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This commit updates the python code to work with python 3 and with a
newer version of matplotlib.
- zip_longest -> izip_longest
- fix tick marks for log plots
- scipy.misc -> scipy.special
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This commit adds a script called print-event-rate which calculates the
event rate per year from the GENIE MCPL files. The livetime comes from
the autosno run_info.log file. The output of the script is a latex table
that I included in my thesis.
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This commit updates the chi2 and dm-search scripts to add the ability to
pass a run list on the comand line.
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This commit updates get_events() to calculate the livetime based on both
the number of pulse gt events and the 10 MHz clock and to return it in a
dictionary stored with the dataframe.
I also update dm-search so that the results are now reported as a
function of events/cm^3/s.
Also updated radius cut to be the AV radius.
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This commit updates both the chi2 and dm-search scripts to run nlopt at
the end of the MCMC to find the best fit point.
The reason for this is that for the dm-search script, we rely on the
best fit point being correct when doing the discovery threshold
analysis. To really get the best fit point with the MCMC we would need
to run a ridiculous number of steps, so it's better to run a fewer
amount of steps to get close and then run the minimization from there.
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This commit fixes an unnecessary call to set the index on the ev
dataframe. This was causing issues when trying to process run 11903
since it didn't have any fits to merge into the ev dataframe.
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This commit updates the retrigger cut to cut events where the previous
event is missing, so that even if I forget to run the analysis with all
the orphan events included, we will cut events potentially coming after
an instrumental or muon that got cut by the junk cut.
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This commit updates get_events() to only cut orphans instead of all JUNK
events before calculating time differences. The reason is that some
large instrumental events (or muons) can get flagged as JUNK events
since they sometimes have multiple hits from the same PMT. If we remove
them before calculating the time difference, the follower might not get
cut. See run 10040 GTID 349491 for an example.
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This commit fixes a typo in submit-grid-jobs-queue by fixing get_job()
-> get_entry(). I also decided not to remove held jobs for now, because
it's easier for me to see how many are held when running condor_q.
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This commit updates the chi2 script to add scale factors for the
atmospheric neutrino scale and muon scale parameters. The reason for
this is that these parameters should now have a much easier
interpretation than before. Now, the atmospheric neutrino scale
parameter is relative to the expected atmospheric neutrino flux (i.e. we
expect the fit to return something close to 1), and the muon scale
parameter is the total number of expected muons in our sample, which is
exactly what the data cleaning analysis gives us.
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This commit updates the chi2 script to skip minimzing the likelihood
before running the MCMC. The reason for this is that I was occasionally
seeing a linear algebra error about a singular matrix when running the
MCMC with the KDEMove which I suspect is caused by too many points being
in the same place.
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This commit updates the chi2 script to only merge the weights dataframe
with the Monte Carlo dataframe after we have selected the universe for
the weights. This *greatly* reduces the memory usage.
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This commit fixes the chi2 analysis so that it is no longer biased.
Previously, the chi2 analysis pull plots showed a consistent bias. At
first, I thought this was due to the fact that the posterior wasn't
gaussian, but even after switching to percentile plots based on the
algorithm outlined in "Validating Bayesian Inference Algorithms with
Simulation-Based Calibration", I was still seeing a bias. I finally
tracked it down to the fact that I was applying the energy scale
parameters to the data instead of the Monte Carlo. Therefore, in this
commit I update the posterior to now apply the energy scale parameters
to the Monte Carlo instead of the data. This has the slight disadvantage
that the final histograms will be binned in the biased energy, but
that's not really a big deal.
In addition, this commit contains several other updates:
- switch to plotting percentile plots based on the algorithm in
"Validating Bayesian Inference Algorithms with Simulation-Based
Calibration" instead of pull plots
- apply both the energy scale and resolution at the individual particle
level, i.e. there is no longer an energy resolution term for electron
+ muon fits
- separate pull plots and coverage plots. Previously I was making both
the p-value coverage plots and the pull plots at the same time.
However, the pull plots shouldn't have anything to do with the GENIE
weights whereas the p-value coverage plots should draw samples
weighted by the GENIE weights. In addition, for the pull plots we draw
new truth parameters on every iteration whereas for the p-value
coverage plots we only draw them once.
- switch to using KDEMove() for the MCMC since I think it samples
multimodal distributions a lot better than the default emcee move.
- I now correct for the reconstruction energy bias in plot-michel and
plot-muons
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This commit updates how the energy bias is applied when we correct for
the energy bias in correct_energy_bias(). The correct way to apply this
correction is to compute:
T_corrected = T_reco/(1+bias)
whereas previously we were multiplying by (1-bias).
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