Name | sleepeegpy JSON |
Version |
0.5.1
JSON |
| download |
home_page | |
Summary | Sleep EEG preprocessing, analysis and visualization |
upload_time | 2023-12-12 13:44:04 |
maintainer | |
docs_url | None |
author | |
requires_python | <3.12,>=3.10 |
license | MIT |
keywords |
sleep
eeg
|
VCS |
|
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
|
coveralls test coverage |
No coveralls.
|
# sleepeegpy
**sleepeegpy** is a high-level package built on top of [mne-python](https://mne.tools/stable/index.html), [yasa](https://raphaelvallat.com/yasa/build/html/index.html) for preprocessing, analysis and visualisation of sleep EEG data.
## Installation
0. Make sure you have [Python](https://www.python.org/downloads/) version installed. Requires Python 3.10 or higher.
1. Create a Python virtual environment, for more info you can refer to python [venv](https://docs.python.org/3/tutorial/venv.html), [virtualenv](https://virtualenv.pypa.io/en/latest/user_guide.html) or [conda](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).
2. Activate the environment
3.
```
pip install sleepeegpy
```
4. [Download](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/NirLab-TAU/sleepeegpy/tree/main/notebooks) notebooks.
## Quickstart
1. Open the complete pipeline notebook in the created environment.
2. Follow the notebook's instructions.
## RAM requirements
For overnight, high density (256 channels) EEG recordings downsampled to 250 Hz expect at least 64 GB RAM expenditure for cleaning, spectral analyses and event detection.
## Retrieve example dataset
```
odie = pooch.create(
path=pooch.os_cache("sleepeegpy_dataset"),
base_url="doi:10.5281/zenodo.10362189",
)
odie.load_registry_from_doi()
bad_channels = odie.fetch("bad_channels.txt")
annotations = odie.fetch("annotations.txt")
path_to_eeg = odie.fetch("resampled_raw.fif")
for i in range(1,4):
odie.fetch(f"resample_raw-{i}.fif")
```
Raw data
{
"_id": null,
"home_page": "",
"name": "sleepeegpy",
"maintainer": "",
"docs_url": null,
"requires_python": "<3.12,>=3.10",
"maintainer_email": "",
"keywords": "sleep,eeg",
"author": "",
"author_email": "Gennadiy Belonosov <gennadiyb@mail.tau.ac.il>",
"download_url": "https://files.pythonhosted.org/packages/ff/67/6cdd6bc437013051df20be6482a8c96a65cfed652ceff3c90d2fffe9b272/sleepeegpy-0.5.1.tar.gz",
"platform": null,
"description": "# sleepeegpy\r\n**sleepeegpy** is a high-level package built on top of [mne-python](https://mne.tools/stable/index.html), [yasa](https://raphaelvallat.com/yasa/build/html/index.html) for preprocessing, analysis and visualisation of sleep EEG data.\r\n## Installation\r\n0. Make sure you have [Python](https://www.python.org/downloads/) version installed. Requires Python 3.10 or higher.\r\n1. Create a Python virtual environment, for more info you can refer to python [venv](https://docs.python.org/3/tutorial/venv.html), [virtualenv](https://virtualenv.pypa.io/en/latest/user_guide.html) or [conda](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).\r\n2. Activate the environment\r\n3. \r\n ```\r\n pip install sleepeegpy\r\n ```\r\n4. [Download](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/NirLab-TAU/sleepeegpy/tree/main/notebooks) notebooks.\r\n\r\n## Quickstart\r\n1. Open the complete pipeline notebook in the created environment.\r\n2. Follow the notebook's instructions. \r\n\r\n## RAM requirements\r\nFor overnight, high density (256 channels) EEG recordings downsampled to 250 Hz expect at least 64 GB RAM expenditure for cleaning, spectral analyses and event detection.\r\n\r\n## Retrieve example dataset\r\n```\r\nodie = pooch.create(\r\n path=pooch.os_cache(\"sleepeegpy_dataset\"),\r\n base_url=\"doi:10.5281/zenodo.10362189\",\r\n)\r\nodie.load_registry_from_doi()\r\nbad_channels = odie.fetch(\"bad_channels.txt\")\r\nannotations = odie.fetch(\"annotations.txt\")\r\npath_to_eeg = odie.fetch(\"resampled_raw.fif\")\r\nfor i in range(1,4):\r\n odie.fetch(f\"resample_raw-{i}.fif\")\r\n```\r\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Sleep EEG preprocessing, analysis and visualization",
"version": "0.5.1",
"project_urls": {
"Documentation": "https://nirlab-tau.github.io/sleepeegpy/",
"Homepage": "https://github.com/NirLab-TAU/sleepeegpy"
},
"split_keywords": [
"sleep",
"eeg"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "5c367711e928f7bea462e24aeaa9df087134230c96abd26cd3661ebc15c6cba3",
"md5": "4cd6afa57d0fc7d231e215417dad5038",
"sha256": "7d6deb4307f4771fe4f1de7de557f754350dc19e329d2faad72c325021464b8a"
},
"downloads": -1,
"filename": "sleepeegpy-0.5.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "4cd6afa57d0fc7d231e215417dad5038",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.12,>=3.10",
"size": 26890,
"upload_time": "2023-12-12T13:44:03",
"upload_time_iso_8601": "2023-12-12T13:44:03.641415Z",
"url": "https://files.pythonhosted.org/packages/5c/36/7711e928f7bea462e24aeaa9df087134230c96abd26cd3661ebc15c6cba3/sleepeegpy-0.5.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ff676cdd6bc437013051df20be6482a8c96a65cfed652ceff3c90d2fffe9b272",
"md5": "5a39979058d9e55be553c9910bc8fe2c",
"sha256": "85f62174e19048f96bb2ee2c9dd88f97c97a0a712ac78eb560f87cd9ec486bb5"
},
"downloads": -1,
"filename": "sleepeegpy-0.5.1.tar.gz",
"has_sig": false,
"md5_digest": "5a39979058d9e55be553c9910bc8fe2c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.12,>=3.10",
"size": 26063,
"upload_time": "2023-12-12T13:44:04",
"upload_time_iso_8601": "2023-12-12T13:44:04.892412Z",
"url": "https://files.pythonhosted.org/packages/ff/67/6cdd6bc437013051df20be6482a8c96a65cfed652ceff3c90d2fffe9b272/sleepeegpy-0.5.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-12-12 13:44:04",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "NirLab-TAU",
"github_project": "sleepeegpy",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "sleepeegpy"
}