diff --git a/ensemble_md/analysis/analyze_free_energy.py b/ensemble_md/analysis/analyze_free_energy.py
index 0d2d1a0e..cc005cc0 100644
--- a/ensemble_md/analysis/analyze_free_energy.py
+++ b/ensemble_md/analysis/analyze_free_energy.py
@@ -189,6 +189,8 @@ def _combine_df_adjacent(df_adjacent, state_ranges, df_err_adjacent=None, err_ty
A list of lists free energy differences between adjacent states for all replicas.
state_ranges : list
A list of lists of showing the state indices sampled by each replica.
+ n_tot : int
+ Number of lambda states
df_err_adjacent : list, Optional
A list of lists of uncertainties corresponding to the values of :code:`df_adjacent`. Notably, if
:code:`df_err_adjacent` is :code:`None`, simple means will be used. Otherwise, inverse-variance weighted
@@ -247,7 +249,37 @@ def _combine_df_adjacent(df_adjacent, state_ranges, df_err_adjacent=None, err_ty
return df, df_err, overlap_bool
-def calculate_free_energy(data, state_ranges, df_method="MBAR", err_method="propagate", n_bootstrap=None, seed=None):
+def _calculate_df(estimators):
+ """
+ An internal function used in :func:`calculate_free_energy` to calculate a list of free energies between adjacent
+ states for all replicas.
+
+ Parameters
+ ----------
+ estimators : list
+ A list of estimators fitting the input data for all replicas. With this, the user
+ can access all the free energies and their associated uncertainties for all states and replicas.
+ In our code, these estimators come from the function :func:`_apply_estimators`.
+
+ Returns
+ -------
+ df : float
+ Free energy differences between for specified replica.
+ df_err : float
+ Uncertainties corresponding to the values in :code:`df`.
+
+ See also
+ --------
+ :func:`calculate_free_energy`
+ """
+ l = np.linspace(0, 1, num=len(estimators[0].index))
+ estimators[0].index = l
+ estimators[0].columns = l
+ est = estimators[0].loc[0, 1]
+ return est
+
+
+def calculate_free_energy(data, state_ranges, df_method="MBAR", err_method="propagate", n_bootstrap=None, seed=None, MTREXEE=False): # noqa: E501
"""
Caculates the averaged free energy profile with the chosen method given :math:`u_{nk}` or :math:`dH/dλ` data
obtained from all replicas of the REXEE simulation. Available methods include TI, BAR, and MBAR. TI
@@ -275,6 +307,8 @@ def calculate_free_energy(data, state_ranges, df_method="MBAR", err_method="prop
seed : int, Optional
The random seed for bootstrapping. Only relevant when :code:`err_method` is :code:`"bootstrap"`.
The default is :code:`None`.
+ MTREXEE : bool
+ Whether this is a MT-REXEE simulation or not
Returns
-------
@@ -299,10 +333,17 @@ def calculate_free_energy(data, state_ranges, df_method="MBAR", err_method="prop
>>> f, _, _ = analyze_free_energy.calculate_free_energy(data_list, state_ranges, "MBAR", "propagate")
"""
n_sim = len(data)
- n_tot = state_ranges[-1][-1] + 1
+ if MTREXEE is False:
+ n_tot = state_ranges[-1][-1] + 1
+ else:
+ n_tot = state_ranges[-1] + 1
estimators = _apply_estimators(data, df_method)
- df_adjacent, df_err_adjacent = _calculate_df_adjacent(estimators)
- df, df_err, overlap_bool = _combine_df_adjacent(df_adjacent, state_ranges, df_err_adjacent, err_type='propagate')
+ print(estimators)
+ if MTREXEE is False:
+ df_adjacent, df_err_adjacent = _calculate_df_adjacent(estimators)
+ df, df_err, overlap_bool = _combine_df_adjacent(df_adjacent, state_ranges, df_err_adjacent, err_type='propagate') # noqa: E501
+ else:
+ df, df_err = _calculate_df(estimators)
if err_method == 'bootstrap':
if seed is not None:
@@ -314,15 +355,18 @@ def calculate_free_energy(data, state_ranges, df_method="MBAR", err_method="prop
for b in range(n_bootstrap):
sampled_data = [sampled_data_all[i].iloc[b * len(data[i]):(b + 1) * len(data[i])] for i in range(n_sim)]
bootstrap_estimators = _apply_estimators(sampled_data, df_method)
- df_adjacent, df_err_adjacent = _calculate_df_adjacent(bootstrap_estimators)
- df_sampled, _, overlap_bool = _combine_df_adjacent(df_adjacent, state_ranges, df_err_adjacent, err_type='propagate') # doesn't matter what value err_type here is # noqa: E501
+ if MTREXEE is False:
+ df_adjacent, df_err_adjacent = _calculate_df_adjacent(bootstrap_estimators)
+ df_sampled, _, overlap_bool = _combine_df_adjacent(df_adjacent, state_ranges, df_err_adjacent, err_type='propagate') # doesn't matter what value err_type here is # noqa: E501
+ else:
+ df_sampled, _ = _calculate_df(bootstrap_estimators)
df_bootstrap.append(df_sampled)
error_bootstrap = np.std(df_bootstrap, axis=0, ddof=1)
# Replace the value in df_err with value in error_bootstrap if df_err corresponds to
# the df between overlapping states
for i in range(n_tot - 1):
- if overlap_bool[i] is True:
+ if MTREXEE is True or overlap_bool[i] is True:
print(f'Replaced the propagated error with the bootstrapped error for states {i} and {i + 1}: {df_err[i]:.5f} -> {error_bootstrap[i]:.5f}.') # noqa: E501
df_err[i] = error_bootstrap[i]
elif err_method == 'propagate':
diff --git a/ensemble_md/analysis/analyze_traj.py b/ensemble_md/analysis/analyze_traj.py
index 45af0bb1..cb917f63 100644
--- a/ensemble_md/analysis/analyze_traj.py
+++ b/ensemble_md/analysis/analyze_traj.py
@@ -539,7 +539,15 @@ def plot_state_hist(trajs, state_ranges, fig_name, stack=True, figsize=None, pre
hist, bins = np.histogram(traj, bins=np.arange(lower_bound, upper_bound + 1, 1))
hist_data.append(hist)
if save_hist is True:
- np.save('hist_data.npy', hist_data)
+ if len(fig_name.split('/')) > 1:
+ dir_list = []
+ for i in fig_name.split('/')[:-1]:
+ dir_list.append(i)
+ dir_list.append('/')
+ dir_path = ''.join(dir_list)
+ np.save(f'{dir_path}/hist_data.npy', hist_data)
+ else:
+ np.save('hist_data.npy', hist_data)
# Use the same bins for all histograms
bins = bins[:-1] # Remove the last bin edge because there are n+1 bin edges for n bins
@@ -685,6 +693,8 @@ def plot_transit_time(trajs, N, fig_prefix=None, dt=None, folder='.'):
units : str
The units of the time.
"""
+ import pandas as pd
+
if dt is None:
x = np.arange(len(trajs[0]))
units = 'step'
@@ -824,6 +834,14 @@ def plot_transit_time(trajs, N, fig_prefix=None, dt=None, folder='.'):
plt.savefig(f'{folder}/hist_{fig_names[t]}', dpi=600)
else:
plt.savefig(f'{folder}/{fig_prefix}_hist_{fig_names[t]}', dpi=600)
+ # Save to csv
+ sim_list, rt_list = [], []
+ for n in range(len(t_roundtrip_list)):
+ for rt in t_roundtrip_list[n]:
+ sim_list.append(n)
+ rt_list.append(rt)
+ df_rt = pd.DataFrame({'Sim': sim_list, 'Round Trip Time': rt_list})
+ df_rt.to_csv(f'{folder}/roundtrip_times.csv')
return t_0k_list, t_k0_list, t_roundtrip_list, units
@@ -1330,3 +1348,146 @@ def get_delta_w_updates(log_file, plot=False):
plt.savefig('delta_w_updates.png', dpi=600)
return t_updates, delta_w_updates, equil
+
+
+def end_states_only_traj(working_dir, n_sim, n_iter, l0_states, l1_states, swap_rep_pattern, ps_per_frame):
+ """
+ Create a trajectory which is a concatenation off all frames for each unique end state.
+
+ Parameters
+ ----------
+ working_dir : str
+ path for the current working directory
+ n_sim : int
+ the number of simulations run
+ n_iter : int
+ the number of iterations run
+ l0_states : list of int
+ the lambda states which correspond to lambda=0
+ l1_states : list of int
+ the lambda states which correspond to lambda=1
+ swap_rep_pattern : list of int
+ the replica swapping pattern which will indicate which end states are common
+ ps_per_frame : float
+ the timestep to convert the time in the GROMACS dh/dl file to frames in the trajecotry
+
+ Returns
+ -------
+ None
+ """
+ import pandas as pd
+ import os
+ import mdtraj as md
+
+ # Determine how many end states are present, which simulations and lambdas those end states correspond to
+ state_name = ['A']
+ considered_swaps = [[0, 0]]
+ cat = ord('A') + 1
+ for swap in swap_rep_pattern:
+ part_1, part_2 = swap
+ if part_1 in considered_swaps and part_2 in considered_swaps:
+ continue
+ elif part_1 in considered_swaps:
+ index = considered_swaps.index(part_1)
+ state_name.append(state_name[index])
+ considered_swaps.append(part_2)
+ elif part_2 in considered_swaps:
+ index = considered_swaps.index(part_2)
+ state_name.append(state_name[index])
+ considered_swaps.append(part_1)
+ else:
+ state_name.append(chr(cat))
+ state_name.append(chr(cat))
+ considered_swaps.append(part_1)
+ considered_swaps.append(part_2)
+ cat += 1
+ for i in range(n_sim):
+ for j in [0, 1]:
+ if [i, j] not in considered_swaps:
+ state_name.append(chr(cat))
+ considered_swaps.append([i, j])
+ cat += 1
+
+ # Determine which frames correspond to which end states
+ state_frame_df = pd.DataFrame()
+ for n in range(n_sim):
+ for i in range(n_iter):
+ l0_frame, l1_frame = [], []
+ dhdl_file = open(f'{working_dir}/sim_{n}/iteration_{i}/dhdl.xvg', 'r').readlines()
+ start = True
+ for line in dhdl_file:
+ split_line = line.split(' ')
+ while '' in split_line:
+ split_line.remove('')
+ if '#' not in split_line[0] and '@' not in split_line[0]:
+ time = float(split_line[0])
+ if start:
+ start_time = time
+ start = False
+ state = float(split_line[1])
+ if time % ps_per_frame == 0:
+ if state in l0_states:
+ l0_frame.append(int((time-start_time)/ps_per_frame))
+ elif state in l1_states:
+ l1_frame.append(int((time-start_time)/ps_per_frame))
+ if len(l0_frame) != 0:
+ df_0 = pd.DataFrame({'Sim': n, 'Iteration': i, 'Frame': l0_frame, 'Lambda': 0})
+ state_frame_df = pd.concat([state_frame_df, df_0])
+ if len(l1_frame) != 0:
+ df_1 = pd.DataFrame({'Sim': n, 'Iteration': i, 'Frame': l1_frame, 'Lambda': 1})
+ state_frame_df = pd.concat([state_frame_df, df_1])
+
+ # Concatenate all frames from each set of trajectories for each end state
+ unique_states = list(set(state_name))
+ for state in unique_states:
+ indices = [i for i, value in enumerate(state_name) if value == state]
+ for i, index in enumerate(indices):
+ rep, lambda_rep = considered_swaps[index]
+ started = False
+ if os.path.exists(f'{working_dir}/sim_{rep}/iteration_0/confout_backup.gro'):
+ name = 'confout_backup'
+ else:
+ name = 'confout'
+ for iteration in range(n_iter):
+ frames_select = state_frame_df[(state_frame_df['Sim'] == rep) & (state_frame_df['Iteration'] == iteration) & (state_frame_df['Lambda'] == lambda_rep)]['Frame'].to_numpy() # noqa: E501
+ if len(frames_select) != 0:
+ if not started:
+ traj = md.load(f'{working_dir}/sim_{rep}/iteration_{iteration}/traj.trr', top=f'{working_dir}/sim_{rep}/iteration_0/{name}.gro') # noqa: E501
+ started = True
+ else:
+ traj_add = md.load(f'{working_dir}/sim_{rep}/iteration_{iteration}/traj.trr', top=f'{working_dir}/sim_{rep}/iteration_0/{name}.gro') # noqa: E501
+ traj = md.join(traj, traj_add)
+ traj.save_xtc(f'{working_dir}/analysis/{state}_{rep}.xtc')
+
+
+def concat_sim_traj(working_dir, n_sim, n_iter):
+ """
+ Create a trajectory which is a concatenation off each iterations trajectory
+
+ Parameters
+ ----------
+ working_dir : str
+ path for the current working directory
+ n_sim : int
+ the number of simulations run
+ n_iter : int
+ the number of iterations run
+
+ Returns
+ -------
+ None
+ """
+ import mdtraj as md
+ import os
+
+ for rep in range(n_sim):
+ if os.path.exists(f'{working_dir}/sim_{rep}/iteration_0/confout_backup.gro'):
+ name = 'confout_backup'
+ else:
+ name = 'confout'
+
+ traj = md.load(f'{working_dir}/sim_{rep}/iteration_0/traj.trr', top=f'{working_dir}/sim_{rep}/iteration_0/{name}.gro') # noqa: E501
+ for iteration in range(1, n_iter):
+ traj_add = md.load(f'{working_dir}/sim_{rep}/iteration_{iteration}/traj.trr', top=f'{working_dir}/sim_{rep}/iteration_0/{name}.gro') # noqa: E501
+ traj = md.join([traj, traj_add])
+ traj.save_xtc(f'{working_dir}/analysis/sim{rep}_concat.xtc')
diff --git a/ensemble_md/cli/analyze_REXEE.py b/ensemble_md/cli/analyze_REXEE.py
index 62dfbd63..5a7e8e6c 100644
--- a/ensemble_md/cli/analyze_REXEE.py
+++ b/ensemble_md/cli/analyze_REXEE.py
@@ -25,6 +25,7 @@
warnings.simplefilter(action='ignore', category=UserWarning)
from ensemble_md.utils import utils # noqa: E402
+from ensemble_md.utils import gmx_parser # noqa: E402
from ensemble_md.analysis import analyze_traj # noqa: E402
from ensemble_md.analysis import analyze_matrix # noqa: E402
from ensemble_md.analysis import msm_analysis # noqa: E402
@@ -138,12 +139,13 @@ def main():
counts = [analyze_traj.traj2transmtx(rep_trajs[i], REXEE.n_sim, normalize=False) for i in range(len(rep_trajs))]
reps_mtx = np.sum(counts, axis=0) # First sum up the counts. This should be symmetric if n_ex=1. Otherwise it might not be. # noqa: E501
reps_mtx /= np.sum(reps_mtx, axis=1)[:, None] # and then normalize each row
+ np.save(f'{args.dir}/reps_transmtx_allconfigs.npy', reps_mtx)
analyze_matrix.plot_matrix(reps_mtx, f'{args.dir}/rep_transmtx_allconfigs.png')
# 1-3. Calculate the spectral gap for the replica-space transition amtrix
- print('1-3. Calculating the spectral gap of the replica-space transition matrix ...')
- spectral_gap, spectral_gap_err, eig_vals = analyze_matrix.calc_spectral_gap(reps_mtx)
- print(f'The spectral gap of the replica-space transition matrix: {spectral_gap:.3f}')
+# print('1-3. Calculating the spectral gap of the replica-space transition matrix ...')
+# spectral_gap, spectral_gap_err, eig_vals = analyze_matrix.calc_spectral_gap(reps_mtx)
+# print(f'The spectral gap of the replica-space transition matrix: {spectral_gap:.3f}')
# Section 2. Analysis based on transitions between states
print('\n[ Section 2. Analysis based on transitions between states ]')
@@ -180,35 +182,36 @@ def main():
rmse = analyze_traj.calc_hist_rmse(hist_data, REXEE.state_ranges)
print(f'The RMSE of accumulated histogram counts of the state index: {rmse:.0f}')
- # 2-4. Stitch the time series of state index for different replicas
- if os.path.isfile(args.state_trajs_for_sim) is True:
- print('\n2-4. Reading in the stitched time series of state index for different replicas ...')
- state_trajs_for_sim = np.load(args.state_trajs_for_sim)
- else:
- # This may take a while.
- print('2-4. Stitching time series of state index for each replica ...')
- shifts = list(REXEE.s * np.arange(REXEE.n_sim))
- dhdl_files = [natsort.natsorted(glob.glob(f'sim_{i}/iteration_*/*dhdl*xvg')) for i in range(REXEE.n_sim)]
- state_trajs_for_sim = analyze_traj.stitch_time_series_for_sim(dhdl_files, shifts)
-
- # 2-5. Plot the time series of state index for different state sets
- print('\n2-5. Plotting the time series of state index for different state sets ...')
- analyze_traj.plot_state_trajs(
- state_trajs_for_sim,
- REXEE.state_ranges,
- f'{args.dir}/state_trajs_for_sim.png',
- title_prefix='State set'
- )
-
- # 2-6. Plot the histograms of state index for different state sets
- print('\n2-6. Plotting the histograms of state index for different state sets')
- analyze_traj.plot_state_hist(
- state_trajs_for_sim,
- REXEE.state_ranges,
- f'{args.dir}/state_hist_for_sim.png',
- prefix='State set',
- subplots=True
- )
+ if REXEE.proposal != 'random_range': # Need to FIX THIS FOR RANDOM-Range
+ # 2-4. Stitch the time series of state index for different replicas
+ if os.path.isfile(args.state_trajs_for_sim) is True:
+ print('\n2-4. Reading in the stitched time series of state index for different replicas ...')
+ state_trajs_for_sim = np.load(args.state_trajs_for_sim)
+ else:
+ # This may take a while.
+ print('2-4. Stitching time series of state index for each replica ...')
+ shifts = list(REXEE.s * np.arange(REXEE.n_sim))
+ dhdl_files = [natsort.natsorted(glob.glob(f'sim_{i}/iteration_*/*dhdl*xvg')) for i in range(REXEE.n_sim)]
+ state_trajs_for_sim = analyze_traj.stitch_time_series_for_sim(dhdl_files, shifts)
+
+ # 2-5. Plot the time series of state index for different state sets
+ print('\n2-5. Plotting the time series of state index for different state sets ...')
+ analyze_traj.plot_state_trajs(
+ state_trajs_for_sim,
+ REXEE.state_ranges,
+ f'{args.dir}/state_trajs_for_sim.png',
+ title_prefix='State set'
+ )
+
+ # 2-6. Plot the histograms of state index for different state sets
+ print('\n2-6. Plotting the histograms of state index for different state sets')
+ analyze_traj.plot_state_hist(
+ state_trajs_for_sim,
+ REXEE.state_ranges,
+ f'{args.dir}/state_hist_for_sim.png',
+ prefix='State set',
+ subplots=True
+ )
# 2-7. Plot the overall state transition matrices calculated from the state-space trajectories
print('\n2-7. Plotting the overall state transition matrices from different trajectories ...')
@@ -390,60 +393,35 @@ def main():
section_idx += 1
print(f'\n[ Section {section_idx}. Free energy calculations ]')
- # 4-1. Subsampling the data
- data_list = [] # either a list of u_nk or a list of dhdl
- if REXEE.df_data_type == 'u_nk':
- if os.path.isfile(f'{args.dir}/u_nk_data.pickle') is True:
- print('Loading the preprocessed data u_nk ...')
- with open(f'{args.dir}/u_nk_data.pickle', 'rb') as handle:
- data_all = pickle.load(handle)
- data_list, t_idx_list, g_list = data_all[0], data_all[1], data_all[2]
- else: # should always be 'dhdl'
- if os.path.isfile(f'{args.dir}/dHdl_data.pickle') is True:
- print('Loading the preprocessed data dHdl ...')
- with open(f'{args.dir}/dHdl_data.pickle', 'rb') as handle:
- data_all = pickle.load(handle)
- data_list, t_idx_list, g_list = data_all[0], data_all[1], data_all[2]
-
- if data_list == []:
- files_list = [natsort.natsorted(glob.glob(f'sim_{i}/iteration_*/*dhdl*xvg')) for i in range(REXEE.n_sim)]
- data_list, t_idx_list, g_list = analyze_free_energy.preprocess_data(files_list, REXEE.temp, REXEE.df_data_type, REXEE.df_spacing) # noqa: E501
-
- data_all = [data_list, t_idx_list, g_list]
- with open(f'{args.dir}/{REXEE.df_data_type}_data.pickle', 'wb') as handle:
- pickle.dump(data_all, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
- # 4-2. Calculate the free energy profile
- f, f_err, estimators = analyze_free_energy.calculate_free_energy(data_list, REXEE.state_ranges, REXEE.df_method, REXEE.err_method, REXEE.n_bootstrap, REXEE.seed) # noqa: E501
-
- print('Plotting the full-range free energy profile ...')
- analyze_free_energy.plot_free_energy(f, f_err, f'{args.dir}/free_energy_profile.png')
-
- print('The full-range free energy profile averaged over all replicas:')
- print(f" {', '.join(f'{f[i]:.3f} +/- {f_err[i]:.3f} kT' for i in range(REXEE.n_tot))}")
- print(f'The free energy difference between the coupled and decoupled states: {f[-1]:.3f} +/- {f_err[-1]:.3f} kT') # noqa: E501
-
- if REXEE.df_ref is not None:
- rmse_list = analyze_free_energy.calculate_df_rmse(estimators, REXEE.df_ref, REXEE.state_ranges)
- for i in range(REXEE.n_sim):
- print(f'RMSE of the free energy profile for alchemical range {i} (states {REXEE.state_ranges[i][0]} to {REXEE.state_ranges[i][-1]}): {rmse_list[i]:.2f} kT') # noqa: E501
-
- # 4-3. Recalculate the free energy profile if subsampling_avg is True
- if REXEE.subsampling_avg is True:
- print('\nUsing averaged start index of the equilibrated data and the avearged statistic inefficiency to re-perform free energy calculations ...') # noqa: E501
- t_avg = int(np.mean(t_idx_list)) + 1 # Using the ceiling function to be a little more conservative
- g_avg = np.array(g_list).prod() ** (1/len(g_list)) # geometric mean
- print(f'Averaged start index: {t_avg}')
- print(f'Averaged statistical inefficiency: {g_avg:.2f}')
-
- data_list, t_idx_list, g_list = analyze_free_energy.preprocess_data(files_list, REXEE.temp, REXEE.df_data_type, REXEE.df_spacing, t_avg, g_avg) # noqa: E501
- data_all = [data_list, t_idx_list, g_list]
- with open(f'{args.dir}/{REXEE.df_data_type}_data_avg_subsampling.pickle', 'wb') as handle:
- pickle.dump(data_all, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
+ if REXEE.modify_coords is False:
+ # 4-1. Subsampling the data
+ data_list = [] # either a list of u_nk or a list of dhdl
+ if REXEE.df_data_type == 'u_nk':
+ if os.path.isfile(f'{args.dir}/u_nk_data.pickle') is True:
+ print('Loading the preprocessed data u_nk ...')
+ with open(f'{args.dir}/u_nk_data.pickle', 'rb') as handle:
+ data_all = pickle.load(handle)
+ data_list, t_idx_list, g_list = data_all[0], data_all[1], data_all[2]
+ else: # should always be 'dhdl'
+ if os.path.isfile(f'{args.dir}/dHdl_data.pickle') is True:
+ print('Loading the preprocessed data dHdl ...')
+ with open(f'{args.dir}/dHdl_data.pickle', 'rb') as handle:
+ data_all = pickle.load(handle)
+ data_list, t_idx_list, g_list = data_all[0], data_all[1], data_all[2]
+
+ if data_list == []:
+ files_list = [natsort.natsorted(glob.glob(f'sim_{i}/iteration_*/*dhdl*xvg')) for i in range(REXEE.n_sim)] # noqa: E501
+ data_list, t_idx_list, g_list = analyze_free_energy.preprocess_data(files_list, REXEE.temp, REXEE.df_data_type, REXEE.df_spacing) # noqa: E501
+
+ data_all = [data_list, t_idx_list, g_list]
+ with open(f'{args.dir}/{REXEE.df_data_type}_data.pickle', 'wb') as handle:
+ pickle.dump(data_all, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+ # 4-2. Calculate the free energy profile
f, f_err, estimators = analyze_free_energy.calculate_free_energy(data_list, REXEE.state_ranges, REXEE.df_method, REXEE.err_method, REXEE.n_bootstrap, REXEE.seed) # noqa: E501
+
print('Plotting the full-range free energy profile ...')
- analyze_free_energy.plot_free_energy(f, f_err, f'{args.dir}/free_energy_profile_avg_subsampling.png')
+ analyze_free_energy.plot_free_energy(f, f_err, f'{args.dir}/free_energy_profile.png')
print('The full-range free energy profile averaged over all replicas:')
print(f" {', '.join(f'{f[i]:.3f} +/- {f_err[i]:.3f} kT' for i in range(REXEE.n_tot))}")
@@ -454,7 +432,114 @@ def main():
for i in range(REXEE.n_sim):
print(f'RMSE of the free energy profile for alchemical range {i} (states {REXEE.state_ranges[i][0]} to {REXEE.state_ranges[i][-1]}): {rmse_list[i]:.2f} kT') # noqa: E501
- # Section 4. Calculate the time spent in GROMACS (This could take a while.)
+ # 4-3. Recalculate the free energy profile if subsampling_avg is True
+ if REXEE.subsampling_avg is True:
+ print('\nUsing averaged start index of the equilibrated data and the avearged statistic inefficiency to re-perform free energy calculations ...') # noqa: E501
+ t_avg = int(np.mean(t_idx_list)) + 1 # Using the ceiling function to be a little more conservative
+ g_avg = np.array(g_list).prod() ** (1/len(g_list)) # geometric mean
+ print(f'Averaged start index: {t_avg}')
+ print(f'Averaged statistical inefficiency: {g_avg:.2f}')
+
+ data_list, t_idx_list, g_list = analyze_free_energy.preprocess_data(files_list, REXEE.temp, REXEE.df_data_type, REXEE.df_spacing, t_avg, g_avg) # noqa: E501
+ data_all = [data_list, t_idx_list, g_list]
+ with open(f'{args.dir}/{REXEE.df_data_type}_data_avg_subsampling.pickle', 'wb') as handle:
+ pickle.dump(data_all, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+ f, f_err, estimators = analyze_free_energy.calculate_free_energy(data_list, REXEE.state_ranges, REXEE.df_method, REXEE.err_method, REXEE.n_bootstrap, REXEE.seed) # noqa: E501
+ print('Plotting the full-range free energy profile ...')
+ analyze_free_energy.plot_free_energy(f, f_err, f'{args.dir}/free_energy_profile_avg_subsampling.png')
+
+ print('The full-range free energy profile averaged over all replicas:')
+ print(f" {', '.join(f'{f[i]:.3f} +/- {f_err[i]:.3f} kT' for i in range(REXEE.n_tot))}")
+ print(f'The free energy difference between the coupled and decoupled states: {f[-1]:.3f} +/- {f_err[-1]:.3f} kT') # noqa: E501
+
+ if REXEE.df_ref is not None:
+ rmse_list = analyze_free_energy.calculate_df_rmse(estimators, REXEE.df_ref, REXEE.state_ranges)
+ for i in range(REXEE.n_sim):
+ print(f'RMSE of the free energy profile for alchemical range {i} (states {REXEE.state_ranges[i][0]} to {REXEE.state_ranges[i][-1]}): {rmse_list[i]:.2f} kT') # noqa: E501
+
+ else: # MT-REXEE means each simulation is a seperate transformation
+ for sim in range(REXEE.n_sim):
+ print(f'Computing Free Energy for Simulation {sim}')
+ # 4-1. Subsampling the data
+ data_list = [] # either a list of u_nk or a list of dhdl
+ if REXEE.df_data_type == 'u_nk':
+ if os.path.isfile(f'{args.dir}/u_nk_data_{sim}.pickle') is True:
+ print('Loading the preprocessed data u_nk ...')
+ with open(f'{args.dir}/u_nk_data_{sim}.pickle', 'rb') as handle:
+ data_all = pickle.load(handle)
+ data_list, t_idx_list, g_list = data_all[0], data_all[1], data_all[2]
+ else: # should always be 'dhdl'
+ if os.path.isfile(f'{args.dir}/dHdl_data_{sim}.pickle') is True:
+ print('Loading the preprocessed data dHdl ...')
+ with open(f'{args.dir}/dHdl_data_{sim}.pickle', 'rb') as handle:
+ data_all = pickle.load(handle)
+ data_list, t_idx_list, g_list = data_all[0], data_all[1], data_all[2]
+
+ if data_list == []:
+ files_list = [natsort.natsorted(glob.glob(f'sim_{sim}/iteration_*/*dhdl*xvg'))]
+ data_list, t_idx_list, g_list = analyze_free_energy.preprocess_data(files_list, REXEE.temp, REXEE.df_data_type, REXEE.df_spacing) # noqa: E501
+
+ data_all = [data_list, t_idx_list, g_list]
+ with open(f'{args.dir}/{REXEE.df_data_type}_data_{sim}.pickle', 'wb') as handle:
+ pickle.dump(data_all, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+ # 4-2. Calculate the free energy profile
+ f, f_err, estimators = analyze_free_energy.calculate_free_energy(data_list, REXEE.state_ranges[sim], REXEE.df_method, REXEE.err_method, REXEE.n_bootstrap, REXEE.seed, MTREXEE=True) # noqa: E501
+
+ print('Plotting the full-range free energy profile ...')
+ analyze_free_energy.plot_free_energy(f, f_err, f'{args.dir}/free_energy_profile_{sim}.png')
+
+ print('The full-range free energy profile averaged over all replicas:')
+ print(f" {', '.join(f'{f[i]:.3f} +/- {f_err[i]:.3f} kT' for i in range(REXEE.n_tot))}")
+ print(f'The free energy difference between the coupled and decoupled states: {f[-1]:.3f} +/- {f_err[-1]:.3f} kT') # noqa: E501
+
+ if REXEE.df_ref is not None:
+ rmse_list = analyze_free_energy.calculate_df_rmse(estimators, REXEE.df_ref, REXEE.state_ranges[sim]) # noqa: E501
+ for i in range(REXEE.n_sim):
+ print(f'RMSE of the free energy profile for alchemical range {i} (states {REXEE.state_ranges[i][0]} to {REXEE.state_ranges[i][-1]}): {rmse_list[i]:.2f} kT') # noqa: E501
+
+ # 4-3. Recalculate the free energy profile if subsampling_avg is True
+ if REXEE.subsampling_avg is True:
+ print('\nUsing averaged start index of the equilibrated data and the avearged statistic inefficiency to re-perform free energy calculations ...') # noqa: E501
+ t_avg = int(np.mean(t_idx_list)) + 1 # Using the ceiling function to be a little more conservative # noqa: E501
+ g_avg = np.array(g_list).prod() ** (1/len(g_list)) # geometric mean
+ print(f'Averaged start index: {t_avg}')
+ print(f'Averaged statistical inefficiency: {g_avg:.2f}')
+
+ data_list, t_idx_list, g_list = analyze_free_energy.preprocess_data(files_list, REXEE.temp, REXEE.df_data_type, REXEE.df_spacing, t_avg, g_avg) # noqa: E501
+ data_all = [data_list, t_idx_list, g_list]
+ with open(f'{args.dir}/{REXEE.df_data_type}_data_avg_subsampling_{sim}.pickle', 'wb') as handle:
+ pickle.dump(data_all, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+ f, f_err, estimators = analyze_free_energy.calculate_free_energy(data_list, REXEE.state_ranges[sim], REXEE.df_method, REXEE.err_method, REXEE.n_bootstrap, REXEE.seed) # noqa: E501
+ print('Plotting the full-range free energy profile ...')
+ analyze_free_energy.plot_free_energy(f, f_err, f'{args.dir}/free_energy_profile_avg_subsampling_{sim}.png') # noqa: E501
+
+ print('The full-range free energy profile averaged over all replicas:')
+ print(f" {', '.join(f'{f[i]:.3f} +/- {f_err[i]:.3f} kT' for i in range(REXEE.n_tot))}")
+ print(f'The free energy difference between the coupled and decoupled states: {f[-1]:.3f} +/- {f_err[-1]:.3f} kT') # noqa: E501
+
+ if REXEE.df_ref is not None:
+ rmse_list = analyze_free_energy.calculate_df_rmse(estimators, REXEE.df_ref, REXEE.state_ranges[sim]) # noqa: E501
+ for i in range(REXEE.n_sim):
+ print(f'RMSE of the free energy profile for alchemical range {i} (states {REXEE.state_ranges[i][0]} to {REXEE.state_ranges[i][-1]}): {rmse_list[i]:.2f} kT') # noqa: E501
+
+ # Section 5. Process trajecotries for MT-REXEE
+ if REXEE.modify_coords is not None:
+ # Section 5.1. Create end-state trajecotries for each simulation
+ print('5.1. Create end-state trajecotries for each simulation')
+ l0, l1, ps_per_frame = gmx_parser.get_end_states(f'{REXEE.working_dir}/sim_0/iteration_0/expanded.mdp')
+ n_sim, n_iter = np.shape(rep_trajs)
+ if REXEE.swap_rep_pattern is None:
+ raise Exception('MT-REXEE trajectory analysis requires swap_rep_pattern to be defined')
+ analyze_traj.end_states_only_traj(REXEE.working_dir, n_sim, n_iter, l0, l1, REXEE.swap_rep_pattern, ps_per_frame) # noqa: E501
+
+ # Section 5.2. Create concatenated trajectories for each individual simulation
+ print('5.2. Create concatenated trajectories for each individual simulation')
+ analyze_traj.concat_sim_traj(REXEE.working_dir, n_sim, n_iter)
+
+ # Section 6. Calculate the time spent in GROMACS (This could take a while.)
t_wall_tot, t_sync, _ = utils.analyze_REXEE_time()
print(f'\nTotal wall time GROMACS spent to finish all iterations: {utils.format_time(t_wall_tot)}')
print(f'Total time spent in syncrhonizing all replicas: {utils.format_time(t_sync)}')
diff --git a/ensemble_md/cli/run_REXEE.py b/ensemble_md/cli/run_REXEE.py
index 804b2ce4..325233a0 100644
--- a/ensemble_md/cli/run_REXEE.py
+++ b/ensemble_md/cli/run_REXEE.py
@@ -138,7 +138,9 @@ def main():
REXEE.g_vecs = [list(i) for i in np.load(args.g_vecs)]
if REXEE.fixed_weights is not True and os.path.isfile(args.equil) is True:
REXEE.equil = np.load(args.equil)
- print(REXEE.equil)
+ print(f'equil: {REXEE.equil}')
+ if REXEE.proposal == 'random_range' and os.path.isfile('track_swap_frame.npy'):
+ REXEE.track_swap_frame = list(np.load('track_swap_frame.npy'))
else:
start_idx = None
@@ -173,7 +175,8 @@ def main():
states = copy.deepcopy(states_)
weights = copy.deepcopy(weights_)
counts = copy.deepcopy(counts_)
- swap_pattern, swap_list = REXEE.get_swapping_pattern(dhdl_files, states_) # swap_list will only be used for modify_coords # noqa: E501
+ swap_pattern, swap_list, swap_index, states = REXEE.get_swapping_pattern(dhdl_files, states_, i) # swap_list will only be used for modify_coords # noqa: E501
+ print(f'swap pattern: {swap_pattern}')
# 3-3. Perform weight correction/weight combination
if wl_delta != [None for i in range(REXEE.n_sim)]: # weight-updating
@@ -267,6 +270,15 @@ def main():
# In run_REXEE(i, swap_pattern), where the tpr files will be generated, we use the top file at the
# level of the simulation (the file that will be shared by all simulations). For the gro file, we
# pass swap_pattern to the function to figure it out internally.
+
+ if REXEE.proposal == 'random_range':
+ # 3-5. Keep track of the frames used for swapping in each trajectory
+ track_frame = np.full(REXEE.n_sim, -1)
+ for s in range(len(swap_list)):
+ swap = swap_list[s]
+ track_frame[swap[0]] = swap_index[s][0]
+ track_frame[swap[1]] = swap_index[s][1]
+ REXEE.track_swap_frame.append(track_frame)
else:
swap_pattern, swap_list = None, None
@@ -304,6 +316,13 @@ def main():
if REXEE.modify_coords_fn is not None:
try:
if rank == 0:
+ # If previous swaps were performed undo them
+ for j in range(REXEE.n_sim):
+ gro_backup = f'{REXEE.working_dir}/sim_{j}/iteration_{i-1}/confout_backup.gro'
+ gro = f'{REXEE.working_dir}/sim_{j}/iteration_{i-1}/confout.gro'
+ if os.path.exists(gro_backup):
+ os.rename(gro_backup, gro)
+
for j in range(len(swap_list)):
print('\nModifying the coordinates of the following output GRO files ...')
# gro_1 and gro_2 are the simlation outputs (that we want to back up) and the inputs to modify_coords # noqa: E501
@@ -311,18 +330,14 @@ def main():
gro_2 = f'{REXEE.working_dir}/sim_{swap_list[j][1]}/iteration_{i-1}/confout.gro'
print(f' - {gro_1}\n - {gro_2}')
- # Check that swap was not performed before checkpoint was created
- if os.path.exists(gro_1.split('.gro')[0] + '_backup.gro') and os.path.exists(gro_2.split('.gro')[0] + '_backup.gro'): # noqa: E501
- print('\nSwap already performed')
- else:
- # Now we rename gro_1 and gro_2 to back them up
- gro_1_backup = gro_1.split('.gro')[0] + '_backup.gro'
- gro_2_backup = gro_2.split('.gro')[0] + '_backup.gro'
- os.rename(gro_1, gro_1_backup)
- os.rename(gro_2, gro_2_backup)
-
- # Here we input gro_1_backup and gro_2_backup and modify_coords_fn will save the modified gro files as gro_1 and gro_2 # noqa: E501
- REXEE.modify_coords_fn(gro_1_backup, gro_2_backup) # the order should not matter
+ # Now we rename gro_1 and gro_2 to back them up
+ gro_1_backup = gro_1.split('.gro')[0] + '_backup.gro'
+ gro_2_backup = gro_2.split('.gro')[0] + '_backup.gro'
+ os.rename(gro_1, gro_1_backup)
+ os.rename(gro_2, gro_2_backup)
+
+ # Here we input gro_1_backup and gro_2_backup and modify_coords_fn will save the modified gro files as gro_1 and gro_2 # noqa: E501
+ REXEE.modify_coords_fn(gro_1_backup, gro_2_backup, swap_index[j]) # the order should not matter # noqa: E501
except Exception:
print('\n--------------------------------------------------------------------------\n')
print(f'\nAn error occurred on rank 0:\n{traceback.format_exc()}')
@@ -342,6 +357,8 @@ def main():
np.save(args.ckpt, REXEE.rep_trajs)
if REXEE.fixed_weights is not True:
np.save(args.equil, REXEE.equil)
+ if REXEE.proposal == 'random_range':
+ np.save('track_swap_frame.npy', REXEE.track_swap_frame)
# Save the npy files at the end of the simulation anyway.
if rank == 0:
diff --git a/ensemble_md/replica_exchange_EE.py b/ensemble_md/replica_exchange_EE.py
index f0c13681..45f30c0a 100644
--- a/ensemble_md/replica_exchange_EE.py
+++ b/ensemble_md/replica_exchange_EE.py
@@ -196,8 +196,8 @@ def set_params(self, analysis):
self.warnings.append(f'Warning: Parameter "{i}" specified in the input YAML file is not recognizable.')
# Step 4: Check if the parameters in the YAML file are well-defined
- if self.proposal not in [None, 'single', 'neighboring', 'exhaustive']: # deprecated option: multiple
- raise ParameterError("The specified proposal scheme is not available. Available options include 'single', 'neighboring', and 'exhaustive'.") # noqa: E501
+ if self.proposal not in [None, 'single', 'neighboring', 'exhaustive', 'random_range']: # deprecated option: multiple # noqa: E501
+ raise ParameterError("The specified proposal scheme is not available. Available options include 'single', 'neighboring', 'exhaustive', 'random_range'.") # noqa: E501
if self.df_method not in [None, 'TI', 'BAR', 'MBAR']:
raise ParameterError("The specified free energy estimator is not available. Available options include 'TI', 'BAR', and 'MBAR'.") # noqa: E501
@@ -471,6 +471,9 @@ def set_params(self, analysis):
else:
self.modify_coords_fn = None
+ # 7-13. A list of the frames actually utilized for the swaps in each iteration.
+ self.track_swap_frame = []
+
# Step 8. Check the executables
if analysis is False:
self.check_gmx_executable()
@@ -790,8 +793,7 @@ def extract_final_log_info(self, log_files):
return wl_delta, weights, counts
- @staticmethod
- def identify_swappable_pairs(states, state_ranges, neighbor_exchange, add_swappables=None):
+ def identify_swappable_pairs(self, states, state_ranges, dhdl_files, iteration=None): # noqa: E501
"""
Identifies swappable pairs. By definition, a pair of simulation is considered swappable only if
their last sampled states are in the alchemical ranges of both simulations. This is required
@@ -808,13 +810,9 @@ def identify_swappable_pairs(states, state_ranges, neighbor_exchange, add_swappa
A list of global state indices for all replicas. The input list can be a list updated by
:obj:`.get_swapping_pattern`, especially in the case where there is a need to re-identify the
swappable pairs after an attempted swap is accepted.
- neighbor_exchange : bool
- Whether to exchange only between neighboring replicas.
- add_swappables: list
- A list of lists that additionally consider states (in global indices) that can be swapped.
- For example, :code:`add_swappables=[[4, 5], [14, 15]]` means that if a replica samples state 4,
- it can be swapped with another replica that samples state 5 and vice versa. The same logic applies
- to states 14 and 15. This parameter is only relevant to MT-REXEE simulations.
+ iteration : int
+ The iteration number will determine which direction the swaps will be performed. This option is
+ only necessary when using the "random_range" proposal scheme.
Returns
-------
@@ -838,28 +836,42 @@ def identify_swappable_pairs(states, state_ranges, neighbor_exchange, add_swappa
"""
n_sim = len(states)
sim_idx = list(range(n_sim))
- all_pairs = list(combinations(sim_idx, 2))
+ states_for_swap = []
+ if self.proposal == 'random_range':
+ potential_swappables = []
+ for n in np.arange(0, n_sim-1, 1):
+ potential_swappables.append([n, n+1])
+ swappables, swap_index = [], []
+ for swap in potential_swappables:
+ index, state = self._deter_swap_index(swap, dhdl_files, self.add_swappables)
+ if len(index) == 2:
+ swap_index.append(index)
+ swappables.append(swap)
+ states_for_swap.append(state)
+ else:
+ all_pairs = list(combinations(sim_idx, 2))
- # First, we identify pairs of replicas with overlapping ranges
- swappables = [i for i in all_pairs if set(state_ranges[i[0]]).intersection(set(state_ranges[i[1]])) != set()] # noqa: E501
+ # First, we identify pairs of replicas with overlapping ranges
+ swappables = [i for i in all_pairs if set(state_ranges[i[0]]).intersection(set(state_ranges[i[1]])) != set()] # noqa: E501
- # Then, from these pairs, we exclude the ones whose the last sampled states are not present in both alchemical ranges # noqa: E501
- # In this case, U^i_n, U_^j_m, g^i_n, and g_^j_m are unknown and the acceptance cannot be calculated.
- swappables = [i for i in swappables if states[i[0]] in state_ranges[i[1]] and states[i[1]] in state_ranges[i[0]]] # noqa: E501
+ # Then, from these pairs, we exclude the ones whose the last sampled states are not present in both alchemical ranges # noqa: E501
+ # In this case, U^i_n, U_^j_m, g^i_n, and g_^j_m are unknown and the acceptance cannot be calculated.
+ swappables = [i for i in swappables if states[i[0]] in state_ranges[i[1]] and states[i[1]] in state_ranges[i[0]]] # noqa: E501
- # Expand the definition of swappable pairs when add_swappables is specified
- if add_swappables is not None:
- all_paired_states = [[states[p[0]], states[p[1]]] for p in all_pairs]
- for i in all_paired_states:
- if i in add_swappables:
- pair = all_pairs[all_paired_states.index(i)]
- if pair not in swappables:
- swappables.append(pair)
+ # Expand the definition of swappable pairs when add_swappables is specified
+ if self.add_swappables is not None:
+ all_paired_states = [[states[p[0]], states[p[1]]] for p in all_pairs]
+ for i in all_paired_states:
+ if i in self.add_swappables:
+ pair = all_pairs[all_paired_states.index(i)]
+ if pair not in swappables:
+ swappables.append(pair)
- if neighbor_exchange is True:
- swappables = [i for i in swappables if np.abs(i[0] - i[1]) == 1]
+ if self.proposal == 'neighboring':
+ swappables = [i for i in swappables if np.abs(i[0] - i[1]) == 1]
+ swap_index = []
- return swappables
+ return swappables, swap_index, states_for_swap
@staticmethod
def propose_swap(swappables):
@@ -884,7 +896,7 @@ def propose_swap(swappables):
return swap
- def get_swapping_pattern(self, dhdl_files, states):
+ def get_swapping_pattern(self, dhdl_files, states, iteration=None):
"""
Generates a list (:code:`swap_pattern`) that represents how the configurations should be swapped in the
next iteration. The indices of the output list correspond to the simulation/replica indices, and the
@@ -908,6 +920,9 @@ def get_swapping_pattern(self, dhdl_files, states):
A list of last sampled states (in global indices) of ALL simulations. :code:`states[i]=j` means that
the configuration in replica :code:`i` is at state :code:`j` at the time when the exchange is performed.
This list can be generated :obj:`.extract_final_dhdl_info`.
+ iteration : int
+ The iteration number will determine which direction the swaps will be performed. This option is only
+ necessary when using the "random_range" proposal scheme.
Returns
-------
@@ -917,7 +932,11 @@ def get_swapping_pattern(self, dhdl_files, states):
A list of tuples showing the accepted swaps.
"""
swap_list = []
- if self.proposal == 'exhaustive':
+ if self.proposal == 'random_range':
+ swap_index = []
+ n_ex = int(np.floor(self.n_sim / 2)) # This is the maximum, not necessarily the number that will always be reached. # noqa
+ n_ex_FR = 0
+ elif self.proposal == 'exhaustive':
n_ex = int(np.floor(self.n_sim / 2)) # This is the maximum, not necessarily the number that will always be reached. # noqa
n_ex_exhaustive = 0 # The actual number of swaps atttempted.
else:
@@ -935,7 +954,8 @@ def get_swapping_pattern(self, dhdl_files, states):
swap_pattern = list(range(self.n_sim)) # Can be regarded as the indices of DHDL files/configurations
state_ranges = copy.deepcopy(self.state_ranges)
# states_copy = copy.deepcopy(states) # only for re-identifying swappable pairs given updated state_ranges --> was needed for the multiple exchange proposal scheme # noqa: E501
- swappables = ReplicaExchangeEE.identify_swappable_pairs(states, state_ranges, self.proposal == 'neighboring', self.add_swappables) # noqa: E501
+ swappables, swap_index, states_for_swap = self.identify_swappable_pairs(states, state_ranges, dhdl_files, iteration) # noqa: E501
+ all_swappables = swappables.copy()
# Note that if there is only 1 swappable pair, then it will still be the only swappable pair
# after an attempted swap is accepted. Therefore, there is no need to perform multiple swaps or re-identify
@@ -944,16 +964,17 @@ def get_swapping_pattern(self, dhdl_files, states):
if n_ex > 1:
n_ex = 1 # n_ex is set back to 1 since there is only 1 swappable pair.
- print(f"Swappable pairs: {swappables}")
+ states_modified = states
+ swap_index_accept = []
for i in range(n_ex):
# Update the list of swappable pairs starting from the 2nd attempted swap for the exhaustive swap method.
- if self.proposal == 'exhaustive' and i >= 1:
+ if (self.proposal == 'exhaustive' or self.proposal == 'random_range') and i >= 1: # noqa: E501
# Note that this should be done regardless of the acceptance/rejection of the previously drawn pairs.
# Also note that at this point, swap is still the last attempted swap.
swappables = [i for i in swappables if set(i).intersection(set(swap)) == set()] # noqa: F821
print(f'\nRemaining swappable pairs: {swappables}')
- if len(swappables) == 0 and self.proposal == 'exhaustive':
+ if len(swappables) == 0 and (self.proposal == 'exhaustive' or self.proposal == 'random_range'): # noqa: E501
# This should only happen when the method of exhaustive swaps is used.
if i == 0:
self.n_empty_swappable += 1
@@ -962,8 +983,11 @@ def get_swapping_pattern(self, dhdl_files, states):
else:
if self.proposal == 'exhaustive':
n_ex_exhaustive += 1
+ if self.proposal == 'random_range':
+ n_ex_FR += 1
swap = ReplicaExchangeEE.propose_swap(swappables)
+
print(f'\nProposed swap: {swap}')
if swap == []: # the same as len(swappables) == 0, self.proposal must not be exhaustive if this line is reached. # noqa: E501
self.n_empty_swappable += 1
@@ -971,7 +995,7 @@ def get_swapping_pattern(self, dhdl_files, states):
break # no need to re-identify swappable pairs and draw new samples
else:
self.n_swap_attempts += 1
- if self.verbose is True and self.proposal != 'exhaustive':
+ if self.verbose is True and self.proposal != 'exhaustive' and self.proposal != 'random_range': # noqa: E501
print(f'A swap ({i + 1}/{n_ex}) is proposed between the configurations of Simulation {swap[0]} (state {states[swap[0]]}) and Simulation {swap[1]} (state {states[swap[1]]}) ...') # noqa: E501
if self.modify_coords_fn is not None:
@@ -993,6 +1017,16 @@ def get_swapping_pattern(self, dhdl_files, states):
if swap_bool is True:
swap_list.append(swap)
+ if self.proposal == "random_range":
+ for p, p_swap in enumerate(all_swappables):
+ if p_swap == swap:
+ break
+ swap_index_accept.append(swap_index[p])
+ states_modified[swap[0]] = states_for_swap[p][0]
+ states_modified[swap[1]] = states_for_swap[p][1]
+ else:
+ swap_index_accept.append([-1, -1])
+ # Determine which
# The assignments need to be done at the same time in just one line.
# states[swap[0]], states[swap[1]] = states[swap[1]], states[swap[0]]
# weights[swap[0]], weights[swap[1]] = weights[swap[1]], weights[swap[0]]
@@ -1024,7 +1058,7 @@ def get_swapping_pattern(self, dhdl_files, states):
for i in range(self.n_sim):
self.rep_trajs[i].append(self.configs.index(i))
- return swap_pattern, swap_list
+ return swap_pattern, swap_list, swap_index_accept, states_modified
def calc_prob_acc(self, swap, dhdl_files, states, shifts):
"""
@@ -1127,6 +1161,59 @@ def accept_or_reject(self, prob_acc):
print(" Swap rejected! ")
return swap_bool
+ def _deter_swap_index(self, swap, dhdl_files, add_swappables):
+ """
+ Determine which frames in the trajectory are sampling compatible lambda states to engage in a swap
+
+ Parameters
+ ----------
+ swap : list
+ The simulations for which the swap is occuring
+ dhdl_files : list
+ A list of paths to the DHDL files. The indicies in the DHDL filenames should be in an ascending order, e.g.
+ :code:`[dhdl_0.xvg, dhdl_1.xvg, ..., dhdl_N.xvg]`.
+ add_swappables: list
+ A list of lists that additionally consider states (in global indices) that can be swapped.
+ For example, :code:`add_swappables=[[4, 5], [14, 15]]` means that if a replica samples state 4,
+ it can be swapped with another replica that samples state 5 and vice versa. The same logic applies
+ to states 14 and 15. This parameter is only relevant to MT-REXEE simulations.
+
+ Returns
+ -------
+ swap_bool : bool
+ A boolean variable indicating whether the swap should be accepted.
+ """
+ # Determine possible lambda states that swap partner i can be in to allow a swap
+ swappable_global_states, swap_state = [], []
+ for swappable in add_swappables:
+ A, B = swappable
+ if (A >= (swap[0] * self.s) and A < ((swap[0] + 1) * self.s) and B >= (swap[1] * self.s) and B < ((swap[1] + 1) * self.s)) or (B >= (swap[0] * self.s) and B < ((swap[0] + 1) * self.s) and A >= (swap[1] * self.s) and A < ((swap[1] + 1) * self.s)): # noqa: E501
+ swappable_global_states.append(A)
+ swappable_global_states.append(B)
+ convert_to_frames = int(self.template["nstxout"]/self.template["nstdhdl"])
+ swap_index = []
+ for i in range(2):
+ n = swap[i]
+ # Determine all frames for which swap partner i is in those states
+ potential_swap_index, state_global_list = [], []
+ headers = get_headers(dhdl_files[n])
+ state_local = list(extract_dataframe(dhdl_files[n], headers=headers)['Thermodynamic state']) # local index for all states # noqa: E501
+ for s, state in enumerate(state_local[::convert_to_frames]):
+ state_global = state + (n * self.s) # global index of the last state
+ if state_global in swappable_global_states:
+ potential_swap_index.append(s)
+ state_global_list.append(state_global)
+ # Select a random frame which is in the last 50% of the trajectory to have the swap occur
+ potential_swap_index = np.array(potential_swap_index)
+ potential_swap_index = potential_swap_index[potential_swap_index > (len(state_local)/(2*convert_to_frames))] # noqa: E501
+ if len(potential_swap_index) != 0:
+ index = np.random.choice(potential_swap_index)
+ swap_state.append(state_global_list[np.where(potential_swap_index == index)[0][0]])
+ swap_index.append(index)
+ else:
+ break
+ return swap_index, swap_state
+
def get_averaged_weights(self, log_files):
"""
For each replica, calculates the averaged weights (and the associated error) from the time series
@@ -1522,7 +1609,7 @@ def run_REXEE(self, n, swap_pattern=None):
# rank 3 that has not been generated, which will lead to an I/O error.
comm.barrier()
- def default_coords_fn(self, molA_file_name, molB_file_name):
+ def default_coords_fn(self, molA_file_name, molB_file_name, swap_index):
"""
Swaps coordinates between two GRO files.
@@ -1538,8 +1625,8 @@ def default_coords_fn(self, molA_file_name, molB_file_name):
molB_dir = molB_file_name.rsplit('/', 1)[0] + '/'
# Load trajectory trr for higher precison coordinates
- molA = md.load_trr(f'{molA_dir}/traj.trr', top=molA_file_name).slice(-1) # Load last frame of trr trajectory
- molB = md.load_trr(f'{molB_dir}/traj.trr', top=molB_file_name).slice(-1)
+ molA = md.load_trr(f'{molA_dir}/traj.trr', top=molA_file_name).slice(swap_index[0]) # Load last frame of trr trajectory # noqa: E501
+ molB = md.load_trr(f'{molB_dir}/traj.trr', top=molB_file_name).slice(swap_index[1])
# Load the coordinate swapping map
connection_map = pd.read_csv('residue_connect.csv')
@@ -1591,6 +1678,7 @@ def default_coords_fn(self, molA_file_name, molB_file_name):
# Print box size
molB_new.write(molA_file[-1])
+ molB_new.close()
# Print new coordinates to file
# Reprint preamble text
@@ -1608,6 +1696,7 @@ def default_coords_fn(self, molA_file_name, molB_file_name):
# Print box size
molA_new.write(molB_file[-1])
+ molA_new.close()
# Rename temp files
os.rename('A_hybrid_swap.gro', molB_dir + '/confout.gro')
diff --git a/ensemble_md/tests/data/dhdl/random-range/dhdl_0.xvg b/ensemble_md/tests/data/dhdl/random-range/dhdl_0.xvg
new file mode 100644
index 00000000..d98bdc58
--- /dev/null
+++ b/ensemble_md/tests/data/dhdl/random-range/dhdl_0.xvg
@@ -0,0 +1,238 @@
+# This file was created Sun Jan 19 05:14:48 2025
+# Created by:
+# :-) GROMACS - gmx mdrun, 2022.5-dev (-:
+#
+# Executable: /projects/anfr8476/pkgs/gromacs-2022.5-wl/bin/gmx
+# Data prefix: /projects/anfr8476/pkgs/gromacs-2022.5-wl
+# Working dir: /gpfs/alpine1/scratch/anfr8476/EEXE/test_forced_swap/small_mol/forced_random/solv/rep1/sim_0/iteration_0
+# Command line:
+# gmx mdrun -s sys_EE.tpr -ntomp 6 -ntmpi 1
+# gmx mdrun is part of G R O M A C S:
+#
+# Great Red Owns Many ACres of Sand
+#
+@ title "dH/d\xl\f{} and \xD\f{}H"
+@ xaxis label "Time (ps)"
+@ yaxis label "dH/d\xl\f{} and \xD\f{}H (kJ/mol [\xl\f{}]\S-1\N)"
+@TYPE xy
+@ subtitle "T = 300 (K) "
+@ view 0.15, 0.15, 0.75, 0.85
+@ legend on
+@ legend box on
+@ legend loctype view
+@ legend 0.78, 0.8
+@ legend length 2
+@ s0 legend "Thermodynamic state"
+@ s1 legend "Total Energy (kJ/mol)"
+@ s2 legend "dH/d\xl\f{} coul-lambda = 0.0000"
+@ s3 legend "dH/d\xl\f{} vdw-lambda = 0.0000"
+@ s4 legend "dH/d\xl\f{} bonded-lambda = 0.0000"
+@ s5 legend "\xD\f{}H \xl\f{} to (0.0000, 0.0000, 0.0000)"
+@ s6 legend "\xD\f{}H \xl\f{} to (0.1000, 0.1000, 0.1000)"
+@ s7 legend "\xD\f{}H \xl\f{} to (0.3000, 0.3000, 0.3000)"
+@ s8 legend "\xD\f{}H \xl\f{} to (0.6000, 0.6000, 0.6000)"
+@ s9 legend "\xD\f{}H \xl\f{} to (0.7000, 0.7000, 0.7000)"
+@ s10 legend "\xD\f{}H \xl\f{} to (0.8500, 0.8500, 0.8500)"
+@ s11 legend "\xD\f{}H \xl\f{} to (1.0000, 1.0000, 1.0000)"
+@ s12 legend "pV (kJ/mol)"
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diff --git a/ensemble_md/tests/data/dhdl/random-range/dhdl_1.xvg b/ensemble_md/tests/data/dhdl/random-range/dhdl_1.xvg
new file mode 100644
index 00000000..18bfd217
--- /dev/null
+++ b/ensemble_md/tests/data/dhdl/random-range/dhdl_1.xvg
@@ -0,0 +1,238 @@
+# This file was created Sun Jan 19 05:14:48 2025
+# Created by:
+# :-) GROMACS - gmx mdrun, 2022.5-dev (-:
+#
+# Executable: /projects/anfr8476/pkgs/gromacs-2022.5-wl/bin/gmx
+# Data prefix: /projects/anfr8476/pkgs/gromacs-2022.5-wl
+# Working dir: /gpfs/alpine1/scratch/anfr8476/EEXE/test_forced_swap/small_mol/forced_random/solv/rep1/sim_1/iteration_0
+# Command line:
+# gmx mdrun -s sys_EE.tpr -ntomp 6 -ntmpi 1
+# gmx mdrun is part of G R O M A C S:
+#
+# Great Red Owns Many ACres of Sand
+#
+@ title "dH/d\xl\f{} and \xD\f{}H"
+@ xaxis label "Time (ps)"
+@ yaxis label "dH/d\xl\f{} and \xD\f{}H (kJ/mol [\xl\f{}]\S-1\N)"
+@TYPE xy
+@ subtitle "T = 300 (K) "
+@ view 0.15, 0.15, 0.75, 0.85
+@ legend on
+@ legend box on
+@ legend loctype view
+@ legend 0.78, 0.8
+@ legend length 2
+@ s0 legend "Thermodynamic state"
+@ s1 legend "Total Energy (kJ/mol)"
+@ s2 legend "dH/d\xl\f{} coul-lambda = 0.0000"
+@ s3 legend "dH/d\xl\f{} vdw-lambda = 0.0000"
+@ s4 legend "dH/d\xl\f{} bonded-lambda = 0.0000"
+@ s5 legend "\xD\f{}H \xl\f{} to (0.0000, 0.0000, 0.0000)"
+@ s6 legend "\xD\f{}H \xl\f{} to (0.1000, 0.1000, 0.1000)"
+@ s7 legend "\xD\f{}H \xl\f{} to (0.3000, 0.3000, 0.3000)"
+@ s8 legend "\xD\f{}H \xl\f{} to (0.6000, 0.6000, 0.6000)"
+@ s9 legend "\xD\f{}H \xl\f{} to (0.7000, 0.7000, 0.7000)"
+@ s10 legend "\xD\f{}H \xl\f{} to (0.8500, 0.8500, 0.8500)"
+@ s11 legend "\xD\f{}H \xl\f{} to (1.0000, 1.0000, 1.0000)"
+@ s12 legend "pV (kJ/mol)"
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diff --git a/ensemble_md/tests/data/dhdl/random-range/dhdl_2.xvg b/ensemble_md/tests/data/dhdl/random-range/dhdl_2.xvg
new file mode 100644
index 00000000..ce322e0b
--- /dev/null
+++ b/ensemble_md/tests/data/dhdl/random-range/dhdl_2.xvg
@@ -0,0 +1,238 @@
+# This file was created Sun Jan 19 05:14:48 2025
+# Created by:
+# :-) GROMACS - gmx mdrun, 2022.5-dev (-:
+#
+# Executable: /projects/anfr8476/pkgs/gromacs-2022.5-wl/bin/gmx
+# Data prefix: /projects/anfr8476/pkgs/gromacs-2022.5-wl
+# Working dir: /gpfs/alpine1/scratch/anfr8476/EEXE/test_forced_swap/small_mol/forced_random/solv/rep1/sim_2/iteration_0
+# Command line:
+# gmx mdrun -s sys_EE.tpr -ntomp 6 -ntmpi 1
+# gmx mdrun is part of G R O M A C S:
+#
+# Great Red Owns Many ACres of Sand
+#
+@ title "dH/d\xl\f{} and \xD\f{}H"
+@ xaxis label "Time (ps)"
+@ yaxis label "dH/d\xl\f{} and \xD\f{}H (kJ/mol [\xl\f{}]\S-1\N)"
+@TYPE xy
+@ subtitle "T = 300 (K) "
+@ view 0.15, 0.15, 0.75, 0.85
+@ legend on
+@ legend box on
+@ legend loctype view
+@ legend 0.78, 0.8
+@ legend length 2
+@ s0 legend "Thermodynamic state"
+@ s1 legend "Total Energy (kJ/mol)"
+@ s2 legend "dH/d\xl\f{} coul-lambda = 0.0000"
+@ s3 legend "dH/d\xl\f{} vdw-lambda = 0.0000"
+@ s4 legend "dH/d\xl\f{} bonded-lambda = 0.0000"
+@ s5 legend "\xD\f{}H \xl\f{} to (0.0000, 0.0000, 0.0000)"
+@ s6 legend "\xD\f{}H \xl\f{} to (0.1000, 0.1000, 0.1000)"
+@ s7 legend "\xD\f{}H \xl\f{} to (0.3000, 0.3000, 0.3000)"
+@ s8 legend "\xD\f{}H \xl\f{} to (0.6000, 0.6000, 0.6000)"
+@ s9 legend "\xD\f{}H \xl\f{} to (0.7000, 0.7000, 0.7000)"
+@ s10 legend "\xD\f{}H \xl\f{} to (0.8500, 0.8500, 0.8500)"
+@ s11 legend "\xD\f{}H \xl\f{} to (1.0000, 1.0000, 1.0000)"
+@ s12 legend "pV (kJ/mol)"
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+0.2000 0 -22205.363 208.35834 10.282860 -0.46683121 0.0000000 18.773513 48.903528 117.83817 173.41451 416.95430 5640.4134 1.2313271
+0.4000 0 -22230.637 288.71664 1.9099225 6.2252722 0.0000000 22.920607 50.550151 83.746708 101.03598 164.57543 715.17532 1.2311585
+0.6000 0 -22256.014 239.57758 -2.9888496 1.7805481 0.0000000 19.399810 46.887735 96.619556 129.08773 236.28088 700.56508 1.2288349
+0.8000 0 -22239.547 105.53345 -12.514267 0.61477399 0.0000000 8.8697797 25.488627 59.626191 81.801600 160.68930 729.38869 1.2288287
+1.0000 1 -22261.324 55.237103 2.2850833 8.2886038 -6.6394514 0.0000000 13.019698 34.932585 45.041960 67.872859 113.58219 1.2288629
+1.2000 2 -22209.996 32.706604 -22.406910 1.4253063 9.3961608 2.4503953 0.0000000 11.537623 19.771396 40.603308 87.112774 1.2313678
+1.4000 3 -22329.201 13.858295 -2.8904102 0.95646811 36.308473 13.125441 -0.087635827 0.0000000 1.4073306 4.5250042 10.640149 1.2337381
+1.6000 4 -22306.979 11.701923 23.073025 2.1789384 153.65580 42.207338 -0.45141762 -2.9801425 0.0000000 7.5745095 22.724420 1.2336458
+1.8000 3 -22244.703 22.004276 -15.585249 -1.1995517 44.151619 21.879227 4.6925500 0.0000000 1.0414094 4.5935347 11.360973 1.2354945
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diff --git a/ensemble_md/tests/data/dhdl/random-range/dhdl_3.xvg b/ensemble_md/tests/data/dhdl/random-range/dhdl_3.xvg
new file mode 100644
index 00000000..8258655a
--- /dev/null
+++ b/ensemble_md/tests/data/dhdl/random-range/dhdl_3.xvg
@@ -0,0 +1,238 @@
+# This file was created Sun Jan 19 05:14:48 2025
+# Created by:
+# :-) GROMACS - gmx mdrun, 2022.5-dev (-:
+#
+# Executable: /projects/anfr8476/pkgs/gromacs-2022.5-wl/bin/gmx
+# Data prefix: /projects/anfr8476/pkgs/gromacs-2022.5-wl
+# Working dir: /gpfs/alpine1/scratch/anfr8476/EEXE/test_forced_swap/small_mol/forced_random/solv/rep1/sim_3/iteration_0
+# Command line:
+# gmx mdrun -s sys_EE.tpr -ntomp 6 -ntmpi 1
+# gmx mdrun is part of G R O M A C S:
+#
+# Great Red Owns Many ACres of Sand
+#
+@ title "dH/d\xl\f{} and \xD\f{}H"
+@ xaxis label "Time (ps)"
+@ yaxis label "dH/d\xl\f{} and \xD\f{}H (kJ/mol [\xl\f{}]\S-1\N)"
+@TYPE xy
+@ subtitle "T = 300 (K) "
+@ view 0.15, 0.15, 0.75, 0.85
+@ legend on
+@ legend box on
+@ legend loctype view
+@ legend 0.78, 0.8
+@ legend length 2
+@ s0 legend "Thermodynamic state"
+@ s1 legend "Total Energy (kJ/mol)"
+@ s2 legend "dH/d\xl\f{} coul-lambda = 0.0000"
+@ s3 legend "dH/d\xl\f{} vdw-lambda = 0.0000"
+@ s4 legend "dH/d\xl\f{} bonded-lambda = 0.0000"
+@ s5 legend "\xD\f{}H \xl\f{} to (0.0000, 0.0000, 0.0000)"
+@ s6 legend "\xD\f{}H \xl\f{} to (0.1000, 0.1000, 0.1000)"
+@ s7 legend "\xD\f{}H \xl\f{} to (0.3000, 0.3000, 0.3000)"
+@ s8 legend "\xD\f{}H \xl\f{} to (0.6000, 0.6000, 0.6000)"
+@ s9 legend "\xD\f{}H \xl\f{} to (0.7000, 0.7000, 0.7000)"
+@ s10 legend "\xD\f{}H \xl\f{} to (0.8500, 0.8500, 0.8500)"
+@ s11 legend "\xD\f{}H \xl\f{} to (1.0000, 1.0000, 1.0000)"
+@ s12 legend "pV (kJ/mol)"
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+33.0000 2 -22017.219 -22.695503 -4.6228757 1.4161190 14.275922 8.0670286 0.0000000 -0.84567538 2.3140801 11.139328 26.410578 1.2461213
+33.2000 4 -22065.723 27.931042 1.3973634 8.5627480 14.688402 7.7817708 -1.1898447 -2.8403174 0.0000000 8.1006193 22.228830 1.2480443
+33.4000 5 -22160.598 39.616856 29.708416 0.99082071 3.8774417 -1.9139990 -9.5654533 -10.709160 -7.9257826 0.0000000 13.894952 1.2466638
+33.6000 3 -22162.850 13.980423 13.355594 2.6221437 16.653367 9.2445562 0.023906980 0.0000000 4.1485570 15.341094 34.473395 1.2454400
+33.8000 2 -22146.199 -27.852276 14.323059 2.6225243 14.061259 7.0084105 0.0000000 10.015352 21.291902 51.399325 112.16304 1.2448322
+34.0000 1 -22072.469 -35.445724 -4.1826930 6.5241203 3.9549190 0.0000000 -3.8797972 2.7333339 9.5765717 26.731930 57.454294 1.2425225
+34.2000 0 -22031.475 -50.204124 -8.5319729 8.9145374 0.0000000 -4.1902770 -7.6917397 4.3690097 17.839681 66.830272 319.11855 1.2441996
+34.4000 1 -22000.117 -42.970192 -8.0624084 6.3972640 5.1898994 0.0000000 -5.9403648 -1.8496418 4.2287769 20.340169 50.554482 1.2450097
+34.6000 0 -22040.340 -45.388039 -10.389755 0.55460787 0.0000000 -4.6132113 -7.8822690 12.652130 37.386197 158.94764 3277.2466 1.2459781
+34.8000 1 -22076.617 -34.879288 6.6402822 4.4004326 3.2922079 0.0000000 -0.56785055 24.050902 48.327342 145.51661 1185.0780 1.2465352
+35.0000 0 -22014.766 -53.154316 -3.5881658 7.0886173 0.0000000 -4.1628370 -7.2208319 9.2491417 28.403895 114.98695 1254.5789 1.2507077
+35.2000 0 -21906.758 -80.823502 -8.8612642 1.1127642 0.0000000 -7.7702965 -17.283260 -16.447283 -11.344230 2.7079682 28.749777 1.2526543
+35.4000 2 -21892.178 -29.307768 -2.1332095 8.7433128 15.228085 8.2679921 0.0000000 2.2707289 7.7824038 23.018889 59.632145 1.2462796
+35.6000 1 -21845.686 -29.224909 5.1444516 2.7135811 2.8653404 0.0000000 -0.84266322 19.054878 39.006619 115.73956 684.11747 1.2501708
+35.8000 0 -21803.031 -71.172623 0.077710271 1.2666876 0.0000000 -5.7682113 -9.8085269 9.2917674 27.434349 80.794308 265.27930 1.2540058
+36.0000 1 -21800.367 -30.600681 -6.9443460 4.0521536 3.9936437 0.0000000 -4.0242804 2.8706335 11.200432 37.798958 123.12600 1.2515309
+36.2000 0 -21825.709 -26.062082 -1.9265573 0.95929611 0.0000000 -2.1036288 -1.9800929 17.447680 34.789012 86.483650 223.23977 1.2523506
+36.4000 0 -21870.131 -65.543236 -10.179163 1.7231570 0.0000000 -6.1238639 -12.808933 -9.5106164 -3.4643372 14.534138 67.409227 1.2539344
+36.6000 3 -21855.412 15.205326 8.7067738 5.5651097 17.866745 9.9092194 0.22170620 0.0000000 4.0898400 15.120179 34.008450 1.2560135
+36.8000 2 -21870.010 -25.636562 48.412579 2.9135363 5.7955803 1.2423785 0.0000000 33.820940 69.390996 216.43881 1518.8133 1.2542375
+37.0000 1 -21772.061 -49.890038 -2.4179029 0.10851029 6.1351810 0.0000000 -6.7617408 1.6034165 14.191683 62.353880 314.38143 1.2563856
+37.2000 0 -21827.781 -53.981449 -5.5677691 2.0474875 0.0000000 -4.8040174 -8.1062984 12.275547 34.565968 125.49207 1032.9154 1.2519917
+37.4000 1 -21789.367 -38.590721 -9.8855639 0.52847409 5.4006326 0.0000000 -7.0906462 -7.1119065 -3.5151388 6.6735862 25.363816 1.2512425
+37.6000 3 -21737.291 9.4562874 6.7480464 1.0326600 13.737624 8.4685962 1.3245503 0.0000000 2.5773635 10.171761 23.898382 1.2520005
+37.8000 4 -21669.506 20.223783 -4.8343191 5.4714918 12.938835 7.9033328 0.96416798 -1.4692623 0.0000000 4.6264264 12.651081 1.2521288
+38.0000 6 -21601.133 57.362335 6.7386813 0.68992031 7.2772713 0.97704010 -8.0537335 -12.700614 -11.741553 -7.6740696 0.0000000 1.2520666
+38.2000 4 -21708.410 22.003983 30.077187 1.0235604 8.6374704 2.8592926 -4.5780712 -4.0819582 0.0000000 11.401113 32.687166 1.2486092
+38.4000 3 -21688.703 19.627750 12.591658 0.31133509 26.236267 15.624955 2.1216954 0.0000000 4.8528357 19.979034 59.074318 1.2469311
+38.6000 3 -21703.365 19.232702 0.58847606 9.4736414 23.334377 13.217519 1.2024267 0.0000000 4.1477090 15.517947 35.053402 1.2486875
+38.8000 4 -21785.955 28.679409 7.3418531 1.3506662 17.981277 10.373171 0.13201094 -2.7115504 0.0000000 8.1929082 22.704357 1.2478460
+39.0000 4 -21766.578 24.696751 9.4046907 8.3398838 10.460389 4.4884635 -3.0398489 -3.2665102 0.0000000 8.9640715 25.013117 1.2482303
+39.2000 4 -21763.445 16.242535 -8.9392366 0.80375636 15.105017 10.490253 3.6890434 -0.36200088 0.0000000 2.2691651 6.7861445 1.2512314
+39.4000 6 -21734.457 54.607525 1.5196505 1.0491375 6.7074071 1.2318085 -6.5420395 -10.674909 -9.9449733 -6.5978720 0.0000000 1.2533427
+39.6000 6 -21803.078 68.373940 7.0824919 3.7850811 3.0518581 -3.2101297 -11.899762 -15.561233 -14.145496 -9.1836575 0.0000000 1.2522539
+39.8000 6 -21822.492 73.619293 3.6315372 2.5494051 6.5356759 -0.81135422 -10.981742 -15.695559 -14.378847 -9.3617561 0.0000000 1.2536534
+40.0000 4 -21829.297 40.265335 -0.42265129 0.77138031 27.154670 16.413176 2.0569118 -2.8656206 0.0000000 8.9714997 24.397801 1.2565694
diff --git a/ensemble_md/tests/test_analyze_traj.py b/ensemble_md/tests/test_analyze_traj.py
index b47b3fd9..66a7959a 100644
--- a/ensemble_md/tests/test_analyze_traj.py
+++ b/ensemble_md/tests/test_analyze_traj.py
@@ -595,7 +595,7 @@ def test_plot_state_hist(mock_plt):
}
# Clean up
- os.remove('hist_data.npy')
+ os.remove('ensemble_md/tests/data/hist_data.npy')
def test_calc_hist_rmse():
diff --git a/ensemble_md/tests/test_replica_exchange_EE.py b/ensemble_md/tests/test_replica_exchange_EE.py
index 25ab5c45..ecdf5d4c 100644
--- a/ensemble_md/tests/test_replica_exchange_EE.py
+++ b/ensemble_md/tests/test_replica_exchange_EE.py
@@ -90,7 +90,7 @@ def test_set_params_error(self, params_dict):
params_dict['n_sim'] = 4 # so params_dict can be read without failing in the assertions below
# 2. Available options
- check_param_error(params_dict, 'proposal', "The specified proposal scheme is not available. Available options include 'single', 'neighboring', and 'exhaustive'.", 'cool', 'exhaustive') # noqa: E501
+ check_param_error(params_dict, 'proposal', "The specified proposal scheme is not available. Available options include 'single', 'neighboring', 'exhaustive', 'random_range'.", 'cool', 'exhaustive') # noqa: E501
check_param_error(params_dict, 'df_method', "The specified free energy estimator is not available. Available options include 'TI', 'BAR', and 'MBAR'.") # noqa: E501
check_param_error(params_dict, 'err_method', "The specified method for error estimation is not available. Available options include 'propagate', and 'bootstrap'.") # noqa: E501
@@ -652,22 +652,50 @@ def test_identify_swappable_pairs(self, params_dict):
REXEE = get_REXEE_instance(params_dict)
REXEE.state_ranges = [list(range(i, i + 5)) for i in range(REXEE.n_sim)] # 5 states per replica
states = [4, 2, 2, 7] # This would lead to the swappables: [(0, 1), (0, 2), (1, 2)] in the standard case
+ dhdl_files = [os.path.join(input_path, f"dhdl/dhdl_{i}.xvg") for i in range(4)]
# Case 1: Any case that is not neighboring swap has the same definition for the swappable pairs
- swappables = REXEE.identify_swappable_pairs(states, REXEE.state_ranges, REXEE.proposal == 'neighboring')
+ swappables, swap_index, states_for_swap = REXEE.identify_swappable_pairs(states, REXEE.state_ranges, dhdl_files) # noqa: E501
assert swappables == [(0, 1), (0, 2), (1, 2)]
+ assert swap_index == []
+ assert states_for_swap == []
# Case 2: Neighboring exchange
REXEE.proposal = 'neighboring'
- swappables = REXEE.identify_swappable_pairs(states, REXEE.state_ranges, REXEE.proposal == 'neighboring')
+ swappables, swap_index, states_for_swap = REXEE.identify_swappable_pairs(states, REXEE.state_ranges, dhdl_files) # noqa: E501
assert swappables == [(0, 1), (1, 2)]
+ assert swap_index == []
+ assert states_for_swap == []
# Case 3: Non-neighboring exchange, with add_swappables
REXEE.proposal = 'exhaustive'
REXEE.add_swappables = [[3, 7], [4, 7]]
states = [4, 3, 2, 7] # Without add_swappables, the swappables would be [(0, 1), (0, 2), (1, 2)]
- swappables = REXEE.identify_swappable_pairs(states, REXEE.state_ranges, REXEE.proposal == 'neighboring', REXEE.add_swappables) # noqa: E501
+ swappables, swap_index, states_for_swap = REXEE.identify_swappable_pairs(states, REXEE.state_ranges, dhdl_files) # noqa: E501
assert swappables == [(0, 1), (0, 2), (1, 2), (0, 3), (1, 3)]
+ assert swap_index == []
+ assert states_for_swap == []
+
+ # Case 4: Random Range
+ REXEE.proposal = 'random_range'
+ REXEE.state_ranges = [list(range(i, i + 7)) for i in [0, 7, 14, 21]]
+ states = [[6, 7], [13, 14], [20, 21]]
+ REXEE = get_REXEE_instance(params_dict)
+ REXEE.n_tot = 28
+ REXEE.s = 7
+ REXEE.template['nstdhdl'] = 100
+ REXEE.template['nstxout'] = 1000
+ REXEE.template['n_sim'] = 4
+
+ dhdl_files = [
+ os.path.join(input_path, f"dhdl/random-range/dhdl_{i}.xvg") for i in range(REXEE.n_sim)
+ ]
+ swappables, swap_index, states_for_swap = REXEE.identify_swappable_pairs(states, REXEE.state_ranges, dhdl_files)
+
+ #assert swappables[0] == [0, 1]
+ assert swap_index[0][0] in [14, 15, 16]
+ assert swap_index[0][1] in [10, 12, 16, 19]
+ assert states_for_swap[0] == [6, 7]
def test_propose_swap(self, params_dict):
random.seed(0)
@@ -687,24 +715,29 @@ def test_get_swapping_pattern(self, params_dict):
REXEE.verbose = False
states = [0, 6, 7, 8] # No swappable pairs
f = copy.deepcopy(dhdl_files)
- pattern, swap_list = REXEE.get_swapping_pattern(f, states)
+ pattern, swap_list, swap_index_accept, states_modified = REXEE.get_swapping_pattern(f, states)
assert REXEE.n_empty_swappable == 1
assert REXEE.n_swap_attempts == 0
assert REXEE.n_rejected == 0
assert pattern == [0, 1, 2, 3]
assert swap_list == []
+ assert swap_index_accept == []
+ assert states_modified == states
# Test 2: Empty swap list, neighboring proposal
REXEE = get_REXEE_instance(params_dict)
REXEE.proposal = 'neighboring' # n_ex will be set to 1 automatically.
states = [0, 6, 7, 8] # No swappable pairs
f = copy.deepcopy(dhdl_files)
- pattern, swap_list = REXEE.get_swapping_pattern(f, states)
+ pattern, swap_list, swap_index_accept, states_modified = REXEE.get_swapping_pattern(f, states)
+ print(f'2: {swap_index_accept} {states_modified}')
assert REXEE.n_empty_swappable == 1
assert REXEE.n_swap_attempts == 0
assert REXEE.n_rejected == 0
assert pattern == [0, 1, 2, 3]
assert swap_list == []
+ assert swap_index_accept == []
+ assert states_modified == states
# Test 3: Single swap (proposal = 'single')
random.seed(0)
@@ -713,11 +746,13 @@ def test_get_swapping_pattern(self, params_dict):
REXEE.proposal = 'single' # n_ex will be set to 1 automatically.
states = [5, 2, 2, 8] # swappable pairs: [(0, 1), (0, 2), (1, 2)], swap = (1, 2), accept
f = copy.deepcopy(dhdl_files)
- pattern, swap_list = REXEE.get_swapping_pattern(f, states)
+ pattern, swap_list, swap_index_accept, states_modified = REXEE.get_swapping_pattern(f, states)
assert REXEE.n_swap_attempts == 1
assert REXEE.n_rejected == 0
assert pattern == [0, 2, 1, 3]
assert swap_list == [(1, 2)]
+ assert swap_index_accept == [[-1, -1]]
+ assert states_modified == states
# Test 4: Neighboring swap
random.seed(0)
@@ -725,11 +760,13 @@ def test_get_swapping_pattern(self, params_dict):
REXEE.proposal = 'neighboring' # n_ex will be set to 1 automatically.
states = [5, 2, 2, 8] # swappable pairs: [(0, 1), (0, 2), (1, 2)], swap = (1, 2), accept
f = copy.deepcopy(dhdl_files)
- pattern, swap_list = REXEE.get_swapping_pattern(f, states)
+ pattern, swap_list, swap_index_accept, states_modified = REXEE.get_swapping_pattern(f, states)
assert REXEE.n_swap_attempts == 1
assert REXEE.n_rejected == 0
assert pattern == [0, 2, 1, 3]
assert swap_list == [(1, 2)]
+ assert swap_index_accept == [[-1, -1]]
+ assert states_modified == states
# Test 5: Exhaustive swaps that end up in a single swap
random.seed(0)
@@ -737,11 +774,13 @@ def test_get_swapping_pattern(self, params_dict):
REXEE.proposal = 'exhaustive'
states = [5, 2, 2, 8] # swappable pairs: [(0, 1), (0, 2), (1, 2)], swap = (1, 2), accept
f = copy.deepcopy(dhdl_files)
- pattern, swap_list = REXEE.get_swapping_pattern(f, states)
+ pattern, swap_list, swap_index_accept, states_modified = REXEE.get_swapping_pattern(f, states)
assert REXEE.n_swap_attempts == 1
assert REXEE.n_rejected == 0
assert pattern == [0, 2, 1, 3]
assert swap_list == [(1, 2)]
+ assert swap_index_accept == [[-1, -1]]
+ assert states_modified == states
# Test 6: Exhaustive swaps that involve multiple attempted swaps
random.seed(0)
@@ -749,11 +788,13 @@ def test_get_swapping_pattern(self, params_dict):
REXEE.proposal = 'exhaustive'
states = [4, 2, 4, 3] # swappable pairs: [(0, 1), (0, 2), (0, 3), (1, 2), (2, 3)]; swap 1: (2, 3), accepted; swap 2: (0, 1), accept # noqa: E501
f = copy.deepcopy(dhdl_files)
- pattern, swap_list = REXEE.get_swapping_pattern(f, states)
+ pattern, swap_list, swap_index_accept, states_modified = REXEE.get_swapping_pattern(f, states)
assert REXEE.n_swap_attempts == 2 # \Delta is negative for both swaps -> both accepted
assert REXEE.n_rejected == 0
assert pattern == [1, 0, 3, 2]
assert swap_list == [(2, 3), (0, 1)]
+ assert swap_index_accept == [[-1, -1], [-1, -1]]
+ assert states_modified == states
# Test 7: REXEE.proposal is set to exhaustive but there is only one swappable pair anyway.
random.seed(0)
@@ -761,11 +802,13 @@ def test_get_swapping_pattern(self, params_dict):
REXEE.proposal = 'exhaustive'
states = [0, 2, 2, 8] # swappable pair: [(1, 2)], swap: (1, 2), accept
f = copy.deepcopy(dhdl_files)
- pattern, swap_list = REXEE.get_swapping_pattern(f, states)
+ pattern, swap_list, swap_index_accept, states_modified = REXEE.get_swapping_pattern(f, states)
assert REXEE.n_swap_attempts == 1
assert REXEE.n_rejected == 0
assert pattern == [0, 2, 1, 3]
assert swap_list == [(1, 2)]
+ assert swap_index_accept == [[-1, -1]]
+ assert states_modified == states
# Test 8: modify_coords_fn is not None, so swap_bool is always True
random.seed(0)
@@ -773,11 +816,13 @@ def test_get_swapping_pattern(self, params_dict):
REXEE.modify_coords_fn = 'Cool'
states = [5, 2, 2, 8] # swappable pairs: [(0, 1), (0, 2), (1, 2)], swap = (1, 2), accept
f = copy.deepcopy(dhdl_files)
- pattern, swap_list = REXEE.get_swapping_pattern(f, states)
+ pattern, swap_list, swap_index_accept, states_modified = REXEE.get_swapping_pattern(f, states)
assert REXEE.n_swap_attempts == 1
assert REXEE.n_rejected == 0
assert pattern == [0, 2, 1, 3]
assert swap_list == [(1, 2)]
+ assert swap_index_accept == [[-1, -1]]
+ assert states_modified == states
def test_calc_prob_acc(self, capfd, params_dict):
# k = 1.380649e-23; NA = 6.0221408e23; T = 298; kT = k * NA * T / 1000 = 2.4777098766670016
@@ -815,6 +860,7 @@ def test_accept_or_reject(self, params_dict):
assert swap_bool_2 is False
assert swap_bool_3 is True
+
def test_weight_correction(self, params_dict):
REXEE = get_REXEE_instance(params_dict)
@@ -916,7 +962,7 @@ def check_file(true_output, test_output):
os.system(f'cp {input_path}/coord_swap/residue_connect.csv .')
os.system(f'cp {input_path}/coord_swap/atom_name_mapping.csv .')
os.system(f'cp {input_path}/coord_swap/residue_swap_map.csv .')
- REXEE.default_coords_fn(f'{input_path}/coord_swap/sim_A/confout_backup.gro', f'{input_path}/coord_swap/sim_B/confout_backup.gro') # noqa: E501
+ REXEE.default_coords_fn(f'{input_path}/coord_swap/sim_A/confout_backup.gro', f'{input_path}/coord_swap/sim_B/confout_backup.gro', [-1, -1]) # noqa: E501
true_output_A = open(f'{input_path}/coord_swap/output_A.gro', 'r').readlines()
test_output_A = open(f'{input_path}/coord_swap/sim_B/confout.gro', 'r').readlines()
@@ -932,7 +978,7 @@ def check_file(true_output, test_output):
os.system(f'cp {input_path}/coord_swap/residue_connect_alt.csv residue_connect.csv')
os.system(f'cp {input_path}/coord_swap/atom_name_mapping_alt.csv atom_name_mapping.csv')
os.system(f'cp {input_path}/coord_swap/residue_swap_map_alt.csv residue_swap_map.csv')
- REXEE.default_coords_fn(f'{input_path}/coord_swap/sim_C/confout_backup.gro', f'{input_path}/coord_swap/sim_D/confout_backup.gro') # noqa: E501
+ REXEE.default_coords_fn(f'{input_path}/coord_swap/sim_C/confout_backup.gro', f'{input_path}/coord_swap/sim_D/confout_backup.gro', [-1, -1]) # noqa: E501
true_output_C = open(f'{input_path}/coord_swap/output_C.gro', 'r').readlines()
test_output_C = open(f'{input_path}/coord_swap/sim_D/confout.gro', 'r').readlines()
diff --git a/ensemble_md/utils/coordinate_swap.py b/ensemble_md/utils/coordinate_swap.py
index f529377d..84510275 100644
--- a/ensemble_md/utils/coordinate_swap.py
+++ b/ensemble_md/utils/coordinate_swap.py
@@ -900,7 +900,7 @@ def get_names(input, resname):
continue
while '' in line_sep:
line_sep.remove('')
- if line_sep[0] == '\n':
+ if line_sep[0] == '\n' or line == '[ bonds ]\n':
start_line = l+2
break
if line_sep[3] == resname:
diff --git a/ensemble_md/utils/gmx_parser.py b/ensemble_md/utils/gmx_parser.py
index 4ee8eb36..13068ec1 100644
--- a/ensemble_md/utils/gmx_parser.py
+++ b/ensemble_md/utils/gmx_parser.py
@@ -438,9 +438,28 @@ def deter_atom_order(mol_file, resname):
split_line.remove('')
if resname in split_line[0]:
if len(split_line[1]) > 5:
- split_line = coordinate_swap.sep_merge(split_line)
+ split_line = coordinate_swap._sep_merge(split_line)
atom_order.append(split_line[1])
elif len(atom_order) != 0:
break
return atom_order
+
+
+def get_end_states(mdp_path):
+ mdp = MDP(mdp_path)
+ end_0, end_1 = [], []
+ coul_lambda = mdp['coul_lambdas']
+ vdw_lambda = mdp['vdw_lambdas']
+ n = 0
+ for vdw, coul in zip(coul_lambda, vdw_lambda):
+ if vdw == 0.0 and coul == 0.0:
+ end_0.append(n)
+ elif vdw == 1.0 and coul == 1.0:
+ end_1.append(n)
+ n += 1
+ dt = mdp['dt']
+ steps_per_frame = mdp['nstxout']
+ ps_per_frame = dt*steps_per_frame
+
+ return end_0, end_1, ps_per_frame