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
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
|
#!/usr/bin/env python3
# vim:tabstop=4:softtabstop=4:shiftwidth=4:textwidth=160:smarttab:expandtab
import getopt
import itertools
import matplotlib.pyplot as plt
import numpy as np
import os
import re
import sys
from dfatool.dfatool 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
USAGE
mimosa-etv [--skip <count>] [--threshold <power>] [--plot] [--stat] <voltage> <shunt> <file>
DESCRIPTION
mimosa-etv analyzes measurements taken via MIMOSA. Data can be plotted or aggregated on stdout.
OPTIONS
--skip <count>
Skip the first <count> data samples.
--threshold <watts>|mean
Partition data into points with mean power >= <watts> and points with
mean power < <watts>, and print some statistics. higher power is handled
as peaks, whereas low-power measurements constitute the baseline.
If the threshold is set to "mean", the mean power of all measurements
will be used
--threshold-peakcount <num>
Automatically determine threshold so that there are exactly <num> peaks.
A peaks is a group of consecutive measurements with mean power >= threshold
--plot
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:
bs_test = np.mean([lower, upper])
peakcount = itertools.groupby(data, lambda x: x >= bs_test)
peakcount = filter(lambda x: x[0] == True, peakcount)
peakcount = sum(1 for i in peakcount)
direction = direction_function(peakcount, bs_test)
if direction == 0:
return bs_test
elif direction == 1:
lower = bs_test
else:
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)
peakcount = filter(lambda x: x[0] == True, peakcount)
peakcount = sum(1 for i in peakcount)
if check_function(peakcount, power) == 0:
return power
return None
if __name__ == "__main__":
try:
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)
opt[optname] = parameter
if "help" in opt:
show_help()
sys.exit(0)
if "skip" in opt:
opt["skip"] = int(opt["skip"])
else:
opt["skip"] = 0
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"])
except getopt.GetoptError as err:
print(err)
sys.exit(2)
except IndexError:
print("Usage: mimosa-etv <duration>")
sys.exit(2)
except ValueError:
print("Error: duration or skip is not a number")
sys.exit(2)
voltage, shunt, inputfile = args
voltage = float(voltage)
shunt = int(shunt)
mim = MIMOSA(voltage, shunt)
charges, triggers = mim.load_file(inputfile)
currents = mim.charge_to_current_nocal(charges) * 1e-6
powers = currents * voltage
if "threshold-peakcount" in opt:
bs_mean = np.mean(powers)
# Finding the correct threshold is tricky. If #peaks < peakcont, our
# current threshold may be too low (extreme case: a single peak
# containing all measurements), but it may also be too high (extreme
# case: a single peak containing just one data point). Similarly,
# #peaks > peakcount may be due to baseline noise causing lots of
# small peaks, or due to peak noise (if the threshold is already rather
# high).
# For now, we first try a simple binary search:
# The threshold is probably somewhere around the mean, so if
# #peaks != peakcount and threshold < mean, we go up, and if
# #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 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
)
if threshold != None:
print(
"Threshold set to {:.0f} µW : {:.9f}".format(
threshold * 1e6, threshold
)
)
opt["threshold"] = threshold
else:
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"]
)
)
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"]]),
)
)
peaks = []
peak_start = -1
for i, dp in enumerate(powers):
if dp >= opt["threshold"] and peak_start == -1:
peak_start = i
elif dp < opt["threshold"] and peak_start != -1:
peaks.append((peak_start, i))
peak_start = -1
total_energy = 0
delta_energy = 0
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_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,
)
)
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:
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:
timestamps = np.arange(len(powers)) * 1e-5
(pwrhandle,) = plt.plot(timestamps, powers, "b-", label="U*I", markersize=1)
plt.legend(handles=[pwrhandle])
plt.xlabel("Time [s]")
plt.ylabel("Power [W]")
plt.grid(True)
plt.show()
|
