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     Geant4 - an Object-Oriented Toolkit for Simulation in HEP
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                            Example Par04
                            -------------

 This example demonstrates how to use the Machine Learning (ML) inference
 to create energy deposits as a fast simulation model using
<a href="https://github.com/microsoft/onnxruntime">ONNX runtime</a>
 and <a href="https://github.com/lwtnn/lwtnn">LWTNN</a> libraries.

 The model used in this example was trained externally (in Python) on data
 from this examples' full simulation and can be applied to perform fast simulation

 The geometry used in the example is a cylindrical setup of layers: tungsten
 absorber and silicon as the active material. 3D readout geometry (cylindrical)
 is defined dynamically,  based on the particle direction at the entrance to the
 calorimeter. This is set using a fast simulation model that is triggered at
 detector entrance. Analysis of energy deposits is done in the event action,
 ntuple with hits is stored.

 ## 1. Detector description

 The detector consists of cylindrical layers of passive and active material,
 tungsten and silicon, respectively.

 Fast simulation is attached to the region of the detector.

 Input macro can specify which layer is considered an active layer (sensitive
 detector is attached to it). For fast simulation both layers should be marked
 as sensitive. It is connected to the wway the deposits are created: position is
 centre of the layer, which may often fall within the absorber (which is thicker
 than the active material). In a realistic detector setup, the positions used in
 fast simulation would be calculated properly, to deposit energy within the active
 material.

 ## 2. Sensitive detector

 Sensitive detector inherits from both base classes:
 - G4VSensitiveDetector: for processing of detailed/non-fast simulation hits
 - G4VFastSimSensitiveDetector: for processing of fast sim (G4FastSim) hits.
 Hits are placed in the same hit collection, with a different flag to distinguish
 between those originated in the full simulation, and those from the fast
 simulation.
 During visualisation, hits are represented as volumes of different colour:
 green for full simulation and red for fast simulation.

 ## 3. Primary generation

 Particle gun is used as a primary generator. 10 GeV electron is used by default.
 By default particles are generated along y axis. Those values
 can be changed using /gun/ UI commands.

 ## 4. Physics List

 FTFP_BERT modular physics list is used. On top of it, fast simulation physics
 is registered for selected particles (electrons, positrons).


 ## 5. User actions

 - Par04RunAction : run action used for initialization and termination
                    of the run. Histograms for analysis of shower development
                    in the detector are created.

 - Par04EventAction : event action used for initialization and termination
                      of the event. Analysis of shower development is performed
                      on event-by-event basis.

 ## 6. ML Inference

 - Par04MLFastSimModel : model used for parametrisation of źelectrons, positrons,
      and gammas. Energy is deposited and
      distributed according to inferred values from the ML model.
      This class triggers the inference setup, asks for values,
      and deposits energies at given positions.

 - Par04InferenceSetup : this class is used to initialize the inference parameters
       (user application specific) such as the inference library,
      the path and name of the inference model and the size of
      the input inference vector(latent dimension and and condition size).
      This class constructs this vector and triggers the interface
      corresponding to the specified input inference library.
      After the inference, the post processing step consists of
      scaling back inferred values to the original range.

 - Par04InferenceInterface : is a base class that allows to read in the ML model, configure
       and execute inference.

 - Par04OnnxInference and Par04LWTNNInference : inference library specific classes that inherit
      from the base class Par04InferenceInterface.


 ## 7. Output

 The execution of the program (examplePar04) produces an output with histograms.
 Ntuples are also stored. They are not merged if the application is run on multiple threads.

 The macro file examplePar04.in is used to run full simulation. It will simulate 100
 events, for single 10 GeV electron beams.
 If CMake is able to find inference libraries (lwtnn and/or ONNX Runtime), a configuration
 macro will be available for that library (examplePar04_lwtnn.in and/or examplePar04_onnx.in).
 It will use a trained model to run inference and create showers in the detector by directly
 depositing energy.

 ## 8. How to build and run the example

- LWTNN and ONNX Runtime are available on LCG. In order to use them, one can setup the envirnment:
  % source /cvmfs/sft.cern.ch/lcg/views/LCG_100/x86_64-centos7-gcc10-opt/setup.sh

- Compile and link to generate the executable (in your CMAKE build directory):
  % cmake <Par04_SOURCE>
  % make

- Execute the application (in batch mode):
  % ./examplePar04 -m examplePar04.in
  which produces two root file for full simulation.

- Execute the application (in interactive mode):
  % ./examplePar04
  which allows to visualize hits.

- If ONNX Runtime is available:
  % ./examplePar04 -m examplePar04_onnx.in

- If LWTNN is available:
  % ./examplePar04 -m examplePar04_lwtnn.in

 By default, CMake will attempt to build fast simulation with ONNX Runtime and LWTNN. However, if none
 of those libraries is found, it will proceed with full simulation only. The search can be switched
 off manually switching CMake flag INFERENCE_LIB to OFF (-DINFERENCE_LIB=OFF)

 ## 9. Macros

 vis.mac - Allows to run visualization. It will be automatically run in interactive mode, if no
           argument is passed to the executable (examplePar04)

 examplePar04.in - Runs full simulation. It will run 100 events with single electrons, 10 GeV and
                   along y axis.

 examplePar04_onnx.in - Available only if ONNX Runtime is found by CMake. Runs fast simulation with
                        a NN stored in onnx file.

 examplePar04_lwtnn.in - Available only if LWTNN is found by CMake. Runs fast simulation with
                        a NN stored in json file.

 ## 10. UI commands

 UI commands useful in this example:

- activation/disactivation of the fast simulation model:
   /param/ActivateModel inferenceModel
   /param/InActivateModel inferenceModel

- particle gun commands
   /gun/particle e-
   /gun/energy 10 GeV
   /gun/direction 0 1 0
   /gun/position 0 0 0

 UI commands defined in this example:
 - detector settings
    /Par04/detector/setDetectorInnerRadius 80 cm
    /Par04/detector/setDetectorLength 2 m
    /Par04/detector/setNbOfLayers 90
    /Par04/detector/setAbsorber 0 G4_W 1.4 mm false
    /Par04/detector/setAbsorber 1 G4_Si 0.3 mm true

 - readout mesh
    /Par04/mesh/setSizeOfRhoCells 2.325 mm
    /Par04/mesh/setSizeOfZCells 3.4 mm
    /Par04/mesh/setNbOfRhoCells 18
    /Par04/mesh/setNbOfPhiCells 50
    /Par04/mesh/setNbOfZCells 45

 - inference setup
    /Par04/inference/setSizeLatentVector 10
    /Par04/inference/setSizeConditionVector 4
    /Par04/inference/setModelPathName MLModels/Generator.onnx
    /Par04/inference/setProfileFlag 0
    /Par04/inference/setOptimizationFlag 0
    /Par04/inference/setInferenceLibrary ONNX
    /Par04/inference/setSizeOfRhoCells 2.325 mm
    /Par04/inference/setSizeOfZCells 3.4 mm
    /Par04/inference/setNbOfRhoCells 18
    /Par04/inference/setNbOfPhiCells 50
    /Par04/inference/setNbOfZCells 45

