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https://github.com/locationtech-labs/geopyspark

GeoTrellis for PySpark
https://github.com/locationtech-labs/geopyspark

big-data geospatial geotrellis python spark tile-server

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GeoTrellis for PySpark

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README 

        
GeoPySpark

**********



.. image:: https://travis-ci.org/locationtech-labs/geopyspark.svg?branch=master

   :target: https://travis-ci.org/locationtech-labs/geopyspark



.. image:: https://readthedocs.org/projects/geopyspark/badge/?version=latest

   :target: https://geopyspark.readthedocs.io/en/latest/?badge=latest



.. image:: https://badges.gitter.im/locationtech-labs/geopyspark.png

   :target: https://gitter.im/geotrellis/geotrellis

   



**GeoPySpark is not currently under active development. We will try and address PRs and Issues, but it may take some time as most

of our resources are devoted to other projects now. There is a chance that this project will be revisted in the future, so it is by no means dead.**



GeoPySpark is a Python bindings library for `GeoTrellis `_, a Scala

library for working with geospatial data in a distributed environment.

By using `PySpark `_, GeoPySpark is

able to provide an interface into the GeoTrellis framework.



Links

-----



 * `Documentation `_

 * `Gitter `_



A Quick Example

---------------



Here is a quick example of GeoPySpark. In the following code, we take NLCD data

of the state of Pennsylvania from 2011, and do a masking operation on it with

a Polygon that represents an area of interest. This masked layer is then saved.



If you wish to follow along with this example, you will need to download the

NLCD data and unzip it.. Running these two commands will complete these tasks

for you:



.. code:: console



   curl -o /tmp/NLCD2011_LC_Pennsylvania.zip "https://s3-us-west-2.amazonaws.com/prd-tnm/StagedProducts/NLCD/data/2011/landcover/states/NLCD2011_LC_Pennsylvania.zip?ORIG=513_SBDDG"

   unzip -d /tmp /tmp/NLCD2011_LC_Pennsylvania.zip



.. code:: python



  import geopyspark as gps



  from pyspark import SparkContext

  from shapely.geometry import box



  # Create the SparkContext

  conf = gps.geopyspark_conf(appName="geopyspark-example", master="local[*]")

  sc = SparkContext(conf=conf)



  # Read in the NLCD tif that has been saved locally.

  # This tif represents the state of Pennsylvania.

  raster_layer = gps.geotiff.get(layer_type=gps.LayerType.SPATIAL,

                                 uri='/tmp/NLCD2011_LC_Pennsylvania.tif',

                                 num_partitions=100)



  # Tile the rasters within the layer and reproject them to Web Mercator.

  tiled_layer = raster_layer.tile_to_layout(layout=gps.GlobalLayout(), target_crs=3857)



  # Creates a Polygon that covers roughly the north-west section of Philadelphia.

  # This is the region that will be masked.

  area_of_interest = box(-75.229225, 40.003686, -75.107345, 40.084375)



  # Mask the tiles within the layer with the area of interest

  masked = tiled_layer.mask(geometries=area_of_interest)



  # We will now pyramid the masked TiledRasterLayer so that we can use it in a TMS server later.

  pyramided_mask = masked.pyramid()



  # Save each layer of the pyramid locally so that it can be accessed at a later time.

  for pyramid in pyramided_mask.levels.values():

      gps.write(uri='file:///tmp/pa-nlcd-2011',

                layer_name='north-west-philly',

                tiled_raster_layer=pyramid)



For additional examples, check out the `Jupyter notebook demos <./notebook-demos>`_.



Requirements

------------



============ ============

Requirement  Version

============ ============

Java         >=1.8

Scala        >=2.11

Python       3.3 - 3.6

Spark        >=2.1.1

============ ============



Java 8 and Scala 2.11 are needed for GeoPySpark to work, as they are required by

GeoTrellis. In addition, Spark needs to be installed and configured with the

environment variable ``SPARK_HOME`` set.



You can test to see if Spark is installed properly by running the following in

the terminal:



.. code:: console



   > echo $SPARK_HOME

   /usr/local/bin/spark



If the return is a path leading to your Spark folder, then it means that Spark

has been configured correctly. If ``SPARK_HOME`` is unset or empty, you'll need to add it

to your ``PATH`` after noting where Spark is installed on your system. For example,

a MacOS installation of Spark 2.3.0 via HomeBrew would set ``SPARK_HOME`` as follows:



.. code:: bash



   # In ~/.bash_profile

   export SPARK_HOME=/usr/local/Cellar/apache-spark/2.3.0/libexec/



Installation

------------



Before installing, check the above `Requirements`_ table to make sure that the

requirements are met.



Installing From Pip

~~~~~~~~~~~~~~~~~~~



To install via ``pip`` open the terminal and run the following:



.. code:: console



   pip install geopyspark

   geopyspark install-jar



The first command installs the python code and the `geopyspark` command

from PyPi. The second downloads the backend jar file, which is too large

to be included in the pip package, and installs it to the GeoPySpark

installation directory. For more information about the ``geopyspark``

command, see the `GeoPySpark CLI`_ section.



Installing From Source

~~~~~~~~~~~~~~~~~~~~~~



If you would rather install from source, clone the GeoPySpark repo and enter it.



.. code:: console



   git clone https://github.com/locationtech-labs/geopyspark.git

   cd geopyspark

   make install



This will assemble the backend-end ``jar`` that contains the Scala code,

move it to the ``jars`` sub-package, and then runs the ``setup.py`` script.



Note:

  If you have altered the global behavior of ``sbt`` this install may

  not work the way it was intended.



Uninstalling

~~~~~~~~~~~~



To uninstall GeoPySpark, run the following in the terminal:



.. code:: console



   pip uninstall geopyspark

   rm .local/bin/geopyspark



Contact and Support

-------------------



If you need help, have questions, or like to talk to the developers (let us

know what you're working on!) you can contact us at:



 * `Gitter `_

 * `Mailing list `_



As you may have noticed from the above links, those are links to the GeoTrellis

gitter channel and mailing list. This is because this project is currently an

offshoot of GeoTrellis, and we will be using their mailing list and gitter

channel as a means of contact. However, we will form our own if there is a need

for it.



GeoPySpark CLI

--------------



When GeoPySpark is installed, it comes with a script which can be accessed

from anywhere on you computer. This script is used to facilitate management

of the GeoPySpark jar file that must be installed in order for GeoPySpark to

work correctly. Here are the available commands:



.. code:: console



   geopyspark -h, --help // return help string and exit

   geopyspark install-jar // downloads jar file to default location, which is geopyspark install dir

   geopyspark install-jar -p, --path [download/path] //downloads the jar file to location specified

   geopyspark jar-path //returns the relative path of the jar file

   geopyspark jar-path -a, --absolute //returns the absolute path of the jar file



``geopyspark install-jar`` is only needed when installing GeoPySpark through

``pip``; and it **must** be ran before using GeoPySpark. If no path is selected,

then the jar will be installed wherever GeoPySpark was installed.



The second and third commands are for getting the location of the jar file.

These can be used regardless of installation method. However, if installed

through ``pip``, then the jar must be downloaded first or these commands

will not work.



Developing GeoPySpark

---------------------



Contributing

~~~~~~~~~~~~



Feedback and contributions to GeoPySpark are always welcomed.

A CLA is required for contribution, see `Contributing `_ for more

information.



Installing for Developers

~~~~~~~~~~~~~~~~~~~~~~~~~



.. code:: console



   make build

   pip install -e .



``make build`` will assemble the back-end ``jar`` and move it the ``jars``

sub-package. The second command will install GeoPySpark in "editable" mode.

Meaning any changes to the source files will also appear in your system

installation.



Within a virtualenv

===================



It's possible that you may run into issues when performing the ``pip install -e .``

described above with a Python virtualenv active. If you're having trouble with

Python finding installed libraries within the virtualenv, try adding the virtualenv

site-packages directory to your PYTHONPATH:



.. code:: console



   workon 

   export PYTHONPATH=$VIRTUAL_ENV/lib//site-packages



Replace ```_ testing

framework to run its unittests. If you wish to run GeoPySpark's unittests,

then you must first clone this repository to your machine. Once complete,

go to the root of the library and run the following command:



.. code:: console



   pytest



This will then run all of the tests present in the GeoPySpark library.



**Note**: The unittests require additional dependencies in order to pass fully.

`pyproj `_, `colortools `_,

and `matplotlib `_  (only for >=Python3.4) are needed to

ensure that all of the tests pass.



Make Targets

============



 - **install** - install GeoPySpark python package locally

 - **wheel** - build python GeoPySpark wheel for distribution

 - **pyspark** - start pyspark shell with project jars

 - **build** - builds the backend jar and moves it to the jars sub-package

 - **clean** - remove the wheel, the backend jar file, and clean the

   geotrellis-backend directory



Developing GeoPySpark With GeoNotebook

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



**Note**: Before begining this section, it should be noted that python-mapnik,

a dependency for GeoNotebook, has been found to be difficult to install. If

problems are encountered during installation, a possible work around would be

to run ``make wheel`` and then do ``docker cp`` the ``wheel`` into the

GeoPySpark docker container and install it from there.



`GeoNotebook `_ is a Jupyter

notebook extension that specializes in working with geospatial data. GeoPySpark

can be used with this notebook; which allows for a more interactive experience

when using the library. For this section, we will be installing both tools in a

virtual environment. It is recommended that you start with a new environment

before following this guide.



Because there's already documentation on how to install GeoPySpark in a virtual

environment, we won't go over it here. As for GeoNotebook, it also has a section

on `installation `_

so that will not be covered here either.



Once you've setup both GeoPySpark and GeoNotebook, all that needs to be done

is go to where you want to save/have saved your notebooks and execute this

command:



.. code:: console



   jupyter notebook



This will open up the jupyter hub and will allow you to work on your notebooks.



It is also possible to develop with both GeoPySpark and GeoNotebook in editable mode.

To do so you will need to re-install and re-register GeoNotebook with Jupyter.



.. code:: console



   pip uninstall geonotebook

   git clone --branch feature/geotrellis https://github.com/geotrellis/geonotebook ~/geonotebook

   pip install -r ~/geonotebook/prerequirements.txt

   pip install -r ~/geonotebook/requirements.txt

   pip install -e ~/geonotebook

   jupyter serverextension enable --py geonotebook

   jupyter nbextension enable --py geonotebook

   make notebook



The default ``Geonotebook (Python 3)`` kernel will require the following environment variables to be defined:



.. code:: console



   export PYSPARK_PYTHON="/usr/local/bin/python3"

   export SPARK_HOME="/usr/local/apache-spark/2.1.1/libexec"

   export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.4-src.zip:${SPARK_HOME}/python/lib/pyspark.zip"



Make sure to define them to values that are correct for your system.

The ``make notebook`` command also makes used of ``PYSPARK_SUBMIT_ARGS`` variable defined in the ``Makefile``.



GeoNotebook/GeoTrellis integration in currently in active development and not part of GeoNotebook master.

The latest development is on a ``feature/geotrellis`` branch at ````.



Side Note For Developers

========================



An optional (but recommended!) step for developers is to place these

two lines of code at the top of your notebooks.



.. code:: console



   %load_ext autoreload

   %autoreload 2



This will make it so that you don't have to leave the notebook for your changes

to take affect. Rather, you just have to reimport the module and it will be

updated. However, there are a few caveats when using ``autoreload`` that can be

read `here `_.



Using ``pip install -e`` in conjunction with ``autoreload`` should cover any

changes made, though, and will make the development experience much less

painful.