Build Cython Extensions

This skill provides guidance for building Cython extensions and resolving compatibility issues, with particular focus on numpy version compatibility problems.

When to Use This Skill Building or compiling Cython extensions (.pyx files) Fixing numpy compatibility issues in Cython code Migrating Cython projects to work with numpy 2.0+ Resolving deprecated numpy type errors (np.int, np.float, np.bool, etc.) Troubleshooting Cython compilation failures Key File Types to Examine

When working with Cython projects, always examine ALL relevant file types:

Extension Description Must Check .pyx Cython implementation files Critical - Often contain numpy calls .pxd Cython declaration files Yes - May contain type declarations .py Python files Yes - May use deprecated types setup.py Build configuration Yes - Defines compilation settings .c / .cpp Generated C/C++ files Only if debugging compilation

Critical Pitfall: Never limit searches to only .py files when fixing numpy compatibility. The .pyx files are Cython source code and frequently contain the same deprecated numpy type references.

Approach for Numpy 2.0+ Compatibility Deprecated Types to Replace Deprecated Replacement np.int np.int_ or int np.float np.float64 or float np.bool np.bool_ or bool np.complex np.complex128 or complex np.object np.object_ or object np.str np.str_ or str Search Strategy

Search without file type restrictions to capture all occurrences:

Grep for patterns like "np.int[^0-9_]" across all files

Explicitly search Cython files:

Search specifically in .pyx and .pxd files

Check import statements in .pyx files - they often import numpy and use deprecated types

Fix and Recompile Workflow Identify all .pyx files in the project Search each file for deprecated numpy types Apply fixes to ALL files (both .py and .pyx) Recompile the Cython extensions after making changes to .pyx files Run verification tests Verification Strategy Import Testing Is Insufficient

Simply testing that a compiled module imports successfully does not verify the code works correctly. A module can import but fail when its functions are called.

Recommended Verification Steps Identify all Cython modules in the project For each module: Verify import succeeds Call at least one core function from each module Pass actual data to exercise numpy operations Run the project's test suite if available Create a verification script that exercises key functionality:

Example verification pattern

import numpy as np from module import cython_function

Test with actual numpy arrays

test_data = np.array([1, 2, 3], dtype=np.int64) result = cython_function(test_data) assert result is not None

Test Coverage Awareness Repository tests may not cover all Cython code paths Passing tests does not guarantee all Cython functionality works Explicitly test functions that use numpy types Common Pitfalls Narrow Search Scope: Using file type filters (e.g., type: "py") that exclude .pyx files Premature Success Declaration: Assuming success after imports work or basic tests pass Missing Recompilation: Forgetting to recompile after fixing .pyx files Incomplete Pattern Matching: Missing variations like numpy.int vs np.int Ignoring Warning Signs: If compilation succeeds "surprisingly" easily, verify the compiled code actually runs correctly Systematic Workflow

Discovery Phase

List all .pyx, .pxd, and .py files Identify the build system (setup.py, pyproject.toml, etc.) Check numpy version requirements

Analysis Phase

Search ALL source files for deprecated patterns Document every occurrence before fixing Note which files need recompilation

Fix Phase

Apply fixes to all identified locations Ensure consistency in replacement types Update any type annotations or docstrings

Build Phase

Clean previous build artifacts Recompile all Cython extensions Watch for compilation warnings

Verification Phase

Test each Cython module individually Run the full test suite Execute functions with real numpy data Verify no runtime AttributeError for numpy types