Sardana integration into the Jupyter ecosystem.

This is still a WIP. For a list of missing features/ideas see TODO.md.

Note: The old plotting mechanism was made using PlotlyJS, you can see how it worked, here.

Create the image:

docker build --label jupysar-demo --tag jupysar-demo -f ./docker/Dockerfile .

Run the container:

sudo docker run -dp 8888:8888 -dp 8050:8050 --name jupysar-demo jupysar-demo

• Wait some seconds
• Open localhost:8888
• Wait for a popup to come up, then select "Build"
• Wait for another popup to come, select "Save and Reload"
• Open the Notebook /examples/example_macros.ipynib with Sardana Kernel selected
• Follow the guide

You can install the necessary dependencies using conda.

conda env create -f environment.yml
conda activate sardana-jupyter
./scripts/setup.bash

Currently the Jupyter extension only instantiate the MacroServer part. You can still use it on its own but it may be not so interesting...

In order to use the full Sardana system you will need to run your Pool instance as a Tango device server and point to it in the configuration file (see the "Configuration" section below).

Then to start the Jupter Lab you just need:

jupyter lab examples/example_macros.ipynb

And then select the Sardana Kernel Notebook.

sardana-jupyter can be configured using a YAML file. The file location must be set using the SARDANA_JUPYTER_CONF environment variable. You can use the example configuration file as a template.

cp ./examples/sardana-jupyter.yml $HOME/sardana-jupyter.yml
export SARDANA_JUPYTER_CONF=$HOME/sardana-jupyter.yml

In the file you can set the following keys:

• name - name of your jupyter macroserver instance e.g. test, dummy, etc.
• poolNames - pool(s) you would like to connect to e.g. Pool_test_1
• macroPath - path(s) to your macros e.g. <install-dir>/sardana/macroserver/macros/examples
• recorderPath = path(s) to your recorders e.g. <install-dir>/sardana/macroserver/recorders/examples

Note: You should have ran sar_demo before.

Run kernel tests:

python test/__init__.py