Finished updating analyze_free_energy.py, analyze_matrix.py and analy…

…ze_traj.py

ensemble_md/analysis/analyze_free_energy.py

@@ -8,7 +8,7 @@
 #                                                                  #
 ####################################################################
 """
-The :obj:`.analyze_free_energy` module provides functions for performing free energy calculations for EEXE simulations.
+The :obj:`.analyze_free_energy` module provides functions for performing free energy calculations for REXEE simulations.
 """
 import alchemlyb
 import numpy as np
@@ -26,7 +26,7 @@
 
 def preprocess_data(files_list, temp, data_type, spacing=1, t=None, g=None):
     """
-    This function preprocesses :math:`u_{nk}`/:math:`dH/dλ` data for all replicas in an EEXE simulation.
+    This function preprocesses :math:`u_{nk}`/:math:`dH/dλ` data for all replicas in an REXEE simulation.
     For each replica, it reads in :math:`u_{nk}`/:math:`dH/dλ` data from all iterations, concatenate
     them, remove the equilibrium region and and decorrelate the concatenated data. Notably,
     the data preprocessing protocol is basically the same as the one adopted in
@@ -116,7 +116,7 @@ def _apply_estimators(data, df_method="MBAR"):
     Parameters
     ----------
     data : pd.Dataframe
-        A list of dHdl or u_nk dataframes obtained from all replicas of the EEXE simulation of interest.
+        A list of dHdl or u_nk dataframes obtained from all replicas of the REXEE simulation of interest.
         Preferrably, the dHdl or u_nk data should be preprocessed by the function proprocess_data.
     df_method : str
         The selected free energy estimator. Options include "MBAR", "BAR" and "TI".
@@ -222,13 +222,13 @@ def _calculate_weighted_df(df_adjacent, df_err_adjacent, state_ranges, propagate
 def calculate_free_energy(data, state_ranges, df_method="MBAR", err_method='propagate', n_bootstrap=None, seed=None):
     """
     Caculates the averaged free energy profile with the chosen method given dHdl or u_nk data obtained from
-    all replicas of the EEXE simulation of interest. Available methods include TI, BAR, and MBAR. TI
+    all replicas of the REXEE simulation of interest. Available methods include TI, BAR, and MBAR. TI
     requires dHdl data while the other two require u_nk data.
 
     Parameters
     ----------
     data : pd.Dataframe
-        A list of dHdl or u_nk dataframes obtained from all replicas of the EEXE simulation of interest.
+        A list of dHdl or u_nk dataframes obtained from all replicas of the REXEE simulation of interest.
         Preferrably, the dHdl or u_nk data should be preprocessed by the function proprocess_data.
     state_ranges : list
         A list of lists of intergers that represents the alchemical states that can be sampled by different replicas.

ensemble_md/analysis/analyze_matrix.py

@@ -8,7 +8,7 @@
 #                                                                  #
 ####################################################################
 """
-The :obj:`.analyze_matrix` module provides methods for analyzing matrices obtained from EEXE.
+The :obj:`.analyze_matrix` module provides methods for analyzing matrices obtained from REXEE.
 """
 import numpy as np
 import seaborn as sns
@@ -175,7 +175,7 @@ def split_transmtx(trans_mtx, n_sim, n_sub):
     trans_mtx : np.ndarray
         The input state transition matrix to split
     n_sim : int
-        The number of replicas in EEXE.
+        The number of replicas in REXEE.
     n_sub : int
         The number of states for each replica.
 

ensemble_md/analysis/analyze_traj.py

@@ -8,7 +8,7 @@
 #                                                                  #
 ####################################################################
 """
-The :obj:`.analyze_traj` module provides methods for analyzing trajectories in EEXE.
+The :obj:`.analyze_traj` module provides methods for analyzing trajectories in REXEE.
 """
 import numpy as np
 import matplotlib.pyplot as plt
@@ -256,7 +256,7 @@ def traj2transmtx(traj, N, normalize=True):
     """
     Computes the transition matrix given a trajectory. For example, if a state-space
     trajectory from a EXE or HREX simulation given, a state transition matrix is returned.
-    If a trajectory showing transitions between replicas in a EEXE simulation is given,
+    If a trajectory showing transitions between replicas in a REXEE simulation is given,
     a replica transition matrix is returned.
 
     Parameters
@@ -444,7 +444,7 @@ def plot_state_hist(trajs, state_ranges, fig_name, stack=True, figsize=None, pre
         A list of state index time series either from different continuous trajectories or from different
         alchemical ranges (i.e. from different simulation folders).
     state_ranges : list
-        A list of lists of state indices. (Like the attribute :code:`state_ranges` in :obj:`.EnsembleEXE`.)
+        A list of lists of state indices. (Like the attribute :code:`state_ranges` in :obj:`.ReplicaExchangeEE`.)
     fig_name : str
         The file name of the png file to be saved (with the extension).
     stack : bool
@@ -573,7 +573,7 @@ def calculate_hist_rmse(hist_data, state_ranges):
     hist_data : list
         The histogram data of the state index for each trajectory.
     state_ranges : list
-        A list of lists of state indices. (Like the attribute :code:`state_ranges` in :obj:`.EnsembleEXE`.)
+        A list of lists of state indices. (Like the attribute :code:`state_ranges` in :obj:`.ReplicaExchangeEE`.)
 
     Returns
     -------
@@ -770,7 +770,7 @@ def plot_g_vecs(g_vecs, refs=None, refs_err=None, plot_rmse=True):
     g_vecs : np.array
         The alchemical weights of all states as a function of iteration index. The shape should
         be (n_iterations, n_states). Such an array can be directly read from :code:`g_vecs.npy`
-        saved by :code:`run_EEXE`.
+        saved by :code:`run_REXEE`.
     refs : np.array
         The reference values of the alchemical weights.
     refs_err : list or np.array
@@ -822,16 +822,16 @@ def plot_g_vecs(g_vecs, refs=None, refs_err=None, plot_rmse=True):
         plt.savefig('g_vecs_rmse.png', dpi=600)
 
 
-def get_swaps(EEXE_log='run_EEXE_log.txt'):
+def get_swaps(REXEE_log='run_REXEE_log.txt'):
     """
     For each replica, identifies the states where exchanges were proposed and accepted.
     (Todo: We should be able to only use :code:`rep_trajs.npy` and :code:`state_trajs.npy`
-    instead of parsing the EEXE log file to reach the same goal.)
+    instead of parsing the REXEE log file to reach the same goal.)
 
     Parameters
     ----------
-    EEXE_log : str
-        The output log file of the EEXE simulation.
+    REXEE_log : str
+        The output log file of the REXEE simulation.
 
     Returns
     -------
@@ -846,7 +846,7 @@ def get_swaps(EEXE_log='run_EEXE_log.txt'):
         keys being the global state indices and values being the number of
         accepted swaps that occurred in the state indicated by the key.
     """
-    f = open(EEXE_log, 'r')
+    f = open(REXEE_log, 'r')
     lines = f.readlines()
     f.close()