ndshapecheck

ndshapecheck is a simple library for checking the shape of numpy arrays. To get started, see the examples below. For more information, please visit the documentation.

Contributions are welcome! Please read the contribution guide for more information.

Quickstart

Requirements

• Python 3.10 or newer

Installation

Install the library with:

pip install ndshapecheck

Basic Usage

Import the library:

from ndshapecheck import ShapeCheck

Create a ShapeCheck instance and verify shapes:

import numpy as np
sc = ShapeCheck()

assert sc('A,B,3').check(np.zeros(10, 10, 3)), sc.why # A = 10, B = 10
assert sc('A,C*,3').check(np.zeros(10, 3, 2, 3, 3)), sc.why # C*=3, 2, 3

Notes

• Symbol Binding: ShapeCheck objects remember symbol values between checks.

  In the example above, the first call sets A=10 and B=10. Subsequent checks using these symbols will fail if a value other than 10 is found within the shape.

• Shape Rules: Shape rules are comma-separated strings. Each element is either:

  • A non-negative integer (e.g. 3, 5)
  • A case-sensitive symbolic name (e.g. A, B1, my_dim)

• Quantifiers: You can append standard regular expression quantifiers to symbols:

  • * : zero or more dimensions
  • + : one or more dimensions
  • ? : optional dimension

• Important: A, A*, A? and A+ are treated as separate symbols. Example:

  sc('A*,A').check(np.zeros((5, 10, 10)))
  # Assigns A* = (5,10) and A = 10 separately

• Important: If an optional symbol is omitted, it must also be omitted in future checks. Example:

  sc('optional_batch?,n_features').check((12,))  # no batch dimension
  sc('optional_batch?,n_labels').check((10, 3))  # fails. batch dimension should be omitted

• Error Messages: If a shape check fails, sc.why contains a message string explaining why.