WATCHStat is a software package that is used to:

• generate simulated WATCHMAN experiments assuming poisson statistics for signal/backgrounds
• Run analyses on the simulated experiments to evaluate WATCHMAN's expected reactor monitoring performance
• Plot the results of analyses for straightforward evaluation and presentation

The library is written in a way that Each step could be independently utilized in someone else's code if desired. However, the main.py provides a base script for performing the first two steps and generating output that can be used in the third step.

USAGE

python main.py --help

QUICK START

The following is a quick-start guide to using the main script to run the Dwell Time analysis. Before starting, make sure you have the following information:

• Your WATCHMAN detector's specifications. Currently needed specifications for the code that generates an experiment's signal/background rates is:
  • Photocoverage
  • Buffer volume
  • PMT type (low_radioactivity or normal_radioactivity)
  • PMT orientation ("center_point" or "normal_point")
  • Are PMT shields used ("true" or "false")

1. Enter the ./DB/ directory and start a new JSON file with the same structure as the BoulbySignalBackground.json default.

2. Enter the signal/background rates and WATCHMAN detector configuration as is done for entries in the BoulbySignalBackground.json default file.

3. Enter the lib/config/ directory and open DBConfig.py. Configure the database reader to read from your JSON file and to look for the WATCHMAN detector configuration that you want to simulate.

4. in the lib/config/ directory, open schedule_config.py. Configure the reactor operation schedule to your liking. The methodology behind this config file is to specify the length of outages, spacing between outages, and when the first long outage occurs. The structure of the operation schedule is formed automatically around these dimensions. Default is the optimal Hartlepool operational structure based on past data.

5. Go to WATCHStat's home directory and run the following: $ python main.py -d -j -10 --debug

   This will do several things:

   • Show various plots giving details on what your generated operation schedule looks like
   • Proceed to generate the simulated experiments and run the dwell time analysis on each experiment
   • Save the results of all 3-sigma observation days to the file jobresults/results_j10.json

6. Open python on the command line (I like to use ipython) and do: $ import json $ with open("./jobresults/results_j10.json","r") as f: thedata = json.load(f)

We have now loaded our results JSON as a python dictionary. To plot the cumulative distribution of the dwell time results, do:

$ import plot.CLGraph as clg $ ourgrapher = clg.DwellTimeCL(thedata)

Upon generating the ourgrapher object, a plot of the cumulative distribution should come up. To see possible methods you can use to tweak the graph, do

$ ourgrapher. [HIT THE TAB BUTTON]

And to see what a method does, do $ ourgrapher.amethod?