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CircleCI PyPI version Python 3.6 GitHub

Repo2Data

Repo2Data is a python3 package that automatically fetches data from a remote server, and decompresses it if needed. Supported web data sources are amazon s3, datalad, osf, Google drive, raw http(s) or specific python lib datasets (sklearn.datasets.load, nilearn.datasets.fetch etc...).

Input

A data_requirement.json configuration file explaining what should be read, where should you store the data, and a project name (name of the folder where file will be downloaded).

{ "src": "https://github.com/SIMEXP/Repo2Data/archive/master.zip",
  "dst": "./data",
  "projectName": "repo2data_out"}

src is where you configure the upstream location for your data.

dst specifies where (which folder) the data should be downloaded.

projectName is the name of the directory where the data will be saved, such that you can access it at {dst}/{projectName}

Output

The content of the server inside the specified folder.

Execution

The tool can be executed through bash or imported as a python API.

Bash

If data_requirement.json is inside current directory, you can call the following on the command line:

repo2data

Python API

After defining the data_requirement.json and importing the module, first instanciate the Repo2Data object with:

from repo2data.repo2data import Repo2Data

# define data requirement path
data_req_path = os.path.join("data_requirement.json")
# download data
repo2data = Repo2Data(data_req_path)

You can then fetch the data with the install method, which returns a list to the output directory(ies) where the data was downloaded:

data_path = repo2data.install()

Examples of data_requirement.json

archive file

Repo2Data will use wget if it detects a http link. If this file is an archive, it will be automatically be decompressed using patool. Please unsure that you download the right package to unarchive the data (ex: /usr/bin/tar for .tar.gz).

{ "src": "https://github.com/SIMEXP/Repo2Data/archive/master.tar.gz",
  "dst": "./data",
  "projectName": "repo2data_wget"}
Google Drive

It can also download a file from Google Drive using gdown. You will need to make sure that your file is available publically, and get the project ID (a chain of 33 characters that you can find on the url). Then you can construct the url with this ID: https://drive.google.com/uc?id=${PROJECT_ID}

For example:

{ "src": "https://drive.google.com/uc?id=1_zeJqQP8umrTk-evSAt3wCLxAkTKo0lC",
  "dst": "./data",
  "projectName": "repo2data_gdrive"}
library data-package

You will need to put the script to import and download the data in the src field, the lib should be installed on the host machine.

Any lib function to fetch data needs a parameter so it know where is the output directory. To avoid dupplication of the destination parameter, please replace the parameter for the output dir in the function by _dst.

For example write tf.keras.datasets.mnist.load_data(path=_dst) instead of tf.keras.datasets.mnist.load_data(path=/path/to/your/data). Repo2Data will then automatically replace _dst by the one provided in the dst field.

{ "src": "import tensroflow as tf; tf.keras.datasets.mnist.load_data(path=_dst)",
  "dst": "./data",
  "projectName": "repo2data_lib"}
datalad

The src should be point to a .git link if using datalad, Repo2Data will then just call datalad get.

{ "src": "https://github.com/OpenNeuroDatasets/ds000005.git",
  "dst": "./data",
  "projectName": "repo2data_datalad"}
s3

To download an amazon s3 link, Repo2Data uses aws s3 sync --no-sign-request command. So you should provide the s3:// bucket link of the data:

{ "src": "s3://openneuro.org/ds000005",
  "dst": "./data",
  "projectName": "repo2data_s3"}
osf

Repo2Data uses osfclient osf -p PROJECT_ID clone command. You will need to give the link to the public project containing your data https://osf.io/.../:

{ "src": "https://osf.io/fuqsk/",
  "dst": "./data",
  "projectName": "repo2data_osf"}

If you need to download a single file, or a list of files, you can do this using the remote_filepath field wich runs osf -p PROJECT_ID fetch -f file. For example to download two files (https://osf.io/aevrb/ and https://osf.io/bvuh6/), use a relative path to the root of the project:

{ "src": "https://osf.io/fuqsk/",
  "remote_filepath": ["hello.txt", "test-subfolder/hello-from-subfolder.txt"],
  "dst": "./data",
  "projectName": "repo2data_osf_multiple"}
zenodo

The public data repository zenodo is also supported using zenodo_get. Make sure your project is public and it has a DOI with the form 10.5281/zenodo.XXXXXXX:

{ "src": "10.5281/zenodo.6482995",
  "dst": "./data",
  "projectName": "repo2data_zenodo"}
multiple data

If you need to download many data at once, you can create a list of json. For example, to download different files from a repo :

{
  "authors": {
    "src": "https://github.com/tensorflow/tensorflow/blob/master/AUTHORS",
    "dst": "./data",
    "projectName": "repo2data_multiple1"
  },
  "license": {
    "src": "https://github.com/tensorflow/tensorflow/blob/master/LICENSE",
    "dst": "./data",
    "projectName": "repo2data_multiple2"
  }
}

Install

Docker (recommended)

This is the recommended way of using Repo2Data, because it encapsulate all the dependencies inside the container. It also features scikit-learn and nilearn to pull data from.

Clone this repo and build the docker image yourself :

git clone https://github.com/SIMEXP/Repo2Data
sudo docker build --tag repo2data ./Repo2Data/

pip

To install Datalad you will need the latest version of git-annex, please use the package from neuro-debian :

wget -O- http://neuro.debian.net/lists/stretch.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
sudo apt-key adv --recv-keys --keyserver hkp://ipv4.pool.sks-keyservers.net:80 0xA5D32F012649A5A9

If you have troubles to download the key, please look at this issue.

You can now install with pip:

python3 -m pip install repo2data

Usage

After creating the data_requirement.json, just use repo2data without any option:

repo2data

requirement in another directory

If the data_requirement.json is in another directory, use the -r option:

repo2data -r /PATH/TO/data_requirement.json

github repo url as input

Given a valid https github repository with a data_requirement.json at HEAD branch (under a binder directory or at its root), you can do:

repo2data -r GITHUB_REPO

An example of a valid GITHUB_REPO is: https://github.com/ltetrel/repo2data-caching-s3

Trigger re-fetch

When you re-run Repo2Data with the same destination, it will automatically look for an existing data_requirement.json file in the downloaded folder. If the configured data_requirement.json is the same (i.e. the JSON dict has the same fields) then it will not re-download.

To force the re-fetch (update existing files, add new files but keep the old files), you can add a new field or update an existing one in the data_requirement.json. For example replace:

{ "src": "https://github.com/SIMEXP/Repo2Data/archive/master.zip",
  "dst": "./data",
  "projectName": "repo2data_out"}

by

{ "src": "https://github.com/SIMEXP/Repo2Data/archive/master.zip",
  "dst": "./data",
  "projectName": "repo2data_out",
  "version": "1.1"}

This is especially usefull when the provenance link always stay the same (osf, google drive...).

make dst field optionnal

using dataLayout field

In the case you have a fixed known layout for the data folder within a github repository, the dst field is not needed anymore. To define what kind of layout you want, you can use the dataLayout field. For now we just support the neurolibre layout:

{ "src": "https://github.com/SIMEXP/Repo2Data/archive/master.zip",
  "dataLayout": "neurolibre"}

If you need another data layout (like YODA or cookiecutter-data-science) you can create a feature request.

for administrator

You can disable the field dst by using the option repo2data --server

In this case Repo2Data will put the data inside the folder ./data from where it is run. This is usefull if you want to have full control over the destination (you are a server admin and don't want your users to control the destination).

Docker

You will need to create a folder on your machine (containing a data_requirement.json) that the Docker container will access so Repo2Data can pull the data inside it, after you can use:

sudo docker run -v /PATH/TO/FOLDER:/data repo2data

(the container will run with --server enabled, so all the data in the container will be at /data)

A requirement from a github repo is also supported (so you don't need any data_requirement.json inside your host folder):

sudo docker run -v /PATH/TO/FOLDER:/data repo2data -r GITHUB_REPO

Docker mounts the host (your machine) folder into the container folder as -v host_folder:container_folder, so don't override :/data.