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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
|
#!/usr/bin/env python3
# vim:tabstop=4 softtabstop=4 shiftwidth=4 textwidth=160 smarttab expandtab colorcolumn=160
#
# Copyright (C) 2023 Birte Kristina Friesel
#
# SPDX-License-Identifier: GPL-2.0-or-later
"""plot-conversion-efficiency - Show several conversion efficiency plots at once
DESCRIPTION
fixme
OPTIONS
"""
import argparse
import bisect
import numpy as np
from matplotlib import cm
import matplotlib.pyplot as plt
matplotlib_theme = "fast"
def neighbouring_avg(data, timestamp, eps=0.1):
samples = list()
range_left = bisect.bisect_left(data[:, 0], timestamp - eps)
range_right = bisect.bisect_right(data[:, 0], timestamp + eps)
samples = data[range_left:range_right, 1]
if not len(samples):
return None
return np.mean(samples)
def load_korad(filename, skip=None, limit=None):
if filename.endswith(".xz"):
import lzma
with lzma.open(filename, "rt") as f:
log_data = f.read()
else:
with open(filename, "r") as f:
log_data = f.read()
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, 3))
skip_index = 0
limit_index = data_count
for i, line in enumerate(data_lines):
fields = line.split()
if len(fields) == 3:
timestamp, voltage, current = map(float, fields)
elif len(fields) == 5:
timestamp, voltage, current, max_voltage, max_current = map(float, fields)
else:
raise RuntimeError('cannot parse line "{}"'.format(line))
if i == 0:
first_timestamp = timestamp
timestamp = timestamp - first_timestamp
if skip is not None and timestamp < skip:
skip_index = i + 1
continue
if limit is not None and timestamp > limit:
limit_index = i - 1
break
data[i] = [timestamp, current, voltage]
data = data[skip_index:limit_index]
return data
def load_ads1115(filename, in_channel, out_channel):
readings = list()
first_timestamp = None
with open(filename, "r") as f:
for line in f:
if line.startswith("#"):
continue
timestamp, channel, voltage = line.split()
timestamp = float(timestamp)
channel = int(channel)
voltage = float(voltage)
if abs(voltage) > 1:
if first_timestamp is None:
first_timestamp = timestamp
timestamp -= first_timestamp
readings.append((timestamp, channel, voltage))
vin_t = list()
vout_t = list()
vout_vin = list()
last_vin = None
last_vout = None
for timestamp, channel, voltage in readings:
if channel == in_channel:
vin_t.append((timestamp, voltage))
if last_vin is not None and last_vout is not None:
vout_vin.append((np.mean((last_vin, voltage)), last_vout))
last_vin = voltage
elif channel == out_channel:
vout_t.append((timestamp, voltage))
if last_vin is not None and last_vout is not None:
vout_vin.append((last_vin, np.mean((last_vout, voltage))))
last_vout = voltage
return np.array(vin_t), np.array(vout_t), np.array(vout_vin)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
)
parser.add_argument(
"--skip",
metavar="N",
type=float,
default=0,
help="Skip the first N seconds of data. This is useful to avoid startup code influencing the results of a long-running measurement",
)
parser.add_argument(
"--limit",
type=float,
metavar="N",
help="Limit analysis to the first N seconds of data",
)
parser.add_argument(
"--in-channel",
metavar="CHANNEL",
type=int,
default=0,
help="ADS1115 input channel",
)
parser.add_argument(
"--out-channel",
metavar="CHANNEL",
type=int,
default=2,
help="ADS1115 output channel",
)
parser.add_argument(
"--dark-mode", action="store_true", help="Show plots on a dark background"
)
parser.add_argument("--title", type=str, help="Plot title")
parser.add_argument(
"files",
type=str,
nargs="+",
help="Pairs of <current[A]>:<korad filename>:<ads1115 filename>",
)
args = parser.parse_args()
if args.dark_mode:
global matplotlib_theme
matplotlib_theme = "dark_background"
cmap = cm.get_cmap("winter")
handles = list()
for i, pair in enumerate(args.files):
out_current, korad_file, ads1115_file = pair.split(":")
out_current = float(out_current)
iin_t = load_korad(korad_file, args.skip, args.limit)
vin_t, vout_t, vout_vin = load_ads1115(
ads1115_file, args.in_channel, args.out_channel
)
voltage_in = list()
power_in = list()
power_out = list()
for timestamp, in_current, in_voltage in iin_t:
v_in = neighbouring_avg(vin_t, timestamp)
v_out = neighbouring_avg(vout_t, timestamp)
if v_in is None or v_out is None:
continue
voltage_in.append(v_in)
power_in.append(v_in * in_current)
power_out.append(v_out * out_current)
voltage_in = np.array(voltage_in)
power_in = np.array(power_in)
power_out = np.array(power_out)
(handle,) = plt.plot(
voltage_in,
power_out / power_in,
linestyle="none",
marker="s",
color=cmap(i / len(args.files)),
markersize=2,
label=f"{out_current:0.2f} A",
)
handles.append(handle)
plt.legend(handles=handles, title="Output Current")
if args.title:
plt.title(args.title)
plt.xlabel("Input Voltage [V]")
plt.ylabel("Conversion Efficiency [%]")
plt.show()
if __name__ == "__main__":
main()
|
