def start(self):
self.log.info("Writing DataSet: %s" % self.output)
dataset = DataSet(self.output, mode="w", name="VectorApp-%s" % self.method)
for i, (data, file) in enumerate(self.yield_transform(with_filenames=True)):
dataset[i] = data
dataset.set_trajfn(i, file)
dataset.close()
def _yield_input(self, with_filenames=False):
if self.source == "vector":
for data in self.vectorapp.yield_transform(with_filenames):
yield data
else:
dataset = DataSet(self.input)
for key in dataset.keys():
if with_filenames:
yield dataset[key], dataset.get_trajfn(key)
else:
yield dataset[key]
self.input_provenance = dataset.provenance
dataset.close()
def start(self):
if not os.path.exists(self.input):
self.error('No such file or directory: %s' % self.input)
if not tables.is_pytables_file(self.input):
self.error('Unrecognized format')
try:
ds = DataSet(self.input, 'r')
self.print_dataset(ds)
ds.close()
except UnrecognizedFormatError:
handle = tables.open_file(self.input)
self.print_model(handle)
handle.close()
def _yield_input(self, with_filenames=False):
if self.source == 'tICA':
for data in self.ticaapp.yield_transform(with_filenames):
yield data
elif self.source == 'vector':
for data in self.vector.yield_transform(with_filenames):
yield data
elif self.source == 'precomputed':
dataset = DataSet(self.input)
for key in dataset.keys():
if with_filenames:
yield dataset[key], dataset.get_trajfn(key)
else:
yield dataset[key]
self.input_provenance = dataset.provenance
dataset.close()
else:
raise RuntimeError(self.source)
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()