Skip to main content

Library with basic social science functions

Project description

https://github.com/gesiscss/face2face/blob/master/svg_data/pytest_passing.svg https://github.com/gesiscss/face2face/blob/master/svg_data/coverage.svg https://notebooks.gesis.org/binder/badge.svg

Face-to-face interaction analysis toolkit

Face2face is a toolbox that contains multiple methods for the basic analysis of sociopatterns data sets and networks.

Installation

You have multiple options for the installation on your local machine.

If you want to become a contributor in this toolbox and you want to apply changes or add new functions to the toolbox you can take a look on the development guide from the website.

If you just want to install the toolbox on your system to work with it, you can do so with:

$ pip install face2face

Tutorials

If you are new to this library and you want to learn about how to use the included methods or you want to learn how they work you can use the tutorials. For a short description of the different tutorials you can find more information in the tutorial README [Tutorial README](/tutorial/README.md). If you want to test the tutorials right now you can click on the binder badge on top of this README.md to open the tutorials in the Gesis Notebooks.

[Here](https://github.com/gesiscss/face2face/tree/master/tutorial) you get to the tutorial folder.

Short summary

With the methods from the import module you can import your own tij- and metadata data sets or you can use predefined data sets which are included in the installed library. With the Data Object you created with your or the predefined data sets you can directly start with the analysis of the probability for different contact durations and visualize them. You can also use the Object to create a network and analyze this network in terms of basic key figures about the underlying network. Furthermore you can use this library to analyze and visualize the homophily of the given data set.

Current State

The face2face toolbox at its current state is an alpha version. If you find any bugs or you have suggestions about how to extend the toolbox with useful new functionalities please let us know by creating a new issue thread on our <a href=”https://github.com/gesiscss/face-to-face-interaction-analysis-toolkit/issues”>GitHub Page</a> or contribute a fix or additional methods by yourself as described in the developer guide in the online documentation.

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

face2face-0.1.1.1.1.Alpha.tar.gz (180.9 kB view details)

Uploaded Source

Built Distribution

face2face-0.1.1.1.1a0-py3-none-any.whl (212.9 kB view details)

Uploaded Python 3

File details

Details for the file face2face-0.1.1.1.1.Alpha.tar.gz.

File metadata

  • Download URL: face2face-0.1.1.1.1.Alpha.tar.gz
  • Upload date:
  • Size: 180.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/49.6.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3

File hashes

Hashes for face2face-0.1.1.1.1.Alpha.tar.gz
Algorithm Hash digest
SHA256 e1c050377bc4b06f932e36ba35f370394c24881a4de27567192b04982b4f3c0c
MD5 ca0cc83d777a539d719e00e4b4db5021
BLAKE2b-256 5a2f13dbdc5705be6f59f6d8d0b4e5e44be75a41839a1d08feedadd6c9772057

See more details on using hashes here.

File details

Details for the file face2face-0.1.1.1.1a0-py3-none-any.whl.

File metadata

  • Download URL: face2face-0.1.1.1.1a0-py3-none-any.whl
  • Upload date:
  • Size: 212.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/49.6.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3

File hashes

Hashes for face2face-0.1.1.1.1a0-py3-none-any.whl
Algorithm Hash digest
SHA256 dffec9ebd60f47823fac5068d539cdcd0cb4c2b11da2ea4b63cfde51dc43a434
MD5 3c3e4e035c9d518917a269e79b50c24f
BLAKE2b-256 a7488d48749db0f07baffa59aad3bc4543c9a928e682123aa4c07160ec6eb18f

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