Skip to main content

A small package for handling process models given as lists with their own row number and the row number of previous activityReturning a Petri Net for Process Mining / Conformance Checks

Project description

Welcome to Petrinets 4 Process Mining

handling Process Lists with a simple small package V0.0.5.3

Content:

  • Author Information
  • Description
  • Classes
  • Usage / Examples
  • Other Stuff / Last Words
  • License Information

Author Information

Description

The purpose of this package is it to offer an easy way to create a petrinet, you can use for process mining, without having a full PNML file. All you need is a simple list of your process steps, the number of your step and the number of all Previous Steps.

This is just the first Version of the Package. I will try to develop more possible outputs when i got time.

Classes

The package offers the following classes:

  • Petrinet(labels,[places, transitions, edges, inc_gateways, exc_gateways, parall_gateways])
    • output([tablename])
    • cel_out([tablename])

Usage / Examples

It's very easy to use the package. Just import the package

create your activity list with an activity, position of the activity, all direct previous activity ranges

call the Petrinet function with your list and create your outputs

from pn4pm_nano import petrinets as pn

list = [['Activity1',0,[-1]],['Activity2',1,[0]],['A....']]

petriobject = pn.Petrinet(list) print(petriobject.out()) print(petriobject.cel_out()) print(petrionject.cel_out("YourActivityTable"))

For silencing the activities just give an empty string as name

['',0,[-1]] In this case a transition is created but not returned in the frontend

Other Stuff / Last Words

If you have some issues with the package or ideas what functionalities could be added, feel free to contact me.

Changes to Last Version

  • multiple changes on the calculations - enabled the handling of loops [0.0.5 - 0.0.5.3]
  • Added the handling of duplicated activities in the petrinet Input [0.0.4 - 0.0.5]
  • Added Handling of 'Silent' Activitys and Bug Fix in the cel_out() function [0.0.3.2 -> 0.0.4]
  • Bug Fix in function cel_out() [0.0.3.1 -> 0.0.3.2]
  • Bug Fix after calling the class Petrinet the second time [0.0.1 -> 0.0.3]

License Information

This Project is using the MIT License

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

pn4pm_nano-0.0.5.3.tar.gz (9.3 kB view details)

Uploaded Source

Built Distribution

pn4pm_nano-0.0.5.3-py3-none-any.whl (10.1 kB view details)

Uploaded Python 3

File details

Details for the file pn4pm_nano-0.0.5.3.tar.gz.

File metadata

  • Download URL: pn4pm_nano-0.0.5.3.tar.gz
  • Upload date:
  • Size: 9.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.50.1 CPython/3.8.5

File hashes

Hashes for pn4pm_nano-0.0.5.3.tar.gz
Algorithm Hash digest
SHA256 ac44850f70a295e09f53d9f030aaa2cf75eb96ae4fedc92e45d4a53b306148d6
MD5 7d27ac69c088258261d2e73df32c3941
BLAKE2b-256 968f9ef95bd47f6066265e46106465169f5bcfedd43cf1325f4fdeea3977c12b

See more details on using hashes here.

Provenance

File details

Details for the file pn4pm_nano-0.0.5.3-py3-none-any.whl.

File metadata

  • Download URL: pn4pm_nano-0.0.5.3-py3-none-any.whl
  • Upload date:
  • Size: 10.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.50.1 CPython/3.8.5

File hashes

Hashes for pn4pm_nano-0.0.5.3-py3-none-any.whl
Algorithm Hash digest
SHA256 77dd7351b78025c35571272e2158228922f2589fe09a9cb5847a190e98255690
MD5 f0964614abb412056c1c941a17f21367
BLAKE2b-256 0b2bf978f191138b5399f2618bee31d1fb8d8327d6ff92f022197a49ce2069de

See more details on using hashes here.

Provenance

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