TrkAna
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 the latest version of uproot4
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 = tree.arrays(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")
plt.hist(df["deent.mom"], bins=60, range=(95,110))
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 |
|---|---|---|---|---|
| v04_00_00 | v04_00_00 | SimJob MDC2020z --> Offline v10_23_01 | sl7-prof-e28-p049 | breaking changes: demfit[] branch, new genealogy branches |
| (not built) | v03_00_01 | N/A | N/A | tag before major breaking change, just code tidying so no associated musing |
| v03_00_00 | v03_00_00 | SimJob MDC2020y --> Offline v10_22_01 | sl7-prof-e20-p040 | breaking changes: branch names now three characters; uses KinKal fits |
| v02_03_00 | v02_03_00 | SimJob MDC2020w --> Offline v10_21_00 | sl7-prof-e20-p039 | |
| v02_02_00 | v02_02_00 | SimJob MDC2020v --> Offline v10_20_00 | sl7-prof-e20-p035 | |
| v02_01_00 | v02_01_00 | SimJob MDC2020u --> Production v00_10_00 --> Offline v10_18_00 | sl7-prof-e20-p033 | |
| v02_00_01 | v02_00_01 | SimJob MDC2020t --> Production v00_09_11 --> Offline v10_17_00 | sl7-prof-e20-p026 | |
| (not built) | v02_00_00 | N/A | N/A | failed build due to incompatible KinKal version |
| 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
The TrkAna tree structure is documented in google sheet (spreadsheet). There are separate sheets for different major version numbers. Links to the existing sheets are below. These are viewable by anyone; if you would like to comment or edit these sheets please send a message to the #TrkAna slack channel. Note that the sheets have multiple tabs, at the bottom of the page. The main tab lists the branches, while the other tabs list the detailed content (leafs) of the individual branches, by name.
Hit Level Information
By setting the diagLevel to 2 when running TrkAna, you will fill hit level information branches.
If you are processing Monte Carlo information, hit level Monte Carlo truth branches will also be added.
Adding these increases the size of the output file by an order of magnitude.
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