def _onopeningtracks(controller, models, **_):
cls = BeadSubtractionTask
info = dict(self.store.data)
for i, (isarchive, proc) in enumerate(models):
name = trackname(proc.model[0])
if isarchive or not self.config.automate:
continue
track = next(iter(proc.run())).track
info[proc.model[0]] = self.config(track.beads)
if not len(info[proc.model[0]]):
ctrl.display.update(
"message",
message=
(f"No fixed beads to subtract: {name} [{i}/{len(models)}]",
"normal"))
continue
ctrl.display.update(
"message",
message=(
f"Subtracting fixed beads: {name} [{i}/{len(models)}]",
"normal"))
proc.add(cls(beads=[i[-1] for i in info[proc.model[0]]]),
controller.processortype(cls),
index=self.tasksmodel.config.defaulttaskindex(
proc.model, cls))
if len(info) != len(self.store.data):
ctrl.display.update(self.store, data=info)
def dataframe(self, frame) -> pd.DataFrame:
"return all data from a frame"
fields = RampEventTuple.fields() if self.events else RampCycleTuple.fields()
lst = list(self.stats(frame))
data = pd.DataFrame({
j: [k[i] for k in lst] for i,j in enumerate(fields) # type: ignore
})
data = self.status(data)
data['track'] = trackname(frame.track)
data['modification'] = frame.track.pathinfo.modification
return data
def _body_tracks(current):
if not current.roots:
return ""
reftrack = '|'.join(f"{i+1}:{trackname(j).replace('|', '_')}"
for i, j in enumerate(current.roots))
line = """
%(tracksel[{i}])b {i}-{j: <20} %(tracktag[{i}])250s %(beadmask[{i}])ocsvι"""
return (f"""
ㄩㄩ Tracks
Reference Track %(reftrack)|0:none|{reftrack}|
!! <b>Group</b> <b>Discarded beads</b>""" + "".join(
line.format(i=i, j=trackname(j))
for i, j in enumerate(current.roots)))
def _export_tracks(
processors) -> List[Tuple[pd.DataFrame, Dict[str, Any]]]:
tracks = pd.DataFrame(
dict(trackid=list(range(len(processors))),
track=[trackname(i.model[0]) for i in processors],
tasks=[
dumps(j.model,
ensure_ascii=False,
indent=4,
sort_keys=True) for j in processors
]))
return [(tracks,
dict(startrow=5,
sheet_name="Tracks",
freeze_panes=(5, len(tracks))))]
def compute(self, _: bool):
"compute base dataframes"
out: List[pd.DataFrame] = [self._bead] if hasattr(self, '_bead') else []
itr = self._computations('_bead', (Exception, pd.DataFrame), False)
for proc, info in itr:
data = pd.DataFrame({
'track': [trackname(proc.model[0])],
self._NAME: (
info.errkey() if hasattr(info, 'errkey') else
'bug' if not isinstance(info, pd.DataFrame) else
'ok' if info.shape[0] else
'empty'
)
})
out.append(self._compute_update(itr.send(data), 1.))
if not out:
return
self._bead = _concat(out)
self._bead = self._compute_tags(self._bead)
def _export_plot(mdl, plot, processors,
sheetnames) -> List[Tuple[pd.DataFrame, Dict[str, Any]]]:
plot.compute(True)
tracks = {id(i.model[0]): trackname(i.model[0]) for i in processors}
trackids = {id(j.model[0]): i for i, j in enumerate(processors)}
cnv = dict(mdl.theme.xaxistag, **mdl.theme.yaxistag)
cnv.pop('bead')
info: List[Tuple[pd.DataFrame, Dict[str, Any]]] = []
for name in ('bead', 'peak'):
sheet = getattr(plot, f'_{name}', None)
if not isinstance(sheet, pd.DataFrame):
continue
info.append(((sheet.assign(
track=sheet.trackid.apply(tracks.__getitem__),
trackid=sheet.trackid.apply(trackids.__getitem__))),
dict(header=[cnv.get(k, k) for k in sheet.columns],
sheet_name=sheetnames.get(
name, f"{name.capitalize()} statistics"),
freeze_panes=(1, len(sheet)))))
return info
def trackname(track: Track) -> str:
"returns data track name"
return trackname(track)