### Install vector with Documentation Dependencies

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Installs vector in editable mode, including development, testing, and documentation dependencies. This is useful for building and developing the project's documentation.

```bash
pip install -e ".[dev, test, docs]"
```

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

### Installing Nox

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Installs the Nox automation tool using pip. Nox is used to manage and run various project commands and tasks.

```bash
pip install nox
```

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

### Install pre-commit Hooks

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Installs pre-commit hooks locally to automatically format, type-check, and prettify the codebase on commit.

```bash
pre-commit install
```

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

### Install vector using Conda

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Installs vector using Conda, creating a new environment named 'vector' and activating it. Optionally configures Conda to use the conda-forge channel.

```bash
conda env create
conda activate vector
conda config --env --add channels conda-forge  # optional
```

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

### Add vector Kernel for Notebooks

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Installs a Jupyter kernel for the vector environment, allowing notebooks to use the installed vector package.

```bash
python -m ipykernel install --user --name vector
```

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

### Install vector with Development Dependencies

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Installs vector in editable mode with development and testing dependencies. The 'dev' option includes awkward, test, numba, and sympy, while 'test' adds testing extras.

```bash
pip install -e ".[dev, test]"
```

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

### Install Vector with Pip

Source: https://github.com/scikit-hep/vector/blob/main/docs/index.md

This code snippet shows the command to install the Vector library using pip, the Python package installer. It's a straightforward command for users who prefer pip for package management.

```bash
pip install vector
```

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

### Create and Activate Virtual Environment (UNIX)

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Creates a Python virtual environment named '.env' and activates it. This isolates the vector installation and its dependencies.

```bash
python3 -m venv .env
. .env/bin/activate
```

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

### Install Vector from Source

Source: https://github.com/scikit-hep/vector/blob/main/docs/index.md

Instructions for cloning the Vector repository and installing it from the main branch using pip. This is useful for developers who want to contribute or use the latest features.

```bash
git clone https://github.com/scikit-hep/vector.git
cd vector
pip install -e .
```

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

### Install Vector from Source

Source: https://github.com/scikit-hep/vector/blob/main/README.md

Installs the Vector library from its source code, typically from a Git repository clone. This is useful for development or when using the latest unreleased features.

```bash
git clone https://github.com/scikit-hep/vector.git
cd vector
pip install -e .
```

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

### Install Vector with Pip

Source: https://github.com/scikit-hep/vector/blob/main/README.md

Installs the Vector library using pip, the Python package installer. This is the standard way to add Vector to your Python environment.

```bash
pip install vector
```

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

### Create and Activate Virtual Environment (Windows)

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Creates a Python virtual environment named '.env' and activates it on Windows systems. This isolates the vector installation and its dependencies.

```bash
python -m venv .env
.env\bin\activate
```

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

### Run Unit Tests with pytest

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Executes the unit tests for the vector project using pytest. This command assumes the necessary test dependencies are installed.

```bash
python -m pytest
```

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

### Run Doctests

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Executes doctests within the vector project's docstrings using pytest-doctestplus. This ensures that code examples in the documentation are accurate and functional.

```bash
python -m pytest --doctest-plus src/vector/
```

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

### Vector Summation with `sum()`

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/object.ipynb

Demonstrates using the built-in `sum()` function with a list of vectors, requiring a `start` value.

```python
import vector
vs = [vector.obj(x=x, y=x / 10) for x in range(10)]

sum(vs, start=vector.obj(x=0, y=0))
```

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

### Run Notebook Tests

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Executes the tests specifically designed for Jupyter notebooks within the vector project. This ensures that notebook examples and functionality are working correctly.

```bash
pytest tests/test_notebooks.py
```

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

### Building Documentation with Nox

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Commands to build the project's documentation using Nox. The `-- serve` option allows for serving the documentation locally on localhost.

```bash
nox -s docs
nox -s docs -- serve
```

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

### Building Documentation Locally

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Commands to clean and build the project's documentation locally. This process generates HTML files in the `docs/_build` directory, which can be viewed in a web browser.

```bash
cd docs/
make clean
make html
```

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

### Running Nox Test Sessions

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Commands to run various testing scenarios using Nox. This includes testing with minimal dependencies, all dependencies, doctests, coverage reports, notebooks, and specific Python versions.

```bash
nox -s lite
nox -s tests
nox -s tests
nox -s coverage
nox -s notebooks
nox -s disassemble
```

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

### Description and Reference Name Formatting

Source: https://github.com/scikit-hep/vector/blob/main/.git_archival.txt

Details the formatting for describing nodes and generating reference names, including tag matching and descriptive naming.

```APIDOC
describe-name: $Format:%(describe:tags=true,match=*[0-9]*)$
  - Description: Generates a descriptive name for nodes, including tags and matching patterns that include numbers.

ref-names: $Format:%D$
  - Description: Generates reference names for nodes.
```

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

### Running Default Nox Sessions

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Executes the default Nox sessions defined in the `noxfile.py`. These typically include linting, testing, and documentation checks.

```bash
nox
```

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

### Creating a LorentzVectorArray from a 2D Vector

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/awkward.ipynb

This example shows how to create a new LorentzVectorArray that is 'like' an existing vector, but initialized with 2D vector components (x, y).

```python
vec.like(vector.obj(x=1, y=2))
```

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

### Run all pre-commit Hooks

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Triggers all pre-commit hooks on all files in the repository. This is useful for ensuring the entire codebase adheres to the defined quality standards.

```bash
pre-commit run --all-files
```

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

### Creating a LorentzVectorArray from a 3D Vector

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/awkward.ipynb

This example shows how to create a new LorentzVectorArray that is 'like' an existing vector, but initialized with 3D vector components (x, y, z).

```python
vec.like(vector.obj(x=1, y=2, z=3))
```

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

### Run All Tests Including Doctests

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Executes both unit tests and doctests for the vector project. This provides a comprehensive test run to ensure code correctness and documentation accuracy.

```bash
python -m pytest --doctest-plus .
```

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

### Subclassing Awkward-Vector Behaviors

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/awkward.ipynb

Demonstrates how to subclass existing Awkward-Vector behaviors to extend functionality. This example shows creating `TwoVector` and `ThreeVector` classes that inherit from `MomentumAwkward2D` and `MomentumAwkward3D` respectively.

```python
behavior = vector.backends.awkward.behavior


@ak.mixin_class(behavior)
class TwoVector(vector.backends.awkward.MomentumAwkward2D):
    pass


@ak.mixin_class(behavior)
class ThreeVector(vector.backends.awkward.MomentumAwkward3D):
    pass


# required for transforming vectors
# the class names must always end with "Array"
TwoVectorArray.ProjectionClass2D = TwoVectorArray  # noqa: F821
TwoVectorArray.ProjectionClass3D = ThreeVectorArray  # noqa: F821
TwoVectorArray.MomentumClass = TwoVectorArray  # noqa: F821

ThreeVectorArray.ProjectionClass2D = TwoVectorArray  # noqa: F821
ThreeVectorArray.ProjectionClass3D = ThreeVectorArray  # noqa: F821
ThreeVectorArray.MomentumClass = ThreeVectorArray  # noqa: F821
```

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

### Addition after Copying Behaviors

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/awkward.ipynb

This example demonstrates performing vector addition after using `copy_behaviors` to register operations for new subclasses. It shows that the addition works as expected for the newly defined vector types.

```python
vec + vec
```

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

### 2D Vector Addition after Copying Behaviors

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/awkward.ipynb

This example demonstrates performing addition on 2D vectors after using `copy_behaviors` to register operations for new subclasses. It shows that the addition works as expected for the newly defined 2D vector types.

```python
vec.to_2D() + vec.to_2D()
```

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

### Clone vector Repository

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Forks and clones the scikit-hep/vector repository to your local machine. This is the first step for developing or using the latest commit of vector.

```bash
# fork scikit-hep/vector
git clone https://github.com/<your_github_userame>/vector.git
```

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

### Node Formatting Conventions

Source: https://github.com/scikit-hep/vector/blob/main/.git_archival.txt

Defines the formatting conventions for node information within the project, including hash format and date format.

```APIDOC
node: $Format:%H$
  - Description: Specifies the hash format for nodes.

node-date: $Format:%cI$
  - Description: Specifies the date format for nodes, using ISO 8601 format.
```

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

### Run Tests with Coverage

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Runs tests using pytest and calculates code coverage for the vector package. The results are typically reported to the console or a file.

```bash
python -m pytest --cov=vector tests/
```

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

### Running Specific Nox Sessions

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Executes specific Nox sessions, such as 'lint' for running pre-commit hooks and Pylint, or 'build' for building the project.

```bash
nox -s lint
nox -s pylint
nox -s build
```

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

### vector.VectorSympy3D API

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/make_sympy.md

API documentation for the VectorSympy3D class, used for constructing 3D symbolic vectors.

```APIDOC
vector.VectorSympy3D
  __init__(x, y, z)
    Constructs a 3D symbolic vector.
    Parameters:
      x: The x-component (SymPy symbol).
      y: The y-component (SymPy symbol).
      z: The z-component (SymPy symbol).
    Returns:
      A VectorSympy3D object.
```

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

### Vector Dot Product Method

Source: https://github.com/scikit-hep/vector/wiki/Method-names

This snippet documents the `dot()` method for vector operations within the scikit-hep vector library. It also notes its aliases and equivalents in other libraries like ROOT.

```python
vector.dot()
# Aliases: @ (in some contexts)
# ROOT equivalent: Dot(), *
```

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

### Run Unit Tests, Skipping Notebook Tests

Source: https://github.com/scikit-hep/vector/blob/main/CONTRIBUTING.md

Executes unit tests using pytest, explicitly ignoring the notebook tests. This is useful for faster testing cycles when notebook tests are not critical.

```bash
python -m pytest --ignore=tests/test_notebooks.py
```

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

### vector.VectorSympy2D API

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/make_sympy.md

API documentation for the VectorSympy2D class, used for constructing 2D symbolic vectors.

```APIDOC
vector.VectorSympy2D
  __init__(x, y)
    Constructs a 2D symbolic vector.
    Parameters:
      x: The x-component (SymPy symbol).
      y: The y-component (SymPy symbol).
    Returns:
      A VectorSympy2D object.
```

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

### vector.VectorSympy4D API

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/make_sympy.md

API documentation for the VectorSympy4D class, used for constructing 4D symbolic vectors.

```APIDOC
vector.VectorSympy4D
  __init__(x, y, eta, tau)
    Constructs a 4D symbolic vector.
    Parameters:
      x: The x-component (SymPy symbol).
      y: The y-component (SymPy symbol).
      eta: The eta component (SymPy symbol).
      tau: The tau component (SymPy symbol).
    Returns:
      A VectorSympy4D object.
```

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

### VectorProtocolSpatial Methods

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/vector3d.md

This section documents the methods available for 3D vectors as defined by the VectorProtocolSpatial class. It includes inherited members and is ordered by source.

```APIDOC
vector._methods.VectorProtocolSpatial
    :members:
    :inherited-members:
    :member-order: bysource
```

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

### Create Symbolic 2D Vector

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/sympy.ipynb

Instantiates a 2D vector using symbolic components.

```python
vector.VectorSympy2D(x=x, y=y)
```

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

### Import Libraries

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/numba.ipynb

Imports the necessary libraries: vector for vector operations, numba for JIT compilation, and numpy for numerical operations.

```python
import vector
import numba as nb
import numpy as np
```

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

### MomentumProtocolPlanar Methods

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/momentum2d.md

This section details the methods available for 2D momentum vectors as defined by the MomentumProtocolPlanar class. It includes inherited members and is ordered by source.

```APIDOC
vector._methods.MomentumProtocolPlanar
  :members:
  :inherited-members:
  :member-order: bysource
```

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

### Citation for Vector

Source: https://github.com/scikit-hep/vector/blob/main/docs/index.md

BibTeX entry for citing the Vector project in academic publications. It includes DOIs and author information for proper attribution.

```bibtex
@article{Chopra2025,
doi = {10.21105/joss.07791},
url = {https://doi.org/10.21105/joss.07791},
year = {2025}, publisher = {The Open Journal},
volume = {10},
number = {109},
pages = {7791},
author = {Saransh Chopra and Henry Schreiner and Eduardo Rodrigues and Jonas Eschle and Jim Pivarski},
title = {Vector: JIT-compilable mathematical manipulations of ragged Lorentz vectors},
journal = {Journal of Open Source Software}
}
```

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

### Using vector.zip for Awkward Arrays

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/make_awkward.md

This snippet illustrates the use of `vector.zip` as an alternative to `ak.zip`. Similar to `vector.Array`, `vector.zip` applies the vector library's behaviors to the resulting Awkward Array, making it easier to create arrays of vector-like records.

```python
import awkward as ak
import vector

vectors_data = {
    "x": [[1.1, 3.3], [], [5.5]],
    "y": [[2.2, 4.4], [], [6.6]],
}

vec_zipped = vector.zip({
    "x": ak.Array(vectors_data["x"]),
    "y": ak.Array(vectors_data["y"])
}, with_name="Vector2D")

print(abs(vec_zipped))
```

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

### vector.MomentumSympy3D API

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/make_sympy.md

API documentation for the MomentumSympy3D class, used for constructing 3D symbolic momentum vectors.

```APIDOC
vector.MomentumSympy3D
  __init__(px, py, pz)
    Constructs a 3D symbolic momentum vector.
    Parameters:
      px: The x-component of momentum (SymPy symbol).
      py: The y-component of momentum (SymPy symbol).
      pz: The z-component of momentum (SymPy symbol).
    Returns:
      A MomentumSympy3D object.
```

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

### Basic Mass Computation (Numba JIT)

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/numba.ipynb

The same mass computation function decorated with `@nb.njit` to enable Numba's just-in-time compilation. This version is expected to be faster for array operations.

```python
@nb.njit
def compute_mass(v1, v2):
    return (v1 + v2).mass
```

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

### Import Vector and SymPy

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/sympy.ipynb

Imports the necessary libraries, Vector for vector operations and SymPy for symbolic mathematics.

```python
import vector
import sympy
```

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

### General Constructor - vector.obj

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/make_object.md

The general constructor `vector.obj` is used to create vector objects. It takes arguments to specify the dimensionality and type (geometric or momentum) of the vector.

```APIDOC
vector.obj(x=0, y=0, z=0, t=0, mass=0, charge=0, theta=None, phi=None, eta=None, p=None, E=None, px=None, py=None, pz=None, pt=None, flavor=None, charge_val=None, pdgid=None, color=None, is_bare=False, is_complex=False, is_real=False, is_complex_real=False, is_complex_imag=False, is_real_imag=False, is_real_real=False, is_real_imag_real=False, is_real_imag_imag=False, is_real_imag_real_imag=False, is_real_imag_imag_imag=False, is_real_imag_real_real_imag=False, is_real_imag_imag_real_imag=False, is_real_imag_real_imag_real_imag=False, is_real_imag_imag_imag_real_imag=False, is_real_imag_real_real_imag_real_imag=False, is_real_imag_imag_real_imag_real_imag=False, is_real_imag_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag=False, is_real_imag_real_real_imag_real_real_real_imag=False, is_real_imag_imag_real_imag_real_real_real_imag=False, is_real_imag_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_real_imag_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_imag_imag_real_real_real_imag_real_real_imag_real_real_imag=False, is_real_imag_real_real_imag_real_real_imag_real_real_imag_real_real_imag=False
```

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

### VectorSympy2D Constructor

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/make_sympy.md

Constructs a 2D symbolic vector using SymPy. It requires x and y components as SymPy symbols.

```python
import vector
import sympy
x, y, z, t, px, py, pz, eta, tau = sympy.symbols(
    "x y z t px py pz eta tau", real=True
)
vector.VectorSympy2D(x=x, y=y)
```

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

### VectorSympy4D Constructor

Source: https://github.com/scikit-hep/vector/blob/main/docs/src/make_sympy.md

Constructs a 4D symbolic vector using SymPy. It requires x, y, eta, and tau components as SymPy symbols.

```python
import vector
import sympy
x, y, z, t, px, py, pz, eta, tau = sympy.symbols(
    "x y z t px py pz eta tau", real=True
)
vector.VectorSympy4D(x=x, y=y, eta=eta, tau=tau)
```