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A tool for consistency based analysis of influence graphs and observed systems behavio.

Project description

Installation

You can install iggy by running:

$ pip install --user iggy

On Linux the executable script can then be found in ~/.local/bin

and on MacOS the script is under /Users/YOURUSERNAME/Library/Python/2.7/bin.

Usage

Typical usage is:

$ iggy.py network.sif observation.obs --show_colorings 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_colorings SHOW_COLORINGS]
               [--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_colorings SHOW_COLORINGS
                                number of colorings 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: iggy_demo_data.tar.gz

Project details


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