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Refined the docstrings of analyze_free_energy,py
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wehs7661 committed Apr 18, 2024
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3 changes: 3 additions & 0 deletions docs/conf.py
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Expand Up @@ -187,5 +187,8 @@

intersphinx_mapping = {
'python': ('https://docs.python.org/3', None),
'numpy': ('https://numpy.org/doc/stable/', None),
'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),
'pymbar': ('https://pymbar.readthedocs.io/en/latest/', None),
'alchemlyb': ('https://alchemlyb.readthedocs.io/en/latest/', None),
}
184 changes: 111 additions & 73 deletions ensemble_md/analysis/analyze_free_energy.py
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Expand Up @@ -27,35 +27,34 @@

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 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
:func:`alchemlyb.subsampling.equilibrium_detection`.
This function preprocesses :math:`u_{nk}` or :math:`dH/dλ` data for all replicas in an REXEE simulation.
For each replica, it reads in :math:`u_{nk}` or :math:`dH/dλ` data from all iterations, concatenate
them, remove the equilibrium region and and decorrelate the concatenated data.
Parameters
----------
files_list : list
A list of lists of naturally sorted dhdl file names from all iterations for different replicas.
:code:`files[i]` should be the list of dhdl file names from all iterations of replica :code:`i`.
A list of lists of naturally sorted DHDL file names from all iterations for different replicas.
Specifically, :code:`files[i]` should be the list of DHDL file names from all iterations of replica :code:`i`.
temp : float
The simulation temperature in Kelvin. We assume all replicas were performed at the same temperature.
data_type : str
The type of energy data to be procssed. Should be either :code:`'u_nk'` or :code:`'dhdl'`.
spacing : int
The spacing (number of data points) to consider when subsampling the data, which is assumed to
be the same for all replicas.
t : int
spacing : int, Optional
The spacing (in the number of data points) to consider when subsampling the data, which is assumed to
be the same for all replicas. The default is 1.
t : int, Optional
The user-specified index that indicates the start of equilibrated data. If this parameter is not specified,
the function will estimate it using :code:`pymbar.timeseries.detect_equilibration`.
g : int
The user-specified index that indicates the start of equilibrated data. If this parameter is not specified,
the function will estimate it using :code:`pymbar.timeseries.detect_equilibration`.
the function will estimate it using :func:`pymbar.timeseries.detect_equilibration`. The default
is :code:`None`.
g : int, Optional
The user-specified statistical inefficiency. If this parameter is not specified, the function will estimate
it using :func:`pymbar.timeseries.detect_equilibration`. The default is :code:`None`.
Returns
-------
preprocessed_data_all : pd.Dataframe
A list of preprocessed :math:`u_{nk}`/:math:`dH/dλ` data for all replicas that can serve as the
preprocessed_data_all : pandas.Dataframe
A list of preprocessed :math:`u_{nk}` or :math:`dH/dλ` data for all replicas that can serve as the
input to free energy estimators.
t_list : list
A list of indices indicating the start of equilibrated data for different replicas. This list will
Expand All @@ -78,7 +77,7 @@ def preprocess_data(files_list, temp, data_type, spacing=1, t=None, g=None):
n_sim = len(files_list)
preprocessed_data_all, t_list, g_list = [], [], []
for i in range(n_sim):
print(f'Reading dhdl files of alchemical range {i} ...')
print(f'Reading dhdl files of state range {i} ...')
print(f'Collecting {data_type} data from all iterations ...')
data = alchemlyb.concat([extract_fn(xvg, T=temp) for xvg in files_list[i]])
data_series = convert_fn(data)
Expand Down Expand Up @@ -112,21 +111,27 @@ def preprocess_data(files_list, temp, data_type, spacing=1, t=None, g=None):

def _apply_estimators(data, df_method="MBAR"):
"""
An internal function that generates a list of estimators fitting the input data.
An internal function used in :func:`calculate_free_energy` to generate a list of estimators fitting the input data.
Parameters
----------
data : pd.Dataframe
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".
data : pandas.Dataframe
A list of :math:`dH/dλ` or :math:`u_{nk}` dataframes obtained from all replicas of the REXEE simulation
Preferrably, the :math:`dH/dλ` or :math:`u_{nk}` data should be preprocessed by the function
:func:`preprocess_data`.
df_method : str, Optional
The selected free energy estimator. Options include :code:`"MBAR"`, :code:`"BAR"` and :code:`"TI"`.
The default is :code:`"MBAR"`.
Returns
-------
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.
See also
--------
:func:`calculate_free_energy`
"""
n_sim = len(data)
estimators = [] # A list of objects of the corresponding class in alchemlyb.estimators
Expand All @@ -145,21 +150,26 @@ def _apply_estimators(data, df_method="MBAR"):

def _calculate_df_adjacent(estimators):
"""
An Internal function that calculates at list of free energy between adjacent
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_adjacent : list
A list of lists free energy differences between adjacent states for all replicas.
df_err_adjacent : list
A list of lists of uncertainties corresponding to the values of :code:`df_adjacent`.
A list of lists of uncertainties corresponding to the values in :code:`df_adjacent`.
See also
--------
:func:`calculate_free_energy`
"""
n_sim = len(estimators)
df_adjacent = [list(np.array(estimators[i].delta_f_)[:-1, 1:].diagonal()) for i in range(n_sim)]
Expand All @@ -168,35 +178,40 @@ def _calculate_df_adjacent(estimators):
return df_adjacent, df_err_adjacent


def _combine_df_adjacent(df_adjacent, df_err_adjacent, state_ranges, err_type):
def _combine_df_adjacent(df_adjacent, state_ranges, df_err_adjacent=None, err_type="propagate"):
"""
An internal function that combines the free energy differences between adjacent states
in different state ranges using either simple means or inverse-variance weighted means.
Specifically, if :code:`df_err_adjacent` is :code:`None`, simple means will be used.
Otherwise, inverse-variance weighted means will be used.
An internal function used in :func:`calculate_free_energy` to combine the free energy differences between
adjacent states in different state ranges using either simple means or inverse-variance weighted means.
Parameters
----------
df_adjacent : list
A list of lists free energy differences between adjacent states for all replicas.
df_err_adjacent : list
A list of lists of uncertainties corresponding to the values of :code:`df_adjacent`.
state_ranges : list
A list of lists of intergers that represents the alchemical states that can be sampled by different replicas.
err_type : str
A list of lists of showing the state indices sampled by each replica.
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
means will be used. The default is :code:`None`
err_type : str, Optional
How the error of the combined free energy differences should be calculated. Available options include
"propagate" and "std". Note that the option "propagate" is only available when :code:`df_err_adjacent`
is not :code:`None`.
:code:`"propagate"` and :code:`"std"`. Note that the option :code:`"propagate"` is only available when
:code:`df_err_adjacent` is not :code:`None`.
Returns
-------
df : list
A list of free energy differences between states i and i + 1 for the entire state range.
A list of free energy differences between states :math:`i` and :math:`i + 1` for the entire state range.
df_err : list
A list of uncertainties of the free energy differences for the entire state range.
overlap_bool : list
overlap_bool[i] = True means that the i-th free energy difference (i.e. df[i]) was available
in multiple replicas.
A list of boolean values indicating whether a free energy difference was available in multiple replicas.
For example, :code:`overlap_bool[i] = True` means that the :math:`i`-th free energy difference (i.e.
:code:`df[i]`) was available in multiple replicas.
See also
--------
:func:`calculate_free_energy`
"""
n_tot = state_ranges[-1][-1] + 1
df, df_err, overlap_bool = [], [], []
Expand Down Expand Up @@ -232,46 +247,62 @@ def _combine_df_adjacent(df_adjacent, df_err_adjacent, state_ranges, err_type):
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_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 REXEE simulation of interest. Available methods include TI, BAR, and MBAR. TI
requires dHdl data while the other two require u_nk data.
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
requires :math:`dH/dλ` data while the other two require :math:`u_{nk}` data.
Parameters
----------
data : pd.Dataframe
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.
data : pandas.Dataframe
A list of :math:`u_{nk}` or :math:`dH/dλ` dataframes obtained from all replicas of the REXEE simulation.
Preferrably, the :math:`u_{nk}` or :math:`dH/dλ` data should be preprocessed by the function
:func:`proprocess_data`.
state_ranges : list
A list of lists of intergers that represents the alchemical states that can be sampled by different replicas.
df_method : str
The method used to calculate the free energy profile. Available choices include "TI", "BAR", and "MBAR".
err_method : str
A list of lists of showing the state indices sampled by each replica.
df_method : str, Optional
The method used to calculate the free energy profile. Available choices include :code:`"TI"`,
:code:`"BAR"`, and :code:`"MBAR"`. The default is :code:`"MBAR"`.
err_method : str, Optional
The method used to estimate the uncertainty of the free energy combined across multiple replicas.
Available options include "propagate" and "bootstrap". The bootstrapping method is more accurate
but much more computationally expensive than simple error propagation.
n_bootstrap : int
Available options include :code:`"propagate"` and :code:`"bootstrap"`. The bootstrapping method is
more accurate but much more computationally expensive than simple error propagation.
n_bootstrap : int, Optional
The number of bootstrap iterations. This parameter is used only when the boostrapping method is chosen to
estimate the uncertainties of the free energies.
seed : int
The random seed for bootstrapping.
estimate the uncertainties of the free energies. The default is :code:`None`. In the CLI :code:`analyze_REXEE`,
this number is set by the YAML parameter :code:`n_bootstrap`.
seed : int, Optional
The random seed for bootstrapping. Only relevant when :code:`err_method` is :code:`"bootstrap"`.
The default is :code:`None`.
Returns
-------
f : list
The full-range free energy profile.
f_err : list
The uncertainty corresponding to the values in :code:`f`.
The uncertainties corresponding to the values in :code:`f`.
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.
Example
-------
In the CLI :code:`analyze_REXEE`, lines like below are used:
>>> import glob
>>> import natsort
>>> from ensemble_md.analysis import analyze_free_energy
>>> state_ranges = [[0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6]]
>>> file_list = [natsort.natsorted(glob.glob(f'sim_{i}/iteration_*/dhdl*xvg')) for i in range(4)]
>>> data_list, _, _ = analyze_free_energy.preprocess_data(file_list, temp=300, data_type='u_nk')
>>> f, _, _ = analyze_free_energy.calculate_free_energy(data_list, state_ranges, "MBAR", "propagate")
"""
n_sim = len(data)
n_tot = state_ranges[-1][-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, df_err_adjacent, state_ranges, err_type='propagate')
df, df_err, overlap_bool = _combine_df_adjacent(df_adjacent, state_ranges, df_err_adjacent, err_type='propagate')

if err_method == 'bootstrap':
if seed is not None:
Expand All @@ -284,7 +315,7 @@ def calculate_free_energy(data, state_ranges, df_method="MBAR", err_method='prop
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, df_err_adjacent, state_ranges, err_type='propagate') # doesn't matter what value err_type here is # noqa: E501
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
df_bootstrap.append(df_sampled)
error_bootstrap = np.std(df_bootstrap, axis=0, ddof=1)

Expand All @@ -309,24 +340,29 @@ def calculate_free_energy(data, state_ranges, df_method="MBAR", err_method='prop

def calculate_df_rmse(estimators, df_ref, state_ranges):
"""
Calculates the RMSE values of the free energy profiles of different alchemical ranges given the reference free
Calculates the RMSE values of the free energy profiles of different state ranges given the reference free
energy profile for the whole range of states.
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.
The estimators should be generated by the function :func:`calculate_free_energy`.
df_ref : list
A list of values corresponding to the free energies of the whole range of states. The length
of the list should be equal to the number of states in total.
state_ranges : list
A list of lists of intergers that represents the alchemical states that can be sampled by different replicas.
A list of lists of showing the state indices sampled by each replica.
Returns
-------
rmse_list : list
A list of RMSE values of the free energy profiles of different alchemical ranges.
A list of RMSE values of the free energy profiles of different state ranges.
See also
--------
:func:`calculate_free_energy`
"""
n_sim = len(estimators)
df_ref = np.array(df_ref)
Expand All @@ -344,16 +380,16 @@ def calculate_df_rmse(estimators, df_ref, state_ranges):

def plot_free_energy(f, f_err, fig_name):
"""
Plot the free energy profile with error bars.
Plots the free energy profile with error bars.
Parameters
----------
f : list
The full-range free energy profile.
f_err : list
The uncertainty corresponding to the values in :code:`f`.
The uncertainties corresponding to the values in :code:`f`.
fig_name : str
The file name of the png file to be saved (with the extension).
The file path to save the figure.
"""
plt.figure()
plt.plot(range(len(f)), f, 'o-', c='#1f77b4')
Expand All @@ -366,24 +402,26 @@ def plot_free_energy(f, f_err, fig_name):

def average_weights(g_vecs, frac):
"""
Average the differences between the weights of the coupled and uncoupled states.
This can be an estimate of the free energy difference between two end states.
Given the time series of the whole range of alchemical weights, averages the
weight differences between the the coupled and decoupled states. This can be
an estimate of the free energy difference between two end states. This function
is only relevant for weight-updating REXEE simulations.
Parameters
----------
g_vecs : np.array
g_vecs : numpy.ndarray
An array of alchemical weights of the whole range of states as a function of
simulation time, which is typically generated by :obj:`.combine_weights`.
frac : float
The fraction of g_vecs to average over. frac=0.2 means average the last 20% of
the weight vectors.
The fraction of :code:`g_vecs` to average over. :code:`frac=0.2` means average
the last 20% of the weight vectors will be averaged.
Returns
-------
dg_avg : float
The averaged difference in the weights between the coupled and uncoupled states.
The averaged weight difference between the coupled and decoupled states.
dg_avg_err : float
The error of :code:`dg_avg`.
The errors corresponding to the value of :code:`dg_avg`.
"""
N = len(g_vecs)
dg = []
Expand Down
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