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| author | Daniel Friesel <derf@finalrewind.org> | 2019-03-28 16:44:58 +0100 |
|---|---|---|
| committer | Daniel Friesel <derf@finalrewind.org> | 2019-03-28 16:44:58 +0100 |
| commit | 8c8d157ac537a9c542fe666b5f75c44fad7aac58 (patch) | |
| tree | 9205a6e97ad29f107eb11249018b944b810cef35 | |
| parent | c700a422ef495b90f1a8e7dc2576bf5b8cb9eeb5 (diff) | |
cycle analysis: use median to protect against outliers
| -rw-r--r-- | lib/data_parameters.py | 14 |
1 files changed, 8 insertions, 6 deletions
diff --git a/lib/data_parameters.py b/lib/data_parameters.py index 7f7368d..9a6429f 100644 --- a/lib/data_parameters.py +++ b/lib/data_parameters.py @@ -117,7 +117,7 @@ class Protolog: text_{nop,ser,serdes} : whole-program Text Segment (code/Flash) size """ - def _mean_cycles(data, key): + def _median_cycles(data, key): # There should always be more than just one measurement -- otherwise # something went wrong if len(data[key]) <= 1: @@ -130,7 +130,9 @@ class Protolog: # bogus data if val > 10_000_000: return np.nan - return max(0, int(np.mean(data[key][1:]) - np.mean(data['nop'][1:]))) + # All measurements in data[key] cover the same instructions, so they + # should be identical. + return max(0, int(np.median(data[key][1:]) - np.median(data['nop'][1:]))) idem = lambda x: x datamap = [ @@ -138,10 +140,10 @@ class Protolog: ['bss_ser', 'bss_size_ser', idem], ['bss_serdes', 'bss_size_serdes', idem], ['callcycles_raw', 'callcycles', idem], - ['cycles_ser', 'cycles', lambda x: Protolog._mean_cycles(x, 'ser')], - ['cycles_des', 'cycles', lambda x: Protolog._mean_cycles(x, 'des')], - ['cycles_enc', 'cycles', lambda x: Protolog._mean_cycles(x, 'enc')], - ['cycles_dec', 'cycles', lambda x: Protolog._mean_cycles(x, 'dec')], + ['cycles_ser', 'cycles', lambda x: Protolog._median_cycles(x, 'ser')], + ['cycles_des', 'cycles', lambda x: Protolog._median_cycles(x, 'des')], + ['cycles_enc', 'cycles', lambda x: Protolog._median_cycles(x, 'enc')], + ['cycles_dec', 'cycles', lambda x: Protolog._median_cycles(x, 'dec')], #['cycles_ser_arr', 'cycles', lambda x: np.array(x['ser'][1:]) - np.mean(x['nop'][1:])], #['cycles_des_arr', 'cycles', lambda x: np.array(x['des'][1:]) - np.mean(x['nop'][1:])], #['cycles_enc_arr', 'cycles', lambda x: np.array(x['enc'][1:]) - np.mean(x['nop'][1:])], |