| 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
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"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",
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