Allows object oriented running of code/commands
commandRunner is yet another package created to handle running commands, scripts or programs on the command line. The principle class lets you run anything locally on your machine. Later classes are targetted at Analytics and data processing platforms such as Grid Engine and HADOOP. The class attempts to run commands in a moderately thread safe way by requiring that you provide with sufficient information that it can build a uniquely labelled temp directory for all input and output files. This means that this can play nicely with things like Celery workers.
This release supports running commands on localhost. It also uses interpolation for the commands with the same syntax and python templates
In the future we’ll provide classes to run commands over RServe, Grid Engine, Hadoop, Octave, and SAS Server.
This is the basic usages:
from commandRunner import * r = commandRunner(tmp_id="ID_STRING", tmp_path=,/tmp/" in_glob=.in", out_glob=.out", command="ls /tmp > $OUTPUT", input_data="STRING OF DATA") r.prepare() exit_status = r.run_cmd() r.tidy() print(r.output_data)
__init__ initalises all the class variables needed and performs the command string interpolation.
r.prepare() builds a temporary directory and makes any input file which is needed. In this instance “ID_STRING”, and a path where temporary files can be placed are used to create a tempdir called /tmp/ID_STRING/. Next it takes and string of data and makes and input file given the provided input file ending (.in) which would be /tmp/ID_STRING/ID_STRING.in and this file would contain “STRING OF DATA”.
tmp_id, tmp_path and command are required.
in_glob is only required if the command contains $INPUT and input data is given
r.run_cmd() runs the command string provided. First anything labelled $OUTPUT of $INPUT will be replaced with the path to the temporary files the process will generate. In this instance “ls /tmp > $OUTPUT” will become “ls /tmp > /tmp/ID_STRING/ID_STRING.out”. Any command will be run so this is potentially very dangerous. The exit status of the command is returned
r.tidy() cleans up deleting any input and output files and the temporary working directory. Any data in the output file is read in to r.output_data
Run tests with:
- Implement rserveRunner for running commands in r
- Implement geRunner for running commands on Grid Engine
- Implement hadoopRunner for running command on Hadoop
- Refactor commandRunner to abstract base class
- Move commandRunner tests out of localRunner tests
- Implement sasRunner for a SAS backend
- Implement octaveRunner for Octave backend
- matlab? mathematica?