Cardiac Tissue model (myocytes) from Colleen Clancy (C++ code)

[mguidon]

User story

As a user I want to create a pipeline consisting of the cardiac tissue model from Colleen Clancy and a post-processing service that reproduces the plots in the official MVP use case. I am able to edit all necessary input settings directly in the frontend. Optionally, I would like to have the option to choose a compiler and compiler flags in case I want to build the kernel first.

Definition of Done

• The model is available in the list of services
• The required pipeline can be created
• The code is containerized as binary and as code only with compilation performed on the fly
• For the second part of the pipeline, either python or octave/matlab will be used
• check if there is a need/possibility to have settings (property window)
• the results of the python postpro script should be mapped as time series to the outputs, such that they can be visualized with the 2d plot service (item was modified)

Use case
There is documentation/code/Kepler example for this case in filesrv. In addition, the full workflow is available on MaG's VM.

[mguidon]

for the future: https://mybinder.org/

[esraneufeld]

the option to first compile the code is lower priority than the rest of this case.

[sanderegg]

• upload 2 input data files to S3 (File Upload S3 and Blackfynn #91 Properties/Settings Input (Window) #20 )
  • filereader node (Properties/Settings Input (Window) #20 )
• allow defining 6 parameters (Properties/Settings Input (Window) #20 )
  (- [ ] optional parameter as file that disables the 6 parameters)
• set the pipeline configuration in the workbench-database (Pipeline creation #26 )
• solver(s) must be dockerized and available as dynamic service(s)
• label docker image with ports configuration (services) (Get list of services in frontend from director #100)
• list of computational services (Get list of services in frontend from director #100)
  • director
  • frontend
• pipeline control (connect pipeline control frontend-director #101)
  • director
  • frontend
• connect logger frontend-services (connect pipeline control frontend-director #101)
  (- [ ] push logs to database)
• 2D viewer: (Analysis and visualization (2D) #4 )
  • convert matlab files to python / plotly
  • add the 6 plots to the jupyter node
  • connect to S3/database
  • dockerize - make available as service
• user registration (invite external user) (Multi-User Framework Setup #19 )

(- [ ] automatic deployment of master (IT) (#99 ))
(- [ ] web access to the frontend (#98 ))

[drniiken]

For the very moment, we are happy with the Jupyter based 2D plot visualization.