import numpy as np import logging logger = logging.getLogger(__name__) class DataProcessor: def __init__(self, sync_data, energy_data): """ Creates DataProcessor object. :param sync_data: input timestamps (SigrokResult) :param energy_data: List of EnergyTrace datapoints """ self.reduced_timestamps = [] self.modified_timestamps = [] self.plot_data_x = [] self.plot_data_y = [] self.sync_data = sync_data self.energy_data = energy_data self.start_offset = 0 self.power_sync_watt = 0.011 self.power_sync_len = 0.7 self.power_sync_max_outliers = 2 def run(self): """ Main Function to remove unwanted data, get synchronization points, add the offset and add drift. :return: None """ # remove Dirty Data from previously running program (happens if logic Analyzer Measurement starts earlier than # the HW Reset from energytrace) use_data_after_index = 0 for x in range(1, len(self.sync_data.timestamps)): if self.sync_data.timestamps[x] - self.sync_data.timestamps[x - 1] > 1.3: use_data_after_index = x break time_stamp_data = self.sync_data.timestamps[use_data_after_index:] last_data = [0, 0, 0, 0] # clean timestamp data, if at the end strange ts got added somehow time_stamp_data = self.removeTooFarDatasets(time_stamp_data) self.reduced_timestamps = time_stamp_data # NEW datasync_timestamps = [] sync_start = 0 outliers = 0 pre_outliers_ts = None # TODO only consider the first few and the last few seconds for sync points for i, energytrace_dataset in enumerate(self.energy_data): usedtime = energytrace_dataset[0] - last_data[0] # in microseconds timestamp = energytrace_dataset[0] usedenergy = energytrace_dataset[3] - last_data[3] power = usedenergy / usedtime * 1e-3 # in watts if power > 0: if power > self.power_sync_watt: if sync_start is None: sync_start = timestamp outliers = 0 else: # Sync point over or outliers if outliers == 0: pre_outliers_ts = timestamp outliers += 1 if outliers > self.power_sync_max_outliers: if sync_start is not None: if ( pre_outliers_ts - sync_start ) / 1_000_000 > self.power_sync_len: datasync_timestamps.append( ( sync_start / 1_000_000, pre_outliers_ts / 1_000_000, ) ) sync_start = None last_data = energytrace_dataset self.plot_data_x.append(timestamp / 1_000_000) self.plot_data_y.append(power) if power > self.power_sync_watt: if (self.energy_data[-1][0] - sync_start) / 1_000_000 > self.power_sync_len: datasync_timestamps.append( (sync_start / 1_000_000, pre_outliers_ts / 1_000_000) ) # print(datasync_timestamps) # time_stamp_data contains an entry for each level change on the Logic Analyzer input. # So, time_stamp_data[0] is the first low-to-high transition, time_stamp_data[2] the second, etc. # -> time_stamp_data[-8] is the low-to-high transition indicating the first after-measurement sync pulse start_offset = datasync_timestamps[0][1] - time_stamp_data[2] start_timestamp = datasync_timestamps[0][1] end_offset = datasync_timestamps[-2][0] - (time_stamp_data[-8] + start_offset) end_timestamp = datasync_timestamps[-2][0] logger.debug( f"Measurement area: ET timestamp range [{start_timestamp}, {end_timestamp}]" ) logger.debug( f"Measurement area: LA timestamp range [{time_stamp_data[2]}, {time_stamp_data[-8]}]" ) logger.debug(f"Start/End offsets: {start_offset} / {end_offset}") if end_offset > 10: logger.warning( f"synchronization end_offset == {end_offset}. It should be no more than a few seconds." ) with_offset = np.array(time_stamp_data) + start_offset with_drift = self.addDrift( with_offset, end_timestamp, end_offset, start_timestamp ) self.modified_timestamps = with_drift def removeTooFarDatasets(self, input_timestamps): """ Removing datasets, that are to far away at ethe end :param input_timestamps: List of timestamps (float list) :return: List of modified timestamps (float list) """ modified_timestamps = [] for i, x in enumerate(input_timestamps): # print(x - input_timestamps[i - 1], x - input_timestamps[i - 1] < 2.5) if x - input_timestamps[i - 1] < 1.6: modified_timestamps.append(x) else: break return modified_timestamps def addDrift(self, input_timestamps, end_timestamp, end_offset, start_timestamp): """ Add drift to datapoints :param input_timestamps: List of timestamps (float list) :param end_timestamp: Timestamp of first EnergyTrace datapoint at the second-to-last sync point :param end_offset: the time between end_timestamp and the timestamp of synchronisation signal :param start_timestamp: Timestamp of last EnergyTrace datapoint at the first sync point :return: List of modified timestamps (float list) """ endFactor = (end_timestamp + end_offset - start_timestamp) / ( end_timestamp - start_timestamp ) + 0.0001 # print( # f"({end_timestamp} + {end_offset} - {start_timestamp}) / ({end_timestamp} - {start_timestamp}) == {endFactor}" # ) # Manuelles endFactor += 0.0001 macht es merklich besser # print(f"endFactor = {endFactor}") modified_timestamps_with_drift = ( (input_timestamps - start_timestamp) * endFactor ) + start_timestamp return modified_timestamps_with_drift def plot(self, annotateData=None): """ Plots the power usage and the timestamps by logic analyzer :param annotateData: List of Strings with labels, only needed if annotated plots are wished :return: None """ def calculateRectangleCurve(timestamps, min_value=0, max_value=0.160): import numpy as np data = [] for ts in timestamps: data.append(ts) data.append(ts) a = np.empty((len(data),)) a[1::4] = max_value a[2::4] = max_value a[3::4] = min_value a[4::4] = min_value return data, a # plotting by columns import matplotlib.pyplot as plt fig, ax = plt.subplots() if annotateData: annot = ax.annotate( "", xy=(0, 0), xytext=(20, 20), textcoords="offset points", bbox=dict(boxstyle="round", fc="w"), arrowprops=dict(arrowstyle="->"), ) annot.set_visible(True) rectCurve_with_drift = calculateRectangleCurve( self.modified_timestamps, max_value=max(self.plot_data_y) ) plt.plot(self.plot_data_x, self.plot_data_y, label="Leistung") plt.plot( rectCurve_with_drift[0], rectCurve_with_drift[1], "-g", label="Synchronisationsignale mit Driftfaktor", ) plt.xlabel("Zeit [s]") plt.ylabel("Leistung [W]") leg = plt.legend() def getDataText(x): # print(x) for i, xt in enumerate(self.modified_timestamps): if xt > x: return "Value: %s" % annotateData[i - 5] def update_annot(x, y, name): annot.xy = (x, y) text = name annot.set_text(text) annot.get_bbox_patch().set_alpha(0.4) def hover(event): if event.xdata and event.ydata: annot.set_visible(False) update_annot(event.xdata, event.ydata, getDataText(event.xdata)) annot.set_visible(True) fig.canvas.draw_idle() if annotateData: fig.canvas.mpl_connect("motion_notify_event", hover) plt.show() def getPowerBetween(self, start, end, state_sleep): # 0.001469 """ calculates the powerusage in interval NOT SIDE EFFECT FREE, DON'T USE IT EVERYWHERE :param start: Start timestamp of interval :param end: End timestamp of interval :param state_sleep: Length in seconds of one state, needed for cutting out the UART Sending cycle :return: power measurements in W """ first_index = 0 all_power = [] for ind in range(self.start_offset, len(self.plot_data_x)): first_index = ind if self.plot_data_x[ind] > start: break nextIndAfterIndex = None for ind in range(first_index, len(self.plot_data_x)): nextIndAfterIndex = ind if ( self.plot_data_x[ind] > end or self.plot_data_x[ind] > start + state_sleep ): self.start_offset = ind - 1 break all_power.append(self.plot_data_y[ind]) # TODO Idea remove datapoints that are too far away def removeSD_Mean_Values(arr): import numpy elements = numpy.array(arr) mean = numpy.mean(elements, axis=0) sd = numpy.std(elements, axis=0) return [x for x in arr if (mean - 1 * sd < x < mean + 1.5 * sd)] if len(all_power) > 10: # all_power = removeSD_Mean_Values(all_power) pass # TODO algorithm relocate datapoint pre_fix_len = len(all_power) if len(all_power) == 0: # print("PROBLEM") all_power.append(self.plot_data_y[nextIndAfterIndex]) elif len(all_power) == 1: # print("OKAY") pass return np.array(all_power) def getStatesdfatool(self, state_sleep, with_traces=False, algorithm=False): """ Calculates the length and energy usage of the states :param state_sleep: Length in seconds of one state, needed for cutting out the UART Sending cycle :param algorithm: possible usage of accuracy algorithm / not implemented yet :returns: returns list of states and transitions, starting with a transition and ending with astate Each element is a dict containing: * `isa`: 'state' or 'transition' * `W_mean`: Mittelwert der Leistungsaufnahme * `W_std`: Standardabweichung der Leistungsaufnahme * `s`: Dauer """ if algorithm: raise NotImplementedError end_transition_ts = None timestamps_sync_start = 0 energy_trace_new = list() for ts_index in range( 0 + timestamps_sync_start, int(len(self.modified_timestamps) / 2) ): start_transition_ts = self.modified_timestamps[ts_index * 2] start_transition_ts_timing = self.reduced_timestamps[ts_index * 2] if end_transition_ts is not None: power = self.getPowerBetween( end_transition_ts, start_transition_ts, state_sleep ) # print("STATE", end_transition_ts * 10 ** 6, start_transition_ts * 10 ** 6, (start_transition_ts - end_transition_ts) * 10 ** 6, power) if ( (start_transition_ts - end_transition_ts) * 10 ** 6 > 900_000 and np.mean(power) > self.power_sync_watt * 0.9 and ts_index > 10 ): # remove last transition and stop (upcoming data only sync) del energy_trace_new[-1] break pass state = { "isa": "state", "W_mean": np.mean(power), "W_std": np.std(power), "s": ( start_transition_ts_timing - end_transition_ts_timing ), # * 10 ** 6, } if with_traces: state["uW"] = power * 1e6 energy_trace_new.append(state) energy_trace_new[-2]["W_mean_delta_next"] = ( energy_trace_new[-2]["W_mean"] - energy_trace_new[-1]["W_mean"] ) # get energy end_transition_ts end_transition_ts = self.modified_timestamps[ts_index * 2 + 1] power = self.getPowerBetween( start_transition_ts, end_transition_ts, state_sleep ) # print("TRANS", start_transition_ts * 10 ** 6, end_transition_ts * 10 ** 6, (end_transition_ts - start_transition_ts) * 10 ** 6, power) end_transition_ts_timing = self.reduced_timestamps[ts_index * 2 + 1] transition = { "isa": "transition", "W_mean": np.mean(power), "W_std": np.std(power), "s": ( end_transition_ts_timing - start_transition_ts_timing ), # * 10 ** 6, "count_dp": len(power), } if with_traces: transition["uW"] = power * 1e6 if (end_transition_ts - start_transition_ts) * 10 ** 6 > 2_000_000: # TODO Last data set corrupted? HOT FIX!!!!!!!!!!!! REMOVE LATER # for x in range(4): # del energy_trace_new[-1] # break pass energy_trace_new.append(transition) # print(start_transition_ts, "-", end_transition_ts, "-", end_transition_ts - start_transition_ts) return energy_trace_new