def get_output(self, dimensions=slice(0, None), stride=1, skip=0, chunk=0):
if isinstance(dimensions, int):
ndim = 1
dimensions = slice(dimensions, dimensions + 1)
elif isinstance(dimensions, (list, np.ndarray, tuple, slice)):
if hasattr(dimensions, 'ndim') and dimensions.ndim > 1:
raise ValueError('dimension indices can\'t have more than one dimension')
ndim = len(np.zeros(self.ndim)[dimensions])
else:
raise ValueError('unsupported type (%s) of "dimensions"' % type(dimensions))
assert ndim > 0, "ndim was zero in %s" % self.__class__.__name__
# create iterator
if self.in_memory and not self._mapping_to_mem_active:
from pyemma.coordinates.data.data_in_memory import DataInMemory
assert self._Y is not None
it = DataInMemory(self._Y)._create_iterator(skip=skip, chunk=chunk,
stride=stride, return_trajindex=True)
else:
it = self._create_iterator(skip=skip, chunk=chunk, stride=stride, return_trajindex=True)
with it:
# allocate memory
try:
# TODO: avoid having a copy here, if Y is already filled
trajs = [np.empty((l, ndim), dtype=self.output_type())
for l in it.trajectory_lengths()]
except MemoryError:
self._logger.exception("Could not allocate enough memory to map all data."
" Consider using a larger stride.")
return
if self._logger_is_active(self._loglevel_DEBUG):
self._logger.debug("get_output(): dimensions=%s" % str(dimensions))
self._logger.debug("get_output(): created output trajs with shapes: %s"
% [x.shape for x in trajs])
# fetch data
self.logger.debug("nchunks :%s, chunksize=%s" % (it._n_chunks, it.chunksize))
self._progress_register(it._n_chunks,
description='getting output of %s' % self.__class__.__name__,
stage=1)
for itraj, chunk in it:
L = len(chunk)
if L > 0:
trajs[itraj][it.pos:it.pos + L, :] = chunk[:, dimensions]
# update progress
self._progress_update(1, stage=1)
return trajs
def get_output(self,
dimensions=slice(0, None),
stride=1,
skip=0,
chunk=None):
"""Maps all input data of this transformer and returns it as an array or list of arrays
Parameters
----------
dimensions : list-like of indexes or slice, default=all
indices of dimensions you like to keep.
stride : int, default=1
only take every n'th frame.
skip : int, default=0
initially skip n frames of each file.
chunk: int, default=None
How many frames to process at once. If not given obtain the chunk size
from the source.
Returns
-------
output : list of ndarray(T_i, d)
the mapped data, where T is the number of time steps of the input data, or if stride > 1,
floor(T_in / stride). d is the output dimension of this transformer.
If the input consists of a list of trajectories, Y will also be a corresponding list of trajectories
"""
if isinstance(dimensions, int):
ndim = 1
dimensions = slice(dimensions, dimensions + 1)
elif isinstance(dimensions, (list, np.ndarray, tuple, slice)):
if hasattr(dimensions, 'ndim') and dimensions.ndim > 1:
raise ValueError(
'dimension indices can\'t have more than one dimension')
ndim = len(np.zeros(self.ndim)[dimensions])
else:
raise ValueError('unsupported type (%s) of "dimensions"' %
type(dimensions))
assert ndim > 0, "ndim was zero in %s" % self.__class__.__name__
if chunk is None:
chunk = self.chunksize
# create iterator
if self.in_memory and not self._mapping_to_mem_active:
from pyemma.coordinates.data.data_in_memory import DataInMemory
assert self._Y is not None
it = DataInMemory(self._Y)._create_iterator(skip=skip,
chunk=chunk,
stride=stride,
return_trajindex=True)
else:
it = self._create_iterator(skip=skip,
chunk=chunk,
stride=stride,
return_trajindex=True)
with it:
# allocate memory
try:
# TODO: avoid having a copy here, if Y is already filled
trajs = [
np.empty((l, ndim), dtype=self.output_type())
for l in it.trajectory_lengths()
]
except MemoryError:
self._logger.exception(
"Could not allocate enough memory to map all data."
" Consider using a larger stride.")
return
if self._logger_is_active(self._loglevel_DEBUG):
self._logger.debug("get_output(): dimensions=%s" %
str(dimensions))
self._logger.debug(
"get_output(): created output trajs with shapes: %s" %
[x.shape for x in trajs])
# fetch data
self.logger.debug("nchunks :%s, chunksize=%s" %
(it.n_chunks, it.chunksize))
self._progress_register(it.n_chunks,
description='getting output of %s' %
self.__class__.__name__,
stage=1)
for itraj, chunk in it:
L = len(chunk)
if L > 0:
trajs[itraj][it.pos:it.pos + L, :] = chunk[:, dimensions]
# update progress
self._progress_update(1, stage=1)
return trajs