Sp00kyVectors 0.1.13

A spooky vector analysis library

Sp00kyVectors: Vector Analysis Wrapper for Python

Welcome to Sp00kyVectors, the software powering your Tricorder. 🛸

These eerily intuitive Python modules work seamlessly as one toolkit for:

• 🧲 Data ingestion
• 🧼 Cleaning
• 🧮 Vector analysis
• 📊 Statistical computation
• 🧠 Bespoke neural net creation
• 🌌 Visualizations 🪄👻

Perfect for any away missions 🖖

100% open-source and always summoning new engineers to help!

🧼 Analysis Examples

on-the-go data manipulation across space, time, and spreadsheets:

Before	After

🧹 Dirty Data

Load without worry

Easily load and align mismatched CSV files-hello IoT. This utility intelligently collects, normalizes, and organizes messy datasets — so you can focus on the analysis, not the cleanup. 🚀

Vector.load_folder(path) loads a folder of CSV files with potentially mismatched or missing columns,
aligns all columns based on their headers, and combines them into a single clean DataFrame.
Missing columns in any file are automatically filled with NaN values to maintain consistency.

Perfect for messy datasets where CSVs don't share the exact same structure!

Cleaning is done one layer up with sp00kyDF.get_clean_df() ✨🧹

This method returns a cleaned version of the DataFrame by performing the following steps:

1. 🧩 Removes duplicate rows (performed twice to ensure thorough cleaning)
2. 🚫📊 Clips outlier values based on the Z-score method (an Interquartile Range (IQR) method is also available)
3. 🏷️ Standardizes column names for consistency
4. ❌🕳️ (Optionally drops null values — currently commented out)

Finally, it returns the cleaned DataFrame ready for analysis. 🎯

🎛️⚙️✨ Granular Control

🧠 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/LilaShiba/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. 🕸️

Student Opportunities 🎓💻

Learning to code, using GitHub, or just curious? Reach out and join the team!
We’re currently looking for volunteers of all skill levels. Everyone’s welcome!