def _v1(data: dict) -> dict:
mods = dict(symmetry=lambda val: 'both' if val else RESET)
modifyclasses(data, "peakcalling.processor.fittohairpin.FitToHairpinTask",
mods,
"peakcalling.processor.fittoreference.FitToReferenceTask",
mods)
return data
def _v0task(data: dict) -> dict:
modifyclasses(
data, "eventdetection.processor.ExtremumAlignmentTask",
dict(edge=lambda val: 'right' if val else RESET,
phase=RESET,
factor=RESET))
return data
def _v8(data: dict) -> dict:
modifyclasses(
data, r'.*model.task.*',
dict(__name__=lambda x: x.replace('model.task', 'taskmodel')),
r'.*model.level.*',
dict(__name__=lambda x: x.replace('model.', 'taskmodel.')),
r'.*model.__scripting__.*',
dict(__name__=lambda x: x.replace('model.', 'taskmodel.')))
return data
def _modify(self, data:dict) -> dict:
mods = tuple(self.modifications)
if self.path is not None: # type: ignore
def _pathpatch(val):
# pylint: disable=not-callable
val[CNT] = self.path(cast(Sequence[str], val[CNT])) # type: ignore
return val
mods += "taskmodel.track.TrackReaderTask", dict(path = _pathpatch)
modifyclasses(data, *mods)
return data
def _v5(data: dict) -> dict:
mdl = 'eventdetection.merging.'
args = zip(
('HeteroscedasticEventMerger', 'PopulationMerger', 'RangeMerger'),
('stats', 'pop', 'range'))
def _multi(itm):
itm.update({
k: i
for i, (j, k) in product(itm.pop('merges', ()), args)
if i[TPE] == mdl + j
})
modifyclasses(data, mdl + "MultiMerger", dict(__call__=_multi))
return data
def _v9(data: dict) -> dict:
modifyclasses(
data, ".*cleaning.datacleaning.*",
dict(__name__=lambda x: x.replace("cleaning.datacleaning",
"cleaning._core")))
return data
def _v7(data: dict) -> dict:
modifyclasses(
data, 'peakfinding.processor.singlestrand.SingleStrandTask',
dict(__name__='peakfinding.processor.peakfiltering.SingleStrandTask'))
return data
def _v6(data: dict) -> dict:
modifyclasses(data, 'cleaning.beadsubtraction.BeadSubtractionTask',
dict(__name__='cleaning.processor.BeadSubtractionTask'))
return data
def _v3(data: dict) -> dict:
repl = lambda x: x.replace('Min', '')
modifyclasses(data, "peakfinding.selector.PeakSelector",
dict(align=DELETE), r"cleaning.datacleaning.Min(\w+)",
dict(__name__=repl))
return data
def _v2(data: dict) -> dict:
repl = lambda x: (x.replace('.histogram.', '.groupby.histogramfitter.').
replace('.ByZeroCrossing', '.ByHistogram'))
modifyclasses(data, r"peakfinding.histogram.(\w+)", dict(__name__=repl))
return data
def _v11(data: dict) -> dict:
modifyclasses(data, "peakcalling.view._model.*",
dict(__name__=lambda x: x.replace('view._model', 'model')))
return data
def _v10(data: dict) -> dict:
modifyclasses(data, "data.Track",
dict(_rawprecisions=DELETE, rawprecisions=DELETE))
return data