This repository can be found at: http://vandyastroml.github.io/2017_Spring_Vandy_Computational_Workshop.html
Vandy Computational Workshop
Repository for the Computational Workshop Series Fall 2016-Spring 2017 taught at Vanderbilt University
You can run all the notebooks interactively by clicking on the following link:
Repository template taken from "Cookiecutter Data Science"
├── 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` │ ├── 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.org
This is folder, in which you can annotate the notebooks, run the code, etc.
Simply run the file "nb_copy.sh" in the main directory, followed by the week number to access the live version of the notebook of that week.
./nb_copy.sh week_number ow_opt
week_num: number of the week of the iPython notebook to copy
ow_opt': if "y", it will overwrite the notebooks innb_copy_path'.
week_numbermust be followed by a `0' if the week number is below 10.
Getting the help menu of the executable
:$ ./nb_copy.sh -h How to run: ./nb_copy.sh week_num overwrite_opt * week_num: number of the week of the iPython notebook to copy * ow_opt: if 'true', it will overwrite the notebooks in 'nb_copy_path'
:$ ./nb_copy.sh How to run: ./nb_copy.sh week_num overwrite_opt * week_num: number of the week of the iPython notebook to copy * ow_opt: if 'true', it will overwrite the notebooks in 'nb_copy_path'
Copying the new directory of "Week 04" (with overwriting)
:$ ./nb_copy.sh 04 y git pull Already up-to-date. cp -rp ./notebooks/Week_04 ./notebooks_notes/ jupyter notebook ./notebooks_notes/Week_04/*.ipynb
Copying the new directory of "Week 04" (without overwriting)
In case you had already made some notes on an existing notebook
:$ ./nb_copy.sh 04 git pull Already up-to-date. jupyter notebook ./notebooks_notes/Week_04/*.ipynb