add kconfig benchmark loader and model generation

1 files changed, 48 insertions, 10 deletions

diff --git a/lib/loader.py b/lib/loader.py
index 57b3d30..3f0662e 100644
--- a/lib/loader.py
+++ b/lib/loader.py
@@ -10,6 +10,7 @@ import re
 import struct
 import tarfile
 import hashlib
+import kconfiglib
 from multiprocessing import Pool
 from .utils import running_mean, soft_cast_int
 
@@ -1090,7 +1091,7 @@ def _add_trace_data_to_aggregate(aggregate, key, element):
 
 
 def pta_trace_to_aggregate(traces, ignore_trace_indexes=[]):
-    u"""
+    """
     Convert preprocessed DFA traces from peripherals/drivers to by_name aggregate for PTAModel.
 
     arguments:
@@ -1296,7 +1297,7 @@ class EnergyTraceLog:
         return self._ts_to_index(timestamp, mid_index, right_index)
 
     def analyze_states(self, traces, offline_index: int):
-        u"""
+        """
         Split log data into states and transitions and return duration, energy, and mean power for each element.
 
         :param traces: expected traces, needed to synchronize with the measurement.
@@ -1630,7 +1631,7 @@ class MIMOSA:
         self.errors = list()
 
     def charge_to_current_nocal(self, charge):
-        u"""
+        """
         Convert charge per 10µs (in pJ) to mean currents (in µA) without accounting for calibration.
 
         :param charge: numpy array of charges (pJ per 10µs) as returned by `load_data` or `load_file`
@@ -1642,7 +1643,7 @@ class MIMOSA:
         return charge * ua_step
 
     def _load_tf(self, tf):
-        u"""
+        """
         Load MIMOSA log data from an open `tarfile` instance.
 
         :param tf: `tarfile` instance
@@ -1663,7 +1664,7 @@ class MIMOSA:
         return charges, triggers
 
     def load_data(self, raw_data):
-        u"""
+        """
         Load MIMOSA log data from a MIMOSA log file passed as raw byte string
 
         :param raw_data: MIMOSA log file, passed as raw byte string
@@ -1675,7 +1676,7 @@ class MIMOSA:
                 return self._load_tf(tf)
 
     def load_file(self, filename):
-        u"""
+        """
         Load MIMOSA log data from a MIMOSA log file
 
         :param filename: MIMOSA log file
@@ -1686,7 +1687,7 @@ class MIMOSA:
             return self._load_tf(tf)
 
     def currents_nocal(self, charges):
-        u"""
+        """
         Convert charges (pJ per 10µs) to mean currents without accounting for calibration.
 
         :param charges: numpy array of charges (pJ per 10µs)
@@ -1743,7 +1744,7 @@ class MIMOSA:
         return trigidx
 
     def calibration_edges(self, currents):
-        u"""
+        """
         Return start/stop indexes of calibration measurements.
 
         :param currents: uncalibrated currents as reported by MIMOSA. For best results,
@@ -1780,7 +1781,7 @@ class MIMOSA:
         )
 
     def calibration_function(self, charges, cal_edges):
-        u"""
+        """
         Calculate calibration function from previously determined calibration edges.
 
         :param charges: raw charges from MIMOSA
@@ -1870,7 +1871,7 @@ class MIMOSA:
         return calfunc, caldata
 
     def analyze_states(self, charges, trigidx, ua_func):
-        u"""
+        """
         Split log data into states and transitions and return duration, energy, and mean power for each element.
 
         :param charges: raw charges (each element describes the charge in pJ transferred during 10 µs)
@@ -1940,3 +1941,40 @@ class MIMOSA:
             previdx = idx
             is_state = not is_state
         return iterdata
+
+
+class KConfigAttributes:
+    def __init__(self, kconfig_path, datadir):
+        experiments = list()
+        for direntry in os.listdir(datadir):
+            config_path = f"{datadir}/{direntry}/.config"
+            attr_path = f"{datadir}/{direntry}/attributes.json"
+            if os.path.exists(attr_path):
+                experiments.append((config_path, attr_path))
+
+        kconf = kconfiglib.Kconfig(kconfig_path)
+
+        self.symbols = sorted(
+            map(
+                lambda sym: sym.name,
+                filter(
+                    lambda sym: kconfiglib.TYPE_TO_STR[sym.type] == "bool",
+                    kconf.syms.values(),
+                ),
+            )
+        )
+
+        self.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 = tuple(
+                map(lambda sym: kconf.syms[sym].tri_value == 2, self.symbols)
+            )
+            config_vectors.add(config_vector)
+            self.data.append((config_vector, attr))