diff options
Diffstat (limited to 'lib/loader.py')
| -rw-r--r-- | lib/loader.py | 2008 |
1 files changed, 2008 insertions, 0 deletions
diff --git a/lib/loader.py b/lib/loader.py new file mode 100644 index 0000000..e8b5090 --- /dev/null +++ b/lib/loader.py @@ -0,0 +1,2008 @@ +#!/usr/bin/env python3 + +import csv +import io +import json +import logging +import numpy as np +import os +import re +import struct +import tarfile +import hashlib +from multiprocessing import Pool +from .utils import running_mean, soft_cast_int + +logger = logging.getLogger(__name__) + +try: + from .pubcode import Code128 + import zbar + + zbar_available = True +except ImportError: + zbar_available = False + + +arg_support_enabled = True + + +class KeysightCSV: + """Simple loader for Keysight CSV data, as exported by the windows software.""" + + def __init__(self): + """Create a new KeysightCSV object.""" + pass + + def load_data(self, filename: str): + """ + Load log data from filename, return timestamps and currents. + + Returns two one-dimensional NumPy arrays: timestamps and corresponding currents. + """ + with open(filename) as f: + for i, _ 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_mimosa(measurement): + setup = measurement["setup"] + mim = MIMOSA( + float(setup["mimosa_voltage"]), + int(setup["mimosa_shunt"]), + with_traces=measurement["with_traces"], + ) + try: + charges, triggers = mim.load_data(measurement["content"]) + trigidx = mim.trigger_edges(triggers) + except EOFError as e: + mim.errors.append("MIMOSA logfile error: {}".format(e)) + trigidx = list() + + if len(trigidx) == 0: + mim.errors.append("MIMOSA log has no triggers") + return { + "fileno": measurement["fileno"], + "info": measurement["info"], + "has_datasource_error": len(mim.errors) > 0, + "datasource_errors": mim.errors, + "expected_trace": measurement["expected_trace"], + "repeat_id": measurement["repeat_id"], + } + + 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, + "energy_trace": mim.analyze_states(charges, trigidx, vcalfunc), + "has_datasource_error": len(mim.errors) > 0, + "datasource_errors": mim.errors, + } + + for key in ["expected_trace", "repeat_id"]: + if key in measurement: + processed_data[key] = measurement[key] + + return processed_data + + +def _preprocess_etlog(measurement): + setup = measurement["setup"] + etlog = EnergyTraceLog( + float(setup["voltage"]), + int(setup["state_duration"]), + measurement["transition_names"], + with_traces=measurement["with_traces"], + ) + try: + etlog.load_data(measurement["content"]) + states_and_transitions = etlog.analyze_states( + measurement["expected_trace"], measurement["repeat_id"] + ) + except EOFError as e: + etlog.errors.append("EnergyTrace logfile error: {}".format(e)) + + processed_data = { + "fileno": measurement["fileno"], + "repeat_id": measurement["repeat_id"], + "info": measurement["info"], + "expected_trace": measurement["expected_trace"], + "energy_trace": states_and_transitions, + "has_datasource_error": len(etlog.errors) > 0, + "datasource_errors": etlog.errors, + } + + return processed_data + + +class TimingData: + """ + Loader for timing model traces measured with on-board timers using `harness.OnboardTimerHarness`. + + Excpets a specific trace format and UART log output (as produced by + generate-dfa-benchmark.py). Prunes states from output. (TODO) + """ + + def __init__(self, filenames): + """ + Create a new TimingData object. + + Each filenames element corresponds to a measurement run. + """ + self.filenames = filenames.copy() + self.traces_by_fileno = [] + self.setup_by_fileno = [] + self.preprocessed = False + self._parameter_names = None + self.version = 0 + + def _concatenate_analyzed_traces(self): + self.traces = [] + for trace_group in self.traces_by_fileno: + for trace in trace_group: + # TimingHarness logs states, but does not aggregate any data for them at the moment -> throw all states away + transitions = list( + filter(lambda x: x["isa"] == "transition", trace["trace"]) + ) + self.traces.append({"id": trace["id"], "trace": transitions}) + for i, trace in enumerate(self.traces): + trace["orig_id"] = trace["id"] + trace["id"] = i + for log_entry in trace["trace"]: + paramkeys = sorted(log_entry["parameter"].keys()) + if "param" not in log_entry["offline_aggregates"]: + log_entry["offline_aggregates"]["param"] = list() + if "duration" in log_entry["offline_aggregates"]: + for i in range(len(log_entry["offline_aggregates"]["duration"])): + paramvalues = list() + for paramkey in paramkeys: + if type(log_entry["parameter"][paramkey]) is list: + paramvalues.append( + soft_cast_int(log_entry["parameter"][paramkey][i]) + ) + else: + paramvalues.append( + soft_cast_int(log_entry["parameter"][paramkey]) + ) + if arg_support_enabled and "args" in log_entry: + paramvalues.extend(map(soft_cast_int, log_entry["args"])) + log_entry["offline_aggregates"]["param"].append(paramvalues) + + def _preprocess_0(self): + for filename in self.filenames: + with open(filename, "r") as f: + log_data = json.load(f) + self.traces_by_fileno.extend(log_data["traces"]) + self._concatenate_analyzed_traces() + + def get_preprocessed_data(self): + """ + Return a list of DFA traces annotated with timing and parameter data. + + Suitable for the PTAModel constructor. + See PTAModel(...) docstring for format details. + """ + if self.preprocessed: + return self.traces + if self.version == 0: + self._preprocess_0() + self.preprocessed = True + return self.traces + + +def sanity_check_aggregate(aggregate): + for key in aggregate: + if "param" not in aggregate[key]: + raise RuntimeError("aggregate[{}][param] does not exist".format(key)) + if "attributes" not in aggregate[key]: + raise RuntimeError("aggregate[{}][attributes] does not exist".format(key)) + for attribute in aggregate[key]["attributes"]: + if attribute not in aggregate[key]: + raise RuntimeError( + "aggregate[{}][{}] does not exist, even though it is contained in aggregate[{}][attributes]".format( + key, attribute, key + ) + ) + param_len = len(aggregate[key]["param"]) + attr_len = len(aggregate[key][attribute]) + if param_len != attr_len: + raise RuntimeError( + "parameter mismatch: len(aggregate[{}][param]) == {} != len(aggregate[{}][{}]) == {}".format( + key, param_len, key, attribute, attr_len + ) + ) + + +class RawData: + """ + Loader for hardware model traces measured with MIMOSA. + + Expects a specific trace format and UART log output (as produced by the + dfatool benchmark generator). Loads data, prunes bogus measurements, and + provides preprocessed data suitable for PTAModel. Results are cached on the + file system, making subsequent loads near-instant. + """ + + def __init__(self, filenames, with_traces=False): + """ + Create a new RawData object. + + Each filename element corresponds to a measurement run. + It must be a tar archive with the following contents: + + Version 0: + + * `setup.json`: measurement setup. Must contain the keys `state_duration` (how long each state is active, in ms), + `mimosa_voltage` (voltage applied to dut, in V), and `mimosa_shunt` (shunt value, in Ohm) + * `src/apps/DriverEval/DriverLog.json`: PTA traces and parameters for this benchmark. + Layout: List of traces, each trace has an 'id' (numeric, starting with 1) and 'trace' (list of states and transitions) element. + Each trace has an even number of elements, starting with the first state (usually `UNINITIALIZED`) and ending with a transition. + Each state/transition must have the members `.parameter` (parameter values, empty string or None if unknown), `.isa` ("state" or "transition") and `.name`. + Each transition must additionally contain `.plan.level` ("user" or "epilogue"). + Example: `[ {"id": 1, "trace": [ {"parameter": {...}, "isa": "state", "name": "UNINITIALIZED"}, ...] }, ... ] + * At least one `*.mim` file. Each file corresponds to a single execution of the entire benchmark (i.e., all runs described in DriverLog.json) and starts with a MIMOSA Autocal calibration sequence. + MIMOSA files are parsed by the `MIMOSA` class. + + Version 1: + + * `ptalog.json`: measurement setup and traces. Contents: + `.opt.sleep`: state duration + `.opt.pta`: PTA + `.opt.traces`: list of sub-benchmark traces (the benchmark may have been split due to code size limitations). Each item is a list of traces as returned by `harness.traces`: + `.opt.traces[]`: List of traces. Each trace has an 'id' (numeric, starting with 1) and 'trace' (list of states and transitions) element. + Each state/transition must have the members '`parameter` (dict with normalized parameter values), `.isa` ("state" or "transition") and `.name` + Each transition must additionally contain `.args` + `.opt.files`: list of coresponding MIMOSA measurements. + `.opt.files[]` = ['abc123.mim', ...] + `.opt.configs`: .... + * MIMOSA log files (`*.mim`) as specified in `.opt.files` + + Version 2: + + * `ptalog.json`: measurement setup and traces. Contents: + `.opt.sleep`: state duration + `.opt.pta`: PTA + `.opt.traces`: list of sub-benchmark traces (the benchmark may have been split due to code size limitations). Each item is a list of traces as returned by `harness.traces`: + `.opt.traces[]`: List of traces. Each trace has an 'id' (numeric, starting with 1) and 'trace' (list of states and transitions) element. + Each state/transition must have the members '`parameter` (dict with normalized parameter values), `.isa` ("state" or "transition") and `.name` + Each transition must additionally contain `.args` and `.duration` + * `.duration`: list of durations, one per repetition + `.opt.files`: list of coresponding EnergyTrace measurements. + `.opt.files[]` = ['abc123.etlog', ...] + `.opt.configs`: .... + * EnergyTrace log files (`*.etlog`) as specified in `.opt.files` + + If a cached result for a file is available, it is loaded and the file + is not preprocessed, unless `with_traces` is set. + + tbd + """ + self.with_traces = with_traces + self.filenames = filenames.copy() + self.traces_by_fileno = [] + self.setup_by_fileno = [] + self.version = 0 + self.preprocessed = False + self._parameter_names = None + self.ignore_clipping = False + self.pta = None + + with tarfile.open(filenames[0]) as tf: + for member in tf.getmembers(): + if member.name == "ptalog.json" and self.version == 0: + self.version = 1 + # might also be version 2 + # depends on whether *.etlog exists or not + elif ".etlog" in member.name: + self.version = 2 + break + + self.set_cache_file() + if not with_traces: + self.load_cache() + + def set_cache_file(self): + cache_key = hashlib.sha256("!".join(self.filenames).encode()).hexdigest() + self.cache_dir = os.path.dirname(self.filenames[0]) + "/cache" + self.cache_file = "{}/{}.json".format(self.cache_dir, cache_key) + + def load_cache(self): + if os.path.exists(self.cache_file): + with open(self.cache_file, "r") as f: + cache_data = json.load(f) + self.filenames = cache_data["filenames"] + self.traces = cache_data["traces"] + self.preprocessing_stats = cache_data["preprocessing_stats"] + if "pta" in cache_data: + self.pta = cache_data["pta"] + self.setup_by_fileno = cache_data["setup_by_fileno"] + self.preprocessed = True + + def save_cache(self): + if self.with_traces: + return + try: + os.mkdir(self.cache_dir) + except FileExistsError: + pass + with open(self.cache_file, "w") as f: + cache_data = { + "filenames": self.filenames, + "traces": self.traces, + "preprocessing_stats": self.preprocessing_stats, + "pta": self.pta, + "setup_by_fileno": self.setup_by_fileno, + } + json.dump(cache_data, f) + + 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_2(self, processed_data): + """ + Check if a dfatool v2 measurement is valid. + + processed_data layout: + 'fileno' : measurement['fileno'], + 'info' : measurement['info'], + 'energy_trace' : etlog.analyze_states() + A sequence of unnamed, unparameterized states and transitions with + power and timing data + 'expected_trace' : trace from PTA DFS (with parameter data) + etlog.analyze_states returns a list of (alternating) states and transitions. + Each element is a dict containing: + - isa: 'state' oder 'transition' + - W_mean: Mittelwert der (kalibrierten) Leistungsaufnahme + - W_std: Standardabweichung der (kalibrierten) Leistungsaufnahme + - s: duration + + if isa == 'transition': + - W_mean_delta_prev: Differenz zwischen W_mean und W_mean des vorherigen Zustands + - W_mean_delta_next: Differenz zwischen W_mean und W_mean des Folgezustands + """ + + # Check for low-level parser errors + if processed_data["has_datasource_error"]: + processed_data["error"] = "; ".join(processed_data["datasource_errors"]) + return False + + # Note that the low-level parser (EnergyTraceLog) already checks + # whether the transition count is correct + + return True + + def _measurement_is_valid_01(self, processed_data): + """ + Check if a dfatool v0 or v1 measurement is valid. + + processed_data layout: + 'fileno' : measurement['fileno'], + 'info' : measurement['info'], + 'triggers' : len(trigidx), + 'first_trig' : trigidx[0] * 10, + 'calibration' : caldata, + 'energy_trace' : mim.analyze_states(charges, trigidx, vcalfunc) + A sequence of unnamed, unparameterized states and transitions with + power and timing data + 'expected_trace' : trace from PTA DFS (with parameter data) + mim.analyze_states returns a list of (alternating) states and transitions. + Each element is a dict containing: + - isa: 'state' oder 'transition' + - clip_rate: range(0..1) Anteil an Clipping im Energieverbrauch + - raw_mean: Mittelwert der Rohwerte + - raw_std: Standardabweichung der Rohwerte + - uW_mean: Mittelwert der (kalibrierten) Leistungsaufnahme + - uW_std: Standardabweichung der (kalibrierten) Leistungsaufnahme + - us: Dauer + + Nur falls isa == 'transition': + - timeout: Dauer des vorherigen Zustands + - uW_mean_delta_prev: Differenz zwischen uW_mean und uW_mean des vorherigen Zustands + - uW_mean_delta_next: Differenz zwischen uW_mean und uW_mean des Folgezustands + """ + setup = self.setup_by_fileno[processed_data["fileno"]] + if "expected_trace" in processed_data: + traces = processed_data["expected_trace"] + else: + traces = self.traces_by_fileno[processed_data["fileno"]] + state_duration = setup["state_duration"] + + # Check MIMOSA error + if processed_data["has_datasource_error"]: + processed_data["error"] = "; ".join(processed_data["datasource_errors"]) + return False + + # 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["energy_trace"][offline_idx] + online_trace_part = traces[online_run_idx]["trace"][online_trace_part_idx] + + if self._parameter_names is None: + self._parameter_names = sorted(online_trace_part["parameter"].keys()) + + if sorted(online_trace_part["parameter"].keys()) != self._parameter_names: + processed_data[ + "error" + ] = "Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) has inconsistent parameter set: should be {param_want:s}, is {param_is:s}".format( + off_idx=offline_idx, + on_idx=online_run_idx, + on_sub=online_trace_part_idx, + on_name=online_trace_part["name"], + param_want=self._parameter_names, + param_is=sorted(online_trace_part["parameter"].keys()), + ) + + 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 + and not self.ignore_clipping + ): + 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" + and len(traces[online_run_idx]["trace"]) > online_trace_part_idx + 1 + ): + 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_online_and_offline(self, measurement): + # Edits self.traces_by_fileno[measurement['fileno']][*]['trace'][*]['offline'] + # and self.traces_by_fileno[measurement['fileno']][*]['trace'][*]['offline_aggregates'] in place + # (appends data from measurement['energy_trace']) + # If measurement['expected_trace'] exists, it is edited in place instead + online_datapoints = [] + if "expected_trace" in measurement: + traces = measurement["expected_trace"] + traces = self.traces_by_fileno[measurement["fileno"]] + else: + 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["energy_trace"][offline_idx] + online_trace_part = traces[online_run_idx]["trace"][online_trace_part_idx] + + if "offline" not 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()) + + paramvalues = list() + + for paramkey in paramkeys: + if type(online_trace_part["parameter"][paramkey]) is list: + paramvalues.append( + soft_cast_int( + online_trace_part["parameter"][paramkey][ + measurement["repeat_id"] + ] + ) + ) + else: + paramvalues.append( + soft_cast_int(online_trace_part["parameter"][paramkey]) + ) + + # NB: Unscheduled transitions do not have an 'args' field set. + # However, they should only be caused by interrupts, and + # interrupts don't have args anyways. + if arg_support_enabled and "args" in online_trace_part: + paramvalues.extend(map(soft_cast_int, online_trace_part["args"])) + + if "offline_aggregates" not in online_trace_part: + online_trace_part["offline_attributes"] = [ + "power", + "duration", + "energy", + ] + online_trace_part["offline_aggregates"] = { + "power": [], + "duration": [], + "power_std": [], + "energy": [], + "paramkeys": [], + "param": [], + } + if online_trace_part["isa"] == "transition": + online_trace_part["offline_attributes"].extend( + ["rel_energy_prev", "rel_energy_next", "timeout"] + ) + online_trace_part["offline_aggregates"]["rel_energy_prev"] = [] + online_trace_part["offline_aggregates"]["rel_energy_next"] = [] + online_trace_part["offline_aggregates"]["timeout"] = [] + + # 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(paramvalues) + if online_trace_part["isa"] == "transition": + 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) + ) + online_trace_part["offline_aggregates"]["timeout"].append( + offline_trace_part["timeout"] + ) + + def _merge_online_and_etlog(self, measurement): + # Edits self.traces_by_fileno[measurement['fileno']][*]['trace'][*]['offline'] + # and self.traces_by_fileno[measurement['fileno']][*]['trace'][*]['offline_aggregates'] in place + # (appends data from measurement['energy_trace']) + 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["energy_trace"][offline_idx] + online_trace_part = traces[online_run_idx]["trace"][online_trace_part_idx] + + if "offline" not 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()) + + paramvalues = list() + + for paramkey in paramkeys: + if type(online_trace_part["parameter"][paramkey]) is list: + paramvalues.append( + soft_cast_int( + online_trace_part["parameter"][paramkey][ + measurement["repeat_id"] + ] + ) + ) + else: + paramvalues.append( + soft_cast_int(online_trace_part["parameter"][paramkey]) + ) + + # NB: Unscheduled transitions do not have an 'args' field set. + # However, they should only be caused by interrupts, and + # interrupts don't have args anyways. + if arg_support_enabled and "args" in online_trace_part: + paramvalues.extend(map(soft_cast_int, online_trace_part["args"])) + + if "offline_aggregates" not in online_trace_part: + online_trace_part["offline_aggregates"] = { + "offline_attributes": ["power", "duration", "energy"], + "duration": list(), + "power": list(), + "power_std": list(), + "energy": list(), + "paramkeys": list(), + "param": list(), + } + + offline_aggregates = online_trace_part["offline_aggregates"] + + # if online_trace_part['isa'] == 'transitions': + # online_trace_part['offline_attributes'].extend(['rel_energy_prev', 'rel_energy_next']) + # offline_aggregates['rel_energy_prev'] = list() + # offline_aggregates['rel_energy_next'] = list() + + offline_aggregates["duration"].append(offline_trace_part["s"] * 1e6) + offline_aggregates["power"].append(offline_trace_part["W_mean"] * 1e6) + offline_aggregates["power_std"].append(offline_trace_part["W_std"] * 1e6) + offline_aggregates["energy"].append( + offline_trace_part["W_mean"] * offline_trace_part["s"] * 1e12 + ) + offline_aggregates["paramkeys"].append(paramkeys) + offline_aggregates["param"].append(paramvalues) + + # if online_trace_part['isa'] == 'transition': + # offline_aggregates['rel_energy_prev'].append(offline_trace_part['W_mean_delta_prev'] * offline_trace_part['s'] * 1e12) + # offline_aggregates['rel_energy_next'].append(offline_trace_part['W_mean_delta_next'] * offline_trace_part['s'] * 1e12) + + def _concatenate_traces(self, list_of_traces): + """ + Concatenate `list_of_traces` (list of lists) into a single trace while adjusting trace IDs. + + :param list_of_traces: List of list of traces. + :returns: List of traces with ['id'] in ascending order and ['orig_id'] as previous ['id'] + """ + + trace_output = list() + for trace in list_of_traces: + trace_output.extend(trace.copy()) + for i, trace in enumerate(trace_output): + trace["orig_id"] = trace["id"] + trace["id"] = i + return trace_output + + def get_preprocessed_data(self): + """ + Return a list of DFA traces annotated with energy, timing, and parameter data. + The list is cached on disk, unless the constructor was called with `with_traces` set. + + Each DFA trace contains the following elements: + * `id`: Numeric ID, starting with 1 + * `total_energy`: Total amount of energy (as measured by MIMOSA) in the entire trace + * `orig_id`: Original trace ID. May differ when concatenating multiple (different) benchmarks into one analysis, i.e., when calling RawData() with more than one file argument. + * `trace`: List of the individual states and transitions in this trace. Always contains an even number of elements, staring with the first state (typically "UNINITIALIZED") and ending with a transition. + + Each trace element (that is, an entry of the `trace` list mentioned above) contains the following elements: + * `isa`: "state" or "transition" + * `name`: name + * `offline`: List of offline measumerents for this state/transition. Each entry contains a result for this state/transition during one benchmark execution. + Entry contents: + - `clip_rate`: rate of clipped energy measurements, 0 .. 1 + - `raw_mean`: mean raw MIMOSA value + - `raw_std`: standard deviation of raw MIMOSA value + - `uW_mean`: mean power draw, uW + - `uw_std`: standard deviation of power draw, uW + - `us`: state/transition duration, us + - `uW_mean_delta_prev`: (only for transitions) difference between uW_mean of this transition and uW_mean of previous state + - `uW_mean_elta_next`: (only for transitions) difference between uW_mean of this transition and uW_mean of next state + - `timeout`: (only for transitions) duration of previous state, us + * `offline_aggregates`: Aggregate of `offline` entries. dict of lists, each list entry has the same length + - `duration`: state/transition durations ("us"), us + - `energy`: state/transition energy ("us * uW_mean"), us + - `power`: mean power draw ("uW_mean"), uW + - `power_std`: standard deviations of power draw ("uW_std"), uW^2 + - `paramkeys`: List of lists, each sub-list contains the parameter names corresponding to the `param` entries + - `param`: List of lists, each sub-list contains the parameter values for this measurement. Typically, all sub-lists are the same. + - `rel_energy_prev`: (only for transitions) transition energy relative to previous state mean power, pJ + - `rel_energy_next`: (only for transitions) transition energy relative to next state mean power, pJ + - `timeout`: (only for transitions) duration of previous state, us + * `offline_attributes`: List containing the keys of `offline_aggregates` which are meant to be part of themodel. + This list ultimately decides which hardware/software attributes the model describes. + If isa == state, it contains power, duration, energy + If isa == transition, it contains power, duration, energy, rel_energy_prev, rel_energy_next, timeout + * `online`: List of online estimations for this state/transition. Each entry contains a result for this state/transition during one benchmark execution. + Entry contents for isa == state: + - `time`: state/transition + Entry contents for isa == transition: + - `timeout`: Duration of previous state, measured using on-board timers + * `parameter`: dictionary describing parameter values for this state/transition. Parameter values refer to the begin of the state/transition and do not account for changes made by the transition. + * `plan`: Dictionary describing expected behaviour according to schedule / offline model. + Contents for isa == state: `energy`, `power`, `time` + Contents for isa == transition: `energy`, `timeout`, `level`. + If level is "user", the transition is part of the regular driver API. If level is "epilogue", it is an interrupt service routine and not called explicitly. + Each transition also contains: + * `args`: List of arguments the corresponding function call was called with. args entries are strings which are not necessarily numeric + * `code`: List of function name (first entry) and arguments (remaining entries) of the corresponding function call + """ + if self.preprocessed: + return self.traces + if self.version == 0: + self._preprocess_012(0) + elif self.version == 1: + self._preprocess_012(1) + elif self.version == 2: + self._preprocess_012(2) + self.preprocessed = True + self.save_cache() + return self.traces + + def _preprocess_012(self, version): + """Load raw MIMOSA data and turn it into measurements which are ready to be analyzed.""" + offline_data = [] + for i, filename in enumerate(self.filenames): + + if version == 0: + + 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": + offline_data.append( + { + "content": tf.extractfile(member).read(), + "fileno": i, + "info": member, + "setup": self.setup_by_fileno[i], + "with_traces": self.with_traces, + } + ) + + elif version == 1: + + new_filenames = list() + with tarfile.open(filename) as tf: + ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json"))) + self.pta = ptalog["pta"] + + # Benchmark code may be too large to be executed in a single + # run, so benchmarks (a benchmark is basically a list of DFA runs) + # may be split up. To accomodate this, ptalog['traces'] is + # a list of lists: ptalog['traces'][0] corresponds to the + # first benchmark part, ptalog['traces'][1] to the + # second, and so on. ptalog['traces'][0][0] is the first + # trace (a sequence of states and transitions) in the + # first benchmark part, ptalog['traces'][0][1] the second, etc. + # + # As traces are typically repeated to minimize the effect + # of random noise, observations for each benchmark part + # are also lists. In this case, this applies in two + # cases: traces[i][j]['parameter'][some_param] is either + # a value (if the parameter is controlld by software) + # or a list (if the parameter is known a posteriori, e.g. + # "how many retransmissions did this packet take?"). + # + # The second case is the MIMOSA energy measurements, which + # are listed in ptalog['files']. ptalog['files'][0] + # contains a list of files for the first benchmark part, + # ptalog['files'][0][0] is its first iteration/repetition, + # ptalog['files'][0][1] the second, etc. + + for j, traces in enumerate(ptalog["traces"]): + new_filenames.append("{}#{}".format(filename, j)) + self.traces_by_fileno.append(traces) + self.setup_by_fileno.append( + { + "mimosa_voltage": ptalog["configs"][j]["voltage"], + "mimosa_shunt": ptalog["configs"][j]["shunt"], + "state_duration": ptalog["opt"]["sleep"], + } + ) + for repeat_id, mim_file in enumerate(ptalog["files"][j]): + member = tf.getmember(mim_file) + offline_data.append( + { + "content": tf.extractfile(member).read(), + "fileno": j, + "info": member, + "setup": self.setup_by_fileno[j], + "repeat_id": repeat_id, + "expected_trace": ptalog["traces"][j], + "with_traces": self.with_traces, + } + ) + self.filenames = new_filenames + + elif version == 2: + + new_filenames = list() + with tarfile.open(filename) as tf: + ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json"))) + self.pta = ptalog["pta"] + + # Benchmark code may be too large to be executed in a single + # run, so benchmarks (a benchmark is basically a list of DFA runs) + # may be split up. To accomodate this, ptalog['traces'] is + # a list of lists: ptalog['traces'][0] corresponds to the + # first benchmark part, ptalog['traces'][1] to the + # second, and so on. ptalog['traces'][0][0] is the first + # trace (a sequence of states and transitions) in the + # first benchmark part, ptalog['traces'][0][1] the second, etc. + # + # As traces are typically repeated to minimize the effect + # of random noise, observations for each benchmark part + # are also lists. In this case, this applies in two + # cases: traces[i][j]['parameter'][some_param] is either + # a value (if the parameter is controlld by software) + # or a list (if the parameter is known a posteriori, e.g. + # "how many retransmissions did this packet take?"). + # + # The second case is the MIMOSA energy measurements, which + # are listed in ptalog['files']. ptalog['files'][0] + # contains a list of files for the first benchmark part, + # ptalog['files'][0][0] is its first iteration/repetition, + # ptalog['files'][0][1] the second, etc. + + # generate-dfa-benchmark uses TimingHarness to obtain timing data. + # Data is placed in 'offline_aggregates', which is also + # where we are going to store power/energy data. + # In case of invalid measurements, this can lead to a + # mismatch between duration and power/energy data, e.g. + # where duration = [A, B, C], power = [a, b], B belonging + # to an invalid measurement and thus power[b] corresponding + # to duration[C]. At the moment, this is harmless, but in the + # future it might not be. + if "offline_aggregates" in ptalog["traces"][0][0]["trace"][0]: + for trace_group in ptalog["traces"]: + for trace in trace_group: + for state_or_transition in trace["trace"]: + offline_aggregates = state_or_transition.pop( + "offline_aggregates", None + ) + if offline_aggregates: + state_or_transition[ + "online_aggregates" + ] = offline_aggregates + + for j, traces in enumerate(ptalog["traces"]): + new_filenames.append("{}#{}".format(filename, j)) + self.traces_by_fileno.append(traces) + self.setup_by_fileno.append( + { + "voltage": ptalog["configs"][j]["voltage"], + "state_duration": ptalog["opt"]["sleep"], + } + ) + for repeat_id, etlog_file in enumerate(ptalog["files"][j]): + member = tf.getmember(etlog_file) + offline_data.append( + { + "content": tf.extractfile(member).read(), + "fileno": j, + "info": member, + "setup": self.setup_by_fileno[j], + "repeat_id": repeat_id, + "expected_trace": ptalog["traces"][j], + "with_traces": self.with_traces, + "transition_names": list( + map( + lambda x: x["name"], + ptalog["pta"]["transitions"], + ) + ), + } + ) + self.filenames = new_filenames + # TODO remove 'offline_aggregates' from pre-parse data and place + # it under 'online_aggregates' or similar instead. This way, if + # a .etlog file fails to parse, its corresponding duration data + # will not linger in 'offline_aggregates' and confuse the hell + # out of other code paths + + with Pool() as pool: + if self.version <= 1: + measurements = pool.map(_preprocess_mimosa, offline_data) + elif self.version == 2: + measurements = pool.map(_preprocess_etlog, offline_data) + + num_valid = 0 + for measurement in measurements: + + if "energy_trace" not in measurement: + logger.warning( + "Skipping {ar:s}/{m:s}: {e:s}".format( + ar=self.filenames[measurement["fileno"]], + m=measurement["info"].name, + e="; ".join(measurement["datasource_errors"]), + ) + ) + continue + + if version == 0: + # Strip the last state (it is not part of the scheduled measurement) + measurement["energy_trace"].pop() + elif version == 1: + # The first online measurement is the UNINITIALIZED state. In v1, + # it is not part of the expected PTA trace -> remove it. + measurement["energy_trace"].pop(0) + + if version == 0 or version == 1: + if self._measurement_is_valid_01(measurement): + self._merge_online_and_offline(measurement) + num_valid += 1 + else: + logger.warning( + "Skipping {ar:s}/{m:s}: {e:s}".format( + ar=self.filenames[measurement["fileno"]], + m=measurement["info"].name, + e=measurement["error"], + ) + ) + elif version == 2: + if self._measurement_is_valid_2(measurement): + self._merge_online_and_etlog(measurement) + num_valid += 1 + else: + logger.warning( + "Skipping {ar:s}/{m:s}: {e:s}".format( + ar=self.filenames[measurement["fileno"]], + m=measurement["info"].name, + e=measurement["error"], + ) + ) + logger.info( + "{num_valid:d}/{num_total:d} measurements are valid".format( + num_valid=num_valid, num_total=len(measurements) + ) + ) + if version == 0: + self.traces = self._concatenate_traces(self.traces_by_fileno) + elif version == 1: + self.traces = self._concatenate_traces( + map(lambda x: x["expected_trace"], measurements) + ) + self.traces = self._concatenate_traces(self.traces_by_fileno) + elif version == 2: + self.traces = self._concatenate_traces(self.traces_by_fileno) + self.preprocessing_stats = { + "num_runs": len(measurements), + "num_valid": num_valid, + } + + +def _add_trace_data_to_aggregate(aggregate, key, element): + # Only cares about element['isa'], element['offline_aggregates'], and + # element['plan']['level'] + if key not in aggregate: + aggregate[key] = {"isa": element["isa"]} + for datakey in element["offline_aggregates"].keys(): + aggregate[key][datakey] = [] + if element["isa"] == "state": + aggregate[key]["attributes"] = ["power"] + else: + # TODO do not hardcode values + aggregate[key]["attributes"] = [ + "duration", + "energy", + "rel_energy_prev", + "rel_energy_next", + ] + # Uncomment this line if you also want to analyze mean transition power + # aggrgate[key]['attributes'].append('power') + if "plan" in element and element["plan"]["level"] == "epilogue": + aggregate[key]["attributes"].insert(0, "timeout") + attributes = aggregate[key]["attributes"].copy() + for attribute in attributes: + if attribute not in element["offline_aggregates"]: + aggregate[key]["attributes"].remove(attribute) + for datakey, dataval in element["offline_aggregates"].items(): + aggregate[key][datakey].extend(dataval) + + +def pta_trace_to_aggregate(traces, ignore_trace_indexes=[]): + u""" + Convert preprocessed DFA traces from peripherals/drivers to by_name aggregate for PTAModel. + + arguments: + traces -- [ ... Liste von einzelnen Läufen (d.h. eine Zustands- und Transitionsfolge UNINITIALIZED -> foo -> FOO -> bar -> BAR -> ...) + Jeder Lauf: + - id: int Nummer des Laufs, beginnend bei 1 + - trace: [ ... Liste von Zuständen und Transitionen + Jeweils: + - name: str Name + - isa: str state // transition + - parameter: { ... globaler Parameter: aktueller wert. null falls noch nicht eingestellt } + - args: [ Funktionsargumente, falls isa == 'transition' ] + - offline_aggregates: + - power: [float(uW)] Mittlere Leistung während Zustand/Transitions + - power_std: [float(uW^2)] Standardabweichung der Leistung + - duration: [int(us)] Dauer + - energy: [float(pJ)] Energieaufnahme des Zustands / der Transition + - clip_rate: [float(0..1)] Clipping + - paramkeys: [[str]] Name der berücksichtigten Parameter + - param: [int // str] Parameterwerte. Quasi-Duplikat von 'parameter' oben + Falls isa == 'transition': + - timeout: [int(us)] Dauer des vorherigen Zustands + - rel_energy_prev: [int(pJ)] + - rel_energy_next: [int(pJ)] + ] + ] + ignore_trace_indexes -- list of trace indexes. The corresponding taces will be ignored. + + returns a tuple of three elements: + by_name -- measurements aggregated by state/transition name, annotated with parameter values + parameter_names -- list of parameter names + arg_count -- dict mapping transition names to the number of arguments of their corresponding driver function + + by_name layout: + Dictionary with one key per state/transition ('send', 'TX', ...). + Each element is in turn a dict with the following elements: + - isa: 'state' or 'transition' + - power: list of mean power measurements in µW + - duration: list of durations in µs + - power_std: list of stddev of power per state/transition + - energy: consumed energy (power*duration) in pJ + - paramkeys: list of parameter names in each measurement (-> list of lists) + - param: list of parameter values in each measurement (-> list of lists) + - attributes: list of keys that should be analyzed, + e.g. ['power', 'duration'] + additionally, only if isa == 'transition': + - timeout: list of duration of previous state in µs + - rel_energy_prev: transition energy relative to previous state mean power in pJ + - rel_energy_next: transition energy relative to next state mean power in pJ + """ + arg_count = dict() + by_name = dict() + parameter_names = sorted(traces[0]["trace"][0]["parameter"].keys()) + for run in traces: + if run["id"] not in ignore_trace_indexes: + for elem in run["trace"]: + if ( + elem["isa"] == "transition" + and not elem["name"] in arg_count + and "args" in elem + ): + arg_count[elem["name"]] = len(elem["args"]) + if elem["name"] != "UNINITIALIZED": + _add_trace_data_to_aggregate(by_name, elem["name"], elem) + for elem in by_name.values(): + for key in elem["attributes"]: + elem[key] = np.array(elem[key]) + return by_name, parameter_names, arg_count + + +class EnergyTraceLog: + """ + EnergyTrace log loader for DFA traces. + + Expects an EnergyTrace log file generated via msp430-etv / energytrace-util + and a dfatool-generated benchmark. An EnergyTrace log consits of a series + of measurements. Each measurement has a timestamp, mean current, voltage, + and cumulative energy since start of measurement. Each transition is + preceded by a Code128 barcode embedded into the energy consumption by + toggling a LED. + + Note that the baseline power draw of board and peripherals is not subtracted + at the moment. + """ + + def __init__( + self, + voltage: float, + state_duration: int, + transition_names: list, + with_traces=False, + ): + """ + Create a new EnergyTraceLog object. + + :param voltage: supply voltage [V], usually 3.3 V + :param state_duration: state duration [ms] + :param transition_names: list of transition names in PTA transition order. + Needed to map barcode synchronization numbers to transitions. + """ + self.voltage = voltage + self.state_duration = state_duration * 1e-3 + self.transition_names = transition_names + self.with_traces = with_traces + self.errors = list() + + # TODO auto-detect + self.led_power = 10e-3 + + # multipass/include/object/ptalog.h#startTransition + self.module_duration = 5e-3 + + # multipass/include/object/ptalog.h#startTransition + self.quiet_zone_duration = 60e-3 + + # TODO auto-detect? + # Note that we consider barcode duration after start, so only the + # quiet zone -after- the code is relevant + self.min_barcode_duration = 57 * self.module_duration + self.quiet_zone_duration + self.max_barcode_duration = 68 * self.module_duration + self.quiet_zone_duration + + def load_data(self, log_data): + """ + Load log data (raw energytrace .txt file, one line per event). + + :param log_data: raw energytrace log file in 4-column .txt format + """ + + if not zbar_available: + logger.error("zbar module is not available") + self.errors.append( + 'zbar module is not available. Try "apt install python3-zbar"' + ) + return list() + + lines = log_data.decode("ascii").split("\n") + data_count = sum(map(lambda x: len(x) > 0 and x[0] != "#", lines)) + data_lines = filter(lambda x: len(x) > 0 and x[0] != "#", lines) + + data = np.empty((data_count, 4)) + + for i, line in enumerate(data_lines): + fields = line.split(" ") + if len(fields) == 4: + timestamp, current, voltage, total_energy = map(int, fields) + elif len(fields) == 5: + # cpustate = fields[0] + timestamp, current, voltage, total_energy = map(int, fields[1:]) + else: + raise RuntimeError('cannot parse line "{}"'.format(line)) + data[i] = [timestamp, current, voltage, total_energy] + + self.interval_start_timestamp = data[:-1, 0] * 1e-6 + self.interval_duration = (data[1:, 0] - data[:-1, 0]) * 1e-6 + self.interval_power = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / ( + (data[1:, 0] - data[:-1, 0]) * 1e-6 + ) + + m_duration_us = data[-1, 0] - data[0, 0] + + self.sample_rate = data_count / (m_duration_us * 1e-6) + + logger.debug( + "got {} samples with {} seconds of log data ({} Hz)".format( + data_count, m_duration_us * 1e-6, self.sample_rate + ) + ) + + return ( + self.interval_start_timestamp, + self.interval_duration, + self.interval_power, + ) + + def ts_to_index(self, timestamp): + """ + Convert timestamp in seconds to interval_start_timestamp / interval_duration / interval_power index. + + Returns the index of the interval which timestamp is part of. + """ + return self._ts_to_index(timestamp, 0, len(self.interval_start_timestamp)) + + def _ts_to_index(self, timestamp, left_index, right_index): + if left_index == right_index: + return left_index + if left_index + 1 == right_index: + return left_index + + mid_index = left_index + (right_index - left_index) // 2 + + # I'm feeling lucky + if ( + timestamp > self.interval_start_timestamp[mid_index] + and timestamp + <= self.interval_start_timestamp[mid_index] + + self.interval_duration[mid_index] + ): + return mid_index + + if timestamp <= self.interval_start_timestamp[mid_index]: + return self._ts_to_index(timestamp, left_index, mid_index) + + return self._ts_to_index(timestamp, mid_index, right_index) + + def analyze_states(self, traces, offline_index: int): + u""" + Split log data into states and transitions and return duration, energy, and mean power for each element. + + :param traces: expected traces, needed to synchronize with the measurement. + traces is a list of runs, traces[*]['trace'] is a single run + (i.e. a list of states and transitions, starting with a transition + and ending with a state). + :param offline_index: This function uses traces[*]['trace'][*]['online_aggregates']['duration'][offline_index] to find sync codes + + :param charges: raw charges (each element describes the charge in pJ transferred during 10 µs) + :param trigidx: "charges" indexes corresponding to a trigger edge, see `trigger_edges` + :param ua_func: charge(pJ) -> current(µA) function as returned by `calibration_function` + + :returns: maybe returns list of states and transitions, both starting andending with a state. + Each element is a dict containing: + * `isa`: 'state' or 'transition' + * `clip_rate`: range(0..1) Anteil an Clipping im Energieverbrauch + * `raw_mean`: Mittelwert der Rohwerte + * `raw_std`: Standardabweichung der Rohwerte + * `uW_mean`: Mittelwert der (kalibrierten) Leistungsaufnahme + * `uW_std`: Standardabweichung der (kalibrierten) Leistungsaufnahme + * `us`: Dauer + if isa == 'transition, it also contains: + * `timeout`: Dauer des vorherigen Zustands + * `uW_mean_delta_prev`: Differenz zwischen uW_mean und uW_mean des vorherigen Zustands + * `uW_mean_delta_next`: Differenz zwischen uW_mean und uW_mean des Folgezustands + """ + + first_sync = self.find_first_sync() + + energy_trace = list() + + expected_transitions = list() + for trace_number, trace in enumerate(traces): + for state_or_transition_number, state_or_transition in enumerate( + trace["trace"] + ): + if state_or_transition["isa"] == "transition": + try: + expected_transitions.append( + ( + state_or_transition["name"], + state_or_transition["online_aggregates"]["duration"][ + offline_index + ] + * 1e-6, + ) + ) + except IndexError: + self.errors.append( + 'Entry #{} ("{}") in trace #{} has no duration entry for offline_index/repeat_id {}'.format( + state_or_transition_number, + state_or_transition["name"], + trace_number, + offline_index, + ) + ) + return energy_trace + + next_barcode = first_sync + + for name, duration in expected_transitions: + bc, start, stop, end = self.find_barcode(next_barcode) + if bc is None: + logger.error('did not find transition "{}"'.format(name)) + break + next_barcode = end + self.state_duration + duration + logger.debug( + '{} barcode "{}" area: {:0.2f} .. {:0.2f} / {:0.2f} seconds'.format( + offline_index, bc, start, stop, end + ) + ) + if bc != name: + logger.error('mismatch: expected "{}", got "{}"'.format(name, bc)) + logger.debug( + "{} estimated transition area: {:0.3f} .. {:0.3f} seconds".format( + offline_index, end, end + duration + ) + ) + + transition_start_index = self.ts_to_index(end) + transition_done_index = self.ts_to_index(end + duration) + 1 + state_start_index = transition_done_index + state_done_index = ( + self.ts_to_index(end + duration + self.state_duration) + 1 + ) + + logger.debug( + "{} estimated transitionindex: {:0.3f} .. {:0.3f} seconds".format( + offline_index, + transition_start_index / self.sample_rate, + transition_done_index / self.sample_rate, + ) + ) + + transition_power_W = self.interval_power[ + transition_start_index:transition_done_index + ] + + transition = { + "isa": "transition", + "W_mean": np.mean(transition_power_W), + "W_std": np.std(transition_power_W), + "s": duration, + "s_coarse": self.interval_start_timestamp[transition_done_index] + - self.interval_start_timestamp[transition_start_index], + } + + if self.with_traces: + transition["uW"] = transition_power_W * 1e6 + + energy_trace.append(transition) + + if len(energy_trace) > 1: + energy_trace[-1]["W_mean_delta_prev"] = ( + energy_trace[-1]["W_mean"] - energy_trace[-2]["W_mean"] + ) + + state_power_W = self.interval_power[state_start_index:state_done_index] + state = { + "isa": "state", + "W_mean": np.mean(state_power_W), + "W_std": np.std(state_power_W), + "s": self.state_duration, + "s_coarse": self.interval_start_timestamp[state_done_index] + - self.interval_start_timestamp[state_start_index], + } + + if self.with_traces: + state["uW"] = state_power_W * 1e6 + + energy_trace.append(state) + + energy_trace[-2]["W_mean_delta_next"] = ( + energy_trace[-2]["W_mean"] - energy_trace[-1]["W_mean"] + ) + + expected_transition_count = len(expected_transitions) + recovered_transition_ount = len(energy_trace) // 2 + + if expected_transition_count != recovered_transition_ount: + self.errors.append( + "Expected {:d} transitions, got {:d}".format( + expected_transition_count, recovered_transition_ount + ) + ) + + return energy_trace + + def find_first_sync(self): + # LED Power is approx. self.led_power W, use self.led_power/2 W above surrounding median as threshold + sync_threshold_power = ( + np.median(self.interval_power[: int(3 * self.sample_rate)]) + + self.led_power / 3 + ) + for i, ts in enumerate(self.interval_start_timestamp): + if ts > 2 and self.interval_power[i] > sync_threshold_power: + return self.interval_start_timestamp[i - 300] + return None + + def find_barcode(self, start_ts): + """ + Return absolute position and content of the next barcode following `start_ts`. + + :param interval_ts: list of start timestamps (one per measurement interval) [s] + :param interval_power: mean power per measurement interval [W] + :param start_ts: timestamp at which to start looking for a barcode [s] + """ + + for i, ts in enumerate(self.interval_start_timestamp): + if ts >= start_ts: + start_position = i + break + + # Lookaround: 100 ms in both directions + lookaround = int(0.1 * self.sample_rate) + + # LED Power is approx. self.led_power W, use self.led_power/2 W above surrounding median as threshold + sync_threshold_power = ( + np.median( + self.interval_power[ + start_position - lookaround : start_position + lookaround + ] + ) + + self.led_power / 3 + ) + + logger.debug( + "looking for barcode starting at {:0.2f} s, threshold is {:0.1f} mW".format( + start_ts, sync_threshold_power * 1e3 + ) + ) + + sync_area_start = None + sync_start_ts = None + sync_area_end = None + sync_end_ts = None + for i, ts in enumerate(self.interval_start_timestamp): + if ( + sync_area_start is None + and ts >= start_ts + and self.interval_power[i] > sync_threshold_power + ): + sync_area_start = i - 300 + sync_start_ts = ts + if ( + sync_area_start is not None + and sync_area_end is None + and ts > sync_start_ts + self.min_barcode_duration + and ( + ts > sync_start_ts + self.max_barcode_duration + or abs(sync_threshold_power - self.interval_power[i]) + > self.led_power + ) + ): + sync_area_end = i + sync_end_ts = ts + break + + barcode_data = self.interval_power[sync_area_start:sync_area_end] + + logger.debug( + "barcode search area: {:0.2f} .. {:0.2f} seconds ({} samples)".format( + sync_start_ts, sync_end_ts, len(barcode_data) + ) + ) + + bc, start, stop, padding_bits = self.find_barcode_in_power_data(barcode_data) + + if bc is None: + return None, None, None, None + + start_ts = self.interval_start_timestamp[sync_area_start + start] + stop_ts = self.interval_start_timestamp[sync_area_start + stop] + + end_ts = ( + stop_ts + self.module_duration * padding_bits + self.quiet_zone_duration + ) + + # barcode content, barcode start timestamp, barcode stop timestamp, barcode end (stop + padding) timestamp + return bc, start_ts, stop_ts, end_ts + + def find_barcode_in_power_data(self, barcode_data): + + min_power = np.min(barcode_data) + max_power = np.max(barcode_data) + + # zbar seems to be confused by measurement (and thus image) noise + # inside of barcodes. As our barcodes are only 1px high, this is + # likely not trivial to fix. + # -> Create a black and white (not grayscale) image to avoid this. + # Unfortunately, this decreases resilience against background noise + # (e.g. a not-exactly-idle peripheral device or CPU interrupts). + image_data = np.around( + 1 - ((barcode_data - min_power) / (max_power - min_power)) + ) + image_data *= 255 + + # zbar only returns the complete barcode position if it is at least + # two pixels high. For a 1px barcode, it only returns its right border. + + width = len(image_data) + height = 2 + + image_data = bytes(map(int, image_data)) * height + + # img = Image.frombytes('L', (width, height), image_data).resize((width, 100)) + # img.save('/tmp/test-{}.png'.format(os.getpid())) + + zbimg = zbar.Image(width, height, "Y800", image_data) + scanner = zbar.ImageScanner() + scanner.parse_config("enable") + + if scanner.scan(zbimg): + (sym,) = zbimg.symbols + content = sym.data + try: + sym_start = sym.location[1][0] + except IndexError: + sym_start = 0 + sym_end = sym.location[0][0] + + match = re.fullmatch(r"T(\d+)", content) + if match: + content = self.transition_names[int(match.group(1))] + + # PTALog barcode generation operates on bytes, so there may be + # additional non-barcode padding (encoded as LED off / image white). + # Calculate the amount of extra bits to determine the offset until + # the transition starts. + padding_bits = len(Code128(sym.data, charset="B").modules) % 8 + + # sym_start leaves out the first two bars, but we don't do anything about that here + # sym_end leaves out the last three bars, each of which is one padding bit long. + # as a workaround, we unconditionally increment padding_bits by three. + padding_bits += 3 + + return content, sym_start, sym_end, padding_bits + else: + logger.warning("unable to find barcode") + return None, None, None, None + + +class MIMOSA: + """ + MIMOSA log loader for DFA traces with auto-calibration. + + Expects a MIMOSA log file generated via dfatool and a dfatool-generated + benchmark. A MIMOSA log consists of a series of measurements. Each measurement + gives the total charge (in pJ) and binary buzzer/trigger value during a 10µs interval. + + There must be a calibration run consisting of at least two seconds with disconnected DUT, + two seconds with 1 kOhm (984 Ohm), and two seconds with 100 kOhm (99013 Ohm) resistor at + the start. The first ten seconds of data are reserved for calbiration and must not contain + measurements, as trigger/buzzer signals are ignored in this time range. + + Resulting data is a list of state/transition/state/transition/... measurements. + """ + + def __init__(self, voltage: float, shunt: int, with_traces=False): + """ + Initialize MIMOSA loader for a specific voltage and shunt setting. + + :param voltage: MIMOSA DUT supply voltage (V) + :para mshunt: MIMOSA Shunt (Ohms) + """ + self.voltage = voltage + self.shunt = shunt + self.with_traces = with_traces + self.r1 = 984 # "1k" + self.r2 = 99013 # "100k" + self.errors = list() + + def charge_to_current_nocal(self, charge): + u""" + Convert charge per 10µs (in pJ) to mean currents (in µA) without accounting for calibration. + + :param charge: numpy array of charges (pJ per 10µs) as returned by `load_data` or `load_file` + + :returns: numpy array of mean currents (µA per 10µs) + """ + ua_max = 1.836 / self.shunt * 1000000 + ua_step = ua_max / 65535 + return charge * ua_step + + def _load_tf(self, tf): + u""" + Load MIMOSA log data from an open `tarfile` instance. + + :param tf: `tarfile` instance + + :returns: (numpy array of charges (pJ per 10µs), numpy array of triggers (0/1 int, per 10µs)) + """ + 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("<I", content) + i = 0 + for word in iterator: + charges[i] = word[0] >> 4 + triggers[i] = (word[0] & 0x08) >> 3 + i += 1 + return charges, triggers + + def load_data(self, raw_data): + u""" + Load MIMOSA log data from a MIMOSA log file passed as raw byte string + + :param raw_data: MIMOSA log file, passed as raw byte string + + :returns: (numpy array of charges (pJ per 10µs), numpy array of triggers (0/1 int, per 10µs)) + """ + with io.BytesIO(raw_data) as data_object: + with tarfile.open(fileobj=data_object) as tf: + return self._load_tf(tf) + + def load_file(self, filename): + u""" + Load MIMOSA log data from a MIMOSA log file + + :param filename: MIMOSA log file + + :returns: (numpy array of charges (pJ per 10µs), numpy array of triggers (0/1 int, per 10µs)) + """ + with tarfile.open(filename) as tf: + return self._load_tf(tf) + + def currents_nocal(self, charges): + u""" + Convert charges (pJ per 10µs) to mean currents without accounting for calibration. + + :param charges: numpy array of charges (pJ per 10µs) + + :returns: numpy array of currents (mean µA per 10µs)""" + ua_max = 1.836 / self.shunt * 1000000 + ua_step = ua_max / 65535 + return charges.astype(np.double) * ua_step + + def trigger_edges(self, triggers): + """ + Return indexes of trigger edges (both 0->1 and 1->0) in log data. + + Ignores the first 10 seconds, which are used for calibration and may + contain bogus triggers due to DUT resets. + + :param triggers: trigger array (int, 0/1) as returned by load_data + + :returns: list of int (trigger indices, e.g. [2000000, ...] means the first trigger appears in charges/currents interval 2000000 -> 20s after start of measurements. Keep in mind that each interval is 10µs long, not 1µs, so index values are not µs timestamps) + """ + trigidx = [] + + if len(triggers) < 1000000: + self.errors.append("MIMOSA log is too short") + return trigidx + + prevtrig = triggers[999999] + + # if the first trigger is high (i.e., trigger/buzzer pin is active before the benchmark starts), + # something went wrong and are unable to determine when the first + # transition starts. + if prevtrig != 0: + self.errors.append( + "Unable to find start of first transition (log starts with trigger == {} != 0)".format( + prevtrig + ) + ) + + # if the last trigger is high (i.e., trigger/buzzer pin is active when the benchmark ends), + # it terminated in the middle of a transition -- meaning that it was not + # measured in its entirety. + if triggers[-1] != 0: + self.errors.append("Log ends during a transition".format(prevtrig)) + + # 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 charge/current + # interval belonging to this trigger comes two intervals (20µs) later + trigidx.append(i + 2) + prevtrig = trig + return trigidx + + def calibration_edges(self, currents): + u""" + Return start/stop indexes of calibration measurements. + + :param currents: uncalibrated currents as reported by MIMOSA. For best results, + it may help to use a running mean, like so: + `currents = running_mean(currents_nocal(..., 10))` + + :returns: indices of calibration events in MIMOSA data: + (disconnect start, disconnect stop, R1 (1k) start, R1 (1k) stop, R2 (100k) start, R2 (100k) stop) + indices refer to charges/currents arrays, so 0 refers to the first 10µs interval, 1 to the second, and so on. + """ + 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): + u""" + Calculate calibration function from previously determined calibration edges. + + :param charges: raw charges from MIMOSA + :param cal_edges: calibration edges as returned by calibration_edges + + :returns: (calibration_function, calibration_data): + calibration_function -- charge in pJ (float) -> current in uA (float). + Converts the amount of charge in a 10 µs interval to the + mean current during the same interval. + calibration_data -- dict containing the following keys: + edges -- calibration points in the log file, in µs + offset -- ... + offset2 -- ... + slope_low -- ... + slope_high -- ... + add_low -- ... + add_high -- .. + r0_err_uW -- mean error of uncalibrated data at "∞ Ohm" in µW + r0_std_uW -- standard deviation of uncalibrated data at "∞ Ohm" in µW + r1_err_uW -- mean error of uncalibrated data at 1 kOhm + r1_std_uW -- stddev at 1 kOhm + r2_err_uW -- mean error at 100 kOhm + r2_std_uW -- stddev at 100 kOhm + """ + 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_r1_mean = np.mean(chg_r1) + cal_r2_mean = np.mean(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: + logger.warning("0 uA == %.f uA during calibration" % (ua_r2)) + b_lower = 0 + + b_upper = (ua_r1 - ua_r2) / (cal_r1_mean - cal_r2_mean) + + a_lower = -b_lower * cal_0_mean + a_upper = -b_upper * cal_r2_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): + u""" + Split log data into states and transitions and return duration, energy, and mean power for each element. + + :param charges: raw charges (each element describes the charge in pJ transferred during 10 µs) + :param trigidx: "charges" indexes corresponding to a trigger edge, see `trigger_edges` + :param ua_func: charge(pJ) -> current(µA) function as returned by `calibration_function` + + :returns: list of states and transitions, both starting andending with a state. + Each element is a dict containing: + * `isa`: 'state' or 'transition' + * `clip_rate`: range(0..1) Anteil an Clipping im Energieverbrauch + * `raw_mean`: Mittelwert der Rohwerte + * `raw_std`: Standardabweichung der Rohwerte + * `uW_mean`: Mittelwert der (kalibrierten) Leistungsaufnahme + * `uW_std`: Standardabweichung der (kalibrierten) Leistungsaufnahme + * `us`: Dauer + if isa == 'transition, it also contains: + * `timeout`: Dauer des vorherigen Zustands + * `uW_mean_delta_prev`: Differenz zwischen uW_mean und uW_mean des vorherigen Zustands + * `uW_mean_delta_next`: Differenz zwischen uW_mean und uW_mean des Folgezustands + """ + previdx = 0 + is_state = True + iterdata = [] + + # The last state (between the last transition and end of file) may also + # be important. Pretend it ends when the log ends. + trigger_indices = trigidx.copy() + trigger_indices.append(len(charges)) + + 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: + 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 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) + 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 |