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#!/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]))
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