Skip to main content

Windowed multiprocessing wrapper for rasterio

Project description

rio-mucho
=========

Parallel processing wrapper for rasterio

|PyPI| |Build Status| |Coverage Status|

Install
-------

From pypi:

``pip install rio-mucho``

From github (usually for a branch / dev):

``pip install pip install git+ssh://git@github.com/mapbox/rio-mucho.git@<branch>#egg=riomucho``

Development:

::

git clone git@github.com:mapbox/rio-mucho.git
cd rio-mucho
pip install -e .

Usage
-----

.. code:: python

with riomucho.RioMucho([{inputs}], {output}, {run function},
windows={windows},
global_args={global arguments},
options={options to write}) as rios:

rios.run({processes})

Arguments
~~~~~~~~~

``inputs``
^^^^^^^^^^

An list of file paths to open and read.

``output``
^^^^^^^^^^

What file to write to.

``run_function``
^^^^^^^^^^^^^^^^

A function to be applied to each window chunk. This should have input
arguments of:

1. A data input, which can be one of:

- A list of numpy arrays of shape (x,y,z), one for each file as
specified in input file list ``mode="simple_read" [default]``
- A numpy array of shape ({*n* input files x *n* band count}, {window
rows}, {window cols}) ``mode=array_read"``
- A list of open sources for reading ``mode="manual_read"``

2. A ``rasterio`` window tuple
3. A ``rasterio`` window index (``ij``)
4. A global arguments object that you can use to pass in global
arguments

This should return:

1. An output array of ({count}, {window rows}, {window cols}) shape, and
of the correct data type for writing

.. code:: python

def basic_run({data}, {window}, {ij}, {global args}):
## do something
return {out}

Keyword arguments
~~~~~~~~~~~~~~~~~

``windows={windows}``
^^^^^^^^^^^^^^^^^^^^^

A list of ``rasterio`` (window, ij) tuples to operate on.
``[Default = src[0].block_windows()]``

``global_args={global arguments}``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Since this is working in parallel, any other objects / values that you
want to be accessible in the ``run_function``. ``[Default = {}]``

.. code:: python

global_args = {
'divide_value': 2
}

``options={keyword args}``
^^^^^^^^^^^^^^^^^^^^^^^^^^

The options to pass to the writing output. ``[Default = srcs[0].meta``

Example
-------

.. code:: python

import riomucho, rasterio, numpy

def basic_run(data, window, ij, g_args):
## do something
out = np.array(
[d[0] /= global_args['divide'] for d in data]
)
return out

# get windows from an input
with rasterio.open('/tmp/test_1.tif') as src:
## grabbing the windows as an example. Default behavior is identical.
windows = [[window, ij] for ij, window in src.block_windows()]
options = src.meta
# since we are only writing to 2 bands
options.update(count=2)

global_args = {
'divide': 2
}

processes = 4

# run it
with riomucho.RioMucho(['input1.tif','input2.tif'], 'output.tif', basic_run,
windows=windows,
global_args=global_args,
options=options) as rm:

rm.run(processes)

Utility functions
-----------------

\`riomucho.utils.array\_stack([array, array, array,...])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Given a list of ({depth}, {rows}, {cols}) numpy arrays, stack into a
single (l{list length \* each image depth}, {rows}, {cols}) array. This
is useful for handling variation between ``rgb`` inputs of a single
file, or separate files for each.

One RGB file
^^^^^^^^^^^^

.. code:: python

files = ['rgb.tif']
open_files = [rasterio.open(f) for f in files]
rgb = `riomucho.utils.array_stack([src.read() for src in open_files])

Separate RGB files
^^^^^^^^^^^^^^^^^^

.. code:: python

files = ['r.tif', 'g.tif', 'b.tif']
open_files = [rasterio.open(f) for f in files]
rgb = `riomucho.utils.array_stack([src.read() for src in open_files])

.. |PyPI| image:: https://img.shields.io/pypi/v/rio-mucho.svg?maxAge=2592000?style=plastic
:target:
.. |Build Status| image:: https://travis-ci.org/mapbox/rio-mucho.svg?branch=master
:target: https://travis-ci.org/mapbox/rio-mucho
.. |Coverage Status| image:: https://coveralls.io/repos/mapbox/rio-mucho/badge.svg?branch=master&service=github
:target: https://coveralls.io/github/mapbox/rio-mucho?branch=master


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
rio_mucho-1.0.0-py2-none-any.whl (5.8 kB) Copy SHA256 hash SHA256 Wheel py2
rio_mucho-1.0.0-py3-none-any.whl (5.8 kB) Copy SHA256 hash SHA256 Wheel py3
rio-mucho-1.0.0.tar.gz (5.8 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page