pySPACE Package¶
pySPACE
Package¶
pySPACE API¶
Welcome to the main module of pySPACE, the Signal Processing And Classification Environment in Python. For a complete and up-to date documentation of the release, we refer to our git project web page.
This module contains the basic imports for using pySPACE and sets the default configuration.
Documentation
Documentation is done with Sphinx
and some helper functions coming with the software for more customization.
The folder that contains all the documentation is called docs
.
To compile you first have to install Sphinx 1.1 or a better version.
The documentation can be created by running make html
in the docs
directory
(therefore we have a Makefile
in the docs
folder).
To view the documentation open the index.html
in the .build/html
folder.
Structure of the Software and First Steps
This software is structured into six main parts on the top level which give you a hint of
where you might like to go. pySPACE is a highly modular framework, so all possible
tasks and algorithms are called missions
, input and output are defined
as resources
.
To start the software, go to the run
package, to get an idea of
what you can do, see the documentation of missions
(nodes and operations)
and the documentation in Getting started, the Overview and maybe some Tutorials.
Where to Go to Integrate Your Own Extensions
If you want to integrate your own application, you will probably have to create new
missions
. If your algorithm can handle single data_types
or only works with one dataset
or a combination of training and test set, you can integrate it easily by defining it in the
nodes
package.
Otherwise you will have to integrate it into the
operations
package, which is also a subpackage of the
missions
package.
Last but not least, you may want to write only a wrapper for your algorithm and
even implement your algorithm in a different language like C++ or even Matlab.
So the wrapper is integrated as mentioned before and the real algorithm
implementation can be done in the support
package.
If you should realize, that this software is unable to load, store or
process your special type of data, you should have a look into the
resources
package.
Here different stages of accumulation of data are defined,
but most probably, you will be interested in
dataset_defs
.
Very likely you will not have to define a complete new dataset type but
only add the functionality you need to the existing ones.
When integrating new components you should also write small
unittests
in tests
.
In case you need some of the tools
please have a look at the
respective documentation, too. This is also true for
environments
similar holds, e.g., if you want to implement
new backend
or have an own live
application.
The following methods constitute the interface of the package:
load_configuration
Load a configuration from the specified YAML configuration file.
create_operation_from_file
Creates the operation for the file operation_filename create_operation
Creates the operation for the given operation spec
run_operation
Runs the given operation on the backend run_operation_chain
Runs the given operation chain on the backend
create_backend
Creates the backend object
based on the given options
Subpackages Summary¶
environments |
Contains everything to make missions possible, e.g., definitions of backends and processing chains |
missions |
Modules for data processing including large tasks (operations), signal processing algorithms (nodes) and interfaces to external packages |
resources |
Everything that can be input and output of pySPACE. |
run |
The name tells you everything: run contains everything that can be executed |
tests |
Collection of pySPACE system and unit tests |
tools |
Several communication, file, memory, and performance tools used in pySPACE |