rio-mucho

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://[e-mail]/mapbox/rio-mucho.git@<branch>#egg=riomucho

Development:

::

git clone [e-mail]: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: https://travis-ci.org/mapbox/rio-mucho
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   :target: https://coveralls.io/github/mapbox/rio-mucho?branch=master