def _build_gate_alphabetainftau(g, q10tempadjustmentName, neuroml_dt):
if len(g.openstates) != 1 or len(g.closedstates) != 1 or len(g.transitions) != 2:
raise NeuroUnitsImportNeuroMLNotImplementedException("Non-Standard Gate/Transtions setup")
state_name = g.name
alphaTr = SeqUtils.filter_expect_single(g.transitions, lambda s: s.name == "alpha")
betaTr = SeqUtils.filter_expect_single(g.transitions, lambda s: s.name == "beta")
term_name_alpha = "%s_%s" % (state_name, "alpha")
term_name_beta = "%s_%s" % (state_name, "beta")
term_name_inf = "%s_%s" % (state_name, "inf")
term_name_tau = "%s_%s" % (state_name, "tau")
def remap_gate_eqn(s):
# Remap Alpha/Beta terms into the appropriate Units
# when they are used in inf/tau equations:
alpha_repl = "((%s) * (%s) )" % (term_name_alpha, neuroml_dt)
beta_repl = "((%s) * (%s) )" % (term_name_beta, neuroml_dt)
s = s.replace("alpha", alpha_repl).replace("beta", beta_repl)
# Remap voltage terms to __VGATE__ (since they might be subject
# to offset later on:
s = re.sub(r"""\bV\b""", "__VGate__", s)
s = re.sub(r"""\bv\b""", "__VGate__", s)
return s
# Write the alpha/beta terms:
e1 = "%s = (%s) * (1/%s)" % (term_name_alpha, remap_gate_eqn(alphaTr.getEqn()), neuroml_dt)
e2 = "%s = (%s) * (1/%s)" % (term_name_beta, remap_gate_eqn(betaTr.getEqn()), neuroml_dt)
# Time courses should always be divided by rate_adjustment term!
if len(g.time_courses) != 0:
tc = SeqUtils.expect_single(g.time_courses)
tc_eqn = "%s = %s * (1/%s) *(%s)" % (
term_name_tau,
remap_gate_eqn(tc.getEqn()),
q10tempadjustmentName,
neuroml_dt,
)
else:
tc_eqn = "%s = 1/(%s* (%s+%s))" % (term_name_tau, q10tempadjustmentName, term_name_alpha, term_name_beta)
if len(g.steady_states) != 0:
ss = SeqUtils.expect_single(g.steady_states)
ss_eqn = "%s = %s" % (term_name_inf, remap_gate_eqn(ss.getEqn()))
else:
ss_eqn = "%s = %s/(%s+%s)" % (term_name_inf, term_name_alpha, term_name_alpha, term_name_beta)
# The state-equations
state_eqn = "%s' = (%s-%s)/(%s) " % (state_name, term_name_inf, state_name, term_name_tau)
# Set the initial value of the state-variable to be the same
# as the steady-state value:
initial_cond = "<=> INITIAL %s:%s" % (state_name, term_name_inf)
return [e1, e2, tc_eqn, ss_eqn, state_eqn, initial_cond]
def _res_assignments(self, o, **kwargs):
removed = []
for aKey in o._eqn_assignment.keys():
a = o._eqn_assignment[aKey]
alhs = a.lhs
fixed_value = self.visit(a.rhs_map)
if fixed_value:
sym_suffix = '_as_symconst'
sym_suffix = ''
s = ast.SymbolicConstant(symbol=aKey.symbol
+ sym_suffix, value=fixed_value)
#ReplaceNode(a.lhs, s).visit(o)
ReplaceNode.replace_and_check(srcObj=a.lhs, dstObj=s, root = o)
o._symbolicconstants[aKey.symbol] = s
from neurounits.misc import SeqUtils
old_ass = SeqUtils.filter_expect_single( o._eqn_assignment, lambda o:o.symbol == aKey.symbol )
del o._eqn_assignment[ old_ass ] #o.get_terminal_obj(aKey.symbol) ]
#del o._eqn_assignment[ o.get_terminal_obj(aKey.symbol) ]
removed.append(alhs)
# Double check they have gone:
for a in removed:
nc = EqnsetVisitorNodeCollector()
nc.visit(o)
assert not a in nc.all()
def VisitRegimeDispatchMap(self,o,**kwargs):
rt_graph = o.get_rt_graph()
rhs_functors = dict([(regime, self.visit(rhs)) for (regime,rhs) in o.rhs_map.items()])
from neurounits.misc import SeqUtils
try:
default = SeqUtils.filter_expect_single(rhs_functors.keys(), lambda r:r.name == None)
assert not None in rhs_functors
rhs_functors[None] = rhs_functors[default]
except ValueError:
pass
def f3(state_data, **kw):
regime_states = state_data.rt_regimes
#print 'Getting regime_state for RT graph:', repr(rt_graph)
curr_state = regime_states[rt_graph]
if curr_state in rhs_functors:
rhs_functor = rhs_functors[curr_state]
else:
rhs_functor = rhs_functors[None]
return rhs_functor(state_data=state_data, **kw)
return f3
assert len(o.rhs_map) == 1
return self.visit(o.rhs_map.values()[0])
def _build_gate_alphabetainftau(g, q10tempadjustmentName, neuroml_dt):
if len(g.openstates) != 1 or len(g.closedstates) != 1 or len(
g.transitions) != 2:
raise NeuroUnitsImportNeuroMLNotImplementedException(
'Non-Standard Gate/Transtions setup')
state_name = g.name
alphaTr = SeqUtils.filter_expect_single(g.transitions,
lambda s: s.name == "alpha")
betaTr = SeqUtils.filter_expect_single(g.transitions,
lambda s: s.name == "beta")
term_name_alpha = "%s_%s" % (state_name, 'alpha')
term_name_beta = "%s_%s" % (state_name, 'beta')
term_name_inf = "%s_%s" % (state_name, 'inf')
term_name_tau = "%s_%s" % (state_name, 'tau')
def remap_gate_eqn(s):
# Remap Alpha/Beta terms into the appropriate Units
# when they are used in inf/tau equations:
alpha_repl = "((%s) * (%s) )" % (term_name_alpha, neuroml_dt)
beta_repl = "((%s) * (%s) )" % (term_name_beta, neuroml_dt)
s = s.replace('alpha', alpha_repl).replace('beta', beta_repl)
# Remap voltage terms to __VGATE__ (since they might be subject
# to offset later on:
s = re.sub(r"""\bV\b""", '__VGate__', s)
s = re.sub(r"""\bv\b""", '__VGate__', s)
return s
# Write the alpha/beta terms:
e1 = '%s = (%s) * (1/%s)' % (term_name_alpha,
remap_gate_eqn(alphaTr.getEqn()), neuroml_dt)
e2 = '%s = (%s) * (1/%s)' % (term_name_beta,
remap_gate_eqn(betaTr.getEqn()), neuroml_dt)
# Time courses should always be divided by rate_adjustment term!
if len(g.time_courses) != 0:
tc = SeqUtils.expect_single(g.time_courses)
tc_eqn = '%s = %s * (1/%s) *(%s)' % (
term_name_tau, remap_gate_eqn(
tc.getEqn()), q10tempadjustmentName, neuroml_dt)
else:
tc_eqn = '%s = 1/(%s* (%s+%s))' % (term_name_tau,
q10tempadjustmentName,
term_name_alpha, term_name_beta)
if len(g.steady_states) != 0:
ss = SeqUtils.expect_single(g.steady_states)
ss_eqn = '%s = %s' % (term_name_inf, remap_gate_eqn(ss.getEqn()))
else:
ss_eqn = '%s = %s/(%s+%s)' % (term_name_inf, term_name_alpha,
term_name_alpha, term_name_beta)
# The state-equations
state_eqn = "%s' = (%s-%s)/(%s) " % (state_name, term_name_inf, state_name,
term_name_tau)
# Set the initial value of the state-variable to be the same
# as the steady-state value:
initial_cond = "<=> INITIAL %s:%s" % (state_name, term_name_inf)
return [e1, e2, tc_eqn, ss_eqn, state_eqn, initial_cond]
