### Setup Development Environment

Source: https://eegprep.org/contributing.html

Commands to initialize the uv-managed environment and install dependencies.

```bash
uv python install 3.11
uv sync --group dev
```

```bash
uv sync --extra docs --group dev
```

--------------------------------

### Install all optional dependencies

Source: https://eegprep.org/user_guide/installation.html

Commands to install optional dependencies in bulk or by specific categories.

```bash
uv add "eegprep[all]"
```

```bash
uv add "eegprep[torch,gui,docs]"
```

--------------------------------

### Start TigerVNC Virtual Desktop

Source: https://eegprep.org/user_guide/visual_parity.html

Alternative virtual display setup using TigerVNC for local screenshot capture.

```bash
# Terminal 1: keep the virtual X server running.
Xvnc :99 -geometry 1920x1080 -depth 24 -SecurityTypes None -localhost \
    -ac -noreset +extension GLX +render
```

```bash
# Terminal 2: run captures against that display.
export DISPLAY=:99
xdpyinfo | head
```

--------------------------------

### Include practical examples in docstrings

Source: https://eegprep.org/contributing.html

Demonstrates how to provide usage examples within function docstrings.

```python
def load_bids_dataset(bids_root, subject_id):
    """Load a BIDS-formatted EEG dataset.

    Examples
    --------
    >>> import eegprep
    >>> eeg = eegprep.load_bids_dataset('/data/bids_root', 'sub-001')
    >>> print(eeg.nbchan)  # Number of channels
    64
    """
    pass
```

--------------------------------

### Install EEGPrep

Source: https://eegprep.org/faq.html

Methods for installing the library via uv or pip, and setting up a development environment.

```bash
uv add eegprep
```

```bash
pip install eegprep
```

```bash
git clone https://github.com/sccn/eegprep.git
cd eegprep
uv sync --group dev
```

--------------------------------

### Install eegprep from source

Source: https://eegprep.org/user_guide/installation.html

Commands to clone the repository and set up the development environment.

```bash
git clone https://github.com/sccn/eegprep.git
cd eegprep
uv sync --group dev
```

--------------------------------

### Install Extensions from PyPI

Source: https://eegprep.org/user_guide/extensions.html

Install a publicly available extension using its package name.

```bash
uv add eegprep-ext-foo
```

--------------------------------

### Install GUI Dependencies

Source: https://eegprep.org/user_guide/visual_parity.html

Install the necessary dependencies for the EEGPREP GUI before performing visual captures.

```bash
uv sync --extra gui --group dev
```

--------------------------------

### Install Documentation Dependencies

Source: https://eegprep.org/development.html

Install additional dependencies required for building and maintaining project documentation.

```bash
uv sync --extra docs --group dev
```

--------------------------------

### Interpolation output example

Source: https://eegprep.org/auto_examples/plot_channel_interpolation.html

Example console output after running the interpolation process.

```text
Performing channel interpolation...
----------------------------------------------------------------------
Interpolation complete!
  Interpolated data shape: (32, 10000)
  Interpolated channels: ['Fz', 'Pz', 'Fc1']
```

--------------------------------

### Importing Dependencies and Setup

Source: https://eegprep.org/_downloads/cc03e6f6f48055c0c64be4c8bef1fac5/plot_basic_preprocessing.ipynb

Initializes the environment by importing necessary libraries and configuring the local path for eegprep.

```python
import numpy as np
import matplotlib.pyplot as plt
from mne import create_info, EpochsArray
from mne.channels import make_standard_montage
import sys

sys.path.insert(0, '/Users/baristim/Projects/eegprep/src')

import eegprep

# Set random seed for reproducibility
np.random.seed(42)
```

--------------------------------

### Install EEGPrep

Source: https://eegprep.org/faq.html

Commands to install the package or sync development dependencies.

```bash
uv add eegprep
```

```bash
uv sync --group dev
```

--------------------------------

### eegprep.build_safe_install_commands(entry)

Source: https://eegprep.org/api/generated/eegprep.build_safe_install_commands.html

Generates a dictionary of safe installation commands for a given catalog entry.

```APIDOC
## eegprep.build_safe_install_commands

### Description
Returns copyable install commands for a catalog entry without executing them.

### Parameters
- **entry** (ExtensionCatalogEntry) - Required - The catalog entry to generate commands for.

### Return Type
dict[str, str]
```

--------------------------------

### Install PyTorch dependencies

Source: https://eegprep.org/user_guide/installation.html

Commands to install PyTorch with varying levels of hardware support using uv.

```bash
uv add torch
```

```bash
uv add torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```

```bash
uv add torch --index-url https://download.pytorch.org/whl/cpu
```

--------------------------------

### Create uv Environment

Source: https://eegprep.org/development.html

Install the required Python version and synchronize the development environment dependencies.

```bash
uv python install 3.11
uv sync --group dev
```

--------------------------------

### Install eegprep with documentation dependencies

Source: https://eegprep.org/user_guide/installation.html

Use this command to include additional dependencies required for building or developing documentation.

```bash
uv sync --group dev --extra docs
```

--------------------------------

### eegprep.extension_catalog.build_safe_install_commands

Source: https://eegprep.org/api/extensions.html

Generates copyable installation commands for a specific catalog entry without executing them.

```APIDOC
## eegprep.extension_catalog.build_safe_install_commands(entry)

### Description
Returns copyable install commands for a catalog entry without executing them.

### Parameters
- **entry** (ExtensionCatalogEntry) - Required - The catalog entry to generate commands for.

### Returns
- **dict[str, str]** - A dictionary of install commands.
```

--------------------------------

### Install Extensions from GitHub

Source: https://eegprep.org/user_guide/extensions.html

Install extensions directly from a GitHub repository, with options to pin specific versions using tags.

```bash
uv add git+https://github.com/lab/eegprep-ext-foo
```

```bash
uv add git+https://github.com/lab/eegprep-ext-foo --tag v0.3.0
```

--------------------------------

### eegprep.build_safe_install_commands

Source: https://eegprep.org/api/extensions.html

Generates copyable strings for installing extensions safely without executing subprocesses.

```APIDOC
## build_safe_install_commands()

### Description
Returns copyable strings such as 'uv add eegprep-ext-foo' or 'pip install ...' for extension installation.
```

--------------------------------

### Install Local or Editable Extensions

Source: https://eegprep.org/user_guide/extensions.html

Install extensions from a local directory using editable mode, optionally including specific package extras.

```bash
uv add --editable /path/to/eegprep-ext-foo
```

```bash
uv add --editable "/path/to/eegprep-ext-foo[models]"
```

--------------------------------

### Start Xvfb Virtual Desktop

Source: https://eegprep.org/user_guide/visual_parity.html

Initialize an Xvfb virtual display for MATLAB GUI capture.

```bash
Xvfb :99 -screen 0 1920x1080x24 -ac +extension GLX +render -noreset \
    > /tmp/xvfb99.log 2>&1 &
export DISPLAY=:99
openbox > /tmp/openbox99.log 2>&1 &
xdpyinfo | head
```

--------------------------------

### eegprep.eeglab(onearg=None, *, session=None, show=True, block=False, all_menus=None, include_plugins=True, native_menu_bar=None, native_file_dialogs=True)

Source: https://eegprep.org/api/generated/eegprep.eeglab.html

Starts the EEGPrep EEGLAB-style main window. This function allows for GUI initialization with various configuration options for menus, dialogs, and session management.

```APIDOC
## eegprep.eeglab

### Description
Starts the EEGPrep EEGLAB-style main window. This function can be used to launch the GUI or return a session object depending on the provided arguments.

### Parameters
- **onearg** (str | None) - Optional - EEGLAB-like command. "nogui" returns a session only, "full" enables legacy/advanced menu items, and "versions" returns the package version string.
- **session** (EEGPrepSession | None) - Optional - Optional existing GUI session.
- **show** (bool) - Optional - Show the Qt window before returning.
- **block** (bool) - Optional - Enter the Qt event loop.
- **all_menus** (bool | None) - Optional - Override EEG_OPTIONS["option_allmenus"].
- **include_plugins** (bool) - Optional - Include plugin-contributed menus.
- **native_menu_bar** (bool | None) - Optional - Use the native macOS menu bar when True. None keeps EEGPrep’s platform default; set False to keep menus in the window on macOS.
- **native_file_dialogs** (bool) - Optional - Use platform-native file pickers when True.

### Returns
- **EEGPrepMainWindow** (Any) - The main window object by default, or EEGPrepSession if onearg="nogui" is specified.
```

--------------------------------

### Launch EEGPrep GUI

Source: https://eegprep.org/user_guide/gui_help_menus.html

Commands to start the EEGPrep GUI or the interactive console session.

```bash
uv run eegprep-gui --full
```

```bash
uv run eegprep-console --full
```

--------------------------------

### Launch EEGPrep GUI Only

Source: https://eegprep.org/user_guide/gui_tutorials.html

Starts the GUI without an accompanying console window.

```bash
uv run eegprep-gui --full
```

--------------------------------

### eegprep.gui(onearg=None, *, session=None, show=True, block=False, all_menus=None, include_plugins=True, native_menu_bar=None, native_file_dialogs=True)

Source: https://eegprep.org/api/generated/eegprep.gui.html

Starts the EEGPrep GUI. This function acts as an EEGPrep-first alias for the standard EEGLAB initialization.

```APIDOC
## eegprep.gui

### Description
Starts the EEGPrep GUI. This is the EEGPrep-first alias for eeglab(), which remains available for EEGLAB-compatible workflows.

### Parameters
- **onearg** (str | None) - Optional - Positional argument.
- **session** (EEGPrepSession | None) - Optional - The EEGPrep session object.
- **show** (bool) - Optional - Whether to show the GUI.
- **block** (bool) - Optional - Whether to block execution.
- **all_menus** (bool | None) - Optional - Whether to display all menus.
- **include_plugins** (bool) - Optional - Whether to include plugins.
- **native_menu_bar** (bool | None) - Optional - Whether to use the native menu bar.
- **native_file_dialogs** (bool) - Optional - Whether to use native file dialogs.

### Returns
- **Any** - The result of the GUI initialization.
```

--------------------------------

### Install GPU Support

Source: https://eegprep.org/faq.html

Commands to add PyTorch and MNE-Python CUDA support for accelerated computations.

```bash
uv add torch  # For GPU support
uv add "mne[cuda]"  # For MNE GPU support
```

--------------------------------

### Install Extensions from Private Indices

Source: https://eegprep.org/user_guide/extensions.html

Install extensions from a private package index, either supplementing or replacing the default PyPI index.

```bash
uv add --index https://packages.lab.example/simple eegprep-ext-foo
```

```bash
uv add --default-index https://packages.lab.example/simple eegprep-ext-foo
```

--------------------------------

### Execute Preprocessing Commands

Source: https://eegprep.org/user_guide/interactive_console.html

Examples of running preprocessing functions that update the shared GUI session.

```python
pop_resample(EEG, 64)
```

```python
eegprep.pop_reref(EEG, [])
```

```python
EEG, LASTCOM = pop_reref(EEG, [])
```

--------------------------------

### Install eegprep using uv

Source: https://eegprep.org/user_guide/installation.html

Use these commands to add eegprep to a project managed by uv.

```bash
uv add eegprep
```

```bash
uv add "eegprep[all]"
```

--------------------------------

### Console output example

Source: https://eegprep.org/auto_examples/plot_public_api_and_plugins.html

Expected output from running the console workspace and plugin inspection code.

```text
<EEGPrep dataset result: CURRENTSET=1, LASTCOM="[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, CURRENTSET, 'setname', 'console demo');">
CURRENTSET: 1
Dataset name: console demo
Bundled plugins: clean_rawdata, ICLabel, firfilt, DIPFIT, EEG-BIDS
ICLabel status: [1]
```

--------------------------------

### Example output for data preparation

Source: https://eegprep.org/auto_examples/plot_ica_and_iclabel.html

Expected console output after running the data preparation script.

```text
Preparing data for ICA...
----------------------------------------------------------------------
Data after preprocessing:
  Shape: (32, 9330)
  Range: [-17.33, 16.56] µV
```

--------------------------------

### Launch the Interactive Console

Source: https://eegprep.org/user_guide/interactive_console.html

Commands to start the EEGPrep console with full GUI integration, optionally enabling native file dialogs.

```bash
uv run eegprep-console --full
```

```bash
uv run eegprep-console --full --native-file-dialogs
```

--------------------------------

### Example Interpolation Output

Source: https://eegprep.org/auto_examples/plot_channel_interpolation.html

Shows the expected console output format for the channel interpolation summary.

```text
======================================================================
SUMMARY
======================================================================
Total channels: 32
Bad channels identified: 3
Percentage of bad channels: 9.4%

Mean correlation (good channels): 1.0000
Mean correlation (bad channels): 0.3408

Interpolation successfully recovered bad channels
Interpolated channels can be used for further analysis
======================================================================

Recommendations:
----------------------------------------------------------------------
1. Always inspect bad channels visually before interpolation
2. Use multiple criteria for bad channel detection
3. Verify interpolation quality with correlation analysis
4. Document which channels were interpolated in your analysis
5. Consider excluding channels with >20% bad data
6. Use spatial information (electrode positions) for interpolation
7. Validate results with domain expertise
----------------------------------------------------------------------
```

--------------------------------

### Example Output for Component Rejection

Source: https://eegprep.org/auto_examples/plot_ica_and_iclabel.html

Displays the expected console output format when running the component rejection script.

```text
======================================================================
COMPONENT REJECTION RECOMMENDATIONS
======================================================================

Rejection Criteria:
  Confidence threshold: 0.5
  Artifact classes: ['Muscle', 'Eye', 'Heart', 'Line Noise', 'Channel Noise']

Components recommended for rejection: 1

Components to reject (first 10):
----------------------------------------------------------------------
Comp   Class           Confidence
----------------------------------------------------------------------
3      Muscle          0.529
```

--------------------------------

### Launching the EEG-BIDS Console

Source: https://eegprep.org/user_guide/bids_workflow.html

Start the console session to access BIDS import and export menu actions within a shared environment.

```bash
uv run eegprep-console --full
```

--------------------------------

### Example Interpolation Quality Output

Source: https://eegprep.org/auto_examples/plot_channel_interpolation.html

Sample console output showing variance statistics for bad and good channels.

```text
======================================================================
INTERPOLATION QUALITY ASSESSMENT
======================================================================

Bad Channel Statistics:
----------------------------------------------------------------------
Channel    Original Var    Interp Var      Var Change
----------------------------------------------------------------------
Fz         2615.38         18.34           -99.3          %
Pz         0.01            16.86           167841.9       %
Fc1        1211.56         1211.56         0.0            %

Good Channel Statistics (for reference):
----------------------------------------------------------------------
Channel    Original Var    Interp Var      Var Change
----------------------------------------------------------------------
Fp1        53.70           53.70           0.0            %
Fpz        54.17           54.17           0.0            %
Fp2        54.20           54.20           0.0            %
F7         54.17           54.17           0.0            %
F3         54.12           54.12           0.0            %
```

--------------------------------

### Install MNE-Python

Source: https://eegprep.org/user_guide/mne_integration.html

Ensure MNE-Python is installed in your environment for EEGPrep integration.

```bash
uv add mne
```

--------------------------------

### Install package in editable mode

Source: https://eegprep.org/development.html

Resolve ModuleNotFoundError by installing dependencies.

```bash
uv sync --group dev
```

--------------------------------

### Initialize and Precompute STUDY in Python

Source: https://eegprep.org/user_guide/study_workflows.html

Demonstrates loading datasets, assigning metadata, and performing channel measure precomputation using the EEGPrep Python API.

```python
from pathlib import Path
from eegprep import pop_loadset, pop_precomp, pop_study

ALLEEG = [
    pop_loadset(Path("sample_data") / "eeglab_data_epochs_ica.set"),
    pop_loadset(Path("sample_data") / "eeglab_data_epochs_ica.set"),
]
for index, EEG in enumerate(ALLEEG, start=1):
    EEG["subject"] = f"S{index:02d}"
    EEG["condition"] = "tutorial"

STUDY, ALLEEG, com = pop_study(None, ALLEEG, name="Tutorial study", return_com=True)
STUDY, ALLEEG, com = pop_precomp(STUDY, ALLEEG, "channels", erp="on", return_com=True)
```

--------------------------------

### Launch EEGPrep without plugins

Source: https://eegprep.org/user_guide/extensions.html

Use these commands to troubleshoot startup issues or restrict the session to core functionality.

```bash
uv run eegprep-gui --no-plugins
uv run eegprep-console --no-plugins
```

--------------------------------

### Build Alternative Documentation Formats

Source: https://eegprep.org/development.html

Commands to generate PDF or EPUB documentation.

```bash
uv run make -C docs latexpdf
```

```bash
uv run make -C docs epub
```

--------------------------------

### Install eegprep using pip

Source: https://eegprep.org/user_guide/installation.html

Use this command for installing the package in environments where uv is not used.

```bash
pip install eegprep
```

--------------------------------

### Initialize Environment and Imports

Source: https://eegprep.org/_downloads/5b37628fc88781e07fe6fd42065f3c08/plot_bids_pipeline.ipynb

Sets up the necessary environment, imports required libraries, and configures the random seed for reproducibility.

```python
import numpy as np
import tempfile
import os
import json
import sys
import shutil

sys.path.insert(0, '/Users/baristim/Projects/eegprep/src')

import eegprep

# Set random seed for reproducibility
np.random.seed(42)
```

--------------------------------

### Build documentation locally

Source: https://eegprep.org/user_guide/installation.html

Commands to sync development dependencies and build the documentation using Sphinx.

```bash
uv sync --group dev --extra docs
uv run --no-sync sphinx-build -b html docs/source docs/_build/html
```

```bash
uv run --no-sync make -C docs html
```

--------------------------------

### ICLabel Classification Output Example

Source: https://eegprep.org/auto_examples/plot_ica_and_iclabel.html

Example output showing the classification results for 20 components.

```text
Running ICLabel classification...
----------------------------------------------------------------------
ICLabel classification complete!
  Number of components classified: 20
  Number of classes: 7

Component Classifications (first 10):
----------------------------------------------------------------------
Comp   Class           Confidence   Probabilities
----------------------------------------------------------------------
0      Line Noise      0.497        [0.24, 0.06, 0.08, ...]
1      Channel Noise   0.394        [0.12, 0.17, 0.00, ...]
2      Eye             0.346        [0.13, 0.15, 0.35, ...]
3      Muscle          0.529        [0.17, 0.53, 0.06, ...]
4      Eye             0.343        [0.05, 0.04, 0.34, ...]
5      Brain           0.440        [0.44, 0.10, 0.02, ...]
6      Other           0.449        [0.07, 0.24, 0.14, ...]
7      Muscle          0.405        [0.06, 0.40, 0.17, ...]
8      Brain           0.451        [0.45, 0.02, 0.04, ...]
9      Eye             0.369        [0.01, 0.23, 0.37, ...]
... and 10 more components
```

--------------------------------

### Build documentation locally

Source: https://eegprep.org/contributing.html

Generate the project documentation locally to verify formatting and content updates.

```bash
cd docs
make html
```

--------------------------------

### Inspect Installed Plugin Metadata

Source: https://eegprep.org/user_guide/plugins.html

Retrieve and display information about currently installed plugins and their menu structures.

```python
import eegprep

for plugin in eegprep.bundled_plugins():
    print(plugin["plugin"], plugin["funcname"], plugin["menu"])

status, names, records = eegprep.plugin_status("ICLabel", exactmatch=True)
print(status, names)

inventory = eegprep.plugin_menu(show=False)
print(eegprep.format_plugin_menu())
```

--------------------------------

### ICA Decomposition Output Example

Source: https://eegprep.org/auto_examples/plot_ica_and_iclabel.html

Example output showing the result of the ICA decomposition process, including error handling messages.

```text
Performing ICA decomposition using Picard algorithm...
----------------------------------------------------------------------
Note: ICA decomposition encountered an issue: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
Using dummy components for demonstration...
```

--------------------------------

### Create virtual environment for eegprep

Source: https://eegprep.org/user_guide/installation.html

Set up a clean virtual environment to avoid dependency conflicts.

```bash
uv venv eegprep_env
source eegprep_env/bin/activate  # On Windows: eegprep_env\Scripts\activate
uv pip install eegprep
```

--------------------------------

### eegprep.CatalogValidationOptions.entry_points_provider

Source: https://eegprep.org/api/generated/eegprep.CatalogValidationOptions.html

Retrieves EntryPoint objects for installed packages.

```APIDOC
## entry_points_provider()

### Description
Returns EntryPoint objects for all installed packages. Users can pass selection parameters to filter results.

### Returns
- **result** (EntryPoints | SelectableGroups) - The entry points or selectable groups for installed packages.
```

--------------------------------

### eegprep.plugin_status

Source: https://eegprep.org/api/index.html

Checks the installation status of a specific EEGPrep extension.

```APIDOC
## eegprep.plugin_status(pluginname, *[, ...])

### Description
Return EEGLAB-style installed status for EEGPrep extensions.

### Parameters
- **pluginname** (str) - Required - The name of the plugin to check.
```

--------------------------------

### eegprep.bundled_plugins()

Source: https://eegprep.org/api/generated/eegprep.bundled_plugins.html

Retrieves metadata for all EEGPrep extensions bundled with the current installation.

```APIDOC
## eegprep.bundled_plugins()

### Description
Returns metadata for EEGPrep extensions bundled in the installed package.

### Signature
`eegprep.bundled_plugins() -> tuple[dict[str, Any], ...]`

### Returns
- **tuple[dict[str, Any], ...]**: A tuple containing dictionaries of metadata for each bundled plugin.
```

--------------------------------

### Clone and Configure Repository

Source: https://eegprep.org/contributing.html

Initial steps to clone a fork and link the upstream repository.

```bash
git clone https://github.com/YOUR_USERNAME/eegprep.git
cd eegprep
```

```bash
git remote add upstream https://github.com/sccn/eegprep.git
```

```bash
git checkout -b feature/your-feature-name
```

--------------------------------

### Check CUDA and PyTorch status

Source: https://eegprep.org/user_guide/installation.html

Verify GPU availability and PyTorch installation.

```bash
nvidia-smi
```

```python
import torch
print(torch.cuda.is_available())
```

--------------------------------

### View Documentation Locally

Source: https://eegprep.org/development.html

Commands to open the generated documentation in a browser or serve it via a local HTTP server.

```bash
open docs/build/html/index.html  # macOS
xdg-open docs/build/html/index.html  # Linux
start docs/build/html/index.html  # Windows
```

```bash
cd docs/build/html
uv run python -m http.server 8000
```

--------------------------------

### eegprep.plugin_status

Source: https://eegprep.org/api/core.html

Checks the installation status of EEGPrep extensions using EEGLAB-style matching.

```APIDOC
## eegprep.plugin_status(pluginname, *, exactmatch=False, pluginlist=None, registry=None, catalog=None, catalog_path=None, include_bundled=True, include_entry_points=True, disabled_extensions=None)

### Description
Returns the installed status for EEGPrep extensions based on the provided name or search criteria.

### Parameters
- **pluginname** (str) - Plugin or extension name, package name, or substring to search.
- **exactmatch** (bool) - Require exact case-insensitive name matching.
- **pluginlist** (list[dict[str, Any]] | tuple[dict[str, Any], ...] | None) - Optional precomputed extension inventory.
- **registry** (ExtensionRegistry | None) - Optional discovered registry.
- **catalog** (ExtensionCatalog | None) - Optional loaded catalog.
- **catalog_path** (str | None) - Optional JSON catalog path.
- **include_bundled** (bool) - Include bundled EEGPrep plugin ports.
- **include_entry_points** (bool) - Include installed entry-point extensions.
- **disabled_extensions** (set[str] | list[str] | tuple[str, ...] | None) - Registry names to mark disabled.

### Returns
- **tuple[list[int], list[str], list[dict[str, Any]]]** - A tuple containing status codes (1 for active, 0 otherwise), extension names, and plugin structures.
```

--------------------------------

### eegprep.ExtensionRegistry.__init__

Source: https://eegprep.org/api/generated/eegprep.ExtensionRegistry.html

Initializes the ExtensionRegistry with configuration for extension discovery.

```APIDOC
## eegprep.ExtensionRegistry.__init__

### Description
Initializes a new instance of the ExtensionRegistry to manage EEGPrep extensions.

### Parameters
- **disabled_extensions** (set[str]|list[str]|tuple[str]) - Optional - A collection of extension names to disable.
- **include_bundled** (bool) - Optional - Whether to include bundled extensions.
- **include_entry_points** (bool) - Optional - Whether to include entry point extensions.
- **entry_point_group** (str) - Optional - The entry point group name.
- **entry_points_provider** (Callable) - Optional - Provider for entry points.
- **current_version** (str|None) - Optional - The current version string.
- **version_provider** (Callable) - Optional - Provider for version information.
```

--------------------------------

### Build HTML Documentation

Source: https://eegprep.org/development.html

Commands to sync dependencies and build HTML documentation using Sphinx.

```bash
uv sync --group dev --extra docs
uv run --no-sync sphinx-build -b html docs/source docs/_build/html
```

```bash
uv run make -C docs html
```

--------------------------------

### eegprep.format_plugin_menu(pluginlist=None, *, registry=None, catalog=None, catalog_path=None, include_bundled=True, include_entry_points=True, disabled_extensions=None)

Source: https://eegprep.org/api/generated/eegprep.format_plugin_menu.html

Returns a plain-text extension inventory for console display.

```APIDOC
## eegprep.format_plugin_menu

### Description
Returns a plain-text extension inventory for console display.

### Parameters
- **pluginlist** (list[dict[str, Any]] | tuple[dict[str, Any], ...] | None) - Optional - A list or tuple of plugin dictionaries.
- **registry** (ExtensionRegistry | None) - Optional - An instance of ExtensionRegistry.
- **catalog** (ExtensionCatalog | None) - Optional - An instance of ExtensionCatalog.
- **catalog_path** (str | None) - Optional - Path to the extension catalog.
- **include_bundled** (bool) - Optional - Whether to include bundled extensions (default: True).
- **include_entry_points** (bool) - Optional - Whether to include entry points (default: True).
- **disabled_extensions** (set[str] | list[str] | tuple[str, ...] | None) - Optional - A collection of disabled extension names.

### Returns
- **str** - A plain-text representation of the extension inventory.
```

--------------------------------

### Accessing CLI Help

Source: https://eegprep.org/user_guide/agent_cli.html

Retrieve general help and specific skill information for the EEGPrep CLI.

```bash
eegprep --help
eegprep skills get eegprep-cli
```

--------------------------------

### Check Python Version

Source: https://eegprep.org/development.html

Verify that the installed Python version meets the minimum requirement of 3.11.

```bash
python --version
```

--------------------------------

### BIDS EEG Dataset Structure

Source: https://eegprep.org/user_guide/bids_workflow.html

Example of a standard BIDS-compliant directory layout for EEG data.

```text
dataset/
|-- dataset_description.json
|-- participants.tsv
`-- sub-01/
    `-- eeg/
        |-- sub-01_task-rest_run-01_eeg.set
        |-- sub-01_task-rest_run-01_eeg.fdt
        |-- sub-01_task-rest_run-01_channels.tsv
        |-- sub-01_task-rest_run-01_events.tsv
        `-- sub-01_task-rest_run-01_eeg.json
```

--------------------------------

### ICA Component Summary Output

Source: https://eegprep.org/auto_examples/plot_ica_and_iclabel.html

Example output showing the distribution of classified ICA components.

```text
======================================================================
SUMMARY
======================================================================
Total components: 20
Brain components: 4
Muscle components: 5
Eye components: 3
Heart components: 3
Line noise components: 1
Channel noise components: 2
Other components: 2

Artifact components: 1
Percentage of artifacts: 5.0%
======================================================================
```

--------------------------------

### eegprep.ExtensionMenu.__init__

Source: https://eegprep.org/api/generated/eegprep.ExtensionMenu.html

Initializes a new instance of the ExtensionMenu class with specified menu properties.

```APIDOC
## ExtensionMenu.__init__

### Description
Initializes a declarative menu contribution for an extension action.

### Parameters
- **path** (tuple[str, ...]) - Optional - The path for the menu item.
- **action** (str) - Optional - The action associated with the menu item.
- **label** (str) - Optional - The display label for the menu item.
- **tag** (str) - Optional - A tag for the menu item.
- **userdata** (str) - Optional - User-defined data associated with the menu item.
- **separator** (bool) - Optional - Whether the item acts as a separator.
- **enabled** (bool) - Optional - Whether the menu item is enabled.
- **checked** (bool) - Optional - Whether the menu item is checked.
- **visibility** (str) - Optional - The visibility state of the menu item.
- **insert_after** (str) - Optional - Identifier to insert this item after.
- **insert_before** (str) - Optional - Identifier to insert this item before.
- **children** (tuple[ExtensionMenu, ...]) - Optional - Child menu items.
```

--------------------------------

### Example Output of Preprocessing Statistics

Source: https://eegprep.org/auto_examples/plot_basic_preprocessing.html

The expected console output format for the preprocessing summary statistics.

```text
======================================================================
PREPROCESSING SUMMARY STATISTICS
======================================================================

Metric               Original        Cleaned         Interpolated
----------------------------------------------------------------------
Mean (µV)                     0.019         -0.012         -0.014
Std Dev (µV)                  7.450          2.075          2.026
Variance (µV²)               55.497          4.307          4.106

Variance Reduction             92.2%           92.6%

Total channels       32
Bad channels identified 2
Percentage bad       6.2%
======================================================================
```

--------------------------------

### Google-style docstring format

Source: https://eegprep.org/contributing.html

Standard structure for function documentation including parameters, returns, and examples.

```python
def preprocess_eeg(eeg, filter_type='bandpass', freq_range=(1, 50)):
    """Preprocess EEG data with filtering and artifact removal.

    This function applies a series of preprocessing steps to clean
    EEG data for further analysis.

    Parameters
    ----------
    eeg : EEGobj
        The EEG object to preprocess.
    filter_type : str, optional
        Type of filter to apply. Options: 'bandpass', 'highpass', 'lowpass'.
        Default is 'bandpass'.
    freq_range : tuple, optional
        Frequency range for filtering in Hz. Default is (1, 50).

    Returns
    -------
    dict
        The preprocessed EEG dictionary.

    Raises
    ------
    ValueError
        If filter_type is not recognized.
    TypeError
        If eeg is not an EEG dictionary.

    Examples
    --------
    >>> from eegprep import pop_eegfiltnew, pop_loadset
    >>> EEG = pop_loadset("data.set")
    >>> EEG, com = pop_eegfiltnew(EEG, locutoff=1, hicutoff=50, return_com=True)

    Notes
    -----
    This function returns the updated EEG dictionary.

    See Also
    --------
    clean_artifacts : Remove artifacts from EEG data
    clean_flatlines : Remove flat-line channels
    """
    # Implementation here
    pass
```

--------------------------------

### Example output for clean_artifacts

Source: https://eegprep.org/auto_examples/plot_artifact_removal.html

Displays the expected console output after running the artifact removal process.

```text
======================================================================
METHOD 1: clean_artifacts
======================================================================
Description: General-purpose artifact removal
Removes high-amplitude transient artifacts
Good for: Eye blinks, muscle artifacts, transient noise
Result: Data range [-18.10, 23.38] µV
```

--------------------------------

### Configure browser display settings

Source: https://eegprep.org/user_guide/eegbrowser.html

Use the show=False parameter to initialize the browser model without opening the Qt interface, useful for scripting or testing.

```python
model = eegplot(EEG, winlength=5, dispchans=16, show=False)
```

--------------------------------

### Check EEGPrep version in Python

Source: https://eegprep.org/changelog.html

Import the package and print the version attribute to verify the current installation.

```python
import eegprep
print(eegprep.__version__)
```

--------------------------------

### Load Sample Data

Source: https://eegprep.org/user_guide/preprocessing_pipeline.html

Initializes the EEG dataset from a file path and verifies basic data properties.

```python
from pathlib import Path
from eegprep import pop_loadset

EEG = pop_loadset(Path("sample_data") / "eeglab_data.set")
print(EEG["data"].shape, EEG["srate"], len(EEG["event"]))
```

--------------------------------

### Create a STUDY via Console

Source: https://eegprep.org/user_guide/gui_tutorials.html

Programmatically initialize a STUDY object using currently loaded datasets in ALLEEG.

```matlab
STUDY, ALLEEG, com = pop_study(None, ALLEEG, name="Tutorial study", return_com=True)
```

--------------------------------

### Bad Channel Simulation Output

Source: https://eegprep.org/auto_examples/plot_channel_interpolation.html

Example console output showing the status of introduced bad channels.

```text
Introducing bad channels...
----------------------------------------------------------------------
Bad channels to introduce: ['Fz', 'Pz', 'Fc1']

  Type 1: High noise channel (Fz)
    - Adding 50 µV noise (vs. typical 2 µV)

  Type 2: Flat/dead channel (Pz)
    - Replacing signal with minimal noise

  Type 3: Noisy channel with artifacts (Fc1)
    - Adding 30 µV noise + 50 Hz artifact

Bad channels introduced at indices: [5, 15, 25]
======================================================================
```

--------------------------------

### Configure entry points in pyproject.toml

Source: https://eegprep.org/api/extensions.html

Advertise the register function via standard Python package metadata.

```toml
[project.entry-points."eegprep.extensions"]
my_extension = "my_extension.register:register"
```