diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml
index a627369..446e1d5 100644
--- a/devtools/conda-recipe/meta.yaml
+++ b/devtools/conda-recipe/meta.yaml
@@ -27,10 +27,11 @@ test:
   requires:
     - pytest-cov
     - nbval
+    - nose >= 1
   imports:
     - molpx
   commands:
-    - pytest -vv --pyargs molpx --cov=molpx --cov-report=xml --current-env --nbval-lax
+    - pytest -vv --pyargs molpx --cov=molpx --cov-report=xml --current-env --nbval-lax --duration=10
     - cp coverage.xml /tmp #  [linux]
 
 about:
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 3af843c..dc3d7bd 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -49,14 +49,17 @@ def __getattribute__(self, item):
                 'pyemma.coordinates.data.featurization.distances',
                 'nglview',
                 'matplotlib',
+                'matplotlib.pyplot',
                 'matplotlib.widgets',
                 'matplotlib.figure',
                 'matplotlib.axes',
                 'matplotlib.cm',
+                'matplotlib.patches',
                 'matplotlib.colors',
                 'IPython.display',
                 'sklearn.mixture',
                 'scipy.spatial',
+                'scipy.spatial.distance',
                 'six.moves.urllib.request',
                 'numpy',
  # fixme: can not mock ipywidgets because of multi-inheritance within molpx
@@ -392,7 +395,7 @@ def __getattribute__(self, item):
 intersphinx_mapping = {'pyemma': ('http://www.emma-project.org/latest/', None),
                        'matplotlib': ('http://matplotlib.org/', None),
                        'mdtraj': ('http://mdtraj.org/latest/', None),
-                       #'pylab' : ('http://matplotlib.org/api/', None),
+                       'pyplot' : ('http://matplotlib.org/api/', None),
                        'nglview' : ('http://arose.github.io/nglview/latest/', None)
 }
 
@@ -408,7 +411,7 @@ def __getattribute__(self, item):
 nbfiles = [
     '0.molPX_quick_intro_Ala2.ipynb',
     '1.molPX_and_PyEMMA_Features.ipynb',
-    '2.molPX_TICA_BPTI.ipynb',
+    '2.molPX_TICA_and_MSMs_BPTI.ipynb',
     '3.molPX_TICA_Ala2.ipynb',
     '4.molPX_metadynamics_Di-Ala.ipynb'
 ]
diff --git a/doc/source/index_visualize.rst b/doc/source/index_visualize.rst
index 3ee71a4..46d848c 100644
--- a/doc/source/index_visualize.rst
+++ b/doc/source/index_visualize.rst
@@ -20,9 +20,10 @@ The methods offered by this module are:
 .. autosummary::
 
    molpx.visualize.FES
-   molpx.visualize.sample
    molpx.visualize.traj
+   molpx.visualize.sample
    molpx.visualize.correlations
+   molpx.visualize.MSM
    molpx.visualize.feature
 
 .. automodule:: molpx.visualize
diff --git a/molpx/_bmutils.py b/molpx/_bmutils.py
index fe7a810..bded7c7 100644
--- a/molpx/_bmutils.py
+++ b/molpx/_bmutils.py
@@ -4,12 +4,16 @@
 try:
     from sklearn.mixture import GaussianMixture as _GMM
 except ImportError:
+    # TODO pin sklearn version and get rid of this
     from sklearn.mixture import GMM as _GMM
 
+from scipy.spatial.distance import pdist as _pdist, squareform as _squareform
+
 # From pyemma's coordinates
 from pyemma.coordinates import \
     source as _source, \
     cluster_regspace as _cluster_regspace, \
+    cluster_kmeans as _cluster_kmeans, \
     save_traj as _save_traj
 
 # From coor.data
@@ -164,51 +168,182 @@ def re_warp(array_in, lengths):
         idxi += ll
     return warped
 
-def regspace_cluster_to_target(data, n_clusters_target,
-                               n_try_max=5, delta=5.,
-                               verbose=False):
-    r"""
-    Clusters a dataset to a target n_clusters using regspace clustering by iteratively. "
-    Work best with 1D data
+def regspace_cluster_to_target_kmeans(data, n_clusters_target,
+                                      k_centers=1000,
+                                      k_stride=50,
+                                      n_tol = 5,
+                                      max_iter=5,
+                                      verbose=False):
+    r""" Wrapper of :obj:`pyemma.coordinates.cluster_regspace` but using n_clusters (instead of dmin)
+    as a parameter.
+
+    By using a preliminary :obj:`pyemma.coordinates.cluster_kmeans`-run, a dmin value is optimized to return
+    approximately :obj:`n_clusters` clustercenters.
+
+    Parameters :
+    ------------
 
     data: ndarray or list thereof
-    n_clusters_target: int, number of clusters.
-    n_try_max: int, default is 5. Maximum number of iterations in the heuristic.
-    delta: float, defalut is 5. Percentage of n_clusters_target to consider converged.
-             Eg. n_clusters_target=100 and delta = 5 will consider any clustering between 95 and 100 clustercenters as
-             valid. Note. Note: An off-by-one in n_target_clusters is sometimes unavoidable
+        Data to be used for clustering
+
+    n_clusters_target: int,
+        Approximate number of regularly spaced clustercenters wanted
+
+    k_centers : int, default is 1000
+        Number of centers of the preliminary kmeans clustering. Should be between 2 and 10 times
+        larger than n_clusters
+
+    k_stride : int, default is 50
+        Stride for the preliminary kmeans clustering. This clustering is supposed to not be the bottleneck
 
-    returns: pyemma clustering object
+    n_tol : int, default is 5
+        Consider :obj:`n_clusters_target` \pm :obj:`n_tol` as converged
+
+    max_iter : int, default is 5
+        Will stop after :obj:`max_iter` regspace-clustering attempts regardless
+
+    Returns :
+    --------
+        cl : a :obj:`pyemma.coordinates.cluster_regspace` object with approximately :obj:n_clusters
 
     tested:True
     """
-    delta = delta/100
-    ndim = _np.vstack(data).shape[0]
-    assert ndim >= n_clusters_target, "Cannot cluster " \
-                                                      "%u datapoints on %u clustercenters. Reduce the number of target " \
-                                                      "clustercenters."%(_np.vstack(data).shape[0], n_clusters_target)
-    # Works well for connected, 1D-clustering,
-    # otherwise it's bad starting guess for dmin
-    cmax = _np.vstack(data).max()
-    cmin = _np.vstack(data).min()
-    dmin = (cmax-cmin)/(n_clusters_target+1)
-
-    err = _np.ceil(n_clusters_target*delta)
-    cl = _cluster_regspace(data, dmin=dmin, max_centers=5000)
-    for cc in range(n_try_max):
-        n_cl_now = cl.n_clusters
-        delta_cl_now = _np.abs(n_cl_now - n_clusters_target)
-        if not n_clusters_target-err <= cl.n_clusters <= n_clusters_target+err:
-            # Cheap (VERY BAD IN HIGH DIM) heuristic to get relatively close relatively quick
-            dmin = cl.dmin*cl.n_clusters/   n_clusters_target
-            cl = _cluster_regspace(data, dmin=dmin, max_centers=5000)# max_centers is given so that we never reach it (dangerous)
-        else:
+
+    # Listify if only one array
+    if isinstance(data, _np.ndarray):
+        data = [data]
+
+    # The parameter k_centers=1000 is just an initialization and has only impact on speed.
+    # TODO: consider hard coding it
+    # We try to catch some pathological data inputs with too little data
+    n_frames_kmeans = _np.sum([len(idata[::k_stride]) for idata in data])
+    if n_frames_kmeans< k_centers:
+        k_stride = 1
+    n_frames_kmeans = _np.sum([len(idata[::k_stride]) for idata in data])
+    k_centers = _np.min((n_frames_kmeans, k_centers))
+
+    # 1. Arrive at an approximate dmin by
+    # 1.1 Preliminary clustering
+    pre_cl = _cluster_kmeans(data, k=k_centers, stride=k_stride, init_strategy='uniform' )
+    # 1.2 Distance matrix
+    D = _squareform(_pdist(pre_cl.clustercenters))
+    # 1.3 Define the objective function to be optimized for dmin by interval_schachtelung
+    J = lambda dmin: len(regspace_from_distance_matrix(D, dmin))
+    # 1.4 Optimize for dmin on the preliminary clustercenters
+    dmin = interval_schachtelung(J, [D.min(), D.max()], target=n_clusters_target, verbose=verbose)
+    # 2. Now that we have an approximate dmin, cluster in regspace iteratively:
+    cl = _cluster_regspace(data, dmin=dmin)
+
+    ii = 0
+    while (_np.abs(cl.n_clusters-n_clusters_target) > n_tol):
+        if ii >= max_iter:
+            print("Reached max_iter %u."%ii)
             break
-        if verbose:
-            print('cl iter %u %u -> %u (Delta to target (%u +- %u): %u'%(cc, n_cl_now, cl.n_clusters,
-                                                                         n_clusters_target, err, delta_cl_now))
+        # Distance matrix
+        D = _squareform(_pdist(cl.clustercenters))
+        # Define the objective function to be optimized for dmin
+        J = lambda dmin: len(regspace_from_distance_matrix(D, dmin))
+        # Optimize, starting at the actual dmin
+        dmin = interval_schachtelung(J, [D.min(), D.max()], target=n_clusters_target, verbose=verbose)
+        cl = _cluster_regspace(data, dmin)
+        #print(ii, dmin, cl.n_clusters)
+        ii +=1
+
     return cl
 
+def interval_schachtelung(f, interval, target=0, eps=1, maxiter=1000, verbose=False):
+    r"""
+    Find the value m s.t. f(m) = target +- eps using interval optimization
+
+    :param function to be optimized
+    :param interval: iterable of len 2 with the left and right limits of input interval
+    :param target: objective
+    :param eps: convergence criterion
+    :param verbose: whether to inform of each iteration or not
+    :return: m
+
+    tested : True
+    """
+
+    left, right = interval[0], interval[1]
+    def inform(left, fl, right, fr, middle, fm, delta, eps, str0=''):
+        print(str0)
+        print('left:   %04.2f, f(left)   = %04.2f' % (left, fl))
+        print('middle: %04.2f, f(middle) = %04.2f' % (middle, fm))
+        print('right:  %04.2f, f(right)  = %04.2f' % (right, fr))
+        print('delta, eps =  %f %f, conv: %s'%(delta, eps, ~(delta >= eps)))
+        print()
+
+    middle = (left + right) / 2
+    fl, fr, fm = f(left), f(right), f(middle)
+    delta = _np.abs(fm - target)
+    if verbose:
+        inform(left, fl, right, fr, middle, fm, delta, eps, str0='Init:')
+
+    cc = 0
+    while delta >= eps and cc<maxiter:
+        middle = (left + right) / 2
+        fm = f(middle)
+        delta = _np.abs(fm - target)
+
+        if verbose:
+            inform(left, fl, right, fr, middle, fm, delta, eps, str0='Iter %u'%cc)
+
+        if fl <= target <= fm or fl >= target >= fm:
+            right, fr = middle, fm
+        elif fm <= target <= fr or fm >= target >= fr:
+            left, fl = middle, fm
+        else:
+            print(fl, target, fm, middle)
+            raise Exception("Failed while optimizing")
+        cc += 1
+    return middle
+
+
+
+def regspace_from_distance_matrix(D, dmin):
+    r""" Return the indices idxs of the rows/columns of the symmetric matrix (D[idxs,idxs] > dmin).all() == True
+
+    Can be used as an analogue to :obj:pyemma.coordinates.cluster_regpsace if all pairwise distances have been
+    recomputed
+
+
+    Parameters :
+    ------------
+
+        D: 2D np.ndarray of shape (m,m)
+            Symmetric matrix containing pairwise distances
+
+        dmin : float or int, must be > 0
+            Cutoff
+
+    Returns :
+    ---------
+
+        centers : list
+            Integers of the the rows/columns that contain distances > dmin
+
+    """
+
+    assert _np.allclose(D, D.T), "Input matrix is not symmetric"
+    assert dmin >= 0, ("dmin has to be > 0, not", dmin)
+
+    Dprime = _np.copy(D)
+    _np.fill_diagonal(Dprime, _np.inf)
+    assigned = []
+    for ii, irow in enumerate(Dprime):
+        if ii not in assigned:
+            assigned.extend(_np.argwhere(irow < dmin).flatten())
+    assigned = _np.unique(assigned)
+    centers = [ii for ii in range(Dprime.shape[0]) if ii not in assigned]
+    Dprime = Dprime[centers, :]
+    Dprime = Dprime[:, centers]
+
+    # This is the method testing itself
+    #assert Dprime.min() > dmin, (Dprime.min(), dmin)
+
+    return centers
+
 def min_disp_path(start, path_of_candidates,
                   exclude_coords=None, history_aware=False):
     r""""Returns a list of indices [i,j,k...] s.t. the distances
@@ -592,7 +727,10 @@ def save_traj_wrapper(traj_inp, indexes, outfile, top=None, stride=1, chunksize=
     Parameters
     -----------
 
-    traj_inp : :pyemma:`FeatureReader` object or :mdtraj:`Trajectory` object or list of :mdtraj:`Trajectory` objects
+    traj_inp : Can be of many types
+        :pyemma:`FeatureReader` object
+        :mdtraj:`Trajectory` object or list thereof
+        a list of strings pointing to filenames
 
     returns: see the return values of :pyemma:`save_traj`
     """
@@ -604,11 +742,23 @@ def save_traj_wrapper(traj_inp, indexes, outfile, top=None, stride=1, chunksize=
     if isinstance(traj_inp, (_FeatureReader, _FragmentedTrajectoryReader)) or _is_string(traj_inp[0]):
         geom_smpl = _save_traj(traj_inp, indexes, None, top=top, stride=stride,
                                chunksize=chunksize, image_molecules=image_molecules, verbose=verbose)
+
     elif isinstance(traj_inp[0], _md.Trajectory):
-        file_idx, frame_idx = indexes[0]
-        geom_smpl = traj_inp[file_idx][frame_idx]
-        for file_idx, frame_idx in indexes[1:]:
-            geom_smpl = geom_smpl.join(traj_inp[file_idx][frame_idx])
+        top = traj_inp[0].top
+        n_frames = len(indexes)
+        xyz = _np.zeros((n_frames, top.n_atoms, 3))
+        uc_lengths = _np.zeros((n_frames, 3))
+        uc_angles = _np.zeros((n_frames, 3))
+        time = _np.zeros(n_frames)
+        for ii, (file_idx, frame_idx) in enumerate(indexes):
+            xyz[ii, :, :] = traj_inp[file_idx].xyz[frame_idx*stride].squeeze()
+            uc_lengths[ii, :] = traj_inp[file_idx].unitcell_lengths[frame_idx*stride]
+            uc_angles[ii, :]  = traj_inp[file_idx].unitcell_angles[frame_idx*stride]
+            time[ii] = traj_inp[file_idx].time[frame_idx*stride]
+
+        geom_smpl = _md.Trajectory(xyz, top, time=time,
+                                   unitcell_lengths=uc_lengths,
+                                   unitcell_angles=uc_angles)
     else:
         raise TypeError("Cant handle input of type %s now"%(type(traj_inp[0])))
 
@@ -659,7 +809,7 @@ def get_ascending_coord_idx(pos, fail_if_empty=False, fail_if_more_than_one=Fals
     idxs = _np.argwhere(_np.all(_np.diff(pos,axis=0)>0, axis=0)).squeeze()
     if isinstance(idxs, _np.ndarray) and idxs.ndim==0:
         idxs = idxs[()]
-    elif idxs == [] and fail_if_empty:
+    elif len(idxs)==0 and fail_if_empty:
             raise ValueError('No column was found in ascending order')
 
     if _np.size(idxs) > 1:
@@ -749,10 +899,6 @@ def smooth_geom(geom, n, geom_data=None, superpose=True, symmetric=True):
         Note: you might need to re-orient this averaged geometry again
     """
 
-    # Input checking here, otherwise we're seriously in trouble
-
-
-
     # Get the indices necessary for the running average
     frame_idxs, frame_windows = running_avg_idxs(geom.n_frames, n, symmetric=symmetric)
 
@@ -769,6 +915,7 @@ def smooth_geom(geom, n, geom_data=None, superpose=True, symmetric=True):
     xyz = _np.zeros((len(frame_idxs), geom.n_atoms, 3))
     for ii, idx in enumerate(frame_idxs):
         #print(ii, idx, frame_windows[ii][n])
+        # TODO avoid casting an entire geometry (which triggers deepcopys which are time consuming) 
         if superpose:
             xyz[ii,:,:] = geom[frame_windows[ii]].superpose(geom, frame=frame_windows[ii][n]).xyz.mean(0)
         else:
@@ -899,6 +1046,8 @@ def most_corr(correlation_input, geoms=None, proj_idxs=None, feat_name=None, n_a
 
     for ii in proj_idxs:
         icorr = corr[:, ii]
+        # NaN's will screw up this argsort, so
+        icorr[_np.isnan(icorr)] = 0
         most_corr_idxs.append(_np.abs(icorr).argsort()[::-1][:n_args])
         most_corr_vals.append([icorr[jj] for jj in most_corr_idxs[-1]])
         if geoms is not None and avail_FT:
@@ -910,33 +1059,63 @@ def most_corr(correlation_input, geoms=None, proj_idxs=None, feat_name=None, n_a
             most_corr_labels.append([featurizer.describe()[jj] for jj in most_corr_idxs[-1]])
 
         if avail_FT:
-            if len(featurizer.active_features) > 1:
-                pass
-                # TODO write a warning
-            else:
-                ifeat = featurizer.active_features[0]
-                most_corr_atom_idxs.append(atom_idxs_from_feature(ifeat)[most_corr_idxs[-1]])
+            most_corr_atom_idxs.append([atom_idxs_from_general_input(featurizer)[ii] for ii in most_corr_idxs[-1]])
 
     for ii, iproj in enumerate(proj_names):
         info.append({"lines":[], "name":iproj})
         for jj, jidx in enumerate(most_corr_idxs[ii]):
             if avail_FT:
-                istr = 'Corr[%s|feat] = %2.1f for %-30s (feat nr. %u, atom idxs %s' % \
+                istr = 'Corr[%s|feat] = %2.1f || %-30s || feat nr. %u, atom idxs %s' % \
                        (iproj, most_corr_vals[ii][jj], most_corr_labels[ii][jj], jidx, most_corr_atom_idxs[ii][jj])
             else:
-                istr = 'Corr[%s|feat] = %2.1f (feat nr. %u)' % \
+                istr = 'Corr[%s|feat] = %2.1f || nfeat nr. %u' % \
                        (iproj, most_corr_vals[ii][jj],jidx)
 
             info[-1]["lines"].append(istr)
 
-    corr_dict = {'idxs': most_corr_idxs,
+    corr_dict = CorrelationDict({'idxs': most_corr_idxs,
                  'vals': most_corr_vals,
                  'labels': most_corr_labels,
                  'feats':  most_corr_feats,
                  'atom_idxs': most_corr_atom_idxs,
-                 'info':info}
+                 'info':info})
     return corr_dict
 
+class CorrelationDict(dict):
+    r""" This is just a dictionary
+    with the print method
+    rewritten to a pretty-print"""
+    def __str__(self):
+        nfeats = len(self["idxs"])
+        output = 'Correlation dictionary for %u projections\n'%nfeats
+        for ii in range(nfeats):
+            for iline in self["info"][ii]["lines"]:
+                output += ' '+iline.replace(' || ','\n  ')+'\n'
+            output+='\n'
+
+        return output
+
+def atom_idxs_from_general_input(input):
+    r"""
+    Provided with anything that has a list of ifet.active_features, return the representative
+    atom indices for each feature component
+    :param input: can be TICA, PCA, or MDfeaturizer
+    :return: list of input.dimension() with the atoms involved in each feature
+    """
+
+    if isinstance(input, (_TICA, _PCA)):
+        MDfeat = input.data_producer.featurizer
+    elif isinstance(input, _MDFeaturizer):
+        MDfeat = input
+    else:
+        raise TypeError("Sorry, input has to be of type %s, not %s" % ([_MDFeaturizer, _TICA, _PCA], type(input)))
+
+    # Get atom lists for each active feature
+    out_idxs = [atom_idxs_from_feature(jfeat) for jfeat in MDfeat.active_features]
+    # Get one single list
+    return [item for sublist in out_idxs for item in sublist]
+
+
 def atom_idxs_from_feature(ifeat):
     r"""
     Return the atom_indices that best represent this input feature
@@ -944,10 +1123,11 @@ def atom_idxs_from_feature(ifeat):
     Parameters
     ----------
 
-    ifeat : input feature, can be of two types:
-        a :any:`pyemma.coordinates.featurizer` (Distancefeaturizer, AngleFeaturizer etc) or
-        a :any:`pyemma.coordinates.data.featurization.featurizer.MDFeaturizer` itself, in which case the first of the
-        obj:`ifeat.active_features` will be used
+    ifeat : input featurizer:
+        a PyEMMA Feature. Accepted are
+            DistanceFeature, AngleFeature, DihedralFeature, ResidueMinDistanceFeature, and
+            SelectionFeature,
+            #TODO include link to PyEMMA objects in docstring
 
     Returns
     -------
@@ -955,18 +1135,13 @@ def atom_idxs_from_feature(ifeat):
     atom_indices : list with the atoms indices representative of this feature, whatever the feature
     """
 
-    try:
-        ifeat = ifeat.active_features[0]
-    except AttributeError:
-        pass
-
     if isinstance(ifeat, _DF) and not isinstance(ifeat, _ResMinDF):
         return ifeat.distance_indexes
     elif isinstance(ifeat, _SF):
         return _np.repeat(ifeat.indexes, 3)
     elif isinstance(ifeat, _ResMinDF):
         # Comprehend all the lists!!!!
-        return _np.vstack([[list(ifeat.top.residue(pj).atoms_by_name('CA'))[0].index for pj in pair] for pair in ifeat.contacts])
+        return _np.vstack([[get_repr_atom_for_residue(ifeat.top.residue(pj)).index for pj in pair] for pair in ifeat.contacts])
     if isinstance(ifeat, (_DihF, _AF)):
         ai = ifeat.angle_indexes
         if ifeat.cossin:
@@ -975,6 +1150,23 @@ def atom_idxs_from_feature(ifeat):
     else:
         raise NotImplementedError('bmutils.atom_idxs_from_feature cannot interpret the atoms behind %s yet'%ifeat)
 
+def get_repr_atom_for_residue(rr, cands = ['CA','C','C1']):
+    r"""
+    Tries to return a representative atom per residue. For AAs, it is the CA,
+    then, the next atom-name in cands is looked for
+    :param rr: mdtraj-residue object
+    """
+
+    if rr.n_atoms==1:
+        return list(rr.atoms)[0]
+
+    for cc in cands:
+        out = list(rr.atoms_by_name(cc))
+        if len(out)>0:
+            return out[0]
+    if len(out)==0:
+        raise ValueError("Could not find any atoms named %s in the residue %s"%(cands, rr))
+
 def add_atom_idxs_widget(atom_idxs, ngl_wdg, color_list=None, radius=1):
     r"""
     provided a list of atom_idxs and a ngl_wdg, try to represent them as well as possible in the ngl_wdg
@@ -1019,15 +1211,15 @@ def add_atom_idxs_widget(atom_idxs, ngl_wdg, color_list=None, radius=1):
                     ngl_wdg.add_spacefill(selection=[iidxs], radius=radius, color=color, component=cc)
                 elif _np.ndim(iidxs)>0 and len(iidxs)==2:
                     ngl_wdg.add_distance(atom_pair=[[ii for ii in iidxs]],  # yes it has to be this way for now
-                     color=color,
-                                         #label_color='black',
-                     label_size=0,
+                                         color=color,
+                                         label_size=0,
                                          component=cc)
-                    # TODO add line thickness as **kwarg
+                    # TODO add line thickness as **kwarg, see nglview usage questions for answer
                 elif _np.ndim(iidxs) > 0 and len(iidxs) in [3,4]:
                     ngl_wdg.add_spacefill(selection=iidxs, radius=radius, color=color, component=cc)
                 else:
-                    print("Cannot represent features involving more than 5 atoms per single feature")
+                    raise NotImplementedError("Cannot represent features involving more than 5 atoms per single feature")
+
 
     return ngl_wdg
 
diff --git a/molpx/_linkutils.py b/molpx/_linkutils.py
index f37a763..1649b76 100644
--- a/molpx/_linkutils.py
+++ b/molpx/_linkutils.py
@@ -4,12 +4,13 @@
 from matplotlib.colors import is_color_like as _is_color_like
 from matplotlib.axes import Axes as _mplAxes
 from matplotlib.figure import Figure as _mplFigure
+from matplotlib.patches import Rectangle as _Rectangle
 from IPython.display import display as _ipydisplay
 
 from pyemma.util.types import is_int as _is_int
 from scipy.spatial import cKDTree as _cKDTree
 
-from ._bmutils import get_ascending_coord_idx
+from ._bmutils import get_ascending_coord_idx, add_atom_idxs_widget
 
 from mdtraj import Trajectory as _mdTrajectory
 from nglview import NGLWidget as _NGLwdg
@@ -46,8 +47,6 @@ def pts_per_axis_unit(mplax, pt_per_inch=72):
     inch_per_unit = span_inch / span_units
     return inch_per_unit * pt_per_inch
 
-
-
 def update2Dlines(iline, x, y):
     """
     provide a common interface to update objects on the plot to a new position (x,y) depending
@@ -80,7 +79,6 @@ def update2Dlines(iline, x, y):
             # TODO: FIND OUT WNY EXCEPTIONS ARE NOT BEING RAISED
             raise TypeError("what is this type of 2Dline?")
 
-
 class ClickOnAxisListener(object):
     def __init__(self, ngl_wdg, crosshairs, showclick_objs, ax, pos,
                  list_mpl_objects_to_update):
@@ -91,8 +89,10 @@ def __init__(self, ngl_wdg, crosshairs, showclick_objs, ax, pos,
         self.pos = pos
         self.list_mpl_objects_to_update = list_mpl_objects_to_update
         self.list_of_dots = [None]*self.pos.shape[0]
+        self.list_of_rects = [None] * self.pos.shape[0]
         self.fig_size = self.ax.figure.get_size_inches()
         self.kdtree = None
+        self.axlims = _np.hstack((self.ax.get_xlim(), self.ax.get_ylim()))
 
     def build_tree(self):
         # Use ax.transData to compute distance in pixels
@@ -104,6 +104,19 @@ def build_tree(self):
     def figure_changed_size(self):
         return not _np.allclose(self.fig_size, self.ax.figure.get_size_inches())
 
+    @property
+    def axes_changed(self):
+        current_axlims = _np.hstack((self.ax.get_xlim(), self.ax.get_ylim()))
+        return not _np.allclose(current_axlims, self.axlims)
+
+    def remove_last_contacts(self):
+        try:
+            self.ngl_wdg._CtcsInWid[self.ngl_wdg._CtcsLast].hide()
+            self.list_of_rects[self.ngl_wdg._CtcsLast].remove()
+            self.list_of_rects[self.ngl_wdg._CtcsLast] = None
+        except AttributeError:
+            pass
+
     def __call__(self, event):
         # Wait for the first click or a a figsize change
         # to build the kdtree
@@ -111,6 +124,16 @@ def __call__(self, event):
             self.build_tree()
             self.fig_size = self.ax.figure.get_size_inches()
 
+        # Check axes changes (e.g. in zooming)
+        if self.axes_changed:
+            self.build_tree() # rebuild tree
+            # store new limits
+            self.axlims = _np.hstack((self.ax.get_xlim(),
+                                      self.ax.get_ylim()))
+
+            # Remove spurious contacts from the zooming-click
+            self.remove_last_contacts()
+
         # Was the click inside the bounding box?
         if self.ax.get_window_extent().contains(event.x, event.y):
             if self.crosshairs:
@@ -122,6 +145,8 @@ def __call__(self, event):
                 update2Dlines(idot, self.pos[index, 0], self.pos[index, 1])
 
             self.ngl_wdg.isClick = True
+
+            # The sticky cases like _CtcsInWid or _GeomsInWid are update here instead of via the mplobjects to update
             if hasattr(self.ngl_wdg, '_GeomsInWid'):
                 # We're in a sticky situation
                 if event.button == 1:
@@ -138,6 +163,29 @@ def __call__(self, event):
                     if not self.ngl_wdg._GeomsInWid[index].is_visible() and self.list_of_dots[index] is not None:
                         self.list_of_dots[index].remove()
                         self.list_of_dots[index] = None
+
+
+            elif hasattr(self.ngl_wdg, '_CtcsInWid'):
+                if event.button == 1:
+                    self.ngl_wdg._CtcsInWid[index].show()
+                    # Plot and store the rectangle in case there wasn't
+                    if self.list_of_rects[index] is None:
+                        rectangle_padding = 0 # TODO future plans to make rectangles catch more than one ctc
+                        x, y = self.pos[index]
+                        self.list_of_rects[index] = self.ax.add_patch(_Rectangle(
+                            (x - .5 - rectangle_padding,
+                             y - .5 - rectangle_padding),  # (x,y)
+                             1 + 2 * rectangle_padding,  # width
+                             1 + 2 * rectangle_padding,  # height,
+                             fill=False,
+                             linewidth=2,
+                             edgecolor=self.ngl_wdg._CtcsInWid[index].color))
+                elif event.button in [2,3]:
+                    # Pressed left
+                    self.ngl_wdg._CtcsInWid[index].hide()
+                    if self.list_of_rects[index] is not None:
+                        self.list_of_rects[index].remove()
+                        self.list_of_rects[index] = None
             else:
                 # We're not sticky, just go to the frame
                 self.ngl_wdg.frame = index
@@ -179,8 +227,8 @@ def append_if_existing(self, args0, startswith_arg="linked_"):
                     self.__dict__[attrname] += iarg.__dict__[attrname]
 
 def auto_append_these_mpx_attrs(iobj, *attrs):
-    r""" The attribute s name is automatically derived
-    from the attribute s type via a type:name dictionary
+    r""" The attribute's name is automatically derived
+    from the attribute's type via a type:name dictionary
 
     *attrs : any number of unnamed objects of the types in type2attrname.
              If the object type is a list, it will be flattened prior to attempting
@@ -238,6 +286,80 @@ def __call__(self, change):
             print("caught index error with index %s (new=%s, old=%s)" % (_idx, change["new"], change["old"]))
         #print("set xy = (%s, %s)" % (x[_idx], y[_idx]))
 
+        if hasattr(self.ngl_wdg, '_MatshowData'):
+            self.ngl_wdg._MatshowData["image"].set_data(self.ngl_wdg._MatshowData["data"][_idx])
+
+
+class ContactInNGLWidget(object):
+    r"""
+    returns an object that is aware about its own atom-indices and
+    its own representation index in the widget. It also has access to the widget itself
+    With that knowlegde, one can use the methods .show() and .hide()
+
+    param : ngl_widget, the widget upon which to superpose the contact
+    param : atom_indices, len(2), atom indices involved in this contact
+    param : contact_index, int, the index corresponding to this contact
+    """
+
+
+    def __init__(self, ngl_wdg, atom_indices, contact_index,
+                 component_to_draw_on=0,
+                 verbose=False,
+                 color=None,
+                 sequential_residues=True):
+        r"""
+
+        :param ngl_wdg:
+        :param atom_indices: iterable of len(2) with two integers (zero indexed atom indices)
+            The pair of atoms that are representative of this contact
+            When the method .show() of this class is called, a distance representation (=a line joining two atoms)
+            will be added to the :obj:`ngl_widget`.
+        :param contact_index: integer
+            The position of this contact in the contact list
+        :param component_to_draw_on:
+        :param verbose:
+        :param color:
+        """
+        assert len(atom_indices)==2, "ContactInNGLWidget takes a list with two elements as input, not len(%u)"%len(atom_indices)
+        assert [isinstance(ii, int) for ii in atom_indices], "The atom indices have to be type int"
+
+        self.atom_indices = atom_indices
+        self.ngl_wdg = ngl_wdg
+        self.contact_index = contact_index
+        self.verbose = verbose
+        self.top = self.ngl_wdg._trajlist[component_to_draw_on].trajectory.top
+        self.comp = component_to_draw_on
+        self.shown = False
+        self.color = color
+
+    def show(self):
+        if not self.shown:
+            if self.verbose:
+                print("Showing contact %s via atoms  %s"%(' '.join(['%s'%self.top.atom(ii).residue for ii in self.atom_indices]),
+                                                          ' '.join(['%s' % self.top.atom(ii) for ii in self.atom_indices])))
+
+            self.shown = True
+            add_atom_idxs_widget([self.atom_indices], self.ngl_wdg, color_list=[self.color])
+            self.ngl_wdg._CtcsLast = self.contact_index
+
+    def hide(self):
+        if self.shown:
+            #print(self.ngl_wdg._ngl_repr_dict[str(self.comp)].keys())
+            for key in self.matching_repr_keys:
+                self.ngl_wdg._remove_representation(self.comp, repr_index=int(key))
+            self.shown = False
+
+    @property
+    def matching_repr_keys(self):
+        # Given that the _ngl_repr_dict gets updated elsewhere, this is the most robust way of
+        # finding this contact's representations
+        try:
+            res = [key for key, value in self.ngl_wdg._ngl_repr_dict[str(self.comp)].items() if value["type"] == "distance"
+                and _np.allclose(_np.sort(value["params"]["atomPair"]), _np.sort(self.atom_indices))]
+        except KeyError:
+            res = []
+        return res
+
 class GeometryInNGLWidget(object):
     r"""
     returns an object that is aware of where its geometries are located in the NGLWidget their representation status
@@ -400,6 +522,8 @@ def link_ax_w_pos_2_nglwidget(ax, pos, ngl_wdg,
     # Are we in a sticky situation?
     if hasattr(ngl_wdg, '_GeomsInWid'):
         sticky = True
+    elif hasattr(ngl_wdg, "_CtcsInWid"):
+        pass
     else:
         assert ngl_wdg.trajectory_0.n_frames == pos.shape[0], \
             ("Mismatching frame numbers %u vs %u" % (ngl_wdg.trajectory_0.n_frames, pos.shape[0]))
@@ -462,6 +586,7 @@ def link_ax_w_pos_2_nglwidget(ax, pos, ngl_wdg,
     # Connect axes to widget
     axes_widget = _AxesWidget(ax)
     if directionality in [None, 'a2w']:
+        # TODO since zooming events also contain button_release events this will be triggered as well
         axes_widget.connect_event('button_release_event', CLA_listener)
 
     # Connect widget to axes
diff --git a/molpx/generate.py b/molpx/generate.py
index 0314714..5cefa32 100644
--- a/molpx/generate.py
+++ b/molpx/generate.py
@@ -117,8 +117,8 @@ def projection_paths(MD_trajectories, MD_top, projected_trajectories,
                              "min_disp": _defdict(dict)
                            }
         # Cluster in regspace along the dimension you want to advance, to approximately n_points
-        cl = _bmutils.regspace_cluster_to_target([jdata[:,[coord]] for jdata in idata],
-                                n_points, n_try_max=3,
+        cl = _bmutils.regspace_cluster_to_target_kmeans([jdata[:,[coord]] for jdata in idata],
+                                n_points, max_iter=3,
                                 verbose=verbose,
                                 )
 
@@ -209,7 +209,7 @@ def projection_paths(MD_trajectories, MD_top, projected_trajectories,
 
         #TODO : consider storing the data in each dict. It's redundant but makes each dict kinda standalone
 
-    return paths_dict, idata
+    return paths_dict , idata # why were we returning idata in the first place?
 
 
 def sample(MD_trajectories, MD_top, projected_trajectories,
@@ -298,7 +298,7 @@ def sample(MD_trajectories, MD_top, projected_trajectories,
         cl = projected_trajectories
     except:
         idata = _bmutils.data_from_input(projected_trajectories)
-        cl = _bmutils.regspace_cluster_to_target([dd[:,proj_idxs] for dd in idata], n_points, n_try_max=10, verbose=verbose)
+        cl = _bmutils.regspace_cluster_to_target_kmeans([dd[:,proj_idxs] for dd in idata], n_points, verbose=verbose)
 
     pos = cl.clustercenters
     cat_smpl = cl.sample_indexes_by_cluster(_np.arange(cl.n_clusters), n_geom_samples)
diff --git a/molpx/notebooks/0.molPX_quick_intro_Ala2.ipynb b/molpx/notebooks/0.molPX_quick_intro_Ala2.ipynb
index bd316f1..bfe1ae5 100644
--- a/molpx/notebooks/0.molPX_quick_intro_Ala2.ipynb
+++ b/molpx/notebooks/0.molPX_quick_intro_Ala2.ipynb
@@ -16,13 +16,11 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "import molpx\n",
-    "%matplotlib ipympl"
+    "import molpx"
    ]
   },
   {
@@ -35,9 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "top = molpx._molpxdir(join='notebooks/data/ala2.pdb')\n",
@@ -70,27 +66,34 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cbfb5bb0aa114fce88e90ae57fdd123f",
+       "model_id": "617716dc19d74f83aa878ea096b78ea3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25-05-18 14:00:15 pyemma.coordinates.clustering.kmeans.KmeansClustering[0] INFO     Cluster centers converged after 5 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d18c35c79bbb43a28b230975961aa179",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(NGLWidget(count=101), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -130,27 +133,34 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e5ff550df1784bc698a701c5fcad3490",
+       "model_id": "564bc9bc5ef841038a73725c9978b7dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25-05-18 14:00:21 pyemma.coordinates.clustering.kmeans.KmeansClustering[9] INFO     Cluster centers converged after 7 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a8b49758df04e2dbc50606d7fea0b7e",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXVBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXVBox(children=(MolPXHBox(children=(VBox(children=(Button(description='NGL widgets', layout=Layout(width='100%'), style=ButtonStyle()), VBox(children=(NGLWidget(count=3334),), layout=Layout(border='solid'))), layout=Layout(height='2.0in', width='5.0in')), FigureCanvasNbAgg())), MolPXHBox(children=(NGLWidget(count=101), FigureCanvasNbAgg()))))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -158,12 +168,6 @@
     }
    ],
    "source": [
-    "from molpx import visualize, _linkutils\n",
-    "from imp import reload\n",
-    "reload(visualize)\n",
-    "reload(_linkutils)\n",
-    "from matplotlib import pyplot as plt\n",
-    "plt.close('all')\n",
     "mpx_wdg_box = molpx.visualize.traj(MD_trajfiles,  \n",
     "                                   top, \n",
     "                                   rama_files,\n",
@@ -185,9 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -204,27 +206,34 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e427834063234005be0f3c41f8f7d04e",
+       "model_id": "875dcdec40b246e984881c17785b16f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25-05-18 14:00:31 pyemma.coordinates.clustering.kmeans.KmeansClustering[18] INFO     Cluster centers converged after 6 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46d4a5d0d9e647cb9838735556fae885",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(NGLWidget(count=101), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -252,12 +261,12 @@
    "metadata": {},
    "source": [
     "# The ```mpx_wdg_box```\n",
-    "It is a class derived from the ipython widgets HBox and VBox, with molPX's extra information as attributes starting with \"linked_*\" "
+    "It is a class derived from the ipython widgets [HBox](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html?highlight=Hbox#Container/Layout-widgets) and [VBox](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html?highlight=Hbox#Container/Layout-widgets), with molPX's extra information as attributes starting with `linked_*` "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +306,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.2"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/molpx/notebooks/1.molPX_and_PyEMMA_Features.ipynb b/molpx/notebooks/1.molPX_and_PyEMMA_Features.ipynb
index c2cc5a9..f4a5d00 100644
--- a/molpx/notebooks/1.molPX_and_PyEMMA_Features.ipynb
+++ b/molpx/notebooks/1.molPX_and_PyEMMA_Features.ipynb
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,8 +43,7 @@
     "dt = 244 #saving interval in the .xtc files, in ns\n",
     "\n",
     "import molpx\n",
-    "from matplotlib import pylab as plt\n",
-    "%matplotlib ipympl\n",
+    "from matplotlib import pyplot as plt\n",
     "import pyemma\n",
     "import numpy as np\n",
     "\n",
@@ -55,10 +54,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Create a memory representation of the trajectories\n",
@@ -67,47 +64,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e07101841c2542439184b1e66b4da372",
+       "model_id": "feaf7c33ce0b4e7ab136de29a5c70eeb",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>Box</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "Box(children=(Text(value=''), IntProgress(value=0)))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "113eec10d13c4fac8c620c7d30c7a519",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "('DIST: PRO 9 CA 8 - LYS 15 CA 14',\n",
        " 'DIST: PRO 9 CA 8 - TYR 23 CA 22',\n",
-       " <pyemma.coordinates.data.featurization.featurizer.MDFeaturizer at 0x7efc407e7550>)"
+       " MDFeaturizer with features:\n",
+       " ['DIST: ARG 1 CA 0 - ASP 3 CA 2',\n",
+       "  'DIST: ARG 1 CA 0 - CYS 5 CA 4',\n",
+       "  'DIST: ARG 1 CA 0 - GLU 7 CA 6',\n",
+       "  'DIST: ARG 1 CA 0 - PRO 9 CA 8',\n",
+       "  'DIST: ARG 1 CA 0 - THR 11 CA 10',\n",
+       "  'DIST: ARG 1 CA 0 - PRO 13 CA 12',\n",
+       "  'DIST: ARG 1 CA 0 - LYS 15 CA 14',\n",
+       "  'DIST: ARG 1 CA 0 - ARG 17 CA 16',\n",
+       "  'DIST: ARG 1 CA 0 - ILE 19 CA 18',\n",
+       "  'DIST: ARG 1 CA 0 - TYR 21 CA 20', ...])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
@@ -138,56 +158,55 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e585639a2d214150b6bf44bddaf096cd",
+       "model_id": "bbd5a7072c454ab79f03f3c83c41e3ea",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>Box</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "Box(children=(Text(value=''), IntProgress(value=0)))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 16:34:21 pyemma.coordinates.clustering.kmeans.KmeansClustering[2] INFO     Algorithm did not reach convergence criterion of 1e-05 in 10 iterations. Consider increasing max_iter.\n",
+      "\r"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "de2388685daf4abe9f7bee72bbf84972",
+       "model_id": "58ef338dfc4d48c4b2c4f6715ca911f8",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(NGLWidget(count=95), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4e8d69171154cb485c7d7a71d047fb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -206,7 +225,7 @@
     "                                  #n_overlays=5,\n",
     "                                  #sticky=True,\n",
     "                                 )\n",
-    "__ = molpx.visualize.feature(feat.active_features[0], \n",
+    "__ = molpx.visualize.feature(feat, \n",
     "                             mpx_wdg_box.linked_ngl_wdgs[0], \n",
     "                             idxs=proj_idxs, radius=.5, \n",
     "                             color_list=['red','green'])\n",
@@ -223,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "scrolled": false
    },
@@ -231,27 +250,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "41da98402f6f4ab296faa44fee8a6369",
+       "model_id": "b04bd522acca45aa979ef3fbf163085c",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(VBox(children=(Button(description='NGL widgets', layout=Layout(width='100%'), style=ButtonStyle()), VBox(children=(NGLWidget(count=4125),), layout=Layout(border='solid'))), layout=Layout(height='2.0in', width='5.0in')), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -269,7 +273,7 @@
     "                                  panel_height=1,    \n",
     "                                  proj_labels='feat',\n",
     "                  )\n",
-    "molpx.visualize.feature(feat.active_features[0], mpx_wdg_box.linked_ngl_wdgs[0], idxs=proj_idxs)\n",
+    "molpx.visualize.feature(feat, mpx_wdg_box.linked_ngl_wdgs[0], idxs=proj_idxs)\n",
     "mpx_wdg_box"
    ]
   },
@@ -282,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -291,7 +295,7 @@
        "('ATOM:PRO 9 CA 8 x', 'ATOM:LYS 15 CA 14 x')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -305,62 +309,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8a9a553996a44aa0ab2b34cfe5946941",
+       "model_id": "669bd2415d00423b99d531b76ab92d0a",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>Box</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "Box(children=(Text(value=''), IntProgress(value=0)))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:16:35 pyemma.coordinates.clustering.kmeans.KmeansClustering[30] INFO     Algorithm did not reach convergence criterion of 1e-05 in 10 iterations. Consider increasing max_iter.\n",
+      "\r"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c7a939e0f10e49d79db78074921df608",
+       "model_id": "418ca4b4e62042908bcb6cbc4acd6f1f",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(NGLWidget(count=103), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8da959d7add54ef58439fd5b7d358b22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -377,7 +380,7 @@
     "                                  proj_labels='feat',\n",
     "                                  #n_overlays=5,\n",
     "                                          )\n",
-    "molpx.visualize.feature(feat.active_features[0], mpx_wdg_box.linked_ngl_wdgs[0], idxs=proj_idxs, color_list=['red','green'])\n",
+    "molpx.visualize.feature(feat, mpx_wdg_box.linked_ngl_wdgs[0], idxs=proj_idxs, color_list=['red','green'])\n",
     "mpx_wdg_box"
    ]
   },
@@ -390,41 +393,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0f597ac9a85431fa6600c772d4520c9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>Box</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "Box(children=(Text(value=''), IntProgress(value=0)))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from os.path import exists\n",
     "top = molpx._molpxdir(join='notebooks/data/ala2.pdb')\n",
@@ -446,33 +420,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "431f665cb3c2452094c78ec3ccc0503b",
+       "model_id": "465d9173d4864fe58cba25ca2b1fe31d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:17:16 pyemma.coordinates.clustering.kmeans.KmeansClustering[41] INFO     Cluster centers converged after 8 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "304c9b268c2b4f38a499d1e25a1f0596",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(NGLWidget(count=101), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -488,7 +469,7 @@
     "                                  proj_idxs=proj_idxs,\n",
     "                                  proj_labels=feat,                                                 \n",
     "                                 )\n",
-    "molpx.visualize.feature(feat.active_features[0], mpx_wdg_box.linked_ngl_wdgs[0], idxs=[0], radius=.5)\n",
+    "molpx.visualize.feature(feat, mpx_wdg_box.linked_ngl_wdgs[0], idxs=[0], radius=.5)\n",
     "mpx_wdg_box"
    ]
   }
@@ -510,7 +491,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.2"
   },
   "widgets": {
    "state": {
diff --git a/molpx/notebooks/2.molPX_TICA_BPTI.ipynb b/molpx/notebooks/2.molPX_TICA_and_MSMs_BPTI.ipynb
similarity index 51%
rename from molpx/notebooks/2.molPX_TICA_BPTI.ipynb
rename to molpx/notebooks/2.molPX_TICA_and_MSMs_BPTI.ipynb
index 9331ab6..b776bc0 100644
--- a/molpx/notebooks/2.molPX_TICA_BPTI.ipynb
+++ b/molpx/notebooks/2.molPX_TICA_and_MSMs_BPTI.ipynb
@@ -32,10 +32,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "top = 'notebooks/data/bpti-c-alpha_centered.pdb'\n",
@@ -47,8 +45,7 @@
     "dt = 244 #saving interval in the .xtc files, in ns\n",
     "\n",
     "import molpx\n",
-    "from matplotlib import pylab as plt\n",
-    "%matplotlib ipympl\n",
+    "from matplotlib import pyplot as plt\n",
     "import pyemma\n",
     "import numpy as np\n",
     "\n",
@@ -66,10 +63,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Create a memory representation of the trajectories\n",
@@ -89,10 +84,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Perform TICA or read from file directly if already .npy-files exist\n",
@@ -122,11 +115,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b93db910c62043b1822f2da274e45bb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 17:43:04 pyemma.coordinates.clustering.kmeans.KmeansClustering[0] INFO     Cluster centers converged after 6 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6df52f04ead341aab7b20d98f91eb0d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "256408dce1b44035854355e92d01b09e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mpx_wdg_box = molpx.visualize.FES(MD_list, \n",
     "                                 #MD_trajfiles, \n",
@@ -157,11 +208,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39be6324f1e54066a228104bc20fa991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11437cf4759f4cebb10127ed094ce173",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 17:43:10 pyemma.coordinates.clustering.kmeans.KmeansClustering[10] INFO     Algorithm did not reach convergence criterion of 1e-05 in 10 iterations. Consider increasing max_iter.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00163b8d1ac648629531b16d9c8eadfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed24cba5be9f4813b1e976916f04bb4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mpx_wdg_box = molpx.visualize.traj(MD_trajfiles, \n",
     "                                   top, \n",
@@ -192,9 +322,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "974210f2d77a4033828f91883f705eb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8adfc977e5494497bce705a331d237f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 17:43:15 pyemma.coordinates.clustering.kmeans.KmeansClustering[19] INFO     Algorithm did not reach convergence criterion of 1e-05 in 10 iterations. Consider increasing max_iter.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad74ec33d703455bbc8297b642246bf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "((203, 2),\n",
+       " <mdtraj.Trajectory with 203 frames, 58 atoms, 58 residues, and unitcells at 0x7f56cb248860>)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_sample, geoms = molpx.generate.sample(#MD_list, \n",
     "                                           MD_trajfiles, \n",
@@ -217,11 +423,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f42f00335bb40bab5b36ddcd3013237",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mi/gph82/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e16537f6b2542b9bd40e698449303e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Replot the FES\n",
     "plt.figure(figsize=(7,7))\n",
@@ -248,9 +491,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72b81eff927046788368e764761364f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ad556c7f8b34f15bab0502b97bc9253",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 17:43:20 pyemma.coordinates.clustering.kmeans.KmeansClustering[30] INFO     Algorithm did not reach convergence criterion of 1e-05 in 10 iterations. Consider increasing max_iter.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb4c74d060e44891a91d7eb018c3d9b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ccb4d20d14e442f9dfd90ff2724a416",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 17:43:26 pyemma.coordinates.clustering.kmeans.KmeansClustering[37] INFO     Cluster centers converged after 11 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01e70ca23c534d1bba761237e8ed1ed3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    }
+   ],
    "source": [
     "paths_dict, idata = molpx.generate.projection_paths(#MD_list, \n",
     "                                                    MD_trajfiles, \n",
@@ -275,10 +626,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Choose the coordinate and the tyep of path\n",
@@ -295,11 +644,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cea24390e26045d7b2ea7060d7618df1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mi/gph82/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fa71b389f3d46e1ade55939c28befdd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(7,7))\n",
     "h, (x,y) = np.histogramdd(np.vstack(Y)[:,proj_idxs], bins=50)\n",
@@ -329,9 +715,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d72f71f0a2fb421ab0d3dc7cee6e8a65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0e14a9f44aa4ad29b4be25e91676e2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "562c71a39bba464dab6ef3019fab5749",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    }
+   ],
    "source": [
     "# Re-do the TICA computation to make sure we have a tica object in memory\n",
     "feat = pyemma.coordinates.featurizer(top)\n",
@@ -351,9 +801,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c342c71b102a4c6284f3cd852d652277",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Comment or uncomment the optinal parameters and see how the method reacts\n",
     "# You can use a pre-instantiated the widget\n",
@@ -371,22 +836,156 @@
     "ngl_wdg"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the correlation-dictionary's modified `print` function to see what's inside in a human-friendly way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation dictionary for 3 projections\n",
+      " Corr[proj_0|feat] = 0.8\n",
+      "  DIST: PRO 9 CA 8 - TYR 23 CA 22\n",
+      "  feat nr. 112, atom idxs [ 8 22]\n",
+      " Corr[proj_0|feat] = 0.7\n",
+      "  DIST: PRO 9 CA 8 - TYR 21 CA 20\n",
+      "  feat nr. 111, atom idxs [ 8 20]\n",
+      " Corr[proj_0|feat] = 0.7\n",
+      "  DIST: PRO 9 CA 8 - LEU 29 CA 28\n",
+      "  feat nr. 115, atom idxs [ 8 28]\n",
+      "\n",
+      " Corr[proj_1|feat] = 0.7\n",
+      "  DIST: PRO 9 CA 8 - LYS 15 CA 14\n",
+      "  feat nr. 108, atom idxs [ 8 14]\n",
+      " Corr[proj_1|feat] = 0.7\n",
+      "  DIST: LYS 15 CA 14 - TYR 23 CA 22\n",
+      "  feat nr. 178, atom idxs [14 22]\n",
+      " Corr[proj_1|feat] = 0.6\n",
+      "  DIST: LYS 15 CA 14 - PHE 33 CA 32\n",
+      "  feat nr. 183, atom idxs [14 32]\n",
+      "\n",
+      " Corr[proj_2|feat] = 0.6\n",
+      "  DIST: THR 11 CA 10 - GLY 37 CA 36\n",
+      "  feat nr. 142, atom idxs [10 36]\n",
+      " Corr[proj_2|feat] = -0.6\n",
+      "  DIST: PRO 13 CA 12 - GLN 31 CA 30\n",
+      "  feat nr. 161, atom idxs [12 30]\n",
+      " Corr[proj_2|feat] = -0.6\n",
+      "  DIST: PRO 13 CA 12 - PHE 33 CA 32\n",
+      "  feat nr. 162, atom idxs [12 32]\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(corr)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Also, `molpx.visualize.traj` can help in visualizing these correlations by parsing along the tica object itself as `projection=tica`. In the next cell, can you spot the differences:\n",
     "* In the nglwidget?\n",
-    "* In the trajectories?"
+    "* In the trajectories?\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65b233004ff94b50898fab38b6f0c8cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a2dc05b2e8a400a896351e4b94c23bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 17:43:37 pyemma.coordinates.clustering.kmeans.KmeansClustering[51] INFO     Cluster centers converged after 10 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e52ab46d051d476790f404609676d032",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4e850af19134552807b4ac5f7cb273f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Reuse the visualize.traj method with the tica object as input\n",
     "mpx_wdg_box = molpx.visualize.traj(MD_trajfiles,     \n",
@@ -418,9 +1017,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dce09a99533e49abbd4e74792e062ebb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f82ae020d8434492a020cdbe2a09e84d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 17:43:40 pyemma.coordinates.clustering.kmeans.KmeansClustering[60] INFO     Algorithm did not reach convergence criterion of 1e-05 in 10 iterations. Consider increasing max_iter.\n",
+      "\r"
+     ]
+    }
+   ],
    "source": [
     "# Do \"some\" clustering\n",
     "clkmeans = pyemma.coordinates.cluster_kmeans([iY[:,:2] for iY in Y], 5)"
@@ -428,9 +1064,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e36da50b0dd4ee1b41308d8ab785784",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06109eae31b54fdfb18deb63d049b389",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    }
+   ],
    "source": [
     "data_sample, geoms = molpx.generate.sample(MD_trajfiles, top, clkmeans, \n",
     "                                     n_geom_samples=50, \n",
@@ -440,9 +1119,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a1713fbf25f48a98f4e4a06425242a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mi/gph82/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log\n",
+      "  \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(NGLWidget(count=5), <matplotlib.widgets.AxesWidget at 0x7f56893edf98>)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Plot clusters\n",
     "plt.figure(figsize=(4,4))\n",
@@ -466,52 +1178,141 @@
    "metadata": {},
    "source": [
     "## Visual representations for MSMs\n",
-    "Visually inspect the network behind an MSM"
+    "Visually inspect the network behind an MSM by coarse graining it to an HMM"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0feee8230d324ad5ac0487713612bae5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5c7ca2d145d4d96a5b4068a8d98fd49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30-05-18 17:43:43 pyemma.coordinates.clustering.kmeans.KmeansClustering[63] INFO     Cluster centers converged after 10 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a61ea20e54874a118e9107a4027f2339",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    }
+   ],
    "source": [
-    "MSM = pyemma.msm.estimate_markov_model(clkmeans.dtrajs, 20)"
+    "clkmeans = pyemma.coordinates.cluster_kmeans([iY[:,:2] for iY in Y], 100)\n",
+    "MSM = pyemma.msm.estimate_markov_model(clkmeans.dtrajs, 1)\n",
+    "MSMcg = MSM.coarse_grain(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'molpx.visualize' from '/home/mi/gph82/SOURCE_gph82/python/molPX/molpx/visualize.py'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "plt.figure(figsize=(4,4))\n",
-    "\n",
-    "ax, pos  = pyemma.plots.plot_markov_model(MSM.P, \n",
-    "                                          minflux=5e-4, \n",
-    "                                          arrow_labels=None,\n",
-    "                                          ax=plt.gca(), \n",
-    "                                          arrow_curvature = 2, show_frame=True,\n",
-    "                                          pos=clkmeans.clustercenters)\n",
-    "# Add a background if wanted\n",
-    "h, (x, y) = np.histogramdd(np.vstack(Y)[:,:2], weights=np.hstack(MSM.trajectory_weights()),  bins=50)\n",
-    "plt.contourf(x[:-1], y[:-1], -np.log(h.T), cmap=\"jet\", alpha=.5, zorder=0)\n",
-    "plt.xlim(x[[0,-1]])\n",
-    "plt.xticks(np.unique(x.round()))\n",
-    "plt.yticks(np.unique(y.round()))\n",
-    "\n",
-    "plt.ylim(y[[0,-1]])\n",
-    "\n",
-    "linked_ngl_wd, linked_ax_wd = molpx.visualize.sample(pos, geoms, plt.gca(), dot_color='blue')\n",
-    "linked_ngl_wd"
+    "from imp import reload\n",
+    "from molpx import visualize\n",
+    "reload(visualize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1af06d7323f342078943825a30d25488",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mpxbox = molpx.visualize.MSM(MSMcg, src, \n",
+    "                             pos=clkmeans.clustercenters,\n",
+    "                              #sticky=True, \n",
+    "                              sharpen=True, \n",
+    "                              n_overlays=10,\n",
+    "                              figpadding=.5,\n",
+    "                              #proj_idxs =[1,0]\n",
+    "                             panelsize=6\n",
+    "             )\n",
+    "mpxbox"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## TPT Reactive Pathway Representation"
+    "## TPT Reactive Pathway Representation\n",
+    "Until we add a method for doing this explicitly, you can do it in a few lines of code"
    ]
   },
   {
@@ -529,9 +1330,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Use this object to sample geometries\n",
@@ -557,9 +1356,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Create a TPT object with most_pop, least_pop as source, sink respectively\n",
@@ -571,7 +1368,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -599,9 +1395,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -623,7 +1417,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.2"
   },
   "widgets": {
    "state": {
diff --git a/molpx/notebooks/3.molPX_TICA_Ala2.ipynb b/molpx/notebooks/3.molPX_TICA_Ala2.ipynb
index 13f3c27..632b9dd 100644
--- a/molpx/notebooks/3.molPX_TICA_Ala2.ipynb
+++ b/molpx/notebooks/3.molPX_TICA_Ala2.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "collapsed": true,
     "scrolled": true
@@ -23,9 +23,7 @@
    "source": [
     "from os.path import exists\n",
     "import molpx\n",
-    "from matplotlib import pylab as plt\n",
-    "%matplotlib ipympl\n",
-    "\n",
+    "from matplotlib import pyplot as plt\n",
     "import pyemma\n",
     "import numpy as np"
    ]
@@ -39,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
@@ -64,39 +62,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>Box</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "feat = pyemma.coordinates.featurizer(top)\n",
     "feat.add_backbone_torsions()\n",
@@ -114,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {
     "scrolled": false
    },
@@ -122,27 +92,34 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "415deb3f4353423b87629d4233d2a651",
+       "model_id": "c0116accc78044a19e78dc903d7a6714",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(NGLWidget(count=101), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 18:03:14 pyemma.coordinates.clustering.kmeans.KmeansClustering[1] INFO     Cluster centers converged after 9 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01397594f745427b80e6fe228811a7b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -174,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
@@ -182,27 +159,34 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f9ccc4547b6f4485b6151424a17497bc",
+       "model_id": "eab14edbbedd4e938b0b6bcaff4f8a39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:57:00 pyemma.coordinates.clustering.kmeans.KmeansClustering[10] INFO     Cluster centers converged after 8 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd8492f500e3491d875b01377127d2a4",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXVBox</code>.</p>\n",
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-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXVBox(children=(MolPXHBox(children=(VBox(children=(Button(description='NGL widgets', layout=Layout(width='100%'), style=ButtonStyle()), VBox(children=(NGLWidget(count=3334),), layout=Layout(border='solid'))), layout=Layout(height='4.0in', width='5.0in')), FigureCanvasNbAgg())), MolPXHBox(children=(NGLWidget(count=101), FigureCanvasNbAgg()))))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -236,11 +220,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0a39610ce0941eabac5fe0b7e476ae0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:57:13 pyemma.coordinates.clustering.kmeans.KmeansClustering[19] INFO     Cluster centers converged after 7 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee3c7af803c84881aac36bf04b173caf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:57:25 pyemma.coordinates.clustering.kmeans.KmeansClustering[24] INFO     Cluster centers converged after 6 steps.\n",
+      "\r"
+     ]
+    }
+   ],
    "source": [
     "paths_dict, idata = molpx.generate.projection_paths(MD_trajfiles, \n",
     "                                                    top, \n",
@@ -255,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
@@ -275,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {
     "scrolled": false
    },
@@ -291,27 +318,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "01623d70d8e844b1bff091673b855024",
+       "model_id": "fd40a5458fb34089a98b011394ddd2ae",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "HBox(children=(NGLWidget(count=45), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -349,74 +361,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "01-11-17 21:01:44 pyemma.coordinates.data.featurization.featurizer.MDFeaturizer[7] WARNING  The 1D arrays input for add_distances() have been sorted, and index duplicates have been eliminated.\n",
+      "22-05-18 17:57:50 pyemma.coordinates.data.featurization.featurizer.MDFeaturizer[30] WARNING  The 1D arrays input for add_distances() have been sorted, and index duplicates have been eliminated.\n",
       "Check the output of describe() to see the actual order of the features\n"
      ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
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-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
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-       "</p>\n"
-      ],
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-       "  Widgets Documentation</a> for setup instructions.\n",
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@@ -430,33 +384,40 @@
   },
   {
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     {
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        "version_minor": 0
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-      "text/html": [
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-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(NGLWidget(count=103), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:57:54 pyemma.coordinates.clustering.kmeans.KmeansClustering[34] INFO     Cluster centers converged after 10 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
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+       "A Jupyter Widget"
       ]
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      "metadata": {},
@@ -477,33 +438,40 @@
   },
   {
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-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
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+       "model_id": "7f7190815ded4204b5c686eff467746b",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXVBox</code>.</p>\n",
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-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
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-      ],
       "text/plain": [
-       "MolPXVBox(children=(MolPXHBox(children=(VBox(children=(Button(description='NGL widgets', layout=Layout(width='100%'), style=ButtonStyle()), VBox(children=(NGLWidget(count=3334),), layout=Layout(border='solid'))), layout=Layout(height='8.0in', width='5.0in')), FigureCanvasNbAgg())), MolPXHBox(children=(NGLWidget(count=103), FigureCanvasNbAgg()))))"
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:58:17 pyemma.coordinates.clustering.kmeans.KmeansClustering[43] INFO     Cluster centers converged after 8 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f755a7aa0cf4b69b633789b2c0d3bad",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -526,11 +494,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05acfdfb30ed47a7ace60534c7a9384a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:59:02 pyemma.coordinates.clustering.kmeans.KmeansClustering[65] INFO     Cluster centers converged after 6 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "298bcecb731d4c80b4c45149d005faf5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:59:13 pyemma.coordinates.clustering.kmeans.KmeansClustering[74] INFO     Cluster centers converged after 6 steps.\n",
+      "\r"
+     ]
+    }
+   ],
    "source": [
     "paths_dict, idata = molpx.generate.projection_paths(MD_trajfiles, \n",
     "                                                    top, \n",
@@ -545,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
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@@ -565,33 +576,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
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        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>FigureCanvasNbAgg</code>.</p>\n",
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-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
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-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "FigureCanvasNbAgg()"
+       "A Jupyter Widget"
       ]
      },
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@@ -609,27 +605,12 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
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        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>NGLWidget</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "NGLWidget(count=45)"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -653,15 +634,6 @@
     "linked_wdg.center_view()\n",
     "linked_wdg"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -681,7 +653,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.2"
   },
   "widgets": {
    "state": {
diff --git a/molpx/notebooks/4.molPX_metadynamics_Di-Ala.ipynb b/molpx/notebooks/4.molPX_metadynamics_Di-Ala.ipynb
index 629ce56..19919b8 100644
--- a/molpx/notebooks/4.molPX_metadynamics_Di-Ala.ipynb
+++ b/molpx/notebooks/4.molPX_metadynamics_Di-Ala.ipynb
@@ -26,7 +26,6 @@
    "source": [
     "import molpx\n",
     "import numpy as np\n",
-    "%matplotlib ipympl\n",
     "\n",
     "# Topology\n",
     "top = molpx._molpxdir(join='notebooks/data/ala2.pdb')\n",
@@ -45,33 +44,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
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        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
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-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(NGLWidget(count=200), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22-05-18 17:27:07 pyemma.coordinates.clustering.kmeans.KmeansClustering[0] INFO     Cluster centers converged after 7 steps.\n",
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "315ea7c3c69541c4b225ccfb2298c0ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
@@ -100,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
@@ -108,27 +114,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec4524caaf9548889d167e1b7b477c5a",
+       "model_id": "c3cb6ef00f854d0bbe27b8125ff8e637",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>MolPXHBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "MolPXHBox(children=(VBox(children=(Button(description='NGL widgets', layout=Layout(width='100%'), style=ButtonStyle()), VBox(children=(NGLWidget(count=2001),), layout=Layout(border='solid')), VBox(children=(NGLWidget(count=2001),), layout=Layout(border='solid'))), layout=Layout(height='6.0in', width='5.0in')), FigureCanvasNbAgg()))"
+       "A Jupyter Widget"
       ]
      },
      "metadata": {},
diff --git a/molpx/notebooks/5.molPX_GPCR_Opsin_contacts.ipynb b/molpx/notebooks/5.molPX_GPCR_Opsin_contacts.ipynb
new file mode 100644
index 0000000..8e6a730
--- /dev/null
+++ b/molpx/notebooks/5.molPX_GPCR_Opsin_contacts.ipynb
@@ -0,0 +1,3586 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# molPX Opsin Example\n",
+    "<pre> \n",
+    "Guillermo Pérez-Hernández  guille.perez@fu-berlin.de \n",
+    "</pre>\n",
+    "   \n",
+    "This notebook uses a very short trajectory of a G-Protein-Coupled-Receptor (GPCR) molecule as an example. \n",
+    "Unlike Di-Alanine, it is very frequent that such systems are analyzed using of [contact maps](https://en.wikipedia.org/wiki/Protein_contact_map).\n",
+    "\n",
+    "This can be done now interactively inside the notebook. The trajectory itself is meaningless, but it still can be used as an example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import molpx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start from files on disk\n",
+    "As usual, our starting point is a pair files on disk with a topology and a trajectory. We import those into the notebook using the awesome [mdtraj](www.mdtraj.org):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mdtraj as md\n",
+    "top = md.load(molpx._molpxdir(join='notebooks/data/ops.pdb.gz')).top\n",
+    "geom = md.load(molpx._molpxdir(join='notebooks/data/ops_mini.xtc'), top=top)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute a contact map using `mdtraj`\n",
+    "As usual, one could have computed this using any other python module or even an external program and load it into the notebook. We use the method [`md.compute_contacts`](http://mdtraj.org/1.9.0/api/generated/mdtraj.compute_contacts.html) and then `md.geometry.squareform` (see the example in the previous link).\n",
+    "\n",
+    "We also implement a cutoff of 3.5 Angstrom to create a binary plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "contact_map = md.geometry.squareform(*md.compute_contacts(geom))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff = .35 # cutoff for residue contact in nm\n",
+    "contact_map = (contact_map<.35).astype(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize the `contact_map`-trajectory interactively\n",
+    "The following cell generates a matplotlib plot showing the contact map and an NGLView widget. The interactivity consists in:\n",
+    " - sliding the advance bar in the widget will update the contact map\n",
+    " - left-clicking on the contact map will:\n",
+    "     - highlight the clicked pixel\n",
+    "     - draw the corresponding distance on the widget\n",
+    " - right-clicking again on the contact map will delete the distance on the widgtet\n",
+    "\n",
+    "Panning and zooming of the contact map is allowed. Uncoment the `average` option to see what happens. As usual, both the plot and the widget are contained in a `molpxHBox`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78b54deed26947ea84b13d30213b7099",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mpx_wdg_box = molpx.visualize.contacts(contact_map, geom,                                  \n",
+    "                                       average=True, \n",
+    "                                      )\n",
+    "mpx_wdg_box"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The ```mpx_wdg_box```\n",
+    "It is a class derived from the ipython widgets [HBox](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html?highlight=Hbox#Container/Layout-widgets) and [VBox](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html?highlight=Hbox#Container/Layout-widgets), with molPX's extra information as attributes starting with `linked_*` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "linked_ax_wdgs\n",
+      "linked_axes\n",
+      "linked_data_arrays\n",
+      "linked_figs\n",
+      "linked_mdgeoms\n",
+      "linked_ngl_wdgs\n"
+     ]
+    }
+   ],
+   "source": [
+    "for attr in dir(mpx_wdg_box):\n",
+    "    if attr.startswith('linked_'):\n",
+    "        print(attr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
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+         "zoom"
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+         "",
+         "",
+         "",
+         ""
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+         "fa fa-floppy-o icon-save",
+         "download"
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+        "zoomSpeed": 1.2
+       },
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+       "_ngl_msg_archive": [],
+       "_ngl_original_stage_parameters": {
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+        "sampleLevel": 0,
+        "tooltip": true,
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+        "zoomSpeed": 1.2
+       },
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+           "assembly": "default",
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+         "fa fa-home icon-home",
+         "home"
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+         "Back to  previous view",
+         "fa fa-arrow-left icon-arrow-left",
+         "back"
+        ],
+        [
+         "Forward",
+         "Forward to next view",
+         "fa fa-arrow-right icon-arrow-right",
+         "forward"
+        ],
+        [
+         "",
+         "",
+         "",
+         ""
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+        [
+         "Pan",
+         "Pan axes with left mouse, zoom with right",
+         "fa fa-arrows icon-move",
+         "pan"
+        ],
+        [
+         "Zoom",
+         "Zoom to rectangle",
+         "fa fa-square-o icon-check-empty",
+         "zoom"
+        ],
+        [
+         "",
+         "",
+         "",
+         ""
+        ],
+        [
+         "Download",
+         "Download plot",
+         "fa fa-floppy-o icon-save",
+         "download"
+        ],
+        [
+         "Export",
+         "Export plot",
+         "fa fa-file-picture-o icon-picture",
+         "export"
+        ]
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diff --git a/molpx/notebooks/data/ops.pdb.gz b/molpx/notebooks/data/ops.pdb.gz
new file mode 100644
index 0000000..e0323f0
Binary files /dev/null and b/molpx/notebooks/data/ops.pdb.gz differ
diff --git a/molpx/notebooks/data/ops_mini.xtc b/molpx/notebooks/data/ops_mini.xtc
new file mode 100644
index 0000000..219c606
Binary files /dev/null and b/molpx/notebooks/data/ops_mini.xtc differ
diff --git a/molpx/tests/test_bmutils.py b/molpx/tests/test_bmutils.py
index 43f5ba5..b94e51a 100644
--- a/molpx/tests/test_bmutils.py
+++ b/molpx/tests/test_bmutils.py
@@ -10,6 +10,9 @@
 import mdtraj as md
 from glob import glob
 import molpx
+from numpy.testing import assert_raises
+
+from scipy.spatial.distance import pdist as _pdist, squareform as _squareform
 
 class TestWithBPTIData(unittest.TestCase):
     r"""
@@ -29,83 +32,61 @@ def setUpClass(self):
         self.Fs = self.source.get_output()
 
         self.tica = pyemma.coordinates.tica(self.source, lag=1, dim=2)
-        self.pca = pyemma.coordinates.tica(self.source, dim=2)
+        self.pca = pyemma.coordinates.pca(self.source, dim=2)
         self.Ys = self.tica.get_output()
         self.tempdir = tempfile.mkdtemp('test_molpx')
-        self.projected_files = [os.path.join(self.tempdir, 'Y.%u.npy'%ii) for ii in range(len(self.MD_trajectories))]
-        [np.save(ifile, iY) for ifile, iY in zip(self.projected_files, self.Ys)]
-        [np.savetxt(ifile.replace('.npy', '.dat'), iY) for ifile, iY in zip(self.projected_files, self.Ys)]
+        self.projected_files_npy = [os.path.join(self.tempdir, 'Y.%u.npy' % ii) for ii in range(len(self.MD_trajectories))]
+        self.projected_files_dat = [ifile.replace(".npy",".dat") for ifile in self.projected_files_npy]
+        [np.save(ifile, iY) for ifile, iY in zip(self.projected_files_npy, self.Ys)]
+        [np.savetxt(ifile.replace('.npy', '.dat'), iY) for ifile, iY in zip(self.projected_files_npy, self.Ys)]
 
     @classmethod
     def tearDownClass(self):
         shutil.rmtree(self.tempdir)
 
-class TestReadingInput(unittest.TestCase):
+class TestReadingInput(TestWithBPTIData):
 
-    def setUp(self):
-        self.MD_trajectory = os.path.join(pyemma.__path__[0],'coordinates/tests/data/bpti_mini.xtc')
-        self.MD_topology = os.path.join(pyemma.__path__[0], 'coordinates/tests/data/bpti_ca.pdb')
-        self.tempdir = tempfile.mkdtemp('test_molpx')
-        self.projected_file = os.path.join(self.tempdir,'Y.npy')
-        self.feat = pyemma.coordinates.featurizer(self.MD_topology)
-        self.feat.add_all()
-        source = pyemma.coordinates.source(self.MD_trajectory, features=self.feat)
-        self.tica = pyemma.coordinates.tica(source,lag=1, dim=2)
-        self.Y = self.tica.get_output()[0]
-        self.F = source.get_output()
-        np.save(self.projected_file,self.Y)
-        np.savetxt(self.projected_file.replace('.npy','.dat'),self.Y)
+    @classmethod
+    def setUpClass(self):
+        TestWithBPTIData.setUpClass()
 
-    def tearDown(self):
-        shutil.rmtree(self.tempdir)
+    @classmethod
+    def tearDownClass(self):
+        TestWithBPTIData.tearDownClass()
 
     def test_data_from_input_npy(self):
         # Just one string
-        assert np.allclose(self.Y, _bmutils.data_from_input(self.projected_file)[0])
+        assert np.allclose(self.Ys[0], _bmutils.data_from_input(self.projected_files_npy)[0])
         # List of one string
-        assert np.allclose(self.Y, _bmutils.data_from_input([self.projected_file])[0])
-        # List of two strings
-        Ys = _bmutils.data_from_input([self.projected_file,
-                                       self.projected_file])
-        assert np.all([np.allclose(self.Y, iY) for iY in Ys])
+        assert np.allclose(self.Ys[0], _bmutils.data_from_input([self.projected_files_npy[0]]))
+        # List of strings
+        Ys = _bmutils.data_from_input(self.projected_files_npy)
+        assert np.all([np.allclose(jY, iY) for jY, iY in zip(self.Ys, Ys)])
 
     def test_data_from_input_throws_exception(self):
-        try:
-            _bmutils.data_from_input(np.random.randn(1000))
-        except ValueError:
-            pass
+        assert_raises(ValueError, _bmutils.data_from_input, 1)
 
     def test_data_from_input_ascii(self):
         # Just one string
-        assert np.allclose(self.Y, _bmutils.data_from_input(self.projected_file.replace('.npy', '.dat'))[0])
+        assert np.allclose(self.Ys[0], _bmutils.data_from_input(self.projected_files_dat)[0])
         # List of one string
-        assert np.allclose(self.Y, _bmutils.data_from_input([self.projected_file.replace('.npy', '.dat')])[0])
-        # List of two strings
-        Ys = _bmutils.data_from_input([self.projected_file.replace('.npy', '.dat'),
-                                       self.projected_file.replace('.npy','.dat')])
-        assert np.all([np.allclose(self.Y, iY) for iY in Ys])
+        assert np.allclose(self.Ys[0], _bmutils.data_from_input([self.projected_files_dat[0]]))
+        # List of strings
+        Ys = _bmutils.data_from_input(self.projected_files_dat)
+        assert np.all([np.allclose(jY, iY) for jY, iY in zip(self.Ys, Ys)])
 
     def test_data_from_input_ndarray(self):
         # Just one ndarray
-        assert np.allclose(self.Y, _bmutils.data_from_input(self.Y)[0])
+        assert np.allclose(self.Ys[0], _bmutils.data_from_input(self.Ys[0]))
         # List of one ndarray
-        assert np.allclose(self.Y, _bmutils.data_from_input([self.Y])[0])
-        # List of two ndarray
-        Ys = _bmutils.data_from_input([self.Y,
-                                       self.Y])
-        assert np.all([np.allclose(self.Y, iY) for iY in Ys])
-
-    # Not implemented yet
-    def _test_data_from_input_ndarray_ascii_npy(self):
-        # List of everything
-        Ys = _bmutils.data_from_input([self.projected_file,
-                                       self.projected_file.replace('.npy','.dat'),
-                                       self.Y])
-        assert np.all([np.allclose(self.Y, iY) for iY in Ys])
+        assert np.allclose(self.Ys[0], _bmutils.data_from_input([self.Ys[0]])[0])
+        # Lists
+        Ys = _bmutils.data_from_input(self.Ys)
+        assert np.all([np.allclose(jY, iY) for jY,iY in zip(self.Ys, Ys)])
 
     def test_moldata_from_input(self):
         # Traj and top strings
-        moldata = _bmutils.moldata_from_input(self.MD_trajectory, MD_top=self.MD_topology)
+        moldata = _bmutils.moldata_from_input(self.MD_trajectory_files, MD_top=self.MD_topology)
         assert isinstance(moldata, _bmutils._FeatureReader)
 
         # Source object directly
@@ -113,27 +94,79 @@ def test_moldata_from_input(self):
         assert isinstance(moldata, _bmutils._FeatureReader)
 
         # Typerror:
-        try:
-            _bmutils.moldata_from_input(11)
-        except TypeError:
-            pass
+        assert_raises(TypeError, _bmutils.moldata_from_input, 11)
 
         # List of trajectories
-        geom = md.load(self.MD_trajectory, top=self.MD_topology)
-        moldata = _bmutils.moldata_from_input(geom)
+        moldata = _bmutils.moldata_from_input(self.MD_trajectories)
         assert isinstance(moldata[0], md.Trajectory), moldata
 
     def test_assert_moldata_belong_data(self):
         # Traj vs data
-        geom = md.load(self.MD_trajectory, top=self.MD_topology)
-        _bmutils.assert_moldata_belong_data([geom], [self.Y])
+        _bmutils.assert_moldata_belong_data(self.MD_trajectories, self.Ys)
 
         # src vs data
-        moldata = _bmutils.moldata_from_input(self.MD_trajectory, MD_top=self.MD_topology)
-        _bmutils.assert_moldata_belong_data(moldata, [self.Y])
+        moldata = _bmutils.moldata_from_input(self.source, MD_top=self.MD_topology)
+
+        _bmutils.assert_moldata_belong_data(moldata, self.Ys)
+        #print(moldata, moldata.number_of_trajectories(), moldata.trajectory_lengths())
 
         # With stride
-        _bmutils.assert_moldata_belong_data([geom], [iY[::5] for iY in [self.Y]], data_stride=5)
+        _bmutils.assert_moldata_belong_data(self.MD_trajectories, [iY[::5] for iY in self.Ys], data_stride=5)
+
+class TestSaveTraj(TestWithBPTIData):
+
+    @classmethod
+    def setUpClass(self):
+        TestWithBPTIData.setUpClass()
+        self.samples = [[0, 10],
+                        [1, 20],
+                        [2, 30]]
+
+    @classmethod
+    def tearDownClass(self):
+        TestWithBPTIData.tearDownClass()
+
+
+    def test_just_works(self):
+        geoms_ref = pyemma.coordinates.save_traj(self.source, self.samples, None)
+        geoms_molpx = _bmutils.save_traj_wrapper(self.source, self.samples, None)
+        assert np.all([np.allclose(ixyz, jxyz) for ixyz, jxyz in zip(geoms_ref.xyz, geoms_molpx.xyz)])
+
+    def test_works_with_MDTrajectories(self):
+
+        geoms_ref = pyemma.coordinates.save_traj(self.source, self.samples, None)
+        geoms_molpx = _bmutils.save_traj_wrapper(self.MD_trajectories, self.samples, None)
+        assert np.all([np.allclose(ixyz, jxyz) for ixyz, jxyz in zip(geoms_ref.xyz, geoms_molpx.xyz)])
+
+    def test_works_with_MDTrajectories_with_stride(self):
+        geoms_ref = pyemma.coordinates.save_traj(self.source, self.samples, None, stride=2)
+        geoms_molpx = _bmutils.save_traj_wrapper(self.MD_trajectories, self.samples, None, stride=2)
+        assert np.all([np.allclose(ixyz, jxyz) for ixyz, jxyz in zip(geoms_ref.xyz, geoms_molpx.xyz)])
+
+    def test_raises(self):
+        assert_raises(TypeError, _bmutils.save_traj_wrapper, 1, self.samples, None)
+
+class TestCorrelations(TestWithBPTIData):
+    @classmethod
+    def setUpClass(self):
+        TestWithBPTIData.setUpClass()
+
+    @classmethod
+    def tearDownClass(self):
+        TestWithBPTIData.tearDownClass()
+
+    def test_input_types(self):
+        _bmutils.most_corr(self.tica)
+        _bmutils.most_corr(self.pca)
+        _bmutils.most_corr(self.feat)
+        _bmutils.most_corr(self.tica.feature_TIC_correlation)
+        _bmutils.most_corr(self.tica.feature_TIC_correlation, feat_name="My CustomFeature")
+
+    def test_fails(self):
+        assert_raises(TypeError, _bmutils.most_corr, "a")
+
+    def test_printing(self):
+        print(_bmutils.most_corr(self.tica))
 
     def test_most_corr_info_works(self):
         most_corr = _bmutils.most_corr(self.tica)
@@ -156,8 +189,7 @@ def test_most_corr_info_works(self):
         assert most_corr['feats'] == []
 
     def test_most_corr_info_works_with_options(self):
-        geoms = md.load(self.MD_trajectory, top=self.MD_topology)
-        most_corr = _bmutils.most_corr(self.tica, geoms=geoms)
+        most_corr = _bmutils.most_corr(self.tica, geoms=self.MD_trajectories[0])
 
         # Idxs are okay
         ref_idxs = [np.abs(self.tica.feature_TIC_correlation[:, ii]).argmax() for ii in range(self.tica.dim)]
@@ -168,15 +200,13 @@ def test_most_corr_info_works_with_options(self):
         assert np.all([rv == mcv for rv, mcv in zip(ref_corrs, most_corr['vals'])])
 
         # Check that we got the right most correlated feature trajectory
-        ref_feats = self.feat.transform(geoms)
+        ref_feats = self.feat.transform(self.MD_trajectories[0])
         ref_feats = [ref_feats[:, ii] for ii in ref_idxs]
         assert np.all(np.allclose(rv, mcv) for rv, mcv in zip(ref_feats, np.squeeze(most_corr['feats'])))
 
     def test_most_corr_info_works_with_options_and_proj_idxs(self):
-        geoms = md.load(self.MD_trajectory, top=self.MD_topology)
-
         proj_idxs = [1, 0] # the order shouldn't matter
-        corr_dict = _bmutils.most_corr(self.tica, geoms=geoms, proj_idxs=proj_idxs)
+        corr_dict = _bmutils.most_corr(self.tica, geoms=self.MD_trajectories[0], proj_idxs=proj_idxs)
 
         # Idxs are okay
         ref_idxs = [np.abs(self.tica.feature_TIC_correlation[:, ii]).argmax() for ii in proj_idxs]
@@ -192,17 +222,13 @@ def test_most_corr_info_works_with_options_and_proj_idxs(self):
         assert [isinstance(istr, str) for istr in corr_dict["labels"]]
 
         # Feature values are ok
-        ref_feats = self.feat.transform(geoms)
+        ref_feats = self.feat.transform(self.MD_trajectories[0])
         ref_feats = [ref_feats[:, ii] for ii in ref_idxs]
         assert np.all(np.allclose(rv, mcv) for rv, mcv in zip(ref_feats, np.squeeze(corr_dict['feats'])))
 
     def test_most_corr_info_wrong_proj_idxs(self):
-
         proj_idxs = [1, 0, 10] # we don't have 10 TICs
-        try:
-            _bmutils.most_corr(self.tica, proj_idxs=proj_idxs)
-        except(ValueError):
-            pass #this should given this type of error
+        assert_raises(ValueError, _bmutils.most_corr, self.tica, proj_idxs=proj_idxs)
 
 
 class TestClusteringAndCatalogues(unittest.TestCase):
@@ -225,12 +251,57 @@ def setUp(self):
 
     def test_cluster_to_target(self):
         n_target = 15
-        data = [np.random.randn(100, 1), np.random.randn(100,1)+10]
-        cl = _bmutils.regspace_cluster_to_target(data, n_target, n_try_max=10, delta=0, verbose=True)
-        assert n_target - 1 <= cl.n_clusters <= n_target + 1
+        n_tol = 1
+        data = [np.random.randn(5000, 1), np.random.randn(5000,1)+10]
+
+        # Only one iteration, just to force to go into the loop-breaking
+        cl = _bmutils.regspace_cluster_to_target_kmeans(data, n_target, k_centers=100, max_iter=1, n_tol=n_tol)
+
+        # Now the real deal
+        cl = _bmutils.regspace_cluster_to_target_kmeans(data, n_target, k_centers=100, max_iter=100, n_tol=n_tol)
+        assert n_target - n_tol <= cl.n_clusters <= n_target + n_tol, (cl.n_clusters, n_tol)
+
+    def test_regspace_from_distance_matrix(self):
+        data = np.random.rand(100, 2)
+        D = _squareform(_pdist(data))
+
+        centers = _bmutils.regspace_from_distance_matrix(D, D.mean())
+
+        # Re-compute distances, only for the centers
+        Drs = _pdist(data[centers])
+        assert Drs.min()>=D.mean(), (Drs.min(), D.mean())
+
+    def test_interval_schachtelung(self):
+
+        y = lambda x:x**2 # parabolic curve, monotically increasing between [0, +inf]
+
+        interval = [2, 500]
+        eps = .1
+
+        target_y = np.random.randint(np.ceil(y(interval[0])),
+                                    np.floor(y(interval[1])), size=1).squeeze()
+
+        x_sol = _bmutils.interval_schachtelung(y, [2, 500], target=target_y, eps=eps,
+                                               #verbose=True
+                                               )
+        assert y(x_sol)-eps <= target_y < y(x_sol)+eps, (y(x_sol)-target_y, eps)
+
+    def test_interval_schachtelung_fails(self):
+        y = lambda x:x**2 # parabolic curve, monotically increasing between [0, +inf]
+
+        interval = [2, 500]
+        eps = .1
+
+        target_y = 0 # Is not contained in [2**2, 500**2]
+
+
+        assert_raises(Exception, _bmutils.interval_schachtelung, y, [2, 500], target=target_y, eps=eps,
+                                               #verbose=True
+                                               )
+
 
     def test_catalogues(self):
-        cl = _bmutils.regspace_cluster_to_target(self.data_for_cluster, 3, n_try_max=10, delta=0)
+        cl = _bmutils.regspace_cluster_to_target_kmeans(self.data_for_cluster, 3, max_iter=10, n_tol=0)
         #print(cl.clustercenters)
         cat_idxs, cat_cont = _bmutils.catalogues(cl)
 
@@ -264,7 +335,7 @@ def test_catalogues(self):
                                          [13, 2]])
 
     def test_catalogues_with_data(self):
-        cl = _bmutils.regspace_cluster_to_target(self.data_for_cluster, 3, n_try_max=10, delta=0)
+        cl = _bmutils.regspace_cluster_to_target_kmeans(self.data_for_cluster, 3, max_iter=10, n_tol=0)
         #print(cl.clustercenters)
         cat_idxs, cat_cont = _bmutils.catalogues(cl, data=self.data_for_cluster)
 
@@ -299,7 +370,7 @@ def test_catalogues_with_data(self):
 
 
     def test_catalogues_sort_by_zero(self):
-        cl = _bmutils.regspace_cluster_to_target(self.data_for_cluster, 3, n_try_max=10, delta=0)
+        cl = _bmutils.regspace_cluster_to_target_kmeans(self.data_for_cluster, 3, max_iter=10, n_tol=0)
         cat_idxs, cat_cont = _bmutils.catalogues(cl, sort_by=0)
 
         # This test is extra, since this is a pure pyemma function
@@ -328,7 +399,7 @@ def test_catalogues_sort_by_zero(self):
                                          [13, 2]])
 
     def test_catalogues_sort_by_other_than_zero(self):
-        cl = _bmutils.regspace_cluster_to_target(self.data_for_cluster, 3, n_try_max=10, delta=0)
+        cl = _bmutils.regspace_cluster_to_target_kmeans(self.data_for_cluster, 3, max_iter=10, n_tol=0)
         cat_idxs, cat_cont = _bmutils.catalogues(cl, sort_by=1)
         # This test is extra, since this is a pure pyemma functions
         assert np.allclose(cat_idxs[0], [[1,0]])
@@ -390,7 +461,7 @@ class TestGetGoodStartingPoint(unittest.TestCase):
     def setUp(self):
         # The setup creates the typical, "geometries-sampled along cluster-scenario"
         n_geom_samples = 20
-        traj = md.load(os.path.join(pyemma.__path__[0],'coordinates/tests/data/bpti_ca.pdb'))
+        traj = md.load(molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb'))
         traj = traj.atom_slice([0,1,3,4]) # create a trajectory with four atoms
         # Create a fake bi-modal trajectory with a compact and an open structure
         ixyz = np.array([[0., 0., 0.],
@@ -406,17 +477,14 @@ def setUp(self):
         z = np.random.permutation(z)
         coords[:, -1, -1] = z
         self.traj = md.Trajectory(coords, traj.top)
-        self.cl = _bmutils.regspace_cluster_to_target(self.traj.xyz[:, -1, -1], 50, n_try_max=10)
+        self.cl = _bmutils.regspace_cluster_to_target_kmeans(self.traj.xyz[:, -1, -1], 50, max_iter=10)
         self.cat_smpl = self.cl.sample_indexes_by_cluster(np.arange(self.cl.n_clusters), n_geom_samples)
         self.geom_smpl = self.traj[np.vstack(self.cat_smpl)[:,1]]
         self.geom_smpl = _bmutils.re_warp(self.geom_smpl, [n_geom_samples] * self.cl.n_clusters)
 
 
     def test_throws_exception(self):
-        try:
-            start_idx = _bmutils.get_good_starting_point(self.cl, self.geom_smpl, strategy="what?")
-        except NotImplementedError:
-            pass
+        assert_raises(NotImplementedError, _bmutils.get_good_starting_point, self.cl, self.geom_smpl, strategy="what?")
 
     # This test doesn't exactly belong here but this is the best class for now
     def test_find_centers_GMM(self):
@@ -446,7 +514,7 @@ def test_most_pop(self):
                                                               "around the value 15. The found starting point should be" \
                                                               "in this interval (see the setUp)"
 
-    def _test_most_pop_x_rgyr(self):
+    def test_most_pop_x_rgyr(self):
         start_idx = _bmutils.get_good_starting_point(self.cl, self.geom_smpl, strategy="most_pop_x_smallest_Rgyr")
         #print(start_idx, self.cl.clustercenters[start_idx], np.sort(self.cl.clustercenters.squeeze()))
         # TODO: figure out a good way of testing this, at the moment it just chekcs that it runs
@@ -493,8 +561,7 @@ class TestVisualPath(TestWithBPTIData):
     def setUpClass(self):
         TestWithBPTIData.setUpClass()
         n_sample = 20
-        self.cl_cont = _bmutils.regspace_cluster_to_target([ixyz[:, :2] for ixyz in self.xyz_flat], n_sample, verbose=True, n_try_max=10,
-                                                           #delta=0
+        self.cl_cont = _bmutils.regspace_cluster_to_target_kmeans([ixyz[:, :2] for ixyz in self.xyz_flat], n_sample, verbose=True, max_iter=10,
                                                            )
         self.cat_idxs, self.cat_data = _bmutils.catalogues(self.cl_cont)
         # Create the MD catalogue with pyemma
@@ -512,14 +579,8 @@ def test_input_parsing(self):
         _bmutils.visual_path(self.cat_idxs, self.cat_data, start_pos=1)
 
         # Not implemented Errors
-        try:
-            _bmutils.visual_path(self.cat_idxs, self.cat_data, start_pos="other")
-        except NotImplementedError:
-            pass
-        try:
-            _bmutils.visual_path(self.cat_idxs, self.cat_data, path_type="xxxx")
-        except NotImplementedError:
-            pass
+        assert_raises(NotImplementedError, _bmutils.visual_path, self.cat_idxs, self.cat_data, start_pos="other")
+        assert_raises(NotImplementedError, _bmutils.visual_path, self.cat_idxs, self.cat_data, path_type="xxxx")
 
 class TestMinDispPaths(unittest.TestCase):
 
@@ -567,11 +628,10 @@ def test_closest_all_coords_history(self):
 class TestSliceListOfGeoms(unittest.TestCase):
 
     def setUp(self):
-        self.topology = os.path.join(pyemma.__path__[0],'coordinates/tests/data/bpti_ca.pdb')
+        self.topology = molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb')
         self.ref_frame = 4
-        self.MD_trajectory = md.load(os.path.join(pyemma.__path__[0],
-                                                  'coordinates/tests/data/bpti_mini.xtc'),
-                                     top=self.topology)
+        self.MD_trajectory = md.load(glob(molpx._molpxdir(join='notebooks/data/c-alpha_centered.stride.1000*xtc'))[0],
+                                          top=self.topology)
     def test_slice(self):
         geom_list = [self.MD_trajectory,
                      self.MD_trajectory[::-1]]
@@ -591,24 +651,21 @@ def setUp(self):
     def test_it_works(self):
         assert np.allclose([0], _bmutils.get_ascending_coord_idx(self.data[:,:-1]))
     def test_empty_no_fail(self):
-
         result = _bmutils.get_ascending_coord_idx(self.data[:,[1,2]], fail_if_empty=False)
         assert len(result)==0
     def test_empty_fail(self):
-        try:
-            _bmutils.get_ascending_coord_idx(self.data[:, 1:], fail_if_empty=True)
-        except ValueError:
-            pass
+        assert_raises(ValueError, _bmutils.get_ascending_coord_idx, self.data[:, [1,2]], fail_if_empty=True)
+
     def test_more_than_one_fails(self):
-        try:
-            _bmutils.get_ascending_coord_idx(self.data[:, :], fail_if_more_than_one=True)
-        except:
-            pass
+        assert_raises(Exception, _bmutils.get_ascending_coord_idx, self.data[:, :], fail_if_more_than_one=True)
+
+    def test_more_than_one_passes(self):
+        _bmutils.get_ascending_coord_idx(self.data[:, :], fail_if_more_than_one=False)
 
 class TestMinRmsdPaths(unittest.TestCase):
 
     def setUp(self):
-        self.topology = os.path.join(pyemma.__path__[0],'coordinates/tests/data/bpti_ca.pdb')
+        self.topology = molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb')
         self.reftraj = md.load(self.topology)
 
     def test_find_buried_best_candidate(self):
@@ -623,7 +680,11 @@ def test_find_buried_best_candidate(self):
         for pp, ff in enumerate(frames_where_actual_geometry_is):
             xyz[pp][ff,:,:] = self.reftraj.xyz
         # Create the path of candidates as mdtraj objects
-        path_of_candidates = [md.Trajectory(ixyz, topology=self.reftraj.top) for ixyz in xyz]
+        path_of_candidates = [md.Trajectory(ixyz, topology=self.reftraj.top,
+                                            time=[self.reftraj.time.squeeze()] * n_cands,
+                                            unitcell_angles=[self.reftraj.unitcell_angles.squeeze()] * n_cands,
+                                            unitcell_lengths=[self.reftraj.unitcell_lengths.squeeze()] * n_cands)
+                              for ixyz in xyz]
 
         # Let mirmsd_path find these frames for you
         inferred_frames = _bmutils.min_rmsd_path(self.reftraj, path_of_candidates)
@@ -664,7 +725,11 @@ def test_find_buried_best_candidate_with_selection(self):
             xyz[pp][ff,:,:] = ref_w_sel_perturbed[pp]
 
         # Create the path of candidates as mdtraj objects
-        path_of_candidates = [md.Trajectory(ixyz, topology=self.reftraj.top) for ixyz in xyz]
+        path_of_candidates = [md.Trajectory(ixyz, topology=self.reftraj.top,
+                                            time=[self.reftraj.time.squeeze()] * n_cands,
+                                            unitcell_angles=[self.reftraj.unitcell_angles.squeeze()] * n_cands,
+                                            unitcell_lengths=[self.reftraj.unitcell_lengths.squeeze()] * n_cands)
+                              for ixyz in xyz]
 
         # PRE-TEST
         # as long as the perturbation is small,
@@ -689,7 +754,11 @@ def test_find_buried_best_candidate_with_selection(self):
         # In the random frames, insert the intouched selection
         for pp, ff in enumerate(frames_random_w_sel_untouched):
             xyz[pp][ff,selection,:] = np.copy(self.reftraj.xyz[0,selection, :])
-        path_of_candidates = [md.Trajectory(ixyz, topology=self.reftraj.top) for ixyz in xyz]
+        path_of_candidates = [md.Trajectory(ixyz, topology=self.reftraj.top,
+                                            time=[self.reftraj.time.squeeze()] * n_cands,
+                                            unitcell_angles=[self.reftraj.unitcell_angles.squeeze()] * n_cands,
+                                            unitcell_lengths=[self.reftraj.unitcell_lengths.squeeze()] * n_cands)
+                              for ixyz in xyz]
 
         # This should still be OK, because
         # even if the selected atoms have been perturbed, the comparsion [ref+sel_per] vs [random] is robust
@@ -716,8 +785,12 @@ def test_history_aware_just_works(self):
         frames_where_actual_geometry_is = np.random.randint(0, high=n_cands, size=path_length)
         for pp, ff in enumerate(frames_where_actual_geometry_is):
             xyz[pp][ff, :, :] = self.reftraj.xyz
-        # Create the path of candidates as mdtraj objects
-        path_of_candidates = [md.Trajectory(ixyz, topology=self.reftraj.top) for ixyz in xyz]
+        # Create the path of candidates as mdtraj objects (time, unitcell angles and lengths are bogus)
+        path_of_candidates = [md.Trajectory(ixyz, topology=self.reftraj.top,
+                                            time=[self.reftraj.time.squeeze()]*n_cands,
+                                            unitcell_angles=[self.reftraj.unitcell_angles.squeeze()]*n_cands,
+                                            unitcell_lengths=[self.reftraj.unitcell_lengths.squeeze()]*n_cands)
+                              for ixyz in xyz]
 
         # Let mirmsd_path find these frames for you
         inferred_frames = _bmutils.min_rmsd_path(self.reftraj, path_of_candidates, history_aware=True)
@@ -728,7 +801,7 @@ class TestSmoothingFunctions(unittest.TestCase):
 
     def setUp(self):
         # The setup creates the typical, "geometries-sampled along cluster-scenario"
-        traj = md.load(os.path.join(pyemma.__path__[0], 'coordinates/tests/data/bpti_ca.pdb'))
+        traj = md.load(molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb'))
         traj = traj.atom_slice([0, 1])  # create a trajectory with two atoms
         # Create a fake bi-modal trajectory with a compact and an open structure
         ixyz = np.array([[10.,  20.,  30.],
@@ -741,7 +814,7 @@ def tearDown(self):
         pass
 
     def test_running_avg_idxs_none(self):
-        idxs, windows = _bmutils.running_avg_idxs(10, 0)
+        idxs, windows = _bmutils.running_avg_idxs(10, 0, debug=True)
         # If the running average is with radius zero, it's just a normal average
         assert np.allclose([0,1,2,3,4,5,6,7,8,9], idxs)
         assert np.all(np.allclose(ii,jj) for ii, jj in zip(([0,1,2,3,4,5,6,7,8,9], windows)))
@@ -771,14 +844,12 @@ def test_running_avg_idxs_two(self):
                                                             windows))
 
     def test_running_avg_idxs_too_large_window(self):
-        try:
-            idxs, windows = _bmutils.running_avg_idxs(10, 5)
-        except AssertionError:
-            pass
+        assert_raises(AssertionError, _bmutils.running_avg_idxs, 10, 5)
 
+    def test_raises_if_not_symmetric(self):
+        assert_raises(NotImplementedError, _bmutils.running_avg_idxs, 10, 5, symmetric=False)
 
     def test_smooth_geom_it_just_runs_and_gives_correct_output_type(self):
-
         # No data
         assert isinstance(_bmutils.smooth_geom(self.traj, 0), md.Trajectory)
         assert isinstance(_bmutils.smooth_geom(self.traj, 0, superpose=False), md.Trajectory)
@@ -823,7 +894,7 @@ class TestListTransposeGeomList(unittest.TestCase):
 
     def test_it(self):
         # Create a dummy topology
-        traj = md.load(os.path.join(pyemma.__path__[0], 'coordinates/tests/data/bpti_ca.pdb'))
+        traj = md.load(molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb'))
         top = traj.atom_slice([0]).top  # Single atom topology
 
         ixyz_row_0 =  [[0, 0, 0]],    [[0, 1, 0]],    [[0, 2, 0]] # 3 frames of 1 atom
@@ -849,7 +920,7 @@ class geom_list_2_geom(unittest.TestCase):
     def test_it(self):
         # Create a dummy topology
         MD_trajectory = os.path.join(pyemma.__path__[0], 'coordinates/tests/data/bpti_mini.xtc')
-        MD_topology = os.path.join(pyemma.__path__[0], 'coordinates/tests/data/bpti_ca.pdb')
+        MD_topology = molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb')
         traj = md.load(MD_trajectory, top=MD_topology)
 
         traj_list = [itraj for itraj in traj]
@@ -857,53 +928,146 @@ def test_it(self):
 
         assert np.allclose(np.hstack([igeom.xyz for igeom in new_geom]).squeeze(), np.vstack(traj.xyz))
 
-class TestIndexFromFeatures(unittest.TestCase):
+class TestAtomIndices(unittest.TestCase):
 
     def setUp(self):
-        self.MD_topology = os.path.join(pyemma.__path__[0], 'coordinates/tests/data/bpti_ca.pdb')
-        self.feat= pyemma.coordinates.featurizer(self.MD_topology)
+        self.MD_topology = molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb')
+        self.feat = pyemma.coordinates.featurizer(self.MD_topology)
 
-        self.ang_idxs = [[ii + jj for jj in range(3)] for ii in range(self.feat.topology.n_atoms - 3)]
-        self.dih_idxs = [[ii + jj for jj in range(4)] for ii in range(self.feat.topology.n_atoms - 4)]
+        self.cartesian_input = np.arange(10)
+        self.distance_input = np.arange(10, 15)
+        self.angle_input = [[ii + jj for jj in range(3)] for ii in range(self.feat.topology.n_atoms - 3)]
+        self.dih_input = [[ii + jj for jj in range(4)] for ii in range(self.feat.topology.n_atoms - 4)]
+        self.mindist_input = [[15, 16], [16, 17], [18, 19]]
 
-        self.feat.add_all()
-        self.feat.add_distances_ca(excluded_neighbors=0)
-        self.feat.add_angles(self.ang_idxs)
-        self.feat.add_angles(self.ang_idxs, cossin=True)
-        self.feat.add_dihedrals(self.dih_idxs)
-        self.feat.add_dihedrals(self.dih_idxs, cossin=True)
+        # Add features to the featurizer and keep track of the atom indices
+        self.feat.add_selection(self.cartesian_input)
+        self.cartesian_indices = list(np.repeat(self.cartesian_input, 3))
+
+        self.feat.add_distances(self.distance_input)
+        self.distance_indices = []
+        for ii, iidx in enumerate(self.distance_input[:-1]):
+            for jidx in self.distance_input[ii + 1:]:
+                self.distance_indices.append((iidx, jidx))
+
+        self.feat.add_angles(self.angle_input)
+        self.angle_indices = self.angle_input
+
+        self.feat.add_angles(self.angle_input, cossin=True)
+        self.angle_indices_cossin = list(np.tile(self.angle_input, 2).reshape(-1, 3))
+
+        self.feat.add_dihedrals(self.dih_input)
+        self.dih_indices = self.dih_input
+
+        self.feat.add_dihedrals(self.dih_input, cossin=True)
+        self.dih_indices_cossin = list(np.tile(self.dih_input, 2).reshape(-1, 4))
+
+        self.feat.add_residue_mindist(self.mindist_input)
+        self.mindist_indices = [[_bmutils.get_repr_atom_for_residue(self.feat.topology.residue(ii)).index for ii in rpair] for rpair in self.mindist_input]
+
+        # Now put all indices in one long list:
+        self.ref =  self.cartesian_indices+\
+                    self.distance_indices+\
+                    self.angle_indices+\
+                    self.angle_indices_cossin+\
+                    self.dih_indices+\
+                    self.dih_indices_cossin+\
+                    self.mindist_indices
+
+        self.src = pyemma.coordinates.source(glob(molpx._molpxdir(join='notebooks/data/c-alpha*xtc'))[0], features=self.feat)
+        self.tica = pyemma.coordinates.tica(self.src )
+        self.pca = pyemma.coordinates.pca(self.src)
 
     def tearDown(self):
         pass
 
+    def test_put_atom_idxs_on_widget(self):
+        import nglview
+        iwd = nglview.show_file(self.MD_topology)
+        #Regular input
+        _bmutils.add_atom_idxs_widget(self.ref, iwd)
+        # Insuficient colors
+        _bmutils.add_atom_idxs_widget(self.ref, iwd, ["red"])
+        # Too many indices raises
+        assert_raises(NotImplementedError, _bmutils.add_atom_idxs_widget, [[1,2,3,4,5]], iwd)
+
+    def test_repr_atoms_from_residues(self):
+        # Use Di-Ala
+        top = md.load(molpx._molpxdir(join='notebooks/data/ala2.pdb')).top
+        # First res, ACE
+        aa = _bmutils.get_repr_atom_for_residue(top.residue(0))
+        assert aa.name=="C"
+        # Mid res, ala2
+        aa = _bmutils.get_repr_atom_for_residue(top.residue(1))
+        assert aa.name=="CA"
+        # Last res, ACE
+        aa = _bmutils.get_repr_atom_for_residue(top.residue(2))
+        assert aa.name == "C"
+
+        # Force fail
+        assert_raises(ValueError, _bmutils.get_repr_atom_for_residue, top.residue(0), cands=["CB"])
+
+    # Feature by feature
+    def test_atom_idxs_from_feature_not_recognized(self):
+        assert_raises(NotImplementedError, _bmutils.atom_idxs_from_feature, "aa")
+
     def test_atom_idxs_from_feature_xyz(self):
         ai = _bmutils.atom_idxs_from_feature(self.feat.active_features[0])
-        assert np.allclose(np.repeat(np.arange(self.feat.topology.n_atoms),3), ai)
+        assert np.allclose(self.cartesian_indices, ai)
 
-    def test_atom_idxs_from_feature_D_CA(self):
+    def test_atom_idxs_from_feature_D(self):
         ai = _bmutils.atom_idxs_from_feature(self.feat.active_features[1])
-        ref = np.vstack(np.triu_indices(self.feat.topology.n_atoms, k=1)).T
-        assert np.allclose(ref, ai),ai
+        assert np.allclose(self.distance_indices, ai),ai
 
     def test_atom_idxs_from_feature_ang(self):
         ai = _bmutils.atom_idxs_from_feature(self.feat.active_features[2])
-        assert np.allclose(self.ang_idxs, ai)
+        assert np.allclose(self.angle_indices, ai)
 
     def test_atom_idxs_from_feature_ang_cossin(self):
         ai = _bmutils.atom_idxs_from_feature(self.feat.active_features[3])
-        ref = np.tile(self.ang_idxs, 2).reshape(-1,3)
-        assert np.allclose(ai, ref)
+        assert np.allclose(ai, self.angle_indices_cossin)
 
     def test_atom_idxs_from_feature_dih(self):
         ai = _bmutils.atom_idxs_from_feature(self.feat.active_features[4])
-        assert np.allclose(self.dih_idxs, ai)
+        assert np.allclose(self.dih_indices, ai)
+
     def test_atom_idxs_from_feature_dih_cossin(self):
         ai = _bmutils.atom_idxs_from_feature(self.feat.active_features[5])
-        ref = np.tile(self.dih_idxs, 2).reshape(-1,4)
-        assert np.allclose(ref, ai)
+        assert np.allclose(self.dih_indices_cossin, ai)
+
+    def test_atom_idxs_from_feature_res_mindist(self):
+        ai = _bmutils.atom_idxs_from_feature(self.feat.active_features[6])
+        assert np.allclose(self.mindist_indices,ai)
+
+    def test_all_features_together(self):
+        ai = _bmutils.atom_idxs_from_general_input(self.feat)
+
+        assert len(ai)==self.feat.dimension()
+        assert all([np.allclose(aa, rr) for aa, rr in zip(ai, self.ref)])
+
+    # Test the general input
+    def test_general_input_just_runs(self):
+        _bmutils.atom_idxs_from_general_input(self.feat)
+        _bmutils.atom_idxs_from_general_input(self.tica)
+        _bmutils.atom_idxs_from_general_input(self.pca)
+        assert_raises(TypeError, _bmutils.atom_idxs_from_general_input, "aa")
+
+    def test_general_input_produces_the_right_indices(self):
+        ai  = _bmutils.atom_idxs_from_general_input(self.feat)
+        assert all([np.allclose(aa, rr) for aa, rr in zip(ai, self.ref)])
+        ai  = _bmutils.atom_idxs_from_general_input(self.tica)
+        assert all([np.allclose(aa, rr) for aa, rr in zip(ai, self.ref)])
+        ai = _bmutils.atom_idxs_from_general_input(self.pca)
+        assert all([np.allclose(aa, rr) for aa, rr in zip(ai, self.ref)])
+
+def test_colors():
+    _bmutils.matplotlib_colors_no_blue()
 
 class TestInputManipulationShaping(unittest.TestCase):
 
+    def test_listify_if_int(self):
+        assert isinstance(_bmutils.listify_if_int(0), list)
+
     def test_listify(self):
         input = [1,2,3,4]
         output = _bmutils.listify_if_not_list(input)
@@ -942,8 +1106,11 @@ def test_labelize(self):
         assert labels[0] == feat.describe()[0]
         assert labels[1] == feat.describe()[1]
 
+        # Raises Error
+        assert_raises(TypeError, _bmutils.labelize,1, [0,1])
+
     def test_superpose_list_of_geoms(self):
-        geom = md.load(os.path.join(pyemma.__path__[0], 'coordinates/tests/data/bpti_ca.pdb'))
+        geom = md.load(molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb'))
 
         # Nothing happens
         _bmutils.superpose_to_most_compact_in_list(False, [geom])
diff --git a/molpx/tests/test_generate.py b/molpx/tests/test_generate.py
new file mode 100644
index 0000000..5313bc2
--- /dev/null
+++ b/molpx/tests/test_generate.py
@@ -0,0 +1,80 @@
+__author__ = 'gph82'
+
+import unittest
+import pyemma
+import numpy as np
+import molpx
+from matplotlib import pyplot as plt
+from .test_bmutils import TestWithBPTIData
+
+
+class TestSample(TestWithBPTIData):
+
+    @classmethod
+    def setUpClass(self):
+        TestWithBPTIData.setUpClass()
+
+    @classmethod
+    def tearDownClass(self):
+        TestWithBPTIData.tearDownClass()
+
+    def test_just_runs_input_file_one(self):
+        molpx.generate.sample(self.MD_trajectory_files[0], self.MD_topology_file, self.projected_files_npy[0])
+
+    def test_just_runs_input_file_many(self):
+        molpx.generate.sample(self.MD_trajectory_files, self.MD_topology_file, self.projected_files_npy)
+
+    def test_just_runs_input_objects(self):
+        molpx.generate.sample(self.MD_trajectories, self.MD_topology, self.Ys)
+
+    def test_gen_n_samples(self):
+        molpx.generate.sample(self.MD_trajectories, self.MD_topology, self.Ys, n_geom_samples=5)
+
+    def test_gen_and_keep_n_samples(self):
+        molpx.generate.sample(self.MD_trajectories, self.MD_topology, self.Ys, n_geom_samples=5, keep_all_samples=True)
+
+    def test_atom_selections(self):
+        __, geom_smpl = molpx.generate.sample(self.MD_trajectories, self.MD_topology, self.Ys, atom_selection=np.array([2,4,6,8]))
+        assert geom_smpl.n_atoms == 4
+
+    def test_use_cl_as_input(self):
+        cl = pyemma.coordinates.cluster_kmeans(self.Ys, 10)
+        molpx.generate.sample(self.MD_trajectories, self.MD_topology, cl)
+
+    def test_right_data_are_returned(self):
+        # The only way to test this easily is inputting the clusterobject oneself
+        cl = pyemma.coordinates.cluster_kmeans(self.Ys, 10)
+        pos, geom_sampl = molpx.generate.sample(self.MD_trajectories, self.MD_topology, cl)
+        output_assignments = cl.assign(self.tica.transform(self.feat.transform(geom_sampl))[:,:2])
+        for ii, oa in enumerate(output_assignments):
+            # Each frame of the output geometries must've been assigned to each clustercenter
+            assert ii == oa
+            # The output sample corresponds to that of the input (in projected space)
+            assert np.allclose(cl.clustercenters[ii], pos[oa])
+
+    def test_return_data(self):
+        molpx.generate.sample(self.MD_trajectories, self.MD_topology, self.Ys, return_data=True)
+
+class TestProjectionPath(TestWithBPTIData):
+
+    @classmethod
+    def setUpClass(self):
+        TestWithBPTIData.setUpClass()
+
+    @classmethod
+    def tearDownClass(self):
+        TestWithBPTIData.tearDownClass()
+
+    def test_just_runs(self):
+        molpx.generate.projection_paths(self.MD_trajectories, self.MD_topology, self.Ys)
+
+    def test_just_runs_one_proj_idx(self):
+        molpx.generate.projection_paths(self.MD_trajectories, self.MD_topology, self.Ys, proj_idxs=1)
+
+    def _test_right_geoms_are_returned(self):
+        #Each individual method of generate.projection_path has already been tested.
+        # TODO tomorrow, use ideas from TestMinRmsdPaths
+        pass
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/molpx/tests/test_linkutils.py b/molpx/tests/test_linkutils.py
index 2d4e310..6f48aed 100644
--- a/molpx/tests/test_linkutils.py
+++ b/molpx/tests/test_linkutils.py
@@ -7,15 +7,13 @@
 from glob import glob
 from matplotlib.backend_bases import MouseEvent
 import mdtraj as md
-import matplotlib.pyplot as plt
 #plt.switch_backend('Agg') # allow tests
 
 
 import nglview
+from numpy.testing import assert_raises
 from scipy.spatial import cKDTree as _cKDTree
 
-
-
 class TestLinkAxWPos2NGLWidget(unittest.TestCase):
 
     @classmethod
@@ -31,41 +29,38 @@ def setUpClass(self):
         self.pos[:,1] = np.random.randn(self.n_sample)
         self.geom = self.MD_trajectories[0][:20]
         self.ngl_wdg = nglview.show_mdtraj(self.geom)
+        import matplotlib.pyplot as plt
+        self.plt = plt
 
     def test_just_works(self):
-        plt.figure()
-        __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(plt.gca(), self.pos, self.ngl_wdg)
+        self.plt.figure()
+        __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(self.plt.gca(), self.pos, self.ngl_wdg)
 
     def test_just_works_bandwidth(self):
-        plt.figure()
-        __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(plt.gca(), self.pos, self.ngl_wdg,
+        self.plt.figure()
+        __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(self.plt.gca(), self.pos, self.ngl_wdg,
                                                         band_width=[0.1, .1])
     def test_just_works_exclude_coord(self):
-        plt.figure()
-        __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(plt.gca(), self.pos, self.ngl_wdg,
+        self.plt.figure()
+        __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(self.plt.gca(), self.pos, self.ngl_wdg,
                                                         exclude_coord=1)
 
     def test_force_exceptions(self):
-        plt.figure()
-        try:
-            __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(plt.gca(), self.pos, self.ngl_wdg,
+        self.plt.figure()
+        assert_raises(TypeError, molpx._linkutils.link_ax_w_pos_2_nglwidget, self.plt.gca(), self.pos, self.ngl_wdg,
                                                             dot_color=2)
-        except TypeError:
-            pass
 
-        plt.figure()
-        try:
-            pos_rnd = np.random.randn(self.n_sample, 2)
-            __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(plt.gca(), pos_rnd, self.ngl_wdg,
+        self.plt.figure()
+        pos_rnd = np.random.randn(self.n_sample, 2)
+        assert_raises(ValueError, molpx._linkutils.link_ax_w_pos_2_nglwidget, self.plt.gca(), pos_rnd, self.ngl_wdg,
                                                             band_width=[.1, .1])
-        except ValueError:
-            pass
 
     def test_just_works_radius(self):
-        plt.figure()
-        __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(plt.gca(), self.pos, self.ngl_wdg,
+        self.plt.figure()
+        __ = molpx._linkutils.link_ax_w_pos_2_nglwidget(self.plt.gca(), self.pos, self.ngl_wdg,
                                                         band_width=[.1, .1],
                                                         radius=True)
+
 class TestGeometryInNGLWidget(unittest.TestCase):
 
     # TODO abstact this to a test class
@@ -130,9 +125,91 @@ def test_show_and_hide_just_runs_and_changes_quickhands(self):
         # Now we hide many times, should arrive at the end without anything happening
         [giw.hide() for ii in range(10)]
 
+class TestContactInNGLWidget(unittest.TestCase):
+
+    # TODO abstact this to a test class
+    @classmethod
+    def setUpClass(self):
+        self.MD_trajectory_files = glob(molpx._molpxdir(join='notebooks/data/c-alpha_centered.stride.1000*xtc'))
+        self.MD_topology_file = molpx._molpxdir(join='notebooks/data/bpti-c-alpha_centered.pdb')
+        self.MD_topology = md.load(self.MD_topology_file).top
+        self.MD_trajectories = [md.load(ff, top=self.MD_topology_file) for ff in self.MD_trajectory_files]
+
+        self.n_sample = 20
+        self.pos = np.zeros((self.n_sample,2))
+        self.pos[:,0] = np.linspace(0,1,self.n_sample)
+        self.pos[:,1] = np.random.randn(self.n_sample)
+        self.geom = self.MD_trajectories[0][:20]
+
+    def test_just_works(self):
+        self.ngl_wdg = nglview.show_mdtraj(self.geom)
+        molpx._linkutils.ContactInNGLWidget(self.ngl_wdg, [0, 1], 0)
+
+    def _test_right_atoms_are_represented(self):
+        self.ngl_wdg = nglview.show_mdtraj(self.geom)
+        ctcNwid = molpx._linkutils.ContactInNGLWidget(self.ngl_wdg, [0, 1], 0)
+
+        # TODO cannot be tested since the representation is yet to be shown!
+        # we have to find this out!
+        #irepr = [value for value in self.ngl_wdg._ngl_repr_dict["0"].items() if value["type"] == "distance"]
+        #assert len(irepr)==1
+        #assert np.allclose(np.sort(irepr["params"]["atomPair"]), [0,1])
+
+    def test_method_show_and_hide(self):
+        self.ngl_wdg = nglview.show_mdtraj(self.geom)
+        self.ngl_wdg.display()
+        ctcInwid = molpx._linkutils.ContactInNGLWidget(self.ngl_wdg, [0, 1], 0, verbose=True)
+        ctcInwid.show()
+        ctcInwid.hide() # Hide isn't really doing anything, since nothing is really shown
+        # TODO find out how to force the presentation on nglview from terminal
+
+    def _test_quickhands(self):
+        giw = molpx._linkutils.GeometryInNGLWidget(self.geom, self.ngl_wdg)
+        assert giw.is_empty()
+        assert giw.have_repr == []
+        assert ~giw.all_reps_are_on()
+        assert giw.all_reps_are_off()
+        assert ~giw.any_rep_is_on()
+        assert ~giw.is_visible()
+
+
+    def _test_show_just_runs_and_changes_quickhands(self):
+        giw = molpx._linkutils.GeometryInNGLWidget(self.geom, self.ngl_wdg)
+        giw.show()
+        assert ~giw.is_empty()
+        assert len(giw.have_repr) == 1
+        assert giw.all_reps_are_on()
+        assert ~giw.all_reps_are_off()
+        assert giw.any_rep_is_on()
+        assert giw.is_visible()
+
+    def _test_show_and_hide_just_runs_and_changes_quickhands(self):
+        # We initialize with two frames, it's easy to test the ends
+        giw = molpx._linkutils.GeometryInNGLWidget(self.geom[:2], self.ngl_wdg)
+        giw.show()
+        giw.hide()
+        assert ~giw.is_empty()
+        assert len(giw.have_repr) == 1 #should still be one
+        assert ~giw.all_reps_are_on() # but it´s off
+        assert giw.all_reps_are_off()
+        assert ~giw.any_rep_is_on()
+        assert ~giw.is_visible()
+        assert giw.any_rep_is_off()
+        # Now we show again
+        giw.show() # Turn on the one we had already, does not change the have_repr
+        assert len(giw.have_repr)==1
+        giw.show()
+        assert len(giw.have_repr)==2
+        # Turn on a new one, which isn't there nothing should happen
+        [giw.show() for ii in range(10)]
+        # Now we hide many times, should arrive at the end without anything happening
+        [giw.hide() for ii in range(10)]
+
+
 class TestUpdate2DLines(unittest.TestCase):
 
     def test_all(self):
+        import matplotlib.pyplot as plt
         plt.plot(0,0)
         line = plt.gca().lines[0]
         for ii, attr in enumerate(['lineh', 'linev', 'dot']):
@@ -140,18 +217,17 @@ def test_all(self):
             molpx._linkutils.update2Dlines(line, ii, ii)
 
     def test_force_exceptions(self):
+        import matplotlib.pyplot as plt
         plt.plot(0, 0)
         line = plt.gca().lines[0]
+        # This passes interactively in the console...?
         try:
-            molpx._linkutils.update2Dlines(line,0,0)
-        except AttributeError:
+            assert_raises(AttributeError, molpx._linkutils.update2Dlines, line,0,0)
+        except AssertionError:
             pass
 
         setattr(line, "whatisthis", "non_existing_line")
-        try:
-            molpx._linkutils.update2Dlines(line,0,0)
-        except TypeError:
-            pass
+        assert_raises(TypeError, molpx._linkutils.update2Dlines, line,0,0)
 
 class TestClickOnAxisListener(unittest.TestCase):
     @classmethod
@@ -164,11 +240,13 @@ def setUpClass(self):
 
         self.pos = np.random.rand(self.MD_trajectory.n_frames, 2)
         self.kdtree = _cKDTree(self.pos)
+        import matplotlib.pyplot as plt
+        self.plt = plt
 
     def just_runs(self, ngl_wdg, button=None):
         # Create the linked objects
-        plt.plot(self.pos[:,0], self.pos[:,1])
-        iax = plt.gca()
+        self.plt.plot(self.pos[:,0], self.pos[:,1])
+        iax = self.plt.gca()
 
         # Prepare a mouse event in the middle of the plot
         x, y = np.array(iax.get_window_extent()).mean(0)
@@ -184,7 +262,7 @@ def just_runs(self, ngl_wdg, button=None):
                                              [lineh],
                                              iax, self.pos,
                                              [dot]
-                                             )(MouseEvent(" ", plt.gcf().canvas, x,y,
+                                             )(MouseEvent(" ", self.plt.gcf().canvas, x,y,
                                                           button=button, key=None, step=0,
                                                           dblclick=False,
                                                           guiEvent=None))
@@ -194,13 +272,13 @@ def test_just_runs(self):
 
     def test_just_runs_sticky(self):
         # Create the linked objects (for sticky case, better use _sample
-        ngl_wdg, __ = molpx.visualize.sample(self.pos, self.MD_trajectory, plt.gca(), sticky=True)
+        ngl_wdg, __ = molpx.visualize.sample(self.pos, self.MD_trajectory, self.plt.gca(), sticky=True)
         self.just_runs(ngl_wdg, button=1)
         [self.just_runs(ngl_wdg, button=2) for ii in range(5)]
 
     def test_just_runs_recomputes_kdtree(self):
-        plt.plot(self.pos[:, 0], self.pos[:, 1])
-        iax = plt.gca()
+        self.plt.plot(self.pos[:, 0], self.pos[:, 1])
+        iax = self.plt.gca()
 
         # Prepare a mouse event in the middle of the plot
         x, y = np.array(iax.get_window_extent()).mean(0)
@@ -223,6 +301,81 @@ def test_just_runs_recomputes_kdtree(self):
                     dblclick=False,
                     guiEvent=None))
 
+        # Change axis lims to trigger recomputation
+        old_xlim = iax.get_xlim()
+        new_xmin = old_xlim[0]+abs(np.diff(iax.get_xlim())*.10)
+        iax.set_xlim(new_xmin, old_xlim[1])
+        # Recompute the position of the new click
+        x, y = np.array(iax.get_window_extent()).mean(0)
+        # Send an event
+        CLAL(MouseEvent(" ", CLAL.ax.figure.canvas, x, y,
+                        button=1, key=None, step=0,
+                        dblclick=False,
+                        guiEvent=None))
+
+    def test_click_w_contacts(self):
+        # Plot contact map
+        ctcs, idxs = md.compute_contacts(self.MD_trajectory)
+        ctcs = md.geometry.squareform(ctcs, idxs)
+        self.plt.imshow(ctcs[0])
+        iax = self.plt.gca()
+
+        # Create matching "positions" array
+        nres = ctcs.shape[-1]
+        positions = np.vstack(np.unravel_index(range(nres ** 2), (nres, nres))).T
+
+        # Create widget and monkey-patch its _CtcsInWid attribute
+        ngl_wdg = nglview.show_mdtraj(self.MD_trajectory)
+        ngl_wdg._CtcsInWid = [molpx._linkutils.ContactInNGLWidget(ngl_wdg, [0, 1], 0)]
+
+        # Create the CLA object linking the wid and the axis via "positions"
+        CLAL = molpx._linkutils.ClickOnAxisListener(ngl_wdg, True,
+                                             [],
+                                             iax, positions,
+                                             []
+                                             )
+
+        # Get the left, uppermost pixel of the image (matplotlib voodoo,
+        # see https://matplotlib.org/users/transforms_tutorial.html
+        x, y = iax.get_window_extent().x0, iax.get_window_extent().y1
+
+        # Before the click, this should just pass
+        CLAL.remove_last_contacts()
+
+        # Now we instantiate a mouseclick on the first contact (0,1)
+        ME = MouseEvent(" ", CLAL.ax.figure.canvas, x, y,
+                        button=1, key=None, step=0,
+                        dblclick=False,
+                        guiEvent=None)
+
+        # Make sure the simulated click was actually on the canvas
+        assert CLAL.ax.get_window_extent().contains(ME.x, ME.y)
+
+        # Send the mouseclick to the CLAL
+        CLAL(ME)
+
+        # Assert that a rectangle was created
+        assert CLAL.list_of_rects[0] is not None, CLAL.list_of_rects
+
+        # This should remove that rectangle and the contacts
+        CLAL.remove_last_contacts()
+        # Check that "remove" worked:
+        assert ~ngl_wdg._CtcsInWid[0].shown
+
+        # Now click again one on left and one on right click:
+        ME = MouseEvent(" ", CLAL.ax.figure.canvas, x, y,
+                        button=1, key=None, step=0,
+                        dblclick=False,
+                        guiEvent=None)
+        ME_right = MouseEvent(" ", CLAL.ax.figure.canvas, x, y,
+                        button=2, key=None, step=0,
+                        dblclick=False,
+                        guiEvent=None)
+        CLAL(ME)
+        CLAL(ME_right)
+
+
+
 class TestChangeInNGLWidgetListener(unittest.TestCase):
     @classmethod
     def setUpClass(self):
@@ -233,8 +386,10 @@ def setUpClass(self):
         self.MD_trajectory = self.MD_trajectories[0]
 
         self.pos = np.random.rand(self.MD_trajectory.n_frames, 2)
-        plt.plot(self.pos[:,0], self.pos[:,1])
-        iax = plt.gca()
+        from matplotlib import pyplot as plt
+        self.plt = plt
+        self.plt.plot(self.pos[:,0], self.pos[:,1])
+        iax = self.plt.gca()
         # Prepare event
         self.lineh = iax.axhline(iax.get_ybound()[0])
         setattr(self.lineh, 'whatisthis', 'lineh')
@@ -248,5 +403,18 @@ def test_just_runs_past_last_frame(self):
         molpx._linkutils.ChangeInNGLWidgetListener(self.ngl_wdg, [self.lineh, self.dot], self.pos)({"new":self.pos.shape[0]+1,
                                                                                                     "old":1})
 
+    def test_update_contact_map(self):
+        from matplotlib import pyplot as _plt
+        contact_map = md.geometry.squareform(*md.compute_contacts(self.MD_trajectories[0]))
+        _plt.figure()
+        iax = _plt.gca()
+        self.ngl_wdg._MatshowData = {"image" : iax.matshow(contact_map[0]),
+                                     "data"  : contact_map}
+        CINL = molpx._linkutils.ChangeInNGLWidgetListener(self.ngl_wdg, [], None)
+        # Run trough all available contact maps
+        for ii in range(len(contact_map)):
+            CINL({"new":ii})
+
+
 if __name__ == '__main__':
     unittest.main()
diff --git a/molpx/tests/test_molPX.py b/molpx/tests/test_molPX.py
deleted file mode 100644
index 27d081e..0000000
--- a/molpx/tests/test_molPX.py
+++ /dev/null
@@ -1,60 +0,0 @@
-__author__ = 'gph82'
-
-import unittest
-import pyemma
-import os
-import tempfile
-import numpy as np
-import shutil
-import molpx
-from matplotlib import pyplot as plt
-#plt.switch_backend('Agg') # allow tests
-
-
-class MyTestCase(unittest.TestCase):
-
-    def setUp(self):
-        self.MD_trajectory = os.path.join(pyemma.__path__[0],'coordinates/tests/data/bpti_mini.xtc')
-        self.topology = os.path.join(pyemma.__path__[0],'coordinates/tests/data/bpti_ca.pdb')
-        self.tempdir = tempfile.mkdtemp('test_molpx')
-        self.projected_file = os.path.join(self.tempdir,'Y.npy')
-        feat = pyemma.coordinates.featurizer(self.topology)
-        feat.add_all()
-        source = pyemma.coordinates.source(self.MD_trajectory, features=feat)
-        self.tica = pyemma.coordinates.tica(source,lag=1, dim=2)
-        Y = self.tica.get_output()[0]
-        print(self.tempdir)
-        np.save(self.projected_file,Y)
-
-    def tearDown(self):
-        shutil.rmtree(self.tempdir)
-
-    def test_generate_paths(self):
-        molpx.generate.projection_paths(self.MD_trajectory, self.topology, self.projected_file)
-
-    def test_generate_sample(self):
-        molpx.generate.sample(self.MD_trajectory, self.topology, self.projected_file)
-
-    def test_generate_sample_atom_selections(self):
-        molpx.generate.sample(self.MD_trajectory, self.topology, self.projected_file, atom_selection='symbol != H')
-
-        __, geom_smpl = molpx.generate.sample(self.MD_trajectory, self.topology, self.projected_file, atom_selection=np.array([2,4,6,8]))
-        assert geom_smpl[0].n_atoms == 4
-
-    # Cannot get the widget to run outside the notebook because it needs an interact bar
-    def test_visualize_qsample(self):
-        pos, geom_smpl = molpx.generate.sample(self.MD_trajectory, self.topology, self.projected_file)
-        plt.figure()
-        __ = molpx.visualize.sample(pos, geom_smpl, plt.gca())
-
-    def test_visualize_path_w_tica(self):
-        paths_dict, idata = molpx.generate.projection_paths(self.MD_trajectory, self.topology, self.projected_file)
-        plt.figure()
-        path_type = 'min_disp'
-        igeom = paths_dict[0][path_type]["geom"]
-        ipath = paths_dict[0][path_type]["proj"]
-        __ = molpx.visualize.sample(ipath, igeom, plt.gca(), projection=self.tica)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/molpx/tests/test_visualize.py b/molpx/tests/test_visualize.py
index 24400b6..80518ad 100644
--- a/molpx/tests/test_visualize.py
+++ b/molpx/tests/test_visualize.py
@@ -9,7 +9,8 @@
 import mdtraj as md
 import matplotlib.pyplot as plt
 import nglview
-#plt.switch_backend('Agg') # allow tests
+from numpy.testing import assert_raises
+import pyemma
 
 from .test_bmutils import TestWithBPTIData
 
@@ -33,8 +34,8 @@ def test_simplest_inputs_memory_small_max_frames(self):
             visualize.traj(self.MD_trajectories, self.MD_topology, self.Ys, max_frames=3)
 
     def test_simplest_inputs_disk(self):
-        visualize.traj(self.MD_trajectory_files, self.MD_topology_file, self.projected_files)
-        visualize.traj(self.MD_trajectory_files, self.MD_topology_file, [ifile.replace('.npy', '.dat') for ifile in self.projected_files])
+        visualize.traj(self.MD_trajectory_files, self.MD_topology_file, self.projected_files_npy)
+        visualize.traj(self.MD_trajectory_files, self.MD_topology_file, [ifile.replace('.npy', '.dat') for ifile in self.projected_files_npy])
 
     def test_simplest_inputs_memory_and_proj(self):
         visualize.traj(self.MD_trajectories, self.MD_topology, self.Ys, projection=self.tica)
@@ -85,6 +86,40 @@ def test_weights_on_biased_FES(self):
         visualize.FES(self.metad_trajectory_files, self.ala2_topology_file, self.metad_colvar_files,
                       proj_idxs=[1,2], weights=weights)
 
+class TestFES(TestWithBPTIData):
+
+    @classmethod
+    def setUpClass(self):
+        TestWithBPTIData.setUpClass()
+
+    def test_just_works_min_input_disk(self):
+        molpx.visualize.FES(self.MD_trajectory_files,
+                            self.MD_topology_file,
+                            self.projected_files_npy)
+
+    def test_just_works_min_input_memory(self):
+        molpx.visualize.FES(self.MD_trajectories,
+                            self.MD_topology,
+                            self.Ys)
+
+    def test_overlays(self):
+        molpx.visualize.FES(self.MD_trajectories,
+                            self.MD_topology,
+                            self.Ys,
+                            n_overlays=5)
+
+    def test_1D(self):
+        molpx.visualize.FES(self.MD_trajectories,
+                            self.MD_topology,
+                            self.Ys,
+                            proj_idxs=[0])
+
+    def test_with_weigths(self):
+        weights = [np.ones(len(iY)) for iY in self.Ys]
+        molpx.visualize.FES(self.MD_trajectories,
+                            self.MD_topology,
+                            self.Ys, weights=weights)
+
 class TestNGLWidgetWrapper(unittest.TestCase):
 
     def setUp(self):
@@ -103,7 +138,6 @@ def test_widget_wrapper_w_instantiated_wdg(self):
 
 def test_colors():
     _bmutils.matplotlib_colors_no_blue()
-
 class TestSample(TestWithBPTIData):
 
     @classmethod
@@ -115,6 +149,10 @@ def setUpClass(self):
         self.pos[:,1] = np.random.rand(self.n_sample)
         self.geom = self.MD_trajectories[0][:self.n_sample]
 
+    @classmethod
+    def tearDownClass(self):
+        TestWithBPTIData.tearDownClass()
+
     def test_sample_not_sticky_just_works(self):
         plt.figure()
         __ = molpx.visualize.sample(self.pos, self.geom, plt.gca())
@@ -170,34 +208,6 @@ def test_sample_sticky_just_works_list_geom_small_molecule(self):
                                     color_list=['r', 'b', 'g', 'magenta'],
                                     sticky=True)
 
-class TestFES(TestWithBPTIData):
-
-    @classmethod
-    def setUpClass(self):
-        TestWithBPTIData.setUpClass()
-
-    def test_just_works_min_input_disk(self):
-        molpx.visualize.FES(self.MD_trajectory_files,
-                            self.MD_topology_file,
-                            self.projected_files)
-
-    def test_just_works_min_input_memory(self):
-        molpx.visualize.FES(self.MD_trajectories,
-                            self.MD_topology,
-                            self.Ys)
-
-    def test_overlays(self):
-        molpx.visualize.FES(self.MD_trajectories,
-                            self.MD_topology,
-                            self.Ys,
-                            n_overlays=5)
-
-    def test_1D(self):
-        molpx.visualize.FES(self.MD_trajectories,
-                            self.MD_topology,
-                            self.Ys,
-                            proj_idxs=[0])
-
 class TestCorrelations(TestWithBPTIData):
 
     @classmethod
@@ -222,6 +232,11 @@ def test_correlations_input_warn(self):
     def test_correlations_inputs_verbose(self):
         visualize.correlations(self.tica, verbose=True)
 
+    def test_correlations_inputs_verbose_and_widget(self):
+        visualize.correlations(self.tica,
+                               geoms=self.MD_trajectories[0],
+                               verbose=True)
+
     def test_correlations_inputs_color_list_parsing(self):
         visualize.correlations(self.tica, proj_color_list=['green'])
 
@@ -231,30 +246,120 @@ def test_correlations_inputs_FAIL_color_list_parsing(self):
         except TypeError:
             pass
 
-class TestFeature(TestWithBPTIData):
+class TestMSM(TestWithBPTIData):
+    @classmethod
+    def setUpClass(self):
+        TestWithBPTIData.setUpClass()
+        self.cl = pyemma.coordinates.cluster_kmeans([iY[:, :2] for iY in self.Ys], 5)
+        self.iMSM = pyemma.msm.estimate_markov_model(self.cl.dtrajs, 1)
+        self.iHMM = self.iMSM.coarse_grain(3)
 
+    @classmethod
+    def tearDownClass(self):
+        TestWithBPTIData.tearDownClass()
+
+    def test_just_runs_MSM(self):
+        visualize.MSM(self.iMSM, self.MD_trajectories)
+        # More overlays
+        visualize.MSM(self.iMSM, self.MD_trajectories, n_overlays=10)
+
+    def test_just_runs_HMM(self):
+        visualize.MSM(self.iHMM, self.MD_trajectories)
+        visualize.MSM(self.iHMM, self.MD_trajectories, n_overlays=10)
+        visualize.MSM(self.iHMM, self.MD_trajectories, sharpen=True)
+
+    def test_just_runs_position_input(self):
+        visualize.MSM(self.iMSM, self.MD_trajectories, pos=self.cl.clustercenters)
+        visualize.MSM(self.iHMM, self.MD_trajectories, pos=self.cl.clustercenters)
+
+        visualize.MSM(self.iHMM, self.MD_trajectories, pos=self.cl.clustercenters, sharpen=True)
+        visualize.MSM(self.iHMM, self.MD_trajectories, pos=self.cl.clustercenters[:self.iHMM.nstates])
+        # test assert
+        assert_raises(TypeError, visualize.MSM, self.iMSM, self.MD_trajectories, pos=['a'])
+        assert_raises(AssertionError, visualize.MSM, self.iMSM, self.MD_trajectories, pos=self.cl.clustercenters[:-1])
+
+    def test_source_inputs(self):
+        visualize.MSM(self.iMSM, self.MD_trajectory_files, top=self.MD_topology_file)
+        visualize.MSM(self.iMSM, self.MD_trajectory_files, top=self.MD_topology)
+        visualize.MSM(self.iMSM, self.source)
+
+
+    def test_returns_the_right_things_MSM(self):
+        # Returning the right things should be guaranteed by all the
+        # lower-level methods, which are also unit-tested. Still, here we go
+        mpxbox = visualize.MSM(self.iMSM, self.MD_trajectories, n_overlays=3)
+        # We re-featurize, re-tic-transform, and re-cl-assign the output geoms
+        for igeoms in mpxbox.linked_mdgeoms:
+            out_assign = self.cl.assign(self.tica.transform(self.feat.transform(igeoms))[:,:2])
+            assert np.allclose(out_assign, np.arange(self.cl.n_clusters))
+
+        # Now with sticky
+        mpxbox = visualize.MSM(self.iMSM, self.MD_trajectories, n_overlays=3, sticky=True)
+        # We re-featurize, re-tic-transform, and re-cl-assign the output geoms
+        for igeoms in mpxbox.linked_mdgeoms:
+            out_assign = self.cl.assign(self.tica.transform(self.feat.transform(igeoms))[:, :2])
+            assert np.allclose(out_assign, np.arange(self.cl.n_clusters))
+
+    def test_returns_the_right_things_HMSM_sharpen(self):
+        # Returning the right things should be guaranteed by all the
+        # lower-level methods, which are also unit-tested. Still, here we go
+        mpxbox = visualize.MSM(self.iHMM, self.MD_trajectories, n_overlays=3, sharpen=True)
+        # We re-featurize, re-tic-transform, and re-cl-assign the output geoms
+        out_set = np.argmax(self.iHMM.observation_probabilities, axis=1)
+        for igeoms in mpxbox.linked_mdgeoms:
+            out_assign = self.cl.assign(self.tica.transform(self.feat.transform(igeoms))[:,:2])
+            assert np.allclose(out_assign, out_set)
+
+
+class TestFeature(TestWithBPTIData):
     @classmethod
     def setUpClass(self):
         TestWithBPTIData.setUpClass()
 
+    @classmethod
+    def tearDownClass(self):
+        TestWithBPTIData.tearDownClass()
+
     def test_feature(self):
         plt.figure()
         iwd = nglview.show_mdtraj(self.MD_trajectories[0])
-        visualize.feature(self.feat.active_features[0], iwd)
+        visualize.feature(self.feat, iwd)
 
     def test_feature_color_list(self):
         plt.figure()
         iwd = nglview.show_mdtraj(self.MD_trajectories[0])
-        visualize.feature(self.feat.active_features[0], iwd,
+        visualize.feature(self.feat, iwd,
                           idxs=[0,1],
                           color_list=['blue'])
         try:
-            visualize.feature(self.feat.active_features[0], iwd,
+            visualize.feature(self.feat, iwd,
                               idxs=[0,1],
                               color_list='blue')
         except TypeError:
             pass
 
+class Contacts(TestWithBPTIData):
+    @classmethod
+    def setUpClass(self):
+        TestWithBPTIData.setUpClass()
+        self.geom = self.MD_trajectories[0]
+        ctcs, res_idxs = md.compute_contacts(self.geom)
+        self.ctcs = md.geometry.squareform(ctcs, res_idxs)
+
+    def test_just_runs(self):
+        visualize.contacts(self.ctcs, self.geom)
+
+    def test_one_ctcframe(self):
+        # this should fail
+        try:
+            visualize.contacts(self.ctcs.mean(0), self.geom)
+        except AssertionError:
+            pass
+        # This should pass
+        visualize.contacts(self.ctcs.mean(0), self.geom, average=True)
+
+    def test_raises(self):
+        assert_raises(NotImplementedError, visualize.contacts, self.ctcs.mean(0), self.geom, average=True, residue_indices=[1,2,3])
 
 class TestBoxMe(unittest.TestCase):
 
diff --git a/molpx/visualize.py b/molpx/visualize.py
index cbff1c4..e93b7cd 100644
--- a/molpx/visualize.py
+++ b/molpx/visualize.py
@@ -77,6 +77,7 @@ def FES(MD_trajectories, MD_top, projected_trajectories,
         proj_labels='proj',
         n_overlays=1,
         atom_selection=None,
+        verbose=False,
         **sample_kwargs):
     r"""
     Return a molecular visualization widget connected with a free energy plot.
@@ -132,9 +133,12 @@ def FES(MD_trajectories, MD_top, projected_trajectories,
         If :obj:`MD_trajectories` is already a (list of) :obj:`mdtraj.Trajectory` objects, the atom-slicing can be
         done by the user place before calling this method.
 
+    verbose : bool, default is False
+        Be verbose while computing the FES
+
     sample_kwargs : dictionary of named arguments, optional
-        named arguments for the function :obj:`molpx.visualize.sample`. Non-expert users can safely ignore this option. Examples
-        are :obj:`superpose` or :obj:`proj_idxs`
+        named arguments for the function :obj:`molpx.visualize.sample`. Non-expert users can safely ignore this option.
+        Examples are :obj:`superpose` or :obj:`proj_idxs`
 
     Returns
     -------
@@ -148,13 +152,13 @@ def FES(MD_trajectories, MD_top, projected_trajectories,
         information being displayed.
 
         linked_axes :
-            list with all the :obj:`pylab.Axis`-objects contained in the :obj:`widgetbox`
+            list with all the :obj:`pyplot.Axis`-objects contained in the :obj:`widgetbox`
 
         linked_ax_wdgs :
             list with all the :obj:`matplotlib.widgets.AxesWidget`objects contained in the :obj:`widgetbox`
 
         linked_figs :
-            list with all the :obj:`pylab.Figure`-objects contained in the :obj:`widgetbox`
+            list with all the :obj:`pyplot.Figure`-objects contained in the :obj:`widgetbox`
 
         linked_ngl_wdgs :
             list with all the :obj:`nglview.NGLWidget`-objects contained in the :obj:`widgetbox`
@@ -166,7 +170,7 @@ def FES(MD_trajectories, MD_top, projected_trajectories,
             list with all the :obj:`mdtraj.Trajectory`-objects contained in the :obj:`widgetbox`
 
     """
-    from matplotlib import pylab as _plt
+    from matplotlib import pyplot as _plt
     # Prepare the overlay option
     n_overlays = _np.min([n_overlays,50])
     if n_overlays>1:
@@ -184,7 +188,8 @@ def FES(MD_trajectories, MD_top, projected_trajectories,
                                                return_data=True,
                                                n_geom_samples=n_overlays,
                                                keep_all_samples=keep_all_samples,
-                                               proj_stride=proj_stride
+                                               proj_stride=proj_stride,
+                                               verbose=verbose
                                                )
 
     data = _np.vstack(data)
@@ -220,8 +225,6 @@ def FES(MD_trajectories, MD_top, projected_trajectories,
     _linkutils.auto_append_these_mpx_attrs(outbox, geoms, ax, _plt.gcf(), ngl_wdg, axes_wdg, data_sample)
 
     return outbox
-
-
 def _box_me(tuple_in, auto_resize=True):
     r"""
     A wrapper that tries to put in an HBox whatever it s in
@@ -238,7 +241,8 @@ def _box_me(tuple_in, auto_resize=True):
 
     :return: obj:IPywdigets.HBox:, if possible
     """
-
+    # TODO THIS IS UNUSED
+    # TODO EITHER USE IT OR REMOVE IT BEFORE RELEASING
     widgets_and_canvas = []
     size_inches = []
     for obj in tuple_in:
@@ -275,7 +279,6 @@ def _box_me(tuple_in, auto_resize=True):
 
     return _linkutils._HBox(tuple_out)
 
-
 def _plot_ND_FES(data, ax_labels, weights=None, bins=50, figsize=(4,4)):
     r""" A wrapper for pyemmas FESs plotting function that can also plot 1D
 
@@ -289,14 +292,14 @@ def _plot_ND_FES(data, ax_labels, weights=None, bins=50, figsize=(4,4)):
     Returns
     -------
 
-    ax : :obj:`pylab.Axis` object
+    ax : :obj:`pyplot.Axis` object
 
     FES_data : numpy nd.array containing the FES (only for 1D data)
 
     edges : tuple containimg the axes along which FES is to be plotted (only in the 1D case so far, else it's None)
 
     """
-    from matplotlib import pylab as _plt
+    from matplotlib import pyplot as _plt
     _plt.figure(figsize=figsize)
     ax = _plt.gca()
     idata = _np.vstack(data)
@@ -336,6 +339,7 @@ def traj(MD_trajectories,
          tunits = 'frames',
          traj_selection = None,
          projection = None,
+         input_feature_traj = None,
          n_feats = 1,
          ):
     r"""Link one or many :obj:`projected trajectories`, [Y_0(t), Y_1(t)...], with the :obj:`MD_trajectories` that
@@ -346,7 +350,6 @@ def traj(MD_trajectories,
 
     MD_trajectories : str, or list of strings with the filename(s) the the molecular dynamics (MD) trajectories.
         Any file extension that :py:obj:`mdtraj` (.xtc, .dcd etc) can read is accepted.
-
         Alternatively, a single :obj:`mdtraj.Trajectory` object or a list of them can be given as input.
 
     MD_top : str to topology filename or directly :obj:`mdtraj.Topology` object
@@ -398,16 +401,20 @@ def traj(MD_trajectories,
 
     traj_selection : None, int, iterable of ints, default is None
         Don't plot all trajectories but only few of them. The default None implies that all trajs will be plotted.
-        Note: the data used for the FES will always include all trajectories, regardless of this value
+        Note: the data used for the FES will only include these trajectories
 
     projection : object that generated the projection, default is None
         The projected coordinates may come from a variety of sources. When working with :obj:`pyemma` a number of objects
         might have generated this projection, like a
             :obj:`pyemma.coordinates.transform.TICA` or a
             :obj:`pyemma.coordinates.transform.PCA`
-        Pass this object along and observe and the features that are most correlated with the projections
+        Pass this object along and observe the features that are most correlated with the projections
         will be plotted for the active trajectory, allowing the user to establish a visual connection between the
         projected coordinate and the original features (distances, angles, contacts etc)
+        These trajectories will be re-computed by applyiing
+        :obj:`projection.transform(MD_trajectories)', unless :obj:`input_feature_traj` is parsed
+
+    input_feature_traj : TODO
 
     n_feats : int, default is 1
         If a :obj:`projection` is passed along, the first n_feats features that most correlate the
@@ -421,9 +428,9 @@ def traj(MD_trajectories,
         return _plt.gca(), _plt.gcf(), widget, geoms
 
     ax :
-        :obj:`pylab.Axis` object
+        :obj:`pyplot.Axis` object
     fig :
-        :obj:`pylab.Figure` object
+        :obj:`pyplot.Figure` object
     ngl_wdg :
         :obj:`nglview.NGLWidget`
     geoms:
@@ -431,7 +438,7 @@ def traj(MD_trajectories,
 
 
     """
-    from matplotlib import pylab as _plt
+    from matplotlib import pyplot as _plt
     smallfontsize = int(_rcParams['font.size'] / 1.5)
     proj_idxs = _bmutils.listify_if_int(proj_idxs)
 
@@ -481,17 +488,18 @@ def traj(MD_trajectories,
     for proj_counter, __ in enumerate(proj_idxs):
         ylims[0, proj_counter] = _np.min([idata[:,proj_counter].min() for idata in data])
         ylims[1, proj_counter] = _np.max([idata[:,proj_counter].max() for idata in data])
-
+    
     ylabels = _bmutils.labelize(proj_labels, proj_idxs)
 
     # Do we have usable projection information?
-    corr_dicts = [[]]*n_trajs
+    corr_dicts = [[]]*len(data)
     if projection is not None:
         corr_dicts = [_bmutils.most_corr(projection, geoms=igeom, proj_idxs=proj_idxs, n_args=n_feats)
                       for igeom in geoms]
         if corr_dicts[0]["feats"] != []:
-            colors = _bmutils.matplotlib_colors_no_blue(ncycles=int(_np.ceil(_np.max(proj_idxs)/6.))) # Hack
+            colors = _bmutils.matplotlib_colors_no_blue(ncycles=int(_np.ceil((_np.max(proj_idxs)+1)/6.))) # Hack
             colors = [colors[ii] for ii in proj_idxs]
+            #colors = ['red']*10 # for the paper, to be deleted later
         else:
             n_feats=0
     else:
@@ -626,7 +634,6 @@ def traj(MD_trajectories,
 
     return mpx_wdg_box
 
-
 def correlations(correlation_input,
                  geoms=None,
                  proj_idxs=None,
@@ -643,7 +650,7 @@ def correlations(correlation_input,
 
     correlation_input : numpy ndarray or some PyEMMA objects
 
-        if array :
+        if array : 
             (m,m) correlation matrix, with a row for each feature and a column for each projection
 
         if PyEMMA-object :
@@ -701,7 +708,8 @@ def correlations(correlation_input,
     corr_dict and ngl_wdg
 
     corr_dict :
-        Dictionary with items:
+        Dictionary containing correlation information. For an overview, just issue `print(corr_dict)`. The
+        values are stored under the following keys.
 
         idxs :
             List of length len(proj_idxs) with lists of length n_feat with the idxs of the most correlated features
@@ -761,14 +769,12 @@ def correlations(correlation_input,
         for ii, line in enumerate(corr_dict["info"]):
             print('%s is most correlated with '%(line["name"] ))
             for line in line["lines"]:
-                # TODO: this is for when tica is there but no featurizer is there
                 if widget is not None and len(corr_dict["atom_idxs"]) != 0:
                     line += ' (in %s in the widget)'%(proj_color_list[ii])
                 print(line)
 
     return corr_dict, widget
 
-
 def feature(feat,
             widget,
             idxs=0,
@@ -783,7 +789,7 @@ def feature(feat,
     ----------
 
     featurizer : py:obj:`_MDFeautrizer`
-        A PyEMMA MDFeaturizer object (either a feature or a featurizer, works with both)
+        A PyEMMA MDfeaturizer object with any number of .active_features()
 
     widget : None or nglview widget
         Provide an already existing widget to visualize the correlations on top of. This is only for expert use,
@@ -813,7 +819,7 @@ def feature(feat,
     """
 
     idxs = _bmutils.listify_if_int(idxs)
-    atom_idxs = _bmutils.atom_idxs_from_feature(feat)[idxs]
+    atom_idxs = _bmutils.atom_idxs_from_general_input(feat)[slice(*idxs)]
 
     if color_list is None:
         color_list = ['blue'] * len(idxs)
@@ -829,7 +835,6 @@ def feature(feat,
 
     return widget
 
-
 def sample(positions, geom, ax,
            plot_path=False,
            clear_lines=True,
@@ -931,9 +936,14 @@ def sample(positions, geom, ax,
     axes_wdg: :obj:`~matplotlib.Axes.AxesWidget`
 
     """
+    # Make a copy of the geometry, otherwise the input gets destroyed
+    if isinstance(geom, list):
+        copy_geom = [gg[:] for gg in geom]
+    elif isinstance(geom, _md.Trajectory):
+        copy_geom = geom[:]
 
     if not sticky:
-        return _sample(positions, geom, ax,
+        return _sample(positions, copy_geom, ax,
                        plot_path = plot_path,
                        clear_lines = clear_lines,
                        n_smooth = n_smooth,
@@ -944,11 +954,11 @@ def sample(positions, geom, ax,
                        **link_ax2wdg_kwargs)
     else:
 
-        if isinstance(geom, _md.Trajectory):
-            geom=[geom]
+        if isinstance(copy_geom, _md.Trajectory):
+            copy_geom=[copy_geom]
 
         # The method takes care of whatever superpose
-        geom = _bmutils.superpose_to_most_compact_in_list(superpose, geom)
+        copy_geom = _bmutils.superpose_to_most_compact_in_list(superpose, copy_geom)
 
         if color_list is None:
             sticky_colors_hex = ['Element' for ii in range(len(positions))]
@@ -964,7 +974,7 @@ def sample(positions, geom, ax,
             raise TypeError('argument color_list should be either None, "random", or a list of len(pos)=%u, '
                             'instead of type %s and len %u' % (len(positions), type(color_list), len(color_list)))
         sticky_rep = 'cartoon'
-        if geom[0].top.n_residues < 10:
+        if copy_geom[0].top.n_residues < 10:
             sticky_rep = 'ball+stick'
         if list_of_repr_dicts is None:
             list_of_repr_dicts = [{'repr_type': sticky_rep, 'selection': 'all'}]
@@ -974,7 +984,7 @@ def sample(positions, geom, ax,
         # Prepare Geometry_in_widget_list
         ngl_wdg._GeomsInWid = [_linkutils.GeometryInNGLWidget(igeom, ngl_wdg,
                                                           color_molecule_hex= cc,
-                                                          list_of_repr_dicts=list_of_repr_dicts) for igeom, cc in zip(_bmutils.transpose_geom_list(geom), sticky_colors_hex)]
+                                                          list_of_repr_dicts=list_of_repr_dicts) for igeom, cc in zip(_bmutils.transpose_geom_list(copy_geom), sticky_colors_hex)]
 
         axes_wdg = _linkutils.link_ax_w_pos_2_nglwidget(ax,
                                    positions,
@@ -986,7 +996,6 @@ def sample(positions, geom, ax,
 
         return ngl_wdg, axes_wdg
 
-
 def _sample(positions, geoms, ax,
             plot_path=False,
             clear_lines=True,
@@ -1061,7 +1070,7 @@ def _sample(positions, geoms, ax,
 
     """
 
-    assert isinstance(geoms, (list, _md.Trajectory))
+    assert isinstance(geoms, (list, _md.Trajectory)), type(geoms)
 
     # Dow I need to smooth things out?
     if n_smooth > 0:
@@ -1090,7 +1099,7 @@ def _sample(positions, geoms, ax,
         [ax.lines.pop() for ii in range(len(ax.lines))]
     # Plot the path on top of it
     if plot_path:
-        ax.plot(positions[:,0], positions[:,1], '-g', lw=3)
+        ax.plot(positions[:,0], positions[:,1], '-k', lw=3)
 
     # Link the axes ngl_wdg with the ngl ngl_wdg
     axes_wdg = _linkutils.link_ax_w_pos_2_nglwidget(ax,
@@ -1114,3 +1123,269 @@ def _sample(positions, geoms, ax,
 
     return ngl_wdg, axes_wdg
 
+def contacts(contact_map, input, residue_indices=None, average=False, panelsize=4):
+    r"""
+    Return a plot of the contact map and a linked :obj:`nglview.NGLWidget`. Clicking on
+    a pixel of interest on the contact map will a) highlight that pixel and b)
+    add lines in the widget ,connecting the corresponding atoms. Also, any updates in
+    the widget's frame, via the sliding bar, will update the shown contact map (in
+     case more than one contact map was provided)
+
+    Parameters
+    ----------
+    contact_map : square nd.array or iterable thereof.
+        These square arrays contain the contact map(s)
+
+    input : :obj:`mdtraj.Trajectory` object or a list thereof.
+        An :obj:`nglview.NGLWidget` will be instantiated with this input
+
+    residue_indices : boolean or iterable of integers
+        Residue indices corresponding to the :obj:`contact_map`. If None, an array
+        (0,1,...n_residues) will be created.
+        # TODO if not None, a NotImplementedError will be raised, because the relabeling of
+        zoomable plots is not yet implemented by molpx)
+
+    average : boolean, default is False
+        Plot only the average of the contact maps provided in :obj:`contact_map`. If only one
+        such map is given, this keyword has no effect. If average is false but the
+        number of frames in :obj:`input` and :obj:`contact_map` don match, an exception is thrown.
+
+    panelsize : int, default is 4
+        The size of the figure and widget that will be outputted inside the molpxbox
+
+    Returns
+    --------
+
+    mpxbox : An :obj:`nglview.NGLWidget`
+    """
+
+    from matplotlib import pyplot as _plt
+    # Add one axis to the input if necessary
+    if _np.ndim(contact_map)==2:
+        contact_map = _np.array(contact_map, ndmin=3)
+
+    # Check that the number of frames match if no average is requested
+    if _np.ndim(contact_map)==3 and not average:
+        assert len(contact_map) == input.n_frames, "If average is False, the number of contact maps (%u) must " \
+                                                   "match the number of frames in input (%u)" % (
+                                                   len(contact_map), input.n_frames)
+    # Assert squaredness
+    assert all([ict.shape[0] == ict.shape[1] for ict in contact_map]), "The input has to be a square matrix"
+
+    # Needed arrays
+    nres = contact_map[0].shape[0]
+    positions = _np.vstack(_np.unravel_index(range(nres**2), (nres,nres))).T
+    if residue_indices is None:
+        residue_pairs = positions
+    else:
+        raise NotImplementedError("This feature is not implemented yet!")
+
+    # Create a color list
+    cmap = _get_cmap('rainbow')
+    cmap_table = _np.linspace(0, 1, len(positions))
+    sticky_colors_hex = [_rgb2hex(cmap(ii)) for ii in _np.random.permutation(cmap_table)]
+
+    # Instantiate widget
+    iwd = _nglwidget_wrapper(input)
+
+    # Do the plot
+    _plt.ioff()
+    _plt.figure(figsize=(panelsize, panelsize))
+    iax = _plt.gca()
+    # _plt.plot(positions[:,0], positions[:,1], ' ok')
+    # Make the average if wanted
+    if average:
+        iax.matshow(_np.average(contact_map, axis=0))
+    else:
+        # Monkey-Patch the matshow_data into the object
+        iwd._MatshowData = {"image" : iax.matshow(contact_map[0]),
+                            "data"  : contact_map}
+    _plt.ion()
+
+
+    # TODO: if residues is not None,
+    # TODO make sure that zooming works even if a sub-set of res_idxs is given
+    """
+    # Relabel the plot
+    for axtype in ['x', 'y']:
+        tic_idxs = [int(tl) for tl in getattr(iax, 'get_%sticks'%axtype)()[1:-1]]
+        tic_labels = ['']+['%u'%residue_idxs[ii] for ii in tic_idxs]+['']
+        getattr(iax,'set_%sticklabels'%axtype)(tic_labels)
+    """
+
+    # Monkey-Patch the ContactInNGLWidgets into the widget
+    iwd._CtcsInWid = [_linkutils.ContactInNGLWidget
+        (iwd, [_bmutils.get_repr_atom_for_residue(input.top.residue(aa)).index for aa in [ii,jj]], rp_idx,
+         #verbose=True,
+         color= sticky_colors_hex[rp_idx]
+         )
+                      for rp_idx, (ii,jj) in enumerate(residue_pairs)]
+
+    # Turn axes into a widget
+    axes_wdg = _linkutils.link_ax_w_pos_2_nglwidget(iax,
+                                                    positions,
+                                                    iwd,
+                                                    crosshairs=False,
+                                                    dot_color='None',
+                                                    #**link_ax2wdg_kwargs
+                                                    )
+
+    iwd._set_size(*['%fin' % inches for inches in iax.get_figure().get_size_inches()])
+    #iax.figure.tight_layout()
+    axes_wdg.canvas.set_window_title("Contact Map")
+    outbox = _linkutils.MolPXHBox([iwd, axes_wdg.canvas])
+    _linkutils.auto_append_these_mpx_attrs(outbox, input, iax, _plt.gcf(), iwd, axes_wdg, positions)
+
+    return outbox
+
+
+def MSM(msm_obj, traj_inp,
+        pos=None,
+        sharpen=False,
+        n_overlays=1,
+        top=None,
+        sticky=False,
+        panelsize=6,
+        **networkplot_kwargs):
+
+    r"""
+    Visualize an MSM or an HMM as a network of nodes and egdes, together with an :obj:~`nglview.NGLWidget`
+    containing representative structures of node/state. Clicking on the node will update the widget.
+
+    Parameters
+    ----------
+    msm_obj: input MSM-object
+        One of PyEMMA's MSM-objects, either a "normal" MSM (:obj:`~pyemma.msm.MaximumLikelihoodMSM`) or a hidden MSM (:obj:`~pyemma.msm.MaximumLikelihoodHMSM`)
+
+    traj_inp : trajectory input
+        Where to get the geometries from. It must be the same input with which the :obj:`msm_obj` was built.
+        No checks are done by the method as to whether this is true, i.e. *rubbish-in->rubbish-out*.
+        It can be of three different types (and lists thereof):
+
+           * filenames (for which a :obj:`top` is needed, see below)
+           * :obj:`mdtraj.Trajectory` objects
+           * a PyEMMA's :obj:`~pyemma.coordinates.data.feature_reader.FeatureReader`
+
+    pos : node positions, either None or a numpy ndarray of ndim=2
+        By default, node positions are optimized to represent connectivity
+        (see PyEMMA's :obj:`~pyemma.plots.plot_markov_model`). However, the user can override with
+        custom node-positions by passing an array as :obj:`pos`. In many cases, it is useful for that array to be
+        the clustercenter-positions with which the MSM/HMM was constructed: :obj:`pos=cl.clustercenters`.
+        The input in :obj:`pos` has to be compatible with the provided :obj:`msm_obj`, i.e. have the necessary
+        number entries. The error messages will inform about what's wrong.
+
+    sharpen : boolean, default is False,
+        This keyword only has effect for an HMM as an :obj:`input_msm`.
+        By default, the method samples from the distribution of microstate inside each macrostate, using the object's
+        :obj:`~pyemma.msm.MaximumLikelihoodHMSM.sample_by_observation_probabilities`- method. This can lead
+        to fuzzy samples where the overlay of molecular structures is not very informative.
+
+        If :obj:`sharpen` is True, only the microstate that maximizes each macrostate's probabilites
+        (i.e., its argmax) will be sampled. Produces a more *sharpened* sample,
+        which is less representative of the whole set but very representative of the most probable
+        microstate within that set.
+
+    n_overlays : int, default is 1
+        Number of structures to represent simultaneously for each node of the network
+
+    top : str or :obj:`mdtraj.Topology`, default is None
+        If the filenames in :obj:`traj_inp` need a topology, here's where you pass it along
+
+    sticky : bool, default is False
+        Behaviour of the mouseclick when clicking a node in the network.
+        If True, left click adds-structures, right-click deletes them
+
+    panelsize : int, default is 6
+        Size of the network figure, in inches. If  :obj:`pos` is provided, the panelsize will be adapted
+        slightly to match the proportions of :obj:`pos`
+
+    networkplot_kwargs : named keyword arguments for :obj:`~pyemma.plots.plot_markov_model`
+
+    Returns :
+    ---------
+
+    mpxbox : A :obj:`~molpx.linkutils.MolPXHBox`-object. It contains the :obj:`nglview.NGLWidget` and the network
+    plot. Check the :obj:`mpxbox_linked*` attributes to see what the object contains
+
+    """
+    from pyemma.msm.estimators.maximum_likelihood_hmsm import MaximumLikelihoodHMSM as _MLHMSM
+    from pyemma.msm.estimators.maximum_likelihood_msm  import MaximumLikelihoodMSM as _MLMSM
+    from pyemma.plots import plot_markov_model as _pyemma_plt_msm
+    from pyemma.util.discrete_trajectories import  sample_indexes_by_state as _sample_indexes_by_state
+    from matplotlib import pyplot as _plt
+
+    assert isinstance(msm_obj, (_MLHMSM, _MLMSM)), "Allowed input types are %s, not %s" % ((_MLHMSM, _MLMSM), type(MSM))
+
+    # Input parsing of the position object (#TODO reduce code?)
+    if pos is None:
+       pass
+    elif isinstance(pos, _np.ndarray):
+            if isinstance(msm_obj, _MLMSM):
+                assert len(pos) == msm_obj.nstates, \
+                    ("Number of input positions (%u) "
+                     "does not match number %u active states of the MSM. Try slicing the input positions with "
+                     "MSM.active_set" % (len(pos), msm_obj.nstates))
+            elif isinstance(msm_obj, _MLHMSM):
+                assert len(pos) == msm_obj.nstates or len(pos) == msm_obj.nstates_obs, \
+                    ("With the input HMSM, the input positions have to have either "
+                               "%u entries (number of coarse states in the HMSM) or %u entries (number of observed states of the HMSM). "
+                               "You have provided neither: %u" % (msm_obj.nstates, msm_obj.nstates_obs, len(pos)))
+    else:
+        raise TypeError("The object_for_positions has the wrong type %s" % type(pos))
+
+    # MSM without coarse-graining
+    if isinstance(msm_obj, _MLMSM):
+        sample_frames = _np.vstack(msm_obj.sample_by_state(n_overlays))
+
+    # HMM
+    else:
+        if sharpen:
+            active_state_indexes = msm_obj.observable_state_indexes
+            subset = _np.argmax(msm_obj.observation_probabilities, axis=1)
+            sample_frames = _np.vstack(_sample_indexes_by_state(active_state_indexes, n_overlays,
+                                                                subset=subset, replace=True))
+            # The user gave one position entry per metastable set
+            if pos is not None and len(pos)==msm_obj.nstates:
+                pass # im leaving this case for clarity, in theory it could be removed
+            # The user gave a full array of positions that matches the total number of microstates
+            # and wants the method to choose automagically the positions that match the argmax(PDF)
+            elif pos is not None and len(pos)==msm_obj.nstates_obs:
+                pos = pos[subset]
+            # Any other case has ben caught before by the above ValueErrors
+        else:
+            sample_frames = _np.vstack(msm_obj.sample_by_observation_probabilities(n_overlays))
+            # The user gave one position entry per metastable set
+            if pos is not None and len(pos)==msm_obj.nstates:
+                pass  # im leaving this case for clarity, in theory it could be removed
+            # The user gave a full array of positions that matches the total number of microstates
+            # and wants the method to wheight them using the observation probabilities
+            elif pos is not None and len(pos)==msm_obj.nstates_obs:
+                isample_pos = []
+                for idist in msm_obj.observation_probabilities:
+                    isample_pos.append(_np.average(pos, weights=idist, axis=0))
+                pos = _np.vstack(isample_pos)
+
+    sample_geoms = _bmutils.save_traj_wrapper(traj_inp, sample_frames, None, top=top)
+    sample_geoms = _bmutils.re_warp(sample_geoms, n_overlays)
+    sample_geoms = _bmutils.transpose_geom_list(sample_geoms)
+
+    _plt.ioff()
+    ifig, pos = _pyemma_plt_msm(msm_obj.P, pos=pos,
+                                **networkplot_kwargs,
+                                )
+    # Conserve the proportion of the circles in the MSM plot
+    figw, figh = ifig.get_size_inches()
+    ifig.set_size_inches((panelsize, panelsize * figh / figw))
+    iax = ifig.gca()
+
+    ngl_wdg, axes_wdg = sample(pos, sample_geoms, iax,
+                               sticky=sticky,
+                               crosshairs=False,
+                               )#, clear_lines=False, **sample_kwargs)
+    ngl_wdg._set_size(*['%fin' % inches for inches in ifig.get_size_inches()])
+    ifig.tight_layout()
+    outbox = _linkutils.MolPXHBox([ngl_wdg, ifig.canvas])
+    _linkutils.auto_append_these_mpx_attrs(outbox, sample_geoms, _plt.gca(), ifig, ngl_wdg, axes_wdg, pos)
+    _plt.ion()
+
+    return outbox
\ No newline at end of file