def test_conv12():
x = Symbol("x")
y = Symbol("y")
assert sinh(x / 3) == sinh(sympy.Symbol("x") / 3)
assert cosh(x / 3) == cosh(sympy.Symbol("x") / 3)
assert tanh(x / 3) == tanh(sympy.Symbol("x") / 3)
assert coth(x / 3) == coth(sympy.Symbol("x") / 3)
assert asinh(x / 3) == asinh(sympy.Symbol("x") / 3)
assert acosh(x / 3) == acosh(sympy.Symbol("x") / 3)
assert atanh(x / 3) == atanh(sympy.Symbol("x") / 3)
assert acoth(x / 3) == acoth(sympy.Symbol("x") / 3)
assert sinh(x / 3)._sympy_() == sympy.sinh(sympy.Symbol("x") / 3)
assert cosh(x / 3)._sympy_() == sympy.cosh(sympy.Symbol("x") / 3)
assert tanh(x / 3)._sympy_() == sympy.tanh(sympy.Symbol("x") / 3)
assert coth(x / 3)._sympy_() == sympy.coth(sympy.Symbol("x") / 3)
assert asinh(x / 3)._sympy_() == sympy.asinh(sympy.Symbol("x") / 3)
assert acosh(x / 3)._sympy_() == sympy.acosh(sympy.Symbol("x") / 3)
assert atanh(x / 3)._sympy_() == sympy.atanh(sympy.Symbol("x") / 3)
assert acoth(x / 3)._sympy_() == sympy.acoth(sympy.Symbol("x") / 3)
def test_conv12b():
x = sympy.Symbol("x")
y = sympy.Symbol("y")
assert sympify(sympy.sinh(x/3)) == sinh(Symbol("x") / 3)
assert sympify(sympy.cosh(x/3)) == cosh(Symbol("x") / 3)
assert sympify(sympy.tanh(x/3)) == tanh(Symbol("x") / 3)
assert sympify(sympy.coth(x/3)) == coth(Symbol("x") / 3)
assert sympify(sympy.asinh(x/3)) == asinh(Symbol("x") / 3)
assert sympify(sympy.acosh(x/3)) == acosh(Symbol("x") / 3)
assert sympify(sympy.atanh(x/3)) == atanh(Symbol("x") / 3)
assert sympify(sympy.acoth(x/3)) == acoth(Symbol("x") / 3)
def test_conv12():
x = Symbol("x")
y = Symbol("y")
assert sinh(x/3) == sinh(sympy.Symbol("x") / 3)
assert cosh(x/3) == cosh(sympy.Symbol("x") / 3)
assert tanh(x/3) == tanh(sympy.Symbol("x") / 3)
assert coth(x/3) == coth(sympy.Symbol("x") / 3)
assert asinh(x/3) == asinh(sympy.Symbol("x") / 3)
assert acosh(x/3) == acosh(sympy.Symbol("x") / 3)
assert atanh(x/3) == atanh(sympy.Symbol("x") / 3)
assert acoth(x/3) == acoth(sympy.Symbol("x") / 3)
assert sinh(x/3)._sympy_() == sympy.sinh(sympy.Symbol("x") / 3)
assert cosh(x/3)._sympy_() == sympy.cosh(sympy.Symbol("x") / 3)
assert tanh(x/3)._sympy_() == sympy.tanh(sympy.Symbol("x") / 3)
assert coth(x/3)._sympy_() == sympy.coth(sympy.Symbol("x") / 3)
assert asinh(x/3)._sympy_() == sympy.asinh(sympy.Symbol("x") / 3)
assert acosh(x/3)._sympy_() == sympy.acosh(sympy.Symbol("x") / 3)
assert atanh(x/3)._sympy_() == sympy.atanh(sympy.Symbol("x") / 3)
assert acoth(x/3)._sympy_() == sympy.acoth(sympy.Symbol("x") / 3)
def test_conv12b():
x = sympy.Symbol("x")
y = sympy.Symbol("y")
assert sympify(sympy.sinh(x / 3)) == sinh(Symbol("x") / 3)
assert sympify(sympy.cosh(x / 3)) == cosh(Symbol("x") / 3)
assert sympify(sympy.tanh(x / 3)) == tanh(Symbol("x") / 3)
assert sympify(sympy.coth(x / 3)) == coth(Symbol("x") / 3)
assert sympify(sympy.asinh(x / 3)) == asinh(Symbol("x") / 3)
assert sympify(sympy.acosh(x / 3)) == acosh(Symbol("x") / 3)
assert sympify(sympy.atanh(x / 3)) == atanh(Symbol("x") / 3)
assert sympify(sympy.acoth(x / 3)) == acoth(Symbol("x") / 3)
def diffable_sign(x):
"""
!Returns shit if x is very close to but not equal to zero!
if x > 0:
return 1
if x < 0:
return -1
if x == 0:
return 0
:type x: Union[float, Symbol]
:return: sign(x)
:rtype: Union[float, Symbol]
"""
# return x/(-VERY_SMALL_NUMBER + diffable_abs(x))
return (tanh(x * 1e105))
def conditional(observable, threshold, value_if, value_else, width=None):
"""
Provides an smoothed and thus integrator-friendly version of a conditional statement. For most purposes, you can imagine this being equivalent to:
.. code-block:: Python
def conditional(observable,threshold,value_if,value_else):
if observable<threshold:
return value_if
else:
return value_else
The import difference is that this is smooth and evaluated at runtime.
`width` controls the steepness of the sigmoidal used to implement this. If not specified, this will be guessed – from the threshold if possible.
"""
if width is None:
if sympify(threshold).is_number and threshold != 0:
width = threshold / 100000
else:
width = 1e-5
return value_if + (1 + tanh(
(observable - threshold) / width)) / 2 * (value_else - value_if)
import numpy as np
from jitcode import jitcode, y
from symengine import exp, log, sqrt, tanh, sympify
def wrapper():
tDrugApplication = 10000
INaFRedMed = 1
ICaLRedMed = 1
IKrRedMed = 1
IKsRedMed = 1
return Paci2018(tDrugApplication, INaFRedMed,
ICaLRedMed, IKrRedMed, IKsRedMed)
sigmoid = lambda x: (tanh(x)+1)/2
SHARPN_COEFF = 1e5
def conditional(trigger,threshold,untriggered_value,triggered_value,sharpness=None):
if sharpness is None:
if sympify(threshold).is_number and threshold:
sharpness = SHARPN_COEFF*abs(threshold)
else:
sharpness = SHARPN_COEFF
return untriggered_value + sigmoid((trigger-threshold)*sharpness)*(triggered_value-untriggered_value)
def Paci2018(tDrugApplication, INaFRedMed,ICaLRedMed, IKrRedMed, IKsRedMed):
dY = [None]*23
'''
Parameters from optimizer
ε = [0.03966, 0.03184, 0.02847]
ν = [1, 2.033, 3.066]
μ = [0.16115668456085775, 0.14093420256851111, 0.11465065353644151]
ybar_0 = 0
τ = 1.7735
ζ = [0.017940997406325931, 0.015689701773967984, 0.012763648066925721]
ydot = [y(i) for i in range(3, 6)]
y_tot = lambda time: sum(y(i, time) for i in range(3))
ydot_tot = lambda time: sum(y(i, time) for i in range(3, 6))
yddot_tot = lambda time: sum(dy(i, time) for i in range(3, 6))
f = {y(i): ydot[i] for i in range(3)}
f.update({
ydot[i]: μ[i] * sech(ybar_0 - y_tot(t - τ))**2 *
(yddot_tot(t - τ) + 2 * ydot_tot(t - τ)**2 * tanh(ybar_0 - y_tot(t - τ))) -
2 * ζ[i] * abs(y_tot(t)) * ydot_tot(t) - ε[i] * ν[i] * ydot[i] -
ν[i]**2 * y(i)
for i in range(3)
})
DDE = jitcdde(f, verbose=False)
np.random.seed(23)
DDE.constant_past(np.random.normal(0, 1, 6))
DDE.adjust_diff()
for time in DDE.t + np.arange(0.1, 100, 0.1):
print(time, *DDE.integrate(time))
def tau_h(self, V):
return self.th0 + self.th1 * (1 - sym_backend.tanh(
(V - self.vht) / self.dvht)**2)
def tau_n(self, V):
return self.tn0 + self.tn1 * (1 - sym_backend.tanh(
(V - self.vnt) / self.dvnt)**2)
def tau_x(self, V, theta, sigma, t0, t1):
return t0 + t1 * (1.0 - sym_backend.tanh((V - theta) / sigma)**2)
def u0(self, V):
return 0.5 * (1 - sym_backend.tanh(0.5 * (V + 78) / 6.0))
def m0(self, V):
return 0.5 * (1 + sym_backend.tanh((V - self.vm) / self.dvm))
def tau_x(self, Vm, V_0, sigma_x, tau_x_0, tau_x_1):
return tau_x_0 + tau_x_1 * (1 - (sym_backend.tanh(
(Vm - V_0) / sigma_x))**2)
def r_inf(self, V):
return 0.5 * (1.0 - sym_backend.tanh(-0.5 * (V - self.Vp) / self.Kp))
def v0(self, V):
return 0.5 * (1 - sym_backend.tanh(0.5 * (V + 25.0) / 12.0))
def s0(self, V):
return 0.5 * (1 - sym_backend.tanh(-0.5 * (V + 20.0) / 6.5))
def tu(self, V):
return 0.27/(sym_backend.exp((V+46)/5.0)+sym_backend.exp(-(V+238)/37.5)) \
+5.1/2*(1+sym_backend.tanh((V+57)/3))
def n0(self, V):
return 0.5 * (1 + sym_backend.tanh((V - self.vn) / self.dvn))
def z0(self, V):
return 0.5 * (1 - sym_backend.tanh(-0.5 * (V + 60) / 8.5))
difference = Symbol("difference")
factor_μ = Symbol("factor_μ")
factor_ζ = Symbol("factor_ζ")
ydot = [y(i) for i in range(3, 6)]
y_tot = sum(current_y(i) for i in range(3))
ydot_tot = sum(current_y(i) for i in range(3, 6))
y_past = sum(past_y(t - τ, i, anchors_past) for i in range(3))
ydot_past = sum(past_y(t - τ, i, anchors_past) for i in range(3, 6))
yddot_past = sum(past_dy(t - τ, i, anchors_past) for i in range(3, 6))
helpers = {
(anchors_past, anchors(t - τ)),
(difference, ybar_0 - y_past),
(factor_μ,
sech(difference)**2 * (yddot_past + 2 * ydot_past**2 * tanh(difference))),
(factor_ζ, 2 * abs(y_tot) * ydot_tot),
}
f = {y(i): ydot[i] for i in range(3)}
f.update({
ydot[i]:
μ[i] * factor_μ - ζ[i] * factor_ζ - ε[i] * ν[i] * ydot[i] - ν[i]**2 * y(i)
for i in range(3)
})
DDE = jitcdde(f, helpers=helpers, verbose=False)
np.random.seed(23)
DDE.constant_past(np.random.normal(0, 1, 6))
DDE.adjust_diff()
def h0(self, V):
return 0.5 * (1 + sym_backend.tanh((V - self.vh) / self.dvh))
def tau_m(self, V):
return self.tm0 + self.tm1 * (1 - sym_backend.tanh(
(V - self.vmt) / self.dvmt)**2)
def x_eqm(self, V, theta, sigma):
return 0.5 * (1.0 - sym_backend.tanh(0.5 * (V - theta) / sigma))
