#!/usr/bin/env python3 import csv from itertools import chain, combinations import io import json import numpy as np import os from scipy.cluster.vq import kmeans2 import struct import sys import tarfile from multiprocessing import Pool def running_mean(x, N): cumsum = np.cumsum(np.insert(x, 0, 0)) return (cumsum[N:] - cumsum[:-N]) / N def is_numeric(n): if n == None: return False try: int(n) return True except ValueError: return False def _soft_cast_int(n): if n == None or n == '': return None try: return int(n) except ValueError: return n def float_or_nan(n): if n == None: return np.nan try: return float(n) except ValueError: return np.nan def _param_dict_to_list(param_dict): paramkeys = sorted(param_dict.keys()) paramvalue = [_soft_cast_int(param_dict[x]) for x in paramkeys] return paramvalue def append_if_set(aggregate, data, key): if key in data: aggregate.append(data[key]) def mean_or_none(arr): if len(arr): return np.mean(arr) return -1 def aggregate_measures(aggregate, actual): aggregate_array = np.array([aggregate] * len(actual)) return regression_measures(aggregate_array, np.array(actual)) def regression_measures(predicted, actual): if type(predicted) != np.ndarray: raise ValueError('first arg must be ndarray, is {}'.format(type(predicted))) if type(actual) != np.ndarray: raise ValueError('second arg must be ndarray, is {}'.format(type(actual))) deviations = predicted - actual if len(deviations) == 0: return {} measures = { 'mae' : np.mean(np.abs(deviations), dtype=np.float64), 'msd' : np.mean(deviations**2, dtype=np.float64), 'rmsd' : np.sqrt(np.mean(deviations**2), dtype=np.float64), 'ssr' : np.sum(deviations**2, dtype=np.float64), } if np.all(actual != 0): measures['mape'] = np.mean(np.abs(deviations / actual)) * 100 # bad measure if np.all(np.abs(predicted) + np.abs(actual) != 0): measures['smape'] = np.mean(np.abs(deviations) / (( np.abs(predicted) + np.abs(actual)) / 2 )) * 100 return measures def powerset(iterable): s = list(iterable) return chain.from_iterable(combinations(s, r) for r in range(len(s)+1)) class Keysight: def __init__(self): pass def load_data(self, filename): with open(filename) as f: for i, l in enumerate(f): pass timestamps = np.ndarray((i-3), dtype=float) currents = np.ndarray((i-3), dtype=float) # basically seek back to start with open(filename) as f: for _ in range(4): next(f) reader = csv.reader(f, delimiter=',') for i, row in enumerate(reader): timestamps[i] = float(row[0]) currents[i] = float(row[2]) * -1 return timestamps, currents def _preprocess_measurement(measurement): setup = measurement['setup'] mim = MIMOSA(float(setup['mimosa_voltage']), int(setup['mimosa_shunt'])) charges, triggers = mim.load_data(measurement['content']) trigidx = mim.trigger_edges(triggers) triggers = [] cal_edges = mim.calibration_edges(running_mean(mim.currents_nocal(charges[0:trigidx[0]]), 10)) calfunc, caldata = mim.calibration_function(charges, cal_edges) vcalfunc = np.vectorize(calfunc, otypes=[np.float64]) processed_data = { 'fileno' : measurement['fileno'], 'info' : measurement['info'], 'triggers' : len(trigidx), 'first_trig' : trigidx[0] * 10, 'calibration' : caldata, 'trace' : mim.analyze_states(charges, trigidx, vcalfunc) } return processed_data class RawData: def __init__(self, filenames): self.filenames = filenames.copy() self.traces_by_fileno = [] self.setup_by_fileno = [] self.version = 0 self.preprocessed = False def _state_is_too_short(self, online, offline, state_duration, next_transition): # We cannot control when an interrupt causes a state to be left if next_transition['plan']['level'] == 'epilogue': return False # Note: state_duration is stored as ms, not us return offline['us'] < state_duration * 500 def _state_is_too_long(self, online, offline, state_duration, prev_transition): # If the previous state was left by an interrupt, we may have some # waiting time left over. So it's okay if the current state is longer # than expected. if prev_transition['plan']['level'] == 'epilogue': return False # state_duration is stored as ms, not us return offline['us'] > state_duration * 1500 def _measurement_is_valid(self, processed_data): setup = self.setup_by_fileno[processed_data['fileno']] traces = self.traces_by_fileno[processed_data['fileno']] state_duration = setup['state_duration'] # Check trigger count sched_trigger_count = 0 for run in traces: sched_trigger_count += len(run['trace']) if sched_trigger_count != processed_data['triggers']: processed_data['error'] = 'got {got:d} trigger edges, expected {exp:d}'.format( got = processed_data['triggers'], exp = sched_trigger_count ) return False # Check state durations. Very short or long states can indicate a # missed trigger signal which wasn't detected due to duplicate # triggers elsewhere online_datapoints = [] for run_idx, run in enumerate(traces): for trace_part_idx in range(len(run['trace'])): online_datapoints.append((run_idx, trace_part_idx)) for offline_idx, online_ref in enumerate(online_datapoints): online_run_idx, online_trace_part_idx = online_ref offline_trace_part = processed_data['trace'][offline_idx] online_trace_part = traces[online_run_idx]['trace'][online_trace_part_idx] if online_trace_part['isa'] != offline_trace_part['isa']: processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) claims to be {off_isa:s}, but should be {on_isa:s}'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], off_isa = offline_trace_part['isa'], on_isa = online_trace_part['isa']) return False # Clipping in UNINITIALIZED (offline_idx == 0) can happen during # calibration and is handled by MIMOSA if offline_idx != 0 and offline_trace_part['clip_rate'] != 0: processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) was clipping {clip:f}% of the time'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], clip = offline_trace_part['clip_rate'] * 100, ) return False if online_trace_part['isa'] == 'state' and online_trace_part['name'] != 'UNINITIALIZED': online_prev_transition = traces[online_run_idx]['trace'][online_trace_part_idx-1] online_next_transition = traces[online_run_idx]['trace'][online_trace_part_idx+1] try: if self._state_is_too_short(online_trace_part, offline_trace_part, state_duration, online_next_transition): processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) is too short (duration = {dur:d} us)'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], dur = offline_trace_part['us']) return False if self._state_is_too_long(online_trace_part, offline_trace_part, state_duration, online_prev_transition): processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) is too long (duration = {dur:d} us)'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], dur = offline_trace_part['us']) return False except KeyError: pass # TODO es gibt next_transitions ohne 'plan' return True def _merge_measurement_into_online_data(self, measurement): online_datapoints = [] traces = self.traces_by_fileno[measurement['fileno']] for run_idx, run in enumerate(traces): for trace_part_idx in range(len(run['trace'])): online_datapoints.append((run_idx, trace_part_idx)) for offline_idx, online_ref in enumerate(online_datapoints): online_run_idx, online_trace_part_idx = online_ref offline_trace_part = measurement['trace'][offline_idx] online_trace_part = traces[online_run_idx]['trace'][online_trace_part_idx] if not 'offline' in online_trace_part: online_trace_part['offline'] = [offline_trace_part] else: online_trace_part['offline'].append(offline_trace_part) paramkeys = sorted(online_trace_part['parameter'].keys()) paramvalue = [_soft_cast_int(online_trace_part['parameter'][x]) for x in paramkeys] if not 'offline_aggregates' in online_trace_part: online_trace_part['offline_aggregates'] = { 'power' : [], 'duration' : [], 'power_std' : [], 'energy' : [], 'paramkeys' : [], 'param': [], } if online_trace_part['isa'] == 'transition': online_trace_part['offline_aggregates']['timeout'] = [] online_trace_part['offline_aggregates']['rel_energy_prev'] = [] online_trace_part['offline_aggregates']['rel_energy_next'] = [] # Note: All state/transitions are 20us "too long" due to injected # active wait states. These are needed to work around MIMOSA's # relatively low sample rate of 100 kHz (10us) and removed here. online_trace_part['offline_aggregates']['power'].append( offline_trace_part['uW_mean']) online_trace_part['offline_aggregates']['duration'].append( offline_trace_part['us'] - 20) online_trace_part['offline_aggregates']['power_std'].append( offline_trace_part['uW_std']) online_trace_part['offline_aggregates']['energy'].append( offline_trace_part['uW_mean'] * (offline_trace_part['us'] - 20)) online_trace_part['offline_aggregates']['paramkeys'].append(paramkeys) online_trace_part['offline_aggregates']['param'].append(paramvalue) if online_trace_part['isa'] == 'transition': online_trace_part['offline_aggregates']['timeout'].append( offline_trace_part['timeout']) online_trace_part['offline_aggregates']['rel_energy_prev'].append( offline_trace_part['uW_mean_delta_prev'] * (offline_trace_part['us'] - 20)) online_trace_part['offline_aggregates']['rel_energy_next'].append( offline_trace_part['uW_mean_delta_next'] * (offline_trace_part['us'] - 20)) def _concatenate_analyzed_traces(self): self.traces = [] for trace in self.traces_by_fileno: self.traces.extend(trace) def get_preprocessed_data(self, verbose = True): self.verbose = verbose if self.preprocessed: return self.traces if self.version == 0: self.preprocess_0() self.preprocessed = True return self.traces # Loads raw MIMOSA data and turns it into measurements which are ready to # be analyzed. def preprocess_0(self): mim_files = [] for i, filename in enumerate(self.filenames): with tarfile.open(filename) as tf: self.setup_by_fileno.append(json.load(tf.extractfile('setup.json'))) self.traces_by_fileno.append(json.load(tf.extractfile('src/apps/DriverEval/DriverLog.json'))) for member in tf.getmembers(): _, extension = os.path.splitext(member.name) if extension == '.mim': mim_files.append({ 'content' : tf.extractfile(member).read(), 'fileno' : i, 'info' : member, 'setup' : self.setup_by_fileno[i], 'traces' : self.traces_by_fileno[i], }) with Pool() as pool: measurements = pool.map(_preprocess_measurement, mim_files) num_valid = 0 for measurement in measurements: if self._measurement_is_valid(measurement): self._merge_measurement_into_online_data(measurement) num_valid += 1 elif self.verbose: print('[W] Skipping {ar:s}/{m:s}: {e:s}'.format( ar = self.filenames[measurement['fileno']], m = measurement['info'].name, e = measurement['error'])) if self.verbose: print('[I] {num_valid:d}/{num_total:d} measurements are valid'.format( num_valid = num_valid, num_total = len(measurements))) self._concatenate_analyzed_traces() self.preprocessing_stats = { 'num_runs' : len(measurements), 'num_valid' : num_valid } class EnergyModel: def __init__(self, preprocessed_data): self.traces = preprocessed_data self.by_name = {} self.by_arg = {} self.by_param = {} self.by_trace = {} np.seterr('raise') for runidx, run in enumerate(self.traces): # if opts['ignore-trace-idx'] != runidx for i, elem in enumerate(run['trace']): if elem['name'] != 'UNINITIALIZED': self._load_run_elem(i, elem) self._aggregate_to_ndarray(self.by_name) def _aggregate_to_ndarray(self, aggregate): for elem in aggregate.values(): for key in ['power', 'power_std', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: if key in elem: elem[key] = np.array(elem[key]) def _add_data_to_aggregate(self, aggregate, key, element): if not key in aggregate: aggregate[key] = { 'isa' : element['isa'] } for datakey in element['offline_aggregates'].keys(): aggregate[key][datakey] = [] for datakey, dataval in element['offline_aggregates'].items(): aggregate[key][datakey].extend(dataval) def _load_run_elem(self, i, elem): self._add_data_to_aggregate(self.by_name, elem['name'], elem) self._add_data_to_aggregate(self.by_param, (elem['name'], tuple(_param_dict_to_list(elem['parameter']))), elem) def get_static(self): static_model = {} for name, elem in self.by_name.items(): static_model[name] = {} for key in ['power', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: if key in elem: try: static_model[name][key] = np.median(elem[key]) except RuntimeWarning: print('[W] Got no data for {} {}'.format(name, key)) except FloatingPointError as fpe: print('[W] Got no data for {} {}: {}'.format(name, key, fpe)) def static_median_getter(name, key, **kwargs): return static_model[name][key] return static_median_getter def get_static_using_mean(self): static_model = {} for name, elem in self.by_name.items(): static_model[name] = {} for key in ['power', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: if key in elem: try: static_model[name][key] = np.mean(elem[key]) except RuntimeWarning: print('[W] Got no data for {} {}'.format(name, key)) except FloatingPointError as fpe: print('[W] Got no data for {} {}: {}'.format(name, key, fpe)) def static_mean_getter(name, key, **kwargs): return static_model[name][key] return static_mean_getter def get_param_lut(self): lut_model = {} for name, elem in self.by_param.items(): lut_model[name] = {} for key in ['power', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: if key in elem: try: lut_model[name][key] = np.mean(elem[key]) except RuntimeWarning: print('[W] Got no data for {} {}'.format(name, key)) except FloatingPointError as fpe: print('[W] Got no data for {} {}: {}'.format(name, key, fpe)) def lut_median_getter(name, key, param, **kwargs): return lut_model[(name, tuple(param))][key] return lut_median_getter def states(self): return sorted(list(filter(lambda k: self.by_name[k]['isa'] == 'state', self.by_name.keys()))) def transitions(self): return sorted(list(filter(lambda k: self.by_name[k]['isa'] == 'transition', self.by_name.keys()))) def assess(self, model_function): for name, elem in sorted(self.by_name.items()): print('{}:'.format(name)) if elem['isa'] == 'state': predicted_data = np.array(list(map(lambda i: model_function(name, 'power', param=elem['param'][i]), range(len(elem['power']))))) measures = regression_measures(predicted_data, elem['power']) if 'smape' in measures: print(' power: {:.2f}% / {:.0f} µW'.format( measures['smape'], measures['mae'] )) else: print(' power: {:.0f} µW'.format( measures['mae'] )) else: for key in ['duration', 'energy', 'rel_energy_prev', 'rel_energy_next']: predicted_data = np.array(list(map(lambda i: model_function(name, key, param=elem['param'][i]), range(len(elem[key]))))) measures = regression_measures(predicted_data, elem[key]) if 'smape' in measures: print(' {:15s}: {:.2f}% / {:.0f}'.format( key, measures['smape'], measures['mae'] )) else: print(' {:15s}: {:.0f}'.format( key, measures['mae'] )) class MIMOSA: def __init__(self, voltage, shunt): self.voltage = voltage self.shunt = shunt self.r1 = 984 # "1k" self.r2 = 99013 # "100k" def charge_to_current_nocal(self, charge): ua_max = 1.836 / self.shunt * 1000000 ua_step = ua_max / 65535 return charge * ua_step def _load_tf(self, tf): num_bytes = tf.getmember('/tmp/mimosa//mimosa_scale_1.tmp').size charges = np.ndarray(shape=(int(num_bytes / 4)), dtype=np.int32) triggers = np.ndarray(shape=(int(num_bytes / 4)), dtype=np.int8) with tf.extractfile('/tmp/mimosa//mimosa_scale_1.tmp') as f: content = f.read() iterator = struct.iter_unpack('> 4) triggers[i] = (word[0] & 0x08) >> 3 i += 1 return charges, triggers def load_data(self, raw_data): with io.BytesIO(raw_data) as data_object: with tarfile.open(fileobj = data_object) as tf: return self._load_tf(tf) def currents_nocal(self, charges): ua_max = 1.836 / self.shunt * 1000000 ua_step = ua_max / 65535 return charges.astype(np.double) * ua_step def trigger_edges(self, triggers): trigidx = [] prevtrig = triggers[0] # the device is reset for MIMOSA calibration in the first 10s and may # send bogus interrupts -> bogus triggers for i in range(1000000, triggers.shape[0]): trig = triggers[i] if trig != prevtrig: # Due to MIMOSA's integrate-read-reset cycle, the trigger # appears two points (20µs) before the corresponding data trigidx.append(i+2) prevtrig = trig return trigidx def calibration_edges(self, currents): r1idx = 0 r2idx = 0 ua_r1 = self.voltage / self.r1 * 1000000 # first second may be bogus for i in range(100000, len(currents)): if r1idx == 0 and currents[i] > ua_r1 * 0.6: r1idx = i elif r1idx != 0 and r2idx == 0 and i > (r1idx + 180000) and currents[i] < ua_r1 * 0.4: r2idx = i # 2s disconnected, 2s r1, 2s r2 with r1 < r2 -> ua_r1 > ua_r2 # allow 5ms buffer in both directions to account for bouncing relais contacts return r1idx - 180500, r1idx - 500, r1idx + 500, r2idx - 500, r2idx + 500, r2idx + 180500 def calibration_function(self, charges, cal_edges): dis_start, dis_end, r1_start, r1_end, r2_start, r2_end = cal_edges if dis_start < 0: dis_start = 0 chg_r0 = charges[dis_start:dis_end] chg_r1 = charges[r1_start:r1_end] chg_r2 = charges[r2_start:r2_end] cal_0_mean = np.mean(chg_r0) cal_0_std = np.std(chg_r0) cal_r1_mean = np.mean(chg_r1) cal_r1_std = np.std(chg_r1) cal_r2_mean = np.mean(chg_r2) cal_r2_std = np.std(chg_r2) ua_r1 = self.voltage / self.r1 * 1000000 ua_r2 = self.voltage / self.r2 * 1000000 if cal_r2_mean > cal_0_mean: b_lower = (ua_r2 - 0) / (cal_r2_mean - cal_0_mean) else: print('[W] 0 uA == %.f uA during calibration' % (ua_r2)) b_lower = 0 b_upper = (ua_r1 - ua_r2) / (cal_r1_mean - cal_r2_mean) b_total = (ua_r1 - 0) / (cal_r1_mean - cal_0_mean) a_lower = -b_lower * cal_0_mean a_upper = -b_upper * cal_r2_mean a_total = -b_total * cal_0_mean if self.shunt == 680: # R1 current is higher than shunt range -> only use R2 for calibration def calfunc(charge): if charge < cal_0_mean: return 0 else: return charge * b_lower + a_lower else: def calfunc(charge): if charge < cal_0_mean: return 0 if charge <= cal_r2_mean: return charge * b_lower + a_lower else: return charge * b_upper + a_upper + ua_r2 caldata = { 'edges' : [x * 10 for x in cal_edges], 'offset': cal_0_mean, 'offset2' : cal_r2_mean, 'slope_low' : b_lower, 'slope_high' : b_upper, 'add_low' : a_lower, 'add_high' : a_upper, 'r0_err_uW' : np.mean(self.currents_nocal(chg_r0)) * self.voltage, 'r0_std_uW' : np.std(self.currents_nocal(chg_r0)) * self.voltage, 'r1_err_uW' : (np.mean(self.currents_nocal(chg_r1)) - ua_r1) * self.voltage, 'r1_std_uW' : np.std(self.currents_nocal(chg_r1)) * self.voltage, 'r2_err_uW' : (np.mean(self.currents_nocal(chg_r2)) - ua_r2) * self.voltage, 'r2_std_uW' : np.std(self.currents_nocal(chg_r2)) * self.voltage, } #print("if charge < %f : return 0" % cal_0_mean) #print("if charge <= %f : return charge * %f + %f" % (cal_r2_mean, b_lower, a_lower)) #print("else : return charge * %f + %f + %f" % (b_upper, a_upper, ua_r2)) 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): previdx = 0 is_state = True iterdata = [] for idx in trigidx: 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: isa = 'transition' data = { 'isa': isa, 'clip_rate' : np.mean(range_raw == 65535), 'raw_mean': np.mean(range_raw), 'raw_std' : np.std(range_raw), 'uW_mean' : np.mean(range_ua * self.voltage), 'uW_std' : np.std(range_ua * self.voltage), 'us' : (idx - previdx) * 10, } if 'states' in substates: data['substates'] = substates ssum = np.sum(list(map(lambda x : x['duration'], substates['states']))) if ssum != data['us']: print("ERR: 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) data['uW_mean_delta_prev'] = data['uW_mean'] - iterdata[-1]['uW_mean'] # placeholder to avoid extra cases in the analysis data['uW_mean_delta_next'] = data['uW_mean'] data['timeout'] = iterdata[-1]['us'] elif len(iterdata) > 0: # subtract average power of next state # (the state into which this transition leads) iterdata[-1]['uW_mean_delta_next'] = iterdata[-1]['uW_mean'] - data['uW_mean'] iterdata.append(data) previdx = idx is_state = not is_state return iterdata