def exact_mean_open_shut_time(mec, tres):
"""
Calculate exact mean open or shut time from HJC probability density
function.
Parameters
----------
tres : float
Time resolution (dead time).
QAA : array_like, shape (kA, kA)
QFF : array_like, shape (kF, kF)
QAF : array_like, shape (kA, kF)
QAA, QFF, QAF - submatrices of Q.
kA : int
A number of open states in kinetic scheme.
kF : int
A number of shut states in kinetic scheme.
GAF : array_like, shape (kA, kB)
GFA : array_like, shape (kB, kA)
GAF, GFA- transition probabilities
Returns
-------
mean : float
Apparent mean open/shut time.
"""
GAF, GFA = qml.iGs(mec.Q, mec.kA, mec.kF)
expQFF = qml.expQt(mec.QFF, tres)
expQAA = qml.expQt(mec.QAA, tres)
eGAF = qml.eGs(GAF, GFA, mec.kA, mec.kF, expQFF)
eGFA = qml.eGs(GFA, GAF, mec.kF, mec.kA, expQAA)
phiA = qml.phiHJC(eGAF, eGFA, mec.kA)
phiF = qml.phiHJC(eGFA, eGAF, mec.kF)
QexpQF = np.dot(mec.QAF, expQFF)
QexpQA = np.dot(mec.QFA, expQAA)
DARS = qml.dARSdS(tres, mec.QAA, mec.QFF, GAF, GFA, expQFF, mec.kA, mec.kF)
DFRS = qml.dARSdS(tres, mec.QFF, mec.QAA, GFA, GAF, expQAA, mec.kF, mec.kA)
uF, uA = np.ones((mec.kF, 1)), np.ones((mec.kA, 1))
# meanOpenTime = tres + phiA * DARS * QexpQF * uF
meanA = tres + np.dot(phiA, np.dot(np.dot(DARS, QexpQF), uF))[0]
meanF = tres + np.dot(phiF, np.dot(np.dot(DFRS, QexpQA), uA))[0]
return meanA, meanF
def HJC_adjacent_mean_open_to_shut_time_pdf(sht, tres, Q, QAA, QAF, QFF, QFA):
"""
Calculate theoretical HJC (with missed events correction) mean open time
given previous/next gap length (continuous function; CHS96 Eq.3.5).
Parameters
----------
sht : array of floats
Shut time interval.
tres : float
Time resolution.
Q : array, shape (k,k)
Q matrix.
QAA, QAF, QFF, QFA : array_like
Submatrices of Q.
Returns
-------
mp : ndarray of floats
Mean open time given previous gap length.
mn : ndarray of floats
Mean open time given next gap length.
"""
kA, kF = QAA.shape[0], QFF.shape[0]
uA = np.ones((kA))[:,np.newaxis]
uF = np.ones((kF))[:,np.newaxis]
expQFF = qml.expQt(QFF, tres)
expQAA = qml.expQt(QAA, tres)
GAF, GFA = qml.iGs(Q, kA, kF)
eGAF = qml.eGs(GAF, GFA, kA, kF, expQFF)
eGFA = qml.eGs(GFA, GAF, kF, kA, expQAA)
phiA = qml.phiHJC(eGAF, eGFA, kA)
phiF = qml.phiHJC(eGFA, eGAF, kF)
DARS = qml.dARSdS(tres, QAA, QFF, GAF, GFA, expQFF, kA, kF)
eigs, A = qml.eigs(-Q)
Feigvals, FZ00, FZ10, FZ11 = qml.Zxx(Q, eigs, A, kA, QAA, QFA, QAF, expQAA, False)
Froots = asymptotic_roots(tres, QFF, QAA, QFA, QAF, kF, kA)
FR = qml.AR(Froots, tres, QFF, QAA, QFA, QAF, kF, kA)
Q1 = np.dot(np.dot(DARS, QAF), expQFF)
col1 = np.dot(Q1, uF)
row1 = np.dot(phiA, Q1)
mp = []
mn = []
for t in sht:
eGFAt = qml.eGAF(t, tres, Feigvals, FZ00, FZ10, FZ11, Froots,
FR, QFA, expQAA)
denom = np.dot(np.dot(phiF, eGFAt), uA)[0]
nom1 = np.dot(np.dot(phiF, eGFAt), col1)[0]
nom2 = np.dot(np.dot(row1, eGFAt), uA)[0]
mp.append(nom1 / denom)
mn.append(nom2 / denom)
return np.array(mp), np.array(mn)
