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
249
250
251
252
253
254
255
256
257
258
259
260
|
#!/usr/bin/env python3
import getopt
import json
import plotter
import re
import sys
from dfatool import EnergyModel, RawData
from dfatool import soft_cast_int, is_numeric, gplearn_to_function
opts = {}
def print_model_quality(results):
for state_or_tran in results.keys():
print()
for key, result in results[state_or_tran].items():
if 'smape' in result:
print('{:20s} {:15s} {:.2f}% / {:.0f}'.format(
state_or_tran, key, result['smape'], result['mae']))
else:
print('{:20s} {:15s} {:.0f}'.format(
state_or_tran, key, result['mae']))
def format_quality_measures(result):
if 'smape' in result:
return '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
else:
return '{:6} {:9.0f}'.format('', result['mae'])
def model_quality_table(result_lists, info_list):
for state_or_tran in result_lists[0]['by_dfa_component'].keys():
for key in result_lists[0]['by_dfa_component'][state_or_tran].keys():
buf = '{:20s} {:15s}'.format(state_or_tran, key)
for i, results in enumerate(result_lists):
info = info_list[i]
buf += ' ||| '
if info == None or info(state_or_tran, key):
result = results['by_dfa_component'][state_or_tran][key]
buf += format_quality_measures(result)
else:
buf += '{:6}----{:9}'.format('', '')
print(buf)
def model_summary_table(result_list):
buf = 'transition duration'
for results in result_list:
if len(buf):
buf += ' ||| '
buf += format_quality_measures(results['duration_by_trace'])
print(buf)
buf = 'total energy '
for results in result_list:
if len(buf):
buf += ' ||| '
buf += format_quality_measures(results['energy_by_trace'])
print(buf)
buf = 'rel total energy '
for results in result_list:
if len(buf):
buf += ' ||| '
buf += format_quality_measures(results['rel_energy_by_trace'])
print(buf)
buf = 'state-only energy '
for results in result_list:
if len(buf):
buf += ' ||| '
buf += format_quality_measures(results['state_energy_by_trace'])
print(buf)
buf = 'transition timeout '
for results in result_list:
if len(buf):
buf += ' ||| '
buf += format_quality_measures(results['timeout_by_trace'])
print(buf)
def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq):
print('')
print(r'key attribute $1 - \frac{\sigma_X}{...}$')
for state_or_tran in model.by_name.keys():
for attribute in model.attributes(state_or_tran):
print('{} {} {:.8f}'.format(state_or_tran, attribute, model.stats.generic_param_dependence_ratio(state_or_tran, attribute)))
print('')
print(r'key attribute parameter $1 - \frac{...}{...}$')
for state_or_tran in model.by_name.keys():
for attribute in model.attributes(state_or_tran):
for param in model.parameters():
print('{} {} {} {:.8f}'.format(state_or_tran, attribute, param, model.stats.param_dependence_ratio(state_or_tran, attribute, param)))
if state_or_tran in model._num_args:
for arg_index in range(model._num_args[state_or_tran]):
print('{} {} {:d} {:.8f}'.format(state_or_tran, attribute, arg_index, model.stats.arg_dependence_ratio(state_or_tran, attribute, arg_index)))
if __name__ == '__main__':
ignored_trace_indexes = []
discard_outliers = None
safe_functions_enabled = False
function_override = {}
show_models = []
show_quality = []
hwmodel = None
energymodel_export_file = None
try:
optspec = (
'plot-unparam= plot-param= show-models= show-quality= '
'ignored-trace-indexes= discard-outliers= function-override= '
'with-safe-functions hwmodel= export-energymodel='
)
raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' '))
for option, parameter in raw_opts:
optname = re.sub(r'^--', '', option)
opts[optname] = parameter
if 'ignored-trace-indexes' in opts:
ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(',')))
if 0 in ignored_trace_indexes:
print('[E] arguments to --ignored-trace-indexes start from 1')
if 'discard-outliers' in opts:
discard_outliers = float(opts['discard-outliers'])
if 'function-override' in opts:
for function_desc in opts['function-override'].split(';'):
state_or_tran, attribute, *function_str = function_desc.split(' ')
function_override[(state_or_tran, attribute)] = ' '.join(function_str)
if 'show-models' in opts:
show_models = opts['show-models'].split(',')
if 'show-quality' in opts:
show_quality = opts['show-quality'].split(',')
if 'with-safe-functions' in opts:
safe_functions_enabled = True
if 'hwmodel' in opts:
with open(opts['hwmodel'], 'r') as f:
hwmodel = json.load(f)
except getopt.GetoptError as err:
print(err)
sys.exit(2)
raw_data = RawData(args)
preprocessed_data = raw_data.get_preprocessed_data()
model = EnergyModel(preprocessed_data,
ignore_trace_indexes = ignored_trace_indexes,
discard_outliers = discard_outliers,
function_override = function_override,
hwmodel = hwmodel)
if 'plot-unparam' in opts:
for kv in opts['plot-unparam'].split(';'):
state_or_trans, attribute, ylabel = kv.split(':')
fname = 'param_y_{}_{}.pdf'.format(state_or_trans,attribute)
plotter.plot_y(model.by_name[state_or_trans][attribute], xlabel = 'measurement #', ylabel = ylabel, output = fname)
if len(show_models):
print('--- simple static model ---')
static_model = model.get_static()
if 'static' in show_models or 'all' in show_models:
for state in model.states():
print('{:10s}: {:.0f} µW ({:.2f})'.format(
state,
static_model(state, 'power'),
model.stats.generic_param_dependence_ratio(state, 'power')))
for param in model.parameters():
print('{:10s} dependence on {:15s}: {:.2f}'.format(
'',
param,
model.stats.param_dependence_ratio(state, 'power', param)))
for trans in model.transitions():
print('{:10s}: {:.0f} / {:.0f} / {:.0f} pJ ({:.2f} / {:.2f} / {:.2f})'.format(
trans, static_model(trans, 'energy'),
static_model(trans, 'rel_energy_prev'),
static_model(trans, 'rel_energy_next'),
model.stats.generic_param_dependence_ratio(trans, 'energy'),
model.stats.generic_param_dependence_ratio(trans, 'rel_energy_prev'),
model.stats.generic_param_dependence_ratio(trans, 'rel_energy_next')))
print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration')))
static_quality = model.assess(static_model)
if len(show_models):
print('--- LUT ---')
lut_model = model.get_param_lut()
lut_quality = model.assess(lut_model)
if len(show_models):
print('--- param model ---')
param_model, param_info = model.get_fitted(safe_functions_enabled = safe_functions_enabled)
if 'paramdetection' in show_models or 'all' in show_models:
for state in model.states_and_transitions():
for attribute in model.attributes(state):
info = param_info(state, attribute)
print('{:10s} {:10s} non-param stddev {:f}'.format(
state, attribute, model.stats.stats[state][attribute]['std_static']
))
print('{:10s} {:10s} param-lut stddev {:f}'.format(
state, attribute, model.stats.stats[state][attribute]['std_param_lut']
))
for param in sorted(model.stats.stats[state][attribute]['std_by_param'].keys()):
print('{:10s} {:10s} {:10s} stddev {:f}'.format(
state, attribute, param, model.stats.stats[state][attribute]['std_by_param'][param]
))
if info != None:
for param_name in sorted(info['fit_result'].keys(), key=str):
param_fit = info['fit_result'][param_name]['results']
for function_type in sorted(param_fit.keys()):
function_rmsd = param_fit[function_type]['rmsd']
print('{:10s} {:10s} {:10s} mean {:10s} RMSD {:.0f}'.format(
state, attribute, str(param_name), function_type, function_rmsd
))
if 'param' in show_models or 'all' in show_models:
for state in model.states():
for attribute in model.attributes(state):
if param_info(state, attribute):
print('{:10s}: {}'.format(state, param_info(state, attribute)['function']._model_str))
print('{:10s} {}'.format('', param_info(state, attribute)['function']._regression_args))
for trans in model.transitions():
for attribute in model.attributes(trans):
if param_info(trans, attribute):
print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str))
print('{:10s} {:10s} {}'.format('', '', param_info(trans, attribute)['function']._regression_args))
analytic_quality = model.assess(param_model)
if 'tex' in show_models or 'tex' in show_quality:
print_text_model_data(model, static_model, static_quality, lut_model, lut_quality, param_model, param_info, analytic_quality)
if 'table' in show_quality or 'all' in show_quality:
model_quality_table([static_quality, analytic_quality, lut_quality], [None, param_info, None])
if 'summary' in show_quality or 'all' in show_quality:
model_summary_table([static_quality, analytic_quality, lut_quality])
if 'plot-param' in opts:
for kv in opts['plot-param'].split(';'):
state_or_trans, attribute, param_name, *function = kv.split(' ')
if len(function):
function = gplearn_to_function(' '.join(function))
else:
function = None
plotter.plot_param(model, state_or_trans, attribute, model.param_index(param_name), extra_function=function)
if 'export-energymodel' in opts:
if not hwmodel:
print('[E] --export-energymodel requires --hwmodel to be set')
sys.exit(1)
json_model = model.to_json()
with open(opts['export-energymodel'], 'w') as f:
json.dump(json_model, f, indent = 2, sort_keys = True)
sys.exit(0)
|
