def eG(string):
""" Creates missed-events likelihood from specific strings """
from dcprogs.likelihood import MissedEventsG
string = string.lower().rstrip().lstrip()
if 'transpose' in string:
return eG(string.replace('transpose', '')).transpose()
if string == "classic": return MissedEventsG(QMat(string), 1e-4)
if string == "ch82": return MissedEventsG(QMat(string), 0.2)
if string == "cb": return MissedEventsG(QMat(string), 0.2)
if string == "cks": return MissedEventsG(QMat(string), 0.2)
else: raise Exception("Unknown eG model {0}".format(string))
def step(context, n, tau, nmax):
from dcprogs.likelihood.random import qmatrix as random_qmatrix
from dcprogs.likelihood import MissedEventsG
qmatrices, Gs, i = [], [], 10 * n
while len(Gs) != n:
i -= 1
if i == 0:
raise AssertionError('Could not instanciate enough likelihoods.')
qmatrix = random_qmatrix()
G = MissedEventsG(qmatrix, tau, nmax)
try:
G = MissedEventsG(qmatrix, tau, nmax)
except:
continue
else:
Gs.append(G)
qmatrices.append(qmatrix)
if not hasattr(context, 'qmatrices'): context.qmatrices = []
if not hasattr(context, 'likelihoods'): context.likelihoods = []
context.qmatrices.extend(qmatrices)
context.likelihoods.extend(Gs)
def step(context, tau, nmax):
from dcprogs.likelihood import MissedEventsG
if not hasattr(context, "likelihoods"): context.likelihoods = []
for i, qmatrix in enumerate(context.qmatrices):
if qmatrix is None:
context.likelihoods.append(None)
continue
try:
context.likelihoods.append(MissedEventsG(qmatrix, tau, nmax))
except ArithmeticError:
context.likelihoods.append(None)
context.qmatrices[i] = None
continue
except:
print(qmatrix)
raise
from numpy import all, abs, arange
from dcprogs.likelihood import QMatrix, DeterminantEq, MissedEventsG
# Define parameters.
qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0],
[2./3., -1502./3., 0, 500, 0],
[ 15, 0, -2065, 50, 2000],
[ 0, 15000, 4000, -19000, 0],
[ 0, 0, 10, 0, -10] ], 2)
tau = 1e-4
# Create eG from prior knowledge of roots
determinant_eq = DeterminantEq(qmatrix, tau)
af_roots = [( -3045.285776037674, 1), (-162.92946543451328, 1)]
fa_roots = [(-17090.192769236815, 1), (-2058.0812921673496, 1), (-0.24356535498785126, 1)]
eG_from_roots = MissedEventsG(determinant_eq, af_roots, determinant_eq.transpose(), fa_roots)
# Create eG automaticallye
eG_automatic = MissedEventsG(qmatrix, tau)
# Checks the three initialization are equivalent at tau
assert all(abs(eG_from_roots.af(tau) - eG_automatic.af(tau)) < 1e-8)
assert all(abs(eG_from_roots.fa(tau) - eG_automatic.fa(tau)) < 1e-8)
# Checks the three initialization are equivalent at different times
# The functions can be applied to arrays.
times = arange(tau, 10*tau, 0.1*tau)
assert eG_from_roots.af(times).shape == (len(times), 2, 3)
assert eG_from_roots.fa(times).shape == (len(times), 3, 2)
assert all(abs(eG_from_roots.af(times) - eG_automatic.af(times)) < 1e-8)
assert all(abs(eG_from_roots.fa(times) - eG_automatic.fa(times)) < 1e-8)
from dcprogs.likelihood import QMatrix, IdealG, MissedEventsG
# Define parameters.
qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0],
[2./3., -1502./3., 0, 500, 0],
[ 15, 0, -2065, 50, 2000],
[ 0, 15000, 4000, -19000, 0],
[ 0, 0, 10, 0, -10] ], 2)
tau = 1e-4
eG = MissedEventsG(qmatrix, tau)
idealG = IdealG(qmatrix)
tcritical = 5e-3
print("Equilibrium Occupancies\n" \
"=======================\n\n" \
"Ideal Likelihood\n" \
"----------------\n\n" \
" * initial: {ideal_initial!r}\n" \
" * final: {ideal_final!r}\n\n\n" \
"Missed-events Likelihood\n" \
"------------------------\n\n" \
" * initial: {equi_initial!r}\n" \
" * final: {equi_final!r}\n\n\n\n" \
"CHS Occupancies\n" \
"===============\n\n" \
"Missed-events Likelihood\n" \
"------------------------\n\n" \
" * tcritical: {tcritical}\n" \
" * initial: {chs_initial!r}\n" \
from numpy import all, abs, arange
from dcprogs.likelihood import QMatrix, DeterminantEq, MissedEventsG
# Define parameters.
qmatrix = QMatrix([[-3050, 50, 3000, 0, 0], [2. / 3., -1502. / 3., 0, 500, 0],
[15, 0, -2065, 50, 2000], [0, 15000, 4000, -19000, 0],
[0, 0, 10, 0, -10]], 2)
tau = 1e-4
# Create eG from prior knowledge of roots
determinant_eq = DeterminantEq(qmatrix, tau)
af_roots = [(-3045.285776037674, 1), (-162.92946543451328, 1)]
fa_roots = [(-17090.192769236815, 1), (-2058.0812921673496, 1),
(-0.24356535498785126, 1)]
eG_from_roots = MissedEventsG(determinant_eq, af_roots,
determinant_eq.transpose(), fa_roots)
# Create eG automaticallye
eG_automatic = MissedEventsG(qmatrix, tau)
# Checks the three initialization are equivalent at tau
assert all(abs(eG_from_roots.af(tau) - eG_automatic.af(tau)) < 1e-8)
assert all(abs(eG_from_roots.fa(tau) - eG_automatic.fa(tau)) < 1e-8)
# Checks the three initialization are equivalent at different times
# The functions can be applied to arrays.
times = arange(tau, 10 * tau, 0.1 * tau)
assert eG_from_roots.af(times).shape == (len(times), 2, 3)
assert eG_from_roots.fa(times).shape == (len(times), 3, 2)
assert all(abs(eG_from_roots.af(times) - eG_automatic.af(times)) < 1e-8)
assert all(abs(eG_from_roots.fa(times) - eG_automatic.fa(times)) < 1e-8)
