Skip to main content

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

Project 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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

iggy-1.2.tar.gz (26.6 kB view details)

Uploaded Source

File details

Details for the file iggy-1.2.tar.gz.

File metadata

  • Download URL: iggy-1.2.tar.gz
  • Upload date:
  • Size: 26.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for iggy-1.2.tar.gz
Algorithm Hash digest
SHA256 2b941e9c271d0ff9b2c03c9c63938c73a01df94b9f35c0041089a1b2ef685735
MD5 9f87a2fe603242047509142beeedbb2e
BLAKE2b-256 62bc80892a7b04b882bf0a5dc61f7f53bc1d34d79a611455698ab07f91b81632

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page