Merge branch 'master' into decisiontrees

1 files changed, 47 insertions, 13 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index 4c442af..5a6b8f0 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -101,6 +101,9 @@ Options:
 
 --export-energymodel=<model.json>
     Export energy model. Works out of the box for v1 and v2 logfiles. Requires --hwmodel for v0 logfiles.
+
+--no-cache
+    Do not load cached measurement results
 """
 
 import getopt
@@ -142,6 +145,15 @@ def format_quality_measures(result):
 
 
 def model_quality_table(result_lists, info_list):
+    print(
+        "{:20s} {:15s}       {:19s}       {:19s}       {:19s}".format(
+            "key",
+            "attribute",
+            "static".center(19),
+            "parameterized".center(19),
+            "LUT".center(19),
+        )
+    )
     for state_or_tran in result_lists[0]["by_name"].keys():
         for key in result_lists[0]["by_name"][state_or_tran].keys():
             buf = "{:20s} {:15s}".format(state_or_tran, key)
@@ -152,7 +164,7 @@ def model_quality_table(result_lists, info_list):
                     result = results["by_name"][state_or_tran][key]
                     buf += format_quality_measures(result)
                 else:
-                    buf += "{:6}----{:9}".format("", "")
+                    buf += "{:7}----{:8}".format("", "")
             print(buf)
 
 
@@ -300,7 +312,7 @@ if __name__ == "__main__":
 
     try:
         optspec = (
-            "info "
+            "info no-cache "
             "plot-unparam= plot-param= plot-traces= show-models= show-quality= "
             "ignored-trace-indexes= function-override= "
             "export-traces= "
@@ -362,11 +374,18 @@ if __name__ == "__main__":
         sys.exit(2)
 
     raw_data = RawData(
-        args, with_traces=("export-traces" in opt or "plot-traces" in opt)
+        args,
+        with_traces=("export-traces" in opt or "plot-traces" in opt),
+        skip_cache=("no-cache" in opt),
     )
 
     if "info" in opt:
         print(" ".join(raw_data.filenames) + ":")
+        if raw_data.ptalog:
+            options = " --".join(
+                map(lambda kv: f"{kv[0]}={str(kv[1])}", raw_data.ptalog["opt"].items())
+            )
+            print(f"    Options: --{options}")
         if raw_data.version <= 1:
             data_source = "MIMOSA"
         elif raw_data.version == 2:
@@ -420,7 +439,7 @@ if __name__ == "__main__":
             )
             sys.exit(2)
 
-        if len(traces) > 20:
+        if len(traces) > 40:
             print(f"""Truncating plot to 40 of {len(traces)} traces (random sample)""")
             traces = random.sample(traces, 40)
 
@@ -693,7 +712,7 @@ if __name__ == "__main__":
         )
 
     if "overall" in show_quality or "all" in show_quality:
-        print("overall static/param/lut MAE assuming equal state distribution:")
+        print("overall state static/param/lut MAE assuming equal state distribution:")
         print(
             "    {:6.1f}  /  {:6.1f}  /  {:6.1f}  µW".format(
                 model.assess_states(static_model),
@@ -701,15 +720,30 @@ if __name__ == "__main__":
                 model.assess_states(lut_model),
             )
         )
-        print("overall static/param/lut MAE assuming 95% STANDBY1:")
-        distrib = {"STANDBY1": 0.95, "POWERDOWN": 0.03, "TX": 0.01, "RX": 0.01}
-        print(
-            "    {:6.1f}  /  {:6.1f}  /  {:6.1f}  µW".format(
-                model.assess_states(static_model, distribution=distrib),
-                model.assess_states(param_model, distribution=distrib),
-                model.assess_states(lut_model, distribution=distrib),
+        distrib = dict()
+        num_states = len(model.states())
+        p95_state = None
+        for state in model.states():
+            distrib[state] = 1.0 / num_states
+
+        if "STANDBY1" in model.states():
+            p95_state = "STANDBY1"
+        elif "SLEEP" in model.states():
+            p95_state = "SLEEP"
+
+        if p95_state is not None:
+            for state in distrib.keys():
+                distrib[state] = 0.05 / (num_states - 1)
+            distrib[p95_state] = 0.95
+
+            print(f"overall state static/param/lut MAE assuming 95% {p95_state}:")
+            print(
+                "    {:6.1f}  /  {:6.1f}  /  {:6.1f}  µW".format(
+                    model.assess_states(static_model, distribution=distrib),
+                    model.assess_states(param_model, distribution=distrib),
+                    model.assess_states(lut_model, distribution=distrib),
+                )
             )
-        )
 
     if "summary" in show_quality or "all" in show_quality:
         model_summary_table(