Sp00kyVectors 0.1.6

A spooky vector analysis library

🎃 Sp00kyVectors: Haunted Vector Analysis for the Living and the Dead 📈💀

Welcome to Sp00kyVectors, the eerily intuitive Python class for vector analysis, statistical computation, and sinister visualizations — all coded to thrill and analyze! 🪄👻

Whether you’re chasing spectral patterns or just need basic stats with dramatic flair, this library’s got your back (from beyond).

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🧼 Cleaning Example

Before	After

🧠 Features

• 🧮 Vector Magic:

  • Load 1D or 2D arrays into Vector objects
  • X/Y decomposition for 2D data
  • Linear algebra methods like magnitude, angle, dot, and projection

• 📊 Statistical Potions:

  • Mean, median, standard deviation 💀
  • Probability vectors and PDFs 🧪
  • Z-score normalization 🧼
  • Entropy between aligned vectors 🌀
  • Internal entropy of a vector

• 🖼️ Visualizations:

  • Linear and log-scale histogramming
  • Vector plots with tails, heads, and haunted trails
  • Optional "entropy mode" that colors plots based on mysterious disorder 👀

• 🔧 Tools of the Craft:

  • Gaussian kernel smoothing for smoothing out your nightmares
  • Elementwise operations: .normalize(), .project(), .difference(), and more
  • Pretty __repr__ so your print statements conjure elegant summaries

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🧪 Example

from sp00kyvectors import Vector

v = Vector([1, 2, 3, 4, 5])
print(v.mean())  # Output: 3.0

v2 = Vector([1, 1, 1, 1, 6])
print(v.entropy(v2))  # Output: spooky entropy value

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📦 Installation

pip install sp00kyvectors

Or summon it from your own local clone:

git clone https://github.com/yourname/sp00kyvectors.git
cd sp00kyvectors
pip install .

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📚 Documentation

🧪 Class: Vector

✨ Initialization

Create a new Vector from a list or numpy array.

from sp00kyvectors import Vector

v = Vector([1, 2, 3, 4, 5])

If you're working with 2D data:

v2d = Vector([[1, 2], [3, 4], [5, 6]])

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📊 Methods

.mean()

Returns the mean of the vector.

v.mean()  # ➜ 3.0

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.median()

Returns the median.

v.median()  # ➜ 3

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.std()

Returns the standard deviation.

v.std()  # ➜ 1.5811...

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.normalize()

Normalizes the vector using Z-score (zero mean, unit variance).

v_norm = v.normalize()

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.entropy(other: Vector)

Computes Shannon entropy between this vector and another.

v2 = Vector([1, 1, 1, 1, 6])
v.entropy(v2)  # ➜ ~0.72 (varies based on normalization)

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.difference(other: Vector)

Returns a new Vector representing the difference between this vector and another.

v3 = v.difference(v2)

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.project(dim: int)

Projects a 2D vector onto a specific dimension (0 = x, 1 = y).

vx = v2d.project(0)
vy = v2d.project(1)

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🔢 Linear Algebra Methods

.magnitude()

Returns the magnitude (length) of the vector.

v.magnitude()  # ➜ 7.416

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.angle(other: Vector)

Returns the angle between this vector and another, in radians.

v.angle(v2)  # ➜ 0.225 (radians)

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.dot(other: Vector)

Computes the dot product of this vector and another.

v.dot(v2)  # ➜ 24

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.cross(other: Vector)

Computes the cross product (only for 3D vectors).

v3d = Vector([1, 2, 3])
v3d2 = Vector([4, 5, 6])
v3d.cross(v3d2)  # ➜ [-3, 6, -3]

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.projection(other: Vector)

Returns the projection of this vector onto another.

v.projection(v2)  # ➜ Vector with projected values

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📈 Plotting

.histogram(log=False)

Plots a histogram of the vector values. Set log=True for logarithmic scale.

v.histogram()
v.histogram(log=True)

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.plot_vectors(mode="line", entropy=False)

Plots 2D vectors.

• mode: "line", "arrow", or "trail"
• entropy: if True, colorizes vectors by entropy

v2d.plot_vectors(mode="arrow", entropy=True)

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🔮 Utilities

.gaussian_smooth(sigma=1.0)

Applies Gaussian smoothing to the vector.

v_smooth = v.gaussian_smooth(sigma=2.0)

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💀 Dunder Methods

__repr__()

Pretty string representation.

print(v)  # Vector(mean=3.0, std=1.58, ...)

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🛠 Developer Notes

• Internal data is stored as numpy.ndarray
• Methods use scipy.stats, numpy, and matplotlib
• Entropy assumes aligned distributions (normalized first)

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🧛 License

MIT — haunt and hack as you please.

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🕸️ Coming Soon

• 3D support
• More spooky plots
• CLI interface: spookify file.csv --plot

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👻 Contributing

Spirits and sorcerers of all levels are welcome. Open an issue, fork the repo, or summon a pull request.

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🧛 License

MIT — you’re free to haunt this code as you wish as long as money is never involved!

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✨ Stay spooky, and may your vectors always point toward the unknown. 🕸️