# SCLUDAM (**S**tar **CLU**ster **D**etection **A**nd **M**embership estimation)
[](https://travis-ci.com/simonpedrogonzalez/scludam)
[](https://simonpedrogonzalez.github.io/scludam-docs/index.html)
[](https://pypi.org/project/scludam/)
[](https://github.com/simonpedrogonzalez/scludam)
[](https://github.com/simonpedrogonzalez/scludam)
[](https://github.com/simonpedrogonzalez/scludam)
[](https://tldrlegal.com/license/gnu-lesser-general-public-license-v3-(lgpl-3))
**SCLUDAM** (**S**tar **CLU**ster **D**etection **A**nd **M**embership estimation) is a Python package for GAIA catalogues **data fetching**, **star cluster detection** and **star cluster membership estimation**.
### Repository and issues
[https://github.com/simonpedrogonzalez/scludam](https://github.com/simonpedrogonzalez/scludam)
### Authors
- Simón Pedro González.
Email: [simon.pedro.g@gmail.com](simon.pedro.g@gmail.com)
### Features
Included modules and features are:
- **fetcher**: Query builder for easy access to GAIA catalogues data, functions to get catalogues and SIMBAD objects information.
- **stat_tests**: Set of three clusterability tests that can be used to detect the presence of a cluster in a sample.
- **synthetic**: Classes that can be used to generate synthetic astrometric samples by specifying the distributions and parameter values.
- **detection**: Detection of star clusters in a sample using an improved version of the Star Counts algorithm.
- **shdbscan**: Soft clustering based on the **HDBSCAN** algorithm.
- **hkde**: Kernel density estimation with per-observation or per-dimension variable bandwidth.
- **membership**: Membership probability estimation based on **hkde** smoothing.
- **pipeline**: Pipeline for the detection and membership estimation, with default values and convenience functions.
- **plots**: Plot detection and membership estimation results alongside SIMBAD objects for better result interpretation.
--------------------------------------------------------------------------------
### Requirements
**Python 3.9+** is needed to run SCLUDAM. It is recommended to install scludam in a separate environment created to avoid dependencies issues with other preinstalled packages in the base environment. The following dependencies will be installed along with SCLUDAM:
- numpy>=1.26.4,<2.0
- matplotlib>=3.9.4,<4.0
- scipy>=1.13.1,<2.0
- astropy>=6.0.1,<7.0
- astroquery==0.4.6
- pandas>=2.3.1,< 3.0
- hdbscan>=0.8.40
- joblib>=1.1.0
- scikit-learn>=1.1.3
- scikit-image>=0.24.0
- seaborn>=0.13.2,<0.14
- statsmodels>=0.12.2
- diptest>=0.10.0,<0.11.0
### User install in a Conda environment (recommended)
Create a conda environment named ``myscludamenv`` with python3.8 and ``scludam`` installed
```
conda create --name myscludamenv python=3.11 pip --yes
conda activate myscludamenv
python -m pip install scludam
```
### Update scludam in a Conda environment
```
conda activate myscludamenv
python -m pip install -U scludam
python -m pip show scludam
```
### Simple user install
Install from PyPi:
```python -m pip install scludam```
### Simple user update
Update from PyPi:
```python -m pip install -U scludam```
### Dev install
Clone the repo and run the following command in the cloned directory (with your environment activated):
```python -m pip install -e .[dev]```
Raw data
{
"_id": null,
"home_page": "http://packages.python.org/scludam",
"name": "scludam",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "star cluster detection membership probabilities",
"author": "Sim\u00f3n Pedro Gonz\u00e1lez",
"author_email": "simon.pedro.g@gmail.com",
"download_url": null,
"platform": null,
"description": "# SCLUDAM (**S**tar **CLU**ster **D**etection **A**nd **M**embership estimation)\n\n[](https://travis-ci.com/simonpedrogonzalez/scludam)\n[](https://simonpedrogonzalez.github.io/scludam-docs/index.html)\n[](https://pypi.org/project/scludam/)\n[](https://github.com/simonpedrogonzalez/scludam)\n[](https://github.com/simonpedrogonzalez/scludam)\n[](https://github.com/simonpedrogonzalez/scludam)\n[](https://tldrlegal.com/license/gnu-lesser-general-public-license-v3-(lgpl-3))\n\n\n**SCLUDAM** (**S**tar **CLU**ster **D**etection **A**nd **M**embership estimation) is a Python package for GAIA catalogues **data fetching**, **star cluster detection** and **star cluster membership estimation**.\n\n### Repository and issues\n[https://github.com/simonpedrogonzalez/scludam](https://github.com/simonpedrogonzalez/scludam)\n\n### Authors\n- Sim\u00f3n Pedro Gonz\u00e1lez. \nEmail: [simon.pedro.g@gmail.com](simon.pedro.g@gmail.com)\n\n### Features\nIncluded modules and features are:\n\n- **fetcher**: Query builder for easy access to GAIA catalogues data, functions to get catalogues and SIMBAD objects information.\n\n- **stat_tests**: Set of three clusterability tests that can be used to detect the presence of a cluster in a sample.\n\n- **synthetic**: Classes that can be used to generate synthetic astrometric samples by specifying the distributions and parameter values.\n\n- **detection**: Detection of star clusters in a sample using an improved version of the Star Counts algorithm.\n\n- **shdbscan**: Soft clustering based on the **HDBSCAN** algorithm.\n\n- **hkde**: Kernel density estimation with per-observation or per-dimension variable bandwidth.\n\n- **membership**: Membership probability estimation based on **hkde** smoothing.\n\n- **pipeline**: Pipeline for the detection and membership estimation, with default values and convenience functions.\n\n- **plots**: Plot detection and membership estimation results alongside SIMBAD objects for better result interpretation.\n\n--------------------------------------------------------------------------------\n\n### Requirements\n**Python 3.9+** is needed to run SCLUDAM. It is recommended to install scludam in a separate environment created to avoid dependencies issues with other preinstalled packages in the base environment. The following dependencies will be installed along with SCLUDAM:\n\n- numpy>=1.26.4,<2.0\n- matplotlib>=3.9.4,<4.0\n- scipy>=1.13.1,<2.0\n- astropy>=6.0.1,<7.0\n- astroquery==0.4.6\n- pandas>=2.3.1,< 3.0\n- hdbscan>=0.8.40\n- joblib>=1.1.0\n- scikit-learn>=1.1.3\n- scikit-image>=0.24.0\n- seaborn>=0.13.2,<0.14\n- statsmodels>=0.12.2\n- diptest>=0.10.0,<0.11.0\n\n\n### User install in a Conda environment (recommended)\nCreate a conda environment named ``myscludamenv`` with python3.8 and ``scludam`` installed\n```\nconda create --name myscludamenv python=3.11 pip --yes\nconda activate myscludamenv\npython -m pip install scludam\n```\n\n### Update scludam in a Conda environment\n```\nconda activate myscludamenv\npython -m pip install -U scludam\npython -m pip show scludam\n```\n\n### Simple user install\nInstall from PyPi:\n```python -m pip install scludam```\n\n### Simple user update\nUpdate from PyPi:\n```python -m pip install -U scludam```\n\n### Dev install\nClone the repo and run the following command in the cloned directory (with your environment activated):\n```python -m pip install -e .[dev]```\n",
"bugtrack_url": null,
"license": "GPL-3",
"summary": "Star cluster detection and membership estimation based on GAIA data.",
"version": "1.0.10",
"project_urls": {
"Homepage": "http://packages.python.org/scludam"
},
"split_keywords": [
"star",
"cluster",
"detection",
"membership",
"probabilities"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "a77d85f2de38e6475a9d0acbf41d96640dd7bb56f4c685b97f6d9ce4cda29592",
"md5": "e911042ba241eab82904a5368770e887",
"sha256": "2ac6f4ed4eca397fe52c5f77aadf82897fc7ffe01e2be05cbd4278de64cae8da"
},
"downloads": -1,
"filename": "scludam-1.0.10-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e911042ba241eab82904a5368770e887",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 125930,
"upload_time": "2025-08-03T18:51:50",
"upload_time_iso_8601": "2025-08-03T18:51:50.606809Z",
"url": "https://files.pythonhosted.org/packages/a7/7d/85f2de38e6475a9d0acbf41d96640dd7bb56f4c685b97f6d9ce4cda29592/scludam-1.0.10-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-03 18:51:50",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "scludam"
}
JSON