From f433a9fa3a464d611e75b26fcb540dd835f693d7 Mon Sep 17 00:00:00 2001
From: Daniel Friesel <daniel.friesel@uos.de>
Date: Fri, 25 Oct 2019 15:40:13 +0200
Subject: dfatool: More error handling, support a posteriori arguments
---
lib/dfatool.py | 79 +++++++++++++++++++++++++++++++++++++++++++++++++---------
1 file changed, 67 insertions(+), 12 deletions(-)
diff --git a/lib/dfatool.py b/lib/dfatool.py
index dd9ed61..2f5fa29 100644
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -330,9 +330,26 @@ class CrossValidator:
def _preprocess_measurement(measurement):
setup = measurement['setup']
mim = MIMOSA(float(setup['mimosa_voltage']), int(setup['mimosa_shunt']))
- charges, triggers = mim.load_data(measurement['content'])
- trigidx = mim.trigger_edges(triggers)
- triggers = []
+ try:
+ charges, triggers = mim.load_data(measurement['content'])
+ trigidx = mim.trigger_edges(triggers)
+ except EOFError as e:
+ mim.is_error = True
+ mim.errors.append('MIMOSA logfile error: {}'.format(e))
+ trigidx = list()
+
+ if len(trigidx) == 0:
+ mim.is_error = True
+ mim.errors.append('MIMOSA log has no triggers')
+ return {
+ 'fileno' : measurement['fileno'],
+ 'info' : measurement['info'],
+ 'has_mimosa_error' : mim.is_error,
+ 'mimosa_errors' : mim.errors,
+ 'expected_trace' : measurement['expected_trace'],
+ 'repeat_id' : measurement['repeat_id'],
+ }
+
cal_edges = mim.calibration_edges(running_mean(mim.currents_nocal(charges[0:trigidx[0]]), 10))
calfunc, caldata = mim.calibration_function(charges, cal_edges)
vcalfunc = np.vectorize(calfunc, otypes=[np.float64])
@@ -348,8 +365,9 @@ def _preprocess_measurement(measurement):
'mimosa_errors' : mim.errors,
}
- if 'expected_trace' in measurement:
- processed_data['expected_trace'] = measurement['expected_trace']
+ for key in ['expected_trace', 'repeat_id']:
+ if key in measurement:
+ processed_data[key] = measurement[key]
return processed_data
@@ -489,6 +507,7 @@ class RawData:
self.version = 0
self.preprocessed = False
self._parameter_names = None
+ self.ignore_clipping = False
with tarfile.open(filenames[0]) as tf:
for member in tf.getmembers():
@@ -621,7 +640,7 @@ class RawData:
# Clipping in UNINITIALIZED (offline_idx == 0) can happen during
# calibration and is handled by MIMOSA
- if offline_idx != 0 and offline_trace_part['clip_rate'] != 0:
+ if offline_idx != 0 and offline_trace_part['clip_rate'] != 0 and not self.ignore_clipping:
processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) was clipping {clip:f}% of the time'.format(
off_idx = offline_idx, on_idx = online_run_idx,
on_sub = online_trace_part_idx,
@@ -679,13 +698,20 @@ class RawData:
online_trace_part['offline'].append(offline_trace_part)
paramkeys = sorted(online_trace_part['parameter'].keys())
- paramvalue = [soft_cast_int(online_trace_part['parameter'][x]) for x in paramkeys]
+
+ paramvalues = list()
+
+ for paramkey in paramkeys:
+ if type(online_trace_part['parameter'][paramkey]) is list:
+ paramvalues.append(soft_cast_int(online_trace_part['parameter'][paramkey][measurement['repeat_id']]))
+ else:
+ paramvalues.append(soft_cast_int(online_trace_part['parameter'][paramkey]))
# NB: Unscheduled transitions do not have an 'args' field set.
# However, they should only be caused by interrupts, and
# interrupts don't have args anyways.
if arg_support_enabled and 'args' in online_trace_part:
- paramvalue.extend(map(soft_cast_int, online_trace_part['args']))
+ paramvalues.extend(map(soft_cast_int, online_trace_part['args']))
if not 'offline_aggregates' in online_trace_part:
online_trace_part['offline_attributes'] = ['power', 'duration', 'energy']
@@ -715,7 +741,7 @@ class RawData:
online_trace_part['offline_aggregates']['energy'].append(
offline_trace_part['uW_mean'] * (offline_trace_part['us'] - 20))
online_trace_part['offline_aggregates']['paramkeys'].append(paramkeys)
- online_trace_part['offline_aggregates']['param'].append(paramvalue)
+ online_trace_part['offline_aggregates']['param'].append(paramvalues)
if online_trace_part['isa'] == 'transition':
online_trace_part['offline_aggregates']['rel_energy_prev'].append(
offline_trace_part['uW_mean_delta_prev'] * (offline_trace_part['us'] - 20))
@@ -838,13 +864,14 @@ class RawData:
'mimosa_shunt' : ptalog['configs'][j]['shunt'],
'state_duration' : ptalog['opt']['sleep'],
})
- for mim_file in ptalog['files'][j]:
+ for repeat_id, mim_file in enumerate(ptalog['files'][j]):
member = tf.getmember(mim_file)
mim_files.append({
'content' : tf.extractfile(member).read(),
'fileno' : j,
'info' : member,
'setup' : self.setup_by_fileno[j],
+ 'repeat_id' : repeat_id,
'expected_trace' : ptalog['traces'][j],
})
self.filenames = new_filenames
@@ -856,6 +883,13 @@ class RawData:
valid_traces = list()
for measurement in measurements:
+ if not 'energy_trace' in measurement:
+ vprint(self.verbose, '[W] Skipping {ar:s}/{m:s}: {e:s}'.format(
+ ar = self.filenames[measurement['fileno']],
+ m = measurement['info'].name,
+ e = '; '.join(measurement['mimosa_errors'])))
+ continue
+
if version == 0:
# Strip the last state (it is not part of the scheduled measurement)
measurement['energy_trace'].pop()
@@ -1763,12 +1797,27 @@ class PTAModel:
'by_name' : detailed_results
}
- def assess_states(self, model_function, model_attribute = 'power'):
+ def assess_states(self, model_function, model_attribute = 'power', distribution: dict = None):
"""
Calculate overall model error assuming equal distribution of states
"""
+ # TODO calculate mean power draw for distribution and use it to
+ # calculate relative error from MAE combination
model_quality = self.assess(model_function)
- total_error = np.sqrt(sum(map(lambda x: np.square(model_quality['by_name'][x][model_attribute]['mae']), self.states())))
+ num_states = len(self.states())
+ if distribution is None:
+ distribution = dict(map(lambda x: [x, 1/num_states], self.states()))
+
+ if not np.isclose(sum(distribution.values()), 1):
+ raise ValueError('distribution must be a probability distribution with sum 1')
+
+ total_value = None
+ try:
+ total_value = sum(map(lambda x: model_function(x, model_attribute) * distribution[x], self.states()))
+ except KeyError:
+ pass
+
+ total_error = np.sqrt(sum(map(lambda x: np.square(model_quality['by_name'][x][model_attribute]['mae'] * distribution[x]), self.states())))
return total_error
@@ -1961,6 +2010,12 @@ class MIMOSA:
:returns: list of int (trigger indices, e.g. [2000000, ...] means the first trigger appears in charges/currents interval 2000000 -> 20s after start of measurements. Keep in mind that each interval is 10µs long, not 1µs, so index values are not µs timestamps)
"""
trigidx = []
+
+ if len(triggers) < 1000000:
+ self.is_error = True
+ self.errors.append('MIMOSA log is too short')
+ return trigidx
+
prevtrig = triggers[999999]
# if the first trigger is high (i.e., trigger/buzzer pin is active before the benchmark starts),
--
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