saq_analysis

analysis of data from Q-Pix SAQ

So far:

1. make_templates_root.py
   Simulate reset data (aka "templates"). Run many instances in parallel (on noether).
   Produces several .root files containing parameter values and simulated resets (per area).
   Upgrade: write "batch" script that farms out make_templates_root.py jobs to multiple processors
2. templates_merge.py
   Produce a single Pandas DataFrame containing all templates. Store in a pickle file
3. template_compute_chisq.py
   Compute and save the optimal scaling factor and sum of squares of residuals between data and model. Store in a pickle file.
4. template_compute_optimal_parameter_sets.py
   For each set of nuissance parameters, choose the set of diffusion parameters that minimizes the overall chi-squared. Store in a pickle file.
5. template_analysis.py
   Plot the data and best fit models (not yet mature).

Helper functions

• offset_simulation.py A collection of useful helper functions. (import offset_simulation as off)
• pressure_scan_data_to_root.py Convert a collection of .csv files containing reset data into a single .root file
• pressure_scan_data_read_root.py Read in ROOT files containing pressure scan data

To do:

• could likely avoid ROOT altogether and just use Pandas (e.g. for reading/saving pressure scan data)