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fair-umn-hgcal

https://github.com/fair-umn/fair-umn-hgcal

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
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    Scientific vocabulary similarity
    Low similarity (10.5%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: FAIR-UMN
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 1.83 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created about 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

FAIR-UMN-HGCAL (FAIR-UMN)

Repository for analyzing simulated data from HGCAL prototype uploaded on Zenodo. Run the following commands to setup a conda environment.

``` conda env create -f fair_cpu.yml

```

If you are running the code on the a Mac OS with arm64 architecture, use fairmacosm1.yml file instead. One can also manually install the enviroment from scratch using the following commands.

conda env create -n fair_cpu --python=3.6 conda activate fair_cpu pip install numpy scikit-learn scipy awkward ipykernel jupyter h5py pip install matplotlib plotly python -m ipykernel install -n fair_cpu

The tree structure of the directory is shown below. Onde can open and run notebooks from the notebooks folder. The dataloaders for pytorch are stored in src/data/datautils.py and the pytorch related custom functions can be found under src/utils/torchutils.py, where one can define custom functions for training loss and optimizer.

bash ├── CITATION.cff ├── LICENSE ├── README.md ├── data ├── docs │   ├── Makefile │   ├── commands.rst │   ├── conf.py │   ├── getting-started.rst │   ├── index.rst │   └── make.bat ├── fair_cpu.yml ├── fair_macos_m1.yml ├── metadata │   └── hgcal_electron_dataset.json ├── models ├── notebooks │   ├── DNN.ipynb │   ├── GetBinnedResolution.ipynb │   ├── iframe_figures │   │   ├── figure_38.html │   │   ├── figure_39.html │   │   └── figure_40.html │   └── sparkles.ipynb ├── requirements.txt ├── src │   ├── __init__.py │   ├── data │   │   ├── __init__.py │   │   ├── create_test_file.py │   │   └── make_dataset.py │   ├── dataset_utils.py │   ├── features │   │   ├── __init__.py │   │   └── build_features.py │   ├── models │   │   ├── DNN.py │   │   ├── __init__.py │   │   ├── predict_model.py │   │   └── train_model.py │   ├── utils │   │   └── torch_utils.py │   └── visualization │   ├── __init__.py │   └── visualize.py └── training

Project based on the cookiecutter4fair project template.

Owner

  • Name: FAIR-UMN
  • Login: FAIR-UMN
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: UMN
    orcid: https://orcid.org/Your ORCID
title: FAIR-UMN
version: 0.1.0
doi: 10.XXXX/XXXX
date-released: 2023-08-01/04/23

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Dependencies

requirements.txt pypi
  • Sphinx *
  • awscli *
  • click *
  • coverage *
  • flake8 *
  • python-dotenv >=0.5.1
  • zenodo_get *