1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
|
#!/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]))
|
