def test_match_events():
port = Port()
events = [['foo', ''], ['bar', ''], ['baz', ''], ['foo', '']]
times = pd.date_range(start='2018-01-01', periods=4, freq='1s')
port.set(events, times, ['label', 'data'])
indices = match_events(port, 'foo').index.values.tolist()
expected = [1514764800000000000, 1514764803000000000]
assert indices == expected
def update(self):
# Append to main buffer
if self.i.data is not None:
if not self.i.data.empty:
if self._buffer is None:
self._buffer = self.i.data
else:
self._buffer = self._buffer.append(self.i.data)
# Detect onset
matches = match_events(self.i_events, self._event_trigger)
if matches is not None:
for index, row in matches.iterrows():
# Start a new epoch
low = index - self._before
high = index + self._after
self._epochs.append({
'data': self._buffer[low:high],
'meta': {
'onset': index,
'context': row['data'],
'before': self._before.total_seconds(),
'after': self._after.total_seconds()
}
})
# Trim main buffer
if self._buffer is not None:
low = self._buffer.index[-1] - self._before
self._buffer = self._buffer[low:]
# Update epochs
if self._epochs and self.i.ready():
complete = 0
for epoch in self._epochs:
high = epoch['meta']['onset'] + self._after
last = self.i.data.index[-1]
if epoch['data'].empty:
low = epoch['meta']['onset'] - self._before
mask = (self.i.data.index >= low) & (self.i.data.index <=
high)
else:
low = epoch['data'].index[-1]
mask = (self.i.data.index > low) & (self.i.data.index <=
high)
# Append
epoch['data'] = epoch['data'].append(self.i.data[mask])
# Send if we have enough data
if last >= high:
o = getattr(self, 'o_' + str(complete))
o.data = epoch['data']
o.meta = {'epoch': epoch['meta']}
complete += 1
if complete > 0:
del self._epochs[:complete] # Unqueue
self.o = self.o_0 # Bind default output to the first epoch
def update(self):
# Let's get ready
self._clear()
# Are we dealing with continuous data or epochs?
if self._dimensions is None:
port_name = "i_training" if self.fit else "i"
if getattr(self, port_name).ready():
self._dimensions = 2
elif len(list(self.iterate(port_name + "_*"))) > 0:
self._dimensions = 3
# Set the accumulation boundaries
if self._accumulation_start is None:
matches = match_events(self.i_events, self.event_start_accumulation)
if matches is not None:
self._accumulation_start = matches.index.values[0]
self._status = ACCUMULATING
if self._accumulation_stop is None:
matches = match_events(self.i_events, self.event_stop_accumulation)
if matches is not None:
self._accumulation_stop = matches.index.values[0]
# Always buffer a few seconds, in case the start event is coming late
if self._status == IDLE:
start = (now() - self._buffer_size).to_datetime64()
stop = max_time()
self._accumulate(start, stop)
# Accumulate between boundaries
if self._status == ACCUMULATING:
start = self._accumulation_start
stop = self._accumulation_stop if self._accumulation_stop else max_time()
self._accumulate(start, stop)
# Should we start fitting the model?
if self._status < FITTING:
if match_events(self.i_events, self.event_start_training) is not None:
self._status = FITTING
self._task = Task(
self._pipeline, "fit", self._X_train, self._y_train
).start()
# Is the model ready?
if self._status == FITTING:
status = self._task.status()
if status:
if status["success"]:
self._pipeline = status["instance"]
self._status = READY
self.logger.debug(f"Model fitted in {status['time']} seconds")
else:
self.logger.error(
f"An error occured while fitting: {status['exception'].args[0]}"
)
self.logger.debug(
"\nTraceback (most recent call last):\n"
+ "".join(status["traceback"])
)
raise WorkerInterrupt()
# Run the pipeline
if self._status == READY:
self._receive()
if self._X is not None:
args = [self._X]
if self.mode.startswith("fit"):
args.append(self._y)
# TODO: optionally loop through epochs instead of sending them all at once
self._out = getattr(self._pipeline, self.mode)(*args)
# Set output streams
self._send()
def update(self):
# Append to main buffer
if self.i.data is not None:
if not self.i.data.empty:
if self._buffer is None:
self._buffer = self.i.data
else:
self._buffer = self._buffer.append(self.i.data)
# Detect onset
matches = match_events(self.i_events, self._event_trigger)
if matches is not None:
for index, row in matches.iterrows():
# Start a new epoch
low = index - self._before
high = index + self._after
if not self._buffer.index.is_monotonic:
self.logger.warning(
f"Index should be monotonic. Skipping epoch {row['data']}."
)
return
self._epochs.append({
"data": self._buffer[low:high],
"meta": {
"onset": index,
"context": row["data"],
"before": self._before.total_seconds(),
"after": self._after.total_seconds(),
},
})
# Trim main buffer
if self._buffer is not None:
low = self._buffer.index[-1] - self._before
self._buffer = self._buffer[low:]
# Update epochs
if self._epochs and self.i.ready():
complete = 0
for epoch in self._epochs:
high = epoch["meta"]["onset"] + self._after
last = self.i.data.index[-1]
if epoch["data"].empty:
low = epoch["meta"]["onset"] - self._before
mask = (self.i.data.index >= low) & (self.i.data.index <=
high)
else:
low = epoch["data"].index[-1]
mask = (self.i.data.index > low) & (self.i.data.index <=
high)
# Append
epoch["data"] = epoch["data"].append(self.i.data[mask])
# Send if we have enough data
if last >= high:
o = getattr(self, "o_" + str(complete))
o.data = epoch["data"]
o.meta = {"epoch": epoch["meta"]}
complete += 1
if complete > 0:
del self._epochs[:complete] # Unqueue
self.o = self.o_0 # Bind default output to the first epoch
def test_match_empty():
port = Port()
events = [['foo', ''], ['bar', '']]
times = pd.date_range(start='2018-01-01', periods=2, freq='1s')
port.set(events, times, ['label', 'data'])
assert match_events(port, 'baz') == None
