Statistically infer BMHM and SMHM relation for RESOLVE
Author: Mehnaaz Asad (mehnaaz.asad@vanderbilt.edu)
Table of Contents
To use the scripts in this repository, you must have Anaconda installed on the systems that will be running the scripts. This will simplify the processes of installing all the dependencies.
For reference, see: Manage Anaconda Environments
The package counts with a Makefile with useful commands and functions. You must use this Makefile to ensure that you have all of the necessary dependencies, as well the correct conda environment.
You can use the Makefile for running common tasks like updating environments, cleaning extra files, and more.
To show all available functions in the Makefile, run:
make show-help
Available rules:
clean Deletes all build, test, coverage, and Python artifacts
clean-build Remove build artifacts
clean-pyc Removes Python file artifacts
clean-test Remove test and coverage artifacts
environment Set up python interpreter environment - Using environment.yml
lint Lint using flake8
remove_environment Delete python interpreter environment
test_environment Test python environment is setup correctly
update_environment Update python interpreter environmentIn order to properly run the commands of this project, you should install the necessary packages before. For this, you will to have installed Anaconda, because otherwise you will not be able to use this command.
The name of the environment and its dependencies are explicitely shown in the environment.yml file. In order to create the environment, you must run:
make environmentThe main file that lists all of the dependencies for the project can be found as environment.yml.
Once the environment has been installed, you can now activate the environment by typing
source activate resolve_statisticsNote
Depending on your installation of Anaconda, you might have to use the command:
conda activate resolve_statisticsinstead.
You can always update the project's environment. The package dependencies are handled by the environment.yml file, and sometimes these packages need to updaetd.
You can updated the project's environments by running:
make update_environmentThis will update the versions of each of the necessary packages.
Once you are done running the scripts of this project, you should deactivate the environment. To do so, run:
source deactivateNote
Depending on your installation of Anaconda, you might have to use the command:
conda deactivateinstead.
To make it easier to activate the necessary environment, one can use the conda-auto-env package, which activates the necessary environment automatically.
See the link above for more information!
In order to use the scripts, you can execute the next set of commands.
The organization of the project is the following:
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── 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, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── 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`
│
├── environment.yml <- The Anaconda environment requirements file for reproducing the analysis environment.
│ This file is used by Anaconda to create the project environment.
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ │
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.orgProject based on the modified version of cookiecutter data science project template