Python package to analyze collected data from freezing experiments to estimate INP concentration as function of the activation temperature in the immersion freezing mode.
This project can be used from the command line with brr command or can be imported into other python modules.
Requirements for the software and other tools to build, test and push
Clone the repository
$ git clone https://github.com/gfogwill/freezing_experiment_analyzer
$ cd freezing_experiment_analyzerNow let's install the requirements. But before we do that, we strongly recommend creating a virtual environment with a tool such as virtualenv:
$ python -m venv my_venv
$ source my_venv/bin/activate
$ python -m pip install -r requirements.txtNote: Every time you start a new session you need to activate the virtual environment.
$ source my_venv/bin/activateThis can also be done with Conda. Create a new Conda virtual environment named my_venv
The command below will create a new folder called my_venv.
$ conda create --name my_venvActivating the environment named my_venv in Conda
$ conda activate environment_name$ brr infoIf everything is OK you should see the program logo.
Download example data from: https://doi.org/10.5281/zenodo.7080899
Unzip data and store the folders in ./data/raw/
The software works without major problem with the Experiment 1
$ brr analyze --experiment-name experiment_1 --n-cols 5 --n-rows 5
Results are stored in ./reports/experiment_1
In Experiment 2 the software fails to detect properly the circles automatically. But if images are cropped the
software works properly.
$ brr analyze --experiment-name experiment_2 --n-cols 5 --n-rows 5 --crop
In Experiment 3 the experiment was performed with 49 droplets.
$ brr analyze --experiment-name experiment_3 --n-cols 7 --n-rows 7 --crop
In Experiment 4 the experiment there was a moving bubble in oil under the glass slide for droplet number 18. However,
the software worked properly.
$ brr analyze --experiment-name experiment_4 --n-cols 5 --n-rows 5 --crop
In Experiment 5 there is an artifact in the brightness. The software still need to be improved to solve this issue.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final data sets.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ ├── circles.py <- Scripts to detect circles in images
│ ├── cli.py <- Scripts for the Command Line Interface (CLI)
│ ├── localdata.py <- Scripts to load raw data from experiments
│ ├── paths.py <- Definition of the paths of the package
│ ├── prompts.py <- Prompts used by cli.py
│ ├── reports.py <- Scripts to generate reports and plots after the data analysis
│
└── setup.py <- makes project pip installable (pip install -e .) so src can be imported
Project based on the cookiecutter data science project template. #cookiecutterdatascience