Metadata-Version: 2.1
Name: faster-particles
Version: 0.2.0
Summary: Point Proposal Network for particles images and related tools.
Home-page: https://github.com/Temigo/faster-particles
Author: Laura Domine, Ji Won Park, Kazuhiro Terao
Author-email: temigo@gmx.com
License: LICENSE.md
Project-URL: Source, https://github.com/Temigo/faster-particles
Project-URL: Bug Reports, https://github.com/Temigo/faster-particles/issues
Description: # faster-particles
        This package includes the following:
        * Toydata generator
        * LArCV data interface (2D and 3D)
        * Pixel Proposal Network implementation using Tensorflow
        
        ## Installation
        ### Dependencies
        You must install [larcv2](https://github.com/DeepLearnPhysics/larcv2) and its
        own dependencies (ROOT, OpenCV, Numpy) in order to use LArCV data interface.
        To install `larcv2`:
        ```bash
        git clone https://github.com/DeepLearnPhysics/larcv2.git
        cd larcv2
        source configure.sh
        make
        ```
        
        ### Install
        Then install `faster-particles` with Pip:
        ```bash
        pip install faster-particles
        ```
        
        Alternatively, you can also clone the source:
        ```bash
        git clone https://github.com/Temigo/faster-particles.git
        cd faster-particles
        ```
        
        ## Usage
        
        **The following assumes you installed with pip. If you cloned the source, make
        sure you are in the root directory and replace `ppn` with `python faster_particles/bin/ppn.py`.**
        
        To use toydata rather than LArCV data in the following sections, use option `--toydata`.
        LArCV data files can be specified with `--data` option. They can use regex, e.g. `ppn_p[01]*.root`.
        
        ### Training
        The program output is divided between:
        * Output directory: with all the weights
        * Log directory: to store all Tensorflow logs (and visualize them with Tensorboard)
        * Display directory: stores regular snapshots taken during training of PPN1 and PPN2 proposals compared to ground truth.
        
        To train PPN on 1000 steps use:
        ```bash
        ppn train -o output/dir/ -l log/dir/ -d display/dir -n ppn -m 1000 --data path/to/data
        ```
        
        To train the base network (currently only VGG available) on track/shower classification task use:
        ```bash
        ppn train -o output/dir/ -l log/dir/ -d display/dir -n base -m 1000
        ```
        
        To train on 3D data, use the argument `-3d` and don't forget to specify the image size with `-N` argument (e.g. 192 for a compression factor of 4, see `larcvdata_generator.py` for more details).
        
        ### Inference
        To run inference with a minimal score of 0.5 for predicted points:
        ```bash
        ppn demo weights_file.ckpt -d display/dir/ -ms 0.5
        ```
        The display directory will contain snapshots of the results.
        
        More options are available through `ppn train -h` and `ppn demo -h` respectively.
        
        ## Authors
        K.Terao, J.W. Park, L.Domine
        
Keywords: physics
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Description-Content-Type: text/markdown
