Note: This repository, API endpoint and correspondning site is under active development, is not fully (or at all) functional, and is provided as-is
This repository aims to provide and demo tools for researchers in preparation of gaining access to digital archives on-premise at the National Library, or anyone else wanting to access collections without active copyright. There are two main ways to access digital objects: either by using the HTTP API directly or using the provided client written in Python. You can also create a Docker image based on the one below which will have the client installed, or use the install the client using pip or conda in your own container. The data available outside the National Library, currently on https://betalab.kb.se, does not have active copyright.
To install the client module using pip simply run
pip install kblab-client
To install using conda instead use the following
conda install kblab-client
Or add it to your dependencies in environment.yml
dependencies:
- pip:
kblab-client
Then, see examples below.
Start environment using docker. The local directory ./data will be mounted on /data in the container. Any change from within the container will be reflected in the local directory and vice versa.
git clone https://github.com/kungbib/kblab
cd kblab
docker build --tag repository.kb.se/lab/client:local .
docker container run -it repository.kb.se/lab/client:local /bin/bash
d8fg7sjf4i # python
Then, see examples below.
First check out the source code
git clone https://github.com/kungbib/kblab
cd kblab
Then either build and run the Docker image
docker build .
docker run -it <image id> /bin/bash
Or install the required package and python client, optionally creating a virtual environment so as to not mess up you existing one.
python -m venv venv
source venv/bin/activate
pip install -r requirement.txt
(cd client && ./setup.py install)
Then, see examples below.
The API is a simple REST-based API that delivers JSON(-LD-ish describing packages and/or files with the addition of a search endpoint.
Examples
- https://betalab.kb.se/dark-29967/
- https://betalab.kb.se/dark-29967/_view
- https://betalab.kb.se/dark-29967/bib4345612_18620103_0_s_0001_alto.xml
Packages may contain files of type Structure, Content or Meta which contain structure information, content and metadata respectively (see below for examples). The meta and content files are indexed and can be searched through the API. Content is indexed under content and metadata under meta.* and can be accesed either through the web interface or through the API. For example:
Example: Get all packages tagged with SOU created in 1927: tags:SOU meta.created:1927.
Content is indexed under content which is also the default prefix so the query "olof palme" and "carl bildt" will find any package where the phrase "olof palme" and "carl bildt" appear.
Also: see examples below.
The National Library uses a package structure modeled on OAIS. A simplified representation in JSON-LD is provided as part of the response in addition to information about the structure of the material (e.g pages, covers), some metadata, links to physical object, etc.
Indexing is experimental at this point so verify your results.
{
"@id": "#1",
"@type": "Part",
"derived_from": "https://.../1927_1(librisid_13483334).pdf",
"has_part": [
{
"@id": "#1-1",
"@type": "Page",
"has_part": [
{
"@id": "#1-1-1",
"@type": "Area"
"has_part": [
{
"@id": "#1-1-1-1",
"@type": "Text"
}
]
}
]
}
]
}
[
{
"@id": "#1-1-1-1",
"content": "..."
}
]
{
"created": "1923",
"title": "An example"
...
}
from kblab import Archive
# connect to betalab. Use parameter auth=(username, password) for authentication
a = Archive('https://betalab.kb.se', auth=('username', 'hunter2'))
Caveat: if you get an error about "certificate verify failed" you may need to update the root certificates on you platform. You can also add the following lines to your code. Please not that this is NOT ADVISED, it is better to add the correct root certificates.
import kblab
kblab.VERIFY_CA=False
for package_id in a.search({ 'content': 'test' }, max=20):
package = a.get(package_id)
# do something with package
...
for file in package:
content = package.open(f).read()
...
from collections import Counter
from kblab import Archive
from json import load
a = Archive('https://betalab.kb.se/')
c = Counter()
# get 25 issues of Aftonbladet
for package_id in a.search({ 'label': 'AFTONBLADET' }, max=25):
print(package_id)
p = a.get(package_id)
if 'content.json' in p:
for part in load(p.get_raw(fname)):
c.update(part.get('content', '').toupper().split())
for word,count in c:
print(word, count, sep='\t')
When processing large result sets parallelization can be crucial. This can be achieved either through using the multiprocessing module or the map method on the search result and parameter multi=True. A parallelized version in the example above could look like:
from collections import Counter
from kblab import Archive
from json import load
import kblab
a = Archive('https://betalab.kb.se/')
c = Counter()
def count(package_id):
print(package_id)
c = Counter()
p = a.get(package_id)
if 'content.json' in p:
for part in load(p.get_raw(fname)):
c.update(part.get('content', '').toupper().split())
return c
# loop over 25 issues of Aftonbladet
for words in a.search({ 'label': 'AFTONBLADET' }, max=25).map(count, multi=True):
c.update(words)
for word,count in c.items():
print(word, count, sep='\t')
The number of processes is specified by the processes parameter, it defaults to the number of cores on the machine running the program. For optimal performance, and if the order of the result is not important, add parameter ordered=False to map(...).
Parallelization using multiprocessing.Pool would look something like this:
...
from multiprocessing import Pool
def f(package_id):
# same as above
...
with Pool() as pool:
for words in pool.imap(f, a.search({ 'label': 'AFTONBLADET' }, max=25)):
c.update(words)
...
Images in the archive can either be downloaded and dealt with directly in full resolution or they can be cropped and scaled using the IIIF protocol.