# Django 5.2.17 documentation

# 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 data sets that you can use for testing. You can download them here:

    $ wget https://raw.githubusercontent.com/django/django/main/tests/gis_tests/data/cities/cities.{shp,prj,shx,dbf}
    $ wget https://raw.githubusercontent.com/django/django/main/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 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. The `name` property of a `DataSource` 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 obtain `DjangoUnicodeDecodeError` exceptions while reading field values.

  Once you’ve created your `DataSource`, you can find out how many layers of data it contains by accessing the `layer_count` property, or (equivalently) by using the `len()` 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 a `DataSource` object. You never create a `Layer` object directly. Instead, you retrieve them from a `DataSource` object, which is essentially a standard Python container of `Layer` 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 a `for` loop. The `Layer` 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 an `OGRGeomType` that identifies the feature type. We can use it to print out some basic information about each layer in a `DataSource`:

      >>> 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 variable `layer`:

      >>> 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, or `None`. When set with something other than `None`, 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 to `True` then the geometries are converted to `GEOSGeometry` objects. Otherwise, they are returned as `OGRGeometry` 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 a `Feature` object directly. Instead, you retrieve them from a `Layer` object. Each feature consists of a geometry and a set of fields containing additional properties. The geometry of a field is accessible via its `geom` property, which returns an `OGRGeometry` object. A `Feature` behaves like a standard Python container for its fields, which it returns as `Field` objects: you can access a field directly by its index or name, or you can iterate over a feature’s fields, e.g. in a `for` 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 the `Layer.geom_type` property of the `Layer` 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 the `Layer` 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 the `Layer` 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 of `Field`:

      >>> 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 OGR `DataSource` 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, a `buffer` containing WKB data, or an `OGRGeomType` object. These objects are also returned from the `Feature.geom` attribute, when reading vector data from `Layer` (which is in turn a part of a `DataSource`).

  classmethod from_gml(gml_string)

  Constructs an `OGRGeometry` from the given GML string.

  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 a `Polygon`, or the number of geometries in a `GeometryCollection`. Not applicable to other geometry types.

  __iter__()

  Iterates over the points in a `LineString`, the rings in a `Polygon`, or the geometries in a `GeometryCollection`. 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 a `Polygon`, or the geometry at the specified index in a `GeometryCollection`. 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 the coordinate dimension of this geometry. For example, the value would be 2 for two-dimensional geometries.

  Deprecated since version 5.1: The `coord_dim` setter is deprecated. Use `set_3d()` instead.

  is_3d
  New in Django 5.1.

  A boolean indicating if this geometry has Z coordinates.

  set_3d(value)
  New in Django 5.1.

  A method that adds or removes the Z coordinate dimension.

      >>> p = OGRGeometry("POINT (1 2 3)")
      >>> p.is_3d
      True
      >>> p.set_3d(False)
      >>> p.wkt
      "POINT (1 2)"

  is_measured
  New in Django 5.1.

  A boolean indicating if this geometry has M coordinates.

  set_measured(value)
  New in Django 5.1.

  A method to add or remove the M coordinate dimension.

      >>> p = OGRGeometry("POINT (1 2)")
      >>> p.is_measured
      False
      >>> p.set_measured(True)
      >>> p.wkt
      "POINT M (1 2 0)"

  geom_count

  Returns the number of elements in this geometry:

      >>> polygon.geom_count
      1

  has_curve
  New in Django 5.2.

  A boolean indicating if this geometry is or contains a curve geometry.

  get_linear_geometry()
  New in Django 5.2.

  Returns a linear version of the geometry. If no conversion can be made, the original geometry is returned.

  get_curve_geometry()
  New in Django 5.2.

  Returns a curved version of the geometry. If no conversion can be made, the original geometry is returned.

  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 a `SpatialReference` object. It may be set with another `SpatialReference` object, or any input that `SpatialReference` accepts. Example:

      >>> city.geom.srs.name
      'GCS_WGS_1984'

  srid

  Returns or sets the spatial reference identifier corresponding to `SpatialReference` of this geometry. Returns `None` 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, a `SpatialReference` object, or any other input accepted by `SpatialReference` (including spatial reference WKT and PROJ strings, or an integer SRID).

  By default nothing is returned and the geometry is transformed in-place. However, if the `clone` keyword is set to `True` then a transformed clone of this geometry is returned instead.

  intersects(other)

  Returns `True` if this geometry intersects the other, otherwise returns `False`.

  equals(other)

  Returns `True` if this geometry is equivalent to the other, otherwise returns `False`.

  disjoint(other)

  Returns `True` if this geometry is spatially disjoint to (i.e. does not intersect) the other, otherwise returns `False`.

  touches(other)

  Returns `True` if this geometry touches the other, otherwise returns `False`.

  crosses(other)

  Returns `True` if this geometry crosses the other, otherwise returns `False`.

  within(other)

  Returns `True` if this geometry is contained within the other, otherwise returns `False`.

  contains(other)

  Returns `True` if this geometry contains the other, otherwise returns `False`.

  overlaps(other)

  Returns `True` if this geometry overlaps the other, otherwise returns `False`.

  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.

  centroid

  Returns a `Point` representing the centroid of this geometry.

  Changed in Django 5.1:

  `centroid` was promoted from a `Polygon` only attribute to being available on all geometry types.

  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

  m
  New in Django 5.1.

  Returns the M coordinate of this point, or `None` if the Point does not have an M coordinate:

      >>> OGRGeometry("POINT ZM (1 2 3 4)").m
      4.0

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]

  m
  New in Django 5.1.

  Returns a list of M coordinates in this line or `None` if the line does not have M coordinates:

      >>> OGRGeometry("LINESTRING(0 1 2 10, 1 2 3 11, 2 3 4 12)").m
      [10.0, 11.0, 12.0]

class Polygon
  shell

  Returns the shell or exterior ring of this polygon, as a `LinearRing` geometry.

  exterior_ring

  An alias for `shell`.

class GeometryCollection
  add(geom)

  Adds a geometry to this geometry collection. Not applicable to other geometry types.

### `OGRGeomType`

class OGRGeomType(type_input)

  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)

  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)

## 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 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
      >>> proj = "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs "
      >>> wgs84 = SpatialReference(proj)  # PROJ 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 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 is `GEOGCS`).

  local

  Returns `True` if this spatial reference is local (root node is `LOCAL_CS`).

  projected

  Returns `True` if this spatial reference is a projected coordinate system (root node is `PROJCS`).

  wkt

  Returns the WKT representation of this spatial reference.

  pretty_wkt

  Returns the ‘pretty’ representation of the WKT.

  proj

  Returns the PROJ 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 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, and the second parameter defines if a raster should be opened in write mode. For newly-created rasters, the second parameter is ignored and the new raster is always created in write mode.

  The first parameter can take three forms: a string or `Path` representing a file path (filesystem or GDAL virtual filesystem), a dictionary with values defining a new raster, or a bytes object representing a raster file.

  If the input is a file path, the raster is opened from there. If the input is raw data in a dictionary, the parameters `width`, `height`, and `srid` are required. If the input is a bytes object, it will be opened using a GDAL virtual filesystem.

  For a detailed description of how to create rasters using dictionary input, see Creating rasters from data. For a detailed description of how to create rasters in the virtual filesystem, see Using GDAL’s Virtual Filesystem.

  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(
      ...     {  # Creates an in-memory raster
      ...         "srid": 4326,
      ...         "width": 4,
      ...         "height": 4,
      ...         "datatype": 1,
      ...         "bands": [
      ...             {
      ...                 "data": (2, 3),
      ...                 "offset": (1, 1),
      ...                 "size": (2, 2),
      ...                 "shape": (2, 1),
      ...                 "nodata_value": 5,
      ...             }
      ...         ],
      ...     }
      ... )
      >>> rst.srs.srid
      4326
      >>> rst.width, rst.height
      (4, 4)
      >>> rst.bands[0].data()
      array([[5, 5, 5, 5],
             [5, 2, 3, 5],
             [5, 2, 3, 5],
             [5, 5, 5, 5]], dtype=uint8)
      >>> rst_file = open("/path/to/your/raster.tif", "rb")
      >>> rst_bytes = rst_file.read()
      >>> rst = GDALRaster(rst_bytes)
      >>> rst.is_vsi_based
      True
      >>> rst.name  # Stored in a random path in the vsimem filesystem.
      '/vsimem/da300bdb-129d-49a8-b336-e410a9428dad'

  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 an in-memory raster by default (`'MEM'`), but can be altered as needed. For instance, use `GTiff` for a `GeoTiff` 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 other `SpatialReference` or providing any input that is accepted by the `SpatialReference` constructor.

        >>> rst = GDALRaster({"width": 10, "height": 20, "srid": 4326})
        >>> rst.srs.srid
        4326
        >>> rst.srs = 3086
        >>> rst.srs.srid
        3086

  srid

    The Spatial Reference System Identifier (SRID) of the raster. This property is a shortcut to getting or setting the SRID through the `srs` attribute.

        >>> rst = GDALRaster({"width": 10, "height": 20, "srid": 4326})
        >>> rst.srid
        4326
        >>> rst.srid = 3086
        >>> rst.srid
        3086
        >>> rst.srs.srid  # This is equivalent
        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) and `skew` (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` and `y` 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 point object with `x` and `y` members. See `geotransform` 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` and `y` members. In case of north up images, these coefficients are both `0`.

        >>> 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 of `ds_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 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 is `NearestNeighbor`, and the other allowed values are `Bilinear`, `Cubic`, `CubicSpline`, `Lanczos`, `Average`, and `Mode`.

    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 the `gdalwarp` 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(srs, driver=None, name=None, resampling='NearestNeighbour', max_error=0.0)

    Transforms this raster to a different spatial reference system (`srs`), which may be a `SpatialReference` object, or any other input accepted by `SpatialReference` (including spatial reference WKT and PROJ strings, or an integer SRID).

    It calculates the bounds and scale of the current raster in the new spatial reference system and warps the raster using the `warp` 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 the `driver` and `name` arguments.

    The default resampling algorithm is `NearestNeighbour` but can be changed using the `resampling` argument. The default maximum allowed error for resampling is 0.0 and can be changed using the `max_error` argument. Consult the `warp` 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_srs = SpatialReference(4326)
        >>> target = rst.transform(target_srs)
        >>> target.origin
        [-82.98492744885776, 27.601924753080144]

  info

    Returns a string with a summary of the raster. This is equivalent to the gdalinfo command line utility.

  metadata

    The metadata of this raster, represented as a nested dictionary. The first-level key is the metadata domain. The second-level contains the metadata item names and values from each domain.

    To set or update a metadata item, pass the corresponding metadata item to the method using the nested structure described above. Only keys that are in the specified dictionary are updated; the rest of the metadata remains unchanged.

    To remove a metadata item, use `None` as the metadata value.

        >>> rst = GDALRaster({"width": 10, "height": 20, "srid": 4326})
        >>> rst.metadata
        {}
        >>> rst.metadata = {"DEFAULT": {"OWNER": "Django", "VERSION": "1.0"}}
        >>> rst.metadata
        {'DEFAULT': {'OWNER': 'Django', 'VERSION': '1.0'}}
        >>> rst.metadata = {"DEFAULT": {"OWNER": None, "VERSION": "2.0"}}
        >>> rst.metadata
        {'DEFAULT': {'VERSION': '2.0'}}

  vsi_buffer

    A `bytes` representation of this raster. Returns `None` for rasters that are not stored in GDAL’s virtual filesystem.

  is_vsi_based

    A boolean indicating if this raster is stored in GDAL’s virtual filesystem.

### `GDALBand`

class GDALBand

  `GDALBand` instances are not created explicitly, but rather obtained from a `GDALRaster` object, through its `bands` 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`.

  statistics(refresh=False, approximate=False)

    Compute statistics on the pixel values of this band. The return value is a tuple with the following structure: `(minimum, maximum, mean, standard deviation)`.

    If the `approximate` argument is set to `True`, the statistics may be computed based on overviews or a subset of image tiles.

    If the `refresh` argument is set to `True`, the statistics will be computed from the data directly, and the cache will be updated with the result.

    If a persistent cache value is found, that value is returned. For raster formats using Persistent Auxiliary Metadata (PAM) services, the statistics might be cached in an auxiliary file. In some cases this metadata might be out of sync with the pixel values or cause values from a previous call to be returned which don’t reflect the value of the `approximate` argument. In such cases, use the `refresh` argument to get updated values and store them in the cache.

    For empty bands (where all pixel values are “no data”), all statistics are returned as `None`.

    The statistics can also be retrieved directly by accessing the `min`, `max`, `mean`, and `std` properties.

  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).

  mean

    The mean of all pixel values of the band (excluding the “no data” value).

  std

    The standard deviation of all pixel values 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.

    To delete an existing “no data” value, set this property to `None`.

  datatype(as_string=False)

    The data type contained in the band, as an integer constant between 0 (Unknown) and 14. If `as_string` is `True`, the data type is returned as a string. Check out the “GDAL Pixel Type” column in the datatype value table for possible values.

  color_interp(as_string=False)

    The color interpretation for the band, as an integer between 0and 16. If `as_string` is `True`, the data type is returned as a string with the following possible values: `GCI_Undefined`, `GCI_GrayIndex`, `GCI_PaletteIndex`, `GCI_RedBand`, `GCI_GreenBand`, `GCI_BlueBand`, `GCI_AlphaBand`, `GCI_HueBand`, `GCI_SaturationBand`, `GCI_LightnessBand`, `GCI_CyanBand`, `GCI_MagentaBand`, `GCI_YellowBand`, `GCI_BlackBand`, `GCI_YCbCr_YBand`, `GCI_YCbCr_CbBand`, and `GCI_YCbCr_CrBand`. `GCI_YCbCr_CrBand` also represents `GCI_Max` because both correspond to the integer 16, but only `GCI_YCbCr_CrBand` is returned as a string.

  data(data=None, offset=None, size=None, shape=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 the `data` 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 should normally correspond to the total number of pixels in the band, or to the number of pixels for a specific block of pixel values if the `offset` and `size` parameters are provided.

    If the number of items in the input is different from the target pixel block, the `shape` parameter must be specified. The shape is a tuple that specifies the width and height of the input data in pixels. The data is then replicated to update the pixel values of the selected block. This is useful to fill an entire band with a single value, for instance.

    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)
        >>> bnd.data([1], shape=(1, 1))
        >>> bnd.data()
        array([[1, 1, 1, 1],
               [1, 1, 1, 1],
               [1, 1, 1, 1],
               [1, 1, 1, 1]], dtype=uint8)
        >>> bnd.data(range(4), shape=(1, 4))
        array([[0, 0, 0, 0],
               [1, 1, 1, 1],
               [2, 2, 2, 2],
               [3, 3, 3, 3]], dtype=uint8)

  metadata

    The metadata of this band. The functionality is identical to `GDALRaster.metadata`.

### Creating rasters from data

This section describes how to create rasters from scratch using the `ds_input` parameter.

A new raster is created when a `dict` is passed to the `GDALRaster` constructor. The dictionary contains defining parameters of the new raster, such as the origin, size, or spatial reference system. The dictionary can also contain pixel data and information about the format of the new raster. The resulting raster can therefore be file-based or memory-based, depending on the driver specified.

There’s no standard for describing raster data in a dictionary or JSON flavor. The definition of the dictionary input to the `GDALRaster` class is therefore specific to Django. It’s inspired by the geojson format, but the `geojson` standard is currently limited to vector formats.

Examples of using the different keys when creating rasters can be found in the documentation of the corresponding attributes and methods of the `GDALRaster` and `GDALBand` classes.

#### The `ds_input` dictionary

Only a few keys are required in the `ds_input` dictionary to create a raster: `width`, `height`, and `srid`. All other parameters have default values (see the table below). The list of keys that can be passed in the `ds_input` dictionary is closely related but not identical to the `GDALRaster` properties. Many of the parameters are mapped directly to those properties; the others are described below.

The following table describes all keys that can be set in the `ds_input` dictionary.

Key | Default | Usage
--- | --- | ---
`srid` | required | Mapped to the `srid` attribute
`width` | required | Mapped to the `width` attribute
`height` | required | Mapped to the `height` attribute
`driver` | `MEM` | Mapped to the `driver` attribute
`name` | `''` | See below
`origin` | `0` | Mapped to the `origin` attribute
`scale` | `0` | Mapped to the `scale` attribute
`skew` | `0` | Mapped to the `width` attribute
`bands` | `[]` | See below
`nr_of_bands` | `0` | See below
`datatype` | `6` | See below
`papsz_options` | `{}` | See below

name

  String representing the name of the raster. When creating a file-based raster, this parameter must be the file path for the new raster. If the name starts with `/vsimem/`, the raster is created in GDAL’s virtual filesystem.

datatype

  Integer representing the data type for all the bands. Defaults to `6` (Float32). All bands of a new raster are required to have the same datatype. The value mapping is:

  Value | GDAL Pixel Type | Description
  --- | --- | ---
  1 | GDT_Byte | 8 bit unsigned integer
  2 | GDT_UInt16 | 16 bit unsigned integer
  3 | GDT_Int16 | 16 bit signed integer
  4 | GDT_UInt32 | 32 bit unsigned integer
  5 | GDT_Int32 | 32 bit signed integer
  6 | GDT_Float32 | 32 bit floating point
  7 | GDT_Float64 | 64 bit floating point
  12 | GDT_UInt64 | 64 bit unsigned integer (GDAL 3.5+)
  13 | GDT_Int64 | 64 bit signed integer (GDAL 3.5+)
  14 | GDT_Int8 | 8 bit signed integer (GDAL 3.7+)

nr_of_bands

  Integer representing the number of bands of the raster. A raster can be created without passing band data upon creation. If the number of bands isn’t specified, it’s automatically calculated from the length of the `bands` input. The number of bands can’t be changed after creation.

bands

  A list of `band_input` dictionaries with band input data. The resulting band indices are the same as in the list provided. The definition of the band input dictionary is given below. If band data isn’t provided, the raster bands values are instantiated as an array of zeros and the “no data” value is set to `None`.

papsz_options

  A dictionary with raster creation options. The key-value pairs of the input dictionary are passed to the driver on creation of the raster.

  The available options are driver-specific and are described in the documentation of each driver.

  The values in the dictionary are not case-sensitive and are automatically converted to the correct string format upon creation.

  The following example uses some of the options available for the GTiff driver. The result is a compressed raster with an internal tiling scheme. The internal tiles have a block size of 23 by 23:

      >>> GDALRaster(
      ...     {
      ...         "driver": "GTiff",
      ...         "name": "/path/to/new/file.tif",
      ...         "srid": 4326,
      ...         "width": 255,
      ...         "height": 255,
      ...         "nr_of_bands": 1,
      ...         "papsz_options": {
      ...             "compress": "packbits",
      ...             "tiled": "yes",
      ...             "blockxsize": 23,
      ...             "blockysize": 23,
      ...         },
      ...     }
      ... )

#### The `band_input` dictionary

The `bands` key in the `ds_input` dictionary is a list of `band_input` dictionaries. Each `band_input` dictionary can contain pixel values and the “no data” value to be set on the bands of the new raster. The data array can have the full size of the new raster or be smaller. For arrays that are smaller than the full raster, the `size`, `shape`, and `offset` keys control the pixel values. The corresponding keys are passed to the `data()` method. Their functionality is the same as setting the band data with that method. The following table describes the keys that can be used.

Key | Default | Usage
--- | --- | ---
`nodata_value` | `None` | Mapped to the `nodata_value` attribute
`data` | Same as `nodata_value` or `0` | Passed to the `data()` method
`size` | `(with, height)` of raster | Passed to the `data()` method
`shape` | Same as size | Passed to the `data()` method
`offset` | `(0, 0)` | Passed to the `data()` method

### Using GDAL’s Virtual Filesystem

GDAL can access files stored in the filesystem, but also supports virtual filesystems to abstract accessing other kind of files, such as compressed, encrypted, or remote files.

#### Using memory-based Virtual Filesystem

GDAL has an internal memory-based filesystem, which allows treating blocks of memory as files. It can be used to read and write `GDALRaster` objects to and from binary file buffers.

This is useful in web contexts where rasters might be obtained as a buffer from a remote storage or returned from a view without being written to disk.

`GDALRaster` objects are created in the virtual filesystem when a `bytes` object is provided as input, or when the file path starts with `/vsimem/`.

Input provided as `bytes` has to be a full binary representation of a file. For instance:

    # Read a raster as a file object from a remote source.
    >>> from urllib.request import urlopen
    >>> dat = urlopen("https://example.com/raster.tif").read()
    # Instantiate a raster from the bytes object.
    >>> rst = GDALRaster(dat)
    # The name starts with /vsimem/, indicating that the raster lives in the
    # virtual filesystem.
    >>> rst.name
    '/vsimem/da300bdb-129d-49a8-b336-e410a9428dad'

To create a new virtual file-based raster from scratch, use the `ds_input` dictionary representation and provide a `name` argument that starts with `/vsimem/` (for detail of the dictionary representation, see Creating rasters from data). For virtual file-based rasters, the `vsi_buffer` attribute returns the `bytes` representation of the raster.

Here’s how to create a raster and return it as a file in an `HttpResponse`:

    >>> from django.http import HttpResponse
    >>> rst = GDALRaster(
    ...     {
    ...         "name": "/vsimem/temporarymemfile",
    ...         "driver": "tif",
    ...         "width": 6,
    ...         "height": 6,
    ...         "srid": 3086,
    ...         "origin": [500000, 400000],
    ...         "scale": [100, -100],
    ...         "bands": [{"data": range(36), "nodata_value": 99}],
    ...     }
    ... )
    >>> HttpResponse(rast.vsi_buffer, "image/tiff")

#### Using other Virtual Filesystems

Depending on the local build of GDAL other virtual filesystems may be supported. You can use them by prepending the provided path with the appropriate `/vsi*/` prefix. See the GDAL Virtual Filesystems documentation for more details.

##### Compressed rasters

Instead decompressing the file and instantiating the resulting raster, GDAL can directly access compressed files using the `/vsizip/`, `/vsigzip/`, or `/vsitar/` virtual filesystems:

    >>> from django.contrib.gis.gdal import GDALRaster
    >>> rst = GDALRaster("/vsizip/path/to/your/file.zip/path/to/raster.tif")
    >>> rst = GDALRaster("/vsigzip/path/to/your/file.gz")
    >>> rst = GDALRaster("/vsitar/path/to/your/file.tar/path/to/raster.tif")

##### Network rasters

GDAL can support online resources and storage providers transparently. As long as it’s built with such capabilities.

To access a public raster file with no authentication, you can use `/vsicurl/`:

    >>> from django.contrib.gis.gdal import GDALRaster
    >>> rst = GDALRaster("/vsicurl/https://example.com/raster.tif")
    >>> rst.name
    '/vsicurl/https://example.com/raster.tif'

For commercial storage providers (e.g. `/vsis3/`) the system should be previously configured for authentication and possibly other settings (see the GDAL Virtual Filesystems documentation for available options).

#### Security considerations

Since `GDALRaster` always opens new rasters in write mode, it is essential to prevent instantiating one from untrusted input. Otherwise, an attacker might gain the ability to write a file or make a network request.

To mitigate this, spatial lookups prevent `str`, `pathlib.Path`, and `dict` values from reaching `GDALRaster` altogether. To use these types with lookups, wrap them explicitly with `GDALRaster`, indicating that the value is trusted. Bytes are accepted without being wrapped in `GDALRaster` because they are opened through GDAL’s memory-based virtual filesystem.

This protection applies only to spatial lookups. Assigning a `dict` value to a `RasterField` will still open a new raster, and assigning a `str` or `Path` will still fetch and open the referenced raster.

When validating geometry inputs, the `GeometryField` form field will reject raster values. When validating raster inputs, you should write custom validation.

For defense-in-depth strategies for limiting the available raster drivers, see GDAL security considerations.

Changed in Django 5.2.17:

In earlier versions, spatial lookups accepted `str` and `dict` types for new rasters, allowing file writes and network fetches.

## 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`).

## Exceptions

exception GDALException

  The base GDAL exception, indicating a GDAL-related error.

exception SRSException

  An exception raised when an error occurs when constructing or using a spatial reference system object.