We present the publicly available, open source code UCLCHEMCMC, designed to estimate physical parameters of an observed cloud of gas by combining Monte Carlo Markov Chain (MCMC) sampling with chemical and radiative transfer modeling. When given the observed values of different emission lines, UCLCHEMCMC creates and stores chemical and radiative transfer models in an SQL database. These models are then used for the Inference to create the posteriors using the emcee package. The storing of the models is done in order to prevent redundant model calculations in future inferences.
For more details, we will add a link to the ApJ (Accepted) and astro-ph publication as soon as they go live.
Required Packages: pandas numpy corner matplotlib emcee billiard bokeh flask celery Required Software: redis
Compile UCLCHEM:
In directory
/UCLCHEMCMC/src/UCLCHEM/src/
call
make
make python
Compile Spectral Radex:
In directory
/UCLCHEMCMC/src/SpectralRadex
call
python3 setpu.py
To Run UCLCHEMCMC:
In directory
/UCLCHEMCMC/
call
bash runUCLCHEMCMC.sh
OR
in three different terminals, or terminal windows, call the following:
bash ./run-redis.sh
python GUI.py
celery worker -A GUI.celery --loglevel=info
Upon finishing that, open a browser and go to following address
http://localhost:5000/
Once the browser has been opened to localhost, and the Inference tab on the left has been selected. You should see the ability to input the name of a previous session or the option to choose a Coarse or Fine Grid. Inputing a session name and hitting load will take you to the Results page, while selecting a Grid should take you to the following page:
Upon submitting and verifying the Parameter ranges, you will be greeted by the page to choose species and input observed emission line information as seen below.
This is followed by the options page, where a session name can be given, number of walkers can be choosen and the amount of steps the inference should take prior to saving the inference can be input. This then takes you to the final page, the results where you have the option to start the inference as well as see the current Corner Plots of the inference.
