def test_IndexedBase_assumptions():
i = Symbol('i', integer=True)
a = Symbol('a')
A = IndexedBase(a, positive=True)
for c in (A, A[i]):
assert c.is_real
assert c.is_complex
assert not c.is_imaginary
assert c.is_nonnegative
assert c.is_nonzero
assert c.is_commutative
assert log(exp(c)) == c
assert A != IndexedBase(a)
assert A == IndexedBase(a, positive=True, real=True)
assert A[i] != Indexed(a, i)
def __getitem__(self, time):
"""
For indexing discrete time stochastic processes.
Returns
=======
RandomIndexedSymbol
"""
if time not in self.index_set:
raise IndexError("%s is not in the index set of %s" %
(time, self.symbol))
idx_obj = Indexed(self.symbol, time)
distribution = getattr(self, "distribution", None)
pspace_obj = StochasticPSpace(self.symbol, self, distribution)
return RandomIndexedSymbol(idx_obj, pspace_obj)
def pdf(self, *x):
expr, rvs = self.args[0], self.args[1]
marginalise_out = [i for i in random_symbols(expr) if i not in rvs]
if isinstance(expr, CompoundDistribution):
syms = Dummy('x', real=True)
expr = expr.args[0].pdf(syms)
elif isinstance(expr, JointDistribution):
count = len(expr.domain.args)
x = Dummy('x', real=True, finite=True)
syms = tuple(Indexed(x, i) for i in count)
expr = expr.pdf(syms)
else:
syms = tuple(rv.pspace.symbol if isinstance(rv, RandomSymbol
) else rv.args[0]
for rv in rvs)
return Lambda(syms, self.compute_pdf(expr, marginalise_out))(*x)
def test_issue_12283():
x = symbols('x')
X = RandomSymbol(x)
Y = RandomSymbol('Y')
Z = RandomMatrixSymbol('Z', 2, 1)
W = RandomMatrixSymbol('W', 2, 1)
RI = RandomIndexedSymbol(Indexed('RI', 3))
assert pspace(Z) == PSpace()
assert pspace(RI) == PSpace()
assert pspace(X) == PSpace()
assert E(X) == Expectation(X)
assert P(Y > 3) == Probability(Y > 3)
assert variance(X) == Variance(X)
assert variance(RI) == Variance(RI)
assert covariance(X, Y) == Covariance(X, Y)
assert covariance(W, Z) == Covariance(W, Z)
def shift_grid(expr):
if expr.is_Symbol:
return expr
if expr.is_Number:
return expr
if isinstance(expr, Indexed):
b = expr.base
idx = list(expr.indices)
if b.label.name in shift_x:
idx[1] -= hf
if b.label.name in shift_y:
idx[2] -= hf
t = Indexed(b, *idx)
return t
args = tuple([shift_grid(arg) for arg in expr.args])
expr2 = expr.func(*args)
return expr2
def pdf(self, *x):
expr, rvs = self.args[0], self.args[1]
marginalise_out = [i for i in random_symbols(expr) if i not in self.args[1]]
syms = [i.pspace.symbol for i in self.args[1]]
for i in expr.atoms(Indexed):
if isinstance(i, Indexed) and isinstance(i.base, RandomSymbol)\
and i not in rvs:
marginalise_out.append(i)
if isinstance(expr, CompoundDistribution):
syms = Dummy('x', real=True)
expr = expr.args[0].pdf(syms)
elif isinstance(expr, JointDistribution):
count = len(expr.domain.args)
x = Dummy('x', real=True, finite=True)
syms = [Indexed(x, i) for i in count]
expr = expression.pdf(syms)
return Lambda(syms, self.compute_pdf(expr, marginalise_out))(*x)
def shift_grid(expr):
"""
shift all field indices in the input expression to whole number
if it is staggered (i.e. has index that is in the middle of grids)
the result expression can then be converted to C code
"""
if expr.is_Symbol:
return expr
if expr.is_Number:
return expr
if isinstance(expr, Indexed):
b = expr.base
idx = [x-hf if is_half(x) else x for x in list(expr.indices)]
t = Indexed(b, *idx)
return t
args = tuple([shift_grid(arg) for arg in expr.args]) # recursive call
expr2 = expr.func(*args)
return expr2
def test_sample_seed():
x1, x2 = (Indexed('x', i) for i in (1, 2))
pdf = exp(-x1**2 / 2 + x1 - x2**2 / 2 - S.Half) / (2 * pi)
X = JointRV('x', pdf)
libraries = ['scipy', 'numpy', 'pymc3']
for lib in libraries:
try:
imported_lib = import_module(lib)
if imported_lib:
s0, s1, s2 = [], [], []
s0 = list(sample(X, numsamples=10, library=lib, seed=0))
s1 = list(sample(X, numsamples=10, library=lib, seed=0))
s2 = list(sample(X, numsamples=10, library=lib, seed=1))
assert s0 == s1
assert s1 != s2
except NotImplementedError:
continue
def kappac(self, n):
fn = self.fn
r = self.r
z = self.z
y = self.y
W = self.W
Q = self.Q
S = self.S
NBc = self.NBc
NBbc = self.NBbc
j = self.j
L1 = [fn[0] * bell(n, 1, (r))]
for k in range(2, n + 1):
L1.extend([fn[k - 1] * bell(n, k, r)])
L2 = Sum(Indexed('y', j), (j, 0, n - 1))
L22 = lambdify(y, L2)
L3 = L22(L1)
L4 = L3.subs({W(z): Q(z) * S(z) - NBc * NBbc})
L5 = L4.subs({S(z): NBc + NBbc, Q(z): z * z - NBc * NBbc})
return L5
def test_Lambda():
e = Lambda(x, x**2)
assert e(4) == 16
assert e(x) == x**2
assert e(y) == y**2
assert Lambda((), 42)() == 42
assert unchanged(Lambda, (), 42)
assert Lambda((), 42) != Lambda((), 43)
assert Lambda((), f(x))() == f(x)
assert Lambda((), 42).nargs == FiniteSet(0)
assert unchanged(Lambda, (x,), x**2)
assert Lambda(x, x**2) == Lambda((x,), x**2)
assert Lambda(x, x**2) == Lambda(y, y**2)
assert Lambda(x, x**2) != Lambda(y, y**2 + 1)
assert Lambda((x, y), x**y) == Lambda((y, x), y**x)
assert Lambda((x, y), x**y) != Lambda((x, y), y**x)
assert Lambda((x, y), x**y)(x, y) == x**y
assert Lambda((x, y), x**y)(3, 3) == 3**3
assert Lambda((x, y), x**y)(x, 3) == x**3
assert Lambda((x, y), x**y)(3, y) == 3**y
assert Lambda(x, f(x))(x) == f(x)
assert Lambda(x, x**2)(e(x)) == x**4
assert e(e(x)) == x**4
x1, x2 = (Indexed('x', i) for i in (1, 2))
assert Lambda((x1, x2), x1 + x2)(x, y) == x + y
assert Lambda((x, y), x + y).nargs == FiniteSet(2)
p = x, y, z, t
assert Lambda(p, t*(x + y + z))(*p) == t * (x + y + z)
assert Lambda(x, 2*x) + Lambda(y, 2*y) == 2*Lambda(x, 2*x)
assert Lambda(x, 2*x) not in [ Lambda(x, x) ]
raises(TypeError, lambda: Lambda(1, x))
assert Lambda(x, 1)(1) is S.One
raises(SyntaxError, lambda: Lambda((x, x), x + 2))
def shift_index(expr, k, s):
"""
shift the k-th index of all fields in input expression by s
return the shifted expression
:param expr: input expression
k: the index number to be shifted
s: the shifted amount
e.g. k=1, s=1, U[x,y,z] -> U[x,y+1,z]
"""
if expr.is_Symbol:
return expr
if expr.is_Number:
return expr
if isinstance(expr, Indexed):
b = expr.base
idx = list(expr.indices)
idx[k] += s
t = Indexed(b, *idx)
return t
# recursive call
args = tuple([shift_index(arg, k, s) for arg in expr.args])
expr2 = expr.func(*args)
return expr2
def test_indexed_idx_sum():
i = symbols('i', cls=Idx)
r = Indexed('r', i)
assert Sum(r, (i, 0, 3)).doit() == sum([r.xreplace({i: j}) for j in range(4)])
assert Product(r, (i, 0, 3)).doit() == prod([r.xreplace({i: j}) for j in range(4)])
j = symbols('j', integer=True)
assert Sum(r, (i, j, j+2)).doit() == sum([r.xreplace({i: j+k}) for k in range(3)])
assert Product(r, (i, j, j+2)).doit() == prod([r.xreplace({i: j+k}) for k in range(3)])
k = Idx('k', range=(1, 3))
A = IndexedBase('A')
assert Sum(A[k], k).doit() == sum([A[Idx(j, (1, 3))] for j in range(1, 4)])
assert Product(A[k], k).doit() == prod([A[Idx(j, (1, 3))] for j in range(1, 4)])
raises(ValueError, lambda: Sum(A[k], (k, 1, 4)))
raises(ValueError, lambda: Sum(A[k], (k, 0, 3)))
raises(ValueError, lambda: Sum(A[k], (k, 2, oo)))
raises(ValueError, lambda: Product(A[k], (k, 1, 4)))
raises(ValueError, lambda: Product(A[k], (k, 0, 3)))
raises(ValueError, lambda: Product(A[k], (k, 2, oo)))
def pdf(self):
sym = [Indexed(self.symbol, i) for i in range(self.component_count)]
return self.distribution(*sym)
def __getitem__(self, key):
if isinstance(self.pspace, JointPSpace):
if (self.pspace.component_count <= key) == True:
raise ValueError("Index keys for %s can only up to %s." %
(self.name, self.pspace.component_count - 1))
return Indexed(self, key)
def handleSymmetricYukawa(self, fermions, contractArgs, coeff, freeDummies,
coupling):
duplicateFermions = [el for el in fermions if fermions.count(el) == 2]
if len(duplicateFermions) != 2:
return expand(
tensorContract(*contractArgs,
value=coeff,
freeDummies=freeDummies,
doit=True))
expanded = []
duplicateFermion = duplicateFermions[0]
symbs = [el[0].symbol for el in contractArgs]
duplicatePos = [
i for i, el in enumerate(symbs) if el == duplicateFermion
]
for pos in duplicatePos:
duplicateTensor = copy.deepcopy(contractArgs[pos][0])
newDic = {}
for k, v in duplicateTensor.dic.items():
if isinstance(v, Symbol):
newDic[k] = Symbol('df$' + str(v))
elif isinstance(v, Indexed):
newDic[k] = Indexed('df$' + str(v.base), *v.indices)
duplicateTensor.dic = newDic
newcArgs = []
for i, el in enumerate(contractArgs):
if i != pos:
newcArgs.append(el)
else:
if len(contractArgs[pos]) > 1:
newcArgs.append(
(duplicateTensor, *contractArgs[pos][1:]))
else:
newcArgs.append((duplicateTensor, ))
expanded.append(
expand(
tensorContract(*newcArgs,
value=coeff,
freeDummies=freeDummies,
doit=True)))
if expand(expanded[0] + expanded[1]) == 0:
# Antisymmetric Yukawa matrix
# For one generation, the Yukawa operator simply vanishes.
# In this case an error should be raised eventually
if self.model.Fermions[str(duplicateFermion)].gen == 1:
return expand(
tensorContract(*contractArgs,
value=coeff,
freeDummies=freeDummies,
doit=True))
self.model.assumptions[coupling]['antisymmetric'] = True
else:
# Symmetric Yukawa matrix
self.model.assumptions[coupling]['symmetric'] = True
return expanded[0]
def class_key(cls):
return Indexed.class_key()
def test_issue_9594():
i = symbols('i', cls=Idx)
r = Indexed('r', i)
Sum(r, (i, 0, 3)).doit() == [r.subs(i, j) for j in range(4)]
def dse_cse(expr):
"""
Perform common subexpression elimination on sympy expressions.
:param expr: sympy equation or list of equations on which CSE is performed.
:return: A list of the resulting equations after performing CSE
"""
expr = expr if isinstance(expr, list) else [expr]
temps, stencils = cse(expr, numbered_symbols("temp"))
# Restores the LHS
for i in range(len(expr)):
stencils[i] = Eq(expr[i].lhs, stencils[i].rhs)
to_revert = {}
to_keep = []
# Restores IndexedBases if they are collected by CSE and
# reverts changes to simple index operations (eg: t - 1)
for temp, value in temps:
if isinstance(value, IndexedBase):
to_revert[temp] = value
elif isinstance(value, Indexed):
to_revert[temp] = value
elif isinstance(value, Add) and not \
set([t, x, y, z]).isdisjoint(set(value.args)):
to_revert[temp] = value
else:
to_keep.append((temp, value))
# Restores the IndexedBases and the Indexes in the assignments to revert
for temp, value in to_revert.items():
s_dict = {}
for arg in preorder_traversal(value):
if isinstance(arg, Indexed):
new_indices = []
for index in arg.indices:
if index in to_revert:
new_indices.append(to_revert[index])
else:
new_indices.append(index)
if arg.base.label in to_revert:
s_dict[arg] = Indexed(to_revert[value.base.label],
*new_indices)
to_revert[temp] = value.xreplace(s_dict)
subs_dict = {}
# Builds a dictionary of the replacements
for expr in stencils + [assign for temp, assign in to_keep]:
for arg in preorder_traversal(expr):
if isinstance(arg, Indexed):
new_indices = []
for index in arg.indices:
if index in to_revert:
new_indices.append(to_revert[index])
else:
new_indices.append(index)
if arg.base.label in to_revert:
subs_dict[arg] = Indexed(to_revert[arg.base.label],
*new_indices)
elif tuple(new_indices) != arg.indices:
subs_dict[arg] = Indexed(arg.base, *new_indices)
if arg in to_revert:
subs_dict[arg] = to_revert[arg]
stencils = [stencil.xreplace(subs_dict) for stencil in stencils]
to_keep = [Eq(temp[0], temp[1].xreplace(subs_dict)) for temp in to_keep]
# If the RHS of a temporary variable is the LHS of a stencil,
# update the value of the temporary variable after the stencil
new_stencils = []
for stencil in stencils:
new_stencils.append(stencil)
for temp in to_keep:
if stencil.lhs in preorder_traversal(temp.rhs):
new_stencils.append(temp)
break
return to_keep + new_stencils
def test_Indexed_func_args():
i, j = symbols('i j', integer=True)
a = symbols('a')
A = Indexed(a, i, j)
assert A == A.func(*A.args)
def test_not_interable():
i, j = symbols('i j', integer=True)
A = Indexed('A', i, i + j)
assert not iterable(A)
def test_MarginalDistribution():
a1, p1, p2 = symbols('a1 p1 p2', positive=True)
C = Multinomial('C', 2, p1, p2)
B = MultivariateBeta('B', a1, C[0])
MGR = MarginalDistribution(B, (C[0],))
mgrc = Mul(Symbol('B'), Piecewise(ExprCondPair(Mul(Integer(2),
Pow(Symbol('p1', positive=True), Indexed(IndexedBase(Symbol('C')),
Integer(0))), Pow(Symbol('p2', positive=True),
Indexed(IndexedBase(Symbol('C')), Integer(1))),
Pow(factorial(Indexed(IndexedBase(Symbol('C')), Integer(0))), Integer(-1)),
Pow(factorial(Indexed(IndexedBase(Symbol('C')), Integer(1))), Integer(-1))),
Eq(Add(Indexed(IndexedBase(Symbol('C')), Integer(0)),
Indexed(IndexedBase(Symbol('C')), Integer(1))), Integer(2))),
ExprCondPair(Integer(0), True)), Pow(gamma(Symbol('a1', positive=True)),
Integer(-1)), gamma(Add(Symbol('a1', positive=True),
Indexed(IndexedBase(Symbol('C')), Integer(0)))),
Pow(gamma(Indexed(IndexedBase(Symbol('C')), Integer(0))), Integer(-1)),
Pow(Indexed(IndexedBase(Symbol('B')), Integer(0)),
Add(Symbol('a1', positive=True), Integer(-1))),
Pow(Indexed(IndexedBase(Symbol('B')), Integer(1)),
Add(Indexed(IndexedBase(Symbol('C')), Integer(0)), Integer(-1))))
assert MGR(C) == mgrc
def probability(self,
condition,
given_condition=None,
evaluate=True,
**kwargs):
"""
Handles probability queries for discrete Markov chains.
Parameters
==========
condition: Relational
given_condition: Relational/And
Returns
=======
Probability
If the transition probabilities are not available
Expr
If the transition probabilities is MatrixSymbol or Matrix
Note
====
Any information passed at the time of query overrides
any information passed at the time of object creation like
transition probabilities, state space.
Pass the transition matrix using TransitionMatrixOf and state space
using StochasticStateSpaceOf in given_condition using & or And.
"""
check, trans_probs, state_space, given_condition = \
self._preprocess(given_condition, evaluate)
if check:
return Probability(condition, given_condition)
if isinstance(condition, Eq) and \
isinstance(given_condition, Eq) and \
len(given_condition.atoms(RandomSymbol)) == 1:
# handles simple queries like P(Eq(X[i], dest_state), Eq(X[i], init_state))
lhsc, rhsc = condition.lhs, condition.rhs
lhsg, rhsg = given_condition.lhs, given_condition.rhs
if not isinstance(lhsc, RandomIndexedSymbol):
lhsc, rhsc = (rhsc, lhsc)
if not isinstance(lhsg, RandomIndexedSymbol):
lhsg, rhsg = (rhsg, lhsg)
keyc, statec, keyg, stateg = (lhsc.key, rhsc, lhsg.key, rhsg)
if Lt(stateg, trans_probs.shape[0]) == False or Lt(
statec, trans_probs.shape[1]) == False:
raise IndexError(
"No information is avaliable for (%s, %s) in "
"transition probabilities of shape, (%s, %s). "
"State space is zero indexed." %
(stateg, statec, trans_probs.shape[0],
trans_probs.shape[1]))
if keyc < keyg:
raise ValueError(
"Incorrect given condition is given, probability "
"of past state cannot be computed from future state.")
nsteptp = trans_probs**(keyc - keyg)
if hasattr(nsteptp, "__getitem__"):
return nsteptp.__getitem__((stateg, statec))
return Indexed(nsteptp, stateg, statec)
if isinstance(condition, And):
# handle queries like,
# P(Eq(X[i+k], s1) & Eq(X[i+m], s2) . . . & Eq(X[i], sn), Eq(P(X[i]), prob))
conds = condition.args
i, result = -1, 1
while i > -len(conds):
result *= self.probability(conds[i], conds[i-1] & \
TransitionMatrixOf(self, trans_probs) & \
StochasticStateSpaceOf(self, state_space))
i -= 1
if isinstance(given_condition,
(TransitionMatrixOf, StochasticStateSpaceOf)):
return result * Probability(conds[i])
if isinstance(given_condition, Eq):
if not isinstance(given_condition.lhs, Probability) or \
given_condition.lhs.args[0] != conds[i]:
raise ValueError("Probability for %s needed", conds[i])
return result * given_condition.rhs
raise NotImplementedError(
"Mechanism for handling (%s, %s) queries hasn't been "
"implemented yet." % (condition, given_condition))
def __getitem__(self, key):
from sympy.stats.joint_rv import JointPSpace
if isinstance(self.pspace, JointPSpace):
return Indexed(self, key)
def get_sum(min_limit, max_limit, reg):
x, i = symbols('x i')
return Sum(Indexed('x', i), (i, min_limit, max_limit)).doit()
def test_Idx_limits():
i = symbols('i', cls=Idx)
r = Indexed('r', i)
assert SeqFormula(r, (i, 0, 5))[:] == [r.subs(i, j) for j in range(6)]
assert SeqPer((1, 2), (i, 0, 5))[:] == [1, 2, 1, 2, 1, 2]
def __getitem__(self, indices, **kw_args):
if is_sequence(indices):
# Special case needed because M[*my_tuple] is a syntax error.
return Indexed(self, *indices, **kw_args)
else:
return Indexed(self, indices, **kw_args)
def test_Indexed_func_args():
i, j = symbols('i j', integer=True)
a = symbols('a')
A = Indexed(a, i, j)
assert A == A.func(*A.args)
def test_Idx_limits():
i = symbols('i', cls=Idx)
r = Indexed('r', i)
assert SeqFormula(r, (i, 0, 5))[:] == [r.subs(i, j) for j in range(6)]
assert SeqPer((1, 2), (i, 0, 5))[:] == [1, 2, 1, 2, 1, 2]
def test_not_interable():
i, j = symbols("i j", integer=True)
A = Indexed("A", i, i + j)
assert not iterable(A)
def test_complex_indices():
i, j = symbols('i j', integer=True)
A = Indexed('A', i, i + j)
assert A.rank == 2
assert A.indices == (i, i + j)
def generate_coupling_expansions(obj, verbose=True):
"""
generate expansions for coupling.
"""
if verbose:
print('* Generating coupling expansions...')
i = sym.symbols('i_sym') # summation index
psi = obj.psi
eps = obj.eps
ruleA = {'psi': obj.pA['expand']}
ruleB = {'psi': obj.pB['expand']}
rule_trunc = {}
for k in range(obj.miter, obj.miter + 200):
rule_trunc.update({obj.eps**k: 0})
for key in obj.var_names:
if verbose:
print('key in coupling expand', key)
gA = Sum(psi**i * Indexed('g' + key + 'A', i),
(i, 1, obj.miter)).doit()
gB = Sum(psi**i * Indexed('g' + key + 'B', i),
(i, 1, obj.miter)).doit()
iA = Sum(psi**i * Indexed('i' + key + 'A', i),
(i, 0, obj.miter)).doit()
iB = Sum(psi**i * Indexed('i' + key + 'B', i),
(i, 0, obj.miter)).doit()
tmp = gA.subs(ruleA)
tmp = sym.expand(tmp,
basic=False,
deep=True,
power_base=False,
power_exp=False,
mul=False,
log=False,
multinomial=True)
tmp = tmp.subs(rule_trunc)
gA_collected = collect(expand(tmp).subs(rule_trunc), eps)
if verbose:
print('completed gA collected')
tmp = gB.subs(ruleB)
tmp = sym.expand(tmp,
basic=False,
deep=True,
power_base=False,
power_exp=False,
mul=False,
log=False,
multinomial=True)
tmp = tmp.subs(rule_trunc)
gB_collected = collect(expand(tmp).subs(rule_trunc), eps)
if verbose:
print('completed gB collected')
tmp = iA.subs(ruleA)
tmp = sym.expand(tmp,
basic=False,
deep=True,
power_base=False,
power_exp=False,
mul=False,
log=False,
multinomial=True)
tmp = tmp.subs(rule_trunc)
iA_collected = collect(expand(tmp).subs(rule_trunc), eps)
if verbose:
print('completed iA collected')
tmp = iB.subs(ruleB)
tmp = sym.expand(tmp,
basic=False,
deep=True,
power_base=False,
power_exp=False,
mul=False,
log=False,
multinomial=True)
tmp = tmp.subs(rule_trunc)
iB_collected = collect(expand(tmp).subs(rule_trunc), eps)
if verbose:
print('completed iB collected')
obj.g[key + '_epsA'] = 0
obj.g[key + '_epsB'] = 0
obj.i[key + '_epsA'] = 0
obj.i[key + '_epsB'] = 0
for j in range(obj.miter):
obj.g[key + '_epsA'] += eps**j * gA_collected.coeff(eps, j)
obj.g[key + '_epsB'] += eps**j * gB_collected.coeff(eps, j)
obj.i[key + '_epsA'] += eps**j * iA_collected.coeff(eps, j)
obj.i[key + '_epsB'] += eps**j * iB_collected.coeff(eps, j)
return obj.g, obj.i
def test_Lambda():
e = Lambda(x, x**2)
assert e(4) == 16
assert e(x) == x**2
assert e(y) == y**2
assert Lambda((), 42)() == 42
assert unchanged(Lambda, (), 42)
assert Lambda((), 42) != Lambda((), 43)
assert Lambda((), f(x))() == f(x)
assert Lambda((), 42).nargs == FiniteSet(0)
assert unchanged(Lambda, (x, ), x**2)
assert Lambda(x, x**2) == Lambda((x, ), x**2)
assert Lambda(x, x**2) == Lambda(y, y**2)
assert Lambda(x, x**2) != Lambda(y, y**2 + 1)
assert Lambda((x, y), x**y) == Lambda((y, x), y**x)
assert Lambda((x, y), x**y) != Lambda((x, y), y**x)
assert Lambda((x, y), x**y)(x, y) == x**y
assert Lambda((x, y), x**y)(3, 3) == 3**3
assert Lambda((x, y), x**y)(x, 3) == x**3
assert Lambda((x, y), x**y)(3, y) == 3**y
assert Lambda(x, f(x))(x) == f(x)
assert Lambda(x, x**2)(e(x)) == x**4
assert e(e(x)) == x**4
x1, x2 = (Indexed('x', i) for i in (1, 2))
assert Lambda((x1, x2), x1 + x2)(x, y) == x + y
assert Lambda((x, y), x + y).nargs == FiniteSet(2)
p = x, y, z, t
assert Lambda(p, t * (x + y + z))(*p) == t * (x + y + z)
assert Lambda(x, 2 * x) + Lambda(y, 2 * y) == 2 * Lambda(x, 2 * x)
assert Lambda(x, 2 * x) not in [Lambda(x, x)]
raises(BadSignatureError, lambda: Lambda(1, x))
assert Lambda(x, 1)(1) is S.One
raises(BadSignatureError, lambda: Lambda((x, x), x + 2))
raises(BadSignatureError, lambda: Lambda(((x, x), y), x))
raises(BadSignatureError, lambda: Lambda(((y, x), x), x))
raises(BadSignatureError, lambda: Lambda(((y, 1), 2), x))
with warns_deprecated_sympy():
assert Lambda([x, y], x + y) == Lambda((x, y), x + y)
flam = Lambda(((x, y), ), x + y)
assert flam((2, 3)) == 5
flam = Lambda(((x, y), z), x + y + z)
assert flam((2, 3), 1) == 6
flam = Lambda((((x, y), z), ), x + y + z)
assert flam(((2, 3), 1)) == 6
raises(BadArgumentsError, lambda: flam(1, 2, 3))
flam = Lambda((x, ), (x, x))
assert flam(1, ) == (1, 1)
assert flam((1, )) == ((1, ), (1, ))
flam = Lambda(((x, ), ), (x, x))
raises(BadArgumentsError, lambda: flam(1))
assert flam((1, )) == (1, 1)
# Previously TypeError was raised so this is potentially needed for
# backwards compatibility.
assert issubclass(BadSignatureError, TypeError)
assert issubclass(BadArgumentsError, TypeError)
# These are tested to see they don't raise:
hash(Lambda(x, 2 * x))
hash(Lambda(x, x)) # IdentityFunction subclass
def load_coupling_expansions(obj, fn='gA', recompute=False):
i = sym.symbols('i_sym') # summation index
#psi = obj.psi
eps = obj.eps
# for solution of isostables in terms of theta.
obj.pA['expand'] = Sum(eps**i * Indexed('pA', i), (i, 1, obj.miter)).doit()
obj.pB['expand'] = Sum(eps**i * Indexed('pB', i), (i, 1, obj.miter)).doit()
#fname = obj.hodd['dat_fnames'][i]
#file_does_not_exist = not(os.path.exists(fname))
for key in obj.var_names:
obj.g[key + '_epsA'] = []
obj.g[key + '_epsB'] = []
obj.i[key + '_epsA'] = []
obj.i[key + '_epsB'] = []
# check that files exist
val = 0
for key in obj.var_names:
val += not (os.path.isfile(obj.g[key + '_epsA_fname']))
val += not (os.path.isfile(obj.g[key + '_epsB_fname']))
val += not (os.path.isfile(obj.i[key + '_epsA_fname']))
val += not (os.path.isfile(obj.i[key + '_epsB_fname']))
#print(key,obj.g[key+'_epsA_fname'])
if val != 0:
files_do_not_exist = True
else:
files_do_not_exist = False
if recompute or files_do_not_exist:
generate_coupling_expansions(obj)
for key in obj.var_names:
# dump
dill.dump(obj.g[key + '_epsA'],
open(obj.g[key + '_epsA_fname'], 'wb'),
recurse=True)
dill.dump(obj.g[key + '_epsB'],
open(obj.g[key + '_epsB_fname'], 'wb'),
recurse=True)
dill.dump(obj.i[key + '_epsA'],
open(obj.i[key + '_epsA_fname'], 'wb'),
recurse=True)
dill.dump(obj.i[key + '_epsB'],
open(obj.i[key + '_epsB_fname'], 'wb'),
recurse=True)
else:
for key in obj.var_names:
obj.g[key + '_epsA'] = dill.load(
open(obj.g[key + '_epsA_fname'], 'rb'))
obj.g[key + '_epsB'] = dill.load(
open(obj.g[key + '_epsB_fname'], 'rb'))
obj.i[key + '_epsA'] = dill.load(
open(obj.i[key + '_epsA_fname'], 'rb'))
obj.i[key + '_epsB'] = dill.load(
open(obj.i[key + '_epsB_fname'], 'rb'))
# vector of i expansion
obj.i['vecA'] = sym.zeros(obj.dim, 1)
obj.i['vecB'] = sym.zeros(obj.dim, 1)
for i, key in enumerate(obj.var_names):
#print('ia',i,key,obj.i[key+'_epsA'])
#print('ib',i,key,obj.i[key+'_epsB'])
obj.i['vecA'][i] = obj.i[key + '_epsA']
obj.i['vecB'][i] = obj.i[key + '_epsB']
pass
