Code to analyse the results of experiment performed on the Dropfactory platform
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
analysis
csv
datasets
figures
properties
utils
.gitignore
README.md
constants.py

README.md

This repository is associated to the paper "A soft matter discovery robot driven by child-like curiosity" by Jonathan Grizou, Laurie J. Points, Abhishek Sharma and Leroy Cronin. A draft version of the paper is available on Chemarxiv and a brief overview of the scientific approach can be found at https://croningp.github.io/tutorial_icdl_epirob_2017/.

Dropfactory Analysis

Code to analyse the results of experiments performed on the Dropfactory platform

Dropfactory_Station

Repository Organization

Folders usage:

  • analysis contains script that analyse various aspects of the experiment, it is where we test and develop our analysis before generating the final analysis script and plots in the figure folder.
  • csv is for generating csv files from the datasets extracted in the datasets folder. It is useful if you want to perfom analysis in spreadsheet or send to colleague in a sharteable form.
  • datasets is where the data collected from our experiments are gathered, sanitized and stored in a user friendly way. To allow you to reproduce the figures without access to the >500Go data (available upon request), the already processed dataset are available in the datasets release.
  • figures contains the exact code used to analayse the data and generate the figures shown in the paper and SI. A bit more information is provided in the next seciton of this README file.
  • properties is a set of scripts used to infer droplet properties (density, viscocities, and surface tension) from a droplet recipy (the ratio of each oil). See http://www.pnas.org/content/115/5/885 (https://doi.org/10.1073/pnas.1711089115) for more details.
  • utils contains a set of tools useful across our analysis.

Releases usage for large files:

  • The datasets release contains the already extracted datasets enabling you to reproduce all the figures.
  • The SI release contains the paper and all supporting information files explaining the working of the system.
  • The video release contains supplementary videos in mp4 format and gif format for embedding on github. The video are also available on youtube.

Main results

In the following all section and figure number are related to the paper and SI documents in the SI release.

Within the figures folder, you can find:

Associated repositories

Dependencies

This code has been tested under Python 2.7.6 on Ubuntu 14.04 LTS. Despite all our efforts, we cannot guarantee everything will be executable on other OS or Python version.

Aside from the standard libraries, we are using the following libraries. You do not have to install them all, it depends on the task you are performing.

  • numpy: Scientific computing in Python. Version: numpy.version is '1.10.4'

  • scipy: More scientific computing in Python. Version: scipy.version is '0.16.1'

  • sklearn: Machine Learning in Python. Version: sklearn.version is '0.16.1'

  • filetools is a simple file management library

  • matplotlib v2.1.1 and seaborn v0.8.1 for plotting

Author

Jonathan Grizou while working in the CroninGroup.

License

GPL V3