GDAL stands for Geospatial Data Abstraction Library,\nand is a veritable “Swiss army knife” of GIS data functionality. A subset\nof GDAL is the OGR Simple Features Library, which specializes\nin reading and writing vector geographic data in a variety of standard\nformats.
\nGeoDjango provides a high-level Python interface for some of the\ncapabilities of OGR, including the reading and coordinate transformation\nof vector spatial data and minimal support for GDAL’s features with respect\nto raster (image) data.
\n\nThe GDAL/OGR tools described here are designed to help you read in\nyour geospatial data, in order for most of them to be useful you have\nto have some data to work with. If you’re starting out and don’t yet\nhave any data of your own to use, GeoDjango tests contain a number of\nsimple data sets that you can use for testing. You can download them here:
\n$ wget https://raw.githubusercontent.com/django/django/master/tests/gis_tests/data/cities/cities.{shp,prj,shx,dbf}\n$ wget https://raw.githubusercontent.com/django/django/master/tests/gis_tests/data/rasters/raster.tif\nDataSourceLink to this heading#DataSource is a wrapper for the OGR data source object that\nsupports reading data from a variety of OGR-supported geospatial file\nformats and data sources using a simple, consistent interface. Each\ndata source is represented by a DataSource object which contains\none or more layers of data. Each layer, represented by a Layer\nobject, contains some number of geographic features (Feature),\ninformation about the type of features contained in that layer (e.g.\npoints, polygons, etc.), as well as the names and types of any\nadditional fields (Field) of data that may be associated with\neach feature in that layer.
The constructor for DataSource only requires one parameter: the path of\nthe file you want to read. However, OGR\nalso supports a variety of more complex data sources, including\ndatabases, that may be accessed by passing a special name string instead\nof a path. For more information, see the OGR Vector Formats\ndocumentation. The name property of a DataSource\ninstance gives the OGR name of the underlying data source that it is\nusing.
The optional encoding parameter allows you to\nspecify a non-standard encoding of the strings in the source. This is\ntypically useful when you obtain DjangoUnicodeDecodeError exceptions\nwhile reading field values.
Once you’ve created your DataSource, you can find out how many\nlayers of data it contains by accessing the layer_count property,\nor (equivalently) by using the len() function. For information on\naccessing the layers of data themselves, see the next section:
>>> from django.contrib.gis.gdal import DataSource\n>>> ds = DataSource('/path/to/your/cities.shp')\n>>> ds.name\n'/path/to/your/cities.shp'\n>>> ds.layer_count # This file only contains one layer\n1\nReturns the number of layers in the data source.
\nReturns the name of the data source.
\nLayerLink to this heading#Layer is a wrapper for a layer of data in a DataSource object.\nYou never create a Layer object directly. Instead, you retrieve\nthem from a DataSource object, which is essentially a standard\nPython container of Layer objects. For example, you can access a\nspecific layer by its index (e.g. ds[0] to access the first\nlayer), or you can iterate over all the layers in the container in a\nfor loop. The Layer itself acts as a container for geometric\nfeatures.
Typically, all the features in a given layer have the same geometry type.\nThe geom_type property of a layer is an OGRGeomType\nthat identifies the feature type. We can use it to print out some basic\ninformation about each layer in a DataSource:
>>> for layer in ds:\n... print('Layer "%s": %i %ss' % (layer.name, len(layer), layer.geom_type.name))\n...\nLayer "cities": 3 Points\nThe example output is from the cities data source, loaded above, which\nevidently contains one layer, called "cities", which contains three\npoint features. For simplicity, the examples below assume that you’ve\nstored that layer in the variable layer:
>>> layer = ds[0]\nReturns the name of this layer in the data source.
\n>>> layer.name\n'cities'\nReturns the number of features in the layer. Same as len(layer):
>>> layer.num_feat\n3\nReturns the geometry type of the layer, as an OGRGeomType\nobject:
>>> layer.geom_type.name\n'Point'\nReturns the number of fields in the layer, i.e the number of fields of\ndata associated with each feature in the layer:
\n>>> layer.num_fields\n4\nReturns a list of the names of each of the fields in this layer:
\n>>> layer.fields\n['Name', 'Population', 'Density', 'Created']\nReturns a list of the data types of each of the fields in this layer.\nThese are subclasses of Field, discussed below:
>>> [ft.__name__ for ft in layer.field_types]\n['OFTString', 'OFTReal', 'OFTReal', 'OFTDate']\nReturns a list of the maximum field widths for each of the fields in\nthis layer:
\n>>> layer.field_widths\n[80, 11, 24, 10]\nReturns a list of the numeric precisions for each of the fields in\nthis layer. This is meaningless (and set to zero) for non-numeric\nfields:
\n>>> layer.field_precisions\n[0, 0, 15, 0]\nReturns the spatial extent of this layer, as an Envelope\nobject:
>>> layer.extent.tuple\n(-104.609252, 29.763374, -95.23506, 38.971823)\nProperty that returns the SpatialReference associated\nwith this layer:
>>> print(layer.srs)\nGEOGCS["GCS_WGS_1984",\n DATUM["WGS_1984",\n SPHEROID["WGS_1984",6378137,298.257223563]],\n PRIMEM["Greenwich",0],\n UNIT["Degree",0.017453292519943295]]\nIf the Layer has no spatial reference information associated\nwith it, None is returned.
Property that may be used to retrieve or set a spatial filter for this\nlayer. A spatial filter can only be set with an OGRGeometry\ninstance, a 4-tuple extent, or None. When set with something\nother than None, only features that intersect the filter will be\nreturned when iterating over the layer:
>>> print(layer.spatial_filter)\nNone\n>>> print(len(layer))\n3\n>>> [feat.get('Name') for feat in layer]\n['Pueblo', 'Lawrence', 'Houston']\n>>> ks_extent = (-102.051, 36.99, -94.59, 40.00) # Extent for state of Kansas\n>>> layer.spatial_filter = ks_extent\n>>> len(layer)\n1\n>>> [feat.get('Name') for feat in layer]\n['Lawrence']\n>>> layer.spatial_filter = None\n>>> len(layer)\n3\nA method that returns a list of the values of a given field for each\nfeature in the layer:
\n>>> layer.get_fields('Name')\n['Pueblo', 'Lawrence', 'Houston']\nA method that returns a list containing the geometry of each feature\nin the layer. If the optional argument geos is set to True\nthen the geometries are converted to GEOSGeometry\nobjects. Otherwise, they are returned as OGRGeometry objects:
>>> [pt.tuple for pt in layer.get_geoms()]\n[(-104.609252, 38.255001), (-95.23506, 38.971823), (-95.363151, 29.763374)]\nReturns a boolean indicating whether this layer supports the\ngiven capability (a string). Examples of valid capability strings\ninclude: 'RandomRead', 'SequentialWrite', 'RandomWrite',\n'FastSpatialFilter', 'FastFeatureCount', 'FastGetExtent',\n'CreateField', 'Transactions', 'DeleteFeature', and\n'FastSetNextByIndex'.
FeatureLink to this heading#Feature wraps an OGR feature. You never create a Feature\nobject directly. Instead, you retrieve them from a Layer object.\nEach feature consists of a geometry and a set of fields containing\nadditional properties. The geometry of a field is accessible via its\ngeom property, which returns an OGRGeometry object. A Feature\nbehaves like a standard Python container for its fields, which it returns as\nField objects: you can access a field directly by its index or name,\nor you can iterate over a feature’s fields, e.g. in a for loop.
Returns the geometry for this feature, as an OGRGeometry object:
>>> city.geom.tuple\n(-104.609252, 38.255001)\nA method that returns the value of the given field (specified by name)\nfor this feature, not a Field wrapper object:
>>> city.get('Population')\n102121\nReturns the type of geometry for this feature, as an OGRGeomType\nobject. This will be the same for all features in a given layer, and\nis equivalent to the Layer.geom_type property of the\nLayer object the feature came from.
Returns the number of fields of data associated with the feature.\nThis will be the same for all features in a given layer, and is\nequivalent to the Layer.num_fields property of the\nLayer object the feature came from.
Returns a list of the names of the fields of data associated with the\nfeature. This will be the same for all features in a given layer, and\nis equivalent to the Layer.fields property of the Layer\nobject the feature came from.
Returns the feature identifier within the layer:
\n>>> city.fid\n0\nReturns the name of the Layer that the feature came from.\nThis will be the same for all features in a given layer:
>>> city.layer_name\n'cities'\nA method that returns the index of the given field name. This will be\nthe same for all features in a given layer:
\n>>> city.index('Population')\n1\nFieldLink to this heading#Returns the name of this field:
\n>>> city['Name'].name\n'Name'\nReturns the OGR type of this field, as an integer. The\nFIELD_CLASSES dictionary maps these values onto\nsubclasses of Field:
>>> city['Density'].type\n2\nReturns a string with the name of the data type of this field:
\n>>> city['Name'].type_name\n'String'\nReturns the value of this field. The Field class itself\nreturns the value as a string, but each subclass returns the\nvalue in the most appropriate form:
>>> city['Population'].value\n102121\nReturns the width of this field:
\n>>> city['Name'].width\n80\nReturns the numeric precision of this field. This is meaningless (and\nset to zero) for non-numeric fields:
\n>>> city['Density'].precision\n15\nReturns the value of the field as a double (float):
\n>>> city['Density'].as_double()\n874.7\nReturns the value of the field as an integer:
\n>>> city['Population'].as_int()\n102121\nReturns the value of the field as a string:
\n>>> city['Name'].as_string()\n'Pueblo'\nReturns the value of the field as a tuple of date and time components:
\n>>> city['Created'].as_datetime()\n(c_long(1999), c_long(5), c_long(23), c_long(0), c_long(0), c_long(0), c_long(0))\nDriverLink to this heading#The Driver class is used internally to wrap an OGR DataSource driver.
Returns the number of OGR vector drivers currently registered.
\nOGRGeometryLink to this heading#OGRGeometry objects share similar functionality with\nGEOSGeometry objects, and are thin\nwrappers around OGR’s internal geometry representation. Thus,\nthey allow for more efficient access to data when using DataSource.\nUnlike its GEOS counterpart, OGRGeometry supports spatial reference\nsystems and coordinate transformation:
>>> from django.contrib.gis.gdal import OGRGeometry\n>>> polygon = OGRGeometry('POLYGON((0 0, 5 0, 5 5, 0 5))')\nThis object is a wrapper for the OGR Geometry class.\nThese objects are instantiated directly from the given geom_input\nparameter, which may be a string containing WKT, HEX, GeoJSON, a buffer\ncontaining WKB data, or an OGRGeomType object. These objects\nare also returned from the Feature.geom attribute, when\nreading vector data from Layer (which is in turn a part of\na DataSource).
Constructs a Polygon from the given bounding-box (a 4-tuple).
Returns the number of points in a LineString, the\nnumber of rings in a Polygon, or the number of geometries in a\nGeometryCollection. Not applicable to other geometry types.
Iterates over the points in a LineString, the rings in a\nPolygon, or the geometries in a GeometryCollection.\nNot applicable to other geometry types.
Returns the point at the specified index for a LineString, the\ninterior ring at the specified index for a Polygon, or the geometry\nat the specified index in a GeometryCollection. Not applicable to\nother geometry types.
Returns the number of coordinated dimensions of the geometry, i.e. 0\nfor points, 1 for lines, and so forth:
\n>> polygon.dimension\n2\nReturns or sets the coordinate dimension of this geometry. For\nexample, the value would be 2 for two-dimensional geometries.
\nReturns the number of elements in this geometry:
\n>>> polygon.geom_count\n1\nReturns the number of points used to describe this geometry:
\n>>> polygon.point_count\n4\nAlias for point_count.
Alias for point_count.
Returns the type of this geometry, as an OGRGeomType object.
Returns the name of the type of this geometry:
\n>>> polygon.geom_name\n'POLYGON'\nReturns the area of this geometry, or 0 for geometries that do not\ncontain an area:
\n>>> polygon.area\n25.0\nReturns the envelope of this geometry, as an Envelope object.
Returns the envelope of this geometry as a 4-tuple, instead of as an\nEnvelope object:
>>> point.extent\n(0.0, 0.0, 5.0, 5.0)\nThis property controls the spatial reference for this geometry, or\nNone if no spatial reference system has been assigned to it.\nIf assigned, accessing this property returns a SpatialReference\nobject. It may be set with another SpatialReference object,\nor any input that SpatialReference accepts. Example:
>>> city.geom.srs.name\n'GCS_WGS_1984'\nReturns or sets the spatial reference identifier corresponding to\nSpatialReference of this geometry. Returns None if\nthere is no spatial reference information associated with this\ngeometry, or if an SRID cannot be determined.
Returns a GEOSGeometry object\ncorresponding to this geometry.
Returns a string representation of this geometry in GML format:
\n>>> OGRGeometry('POINT(1 2)').gml\n'<gml:Point><gml:coordinates>1,2</gml:coordinates></gml:Point>'\nReturns a string representation of this geometry in HEX WKB format:
\n>>> OGRGeometry('POINT(1 2)').hex\n'0101000000000000000000F03F0000000000000040'\nReturns a string representation of this geometry in JSON format:
\n>>> OGRGeometry('POINT(1 2)').json\n'{ "type": "Point", "coordinates": [ 1.000000, 2.000000 ] }'\nReturns a string representation of this geometry in KML format.
\nReturns the size of the WKB buffer needed to hold a WKB representation\nof this geometry:
\n>>> OGRGeometry('POINT(1 2)').wkb_size\n21\nReturns a buffer containing a WKB representation of this geometry.
Returns a string representation of this geometry in WKT format.
\nReturns the EWKT representation of this geometry.
\nReturns a new OGRGeometry clone of this geometry object.
If there are any rings within this geometry that have not been closed,\nthis routine will do so by adding the starting point to the end:
\n>>> triangle = OGRGeometry('LINEARRING (0 0,0 1,1 0)')\n>>> triangle.close_rings()\n>>> triangle.wkt\n'LINEARRING (0 0,0 1,1 0,0 0)'\nTransforms this geometry to a different spatial reference system. May\ntake a CoordTransform object, a SpatialReference object,\nor any other input accepted by SpatialReference (including\nspatial reference WKT and PROJ.4 strings, or an integer SRID).\nBy default nothing is returned and the geometry is transformed in-place.\nHowever, if the clone keyword is set to True then a transformed\nclone of this geometry is returned instead.
Returns True if this geometry intersects the other, otherwise returns\nFalse.
Returns True if this geometry is equivalent to the other, otherwise returns\nFalse.
Returns True if this geometry is spatially disjoint to (i.e. does\nnot intersect) the other, otherwise returns False.
Returns True if this geometry touches the other, otherwise returns\nFalse.
Returns True if this geometry crosses the other, otherwise returns\nFalse.
Returns True if this geometry is contained within the other, otherwise returns\nFalse.
Returns True if this geometry contains the other, otherwise returns\nFalse.
Returns True if this geometry overlaps the other, otherwise returns\nFalse.
The boundary of this geometry, as a new OGRGeometry object.
The smallest convex polygon that contains this geometry, as a new\nOGRGeometry object.
Returns the region consisting of the difference of this geometry and\nthe other, as a new OGRGeometry object.
Returns the region consisting of the intersection of this geometry and\nthe other, as a new OGRGeometry object.
Returns the region consisting of the symmetric difference of this\ngeometry and the other, as a new OGRGeometry object.
Returns the region consisting of the union of this geometry and\nthe other, as a new OGRGeometry object.
Returns the coordinates of a point geometry as a tuple, the\ncoordinates of a line geometry as a tuple of tuples, and so forth:
\n>>> OGRGeometry('POINT (1 2)').tuple\n(1.0, 2.0)\n>>> OGRGeometry('LINESTRING (1 2,3 4)').tuple\n((1.0, 2.0), (3.0, 4.0))\nAn alias for tuple.
Returns the X coordinate of this point:
\n>>> OGRGeometry('POINT (1 2)').x\n1.0\nReturns the Y coordinate of this point:
\n>>> OGRGeometry('POINT (1 2)').y\n2.0\nReturns the Z coordinate of this point, or None if the\npoint does not have a Z coordinate:
>>> OGRGeometry('POINT (1 2 3)').z\n3.0\nReturns a list of X coordinates in this line:
\n>>> OGRGeometry('LINESTRING (1 2,3 4)').x\n[1.0, 3.0]\nReturns a list of Y coordinates in this line:
\n>>> OGRGeometry('LINESTRING (1 2,3 4)').y\n[2.0, 4.0]\nReturns a list of Z coordinates in this line, or None if the\nline does not have Z coordinates:
>>> OGRGeometry('LINESTRING (1 2 3,4 5 6)').z\n[3.0, 6.0]\nReturns the shell or exterior ring of this polygon, as a LinearRing\ngeometry.
An alias for shell.
Returns a Point representing the centroid of this polygon.
Adds a geometry to this geometry collection. Not applicable to other\ngeometry types.
\nOGRGeomTypeLink to this heading#This class allows for the representation of an OGR geometry type\nin any of several ways:
\n>>> from django.contrib.gis.gdal import OGRGeomType\n>>> gt1 = OGRGeomType(3) # Using an integer for the type\n>>> gt2 = OGRGeomType('Polygon') # Using a string\n>>> gt3 = OGRGeomType('POLYGON') # It's case-insensitive\n>>> print(gt1 == 3, gt1 == 'Polygon') # Equivalence works w/non-OGRGeomType objects\nTrue True\nReturns a short-hand string form of the OGR Geometry type:
\n>>> gt1.name\n'Polygon'\nReturns the number corresponding to the OGR geometry type:
\n>>> gt1.num\n3\nReturns the Django field type (a subclass of GeometryField) to use for\nstoring this OGR type, or None if there is no appropriate Django\ntype:
>>> gt1.django\n'PolygonField'\nEnvelopeLink to this heading#Represents an OGR Envelope structure that contains the\nminimum and maximum X, Y coordinates for a rectangle bounding box.\nThe naming of the variables is compatible with the OGR Envelope\nC structure.
\nThe value of the minimum X coordinate.
\nThe value of the maximum X coordinate.
\nThe value of the minimum Y coordinate.
\nThe value of the maximum Y coordinate.
\nThe upper-right coordinate, as a tuple.
\nThe lower-left coordinate, as a tuple.
\nA tuple representing the envelope.
\nA string representing this envelope as a polygon in WKT format.
\nSpatialReferenceLink to this heading#Spatial reference objects are initialized on the given srs_input,\nwhich 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:
\n>>> wgs84 = SpatialReference('WGS84') # shorthand string\n>>> wgs84 = SpatialReference(4326) # EPSG code\n>>> wgs84 = SpatialReference('EPSG:4326') # EPSG string\n>>> proj4 = '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs '\n>>> wgs84 = SpatialReference(proj4) # PROJ.4 string\n>>> wgs84 = SpatialReference("""GEOGCS["WGS 84",\nDATUM["WGS_1984",\n SPHEROID["WGS 84",6378137,298.257223563,\n AUTHORITY["EPSG","7030"]],\n AUTHORITY["EPSG","6326"]],\n PRIMEM["Greenwich",0,\n AUTHORITY["EPSG","8901"]],\n UNIT["degree",0.01745329251994328,\n AUTHORITY["EPSG","9122"]],\n AUTHORITY["EPSG","4326"]]""") # OGC WKT\nReturns the value of the given string attribute node, None if the node\ndoesn’t exist. Can also take a tuple as a parameter, (target, child),\nwhere child is the index of the attribute in the WKT. For example:
>>> wkt = 'GEOGCS["WGS 84", DATUM["WGS_1984, ... AUTHORITY["EPSG","4326"]]')\n>>> srs = SpatialReference(wkt) # could also use 'WGS84', or 4326\n>>> print(srs['GEOGCS'])\nWGS 84\n>>> print(srs['DATUM'])\nWGS_1984\n>>> print(srs['AUTHORITY'])\nEPSG\n>>> print(srs['AUTHORITY', 1]) # The authority value\n4326\n>>> print(srs['TOWGS84', 4]) # the fourth value in this wkt\n0\n>>> print(srs['UNIT|AUTHORITY']) # For the units authority, have to use the pipe symbol.\nEPSG\n>>> print(srs['UNIT|AUTHORITY', 1]) # The authority value for the units\n9122\nThe attribute value for the given target node (e.g. 'PROJCS').\nThe index keyword specifies an index of the child node to return.
Returns the authority name for the given string target node.
\nReturns the authority code for the given string target node.
\nReturns a clone of this spatial reference object.
\nThis method inspects the WKT of this SpatialReference, and will\nadd EPSG authority nodes where an EPSG identifier is applicable.
\nMorphs this SpatialReference from ESRI’s format to EPSG
\nMorphs this SpatialReference to ESRI’s format.
\nChecks to see if the given spatial reference is valid, if not\nan exception will be raised.
\nImport spatial reference from EPSG code.
\nImport spatial reference from PROJ.4 string.
\nImport spatial reference from WKT.
\nImport spatial reference from XML.
\nReturns the name of this Spatial Reference.
\nReturns the SRID of top-level authority, or None if undefined.
Returns the name of the linear units.
\nReturns the value of the linear units.
\nReturns the name of the angular units.”
\nReturns the value of the angular units.
\nReturns a 2-tuple of the units value and the units name,\nand will automatically determines whether to return the linear\nor angular units.
\nReturns a tuple of the ellipsoid parameters for this spatial\nreference: (semimajor axis, semiminor axis, and inverse flattening)
\nReturns the semi major axis of the ellipsoid for this spatial reference.
\nReturns the semi minor axis of the ellipsoid for this spatial reference.
\nReturns the inverse flattening of the ellipsoid for this spatial reference.
\nReturns True if this spatial reference is geographic\n(root node is GEOGCS).
Returns True if this spatial reference is local\n(root node is LOCAL_CS).
Returns True if this spatial reference is a projected coordinate\nsystem (root node is PROJCS).
Returns the WKT representation of this spatial reference.
\nReturns the ‘pretty’ representation of the WKT.
\nReturns the PROJ.4 representation for this spatial reference.
\nAlias for SpatialReference.proj.
Returns the XML representation of this spatial reference.
\nCoordTransformLink to this heading#Represents a coordinate system transform. It is initialized with two\nSpatialReference, representing the source and target coordinate\nsystems, respectively. These objects should be used when performing\nthe same coordinate transformation repeatedly on different geometries:
>>> ct = CoordTransform(SpatialReference('WGS84'), SpatialReference('NAD83'))\n>>> for feat in layer:\n... geom = feat.geom # getting clone of feature geometry\n... geom.transform(ct) # transforming\nGDALRasterLink to this heading#GDALRaster is a wrapper for the GDAL raster source object that\nsupports reading data from a variety of GDAL-supported geospatial file\nformats and data sources using a simple, consistent interface. Each\ndata source is represented by a GDALRaster object which contains\none or more layers of data named bands. Each band, represented by a\nGDALBand object, contains georeferenced image data. For example, an RGB\nimage is represented as three bands: one for red, one for green, and one for\nblue.
The constructor for GDALRaster accepts two parameters. The first parameter\ndefines the raster source, it is either a path to a file or spatial data with\nvalues defining the properties of a new raster (such as size and name). If the\ninput is a file path, the second parameter specifies if the raster should\nbe opened with write access. If the input is raw data, the parameters width,\nheight, and srid are required. The following example shows how rasters\ncan be created from different input sources (using the sample data from the\nGeoDjango tests, see also the Sample Data section):
>>> from django.contrib.gis.gdal import GDALRaster\n>>> rst = GDALRaster('/path/to/your/raster.tif', write=False)\n>>> rst.name\n'/path/to/your/raster.tif'\n>>> rst.width, rst.height # This file has 163 x 174 pixels\n(163, 174)\n>>> rst = GDALRaster({'srid': 4326, 'width': 1, 'height': 2, 'datatype': 1\n... 'bands': [{'data': [0, 1]}]}) # Creates in-memory raster\n>>> rst.srs.srid\n4326\n>>> rst.width, rst.height\n(1, 2)\n>>> rst.bands[0].data()\narray([[0, 1]], dtype=int8)\nThe name of the source which is equivalent to the input file path or the name\nprovided upon instantiation.
\n>>> GDALRaster({'width': 10, 'height': 10, 'name': 'myraster', 'srid': 4326}).name\n'myraster'\nThe name of the GDAL driver used to handle the input file. For GDALRasters created\nfrom a file, the driver type is detected automatically. The creation of rasters from\nscratch is a in-memory raster by default ('MEM'), but can be altered as\nneeded. For instance, use GTiff for a GeoTiff file. For a list of file types,\nsee also the GDAL Raster Formats list.
An in-memory raster is created through the following example:
\n>>> GDALRaster({'width': 10, 'height': 10, 'srid': 4326}).driver.name\n'MEM'\nA file based GeoTiff raster is created through the following example:
\n>>> import tempfile\n>>> rstfile = tempfile.NamedTemporaryFile(suffix='.tif')\n>>> rst = GDALRaster({'driver': 'GTiff', 'name': rstfile.name, 'srid': 4326,\n... 'width': 255, 'height': 255, 'nr_of_bands': 1})\n>>> rst.name\n'/tmp/tmp7x9H4J.tif' # The exact filename will be different on your computer\n>>> rst.driver.name\n'GTiff'\nThe width of the source in pixels (X-axis).
\n>>> GDALRaster({'width': 10, 'height': 20, 'srid': 4326}).width\n10\nThe height of the source in pixels (Y-axis).
\n>>> GDALRaster({'width': 10, 'height': 20, 'srid': 4326}).height\n20\nThe spatial reference system of the raster, as a\nSpatialReference instance. The SRS can be changed by\nsetting it to an other SpatialReference or providing any input\nthat is accepted by the SpatialReference constructor.
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})\n>>> rst.srs.srid\n4326\n>>> rst.srs = 3086\n>>> rst.srs.srid\n3086\nThe affine transformation matrix used to georeference the source, as a\ntuple of six coefficients which map pixel/line coordinates into\ngeoreferenced space using the following relationship:
\nXgeo = GT(0) + Xpixel*GT(1) + Yline*GT(2)\nYgeo = GT(3) + Xpixel*GT(4) + Yline*GT(5)\nThe same values can be retrieved by accessing the origin\n(indices 0 and 3), scale (indices 1 and 5) and skew\n(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})\n>>> rst.geotransform\n[0.0, 1.0, 0.0, 0.0, 0.0, -1.0]\nCoordinates of the top left origin of the raster in the spatial\nreference system of the source, as a point object with x and y\nmembers.
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})\n>>> rst.origin\n[0.0, 0.0]\n>>> rst.origin.x = 1\n>>> rst.origin\n[1.0, 0.0]\nPixel width and height used for georeferencing the raster, as a as a\npoint object with x and y members. See geotransform\nfor more information.
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})\n>>> rst.scale\n[1.0, -1.0]\n>>> rst.scale.x = 2\n>>> rst.scale\n[2.0, -1.0]\nSkew coefficients used to georeference the raster, as a point object\nwith x and y members. In case of north up images, these\ncoefficients are both 0.
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})\n>>> rst.skew\n[0.0, 0.0]\n>>> rst.skew.x = 3\n>>> rst.skew\n[3.0, 0.0]\nExtent (boundary values) of the raster source, as a 4-tuple\n(xmin, ymin, xmax, ymax) in the spatial reference system of the\nsource.
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})\n>>> rst.extent\n(0.0, -20.0, 10.0, 0.0)\n>>> rst.origin.x = 100\n>>> rst.extent\n(100.0, -20.0, 110.0, 0.0)\nList of all bands of the source, as GDALBand instances.
>>> rst = GDALRaster({"width": 1, "height": 2, 'srid': 4326,\n... "bands": [{"data": [0, 1]}, {"data": [2, 3]}]})\n>>> len(rst.bands)\n2\n>>> rst.bands[1].data()\narray([[ 2., 3.]], dtype=float32)\nReturns a warped version of this raster.
\nThe warping parameters can be specified through the ds_input\nargument. The use of ds_input is analogous to the corresponding\nargument of the class constructor. It is a dictionary with the\ncharacteristics of the target raster. Allowed dictionary key values are\nwidth, height, SRID, origin, scale, skew, datatype, driver, and name\n(filename).
By default, the warp functions keeps most parameters equal to the\nvalues of the original source raster, so only parameters that should be\nchanged need to be specified. Note that this includes the driver, so\nfor file-based rasters the warp function will create a new raster on\ndisk.
\nThe only parameter that is set differently from the source raster is the\nname. The default value of the the raster name is the name of the source\nraster appended with '_copy' + source_driver_name. For file-based\nrasters it is recommended to provide the file path of the target raster.
The resampling algorithm used for warping can be specified with the\nresampling argument. The default is NearestNeighbor, and the\nother allowed values are Bilinear, Cubic, CubicSpline,\nLanczos, Average, and Mode.
The max_error argument can be used to specify the maximum error\nmeasured in input pixels that is allowed in approximating the\ntransformation. The default is 0.0 for exact calculations.
For users familiar with GDAL, this function has a similar\nfunctionality to the gdalwarp command-line utility.
For example, the warp function can be used for aggregating a raster to\nthe double of its original pixel scale:
\n>>> rst = GDALRaster({\n... "width": 6, "height": 6, "srid": 3086,\n... "origin": [500000, 400000],\n... "scale": [100, -100],\n... "bands": [{"data": range(36), "nodata_value": 99}]\n... })\n>>> target = rst.warp({"scale": [200, -200], "width": 3, "height": 3})\n>>> target.bands[0].data()\narray([[ 7., 9., 11.],\n [ 19., 21., 23.],\n [ 31., 33., 35.]], dtype=float32)\nReturns a transformed version of this raster with the specified SRID.
\nThis function transforms the current raster into a new spatial reference\nsystem that can be specified with an srid. It calculates the bounds\nand scale of the current raster in the new spatial reference system and\nwarps the raster using the warp function.
By default, the driver of the source raster is used and the name of the\nraster is the original name appended with\n'_copy' + source_driver_name. A different driver or name can be\nspecified with the driver and name arguments.
The default resampling algorithm is NearestNeighbour but can be\nchanged using the resampling argument. The default maximum allowed\nerror for resampling is 0.0 and can be changed using the max_error\nargument. Consult the warp documentation for detail\non those arguments.
>>> rst = GDALRaster({\n... "width": 6, "height": 6, "srid": 3086,\n... "origin": [500000, 400000],\n... "scale": [100, -100],\n... "bands": [{"data": range(36), "nodata_value": 99}]\n... })\n>>> target = rst.transform(4326)\n>>> target.origin\n[-82.98492744885776, 27.601924753080144]\nGDALBandLink to this heading#GDALBand instances are not created explicitly, but rather obtained\nfrom a GDALRaster object, through its bands\nattribute. The GDALBands contain the actual pixel values of the raster.
The name or description of the band, if any.
\nThe width of the band in pixels (X-axis).
\nThe height of the band in pixels (Y-axis).
\nThe total number of pixels in this band. Is equal to width * height.
The minimum pixel value of the band (excluding the “no data” value).
\nThe maximum pixel value of the band (excluding the “no data” value).
\nThe “no data” value for a band is generally a special marker value used\nto mark pixels that are not valid data. Such pixels should generally not\nbe displayed, nor contribute to analysis operations.
\n\nThe data type contained in the band, as an integer constant between 0\n(Unknown) and 11. If as_string is True, the data type is\nreturned as a string with the following possible values:\nGDT_Unknown, GDT_Byte, GDT_UInt16, GDT_Int16,\nGDT_UInt32, GDT_Int32, GDT_Float32, GDT_Float64,\nGDT_CInt16, GDT_CInt32, GDT_CFloat32, and GDT_CFloat64.
The accessor to the pixel values of the GDALBand. Returns the complete\ndata array if no parameters are provided. A subset of the pixel array can\nbe requested by specifying an offset and block size as tuples.
If NumPy is available, the data is returned as NumPy array. For performance\nreasons, it is highly recommended to use NumPy.
\nData is written to the GDALBand if the data parameter is provided.\nThe input can be of one of the following types - packed string, buffer, list,\narray, and NumPy array. The number of items in the input must correspond to the\ntotal number of pixels in the band, or to the number of pixels for a specific\nblock of pixel values if the offset and size parameters are provided.
For example:
\n>>> rst = GDALRaster({'width': 4, 'height': 4, 'srid': 4326, 'datatype': 1, 'nr_of_bands': 1})\n>>> bnd = rst.bands[0]\n>>> bnd.data(range(16))\n>>> bnd.data()\narray([[ 0, 1, 2, 3],\n [ 4, 5, 6, 7],\n [ 8, 9, 10, 11],\n [12, 13, 14, 15]], dtype=int8)\n>>> bnd.data(offset=(1, 1), size=(2, 2))\narray([[ 5, 6],\n [ 9, 10]], dtype=int8)\n>>> bnd.data(data=[-1, -2, -3, -4], offset=(1, 1), size=(2, 2))\n>>> bnd.data()\narray([[ 0, 1, 2, 3],\n [ 4, -1, -2, 7],\n [ 8, -3, -4, 11],\n [12, 13, 14, 15]], dtype=int8)\n>>> bnd.data(data='\\x9d\\xa8\\xb3\\xbe', offset=(1, 1), size=(2, 2))\n>>> bnd.data()\narray([[ 0, 1, 2, 3],\n [ 4, -99, -88, 7],\n [ 8, -77, -66, 11],\n [ 12, 13, 14, 15]], dtype=int8)\nGDAL_LIBRARY_PATHLink to this heading#A string specifying the location of the GDAL library. Typically,\nthis setting is only used if the GDAL library is in a non-standard\nlocation (e.g., /home/john/lib/libgdal.so).