rio-mucho 1.0rc1

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])

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:target:
.. |Build Status| image:: https://travis-ci.org/mapbox/rio-mucho.svg?branch=master
:target: https://travis-ci.org/mapbox/rio-mucho
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:target: https://coveralls.io/github/mapbox/rio-mucho?branch=master