undr 1.0.1
Download and process Neuromorphic datasets

<p align="center">
    <img src="https://raw.githubusercontent.com/neuromorphicsystems/undr/main/undr.png" width="256">
</p>

# Unified Neuromorphic Datasets Repository

- [Unified Neuromorphic Datasets Repository](#unified-neuromorphic-datasets-repository)
    - [Getting Started](#getting-started)
        - [Install the undr module](#install-the-undr-module)
        - [Generate a default configuration file](#generate-a-default-configuration-file)
        - [Download the datasets](#download-the-datasets)
        - [Generate a BibTex file](#generate-a-bibtex-file)
    - [Python module](#python-module)
        - [Concepts](#concepts)
        - [Python APIs](#python-apis)
    - [Dataset format specification](#dataset-format-specification)
    - [Dataset mirrors](#dataset-mirrors)
        - [Example configuration](#example-configuration)
            - [Apache](#apache)
            - [Nginx](#nginx)
            - [Upload a dataset](#upload-a-dataset)
        - [Download with existing CLI](#download-with-existing-cli)
    - [Build documentation](#build-documentation)
- [compilation](#compilation)

## Getting Started

### Install the undr module

```sh
pip3 install undr
```

Documentation: https://neuromorphicsystems.github.io/undr/

### Generate a default configuration file

```sh
python3 -m undr init
```

The generated _undr.toml_ file is written in TOML (https://github.com/toml-lang/toml). It lists the datasets that will be downloaded or streamed, hence it needs to be ajusted to your needs.

The line `directory = 'datasets'` specifies the directory where downloaded files are stored (relatively to the configuration file). All the files generated by `undr` (directory indexes, downloaded data, temporary files...) are stored in this directory.

Datasets are listed as `[[datasets]]` entries with three mandatory properties: `name`, `url` and `mode`. The optional `server_type` property is used internally to speed up the download process. To discard a dataset, you can either remove it from the configuration file or comment all its lines with `#` signs.

`mode` changes the download strategy on a per-dataset basis, with three possible values:

-   `'remote'` only downloads the dataset's file index. The `undr` Python package can be used to process the dataset files as if they were on your hard drive by streaming them from the server. This option is particularly useful for large datasets that do not fit on your disk but requires a fast internet connection since files are re-downloaded every time they are processed.
-   `'local'` downloads all the dataset files locally but does not decompress them (most datasets are stored as [Brotli](https://github.com/google/brotli/) archives). The `undr` Python library transparently decompresses files in memory when you read them, making this option a good trade-off between disk usage and processing speed.
-   `'local-decompressed'` downloads all the dataset files locally and decompresses them. Decompressed files use a relatively inefficient plain binary file format so this option requires vast amounts of disk space (3 to 5 times as much as the Brotli archives). On the other hand, the plain binary format facilitates processing with other languages such as Matlab or C++.

`undr` also supports hybrid configurations where only part of a dataset is downloaded or decompressed. You may also use local directories without a server. See [NOT DOCUMENTED YET] for details.

### Download the datasets

```sh
python3 -m undr install
```

This command downloads the datasets file indexes. If the `mode` is `'compressed'` or `'decompress'`, it also downloads the dataset files (and possibly decompresses them).

This command can be interrupted at any time with CTRL + C. Re-running it will resume download where it left off.

### Generate a BibTex file

```sh
python3 -m undr bibtex --output datasets.bib
```

The UNDR project does not claim authorship of the datasets. Please use this file to cite the origiinal articles.

## Python module

```sh
pip3 install undr
```

### Concepts

-   priority
-   -index.json
-   files
-   other_files

### Python APIs

| API name | Complexity | Configurability | Parallel processing | Progress display |
| -------- | ---------- | --------------- | ------------------- | ---------------- |
| loop     | simple     | high            | no                  | no               |
| map      | simple     | low             | yes                 | yes              |
| task     | complex    | high            | yes                 | yes              |

All three approches support progress persistence. Progress persistence sightly increases the code complexity but makes it possible to resume processing after a network or power failure.

## Dataset format specification

`-index.json` rationale

-   prepend a special character to make sure the index file is ordered first in ASCII
-   use an unreserved URL character to avoid escaping problems
-   out of the unreserved URL characters (`-` / `.` / `_` / `~`, see https://www.rfc-editor.org/rfc/rfc3986#section-2):
    -   `.` would result in a hidden file on UNIX systems
    -   `_` comes after alpha-numerical characters in ASCII
    -   `~` is a shortcut for the user's home directory in many shells

Many command-line programs treat `-index.json` as a flag, hence a command such as `cat -index.json` returns an error. Prepending `./` to the filename avoids the problem: `cat ./-index.json`.

## Dataset mirrors

### Example configuration

#### Apache

```xml
<VirtualHost *:80>
    Alias / /path/to/local/directory/
    <Directory "/path/to/local/directory/">
        Require all granted
        Options +Indexes
    </Directory>
</VirtualHost>
```

To use another port, remember to edit _/etc/apache2/ports.conf_ as well.

#### Nginx

```nginx
server {
    listen 80;
    location / {
        alias /path/to/local/directory/;
        autoindex on;
        sendfile on;
        tcp_nopush on;
        sendfile_max_chunk 1m;
    }
}
```

#### Upload a dataset

-   `python3 -m undr check-conformity /path/to/dataset`

-   Caveat: An UNDR server can provide multiple compressed files (different formats) for each resource. The Python UNDR library always picks the best compression (smallest encoded size). `check-conformity` only checks the best compression and will not report errors for other compressions.

-   for macOS users (.DS_Store)

Add to _~/.zshrc_:

```sh
# rmdsstore removes .DS_Store files recursively
rmdsstore() {
    if [ $# -eq 0 ]; then
        printf 'usage: rmdsstore directory\n' >&2
        return
    fi
    find "$1" -name ".DS_Store" -delete -print
}
```

Run `rmdsstore /path/to/dataset` before running `python3 -m undr check-conformity /path/to/dataset`.

### Download with existing CLI

```sh
wget --no-parent --recursive --level=inf http://localhost:5432/dvs09/
find . -iname '\*.br' | while read filename; do brotli -d -j "$filename"; done;
```

## Build documentation

```sh
# setup
python3 -m venv .venv
source .venv/bin/activate
python documentation/build.py
```

pip install --upgrade pip setuptools
pip install -r documentation/requirements.txt

# compilation

cd python
pip install .
cd ..
sphinx-build documentation documentation/\_build