def start(self):
self.fit()
if self.mode == 'fit':
self.log.info('Saving fit KCenters model to `%s`' % self.output)
with tables.open_file(self.output, 'w') as f:
self.kcenters.to_pytables(f.root)
elif self.mode in ['fit_predict', 'predict']:
self.log.info('Writing DataSet: %s' % self.output)
dataset = DataSet(self.output, mode='w', name='KCenters')
if self.source == 'precomputed':
dataset.provenance = self.input_provenance
for i, (data, fn) in enumerate(self.yield_transform(with_filenames=True)):
dataset[i] = data
dataset.set_trajfn(i, fn)
dataset.close()
else:
raise RuntimeError(self.mode)
def start(self):
self.fit()
if self.mode == "fit":
self.log.info("Saving fit tICA model to `%s`" % self.output)
with tables.open_file(self.output, "w") as f:
self.tica.to_pytables(f.root)
return
if self.mode in ["fit_transform", "transform"]:
self.log.info("Writing DataSet: %s" % self.output)
dataset = DataSet(self.output, mode="w", name="TICAApp")
if self.source == "precomputed":
dataset.provenance = self.input_provenance
for i, (data, fn) in enumerate(self.yield_transform(with_filenames=True)):
dataset[i] = data
dataset.set_trajfn(i, fn)
dataset.close()
else:
raise RuntimeError()
