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""" Introspection helper functions. """ __all__ = ['opt_func_info'] def opt_func_info(func_name=None, signature=None): """ Returns a dictionary containing the currently supported CPU dispatched features for all optimized functions. Parameters ---------- func_name : str (optional) Regular expression to filter by function name. signature : str (optional) Regular expression to filter by data type. Returns ------- dict A dictionary where keys are optimized function names and values are nested dictionaries indicating supported targets based on data types. Examples -------- Retrieve dispatch information for functions named 'add' or 'sub' and data types 'float64' or 'float32': >>> import numpy as np >>> dict = np.lib.introspect.opt_func_info( ... func_name="add|abs", signature="float64|complex64" ... ) >>> import json >>> print(json.dumps(dict, indent=2)) # may vary (architecture) { "absolute": { "dd": { "current": "SSE41", "available": "SSE41 baseline(SSE SSE2 SSE3)" }, "Ff": { "current": "FMA3__AVX2", "available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)" }, "Dd": { "current": "FMA3__AVX2", "available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)" } }, "add": { "ddd": { "current": "FMA3__AVX2", "available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)" }, "FFF": { "current": "FMA3__AVX2", "available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)" } } } """ import re from numpy._core._multiarray_umath import __cpu_targets_info__ as targets, dtype if func_name is not None: func_pattern = re.compile(func_name) matching_funcs = { k: v for k, v in targets.items() if func_pattern.search(k) } else: matching_funcs = targets if signature is not None: sig_pattern = re.compile(signature) matching_sigs = {} for k, v in matching_funcs.items(): matching_chars = {} for chars, targets in v.items(): if any( sig_pattern.search(c) or sig_pattern.search(dtype(c).name) for c in chars ): matching_chars[chars] = targets # noqa: PERF403 if matching_chars: matching_sigs[k] = matching_chars else: matching_sigs = matching_funcs return matching_sigs