blosc2.lazyexpr#

blosc2.lazyexpr(expression: str | bytes | LazyExpr, operands: dict | None = None, out: NDArray | ndarray = None, where: tuple | list | None = None, local_dict: dict | None = None, global_dict: dict | None = None) → LazyExpr#

Get a LazyExpr from an expression.

Parameters:

  • expression (str or bytes or LazyExpr) – The expression to evaluate. This can be any valid expression that can be ingested by numexpr. If a LazyExpr is passed, the expression will be updated with the new operands.

  • operands (dict) – The dictionary with operands. Supported values are NumPy.ndarray, Python scalars, NDArray, NDField or C2Array instances. If None, the operands will be seeked in the local and global dictionaries.

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

  • where (tuple, list, optional) – A sequence of arguments for the where clause in the expression.

  • 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.

Returns:

out – A LazyExpr is returned.

Return type:

LazyExpr

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 = np.linspace(0, 5, num=size, dtype=dtype).reshape(shape)
>>> a1 = blosc2.asarray(a)
>>> a1[:]
[[0.    0.625 1.25 ]
[1.875 2.5   3.125]
[3.75  4.375 5.   ]]
>>> b1 = blosc2.asarray(b)
>>> expr = 'a1 * b1 + 2'
>>> operands = { 'a': a1, 'b': b1 }
>>> lazy_expr = blosc2.lazyexpr(expr, operands=operands)
>>> f"Lazy expression created: {lazy_expr}"
Lazy expression created: a1 * b1 + 2
>>> lazy_expr[:]
[[ 2.        2.390625  3.5625  ]
[ 5.515625  8.25     11.765625]
[16.0625   21.140625 27.      ]]
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