GDAL API
GDAL stands for Geospatial Data Abstraction Library, and is a veritable “Swiss army knife” of GIS data functionality. A subset of GDAL is the OGR Simple Features Library, which specializes in reading and writing vector geographic data in a variety of standard formats.
GeoDjango provides a high-level Python interface for some of the capabilities of OGR, including the reading and coordinate transformation of vector spatial data and minimal support for GDAL’s features with respect to raster (image) data.
Note
Although the module is named gdal
, GeoDjango only supports some of the capabilities of OGR and GDAL’s raster features at this time.
Overview
Sample Data
The GDAL/OGR tools described here are designed to help you read in your geospatial data, in order for most of them to be useful you have to have some data to work with. If you’re starting out and don’t yet have any data of your own to use, GeoDjango tests contain a number of simple data sets that you can use for testing. You can download them here:
$ wget https://raw.githubusercontent.com/django/django/master/tests/gis_tests/data/cities/cities.{shp,prj,shx,dbf} $ wget https://raw.githubusercontent.com/django/django/master/tests/gis_tests/data/rasters/raster.tif
Vector Data Source Objects
DataSource
DataSource
is a wrapper for the OGR data source object that supports reading data from a variety of OGR-supported geospatial file formats and data sources using a simple, consistent interface. Each data source is represented by a DataSource
object which contains one or more layers of data. Each layer, represented by a Layer
object, contains some number of geographic features (Feature
), information about the type of features contained in that layer (e.g. points, polygons, etc.), as well as the names and types of any additional fields (Field
) of data that may be associated with each feature in that layer.
-
class DataSource(ds_input, encoding='utf-8')
-
The constructor for
DataSource
only requires one parameter: the path of the file you want to read. However, OGR also supports a variety of more complex data sources, including databases, that may be accessed by passing a special name string instead of a path. For more information, see the OGR Vector Formats documentation. Thename
property of aDataSource
instance gives the OGR name of the underlying data source that it is using.The optional
encoding
parameter allows you to specify a non-standard encoding of the strings in the source. This is typically useful when you obtainDjangoUnicodeDecodeError
exceptions while reading field values.Once you’ve created your
DataSource
, you can find out how many layers of data it contains by accessing thelayer_count
property, or (equivalently) by using thelen()
function. For information on accessing the layers of data themselves, see the next section:>>> from django.contrib.gis.gdal import DataSource >>> ds = DataSource('/path/to/your/cities.shp') >>> ds.name '/path/to/your/cities.shp' >>> ds.layer_count # This file only contains one layer 1
-
layer_count
Returns the number of layers in the data source.
-
name
Returns the name of the data source.
-
Layer
-
class Layer
-
Layer
is a wrapper for a layer of data in aDataSource
object. You never create aLayer
object directly. Instead, you retrieve them from aDataSource
object, which is essentially a standard Python container ofLayer
objects. For example, you can access a specific layer by its index (e.g.ds[0]
to access the first layer), or you can iterate over all the layers in the container in afor
loop. TheLayer
itself acts as a container for geometric features.Typically, all the features in a given layer have the same geometry type. The
geom_type
property of a layer is anOGRGeomType
that identifies the feature type. We can use it to print out some basic information about each layer in aDataSource
:>>> for layer in ds: ... print('Layer "%s": %i %ss' % (layer.name, len(layer), layer.geom_type.name)) ... Layer "cities": 3 Points
The example output is from the cities data source, loaded above, which evidently contains one layer, called
"cities"
, which contains three point features. For simplicity, the examples below assume that you’ve stored that layer in the variablelayer
:>>> layer = ds[0]
-
name
Returns the name of this layer in the data source.
>>> layer.name 'cities'
-
num_feat
Returns the number of features in the layer. Same as
len(layer)
:>>> layer.num_feat 3
-
geom_type
Returns the geometry type of the layer, as an
OGRGeomType
object:>>> layer.geom_type.name 'Point'
-
num_fields
Returns the number of fields in the layer, i.e the number of fields of data associated with each feature in the layer:
>>> layer.num_fields 4
-
fields
Returns a list of the names of each of the fields in this layer:
>>> layer.fields ['Name', 'Population', 'Density', 'Created']
Returns a list of the data types of each of the fields in this layer. These are subclasses of
Field
, discussed below:>>> [ft.__name__ for ft in layer.field_types] ['OFTString', 'OFTReal', 'OFTReal', 'OFTDate']
-
field_widths
Returns a list of the maximum field widths for each of the fields in this layer:
>>> layer.field_widths [80, 11, 24, 10]
-
field_precisions
Returns a list of the numeric precisions for each of the fields in this layer. This is meaningless (and set to zero) for non-numeric fields:
>>> layer.field_precisions [0, 0, 15, 0]
-
extent
Returns the spatial extent of this layer, as an
Envelope
object:>>> layer.extent.tuple (-104.609252, 29.763374, -95.23506, 38.971823)
-
srs
Property that returns the
SpatialReference
associated with this layer:>>> print(layer.srs) GEOGCS["GCS_WGS_1984", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]]
If the
Layer
has no spatial reference information associated with it,None
is returned.-
spatial_filter
Property that may be used to retrieve or set a spatial filter for this layer. A spatial filter can only be set with an
OGRGeometry
instance, a 4-tuple extent, orNone
. When set with something other thanNone
, only features that intersect the filter will be returned when iterating over the layer:>>> print(layer.spatial_filter) None >>> print(len(layer)) 3 >>> [feat.get('Name') for feat in layer] ['Pueblo', 'Lawrence', 'Houston'] >>> ks_extent = (-102.051, 36.99, -94.59, 40.00) # Extent for state of Kansas >>> layer.spatial_filter = ks_extent >>> len(layer) 1 >>> [feat.get('Name') for feat in layer] ['Lawrence'] >>> layer.spatial_filter = None >>> len(layer) 3
-
get_fields()
A method that returns a list of the values of a given field for each feature in the layer:
>>> layer.get_fields('Name') ['Pueblo', 'Lawrence', 'Houston']
-
get_geoms(geos=False)
A method that returns a list containing the geometry of each feature in the layer. If the optional argument
geos
is set toTrue
then the geometries are converted toGEOSGeometry
objects. Otherwise, they are returned asOGRGeometry
objects:>>> [pt.tuple for pt in layer.get_geoms()] [(-104.609252, 38.255001), (-95.23506, 38.971823), (-95.363151, 29.763374)]
-
test_capability(capability)
Returns a boolean indicating whether this layer supports the given capability (a string). Examples of valid capability strings include:
'RandomRead'
,'SequentialWrite'
,'RandomWrite'
,'FastSpatialFilter'
,'FastFeatureCount'
,'FastGetExtent'
,'CreateField'
,'Transactions'
,'DeleteFeature'
, and'FastSetNextByIndex'
. -
Feature
-
class Feature
-
Feature
wraps an OGR feature. You never create aFeature
object directly. Instead, you retrieve them from aLayer
object. Each feature consists of a geometry and a set of fields containing additional properties. The geometry of a field is accessible via itsgeom
property, which returns anOGRGeometry
object. AFeature
behaves like a standard Python container for its fields, which it returns asField
objects: you can access a field directly by its index or name, or you can iterate over a feature’s fields, e.g. in afor
loop.-
geom
Returns the geometry for this feature, as an
OGRGeometry
object:>>> city.geom.tuple (-104.609252, 38.255001)
-
get
A method that returns the value of the given field (specified by name) for this feature, not a
Field
wrapper object:>>> city.get('Population') 102121
-
geom_type
Returns the type of geometry for this feature, as an
OGRGeomType
object. This will be the same for all features in a given layer, and is equivalent to theLayer.geom_type
property of theLayer
object the feature came from.-
num_fields
Returns the number of fields of data associated with the feature. This will be the same for all features in a given layer, and is equivalent to the
Layer.num_fields
property of theLayer
object the feature came from.-
fields
Returns a list of the names of the fields of data associated with the feature. This will be the same for all features in a given layer, and is equivalent to the
Layer.fields
property of theLayer
object the feature came from.-
fid
Returns the feature identifier within the layer:
>>> city.fid 0
-
layer_name
Returns the name of the
Layer
that the feature came from. This will be the same for all features in a given layer:>>> city.layer_name 'cities'
-
index
A method that returns the index of the given field name. This will be the same for all features in a given layer:
>>> city.index('Population') 1
-
Field
-
class Field
-
-
name
Returns the name of this field:
>>> city['Name'].name 'Name'
-
type
Returns the OGR type of this field, as an integer. The
FIELD_CLASSES
dictionary maps these values onto subclasses ofField
:>>> city['Density'].type 2
-
type_name
Returns a string with the name of the data type of this field:
>>> city['Name'].type_name 'String'
-
value
Returns the value of this field. The
Field
class itself returns the value as a string, but each subclass returns the value in the most appropriate form:>>> city['Population'].value 102121
-
width
Returns the width of this field:
>>> city['Name'].width 80
-
precision
Returns the numeric precision of this field. This is meaningless (and set to zero) for non-numeric fields:
>>> city['Density'].precision 15
-
as_double()
Returns the value of the field as a double (float):
>>> city['Density'].as_double() 874.7
-
as_int()
Returns the value of the field as an integer:
>>> city['Population'].as_int() 102121
-
as_string()
Returns the value of the field as a string:
>>> city['Name'].as_string() 'Pueblo'
-
as_datetime()
Returns the value of the field as a tuple of date and time components:
>>> city['Created'].as_datetime() (c_long(1999), c_long(5), c_long(23), c_long(0), c_long(0), c_long(0), c_long(0))
-
Driver
-
class Driver(dr_input)
-
The
Driver
class is used internally to wrap an OGRDataSource
driver.-
driver_count
Returns the number of OGR vector drivers currently registered.
-
OGR Geometries
OGRGeometry
OGRGeometry
objects share similar functionality with GEOSGeometry
objects, and are thin wrappers around OGR’s internal geometry representation. Thus, they allow for more efficient access to data when using DataSource
. Unlike its GEOS counterpart, OGRGeometry
supports spatial reference systems and coordinate transformation:
>>> from django.contrib.gis.gdal import OGRGeometry >>> polygon = OGRGeometry('POLYGON((0 0, 5 0, 5 5, 0 5))')
-
class OGRGeometry(geom_input, srs=None)
-
This object is a wrapper for the OGR Geometry class. These objects are instantiated directly from the given
geom_input
parameter, which may be a string containing WKT, HEX, GeoJSON, abuffer
containing WKB data, or anOGRGeomType
object. These objects are also returned from theFeature.geom
attribute, when reading vector data fromLayer
(which is in turn a part of aDataSource
).-
classmethod from_bbox(bbox)
Constructs a
Polygon
from the given bounding-box (a 4-tuple).-
__len__()
Returns the number of points in a
LineString
, the number of rings in aPolygon
, or the number of geometries in aGeometryCollection
. Not applicable to other geometry types.-
__iter__()
Iterates over the points in a
LineString
, the rings in aPolygon
, or the geometries in aGeometryCollection
. Not applicable to other geometry types.-
__getitem__()
Returns the point at the specified index for a
LineString
, the interior ring at the specified index for aPolygon
, or the geometry at the specified index in aGeometryCollection
. Not applicable to other geometry types.-
dimension
Returns the number of coordinated dimensions of the geometry, i.e. 0 for points, 1 for lines, and so forth:
>> polygon.dimension 2
-
coord_dim
Returns or sets the coordinate dimension of this geometry. For example, the value would be 2 for two-dimensional geometries.
-
geom_count
Returns the number of elements in this geometry:
>>> polygon.geom_count 1
-
point_count
Returns the number of points used to describe this geometry:
>>> polygon.point_count 4
-
num_points
Alias for
point_count
.-
num_coords
Alias for
point_count
.-
geom_type
Returns the type of this geometry, as an
OGRGeomType
object.-
geom_name
Returns the name of the type of this geometry:
>>> polygon.geom_name 'POLYGON'
-
area
Returns the area of this geometry, or 0 for geometries that do not contain an area:
>>> polygon.area 25.0
-
envelope
Returns the envelope of this geometry, as an
Envelope
object.-
extent
Returns the envelope of this geometry as a 4-tuple, instead of as an
Envelope
object:>>> point.extent (0.0, 0.0, 5.0, 5.0)
-
srs
This property controls the spatial reference for this geometry, or
None
if no spatial reference system has been assigned to it. If assigned, accessing this property returns aSpatialReference
object. It may be set with anotherSpatialReference
object, or any input thatSpatialReference
accepts. Example:>>> city.geom.srs.name 'GCS_WGS_1984'
-
srid
Returns or sets the spatial reference identifier corresponding to
SpatialReference
of this geometry. ReturnsNone
if there is no spatial reference information associated with this geometry, or if an SRID cannot be determined.-
geos
Returns a
GEOSGeometry
object corresponding to this geometry.-
gml
Returns a string representation of this geometry in GML format:
>>> OGRGeometry('POINT(1 2)').gml '<gml:Point><gml:coordinates>1,2</gml:coordinates></gml:Point>'
-
hex
Returns a string representation of this geometry in HEX WKB format:
>>> OGRGeometry('POINT(1 2)').hex '0101000000000000000000F03F0000000000000040'
-
json
Returns a string representation of this geometry in JSON format:
>>> OGRGeometry('POINT(1 2)').json '{ "type": "Point", "coordinates": [ 1.000000, 2.000000 ] }'
-
kml
Returns a string representation of this geometry in KML format.
-
wkb_size
Returns the size of the WKB buffer needed to hold a WKB representation of this geometry:
>>> OGRGeometry('POINT(1 2)').wkb_size 21
-
wkb
Returns a
buffer
containing a WKB representation of this geometry.-
wkt
Returns a string representation of this geometry in WKT format.
-
ewkt
Returns the EWKT representation of this geometry.
-
clone()
Returns a new
OGRGeometry
clone of this geometry object.-
close_rings()
If there are any rings within this geometry that have not been closed, this routine will do so by adding the starting point to the end:
>>> triangle = OGRGeometry('LINEARRING (0 0,0 1,1 0)') >>> triangle.close_rings() >>> triangle.wkt 'LINEARRING (0 0,0 1,1 0,0 0)'
-
transform(coord_trans, clone=False)
Transforms this geometry to a different spatial reference system. May take a
CoordTransform
object, aSpatialReference
object, or any other input accepted bySpatialReference
(including spatial reference WKT and PROJ.4 strings, or an integer SRID). By default nothing is returned and the geometry is transformed in-place. However, if theclone
keyword is set toTrue
then a transformed clone of this geometry is returned instead.-
intersects(other)
Returns
True
if this geometry intersects the other, otherwise returnsFalse
.-
equals(other)
Returns
True
if this geometry is equivalent to the other, otherwise returnsFalse
.-
disjoint(other)
Returns
True
if this geometry is spatially disjoint to (i.e. does not intersect) the other, otherwise returnsFalse
.-
touches(other)
Returns
True
if this geometry touches the other, otherwise returnsFalse
.-
crosses(other)
Returns
True
if this geometry crosses the other, otherwise returnsFalse
.-
within(other)
Returns
True
if this geometry is contained within the other, otherwise returnsFalse
.-
contains(other)
Returns
True
if this geometry contains the other, otherwise returnsFalse
.-
overlaps(other)
Returns
True
if this geometry overlaps the other, otherwise returnsFalse
.-
boundary()
The boundary of this geometry, as a new
OGRGeometry
object.-
convex_hull
The smallest convex polygon that contains this geometry, as a new
OGRGeometry
object.-
difference()
Returns the region consisting of the difference of this geometry and the other, as a new
OGRGeometry
object.-
intersection()
Returns the region consisting of the intersection of this geometry and the other, as a new
OGRGeometry
object.-
sym_difference()
Returns the region consisting of the symmetric difference of this geometry and the other, as a new
OGRGeometry
object.-
union()
Returns the region consisting of the union of this geometry and the other, as a new
OGRGeometry
object.-
tuple
Returns the coordinates of a point geometry as a tuple, the coordinates of a line geometry as a tuple of tuples, and so forth:
>>> OGRGeometry('POINT (1 2)').tuple (1.0, 2.0) >>> OGRGeometry('LINESTRING (1 2,3 4)').tuple ((1.0, 2.0), (3.0, 4.0))
-
coords
An alias for
tuple
. -
-
class Point
-
-
x
Returns the X coordinate of this point:
>>> OGRGeometry('POINT (1 2)').x 1.0
-
y
Returns the Y coordinate of this point:
>>> OGRGeometry('POINT (1 2)').y 2.0
-
z
Returns the Z coordinate of this point, or
None
if the point does not have a Z coordinate:>>> OGRGeometry('POINT (1 2 3)').z 3.0
-
-
class LineString
-
-
x
Returns a list of X coordinates in this line:
>>> OGRGeometry('LINESTRING (1 2,3 4)').x [1.0, 3.0]
-
y
Returns a list of Y coordinates in this line:
>>> OGRGeometry('LINESTRING (1 2,3 4)').y [2.0, 4.0]
-
z
Returns a list of Z coordinates in this line, or
None
if the line does not have Z coordinates:>>> OGRGeometry('LINESTRING (1 2 3,4 5 6)').z [3.0, 6.0]
-
-
class Polygon
-
-
shell
Returns the shell or exterior ring of this polygon, as a
LinearRing
geometry.-
exterior_ring
An alias for
shell
.-
centroid
Returns a
Point
representing the centroid of this polygon. -
-
class GeometryCollection
-
-
add(geom)
Adds a geometry to this geometry collection. Not applicable to other geometry types.
-
OGRGeomType
-
class OGRGeomType(type_input)
[source] -
This class allows for the representation of an OGR geometry type in any of several ways:
>>> from django.contrib.gis.gdal import OGRGeomType >>> gt1 = OGRGeomType(3) # Using an integer for the type >>> gt2 = OGRGeomType('Polygon') # Using a string >>> gt3 = OGRGeomType('POLYGON') # It's case-insensitive >>> print(gt1 == 3, gt1 == 'Polygon') # Equivalence works w/non-OGRGeomType objects True True
-
name
Returns a short-hand string form of the OGR Geometry type:
>>> gt1.name 'Polygon'
-
num
Returns the number corresponding to the OGR geometry type:
>>> gt1.num 3
-
django
Returns the Django field type (a subclass of GeometryField) to use for storing this OGR type, or
None
if there is no appropriate Django type:>>> gt1.django 'PolygonField'
-
Envelope
-
class Envelope(*args)
[source] -
Represents an OGR Envelope structure that contains the minimum and maximum X, Y coordinates for a rectangle bounding box. The naming of the variables is compatible with the OGR Envelope C structure.
-
min_x
The value of the minimum X coordinate.
-
min_y
The value of the maximum X coordinate.
-
max_x
The value of the minimum Y coordinate.
-
max_y
The value of the maximum Y coordinate.
-
ur
The upper-right coordinate, as a tuple.
-
ll
The lower-left coordinate, as a tuple.
-
tuple
A tuple representing the envelope.
-
wkt
A string representing this envelope as a polygon in WKT format.
-
expand_to_include(*args)
[source]
-
Coordinate System Objects
SpatialReference
-
class SpatialReference(srs_input)
-
Spatial reference objects are initialized on the given
srs_input
, which may be one of the following:- OGC Well Known Text (WKT) (a string)
- EPSG code (integer or string)
- PROJ.4 string
- A shorthand string for well-known standards (
'WGS84'
,'WGS72'
,'NAD27'
,'NAD83'
)
Example:
>>> wgs84 = SpatialReference('WGS84') # shorthand string >>> wgs84 = SpatialReference(4326) # EPSG code >>> wgs84 = SpatialReference('EPSG:4326') # EPSG string >>> proj4 = '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs ' >>> wgs84 = SpatialReference(proj4) # PROJ.4 string >>> wgs84 = SpatialReference("""GEOGCS["WGS 84", DATUM["WGS_1984", SPHEROID["WGS 84",6378137,298.257223563, AUTHORITY["EPSG","7030"]], AUTHORITY["EPSG","6326"]], PRIMEM["Greenwich",0, AUTHORITY["EPSG","8901"]], UNIT["degree",0.01745329251994328, AUTHORITY["EPSG","9122"]], AUTHORITY["EPSG","4326"]]""") # OGC WKT
-
__getitem__(target)
Returns the value of the given string attribute node,
None
if the node doesn’t exist. Can also take a tuple as a parameter, (target, child), where child is the index of the attribute in the WKT. For example:>>> wkt = 'GEOGCS["WGS 84", DATUM["WGS_1984, ... AUTHORITY["EPSG","4326"]]') >>> srs = SpatialReference(wkt) # could also use 'WGS84', or 4326 >>> print(srs['GEOGCS']) WGS 84 >>> print(srs['DATUM']) WGS_1984 >>> print(srs['AUTHORITY']) EPSG >>> print(srs['AUTHORITY', 1]) # The authority value 4326 >>> print(srs['TOWGS84', 4]) # the fourth value in this wkt 0 >>> print(srs['UNIT|AUTHORITY']) # For the units authority, have to use the pipe symbol. EPSG >>> print(srs['UNIT|AUTHORITY', 1]) # The authority value for the units 9122
-
attr_value(target, index=0)
The attribute value for the given target node (e.g.
'PROJCS'
). The index keyword specifies an index of the child node to return.-
auth_name(target)
Returns the authority name for the given string target node.
-
auth_code(target)
Returns the authority code for the given string target node.
-
clone()
Returns a clone of this spatial reference object.
-
identify_epsg()
This method inspects the WKT of this SpatialReference, and will add EPSG authority nodes where an EPSG identifier is applicable.
-
from_esri()
Morphs this SpatialReference from ESRI’s format to EPSG
-
to_esri()
Morphs this SpatialReference to ESRI’s format.
-
validate()
Checks to see if the given spatial reference is valid, if not an exception will be raised.
-
import_epsg(epsg)
Import spatial reference from EPSG code.
-
import_proj(proj)
Import spatial reference from PROJ.4 string.
-
import_user_input(user_input)
-
import_wkt(wkt)
Import spatial reference from WKT.
-
import_xml(xml)
Import spatial reference from XML.
-
name
Returns the name of this Spatial Reference.
-
srid
Returns the SRID of top-level authority, or
None
if undefined.-
linear_name
Returns the name of the linear units.
-
linear_units
Returns the value of the linear units.
-
angular_name
Returns the name of the angular units.”
-
angular_units
Returns the value of the angular units.
-
units
Returns a 2-tuple of the units value and the units name, and will automatically determines whether to return the linear or angular units.
-
ellipsoid
Returns a tuple of the ellipsoid parameters for this spatial reference: (semimajor axis, semiminor axis, and inverse flattening)
-
semi_major
Returns the semi major axis of the ellipsoid for this spatial reference.
-
semi_minor
Returns the semi minor axis of the ellipsoid for this spatial reference.
-
inverse_flattening
Returns the inverse flattening of the ellipsoid for this spatial reference.
-
geographic
Returns
True
if this spatial reference is geographic (root node isGEOGCS
).-
local
Returns
True
if this spatial reference is local (root node isLOCAL_CS
).-
projected
Returns
True
if this spatial reference is a projected coordinate system (root node isPROJCS
).-
wkt
Returns the WKT representation of this spatial reference.
-
pretty_wkt
Returns the ‘pretty’ representation of the WKT.
-
proj
Returns the PROJ.4 representation for this spatial reference.
-
proj4
Alias for
SpatialReference.proj
.-
xml
Returns the XML representation of this spatial reference.
CoordTransform
-
class CoordTransform(source, target)
Represents a coordinate system transform. It is initialized with two SpatialReference
, representing the source and target coordinate systems, respectively. These objects should be used when performing the same coordinate transformation repeatedly on different geometries:
>>> ct = CoordTransform(SpatialReference('WGS84'), SpatialReference('NAD83')) >>> for feat in layer: ... geom = feat.geom # getting clone of feature geometry ... geom.transform(ct) # transforming
Raster Data Objects
GDALRaster
GDALRaster
is a wrapper for the GDAL raster source object that supports reading data from a variety of GDAL-supported geospatial file formats and data sources using a simple, consistent interface. Each data source is represented by a GDALRaster
object which contains one or more layers of data named bands. Each band, represented by a GDALBand
object, contains georeferenced image data. For example, an RGB image is represented as three bands: one for red, one for green, and one for blue.
Note
For raster data there is no difference between a raster instance and its data source. Unlike for the Geometry objects, GDALRaster
objects are always a data source. Temporary rasters can be instantiated in memory using the corresponding driver, but they will be of the same class as file-based raster sources.
-
class GDALRaster(ds_input, write=False)
-
The constructor for
GDALRaster
accepts two parameters. The first parameter defines the raster source, it is either a path to a file or spatial data with values defining the properties of a new raster (such as size and name). If the input is a file path, the second parameter specifies if the raster should be opened with write access. If the input is raw data, the parameterswidth
,height
, andsrid
are required. The following example shows how rasters can be created from different input sources (using the sample data from the GeoDjango tests, see also the Sample Data section):>>> from django.contrib.gis.gdal import GDALRaster >>> rst = GDALRaster('/path/to/your/raster.tif', write=False) >>> rst.name '/path/to/your/raster.tif' >>> rst.width, rst.height # This file has 163 x 174 pixels (163, 174) >>> rst = GDALRaster({'srid': 4326, 'width': 1, 'height': 2, 'datatype': 1 ... 'bands': [{'data': [0, 1]}]}) # Creates in-memory raster >>> rst.srs.srid 4326 >>> rst.width, rst.height (1, 2) >>> rst.bands[0].data() array([[0, 1]], dtype=int8)
GDALRaster
objects can now be instantiated directly from raw data. Setters have been added for the following properties:srs
,geotransform
,origin
,scale
, andskew
.-
name
-
The name of the source which is equivalent to the input file path or the name provided upon instantiation.
>>> GDALRaster({'width': 10, 'height': 10, 'name': 'myraster', 'srid': 4326}).name 'myraster'
-
driver
-
The name of the GDAL driver used to handle the input file. For
GDALRaster
s created from a file, the driver type is detected automatically. The creation of rasters from scratch is a in-memory raster by default ('MEM'
), but can be altered as needed. For instance, useGTiff
for aGeoTiff
file. For a list of file types, see also the GDAL Raster Formats list.An in-memory raster is created through the following example:
>>> GDALRaster({'width': 10, 'height': 10, 'srid': 4326}).driver.name 'MEM'
A file based GeoTiff raster is created through the following example:
>>> import tempfile >>> rstfile = tempfile.NamedTemporaryFile(suffix='.tif') >>> rst = GDALRaster({'driver': 'GTiff', 'name': rstfile.name, 'srid': 4326, ... 'width': 255, 'height': 255, 'nr_of_bands': 1}) >>> rst.name '/tmp/tmp7x9H4J.tif' # The exact filename will be different on your computer >>> rst.driver.name 'GTiff'
-
width
-
The width of the source in pixels (X-axis).
>>> GDALRaster({'width': 10, 'height': 20, 'srid': 4326}).width 10
-
height
-
The height of the source in pixels (Y-axis).
>>> GDALRaster({'width': 10, 'height': 20, 'srid': 4326}).height 20
-
srs
-
The spatial reference system of the raster, as a
SpatialReference
instance. The SRS can be changed by setting it to an otherSpatialReference
or providing any input that is accepted by theSpatialReference
constructor.>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326}) >>> rst.srs.srid 4326 >>> rst.srs = 3086 >>> rst.srs.srid 3086
-
geotransform
-
The affine transformation matrix used to georeference the source, as a tuple of six coefficients which map pixel/line coordinates into georeferenced space using the following relationship:
Xgeo = GT(0) + Xpixel*GT(1) + Yline*GT(2) Ygeo = GT(3) + Xpixel*GT(4) + Yline*GT(5)
The same values can be retrieved by accessing the
origin
(indices 0 and 3),scale
(indices 1 and 5) andskew
(indices 2 and 4) properties.The default is
[0.0, 1.0, 0.0, 0.0, 0.0, -1.0]
.>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326}) >>> rst.geotransform [0.0, 1.0, 0.0, 0.0, 0.0, -1.0]
-
origin
-
Coordinates of the top left origin of the raster in the spatial reference system of the source, as a point object with
x
andy
members.>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326}) >>> rst.origin [0.0, 0.0] >>> rst.origin.x = 1 >>> rst.origin [1.0, 0.0]
-
scale
-
Pixel width and height used for georeferencing the raster, as a as a point object with
x
andy
members. Seegeotransform
for more information.>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326}) >>> rst.scale [1.0, -1.0] >>> rst.scale.x = 2 >>> rst.scale [2.0, -1.0]
-
skew
-
Skew coefficients used to georeference the raster, as a point object with
x
andy
members. In case of north up images, these coefficients are both0
.>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326}) >>> rst.skew [0.0, 0.0] >>> rst.skew.x = 3 >>> rst.skew [3.0, 0.0]
-
extent
-
Extent (boundary values) of the raster source, as a 4-tuple
(xmin, ymin, xmax, ymax)
in the spatial reference system of the source.>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326}) >>> rst.extent (0.0, -20.0, 10.0, 0.0) >>> rst.origin.x = 100 >>> rst.extent (100.0, -20.0, 110.0, 0.0)
-
bands
-
List of all bands of the source, as
GDALBand
instances.>>> rst = GDALRaster({"width": 1, "height": 2, 'srid': 4326, ... "bands": [{"data": [0, 1]}, {"data": [2, 3]}]}) >>> len(rst.bands) 2 >>> rst.bands[1].data() array([[ 2., 3.]], dtype=float32)
-
warp(ds_input, resampling='NearestNeighbour', max_error=0.0)
-
Returns a warped version of this raster.
The warping parameters can be specified through the
ds_input
argument. The use ofds_input
is analogous to the corresponding argument of the class constructor. It is a dictionary with the characteristics of the target raster. Allowed dictionary key values are width, height, SRID, origin, scale, skew, datatype, driver, and name (filename).By default, the warp functions keeps most parameters equal to the values of the original source raster, so only parameters that should be changed need to be specified. Note that this includes the driver, so for file-based rasters the warp function will create a new raster on disk.
The only parameter that is set differently from the source raster is the name. The default value of the the raster name is the name of the source raster appended with
'_copy' + source_driver_name
. For file-based rasters it is recommended to provide the file path of the target raster.The resampling algorithm used for warping can be specified with the
resampling
argument. The default isNearestNeighbor
, and the other allowed values areBilinear
,Cubic
,CubicSpline
,Lanczos
,Average
, andMode
.The
max_error
argument can be used to specify the maximum error measured in input pixels that is allowed in approximating the transformation. The default is 0.0 for exact calculations.For users familiar with
GDAL
, this function has a similar functionality to thegdalwarp
command-line utility.For example, the warp function can be used for aggregating a raster to the double of its original pixel scale:
>>> rst = GDALRaster({ ... "width": 6, "height": 6, "srid": 3086, ... "origin": [500000, 400000], ... "scale": [100, -100], ... "bands": [{"data": range(36), "nodata_value": 99}] ... }) >>> target = rst.warp({"scale": [200, -200], "width": 3, "height": 3}) >>> target.bands[0].data() array([[ 7., 9., 11.], [ 19., 21., 23.], [ 31., 33., 35.]], dtype=float32)
-
transform(srid, driver=None, name=None, resampling='NearestNeighbour', max_error=0.0)
-
Returns a transformed version of this raster with the specified SRID.
This function transforms the current raster into a new spatial reference system that can be specified with an
srid
. It calculates the bounds and scale of the current raster in the new spatial reference system and warps the raster using thewarp
function.By default, the driver of the source raster is used and the name of the raster is the original name appended with
'_copy' + source_driver_name
. A different driver or name can be specified with thedriver
andname
arguments.The default resampling algorithm is
NearestNeighbour
but can be changed using theresampling
argument. The default maximum allowed error for resampling is 0.0 and can be changed using themax_error
argument. Consult thewarp
documentation for detail on those arguments.>>> rst = GDALRaster({ ... "width": 6, "height": 6, "srid": 3086, ... "origin": [500000, 400000], ... "scale": [100, -100], ... "bands": [{"data": range(36), "nodata_value": 99}] ... }) >>> target = rst.transform(4326) >>> target.origin [-82.98492744885776, 27.601924753080144]
-
GDALBand
-
class GDALBand
-
GDALBand
instances are not created explicitly, but rather obtained from aGDALRaster
object, through itsbands
attribute. The GDALBands contain the actual pixel values of the raster.-
description
-
The name or description of the band, if any.
-
width
-
The width of the band in pixels (X-axis).
-
height
-
The height of the band in pixels (Y-axis).
-
pixel_count
-
The total number of pixels in this band. Is equal to
width * height
.
-
min
-
The minimum pixel value of the band (excluding the “no data” value).
-
max
-
The maximum pixel value of the band (excluding the “no data” value).
-
nodata_value
-
The “no data” value for a band is generally a special marker value used to mark pixels that are not valid data. Such pixels should generally not be displayed, nor contribute to analysis operations.
This property can now be set as well.
-
datatype(as_string=False)
-
The data type contained in the band, as an integer constant between 0 (Unknown) and 11. If
as_string
isTrue
, the data type is returned as a string with the following possible values:GDT_Unknown
,GDT_Byte
,GDT_UInt16
,GDT_Int16
,GDT_UInt32
,GDT_Int32
,GDT_Float32
,GDT_Float64
,GDT_CInt16
,GDT_CInt32
,GDT_CFloat32
, andGDT_CFloat64
.
-
data(data=None, offset=None, size=None)
-
The accessor to the pixel values of the
GDALBand
. Returns the complete data array if no parameters are provided. A subset of the pixel array can be requested by specifying an offset and block size as tuples.If NumPy is available, the data is returned as NumPy array. For performance reasons, it is highly recommended to use NumPy.
Data is written to the
GDALBand
if thedata
parameter is provided. The input can be of one of the following types - packed string, buffer, list, array, and NumPy array. The number of items in the input must correspond to the total number of pixels in the band, or to the number of pixels for a specific block of pixel values if theoffset
andsize
parameters are provided.For example:
>>> rst = GDALRaster({'width': 4, 'height': 4, 'srid': 4326, 'datatype': 1, 'nr_of_bands': 1}) >>> bnd = rst.bands[0] >>> bnd.data(range(16)) >>> bnd.data() array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]], dtype=int8) >>> bnd.data(offset=(1, 1), size=(2, 2)) array([[ 5, 6], [ 9, 10]], dtype=int8) >>> bnd.data(data=[-1, -2, -3, -4], offset=(1, 1), size=(2, 2)) >>> bnd.data() array([[ 0, 1, 2, 3], [ 4, -1, -2, 7], [ 8, -3, -4, 11], [12, 13, 14, 15]], dtype=int8) >>> bnd.data(data='\x9d\xa8\xb3\xbe', offset=(1, 1), size=(2, 2)) >>> bnd.data() array([[ 0, 1, 2, 3], [ 4, -99, -88, 7], [ 8, -77, -66, 11], [ 12, 13, 14, 15]], dtype=int8)
-
Settings
GDAL_LIBRARY_PATH
A string specifying the location of the GDAL library. Typically, this setting is only used if the GDAL library is in a non-standard location (e.g., /home/john/lib/libgdal.so
).
© Django Software Foundation and individual contributors
Licensed under the BSD License.
https://docs.djangoproject.com/en/1.9/ref/contrib/gis/gdal/