=====================================
Switching from GDAL's Python bindings
=====================================

This document is written specifically for users of GDAL's Python bindings
(``osgeo.gdal``) who have read about Rasterio's `philosophy <intro.html>`__ and
want to know what switching entails.  The good news is that switching may not
be complicated. This document explains the key similarities and differences
between these two Python packages and highlights the features of Rasterio that
can help in switching.

Mutual Incompatibilities
========================

Rasterio and GDAL's bindings can contend for global GDAL objects. Unless you
have deep knowledge about both packages, choose exactly one of ``import
osgeo.gdal`` or ``import rasterio``.

GDAL's bindings (``gdal`` for the rest of this document) and Rasterio are not
entirely compatible and should not, without a great deal of care, be imported
and used in a single Python program. The reason is that the dynamic library
they each load (these are C extension modules, remember), ``libgdal.so`` on
Linux, ``gdal.dll`` on Windows, has a number of global objects and the two
modules take different approaches to managing these objects.

Static linking of the GDAL library for ``gdal`` and ``rasterio`` can avoid 
this contention, but in practice you will almost never see distributions of
these modules that statically link the GDAL library.

Beyond the issues above, the modules have different styles – ``gdal`` reads and
writes like C while ``rasterio`` is more Pythonic – and don't complement each
other well.

The GDAL Environment
====================

GDAL library functions are excuted in a context of format drivers, error
handlers, and format-specific configuration options that this document will
call the "GDAL Environment." Rasterio has an abstraction for the GDAL
environment, ``gdal`` does not.

With ``gdal``, this context is initialized upon import of the module. This
makes sense because ``gdal`` objects are thin wrappers around functions and
classes in the GDAL dynamic library that generally require registration of
drivers and error handlers.  The ``gdal`` module doesn't have an abstraction
for the environment, but it can be modified using functions like
``gdal.SetErrorHandler()`` and ``gdal.UseExceptions()``.

Rasterio has modules that don't require complete initialization and
configuration of GDAL (``rasterio.dtypes``, ``rasterio.profiles``, and
``rasterio.windows``, for example) and in the interest of reducing overhead
doesn't register format drivers and error handlers until they are needed. The
functions that do need fully initialized GDAL environments will ensure that
they exist. ``rasterio.open()`` is the foremost of this category of functions.
Consider the example code below.

.. code-block:: python

   import rasterio
   # The GDAL environment has no registered format drivers or error
   # handlers at this point.

   with rasterio.open('example.tif') as src:
       # Format drivers and error handlers are registered just before
       # open() executes.

Importing ``rasterio`` does not initialize the GDAL environment. Calling
``rasterio.open()`` does. This is different from ``gdal`` where ``import
osgeo.gdal``, not ``osgeo.gdal.Open()``, initializes the GDAL environment.

Rasterio has an abstraction for the GDAL environment, ``rasterio.Env``, that
can be invoked explicitly for more control over the configuration of GDAL as
shown below.

.. code-block:: python

   import rasterio
   # The GDAL environment has no registered format drivers or error
   # handlers at this point.

   with rasterio.Env(CPL_DEBUG=True, GDAL_CACHEMAX=512):
       # This ensures that all drivers are registered in the global
       # context. Within this block *only* GDAL's debugging messages
       # are turned on and the raster block cache size is set to 512MB.
   
       with rasterio.open('example.tif') as src:
           # Perform GDAL operations in this context.
           # ...
           # Done.

   # At this point, configuration options are set back to their
   # previous (possibly unset) values. The raster block cache size
   # is returned to its default (5% of available RAM) and debugging
   # messages are disabled.

As mentioned previously, ``gdal`` has no such abstraction for the GDAL
environment. The nearest approximation would be something like the code
below.

.. code-block:: python

   from osgeo import gdal

   # Define a new configuration, save the previous configuration,
   # and then apply the new one.
   new_config = {
       'CPL_DEBUG': 'ON', 'GDAL_CACHEMAX': '512'}
   prev_config = {
       key: gdal.GetConfigOption(key) for key in new_config.keys()}
   for key, val in new_config.items():
       gdal.SetConfigOption(key, val)

   # Perform GDAL operations in this context.
   # ...
   # Done.

   # Restore previous configuration.
   for key, val in prev_config.items():
       gdal.SetConfigOption(key, val)

Rasterio achieves this with a single Python statement.

.. code-block:: python

   with rasterio.Env(CPL_DEBUG=True, GDAL_CACHEMAX=512):
       # ...


Format Drivers
==============

``gdal`` provides objects for each of the GDAL format drivers. With Rasterio,
format drivers are represented by strings and are used only as arguments to
functions like ``rasterio.open()``.

.. code-block:: python

   dst = rasterio.open('new.tif', 'w', format='GTiff', **kwargs)

Rasterio uses the same format driver names as GDAL does.

Dataset Identifiers
===================

Rasterio uses URIs to identify datasets, with schemes for different protocols.
The GDAL bindings have their own special syntax.

Unix-style filenames such as ``/var/data/example.tif`` identify dataset files
for both Rasterio and ``gdal``. Rasterio also accepts 'file' scheme URIs
like ``file:///var/data/example.tif``.

Rasterio identifies datasets within ZIP or tar archives using Apache VFS style
identifiers like ``zip:///var/data/example.zip!example.tif`` or
``tar:///var/data/example.tar!example.tif``.

Datasets served via HTTPS are identified using 'https' URIs like
``https://landsat-pds.s3.amazonaws.com/L8/139/045/LC81390452014295LGN00/LC81390452014295LGN00_B1.TIF``.

Datasets on AWS S3 are identified using 's3' scheme identifiers like
``s3://landsat-pds/L8/139/045/LC81390452014295LGN00/LC81390452014295LGN00_B1.TIF``.

With ``gdal``, the equivalent identifiers are respectively
``/vsizip//var/data/example.zip/example.tif``,
``/vsitar//var/data/example.tar/example.tif``,
``/vsicurl/landsat-pds.s3.amazonaws.com/L8/139/045/LC81390452014295LGN00/LC81390452014295LGN00_B1.TIF``,
and
``/vsis3/landsat-pds/L8/139/045/LC81390452014295LGN00/LC81390452014295LGN00_B1.TIF``.

To help developers switch, Rasterio will accept these identifiers and other
format-specific connection strings, too, and dispatch them to the proper format
drivers and protocols.

Dataset Objects
===============

Rasterio and ``gdal`` each have dataset objects. Not the same classes, of 
course, but not radically different ones. In each case, you generally get
dataset objects through an "opener" function: ``rasterio.open()`` or
``gdal.Open()``.

So that Python developers can spend less time reading docs, the dataset object
returned by ``rasterio.open()`` is modeled on Python's file object. It even has
the ``close()`` method that ``gdal`` lacks so that you can actively close
dataset connections.

Bands
=====

``gdal`` has band objects. Rasterio does not and thus never has objects with
dangling dataset pointers. With Rasterio, bands are represented by a numerical
index, starting from 1 (as GDAL does), and are used as arguments to dataset
methods. To read the first band of a dataset as a Numpy ``ndarray``, do this.

.. code-block:: python

   with rasterio.open('example.tif') as src:
       band1 = src.read(1)

Other attributes of GDAL band objects generally surface in Rasterio as tuples
returned by dataset attributes, with one value per band, in order.

.. code-block:: pycon

   >>> src = rasterio.open('example.tif')
   >>> src.indexes
   (1, 2, 3)
   >>> src.dtypes
   ('uint8', 'uint8', 'uint8')
   >>> src.descriptions
   ('Red band', 'Green band', 'Blue band')
   >>> src.units
   ('DN', 'DN', 'DN')

Developers that want read-only band objects for their applications can create
them by zipping these tuples together.

.. code-block:: python

   from collections import namedtuple

   Band = namedtuple('Band', ['idx', 'dtype', 'description', 'units'])

   src = rasterio.open('example.tif')
   bands = [Band(vals) for vals in zip(
       src.indexes, src.dtypes, src.descriptions, src.units)]

Namedtuples are like lightweight classes.

.. code-block:: pycon

   >>> for band in bands:
   ...     print(band.idx)
   ...
   1
   2
   3

Geotransforms
=============

The ``transform`` attribute of a Rasterio dataset object is comparable to the
``GeoTransform`` attribute of a GDAL dataset, but Rasterio's has more power.
It's not just an array of affine transformation matrix elements, it's an
instance of an ``Afine`` class and has many handy methods. For example, the
spatial coordinates of the upper left corner of any raster element is the
product of the dataset's ``transform`` matrix and the ``(column, row)`` index
of the element.

.. code-block:: pycon

   >>> src = rasterio.open('example.tif')
   >>> src.transform * (0, 0)
   (101985.0, 2826915.0)

The affine transformation matrix can be inverted as well.

.. code-block:: pycon

   >>> ~src.transform * (101985.0, 2826915.0)
   (0.0, 0.0)

To help developers switch, ``Affine`` instances can be created from or
converted to the sequences used by ``gdal``.

.. code-block:: pycon

    >>> from rasterio.transform import Affine
    >>> Affine.from_gdal(101985.0, 300.0379266750948, 0.0,
    ...                  2826915.0, 0.0, -300.041782729805).to_gdal()
    ...
    (101985.0, 300.0379266750948, 0.0, 2826915.0, 0.0, -300.041782729805)

Coordinate Reference Systems
============================

The ``crs`` attribute of a Rasterio dataset object is an instance of Rasterio's
``CRS`` class and works well with ``pyproj``.

.. code-block:: pycon

   >>> from pyproj import Proj, transform
   >>> src = rasterio.open('example.tif')
   >>> transform(Proj(src.crs), Proj('+init=epsg:3857'), 101985.0, 2826915.0)
   (-8789636.707871985, 2938035.238323653)

Tags
====

GDAL metadata items are called "tags" in Rasterio. The tag set for a given GDAL
metadata namespace is represented as a dict.

.. code-block:: pycon

   >>> src.tags()
   {'AREA_OR_POINT': 'Area'}
   >>> src.tags(ns='IMAGE_STRUCTURE')
   {'INTERLEAVE': 'PIXEL'}

The semantics of the tags in GDAL's default and ``IMAGE_STRUCTURE`` namespaces
are described in http://www.gdal.org/gdal_datamodel.html. Rasterio uses 
several namespaces of its own: ``rio_creation_kwds`` and ``rio_overviews``,
each with their own semantics.

Offsets and Windows
===================

Rasterio adds an abstraction for subsets or windows of a raster array that
GDAL does not have. A window is a pair of tuples, the first of the pair being
the raster row indexes at which the window starts and stops, the second being
the column indexes at which the window starts and stops. Row before column,
as with ``ndarray`` slices. Instances of ``Window`` are created by passing the
four subset parameters used with ``gdal`` to the class constructor.

.. code-block:: python

   src = rasterio.open('example.tif')

   xoff, yoff = 0, 0
   xsize, ysize = 10, 10
   subset = src.read(1, window=Window(xoff, yoff, xsize, ysize))

Valid Data Masks
================

Rasterio provides an array for every dataset representing its valid data mask
using the same indicators as GDAL: ``0`` for invalid data and ``255`` for valid
data.

.. code-block:: pycon

   >>> src = rasterio.open('example.tif')
   >>> src.dataset_mask()
   array([[0, 0, 0, ..., 0, 0, 0],
          [0, 0, 0, ..., 0, 0, 0],
          [0, 0, 0, ..., 0, 0, 0],
          ...,
          [0, 0, 0, ..., 0, 0, 0],
          [0, 0, 0, ..., 0, 0, 0],
          [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)

Arrays for dataset bands can also be had as a Numpy ``masked_array``.

.. code-block:: pycon

    >>> src.read(1, masked=True)
    masked_array(data =
     [[-- -- -- ..., -- -- --]
      [-- -- -- ..., -- -- --]
      [-- -- -- ..., -- -- --]
      ...,
      [-- -- -- ..., -- -- --]
      [-- -- -- ..., -- -- --]
      [-- -- -- ..., -- -- --]],
                 mask =
     [[ True  True  True ...,  True  True  True]
      [ True  True  True ...,  True  True  True]
      [ True  True  True ...,  True  True  True]
      ...,
      [ True  True  True ...,  True  True  True]
      [ True  True  True ...,  True  True  True]
      [ True  True  True ...,  True  True  True]],
            fill_value = 0)

Where the masked array's ``mask`` is ``True``, the data is invalid and has been
masked "out" in the opposite sense of GDAL's mask.

Errors and Exceptions
=====================

Rasterio always raises Python exceptions when an error occurs and never returns
an error code or ``None`` to indicate an error. ``gdal`` takes the opposite
approach, although developers can turn on exceptions by calling
``gdal.UseExceptions()``.
