def calculate_all_to_all_mfpt(tprob, populations=None):
"""
Calculate the all-states by all-state matrix of mean first passage
times.
This uses the fundamental matrix formalism, and should be much faster
than GetMFPT for calculating many MFPTs.
Parameters
----------
tprob : matrix
transition probability matrix
populations : array_like, float
optional argument, the populations of each state. If not supplied,
it will be computed from scratch
Returns
-------
MFPT : array, float
MFPT in time units of LagTime, square array for MFPT from i -> j
See Also
--------
GetMFPT : function
for calculating a subset of the MFPTs, with functionality for including
a set of sinks
"""
msm_analysis.check_transition(tprob)
if scipy.sparse.issparse(tprob):
tprob = tprob.toarray()
logger.warning('calculate_all_to_all_mfpt does not support sparse linear algebra')
if populations is None:
eigens = msm_analysis.get_eigenvectors(tprob, 5)
if np.count_nonzero(np.imag(eigens[1][:,0])) != 0:
raise ValueError('First eigenvector has imaginary parts')
populations = np.real(eigens[1][:,0])
# ensure that tprob is a transition matrix
msm_analysis.check_transition(tprob)
num_states = len(populations)
if tprob.shape[0] != num_states:
raise ValueError("Shape of tprob and populations vector don't match")
eye = np.transpose( np.matrix(np.ones(num_states)) )
limiting_matrix = eye * populations
#z = scipy.linalg.inv(scipy.sparse.eye(num_states, num_states) - (tprob - limiting_matrix))
z = scipy.linalg.inv(np.eye(num_states) - (tprob - limiting_matrix))
# mfpt[i,j] = z[j,j] - z[i,j] / pi[j]
mfpt = -z
for j in range(num_states):
mfpt[:, j] += z[j, j]
mfpt[:, j] /= populations[j]
return mfpt
def calculate_mfpt(sinks, tprob, lag_time=1.):
"""
Gets the Mean First Passage Time (MFPT) for all states to a *set*
of sinks.
Parameters
----------
sinks : array, int
indices of the sink states
tprob : matrix
transition probability matrix
LagTime : float
the lag time used to create T (dictates units of the answer)
Returns
-------
MFPT : array, float
MFPT in time units of LagTime, for each state (in order of state index)
See Also
--------
calculate_all_to_all_mfpt : function
A more efficient way to calculate all the MFPTs in a network
"""
sinks = _ensure_iterable(sinks)
msm_analysis.check_transition(tprob)
n = tprob.shape[0]
if scipy.sparse.isspmatrix(tprob):
tprob = tprob.tolil()
for state in sinks:
tprob[state,:] = 0.0
tprob[state,state] = 2.0
if scipy.sparse.isspmatrix(tprob):
tprob = tprob - scipy.sparse.eye(n,n)
tprob = tprob.tocsr()
else:
tprob = tprob - np.eye(n)
RHS = -1 * np.ones(n)
for state in sinks:
RHS[state] = 0.0
if scipy.sparse.isspmatrix(tprob):
MFPT = lag_time * scipy.sparse.linalg.spsolve(tprob, RHS)
else:
MFPT = lag_time * np.linalg.solve(tprob, RHS)
return MFPT
def calculate_net_fluxes(sources, sinks, tprob, populations=None, committors=None):
"""
Computes the transition path theory net flux matrix.
Parameters
----------
sources : array_like, int
The set of unfolded/reactant states.
sinks : array_like, int
The set of folded/product states.
tprob : mm_matrix
The transition matrix.
Returns
------
net_fluxes : mm_matrix
The net flux matrix
Optional Parameters
-------------------
populations : nd_array, float
The equilibrium populations, if not provided is re-calculated
committors : nd_array, float
The committors associated with `sources`, `sinks`, and `tprob`.
If not provided, is calculated from scratch. If provided, `sources`
and `sinks` are ignored.
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
if scipy.sparse.issparse(tprob):
dense = False
else:
dense = True
n = tprob.shape[0]
flux = calculate_fluxes(sources, sinks, tprob, populations, committors)
ind = flux.nonzero()
if dense:
net_flux = np.zeros((n, n))
else:
net_flux = scipy.sparse.lil_matrix((n, n))
for k in range(len(ind[0])):
i, j = ind[0][k], ind[1][k]
forward = flux[i, j]
reverse = flux[j, i]
net_flux[i, j] = max(0, forward - reverse)
return net_flux
def calculate_ensemble_mfpt(sources, sinks, tprob, lag_time):
"""
Calculates the average 'Folding Time' of an MSM defined by T and a LagTime.
The Folding Time is the average of the MFPTs (to F) of all the states in U.
Note here 'Folding Time' is defined as the avg MFPT of {U}, to {F}.
Consider this carefully. This is probably NOT the experimental folding time!
Parameters
----------
sources : array, int
indices of the source states
sinks : array, int
indices of the sink states
tprob : matrix
transition probability matrix
lag_time : float
the lag time used to create T (dictates units of the answer)
Returns
-------
avg : float
the average of the MFPTs
std : float
the standard deviation of the MFPTs
References
----------
.. [1] Metzner, P., Schutte, C. & Vanden-Eijnden, E. Transition path theory
for Markov jump processes. Multiscale Model. Simul. 7, 1192–1219
(2009).
.. [2] Berezhkovskii, A., Hummer, G. & Szabo, A. Reactive flux and folding
pathways in network models of coarse-grained protein dynamics. J.
Chem. Phys. 130, 205102 (2009).
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
X = calculate_mfpt(sinks, tprob, lag_time)
times = np.zeros(len(sources))
for i in range(len(sources)):
times[i] = X[sources[i]]
return np.average(times), np.std(times)
def calculate_ensemble_mfpt(sources, sinks, tprob, lag_time):
"""
Calculates the average 'Folding Time' of an MSM defined by T and a LagTime.
The Folding Time is the average of the MFPTs (to F) of all the states in U.
Note here 'Folding Time' is defined as the avg MFPT of {U}, to {F}.
Consider this carefully. This is probably NOT the experimental folding time!
Parameters
----------
sources : array, int
indices of the source states
sinks : array, int
indices of the sink states
tprob : matrix
transition probability matrix
lag_time : float
the lag time used to create T (dictates units of the answer)
Returns
-------
avg : float
the average of the MFPTs
std : float
the standard deviation of the MFPTs
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
X = calculate_mfpt(sinks, tprob, lag_time)
times = np.zeros(len(sources))
for i in range(len(sources)):
times[i] = X[ sources[i] ]
return np.average(times), np.std(times)
def find_top_paths(sources, sinks, tprob, num_paths=10, node_wipe=False, net_flux=None):
r"""
Calls the Dijkstra algorithm to find the top 'NumPaths'.
Does this recursively by first finding the top flux path, then cutting that
path and relaxing to find the second top path. Continues until NumPaths
have been found.
Parameters
----------
sources : array_like, int
The indices of the source states
sinks : array_like, int
Indices of sink states
num_paths : int
The number of paths to find
Returns
-------
Paths : list of lists
The nodes transversed in each path
Bottlenecks : list of tuples
The nodes between which exists the path bottleneck
Fluxes : list of floats
The flux through each path
Optional Parameters
-------------------
node_wipe : bool
If true, removes the bottleneck-generating node from the graph, instead
of just the bottleneck (not recommended, a debugging functionality)
net_flux : sparse matrix
Matrix of the net flux from `sources` to `sinks`, see function `net_flux`.
If not provided, is calculated from scratch. If provided, `tprob` is
ignored.
To Do
-----
-- Add periodic flow check
References
----------
.. [1] Dijkstra, E. W. (1959). "A note on two problems in connexion with
graphs". Numerische Mathematik 1: 269–271. doi:10.1007/BF01386390.
"""
# first, do some checking on the input, esp. `sources` and `sinks`
# we want to make sure all objects are iterable and the sets are disjoint
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
# check to see if we get net_flux for free, otherwise calculate it
if not net_flux:
net_flux = calculate_net_fluxes(sources, sinks, tprob)
# initialize objects
paths = []
fluxes = []
bottlenecks = []
if scipy.sparse.issparse(net_flux):
net_flux = net_flux.tolil()
# run the initial Dijkstra pass
pi, b = Dijkstra(sources, sinks, net_flux)
logger.info("Path Num | Path | Bottleneck | Flux")
i = 1
done = False
while not done:
# First find the highest flux pathway
(path, (b1, b2), flux) = _backtrack(sinks, b, pi, net_flux)
# Add each result to a Paths, Bottlenecks, Fluxes list
if flux == 0:
logger.info("Only %d possible pathways found. Stopping backtrack.", i)
break
paths.append(path)
bottlenecks.append((b1, b2))
fluxes.append(flux)
logger.info("%s | %s | %s | %s ", i, path, (b1, b2), flux)
# Cut the bottleneck, start relaxing from B side of the cut
if node_wipe:
net_flux[:, b2] = 0
logger.info("Wiped node: %s", b2)
else:
net_flux[b1, b2] = 0
G = scipy.sparse.find(net_flux)
Q = [b2]
b, pi, net_flux = _back_relax(b2, b, pi, net_flux)
# Then relax the graph and repeat
# But only if we still need to
if i != num_paths - 1:
while len(Q) > 0:
w = Q.pop()
for v in G[1][np.where(G[0] == w)]:
if pi[v] == w:
b, pi, net_flux = _back_relax(v, b, pi, net_flux)
Q.append(v)
Q = sorted(Q, key=lambda v: b[v])
i += 1
if i == num_paths + 1:
done = True
if flux == 0:
logger.info("Only %d possible pathways found. Stopping backtrack.", i)
done = True
return paths, bottlenecks, fluxes
def calculate_all_to_all_mfpt(tprob, populations=None):
"""
Calculate the all-states by all-state matrix of mean first passage
times.
This uses the fundamental matrix formalism, and should be much faster
than GetMFPT for calculating many MFPTs.
Parameters
----------
tprob : matrix
transition probability matrix
populations : array_like, float
optional argument, the populations of each state. If not supplied,
it will be computed from scratch
Returns
-------
MFPT : array, float
MFPT in time units of LagTime, square array for MFPT from i -> j
See Also
--------
GetMFPT : function
for calculating a subset of the MFPTs, with functionality for including
a set of sinks
References
----------
.. [1] Metzner, P., Schutte, C. & Vanden-Eijnden, E. Transition path theory
for Markov jump processes. Multiscale Model. Simul. 7, 1192–1219
(2009).
.. [2] Berezhkovskii, A., Hummer, G. & Szabo, A. Reactive flux and folding
pathways in network models of coarse-grained protein dynamics. J.
Chem. Phys. 130, 205102 (2009).
"""
msm_analysis.check_transition(tprob)
if scipy.sparse.issparse(tprob):
tprob = tprob.toarray()
logger.warning('calculate_all_to_all_mfpt does not support sparse linear algebra')
if populations is None:
eigens = msm_analysis.get_eigenvectors(tprob, 1)
if np.count_nonzero(np.imag(eigens[1][:, 0])) != 0:
raise ValueError('First eigenvector has imaginary parts')
populations = np.real(eigens[1][:, 0])
# ensure that tprob is a transition matrix
msm_analysis.check_transition(tprob)
num_states = len(populations)
if tprob.shape[0] != num_states:
raise ValueError("Shape of tprob and populations vector don't match")
eye = np.transpose(np.matrix(np.ones(num_states)))
limiting_matrix = eye * populations
#z = scipy.linalg.inv(scipy.sparse.eye(num_states, num_states) - (tprob - limiting_matrix))
z = scipy.linalg.inv(np.eye(num_states) - (tprob - limiting_matrix))
# mfpt[i,j] = z[j,j] - z[i,j] / pi[j]
mfpt = -z
for j in range(num_states):
mfpt[:, j] += z[j, j]
mfpt[:, j] /= populations[j]
return mfpt
def calculate_mfpt(sinks, tprob, lag_time=1.):
"""
Gets the Mean First Passage Time (MFPT) for all states to a *set*
of sinks.
Parameters
----------
sinks : array, int
indices of the sink states
tprob : matrix
transition probability matrix
LagTime : float
the lag time used to create T (dictates units of the answer)
Returns
-------
MFPT : array, float
MFPT in time units of LagTime, for each state (in order of state index)
See Also
--------
calculate_all_to_all_mfpt : function
A more efficient way to calculate all the MFPTs in a network
References
----------
.. [1] Metzner, P., Schutte, C. & Vanden-Eijnden, E. Transition path theory
for Markov jump processes. Multiscale Model. Simul. 7, 1192–1219
(2009).
.. [2] Berezhkovskii, A., Hummer, G. & Szabo, A. Reactive flux and folding
pathways in network models of coarse-grained protein dynamics. J.
Chem. Phys. 130, 205102 (2009).
"""
sinks = _ensure_iterable(sinks)
msm_analysis.check_transition(tprob)
n = tprob.shape[0]
if scipy.sparse.isspmatrix(tprob):
tprob = tprob.tolil()
for state in sinks:
tprob[state, :] = 0.0
tprob[state, state] = 2.0
if scipy.sparse.isspmatrix(tprob):
tprob = tprob - scipy.sparse.eye(n, n)
tprob = tprob.tocsr()
else:
tprob = tprob - np.eye(n)
RHS = -1 * np.ones(n)
for state in sinks:
RHS[state] = 0.0
if scipy.sparse.isspmatrix(tprob):
MFPT = lag_time * scipy.sparse.linalg.spsolve(tprob, RHS)
else:
MFPT = lag_time * np.linalg.solve(tprob, RHS)
return MFPT
def calculate_avg_TP_time(sources, sinks, tprob, lag_time):
"""
Calculates the Average Transition Path Time for MSM with: T, LagTime.
The TPTime is the average of the MFPTs (to F) of all the states
immediately adjacent to U, with the U states effectively deleted.
Note here 'TP Time' is defined as the avg MFPT of all adjacent states to {U},
to {F}, ignoring {U}.
Consider this carefully.
Parameters
----------
sources : array, int
indices of the unfolded states
sinks : array, int
indices of the folded states
tprob : matrix
transition probability matrix
lag_time : float
the lag time used to create T (dictates units of the answer)
Returns
-------
avg : float
the average of the MFPTs
std : float
the standard deviation of the MFPTs
References
----------
.. [1] Metzner, P., Schutte, C. & Vanden-Eijnden, E. Transition path theory
for Markov jump processes. Multiscale Model. Simul. 7, 1192–1219
(2009).
.. [2] Berezhkovskii, A., Hummer, G. & Szabo, A. Reactive flux and folding
pathways in network models of coarse-grained protein dynamics. J.
Chem. Phys. 130, 205102 (2009).
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
n = tprob.shape[0]
if scipy.sparse.issparse(tprob):
T = tprob.tolil()
P = scipy.sparse.lil_matrix((n, n))
else:
P = np.zeros((n, n))
for u in sources:
for i in range(n):
if i not in sources:
P[u, i] = T[u, i]
for u in sources:
T[u, :] = np.zeros(n)
T[:, u] = 0
for i in sources:
N = T[i, :].sum()
T[i, :] = T[i, :] / N
X = calculate_mfpt(sinks, tprob, lag_time)
TP = P * X.T
TPtimes = []
for time in TP:
if time != 0:
TPtimes.append(time)
return np.average(TPtimes), np.std(TPtimes)
def calculate_net_fluxes(sources, sinks, tprob, populations=None, committors=None):
"""
Computes the transition path theory net flux matrix.
Parameters
----------
sources : array_like, int
The set of unfolded/reactant states.
sinks : array_like, int
The set of folded/product states.
tprob : mm_matrix
The transition matrix.
Returns
------
net_fluxes : mm_matrix
The net flux matrix
Optional Parameters
-------------------
populations : nd_array, float
The equilibrium populations, if not provided is re-calculated
committors : nd_array, float
The committors associated with `sources`, `sinks`, and `tprob`.
If not provided, is calculated from scratch. If provided, `sources`
and `sinks` are ignored.
References
----------
.. [1] Metzner, P., Schutte, C. & Vanden-Eijnden, E. Transition path theory
for Markov jump processes. Multiscale Model. Simul. 7, 1192–1219
(2009).
.. [2] Berezhkovskii, A., Hummer, G. & Szabo, A. Reactive flux and folding
pathways in network models of coarse-grained protein dynamics. J.
Chem. Phys. 130, 205102 (2009).
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
if scipy.sparse.issparse(tprob):
dense = False
else:
dense = True
n = tprob.shape[0]
flux = calculate_fluxes(sources, sinks, tprob, populations, committors)
ind = flux.nonzero()
if dense:
net_flux = np.zeros((n, n))
else:
net_flux = scipy.sparse.lil_matrix((n, n))
for k in range(len(ind[0])):
i, j = ind[0][k], ind[1][k]
forward = flux[i, j]
reverse = flux[j, i]
net_flux[i, j] = max(0, forward - reverse)
return net_flux
def calculate_fluxes(sources, sinks, tprob, populations=None, committors=None):
"""
Compute the transition path theory flux matrix.
Parameters
----------
sources : array_like, int
The set of unfolded/reactant states.
sinks : array_like, int
The set of folded/product states.
tprob : mm_matrix
The transition matrix.
Returns
------
fluxes : mm_matrix
The flux matrix
Optional Parameters
-------------------
populations : nd_array, float
The equilibrium populations, if not provided is re-calculated
committors : nd_array, float
The committors associated with `sources`, `sinks`, and `tprob`.
If not provided, is calculated from scratch. If provided, `sources`
and `sinks` are ignored.
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
if scipy.sparse.issparse(tprob):
dense = False
else:
dense = True
# check if we got the populations
if populations is None:
eigens = msm_analysis.get_eigenvectors(tprob, 5)
if np.count_nonzero(np.imag(eigens[1][:,0])) != 0:
raise ValueError('First eigenvector has imaginary components')
populations = np.real(eigens[1][:,0])
# check if we got the committors
if committors is None:
committors = calculate_committors(sources, sinks, tprob)
# perform the flux computation
Indx, Indy = tprob.nonzero()
n = tprob.shape[0]
if dense:
X = np.zeros((n, n))
Y = np.zeros((n, n))
X[(np.arange(n), np.arange(n))] = populations * (1.0 - committors)
Y[(np.arange(n), np.arange(n))] = committors
else:
X = scipy.sparse.lil_matrix((n,n))
Y = scipy.sparse.lil_matrix((n,n))
X.setdiag( populations * (1.0 - committors))
Y.setdiag(committors)
if dense:
fluxes = np.dot(np.dot(X, tprob), Y)
fluxes[(np.arange(n), np.arange(n))] = np.zeros(n)
else:
fluxes = np.dot(np.dot(X.tocsr(), tprob.tocsr()), Y.tocsr())
fluxes = fluxes.tolil()
fluxes.setdiag(np.zeros(n))
return fluxes
def calculate_hub_score(tprob, waypoint):
"""
Calculate the hub score for the states `waypoint`.
The "hub score" is a measure of how well traveled a certain state or
set of states is in a network. Specifically, it is the fraction of
times that a walker visits a state en route from some state A to another
state B, averaged over all combinations of A and B.
Parameters
----------
tprob : matrix
The transition probability matrix
waypoints : int
The indices of the intermediate state(s)
Returns
-------
Hc : float
The hub score for the state composed of `waypoints`
See Also
--------
calculate_fraction_visits : function
Calculate the fraction of times a state is visited on pathways going
from a set of "sources" to a set of "sinks".
calculate_all_hub_scores : function
A more efficient way to compute the hub score for every state in a
network.
Notes
-----
Employs dense linear algebra,
memory use scales as N^2
cycle use scales as N^5
References
----------
..[1] Dickson & Brooks (2012), J. Chem. Theory Comput.,
Article ASAP DOI: 10.1021/ct300537s
"""
msm_analysis.check_transition(tprob)
# typecheck
if type(waypoint) != int:
if hasattr(waypoint, '__len__'):
if len(waypoint) == 1:
waypoint = waypoint[0]
else:
raise ValueError('Must pass waypoints as int or list/array of ints')
else:
raise ValueError('Must pass waypoints as int or list/array of ints')
# find out which states to include in A, B (i.e. everything but C)
N = tprob.shape[0]
states_to_include = list(range(N))
states_to_include.remove(waypoint)
# calculate the hub score
Hc = 0.0
for s1 in states_to_include:
for s2 in states_to_include:
if (s1 != s2) and (s1 != waypoint) and (s2 != waypoint):
Hc += calculate_fraction_visits(tprob, waypoint,
s1, s2, return_cond_Q=False)
Hc /= ((N - 1) * (N - 2))
return Hc
def calculate_committors(sources, sinks, tprob):
"""
Get the forward committors of the reaction sources -> sinks.
Parameters
----------
sources : array_like, int
The set of unfolded/reactant states.
sinks : array_like, int
The set of folded/product states.
tprob : mm_matrix
The transition matrix.
Returns
-------
Q : array_like
The forward committors for the reaction U -> F.
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
if scipy.sparse.issparse(tprob):
dense = False
tprob = tprob.tolil()
else:
dense = True
# construct the committor problem
n = tprob.shape[0]
if dense:
T = np.eye(n) - tprob
else:
T = scipy.sparse.eye(n, n, 0, format='lil') - tprob
T = T.tolil()
for a in sources:
T[a,:] = 0.0 #np.zeros(n)
T[:,a] = 0.0
T[a,a] = 1.0
for b in sinks:
T[b,:] = 0.0 # np.zeros(n)
T[:,b] = 0.0
T[b,b] = 1.0
IdB = np.zeros(n)
IdB[sinks] = 1.0
if dense:
RHS = np.dot(tprob, IdB)
else:
RHS = tprob * IdB
RHS[sources] = 0.0
RHS[sinks] = 1.0
# solve for the committors
if dense == False:
Q = scipy.sparse.linalg.spsolve(T.tocsr(), RHS)
else:
Q = np.linalg.solve(T, RHS)
assert np.all( Q <= 1.0 )
assert np.all( Q >= 0.0 )
return Q
def calculate_avg_TP_time(sources, sinks, tprob, lag_time):
"""
Calculates the Average Transition Path Time for MSM with: T, LagTime.
The TPTime is the average of the MFPTs (to F) of all the states
immediately adjacent to U, with the U states effectively deleted.
Note here 'TP Time' is defined as the avg MFPT of all adjacent states to {U},
to {F}, ignoring {U}.
Consider this carefully.
Parameters
----------
sources : array, int
indices of the unfolded states
sinks : array, int
indices of the folded states
tprob : matrix
transition probability matrix
lag_time : float
the lag time used to create T (dictates units of the answer)
Returns
-------
avg : float
the average of the MFPTs
std : float
the standard deviation of the MFPTs
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
n = tprob.shape[0]
if scipy.sparse.issparse(tprob):
T = tprob.tolil()
P = scipy.sparse.lil_matrix((n, n))
else:
p = np.zeros((n, n))
for u in sources:
for i in range(n):
if i not in sources:
P[u, i] = T[u, i]
for u in sources:
T[u, :] = np.zeros(n)
T[:, u] = 0
for i in sources:
N = T[i, :].sum()
T[i,:] = T[i, :]/N
X = calculate_mfpt(sinks, tprob, lag_time)
TP = P * X.T
TPtimes = []
for time in TP:
if time != 0: TPtimes.append(time)
return np.average(TPtimes), np.std(TPtimes)
def calculate_committors(sources, sinks, tprob):
"""
Get the forward committors of the reaction sources -> sinks.
Parameters
----------
sources : array_like, int
The set of unfolded/reactant states.
sinks : array_like, int
The set of folded/product states.
tprob : mm_matrix
The transition matrix.
Returns
-------
Q : array_like
The forward committors for the reaction U -> F.
References
----------
.. [1] Metzner, P., Schutte, C. & Vanden-Eijnden, E. Transition path theory
for Markov jump processes. Multiscale Model. Simul. 7, 1192–1219
(2009).
.. [2] Berezhkovskii, A., Hummer, G. & Szabo, A. Reactive flux and folding
pathways in network models of coarse-grained protein dynamics. J.
Chem. Phys. 130, 205102 (2009).
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
if scipy.sparse.issparse(tprob):
dense = False
tprob = tprob.tolil()
else:
dense = True
# construct the committor problem
n = tprob.shape[0]
if dense:
T = np.eye(n) - tprob
else:
T = scipy.sparse.eye(n, n, 0, format='lil') - tprob
T = T.tolil()
for a in sources:
T[a, :] = 0.0 # np.zeros(n)
T[:, a] = 0.0
T[a, a] = 1.0
for b in sinks:
T[b, :] = 0.0 # np.zeros(n)
T[:, b] = 0.0
T[b, b] = 1.0
IdB = np.zeros(n)
IdB[sinks] = 1.0
if dense:
RHS = np.dot(tprob, IdB)
else:
RHS = tprob.dot(IdB)
# This should be the same as below
#RHS = tprob * IdB
RHS[sources] = 0.0
RHS[sinks] = 1.0
# solve for the committors
if dense == False:
Q = scipy.sparse.linalg.spsolve(T.tocsr(), RHS)
else:
Q = np.linalg.solve(T, RHS)
epsilon = 0.001
assert np.all(Q <= 1.0 + epsilon)
assert np.all(Q >= 0.0 - epsilon)
return Q
def calculate_fluxes(sources, sinks, tprob, populations=None, committors=None):
"""
Compute the transition path theory flux matrix.
Parameters
----------
sources : array_like, int
The set of unfolded/reactant states.
sinks : array_like, int
The set of folded/product states.
tprob : mm_matrix
The transition matrix.
Returns
------
fluxes : mm_matrix
The flux matrix
Optional Parameters
-------------------
populations : nd_array, float
The equilibrium populations, if not provided is re-calculated
committors : nd_array, float
The committors associated with `sources`, `sinks`, and `tprob`.
If not provided, is calculated from scratch. If provided, `sources`
and `sinks` are ignored.
References
----------
.. [1] Metzner, P., Schutte, C. & Vanden-Eijnden, E. Transition path theory
for Markov jump processes. Multiscale Model. Simul. 7, 1192–1219
(2009).
.. [2] Berezhkovskii, A., Hummer, G. & Szabo, A. Reactive flux and folding
pathways in network models of coarse-grained protein dynamics. J.
Chem. Phys. 130, 205102 (2009).
"""
sources, sinks = _check_sources_sinks(sources, sinks)
msm_analysis.check_transition(tprob)
if scipy.sparse.issparse(tprob):
dense = False
else:
dense = True
# check if we got the populations
if populations is None:
eigens = msm_analysis.get_eigenvectors(tprob, 1)
if np.count_nonzero(np.imag(eigens[1][:, 0])) != 0:
raise ValueError('First eigenvector has imaginary components')
populations = np.real(eigens[1][:, 0])
# check if we got the committors
if committors is None:
committors = calculate_committors(sources, sinks, tprob)
# perform the flux computation
Indx, Indy = tprob.nonzero()
n = tprob.shape[0]
if dense:
X = np.zeros((n, n))
Y = np.zeros((n, n))
X[(np.arange(n), np.arange(n))] = populations * (1.0 - committors)
Y[(np.arange(n), np.arange(n))] = committors
else:
X = scipy.sparse.lil_matrix((n, n))
Y = scipy.sparse.lil_matrix((n, n))
X.setdiag(populations * (1.0 - committors))
Y.setdiag(committors)
if dense:
fluxes = np.dot(np.dot(X, tprob), Y)
fluxes[(np.arange(n), np.arange(n))] = np.zeros(n)
else:
fluxes = (X.tocsr().dot(tprob.tocsr())).dot(Y.tocsr())
# This should be the same as below, but it's a bit messy...
#fluxes = np.dot(np.dot(X.tocsr(), tprob.tocsr()), Y.tocsr())
fluxes = fluxes.tolil()
fluxes.setdiag(np.zeros(n))
return fluxes
def calculate_fraction_visits(tprob, waypoint, source, sink, return_cond_Q=False):
"""
Calculate the fraction of times a walker on `tprob` going from `sources`
to `sinks` will travel through the set of states `waypoints` en route.
Computes the conditional committors q^{ABC^+} and uses them to find the
fraction of paths mentioned above. The conditional committors can be
Note that in the notation of Dickson et. al. this computes h_c(A,B), with
sources = A
sinks = B
waypoint = C
Parameters
----------
tprob : matrix
The transition probability matrix
waypoint : int
The index of the intermediate state
sources : nd_array, int or int
The indices of the source state(s)
sinks : nd_array, int or int
The indices of the sink state(s)
return_cond_Q : bool
Whether or not to return the conditional committors
Returns
-------
fraction_paths : float
The fraction of times a walker going from `sources` -> `sinks` stops
by `waypoints` on its way.
cond_Q : nd_array, float (optional)
Optionally returned (`return_cond_Q`)
See Also
--------
calculate_hub_score : function
Compute the 'hub score', the weighted fraction of visits for an
entire network.
calculate_all_hub_scores : function
Wrapper to compute all the hub scores in a network.
Notes
-----
Employs dense linear algebra,
memory use scales as N^2
cycle use scales as N^3
References
----------
..[1] Dickson & Brooks (2012), J. Chem. Theory Comput.,
Article ASAP DOI: 10.1021/ct300537s
"""
# do some typechecking - we need to be sure that the lumped sources are in
# the second to last row, and the lumped sinks are in the last row
# check `tprob`
msm_analysis.check_transition(tprob)
if type(tprob) != np.ndarray:
try:
tprob = tprob.todense()
except AttributeError as e:
raise TypeError('Argument `tprob` must be convertable to a dense'
'numpy array. \n%s' % e)
# typecheck
for data in [source, sink, waypoint]:
if type(data) == int:
pass
elif hasattr(data, 'len'):
if len(data) == 1:
data = data[0]
else:
raise TypeError('Arguments source/sink/waypoint must be an int')
if (source == waypoint) or (sink == waypoint) or (sink == source):
raise ValueError('source, sink, waypoint must all be disjoint!')
N = tprob.shape[0]
Q = calculate_committors([source], [sink], tprob)
# permute the transition matrix into cannonical form - send waypoint the the
# last row, and source + sink to the end after that
Bsink_indices = [source, sink, waypoint]
perm = np.arange(N)
perm = np.delete(perm, Bsink_indices)
perm = np.append(perm, Bsink_indices)
T = MSMLib.permute_mat(tprob, perm)
# extract P, R
n = N - len(Bsink_indices)
P = T[:n, :n]
R = T[:n, n:]
# calculate the conditional committors ( B = N*R ), B[i,j] is the prob
# state i ends in j, where j runs over the source + sink + waypoint
# (waypoint is position -1)
B = np.dot(np.linalg.inv(np.eye(n) - P), R)
# Not sure if this is sparse or not...
# add probs for the sinks, waypoint / b[i] is P( i --> {C & not A, B} )
b = np.append(B[:, -1].flatten(), [0.0] * (len(Bsink_indices) - 1) + [1.0])
cond_Q = b * Q[waypoint]
epsilon = 1e-6 # some numerical give, hard-coded
assert cond_Q.shape == (N,)
assert np.all(cond_Q <= 1.0 + epsilon)
assert np.all(cond_Q >= 0.0 - epsilon)
assert np.all(cond_Q <= Q[perm] + epsilon)
# finally, calculate the fraction of paths h_C(A,B) (eq. 7 in [1])
fraction_paths = np.sum(T[-3, :] * cond_Q) / np.sum(T[-3, :] * Q[perm])
assert fraction_paths <= 1.0
assert fraction_paths >= 0.0
if return_cond_Q:
cond_Q = cond_Q[np.argsort(perm)] # put back in orig. order
return fraction_paths, cond_Q
else:
return fraction_paths
def _run_trial(arg_dict):
# inject the arg_dict into the local namespace - may be a bad idea...
for key in arg_dict.keys():
exec(key + " = arg_dict['" + key + "']")
# initialize data structures to hold output
distance_to_target = np.zeros(rounds_of_sampling)
obs_distance = np.zeros(rounds_of_sampling)
# the assignments array will hold all of the output of all simulations
assignments = -1.0 * np.ones((rounds_of_sampling * simultaneous_samplers + 1,
max(size_of_intial_data, length_of_sampling_trajs+1) ))
# initialize the "true" transition matrix
if not transition_matrix:
assert num_states > 0
C_rand = np.random.randint( 0, 100, (num_states, num_states) )
C_rand += C_rand.T
T = MSMLib.estimate_transition_matrix( C_rand )
else:
T = transition_matrix
num_states = T.shape[0]
T = sparse.csr_matrix(T)
msm_analysis.check_transition(T)
if observable_function:
try:
obs_goal = observable_function(T)
except Exception as e:
print >> sys.stderr, e
raise Exception("Error evaluating function: %s" % observable_function.__name__)
assignments[0,:size_of_intial_data] = msm_analysis.sample(T, None, size_of_intial_data)
# iterate, adding simulation time
for sampling_round in range(rounds_of_sampling):
# apply the adaptive sampling method - we need to be true to what a
# real simulation would actually see for the counts matrix
mod_assignments = assignments.copy()
mapping = MSMLib.renumber_states( mod_assignments )
C_mod = MSMLib.get_count_matrix_from_assignments( mod_assignments )
T_mod = MSMLib.estimate_transition_matrix(C_mod)
adaptive_sampling_multivariate = SamplerObject.sample(C_mod)
# choose the states to sample from (in the original indexing)
state_inds = np.arange(len(adaptive_sampling_multivariate))
sampler = stats.rv_discrete(name='sampler',
values=[state_inds, adaptive_sampling_multivariate])
starting_states = sampler.rvs( size=simultaneous_samplers )
starting_states = mapping[starting_states]
# start new 'simulations' in each of those states
for i,init_state in enumerate(starting_states):
a_ind = sampling_round * simultaneous_samplers + i + 1
s_ind = length_of_sampling_trajs + 1
assignments[a_ind,:s_ind] = msm_analysis.sample(T, init_state, s_ind)
# build a new MSM from all the simulation so far
C_raw = MSMLib.get_count_matrix_from_assignments( assignments, n_states=num_states )
C_raw = C_raw + C_raw.T # might want to add trimming, etc.
T_pred = MSMLib.estimate_transition_matrix(C_raw)
# calculate the error between the real transition matrix and our best prediction
assert T.shape == T_pred.shape
distance_to_target[sampling_round] = np.sqrt( ((T_pred - T).data ** 2).sum() ) \
/ float(num_states)
if observable_function:
obs_distance[sampling_round] = np.abs(observable_function(T_mod) - obs_goal)
return distance_to_target, obs_distance
