autoformat the code (using black(1)) (30d7cc1b) · Commits · derf / msp430-etv

bin/msp430-etv

+160 −93

Original line number	Diff line number	Diff line
		@@ -15,6 +15,7 @@ import time

		opt = dict()


		def running_mean(x: np.ndarray, N: int) -> np.ndarray:
		"""
		Compute `N` elements wide running average over `x`.
		@@ -31,28 +32,35 @@ def running_mean(x: np.ndarray, N: int) -> np.ndarray:
		cumsum = np.cumsum(boundary_array)
		return (cumsum[N:] - cumsum[:-N]) / N


		def measure_data(filename, duration):
		# libmsp430.so must be available
		if not 'LD_LIBRARY_PATH' in os.environ:
		os.environ['LD_LIBRARY_PATH'] = '{}/var/projects/msp430/MSP430Flasher_1.3.15'.format(os.environ['HOME'])
		if not "LD_LIBRARY_PATH" in os.environ:
		os.environ[
		"LD_LIBRARY_PATH"
		] = "{}/var/projects/msp430/MSP430Flasher_1.3.15".format(os.environ["HOME"])

		# https://github.com/carrotIndustries/energytrace-util must be available
		energytrace_cmd = 'energytrace'
		energytrace_cmd = "energytrace"
		if which(energytrace_cmd) is None:
		energytrace_cmd = '{}/var/source/energytrace-util/energytrace64'.format(os.environ['HOME'])
		energytrace_cmd = "{}/var/source/energytrace-util/energytrace64".format(
		os.environ["HOME"]
		)

		if filename is not None:
		output_handle = open(filename, 'w+')
		output_handle = open(filename, "w+")
		else:
		output_handle = tempfile.TemporaryFile('w+')
		output_handle = tempfile.TemporaryFile("w+")

		energytrace = subprocess.Popen([energytrace_cmd, str(duration)], stdout = output_handle, universal_newlines = True)
		energytrace = subprocess.Popen(
		[energytrace_cmd, str(duration)], stdout=output_handle, universal_newlines=True
		)

		try:
		if duration:
		time.sleep(duration)
		else:
		print('Press Ctrl+C to stop measurement')
		print("Press Ctrl+C to stop measurement")
		while True:
		time.sleep(3600)
		except KeyboardInterrupt:
		@@ -66,8 +74,10 @@ def measure_data(filename, duration):

		return output


		def show_help():
		print('''msp430-etv - MSP430 EnergyTrace Visualizer
		print(
		"""msp430-etv - MSP430 EnergyTrace Visualizer

		USAGE

		@@ -117,7 +127,9 @@ For data measurements (i.e., any invocation not using --load),
		energytrace-util <https://github.com/carrotIndustries/energytrace-util>
		must be available in $PATH and libmsp430.so must be located in the
		LD library search path (e.g. LD_LIBRARY_PATH=../MSP430Flasher).
		''')
		"""
		)


		def peak_search(data, lower, upper, direction_function):
		while upper - lower > 1e-6:
		@@ -134,6 +146,7 @@ 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)
		@@ -143,61 +156,62 @@ def peak_search2(data, lower, upper, check_function):
		return power
		return None

		if __name__ == '__main__':

		if __name__ == "__main__":
		try:
		optspec = ('help load= save= skip= threshold= threshold-peakcount= plot stat histogram=')
		raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' '))
		optspec = "help load= save= skip= threshold= threshold-peakcount= plot stat histogram="
		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 not 'load' in opt:
		if not "load" in opt:
		duration = int(args[0])

		if not 'save' in opt:
		opt['save'] = None
		if not "save" in opt:
		opt["save"] = None

		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: msp430-etv <duration>')
		print("Usage: msp430-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)

		if 'load' in opt:
		with open(opt['load'], 'r') as f:
		if "load" in opt:
		with open(opt["load"], "r") as f:
		log_data = f.read()
		else:
		log_data = measure_data(opt['save'], duration)
		log_data = measure_data(opt["save"], duration)

		lines = log_data.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)
		lines = log_data.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 - opt['skip'], 4))
		data = np.empty((data_count - opt["skip"], 4))

		for i, line in enumerate(data_lines):
		if i >= opt['skip']:
		fields = line.split(' ')
		if i >= opt["skip"]:
		fields = line.split(" ")
		if len(fields) == 4:
		timestamp, current, voltage, total_energy = map(int, fields)
		elif len(fields) == 5:
		@@ -205,24 +219,28 @@ if __name__ == '__main__':
		timestamp, current, voltage, total_energy = map(int, fields[1:])
		else:
		raise RuntimeError('cannot parse line "{}"'.format(line))
		data[i - opt['skip']] = [timestamp, current, voltage, total_energy]
		data[i - opt["skip"]] = [timestamp, current, voltage, total_energy]

		m_duration_us = data[-1, 0] - data[0, 0]
		m_energy_nj = data[-1, 3] - data[0, 3]
		# mV * nA * us = aJ (1e-18 J) -> use factor 1e-6 to get pJ (1e-12 J)

		print('{:d} measurements in {:.2f} s = {:.0f} Hz sample rate'.format(
		data_count, m_duration_us * 1e-6, data_count / (m_duration_us * 1e-6)))
		print(
		"{:d} measurements in {:.2f} s = {:.0f} Hz sample rate".format(
		data_count, m_duration_us * 1e-6, data_count / (m_duration_us * 1e-6)
		)
		)

		print('Reported energy: E = {:f} J'.format(m_energy_nj * 1e-9))
		print("Reported energy: E = {:f} J".format(m_energy_nj * 1e-9))

		# nJ / us = mW -> (nJ * 1e-9) / (us * 1e-6) = W
		# Do not use power = data[:, 1] * data[:, 2] * 1e-12 here: nA values provided by the EnergyTrace library in data[:, 1] are heavily filtered and mostly
		# useless for visualization and calculation. They often do not agree with the nJ values in data[:, 3].
		power = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / ((data[1:, 0] - data[:-1, 0]) * 1e-6)

		power = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / (
		(data[1:, 0] - data[:-1, 0]) * 1e-6
		)

		if 'threshold-peakcount' in opt:
		if "threshold-peakcount" in opt:
		bs_mean = np.mean(power)

		# Finding the correct threshold is tricky. If #peaks < peakcont, our
		@@ -238,42 +256,59 @@ 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']:
		return 0;
		if peakcount == opt["threshold-peakcount"]:
		return 0
		if power < bs_mean:
		return 1;
		return -1;
		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(power)
		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(power)
		print(
		"Threshold set to {:.0f} µW : {:.9f}".format(
		opt["threshold"] * 1e6, opt["threshold"]
		)
		)

		baseline_mean = 0
		if np.any(power < opt['threshold']):
		baseline_mean = np.mean(power[power < opt['threshold']])
		print('Baseline mean: {:.0f} µW : {:.9f}'.format(
		baseline_mean * 1e6, baseline_mean))
		if np.any(power >= opt['threshold']):
		print('Peak mean: {:.0f} µW : {:.9f}'.format(
		np.mean(power[power >= opt['threshold']]) * 1e6,
		np.mean(power[power >= opt['threshold']])))
		if np.any(power < opt["threshold"]):
		baseline_mean = np.mean(power[power < opt["threshold"]])
		print(
		"Baseline mean: {:.0f} µW : {:.9f}".format(
		baseline_mean * 1e6, baseline_mean
		)
		)
		if np.any(power >= opt["threshold"]):
		print(
		"Peak mean: {:.0f} µW : {:.9f}".format(
		np.mean(power[power >= opt["threshold"]]) * 1e6,
		np.mean(power[power >= opt["threshold"]]),
		)
		)

		peaks = []
		peak_start = -1
		for i, dp in enumerate(power):
		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

		@@ -282,41 +317,73 @@ if __name__ == '__main__':
		for peak in peaks:
		duration = data[peak[1] - 1, 0] - data[peak[0], 0]
		total_energy += np.mean(power[peak[0] : peak[1]]) * duration
		delta_energy += (np.mean(power[peak[0] : peak[1]]) - baseline_mean) * duration
		print('{:.2f}ms peak ({:f} -> {:f})'.format(duration * 1000,
		data[peak[0], 0], data[peak[1]-1, 0]))
		print(' {:f} µJ / mean {:f} µW'.format(
		delta_energy += (
		np.mean(power[peak[0] : peak[1]]) - baseline_mean
		) * duration
		print(
		"{:.2f}ms peak ({:f} -> {:f})".format(
		duration * 1000, data[peak[0], 0], data[peak[1] - 1, 0]
		)
		)
		print(
		" {:f} µJ / mean {:f} µW".format(
		np.mean(power[peak[0] : peak[1]]) * duration * 1e6,
		np.mean(power[peak[0] : peak[1]]) * 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)))

		power_from_energy = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / ((data[1:, 0] - data[:-1, 0]) * 1e-6)
		np.mean(power[peak[0] : peak[1]]) * 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)
		)
		)

		power_from_energy = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / (
		(data[1:, 0] - data[:-1, 0]) * 1e-6
		)
		mean_power = running_mean(power_from_energy, 10)

		if 'stat' in opt:
		if "stat" in opt:
		mean_voltage = np.mean(data[:, 2] * 1e-3)
		mean_current = np.mean(data[:, 1] * 1e-9)
		mean_power = np.mean(data[:, 1] * data[:, 2] * 1e-12)
		print('Mean voltage: {:.2f} V : {:.9f}'.format(mean_voltage, mean_voltage))
		print('Mean current: {:.0f} µA : {:.9f}'.format(mean_current * 1e6, mean_current))
		print('Mean power: {:.0f} µW : {:.9f}'.format(mean_power * 1e6, mean_power))
		print('Total energy: {:f} J : {:.9f}'.format(m_energy_nj * 1e-9, m_energy_nj * 1e-9))

		if 'plot' in opt:
		print(
		"Mean voltage: {:.2f} V : {:.9f}".format(mean_voltage, mean_voltage)
		)
		print(
		"Mean current: {:.0f} µA : {:.9f}".format(
		mean_current * 1e6, mean_current
		)
		)
		print(
		"Mean power: {:.0f} µW : {:.9f}".format(mean_power * 1e6, mean_power)
		)
		print(
		"Total energy: {:f} J : {:.9f}".format(
		m_energy_nj * 1e-9, m_energy_nj * 1e-9
		)
		)

		if "plot" in opt:
		# nA * mV = pW
		energyhandle, = plt.plot(data[1:, 0] * 1e-6, power_from_energy, 'b-', label='P=ΔE/Δt', markersize=1)
		meanhandle, = plt.plot(data[1:, 0] * 1e-6, mean_power, 'r-', label='mean(P, 10)', markersize=1)
		(energyhandle,) = plt.plot(
		data[1:, 0] * 1e-6, power_from_energy, "b-", label="P=ΔE/Δt", markersize=1
		)
		(meanhandle,) = plt.plot(
		data[1:, 0] * 1e-6, mean_power, "r-", label="mean(P, 10)", markersize=1
		)
		plt.legend(handles=[energyhandle, meanhandle])
		plt.xlabel('Time [s]')
		plt.ylabel('Power [W]')
		plt.xlabel("Time [s]")
		plt.ylabel("Power [W]")
		plt.grid(True)
		if 'load' in opt:
		plt.title(opt['load'])
		if "load" in opt:
		plt.title(opt["load"])
		plt.show()

		if 'histogram' in opt:
		plt.hist((data[1:, 3] - data[:-1, 3]) * 1e-9, bins=int(opt['histogram']))
		if "histogram" in opt:
		plt.hist((data[1:, 3] - data[:-1, 3]) * 1e-9, bins=int(opt["histogram"]))
		plt.show()

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