Remove legacy analyze-config script

1 files changed, 0 insertions, 185 deletions

diff --git a/bin/analyze-config.py b/bin/analyze-config.py
deleted file mode 100755
index 54fde23..0000000
--- a/bin/analyze-config.py
+++ /dev/null
@@ -1,185 +0,0 @@
-#!/usr/bin/env python3
-
-#  _                                   ____          _
-# | |    ___  __ _  __ _  ___ _   _   / ___|___   __| | ___
-# | |   / _ \/ _` |/ _` |/ __| | | | | |   / _ \ / _` |/ _ \
-# | |__|  __/ (_| | (_| | (__| |_| | | |__| (_) | (_| |  __/
-# |_____\___|\__, |\__,_|\___|\__, |  \____\___/ \__,_|\___|
-#            |___/            |___/
-
-
-"""
-analyze-config - generate NFP model from system config benchmarks
-
-analyze-config generates an NFP model from benchmarks with various system
-configs (.config entries generated from a common Kconfig definition). The
-NFP model is not yet compatible with the type of models generated
-by analyze-archive and analyze-timing
-"""
-
-import argparse
-import hashlib
-import json
-import kconfiglib
-import logging
-import os
-
-import numpy as np
-
-from dfatool.functions import SplitFunction, StaticFunction
-from dfatool.model import AnalyticModel, ModelAttribute
-from dfatool.utils import NpEncoder
-
-
-def make_config_vector(kconf, params, symbols, choices):
-    config_vector = [None for i in params]
-    for i, param in enumerate(params):
-        if param in choices:
-            choice = kconf.choices[choices.index(param)]
-            if choice.selection:
-                config_vector[i] = choice.selection.name
-        else:
-            config_vector[i] = kconf.syms[param].str_value
-    return tuple(config_vector)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
-    )
-    parser.add_argument(
-        "--log-level",
-        metavar="LEVEL",
-        choices=["debug", "info", "warning", "error"],
-        default="warning",
-        help="Set log level",
-    )
-    parser.add_argument(
-        "--with-choice-node",
-        action="store_true",
-        help="Add special decisiontree nodes for Kconfig choice symbols",
-    )
-    parser.add_argument("kconfig_file")
-    parser.add_argument("data_dir")
-
-    args = parser.parse_args()
-
-    if args.log_level:
-        numeric_level = getattr(logging, args.log_level.upper(), None)
-        if not isinstance(numeric_level, int):
-            print(f"Invalid log level: {args.log_level}", file=sys.stderr)
-            sys.exit(1)
-        logging.basicConfig(level=numeric_level)
-
-    experiments = list()
-
-    for direntry in os.listdir(args.data_dir):
-        if "Multipass" in direntry:
-            config_path = f"{args.data_dir}/{direntry}/.config"
-            attr_path = f"{args.data_dir}/{direntry}/attributes.json"
-            if os.path.exists(attr_path):
-                experiments.append((config_path, attr_path))
-
-    kconf = kconfiglib.Kconfig(args.kconfig_file)
-
-    file_hash = hashlib.sha256()
-    with open(args.kconfig_file, "rb") as f:
-        kconfig_data = f.read()
-        while len(kconfig_data) > 0:
-            file_hash.update(kconfig_data)
-            kconfig_data = f.read()
-
-    kconfig_hash = str(file_hash.hexdigest())
-
-    # TODO Optional neben bool auch choices unterstützen.
-    # Später ebenfalls int u.ä. -> dfatool-modeling
-
-    symbols = sorted(
-        map(
-            lambda sym: sym.name,
-            filter(
-                lambda sym: kconfiglib.TYPE_TO_STR[sym.type] == "bool",
-                kconf.syms.values(),
-            ),
-        )
-    )
-
-    if args.with_choice_node:
-        choices = sorted(map(lambda choice: choice.name, kconf.choices))
-    else:
-        choices = list()
-
-    params = sorted(symbols + choices)
-
-    by_name = {
-        "multipass": {
-            "isa": "state",
-            "attributes": ["rom_usage", "ram_usage"],
-            "rom_usage": list(),
-            "ram_usage": list(),
-            "param": list(),
-        }
-    }
-    data = list()
-
-    config_vectors = set()
-
-    for config_path, attr_path in experiments:
-        kconf.load_config(config_path)
-        with open(attr_path, "r") as f:
-            attr = json.load(f)
-
-        config_vector = make_config_vector(kconf, params, symbols, choices)
-
-        config_vectors.add(config_vector)
-        by_name["multipass"]["rom_usage"].append(attr["total"]["ROM"])
-        by_name["multipass"]["ram_usage"].append(attr["total"]["RAM"])
-        by_name["multipass"]["param"].append(config_vector)
-        data.append((config_vector, (attr["total"]["ROM"], attr["total"]["RAM"])))
-
-    print(
-        "Processing {:d} unique configurations of {:d} total".format(
-            len(config_vectors), len(experiments)
-        )
-    )
-
-    print(
-        "std of all data: {:5.0f} Bytes".format(
-            np.std(list(map(lambda x: x[1][0], data)))
-        )
-    )
-
-    model = AnalyticModel(by_name, params, compute_stats=False)
-    model.build_dtree("multipass", "rom_usage", 100)
-    model.build_dtree("multipass", "ram_usage", 20)
-
-    with open("kconfigmodel.json", "w") as f:
-        json_model = model.to_json(with_param_name=True, param_names=params)
-        json_model = json_model["name"]["multipass"]
-        json_model["ram_usage"].update(
-            {
-                "unit": "B",
-                "description": "RAM Usage",
-                "minimize": True,
-            }
-        )
-        json_model["rom_usage"].update(
-            {
-                "unit": "B",
-                "description": "ROM Usage",
-                "minimize": True,
-            }
-        )
-        out_model = {
-            "model": json_model,
-            "modelType": "dfatool-kconfig",
-            "kconfigHash": kconfig_hash,
-            "project": "multipass",
-            "symbols": symbols,
-            "choices": choices,
-        }
-        json.dump(out_model, f, sort_keys=True, cls=NpEncoder)
-
-
-if __name__ == "__main__":
-    main()