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Quickstart

A feel for panchi in a few minutes. For deeper explanations, follow the links into the User Guide. For those that wish to contribute, take a look at Contributing

Installation

pip install panchi

Vectors

import panchi as pan

v = pan.Vector([3, 4])
print(v.magnitude)    # 5.0
print(v.normalize())  # [0.6, 0.8]

u = pan.Vector([1, 2, 3])
w = pan.Vector([4, 5, 6])
print(pan.dot(u, w))    # 32
print(pan.cross(u, w))  # [-3, 6, -3]

Vectors support the arithmetic you would expect: +, -, scalar * and /, and unary - for negation.

Matrices

A = pan.Matrix([[1, 2], [3, 4]])
B = pan.Matrix([[5, 6], [7, 8]])

print(A @ B)          # matrix multiplication
print(A.T)            # transpose
print(A.trace)        # 5
print(A.determinant)  # -2

@ is matrix multiplication, * is scalar multiplication — consistent with standard Python convention.

Factory Functions

I = pan.identity(3)
Z = pan.zero_matrix(2, 3)
D = pan.diagonal([1, 2, 3])
R = pan.rotation_matrix_2d(3.14159 / 2)

Exact Arithmetic

panchi supports exact fractions — write them as strings and they stay exact through every operation:

v = pan.Vector(["1/3", "2/3", "1/2"])
A = pan.exact_matrix([[1, 2, 3], [4, 5, 6], [7, 8, 10]])
inv = pan.inverse(A).inverse
print(A @ inv)  # exact identity matrix, no floating-point drift

See the Exact Arithmetic guide for the full story.

Algorithms

Algorithms return result objects that carry both the answer and the work behind it.

from panchi.algorithms import rref

A = pan.Matrix([[1, 2, 3], [2, 5, 7], [0, 1, 2]])

reduction = rref(A)
print(reduction.result)  # the RREF matrix
print(reduction.rank)    # 3
print(reduction)         # full step-by-step walkthrough

The step-by-step output is panchi's most distinctive feature — every algorithm lets you see exactly what happened, not just the final answer.

Visualizations

panchi can visualize vectors, transformations, and spans right out of the box.

from panchi.visualizations import Animator2D

animator = Animator2D()

# Plot vectors
animator.plot_vectors(pan.Vector([3, 2]), pan.Vector([-1, 3]), labels=["v1", "v2"])

# Animate a linear transformation (grid morph)
animator.animate_transform(pan.Matrix([[0, -1], [1, 0]]))

For video output, use the manim backend (pip install panchi[manim]):

animator = Animator2D(backend="manim", save_path="./videos")
animator.animate_transform(pan.Matrix([[1, 1], [0, 1]]))

See the Visualizations guide for the full feature set.

From here, explore the User Guide for the concepts and math behind each part of the library.