The fit-and-plot set of scripts aims to facilitate the post-processing of TD-DFT(B) and Trajectory Surface Hopping (TSH) simulations performed with the deMon-Nano code on High-Performance Computing clusters.

However, these scripts can be easily adapted for any other software.

• plot-tsh-pops.py : plot averaged populations and fit the initial one with a single exponent
• plot-tsh-occs.py : plot averaged occupations and fit the initial one with a single exponent

• python 3
  • matplotlib
  • numpy
  • scipy
  • pandas

Note: I recommend using virtual environments to avoid compatibility issues.

git clone https://github.com/q-posev/fit_and_plot
cd fit_and_plot

You're ready to go!

The script can be launched using:

python plot-tsh-pops.py -h oly -f 666/ -t 100 -n 9 -s 8 --sum 4 -m chrysene

• -h is the name of the HPC machine (s10 or oly)
• -f is the name of the folder with TSH results (not required for s10)
• -t is the number of trajectories to analyze
• -n is the total number of excited states in the propagation
• -s is the initially excited state (its population will be exponentially fitted)

Note: You need to have a todolist file (with ID-s of trajectories) in the same directory as the script. This is particularly important for results from s10 cluster.

• --sum can be used if accumulated population has to be plotted for low-lying states (in this example, accumulated population of 4 lowest states will be plotted)
• -m or --mol_name or is the name of the molecule (will be added to the output file name)
• -pe or --plt_err can be used to plot statistical error for the fitted population
• -i or --info to output the full list of required and optional arguments

python plot-tsh-occs.py -h s10 -t 100 -n 9 -s 8 --sum 4 -m chrysene --gaps

All arguments are the same as in previous (populations) case.

One additional argument (--gaps or -g) can be used to plot the energy gap between the intially excited state and the one below in energy along each trajectory together with the averaged (over an entire ensemble) value.