diff --git a/doc/source/CHANGELOG.rst b/doc/source/CHANGELOG.rst
index e65e07826..c3969aacc 100644
--- a/doc/source/CHANGELOG.rst
+++ b/doc/source/CHANGELOG.rst
@@ -1,6 +1,22 @@
 Changelog
 =========
 
+2.3.1 (tba)
+-----------
+
+**New features**:
+
+- msm:
+  - ImpliedTimescales: enable insertion/removal of lag times.
+    Avoid recomputing existing models. #1030
+
+**Fixes**:
+
+- coordinates:
+   - If Estimators supporting streaming are used directly, restore previous behaviour. #1034
+     Note that estimators used directly from the API are not affected.
+
+
 2.3 (6-1-2017)
 --------------
 
diff --git a/pyemma/coordinates/clustering/assign.py b/pyemma/coordinates/clustering/assign.py
index 84a4ae483..597105ffb 100644
--- a/pyemma/coordinates/clustering/assign.py
+++ b/pyemma/coordinates/clustering/assign.py
@@ -87,11 +87,12 @@ def describe(self):
 
     @AbstractClustering.data_producer.setter
     def data_producer(self, dp):
-        # check dimensions
-        dim = self.clustercenters.shape[1]
-        if not dim == dp.dimension():
-            raise ValueError('cluster centers have wrong dimension. Have dim=%i'
-                             ', but input has %i' % (dim, dp.dimension()))
+        if dp is not None:
+            # check dimensions
+            dim = self.clustercenters.shape[1]
+            if not dim == dp.dimension():
+                raise ValueError('cluster centers have wrong dimension. Have dim=%i'
+                                 ', but input has %i' % (dim, dp.dimension()))
         AbstractClustering.data_producer.fset(self, dp)
 
     def _estimate(self, iterable, **kw):
diff --git a/pyemma/coordinates/data/_base/streaming_estimator.py b/pyemma/coordinates/data/_base/streaming_estimator.py
index b400c6997..41ce2efec 100644
--- a/pyemma/coordinates/data/_base/streaming_estimator.py
+++ b/pyemma/coordinates/data/_base/streaming_estimator.py
@@ -37,13 +37,15 @@ def __init__(self, chunksize=None):
         self._chunksize = chunksize
 
     def estimate(self, X, **kwargs):
+        # ensure the input is able to provide a stream
         if not isinstance(X, Iterable):
             if isinstance(X, np.ndarray) or \
-                    (isinstance(X, (list, tuple)) and len(X) > 0 and all([isinstance(x, np.ndarray) for x in X])):
+                    (isinstance(X, (list, tuple)) and len(X) > 0 and all((isinstance(x, np.ndarray) for x in X))):
                 X = DataInMemory(X, self.chunksize)
             else:
                 raise ValueError("no np.ndarray or non-empty list of np.ndarrays given")
-
+        # Because we want to use pipelining methods like get_output, we have to set a data producer.
+        self.data_producer = X
         # run estimation
         try:
             super(StreamingEstimator, self).estimate(X, **kwargs)
diff --git a/pyemma/coordinates/data/_base/transformer.py b/pyemma/coordinates/data/_base/transformer.py
index 69944c3fd..efc29f754 100644
--- a/pyemma/coordinates/data/_base/transformer.py
+++ b/pyemma/coordinates/data/_base/transformer.py
@@ -24,6 +24,7 @@
 import six
 from pyemma._ext.sklearn.base import TransformerMixin
 from pyemma.coordinates.data._base.datasource import DataSource, DataSourceIterator
+from pyemma.coordinates.data._base.iterable import Iterable
 from pyemma.coordinates.data._base.random_accessible import RandomAccessStrategy
 from pyemma.coordinates.data._base.streaming_estimator import StreamingEstimator
 from pyemma.coordinates.util.change_notification import (inform_children_upon_change,
@@ -108,6 +109,9 @@ class StreamingTransformer(Transformer, DataSource, NotifyOnChangesMixIn):
 
     r""" Basis class for pipelined Transformers.
 
+    This class derives from DataSource, so follow up pipeline elements can stream
+    the output of this class.
+
     Parameters
     ----------
     chunksize : int (optional)
@@ -116,24 +120,28 @@ class StreamingTransformer(Transformer, DataSource, NotifyOnChangesMixIn):
     """
     def __init__(self, chunksize=1000):
         super(StreamingTransformer, self).__init__(chunksize=chunksize)
-        self._estimated = False
-        self._data_producer = None
+        self.data_producer = None
+        self._Y_source = None
 
     @property
     # overload of DataSource
     def data_producer(self):
+        if not hasattr(self, '_data_producer'):
+            return None
         return self._data_producer
 
     @data_producer.setter
     @inform_children_upon_change
     def data_producer(self, dp):
-        if dp is not self._data_producer:
+        if dp is not self.data_producer:
             # first unregister from current dataproducer
-            if self._data_producer is not None and isinstance(self._data_producer, NotifyOnChangesMixIn):
-                self._data_producer._stream_unregister_child(self)
+            if self.data_producer is not None and isinstance(self.data_producer, NotifyOnChangesMixIn):
+                self.data_producer._stream_unregister_child(self)
             # then register this instance as a child of the new one.
             if dp is not None and isinstance(dp, NotifyOnChangesMixIn):
                 dp._stream_register_child(self)
+        if dp is not None and not isinstance(dp, Iterable):
+            raise ValueError('can not set data_producer to non-iterable class of type {}'.format(type(dp)))
         self._data_producer = dp
         # register random access strategies
         self._set_random_access_strategies()
@@ -215,9 +223,10 @@ def n_frames_total(self, stride=1, skip=0):
 
 class StreamingEstimationTransformer(StreamingTransformer, StreamingEstimator):
     """ Basis class for pipelined Transformers, which perform also estimation. """
-
     def estimate(self, X, **kwargs):
         super(StreamingEstimationTransformer, self).estimate(X, **kwargs)
+        # we perform the mapping to memory exactly here, because a StreamingEstimator on its own
+        # has not output to be mapped. Only the combination of Estimation/Transforming has this feature.
         if self.in_memory and not self._mapping_to_mem_active:
             self._map_to_memory()
         return self
diff --git a/pyemma/coordinates/tests/test_cluster.py b/pyemma/coordinates/tests/test_cluster.py
index a11abcb26..dcda2a227 100644
--- a/pyemma/coordinates/tests/test_cluster.py
+++ b/pyemma/coordinates/tests/test_cluster.py
@@ -65,9 +65,6 @@ def setUpClass(cls):
         cls.rt = coor.cluster_uniform_time(data = cls.X, k = 100)
         cls.cl = [cls.km, cls.rs, cls.rt]
 
-    def setUp(self):
-        pass
-
     def test_chunksize(self):
         for c in self.cl:
             assert types.is_int(c.chunksize)
@@ -138,11 +135,6 @@ def test_output_type(self):
         for c in self.cl:
             assert c.output_type() == np.int32
 
-    def test_parametrize(self):
-        for c in self.cl:
-            # nothing should happen
-            c.parametrize()
-
     def test_save_dtrajs(self):
         extension = ".dtraj"
         outdir = self.dtraj_dir
@@ -170,5 +162,38 @@ def test_trajectory_lengths(self):
             assert c.trajectory_lengths()[0] == c.trajectory_length(0)
 
 
+class TestClusterDirect(TestCluster):
+    # perform all the tests of TestCluster, but use Estimator classes directly without API.
+    @classmethod
+    def setUpClass(cls):
+        from pyemma.coordinates.clustering import KmeansClustering, RegularSpaceClustering, UniformTimeClustering
+        cls.dtraj_dir = tempfile.mkdtemp()
+
+        # generate Gaussian mixture
+        means = [np.array([-3,0]),
+                 np.array([-1,1]),
+                 np.array([0,0]),
+                 np.array([1,-1]),
+                 np.array([4,2])]
+        widths = [np.array([0.3,2]),
+                  np.array([0.3,2]),
+                  np.array([0.3,2]),
+                  np.array([0.3,2]),
+                  np.array([0.3,2])]
+        # continuous trajectory
+        nsample = 1000
+        cls.T = len(means)*nsample
+        cls.X = np.zeros((cls.T, 2))
+        for i in range(len(means)):
+            cls.X[i*nsample:(i+1)*nsample,0] = widths[i][0] * np.random.randn() + means[i][0]
+            cls.X[i*nsample:(i+1)*nsample,1] = widths[i][1] * np.random.randn() + means[i][1]
+        # cluster in different ways
+        cls.km = KmeansClustering(n_clusters=100).estimate(cls.X)
+        cls.rs = RegularSpaceClustering(dmin=0.5).estimate(cls.X)
+        cls.rt = UniformTimeClustering(n_clusters=100).estimate(cls.X)
+        cls.cl = [cls.km, cls.rs, cls.rt]
+        return cls
+
+
 if __name__ == "__main__":
     unittest.main()
\ No newline at end of file
diff --git a/pyemma/coordinates/tests/test_pca.py b/pyemma/coordinates/tests/test_pca.py
index 38e227782..96d4b941c 100644
--- a/pyemma/coordinates/tests/test_pca.py
+++ b/pyemma/coordinates/tests/test_pca.py
@@ -222,5 +222,13 @@ def test_feature_correlation_data(self):
         true_corr = np.corrcoef(feature_traj.T, pca_traj.T)[:nfeat,-npcs:]
         np.testing.assert_allclose(test_corr, true_corr, atol=1.E-8)
 
+    def test_pipelining_sklearn_compat(self):
+        from pyemma.coordinates.transform import PCA
+        t = PCA(dim=2)
+        x = np.random.random((20, 3))
+        y = t.fit_transform(x)
+        y2 = t.get_output()
+        np.testing.assert_allclose(y2[0], y)
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/pyemma/coordinates/tests/test_tica.py b/pyemma/coordinates/tests/test_tica.py
index 1a25ade43..1bd0693aa 100644
--- a/pyemma/coordinates/tests/test_tica.py
+++ b/pyemma/coordinates/tests/test_tica.py
@@ -178,7 +178,15 @@ def test_too_short_trajs(self):
 
     def test_with_skip(self):
         data = np.random.random((100, 10))
-        tica_obj = api.tica(lag=10, dim=1, skip=1)
+        tica_obj = api.tica(data, lag=10, dim=1, skip=1)
+
+    def test_pipelining_sklearn_compat(self):
+        from pyemma.coordinates.transform import TICA
+        t = TICA(1)
+        x = np.random.random((20, 3))
+        y = t.fit_transform(x)
+        y2 = t.get_output()
+        np.testing.assert_allclose(y2[0], y)
 
 
 class TestTICAExtensive(unittest.TestCase):