Metadata-Version: 2.1
Name: toytrack
Version: 0.1.16
Summary: A package for generating toy tracking data.
Home-page: https://github.com/murnanedaniel/ToyTrack
Author: Daniel Murnane
Author-email: daniel.thomas.murnane@cern.ch
License: UNKNOWN
Description: # ToyTrack
        
        [![Documentation Status](https://readthedocs.org/projects/toytrack/badge/?version=latest)](https://toytrack.readthedocs.io/en/latest/?badge=latest)
        
        ToyTrack is a Python library for generating toy tracking events for particle physics. 
        
        **The goal**: To produce a "*good-enough*" event simulation, in as few lines as possible (currently 3 lines), as quickly as possible (currently 0.07 seconds for a 10,000-particle event).
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install ToyTrack.
        
        ```bash
        pip install toytrack
        ```
        
        Optionally, there are Pytorch dataloaders available for convenience. These require the `torch` package.
        
        ```bash
        pip install toytrack[torch]
        ```
        
        # Usage
        
        ## Vanilla Event
        
        ```python
        from toytrack import ParticleGun, Detector, EventGenerator
        
        # Initialize a particle gun which samples uniformly from pt between 2 and 20 GeV, 
        # initial direction phi between -pi and pi, and creation vertex vx and vy between -0.1 and 0.1 cm
        # and which fires a normally-distributed number of particles with mean 20 and standard deviation 5
        particle_gun = ParticleGun(num_particles=[20, 5, 'normal'], pt=[2, 20], pphi=[-np.pi, np.pi], vx=[-0.1, 0.1], vy=[-0.1, 0.1])
        
        # Initialize a detector, a barrel-like detector with inner radius of 0.5 cm, and outer radius of 3 cm,
        # with 10 layers
        detector = Detector(dimension=2).add_from_template('barrel', min_radius=0.5, max_radius=3, number_of_layers=10)
        
        # Initialize an event generator and generate an event
        event = EventGenerator(particle_gun, detector).generate_event()
        
        # Access the particles, hits and tracks as needed
        particles = event.particles
        hits = event.hits
        tracks = event.tracks
        
        # Plot the event
        event.display()
        ```
        
        ![Example Event](https://raw.githubusercontent.com/murnanedaniel/ToyTrack/main/docs/imgs/example_event_vanilla.png)
        
        ## Event with Track Holes
        
        ```python
        ... # as above
        
        # Initialize a detector that randomly drops 10% of hits. If an int N is given, then exactly
        # N hits per track are dropped
        detector = Detector(dimension=2, hole_inefficiency=0.1).add_from_template('barrel', min_radius=0.5, max_radius=3, number_of_layers=10)
        
        ... # as above
        
        # Plot the event
        event.display()
        ```
        
        ![Example Event with Holes](https://raw.githubusercontent.com/murnanedaniel/ToyTrack/main/docs/imgs/example_event_holes.png)
        
        ## Event with Noise Hits
        
        ```python
        ... # as above
        
        # Generate event with between 30% and 70% noise hits per real hits. E.g. If the event has 100 
        # real hits, then between 30 and 70 noise hits will be generated. If an int N is given, then
        # the absolute value of N noise hits are generated
        event = EventGenerator(particle_gun, detector, noise=[0.3, 0.7]).generate_event()
        
        ... # as above
        
        # Plot the event
        event.display()
        ```
        
        ![Example Event with Noise](https://raw.githubusercontent.com/murnanedaniel/ToyTrack/main/docs/imgs/example_event_noise.png)
        
        ## Event with Multiple Particle Guns
        
        ```python
        # Initialize one particle gun which samples uniformly from pt between 2 and 3 GeV, 
        # initial direction phi between -pi and pi, and creation vertex vx and vy between -0.1 and 0.1 cm
        particle_gun_1 = ParticleGun(num_particles=[20, 5, 'normal'], pt=[2, 3], pphi=[-np.pi, np.pi], vx=[-0.1, 0.1], vy=[-0.1, 0.1])
        
        # Initialize another particle gun which samples uniformly from pt between 100 and 200 GeV, 
        # initial direction phi between -pi and pi, and creation vertex vx and vy between -0.1 and 0.1 cm
        particle_gun_2 = ParticleGun(num_particles=1, pt=[100, 200], pphi=[-np.pi, np.pi], vx=[-0.1, 0.1], vy=[-0.1, 0.1])
        
        ... # as above
        
        # Initialize an event generator with a list of particle guns
        event = EventGenerator([particle_gun_1, particle_gun_2], detector).generate_event()
        
        ... # as above
        
        # Plot the event
        event.display()
        ```
        
        ![Example Event with Multiple Particle Guns](https://raw.githubusercontent.com/murnanedaniel/ToyTrack/main/docs/imgs/example_event_multigun.png)
        
        ## Detector with Layer Safety Guarantee
        
        We can ensure that each particle has exactly one hit per layer by setting the `layer_safety_guarantee` flag to `True`.
        
        Consider an event with low pT particles:
        
        ```python
        particle_gun = ParticleGun(num_particles=[20, 5, 'normal'], pt=[1, 3], pphi=[-np.pi, np.pi], vx=[-0.1, 0.1], vy=[-0.1, 0.1])
        
        detector = Detector(dimension=2, layer_safety_guarantee=False).add_from_template('barrel', min_radius=0.5, max_radius=3, number_of_layers=10)
        
        event = EventGenerator(particle_gun, detector).generate_event()
        
        event.display()
        ```
        
        ![Example Event with Low pT Particles](https://raw.githubusercontent.com/murnanedaniel/ToyTrack/main/docs/imgs/example_event_lowpt.png)
        
        ```python
        ... # as above
        
        # Initialize a detector WITH layer safety guarantee
        detector = Detector(dimension=2, layer_safety_guarantee=True).add_from_template('barrel', min_radius=0.5, max_radius=3, number_of_layers=10)
        
        ... # as above
        
        event.display()
        ```
        
        ![Example Event with Layer Safety Guarantee](https://raw.githubusercontent.com/murnanedaniel/ToyTrack/main/docs/imgs/example_event_lowpt_layer_safety.png)
        
        Observe that particles with such low pT that they "curled" before the final layer are now removed.
        
        # Performance
        
        ToyTrack is designed to be fast. The following benchmarks were performed on a 64-core AMD EPYC 7763 (Milan) CPU. 
        
        ![Scaling Study](https://raw.githubusercontent.com/murnanedaniel/ToyTrack/main/docs/imgs/time_scaling.png)
        
        # Data Loading
        
        ## Pytorch Dataset
        
        The `TracksDataset` class is a Pytorch dataset which can be used with a Pytorch dataloader. It can return either hitwise structure or trackwise structure.
        
        ```python
        from toytrack.dataloaders import TracksDataset
        
        config = {
            "detector": {
                "dimension": 2,
                "hole_inefficiency": 0,
                "min_radius": 0.5,
                "max_radius": 3.,
                "number_of_layers": 10,
            },
            "particle_guns": [
                {
                    "num_particles": [20, 5, 'normal'],
                    "pt": [2, 20],
                    "pphi": [-3.14159, 3.14159],
                    "vx": [-0.1, 0.1],
                    "vy": [-0.1, 0.1]
                }
            ],
            "structure": "hitwise"
        }
        
        # initialize dataloader
        dataset = TracksDataset(config)
        
        # iterate over dataset
        for sample in dataset:
            x, mask, pids, event = sample["x"], sample["mask"], sample["pids"], sample["event"]
            # x has shape (num_hits, num_features)
        ```
        
        For trackwise structure:
        
        ```python
        config = {
            ... # as above
            "detector": {
                ... # as above
                "layer_safety_guarantee": True # Must be True for trackwise structure
                ... # as above
            }
            "structure": "trackwise"
        }
        
        # initialize dataloader
        dataset = TracksDataset(config)
        
        # iterate over dataset
        for sample in dataset:
            x, mask, pids, event = sample["x"], sample["mask"], sample["pids"], sample["event"]
            # x has shape (num_tracks, num_hits_per_track, num_features)
        ```
        
        ## Pytorch DataLoader
        
        ```python
        from torch.utils.data import DataLoader
        
        dataloader = DataLoader(dataset, batch_size=100, collate_fn=dataset.collate_fn)
        
        # iterate over dataloader
        for batch in dataloader:
            x, mask, pids, event = batch["x"], batch["mask"], batch["pids"], batch["event"]
            # Do something with the batch (which now has an extra batch dimension of size 100)
        ```
        
        You can also get a straightforward parallelisation, with 
        ```
        dataloader = DataLoader(dataset, batch_size=100, collate_fn=dataset.collate_fn, num_workers=16)
        ```
        
        ## Transformations
        
        You can apply transformations to the dataset by passing a list of transformations to the `TracksDataset` class. For example, to convert a trackwise dataset into patches of 3 hits each, you can do the following:
        
        ```python
        from toytrack.transforms import TrackletPatchify
        
        transform = TrackletPatchify(num_patches_per_track=3)
        dataset = TracksDataset(config, transform=transform)
        
        # iterate over dataset
        for sample in dataset:
            x, mask, pids, event = sample["x"], sample["mask"], sample["pids"], sample["event"]
            # x has shape (num_tracks * num_patches_per_track, num_hits_per_patch, num_features)
        ```
        
        ![Example Event with Tracklet Patchify](https://raw.githubusercontent.com/murnanedaniel/ToyTrack/main/docs/imgs/example_event_tracklet_patches.png)
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: torch
