This project has been archived by its maintainers, and is no longer receiving any updates.
unimath — Mathematical Environment Library in Python
unimath is a Python library that provides an abstract mathematical environment designed to represent and manipulate sets, intervals, and number systems in a symbolic and intuitive way.
The project is continuously growing, with new mathematical structures and operations being added every day.
Developed and maintained by a Mathematics undergraduate student at Marmara University, unimath aims to combine mathematical theory with computational representation, creating a bridge between abstract reasoning and code implementation.
Features
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Abstract representation of Sets, Intervals, and Number Systems
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Support for Vector operations and linear algebra concepts
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Symbolic and numerical computation environment
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Integration with matplotlib for visualization
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Intuitive class structures and error handling for mathematical rigor
Vector
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Magnitude
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Specific vector information
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Inner product
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Angle between two vectors
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Visualization with matplotlib
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Find unit vector
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Cosine value between two vectors
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Projection around two vectors
Matrix
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Precisely defined mathematically
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Transpose
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Special matrix definitions
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Determinant
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Hadamard and classical product
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Symbolic Matris
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(3,3) Sarrus Method
Combinatorics
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Permutation
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Combination
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Posibilty
Calculus
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Find Supremum İnfrimum value
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Σ and Π operations
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Transformation
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Monotonicity of the series
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Symbolid sequance
Physics
- Wave equation analysis and visualization
$$ \psi(x,y) = \frac{2}{\sqrt{L_x L_y}} \sin\left(\frac{n_x \pi x}{L_x}\right) \sin\left(\frac{n_y \pi y}{L_y}\right) $$
- Energy levels of a particle in a two-dimensional cubic box
$$ E = \frac{\pi^2 \hbar^2}{2m} (n_x^2 + n_y^2 + n_z^2) $$
Wave_Equation(Lx=2.0, Ly=1.5, nx=4, ny=1, N=100)
Visualization
unimath includes built-in vector visualization features using matplotlib, allowing users to plot and analyze vector relationships directly in Python. This helps bridge the gap between symbolic manipulation and geometric intuition.
Installation / Download
PyPI (Recommended)
pip install unimath
Git
git clone https://github.com/Jolankaa/unimath
cd unimath
pip install .
Few Examples
import unimath
# Example: Creating a vector
v = unimath.Vector([1,2,3])
print("Magnitude:", v.magnitude())
# For visualization
v.Visualization()
# Example: Creating a matrix
m = unimath.Matrix([[1,2],[3,4]])
print("Determinant:", m.determinant())
Metadata
Release files for unimath 1.4.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| unimath-1.4.5.tar.gz | 14.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| unimath-1.4.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 31.4 kB
Release files / unimath-1.4.5.tar.gz
| Download URL | unimath-1.4.5.tar.gz |
|---|---|
| Size | 14.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
5e55f91358731ab25576f7062fce8d362316e8136239c97331d20e5eb8c88554
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.7
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Release files / unimath-1.4.5-py3-none-any.whl
| Download URL | unimath-1.4.5-py3-none-any.whl |
|---|---|
| Size | 16.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
43df6116b38a4df2ce0d484d9c5ba785e3e7e149674f271f3e73f6fb5404ac37
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8505871cdf81fcf903b67913faa1591ad65a04bd4524d4e084a6926077974d0f
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.7
|