old_contents: """
  MSM Network Plot
  ================
  """
  from msmbuilder.featurizer import DihedralFeaturizer
  from msmbuilder.decomposition import tICA
  from msmbuilder.cluster import MiniBatchKMeans
  from msmbuilder.msm import MarkovStateModel
  
  import numpy as np
  
  import msmexplorer as msme
  from msmexplorer.example_datasets import FsPeptide
  
  rs = np.random.RandomState(42)
  
  # Load Fs Peptide Data
  trajs = FsPeptide().get().trajectories
  
  # Extract Backbone Dihedrals
  featurizer = DihedralFeaturizer(types=['phi', 'psi'])
  diheds = featurizer.fit_transform(trajs)
  
  # Perform Dimensionality Reduction
  tica_model = tICA(lag_time=2, n_components=2)
  tica_trajs = tica_model.fit_transform(diheds)
  
  # Perform Clustering
  clusterer = MiniBatchKMeans(n_clusters=100, random_state=rs)
  clustered_trajs = clusterer.fit_transform(tica_trajs)
  
  # Construct MSM
  msm = MarkovStateModel(lag_time=2)
  msm.fit(clustered_trajs)
  
  # Plot TPT
  pos = dict(zip(range(clusterer.n_clusters), clusterer.cluster_centers_))
  msme.plot_tpaths(msm, [99], [0], pos=pos, node_color='beryl',
                   edge_color='carbon')
  
new_contents: """
  Transition Pathway Plot
  =======================
  """
  from msmbuilder.featurizer import DihedralFeaturizer
  from msmbuilder.decomposition import tICA
  from msmbuilder.cluster import MiniBatchKMeans
  from msmbuilder.msm import MarkovStateModel
  
  import numpy as np
  
  import msmexplorer as msme
  from msmexplorer.example_datasets import FsPeptide
  
  rs = np.random.RandomState(42)
  
  # Load Fs Peptide Data
  trajs = FsPeptide().get().trajectories
  
  # Extract Backbone Dihedrals
  featurizer = DihedralFeaturizer(types=['phi', 'psi'])
  diheds = featurizer.fit_transform(trajs)
  
  # Perform Dimensionality Reduction
  tica_model = tICA(lag_time=2, n_components=2)
  tica_trajs = tica_model.fit_transform(diheds)
  
  # Perform Clustering
  clusterer = MiniBatchKMeans(n_clusters=100, random_state=rs)
  clustered_trajs = clusterer.fit_transform(tica_trajs)
  
  # Construct MSM
  msm = MarkovStateModel(lag_time=2)
  msm.fit(clustered_trajs)
  
  # Plot TPT
  pos = dict(zip(range(clusterer.n_clusters), clusterer.cluster_centers_))
  msme.plot_tpaths(msm, [99], [0], pos=pos, node_color='beryl',
                   edge_color='carbon')
  
subject: Update plot_tpaths.py