Georeferencing

Keyword Arguments

crs (osgeo.osr.SpatialReference, cartopy.crs.CRS or None) – If GDAL OSR SRS, output is in this projection, else AEQD.
re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

wradlib.georef.polar.spherical_to_xyz(r, phi, theta, site, *, re=None, ke=1.3333333333333333, squeeze=False, strict_dims=False)[source]#

wradlib.georef.polar.spherical_to_xyz(obj: DataArray, **kwargs)

wradlib.georef.polar.spherical_to_xyz(obj: Dataset, **kwargs)

Transforms spherical coordinates (r, phi, theta) to cartesian coordinates (x, y, z) centered at site (aeqd).

It takes the shortening of the great circle distance with increasing elevation angle as well as the resulting increase in height into account.

Parameters

r (numpy.ndarray) – Contains the radial distances in meters.
phi (numpy.ndarray) – Contains the azimuthal angles in degree.
theta (numpy.ndarray) – Contains the elevation angles in degree.
site (sequence) – the lon / lat / alt coordinates of the radar location and its altitude a.m.s.l. (in meters)

Keyword Arguments

re (float) – earth’s radius [m], defaults to None (calculating from given latitude)
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.
squeeze (bool) – If True, returns squeezed array. Defaults to False.
strict_dims (bool) – If True, generates output of (theta, phi, r, 3) in any case. If False, dimensions with same length are “merged”.

Returns

xyz (numpy.ndarray) – Array of shape (…, 3). Contains cartesian coordinates.
aeqd (osgeo.osr.SpatialReference) – Destination Spatial Reference System (AEQD-Projection).

wradlib.georef.polar.spherical_to_proj(r, phi, theta, site, *, crs=None, re=None, ke=1.3333333333333333)[source]#

wradlib.georef.polar.spherical_to_proj(obj: DataArray, **kwargs)

wradlib.georef.polar.spherical_to_proj(obj: Dataset, **kwargs)

Transforms spherical coordinates (r, phi, theta) to projected coordinates centered at site in given projection.

It takes the shortening of the great circle distance with increasing elevation angle as well as the resulting increase in height into account.

Parameters

r (numpy.ndarray) – Contains the radial distances.
phi (numpy.ndarray) – Contains the azimuthal angles.
theta (numpy.ndarray) – Contains the elevation angles.
site (sequence) – the lon / lat coordinates of the radar location and its altitude a.m.s.l. (in meters) if site is of length two, altitude is assumed to be zero

Keyword Arguments

crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).
re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

coords (numpy.ndarray) – Array of shape (…, 3). Contains projected map coordinates.

Examples

A few standard directions (North, South, North, East, South, West) with different distances (amounting to roughly 1°) from a site located at 48°N 9°E

>>> r  = np.array([0.,   0., 111., 111., 111., 111.,])*1000
>>> az = np.array([0., 180.,   0.,  90., 180., 270.,])
>>> th = np.array([0.,   0.,   0.,   0.,   0.,  0.5,])
>>> csite = (9.0, 48.0, 0)
>>> coords = spherical_to_proj(r, az, th, csite)
>>> for coord in coords:
...     print( '{0:7.4f}, {1:7.4f}, {2:7.4f}'.format(*coord))
...
 9.0000, 48.0000,  0.0000
 9.0000, 48.0000,  0.0000
 9.0000, 48.9981, 725.7160
10.4872, 47.9904, 725.7160
 9.0000, 47.0017, 725.7160
 7.5131, 47.9904, 1694.2234

Here, the coordinates of the east and west directions won’t come to lie on the latitude of the site because the beam doesn’t travel along the latitude circle but along a great circle.

wradlib.georef.polar.spherical_to_polyvert(r, phi, theta, site, *, crs=None)[source]#

wradlib.georef.polar.spherical_to_polyvert(obj: DataArray, **kwargs)

wradlib.georef.polar.spherical_to_polyvert(obj: Dataset, **kwargs)

Generate 3-D polygon vertices directly from spherical coordinates (r, phi, theta).

This is an alternative to centroid_to_polyvert which does not use centroids, but generates the polygon vertices by simply connecting the corners of the radar bins.

Both azimuth and range arrays are assumed to be equidistant and to contain only unique values. For further information refer to the documentation of spherical_to_xyz.

Parameters

r (numpy.ndarray) – Array of ranges [m]; r defines the exterior boundaries of the range bins! (not the centroids). Thus, values must be positive!
phi (numpy.ndarray) – Array of azimuth angles containing values between 0° and 360°. The angles are assumed to describe the pointing direction fo the main beam lobe! The first angle can start at any values, but make sure the array is sorted continuously positively clockwise and the angles are equidistant. An angle if 0 degree is pointing north.
theta (float) – Elevation angle of scan
site (sequence) – the lon/lat/alt coordinates of the radar location
crs (osgeo.osr.SpatialReference) – Destination Projection

Returns

output (numpy.ndarray) – A 3-d array of polygon vertices with shape(num_vertices, num_vertex_nodes, 2). The last dimension carries the xyz-coordinates either in aeqd or given crs.
aeqd (gdal:osgeo.aeqosr.SpatialReference) – only returned if crs is None

Examples

>>> import wradlib.georef as georef  # noqa
>>> import numpy as np
>>> from matplotlib import collections
>>> import matplotlib.pyplot as pl
>>> #pl.interactive(True)
>>> # define the polar coordinates and the site coordinates in lat/lon
>>> r = np.array([50., 100., 150., 200.]) * 1000
>>> # _check_polar_coords fails in next line
>>> # az = np.array([0., 45., 90., 135., 180., 225., 270., 315., 360.])
>>> az = np.array([0., 45., 90., 135., 180., 225., 270., 315.])
>>> el = 1.0
>>> site = (9.0, 48.0, 0)
>>> polygons, aeqd = georef.spherical_to_polyvert(r, az, el, site)
>>> # plot the resulting mesh
>>> fig = pl.figure()
>>> ax = fig.add_subplot(111)
>>> #polycoll = mpl.collections.PolyCollection(vertices,closed=True, facecolors=None)  # noqa
>>> polycoll = collections.PolyCollection(polygons[...,:2], closed=True, facecolors='None')  # noqa
>>> ret = ax.add_collection(polycoll, autolim=True)
>>> pl.autoscale()
>>> pl.show()

wradlib.georef.polar.spherical_to_centroids(r, phi, theta, site, *, crs=None)[source]#

wradlib.georef.polar.spherical_to_centroids(obj: Dataset, **kwargs)

Generate 3-D centroids of the radar bins from the sperical coordinates (r, phi, theta).

Both azimuth and range arrays are assumed to be equidistant and to contain only unique values. The ranges are assumed to define the exterior boundaries of the range bins (thus they must be positive). The angles are assumed to describe the pointing direction fo the main beam lobe.

For further information refer to the documentation of spherical_to_xyz.

Parameters

r (numpy.ndarray) – Array of ranges [m]; r defines the exterior boundaries of the range bins! (not the centroids). Thus, values must be positive!
phi (numpy.ndarray) – Array of azimuth angles containing values between 0° and 360°. The angles are assumed to describe the pointing direction fo the main beam lobe! The first angle can start at any values, but make sure the array is sorted continuously positively clockwise and the angles are equidistant. An angle if 0 degree is pointing north.
theta (float) – Elevation angle of scan
site (sequence) – the lon/lat/alt coordinates of the radar location
crs (osgeo.osr.SpatialReference) – Destination Projection

Returns

output (numpy.ndarray) – A 3-d array of bin centroids with shape(num_rays, num_bins, 3). The last dimension carries the xyz-coordinates either in aeqd or given crs.
aeqd (osgeo.osr.SpatialReference) – only returned if crs is None

Note

Azimuth angles of 360 deg are internally converted to 0 deg.

wradlib.georef.polar.centroid_to_polyvert(centroid, delta, /)[source]#

Calculates the 2-D Polygon vertices necessary to form a rectangular polygon around the centroid’s coordinates.

The vertices order will be clockwise, as this is the convention used by ESRI’s shapefile format for a polygon.

Parameters

centroid (array-like) – List of 2-D coordinates of the center point of the rectangle.
delta (scalar or numpy.ndarray) – Symmetric distances of the vertices from the centroid in each direction. If delta is scalar, it is assumed to apply to both dimensions.

Returns

vertices (numpy.ndarray) – An array with 5 vertices per centroid.

Note

The function can currently only deal with 2-D data (If you come up with a higher dimensional version of ‘clockwise’ you’re welcome to add it). The data is then assumed to be organized within the centroid array with the last dimension being the 2-D coordinates of each point.

Examples

>>> centroid_to_polyvert([0., 1.], [0.5, 1.5])
array([[-0.5, -0.5],
       [-0.5,  2.5],
       [ 0.5,  2.5],
       [ 0.5, -0.5],
       [-0.5, -0.5]])
>>> centroid_to_polyvert(np.arange(4).reshape((2,2)), 0.5)
array([[[-0.5,  0.5],
        [-0.5,  1.5],
        [ 0.5,  1.5],
        [ 0.5,  0.5],
        [-0.5,  0.5]],

       [[ 1.5,  2.5],
        [ 1.5,  3.5],
        [ 2.5,  3.5],
        [ 2.5,  2.5],
        [ 1.5,  2.5]]])

wradlib.georef.polar.sweep_centroids(nrays, rscale, nbins, elangle, /)[source]#

Construct sweep centroids native coordinates.

Parameters

nrays (int) – number of rays
rscale (float) – length [m] of a range bin
nbins (int) – number of range bins
elangle (float) – elevation angle [deg]

Returns

coordinates (numpy.ndarray) – array of shape (nrays,nbins,3) containing native centroid radar coordinates (slant range, azimuth, elevation)

wradlib.georef.polar.maximum_intensity_projection(data, *, r=None, az=None, angle=None, elev=None, autoext=True)[source]#

Computes the maximum intensity projection along an arbitrary cut through the ppi from polar data.

Parameters

data (numpy.ndarray) – Array containing polar data (azimuth, range)

Keyword Arguments

r (numpy.ndarray) – Array containing range data
az (numpy.ndarray) – Array containing azimuth data
angle (float) – angle of slice, Defaults to 0. Should be between 0 and 180. 0. means horizontal slice, 90. means vertical slice
elev (float) – elevation angle of scan, Defaults to 0.
autoext (bool) – This routine uses numpy.digitize to bin the data. As this function needs bounds, we create one set of coordinates more than would usually be provided by r and az.

Returns

xs (numpy.ndarray) – meshgrid x array
ys (numpy.ndarray) – meshgrid y array
mip (numpy.ndarray) – Array containing the maximum intensity projection (range, range*2)

class wradlib.georef.polar.GeorefPolarMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Polar Methods.

georeference(**kwargs)[source]#

Georeference Dataset/DataArray.

New in version 1.5.

This function adds georeference data to xarray Dataset/DataArray obj.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference, cartopy.crs.CRS or None) – If GDAL OSR SRS, output is in this projection, else AEQD.
re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

spherical_to_xyz(**kwargs)[source]#

Transforms spherical coordinates (r, phi, theta) to cartesian coordinates (x, y, z) centered at site (aeqd).

It takes the shortening of the great circle distance with increasing elevation angle as well as the resulting increase in height into account.

Parameters

Keyword Arguments

re (float) – earth’s radius [m], defaults to None (calculating from given latitude)
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

xyz (xarray.DataArray) – Array of shape (…, 3). Contains cartesian coordinates.
aeqd (osgeo.osr.SpatialReference) – Destination Spatial Reference System (AEQD-Projection).

spherical_to_proj(**kwargs)[source]#

Transforms spherical coordinates (r, phi, theta) to projected coordinates centered at site in given projection.

It takes the shortening of the great circle distance with increasing elevation angle as well as the resulting increase in height into account.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

coords (xarray.DataArray) – Array of shape (…, 3). Contains projected map coordinates.

spherical_to_polyvert(**kwargs)[source]#

Generate 3-D polygon vertices directly from spherical coordinates (r, phi, theta).

This is an alternative to centroid_to_polyvert which does not use centroids, but generates the polygon vertices by simply connecting the corners of the radar bins.

Both azimuth and range arrays are assumed to be equidistant and to contain only unique values. For further information refer to the documentation of spherical_to_xyz.

Currently only works for PPI.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

xyz (xarray.DataArray) – Array of shape (…, 3). Contains cartesian coordinates.
crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).

spherical_to_centroids(**kwargs)[source]#

Generate 3-D centroids of the radar bins from the sperical coordinates (r, phi, theta).

For further information refer to the documentation of spherical_to_xyz.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.
PPI. (Currently only works for) –

Returns

xyz (xarray.DataArray) – Array of shape (…, 3). Contains cartesian coordinates.
crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).

Note

Azimuth angles of 360 deg are internally converted to 0 deg.

Projection Functions#

`reproject`	Transform coordinates from a source projection to a target projection.
`create_osr`	Conveniently supports the construction of osr spatial reference objects
`projstr_to_osr`	Transform a PROJ string to an osr spatial reference object
`epsg_to_osr`	Create osr spatial reference object from EPSG number
`wkt_to_osr`	Create osr spatial reference object from WKT string
`get_default_projection`	Create a default projection object (wgs84)
`get_earth_radius`	Get the radius of the Earth (in km) for a given Spheroid model (sr) at a given position.
`get_radar_projection`	Get the native radar projection which is an azimuthal equidistant projection centered at the site using WGS84.
`get_earth_projection`	Get a default earth projection based on WGS
`get_extent`	Get the extent of 2d coordinates
`GeorefProjectionMethods`	wradlib xarray SubAccessor methods for Georef Projection Methods.

wradlib.georef.projection.reproject(*args, **kwargs)[source]#

wradlib.georef.projection.reproject(obj: Dataset, **kwargs)

wradlib.georef.projection.reproject(obj: DataArray, **kwargs)

Transform coordinates from a source projection to a target projection.

Call signatures:

reproject(C, **kwargs)
reproject(X, Y, **kwargs)
reproject(X, Y, Z, **kwargs)

C is the np array of source coordinates. X, Y and Z specify arrays of x, y, and z coordinate values

Parameters

C (numpy.ndarray) – Array of shape (…,2) or (…,3) with coordinates (x,y) or (x,y,z) respectively
X (numpy.ndarray) – Array of x coordinates
Y (numpy.ndarray) – Array of y coordinates
Z (numpy.ndarray) – Array of z coordinates

Keyword Arguments

src_crs (osgeo.osr.SpatialReference) – defaults to EPSG(4326)
trg_crs (osgeo.osr.SpatialReference) – defaults to EPSG(4326)
area_of_interest (tuple) – tuple of floats (WestLongitudeDeg, SouthLatitudeDeg, EastLongitudeDeg, NorthLatitudeDeg), only gdal>=3

Returns

trans (numpy.ndarray) – Array of reprojected coordinates x,y (…,2) or x,y,z (…,3) depending on input array.
X, Y (numpy.ndarray) – Arrays of reprojected x,y coordinates, shape depending on input array
X, Y, Z (numpy.ndarray) – Arrays of reprojected x,y,z coordinates, shape depending on input array

Examples

wradlib.georef.projection.create_osr(projname, **kwargs)[source]#

Conveniently supports the construction of osr spatial reference objects

Currently, the following projection names (argument projname) are supported:

“aeqd”: Azimuthal Equidistant

needs the following keyword arguments:

lat_0 (latitude at projection center),

lon_0 (longitude at projection center),

x_0 (false Easting, also known as x-offset),

y_0 (false Northing, also known as y-offset)

“dwd-radolan” : RADOLAN Composite Coordinate System

no additional arguments needed.

Polar stereographic projection used by the German Weather Service (DWD) for all Radar composite products. See the final report on the RADOLAN project [DrWeigl et al., 2004] for details.

Parameters

projname (str) – “aeqd” or “dwd-radolan”
kwargs (dict) – depends on projname - see above!

Returns

output (osgeo.osr.SpatialReference) – GDAL/OSR object defining projection

Examples

wradlib.georef.projection.projstr_to_osr(projstr)[source]#

Transform a PROJ string to an osr spatial reference object

Parameters: projstr (str) – PROJ string describing projection
Returns: crs (osgeo.osr.SpatialReference) – GDAL OSR SRS object defining projection

Examples

See PROJ.

wradlib.georef.projection.epsg_to_osr(epsg=None)[source]#

Create osr spatial reference object from EPSG number

Parameters: epsg (int) – EPSG-Number defining the coordinate system
Returns: crs (osgeo.osr.SpatialReference) – GDAL/OSR object defining projection

wradlib.georef.projection.wkt_to_osr(wkt=None)[source]#

Create osr spatial reference object from WKT string

Parameters: wkt (str) – WTK string defining the coordinate reference system
Returns: crs (osgeo.osr.SpatialReference) – GDAL/OSR object defining projection

wradlib.georef.projection.get_default_projection()[source]#: Create a default projection object (wgs84)

wradlib.georef.projection.get_earth_radius(latitude, *, sr=None)[source]#

wradlib.georef.projection.get_earth_radius(obj: DataArray, *, sr=None)

wradlib.georef.projection.get_earth_radius(obj: Dataset, *, sr=None)

Get the radius of the Earth (in km) for a given Spheroid model (sr) at a given position.

\[R^2 = \frac{a^4 \cos(f)^2 + b^4 \sin(f)^2} {a^2 \cos(f)^2 + b^2 \sin(f)^2}\]

Parameters

sr (osgeo.osr.SpatialReference) – spatial reference
latitude (float) – geodetic latitude in degrees

Returns

radius (float) – earth radius in meter

wradlib.georef.projection.get_radar_projection(site)[source]#

Get the native radar projection which is an azimuthal equidistant projection centered at the site using WGS84.

Parameters: site (sequence) – the WGS84 lon / lat coordinates of the radar location
Returns: crs (osgeo.osr.SpatialReference) – projection definition

wradlib.georef.projection.get_earth_projection(model='ellipsoid')[source]#

Get a default earth projection based on WGS

Parameters

model (str) – earth model used, defaults to ellipsoid:

‘ellipsoid’ - WGS84 with ellipsoid heights -> EPSG 4979
‘geoid’ - WGS84 with egm96 geoid heights -> EPSG 4326 + 5773
‘sphere’ - GRS 1980 authalic sphere -> EPSG 4047

Returns

crs (osgeo.osr.SpatialReference) – projection definition

wradlib.georef.projection.get_extent(coords)[source]#

Get the extent of 2d coordinates

Parameters: coords (numpy.ndarray) – coordinates array with shape (…,(x,y))
Returns: extent (tuple) – (xmin, xmax, ymin, ymax)

class wradlib.georef.projection.GeorefProjectionMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Projection Methods.

get_earth_radius(*, sr=None)[source]#

Get the radius of the Earth (in km) for a given Spheroid model (sr) at a given position.

\[R^2 = \frac{a^4 \cos(f)^2 + b^4 \sin(f)^2} {a^2 \cos(f)^2 + b^2 \sin(f)^2}\]

Parameters

obj (xarray.DataArray | xarray.Dataset)
sr (osgeo.osr.SpatialReference) – spatial reference

Returns

radius (float) – earth radius in meter

reproject(**kwargs)[source]#

Transform coordinates from current projection to a target projection.

Parameters

Keyword Arguments

trg_crs (osgeo.osr.SpatialReference) –
coords (dict) – Mapping of coordinates.
area_of_interest (tuple) – tuple of floats (WestLongitudeDeg, SouthLatitudeDeg, EastLongitudeDeg, NorthLatitudeDeg)

Returns

obj (xarray.DataArray | xarray.Dataset) – reprojected Dataset/DataArray

Examples

See Clutter detection by using space-born cloud images.

Raster Functions#

`read_gdal_values`	Read values from a gdal object.
`read_gdal_projection`	Get a projection (OSR object) from a GDAL dataset.
`read_gdal_coordinates`	Get the projected coordinates from a GDAL dataset.
`extract_raster_dataset`	Extract data, coordinates and projection information
`get_raster_extent`	Get the coordinates of the 4 corners of the raster dataset
`get_raster_elevation`	Return surface elevation corresponding to raster dataset
`reproject_raster_dataset`	Reproject/Resample given dataset according to keyword arguments
`merge_raster_datasets`	Merge rasters.
`create_raster_dataset`	Create In-Memory Raster Dataset
`set_raster_origin`	Converts Data and Coordinates Origin
`set_raster_indexing`	Sets Data and Coordinates Indexing Scheme
`set_coordinate_indexing`	Sets Coordinates Indexing Scheme
`raster_to_polyvert`	Get raster polygonal vertices from gdal dataset.

wradlib.georef.raster.read_gdal_values(dataset, *, nodata=None)[source]#

Read values from a gdal object.

Parameters

dataset (osgeo.gdal.Dataset) – raster image with georeferencing
nodata (float) – replace nodata values

Returns

values (numpy.ndarray) – Array of shape (nrows, ncols) or (nbands, nrows, ncols) containing the data values.

Examples

wradlib.georef.raster.read_gdal_projection(dataset)[source]#

Get a projection (OSR object) from a GDAL dataset.

Parameters: dataset (osgeo.gdal.Dataset) – raster image with georeferencing
Returns: crs (osgeo.osr.SpatialReference) – dataset projection object

Examples

See Clutter detection by using space-born cloud images.

wradlib.georef.raster.read_gdal_coordinates(dataset, *, mode='center')[source]#

Get the projected coordinates from a GDAL dataset.

Parameters

dataset (osgeo.gdal.Dataset) – raster image with georeferencing
mode (str) – either ‘center’ or ‘edge’

Returns

coordinates (numpy.ndarray) – Array of shape (nrows,ncols,2) containing xy coordinates. The array indexing follows image convention with origin at upper left pixel. The shape is (nrows+1,ncols+1,2) if mode == edge.

Examples

See Clutter detection by using space-born cloud images.

wradlib.georef.raster.extract_raster_dataset(dataset, *, mode='center', nodata=None)[source]#

Extract data, coordinates and projection information

Parameters

dataset (osgeo.gdal.Dataset) – raster dataset
mode (str) – either ‘center’ or ‘edge’
nodata (float) – replace nodata values

Returns

values (numpy.ndarray) – Array of shape (nrows, ncols) or (nbands, nrows, ncols) containing the data values.
coords (numpy.ndarray) – Array of shape (nrows,ncols,2) containing xy coordinates. The array indexing follows image convention with origin at the upper left pixel (northup). The shape is (nrows+1,ncols+1,2) if mode == edge.
projection (osgeo.osr.SpatialReference) – Spatial reference system of the used coordinates.

wradlib.georef.raster.get_raster_extent(dataset, *, geo=False, window=True)[source]#

Get the coordinates of the 4 corners of the raster dataset

Parameters

dataset (osgeo.gdal.Dataset) – raster image with georeferencing (GeoTransform at least)
geo (bool) – True to get geographical coordinates
window (bool) – True to get the window containing the corners

Returns

extent (numpy.ndarray) – corner coordinates [ul,ll,lr,ur] or window extent [xmin, xmax, ymin, ymax]

wradlib.georef.raster.get_raster_elevation(dataset, *, resample=None, **kwargs)[source]#

Return surface elevation corresponding to raster dataset: The resampling algorithm is chosen based on scale ratio

Parameters

dataset (osgeo.gdal.Dataset) – raster image with georeferencing (GeoTransform at least)
resample (gdal:osgeo.gdalconst.ResampleAlg) – If None the best algorithm is chosen based on scales. GRA_NearestNeighbour = 0, GRA_Bilinear = 1, GRA_Cubic = 2, GRA_CubicSpline = 3, GRA_Lanczos = 4, GRA_Average = 5, GRA_Mode = 6, GRA_Max = 8, GRA_Min = 9, GRA_Med = 10, GRA_Q1 = 11, GRA_Q3 = 12
kwargs (dict) – keyword arguments passed to wradlib.io.dem.get_srtm

Returns

elevation (numpy.ndarray) – Array of shape (rows, cols, 2) containing elevation

wradlib.georef.raster.reproject_raster_dataset(src_ds, **kwargs)[source]#

Reproject/Resample given dataset according to keyword arguments

Parameters

src_ds (osgeo.gdal.Dataset) – raster image with georeferencing (GeoTransform at least)

Keyword Arguments

spacing (float) – float or tuple of two floats pixel spacing of destination dataset, same unit as pixel coordinates
size (int) – tuple of two ints X/YRasterSize of destination dataset
resample (gdal:osgeo.gdalconst.ResampleAlg) – defaults to GRA_Bilinear GRA_NearestNeighbour = 0, GRA_Bilinear = 1, GRA_Cubic = 2, GRA_CubicSpline = 3, GRA_Lanczos = 4, GRA_Average = 5, GRA_Mode = 6, GRA_Max = 8, GRA_Min = 9, GRA_Med = 10, GRA_Q1 = 11, GRA_Q3 = 12
projection_source (osgeo.osr.SpatialReference) – source dataset projection, defaults to None (get projection from src_ds)
projection_target (osgeo.osr.SpatialReference) – destination dataset projection, defaults to None
align (bool or tuple) – If False, there is no destination grid aligment. If True, aligns the destination grid to the next integer multiple of destination grid. If tuple (upper-left x,y-coordinate), the destination grid is aligned to this point.

Returns

dst_ds (osgeo.gdal.Dataset) – reprojected/resampled raster dataset

wradlib.georef.raster.merge_raster_datasets(datasets, **kwargs)[source]#

Merge rasters.

Parameters

datasets (list) – list of osgeo.gdal.Dataset raster images with georeferencing
kwargs (dict) – keyword arguments passed to gdal.Warp()

Returns

dataset (osgeo.gdal.Dataset) – merged raster dataset

wradlib.georef.raster.create_raster_dataset(data, coords, *, crs=None, nodata=-9999)[source]#

Create In-Memory Raster Dataset

Parameters

data (numpy.ndarray) – Array of shape (rows, cols) or (bands, rows, cols) containing the data values.
coords (numpy.ndarray) – Array of shape (nrows, ncols, 2) containing pixel center coordinates or Array of shape (nrows+1, ncols+1, 2) containing pixel edge coordinates
crs (osgeo.osr.SpatialReference) – Spatial reference system of the used coordinates, defaults to None.
nodata (int) – Value of NODATA

Returns

dataset (osgeo.gdal.Dataset) – In-Memory raster dataset

Note

The origin of the provided data and coordinates is UPPER LEFT.

wradlib.georef.raster.set_raster_origin(data, coords, direction)[source]#

Converts Data and Coordinates Origin

Parameters

data (numpy.ndarray) – Array of shape (rows, cols) or (bands, rows, cols) containing the data values.
coords (numpy.ndarray) – Array of shape (rows, cols, 2) containing xy-coordinates.
direction (str) – ‘lower’ or ‘upper’, direction in which to convert data and coordinates.

Returns

data (numpy.ndarray) – Array of shape (rows, cols) or (bands, rows, cols) containing the data values.
coords (numpy.ndarray) – Array of shape (rows, cols, 2) containing xy-coordinates.

wradlib.georef.raster.set_raster_indexing(data, coords, *, indexing='xy')[source]#

Sets Data and Coordinates Indexing Scheme

This converts data and coordinate layout from row-major to column major indexing.

Parameters

data (numpy.ndarray) – Array of shape (…, M, N) containing the data values.
coords (numpy.ndarray) – Array of shape (…, M, N, 2) containing xy-coordinates.
indexing (str) – ‘xy’ or ‘ij’, indexing scheme in which to convert data and coordinates.

Returns

data (numpy.ndarray) – Array of shape (…, N, M) containing the data values.
coords (numpy.ndarray) – Array of shape (…, N, M, 2) containing xy-coordinates.

wradlib.georef.raster.set_coordinate_indexing(coords, *, indexing='xy')[source]#

Sets Coordinates Indexing Scheme

This converts coordinate layout from row-major to column major indexing.

Parameters

coords (numpy.ndarray) – Array of shape (…, M, N, 2) containing xy-coordinates.
indexing (str) – ‘xy’ or ‘ij’, indexing scheme in which to convert data and coordinates.

Returns

coords (numpy.ndarray) – Array of shape (…, N, M, 2) containing xy-coordinates.

wradlib.georef.raster.raster_to_polyvert(dataset)[source]#

Get raster polygonal vertices from gdal dataset.

Parameters: dataset (osgeo.gdal.Dataset) – raster image with georeferencing (GeoTransform at least)
Returns: polyvert (numpy.ndarray) – A N-d array of polygon vertices with shape (…, 5, 2).

Rectangular Grid Functions#

`get_radolan_coords`	Calculates x,y coordinates of radolan grid from lon, lat
`get_radolan_coordinates`	Calculates x/y coordinates of radolan projection of the German Weather Service
`get_radolan_grid`	Calculates x/y coordinates of radolan grid of the German Weather Service
`xyz_to_spherical`
`grid_to_polyvert`	Get polygonal vertices from rectangular grid coordinates.
`GeorefRectMethods`	wradlib xarray SubAccessor methods for Georef Rect Methods.

wradlib.georef.rect.get_radolan_coords(lon, lat, **kwargs)[source]#

Calculates x,y coordinates of radolan grid from lon, lat

Parameters

lon (float or numpy.ndarray) – longitude
lat (float, numpy.ndarray) – latitude

Keyword Arguments

crs (osgeo.osr.SpatialReference | str) – projection of the DWD grid with spheroid model or string trig to use trigonometric formulas for calculation (only for earth model - sphere). Defaults to None (earth model - sphere).

wradlib.georef.rect.get_radolan_coordinates(nrows=None, ncols=None, **kwargs)[source]#

Calculates x/y coordinates of radolan projection of the German Weather Service

Returns the 1D x,y coordinates of the radolan projection for the given grid layout.

Parameters

nrows (int) – number of rows (460, 900 by default, 1100, 1500)
ncols (int) – number of columns (460, 900 by default, 1400)

Keyword Arguments

wgs84 (bool) – if True, output coordinates are in wgs84 lonlat format (default: False)
mode (str) – ‘radolan’ - lower left pixel coordinates ‘center’ - pixel center coordinates ‘edge’ - pixel edge coordinates
crs (osgeo.osr.SpatialReference | str) – projection of the DWD grid with spheroid model or string trig to use trigonometric formulas for calculation (only for earth model - sphere). Defaults to None (earth model - sphere).

Returns

radolan_ccords (tuple) – tuple x and y 1D coordinate numpy.ndarray shape is (nrows,) and (ncols,) if mode=’radolan’ shape is (nrows,) and (ncols,) if mode=’center’ shape is (nrows+1,) and (ncols+1,) if mode=’edge’

wradlib.georef.rect.get_radolan_grid(nrows=None, ncols=None, **kwargs)[source]#

Calculates x/y coordinates of radolan grid of the German Weather Service

Returns the x,y coordinates of the radolan grid positions (lower left corner of every pixel). The radolan grid is a polarstereographic projection, the projection information was taken from RADOLAN-RADVOR-OP Kompositformat_2.2.2 [DWD, 2009]

Coordinates for 900km x 900km grid#
Coordinate	lon	lat	x	y
LowerLeft	3.5889E	46.9526N	-523.4622	-4658.645
LowerRight	14.6209E	47.0705N	376.5378	-4658.645
UpperRight	15.7208E	54.7405N	376.5378	-3758.645
UpperLeft	2.0715E	54.5877N	-523.4622	-3758.645

Coordinates for 1100km x 900km grid#
Coordinate	lon	lat	x	y
LowerLeft	4.6759E	46.1929N	-443.4622	-4758.645
LowerRight	15.4801E	46.1827N	456.5378	-4758.645
UpperRight	17.1128E	55.5342N	456.5378	-3658.645
UpperLeft	3.0889E	55.5482N	-443.4622	-3658.645

Coordinates for 1500km x 1400km grid#
Coordinate	lon	lat	x	y
LowerLeft	2.3419E	43.9336N	-673.4622	-5008.645

Parameters

nrows (int) – number of rows (460, 900 by default, 1100, 1500)
ncols (int) – number of columns (460, 900 by default, 1400)

Keyword Arguments

wgs84 (bool) – if True, output coordinates are in wgs84 lonlat format (default: False)
mode (str) – ‘radolan’ - lower left pixel coordinates ‘center’ - pixel center coordinates ‘edge’ - pixel edge coordinates
crs (osgeo.osr.SpatialReference | str) – projection of the DWD grid with spheroid model or string trig to use trigonometric formulas for calculation (only for earth model - sphere). Defaults to None (earth model - sphere).

Returns

radolan_grid (numpy.ndarray) – Array of xy- or lonlat-grid. shape is (nrows, ncols, 2) if mode=’radolan’ shape is (nrows, ncols, 2) if mode=’center’ shape is (nrows+1, ncols+1, 2) if mode=’edge’

Examples

>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef  # noqa
>>> radolan_grid = georef.get_radolan_grid()
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:]))  # noqa
(900, 900, 2), (-523.4622, -4658.6447)

>>> # using pure trigonometric transformations
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(crs="trig")
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:]))  # noqa
(900, 900, 2), (-523.4622, -4658.6447)

>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(1500, 1400)
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:]))  # noqa
(1500, 1400, 2), (-673.4622, -5008.6447)

>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(900, 900, wgs84=True)
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:]))  # noqa
(900, 900, 2), (3.5889, 46.9526)

See Polar Stereographic Projection.

Raises: TypeError –

wradlib.georef.rect.xyz_to_spherical(*args, **kwargs)[source]#
wradlib.georef.rect.xyz_to_spherical(xyz: ndarray, *, altitude=0, crs=None, ke=1.3333333333333333)
wradlib.georef.rect.xyz_to_spherical(obj: DataArray, **kwargs)
wradlib.georef.rect.xyz_to_spherical(obj: Dataset, **kwargs)

wradlib.georef.rect.grid_to_polyvert(grid, *, ravel=False)[source]#

Get polygonal vertices from rectangular grid coordinates.

Parameters: grid (numpy.ndarray) – grid edge coordinates
Keyword Arguments: ravel (bool) – option to flatten the grid
Returns: polyvert (numpy.ndarray) – A 3-d array of polygon vertices with shape (…, 5, 2).

class wradlib.georef.rect.GeorefRectMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Rect Methods.

xyz_to_spherical(**kwargs)[source]#

Returns spherical representation (r, theta, phi) of given cartesian coordinates (x, y, z) with respect to the reference altitude (asl) considering earth’s geometry (crs).

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference) – projection of the source coordinates (aeqd) with spheroid model defaults to None.
ke (float) – Adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths

Returns

obj (xarray.Dataset) – obj with added spherical coordinates.

Satellite Functions#

`correct_parallax`
`dist_from_orbit`
`GeorefSatelliteMethods`	wradlib xarray SubAccessor methods for Georef Satellite Methods.

wradlib.georef.satellite.correct_parallax(*args, **kwargs)[source]#
wradlib.georef.satellite.correct_parallax(sr_xy: ndarray, nbin, drt, alpha)
wradlib.georef.satellite.correct_parallax(obj: Dataset, drt, **kwargs)

wradlib.georef.satellite.dist_from_orbit(*args, **kwargs)[source]#
wradlib.georef.satellite.dist_from_orbit(sr_alt: float, alpha, beta, r_sr_inv, *, re=6371000)
wradlib.georef.satellite.dist_from_orbit(obj: Dataset, bw_sr, freq, re)

class wradlib.georef.satellite.GeorefSatelliteMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Satellite Methods.

correct_parallax(drt, **kwargs)[source]#

Adjust the geo-locations of the SR pixels

With SR, we refer to precipitation radars based on space-born platforms such as TRMM or GPM.

The sr_xy coordinates of the SR beam footprints need to be in the azimuthal equidistant projection of the ground radar. This ensures that the ground radar is fixed at xy-coordinate (0, 0), and every SR bin has its relative xy-coordinates with respect to the ground radar site.

Parameters

obj (xarray.Dataset)
drt (float) – Gate lenght of SR in meter.

Returns

obj (xarray.Dataset) – obj with added coordinates in ground radar projection and range

dist_from_orbit(bw_sr, freq, re)[source]#

Returns range distances of SR bins (in meters) as seen from the orbit

With SR, we refer to precipitation radars based on space-born platforms such as TRMM or GPM.

Parameters

obj (xarray.Dataset)
bw_sr (float) – Beam width of SR in degree.
freq (int) – Frequency index of PR.
re (float) – earth radius [m]

Returns

obj (xarray.Dataset) – obj with added PR bin range distances from SR platform in orbit.

Vector Functions (GDAL)#

`get_vector_coordinates`	Function iterates over gdal ogr layer features and packs extracted vector coordinate points into nested ndarray
`get_vector_points`	Extract coordinate points from given ogr geometry as generator object
`transform_geometry`	Perform geotransformation to given destination SpatialReferenceSystem
`ogr_create_layer`	Creates OGR.Layer objects in gdal.Dataset object.
`ogr_copy_layer`	Copy OGR.Layer object.
`ogr_copy_layer_by_name`	Copy OGR.Layer object.
`ogr_reproject_layer`	Reproject src_lyr to dst_lyr.
`ogr_add_feature`	Creates OGR.Feature objects in OGR.Layer object.
`ogr_add_geometry`	Copies single OGR.Geometry object to an OGR.Layer object.
`numpy_to_ogr`	Convert a vertex array to gdal/ogr geometry.
`ogr_to_numpy`	Backconvert a gdal/ogr geometry to a numpy vertex array.
`ogr_geocol_to_numpy`	Backconvert a gdal/ogr geometry Collection to a numpy vertex array.
`get_centroid`	Return centroid of a polygon

wradlib.georef.vector.get_vector_coordinates(layer, **kwargs)[source]#

Function iterates over gdal ogr layer features and packs extracted vector coordinate points into nested ndarray

It transforms coordinates to a given destination osr spatial reference if trg_crs is given and a geotransform is necessary.

Parameters

layer (osgeo.ogr.Layer)

Keyword Arguments

src_crs (osgeo.osr.SpatialReference) – Source Projection
trg_crs (osgeo.osr.SpatialReference) – Destination Projection
key (str) – attribute key to extract from layer feature

Returns

shp (numpy.ndarray) – Dimension of subarrays Nx2 extracted shape coordinate points
attrs (list) – List of attributes extracted from features

wradlib.georef.vector.get_vector_points(geom)[source]#

Extract coordinate points from given ogr geometry as generator object

If geometries are nested, function recurses.

Parameters: geom (osgeo.ogr.Geometry)
Yields: result (numpy.ndarray) – expands to Nx2 dimensional nested point arrays

wradlib.georef.vector.transform_geometry(geom, trg_crs, **kwargs)[source]#

Perform geotransformation to given destination SpatialReferenceSystem

It transforms coordinates to a given destination osr spatial reference if a geotransform is neccessary.

Parameters

geom (osgeo.ogr.Geometry)
trg_crs (osgeo.osr.SpatialReference) – Destination Projection

Keyword Arguments

src_crs (osgeo.osr.SpatialReference) – Source Projection

Returns

geom (osgeo.ogr.Geometry) – Transformed Geometry

wradlib.georef.vector.ogr_create_layer(ds, name, *, crs=None, geom_type=None, fields=None)[source]#

Creates OGR.Layer objects in gdal.Dataset object.

Creates one OGR.Layer with given name in given gdal.Dataset object using given OGR.GeometryType and FieldDefinitions

Parameters

ds (osgeo.gdal.Dataset) – object
name (str) – OGRLayer name
crs (osgeo.osr.SpatialReference) – object
geom_type (gdal:osgeo.ogr.GeometryType) – (eg. ogr.wkbPolygon)
fields (list) – list of 2 element tuples (str, gdal:osgeo.ogr.DataType) field name, field type

Returns

out (osgeo.ogr.Layer) – object

wradlib.georef.vector.ogr_copy_layer(src_ds, index, dst_ds, *, reset=True)[source]#

Copy OGR.Layer object.

Copy OGR.Layer object from src_ds gdal.Dataset to dst_ds gdal.Dataset

Parameters

src_ds (osgeo.gdal.Dataset) – object
index (int) – layer index
dst_ds (osgeo.gdal.Dataset) – object
reset (bool) – if True resets src_layer

wradlib.georef.vector.ogr_copy_layer_by_name(src_ds, name, dst_ds, *, reset=True)[source]#

Copy OGR.Layer object.

Copy OGR.Layer object from src_ds gdal.Dataset to dst_ds gdal.Dataset

Parameters

src_ds (osgeo.gdal.Dataset) – object
name (str) – layer name
dst_ds (osgeo.gdal.Dataset) – object
reset (bool) – if True resets src_layer

wradlib.georef.vector.ogr_reproject_layer(src_lyr, dst_lyr, trg_crs, *, src_crs=None)[source]#

Reproject src_lyr to dst_lyr.

Creates one OGR.Layer with given name in given gdal.Dataset object using given OGR.GeometryType and FieldDefinitions

Parameters

src_lyr (osgeo.ogr.Layer) – OGRLayer source layer
dst_lyr (osgeo.ogr.Layer) – OGRLayer destination layer
trg_crs (osgeo.osr.SpatialReference) – Projection Target crs
src_crs (osgeo.osr.SpatialReference) – Projection Source crs

Returns

dst_lyr (osgeo.ogr.Layer) – OGRLayer destination layer

wradlib.georef.vector.ogr_add_feature(ds, src, *, name=None)[source]#

Creates OGR.Feature objects in OGR.Layer object.

OGR.Features are built from numpy src points or polygons.

OGR.Features ‘FID’ and ‘index’ corresponds to source data element

Parameters

ds (osgeo.gdal.Dataset) – object
src (numpy.ndarray) – source data
name (str) – name of wanted Layer

wradlib.georef.vector.ogr_add_geometry(layer, geom, attrs)[source]#

Copies single OGR.Geometry object to an OGR.Layer object.

Given OGR.Geometry is copied to new OGR.Feature and written to given OGR.Layer by given index. Attributes are attached.

Parameters

layer (osgeo.ogr.Layer) – object
geom (osgeo.ogr.Geometry) – object
attrs (list) – attributes referring to layer fields

wradlib.georef.vector.numpy_to_ogr(vert, geom_name)[source]#

Convert a vertex array to gdal/ogr geometry.

Using JSON as a vehicle to efficiently deal with numpy arrays.

Parameters

vert (array-like) – a numpy array of vertices of shape (num vertices, 2)
geom_name (str) – Name of Geometry

Returns

out (osgeo.ogr.Geometry) – object of type geom_name

wradlib.georef.vector.ogr_to_numpy(ogrobj)[source]#

Backconvert a gdal/ogr geometry to a numpy vertex array.

Using JSON as a vehicle to efficiently deal with numpy arrays.

Parameters: ogrobj (osgeo.ogr.Geometry) – object
Returns: out (numpy.ndarray) – a nested ndarray of vertices of shape (num vertices, 2)

wradlib.georef.vector.ogr_geocol_to_numpy(ogrobj)[source]#

Backconvert a gdal/ogr geometry Collection to a numpy vertex array.

This extracts only Polygon geometries!

Using JSON as a vehicle to efficiently deal with numpy arrays.

Parameters: ogrobj (osgeo.ogr.Geometry) – Collection object
Returns: out (numpy.ndarray) – a nested ndarray of vertices of shape (num vertices, 2)

wradlib.georef.vector.get_centroid(polyg)[source]#

Return centroid of a polygon

Parameters: polyg (numpy.ndarray) – of shape (num vertices, 2) or ogr.Geometry object
Returns: out (tuple) – x and y coordinate of the centroid

Xarray Functions#

`as_xarray_dataarray`	Create Xarray DataArray from NumPy Array
`create_xarray_dataarray`	Create Xarray DataArray from Polar Radar Data

wradlib.georef.xarray.as_xarray_dataarray(data, dims, coords)[source]#

Create Xarray DataArray from NumPy Array

New in version 1.3.

Parameters

data (numpy.ndarray) – data array
dims (dict) – dictionary describing xarray dimensions
coords (dict) – dictionary describing xarray coordinates

Returns

dataset (xarray.DataArray) – DataArray

wradlib.georef.xarray.create_xarray_dataarray(data, *, r=None, phi=None, theta=None, site=None, sweep_mode='azimuth_surveillance', rf=1.0, **kwargs)[source]#

Create Xarray DataArray from Polar Radar Data

New in version 1.3.

Parameters

data (numpy.ndarray) – The data array. It is assumed that the first dimension is over the azimuth angles, while the second dimension is over the range bins
r (numpy.ndarray) – The ranges. Units may be chosen arbitrarily, m preferred.
phi (numpy.ndarray) – The azimuth angles in degrees.
theta (numpy.ndarray) – The elevation angles in degrees.
site (tuple) – Tuple of coordinates of the radar site.
sweep_mode (str) – Defaults to ‘azimuth_surveillance’.
rf (float) – factor to scale range, defaults to 1. (no scale)

Keyword Arguments

re (float) – effective earth radius
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. Defaults to 4/3.
dim0 (str) – Name of the first dimension. Defaults to ‘azimuth’.
dim1 (str) – Name of the second dimension. Defaults to ‘range’.

Returns

dataset (xarray.DataArray) – DataArray

class wradlib.georef.GeorefMethods(obj)[source]#

Bases: XarrayMethods, GeorefMiscMethods, GeorefPolarMethods, GeorefProjectionMethods, GeorefRectMethods, GeorefSatelliteMethods

wradlib xarray SubAccessor methods for Georef.

class wradlib.georef.GeorefMiscMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Misc Methods.

bin_altitude(**kwargs)[source]#

Calculates the height of a radar bin taking the refractivity of the atmosphere into account.

Based on [Doviak et al., 1993] the bin altitude is calculated as

\[h = \sqrt{r^2 + (k_e r_e)^2 + 2 r k_e r_e \sin\theta} - k_e r_e\]

Parameters: obj (xarray.DataArray | xarray.Dataset) – DataArray
Returns: z (xarray.DataArray) – DataArray

bin_distance(**kwargs)[source]#

Calculates great circle distance from radar site to radar bin over spherical earth, taking the refractivity of the atmosphere into account.

\[s = k_e r_e \arctan\left( \frac{r \cos\theta}{r \cos\theta + k_e r_e + h}\right)\]

where \(h\) would be the radar site altitude amsl.

Parameters: obj (xarray.DataArray | xarray.Dataset) – DataArray or Dataset
Returns: bin_distance (xarray.DataArray) – DataArray

site_distance(**kwargs)[source]#

Calculates great circle distance from bin at certain altitude to the radar site over spherical earth, taking the refractivity of the atmosphere into account.

Based on [Doviak et al., 1993] the site distance may be calculated as

\[s = k_e r_e \arcsin\left( \frac{r \cos\theta}{k_e r_e + h_n(r, \theta, r_e, k_e)}\right)\]

where \(h_n\) would be provided by bin_altitude.

Parameters: obj (xarray.DataArray | xarray.Dataset) – DataArray or Dataset
Returns: z (xarray.DataArray) – DataArray

class wradlib.georef.GeorefPolarMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Polar Methods.

georeference(**kwargs)[source]#

Georeference Dataset/DataArray.

New in version 1.5.

This function adds georeference data to xarray Dataset/DataArray obj.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference, cartopy.crs.CRS or None) – If GDAL OSR SRS, output is in this projection, else AEQD.
re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

spherical_to_xyz(**kwargs)[source]#

Transforms spherical coordinates (r, phi, theta) to cartesian coordinates (x, y, z) centered at site (aeqd).

It takes the shortening of the great circle distance with increasing elevation angle as well as the resulting increase in height into account.

Parameters

Keyword Arguments

re (float) – earth’s radius [m], defaults to None (calculating from given latitude)
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

xyz (xarray.DataArray) – Array of shape (…, 3). Contains cartesian coordinates.
aeqd (osgeo.osr.SpatialReference) – Destination Spatial Reference System (AEQD-Projection).

spherical_to_proj(**kwargs)[source]#

Transforms spherical coordinates (r, phi, theta) to projected coordinates centered at site in given projection.

It takes the shortening of the great circle distance with increasing elevation angle as well as the resulting increase in height into account.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

coords (xarray.DataArray) – Array of shape (…, 3). Contains projected map coordinates.

spherical_to_polyvert(**kwargs)[source]#

Generate 3-D polygon vertices directly from spherical coordinates (r, phi, theta).

This is an alternative to centroid_to_polyvert which does not use centroids, but generates the polygon vertices by simply connecting the corners of the radar bins.

Both azimuth and range arrays are assumed to be equidistant and to contain only unique values. For further information refer to the documentation of spherical_to_xyz.

Currently only works for PPI.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

xyz (xarray.DataArray) – Array of shape (…, 3). Contains cartesian coordinates.
crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).

spherical_to_centroids(**kwargs)[source]#

Generate 3-D centroids of the radar bins from the sperical coordinates (r, phi, theta).

For further information refer to the documentation of spherical_to_xyz.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.
PPI. (Currently only works for) –

Returns

xyz (xarray.DataArray) – Array of shape (…, 3). Contains cartesian coordinates.
crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).

Note

Azimuth angles of 360 deg are internally converted to 0 deg.

class wradlib.georef.GeorefProjectionMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Projection Methods.

get_earth_radius(*, sr=None)[source]#

Get the radius of the Earth (in km) for a given Spheroid model (sr) at a given position.

\[R^2 = \frac{a^4 \cos(f)^2 + b^4 \sin(f)^2} {a^2 \cos(f)^2 + b^2 \sin(f)^2}\]

Parameters

obj (xarray.DataArray | xarray.Dataset)
sr (osgeo.osr.SpatialReference) – spatial reference

Returns

radius (float) – earth radius in meter

reproject(**kwargs)[source]#

Transform coordinates from current projection to a target projection.

Parameters

Keyword Arguments

trg_crs (osgeo.osr.SpatialReference) –
coords (dict) – Mapping of coordinates.
area_of_interest (tuple) – tuple of floats (WestLongitudeDeg, SouthLatitudeDeg, EastLongitudeDeg, NorthLatitudeDeg)

Returns

obj (xarray.DataArray | xarray.Dataset) – reprojected Dataset/DataArray

Examples

class wradlib.georef.GeorefRectMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Rect Methods.

xyz_to_spherical(**kwargs)[source]#

Returns spherical representation (r, theta, phi) of given cartesian coordinates (x, y, z) with respect to the reference altitude (asl) considering earth’s geometry (crs).

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference) – projection of the source coordinates (aeqd) with spheroid model defaults to None.
ke (float) – Adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths

Returns

obj (xarray.Dataset) – obj with added spherical coordinates.

class wradlib.georef.GeorefSatelliteMethods[source]#

Bases: object

wradlib xarray SubAccessor methods for Georef Satellite Methods.

correct_parallax(drt, **kwargs)[source]#

Adjust the geo-locations of the SR pixels

With SR, we refer to precipitation radars based on space-born platforms such as TRMM or GPM.

Parameters

obj (xarray.Dataset)
drt (float) – Gate lenght of SR in meter.

Returns

obj (xarray.Dataset) – obj with added coordinates in ground radar projection and range

dist_from_orbit(bw_sr, freq, re)[source]#

Returns range distances of SR bins (in meters) as seen from the orbit

With SR, we refer to precipitation radars based on space-born platforms such as TRMM or GPM.

Parameters

obj (xarray.Dataset)
bw_sr (float) – Beam width of SR in degree.
freq (int) – Frequency index of PR.
re (float) – earth radius [m]

Returns

obj (xarray.Dataset) – obj with added PR bin range distances from SR platform in orbit.

wradlib.georef.as_xarray_dataarray(data, dims, coords)[source]#

Create Xarray DataArray from NumPy Array

New in version 1.3.

Parameters

data (numpy.ndarray) – data array
dims (dict) – dictionary describing xarray dimensions
coords (dict) – dictionary describing xarray coordinates

Returns

dataset (xarray.DataArray) – DataArray

wradlib.georef.bin_altitude(r, theta, sitealt, *, re=6371000, ke=1.3333333333333333)[source]#

wradlib.georef.bin_altitude(obj: DataArray, **kwargs)

wradlib.georef.bin_altitude(obj: Dataset, **kwargs)

Calculates the height of a radar bin taking the refractivity of the atmosphere into account.

Based on [Doviak et al., 1993] the bin altitude is calculated as

\[h = \sqrt{r^2 + (k_e r_e)^2 + 2 r k_e r_e \sin\theta} - k_e r_e\]

Parameters

r (numpy.ndarray) – Array of ranges [m]
theta (scalar or numpy.ndarray) – Array broadcastable to the shape of r elevation angles in degrees with 0° at horizontal and +90° pointing vertically upwards from the radar
sitealt (float) – Altitude in [m] a.s.l. of the referencing radar site

Keyword Arguments

re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths

Returns

altitude (numpy.ndarray) – Array of heights of the radar bins in [m]

wradlib.georef.bin_distance(r, theta, sitealt, *, re=6371000, ke=1.3333333333333333)[source]#

wradlib.georef.bin_distance(obj: DataArray, **kwargs)

wradlib.georef.bin_distance(obj: Dataset, **kwargs)

Calculates great circle distance from radar site to radar bin over spherical earth, taking the refractivity of the atmosphere into account.

\[s = k_e r_e \arctan\left( \frac{r \cos\theta}{r \cos\theta + k_e r_e + h}\right)\]

where \(h\) would be the radar site altitude amsl.

Parameters

r (numpy.ndarray) – Array of ranges [m]
theta (scalar or numpy.ndarray) – Array broadcastable to the shape of r elevation angles in degrees with 0° at horizontal and +90° pointing vertically upwards from the radar
sitealt (float) – site altitude [m] amsl.
re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths

Returns

distance (numpy.ndarray) – Array of great circle arc distances [m]

wradlib.georef.centroid_to_polyvert(centroid, delta, /)[source]#

Calculates the 2-D Polygon vertices necessary to form a rectangular polygon around the centroid’s coordinates.

The vertices order will be clockwise, as this is the convention used by ESRI’s shapefile format for a polygon.

Parameters

centroid (array-like) – List of 2-D coordinates of the center point of the rectangle.
delta (scalar or numpy.ndarray) – Symmetric distances of the vertices from the centroid in each direction. If delta is scalar, it is assumed to apply to both dimensions.

Returns

vertices (numpy.ndarray) – An array with 5 vertices per centroid.

Note

Examples

>>> centroid_to_polyvert([0., 1.], [0.5, 1.5])
array([[-0.5, -0.5],
       [-0.5,  2.5],
       [ 0.5,  2.5],
       [ 0.5, -0.5],
       [-0.5, -0.5]])
>>> centroid_to_polyvert(np.arange(4).reshape((2,2)), 0.5)
array([[[-0.5,  0.5],
        [-0.5,  1.5],
        [ 0.5,  1.5],
        [ 0.5,  0.5],
        [-0.5,  0.5]],

       [[ 1.5,  2.5],
        [ 1.5,  3.5],
        [ 2.5,  3.5],
        [ 2.5,  2.5],
        [ 1.5,  2.5]]])

wradlib.georef.correct_parallax(*args, **kwargs)[source]#
wradlib.georef.correct_parallax(sr_xy: ndarray, nbin, drt, alpha)
wradlib.georef.correct_parallax(obj: Dataset, drt, **kwargs)

wradlib.georef.create_osr(projname, **kwargs)[source]#

Conveniently supports the construction of osr spatial reference objects

Currently, the following projection names (argument projname) are supported:

“aeqd”: Azimuthal Equidistant

needs the following keyword arguments:

lat_0 (latitude at projection center),

lon_0 (longitude at projection center),

x_0 (false Easting, also known as x-offset),

y_0 (false Northing, also known as y-offset)

“dwd-radolan” : RADOLAN Composite Coordinate System

no additional arguments needed.

Polar stereographic projection used by the German Weather Service (DWD) for all Radar composite products. See the final report on the RADOLAN project [DrWeigl et al., 2004] for details.

Parameters

projname (str) – “aeqd” or “dwd-radolan”
kwargs (dict) – depends on projname - see above!

Returns

output (osgeo.osr.SpatialReference) – GDAL/OSR object defining projection

Examples

wradlib.georef.create_raster_dataset(data, coords, *, crs=None, nodata=-9999)[source]#

Create In-Memory Raster Dataset

Parameters

data (numpy.ndarray) – Array of shape (rows, cols) or (bands, rows, cols) containing the data values.
coords (numpy.ndarray) – Array of shape (nrows, ncols, 2) containing pixel center coordinates or Array of shape (nrows+1, ncols+1, 2) containing pixel edge coordinates
crs (osgeo.osr.SpatialReference) – Spatial reference system of the used coordinates, defaults to None.
nodata (int) – Value of NODATA

Returns

dataset (osgeo.gdal.Dataset) – In-Memory raster dataset

Note

The origin of the provided data and coordinates is UPPER LEFT.

wradlib.georef.create_xarray_dataarray(data, *, r=None, phi=None, theta=None, site=None, sweep_mode='azimuth_surveillance', rf=1.0, **kwargs)[source]#

Create Xarray DataArray from Polar Radar Data

New in version 1.3.

Parameters

data (numpy.ndarray) – The data array. It is assumed that the first dimension is over the azimuth angles, while the second dimension is over the range bins
r (numpy.ndarray) – The ranges. Units may be chosen arbitrarily, m preferred.
phi (numpy.ndarray) – The azimuth angles in degrees.
theta (numpy.ndarray) – The elevation angles in degrees.
site (tuple) – Tuple of coordinates of the radar site.
sweep_mode (str) – Defaults to ‘azimuth_surveillance’.
rf (float) – factor to scale range, defaults to 1. (no scale)

Keyword Arguments

re (float) – effective earth radius
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. Defaults to 4/3.
dim0 (str) – Name of the first dimension. Defaults to ‘azimuth’.
dim1 (str) – Name of the second dimension. Defaults to ‘range’.

Returns

dataset (xarray.DataArray) – DataArray

wradlib.georef.dist_from_orbit(*args, **kwargs)[source]#
wradlib.georef.dist_from_orbit(sr_alt: float, alpha, beta, r_sr_inv, *, re=6371000)
wradlib.georef.dist_from_orbit(obj: Dataset, bw_sr, freq, re)

wradlib.georef.epsg_to_osr(epsg=None)[source]#

Create osr spatial reference object from EPSG number

Parameters: epsg (int) – EPSG-Number defining the coordinate system
Returns: crs (osgeo.osr.SpatialReference) – GDAL/OSR object defining projection

wradlib.georef.extract_raster_dataset(dataset, *, mode='center', nodata=None)[source]#

Extract data, coordinates and projection information

Parameters

dataset (osgeo.gdal.Dataset) – raster dataset
mode (str) – either ‘center’ or ‘edge’
nodata (float) – replace nodata values

Returns

values (numpy.ndarray) – Array of shape (nrows, ncols) or (nbands, nrows, ncols) containing the data values.
coords (numpy.ndarray) – Array of shape (nrows,ncols,2) containing xy coordinates. The array indexing follows image convention with origin at the upper left pixel (northup). The shape is (nrows+1,ncols+1,2) if mode == edge.
projection (osgeo.osr.SpatialReference) – Spatial reference system of the used coordinates.

wradlib.georef.georeference(obj, **kwargs)[source]#

Georeference Dataset/DataArray.

New in version 1.5.

This function adds georeference data to xarray Dataset/DataArray obj.

Parameters

Keyword Arguments

crs (osgeo.osr.SpatialReference, cartopy.crs.CRS or None) – If GDAL OSR SRS, output is in this projection, else AEQD.
re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

See Clutter detection by using space-born cloud images.

wradlib.georef.get_centroid(polyg)[source]#

Return centroid of a polygon

Parameters: polyg (numpy.ndarray) – of shape (num vertices, 2) or ogr.Geometry object
Returns: out (tuple) – x and y coordinate of the centroid

wradlib.georef.get_default_projection()[source]#: Create a default projection object (wgs84)

wradlib.georef.get_earth_projection(model='ellipsoid')[source]#

Get a default earth projection based on WGS

Parameters

model (str) – earth model used, defaults to ellipsoid:

‘ellipsoid’ - WGS84 with ellipsoid heights -> EPSG 4979
‘geoid’ - WGS84 with egm96 geoid heights -> EPSG 4326 + 5773
‘sphere’ - GRS 1980 authalic sphere -> EPSG 4047

Returns

crs (osgeo.osr.SpatialReference) – projection definition

wradlib.georef.get_earth_radius(latitude, *, sr=None)[source]#

wradlib.georef.get_earth_radius(obj: DataArray, *, sr=None)

wradlib.georef.get_earth_radius(obj: Dataset, *, sr=None)

Get the radius of the Earth (in km) for a given Spheroid model (sr) at a given position.

\[R^2 = \frac{a^4 \cos(f)^2 + b^4 \sin(f)^2} {a^2 \cos(f)^2 + b^2 \sin(f)^2}\]

Parameters

sr (osgeo.osr.SpatialReference) – spatial reference
latitude (float) – geodetic latitude in degrees

Returns

radius (float) – earth radius in meter

wradlib.georef.get_extent(coords)[source]#

Get the extent of 2d coordinates

Parameters: coords (numpy.ndarray) – coordinates array with shape (…,(x,y))
Returns: extent (tuple) – (xmin, xmax, ymin, ymax)

wradlib.georef.get_radar_projection(site)[source]#

Get the native radar projection which is an azimuthal equidistant projection centered at the site using WGS84.

Parameters: site (sequence) – the WGS84 lon / lat coordinates of the radar location
Returns: crs (osgeo.osr.SpatialReference) – projection definition

wradlib.georef.get_radolan_coordinates(nrows=None, ncols=None, **kwargs)[source]#

Calculates x/y coordinates of radolan projection of the German Weather Service

Returns the 1D x,y coordinates of the radolan projection for the given grid layout.

Parameters

nrows (int) – number of rows (460, 900 by default, 1100, 1500)
ncols (int) – number of columns (460, 900 by default, 1400)

Keyword Arguments

wgs84 (bool) – if True, output coordinates are in wgs84 lonlat format (default: False)
mode (str) – ‘radolan’ - lower left pixel coordinates ‘center’ - pixel center coordinates ‘edge’ - pixel edge coordinates
crs (osgeo.osr.SpatialReference | str) – projection of the DWD grid with spheroid model or string trig to use trigonometric formulas for calculation (only for earth model - sphere). Defaults to None (earth model - sphere).

Returns

wradlib.georef.get_radolan_coords(lon, lat, **kwargs)[source]#

Calculates x,y coordinates of radolan grid from lon, lat

Parameters

lon (float or numpy.ndarray) – longitude
lat (float, numpy.ndarray) – latitude

Keyword Arguments

wradlib.georef.get_radolan_grid(nrows=None, ncols=None, **kwargs)[source]#

Calculates x/y coordinates of radolan grid of the German Weather Service

Coordinates for 900km x 900km grid#
Coordinate	lon	lat	x	y
LowerLeft	3.5889E	46.9526N	-523.4622	-4658.645
LowerRight	14.6209E	47.0705N	376.5378	-4658.645
UpperRight	15.7208E	54.7405N	376.5378	-3758.645
UpperLeft	2.0715E	54.5877N	-523.4622	-3758.645

Coordinates for 1100km x 900km grid#
Coordinate	lon	lat	x	y
LowerLeft	4.6759E	46.1929N	-443.4622	-4758.645
LowerRight	15.4801E	46.1827N	456.5378	-4758.645
UpperRight	17.1128E	55.5342N	456.5378	-3658.645
UpperLeft	3.0889E	55.5482N	-443.4622	-3658.645

Coordinates for 1500km x 1400km grid#
Coordinate	lon	lat	x	y
LowerLeft	2.3419E	43.9336N	-673.4622	-5008.645

Parameters

nrows (int) – number of rows (460, 900 by default, 1100, 1500)
ncols (int) – number of columns (460, 900 by default, 1400)

Keyword Arguments

wgs84 (bool) – if True, output coordinates are in wgs84 lonlat format (default: False)
mode (str) – ‘radolan’ - lower left pixel coordinates ‘center’ - pixel center coordinates ‘edge’ - pixel edge coordinates
crs (osgeo.osr.SpatialReference | str) – projection of the DWD grid with spheroid model or string trig to use trigonometric formulas for calculation (only for earth model - sphere). Defaults to None (earth model - sphere).

Returns

Examples

>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef  # noqa
>>> radolan_grid = georef.get_radolan_grid()
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:]))  # noqa
(900, 900, 2), (-523.4622, -4658.6447)

>>> # using pure trigonometric transformations
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(crs="trig")
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:]))  # noqa
(900, 900, 2), (-523.4622, -4658.6447)

>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(1500, 1400)
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:]))  # noqa
(1500, 1400, 2), (-673.4622, -5008.6447)

>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(900, 900, wgs84=True)
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:]))  # noqa
(900, 900, 2), (3.5889, 46.9526)

See Polar Stereographic Projection.

Raises: TypeError –

wradlib.georef.get_raster_elevation(dataset, *, resample=None, **kwargs)[source]#

Return surface elevation corresponding to raster dataset: The resampling algorithm is chosen based on scale ratio

Parameters

dataset (osgeo.gdal.Dataset) – raster image with georeferencing (GeoTransform at least)
resample (gdal:osgeo.gdalconst.ResampleAlg) – If None the best algorithm is chosen based on scales. GRA_NearestNeighbour = 0, GRA_Bilinear = 1, GRA_Cubic = 2, GRA_CubicSpline = 3, GRA_Lanczos = 4, GRA_Average = 5, GRA_Mode = 6, GRA_Max = 8, GRA_Min = 9, GRA_Med = 10, GRA_Q1 = 11, GRA_Q3 = 12
kwargs (dict) – keyword arguments passed to wradlib.io.dem.get_srtm

Returns

elevation (numpy.ndarray) – Array of shape (rows, cols, 2) containing elevation

wradlib.georef.get_raster_extent(dataset, *, geo=False, window=True)[source]#

Get the coordinates of the 4 corners of the raster dataset

Parameters

dataset (osgeo.gdal.Dataset) – raster image with georeferencing (GeoTransform at least)
geo (bool) – True to get geographical coordinates
window (bool) – True to get the window containing the corners

Returns

extent (numpy.ndarray) – corner coordinates [ul,ll,lr,ur] or window extent [xmin, xmax, ymin, ymax]

wradlib.georef.get_vector_coordinates(layer, **kwargs)[source]#

Function iterates over gdal ogr layer features and packs extracted vector coordinate points into nested ndarray

It transforms coordinates to a given destination osr spatial reference if trg_crs is given and a geotransform is necessary.

Parameters

layer (osgeo.ogr.Layer)

Keyword Arguments

src_crs (osgeo.osr.SpatialReference) – Source Projection
trg_crs (osgeo.osr.SpatialReference) – Destination Projection
key (str) – attribute key to extract from layer feature

Returns

shp (numpy.ndarray) – Dimension of subarrays Nx2 extracted shape coordinate points
attrs (list) – List of attributes extracted from features

wradlib.georef.get_vector_points(geom)[source]#

Extract coordinate points from given ogr geometry as generator object

If geometries are nested, function recurses.

Parameters: geom (osgeo.ogr.Geometry)
Yields: result (numpy.ndarray) – expands to Nx2 dimensional nested point arrays

wradlib.georef.grid_to_polyvert(grid, *, ravel=False)[source]#

Get polygonal vertices from rectangular grid coordinates.

Parameters: grid (numpy.ndarray) – grid edge coordinates
Keyword Arguments: ravel (bool) – option to flatten the grid
Returns: polyvert (numpy.ndarray) – A 3-d array of polygon vertices with shape (…, 5, 2).

wradlib.georef.maximum_intensity_projection(data, *, r=None, az=None, angle=None, elev=None, autoext=True)[source]#

Computes the maximum intensity projection along an arbitrary cut through the ppi from polar data.

Parameters

data (numpy.ndarray) – Array containing polar data (azimuth, range)

Keyword Arguments

r (numpy.ndarray) – Array containing range data
az (numpy.ndarray) – Array containing azimuth data
angle (float) – angle of slice, Defaults to 0. Should be between 0 and 180. 0. means horizontal slice, 90. means vertical slice
elev (float) – elevation angle of scan, Defaults to 0.
autoext (bool) – This routine uses numpy.digitize to bin the data. As this function needs bounds, we create one set of coordinates more than would usually be provided by r and az.

Returns

xs (numpy.ndarray) – meshgrid x array
ys (numpy.ndarray) – meshgrid y array
mip (numpy.ndarray) – Array containing the maximum intensity projection (range, range*2)

wradlib.georef.merge_raster_datasets(datasets, **kwargs)[source]#

Merge rasters.

Parameters

datasets (list) – list of osgeo.gdal.Dataset raster images with georeferencing
kwargs (dict) – keyword arguments passed to gdal.Warp()

Returns

dataset (osgeo.gdal.Dataset) – merged raster dataset

wradlib.georef.numpy_to_ogr(vert, geom_name)[source]#

Convert a vertex array to gdal/ogr geometry.

Using JSON as a vehicle to efficiently deal with numpy arrays.

Parameters

vert (array-like) – a numpy array of vertices of shape (num vertices, 2)
geom_name (str) – Name of Geometry

Returns

out (osgeo.ogr.Geometry) – object of type geom_name

wradlib.georef.ogr_add_feature(ds, src, *, name=None)[source]#

Creates OGR.Feature objects in OGR.Layer object.

OGR.Features are built from numpy src points or polygons.

OGR.Features ‘FID’ and ‘index’ corresponds to source data element

Parameters

ds (osgeo.gdal.Dataset) – object
src (numpy.ndarray) – source data
name (str) – name of wanted Layer

wradlib.georef.ogr_add_geometry(layer, geom, attrs)[source]#

Copies single OGR.Geometry object to an OGR.Layer object.

Given OGR.Geometry is copied to new OGR.Feature and written to given OGR.Layer by given index. Attributes are attached.

Parameters

layer (osgeo.ogr.Layer) – object
geom (osgeo.ogr.Geometry) – object
attrs (list) – attributes referring to layer fields

wradlib.georef.ogr_copy_layer(src_ds, index, dst_ds, *, reset=True)[source]#

Copy OGR.Layer object.

Copy OGR.Layer object from src_ds gdal.Dataset to dst_ds gdal.Dataset

Parameters

src_ds (osgeo.gdal.Dataset) – object
index (int) – layer index
dst_ds (osgeo.gdal.Dataset) – object
reset (bool) – if True resets src_layer

wradlib.georef.ogr_copy_layer_by_name(src_ds, name, dst_ds, *, reset=True)[source]#

Copy OGR.Layer object.

Copy OGR.Layer object from src_ds gdal.Dataset to dst_ds gdal.Dataset

Parameters

src_ds (osgeo.gdal.Dataset) – object
name (str) – layer name
dst_ds (osgeo.gdal.Dataset) – object
reset (bool) – if True resets src_layer

wradlib.georef.ogr_create_layer(ds, name, *, crs=None, geom_type=None, fields=None)[source]#

Creates OGR.Layer objects in gdal.Dataset object.

Creates one OGR.Layer with given name in given gdal.Dataset object using given OGR.GeometryType and FieldDefinitions

Parameters

ds (osgeo.gdal.Dataset) – object
name (str) – OGRLayer name
crs (osgeo.osr.SpatialReference) – object
geom_type (gdal:osgeo.ogr.GeometryType) – (eg. ogr.wkbPolygon)
fields (list) – list of 2 element tuples (str, gdal:osgeo.ogr.DataType) field name, field type

Returns

out (osgeo.ogr.Layer) – object

wradlib.georef.ogr_geocol_to_numpy(ogrobj)[source]#

Backconvert a gdal/ogr geometry Collection to a numpy vertex array.

This extracts only Polygon geometries!

Using JSON as a vehicle to efficiently deal with numpy arrays.

Parameters: ogrobj (osgeo.ogr.Geometry) – Collection object
Returns: out (numpy.ndarray) – a nested ndarray of vertices of shape (num vertices, 2)

wradlib.georef.ogr_reproject_layer(src_lyr, dst_lyr, trg_crs, *, src_crs=None)[source]#

Reproject src_lyr to dst_lyr.

Creates one OGR.Layer with given name in given gdal.Dataset object using given OGR.GeometryType and FieldDefinitions

Parameters

src_lyr (osgeo.ogr.Layer) – OGRLayer source layer
dst_lyr (osgeo.ogr.Layer) – OGRLayer destination layer
trg_crs (osgeo.osr.SpatialReference) – Projection Target crs
src_crs (osgeo.osr.SpatialReference) – Projection Source crs

Returns

dst_lyr (osgeo.ogr.Layer) – OGRLayer destination layer

wradlib.georef.ogr_to_numpy(ogrobj)[source]#

Backconvert a gdal/ogr geometry to a numpy vertex array.

Using JSON as a vehicle to efficiently deal with numpy arrays.

Parameters: ogrobj (osgeo.ogr.Geometry) – object
Returns: out (numpy.ndarray) – a nested ndarray of vertices of shape (num vertices, 2)

wradlib.georef.projstr_to_osr(projstr)[source]#

Transform a PROJ string to an osr spatial reference object

Parameters: projstr (str) – PROJ string describing projection
Returns: crs (osgeo.osr.SpatialReference) – GDAL OSR SRS object defining projection

Examples

See PROJ.

wradlib.georef.raster_to_polyvert(dataset)[source]#

Get raster polygonal vertices from gdal dataset.

Parameters: dataset (osgeo.gdal.Dataset) – raster image with georeferencing (GeoTransform at least)
Returns: polyvert (numpy.ndarray) – A N-d array of polygon vertices with shape (…, 5, 2).

wradlib.georef.read_gdal_coordinates(dataset, *, mode='center')[source]#

Get the projected coordinates from a GDAL dataset.

Parameters

dataset (osgeo.gdal.Dataset) – raster image with georeferencing
mode (str) – either ‘center’ or ‘edge’

Returns

Examples

wradlib.georef.read_gdal_projection(dataset)[source]#

Get a projection (OSR object) from a GDAL dataset.

Parameters: dataset (osgeo.gdal.Dataset) – raster image with georeferencing
Returns: crs (osgeo.osr.SpatialReference) – dataset projection object

Examples

See Clutter detection by using space-born cloud images.

wradlib.georef.read_gdal_values(dataset, *, nodata=None)[source]#

Read values from a gdal object.

Parameters

dataset (osgeo.gdal.Dataset) – raster image with georeferencing
nodata (float) – replace nodata values

Returns

values (numpy.ndarray) – Array of shape (nrows, ncols) or (nbands, nrows, ncols) containing the data values.

Examples

See Clutter detection by using space-born cloud images.

wradlib.georef.reproject(*args, **kwargs)[source]#

wradlib.georef.reproject(obj: Dataset, **kwargs)

wradlib.georef.reproject(obj: DataArray, **kwargs)

Transform coordinates from a source projection to a target projection.

Call signatures:

reproject(C, **kwargs)
reproject(X, Y, **kwargs)
reproject(X, Y, Z, **kwargs)

C is the np array of source coordinates. X, Y and Z specify arrays of x, y, and z coordinate values

Parameters

C (numpy.ndarray) – Array of shape (…,2) or (…,3) with coordinates (x,y) or (x,y,z) respectively
X (numpy.ndarray) – Array of x coordinates
Y (numpy.ndarray) – Array of y coordinates
Z (numpy.ndarray) – Array of z coordinates

Keyword Arguments

src_crs (osgeo.osr.SpatialReference) – defaults to EPSG(4326)
trg_crs (osgeo.osr.SpatialReference) – defaults to EPSG(4326)
area_of_interest (tuple) – tuple of floats (WestLongitudeDeg, SouthLatitudeDeg, EastLongitudeDeg, NorthLatitudeDeg), only gdal>=3

Returns

trans (numpy.ndarray) – Array of reprojected coordinates x,y (…,2) or x,y,z (…,3) depending on input array.
X, Y (numpy.ndarray) – Arrays of reprojected x,y coordinates, shape depending on input array
X, Y, Z (numpy.ndarray) – Arrays of reprojected x,y,z coordinates, shape depending on input array

Examples

wradlib.georef.reproject_raster_dataset(src_ds, **kwargs)[source]#

Reproject/Resample given dataset according to keyword arguments

Parameters

src_ds (osgeo.gdal.Dataset) – raster image with georeferencing (GeoTransform at least)

Keyword Arguments

spacing (float) – float or tuple of two floats pixel spacing of destination dataset, same unit as pixel coordinates
size (int) – tuple of two ints X/YRasterSize of destination dataset
resample (gdal:osgeo.gdalconst.ResampleAlg) – defaults to GRA_Bilinear GRA_NearestNeighbour = 0, GRA_Bilinear = 1, GRA_Cubic = 2, GRA_CubicSpline = 3, GRA_Lanczos = 4, GRA_Average = 5, GRA_Mode = 6, GRA_Max = 8, GRA_Min = 9, GRA_Med = 10, GRA_Q1 = 11, GRA_Q3 = 12
projection_source (osgeo.osr.SpatialReference) – source dataset projection, defaults to None (get projection from src_ds)
projection_target (osgeo.osr.SpatialReference) – destination dataset projection, defaults to None
align (bool or tuple) – If False, there is no destination grid aligment. If True, aligns the destination grid to the next integer multiple of destination grid. If tuple (upper-left x,y-coordinate), the destination grid is aligned to this point.

Returns

dst_ds (osgeo.gdal.Dataset) – reprojected/resampled raster dataset

wradlib.georef.set_coordinate_indexing(coords, *, indexing='xy')[source]#

Sets Coordinates Indexing Scheme

This converts coordinate layout from row-major to column major indexing.

Parameters

coords (numpy.ndarray) – Array of shape (…, M, N, 2) containing xy-coordinates.
indexing (str) – ‘xy’ or ‘ij’, indexing scheme in which to convert data and coordinates.

Returns

coords (numpy.ndarray) – Array of shape (…, N, M, 2) containing xy-coordinates.

wradlib.georef.set_raster_indexing(data, coords, *, indexing='xy')[source]#

Sets Data and Coordinates Indexing Scheme

This converts data and coordinate layout from row-major to column major indexing.

Parameters

data (numpy.ndarray) – Array of shape (…, M, N) containing the data values.
coords (numpy.ndarray) – Array of shape (…, M, N, 2) containing xy-coordinates.
indexing (str) – ‘xy’ or ‘ij’, indexing scheme in which to convert data and coordinates.

Returns

data (numpy.ndarray) – Array of shape (…, N, M) containing the data values.
coords (numpy.ndarray) – Array of shape (…, N, M, 2) containing xy-coordinates.

wradlib.georef.set_raster_origin(data, coords, direction)[source]#

Converts Data and Coordinates Origin

Parameters

data (numpy.ndarray) – Array of shape (rows, cols) or (bands, rows, cols) containing the data values.
coords (numpy.ndarray) – Array of shape (rows, cols, 2) containing xy-coordinates.
direction (str) – ‘lower’ or ‘upper’, direction in which to convert data and coordinates.

Returns

data (numpy.ndarray) – Array of shape (rows, cols) or (bands, rows, cols) containing the data values.
coords (numpy.ndarray) – Array of shape (rows, cols, 2) containing xy-coordinates.

wradlib.georef.site_distance(r, theta, binalt, *, re=6371000, ke=1.3333333333333333)[source]#

wradlib.georef.site_distance(obj: DataArray, **kwargs)

wradlib.georef.site_distance(obj: Dataset, **kwargs)

Calculates great circle distance from bin at certain altitude to the radar site over spherical earth, taking the refractivity of the atmosphere into account.

Based on [Doviak et al., 1993] the site distance may be calculated as

\[s = k_e r_e \arcsin\left( \frac{r \cos\theta}{k_e r_e + h_n(r, \theta, r_e, k_e)}\right)\]

where \(h_n\) would be provided by bin_altitude.

Parameters

r (numpy.ndarray) – Array of ranges [m]
theta (scalar or numpy.ndarray) – Array broadcastable to the shape of r elevation angles in degrees with 0° at horizontal and +90° pointing vertically upwards from the radar
binalt (numpy.ndarray) – site altitude [m] amsl. same shape as r.

Keyword Arguments

re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths

Returns

distance (numpy.ndarray) – Array of great circle arc distances [m]

wradlib.georef.spherical_to_centroids(r, phi, theta, site, *, crs=None)[source]#

wradlib.georef.spherical_to_centroids(obj: Dataset, **kwargs)

Generate 3-D centroids of the radar bins from the sperical coordinates (r, phi, theta).

For further information refer to the documentation of spherical_to_xyz.

Parameters

r (numpy.ndarray) – Array of ranges [m]; r defines the exterior boundaries of the range bins! (not the centroids). Thus, values must be positive!
phi (numpy.ndarray) – Array of azimuth angles containing values between 0° and 360°. The angles are assumed to describe the pointing direction fo the main beam lobe! The first angle can start at any values, but make sure the array is sorted continuously positively clockwise and the angles are equidistant. An angle if 0 degree is pointing north.
theta (float) – Elevation angle of scan
site (sequence) – the lon/lat/alt coordinates of the radar location
crs (osgeo.osr.SpatialReference) – Destination Projection

Returns

output (numpy.ndarray) – A 3-d array of bin centroids with shape(num_rays, num_bins, 3). The last dimension carries the xyz-coordinates either in aeqd or given crs.
aeqd (osgeo.osr.SpatialReference) – only returned if crs is None

Note

Azimuth angles of 360 deg are internally converted to 0 deg.

wradlib.georef.spherical_to_polyvert(r, phi, theta, site, *, crs=None)[source]#

wradlib.georef.spherical_to_polyvert(obj: DataArray, **kwargs)

wradlib.georef.spherical_to_polyvert(obj: Dataset, **kwargs)

Generate 3-D polygon vertices directly from spherical coordinates (r, phi, theta).

This is an alternative to centroid_to_polyvert which does not use centroids, but generates the polygon vertices by simply connecting the corners of the radar bins.

Both azimuth and range arrays are assumed to be equidistant and to contain only unique values. For further information refer to the documentation of spherical_to_xyz.

Parameters

r (numpy.ndarray) – Array of ranges [m]; r defines the exterior boundaries of the range bins! (not the centroids). Thus, values must be positive!
phi (numpy.ndarray) – Array of azimuth angles containing values between 0° and 360°. The angles are assumed to describe the pointing direction fo the main beam lobe! The first angle can start at any values, but make sure the array is sorted continuously positively clockwise and the angles are equidistant. An angle if 0 degree is pointing north.
theta (float) – Elevation angle of scan
site (sequence) – the lon/lat/alt coordinates of the radar location
crs (osgeo.osr.SpatialReference) – Destination Projection

Returns

output (numpy.ndarray) – A 3-d array of polygon vertices with shape(num_vertices, num_vertex_nodes, 2). The last dimension carries the xyz-coordinates either in aeqd or given crs.
aeqd (gdal:osgeo.aeqosr.SpatialReference) – only returned if crs is None

Examples

>>> import wradlib.georef as georef  # noqa
>>> import numpy as np
>>> from matplotlib import collections
>>> import matplotlib.pyplot as pl
>>> #pl.interactive(True)
>>> # define the polar coordinates and the site coordinates in lat/lon
>>> r = np.array([50., 100., 150., 200.]) * 1000
>>> # _check_polar_coords fails in next line
>>> # az = np.array([0., 45., 90., 135., 180., 225., 270., 315., 360.])
>>> az = np.array([0., 45., 90., 135., 180., 225., 270., 315.])
>>> el = 1.0
>>> site = (9.0, 48.0, 0)
>>> polygons, aeqd = georef.spherical_to_polyvert(r, az, el, site)
>>> # plot the resulting mesh
>>> fig = pl.figure()
>>> ax = fig.add_subplot(111)
>>> #polycoll = mpl.collections.PolyCollection(vertices,closed=True, facecolors=None)  # noqa
>>> polycoll = collections.PolyCollection(polygons[...,:2], closed=True, facecolors='None')  # noqa
>>> ret = ax.add_collection(polycoll, autolim=True)
>>> pl.autoscale()
>>> pl.show()

wradlib.georef.spherical_to_proj(r, phi, theta, site, *, crs=None, re=None, ke=1.3333333333333333)[source]#

wradlib.georef.spherical_to_proj(obj: DataArray, **kwargs)

wradlib.georef.spherical_to_proj(obj: Dataset, **kwargs)

Transforms spherical coordinates (r, phi, theta) to projected coordinates centered at site in given projection.

It takes the shortening of the great circle distance with increasing elevation angle as well as the resulting increase in height into account.

Parameters

r (numpy.ndarray) – Contains the radial distances.
phi (numpy.ndarray) – Contains the azimuthal angles.
theta (numpy.ndarray) – Contains the elevation angles.
site (sequence) – the lon / lat coordinates of the radar location and its altitude a.m.s.l. (in meters) if site is of length two, altitude is assumed to be zero

Keyword Arguments

crs (osgeo.osr.SpatialReference) – Destination Spatial Reference System (Projection). Defaults to wgs84 (epsg 4326).
re (float) – earth’s radius [m]
ke (float) – adjustment factor to account for the refractivity gradient that affects radar beam propagation. In principle this is wavelength- dependend. The default of 4/3 is a good approximation for most weather radar wavelengths.

Returns

coords (numpy.ndarray) – Array of shape (…, 3). Contains projected map coordinates.

Examples

A few standard directions (North, South, North, East, South, West) with different distances (amounting to roughly 1°) from a site located at 48°N 9°E

>>> r  = np.array([0.,   0., 111., 111., 111., 111.,])*1000
>>> az = np.array([0., 180.,   0.,  90., 180., 270.,])
>>> th = np.array([0.,   0.,   0.,   0.,   0.,  0.5,])
>>> csite = (9.0, 48.0, 0)
>>> coords = spherical_to_proj(r, az, th, csite)
>>> for coord in coords:
...     print( '{0:7.4f}, {1:7.4f}, {2:7.4f}'.format(*coord))
...
 9.0000, 48.0000,  0.0000
 9.0000, 48.0000,  0.0000
 9.0000, 48.9981, 725.7160
10.4872, 47.9904, 725.7160
 9.0000, 47.0017, 725.7160
 7.5131, 47.9904, 1694.2234

Here, the coordinates of the east and west directions won’t come to lie on the latitude of the site because the beam doesn’t travel along the latitude circle but along a great circle.