Warning: | All the maintainers of this project have moved on so this project isn't receiving support anymore. If you wish to revive the project, reach out to me and we'll make it happen. |
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Alternatives: | Check out gutenbergpy. |
This package contains a variety of scripts to make working with the Project Gutenberg body of public domain texts easier.
The functionality provided by this package includes:
- Downloading texts from Project Gutenberg.
- Cleaning the texts: removing all the crud, leaving just the text behind.
- Making meta-data about the texts easily accessible.
The package has been tested with Python 3.7+.
An HTTP interface to this package exists too. Try it out!
This project is on PyPI, so I'd recommend that you just install everything from there using your favourite Python package manager.
pip install gutenberg
If you want to install from source or modify the package, you'll need to clone this repository:
git clone https://github.com/c-w/Gutenberg.git
Now, you should probably install the dependencies for the package and verify your checkout by running the tests.
cd Gutenberg
virtualenv --no-site-packages virtualenv
source virtualenv/bin/activate
pip install -r requirements-dev.pip
pip install .
nose2
Alternatively, you can also run the project via Docker:
docker build -t gutenberg .
docker run -it -v /some/mount/path:/data gutenberg python
This package depends on BSD-DB and you will need to manually install it.
If getting BSD-DB to run on your platform is difficult, take a look at gutenbergpy which only depends on SQLite or MongoDB.
On Linux, you can usually install BSD-DB using your distribution's package manager. For example, on Ubuntu, you can use apt-get:
sudo apt-get install libdb++-dev
export BERKELEYDB_DIR=/usr
pip install .
On Mac, you can install BSD-DB using homebrew:
brew install berkeley-db4
pip install .
On Windows, it's easiest to download a pre-compiled version of BSD-DB from pythonlibs which works great.
For example, if you have Python 3.5 on a 64-bit version of Windows, you
should download bsddb3‑6.2.1‑cp35‑cp35m‑win_amd64.whl
.
After you download the wheel, install it and you're good to go:
pip install bsddb3‑6.2.1‑cp35‑cp35m‑win_amd64.whl
pip install .
Since its v6.x releases, BSD-DB switched to the AGPL3 license which is stricter than this project's Apache v2 license. This means that unless you're happy to comply to the terms of the AGPL3 license, you'll have to install an ealier version of BSD-DB (anything between 4.8.30 and 5.x should be fine). If you are happy to use this project under AGPL3 (or if you have a commercial license for BSD-DB), set the following environment variable before attempting to install BSD-DB:
YES_I_HAVE_THE_RIGHT_TO_USE_THIS_BERKELEY_DB_VERSION=1
As an alternative to the BSD-DB backend, this package can also use Apache Jena Fuseki
for the metadata store. The Apache Jena Fuseki backend is activated by
setting the GUTENBERG_FUSEKI_URL
environment variable to the HTTP
endpoint at which Fuseki is listening. If the Fuseki server has HTTP basic
authentication enabled, the username and password can be provided via the
GUTENBERG_FUSEKI_USER
and GUTENBERG_FUSEKI_PASSWORD
environment
variables.
For local development, the Fuseki server can be run via Docker:
docker run \
--detach \
--publish 3030:3030 \
--env ADMIN_PASSWORD=some-password \
--volume /some/mount/location:/fuseki \
stain/jena-fuseki:3.6.0 \
/jena-fuseki/fuseki-server --loc=/fuseki --update /ds
export GUTENBERG_FUSEKI_URL=http://localhost:3030/ds
export GUTENBERG_FUSEKI_USER=admin
export GUTENBERG_FUSEKI_PASSWORD=some-password
from gutenberg.acquire import load_etext
from gutenberg.cleanup import strip_headers
text = strip_headers(load_etext(2701)).strip()
print(text) # prints 'MOBY DICK; OR THE WHALE\n\nBy Herman Melville ...'
python -m gutenberg.acquire.text 2701 moby-raw.txt
python -m gutenberg.cleanup.strip_headers moby-raw.txt moby-clean.txt
A bunch of meta-data about ebooks can be queried:
from gutenberg.query import get_etexts
from gutenberg.query import get_metadata
print(get_metadata('title', 2701)) # prints frozenset([u'Moby Dick; Or, The Whale'])
print(get_metadata('author', 2701)) # prints frozenset([u'Melville, Hermann'])
print(get_etexts('title', 'Moby Dick; Or, The Whale')) # prints frozenset([2701, ...])
print(get_etexts('author', 'Melville, Hermann')) # prints frozenset([2701, ...])
You can get a full list of the meta-data that can be queried by calling:
from gutenberg.query import list_supported_metadatas
print(list_supported_metadatas()) # prints (u'author', u'formaturi', u'language', ...)
Before you use one of the gutenberg.query
functions you must populate the
local metadata cache. This one-off process will take quite a while to complete
(18 hours on my machine) but once it is done, any subsequent calls to
get_etexts
or get_metadata
will be very fast. If you fail to populate the
cache, the calls will raise an exception.
To populate the cache:
from gutenberg.acquire import get_metadata_cache
cache = get_metadata_cache()
cache.populate()
If you need more fine-grained control over the cache (e.g. where it's stored or
which backend is used), you can use the set_metadata_cache
function to switch
out the backend of the cache before you populate it. For example, to use the
Sqlite cache backend instead of the default Sleepycat backend and store the
cache at a custom location, you'd do the following:
from gutenberg.acquire import set_metadata_cache
from gutenberg.acquire.metadata import SqliteMetadataCache
cache = SqliteMetadataCache('/my/custom/location/cache.sqlite')
cache.populate()
set_metadata_cache(cache)
This project deliberately does not include any natural language processing functionality. Consuming and processing the text is the responsibility of the client; this library merely focuses on offering a simple and easy to use interface to the works in the Project Gutenberg corpus. Any linguistic processing can easily be done client-side e.g. using the TextBlob library.