Data

Tools

Indexes

Applications

Experiments

Open Source Science

2022_dcpt_let

MC particle transport simulations for the 2022 LET-measurements at DCPT

https://github.com/aptg/2022_dcpt_let

Science Score: 49.0%

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

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

MC particle transport simulations for the 2022 LET-measurements at DCPT

Basic Info
  • Host: GitHub
  • Owner: APTG
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 69.9 MB
Statistics
  • Stars: 7
  • Watchers: 5
  • Forks: 1
  • Open Issues: 21
  • Releases: 1
Created over 3 years ago · Last pushed 8 months ago
Metadata Files
Readme Contributing Citation

README.md

DCPT LET-measurements 2022

All data in this repository is only preliminary, and may still be subject to change.

Background

This repository serves as a centralized location for all Monte Carlo (MC) simulations relevant to calculating dose, LET, and other derived quantities for a proton therapy reference setup.

Reference Setup

The primary reference setup explicitly omits detector details. This is intentional. The objective is to ascertain how effectively a detector can gauge the LET at a specific position as if the detector was absent. This concept draws parallels to cavity theory, where the dose in a given point is assessed as though no detector is present.

Detector-Specific Calculations

Certain detectors will necessitate specialized calculations. For such cases:

  • Users can fork this repository and integrate their detector-specific simulations.
  • Alternatively, a dedicated folder can be introduced within this repository for those detector-specific calculations.

Multiple MC codes will be used.

We here always assume beam transport along the positive Z-axis, as is convention for most MC codes, also to minimize confusion during setup in the experimental room. z_iso = 0.0 cm marks the isocenter position.

The DCPT beam model is supplied, describing the proton beam starting at z_iso = -50 cm.

Details

Contributing

You can create new issues, and create new branches based on these issues. The branches will be reviewed before entering the master branch. See also doc/contributing.md for general guidelines.

Credits

To cite this work please referer to the Zenodo dataset https://zenodo.org/records/10641085 It can be cited as:

Bassler, N., Grzanka, L., Christensen, J. B., Villads J, Brki, H., Perrot, Y., Pasariek, L., & Romero-Expsito, M. (2024). MC particle transport simulations for the 2022 LET-measurements at DCPT: v1.0.0 (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10641085

Owner

  • Name: Aarhus Particle Therapy Group
  • Login: APTG
  • Kind: organization
  • Email: niels.bassler@gmail.com
  • Location: Aarhus University, Denmark

GitHub Events

Total
  • Issues event: 24
  • Watch event: 1
  • Delete event: 6
  • Issue comment event: 32
  • Push event: 42
  • Pull request review comment event: 1
  • Pull request review event: 4
  • Pull request event: 13
  • Fork event: 2
  • Create event: 12
Last Year
  • Issues event: 24
  • Watch event: 1
  • Delete event: 6
  • Issue comment event: 32
  • Push event: 42
  • Pull request review comment event: 1
  • Pull request review event: 4
  • Pull request event: 13
  • Fork event: 2
  • Create event: 12

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 15
  • Total pull requests: 6
  • Average time to close issues: 7 months
  • Average time to close pull requests: 2 months
  • Total issue authors: 2
  • Total pull request authors: 2
  • Average comments per issue: 0.87
  • Average comments per pull request: 1.67
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 8
  • Pull requests: 5
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 13 days
  • Issue authors: 2
  • Pull request authors: 2
  • Average comments per issue: 0.5
  • Average comments per pull request: 1.6
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • nbassler (13)
  • grzanka (7)
  • mdsainth (1)
Pull Request Authors
  • grzanka (8)
  • nbassler (4)
  • maiteromexp (1)
  • hbrkic (1)
  • mdsainth (1)
Top Labels
Issue Labels
enhancement (1) good first issue (1) help wanted (1)
Pull Request Labels

Dependencies

requirements.txt pypi
  • matplotlib *
  • mcpl *
  • numpy *
  • pymchelper >=2.5.1