dosertools 1.0.2

dosertools is a Python package for analyzing dripping-onto-substrate extensional rheology (DoSER) videos.

dosertools

dosertools is a Python package for analyzing dripping-onto-substrate extensional rheology (DoSER) videos.
The software converts an input video into a series of binary images, produces a dataset of D/D0 and time values for each frame in the video, and generates values for the elongational relaxation time lambda_E, the diameter at critical time (the onset of elasto-capillary behavior) D(t_c)/D0, and a guess for the quantity of (elongational viscosity / surface tension) eta_E/sigma.

Authors

Rob Learsch and Red Lhota
Contact:
rlearsch (at) caltech (dot) edu

Installation

dosertools comes bundled with a pipfile which creates the python environment it needs to run. Use the package manager pip to install pipenv. You will need to install pipenv if your installation of python doesn't have it. You can do this by typing

pip install pipenv

in your terminal, followed by

pipenv install

in the terminal from your dosertools directory.

See the file named "example-and-settings.md" and "example_script.py" for more information on how to use this package.

Usage

dosertools is expecting experiments that contain a pair of videos - one "experimental" video that contains the fluid transitioning from needle to substrate, and one "background" video that contains only the needle and the literal background of the camera's view. In our testing, we found a background video of 100 frames gives plenty of data to overcome any noise that would be found in a single frame.

dosertools is written for DoSER videos of viscoelastic fluids. It can perform background subtraction and image processing on videos of any type. However, the data processing (calculating elongational relaxation time, elongational viscosity, etc) is done using assumptions that are only true for fluids that exhibit elastocapillary behavior.

dosertools calculates values with machine precision. It is up to the user to decide where to truncate the data produced, based on the specifics of their own experimental setup.

License

MIT License

Copyright (c) 2022 Red C. Lhota and Robert W. Learsch

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.