Metadata-Version: 1.0
Name: iggy
Version: 1.4.1
Summary: A tool for consistency based analysis of influence graphs and observed systems behavior.
Home-page: http://bioasp.github.io/iggy/
Author: Sven Thiele
Author-email: sthiele78@gmail.com
License: GPLv3+
Description: Installation
        ------------
        
        
        You can install iggy by running::
        
        	$ pip install --user iggy
        
        On Linux the executable scripts can then be found in ``~/.local/bin``
        
        and on MacOS the scripts are under ``/Users/YOURUSERNAME/Library/Python/3.2/bin``.
        
        
        Usage
        -----
        
        Typical usage is::
        
        	$ iggy.py network.sif observation.obs --show_labelings 10 --show_predictions
        
        For more options you can ask for help as follows::
        
        	$ iggy.py -h 		
        	usage: iggy.py [-h] [--no_zero_constraints]
                       [--propagate_unambigious_influences] [--no_founded_constraint]
                       [--autoinputs] [--scenfit] [--show_labelings SHOW_LABELINGS]
                       [--show_predictions]
                       networkfile observationfile
        
        	positional arguments:
        	  networkfile           influence graph in SIF format
        	  observationfile       observations in bioquali format
        
        	optional arguments:
        	  -h, --help            show this help message and exit
        	  --no_zero_constraints
        				turn constraints on zero variations OFF, default is ON
        	  --propagate_unambigious_influences
        				turn constraints ON that if all predecessor of a node
        				have the same influence this must have an effect,
        				default is ON
        	  --no_founded_constraint
        				turn constraints OFF that every variation must be
        				explained by an input, default is ON
        	  --autoinputs          compute possible inputs of the network (nodes with
        				indegree 0)
        	  --scenfit             compute scenfit of the data, default is mcos
        	  --show_labelings SHOW_LABELINGS
        				number of labelings to print, default is OFF, 0=all
        	  --show_predictions    show predictions
        
        
        The second script contained is opt_graph.py
        Typical usage is::
        
        	$ opt_graph.py network.sif observations_dir/ --show_repairs 10
        
        For more options you can ask for help as follows::
        
        	$ opt_graph.py -h 	
        	usage: opt_graph.py [-h] [--no_zero_constraints]
        		    [--propagate_unambigious_influences]
        		    [--no_founded_constraint] [--autoinputs]
        		    [--show_repairs SHOW_REPAIRS] [--opt_graph]
        		    networkfile observationfiles
        
        	positional arguments:
        	  networkfile           influence graph in SIF format
        	  observationfiles      directory of observations in bioquali format
        
        	optional arguments:
        	  -h, --help            show this help message and exit
        	  --no_zero_constraints
        				turn constraints on zero variations OFF, default is ON
        	  --propagate_unambigious_influences
        				turn constraints ON that if all predecessor of a node
        				have the same influence this must have an effect,
        				default is ON
        	  --no_founded_constraint
        				turn constraints OFF that every variation must be
        				explained by an input, default is ON
        	  --autoinputs          compute possible inputs of the network (nodes with
        				indegree 0)
        	  --show_repairs SHOW_REPAIRS
        				number of repairs to show, default is OFF, 0=all
        	  --opt_graph           compute opt-graph repairs (allows also adding edges),
        				default is only removing edges
        
        
        Samples
        -------
        
        Sample files available here: demo_data.tar.gz_ 
        
        .. _demo_data.tar.gz: https://bioasp.github.io/iggy/downloads/demo_data.tar.gz
        
Platform: UNKNOWN
