TrkAna: Difference between revisions
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Now, if we want to store, for example, the <code> | Now, if we want to store, for example, the <code>deent</code> branch in the dataframe: | ||
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import matplotlib.pyplot as plt | import matplotlib.pyplot as plt | ||
trkananeg = uproot.open("trkana.root:TrkAnaNeg/trkana") | |||
trkananeg = | df = trkananeg(filter_name=['deent*'],library="pd") | ||
fig, ax = plt.subplots(1,1) | fig, ax = plt.subplots(1,1) | ||
Revision as of 02:16, 26 August 2022
Overview
TrkAna is a track-based ROOT TTree that can be used to help with analysis. Each entry in the TTree corresponds to a single fitted track and contains reconstructed information from the tracker, calorimeter and CRV. There is also the option to write out Monte Carlo truth information and additional information for other track types that might be important to an analysis.
How to Get Help
Feel free to send any questions to the #trkana Slack channel, or the 'Is It Me Or A Bug?' hypernews forum.
There is also a tutorial that is a few years old and will be updated soon.
For Analyzers
How To Create a TrkAna Tree
You can use the TrkAna Musing like so:
setup mu2e muse setup TrkAna mu2e -c TrkAna/fcl/TrkAnaReco.fcl -s mcs.art --TFileName trkana.root
where mcs.art contains the result of the Mu2e reconstruction.
In the trkana.root file, you will see two different TrkAna-related folders:
- TrkAnaNeg contains the results of the negatively-charged tracks, and
- TrkAnaPos contains the results of the positively-charged tracks.
TrkAna is designed to be flexibile so if this does not produce the type of tree you want, then there are fcl parameters that can be changed. A few examples are available here: https://github.com/Mu2e/TrkAna/tree/main/fcl and are given in the table below. Also, you can always ask for help on the #trkana channel on Slack
| fcl file | module label | candidate | supplements | notes |
|---|---|---|---|---|
| TrkAnaReco.fcl | TrkAnaNeg | downstream e-minus | upstream e-minus, downstream mu-minus | |
| TrkAnaPos | downstream e-plus | upstream e-plus, downstream mu-plus | ||
| CrvExpert.fcl | TrkAnaNeg | downstream e-minus | upstream e-minus, downstream mu-minus | adds crvinfo, crvinfomc, crvsummary, crvsummarymc, and crvinfomcplane branches
|
| TrkAnaPos | downstream e-plus | upstream e-plus, downstream mu-plus | ||
| TrkAnaReco_Upstream.fcl | TrkAnaNeg | upstream e-minus | downstream e-minus, upstream mu-minus | |
| TrkAnaPos | upstream e-plus | downstream e-plus, upstream mu-plus | ||
| TrkAnaReco_ceSimReco.fcl | TrkAnaNeg | downstream e-minus | none | for use with reco files generated with ceSimReco.fcl |
| fcl files below are flagged for updating / removal | ||||
| TrkAnaRecoEnsemble-MC.fcl | TrkAnaNeg | downstream e-minus | upstream e-minus, downstream mu-minus | for use with MDC2018 Ensemble datasets |
| TrkAnaPos | downstream e-plus | upstream e-plus, downstream mu-plus | ||
| TrkAnaRecoEnsemble-Data.fcl | TrkAnaNeg | downstream e-minus | upstream e-minus, downstream mu-minus | for use with MDC2018 Ensemble datasets, no MC information |
| TrkAnaPos | downstream e-plus | upstream e-plus, downstream mu-plus | ||
| TrkAnaReco_wTrkQualFilter.fcl | TrkAnaNeg | downstream e-minus | upstream e-minus, downstream mu-minus | an art filter module is run before creating the TrkAna tree |
| TrkAnaPos | downstream e-plus | upstream e-plus, downstream mu-plus | ||
| TrkAnaReco_MultipleTrkQual.fcl | TrkAnaNeg | downstream e-minus | upstream e-minus, downstream mu-minus | an additional TrkQual algorithm is used for each track |
| TrkAnaPos | downstream e-plus | upstream e-plus, downstream mu-plus | ||
How to Analyze a TrkAna Tree
Each of these contain TTrees that you can use ROOT or python
with ROOT
You can use the trkana tree like you would any other ROOT TTree: with the ROOT command line for data exploration, with ROOT macros to make plots etc.
with Python
- Note: the below is for uproot3
The best way to open ROOT files in Python is to use the uproot package. It does not require a working installation of ROOT, so it allows to read a ROOT file on basically any platform that supports Python (e.g. Colaboratory). It also provides a more pythonic bridge between ROOT and Numpy/pandas.
In order to open a ROOT TTree with uproot it's sufficient to write:
import uproot
trkananeg = uproot.open("trkana.root:TrkAnaNeg/trkana") # opens the 'trkana' tree in the 'TrkAnaNeg' folder
Now, if we want to store, for example, the deent branch in the dataframe:
df = trkananeg(filter_name=['deent*'],library="pd")
Once your data is stored in a pandas dataframe or in a numpy array you can plot it with the many plotting libraries available (matplotlib, plot.ly, etc.). This example shows how to plot an histogram with matplotlib:
import uproot
import matplotlib.pyplot as plt
trkananeg = uproot.open("trkana.root:TrkAnaNeg/trkana")
df = trkananeg(filter_name=['deent*'],library="pd")
fig, ax = plt.subplots(1,1)
n, bins, patches = ax.hist(df["deent.mom"],
bins=60,
range=(95,110),
label="Reco. momentum")
A class that allows you to query the dataframe and plot manipulated variables can be found at https://github.com/soleti/mu2e_plotter.
For Developers
If you will be contributing to the development of TrkAna, then you can fork TrkAna on GitHub and build it within Muse Like so:
Once you log in:
setup mu2e cd /your/working/directory git clone git@github.com:<Your GitHub Username>/Offline.git (or muse backing Offline vXX_YY_ZZ) git clone git@github.com:<Your GitHub Username>/TrkAna.git muse setup muse build -j4 --mu2eCompactPrint
Then you can make your developments and put in a pull request.
Version History / TrkAna Musings
This is the list of TrkAna Musings. The top row corresponds to the "current" version.
| TrkAna Musing | TrkAna tag | muse backings | muse stub | notes |
|---|---|---|---|---|
| v01_01_00 | v01_01_00 | SimJob MDC2020r --> Production v00_09_02 --> Offline v10_15_01 | sl7-prof-e20-p023 | adds TrkAnaUtils |
| (not built) | v01_00_01 | N/A | N/A | resolves MDC2020r incompatibility |
| v01_00_00a | v01_00_00 | Production v00_07_00 --> Offline v10_11_00 | sl7-prof-e20-p021 | updated for muse 1-path/2-path change |
| v01_00_00 | v01_00_00 | Production v00_06_00 --> Offline v10_10_01 | sl7-prof-e20-p018 | uses deprecated "muse link" |
Tree Structure
Here is a very rough description of the tree branches and where to find leaf definitions in the repository:
- evtinfo
- information about the event (TrkAna/inc/EventInfo.hh)
- hcnt
- count of various hit types (TrkAna/inc/HitCount.hh)
- tcnt
- count of various track types (TrkAna/inc/TrkCount.hh)
- de
- global fit information for downstream electron track (TrkAna/inc/TrkInfo.hh)
- deent
- local fit information for downstream electron track at tracker entrance (TrkAna/inc/TrkInfo.hh)
- demid
- local fit information for downstream electron track at middle of tracker (TrkAna/inc/TrkInfo.hh)
- dexit
- local fit information for downstream electron track at tracker exit (TrkAna/inc/TrkInfo.hh)
- detch
- calorimeter cluster information for cluster used in downstream electron track fit (tch = TrkCaloHit, TrkAna/inc/TrkCaloHitInfo.hh)
- dequal
- the output values of the TrkQual and TrkPID ANNs
- uetch
- calorimeter cluster information for cluster used in upstream electron track fit (tch = TrkCaloHit, TrkAna/inc/TrkCaloHitInfo.hh)
- ue
- global fit information for upstream electron track (TrkAna/inc/TrkInfo.hh)
- dm
- global fit information for downstream muon track (TrkAna/inc/TrkInfo.hh)
- trigbits
- unsigned int of the triggers
- crvinfo
- information about CRV coincidences (Offline/CRVAnalysis/inc/CrvHitInfoReco.hh)
- bestcrv
- element in crvinfo array that is the best
- demc
- MC information about particle that created downstream track (TrkAna/inc/TrkInfo.hh)
- demcgen
- MC information about particle that started the simulation (TrkAna/inc/GenInfo.hh)
- demcpri
- MC information about particle that would have ultimately created the GenParticle (TrkAna/inc/GenInfo.hh)
- demcent
- MC information about step of particle that created downstream track as it enters the tracker (TrkAna/inc/TrkInfo.hh)
- demcmid
- MC information about step of particle that created downstream track as it passes the middle of the tracker (TrkAna/inc/TrkInfo.hh)
- demcxit
- MC information about step of particle that created downstream track as it leaves the tracker (TrkAna/inc/TrkInfo.hh)
- crvinfomc
- MC information about CRV coincidences (Offline/CRVAnalysis/inc/CrvHitInfoMC.hh)
- detchmc
- MC information about calorimeter cluster used in the downstream electron fit (TrkAna/inc/CaloClusterInfoMC.hh)
- uetchmc
- MC information about calorimeter cluster used in the upstream electron fit (TrkAna/inc/CaloClusterInfoMC.hh)
- detshmc
- MC information about the straw hits used in the downstream electron fit (need diagLevel > 0, TrkAna/inc/TrkStrawHitInfo.hh)
- detrkqual
- the input variables and output value of the track quality artificial neural network (TrkAna/inc/TrkQualInfo.hh)
- evtwt
- the values of all EventWeight objects that were in the art even (e.g. proton bunch intensity = PBIWeight)
By setting the diagLevel to 2, you can get hit level informationTrkAna
- detsh
- reconstructed straw hit information for downstream electron track fit (TrkDiag/inc/TrkStrawHitInfo.hh)
- detsm
- information about straw materials that the downstream electron track fit goes through (TrkAna/inc/TrkStrawMatInfo.hh)
Future Developments
We're always looking for input from developers and analyzers to keep TrkAna up-to-date! Get in touch on the #trkana Slack channel, or with Andy Edmonds directly with any thoughts / comments you have