.rst .pdf

Contents

Help! I don’t know how to X

Dataset-related questions

How to select specific entries

The iotool configuration has an option to select specific event indexes. Here is an example:

iotool:
  dataset:
    event_list: '[18,34,41]'

How to go back to real-world coordinates

Coordinates in lartpc_mlreco3d are assumed to be in the range 0 .. N where N is some integer. This range is in voxel units. What if you want to identify a region based on its real-world coordinates in cm, for example the cathode position?

If you need to go back to absolute detector coordinates, you will need to retrieve the meta information from the file. There is a parser that can do this for you:

iotool:
  dataset:
    schema:
      - parse_meta3d
      - sparse3d_reco

then you will be able to access the meta information from the data blob:

min_x, min_y, min_z = data['meta'][entry][0:3]
max_x, max_y, max_z = data['meta'][entry][3:6]
size_voxel_x, size_voxel_y, size_voxel_z = data['meta'][entry][6:9]

absolute_coords_x = relative_coords_x * size_voxel_x + min_x

How to get true particle information

You need to use the parser parse_particles. For example:

iotool:
  dataset:
    schema:
      particles:
        - parse_particles
        - particle_pcluster
        - cluster3d_pcluster

Then you will be able to access data['particles'][entry] which is a list of objects of type larcv::Particle.

for p in data['particles'][entry]:
    mom = np.array([p.px(), p.py(), p.pz()])
    print(p.id(), p.num_voxels(), mom/np.linalg.norm(mom))

You can see the full list of attributes of larcv::Particle objects here: https://github.com/DeepLearnPhysics/larcv2/blob/develop/larcv/core/DataFormat/Particle.h

How to get true neutrino information

Tip

As of now (6/1/22) you need to build your own copy of larcv2 to have access to the larcv::Neutrino data structure which stores all of the true neutrino information.

$ git clone https://github.com/DeepLearnPhysics/larcv2.git
$ cd larcv2 & git checkout develop
$ source configure.sh & make -j4

If you use lartpc_mlreco3d in command line, you just need to source larcv2/configure.sh before running lartpc_mlreco3d code.

If instead you rely on a notebook, you will need to load the right version of larcv, the one you just built instead of the default one from the Singularity container.

%env LD_LIBRARY_PATH=/path/to/your/larcv2/build/lib:$LD_LIBRARY_PATH

Replace the path with the correct one where you just built larcv2. This cell should be the first one of your notebook (before you import larcv or lartpc_mlreco3d modules).

Assuming you are either using a Singularity container that has the right larcv2 compiled or you followed the note above explaining how to get it by yourself, you can use the parse_neutrinos parser of lartpc_mlreco3d.

iotool:
  dataset:
    schema:
      neutrinos:
        - parse_neutrinos
        - neutrino_mpv
        - cluster3d_pcluster

You can then read data['neutrinos'][entry] which is a list of objects of type larcv::Neutrino. You can check out the header file here for a full list of attributes: https://github.com/DeepLearnPhysics/larcv2/blob/develop/larcv/core/DataFormat/Neutrino.h

A quick example could be:

for neutrino in data['neutrinos'][entry]:
    print(neutrino.pdg_code()) # 12 for nue, 14 for numu
    print(neutrino.current_type(), neutrino.interaction_type())

If you try this, it will print integers for the current type and interaction type. The key to interprete them is in the MCNeutrino header: https://internal.dunescience.org/doxygen/MCNeutrino_8h_source.html

How to read true SimEnergyDeposits (true voxels)

There is a way to retrieve the true voxels and SimEnergyDeposits particle-wise. Add the following block to your configuration under iotool.dataset.schema:

iotool:
  dataset:
    schema:
      simenergydeposits:
        - parse_cluster3d
        - cluster3d_sed

Then you can read it as such (e.g. using analysis tools’ predictor):

predictor.data_blob['simenergydeposits'][entry]

It will have a shape (N, 6) where column 4 contains the SimEnergyDeposit value and column 5 contains the particle ID.

Training-related questions

How to freeze a model

You can freeze the entire model or just a module (subset) of it. The keyword in the configuration file is freeze_weight. If you put it under trainval directly, it will freeze the entire network. If you put it under a module configuration, it will only freeze that module.

How to load partial weights

model_path does not have to be specified at the global level (under trainval section). If it is, then the weights will be loaded for the entire network. But if you want to only load the weights for a submodule of the network, you can also specify model_path under that module’s configuration. It will filter weights names based on the module’s name to make sure to only load weights related to the module.

Tip

If your weights are named differently in your checkpoint file versus in your network, you can use model_name to fix it.

TODO: explain more.

I have another question!

Ping Laura (@Temigo) or someone else in the lartpc_mlreco3d team. We might include your question here if it can be useful to others!

Configuration mlreco.iotools.parsers package