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()