Removed synthesize_transmtx from analyze_matrix.py; Simplified synthe…

…size_transmtx in synthesize_data.py

ensemble_md/analysis/analyze_matrix.py

@@ -122,40 +122,6 @@ def calc_equil_prob(trans_mtx):
     return equil_prob
 
 
-def synthesize_transmtx(trans_mtx, mtx_type='rep', n_frames=100000):
-    """
-    Synthesizes a mock transition matrix by calculating the underlying equilibrium probability
-    of the input transition matrix, synthesizing a trajectory by drawing samples from the equilibrium
-    distribution, and calculating the transition matrix from the trajectory.
-
-    Parameters
-    ----------
-    trans_mtx: np.ndarray
-        The input transition matrix.
-    mtx_type: str
-        The type of the input transition matrix. It can be either 'rep' (replica-space transition matrix)
-        or 'state' (state-space transition matrix).
-    n_frames: int
-        The number of frames of the synthesized trajectory from which the mock transition matrix is calculated.
-
-    Returns
-    -------
-    syn_mtx: np.ndarray
-        The synthesized transition matrix.
-    syn_traj: np.ndarray
-        The synthesized trajectory.
-    diff_mtx: np.ndarray
-        The absolute difference between the input and synthesized transition matrices.
-    """
-    equil_prob = calc_equil_prob(trans_mtx)
-    n_states = len(equil_prob)
-    syn_traj = np.random.choice(n_states, size=n_frames, p=equil_prob.reshape(n_states))
-    syn_mtx = analyze_traj.traj2transmtx(syn_traj, n_states)
-    diff_mtx = np.abs(trans_mtx - syn_mtx)
-
-    return syn_mtx, syn_traj, diff_mtx
-
-
 def calc_spectral_gap(trans_mtx, atol=1e-8, n_bootstrap=50):
     """
     Calculates the spectral gap of the input transition matrix and estimates its

ensemble_md/analysis/synthesize_data.py

@@ -61,7 +61,7 @@ def synthesize_traj(trans_mtx, n_frames=100000, method='transmtx', start=0, seed
     return syn_traj
 
 
-def synthesize_transmtx(trans_mtx, mtx_type='rep', n_frames=100000, seed=None):
+def synthesize_transmtx(trans_mtx, n_frames=100000, seed=None):
     """
     Synthesizes a normalized transition matrix similar to the input transition matrix by first
     generating a trajectory using :code:`synthesize_traj` with :code:`method='transmtx'` and then
@@ -71,9 +71,6 @@ def synthesize_transmtx(trans_mtx, mtx_type='rep', n_frames=100000, seed=None):
     ----------
     trans_mtx: np.ndarray
         The input transition matrix.
-    mtx_type: str
-        The type of the input transition matrix. It can be either 'rep' (replica-space transition matrix)
-        or 'state' (state-space transition matrix).
     n_frames: int
         The number of frames of the synthesized trajectory from which the mock transition matrix is calculated.
         The default value is 100000.
@@ -85,32 +82,20 @@ def synthesize_transmtx(trans_mtx, mtx_type='rep', n_frames=100000, seed=None):
     syn_mtx: np.ndarray
         The synthesized transition matrix.
     syn_traj: np.ndarray
-        The synthesized trajectory/trajectories from which the transition matrix is calculated. Note that
-        if :code:`mtx_type` is 'rep', this will be a list of trajectories, which represent synthesized
-        replica-space trajectories.
+        The synthesized trajectory from which the transition matrix is calculated.
     diff_mtx: np.ndarray
         The input transition matrix subtracted by the synthesized transition matrix.
     """
     N = len(trans_mtx)  # can be the number of states or number of replicas depending on mtx_type
-    if mtx_type == 'state':
-        # Note that here we just use the default values (method='transmtx' and start=0) for synthesize_traj, so that
-        # the synthesized matrix will be similar to the input one. (If equil_prob is used, the resulting matrix may
-        # be very different from the input one, though the equilibrium probabilities and spectral gap should be similar.)
-        # Note that for transition matrix synthesis, the starting state of the synthesized trajectory
-        # should not matter given that the number of frames is large.
-        syn_traj = synthesize_traj(trans_mtx, n_frames, seed=seed)
-        syn_mtx = analyze_traj.traj2transmtx(syn_traj, N)
-    elif mtx_type == 'rep':
-        rep_trajs = np.array([synthesize_traj(trans_mtx, n_frames, start=i, seed=seed) for i in range(N)])
-        counts = [analyze_traj.traj2transmtx(rep_trajs[i], N, normalize=False) for i in range(len(rep_trajs))]
-        syn_mtx = np.sum(counts, axis=0)
-        syn_mtx /= np.sum(syn_mtx, axis=1)[:, None]
-        syn_traj = rep_trajs
-    else:
-        raise ValueError(f'Invalid mtx_type: {mtx_type}. The mtx_type must be either "rep" or "state".')
-
+
+    # Note that here we just use the default values (method='transmtx' and start=0) for synthesize_traj, so that
+    # the synthesized matrix will be similar to the input one. (If equil_prob is used, the resulting matrix may
+    # be very different from the input one, though the equilibrium probabilities and spectral gap should be similar.)
+    # Note that for transition matrix synthesis, the starting state of the synthesized trajectory
+    # should not matter given that the number of frames is large.
+    syn_traj = synthesize_traj(trans_mtx, n_frames, seed=seed)
+    syn_mtx = analyze_traj.traj2transmtx(syn_traj, N)
     diff_mtx = trans_mtx - syn_mtx
 
     return syn_mtx, syn_traj, diff_mtx
 
-