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:

Project Organization

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

Notebook Notes

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

For help:

./nb_copy.sh -h

Parameters:

---

• week_num: number of the week of the iPython notebook to copy
• ow_opt': if "y", it will overwrite the notebooks in nb_copy_path'.

Note:

• The week_number must be followed by a `0' if the week number is below 10.

Example

---

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'

or

:$ ./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