Release Notes¶

Version 0.6¶

Python 2.6 support
Programmable typing
- Allow users to add type inference for external code
Better NumPy type inference
- outer, inner, dot, vdot, tensordot, nonzero, where, binary ufuncs + methods (reduce, accumulate, reduceat, outer)
Type based alias analysis
- Support for strict aliasing
Much faster autojit dispatch when calling from Python
Faster numerical loops through data and stride pre-loading
Integral overflow and underflow checking for conversions from objects
Make Meta dependency optional

SSA-based type inference
- Allows variable reuse
- Allow referring to variables before lexical definition
Support multiple comparisons
Support for template types
List comprehensions
Support for pointers
Many bug fixes
Added user documentation

Changed default compilation approach to ast
Added support for cross-module linking
Added support for closures (can jit inner functions and return them) (see examples/closure.py)
Added support for dtype structures (can access elements of structure with attribute access) (see examples/structures.py)
Added support for extension types (numba classes) (see examples/numbaclasses.py)
Added support for general Python code (use nopython to raise an error if Python C-API is used to avoid unexpected slowness because of lack of implementation defaulting to generic Python)
Fixed many bugs
Added support to detect math operations.
Added with python and with nopython contexts
Added more examples

Many features need to be documented still. Look at examples and tests for more information.

Added an ast approach to compilation
Removed d, f, i, b from numba namespace (use f8, f4, i4, b1)
Changed function to autojit2
Added autojit function to decorate calls to the function and use types of the variable to create compiled versions.
changed keyword arguments to jit and autojit functions to restype and argtypes to be consistent with ctypes module.
Added pycc – a python to shared library compiler