Caterva2 is a service meant for serving Blosc2 and HDF5 datasets among authenticated users, work groups, or the public. There are several interfaces to Caterva2, including a web GUI, a REST API, a Python API, and a command-line client.
It can be used either remotely or locally, as a simple way to access datasets in a directory hierarchy, or to share them with other users in the same network.
The Python API is the recommended way for building your own Caterva2 clients, whereas the web client provides a more user-friendly interface for browsing and accessing datasets.
The main role of the Caterva2 package is to provide a simple and lightweight library to build your own Caterva2 clients. The variety of interfaces available allows you to choose the one that best fits your needs. For example, querying a dataset from source can be accomplished :
- Via the web GUI using a browser
- Via the Python API
client = cat2.Client("https://cat2.cloud/demo")
client.get("@public/examples/tomo-guess-test.b2nd")
- Via the command line client
cat2cli info @public/kevlar/entry/data/data.b2nd
In addition, as Caterva2 supports authentication, all client interfaces expose a way to log in and access private datasets. Administration of authenticated users may be done using the internal mechanics of Caterva2 (see section "User authentication" below).
You may install Caterva2 in several ways:
-
Pre-built wheel from PyPI:
python -m pip install caterva2
-
Wheel built from source code:
git clone https://github.com/ironArray/Caterva2 cd Caterva2 python -m build python -m pip install dist/caterva2-*.whl
-
Developer setup:
git clone https://github.com/ironArray/Caterva2 cd Caterva2 python -m pip install -e .
When a user uses a client (web GUI, REST API, Python API, or command line) to query datasets, the client will connect to a Caterva2 subscriber service, which
accesses the relevant datasets stored either locally or remotely. The subscriber services may be managed via the command line by installing the caterva2
package with the [subscriber]
extra feature (we also wish to use the command line client, so we will also install the clients
extra too):
python -m pip install caterva2 [subscriber, clients]
In general, if you intend to run Caterva2 services, client programs, or the test suite, you need to enable the proper extra features by appending [feature1,feature2...]
to the last argument of pip
commands above. The following extras are supported:
subscriber
for running the Caterva2 subscriber serviceclients
to use Caterva2 client programs (command-line or terminal)blosc2-plugins
to enable extra Blosc2 features like Btune or JPEG 2000 supportplugins
to enable web GUI features like the tomography displaytools
for additional utilities likecat2import
andcat2export
(see below)tests
if you want to run the Caterva2 test suite
After installing with the [tests]
extra, you can quickly check that the package is sane by running the test suite (that comes with the package):
python -m caterva2.tests -v
You may also run tests from source code:
cd Caterva2
python -m pytest -v
Tests will use a copy of Caterva2's root-example
directory. After they finish, state files will be left under the _caterva2_tests
directory for inspection (it will be re-created when tests are run again).
In case you want to run the tests with your own running daemons, you can do:
env CATERVA2_USE_EXTERNAL=1 python -m caterva2.tests -v
Neither root-example
nor _caterva2_tests
will be used in this case.
(Find more detailed step-by-step tutorials in Caterva2 documentation.)
For the purpose of this quick start, let's use the datasets within the root-example
folder:
cd Caterva2
ls -F root-example/
README.md dir2/ ds-1d-fields.b2nd ds-2d-fields.b2nd ds-sc-attr.b2nd
dir1/ ds-1d-b.b2nd ds-1d.b2nd ds-hello.b2frame
Now:
- create a virtual environment and install Caterva2 with the
[subscriber,clients]
extras (see above). - copy the configuration file
caterva2-standalone.sample.toml
tocaterva2.toml
.
For more advanced configuration options, see the fully documented caterva2.sample.toml
file (see also caterva2.toml in Caterva2 tutorials). Subscribers (and clients, to a limited extent) may get their configuration from a caterva2.toml
file at the current directory (or an alternative file given with the --conf
option).
Then run the subscriber:
CATERVA2_SECRET=c2sikrit cat2sub & # subscriber
The CATERVA2_SECRET
environment variable is obligatory and is explained below in the following section.
The Caterva2 subscriber includes some support for authenticating users. To enable it, run the subscriber with the environment variable CATERVA2_SECRET
set to some non-empty, secure string that will be used for various user management operations. Note that new accounts may be registered, but their addresses are not verified. Password recovery does not work either.
To create a user, you can use the cat2adduser
command line client. For example:
cat2adduser [email protected] foobar11
Client queries then require the same user credentials:
- The user will be prompted to login when accessing the web client using a browser
- The Python API client can be authenticated in the following way:
client = cat2.Client("https://cat2.cloud/demo", ('[email protected]', 'foobar11'))
- The command line client can be authenticated with the
--user
and--pass
options
Now that the services are running, we can use the cat2cli
client to talk
to the subscriber. In another shell, let's list all the available roots in the system:
cat2cli --user "[email protected]" --pass "foobar11" roots
@public (subscribed)
@personal (subscribed)
@shared (subscribed)
First let's upload a file from the root-example
folder to the @personal
root:
cat2cli --username [email protected] --password foobar11 upload root-example/ds-1d.b2nd @personal/ds-1d.b2nd
Now, one can list the datasets in the @personal
root and see that the uploaded file appears
cat2cli --username [email protected] --password foobar11 list @personal
>> ds-1d.b2nd
Let's ask the subscriber for more info about the dataset:
cat2cli --username [email protected] --password foobar11 info @personal/ds-1d.b2nd
Getting info for @personal/ds-1d.b2nd
{
'shape': [1000],
'chunks': [100],
'blocks': [10],
'dtype': 'int64',
'schunk': {
'cbytes': 5022,
'chunkshape': 100,
'chunksize': 800,
'contiguous': True,
'cparams': {'codec': 5, 'codec_meta': 0, 'clevel': 1, 'filters': [0, 0, 0, 0, 0, 1], 'filters_meta': [0, 0, 0, 0, 0, 0], 'typesize': 8, 'blocksize': 80, 'nthreads': 1, 'splitmode': 1, 'tuner': 0, 'use_dict': False, 'filters, meta': [[1, 0]]},
'cratio': 1.5929908403026682,
'nbytes': 8000,
'urlpath': '/home/lshaw/Caterva2/_caterva2/sub/personal/2fa87091-84c6-44f9-a57e-7f04290630b1/ds-1d.b2nd',
'vlmeta': {},
'nchunks': 10,
'mtime': None
},
'mtime': '2025-05-29T09:11:26.860956Z'
}
This command returns a JSON object with the dataset's metadata, including its shape, chunks, blocks, data type, and compression parameters. The schunk
field contains information about the underlying Blosc2 super-chunk that stores the dataset's data.
There are more commands available in the cat2cli
client; ask for help with:
cat2cli --help
To see how to use the Python and REST API and web GUI, check out the Caterva2 documentation. You'll also find more information on how to use Caterva2, including tutorials, API references, and examples here.
That's all folks!