def __new__(cls, name, base: CoordSys3D):
x, y, z = symbols('x,y,z', cls=DummyVariable)
q, r, s = symbols('q,r,s', cls=DummyVariable)
tvec: vector.VectorAdd = x * base.i + y * base.j + z * base.k
rvec: vector.VectorAdd = q * base.i + r * base.j + s * base.k
rot_mat = ImmutableDenseMatrix([
[ (q**2 + (r**2 + s**2)*cos(sqrt(q**2 + r**2 + s**2)))/(q**2 + r**2 + s**2), (q*r*(1 - cos(sqrt(q**2 + r**2 + s**2)))*sqrt(q**2 + r**2 + s**2) + s*(q**2 + r**2 + s**2)*sin(sqrt(q**2 + r**2 + s**2)))/(q**2 + r**2 + s**2)**(3/2), (q*s*(1 - cos(sqrt(q**2 + r**2 + s**2)))*sqrt(q**2 + r**2 + s**2) - r*(q**2 + r**2 + s**2)*sin(sqrt(q**2 + r**2 + s**2)))/(q**2 + r**2 + s**2)**(3/2)],
[(q*r*(1 - cos(sqrt(q**2 + r**2 + s**2)))*sqrt(q**2 + r**2 + s**2) - s*(q**2 + r**2 + s**2)*sin(sqrt(q**2 + r**2 + s**2)))/(q**2 + r**2 + s**2)**(3/2), (r**2 + (q**2 + s**2)*cos(sqrt(q**2 + r**2 + s**2)))/(q**2 + r**2 + s**2), (q*(q**2 + r**2 + s**2)*sin(sqrt(q**2 + r**2 + s**2)) + r*s*(1 - cos(sqrt(q**2 + r**2 + s**2)))*sqrt(q**2 + r**2 + s**2))/(q**2 + r**2 + s**2)**(3/2)],
[(q*s*(1 - cos(sqrt(q**2 + r**2 + s**2)))*sqrt(q**2 + r**2 + s**2) + r*(q**2 + r**2 + s**2)*sin(sqrt(q**2 + r**2 + s**2)))/(q**2 + r**2 + s**2)**(3/2), (-q*(q**2 + r**2 + s**2)*sin(sqrt(q**2 + r**2 + s**2)) + r*s*(1 - cos(sqrt(q**2 + r**2 + s**2)))*sqrt(q**2 + r**2 + s**2))/(q**2 + r**2 + s**2)**(3/2), (s**2 + (q**2 + r**2)*cos(sqrt(q**2 + r**2 + s**2)))/(q**2 + r**2 + s**2)]])
# obj = base.locate_new(name, tvec)
obj = CoordSys3D.__new__(cls, name, location=tvec,
rotation_matrix=rot_mat,
vector_names=base._vector_names,
variable_names=base._variable_names,
parent=base)
obj.x, obj.y, obj.z = x, y, z
obj.q, obj.r, obj.s = q, r, s
obj._tvec = tvec
obj._rvec = rvec
obj._rot_mat = rot_mat
return obj
def eval(cls, *_args):
if not _args:
return
u = _args[0]
return ImmutableDenseMatrix([[dy(u)], [-dx(u)]])
def eval(cls, *_args):
if not _args:
return
u = _args[0]
return ImmutableDenseMatrix([[dy(u[2]) - dz(u[1])],
[dz(u[0]) - dx(u[2])],
[dx(u[1]) - dy(u[0])]])
def __new__(cls, u, mapping=None):
if not isinstance(u, (VectorTestFunction, ScalarTestFunction,
ScalarField, VectorField)):
raise TypeError(
'{} must be of type ScalarTestFunction or VectorTestFunction'.
format(str(u)))
if u.space.domain.mapping is None:
raise ValueError(
'The pull-back can be performed only to mapped domains')
space = u.space
kind = space.kind
dim = space.ldim
el = get_logical_test_function(u)
if space.is_broken:
assert mapping is not None
else:
mapping = space.domain.mapping
J = mapping.jacobian
if isinstance(kind, (UndefinedSpaceType, H1SpaceType)):
expr = el
elif isinstance(kind, HdivSpaceType):
expr = (J / J.det()) * ImmutableDenseMatrix(
tuple(el[i] for i in range(dim)))
elif isinstance(kind, HcurlSpaceType):
expr = J.inv().T * ImmutableDenseMatrix(
tuple(el[i] for i in range(dim)))
elif isinstance(kind, L2SpaceType):
expr = J.det() * el
# elif isinstance(kind, UndefinedSpaceType):
# raise ValueError('kind must be specified in order to perform the pull-back transformation')
else:
raise ValueError("Unrecognized kind '{}' of space {}".format(
kind, str(u.space)))
obj = Expr.__new__(cls, u)
obj._expr = expr
obj._kind = kind
obj._test = el
return obj
def _to_matrix_form(expr, *, trials=None, tests=None):
"""
Create a matrix representation of input expression, based on trial and
test functions. We have three options:
1. if both the trial and test functions are given, we treat the expression
as a bilinear form and convert it to a (n_rows x n_cols) rectangular
matrix with n_rows = len(tests) and n_cols = len(trials);
2. if only the test functions are given, we treat the expression as a
linear form and convert it to a (n_rows x 1) matrix (column vector) with
n_rows = len(tests);
3. if neither the trial nor the test functions are given, we treat the
expression as a scalar functional and convert it to a 1x1 matrix.
Parameters
----------
expr : sympy.Expr
Expression corresponding to a bilinear/linear form or functional.
trials : iterable
List of all scalar trial functions (after unpacking vector functions).
tests : iterable
List of all scalar test functions (after unpacking vector functions).
Returns
-------
M : sympy.matrices.immutable.ImmutableDenseMatrix
Matrix representation of input expression.
"""
# Bilinear form
if trials and tests:
M = Matrix.zeros(len(tests), len(trials))
for i, test in enumerate(tests):
subs_i = {v: 0 for v in tests if v != test}
expr_i = expr.subs(subs_i)
for j, trial in enumerate(trials):
subs_j = {u: 0 for u in trials if u != trial}
M[i, j] = expr_i.subs(subs_j)
# Linear form
elif tests:
M = Matrix.zeros(len(tests), 1)
for i, test in enumerate(tests):
subs_i = {v: 0 for v in tests if v != test}
M[i, 0] = expr.subs(subs_i)
# Functional
else:
M = [[expr]]
return ImmutableDenseMatrix(M)
def eval(cls, *_args):
if not _args:
return
u = _args[0]
if isinstance(u, (VectorTestFunction, VectorField)):
raise NotImplementedError('TODO')
return ImmutableDenseMatrix([[dx(dx(u)), dx(dy(u))],
[dx(dy(u)), dy(dy(u))]])
def eval(cls, *_args):
if not _args:
return
u = _args[0]
if isinstance(u, VectorFunction):
raise NotImplementedError('TODO')
return ImmutableDenseMatrix([[dx1(dx1(u)), dx1(dx2(u))],
[dx1(dx2(u)), dx2(dx2(u))]])
def eval(cls, *_args):
if not _args:
return
u = _args[0]
du = (dx1(u), dx2(u), dx3(u))
if isinstance(du[0], (Tuple, Matrix, ImmutableDenseMatrix)):
lines = [list(d[:]) for d in du]
else:
lines = [[d] for d in du]
v = ImmutableDenseMatrix(lines)
return v
def __init__(self, n, link_length = 0.3, k_val = 1):
l = symbols('l') #the link length
k = symbols('k') #the liquid vicousness
q = symbols('q:' + str(n - 1)) #the joint angle
#################################################
################### Question ###################
#################################################
assert(n==3)
# #this is the swimmer matrix part
# F1 = ImmutableDenseMatrix([k[0] * l[0] * (cos(q[0]) * dx0 - sin(q[0]) * dy0 + sin(q[0]) * l[0] * dtheta0),
# 2 * k[0] * l[0] * (sin(q[0]) * dx0 + cos(q[0]) * dy0 - l[0] * (1 + cos(q[0])) * dtheta0 + l[0] * u[0]),
# 2.0 / 3.0 * k[0] * l[0]**3.0 * (dtheta0 - u[0])])
# F2 = ImmutableDenseMatrix([k[0] * l[0] * dx0, 2 * k[0] * l[0] * dy0, 2.0 / 3.0 * k[0] * l[0]**3 * dtheta0])
# F3 = ImmutableDenseMatrix([k[0] * l[0] * (cos(q[1]) * dx0 + sin(q[1]) * dy0 + l[0] * sin(q[1]) * l[0] * dtheta0),
# 2 * k[0] * l[0] * (-sin(q[1]) * dx0 + cos(q[1]) * dy0 + l[0] * (1 + cos(q[1])) * dtheta0 + l[0] * u[1]),
# 2.0 / 3.0 * k[0] * l[0]**3.0 * (dtheta0 + u[1])])
# Q1 = ImmutableDenseMatrix([[cos(q[0]), sin(q[0]), 0],
# [-sin(q[0]), cos(q[0]), 0],
# [l[0] * sin(q[0]), - l[0] * (1 + cos(q[0])), 1]])
# Q2 = ImmutableDenseMatrix([[cos(q[1]), -sin(q[1]), 0],
# [sin(q[1]), cos(q[1]), 0],
# [l[0] * sin(q[1]), l[0] * (1 + cos(q[1])), 1]])
# F = (Q1 * F1 + F2 + Q2 * F3).simplify()
# w1 = ImmutableDenseMatrix(diff(flatten(F), dx0), diff(flatten(F), dy0, diff(flatten(F), dtheta0)))
w1 = ImmutableDenseMatrix(
[
[-1.0/2 * k * l * (cos(2 * q[0]) + cos(2 * q[1]) - 8),
-1.0/2 * k * l * (sin(2 * q[1]) - 2 * cos(q[0]) * sin(q[0])),
-1.0/2 * k * l * ( 2 * l * (cos(q[0])+2) * sin(q[0] + 4 * l * sin(q[1]) + l * sin(2*q[1])))],
[1.0/2 * k * l * (sin(2 * q[0]) - sin(2 * q[1])),
1.0/2 * k * l * (cos(2 * q[0]) + cos(2 * q[1]) + 10),
1.0/2 * k * l**2 * ( -4 * cos(q[0]) - cos(2 * q[0]) + 4 * cos(q[1]) + cos(2 * q[1]))],
[1.0/2 * k * l**2 * ( 4 * sin(q[0]) + sin(2 * q[0]) + 4 * sin(q[1]) + sin(2 * q[1])),
1.0/2 * k * l**2 * ( -4 * cos(q[0]) - cos(2 * q[0]) + 4 * cos(q[1]) + cos(2 * q[1])),
1.0/2 * k * l**3 * ( 8 * cos(q[0]) - cos(2 * q[0]) + 8 * cos(q[1]) + cos(2 * q[1]) + 18)
]
])
w2 = ImmutableDenseMatrix(
[
[2 * k * l**2 * sin(q[0]),
-2 * k * l**2 * sin(q[1])
],
[2 * k * l**2 * cos(q[0]),
2 * k * l**2 * cos(q[1])
],
[-2.0 / 3 * k * l**3 * (3 * cos(q[0]) + 4),
2.0 / 3 * k * l**3 * (3 * cos(q[1]) + 4)
]
])
#A = (w1.inv() * w2).simply()
#A = w1.LUsolve(w2)
#print(A)
#A.simplify()
#pprint(A)
#initialize self objects
self.l = l
self.k = k
self.q = q
self.n = n #the number of links
self.link_length = link_length
self.k_val = k_val
#set the value for all the variables
parameters = (self.k, self.l)
self.parameter_vals = [self.k_val, self.link_length]
#this can potentially be improved, probably through substituting in this function
self.w1_func = lambdify(q + parameters, w1)
self.w2_func = lambdify(q + parameters, w2)
def __init__(self, n, link_length = 0.3, k_val = 1):
l = symbols('l') #the link length
k = symbols('k') #the liquid vicousness
q = symbols('q:' + str(n - 1)) #the joint angle
#l and k are useless now
assert(n==3)
rho=1
a=4
b=1
demonimator = (np.pi**3*rho**3*(-25*a**8 - 186*a**6*b**2 - 218*a**4*b**4 - 42*a**2*b**6 - 9*b**8 + (a - b)*(a + b)*(19*a**6 + 63*a**4*b**2 + a**2*b**4 - 3*b**6)*cos(2*q[1]) -
(a - b)*(a + b)*cos(2*q[0])*(-19*a**6 - 63*a**4*b**2 - a**2*b**4 + 3*b**6 + (13*a**6 - 31*a**4*b**2 - a**2*b**4 + 3*b**6)*cos(2*q[1])) +
32*b**2*cos(q[1])*(-9*a**4*b**2*cos(q[0])**2 - (2*a**3 + a*b**2)**2*sin(q[0])**2) + 32*a**2*b**4*(2*a**2 + b**2)*sin(q[0])*sin(q[1]) + 16*a**2*b**2*(-2*a**4 + a**2*b**2 + b**4)*sin(q[0])*sin(2*q[1]) +
2*cos(q[0])*(-8*a**2*b**2*(4*a**4 + 13*a**2*b**2 + b**4) - 48*a**4*b**4*cos(q[1]) +
(a - b)*(a + b)*(8*a**2*b**2*((4*a**2 - b**2)*cos(2*q[1]) - 2*(2*a**2 + b**2)*sin(q[0])*sin(q[1])) + (-3*a**6 + a**4*b**2 - 17*a**2*b**4 + 3*b**6)*sin(q[0])*sin(2*q[1])))))/8.
nominator = ImmutableDenseMatrix(
np.array([
[(a*np.pi**3*rho**3*(2*b**2*(19*a**6 + 37*a**4*b**2 + 9*a**2*b**4 + 3*b**6)*sin(q[0]) + (a**2 + b**2)*(a**2 + 2*b**2)*(a**4 + 4*a**2*b**2 - b**4)*sin(2*q[0]) - 16*a**6*b**2*sin(q[0] - 2*q[1]) +
8*a**4*b**4*sin(q[0] - 2*q[1]) + 8*a**2*b**6*sin(q[0] - 2*q[1]) + 48*a**4*b**4*sin(q[0] - q[1]) + 24*a**2*b**6*sin(q[0] - q[1]) - 3*a**6*b**2*sin(q[1]) - 21*a**4*b**4*sin(q[1]) -
13*a**2*b**6*sin(q[1]) - 3*b**8*sin(q[1]) + a**8*sin(2*q[1]) + 7*a**6*b**2*sin(2*q[1]) - 3*a**4*b**4*sin(2*q[1]) - 3*a**2*b**6*sin(2*q[1]) - 2*b**8*sin(2*q[1]) + 16*a**4*b**4*sin(q[0] + q[1]) +
8*a**2*b**6*sin(q[0] + q[1]) - a**8*sin(2*(q[0] + q[1])) - 4*a**6*b**2*sin(2*(q[0] + q[1])) + 2*a**4*b**4*sin(2*(q[0] + q[1])) + 4*a**2*b**6*sin(2*(q[0] + q[1])) - b**8*sin(2*(q[0] + q[1])) +
a**6*b**2*sin(2*q[0] + q[1]) + 5*a**4*b**4*sin(2*q[0] + q[1]) + 3*a**2*b**6*sin(2*q[0] + q[1]) - b**8*sin(2*q[0] + q[1]) + 2*b**2*(-9*a**6 + 7*a**4*b**2 - 3*a**2*b**4 + b**6)*sin(q[0] + 2*q[1])))/8.,
(a*np.pi**3*rho**3*(b**2*(3*a**6 + 21*a**4*b**2 + 13*a**2*b**4 + 3*b**6 + 16*a**2*b**2*(2*a**2 + b**2)*cos(q[1]) - (a**2 + b**2)*(a**4 + 4*a**2*b**2 - b**4)*cos(2*q[1]))*sin(q[0]) +
(-a**8 - 7*a**6*b**2 + 3*a**4*b**4 + 3*a**2*b**6 + 2*b**8 + 2*b**2*(a**6 - 3*a**4*b**2 + 7*a**2*b**4 - b**6)*cos(q[1]) + (a**8 + 4*a**6*b**2 - 2*a**4*b**4 - 4*a**2*b**6 + b**8)*cos(2*q[1]))*sin(2*q[0]) -
2*b**2*(19*a**6 + 37*a**4*b**2 + 9*a**2*b**4 + 3*b**6 + 16*a**2*b**2*(2*a**2 + b**2)*cos(q[0]) + (-17*a**6 + 11*a**4*b**2 + a**2*b**4 + b**6)*cos(2*q[0]))*sin(q[1]) +
(a**2 + b**2)*(-a**4 - 4*a**2*b**2 + b**4)*(a**2 + 2*b**2 + b**2*cos(q[0]) + (-a**2 + b**2)*cos(2*q[0]))*sin(2*q[1])))/8.],
[(a*np.pi**3*rho**3*(-24*a**4*b**4 - (a**2 + b**2)*(2*a**2 + b**2)*(a**4 + 4*a**2*b**2 - b**4)*cos(2*q[0]) +
b**2*(-5*a**6 - 31*a**4*b**2 - 3*a**2*b**4 - b**6 - (a**2 + b**2)*(a**4 + 4*a**2*b**2 - b**4)*cos(2*q[0]))*cos(q[1]) + (a - b)*(a + b)*(2*a**6 + 13*a**4*b**2 + b**6)*cos(2*q[1]) +
2*b**2*cos(q[0])*(-21*a**6 - 43*a**4*b**2 - 3*a**2*b**4 - b**6 - 72*a**4*b**2*cos(q[1]) + (15*a**6 - 17*a**4*b**2 - 3*a**2*b**4 + b**6)*cos(2*q[1])) +
b**2*(a**2 + b**2)*(a**4 + 4*a**2*b**2 - b**4)*sin(2*q[0])*sin(q[1]) - 2*b**2*(-9*a**6 + 7*a**4*b**2 - 3*a**2*b**4 + b**6)*sin(q[0])*sin(2*q[1])))/8.,
(a*np.pi**3*rho**3*(-24*a**4*b**4 + (a - b)*(a + b)*(2*a**6 + 13*a**4*b**2 + b**6)*cos(2*q[0]) +
2*b**2*(-21*a**6 - 43*a**4*b**2 - 3*a**2*b**4 - b**6 + (15*a**6 - 17*a**4*b**2 - 3*a**2*b**4 + b**6)*cos(2*q[0]))*cos(q[1]) - (a**2 + b**2)*(2*a**2 + b**2)*(a**4 + 4*a**2*b**2 - b**4)*cos(2*q[1]) +
b**2*cos(q[0])*(-5*a**6 - 31*a**4*b**2 - 3*a**2*b**4 - b**6 - 144*a**4*b**2*cos(q[1]) - (a**2 + b**2)*(a**4 + 4*a**2*b**2 - b**4)*cos(2*q[1])) -
2*b**2*(-9*a**6 + 7*a**4*b**2 - 3*a**2*b**4 + b**6)*sin(2*q[0])*sin(q[1]) + b**2*(a**2 + b**2)*(a**4 + 4*a**2*b**2 - b**4)*sin(q[0])*sin(2*q[1])))/8.],
[(np.pi**3*rho**3*(-3*a**8 - 30*a**6*b**2 - 46*a**4*b**4 - 14*a**2*b**6 - 3*b**8 + 2*(a**8 + 4*a**6*b**2 - 2*a**4*b**4 - 4*a**2*b**6 + b**8)*cos(2*q[0])*cos(q[1])**2 +
(a**2 - b**2)**2*(a**4 + 6*a**2*b**2 + b**4)*cos(2*q[1]) - 16*a**2*b**2*(2*a**2 + b**2)*(-b**2 + (a - b)*(a + b)*cos(q[1]))*sin(q[0])*sin(q[1]) +
2*cos(q[0])*(-24*a**4*b**4*cos(q[1])*(1 + 3*cos(q[1])) - 8*a**2*b**2*(2*a**2 + b**2)**2*sin(q[1])**2 - (a**8 + 4*a**6*b**2 - 2*a**4*b**4 - 4*a**2*b**6 + b**8)*sin(q[0])*sin(2*q[1]))))/8.,
(np.pi**3*rho**3*(3*a**8 + 30*a**6*b**2 + 46*a**4*b**4 + 14*a**2*b**6 + 3*b**8 - (a**2 - b**2)**2*(a**4 + 6*a**2*b**2 + b**4)*cos(2*q[0]) +
8*a**2*b**2*(4*a**4 + 13*a**2*b**2 + b**4 + 6*a**2*b**2*cos(q[0]) - (4*a**4 - 5*a**2*b**2 + b**4)*cos(2*q[0]))*cos(q[1]) -
2*(a**8 + 4*a**6*b**2 - 2*a**4*b**4 - 4*a**2*b**6 + b**8)*cos(q[0])**2*cos(2*q[1]) + 16*a**2*b**2*(2*a**2 + b**2)*(-b**2 + (a - b)*(a + b)*cos(q[0]))*sin(q[0])*sin(q[1]) +
(a**8 + 4*a**6*b**2 - 2*a**4*b**4 - 4*a**2*b**6 + b**8)*sin(2*q[0])*sin(2*q[1])))/8.]
])
)
A = nominator / demonimator
#initialize self objects
self.l = l
self.k = k
self.q = q
self.n = n #the number of links
self.link_length = link_length
self.k_val = k_val
#set the value for all the variables
parameters = (self.k, self.l)
self.parameter_vals = [self.k_val, self.link_length]
#this can potentially be improved, probably through substituting in this function
self.A_func = lambdify(q, A)
def __new__(cls,
name,
transformation=None,
parent=None,
location=None,
rotation_matrix=None,
vector_names=None,
variable_names=None):
"""
The orientation/location parameters are necessary if this system
is being defined at a certain orientation or location wrt another.
Parameters
==========
name : str
The name of the new CoordSys3D instance.
transformation : Lambda, Tuple, str
Transformation defined by transformation equations or chosen
from predefined ones.
location : Vector
The position vector of the new system's origin wrt the parent
instance.
rotation_matrix : SymPy ImmutableMatrix
The rotation matrix of the new coordinate system with respect
to the parent. In other words, the output of
new_system.rotation_matrix(parent).
parent : CoordSys3D
The coordinate system wrt which the orientation/location
(or both) is being defined.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
"""
name = str(name)
Vector = sympy.vector.Vector
BaseVector = sympy.vector.BaseVector
Point = sympy.vector.Point
if not isinstance(name, string_types):
raise TypeError("name should be a string")
if transformation is not None:
if (location is not None) or (rotation_matrix is not None):
raise ValueError("specify either `transformation` or "
"`location`/`rotation_matrix`")
if isinstance(transformation, (Tuple, tuple, list)):
if isinstance(transformation[0], MatrixBase):
rotation_matrix = transformation[0]
location = transformation[1]
else:
transformation = Lambda(transformation[0],
transformation[1])
elif isinstance(transformation, Callable):
x1, x2, x3 = symbols('x1 x2 x3', cls=Dummy)
transformation = Lambda((x1, x2, x3),
transformation(x1, x2, x3))
elif isinstance(transformation, string_types):
transformation = Symbol(transformation)
elif isinstance(transformation, (Symbol, Lambda)):
pass
else:
raise TypeError("transformation: "
"wrong type {0}".format(type(transformation)))
# If orientation information has been provided, store
# the rotation matrix accordingly
if rotation_matrix is None:
rotation_matrix = ImmutableDenseMatrix(eye(3))
else:
if not isinstance(rotation_matrix, MatrixBase):
raise TypeError("rotation_matrix should be an Immutable" +
"Matrix instance")
rotation_matrix = rotation_matrix.as_immutable()
# If location information is not given, adjust the default
# location as Vector.zero
if parent is not None:
if not isinstance(parent, CoordSys3D):
raise TypeError("parent should be a " + "CoordSys3D/None")
if location is None:
location = Vector.zero
else:
if not isinstance(location, Vector):
raise TypeError("location should be a Vector")
# Check that location does not contain base
# scalars
for x in location.free_symbols:
if isinstance(x, BaseScalar):
raise ValueError("location should not contain" +
" BaseScalars")
origin = parent.origin.locate_new(name + '.origin', location)
else:
location = Vector.zero
origin = Point(name + '.origin')
if transformation is None:
transformation = Tuple(rotation_matrix, location)
if isinstance(transformation, Tuple):
lambda_transformation = CoordSys3D._compose_rotation_and_translation(
transformation[0], transformation[1], parent)
r, l = transformation
l = l._projections
lambda_lame = CoordSys3D._get_lame_coeff('cartesian')
lambda_inverse = lambda x, y, z: r.inv() * Matrix(
[x - l[0], y - l[1], z - l[2]])
elif isinstance(transformation, Symbol):
trname = transformation.name
lambda_transformation = CoordSys3D._get_transformation_lambdas(
trname)
if parent is not None:
if parent.lame_coefficients() != (S.One, S.One, S.One):
raise ValueError('Parent for pre-defined coordinate '
'system should be Cartesian.')
lambda_lame = CoordSys3D._get_lame_coeff(trname)
lambda_inverse = CoordSys3D._set_inv_trans_equations(trname)
elif isinstance(transformation, Lambda):
if not CoordSys3D._check_orthogonality(transformation):
raise ValueError("The transformation equation does not "
"create orthogonal coordinate system")
lambda_transformation = transformation
lambda_lame = CoordSys3D._calculate_lame_coeff(
lambda_transformation)
lambda_inverse = None
else:
lambda_transformation = lambda x, y, z: transformation(x, y, z)
lambda_lame = CoordSys3D._get_lame_coeff(transformation)
lambda_inverse = None
if variable_names is None:
if isinstance(transformation, Lambda):
variable_names = ["x1", "x2", "x3"]
elif isinstance(transformation, Symbol):
if transformation.name == 'spherical':
variable_names = ["r", "theta", "phi"]
elif transformation.name == 'cylindrical':
variable_names = ["r", "theta", "z"]
else:
variable_names = ["x", "y", "z"]
else:
variable_names = ["x", "y", "z"]
if vector_names is None:
vector_names = ["i", "j", "k"]
# All systems that are defined as 'roots' are unequal, unless
# they have the same name.
# Systems defined at same orientation/position wrt the same
# 'parent' are equal, irrespective of the name.
# This is true even if the same orientation is provided via
# different methods like Axis/Body/Space/Quaternion.
# However, coincident systems may be seen as unequal if
# positioned/oriented wrt different parents, even though
# they may actually be 'coincident' wrt the root system.
if parent is not None:
obj = super(CoordSys3D, cls).__new__(cls, Symbol(name),
transformation, parent)
else:
obj = super(CoordSys3D, cls).__new__(cls, Symbol(name),
transformation)
obj._name = name
# Initialize the base vectors
_check_strings('vector_names', vector_names)
vector_names = list(vector_names)
latex_vects = [(r'\mathbf{\hat{%s}_{%s}}' % (x, name))
for x in vector_names]
pretty_vects = ['%s_%s' % (x, name) for x in vector_names]
obj._vector_names = vector_names
v1 = BaseVector(0, obj, pretty_vects[0], latex_vects[0])
v2 = BaseVector(1, obj, pretty_vects[1], latex_vects[1])
v3 = BaseVector(2, obj, pretty_vects[2], latex_vects[2])
obj._base_vectors = (v1, v2, v3)
# Initialize the base scalars
_check_strings('variable_names', vector_names)
variable_names = list(variable_names)
latex_scalars = [(r"\mathbf{{%s}_{%s}}" % (x, name))
for x in variable_names]
pretty_scalars = ['%s_%s' % (x, name) for x in variable_names]
obj._variable_names = variable_names
obj._vector_names = vector_names
x1 = BaseScalar(0, obj, pretty_scalars[0], latex_scalars[0])
x2 = BaseScalar(1, obj, pretty_scalars[1], latex_scalars[1])
x3 = BaseScalar(2, obj, pretty_scalars[2], latex_scalars[2])
obj._base_scalars = (x1, x2, x3)
obj._transformation = transformation
obj._transformation_lambda = lambda_transformation
obj._lame_coefficients = lambda_lame(x1, x2, x3)
obj._transformation_from_parent_lambda = lambda_inverse
setattr(obj, variable_names[0], x1)
setattr(obj, variable_names[1], x2)
setattr(obj, variable_names[2], x3)
setattr(obj, vector_names[0], v1)
setattr(obj, vector_names[1], v2)
setattr(obj, vector_names[2], v3)
# Assign params
obj._parent = parent
if obj._parent is not None:
obj._root = obj._parent._root
else:
obj._root = obj
obj._parent_rotation_matrix = rotation_matrix
obj._origin = origin
# Return the instance
return obj
def _to_matrix_form(expr, *, trials=None, tests=None, domain=None):
"""
Create a matrix representation of input expression, based on trial and
test functions. We have three options:
1. if both the trial and test functions are given, we treat the expression
as a bilinear form and convert it to a (n_rows x n_cols) rectangular
matrix with n_rows = len(tests) and n_cols = len(trials);
2. if only the test functions are given, we treat the expression as a
linear form and convert it to a (n_rows x 1) matrix (column vector) with
n_rows = len(tests);
3. if neither the trial nor the test functions are given, we treat the
expression as a scalar functional and convert it to a 1x1 matrix.
The domain is needed when the expression is defined over an interface,
to delete the plus/minus operators and the InterfaceMapping object.
Parameters
----------
expr : sympy.Expr
Expression corresponding to a bilinear/linear form or functional.
trials : iterable
List of all scalar trial functions (after unpacking vector functions).
tests : iterable
List of all scalar test functions (after unpacking vector functions).
domain : Domain
domain of expr
Returns
-------
M : sympy.matrices.immutable.ImmutableDenseMatrix
Matrix representation of input expression.
"""
if not isinstance(domain, Interface):
atoms = expr.atoms(minus, plus)
new_atoms = [e.args[0] for e in atoms]
subs = tuple(zip(atoms, new_atoms))
expr = expr.subs(subs)
mapping = expr.atoms(InterfaceMapping)
if mapping:
mapping = list(mapping)[0]
expr = expr.subs(mapping, mapping.minus)
# Bilinear form
if trials and tests:
M = [[None for j in trials] for i in tests]
for i, test in enumerate(tests):
subs_i = {v: 0 for v in tests if v != test}
expr_i = expr.subs(subs_i)
for j, trial in enumerate(trials):
subs_j = {u: 0 for u in trials if u != trial}
M[i][j] = expr_i.subs(subs_j)
M = Matrix(M)
# Linear form
elif tests:
M = [[None] for i in tests]
for i, test in enumerate(tests):
subs_i = {v: 0 for v in tests if v != test}
M[i][0] = expr.subs(subs_i)
M = Matrix(M)
# Functional
else:
M = [[expr]]
return ImmutableDenseMatrix(M)
def eval(cls, expr, domain):
"""."""
dim = domain.dim
if isinstance(expr, Add):
args = [cls.eval(a, domain=domain) for a in expr.args]
o = args[0]
for arg in args[1:]:
o = o + arg
return o
elif isinstance(expr, Mul):
args = [cls.eval(a, domain=domain) for a in expr.args]
o = args[0]
for arg in args[1:]:
o = o * arg
return o
elif isinstance(expr, Abs):
return Abs(cls.eval(expr.args[0], domain=domain))
elif isinstance(expr, Pow):
base = cls.eval(expr.base, domain=domain)
exp = cls.eval(expr.exp, domain=domain)
return base**exp
elif isinstance(expr, JacobianSymbol):
axis = expr.axis
J = expr.mapping.jacobian_expr
if not axis is None:
if expr.mapping.ldim > 1:
J = J.col_del(axis)
if expr.mapping.ldim == 1:
J = J.eye(1)
if isinstance(domain, Interface):
mapping = expr.mapping
assert mapping.is_plus is not mapping.is_minus
if mapping.is_plus and mapping.is_analytical:
domain = domain.plus
axis = domain.axis
ext = domain.ext
domain = domain.domain
coordinates = domain.coordinates
if isinstance(domain, (NCube, NCubeInterior)):
bounds = domain.min_coords if ext == -1 else domain.max_coords
J = J.subs(coordinates[axis], bounds[axis])
newcoords = [Symbol(c.name + "_plus") for c in coordinates]
subs = list(zip(coordinates, newcoords))
J = J.subs(subs)
return J
elif isinstance(expr, JacobianInverseSymbol):
axis = expr.axis
J = expr.mapping.jacobian_inv_expr
if not axis is None:
J = J.col_del(axis)
if isinstance(domain, Interface):
mapping = expr.mapping
assert mapping.is_plus is not mapping.is_minus
if mapping.is_plus and mapping.is_analytical:
domain = domain.plus
axis = domain.axis
ext = domain.ext
domain = domain.domain
coordinates = domain.coordinates
if isinstance(domain, (NCube, NCubeInterior)):
bounds = domain.min_coords if ext == -1 else domain.max_coords
J = J.subs(coordinates[axis], bounds[axis])
newcoords = [Symbol(c.name + "_plus") for c in coordinates]
subs = list(zip(coordinates, newcoords))
J = J.subs(subs)
return J
elif isinstance(expr, SymbolicDeterminant):
return cls.eval(expr.arg, domain=domain).det().factor()
elif isinstance(expr, SymbolicTrace):
return cls.eval(expr.arg, domain=domain).trace()
elif isinstance(expr, Transpose):
return cls.eval(expr.arg, domain=domain).T
elif isinstance(expr, Inverse):
return cls.eval(expr.arg, domain=domain).inv()
elif isinstance(expr, ScalarFunction):
return expr
elif isinstance(expr, VectorFunction):
return ImmutableDenseMatrix([[expr[i]] for i in range(dim)])
elif isinstance(expr, (minus, plus)):
newexpr = cls.eval(expr.args[0], domain=domain)
return type(expr)(newexpr)
elif isinstance(expr, PullBack):
return cls.eval(expr.expr, domain=domain)
elif isinstance(expr, MatrixElement):
base = cls.eval(expr.base, domain=domain)
return base[expr.indices]
elif isinstance(expr, BasicForm):
# ...
dim = expr.ldim
domains = expr.domain
if not isinstance(domains, Union):
domains = (domains, )
# ...
d_expr = {}
d_int = {}
for d in domains:
d_expr[d] = S.Zero
# ...
if isinstance(expr.expr, Add):
for a in expr.expr.args:
domains = _get_domain(a)
if not isinstance(domains, Union):
domains = (domains, )
# ...
for d in domains:
d_expr[d] += a
# ...
else:
# ...
if isinstance(expr, Functional):
domains = expr.domain
else:
domains = _get_domain(expr.expr)
if isinstance(domains, Union):
domains = list(domains._args)
elif not is_sequence(domains):
domains = (domains, )
# ...
# ...
for d in domains:
d_expr[d] = expr.expr
# ...
trials, tests = _get_trials_tests(expr, flatten=False)
# ... treating interfaces
keys = [k for k in d_expr.keys() if isinstance(k, Interface)]
for interface in keys:
newexpr = d_expr.pop(interface)
ls_int, d_bnd = _split_expr_over_interface(newexpr,
interface,
tests=tests,
trials=trials)
# ...
for a in ls_int:
if (a.target, a.trial, a.test) in d_int:
d_int[a.target, a.trial, a.test] += a.expr
else:
d_int[a.target, a.trial, a.test] = a.expr
for k, v in d_bnd.items():
if k in d_expr.keys():
d_expr[k] += v
else:
d_expr[k] = v
# ...
trials, tests = _get_trials_tests(expr, flatten=True)
d_new = {}
for domain, a in d_expr.items():
newexpr = cls.eval(a, domain=domain)
if newexpr != 0:
# TODO ARA make sure thre is no problem with psydac
# we should always take the interior of a domain
if not isinstance(domain,
(Boundary, Interface, InteriorDomain)):
domain = domain.interior
d_new[domain] = _to_matrix_form(newexpr,
trials=trials,
tests=tests,
domain=domain)
if len(d_new) == 0:
# ... corner case where the expression is zero
for domain, a in d_expr.items():
if not isinstance(domain,
(Boundary, Interface, InteriorDomain)):
domain = domain.interior
d_new[domain] = _to_matrix_form(S.Zero,
trials=trials,
tests=tests,
domain=domain)
if isinstance(domain, Boundary):
return (BoundaryExpression(domain, d_new[domain]), )
elif isinstance(domain, BasicDomain):
return (DomainExpression(domain, d_new[domain]), )
# ... treating subdomains
keys = [k for k in d_new.keys() if isinstance(k, Union)]
for domain in keys:
newexpr = d_new.pop(domain)
d = dict((interior, newexpr) for interior in domain.as_tuple())
# ...
for k, v in d.items():
if k in d_new.keys():
d_new[k] += v
else:
d_new[k] = v
ls = []
for key in d_int:
domain, u, v = key
expr = d_int[domain, u, v]
newexpr = cls.eval(expr, domain=domain)
if newexpr != 0:
newexpr = _to_matrix_form(newexpr,
trials=trials,
tests=tests,
domain=domain)
ls += [InterfaceExpression(domain, u, v, newexpr)]
for domain, newexpr in d_new.items():
if isinstance(domain, Boundary):
ls += [BoundaryExpression(domain, newexpr)]
elif isinstance(domain, BasicDomain):
ls += [DomainExpression(domain, newexpr)]
else:
raise TypeError('not implemented for {}'.format(
type(domain)))
# ...
return tuple(ls)
elif isinstance(expr, Integral):
domain = expr.domain
expr = cls.eval(expr._args[0], domain=domain)
return expr
elif isinstance(expr, NormalVector):
lines = [[expr[i] for i in range(dim)]]
return ImmutableDenseMatrix(lines)
elif isinstance(expr, TangentVector):
lines = [[expr[i] for i in range(dim)]]
return ImmutableDenseMatrix(lines)
elif isinstance(expr, BasicExpr):
return cls.eval(expr.expr, domain=domain)
elif isinstance(expr, _diff_ops):
op = type(expr)
if domain.mapping is None:
new = eval('Logical{0}_{1}d'.format(op, dim))
else:
new = eval('{0}_{1}d'.format(op, dim))
args = [cls.eval(i, domain=domain) for i in expr.args]
return new(*args)
elif isinstance(expr, _generic_ops):
# if i = Dot(...) then type(i) is Grad
op = type(expr)
new = eval('{0}_{1}d'.format(op, dim))
args = [cls.eval(i, domain=domain) for i in expr.args]
return new(*args)
elif isinstance(expr, Trace):
# TODO treate different spaces
if expr.order == 0:
return cls.eval(expr.expr, domain=domain)
elif expr.order == 1:
# TODO give a name to normal vector
normal_vector_name = 'n'
n = NormalVector(normal_vector_name)
M = cls.eval(expr.expr, domain=domain)
if dim == 1:
return M
else:
if isinstance(M, (Add, Mul)):
ls = M.atoms(Tuple)
for i in ls:
M = M.subs(i, Matrix(i))
M = simplify(M)
e = 0
for i in range(0, dim):
e += M[i] * n[i]
return e
else:
raise ValueError(
'> Only traces of order 0 and 1 are available')
elif isinstance(expr, (Matrix, ImmutableDenseMatrix)):
n, m = expr.shape
lines = []
for i in range(0, n):
line = []
for j in range(0, m):
line.append(cls.eval(expr[i, j], domain=domain))
lines.append(line)
return ImmutableDenseMatrix(lines)
elif isinstance(expr, LogicalExpr):
domain = expr.domain
expr = cls(expr.expr, domain=domain)
return LogicalExpr(expr, domain=domain)
return expr
def test_DiscreteMarkovChain():
# pass only the name
X = DiscreteMarkovChain("X")
assert isinstance(X.state_space, Range)
assert X.index_set == S.Naturals0
assert isinstance(X.transition_probabilities, MatrixSymbol)
t = symbols('t', positive=True, integer=True)
assert isinstance(X[t], RandomIndexedSymbol)
assert E(X[0]) == Expectation(X[0])
raises(TypeError, lambda: DiscreteMarkovChain(1))
raises(NotImplementedError, lambda: X(t))
raises(NotImplementedError, lambda: X.communication_classes())
raises(NotImplementedError, lambda: X.canonical_form())
raises(NotImplementedError, lambda: X.decompose())
nz = Symbol('n', integer=True)
TZ = MatrixSymbol('M', nz, nz)
SZ = Range(nz)
YZ = DiscreteMarkovChain('Y', SZ, TZ)
assert P(Eq(YZ[2], 1), Eq(YZ[1], 0)) == TZ[0, 1]
raises(ValueError, lambda: sample_stochastic_process(t))
raises(ValueError, lambda: next(sample_stochastic_process(X)))
# pass name and state_space
# any hashable object should be a valid state
# states should be valid as a tuple/set/list/Tuple/Range
sym, rainy, cloudy, sunny = symbols('a Rainy Cloudy Sunny', real=True)
state_spaces = [(1, 2, 3), [Str('Hello'), sym, DiscreteMarkovChain],
Tuple(1, exp(sym), Str('World'), sympify=False),
Range(-1, 5, 2), [rainy, cloudy, sunny]]
chains = [
DiscreteMarkovChain("Y", state_space) for state_space in state_spaces
]
for i, Y in enumerate(chains):
assert isinstance(Y.transition_probabilities, MatrixSymbol)
assert Y.state_space == state_spaces[i] or Y.state_space == FiniteSet(
*state_spaces[i])
assert Y.number_of_states == 3
with ignore_warnings(
UserWarning): # TODO: Restore tests once warnings are removed
assert P(Eq(Y[2], 1), Eq(Y[0], 2),
evaluate=False) == Probability(Eq(Y[2], 1), Eq(Y[0], 2))
assert E(Y[0]) == Expectation(Y[0])
raises(ValueError, lambda: next(sample_stochastic_process(Y)))
raises(TypeError, lambda: DiscreteMarkovChain("Y", dict((1, 1))))
Y = DiscreteMarkovChain("Y", Range(1, t, 2))
assert Y.number_of_states == ceiling((t - 1) / 2)
# pass name and transition_probabilities
chains = [
DiscreteMarkovChain("Y", trans_probs=Matrix([[]])),
DiscreteMarkovChain("Y", trans_probs=Matrix([[0, 1], [1, 0]])),
DiscreteMarkovChain("Y",
trans_probs=Matrix([[pi, 1 - pi], [sym, 1 - sym]]))
]
for Z in chains:
assert Z.number_of_states == Z.transition_probabilities.shape[0]
assert isinstance(Z.transition_probabilities, ImmutableDenseMatrix)
# pass name, state_space and transition_probabilities
T = Matrix([[0.5, 0.2, 0.3], [0.2, 0.5, 0.3], [0.2, 0.3, 0.5]])
TS = MatrixSymbol('T', 3, 3)
Y = DiscreteMarkovChain("Y", [0, 1, 2], T)
YS = DiscreteMarkovChain("Y", ['One', 'Two', 3], TS)
assert Y.joint_distribution(1, Y[2],
3) == JointDistribution(Y[1], Y[2], Y[3])
raises(ValueError, lambda: Y.joint_distribution(Y[1].symbol, Y[2].symbol))
assert P(Eq(Y[3], 2), Eq(Y[1], 1)).round(2) == Float(0.36, 2)
assert (P(Eq(YS[3], 2), Eq(YS[1], 1)) -
(TS[0, 2] * TS[1, 0] + TS[1, 1] * TS[1, 2] +
TS[1, 2] * TS[2, 2])).simplify() == 0
assert P(Eq(YS[1], 1), Eq(YS[2], 2)) == Probability(Eq(YS[1], 1))
assert P(Eq(YS[3], 3), Eq(
YS[1],
1)) == TS[0, 2] * TS[1, 0] + TS[1, 1] * TS[1, 2] + TS[1, 2] * TS[2, 2]
TO = Matrix([[0.25, 0.75, 0], [0, 0.25, 0.75], [0.75, 0, 0.25]])
assert P(Eq(Y[3], 2),
Eq(Y[1], 1) & TransitionMatrixOf(Y, TO)).round(3) == Float(
0.375, 3)
with ignore_warnings(
UserWarning): ### TODO: Restore tests once warnings are removed
assert E(Y[3], evaluate=False) == Expectation(Y[3])
assert E(Y[3], Eq(Y[2], 1)).round(2) == Float(1.1, 3)
TSO = MatrixSymbol('T', 4, 4)
raises(
ValueError,
lambda: str(P(Eq(YS[3], 2),
Eq(YS[1], 1) & TransitionMatrixOf(YS, TSO))))
raises(TypeError,
lambda: DiscreteMarkovChain("Z", [0, 1, 2], symbols('M')))
raises(
ValueError,
lambda: DiscreteMarkovChain("Z", [0, 1, 2], MatrixSymbol('T', 3, 4)))
raises(ValueError, lambda: E(Y[3], Eq(Y[2], 6)))
raises(ValueError, lambda: E(Y[2], Eq(Y[3], 1)))
# extended tests for probability queries
TO1 = Matrix([[Rational(1, 4), Rational(3, 4), 0],
[Rational(1, 3),
Rational(1, 3),
Rational(1, 3)], [0, Rational(1, 4),
Rational(3, 4)]])
assert P(
And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)),
Eq(Probability(Eq(Y[0], 0)), Rational(1, 4))
& TransitionMatrixOf(Y, TO1)) == Rational(1, 16)
assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), TransitionMatrixOf(Y, TO1)) == \
Probability(Eq(Y[0], 0))/4
assert P(
Lt(X[1], 2) & Gt(X[1], 0),
Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2])
& TransitionMatrixOf(X, TO1)) == Rational(1, 4)
assert P(
Lt(X[1], 2) & Gt(X[1], 0),
Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1])
& TransitionMatrixOf(X, TO1)) == Rational(1, 4)
assert P(
Ne(X[1], 2) & Ne(X[1], 1),
Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2])
& TransitionMatrixOf(X, TO1)) is S.Zero
assert P(
Ne(X[1], 2) & Ne(X[1], 1),
Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1])
& TransitionMatrixOf(X, TO1)) is S.Zero
assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)),
Eq(Y[1], 1)) == 0.1 * Probability(Eq(Y[0], 0))
# testing properties of Markov chain
TO2 = Matrix([[S.One, 0, 0],
[Rational(1, 3),
Rational(1, 3),
Rational(1, 3)], [0, Rational(1, 4),
Rational(3, 4)]])
TO3 = Matrix([[Rational(1, 4), Rational(3, 4), 0],
[Rational(1, 3),
Rational(1, 3),
Rational(1, 3)], [0, Rational(1, 4),
Rational(3, 4)]])
Y2 = DiscreteMarkovChain('Y', trans_probs=TO2)
Y3 = DiscreteMarkovChain('Y', trans_probs=TO3)
assert Y3.fundamental_matrix() == ImmutableMatrix(
[[176, 81, -132], [36, 141, -52], [-44, -39, 208]]) / 125
assert Y2.is_absorbing_chain() == True
assert Y3.is_absorbing_chain() == False
assert Y2.canonical_form() == ([0, 1, 2], TO2)
assert Y3.canonical_form() == ([0, 1, 2], TO3)
assert Y2.decompose() == ([0, 1,
2], TO2[0:1, 0:1], TO2[1:3, 0:1], TO2[1:3, 1:3])
assert Y3.decompose() == ([0, 1, 2], TO3, Matrix(0, 3,
[]), Matrix(0, 0, []))
TO4 = Matrix([[Rational(1, 5),
Rational(2, 5),
Rational(2, 5)], [Rational(1, 10), S.Half,
Rational(2, 5)],
[Rational(3, 5),
Rational(3, 10),
Rational(1, 10)]])
Y4 = DiscreteMarkovChain('Y', trans_probs=TO4)
w = ImmutableMatrix([[Rational(11, 39),
Rational(16, 39),
Rational(4, 13)]])
assert Y4.limiting_distribution == w
assert Y4.is_regular() == True
assert Y4.is_ergodic() == True
TS1 = MatrixSymbol('T', 3, 3)
Y5 = DiscreteMarkovChain('Y', trans_probs=TS1)
assert Y5.limiting_distribution(w, TO4).doit() == True
assert Y5.stationary_distribution(condition_set=True).subs(
TS1, TO4).contains(w).doit() == S.true
TO6 = Matrix([[S.One, 0, 0, 0, 0], [S.Half, 0, S.Half, 0, 0],
[0, S.Half, 0, S.Half, 0], [0, 0, S.Half, 0, S.Half],
[0, 0, 0, 0, 1]])
Y6 = DiscreteMarkovChain('Y', trans_probs=TO6)
assert Y6.fundamental_matrix() == ImmutableMatrix(
[[Rational(3, 2), S.One, S.Half], [S.One, S(2), S.One],
[S.Half, S.One, Rational(3, 2)]])
assert Y6.absorbing_probabilities() == ImmutableMatrix(
[[Rational(3, 4), Rational(1, 4)], [S.Half, S.Half],
[Rational(1, 4), Rational(3, 4)]])
TO7 = Matrix([[Rational(1, 2),
Rational(1, 4),
Rational(1, 4)], [Rational(1, 2), 0,
Rational(1, 2)],
[Rational(1, 4),
Rational(1, 4),
Rational(1, 2)]])
Y7 = DiscreteMarkovChain('Y', trans_probs=TO7)
assert Y7.is_absorbing_chain() == False
assert Y7.fundamental_matrix() == ImmutableDenseMatrix(
[[Rational(86, 75),
Rational(1, 25),
Rational(-14, 75)],
[Rational(2, 25), Rational(21, 25),
Rational(2, 25)],
[Rational(-14, 75),
Rational(1, 25),
Rational(86, 75)]])
# test for zero-sized matrix functionality
X = DiscreteMarkovChain('X', trans_probs=Matrix([[]]))
assert X.number_of_states == 0
assert X.stationary_distribution() == Matrix([[]])
assert X.communication_classes() == []
assert X.canonical_form() == ([], Matrix([[]]))
assert X.decompose() == ([], Matrix([[]]), Matrix([[]]), Matrix([[]]))
assert X.is_regular() == False
assert X.is_ergodic() == False
# test communication_class
# see https://drive.google.com/drive/folders/1HbxLlwwn2b3U8Lj7eb_ASIUb5vYaNIjg?usp=sharing
# tutorial 2.pdf
TO7 = Matrix([[0, 5, 5, 0, 0], [0, 0, 0, 10, 0], [5, 0, 5, 0, 0],
[0, 10, 0, 0, 0], [0, 3, 0, 3, 4]]) / 10
Y7 = DiscreteMarkovChain('Y', trans_probs=TO7)
tuples = Y7.communication_classes()
classes, recurrence, periods = list(zip(*tuples))
assert classes == ([1, 3], [0, 2], [4])
assert recurrence == (True, False, False)
assert periods == (2, 1, 1)
TO8 = Matrix([[0, 0, 0, 10, 0, 0], [5, 0, 5, 0, 0, 0], [0, 4, 0, 0, 0, 6],
[10, 0, 0, 0, 0, 0], [0, 10, 0, 0, 0, 0], [0, 0, 0, 5, 5, 0]
]) / 10
Y8 = DiscreteMarkovChain('Y', trans_probs=TO8)
tuples = Y8.communication_classes()
classes, recurrence, periods = list(zip(*tuples))
assert classes == ([0, 3], [1, 2, 5, 4])
assert recurrence == (True, False)
assert periods == (2, 2)
TO9 = Matrix(
[[2, 0, 0, 3, 0, 0, 3, 2, 0, 0], [0, 10, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 2, 2, 0, 0, 0, 0, 0, 3, 3], [0, 0, 0, 3, 0, 0, 6, 1, 0, 0],
[0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 10, 0, 0, 0, 0],
[4, 0, 0, 5, 0, 0, 1, 0, 0, 0], [2, 0, 0, 4, 0, 0, 2, 2, 0, 0],
[3, 0, 1, 0, 0, 0, 0, 0, 4, 2], [0, 0, 4, 0, 0, 0, 0, 0, 3, 3]]) / 10
Y9 = DiscreteMarkovChain('Y', trans_probs=TO9)
tuples = Y9.communication_classes()
classes, recurrence, periods = list(zip(*tuples))
assert classes == ([0, 3, 6, 7], [1], [2, 8, 9], [5], [4])
assert recurrence == (True, True, False, True, False)
assert periods == (1, 1, 1, 1, 1)
# test canonical form
# see https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf
# example 11.13
T = Matrix([[1, 0, 0, 0, 0], [S(1) / 2, 0, S(1) / 2, 0, 0],
[0, S(1) / 2, 0, S(1) / 2, 0], [0, 0,
S(1) / 2, 0,
S(1) / 2], [0, 0, 0, 0,
S(1)]])
DW = DiscreteMarkovChain('DW', [0, 1, 2, 3, 4], T)
states, A, B, C = DW.decompose()
assert states == [0, 4, 1, 2, 3]
assert A == Matrix([[1, 0], [0, 1]])
assert B == Matrix([[S(1) / 2, 0], [0, 0], [0, S(1) / 2]])
assert C == Matrix([[0, S(1) / 2, 0], [S(1) / 2, 0, S(1) / 2],
[0, S(1) / 2, 0]])
states, new_matrix = DW.canonical_form()
assert states == [0, 4, 1, 2, 3]
assert new_matrix == Matrix([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0],
[S(1) / 2, 0, 0, S(1) / 2, 0],
[0, 0, S(1) / 2, 0,
S(1) / 2], [0, S(1) / 2, 0,
S(1) / 2, 0]])
# test regular and ergodic
# https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf
T = Matrix([[0, 4, 0, 0, 0], [1, 0, 3, 0, 0], [0, 2, 0, 2, 0],
[0, 0, 3, 0, 1], [0, 0, 0, 4, 0]]) / 4
X = DiscreteMarkovChain('X', trans_probs=T)
assert not X.is_regular()
assert X.is_ergodic()
T = Matrix([[0, 1], [1, 0]])
X = DiscreteMarkovChain('X', trans_probs=T)
assert not X.is_regular()
assert X.is_ergodic()
# http://www.math.wisc.edu/~valko/courses/331/MC2.pdf
T = Matrix([[2, 1, 1], [2, 0, 2], [1, 1, 2]]) / 4
X = DiscreteMarkovChain('X', trans_probs=T)
assert X.is_regular()
assert X.is_ergodic()
# https://docs.ufpr.br/~lucambio/CE222/1S2014/Kemeny-Snell1976.pdf
T = Matrix([[1, 1], [1, 1]]) / 2
X = DiscreteMarkovChain('X', trans_probs=T)
assert X.is_regular()
assert X.is_ergodic()
# test is_absorbing_chain
T = Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
X = DiscreteMarkovChain('X', trans_probs=T)
assert not X.is_absorbing_chain()
# https://en.wikipedia.org/wiki/Absorbing_Markov_chain
T = Matrix([[1, 1, 0, 0], [0, 1, 1, 0], [1, 0, 0, 1], [0, 0, 0, 2]]) / 2
X = DiscreteMarkovChain('X', trans_probs=T)
assert X.is_absorbing_chain()
T = Matrix([[2, 0, 0, 0, 0], [1, 0, 1, 0, 0], [0, 1, 0, 1, 0],
[0, 0, 1, 0, 1], [0, 0, 0, 0, 2]]) / 2
X = DiscreteMarkovChain('X', trans_probs=T)
assert X.is_absorbing_chain()
# test custom state space
Y10 = DiscreteMarkovChain('Y', [1, 2, 3], TO2)
tuples = Y10.communication_classes()
classes, recurrence, periods = list(zip(*tuples))
assert classes == ([1], [2, 3])
assert recurrence == (True, False)
assert periods == (1, 1)
assert Y10.canonical_form() == ([1, 2, 3], TO2)
assert Y10.decompose() == ([1, 2, 3], TO2[0:1, 0:1], TO2[1:3,
0:1], TO2[1:3,
1:3])
# testing miscellaneous queries
T = Matrix([[S.Half, Rational(1, 4),
Rational(1, 4)], [Rational(1, 3), 0,
Rational(2, 3)], [S.Half, S.Half, 0]])
X = DiscreteMarkovChain('X', [0, 1, 2], T)
assert P(
Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0),
Eq(P(Eq(X[1], 0)), Rational(1, 4))
& Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12)
assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3)
assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero
assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3)
assert E(X[1]**2, Eq(X[0], 1)) == Rational(8, 3)
assert variance(X[1], Eq(X[0], 1)) == Rational(8, 9)
raises(ValueError, lambda: E(X[1], Eq(X[2], 1)))
raises(ValueError, lambda: DiscreteMarkovChain('X', [0, 1], T))
# testing miscellaneous queries with different state space
X = DiscreteMarkovChain('X', ['A', 'B', 'C'], T)
assert P(
Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0),
Eq(P(Eq(X[1], 0)), Rational(1, 4))
& Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12)
assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3)
assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero
assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3)
a = X.state_space.args[0]
c = X.state_space.args[2]
assert (E(X[1]**2, Eq(X[0], 1)) -
(a**2 / 3 + 2 * c**2 / 3)).simplify() == 0
assert (variance(X[1], Eq(X[0], 1)) -
(2 * (-a / 3 + c / 3)**2 / 3 +
(2 * a / 3 - 2 * c / 3)**2 / 3)).simplify() == 0
raises(ValueError, lambda: E(X[1], Eq(X[2], 1)))
#testing queries with multiple RandomIndexedSymbols
T = Matrix([[Rational(5, 10),
Rational(3, 10),
Rational(2, 10)],
[Rational(2, 10),
Rational(7, 10),
Rational(1, 10)],
[Rational(3, 10),
Rational(3, 10),
Rational(4, 10)]])
Y = DiscreteMarkovChain("Y", [0, 1, 2], T)
assert P(Eq(Y[7], Y[5]), Eq(Y[2], 0)).round(5) == Float(0.44428, 5)
assert P(Gt(Y[3], Y[1]), Eq(Y[0], 0)).round(2) == Float(0.36, 2)
assert P(Le(Y[5], Y[10]), Eq(Y[4], 2)).round(6) == Float(0.583120, 6)
assert Float(P(Eq(Y[10], Y[5]), Eq(Y[4], 1)),
14) == Float(1 - P(Ne(Y[10], Y[5]), Eq(Y[4], 1)), 14)
assert Float(P(Gt(Y[8], Y[9]), Eq(Y[3], 2)),
14) == Float(1 - P(Le(Y[8], Y[9]), Eq(Y[3], 2)), 14)
assert Float(P(Lt(Y[1], Y[4]), Eq(Y[0], 0)),
14) == Float(1 - P(Ge(Y[1], Y[4]), Eq(Y[0], 0)), 14)
assert P(Eq(Y[5], Y[10]), Eq(Y[2], 1)) == P(Eq(Y[10], Y[5]), Eq(Y[2], 1))
assert P(Gt(Y[1], Y[2]), Eq(Y[0], 1)) == P(Lt(Y[2], Y[1]), Eq(Y[0], 1))
assert P(Ge(Y[7], Y[6]), Eq(Y[4], 1)) == P(Le(Y[6], Y[7]), Eq(Y[4], 1))
#test symbolic queries
a, b, c, d = symbols('a b c d')
T = Matrix([[Rational(1, 10),
Rational(4, 10),
Rational(5, 10)],
[Rational(3, 10),
Rational(4, 10),
Rational(3, 10)],
[Rational(7, 10),
Rational(2, 10),
Rational(1, 10)]])
Y = DiscreteMarkovChain("Y", [0, 1, 2], T)
query = P(Eq(Y[a], b), Eq(Y[c], d))
assert query.subs({
a: 10,
b: 2,
c: 5,
d: 1
}).evalf().round(4) == P(Eq(Y[10], 2), Eq(Y[5], 1)).round(4)
assert query.subs({
a: 15,
b: 0,
c: 10,
d: 1
}).evalf().round(4) == P(Eq(Y[15], 0), Eq(Y[10], 1)).round(4)
query_gt = P(Gt(Y[a], b), Eq(Y[c], d))
query_le = P(Le(Y[a], b), Eq(Y[c], d))
assert query_gt.subs({
a: 5,
b: 2,
c: 1,
d: 0
}).evalf() + query_le.subs({
a: 5,
b: 2,
c: 1,
d: 0
}).evalf() == 1
query_ge = P(Ge(Y[a], b), Eq(Y[c], d))
query_lt = P(Lt(Y[a], b), Eq(Y[c], d))
assert query_ge.subs({
a: 4,
b: 1,
c: 0,
d: 2
}).evalf() + query_lt.subs({
a: 4,
b: 1,
c: 0,
d: 2
}).evalf() == 1
def eval(cls, *_args, **kwargs):
"""."""
if not _args:
return
if not len(_args) == 1:
raise ValueError('Expecting one argument')
expr = _args[0]
d_atoms = kwargs.pop('d_atoms', {})
domain = kwargs.pop('domain', None)
expand = kwargs.pop('expand', False)
if isinstance(expr, Add):
args = [
cls.eval(a, d_atoms=d_atoms, domain=domain) for a in expr.args
]
return Add(*args)
elif isinstance(expr, Mul):
coeffs = [a for a in expr.args if isinstance(a, _coeffs_registery)]
stests = [
a for a in expr.args
if not (a in coeffs) and a.atoms(ScalarFunction)
]
vtests = [
a for a in expr.args
if not (a in coeffs) and a.atoms(VectorFunction)
]
vectors = [
a for a in expr.args if
(not (a in coeffs) and not (a in stests) and not (a in vtests))
]
a = S.One
if coeffs:
a = Mul(*coeffs)
b = S.One
if vectors:
args = [cls(i, evaluate=False) for i in vectors]
b = Mul(*args)
sb = S.One
if stests:
args = [
cls.eval(i, d_atoms=d_atoms, domain=domain) for i in stests
]
sb = Mul(*args)
vb = S.One
if vtests:
args = [
cls.eval(i, d_atoms=d_atoms, domain=domain) for i in vtests
]
vb = Mul(*args)
return Mul(a, b, sb, vb)
elif isinstance(expr, _coeffs_registery):
return expr
elif isinstance(expr, Pow):
b = expr.base
e = expr.exp
return Pow(cls.eval(b), e)
elif isinstance(expr, (Matrix, ImmutableDenseMatrix)):
n_rows, n_cols = expr.shape
lines = []
for i_row in range(0, n_rows):
line = []
for i_col in range(0, n_cols):
line.append(
cls.eval(expr[i_row, i_col],
d_atoms=d_atoms,
domain=domain))
lines.append(line)
return ImmutableDenseMatrix(lines)
elif isinstance(expr, DomainExpression):
# TODO to be removed
return cls.eval(expr.expr, d_atoms=d_atoms, domain=domain)
elif isinstance(expr, BilinearForm):
trials = expr.variables[0]
tests = expr.variables[1]
fields = expr.fields
# ... # TODO improve
terminal_expr = TerminalExpr(expr, domain)[0]
# ...
# ...
# Collect all variables in the expression (fields must be excluded)
variables = [
e for e in terminal_expr.atoms(ScalarFunction)
if e not in fields
]
variables += [
e for e in terminal_expr.atoms(IndexedVectorFunction)
if e.base not in fields
]
# ...
# ...
if domain is not None and domain.mapping is not None:
terminal_expr = LogicalExpr(terminal_expr.expr, domain)
variables = [LogicalExpr(e, domain) for e in variables]
trials = [LogicalExpr(e, domain) for e in trials]
tests = [LogicalExpr(e, domain) for e in tests]
# ...
# Prepare dictionary for '_tensorize_atomic_expr', which should
# process all variables but leave fields unchanged:
d_atoms = dict([(v, _split_test_function(v)[v])
for v in variables] + [(f, (f, )) for f in fields])
# ...
expr = cls.eval(terminal_expr, d_atoms=d_atoms, domain=domain)
# ...
# ...
trials = [
a for a in variables
if ((isinstance(a, ScalarFunction) and a in trials) or (
isinstance(a, IndexedVectorFunction) and a.base in trials))
]
tests = [
a for a in variables
if ((isinstance(a, ScalarFunction) and a in tests) or (
isinstance(a, IndexedVectorFunction) and a.base in tests))
]
# ...
expr = _replace_atomic_expr(expr,
trials,
tests,
d_atoms,
logical=True,
expand=expand)
return expr
if expr.atoms(ScalarFunction) or expr.atoms(IndexedVectorFunction):
return _tensorize_atomic_expr(expr, d_atoms)
return cls(expr, evaluate=False)
def __new__(cls, expr, domain, kind='l2', evaluate=True, **options):
# # ...
# tests = expr.atoms((ScalarTestFunction, VectorTestFunction))
# if tests:
# msg = '> Expecting an Expression without test functions'
# raise UnconsistentArgumentsError(msg)
#
# if not isinstance(expr, (Expr, Matrix, ImmutableDenseMatrix)):
# msg = '> Expecting Expr, Matrix, ImmutableDenseMatrix'
# raise UnconsistentArgumentsError(msg)
# # ...
# ...
kind = kind.lower()
if not (kind in ['l2', 'h1', 'h2']):
raise ValueError('> Only L2, H1, H2 norms are available')
# ...
# ...
is_vector = isinstance(
expr, (Matrix, ImmutableDenseMatrix, Tuple, list, tuple))
if is_vector:
expr = ImmutableDenseMatrix(expr)
# ...
# ...
exponent = None
if kind == 'l2' and evaluate:
exponent = 2
if not is_vector:
expr = expr * expr
else:
if not (expr.shape[1] == 1):
raise ValueError(
'Wrong expression for Matrix. must be a row')
v = Tuple(*expr[:, 0])
expr = Dot(v, v)
elif kind == 'h1' and evaluate:
exponent = 2
if not is_vector:
a = Grad(expr)
expr = Dot(a, a)
else:
if not (expr.shape[1] == 1):
raise ValueError(
'Wrong expression for Matrix. must be a row')
v = Tuple(*expr[:, 0])
a = Grad(v)
expr = Inner(a, a)
elif kind == 'h2' and evaluate:
exponent = 2
if not is_vector:
a = Hessian(expr)
expr = Dot(a, a)
else:
raise NotImplementedError('TODO')
# ...
obj = Functional.__new__(cls, expr, domain, evaluate=evaluate)
obj._exponent = exponent
obj._kind = kind
return obj
def __init__(self, transformation=None, orientation=None):
assert transformation.shape == (
4, 4), "Invalid shape of transformation array"
self.trans_sympy = ImmutableDenseMatrix(transformation)
self.params = {'transformation': self.trans_sympy}
self.orientation = orientation
def func(self, *args):
"""
These should never be interpretted as expressions, only roots.
"""
return ImmutableDenseMatrix(*args)
def __new__(cls, name, dim=None, **kwargs):
ldim = kwargs.pop('ldim', cls._ldim)
pdim = kwargs.pop('pdim', cls._pdim)
coordinates = kwargs.pop('coordinates', None)
evaluate = kwargs.pop('evaluate', True)
dims = [dim, ldim, pdim]
for i, d in enumerate(dims):
if isinstance(d,
(tuple, list, Tuple, Matrix, ImmutableDenseMatrix)):
if not len(d) == 1:
raise ValueError(
'> Expecting a tuple, list, Tuple of length 1')
dims[i] = d[0]
dim, ldim, pdim = dims
if dim is None:
assert ldim is not None
assert pdim is not None
assert pdim >= ldim
else:
ldim = dim
pdim = dim
obj = IndexedBase.__new__(cls, name, shape=pdim)
if not evaluate:
return obj
if coordinates is None:
_coordinates = [Symbol(name) for name in ['x', 'y', 'z'][:pdim]]
else:
if not isinstance(coordinates, (list, tuple, Tuple)):
raise TypeError('> Expecting list, tuple, Tuple')
for a in coordinates:
if not isinstance(a, (str, Symbol)):
raise TypeError('> Expecting str or Symbol')
_coordinates = [Symbol(u) for u in coordinates]
obj._name = name
obj._ldim = ldim
obj._pdim = pdim
obj._coordinates = tuple(_coordinates)
obj._jacobian = kwargs.pop('jacobian', JacobianSymbol(obj))
obj._is_minus = None
obj._is_plus = None
lcoords = ['x1', 'x2', 'x3'][:ldim]
lcoords = [Symbol(i) for i in lcoords]
obj._logical_coordinates = Tuple(*lcoords)
# ...
if not (obj._expressions is None):
coords = ['x', 'y', 'z'][:pdim]
# ...
args = []
for i in coords:
x = obj._expressions[i]
x = sympify(x)
args.append(x)
args = Tuple(*args)
# ...
zero_coords = ['x1', 'x2', 'x3'][ldim:]
for i in zero_coords:
x = sympify(i)
args = args.subs(x, 0)
# ...
constants = list(set(args.free_symbols) - set(lcoords))
constants_values = {a.name: Constant(a.name) for a in constants}
# subs constants as Constant objects instead of Symbol
constants_values.update(kwargs)
d = {a: constants_values[a.name] for a in constants}
args = args.subs(d)
obj._expressions = args
obj._constants = tuple(
a for a in constants
if isinstance(constants_values[a.name], Symbol))
args = [obj[i] for i in range(pdim)]
exprs = obj._expressions
subs = list(zip(_coordinates, exprs))
if obj._jac is None and obj._inv_jac is None:
obj._jac = Jacobian(obj).subs(list(zip(args, exprs)))
obj._inv_jac = obj._jac.inv()
elif obj._inv_jac is None:
obj._jac = ImmutableDenseMatrix(sympify(obj._jac)).subs(subs)
obj._inv_jac = obj._jac.inv()
elif obj._jac is None:
obj._inv_jac = ImmutableDenseMatrix(sympify(
obj._inv_jac)).subs(subs)
obj._jac = obj._inv_jac.inv()
else:
obj._jac = ImmutableDenseMatrix(sympify(obj._jac)).subs(subs)
obj._inv_jac = ImmutableDenseMatrix(sympify(
obj._inv_jac)).subs(subs)
else:
obj._jac = Jacobian(obj)
obj._metric = obj._jac.T * obj._jac
obj._metric_det = obj._metric.det()
return obj
def eval(cls, expr, domain, **options):
"""."""
from sympde.expr.evaluation import TerminalExpr, DomainExpression
from sympde.expr.expr import BilinearForm, LinearForm, BasicForm, Norm
from sympde.expr.expr import Integral
types = (ScalarFunction, VectorFunction, DifferentialOperator, Trace,
Integral)
mapping = domain.mapping
dim = domain.dim
assert mapping
# TODO this is not the dim of the domain
l_coords = ['x1', 'x2', 'x3'][:dim]
ph_coords = ['x', 'y', 'z']
if not has(expr, types):
if has(expr, DiffOperator):
return cls(expr, domain, evaluate=False)
else:
syms = symbols(ph_coords[:dim])
if isinstance(mapping, InterfaceMapping):
mapping = mapping.minus
# here we assume that the two mapped domains
# are identical in the interface so we choose one of them
Ms = [mapping[i] for i in range(dim)]
expr = expr.subs(list(zip(syms, Ms)))
if mapping.is_analytical:
expr = expr.subs(list(zip(Ms, mapping.expressions)))
return expr
if isinstance(expr, Symbol) and expr.name in l_coords:
return expr
if isinstance(expr, Symbol) and expr.name in ph_coords:
return mapping[ph_coords.index(expr.name)]
elif isinstance(expr, Add):
args = [cls.eval(a, domain) for a in expr.args]
v = S.Zero
for i in args:
v += i
n, d = v.as_numer_denom()
return n / d
elif isinstance(expr, Mul):
args = [cls.eval(a, domain) for a in expr.args]
v = S.One
for i in args:
v *= i
return v
elif isinstance(expr, _logical_partial_derivatives):
if mapping.is_analytical:
Ms = [mapping[i] for i in range(dim)]
expr = expr.subs(list(zip(Ms, mapping.expressions)))
return expr
elif isinstance(expr, IndexedVectorFunction):
el = cls.eval(expr.base, domain)
el = TerminalExpr(el, domain=domain.logical_domain)
return el[expr.indices[0]]
elif isinstance(expr, MinusInterfaceOperator):
mapping = mapping.minus
newexpr = PullBack(expr.args[0], mapping)
test = newexpr.test
newexpr = newexpr.expr.subs(test, MinusInterfaceOperator(test))
return newexpr
elif isinstance(expr, PlusInterfaceOperator):
mapping = mapping.plus
newexpr = PullBack(expr.args[0], mapping)
test = newexpr.test
newexpr = newexpr.expr.subs(test, PlusInterfaceOperator(test))
return newexpr
elif isinstance(expr, (VectorFunction, ScalarFunction)):
return PullBack(expr, mapping).expr
elif isinstance(expr, Transpose):
arg = cls(expr.arg, domain)
return Transpose(arg)
elif isinstance(expr, grad):
arg = expr.args[0]
if isinstance(mapping, InterfaceMapping):
if isinstance(arg, MinusInterfaceOperator):
a = arg.args[0]
mapping = mapping.minus
elif isinstance(arg, PlusInterfaceOperator):
a = arg.args[0]
mapping = mapping.plus
else:
raise TypeError(arg)
arg = type(arg)(cls.eval(a, domain))
else:
arg = cls.eval(arg, domain)
return mapping.jacobian.inv().T * grad(arg)
elif isinstance(expr, curl):
arg = expr.args[0]
if isinstance(mapping, InterfaceMapping):
if isinstance(arg, MinusInterfaceOperator):
arg = arg.args[0]
mapping = mapping.minus
elif isinstance(arg, PlusInterfaceOperator):
arg = arg.args[0]
mapping = mapping.plus
else:
raise TypeError(arg)
if isinstance(arg, VectorFunction):
arg = PullBack(arg, mapping)
else:
arg = cls.eval(arg, domain)
if isinstance(arg, PullBack) and isinstance(
arg.kind, HcurlSpaceType):
J = mapping.jacobian
arg = arg.test
if isinstance(expr.args[0],
(MinusInterfaceOperator, PlusInterfaceOperator)):
arg = type(expr.args[0])(arg)
if expr.is_scalar:
return (1 / J.det()) * curl(arg)
return (J / J.det()) * curl(arg)
else:
raise NotImplementedError('TODO')
elif isinstance(expr, div):
arg = expr.args[0]
if isinstance(mapping, InterfaceMapping):
if isinstance(arg, MinusInterfaceOperator):
arg = arg.args[0]
mapping = mapping.minus
elif isinstance(arg, PlusInterfaceOperator):
arg = arg.args[0]
mapping = mapping.plus
else:
raise TypeError(arg)
if isinstance(arg, (ScalarFunction, VectorFunction)):
arg = PullBack(arg, mapping)
else:
arg = cls.eval(arg, domain)
if isinstance(arg, PullBack) and isinstance(
arg.kind, HdivSpaceType):
J = mapping.jacobian
arg = arg.test
if isinstance(expr.args[0],
(MinusInterfaceOperator, PlusInterfaceOperator)):
arg = type(expr.args[0])(arg)
return (1 / J.det()) * div(arg)
elif isinstance(arg, PullBack):
return SymbolicTrace(mapping.jacobian.inv().T * grad(arg.test))
else:
raise NotImplementedError('TODO')
elif isinstance(expr, laplace):
arg = expr.args[0]
v = cls.eval(grad(arg), domain)
v = mapping.jacobian.inv().T * grad(v)
return SymbolicTrace(v)
# elif isinstance(expr, hessian):
# arg = expr.args[0]
# if isinstance(mapping, InterfaceMapping):
# if isinstance(arg, MinusInterfaceOperator):
# arg = arg.args[0]
# mapping = mapping.minus
# elif isinstance(arg, PlusInterfaceOperator):
# arg = arg.args[0]
# mapping = mapping.plus
# else:
# raise TypeError(arg)
# v = cls.eval(grad(expr.args[0]), domain)
# v = mapping.jacobian.inv().T*grad(v)
# return v
elif isinstance(expr, (dot, inner, outer)):
args = [cls.eval(arg, domain) for arg in expr.args]
return type(expr)(*args)
elif isinstance(expr, _diff_ops):
raise NotImplementedError('TODO')
# TODO MUST BE MOVED AFTER TREATING THE CASES OF GRAD, CURL, DIV IN FEEC
elif isinstance(expr, (Matrix, ImmutableDenseMatrix)):
n_rows, n_cols = expr.shape
lines = []
for i_row in range(0, n_rows):
line = []
for i_col in range(0, n_cols):
line.append(cls.eval(expr[i_row, i_col], domain))
lines.append(line)
return type(expr)(lines)
elif isinstance(expr, dx):
if expr.atoms(PlusInterfaceOperator):
mapping = mapping.plus
elif expr.atoms(MinusInterfaceOperator):
mapping = mapping.minus
arg = expr.args[0]
arg = cls(arg, domain, evaluate=True)
if isinstance(arg, PullBack):
arg = TerminalExpr(arg, domain=domain.logical_domain)
elif isinstance(arg, MatrixElement):
arg = TerminalExpr(arg, domain=domain.logical_domain)
# ...
if dim == 1:
lgrad_arg = LogicalGrad_1d(arg)
if not isinstance(lgrad_arg, (list, tuple, Tuple, Matrix)):
lgrad_arg = Tuple(lgrad_arg)
elif dim == 2:
lgrad_arg = LogicalGrad_2d(arg)
elif dim == 3:
lgrad_arg = LogicalGrad_3d(arg)
grad_arg = Covariant(mapping, lgrad_arg)
expr = grad_arg[0]
return expr
elif isinstance(expr, dy):
if expr.atoms(PlusInterfaceOperator):
mapping = mapping.plus
elif expr.atoms(MinusInterfaceOperator):
mapping = mapping.minus
arg = expr.args[0]
arg = cls(arg, domain, evaluate=True)
if isinstance(arg, PullBack):
arg = TerminalExpr(arg, domain=domain.logical_domain)
elif isinstance(arg, MatrixElement):
arg = TerminalExpr(arg, domain=domain.logical_domain)
# ..p
if dim == 1:
lgrad_arg = LogicalGrad_1d(arg)
elif dim == 2:
lgrad_arg = LogicalGrad_2d(arg)
elif dim == 3:
lgrad_arg = LogicalGrad_3d(arg)
grad_arg = Covariant(mapping, lgrad_arg)
expr = grad_arg[1]
return expr
elif isinstance(expr, dz):
if expr.atoms(PlusInterfaceOperator):
mapping = mapping.plus
elif expr.atoms(MinusInterfaceOperator):
mapping = mapping.minus
arg = expr.args[0]
arg = cls(arg, domain, evaluate=True)
if isinstance(arg, PullBack):
arg = TerminalExpr(arg, domain=domain.logical_domain)
elif isinstance(arg, MatrixElement):
arg = TerminalExpr(arg, domain=domain.logical_domain)
# ...
if dim == 1:
lgrad_arg = LogicalGrad_1d(arg)
elif dim == 2:
lgrad_arg = LogicalGrad_2d(arg)
elif dim == 3:
lgrad_arg = LogicalGrad_3d(arg)
grad_arg = Covariant(mapping, lgrad_arg)
expr = grad_arg[2]
return expr
elif isinstance(expr, (Symbol, Indexed)):
return expr
elif isinstance(expr, NormalVector):
return expr
elif isinstance(expr, Pow):
b = expr.base
e = expr.exp
expr = Pow(cls(b, domain), cls(e, domain))
return expr
elif isinstance(expr, Trace):
e = cls.eval(expr.expr, domain)
bd = expr.boundary.logical_domain
order = expr.order
return Trace(e, bd, order)
elif isinstance(expr, Integral):
domain = expr.domain
mapping = domain.mapping
assert domain is not None
if expr.is_domain_integral:
J = mapping.jacobian
det = sqrt((J.T * J).det())
else:
axis = domain.axis
J = JacobianSymbol(mapping, axis=axis)
det = sqrt((J.T * J).det())
body = cls.eval(expr.expr, domain) * det
domain = domain.logical_domain
return Integral(body, domain)
elif isinstance(expr, BilinearForm):
tests = [get_logical_test_function(a) for a in expr.test_functions]
trials = [
get_logical_test_function(a) for a in expr.trial_functions
]
body = cls.eval(expr.expr, domain)
return BilinearForm((trials, tests), body)
elif isinstance(expr, LinearForm):
tests = [get_logical_test_function(a) for a in expr.test_functions]
body = cls.eval(expr.expr, domain)
return LinearForm(tests, body)
elif isinstance(expr, Norm):
kind = expr.kind
exponent = expr.exponent
e = cls.eval(expr.expr, domain)
domain = domain.logical_domain
norm = Norm(e, domain, kind, evaluate=False)
norm._exponent = exponent
return norm
elif isinstance(expr, DomainExpression):
domain = expr.target
J = domain.mapping.jacobian
newexpr = cls.eval(expr.expr, domain)
newexpr = TerminalExpr(newexpr, domain=domain)
domain = domain.logical_domain
det = TerminalExpr(sqrt((J.T * J).det()), domain=domain)
return DomainExpression(domain,
ImmutableDenseMatrix([[newexpr * det]]))
elif isinstance(expr, Function):
args = [cls.eval(a, domain) for a in expr.args]
return type(expr)(*args)
return cls(expr, domain, evaluate=False)
def eval(cls, *_args, **kwargs):
"""."""
if not _args:
return
if not len(_args) == 1:
raise ValueError('Expecting one argument')
expr = _args[0]
d_atoms = kwargs.pop('d_atoms', OrderedDict())
mapping = kwargs.pop('mapping', None)
expand = kwargs.pop('expand', False)
if isinstance(expr, Add):
args = [
cls.eval(a, d_atoms=d_atoms, mapping=mapping)
for a in expr.args
]
return Add(*args)
elif isinstance(expr, Mul):
coeffs = [a for a in expr.args if isinstance(a, _coeffs_registery)]
stests = [
a for a in expr.args
if not (a in coeffs) and a.atoms(ScalarTestFunction)
]
vtests = [
a for a in expr.args
if not (a in coeffs) and a.atoms(VectorTestFunction)
]
vectors = [
a for a in expr.args if
(not (a in coeffs) and not (a in stests) and not (a in vtests))
]
a = S.One
if coeffs:
a = Mul(*coeffs)
b = S.One
if vectors:
args = [cls(i, evaluate=False) for i in vectors]
b = Mul(*args)
sb = S.One
if stests:
args = [
cls.eval(i, d_atoms=d_atoms, mapping=mapping)
for i in stests
]
sb = Mul(*args)
vb = S.One
if vtests:
args = [
cls.eval(i, d_atoms=d_atoms, mapping=mapping)
for i in vtests
]
vb = Mul(*args)
return Mul(a, b, sb, vb)
elif isinstance(expr, _coeffs_registery):
return expr
elif isinstance(expr, Pow):
b = expr.base
e = expr.exp
return Pow(cls.eval(b), e)
elif isinstance(expr, (Matrix, ImmutableDenseMatrix)):
n_rows, n_cols = expr.shape
lines = []
for i_row in range(0, n_rows):
line = []
for i_col in range(0, n_cols):
line.append(
cls.eval(expr[i_row, i_col],
d_atoms=d_atoms,
mapping=mapping))
lines.append(line)
return ImmutableDenseMatrix(lines)
elif isinstance(expr, DomainExpression):
# TODO to be removed
target = expr.target
expr = expr.expr
return cls.eval(expr, d_atoms=d_atoms, mapping=mapping)
elif isinstance(expr, BilinearForm):
trials = list(expr.variables[0])
tests = list(expr.variables[1])
# ... # TODO improve
terminal_expr = TerminalExpr(expr)[0]
# ...
# ...
variables = list(terminal_expr.atoms(ScalarTestFunction))
variables += list(terminal_expr.atoms(IndexedTestTrial))
# ...
# ...
if not (mapping is None):
dim = expr.ldim
terminal_expr = LogicalExpr(terminal_expr.expr,
mapping=mapping,
dim=dim)
variables = [
LogicalExpr(e, mapping=mapping, dim=dim) for e in variables
]
trials = [
LogicalExpr(e, mapping=mapping, dim=dim) for e in trials
]
tests = [
LogicalExpr(e, mapping=mapping, dim=dim) for e in tests
]
# ...
d_atoms = OrderedDict()
for a in variables:
new = _split_test_function(a)
d_atoms[a] = new[a]
# ...
expr = cls.eval(terminal_expr, d_atoms=d_atoms, mapping=mapping)
# ...
# ...
trials = [
a for a in variables
if ((isinstance(a, ScalarTestFunction) and a in trials) or (
isinstance(a, IndexedTestTrial) and a.base in trials))
]
tests = [
a for a in variables
if ((isinstance(a, ScalarTestFunction) and a in tests) or (
isinstance(a, IndexedTestTrial) and a.base in tests))
]
# ...
expr = _replace_atomic_expr(expr,
trials,
tests,
d_atoms,
logical=True,
expand=expand)
return expr
if expr.atoms(ScalarTestFunction) or expr.atoms(IndexedTestTrial):
return _tensorize_atomic_expr(expr, d_atoms)
return cls(expr, evaluate=False)
def eval(cls, *_args, **kwargs):
"""."""
if not _args:
return
if not len(_args) == 1:
raise ValueError('Expecting one argument')
expr = _args[0]
n_rows = kwargs.pop('n_rows', None)
n_cols = kwargs.pop('n_cols', None)
dim = kwargs.pop('dim', None)
logical = kwargs.pop('logical', None)
if isinstance(expr, Add):
args = [cls.eval(a, dim=dim, logical=logical) for a in expr.args]
o = args[0]
for arg in args[1:]:
o = o + arg
return o
elif isinstance(expr, Mul):
args = [cls.eval(a, dim=dim, logical=logical) for a in expr.args]
o = args[0]
for arg in args[1:]:
o = o * arg
return o
elif isinstance(expr, Abs):
return Abs(cls.eval(expr.args[0], dim=dim, logical=logical))
elif isinstance(expr, Pow):
base = cls.eval(expr.base, dim=dim, logical=logical)
exp = cls.eval(expr.exp, dim=dim, logical=logical)
return base**exp
elif isinstance(expr, JacobianSymbol):
axis = expr.axis
J = Jacobian(expr.mapping)
if axis is None:
return J
else:
return J.col_del(axis)
elif isinstance(expr, SymbolicDeterminant):
return cls.eval(expr.arg, dim=dim, logical=logical).det().factor()
elif isinstance(expr, SymbolicTrace):
return cls.eval(expr.arg, dim=dim, logical=logical).trace()
elif isinstance(expr, Transpose):
return cls.eval(expr.arg, dim=dim, logical=logical).T
elif isinstance(expr, Inverse):
return cls.eval(expr.arg, dim=dim, logical=logical).inv()
elif isinstance(expr, (ScalarTestFunction, VectorTestFunction)):
return expr
elif isinstance(expr, PullBack):
return cls.eval(expr.expr, dim=dim, logical=True)
elif isinstance(expr, MatrixElement):
base = cls.eval(expr.base, dim=dim, logical=logical)
return base[expr.indices]
elif isinstance(expr, BasicForm):
# ...
dim = expr.ldim
domain = expr.domain
if not isinstance(domain, Union):
logical = domain.mapping is None
domain = (domain, )
# ...
d_expr = OrderedDict()
for d in domain:
d_expr[d] = S.Zero
# ...
if isinstance(expr.expr, Add):
for a in expr.expr.args:
newexpr = cls.eval(a, dim=dim, logical=True)
# ...
try:
domain = _get_domain(a)
if not isinstance(domain, Union):
domain = (domain, )
except:
pass
# ...
for d in domain:
d_expr[d] += newexpr
# ...
else:
newexpr = cls.eval(expr.expr, dim=dim, logical=logical)
# ...
if isinstance(expr, Functional):
domain = expr.domain
else:
domain = _get_domain(expr.expr)
if isinstance(domain, Union):
domain = list(domain._args)
elif not is_sequence(domain):
domain = [domain]
# ...
# ...
for d in domain:
d_expr[d] = newexpr
# ...
trials, tests = _get_trials_tests_flattened(expr)
d_new = OrderedDict()
for domain, newexpr in d_expr.items():
if newexpr != 0:
# TODO ARA make sure thre is no problem with psydac
# we should always take the interior of a domain
if not isinstance(domain,
(Boundary, Interface, InteriorDomain)):
domain = domain.interior
d_new[domain] = _to_matrix_form(newexpr,
trials=trials,
tests=tests)
# ...
ls = []
d_all = OrderedDict()
# ... treating interfaces
keys = [k for k in d_new.keys() if isinstance(k, Interface)]
for interface in keys:
# ...
trials = None
tests = None
if expr.is_bilinear:
trials = list(expr.variables[0])
tests = list(expr.variables[1])
elif expr.is_linear:
tests = list(expr.variables)
# ...
# ...
newexpr = d_new[interface]
ls_int, d_bnd = _split_expr_over_interface(newexpr,
interface,
tests=tests,
trials=trials)
# ...
ls += ls_int
# ...
for k, v in d_bnd.items():
if k in d_all.keys():
d_all[k] += v
else:
d_all[k] = v
# ...
# ... treating subdomains
keys = [k for k in d_new.keys() if isinstance(k, Union)]
for domain in keys:
newexpr = d_new[domain]
d = OrderedDict(
(interior, newexpr) for interior in domain.as_tuple())
# ...
for k, v in d.items():
if k in d_all.keys():
d_all[k] += v
else:
d_all[k] = v
d = OrderedDict()
for k, v in d_new.items():
if not isinstance(k, (Interface, Union)):
d[k] = d_new[k]
for k, v in d_all.items():
if k in d.keys():
d[k] += v
else:
d[k] = v
d_new = d
# ...
for domain, newexpr in d_new.items():
if isinstance(domain, Boundary):
ls += [BoundaryExpression(domain, newexpr)]
elif isinstance(domain, BasicDomain):
ls += [DomainExpression(domain, newexpr)]
else:
raise TypeError('not implemented for {}'.format(
type(domain)))
# ...
return tuple(ls)
elif isinstance(expr, Integral):
dim = expr.domain.dim if dim is None else dim
logical = expr.domain.mapping is None
return cls.eval(expr._args[0], dim=dim, logical=logical)
elif isinstance(expr, NormalVector):
lines = [[expr[i] for i in range(dim)]]
return ImmutableDenseMatrix(lines)
elif isinstance(expr, TangentVector):
lines = [[expr[i] for i in range(dim)]]
return ImmutableDenseMatrix(lines)
elif isinstance(expr, BasicExpr):
return cls.eval(expr.expr, dim=dim, logical=logical)
elif isinstance(expr, _diff_ops):
op = type(expr)
if logical:
new = eval('Logical{0}_{1}d'.format(op, dim))
else:
new = eval('{0}_{1}d'.format(op, dim))
args = [cls.eval(i, dim=dim, logical=logical) for i in expr.args]
return new(*args)
elif isinstance(expr, _generic_ops):
# if i = Dot(...) then type(i) is Grad
op = type(expr)
new = eval('{0}_{1}d'.format(op, dim))
args = [cls.eval(i, dim=dim, logical=logical) for i in expr.args]
return new(*args)
elif isinstance(expr, Trace):
# TODO treate different spaces
if expr.order == 0:
return cls.eval(expr.expr, dim=dim, logical=logical)
elif expr.order == 1:
# TODO give a name to normal vector
normal_vector_name = 'n'
n = NormalVector(normal_vector_name)
M = cls.eval(expr.expr, dim=dim, logical=logical)
if dim == 1:
return M
else:
if isinstance(M, (Add, Mul)):
ls = M.atoms(Tuple)
for i in ls:
M = M.subs(i, Matrix(i))
M = simplify(M)
e = 0
for i in range(0, dim):
e += M[i] * n[i]
return e
else:
raise ValueError(
'> Only traces of order 0 and 1 are available')
elif isinstance(expr, (Matrix, ImmutableDenseMatrix)):
n, m = expr.shape
lines = []
for i in range(0, n):
line = []
for j in range(0, m):
line.append(cls.eval(expr[i, j], dim=dim, logical=logical))
lines.append(line)
return ImmutableDenseMatrix(lines)
elif isinstance(expr, LogicalExpr):
M = expr.mapping
dim = expr.dim
expr = cls(expr.expr, dim=dim)
dim = M.rdim
return LogicalExpr(expr, mapping=M, dim=dim)
return expr