diff options
Diffstat (limited to 'bin')
| -rwxr-xr-x | bin/analyze-archive.py | 135 | ||||
| -rwxr-xr-x | bin/analyze-timing.py | 52 | ||||
| -rwxr-xr-x | bin/cal-hist | 92 | ||||
| -rwxr-xr-x | bin/eval-accounting-overhead.py | 2 | ||||
| -rwxr-xr-x | bin/eval-online-model-accuracy.py | 2 | ||||
| -rwxr-xr-x | bin/eval-outlier-removal.py | 19 | ||||
| -rwxr-xr-x | bin/eval-rel-energy.py | 10 | ||||
| -rwxr-xr-x | bin/generate-dfa-benchmark.py | 33 | ||||
| -rwxr-xr-x | bin/gptest.py | 5 | ||||
| -rwxr-xr-x | bin/gradient | 14 | ||||
| -rwxr-xr-x | bin/keysightdlog.py | 164 | ||||
| -rwxr-xr-x | bin/mim-vs-keysight.py | 2 | ||||
| -rwxr-xr-x | bin/mimosa-etv | 163 | ||||
| -rwxr-xr-x | bin/mimplot | 68 | ||||
| -rwxr-xr-x | bin/test_corrcoef.py | 13 |
15 files changed, 508 insertions, 266 deletions
diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py index 4531d86..8311f5c 100755 --- a/bin/analyze-archive.py +++ b/bin/analyze-archive.py @@ -61,18 +61,25 @@ Options: Specify traces which should be ignored due to bogus data. 1 is the first trace, 2 the second, and so on. ---discard-outliers= - not supported at the moment - --cross-validate=<method>:<count> Perform cross validation when computing model quality. Only works with --show-quality=table at the moment. + If <method> is "montecarlo": Randomly divide data into 2/3 training and 1/3 validation, <count> times. Reported model quality is the average of all validation runs. Data is partitioned without regard for parameter values, so a specific parameter combination may be present in both training and validation sets or just one of them. + If <method> is "kfold": Perform k-fold cross validation with k=<count>. + Divide data into 1-1/k training and 1/k validation, <count> times. + In the first set, items 0, k, 2k, ... ard used for validation, in the + second set, items 1, k+1, 2k+1, ... and so on. + validation, <count> times. Reported model quality is the average of all + validation runs. Data is partitioned without regard for parameter values, + so a specific parameter combination may be present in both training and + validation sets or just one of them. + --function-override=<name attribute function>[;<name> <attribute> <function>;...] Manually specify the function to fit for <name> <attribute>. A function specified this way bypasses parameter detection: It is always assigned, @@ -94,17 +101,22 @@ Options: --export-energymodel=<model.json> Export energy model. Works out of the box for v1 and v2 logfiles. Requires --hwmodel for v0 logfiles. + +--no-cache + Do not load cached measurement results """ import getopt import json +import logging import random import re import sys from dfatool import plotter -from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate -from dfatool.dfatool import gplearn_to_function -from dfatool.dfatool import CrossValidator +from dfatool.loader import RawData, pta_trace_to_aggregate +from dfatool.functions import gplearn_to_function +from dfatool.model import PTAModel +from dfatool.validation import CrossValidator from dfatool.utils import filter_aggregate_by_param from dfatool.automata import PTA @@ -133,6 +145,15 @@ def format_quality_measures(result): def model_quality_table(result_lists, info_list): + print( + "{:20s} {:15s} {:19s} {:19s} {:19s}".format( + "key", + "attribute", + "static".center(19), + "LUT".center(19), + "parameterized".center(19), + ) + ) for state_or_tran in result_lists[0]["by_name"].keys(): for key in result_lists[0]["by_name"][state_or_tran].keys(): buf = "{:20s} {:15s}".format(state_or_tran, key) @@ -143,7 +164,7 @@ def model_quality_table(result_lists, info_list): result = results["by_name"][state_or_tran][key] buf += format_quality_measures(result) else: - buf += "{:6}----{:9}".format("", "") + buf += "{:7}----{:8}".format("", "") print(buf) @@ -279,7 +300,6 @@ def print_html_model_data(model, pm, pq, lm, lq, am, ai, aq): if __name__ == "__main__": ignored_trace_indexes = [] - discard_outliers = None safe_functions_enabled = False function_override = {} show_models = [] @@ -292,11 +312,12 @@ if __name__ == "__main__": try: optspec = ( - "info " + "info no-cache " "plot-unparam= plot-param= plot-traces= show-models= show-quality= " - "ignored-trace-indexes= discard-outliers= function-override= " + "ignored-trace-indexes= function-override= " "export-traces= " "filter-param= " + "log-level= " "cross-validate= " "with-safe-functions hwmodel= export-energymodel=" ) @@ -313,9 +334,6 @@ if __name__ == "__main__": if 0 in ignored_trace_indexes: print("[E] arguments to --ignored-trace-indexes start from 1") - if "discard-outliers" in opt: - discard_outliers = float(opt["discard-outliers"]) - if "function-override" in opt: for function_desc in opt["function-override"].split(";"): state_or_tran, attribute, *function_str = function_desc.split(" ") @@ -344,12 +362,21 @@ if __name__ == "__main__": if "hwmodel" in opt: pta = PTA.from_file(opt["hwmodel"]) + if "log-level" in opt: + numeric_level = getattr(logging, opt["log-level"].upper(), None) + if not isinstance(numeric_level, int): + print(f"Invalid log level: {loglevel}", file=sys.stderr) + sys.exit(1) + logging.basicConfig(level=numeric_level) + except getopt.GetoptError as err: print(err, file=sys.stderr) sys.exit(2) raw_data = RawData( - args, with_traces=("export-traces" in opt or "plot-traces" in opt) + args, + with_traces=("export-traces" in opt or "plot-traces" in opt), + skip_cache=("no-cache" in opt), ) if "info" in opt: @@ -357,9 +384,12 @@ if __name__ == "__main__": if raw_data.version <= 1: data_source = "MIMOSA" elif raw_data.version == 2: - data_source = "MSP430 EnergyTrace" - else: - data_source = "UNKNOWN" + if raw_data.ptalog and "sync" in raw_data.ptalog["opt"]["energytrace"]: + data_source = "MSP430 EnergyTrace, sync={}".format( + raw_data.ptalog["opt"]["energytrace"]["sync"] + ) + else: + data_source = "MSP430 EnergyTrace" print(f" Data source ID: {raw_data.version} ({data_source})") preprocessed_data = raw_data.get_preprocessed_data() @@ -434,7 +464,6 @@ if __name__ == "__main__": parameters, arg_count, traces=preprocessed_data, - discard_outliers=discard_outliers, function_override=function_override, pta=pta, ) @@ -495,21 +524,6 @@ if __name__ == "__main__": model.stats.param_dependence_ratio(state, "power", param), ) ) - if model.stats.has_codependent_parameters(state, "power", param): - print( - "{:24s} co-dependencies: {:s}".format( - "", - ", ".join( - model.stats.codependent_parameters( - state, "power", param - ) - ), - ) - ) - for param_dict in model.stats.codependent_parameter_value_dicts( - state, "power", param - ): - print("{:24s} parameter-aware for {}".format("", param_dict)) for trans in model.transitions(): # Mean power is not a typical transition attribute, but may be present for debugging or analysis purposes @@ -551,6 +565,8 @@ if __name__ == "__main__": if xv_method == "montecarlo": static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) + elif xv_method == "kfold": + static_quality = xv.kfold(lambda m: m.get_static(), xv_count) else: static_quality = model.assess(static_model) @@ -560,6 +576,8 @@ if __name__ == "__main__": if xv_method == "montecarlo": lut_quality = xv.montecarlo(lambda m: m.get_param_lut(fallback=True), xv_count) + elif xv_method == "kfold": + lut_quality = xv.kfold(lambda m: m.get_param_lut(fallback=True), xv_count) else: lut_quality = model.assess(lut_model) @@ -616,21 +634,21 @@ if __name__ == "__main__": if "param" in show_models or "all" in show_models: if not model.stats.can_be_fitted(): - print( - "[!] measurements have insufficient distinct numeric parameters for fitting. A parameter-aware model is not available." + logging.warning( + "measurements have insufficient distinct numeric parameters for fitting. A parameter-aware model is not available." ) for state in model.states(): for attribute in model.attributes(state): if param_info(state, attribute): print( "{:10s}: {}".format( - state, param_info(state, attribute)["function"]._model_str + state, + param_info(state, attribute)["function"].model_function, ) ) print( "{:10s} {}".format( - "", - param_info(state, attribute)["function"]._regression_args, + "", param_info(state, attribute)["function"].model_args ) ) for trans in model.transitions(): @@ -640,19 +658,19 @@ if __name__ == "__main__": "{:10s}: {:10s}: {}".format( trans, attribute, - param_info(trans, attribute)["function"]._model_str, + param_info(trans, attribute)["function"].model_function, ) ) print( "{:10s} {:10s} {}".format( - "", - "", - param_info(trans, attribute)["function"]._regression_args, + "", "", param_info(trans, attribute)["function"].model_args ) ) if xv_method == "montecarlo": analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) + elif xv_method == "kfold": + analytic_quality = xv.kfold(lambda m: m.get_fitted()[0], xv_count) else: analytic_quality = model.assess(param_model) @@ -686,7 +704,7 @@ if __name__ == "__main__": ) if "overall" in show_quality or "all" in show_quality: - print("overall static/param/lut MAE assuming equal state distribution:") + print("overall state static/param/lut MAE assuming equal state distribution:") print( " {:6.1f} / {:6.1f} / {:6.1f} µW".format( model.assess_states(static_model), @@ -694,15 +712,30 @@ if __name__ == "__main__": model.assess_states(lut_model), ) ) - print("overall static/param/lut MAE assuming 95% STANDBY1:") - distrib = {"STANDBY1": 0.95, "POWERDOWN": 0.03, "TX": 0.01, "RX": 0.01} - print( - " {:6.1f} / {:6.1f} / {:6.1f} µW".format( - model.assess_states(static_model, distribution=distrib), - model.assess_states(param_model, distribution=distrib), - model.assess_states(lut_model, distribution=distrib), + distrib = dict() + num_states = len(model.states()) + p95_state = None + for state in model.states(): + distrib[state] = 1.0 / num_states + + if "STANDBY1" in model.states(): + p95_state = "STANDBY1" + elif "SLEEP" in model.states(): + p95_state = "SLEEP" + + if p95_state is not None: + for state in distrib.keys(): + distrib[state] = 0.05 / (num_states - 1) + distrib[p95_state] = 0.95 + + print(f"overall state static/param/lut MAE assuming 95% {p95_state}:") + print( + " {:6.1f} / {:6.1f} / {:6.1f} µW".format( + model.assess_states(static_model, distribution=distrib), + model.assess_states(param_model, distribution=distrib), + model.assess_states(lut_model, distribution=distrib), + ) ) - ) if "summary" in show_quality or "all" in show_quality: model_summary_table( diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py index 4039f45..ddd49ec 100755 --- a/bin/analyze-timing.py +++ b/bin/analyze-timing.py @@ -75,12 +75,14 @@ Options: import getopt import json +import logging import re import sys from dfatool import plotter -from dfatool.dfatool import AnalyticModel, TimingData, pta_trace_to_aggregate -from dfatool.dfatool import gplearn_to_function -from dfatool.dfatool import CrossValidator +from dfatool.loader import TimingData, pta_trace_to_aggregate +from dfatool.functions import gplearn_to_function +from dfatool.model import AnalyticModel +from dfatool.validation import CrossValidator from dfatool.utils import filter_aggregate_by_param from dfatool.parameters import prune_dependent_parameters @@ -170,7 +172,6 @@ def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq): if __name__ == "__main__": ignored_trace_indexes = [] - discard_outliers = None safe_functions_enabled = False function_override = {} show_models = [] @@ -183,8 +184,9 @@ if __name__ == "__main__": try: optspec = ( "plot-unparam= plot-param= show-models= show-quality= " - "ignored-trace-indexes= discard-outliers= function-override= " + "ignored-trace-indexes= function-override= " "filter-param= " + "log-level= " "cross-validate= " "corrcoef param-info " "with-safe-functions hwmodel= export-energymodel=" @@ -202,9 +204,6 @@ if __name__ == "__main__": if 0 in ignored_trace_indexes: print("[E] arguments to --ignored-trace-indexes start from 1") - if "discard-outliers" in opt: - discard_outliers = float(opt["discard-outliers"]) - if "function-override" in opt: for function_desc in opt["function-override"].split(";"): state_or_tran, attribute, *function_str = function_desc.split(" ") @@ -237,6 +236,13 @@ if __name__ == "__main__": else: opt["filter-param"] = list() + if "log-level" in opt: + numeric_level = getattr(logging, opt["log-level"].upper(), None) + if not isinstance(numeric_level, int): + print(f"Invalid log level: {loglevel}", file=sys.stderr) + sys.exit(1) + logging.basicConfig(level=numeric_level) + except getopt.GetoptError as err: print(err) sys.exit(2) @@ -297,30 +303,6 @@ if __name__ == "__main__": model.stats.param_dependence_ratio(trans, "duration", param), ) ) - if model.stats.has_codependent_parameters(trans, "duration", param): - print( - "{:24s} co-dependencies: {:s}".format( - "", - ", ".join( - model.stats.codependent_parameters( - trans, "duration", param - ) - ), - ) - ) - for param_dict in model.stats.codependent_parameter_value_dicts( - trans, "duration", param - ): - print("{:24s} parameter-aware for {}".format("", param_dict)) - # import numpy as np - # safe_div = np.vectorize(lambda x,y: 0. if x == 0 else 1 - x/y) - # ratio_by_value = safe_div(model.stats.stats['write']['duration']['lut_by_param_values']['max_retry_count'], model.stats.stats['write']['duration']['std_by_param_values']['max_retry_count']) - # err_mode = np.seterr('warn') - # dep_by_value = ratio_by_value > 0.5 - # np.seterr(**err_mode) - # Eigentlich sollte hier ein paar mal True stehen, ist aber nicht so... - # und warum ist da eine non-power-of-two Zahl von True-Einträgen in der Matrix? 3 stück ist komisch... - # print(dep_by_value) if xv_method == "montecarlo": static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) @@ -423,14 +405,12 @@ if __name__ == "__main__": "{:10s}: {:10s}: {}".format( trans, attribute, - param_info(trans, attribute)["function"]._model_str, + param_info(trans, attribute)["function"].model_function, ) ) print( "{:10s} {:10s} {}".format( - "", - "", - param_info(trans, attribute)["function"]._regression_args, + "", "", param_info(trans, attribute)["function"].model_args ) ) diff --git a/bin/cal-hist b/bin/cal-hist index ba2ff47..a92ae1e 100755 --- a/bin/cal-hist +++ b/bin/cal-hist @@ -7,7 +7,7 @@ import struct import sys import tarfile import matplotlib.pyplot as plt -from dfatool.dfatool import MIMOSA +from dfatool.loader import MIMOSA from dfatool.utils import running_mean voltage = float(sys.argv[1]) @@ -18,50 +18,74 @@ mim = MIMOSA(voltage, shunt) charges, triggers = mim.load_data(mimfile) trigidx = mim.trigger_edges(triggers) -cal_edges = mim.calibration_edges(running_mean(mim.currents_nocal(charges[0:trigidx[0]]), 10)) +cal_edges = mim.calibration_edges( + running_mean(mim.currents_nocal(charges[0 : trigidx[0]]), 10) +) + +# charges = charges[charges > 20000] +# charges = charges[charges < 21000] -#charges = charges[charges > 20000] -#charges = charges[charges < 21000] def show_hist(data): - bins = np.max(data) - np.min(data) - if bins == 0: - bins = 1 - if bins > 1000: - bins = bins / 10 - #bins = 500 - n, bins, patches = plt.hist(data, bins, normed=0, facecolor='green', alpha=0.75) - plt.grid(True) - plt.show() - print(np.histogram(data, bins=bins)) + bins = np.max(data) - np.min(data) + if bins == 0: + bins = 1 + if bins > 1000: + bins = bins / 10 + # bins = 500 + n, bins, patches = plt.hist(data, bins, normed=0, facecolor="green", alpha=0.75) + plt.grid(True) + plt.show() + print(np.histogram(data, bins=bins)) + -#show_hist(charges[cal_edges[0]:cal_edges[1]]) -#show_hist(charges[cal_edges[4]:cal_edges[5]]) -#show_hist(charges[cal_edges[2]:cal_edges[3]]) -#show_hist(charges[trigidx[7]:trigidx[8]]) -#show_hist(np.array(charges)) +# show_hist(charges[cal_edges[0]:cal_edges[1]]) +# show_hist(charges[cal_edges[4]:cal_edges[5]]) +# show_hist(charges[cal_edges[2]:cal_edges[3]]) +# show_hist(charges[trigidx[7]:trigidx[8]]) +# show_hist(np.array(charges)) -#print(charges[cal_edges[0]:cal_edges[1]]) -#print(charges[cal_edges[4]:cal_edges[5]]) -#print(charges[cal_edges[2]:cal_edges[3]]) +# print(charges[cal_edges[0]:cal_edges[1]]) +# print(charges[cal_edges[4]:cal_edges[5]]) +# print(charges[cal_edges[2]:cal_edges[3]]) -plt.hist(mim.charge_to_current_nocal(charges[cal_edges[2]:cal_edges[3]]) * 1e-3, 100, normed=0, facecolor='blue', alpha=0.8) -plt.xlabel('mA MimosaCMD') -plt.ylabel('#') +plt.hist( + mim.charge_to_current_nocal(charges[cal_edges[2] : cal_edges[3]]) * 1e-3, + 100, + normed=0, + facecolor="blue", + alpha=0.8, +) +plt.xlabel("mA MimosaCMD") +plt.ylabel("#") plt.grid(True) plt.show() -plt.hist(mim.charge_to_current_nocal(charges[cal_edges[4]:cal_edges[5]]) * 1e-3, 100, normed=0, facecolor='blue', alpha=0.8) -plt.xlabel('mA MimosaCMD') -plt.ylabel('#') +plt.hist( + mim.charge_to_current_nocal(charges[cal_edges[4] : cal_edges[5]]) * 1e-3, + 100, + normed=0, + facecolor="blue", + alpha=0.8, +) +plt.xlabel("mA MimosaCMD") +plt.ylabel("#") plt.grid(True) plt.show() -plt.hist(mim.charge_to_current_nocal(charges[cal_edges[0]:cal_edges[1]]) * 1e-3, 100, normed=0, facecolor='blue', alpha=0.8) -plt.xlabel('mA MimosaCMD') -plt.ylabel('#') +plt.hist( + mim.charge_to_current_nocal(charges[cal_edges[0] : cal_edges[1]]) * 1e-3, + 100, + normed=0, + facecolor="blue", + alpha=0.8, +) +plt.xlabel("mA MimosaCMD") +plt.ylabel("#") plt.grid(True) plt.show() -plt.hist(charges[cal_edges[0]:cal_edges[1]], 100, normed=0, facecolor='blue', alpha=0.8) -plt.xlabel('Rohwert MimosaCMD') -plt.ylabel('#') +plt.hist( + charges[cal_edges[0] : cal_edges[1]], 100, normed=0, facecolor="blue", alpha=0.8 +) +plt.xlabel("Rohwert MimosaCMD") +plt.ylabel("#") plt.grid(True) plt.show() diff --git a/bin/eval-accounting-overhead.py b/bin/eval-accounting-overhead.py index 7ea0807..1c03bf8 100755 --- a/bin/eval-accounting-overhead.py +++ b/bin/eval-accounting-overhead.py @@ -13,7 +13,7 @@ providing overhead per transition and getEnergy overhead """ -from dfatool.dfatool import AnalyticModel, TimingData, pta_trace_to_aggregate +from dfatool.loader import AnalyticModel, TimingData, pta_trace_to_aggregate import json import sys diff --git a/bin/eval-online-model-accuracy.py b/bin/eval-online-model-accuracy.py index 202ac28..97fd8e2 100755 --- a/bin/eval-online-model-accuracy.py +++ b/bin/eval-online-model-accuracy.py @@ -28,7 +28,7 @@ import itertools import yaml from dfatool.automata import PTA from dfatool.codegen import get_simulated_accountingmethod -from dfatool.dfatool import regression_measures +from dfatool.model import regression_measures import numpy as np opt = dict() diff --git a/bin/eval-outlier-removal.py b/bin/eval-outlier-removal.py index 14f0e60..c03266d 100755 --- a/bin/eval-outlier-removal.py +++ b/bin/eval-outlier-removal.py @@ -3,7 +3,8 @@ import getopt import re import sys -from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate +from dfatool.loader import RawData, pta_trace_to_aggregate +from dfatool.model import PTAModel opt = dict() @@ -141,12 +142,12 @@ if __name__ == "__main__": if param_i1(state, attribute): print( "{:10s}: {}".format( - state, param_i1(state, attribute)["function"]._model_str + state, param_i1(state, attribute)["function"].model_function ) ) print( "{:10s} {}".format( - "", param_i1(state, attribute)["function"]._regression_args + "", param_i1(state, attribute)["function"].model_args ) ) for trans in m1.transitions(): @@ -162,12 +163,12 @@ if __name__ == "__main__": "{:10s}: {:10s}: {}".format( trans, attribute, - param_i1(trans, attribute)["function"]._model_str, + param_i1(trans, attribute)["function"].model_function, ) ) print( "{:10s} {:10s} {}".format( - "", "", param_i1(trans, attribute)["function"]._regression_args + "", "", param_i1(trans, attribute)["function"].model_args ) ) param_m2, param_i2 = m2.get_fitted() @@ -176,12 +177,12 @@ if __name__ == "__main__": if param_i2(state, attribute): print( "{:10s}: {}".format( - state, param_i2(state, attribute)["function"]._model_str + state, param_i2(state, attribute)["function"].model_function ) ) print( "{:10s} {}".format( - "", param_i2(state, attribute)["function"]._regression_args + "", param_i2(state, attribute)["function"].model_args ) ) for trans in m2.transitions(): @@ -197,12 +198,12 @@ if __name__ == "__main__": "{:10s}: {:10s}: {}".format( trans, attribute, - param_i2(trans, attribute)["function"]._model_str, + param_i2(trans, attribute)["function"].model_function, ) ) print( "{:10s} {:10s} {}".format( - "", "", param_i2(trans, attribute)["function"]._regression_args + "", "", param_i2(trans, attribute)["function"].model_args ) ) diff --git a/bin/eval-rel-energy.py b/bin/eval-rel-energy.py index 8a2be13..aeaf88c 100755 --- a/bin/eval-rel-energy.py +++ b/bin/eval-rel-energy.py @@ -3,7 +3,8 @@ import getopt import re import sys -from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate +from dfatool.loader import RawData, pta_trace_to_aggregate +from dfatool.model import PTAModel opt = dict() @@ -22,7 +23,6 @@ def get_file_groups(args): if __name__ == "__main__": ignored_trace_indexes = [] - discard_outliers = None safe_functions_enabled = False function_override = {} show_models = [] @@ -31,7 +31,7 @@ if __name__ == "__main__": try: optspec = ( "plot-unparam= plot-param= show-models= show-quality= " - "ignored-trace-indexes= discard-outliers= function-override= " + "ignored-trace-indexes= function-override= " "with-safe-functions" ) raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" ")) @@ -47,9 +47,6 @@ if __name__ == "__main__": if 0 in ignored_trace_indexes: print("[E] arguments to --ignored-trace-indexes start from 1") - if "discard-outliers" in opt: - discard_outliers = float(opt["discard-outliers"]) - if "function-override" in opt: for function_desc in opt["function-override"].split(";"): state_or_tran, attribute, *function_str = function_desc.split(" ") @@ -88,7 +85,6 @@ if __name__ == "__main__": arg_count, traces=preprocessed_data, ignore_trace_indexes=ignored_trace_indexes, - discard_outliers=discard_outliers, function_override=function_override, verbose=False, ) diff --git a/bin/generate-dfa-benchmark.py b/bin/generate-dfa-benchmark.py index 478b221..2c53d9f 100755 --- a/bin/generate-dfa-benchmark.py +++ b/bin/generate-dfa-benchmark.py @@ -61,6 +61,10 @@ Options: --energytrace=[k=v,k=v,...] Perform energy measurements using MSP430 EnergyTrace hardware. Includes --timing. + Additional configuration settings: + sync = bar (Barcode mode (default): synchronize measurements via barcodes embedded in the energy trace) + sync = la (Logic Analyzer mode (WIP): An external logic analyzer captures transition timing) + sync = timing (Timing mode (WIP): The on-board cycle counter captures transition timing) --trace-filter=<transition,transition,transition,...>[ <transition,transition,transition,...> ...] Only consider traces whose beginning matches one of the provided transition sequences. @@ -219,17 +223,11 @@ def benchmark_from_runs( ) elif opt["sleep"]: if "energytrace" in opt: - outbuf.write( - "arch.sleep_ms({:d}); // {}\n".format( - opt["sleep"], transition.destination.name - ) - ) + outbuf.write(f"// -> {transition.destination.name}\n") + outbuf.write(runner.sleep_ms(opt["sleep"], opt["arch"])) else: - outbuf.write( - "arch.delay_ms({:d}); // {}\n".format( - opt["sleep"], transition.destination.name - ) - ) + outbuf.write(f"// -> {transition.destination.name}\n") + outbuf.write("arch.delay_ms({:d});\n".format(opt["sleep"])) outbuf.write(harness.stop_run(num_traces)) if dummy: @@ -337,6 +335,7 @@ def run_benchmark( files = list() i = 0 while i < opt["repeat"]: + print(f"""[RUN] flashing benchmark {i+1}/{opt["repeat"]}""") runner.flash(arch, app, run_args) if "mimosa" in opt: monitor = runner.get_monitor( @@ -353,7 +352,6 @@ def run_benchmark( while not harness.done: # possible race condition: if the benchmark completes at this # exact point, it sets harness.done and unsets harness.synced. - # vvv if ( slept > 30 and slept < 40 @@ -372,11 +370,11 @@ def run_benchmark( time.sleep(5) slept += 5 print( - "[RUN] {:d}/{:d} ({:.0f}%), current benchmark at {:.0f}%".format( + "[RUN] {:d}/{:d} ({:.0f}%) at trace {:d}".format( run_offset, runs_total, run_offset * 100 / runs_total, - slept * 100 / run_timeout, + harness.trace_id, ) ) except KeyboardInterrupt: @@ -593,6 +591,9 @@ if __name__ == "__main__": if run_flags is None: run_flags = opt["run"].split() + if "msp430fr" in opt["arch"]: + run_flags.append("cpu_freq=8000000") + runs = list( pta.dfs( opt["depth"], @@ -630,9 +631,13 @@ if __name__ == "__main__": post_transition_delay_us=20, ) elif "energytrace" in opt: + # Use barcode sync by default + gpio_mode = "bar" + if "sync" in opt["energytrace"] and opt["energytrace"]["sync"] != "bar": + gpio_mode = "around" harness = OnboardTimerHarness( gpio_pin=timer_pin, - gpio_mode="bar", + gpio_mode=gpio_mode, pta=pta, counter_limits=runner.get_counter_limits_us(opt["arch"]), log_return_values=need_return_values, diff --git a/bin/gptest.py b/bin/gptest.py index 82b4575..b5012e5 100755 --- a/bin/gptest.py +++ b/bin/gptest.py @@ -2,12 +2,11 @@ import sys import numpy as np -from dfatool.dfatool import ( - PTAModel, +from dfatool.loader import ( RawData, - regression_measures, pta_trace_to_aggregate, ) +from dfatool.model import PTAModel, regression_measures from gplearn.genetic import SymbolicRegressor from multiprocessing import Pool diff --git a/bin/gradient b/bin/gradient index 8280794..ca60949 100755 --- a/bin/gradient +++ b/bin/gradient @@ -7,7 +7,7 @@ import struct import sys import tarfile import matplotlib.pyplot as plt -from dfatool.dfatool import MIMOSA +from dfatool.loader import MIMOSA from dfatool.utils import running_mean voltage = float(sys.argv[1]) @@ -17,17 +17,17 @@ mimfile = sys.argv[3] mim = MIMOSA(voltage, shunt) charges, triggers = mim.load_file(mimfile) -#charges = charges[2000000:3000000] +# charges = charges[2000000:3000000] currents = running_mean(mim.charge_to_current_nocal(charges), 10) * 1e-6 xr = np.arange(len(currents)) * 1e-5 threshold = 1e-5 grad = np.gradient(currents, 2) tp = np.abs(grad) > threshold -plt.plot( xr, currents, "r-") -plt.plot( xr, grad, "y-") -plt.plot( xr[tp], grad[tp], "bo") -plt.xlabel('Zeit [s]') -plt.ylabel('Strom [A]') +plt.plot(xr, currents, "r-") +plt.plot(xr, grad, "y-") +plt.plot(xr[tp], grad[tp], "bo") +plt.xlabel("Zeit [s]") +plt.ylabel("Strom [A]") plt.grid(True) plt.show() diff --git a/bin/keysightdlog.py b/bin/keysightdlog.py new file mode 100755 index 0000000..89264b9 --- /dev/null +++ b/bin/keysightdlog.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 + +import lzma +import matplotlib.pyplot as plt +import numpy as np +import os +import struct +import sys +import xml.etree.ElementTree as ET + + +def plot_y(Y, **kwargs): + plot_xy(np.arange(len(Y)), Y, **kwargs) + + +def plot_xy(X, Y, xlabel=None, ylabel=None, title=None, output=None): + fig, ax1 = plt.subplots(figsize=(10, 6)) + if title != None: + fig.canvas.set_window_title(title) + if xlabel != None: + ax1.set_xlabel(xlabel) + if ylabel != None: + ax1.set_ylabel(ylabel) + plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99) + plt.plot(X, Y, "bo", markersize=2) + if output: + plt.savefig(output) + with open("{}.txt".format(output), "w") as f: + print("X Y", file=f) + for i in range(len(X)): + print("{} {}".format(X[i], Y[i]), file=f) + else: + plt.show() + + +filename = sys.argv[1] + +with open(filename, "rb") as logfile: + lines = [] + line = "" + + if ".xz" in filename: + f = lzma.open(logfile) + else: + f = logfile + + while line != "</dlog>\n": + line = f.readline().decode() + lines.append(line) + xml_header = "".join(lines) + raw_header = f.read(8) + data_offset = f.tell() + raw_data = f.read() + + xml_header = xml_header.replace("1ua>", "X1ua>") + xml_header = xml_header.replace("2ua>", "X2ua>") + dlog = ET.fromstring(xml_header) + channels = [] + for channel in dlog.findall("channel"): + channel_id = int(channel.get("id")) + sense_curr = channel.find("sense_curr").text + sense_volt = channel.find("sense_volt").text + model = channel.find("ident").find("model").text + if sense_volt == "1": + channels.append((channel_id, model, "V")) + if sense_curr == "1": + channels.append((channel_id, model, "A")) + + num_channels = len(channels) + duration = int(dlog.find("frame").find("time").text) + interval = float(dlog.find("frame").find("tint").text) + real_duration = interval * int(len(raw_data) / (4 * num_channels)) + + data = np.ndarray( + shape=(num_channels, int(len(raw_data) / (4 * num_channels))), dtype=np.float32 + ) + + iterator = struct.iter_unpack(">f", raw_data) + channel_offset = 0 + measurement_offset = 0 + for value in iterator: + data[channel_offset, measurement_offset] = value[0] + if channel_offset + 1 == num_channels: + channel_offset = 0 + measurement_offset += 1 + else: + channel_offset += 1 + +if int(real_duration) != duration: + print( + "Measurement duration: {:f} of {:d} seconds at {:f} µs per sample".format( + real_duration, duration, interval * 1000000 + ) + ) +else: + print( + "Measurement duration: {:d} seconds at {:f} µs per sample".format( + duration, interval * 1000000 + ) + ) + +for i, channel in enumerate(channels): + channel_id, channel_model, channel_type = channel + print( + "channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} {:s}".format( + channel_id, + channel_model, + np.min(data[i]), + np.max(data[i]), + np.mean(data[i]), + channel_type, + ) + ) + + if ( + i > 0 + and channel_type == "A" + and channels[i - 1][2] == "V" + and channel_id == channels[i - 1][0] + ): + power = data[i - 1] * data[i] + power = 3.6 * data[i] + print( + "channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} W".format( + channel_id, channel_model, np.min(power), np.max(power), np.mean(power) + ) + ) + min_power = np.min(power) + max_power = np.max(power) + power_border = np.mean([min_power, max_power]) + low_power = power[power < power_border] + high_power = power[power >= power_border] + plot_y(power) + print( + " avg low / high power (delta): {:f} / {:f} ({:f}) W".format( + np.mean(low_power), + np.mean(high_power), + np.mean(high_power) - np.mean(low_power), + ) + ) + # plot_y(low_power) + # plot_y(high_power) + high_power_durations = [] + current_high_power_duration = 0 + for is_hpe in power >= power_border: + if is_hpe: + current_high_power_duration += interval + else: + if current_high_power_duration > 0: + high_power_durations.append(current_high_power_duration) + current_high_power_duration = 0 + print( + " avg high-power duration: {:f} µs".format( + np.mean(high_power_durations) * 1000000 + ) + ) + +# print(xml_header) +# print(raw_header) +# print(channels) +# print(data) +# print(np.mean(data[0])) +# print(np.mean(data[1])) +# print(np.mean(data[0] * data[1])) diff --git a/bin/mim-vs-keysight.py b/bin/mim-vs-keysight.py index c214f2f..c9a7249 100755 --- a/bin/mim-vs-keysight.py +++ b/bin/mim-vs-keysight.py @@ -3,7 +3,7 @@ import numpy as np import sys import matplotlib.pyplot as plt -from dfatool.dfatool import MIMOSA, KeysightCSV +from dfatool.loader import MIMOSA, KeysightCSV from dfatool.utils import running_mean voltage = float(sys.argv[1]) diff --git a/bin/mimosa-etv b/bin/mimosa-etv index e23b46c..9b6e897 100755 --- a/bin/mimosa-etv +++ b/bin/mimosa-etv @@ -8,13 +8,16 @@ import numpy as np import os import re import sys -from dfatool.dfatool import aggregate_measures, MIMOSA +from dfatool.loader import MIMOSA +from dfatool.model import aggregate_measures from dfatool.utils import running_mean opt = dict() + def show_help(): - print('''mimosa-etv - MIMOSA Analyzer and Visualizer + print( + """mimosa-etv - MIMOSA Analyzer and Visualizer USAGE @@ -41,7 +44,9 @@ OPTIONS Show power/time plot --stat Show mean voltage, current, and power as well as total energy consumption. - ''') + """ + ) + def peak_search(data, lower, upper, direction_function): while upper - lower > 1e-6: @@ -58,6 +63,7 @@ def peak_search(data, lower, upper, direction_function): upper = bs_test return None + def peak_search2(data, lower, upper, check_function): for power in np.arange(lower, upper, 1e-6): peakcount = itertools.groupby(data, lambda x: x >= power) @@ -67,38 +73,39 @@ def peak_search2(data, lower, upper, check_function): return power return None -if __name__ == '__main__': + +if __name__ == "__main__": try: - optspec = ('help skip= threshold= threshold-peakcount= plot stat') - raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' ')) + optspec = "help skip= threshold= threshold-peakcount= plot stat" + raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" ")) for option, parameter in raw_opts: - optname = re.sub(r'^--', '', option) + optname = re.sub(r"^--", "", option) opt[optname] = parameter - if 'help' in opt: + if "help" in opt: show_help() sys.exit(0) - if 'skip' in opt: - opt['skip'] = int(opt['skip']) + if "skip" in opt: + opt["skip"] = int(opt["skip"]) else: - opt['skip'] = 0 + opt["skip"] = 0 - if 'threshold' in opt and opt['threshold'] != 'mean': - opt['threshold'] = float(opt['threshold']) + if "threshold" in opt and opt["threshold"] != "mean": + opt["threshold"] = float(opt["threshold"]) - if 'threshold-peakcount' in opt: - opt['threshold-peakcount'] = int(opt['threshold-peakcount']) + if "threshold-peakcount" in opt: + opt["threshold-peakcount"] = int(opt["threshold-peakcount"]) except getopt.GetoptError as err: print(err) sys.exit(2) except IndexError: - print('Usage: mimosa-etv <duration>') + print("Usage: mimosa-etv <duration>") sys.exit(2) except ValueError: - print('Error: duration or skip is not a number') + print("Error: duration or skip is not a number") sys.exit(2) voltage, shunt, inputfile = args @@ -110,7 +117,7 @@ if __name__ == '__main__': currents = mim.charge_to_current_nocal(charges) * 1e-6 powers = currents * voltage - if 'threshold-peakcount' in opt: + if "threshold-peakcount" in opt: bs_mean = np.mean(powers) # Finding the correct threshold is tricky. If #peaks < peakcont, our @@ -126,42 +133,59 @@ if __name__ == '__main__': # #peaks != peakcount and threshold >= mean, we go down. # If that doesn't work, we fall back to a linear search in 1 µW steps def direction_function(peakcount, power): - if peakcount == opt['threshold-peakcount']: + if peakcount == opt["threshold-peakcount"]: return 0 if power < bs_mean: return 1 return -1 + threshold = peak_search(power, np.min(power), np.max(power), direction_function) if threshold == None: - threshold = peak_search2(power, np.min(power), np.max(power), direction_function) + threshold = peak_search2( + power, np.min(power), np.max(power), direction_function + ) if threshold != None: - print('Threshold set to {:.0f} µW : {:.9f}'.format(threshold * 1e6, threshold)) - opt['threshold'] = threshold + print( + "Threshold set to {:.0f} µW : {:.9f}".format( + threshold * 1e6, threshold + ) + ) + opt["threshold"] = threshold else: - print('Found no working threshold') + print("Found no working threshold") - if 'threshold' in opt: - if opt['threshold'] == 'mean': - opt['threshold'] = np.mean(powers) - print('Threshold set to {:.0f} µW : {:.9f}'.format(opt['threshold'] * 1e6, opt['threshold'])) + if "threshold" in opt: + if opt["threshold"] == "mean": + opt["threshold"] = np.mean(powers) + print( + "Threshold set to {:.0f} µW : {:.9f}".format( + opt["threshold"] * 1e6, opt["threshold"] + ) + ) baseline_mean = 0 - if np.any(powers < opt['threshold']): - baseline_mean = np.mean(powers[powers < opt['threshold']]) - print('Baseline mean: {:.0f} µW : {:.9f}'.format( - baseline_mean * 1e6, baseline_mean)) - if np.any(powers >= opt['threshold']): - print('Peak mean: {:.0f} µW : {:.9f}'.format( - np.mean(powers[powers >= opt['threshold']]) * 1e6, - np.mean(powers[powers >= opt['threshold']]))) + if np.any(powers < opt["threshold"]): + baseline_mean = np.mean(powers[powers < opt["threshold"]]) + print( + "Baseline mean: {:.0f} µW : {:.9f}".format( + baseline_mean * 1e6, baseline_mean + ) + ) + if np.any(powers >= opt["threshold"]): + print( + "Peak mean: {:.0f} µW : {:.9f}".format( + np.mean(powers[powers >= opt["threshold"]]) * 1e6, + np.mean(powers[powers >= opt["threshold"]]), + ) + ) peaks = [] peak_start = -1 for i, dp in enumerate(powers): - if dp >= opt['threshold'] and peak_start == -1: + if dp >= opt["threshold"] and peak_start == -1: peak_start = i - elif dp < opt['threshold'] and peak_start != -1: + elif dp < opt["threshold"] and peak_start != -1: peaks.append((peak_start, i)) peak_start = -1 @@ -170,32 +194,55 @@ if __name__ == '__main__': for peak in peaks: duration = (peak[1] - peak[0]) * 1e-5 total_energy += np.mean(powers[peak[0] : peak[1]]) * duration - delta_energy += (np.mean(powers[peak[0] : peak[1]]) - baseline_mean) * duration + delta_energy += ( + np.mean(powers[peak[0] : peak[1]]) - baseline_mean + ) * duration delta_powers = powers[peak[0] : peak[1]] - baseline_mean - print('{:.2f}ms peak ({:f} -> {:f})'.format(duration * 1000, - peak[0], peak[1])) - print(' {:f} µJ / mean {:f} µW'.format( - np.mean(powers[peak[0] : peak[1]]) * duration * 1e6, - np.mean(powers[peak[0] : peak[1]]) * 1e6 )) + print( + "{:.2f}ms peak ({:f} -> {:f})".format(duration * 1000, peak[0], peak[1]) + ) + print( + " {:f} µJ / mean {:f} µW".format( + np.mean(powers[peak[0] : peak[1]]) * duration * 1e6, + np.mean(powers[peak[0] : peak[1]]) * 1e6, + ) + ) measures = aggregate_measures(np.mean(delta_powers), delta_powers) - print(' {:f} µW delta mean = {:0.1f}% / {:f} µW error'.format(np.mean(delta_powers) * 1e6, measures['smape'], measures['rmsd'] * 1e6 )) - print('Peak energy mean: {:.0f} µJ : {:.9f}'.format( - total_energy * 1e6 / len(peaks), total_energy / len(peaks))) - print('Average per-peak energy (delta over baseline): {:.0f} µJ : {:.9f}'.format( - delta_energy * 1e6 / len(peaks), delta_energy / len(peaks))) - - - if 'stat' in opt: + print( + " {:f} µW delta mean = {:0.1f}% / {:f} µW error".format( + np.mean(delta_powers) * 1e6, + measures["smape"], + measures["rmsd"] * 1e6, + ) + ) + print( + "Peak energy mean: {:.0f} µJ : {:.9f}".format( + total_energy * 1e6 / len(peaks), total_energy / len(peaks) + ) + ) + print( + "Average per-peak energy (delta over baseline): {:.0f} µJ : {:.9f}".format( + delta_energy * 1e6 / len(peaks), delta_energy / len(peaks) + ) + ) + + if "stat" in opt: mean_current = np.mean(currents) mean_power = np.mean(powers) - print('Mean current: {:.0f} µA : {:.9f}'.format(mean_current * 1e6, mean_current)) - print('Mean power: {:.0f} µW : {:.9f}'.format(mean_power * 1e6, mean_power)) - - if 'plot' in opt: + print( + "Mean current: {:.0f} µA : {:.9f}".format( + mean_current * 1e6, mean_current + ) + ) + print( + "Mean power: {:.0f} µW : {:.9f}".format(mean_power * 1e6, mean_power) + ) + + if "plot" in opt: timestamps = np.arange(len(powers)) * 1e-5 - pwrhandle, = plt.plot(timestamps, powers, 'b-', label='U*I', markersize=1) + (pwrhandle,) = plt.plot(timestamps, powers, "b-", label="U*I", markersize=1) plt.legend(handles=[pwrhandle]) - plt.xlabel('Time [s]') - plt.ylabel('Power [W]') + plt.xlabel("Time [s]") + plt.ylabel("Power [W]") plt.grid(True) plt.show() diff --git a/bin/mimplot b/bin/mimplot index 2a888ee..a55a875 100755 --- a/bin/mimplot +++ b/bin/mimplot @@ -9,54 +9,52 @@ import struct import sys import tarfile import matplotlib.pyplot as plt -from dfatool.dfatool import MIMOSA +from dfatool.loader import MIMOSA from dfatool.utils import running_mean opt = dict() -if __name__ == '__main__': +if __name__ == "__main__": - try: - optspec = ( - 'export= ' - ) + try: + optspec = "export= " - raw_opts, args = getopt.getopt(sys.argv[1:], '', optspec.split()) + raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split()) - for option, parameter in raw_opts: - optname = re.sub(r'^--', '', option) - opt[optname] = parameter + for option, parameter in raw_opts: + optname = re.sub(r"^--", "", option) + opt[optname] = parameter - if 'export' in opt: - opt['export'] = list(map(int, opt['export'].split(':'))) + if "export" in opt: + opt["export"] = list(map(int, opt["export"].split(":"))) - except getopt.GetoptError as err: - print(err) - sys.exit(2) + except getopt.GetoptError as err: + print(err) + sys.exit(2) - voltage = float(args[0]) - shunt = float(args[1]) - mimfile = args[2] + voltage = float(args[0]) + shunt = float(args[1]) + mimfile = args[2] - mim = MIMOSA(voltage, shunt) + mim = MIMOSA(voltage, shunt) - charges, triggers = mim.load_file(mimfile) - charges = charges[:3000000] + charges, triggers = mim.load_file(mimfile) + charges = charges[:3000000] - currents = running_mean(mim.charge_to_current_nocal(charges), 10) * 1e-6 - powers = currents * voltage - xr = np.arange(len(currents)) * 1e-5 + currents = running_mean(mim.charge_to_current_nocal(charges), 10) * 1e-6 + powers = currents * voltage + xr = np.arange(len(currents)) * 1e-5 - if 'export' in opt: - xr = xr[opt['export'][0] : opt['export'][1]] - currents = currents[opt['export'][0] : opt['export'][1]] - powers = powers[opt['export'][0] : opt['export'][1]] + if "export" in opt: + xr = xr[opt["export"][0] : opt["export"][1]] + currents = currents[opt["export"][0] : opt["export"][1]] + powers = powers[opt["export"][0] : opt["export"][1]] - for pair in zip(xr, powers): - print('{} {}'.format(*pair)) + for pair in zip(xr, powers): + print("{} {}".format(*pair)) - plt.plot( xr, powers, "r-") - plt.xlabel('Time [s]') - plt.ylabel('Power [W]') - plt.grid(True) - plt.show() + plt.plot(xr, powers, "r-") + plt.xlabel("Time [s]") + plt.ylabel("Power [W]") + plt.grid(True) + plt.show() diff --git a/bin/test_corrcoef.py b/bin/test_corrcoef.py index 0b1ca54..ccb3366 100755 --- a/bin/test_corrcoef.py +++ b/bin/test_corrcoef.py @@ -4,8 +4,9 @@ import getopt import re import sys from dfatool import plotter -from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate -from dfatool.dfatool import gplearn_to_function +from dfatool.loader import RawData, pta_trace_to_aggregate +from dfatool.functions import gplearn_to_function +from dfatool.model import PTAModel opt = dict() @@ -110,7 +111,6 @@ def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq): if __name__ == "__main__": ignored_trace_indexes = None - discard_outliers = None safe_functions_enabled = False function_override = {} show_models = [] @@ -119,7 +119,7 @@ if __name__ == "__main__": try: optspec = ( "plot-unparam= plot-param= show-models= show-quality= " - "ignored-trace-indexes= discard-outliers= function-override= " + "ignored-trace-indexes= function-override= " "with-safe-functions" ) raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" ")) @@ -135,9 +135,6 @@ if __name__ == "__main__": if 0 in ignored_trace_indexes: print("[E] arguments to --ignored-trace-indexes start from 1") - if "discard-outliers" in opt: - discard_outliers = float(opt["discard-outliers"]) - if "function-override" in opt: for function_desc in opt["function-override"].split(";"): state_or_tran, attribute, *function_str = function_desc.split(" ") @@ -169,7 +166,6 @@ if __name__ == "__main__": arg_count, traces=preprocessed_data, ignore_trace_indexes=ignored_trace_indexes, - discard_outliers=discard_outliers, function_override=function_override, use_corrcoef=False, ) @@ -179,7 +175,6 @@ if __name__ == "__main__": arg_count, traces=preprocessed_data, ignore_trace_indexes=ignored_trace_indexes, - discard_outliers=discard_outliers, function_override=function_override, use_corrcoef=True, ) |