### Installing Badger using Pip

Source: https://github.com/xopt-org/badger/blob/main/README.md

This command installs the Badger optimization tool using the Pip package installer for Python. Pip is the standard package-management system used to install and manage software packages written in Python.

```shell
pip install badger-opt
```

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

### Installing Linting and Pre-commit Tools (Shell)

Source: https://github.com/xopt-org/badger/blob/main/README.md

This command installs `ruff` for code linting and `pre-commit` for managing Git hooks. These tools ensure code quality and adherence to project formatting standards before commits are made.

```shell
pip install ruff pre-commit
```

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

### Launching Badger GUI

Source: https://github.com/xopt-org/badger/blob/main/README.md

This command launches the graphical user interface (GUI) for the Badger optimization tool. The '-g' flag specifically instructs Badger to start in GUI mode, allowing interactive optimization tasks.

```bash
badger -g
```

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

### Installing Pre-commit Hooks (Shell)

Source: https://github.com/xopt-org/badger/blob/main/README.md

This command installs the Git pre-commit hooks configured for the Badger repository. These hooks automatically run checks (like linting and formatting) on your code before each commit, helping maintain code quality.

```shell
pre-commit install
```

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

### Installing Badger using Conda

Source: https://github.com/xopt-org/badger/blob/main/README.md

This command installs the Badger optimization tool using the Conda package manager from the 'conda-forge' channel. Conda is a cross-platform package and environment manager commonly used for scientific computing.

```shell
conda install -c conda-forge badger-opt
```

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

### Activating Conda Environment and Installing Badger (Shell)

Source: https://github.com/xopt-org/badger/blob/main/README.md

This sequence of commands first activates the newly created 'badger-env' Conda environment, then installs the Badger project in editable mode, including development dependencies. Editable mode allows for direct modifications to the source code without reinstallation.

```shell
conda activate badger-env
pip install -e ".[dev]"
```

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

### Cloning the Badger Repository (Shell)

Source: https://github.com/xopt-org/badger/blob/main/README.md

This command clones the Badger project repository from GitHub to your local machine. It is the first step for developers to obtain the source code and begin contributing or using the project.

```shell
git clone https://github.com/xopt-org/badger.git
```

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

### Citing Badger in Publications (Text Format)

Source: https://github.com/xopt-org/badger/blob/main/README.md

This snippet provides the recommended citation format for the Badger project, suitable for inclusion in academic publications. It includes author information, title, conference proceedings, and DOI.

```text
Zhang, Z., et al. "Badger: The missing optimizer in ACR",
in Proc. IPAC'22, Bangkok. doi:10.18429/JACoW-IPAC2022-TUPOST058
```

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

### Manually Running Pre-commit Checks (Shell)

Source: https://github.com/xopt-org/badger/blob/main/README.md

This command manually executes all configured pre-commit checks across all files in the repository. It's useful for verifying code quality and formatting compliance at any time, not just during a commit.

```shell
pre-commit run --all-files
```

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

### Citing Badger in Publications (BibTeX Format)

Source: https://github.com/xopt-org/badger/blob/main/README.md

This BibTeX entry provides comprehensive metadata for citing the Badger project in academic papers, compatible with LaTeX and other citation management tools. It includes all necessary fields such as authors, title, conference details, and identifiers.

```bibtex
@inproceedings{Badger,
    author       = {Z. Zhang and M. Böse and A.L. Edelen and J.R. Garrahan and Y. Hidaka and C.E. Mayes and S.A. Miskovich and D.F. Ratner and R.J. Roussel and J. Shtalenkova and S. Tomin and G.M. Wang},
    title        = {{Badger: The Missing Optimizer in ACR}},
    booktitle    = {Proc. IPAC'22},
    pages        = {999--1002},
    eid          = {TUPOST058},
    language     = {english},
    keywords     = {interface, controls, GUI, operation, framework},
    venue        = {Bangkok, Thailand},
    series       = {International Particle Accelerator Conference},
    number       = {13},
    publisher    = {JACoW Publishing, Geneva, Switzerland},
    month        = {07},
    year         = {2022},
    issn         = {2673-5490},
    isbn         = {978-3-95450-227-1},
    doi          = {10.18429/JACoW-IPAC2022-TUPOST058},
    url          = {https://jacow.org/ipac2022/papers/tupost058.pdf},
}
```

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

### Creating a Conda Environment for Badger (Shell)

Source: https://github.com/xopt-org/badger/blob/main/README.md

This command creates a new Conda environment named 'badger-env' with Python version 3.12. It isolates project dependencies, preventing conflicts with other Python projects on your system.

```shell
conda create -n badger-env python=3.12
```

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