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
=========
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
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