Gdal python windows install

GDAL/OGR in Python

This Python package and extensions are a number of tools for programming and
manipulating the GDAL Geospatial Data Abstraction Library. Actually, it is
two libraries — GDAL for manipulating geospatial raster data and OGR for
manipulating geospatial vector data — but we’ll refer to the entire package
as the GDAL library for the purposes of this document.

The GDAL project (primarily Even Rouault) maintains SWIG generated Python
bindings for GDAL and OGR. Generally speaking the classes and methods mostly
match those of the GDAL and OGR C++ classes. There is no Python specific
reference documentation, but the GDAL API Tutorial includes Python examples.

Dependencies

• libgdal (3.3.0 or greater) and header files (gdal-devel)
• numpy (1.0.0 or greater) and header files (numpy-devel) (not explicitly
  required, but many examples and utilities will not work without it)

Installation

Unix

The GDAL Python bindings support both distutils and setuptools, with a
preference for using setuptools. If setuptools can be imported, setup will
use that to build an egg by default. If setuptools cannot be imported, a
simple distutils root install of the GDAL package (and no dependency
chaining for numpy) will be made.

easy_install

GDAL can be installed from the Python CheeseShop:

$ sudo easy_install GDAL

It may be necessary to have libgdal and its development headers installed
if easy_install is expected to do a source build because no egg is available
for your specified platform and Python version.

setup.py

Most of setup.py’s important variables are controlled with the setup.cfg
file. In setup.cfg, you can modify pointers to include files and libraries.
The most important option that will likely need to be modified is the
gdal_config parameter. If you installed GDAL from a package, the location
of this program is likely /usr/bin/gdal-config, but it may be in another place
depending on how your packager arranged things.

After modifying the location of gdal-config, you can build and install
with the setup script:

$ python setup.py build
$ python setup.py install

If you have setuptools installed, you can also generate an egg:

$ python setup.py bdist_egg

Building as part of the GDAL library source tree

You can also have the GDAL Python bindings built as part of a source
build by specifying —with-python as part of your configure line:

$ ./configure --with-python

Use the typical make and make install commands to complete the installation:

$ make
$ make install

A note about setuptools

./configure attempts to detect if you have setuptools installed in the tree
of the Python binary it was given (or detected on the execution path), and it
will use an egg build by default in that instance. If you have a need to
use a distutils-only install, you will have to edit setup.py to ensure that
the HAVE_SETUPTOOLS variable is ultimately set to False and proceed with a
typical ‘python setup.py install’ command.

Windows

You will need the following items to complete an install of the GDAL Python
bindings on Windows:

• GDAL Windows Binaries Download the package that best matches your environment.

As explained in the README_EXE.txt file, after unzipping the GDAL binaries you
will need to modify your system path and variables. If you’re not sure how to
do this, read the Microsoft Knowledge Base doc

1. Add the installation directory bin folder to your system PATH, remember
   to put a semicolon in front of it before you add to the existing path.

   C:\gdalwin32-1.7\bin
   

2. Create a new user or system variable with the data folder from
   your installation.

   Name : GDAL_DATA
   Path : C:\gdalwin32-1.7\data
   

Skip down to the Usage section to test your install. Note, a reboot
may be required.

SWIG

The GDAL Python package is built using SWIG. The earliest version of SWIG
that is supported to generate the wrapper code is 1.3.40. It is possible
that usable bindings will build with a version earlier than 1.3.40, but no
development efforts are targeted at versions below it. You should not have
to run SWIG in your development tree to generate the binding code, as it
is usually included with the source. However, if you do need to regenerate,
you can do so with the following make command from within the ./swig/python
directory:

$ make generate

To ensure that all of the bindings are regenerated, you can clean the
bindings code out before the generate command by issuing:

$ make veryclean

Usage

Imports

There are five major modules that are included with the GDAL Python bindings.:

>>> from osgeo import gdal
>>> from osgeo import ogr
>>> from osgeo import osr
>>> from osgeo import gdal_array
>>> from osgeo import gdalconst

Additionally, there are five compatibility modules that are included but
provide notices to state that they are deprecated and will be going away.
If you are using GDAL 1.7 bindings, you should update your imports to utilize
the usage above, but the following will work until GDAL 3.1.

>>> import gdal
>>> import ogr
>>> import osr
>>> import gdalnumeric
>>> import gdalconst

If you have previous code that imported the global module and still need to
support the old import, a simple try…except import can silence the
deprecation warning and keep things named essentially the same as before:

>>> try:
...     from osgeo import gdal
... except ImportError:
...     import gdal

Docstrings

Currently, only the OGR module has docstrings which are generated from the
C/C++ API doxygen materials. Some of the arguments and types might not
match up exactly with what you are seeing from Python, but they should be
enough to get you going. Docstrings for GDAL and OSR are planned for a future
release.

Numpy

One advanced feature of the GDAL Python bindings not found in the other
language bindings (C#, Perl) is integration with the Python numerical array
facilities. The gdal.Dataset.ReadAsArray() method can be used to read raster
data as numerical arrays, ready to use with the Python numerical array
capabilities.

Examples

One example of GDAL/numpy integration is found in the val_repl.py script.

Performance Notes

ReadAsArray expects to make an entire copy of a raster band or dataset unless
the data are explicitly subsetted as part of the function call. For large
data, this approach is expected to be prohibitively memory intensive.