diff options
Diffstat (limited to 'lib/loader.py')
| -rw-r--r-- | lib/loader.py | 71 |
1 files changed, 0 insertions, 71 deletions
diff --git a/lib/loader.py b/lib/loader.py index e8b5090..77b8652 100644 --- a/lib/loader.py +++ b/lib/loader.py @@ -1864,50 +1864,6 @@ class MIMOSA: return calfunc, caldata - """ - def calcgrad(self, currents, threshold): - grad = np.gradient(running_mean(currents * self.voltage, 10)) - # len(grad) == len(currents) - 9 - subst = [] - lastgrad = 0 - for i in range(len(grad)): - # minimum substate duration: 10ms - if np.abs(grad[i]) > threshold and i - lastgrad > 50: - # account for skew introduced by running_mean and current - # ramp slope (parasitic capacitors etc.) - subst.append(i+10) - lastgrad = i - if lastgrad != i: - subst.append(i+10) - return subst - - # TODO konfigurierbare min/max threshold und len(gradidx) > X, binaere - # Sache nach noetiger threshold. postprocessing mit - # "zwei benachbarte substates haben sehr aehnliche werte / niedrige stddev" -> mergen - # ... min/max muessen nicht vorgegeben werden, sind ja bekannt (0 / np.max(grad)) - # TODO bei substates / index foo den offset durch running_mean beachten - # TODO ggf. clustering der 'abs(grad) > threshold' und bestimmung interessanter - # uebergaenge dadurch? - def gradfoo(self, currents): - gradients = np.abs(np.gradient(running_mean(currents * self.voltage, 10))) - gradmin = np.min(gradients) - gradmax = np.max(gradients) - threshold = np.mean([gradmin, gradmax]) - gradidx = self.calcgrad(currents, threshold) - num_substates = 2 - while len(gradidx) != num_substates: - if gradmax - gradmin < 0.1: - # We did our best - return threshold, gradidx - if len(gradidx) > num_substates: - gradmin = threshold - else: - gradmax = threshold - threshold = np.mean([gradmin, gradmax]) - gradidx = self.calcgrad(currents, threshold) - return threshold, gradidx - """ - 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. @@ -1942,27 +1898,6 @@ class MIMOSA: for idx in trigger_indices: range_raw = charges[previdx:idx] range_ua = ua_func(range_raw) - substates = {} - - if previdx != 0 and idx - previdx > 200: - thr, subst = 0, [] # self.gradfoo(range_ua) - if len(subst): - statelist = [] - prevsubidx = 0 - for subidx in subst: - statelist.append( - { - "duration": (subidx - prevsubidx) * 10, - "uW_mean": np.mean( - range_ua[prevsubidx:subidx] * self.voltage - ), - "uW_std": np.std( - range_ua[prevsubidx:subidx] * self.voltage - ), - } - ) - prevsubidx = subidx - substates = {"threshold": thr, "states": statelist} isa = "state" if not is_state: @@ -1981,12 +1916,6 @@ class MIMOSA: if self.with_traces: data["uW"] = range_ua * self.voltage - if "states" in substates: - data["substates"] = substates - ssum = np.sum(list(map(lambda x: x["duration"], substates["states"]))) - if ssum != data["us"]: - logger.warning("duration %d vs %d" % (data["us"], ssum)) - if isa == "transition": # subtract average power of previous state # (that is, the state from which this transition originates) |