UCL_PDR is a PDR code which solves the equilibrium chemistry and temperature of an arbitrary 1D cloud. It was written by Bell et al. 2006 and modified by Priestley et al. 2017. The code is no longer maintained by Viti group, the code remains available online as a reference.
First, get ahold of a copy of sundials version 2.5.0. A copy is provided within this reposistory. We thank the Sundials developers and acknowledge their license and terms. If you use this repository as a mirror, please do so as well. You can install sundials 2.5.0 using a few easy steps:
Go into the sundials directory and run the following commands with the path where you want to install sundials
./configure --prefix $PWD/../sundials
make
make install
First go into the source directory, this allows you to install run the make command:
cd Source/
make
If you get an error with regards to sundials not being found, please edit the INCLUDES and LIBRARIES to absolute paths on your system. It can then be executed via (assure your path is no longer than 256 characters.)
./UCL-PDR INPUT_FILE_PATH
UCL_PDR will then use the contents of INPUT_FILE_PATH to determine the parameters of the model to be run.
For example try:
./UCL-PDR Input/10_1e3.dat
A key input is the cloud file. The name of this file should be supplied in Input/model-parameters.dat and many cloud files exist in Clouds/. These are csv files containing the x co-ordinate and density of a series of points at which UCL_PDR will evaluate the equilibrium state. Scripts/constant_density_cloud.py and Scripts/make_cloud.py provide convenient ways to create new cloud files.
Chemical networks can be changed by copying the relevant files. All networks are stored in Chemical-Networks/, they contain .dat files which should be moved to Datafiles/Chemical-Network/ and .c files which should be moved to Source/ this mostly easily done by invoking
./Scripts/switch-network.sh full-network
where full-network is the name of the desired network in this example.
UCL_PDR produces a huge amount of output however, this can all be compressed into an easily managed .csv file using
python Scripts/make_results_df.py my_prefix
where my_prefix is the file prefix set in Input/model-parameters.dat. This csv file can also be plotted using Scripts/plot_results.py.