"""
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Introspection helper functions.
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"""
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__all__ = ['opt_func_info']
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def opt_func_info(func_name=None, signature=None):
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"""
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Returns a dictionary containing the currently supported CPU dispatched
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features for all optimized functions.
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Parameters
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----------
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func_name : str (optional)
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Regular expression to filter by function name.
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signature : str (optional)
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Regular expression to filter by data type.
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Returns
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-------
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dict
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A dictionary where keys are optimized function names and values are
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nested dictionaries indicating supported targets based on data types.
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Examples
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--------
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Retrieve dispatch information for functions named 'add' or 'sub' and
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data types 'float64' or 'float32':
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>>> import numpy as np
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>>> dict = np.lib.introspect.opt_func_info(
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... func_name="add|abs", signature="float64|complex64"
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... )
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>>> import json
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>>> print(json.dumps(dict, indent=2))
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{
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"absolute": {
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"dd": {
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"current": "SSE41",
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"available": "SSE41 baseline(SSE SSE2 SSE3)"
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},
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"Ff": {
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"current": "FMA3__AVX2",
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"available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)"
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},
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"Dd": {
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"current": "FMA3__AVX2",
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"available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)"
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}
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},
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"add": {
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"ddd": {
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"current": "FMA3__AVX2",
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"available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)"
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},
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"FFF": {
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"current": "FMA3__AVX2",
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"available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)"
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}
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}
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}
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"""
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import re
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from numpy._core._multiarray_umath import __cpu_targets_info__ as targets
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from numpy._core._multiarray_umath import dtype
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if func_name is not None:
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func_pattern = re.compile(func_name)
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matching_funcs = {
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k: v for k, v in targets.items()
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if func_pattern.search(k)
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}
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else:
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matching_funcs = targets
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if signature is not None:
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sig_pattern = re.compile(signature)
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matching_sigs = {}
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for k, v in matching_funcs.items():
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matching_chars = {}
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for chars, targets in v.items():
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if any(
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sig_pattern.search(c) or sig_pattern.search(dtype(c).name)
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for c in chars
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):
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matching_chars[chars] = targets
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if matching_chars:
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matching_sigs[k] = matching_chars
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else:
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matching_sigs = matching_funcs
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return matching_sigs
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