def _eval_as_leading_term(self, x):
from sympy import expand_mul, factor_terms
old = self
self = expand_mul(self)
if not self.is_Add:
return self.as_leading_term(x)
unbounded = [t for t in self.args if t.is_unbounded]
if unbounded:
return self.func._from_args(unbounded)
self = self.func(*[t.as_leading_term(x) for t in self.args]).removeO()
if not self:
# simple leading term analysis gave us 0 but we have to send
# back a term, so compute the leading term (via series)
return old.compute_leading_term(x)
elif not self.is_Add:
return self
else:
plain = self.func(*[s for s, _ in self.extract_leading_order(x)])
rv = factor_terms(plain, fraction=False)
rv_fraction = factor_terms(rv, fraction=True)
# if it simplifies to an x-free expression, return that;
# tests don't fail if we don't but it seems nicer to do this
if x not in rv_fraction.free_symbols:
if rv_fraction.is_zero and plain.is_zero is not True:
return (self - plain)._eval_as_leading_term(x)
return rv_fraction
return rv
def _eval_as_leading_term(self, x):
from sympy import expand_mul, factor_terms
old = self
self = expand_mul(self)
if not self.is_Add:
return self.as_leading_term(x)
unbounded = [t for t in self.args if t.is_unbounded]
self = self.func(*[t.as_leading_term(x) for t in self.args]).removeO()
if not self:
# simple leading term analysis gave us 0 but we have to send
# back a term, so compute the leading term (via series)
return old.compute_leading_term(x)
elif self is S.NaN:
return old.func._from_args(unbounded)
elif not self.is_Add:
return self
else:
plain = self.func(*[s for s, _ in self.extract_leading_order(x)])
rv = factor_terms(plain, fraction=False)
rv_fraction = factor_terms(rv, fraction=True)
# if it simplifies to an x-free expression, return that;
# tests don't fail if we don't but it seems nicer to do this
if x not in rv_fraction.free_symbols:
if rv_fraction.is_zero and plain.is_zero is not True:
return (self - plain)._eval_as_leading_term(x)
return rv_fraction
return rv
def __new__(cls, b, e, evaluate=True):
from sympy.functions.elementary.exponential import exp_polar
from sympy.functions import log
# don't optimize "if e==0; return 1" here; it's better to handle that
# in the calling routine so this doesn't get called
b = _sympify(b)
e = _sympify(e)
if evaluate:
if e is S.Zero:
return S.One
elif e is S.One:
return b
elif S.NaN in (b, e):
if b is S.One: # already handled e == 0 above
return S.One
return S.NaN
else:
if e.func == log:
if len(e.args) == 2:
lbase = e.args[1]
else:
lbase = S.Exp1
if lbase == b:
return e.args[0]
if e is Mul and e.args[1].func == log:
if len(e.args[1].args) == 2:
lbase = e.args[1].args[1]
else:
lbase = S.Exp1
if lbase == b:
return e.args[1].args[0] ** e.args[0]
# recognize base as E
if not e.is_Atom and b is not S.Exp1 and b.func is not exp_polar:
from sympy import numer, denom, log, sign, im, factor_terms
c, ex = factor_terms(e, sign=False).as_coeff_Mul()
den = denom(ex)
if den.func is log and den.args[0] == b:
return S.Exp1**(c*numer(ex))
elif den.is_Add:
s = sign(im(b))
if s.is_Number and s and den == \
log(-factor_terms(b, sign=False)) + s*S.ImaginaryUnit*S.Pi:
return S.Exp1**(c*numer(ex))
obj = b._eval_power(e)
if obj is not None:
return obj
obj = Expr.__new__(cls, b, e)
obj.is_commutative = (b.is_commutative and e.is_commutative)
return obj
def _eval_as_leading_term(self, x):
from sympy import expand_mul, factor_terms
old = self
expr = expand_mul(self)
if not expr.is_Add:
return expr.as_leading_term(x)
infinite = [t for t in expr.args if t.is_infinite]
expr = expr.func(*[t.as_leading_term(x) for t in expr.args]).removeO()
if not expr:
# simple leading term analysis gave us 0 but we have to send
# back a term, so compute the leading term (via series)
return old.compute_leading_term(x)
elif expr is S.NaN:
return old.func._from_args(infinite)
elif not expr.is_Add:
return expr
else:
plain = expr.func(*[s for s, _ in expr.extract_leading_order(x)])
rv = factor_terms(plain, fraction=False)
rv_simplify = rv.simplify()
# if it simplifies to an x-free expression, return that;
# tests don't fail if we don't but it seems nicer to do this
if x not in rv_simplify.free_symbols:
if rv_simplify.is_zero and plain.is_zero is not True:
return (expr - plain)._eval_as_leading_term(x)
return rv_simplify
return rv
def _eval_as_leading_term(self, x):
from sympy import expand_mul, factor_terms
old = self
expr = expand_mul(self)
if not expr.is_Add:
return expr.as_leading_term(x)
infinite = [t for t in expr.args if t.is_infinite]
expr = expr.func(*[t.as_leading_term(x) for t in expr.args]).removeO()
if not expr:
# simple leading term analysis gave us 0 but we have to send
# back a term, so compute the leading term (via series)
return old.compute_leading_term(x)
elif expr is S.NaN:
return old.func._from_args(infinite)
elif not expr.is_Add:
return expr
else:
plain = expr.func(*[s for s, _ in expr.extract_leading_order(x)])
rv = factor_terms(plain, fraction=False)
rv_simplify = rv.simplify()
# if it simplifies to an x-free expression, return that;
# tests don't fail if we don't but it seems nicer to do this
if x not in rv_simplify.free_symbols:
if rv_simplify.is_zero and plain.is_zero is not True:
return (expr - plain)._eval_as_leading_term(x)
return rv_simplify
return rv
def _solsimp(e, t):
no_t, has_t = powsimp(expand_mul(e)).as_independent(t)
no_t = ratsimp(no_t)
has_t = has_t.replace(exp, lambda a: exp(factor_terms(a)))
return no_t + has_t
def contact_forces(self):
"""Returns contact forces on each wheel."""
self.conForceNoncontri = self._kane.auxiliary_eqs.applyfunc(
lambda w: factor_terms(signsimp(w))).subs(self._kdd)
return self.conForceNoncontri
def __new__(cls, b, e, evaluate=None):
if evaluate is None:
evaluate = global_evaluate[0]
from sympy.functions.elementary.exponential import exp_polar
b = _sympify(b)
e = _sympify(e)
if evaluate:
if e is S.Zero:
return S.One
elif e is S.One:
return b
elif e.is_integer and _coeff_isneg(b):
if e.is_even:
b = -b
elif e.is_odd:
return -Pow(-b, e)
if b is S.One:
if e in (S.NaN, S.Infinity, -S.Infinity):
return S.NaN
return S.One
elif S.NaN in (
b,
e): # XXX S.NaN**x -> S.NaN under assumption that x != 0
return S.NaN
else:
# recognize base as E
if not e.is_Atom and b is not S.Exp1 and b.func is not exp_polar:
from sympy import numer, denom, log, sign, im, factor_terms
c, ex = factor_terms(e, sign=False).as_coeff_Mul()
den = denom(ex)
if den.func is log and den.args[0] == b:
return S.Exp1**(c * numer(ex))
elif den.is_Add:
s = sign(im(b))
if s.is_Number and s and den == \
log(-factor_terms(b, sign=False)) + s*S.ImaginaryUnit*S.Pi:
return S.Exp1**(c * numer(ex))
obj = b._eval_power(e)
if obj is not None:
return obj
obj = Expr.__new__(cls, b, e)
obj.is_commutative = (b.is_commutative and e.is_commutative)
return obj
def __new__(cls, b, e, evaluate=None):
if evaluate is None:
evaluate = global_evaluate[0]
from sympy.functions.elementary.exponential import exp_polar
b = _sympify(b)
e = _sympify(e)
if evaluate:
if e is S.Zero:
return S.One
elif e is S.One:
return b
elif e.is_integer and _coeff_isneg(b):
if e.is_even:
b = -b
elif e.is_odd:
return -Pow(-b, e)
if b is S.One:
if e in (S.NaN, S.Infinity, -S.Infinity):
return S.NaN
return S.One
elif S.NaN in (b, e): # XXX S.NaN**x -> S.NaN under assumption that x != 0
return S.NaN
else:
# recognize base as E
if not e.is_Atom and b is not S.Exp1 and b.func is not exp_polar:
from sympy import numer, denom, log, sign, im, factor_terms
c, ex = factor_terms(e, sign=False).as_coeff_Mul()
den = denom(ex)
if den.func is log and den.args[0] == b:
return S.Exp1**(c*numer(ex))
elif den.is_Add:
s = sign(im(b))
if s.is_Number and s and den == \
log(-factor_terms(b, sign=False)) + s*S.ImaginaryUnit*S.Pi:
return S.Exp1**(c*numer(ex))
obj = b._eval_power(e)
if obj is not None:
return obj
obj = Expr.__new__(cls, b, e)
obj.is_commutative = (b.is_commutative and e.is_commutative)
return obj
def test_J():
"""This test verifies that dJ/dt = 2*H."""
qp = phase_space_coordinates()
qp_ = qp.reshape(-1).tolist()
x, y, z = qp[0, :]
p_x, p_y, p_z = qp[1, :]
P_x_ = P_x__(*qp_)
P_y_ = P_y__(*qp_)
mu_ = mu__(x, y, z)
r_squared_ = r_squared__(x, y)
H_qp = H__(*qp_)
X_H = vorpy.symplectic.symplectic_gradient_of(H_qp, qp)
J_qp = J__(*qp_)
# Because X_H gives the vector field defining the time derivative of a solution to the dynamics,
# it follows that X_H applied to J is equal to dJ/dt (where J(t) is J(qp(t)), where qp(t) is a
# solution to Hamilton's equations).
X_H__J = vorpy.manifold.directional_derivative(X_H, J_qp, qp)
#print(f'test_J; X_H__J = {X_H__J}')
#print(f'test_J; 2*H = {sp.expand(2*H_qp)}')
actual_value = X_H__J - sp.expand(2 * H_qp)
#print(f'test_J; X_H__J - 2*H = {actual_value}')
# Annoyingly, this doesn't simplify to 0 automatically, so some manual manipulation has to be done.
# Manipulate the expression to ensure the P_x and P_y terms cancel
actual_value = sp.collect(actual_value, [P_x_, P_y_])
#print(f'test_J; after collect P_x, P_y: X_H__J - 2*H = {actual_value}')
actual_value = sp.Subs(
actual_value, [P_x_, P_y_],
[P_x_._expanded(), P_y_._expanded()]).doit()
#print(f'test_J; after subs P_x, P_y: X_H__J - 2*H = {actual_value}')
# Manipulate the expression to ensure the mu terms cancel
actual_value = sp.factor_terms(actual_value, clear=True, fraction=True)
#print(f'test_J; after factor_terms: X_H__J - 2*H = {actual_value}')
actual_value = sp.collect(actual_value, [r_squared_])
#print(f'test_J; after collect r_squared_: X_H__J - 2*H = {actual_value}')
actual_value = sp.Subs(actual_value, [r_squared_._expanded()],
[r_squared_]).doit()
#print(f'test_J; after subs r_squared: X_H__J - 2*H = {actual_value}')
actual_value = sp.Subs(actual_value, [mu_._expanded()], [mu_]).doit()
#print(f'test_J; after subs mu: X_H__J - 2*H = {actual_value}')
if actual_value != 0:
raise ValueError(
f'Expected X_H__J - 2*H == 0, but actual value was {actual_value}')
print('test_J passed')
def __new__(cls, b, e, evaluate=None):
if evaluate is None:
evaluate = global_evaluate[0]
from sympy.functions.elementary.exponential import exp_polar
# don't optimize "if e==0; return 1" here; it's better to handle that
# in the calling routine so this doesn't get called
b = _sympify(b)
e = _sympify(e)
if evaluate:
if e is S.Zero:
return S.One
elif e is S.One:
return b
elif b is S.One:
if e in (S.NaN, S.Infinity, -S.Infinity):
return S.NaN
return S.One
elif S.NaN in (b, e):
return S.NaN
else:
# recognize base as E
if not e.is_Atom and b is not S.Exp1 and b.func is not exp_polar:
from sympy import numer, denom, log, sign, im, factor_terms
c, ex = factor_terms(e, sign=False).as_coeff_Mul()
den = denom(ex)
if den.func is log and den.args[0] == b:
return S.Exp1**(c * numer(ex))
elif den.is_Add:
s = sign(im(b))
if s.is_Number and s and den == \
log(-factor_terms(b, sign=False)) + s*S.ImaginaryUnit*S.Pi:
return S.Exp1**(c * numer(ex))
obj = b._eval_power(e)
if obj is not None:
return obj
obj = Expr.__new__(cls, b, e)
obj.is_commutative = (b.is_commutative and e.is_commutative)
return obj
def test_issue_12557():
'''
# 3200 seconds to compute the fourier part of issue
import sympy as sym
x,y,z,t = sym.symbols('x y z t')
k = sym.symbols("k", integer=True)
fourier = sym.fourier_series(sym.cos(k*x)*sym.sqrt(x**2),
(x, -sym.pi, sym.pi))
assert fourier == FourierSeries(
sqrt(x**2)*cos(k*x), (x, -pi, pi), (Piecewise((pi**2,
Eq(k, 0)), (2*(-1)**k/k**2 - 2/k**2, True))/(2*pi),
SeqFormula(Piecewise((pi**2, (Eq(_n, 0) & Eq(k, 0)) | (Eq(_n, 0) &
Eq(_n, k) & Eq(k, 0)) | (Eq(_n, 0) & Eq(k, 0) & Eq(_n, -k)) | (Eq(_n,
0) & Eq(_n, k) & Eq(k, 0) & Eq(_n, -k))), (pi**2/2, Eq(_n, k) | Eq(_n,
-k) | (Eq(_n, 0) & Eq(_n, k)) | (Eq(_n, k) & Eq(k, 0)) | (Eq(_n, 0) &
Eq(_n, -k)) | (Eq(_n, k) & Eq(_n, -k)) | (Eq(k, 0) & Eq(_n, -k)) |
(Eq(_n, 0) & Eq(_n, k) & Eq(_n, -k)) | (Eq(_n, k) & Eq(k, 0) & Eq(_n,
-k))), ((-1)**k*pi**2*_n**3*sin(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi**2*_n**3*sin(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2
- pi*k**4) + (-1)**k*pi*_n**2*cos(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi*_n**2*cos(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) - (-1)**k*pi**2*_n*k**2*sin(pi*_n)/(pi*_n**4 -
2*pi*_n**2*k**2 + pi*k**4) +
(-1)**k*pi**2*_n*k**2*sin(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) + (-1)**k*pi*k**2*cos(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi*k**2*cos(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) - (2*_n**2 + 2*k**2)/(_n**4 - 2*_n**2*k**2 + k**4),
True))*cos(_n*x)/pi, (_n, 1, oo)), SeqFormula(0, (_k, 1, oo))))
'''
x = symbols("x", real=True)
k = symbols('k', integer=True)
abs2 = lambda x: Piecewise((-x, x <= 0), (x, x > 0))
assert integrate(abs2(x), (x, -pi, pi)) == pi**2
# attempting a Piecewise rewrite is not automatic so this needs
# help
func = cos(k * x) * sqrt(x**2)
assert integrate(func, (x, -pi, pi)) == Integral(
cos(k * x) * Abs(x), (x, -pi, pi))
assert factor_terms(integrate(func.rewrite(Piecewise),
(x, -pi, pi))) == Piecewise(
(pi**2, Eq(k, 0)),
(((-1)**k - 1) / k**2 * 2, True))
def test_issue_12557():
'''
# 3200 seconds to compute the fourier part of issue
import sympy as sym
x,y,z,t = sym.symbols('x y z t')
k = sym.symbols("k", integer=True)
fourier = sym.fourier_series(sym.cos(k*x)*sym.sqrt(x**2),
(x, -sym.pi, sym.pi))
assert fourier == FourierSeries(
sqrt(x**2)*cos(k*x), (x, -pi, pi), (Piecewise((pi**2,
Eq(k, 0)), (2*(-1)**k/k**2 - 2/k**2, True))/(2*pi),
SeqFormula(Piecewise((pi**2, (Eq(_n, 0) & Eq(k, 0)) | (Eq(_n, 0) &
Eq(_n, k) & Eq(k, 0)) | (Eq(_n, 0) & Eq(k, 0) & Eq(_n, -k)) | (Eq(_n,
0) & Eq(_n, k) & Eq(k, 0) & Eq(_n, -k))), (pi**2/2, Eq(_n, k) | Eq(_n,
-k) | (Eq(_n, 0) & Eq(_n, k)) | (Eq(_n, k) & Eq(k, 0)) | (Eq(_n, 0) &
Eq(_n, -k)) | (Eq(_n, k) & Eq(_n, -k)) | (Eq(k, 0) & Eq(_n, -k)) |
(Eq(_n, 0) & Eq(_n, k) & Eq(_n, -k)) | (Eq(_n, k) & Eq(k, 0) & Eq(_n,
-k))), ((-1)**k*pi**2*_n**3*sin(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi**2*_n**3*sin(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2
- pi*k**4) + (-1)**k*pi*_n**2*cos(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi*_n**2*cos(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) - (-1)**k*pi**2*_n*k**2*sin(pi*_n)/(pi*_n**4 -
2*pi*_n**2*k**2 + pi*k**4) +
(-1)**k*pi**2*_n*k**2*sin(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) + (-1)**k*pi*k**2*cos(pi*_n)/(pi*_n**4 - 2*pi*_n**2*k**2 +
pi*k**4) - (-1)**k*pi*k**2*cos(pi*_n)/(-pi*_n**4 + 2*pi*_n**2*k**2 -
pi*k**4) - (2*_n**2 + 2*k**2)/(_n**4 - 2*_n**2*k**2 + k**4),
True))*cos(_n*x)/pi, (_n, 1, oo)), SeqFormula(0, (_k, 1, oo))))
'''
x = symbols("x", real=True)
k = symbols('k', integer=True)
abs2 = lambda x: Piecewise((-x, x <= 0), (x, x > 0))
assert integrate(abs2(x), (x, -pi, pi)) == pi**2
# attempting a Piecewise rewrite is not automatic so this needs
# help
func = cos(k*x)*sqrt(x**2)
assert integrate(func, (x, -pi, pi)
) == Integral(cos(k*x)*Abs(x), (x, -pi, pi))
assert factor_terms(integrate(func.rewrite(Piecewise), (x, -pi, pi)
)) == Piecewise(
(pi**2, Eq(k, 0)), (((-1)**k - 1)/k**2*2, True))
def _simpsol(soleq):
lhs = soleq.lhs
sol = soleq.rhs
sol = powsimp(sol)
gens = list(sol.atoms(exp))
p = Poly(sol, *gens, expand=False)
gens = [factor_terms(g) for g in gens]
if not gens:
gens = p.gens
syms = [Symbol('C1'), Symbol('C2')]
terms = []
for coeff, monom in zip(p.coeffs(), p.monoms()):
coeff = piecewise_fold(coeff)
if type(coeff) is Piecewise:
coeff = Piecewise(*((ratsimp(coef).collect(syms), cond)
for coef, cond in coeff.args))
else:
coeff = ratsimp(coeff).collect(syms)
monom = Mul(*(g**i for g, i in zip(gens, monom)))
terms.append(coeff * monom)
return Eq(lhs, Add(*terms))
def simp_hyp(expr):
return factor_terms(expand_mul(expr)).rewrite(sin)
def simp_hyp(expr):
return factor_terms(expand_mul(expr)).rewrite(sin)
def __call__(self, equations, variables=None, method_options=None):
method_options = extract_method_options(method_options,
{'simplify': True})
if equations.is_stochastic:
raise UnsupportedEquationsException('Cannot solve stochastic '
'equations with this state '
'updater.')
if variables is None:
variables = {}
# Get a representation of the ODE system in the form of
# dX/dt = M*X + B
varnames, matrix, constants = get_linear_system(equations, variables)
# No differential equations, nothing to do (this occurs sometimes in the
# test suite where the whole model is nothing more than something like
# 'v : 1')
if matrix.shape == (0, 0):
return ''
# Make sure that the matrix M is constant, i.e. it only contains
# external variables or constant variables
t = Symbol('t', real=True, positive=True)
# Check for time dependence
dt_value = variables['dt'].get_value()[0] if 'dt' in variables else None
# This will raise an error if we meet the symbol "t" anywhere
# except as an argument of a locally constant function
for entry in itertools.chain(matrix, constants):
if not is_constant_over_dt(entry, variables, dt_value):
raise UnsupportedEquationsException(
('Expression "{}" is not guaranteed to be constant over a '
'time step').format(sympy_to_str(entry)))
symbols = [Symbol(variable, real=True) for variable in varnames]
solution = sp.solve_linear_system(matrix.row_join(constants), *symbols)
if solution is None or set(symbols) != set(solution.keys()):
raise UnsupportedEquationsException('Cannot solve the given '
'equations with this '
'stateupdater.')
b = sp.ImmutableMatrix([solution[symbol] for symbol in symbols])
# Solve the system
dt = Symbol('dt', real=True, positive=True)
try:
A = (matrix * dt).exp()
except NotImplementedError:
raise UnsupportedEquationsException('Cannot solve the given '
'equations with this '
'stateupdater.')
if method_options['simplify']:
A = A.applyfunc(lambda x:
sp.factor_terms(sp.cancel(sp.signsimp(x))))
C = sp.ImmutableMatrix(A * b) - b
_S = sp.MatrixSymbol('_S', len(varnames), 1)
updates = A * _S + C
updates = updates.as_explicit()
# The solution contains _S[0, 0], _S[1, 0] etc. for the state variables,
# replace them with the state variable names
abstract_code = []
for idx, (variable, update) in enumerate(zip(varnames, updates)):
rhs = update
if rhs.has(I, re, im):
raise UnsupportedEquationsException('The solution to the linear system '
'contains complex values '
'which is currently not implemented.')
for row_idx, varname in enumerate(varnames):
rhs = rhs.subs(_S[row_idx, 0], varname)
# Do not overwrite the real state variables yet, the update step
# of other state variables might still need the original values
abstract_code.append('_' + variable + ' = ' + sympy_to_str(rhs))
# Update the state variables
for variable in varnames:
abstract_code.append('{variable} = _{variable}'.format(variable=variable))
return '\n'.join(abstract_code)
#u5--rear wheel ang. vel., u6--front wheel ang. vel.
(fr, frstar)= kane.kanes_equations(forceList, bodyList)
kdd = kane.kindiffdict()
#path
path1 = '/home/stefenyin'
path2 = '/home/stefenstudy'
if os.path.exists(path1):
path = path1
else:
path = path2
con_forces_noncontri = kane.auxiliary_eqs.applyfunc(
lambda w: factor_terms(signsimp(w))).subs(kdd)
CF = con_forces_noncontri
"""
#==============================================================================
print('building a file and writing the equations into it')
#-----------------
print('building')
path = path + '/bicycle/bi_equations_writing/con_force_nonslip_non_para_input.txt'
try:
f = open(path,'w')
f.write('')
f.close()
del f
def __call__(self, equations, variables=None, method_options=None):
method_options = extract_method_options(method_options,
{'simplify': True})
if equations.is_stochastic:
raise UnsupportedEquationsException("Cannot solve stochastic "
"equations with this state "
"updater.")
if variables is None:
variables = {}
# Get a representation of the ODE system in the form of
# dX/dt = M*X + B
varnames, matrix, constants = get_linear_system(equations, variables)
# No differential equations, nothing to do (this occurs sometimes in the
# test suite where the whole model is nothing more than something like
# 'v : 1')
if matrix.shape == (0, 0):
return ''
# Make sure that the matrix M is constant, i.e. it only contains
# external variables or constant variables
# Check for time dependence
dt_value = variables['dt'].get_value(
)[0] if 'dt' in variables else None
# This will raise an error if we meet the symbol "t" anywhere
# except as an argument of a locally constant function
for entry in itertools.chain(matrix, constants):
if not is_constant_over_dt(entry, variables, dt_value):
raise UnsupportedEquationsException(
f"Expression '{sympy_to_str(entry)}' is not guaranteed to be "
f"constant over a time step.")
symbols = [Symbol(variable, real=True) for variable in varnames]
solution = sp.solve_linear_system(matrix.row_join(constants), *symbols)
if solution is None or set(symbols) != set(solution.keys()):
raise UnsupportedEquationsException("Cannot solve the given "
"equations with this "
"stateupdater.")
b = sp.ImmutableMatrix([solution[symbol] for symbol in symbols])
# Solve the system
dt = Symbol('dt', real=True, positive=True)
try:
A = (matrix * dt).exp()
except NotImplementedError:
raise UnsupportedEquationsException("Cannot solve the given "
"equations with this "
"stateupdater.")
if method_options['simplify']:
A = A.applyfunc(
lambda x: sp.factor_terms(sp.cancel(sp.signsimp(x))))
C = sp.ImmutableMatrix(A * b) - b
_S = sp.MatrixSymbol('_S', len(varnames), 1)
updates = A * _S + C
updates = updates.as_explicit()
# The solution contains _S[0, 0], _S[1, 0] etc. for the state variables,
# replace them with the state variable names
abstract_code = []
for idx, (variable, update) in enumerate(zip(varnames, updates)):
rhs = update
if rhs.has(I, re, im):
raise UnsupportedEquationsException(
"The solution to the linear system "
"contains complex values "
"which is currently not implemented.")
for row_idx, varname in enumerate(varnames):
rhs = rhs.subs(_S[row_idx, 0], varname)
# Do not overwrite the real state variables yet, the update step
# of other state variables might still need the original values
abstract_code.append(f"_{variable} = {sympy_to_str(rhs)}")
# Update the state variables
for variable in varnames:
abstract_code.append(f"{variable} = _{variable}")
return '\n'.join(abstract_code)
def __call__(self, equations, variables=None, simplify=True):
if equations.is_stochastic:
raise UnsupportedEquationsException('Cannot solve stochastic '
'equations with this state '
'updater.')
if variables is None:
variables = {}
# Get a representation of the ODE system in the form of
# dX/dt = M*X + B
varnames, matrix, constants = get_linear_system(equations, variables)
# No differential equations, nothing to do (this occurs sometimes in the
# test suite where the whole model is nothing more than something like
# 'v : 1')
if matrix.shape == (0, 0):
return ''
# Make sure that the matrix M is constant, i.e. it only contains
# external variables or constant variables
t = Symbol('t', real=True, positive=True)
# Check for time dependence
if 'dt' in variables:
dt_value = variables['dt'].get_value()[0]
# This will raise an error if we meet the symbol "t" anywhere
# except as an argument of a locally constant function
t = Symbol('t', real=True, positive=True)
for entry in itertools.chain(matrix, constants):
_check_for_locally_constant(entry, variables, dt_value, t)
symbols = [Symbol(variable, real=True) for variable in varnames]
solution = sp.solve_linear_system(matrix.row_join(constants), *symbols)
if solution is None or set(symbols) != set(solution.keys()):
raise UnsupportedEquationsException('Cannot solve the given '
'equations with this '
'stateupdater.')
b = sp.ImmutableMatrix([solution[symbol]
for symbol in symbols]).transpose()
# Solve the system
dt = Symbol('dt', real=True, positive=True)
try:
A = (matrix * dt).exp()
except NotImplementedError:
raise UnsupportedEquationsException('Cannot solve the given '
'equations with this '
'stateupdater.')
if simplify:
A = A.applyfunc(
lambda x: sp.factor_terms(sp.cancel(sp.signsimp(x))))
C = sp.ImmutableMatrix([A.dot(b)]) - b
_S = sp.MatrixSymbol('_S', len(varnames), 1)
updates = A * _S + C.transpose()
updates = updates.as_explicit()
# The solution contains _S[0, 0], _S[1, 0] etc. for the state variables,
# replace them with the state variable names
abstract_code = []
for idx, (variable, update) in enumerate(zip(varnames, updates)):
rhs = update
for row_idx, varname in enumerate(varnames):
rhs = rhs.subs(_S[row_idx, 0], varname)
# Do not overwrite the real state variables yet, the update step
# of other state variables might still need the original values
abstract_code.append('_' + variable + ' = ' + sympy_to_str(rhs))
# Update the state variables
for variable in varnames:
abstract_code.append(
'{variable} = _{variable}'.format(variable=variable))
return '\n'.join(abstract_code)
def Symplify(A):
return (factor_terms(simplify(A)))
def Symplify(A):
return(factor_terms(simplify(A)))
