blosc2.evaluate#

blosc2.evaluate(ex: str, local_dict: dict | None = None, global_dict: dict | None = None, out: ndarray | NDArray = None, **kwargs: Any) → ndarray | NDArray#

Evaluate a string expression using the Blosc2 compute engine.

This is a drop-in replacement for numexpr.evaluate(), but using the Blosc2 compute engine. This allows for:

  1. Use more functionality (e.g. reductions) than numexpr.

  2. Follow casting rules of NumPy more closely.

  3. Use both NumPy arrays and Blosc2 NDArrays in the same expression.

As NDArrays can be on-disk, the expression can be evaluated without loading the whole array into memory (i.e. using an out-of-core approach).

Parameters:

  • ex (str) – The expression to evaluate.

  • local_dict (dict, optional) – The local dictionary to use when looking for operands in the expression. If not provided, the local dictionary of the caller will be used.

  • global_dict (dict, optional) – The global dictionary to use when looking for operands in the expression. If not provided, the global dictionary of the caller will be used.

  • out (NDArray or NumPy array, optional) – The output array where the result will be stored. If not provided, a new NumPy array will be created and returned.

  • kwargs (Any, optional) – Additional arguments to be passed to numexpr.evaluate() function.

Returns:

out – The result of the expression evaluation. If out is provided, the result will be stored in out and returned at the same time.

Return type:

NumPy or NDArray

Examples

>>> import blosc2
>>> import numpy as np
>>> dtype = np.float64
>>> shape = [3, 3]
>>> size = shape[0] * shape[1]
>>> a = np.linspace(0, 5, num=size, dtype=dtype).reshape(shape)
>>> b = blosc2.linspace(0, 5, num=size, dtype=dtype, shape=shape)
>>> expr = 'a * b + 2'
>>> out = blosc2.evaluate(expr)
>>> out
[[ 2.        2.390625  3.5625  ]
[ 5.515625  8.25     11.765625]
[16.0625   21.140625 27.      ]]
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