def _initialize_vectors(self, q_ind, q_dep, u_ind, u_dep, u_aux):
"""Initialize the coordinate and speed vectors."""
none_handler = lambda x: Matrix(x) if x else Matrix()
# Initialize generalized coordinates
q_dep = none_handler(q_dep)
if not iterable(q_ind):
raise TypeError('Generalized coordinates must be an iterable.')
if not iterable(q_dep):
raise TypeError('Dependent coordinates must be an iterable.')
q_ind = Matrix(q_ind)
self._qdep = q_dep
self._q = Matrix([q_ind, q_dep])
self._qdot = self._q.diff(dynamicsymbols._t)
# Initialize generalized speeds
u_dep = none_handler(u_dep)
if not iterable(u_ind):
raise TypeError('Generalized speeds must be an iterable.')
if not iterable(u_dep):
raise TypeError('Dependent speeds must be an iterable.')
u_ind = Matrix(u_ind)
self._udep = u_dep
self._u = Matrix([u_ind, u_dep])
self._udot = self._u.diff(dynamicsymbols._t)
self._uaux = none_handler(u_aux)
def _initialize_vectors(self, q_ind, q_dep, u_ind, u_dep, u_aux):
"""Initialize the coordinate and speed vectors."""
none_handler = lambda x: Matrix(x) if x else Matrix()
# Initialize generalized coordinates
q_dep = none_handler(q_dep)
if not iterable(q_ind):
raise TypeError('Generalized coordinates must be an iterable.')
if not iterable(q_dep):
raise TypeError('Dependent coordinates must be an iterable.')
q_ind = Matrix(q_ind)
self._qdep = q_dep
self._q = Matrix([q_ind, q_dep])
self._qdot = self.q.diff(dynamicsymbols._t)
# Initialize generalized speeds
u_dep = none_handler(u_dep)
if not iterable(u_ind):
raise TypeError('Generalized speeds must be an iterable.')
if not iterable(u_dep):
raise TypeError('Dependent speeds must be an iterable.')
u_ind = Matrix(u_ind)
self._udep = u_dep
self._u = Matrix([u_ind, u_dep])
self._udot = self.u.diff(dynamicsymbols._t)
self._uaux = none_handler(u_aux)
def __new__(cls,
func,
dtypes,
results=None,
kind='function',
is_static=False):
func = str(func)
if not (iterable(dtypes)):
raise TypeError("Expecting dtypes to be iterable.")
if results:
if not (iterable(results)):
raise TypeError("Expecting results to be iterable.")
if not isinstance(kind, str):
raise TypeError("Expecting a string for kind.")
if not (kind in ['function', 'procedure']):
raise ValueError(
"kind must be one among {'function', 'procedure'}")
if not isinstance(is_static, bool):
raise TypeError('is_static must be a boolean')
return Basic.__new__(cls, func, dtypes, results, kind, is_static)
def reduce_inequalities(inequalities, symbols=[]):
"""Reduce a system of inequalities with rational coefficients.
Examples
========
>>> from sympy.abc import x, y
>>> from sympy import reduce_inequalities
>>> reduce_inequalities(0 <= x + 3, [])
(-3 <= x) & (x < oo)
>>> reduce_inequalities(0 <= x + y*2 - 1, [x])
(x < oo) & (x >= 1 - 2*y)
"""
if not iterable(inequalities):
inequalities = [inequalities]
inequalities = [sympify(i) for i in inequalities]
gens = set().union(*[i.free_symbols for i in inequalities])
if not iterable(symbols):
symbols = [symbols]
symbols = (set(symbols) or gens) & gens
if any(i.is_extended_real is False for i in symbols):
raise TypeError(filldedent('''
inequalities cannot contain symbols that are not real.
'''))
# make vanilla symbol real
recast = {i: Dummy(i.name, extended_real=True)
for i in gens if i.is_extended_real is None}
inequalities = [i.xreplace(recast) for i in inequalities]
symbols = {i.xreplace(recast) for i in symbols}
# prefilter
keep = []
for i in inequalities:
if isinstance(i, Relational):
i = i.func(i.lhs.as_expr() - i.rhs.as_expr(), 0)
elif i not in (True, False):
i = Eq(i, 0)
if i == True:
continue
elif i == False:
return S.false
if i.lhs.is_number:
raise NotImplementedError(
"could not determine truth value of %s" % i)
keep.append(i)
inequalities = keep
del keep
# solve system
rv = _reduce_inequalities(inequalities, symbols)
# restore original symbols and return
return rv.xreplace({v: k for k, v in recast.items()})
def _contains(self, other):
from sympy.matrices import Matrix
from sympy.solvers.solveset import solveset, linsolve
from sympy.utilities.iterables import iterable, cartes
L = self.lamda
if self._is_multivariate():
if not iterable(L.expr):
if iterable(other):
return S.false
return other.as_numer_denom() in self.func(
Lambda(L.variables, L.expr.as_numer_denom()), self.base_set)
if len(L.expr) != len(self.lamda.variables):
raise NotImplementedError(filldedent('''
Dimensions of input and output of Lambda are different.'''))
eqs = [expr - val for val, expr in zip(other, L.expr)]
variables = L.variables
free = set(variables)
if all(i.is_number for i in list(Matrix(eqs).jacobian(variables))):
solns = list(linsolve([e - val for e, val in
zip(L.expr, other)], variables))
else:
syms = [e.free_symbols & free for e in eqs]
solns = {}
for i, (e, s, v) in enumerate(zip(eqs, syms, other)):
if not s:
if e != v:
return S.false
solns[vars[i]] = [v]
continue
elif len(s) == 1:
sy = s.pop()
sol = solveset(e, sy)
if sol is S.EmptySet:
return S.false
elif isinstance(sol, FiniteSet):
solns[sy] = list(sol)
else:
raise NotImplementedError
else:
raise NotImplementedError
solns = cartes(*[solns[s] for s in variables])
else:
# assume scalar -> scalar mapping
solnsSet = solveset(L.expr - other, L.variables[0])
if solnsSet.is_FiniteSet:
solns = list(solnsSet)
else:
raise NotImplementedError(filldedent('''
Determining whether an ImageSet contains %s has not
been implemented.''' % func_name(other)))
for soln in solns:
try:
if soln in self.base_set:
return S.true
except TypeError:
return self.base_set.contains(soln.evalf())
return S.false
def test_iterable_is_sequence():
ordered = [list(), tuple(), Tuple(), Matrix([[]])]
unordered = [set()]
not_sympy_iterable = [{}, '', '']
assert all(is_sequence(i) for i in ordered)
assert all(not is_sequence(i) for i in unordered)
assert all(iterable(i) for i in ordered + unordered)
assert all(not iterable(i) for i in not_sympy_iterable)
assert all(iterable(i, exclude=None) for i in not_sympy_iterable)
def __new__(cls, target, iter, body):
target = _sympify(target)
if not iterable(iter):
raise TypeError("iter must be an iterable")
iter = _sympify(iter)
if not iterable(body):
raise TypeError("body must be an iterable")
body = Tuple(*(_sympify(i) for i in body))
return Basic.__new__(cls, target, iter, body)
def test_iterable():
assert iterable(0) is False
assert iterable(1) is False
assert iterable(None) is False
class Test1(NotIterable):
pass
assert iterable(Test1()) is False
class Test2(NotIterable):
_iterable = True
assert iterable(Test2()) is True
class Test3:
pass
assert iterable(Test3()) is False
class Test4:
_iterable = True
assert iterable(Test4()) is True
class Test5:
def __iter__(self):
yield 1
assert iterable(Test5()) is True
class Test6(Test5):
_iterable = False
assert iterable(Test6()) is False
def partial_velocity(vel_vecs, gen_speeds, frame):
"""Returns a list of partial velocities with respect to the provided
generalized speeds in the given reference frame for each of the supplied
velocity vectors.
The output is a list of lists. The outer list has a number of elements
equal to the number of supplied velocity vectors. The inner lists are, for
each velocity vector, the partial derivatives of that velocity vector with
respect to the generalized speeds supplied.
Parameters
==========
vel_vecs : iterable
An iterable of velocity vectors (angular or linear).
gen_speeds : iterable
An iterable of generalized speeds.
frame : ReferenceFrame
The reference frame that the partial derivatives are going to be taken
in.
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy.physics.vector import partial_velocity
>>> u = dynamicsymbols('u')
>>> N = ReferenceFrame('N')
>>> P = Point('P')
>>> P.set_vel(N, u * N.x)
>>> vel_vecs = [P.vel(N)]
>>> gen_speeds = [u]
>>> partial_velocity(vel_vecs, gen_speeds, N)
[[N.x]]
"""
if not iterable(vel_vecs):
raise TypeError('Velocity vectors must be contained in an iterable.')
if not iterable(gen_speeds):
raise TypeError('Generalized speeds must be contained in an iterable')
vec_partials = []
for vec in vel_vecs:
partials = []
for speed in gen_speeds:
partials.append(vec.diff(speed, frame, var_in_dcm=False))
vec_partials.append(partials)
return vec_partials
def partial_velocity(vel_vecs, gen_speeds, frame):
"""Returns a list of partial velocities with respect to the provided
generalized speeds in the given reference frame for each of the supplied
velocity vectors.
The output is a list of lists. The outer list has a number of elements
equal to the number of supplied velocity vectors. The inner lists are, for
each velocity vector, the partial derivatives of that velocity vector with
respect to the generalized speeds supplied.
Parameters
==========
vel_vecs : iterable
An iterable of velocity vectors (angular or linear).
gen_speeds : iterable
An iterable of generalized speeds.
frame : ReferenceFrame
The reference frame that the partial derivatives are going to be taken
in.
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy.physics.vector import partial_velocity
>>> u = dynamicsymbols('u')
>>> N = ReferenceFrame('N')
>>> P = Point('P')
>>> P.set_vel(N, u * N.x)
>>> vel_vecs = [P.vel(N)]
>>> gen_speeds = [u]
>>> partial_velocity(vel_vecs, gen_speeds, N)
[[N.x]]
"""
if not iterable(vel_vecs):
raise TypeError('Velocity vectors must be contained in an iterable.')
if not iterable(gen_speeds):
raise TypeError('Generalized speeds must be contained in an iterable')
vec_partials = []
for vec in vel_vecs:
partials = []
for speed in gen_speeds:
partials.append(vec.diff(speed, frame, var_in_dcm=False))
vec_partials.append(partials)
return vec_partials
def __new__(cls, target, iter, body):
target = _sympify(target)
if not iterable(iter):
raise TypeError("iter must be an iterable")
if isinstance(iter, list):
# _sympify errors on lists because they are mutable
iter = tuple(iter)
iter = _sympify(iter)
if not isinstance(body, CodeBlock):
if not iterable(body):
raise TypeError("body must be an iterable or CodeBlock")
body = CodeBlock(*(_sympify(i) for i in body))
return Basic.__new__(cls, target, iter, body)
def __new__(cls, target, iter, body):
target = _sympify(target)
if not iterable(iter):
raise TypeError("iter must be an iterable")
if isinstance(iter, list):
# _sympify errors on lists because they are mutable
iter = tuple(iter)
iter = _sympify(iter)
if not isinstance(body, CodeBlock):
if not iterable(body):
raise TypeError("body must be an iterable or CodeBlock")
body = CodeBlock(*(_sympify(i) for i in body))
return Basic.__new__(cls, target, iter, body)
def __new__(cls, name, dtypes, results=None, is_static=False):
name = str(name)
if not (iterable(dtypes)):
raise TypeError("Expecting dtypes to be iterable.")
if results:
if not (iterable(results)):
raise TypeError("Expecting results to be iterable.")
if not isinstance(is_static, bool):
raise TypeError('is_static must be a boolean')
return Basic.__new__(cls, name, dtypes, results, is_static)
def partial_velocity(vel_list, u_list, frame):
"""Returns a list of partial velocities.
For a list of velocity or angular velocity vectors the partial derivatives
with respect to the supplied generalized speeds are computed, in the
specified ReferenceFrame.
The output is a list of lists. The outer list has a number of elements
equal to the number of supplied velocity vectors. The inner lists are, for
each velocity vector, the partial derivatives of that velocity vector with
respect to the generalized speeds supplied.
Parameters
==========
vel_list : list
List of velocities of Point's and angular velocities of ReferenceFrame's
u_list : list
List of independent generalized speeds.
frame : ReferenceFrame
The ReferenceFrame the partial derivatives are going to be taken in.
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy.physics.vector import partial_velocity
>>> u = dynamicsymbols('u')
>>> N = ReferenceFrame('N')
>>> P = Point('P')
>>> P.set_vel(N, u * N.x)
>>> vel_list = [P.vel(N)]
>>> u_list = [u]
>>> partial_velocity(vel_list, u_list, N)
[[N.x]]
"""
if not iterable(vel_list):
raise TypeError('Provide velocities in an iterable')
if not iterable(u_list):
raise TypeError('Provide speeds in an iterable')
list_of_pvlists = []
for i in vel_list:
pvlist = []
for j in u_list:
vel = i.diff(j, frame)
pvlist += [vel]
list_of_pvlists += [pvlist]
return list_of_pvlists
def doprint(self, funcname, args, expr, *, cses=()):
"""
Returns the function definition code as a string.
"""
from sympy.core.symbol import Dummy
funcbody = []
if not iterable(args):
args = [args]
argstrs, expr = self._preprocess(args, expr)
# Generate argument unpacking and final argument list
funcargs = []
unpackings = []
for argstr in argstrs:
if iterable(argstr):
funcargs.append(self._argrepr(Dummy()))
unpackings.extend(self._print_unpacking(argstr, funcargs[-1]))
else:
funcargs.append(argstr)
funcsig = 'def {}({}):'.format(funcname, ', '.join(funcargs))
# Wrap input arguments before unpacking
funcbody.extend(self._print_funcargwrapping(funcargs))
funcbody.extend(unpackings)
for s, e in cses:
if e is None:
funcbody.append('del {}'.format(s))
else:
funcbody.append('{} = {}'.format(s, self._exprrepr(e)))
str_expr = _recursive_to_string(self._exprrepr, expr)
if '\n' in str_expr:
str_expr = '({})'.format(str_expr)
funcbody.append('return {}'.format(str_expr))
funclines = [funcsig]
funclines.extend([' ' + line for line in funcbody])
return '\n'.join(funclines) + '\n'
def _find_repeated(expr):
if not isinstance(expr, (Basic, Unevaluated)):
return
if isinstance(expr, RootOf):
return
if isinstance(expr, Basic) and (expr.is_Atom or expr.is_Order):
if expr.is_Symbol:
excluded_symbols.add(expr)
return
if iterable(expr):
args = expr
else:
if expr in seen_subexp:
for ign in ignore:
if ign in expr.free_symbols:
break
else:
to_eliminate.add(expr)
return
seen_subexp.add(expr)
if expr in opt_subs:
expr = opt_subs[expr]
args = expr.args
list(map(_find_repeated, args))
def dynamicsymbols(names, level=0):
"""Uses symbols and Function for functions of time.
Creates a SymPy UndefinedFunction, which is then initialized as a function
of a variable, the default being Symbol('t').
Parameters
==========
names : str
Names of the dynamic symbols you want to create; works the same way as
inputs to symbols
level : int
Level of differentiation of the returned function; d/dt once of t,
twice of t, etc.
Examples
========
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy import diff, Symbol
>>> q1 = dynamicsymbols('q1')
>>> q1
q1(t)
>>> diff(q1, Symbol('t'))
Derivative(q1(t), t)
"""
esses = symbols(names, cls=Function)
t = dynamicsymbols._t
if iterable(esses):
esses = [reduce(diff, [t] * level, e(t)) for e in esses]
return esses
else:
return reduce(diff, [t] * level, esses(t))
def convert(self, elem, M=None):
"""
Convert ``elem`` into the internal representation.
This method is called implicitly whenever computations involve elements
not in the internal representation.
Examples
========
>>> from sympy.abc import x
>>> from sympy import QQ
>>> F = QQ.old_poly_ring(x).free_module(2)
>>> F.convert([1, 0])
[1, 0]
"""
if isinstance(elem, FreeModuleElement):
if elem.module is self:
return elem
if elem.module.rank != self.rank:
raise CoercionFailed
return FreeModuleElement(
self,
tuple(
self.ring.convert(x, elem.module.ring) for x in elem.data))
elif iterable(elem):
tpl = tuple(self.ring.convert(x) for x in elem)
if len(tpl) != self.rank:
raise CoercionFailed
return FreeModuleElement(self, tpl)
elif _aresame(elem, 0):
return FreeModuleElement(self,
(self.ring.convert(0), ) * self.rank)
else:
raise CoercionFailed
def dimension(*args):
""" Creates a 'dimension' Attribute with (up to 7) extents.
Examples
========
>>> from sympy.printing import fcode
>>> from sympy.codegen.fnodes import dimension, intent_in
>>> dim = dimension('2', ':') # 2 rows, runtime determined number of columns
>>> from sympy.codegen.ast import Variable, integer
>>> arr = Variable('a', integer, attrs=[dim, intent_in])
>>> fcode(arr.as_Declaration(), source_format='free', standard=2003)
'integer*4, dimension(2, :), intent(in) :: a'
"""
if len(args) > 7:
raise ValueError("Fortran only supports up to 7 dimensional arrays")
parameters = []
for arg in args:
if isinstance(arg, Extent):
parameters.append(arg)
elif isinstance(arg, string_types):
if arg == ':':
parameters.append(Extent())
else:
parameters.append(String(arg))
elif iterable(arg):
parameters.append(Extent(*arg))
else:
parameters.append(sympify(arg))
if len(args) == 0:
raise ValueError("Need at least one dimension")
return Attribute('dimension', parameters)
def find_dynamicsymbols(expression, exclude=None):
"""Find all dynamicsymbols in expression.
>>> from sympy.physics.mechanics import dynamicsymbols, find_dynamicsymbols
>>> x, y = dynamicsymbols('x, y')
>>> expr = x + x.diff()*y
>>> find_dynamicsymbols(expr)
set([x(t), y(t), Derivative(x(t), t)])
If the optional ``exclude`` kwarg is used, only dynamicsymbols
not in the iterable ``exclude`` are returned.
>>> find_dynamicsymbols(expr, [x, y])
set([Derivative(x(t), t)])
"""
t_set = set([dynamicsymbols._t])
if exclude:
if iterable(exclude):
exclude_set = set(exclude)
else:
raise TypeError("exclude kwarg must be iterable")
else:
exclude_set = set()
return set([i for i in expression.atoms(AppliedUndef, Derivative) if
i.free_symbols == t_set]) - exclude_set
def _find_opts(expr):
if not isinstance(expr, (Basic, Unevaluated)):
return
if expr.is_Atom or expr.is_Order:
return
if iterable(expr):
list(map(_find_opts, expr))
return
if expr in seen_subexp:
return expr
seen_subexp.add(expr)
list(map(_find_opts, expr.args))
if expr.could_extract_minus_sign():
neg_expr = -expr
if not neg_expr.is_Atom:
opt_subs[expr] = Unevaluated(Mul, (S.NegativeOne, neg_expr))
seen_subexp.add(neg_expr)
expr = neg_expr
if isinstance(expr, (Mul, MatMul)):
muls.add(expr)
elif isinstance(expr, (Add, MatAdd)):
adds.add(expr)
elif isinstance(expr, (Pow, MatPow)):
base, exp = expr.base, expr.exp
if exp.could_extract_minus_sign():
opt_subs[expr] = Unevaluated(Pow, (Pow(base, -exp), -1))
def find_dynamicsymbols(expression, exclude=None):
"""Find all dynamicsymbols in expression.
>>> from sympy.physics.mechanics import dynamicsymbols, find_dynamicsymbols
>>> x, y = dynamicsymbols('x, y')
>>> expr = x + x.diff()*y
>>> find_dynamicsymbols(expr)
set([x(t), y(t), Derivative(x(t), t)])
If the optional ``exclude`` kwarg is used, only dynamicsymbols
not in the iterable ``exclude`` are returned.
>>> find_dynamicsymbols(expr, [x, y])
set([Derivative(x(t), t)])
"""
t_set = set([dynamicsymbols._t])
if exclude:
if iterable(exclude):
exclude_set = set(exclude)
else:
raise TypeError("exclude kwarg must be iterable")
else:
exclude_set = set()
return set([
i for i in expression.atoms(AppliedUndef, sm.Derivative)
if i.free_symbols == t_set
]) - exclude_set
def _form_fr(self, fl):
"""Form the generalized active force."""
if fl != None and (len(fl) == 0 or not iterable(fl)):
raise ValueError('Force pairs must be supplied in an '
'non-empty iterable or None.')
N = self._inertial
# pull out relevant velocities for constructing partial velocities
vel_list, f_list = _f_list_parser(fl, N)
vel_list = [msubs(i, self._qdot_u_map) for i in vel_list]
# Fill Fr with dot product of partial velocities and forces
o = len(self.u)
b = len(f_list)
FR = zeros(o, 1)
partials = partial_velocity(vel_list, self.u, N)
for i in range(o):
FR[i] = sum(partials[j][i] & f_list[j] for j in range(b))
# In case there are dependent speeds
if self._udep:
p = o - len(self._udep)
FRtilde = FR[:p, 0]
FRold = FR[p:o, 0]
FRtilde += self._Ars.T * FRold
FR = FRtilde
self._forcelist = fl
self._fr = FR
return FR
def _walsh_hadamard_transform(seq, inverse=False):
"""Utility function for the Walsh Hadamard Transform"""
if not iterable(seq):
raise TypeError("Expected a sequence of coefficients "
"for Walsh Hadamard Transform")
a = [sympify(arg) for arg in seq]
n = len(a)
if n < 2:
return a
if n&(n - 1):
n = 2**n.bit_length()
a += [S.Zero]*(n - len(a))
h = 2
while h <= n:
hf = h // 2
for i in range(0, n, h):
for j in range(hf):
u, v = a[i + j], a[i + j + hf]
a[i + j], a[i + j + hf] = u + v, u - v
h *= 2
if inverse:
a = [x/n for x in a]
return a
def _form_fr(self, fl):
"""Form the generalized active force."""
if not iterable(fl):
raise TypeError('Force pairs must be supplied in an iterable.')
N = self._inertial
# pull out relevant velocities for constructing partial velocities
vel_list, f_list = _f_list_parser(fl, N)
vel_list = [i.subs(self._qdot_u_map) for i in vel_list]
# Fill Fr with dot product of partial velocities and forces
o = len(self._u)
b = len(f_list)
FR = zeros(o, 1)
partials = partial_velocity(vel_list, self._u, N)
for i in range(o):
FR[i] = sum(partials[j][i] & f_list[j] for j in range(b))
# In case there are dependent speeds
if self._udep:
p = o - len(self._udep)
FRtilde = FR[:p, 0]
FRold = FR[p:o, 0]
FRtilde += self._Ars.T * FRold
FR = FRtilde
self._fr = FR
return FR
def __new__(cls,
name,
dtypes,
results=None,
kind='function',
is_static=False):
if not isinstance(name, (list, tuple)):
raise TypeError("Expecting a list/tuple of strings.")
if not (iterable(dtypes)):
raise TypeError("Expecting dtypes to be iterable.")
for d in dtypes:
if not isinstance(d, UnionType) and not isinstance(d, dict):
raise TypeError("Wrong element in dtypes.")
for d in results:
if not isinstance(d, UnionType):
raise TypeError("Wrong element in dtypes.")
if not isinstance(kind, str):
raise TypeError("Expecting a string for kind.")
if not (kind in ['function', 'procedure']):
raise ValueError(
"kind must be one among {'function', 'procedure'}")
if not isinstance(is_static, bool):
raise TypeError('is_static must be a boolean')
return Basic.__new__(cls, name, dtypes, results, kind, is_static)
def dimension(*args):
""" Creates a 'dimension' Attribute with (up to 7) extents.
Examples
========
>>> from sympy.printing import fcode
>>> from sympy.codegen.fnodes import dimension, intent_in
>>> dim = dimension('2', ':') # 2 rows, runtime determined number of columns
>>> from sympy.codegen.ast import Variable, integer
>>> arr = Variable('a', integer, attrs=[dim, intent_in])
>>> fcode(arr.as_Declaration(), source_format='free', standard=2003)
'integer*4, dimension(2, :), intent(in) :: a'
"""
if len(args) > 7:
raise ValueError("Fortran only supports up to 7 dimensional arrays")
parameters = []
for arg in args:
if isinstance(arg, Extent):
parameters.append(arg)
elif isinstance(arg, str):
if arg == ':':
parameters.append(Extent())
else:
parameters.append(String(arg))
elif iterable(arg):
parameters.append(Extent(*arg))
else:
parameters.append(sympify(arg))
if len(args) == 0:
raise ValueError("Need at least one dimension")
return Attribute('dimension', parameters)
def dynamicsymbols(names, level=0):
"""Uses symbols and Function for functions of time.
Creates a SymPy UndefinedFunction, which is then initialized as a function
of a variable, the default being Symbol('t').
Parameters
==========
names : str
Names of the dynamic symbols you want to create; works the same way as
inputs to symbols
level : int
Level of differentiation of the returned function; d/dt once of t,
twice of t, etc.
Examples
========
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy import diff, Symbol
>>> q1 = dynamicsymbols('q1')
>>> q1
q1(t)
>>> diff(q1, Symbol('t'))
Derivative(q1(t), t)
"""
esses = symbols(names, cls=Function)
t = dynamicsymbols._t
if iterable(esses):
esses = [reduce(diff, [t] * level, e(t)) for e in esses]
return esses
else:
return reduce(diff, [t] * level, esses(t))
def __new__(cls, target, iter, body):
target = _sympify(target)
if not iterable(iter):
raise TypeError("iter must be an iterable")
if type(iter) == tuple:
# this is a hack, since Range does not accept non valued Integers.
# r = Range(iter[0], 10000000, iter[2])
r = Range(0, 10000000, 1)
r._args = iter
iter = r
else:
iter = _sympify(iter)
if not iterable(body):
raise TypeError("body must be an iterable")
body = Tuple(*(_sympify(i) for i in body))
return Basic.__new__(cls, target, iter, body)
def find_dynamicsymbols(expression, exclude=None, reference_frame=None):
"""Find all dynamicsymbols in expression.
If the optional ``exclude`` kwarg is used, only dynamicsymbols
not in the iterable ``exclude`` are returned.
If we intend to apply this function on a vector, the optional
''reference_frame'' is also used to inform about the corresponding frame
with respect to which the dynamic symbols of the given vector is to be
determined.
Parameters
==========
expression : sympy expression
exclude : iterable of dynamicsymbols, optional
reference_frame : ReferenceFrame, optional
The frame with respect to which the dynamic symbols of the
given vector is to be determined.
Examples
========
>>> from sympy.physics.mechanics import dynamicsymbols, find_dynamicsymbols
>>> from sympy.physics.mechanics import ReferenceFrame
>>> x, y = dynamicsymbols('x, y')
>>> expr = x + x.diff()*y
>>> find_dynamicsymbols(expr)
{x(t), y(t), Derivative(x(t), t)}
>>> find_dynamicsymbols(expr, exclude=[x, y])
{Derivative(x(t), t)}
>>> a, b, c = dynamicsymbols('a, b, c')
>>> A = ReferenceFrame('A')
>>> v = a * A.x + b * A.y + c * A.z
>>> find_dynamicsymbols(v, reference_frame=A)
{a(t), b(t), c(t)}
"""
t_set = {dynamicsymbols._t}
if exclude:
if iterable(exclude):
exclude_set = set(exclude)
else:
raise TypeError("exclude kwarg must be iterable")
else:
exclude_set = set()
if isinstance(expression, Vector):
if reference_frame is None:
raise ValueError("You must provide reference_frame when passing a "
"vector expression, got %s." % reference_frame)
else:
expression = expression.to_matrix(reference_frame)
return {
i
for i in expression.atoms(AppliedUndef, Derivative)
if i.free_symbols == t_set
} - exclude_set
def __new__(cls, name, body):
if not isinstance(name, str):
raise TypeError("Module name must be string")
name = Symbol(name)
# body
if not iterable(body):
raise TypeError("body must be an iterable")
body = Tuple(*body)
return Basic.__new__(cls, name, body)
def dynamicsymbols(names, level=0, **assumptions):
"""Uses symbols and Function for functions of time.
Creates a SymPy UndefinedFunction, which is then initialized as a function
of a variable, the default being Symbol('t').
Parameters
==========
names : str
Names of the dynamic symbols you want to create; works the same way as
inputs to symbols
level : int
Level of differentiation of the returned function; d/dt once of t,
twice of t, etc.
assumptions :
- real(bool) : This is used to set the dynamicsymbol as real,
by default is False.
- positive(bool) : This is used to set the dynamicsymbol as positive,
by default is False.
- commutative(bool) : This is used to set the commutative property of
a dynamicsymbol, by default is True.
- integer(bool) : This is used to set the dynamicsymbol as integer,
by default is False.
Examples
========
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy import diff, Symbol
>>> q1 = dynamicsymbols('q1')
>>> q1
q1(t)
>>> q2 = dynamicsymbols('q2', real=True)
>>> q2.is_real
True
>>> q3 = dynamicsymbols('q3', positive=True)
>>> q3.is_positive
True
>>> q4, q5 = dynamicsymbols('q4,q5', commutative=False)
>>> bool(q4*q5 != q5*q4)
True
>>> q6 = dynamicsymbols('q6', integer=True)
>>> q6.is_integer
True
>>> diff(q1, Symbol('t'))
Derivative(q1(t), t)
"""
esses = symbols(names, cls=Function, **assumptions)
t = dynamicsymbols._t
if iterable(esses):
esses = [reduce(diff, [t] * level, e(t)) for e in esses]
return esses
else:
return reduce(diff, [t] * level, esses(t))
def _rebuild(expr):
if not isinstance(expr, (Basic, Unevaluated)):
return expr
if not expr.args:
return expr
if iterable(expr):
new_args = [_rebuild(arg) for arg in expr]
return expr.func(*new_args)
if expr in subs:
return subs[expr]
orig_expr = expr
if expr in opt_subs:
expr = opt_subs[expr]
# If enabled, parse Muls and Adds arguments by order to ensure
# replacement order independent from hashes
if order != 'none':
if isinstance(expr, (Mul, MatMul)):
c, nc = expr.args_cnc()
if c == [1]:
args = nc
else:
args = list(ordered(c)) + nc
elif isinstance(expr, (Add, MatAdd)):
args = list(ordered(expr.args))
else:
args = expr.args
else:
args = expr.args
new_args = list(map(_rebuild, args))
if isinstance(expr, Unevaluated) or new_args != args:
new_expr = expr.func(*new_args)
else:
new_expr = expr
if orig_expr in to_eliminate:
try:
sym = next(symbols)
except StopIteration:
raise ValueError("Symbols iterator ran out of symbols.")
if isinstance(orig_expr, MatrixExpr):
sym = MatrixSymbol(sym.name, orig_expr.rows,
orig_expr.cols)
subs[orig_expr] = sym
replacements.append((sym, new_expr))
return sym
else:
return new_expr
def _number_theoretic_transform(seq, prime, inverse=False):
"""Utility function for the Number Theoretic Transform"""
if not iterable(seq):
raise TypeError("Expected a sequence of integer coefficients "
"for Number Theoretic Transform")
p = as_int(prime)
if not isprime(p):
raise ValueError("Expected prime modulus for "
"Number Theoretic Transform")
a = [as_int(x) % p for x in seq]
n = len(a)
if n < 1:
return a
b = n.bit_length() - 1
if n&(n - 1):
b += 1
n = 2**b
if (p - 1) % n:
raise ValueError("Expected prime modulus of the form (m*2**k + 1)")
a += [0]*(n - len(a))
for i in range(1, n):
j = int(ibin(i, b, str=True)[::-1], 2)
if i < j:
a[i], a[j] = a[j], a[i]
pr = primitive_root(p)
rt = pow(pr, (p - 1) // n, p)
if inverse:
rt = pow(rt, p - 2, p)
w = [1]*(n // 2)
for i in range(1, n // 2):
w[i] = w[i - 1]*rt % p
h = 2
while h <= n:
hf, ut = h // 2, n // h
for i in range(0, n, h):
for j in range(hf):
u, v = a[i + j], a[i + j + hf]*w[ut * j]
a[i + j], a[i + j + hf] = (u + v) % p, (u - v) % p
h *= 2
if inverse:
rv = pow(n, p - 2, p)
a = [x*rv % p for x in a]
return a
def __init__(self, monom, gens=None):
if not iterable(monom):
rep, gens = dict_from_expr(sympify(monom), gens=gens)
if len(rep) == 1 and list(rep.values())[0] == 1:
monom = list(rep.keys())[0]
else:
raise ValueError("Expected a monomial got {}".format(monom))
self.exponents = tuple(map(int, monom))
self.gens = gens
def __new__(cls, name, args, results):
# name
if isinstance(name, str):
name = Symbol(name)
elif not isinstance(name, Symbol):
raise TypeError("Function name must be Symbol or string")
# args
if not iterable(args):
raise TypeError("args must be an iterable")
if not all(isinstance(a, Argument) for a in args):
raise TypeError("All args must be of type Argument")
args = Tuple(*args)
# results
if not iterable(results):
raise TypeError("results must be an iterable")
if not all(isinstance(i, RoutineResult) for i in results):
raise TypeError("All results must be of type RoutineResult")
results = Tuple(*results)
return Basic.__new__(cls, name, args, results)
def __new__(cls, name, args, results):
# name
if isinstance(name, str):
name = Symbol(name)
elif not isinstance(name, Symbol):
raise TypeError("name must be Symbol or string")
# args
if not iterable(args):
raise TypeError("args must be an iterable")
if not all(isinstance(a, Argument) for a in args):
raise TypeError("All args must be of type Argument")
args = Tuple(*args)
# results
if not iterable(results):
raise TypeError("results must be an iterable")
if not all(isinstance(i, RoutineResult) for i in results):
raise TypeError("All results must be of type RoutineResult")
results = Tuple(*results)
return Basic.__new__(cls, name, args, results)
def __new__(cls, *args):
if len(args)==1 and args[0].__class__ is dict:
items = [Tuple(k, v) for k, v in args[0].iteritems()]
elif iterable(args) and all(len(arg) == 2 for arg in args):
items = [Tuple(k, v) for k, v in args]
else:
raise TypeError('Pass Dict args as Dict((k1, v1), ...) or Dict({k1: v1, ...})')
obj = Basic.__new__(cls, *items)
obj._dict = dict(items) # In case Tuple decides it wants to sympify
return obj
def _flatten(arg):
if isinstance(arg, SeqBase):
if isinstance(arg, SeqMul):
return sum(map(_flatten, arg.args), [])
else:
return [arg]
elif iterable(arg):
return sum(map(_flatten, arg), [])
raise TypeError("Input must be Sequences or "
" iterables of Sequences")
def find_dynamicsymbols(expression, exclude=None, reference_frame=None):
"""Find all dynamicsymbols in expression.
If the optional ``exclude`` kwarg is used, only dynamicsymbols
not in the iterable ``exclude`` are returned.
If we intend to apply this function on a vector, the optional
''reference_frame'' is also used to inform about the corresponding frame
with respect to which the dynamic symbols of the given vector is to be
determined.
Parameters
==========
expression : sympy expression
exclude : iterable of dynamicsymbols, optional
reference_frame : ReferenceFrame, optional
The frame with respect to which the dynamic symbols of the
given vector is to be determined.
Examples
========
>>> from sympy.physics.mechanics import dynamicsymbols, find_dynamicsymbols
>>> from sympy.physics.mechanics import ReferenceFrame
>>> x, y = dynamicsymbols('x, y')
>>> expr = x + x.diff()*y
>>> find_dynamicsymbols(expr)
{x(t), y(t), Derivative(x(t), t)}
>>> find_dynamicsymbols(expr, exclude=[x, y])
{Derivative(x(t), t)}
>>> a, b, c = dynamicsymbols('a, b, c')
>>> A = ReferenceFrame('A')
>>> v = a * A.x + b * A.y + c * A.z
>>> find_dynamicsymbols(v, reference_frame=A)
{a(t), b(t), c(t)}
"""
t_set = {dynamicsymbols._t}
if exclude:
if iterable(exclude):
exclude_set = set(exclude)
else:
raise TypeError("exclude kwarg must be iterable")
else:
exclude_set = set()
if isinstance(expression, Vector):
if reference_frame is None:
raise ValueError("You must provide reference_frame when passing a "
"vector expression, got %s." % reference_frame)
else:
expression = expression.to_matrix(reference_frame)
return set([i for i in expression.atoms(AppliedUndef, Derivative) if
i.free_symbols == t_set]) - exclude_set
def postorder_traversal(node, keys=None):
"""
Do a postorder traversal of a tree.
This generator recursively yields nodes that it has visited in a postorder
fashion. That is, it descends through the tree depth-first to yield all of
a node's children's postorder traversal before yielding the node itself.
Parameters
==========
node : SymPy expression
The expression to traverse.
keys : (default None) sort key(s)
The key(s) used to sort args of Basic objects. When None, args of Basic
objects are processed in arbitrary order. If key is defined, it will
be passed along to ordered() as the only key(s) to use to sort the
arguments; if ``key`` is simply True then the default keys of
``ordered`` will be used (node count and default_sort_key).
Yields
======
subtree : SymPy expression
All of the subtrees in the tree.
Examples
========
>>> from sympy import postorder_traversal
>>> from sympy.abc import w, x, y, z
The nodes are returned in the order that they are encountered unless key
is given; simply passing key=True will guarantee that the traversal is
unique.
>>> list(postorder_traversal(w + (x + y)*z)) # doctest: +SKIP
[z, y, x, x + y, z*(x + y), w, w + z*(x + y)]
>>> list(postorder_traversal(w + (x + y)*z, keys=True))
[w, z, x, y, x + y, z*(x + y), w + z*(x + y)]
"""
if isinstance(node, Basic):
args = node.args
if keys:
if keys != True:
args = ordered(args, keys, default=False)
else:
args = ordered(args)
for arg in args:
yield from postorder_traversal(arg, keys)
elif iterable(node):
for item in node:
yield from postorder_traversal(item, keys)
yield node
def __new__(cls, fil, funcs=None):
fil = Symbol(fil)
if not funcs:
funcs = Tuple()
elif iterable(funcs):
funcs = Tuple(*[Symbol(f) for f in funcs])
elif isinstance(funcs, str):
funcs = Tuple(Symbol(funcs))
else:
raise TypeError("Unrecognized funcs type: ", funcs)
return Basic.__new__(cls, fil, funcs)
def flat_indexes(elems):
n = 0
for el in elems:
if iterable(el):
for ndeep in flat_indexes(el):
yield (n,) + ndeep
else:
yield (n,)
n += 1
def __new__(cls, *args):
if len(args) == 1 and isinstance(args[0], (dict, Dict)):
items = [Tuple(k, v) for k, v in args[0].items()]
elif iterable(args) and all(len(arg) == 2 for arg in args):
items = [Tuple(k, v) for k, v in args]
else:
raise TypeError('Pass Dict args as Dict((k1, v1), ...) or Dict({k1: v1, ...})')
elements = frozenset(items)
obj = Basic.__new__(cls, elements)
obj.elements = elements
obj._dict = dict(items) # In case Tuple decides it wants to sympify
return obj
def solve_multipliers(self, op_point=None, sol_type='dict'):
"""Solves for the values of the lagrange multipliers symbolically at
the specified operating point
Parameters
==========
op_point : dict or iterable of dicts, optional
Point at which to solve at. The operating point is specified as
a dictionary or iterable of dictionaries of {symbol: value}. The
value may be numeric or symbolic itself.
sol_type : str, optional
Solution return type. Valid options are:
- 'dict': A dict of {symbol : value} (default)
- 'Matrix': An ordered column matrix of the solution
"""
# Determine number of multipliers
k = len(self.lam_vec)
if k == 0:
raise ValueError("System has no lagrange multipliers to solve for.")
# Compose dict of operating conditions
if isinstance(op_point, dict):
op_point_dict = op_point
elif iterable(op_point):
op_point_dict = {}
for op in op_point:
op_point_dict.update(op)
elif op_point is None:
op_point_dict = {}
else:
raise TypeError("op_point must be either a dictionary or an "
"iterable of dictionaries.")
# Compose the system to be solved
mass_matrix = self.mass_matrix.col_join((-self.lam_coeffs.row_join(
zeros(k, k))))
force_matrix = self.forcing.col_join(self._f_cd)
# Sub in the operating point
mass_matrix = msubs(mass_matrix, op_point_dict)
force_matrix = msubs(force_matrix, op_point_dict)
# Solve for the multipliers
sol_list = mass_matrix.LUsolve(-force_matrix)[-k:]
if sol_type == 'dict':
return dict(zip(self.lam_vec, sol_list))
elif sol_type == 'Matrix':
return Matrix(sol_list)
else:
raise ValueError("Unknown sol_type {:}.".format(sol_type))
def _contains(self, other):
from sympy.matrices import Matrix
from sympy.solvers.solveset import solveset, linsolve
from sympy.utilities.iterables import is_sequence, iterable, cartes
L = self.lamda
if is_sequence(other):
if not is_sequence(L.expr):
return S.false
if len(L.expr) != len(other):
raise ValueError(filldedent('''
Dimensions of other and output of Lambda are different.'''))
elif iterable(other):
raise ValueError(filldedent('''
`other` should be an ordered object like a Tuple.'''))
solns = None
if self._is_multivariate():
if not is_sequence(L.expr):
# exprs -> (numer, denom) and check again
# XXX this is a bad idea -- make the user
# remap self to desired form
return other.as_numer_denom() in self.func(
Lambda(L.variables, L.expr.as_numer_denom()), self.base_set)
eqs = [expr - val for val, expr in zip(other, L.expr)]
variables = L.variables
free = set(variables)
if all(i.is_number for i in list(Matrix(eqs).jacobian(variables))):
solns = list(linsolve([e - val for e, val in
zip(L.expr, other)], variables))
else:
syms = [e.free_symbols & free for e in eqs]
solns = {}
for i, (e, s, v) in enumerate(zip(eqs, syms, other)):
if not s:
if e != v:
return S.false
solns[vars[i]] = [v]
continue
elif len(s) == 1:
sy = s.pop()
sol = solveset(e, sy)
if sol is S.EmptySet:
return S.false
elif isinstance(sol, FiniteSet):
solns[sy] = list(sol)
else:
raise NotImplementedError
else:
raise NotImplementedError
solns = cartes(*[solns[s] for s in variables])
else:
x = L.variables[0]
if isinstance(L.expr, Expr):
# scalar -> scalar mapping
solnsSet = solveset(L.expr - other, x)
if solnsSet.is_FiniteSet:
solns = list(solnsSet)
else:
msgset = solnsSet
else:
# scalar -> vector
for e, o in zip(L.expr, other):
solns = solveset(e - o, x)
if solns is S.EmptySet:
return S.false
for soln in solns:
try:
if soln in self.base_set:
break # check next pair
except TypeError:
if self.base_set.contains(soln.evalf()):
break
else:
return S.false # never broke so there was no True
return S.true
if solns is None:
raise NotImplementedError(filldedent('''
Determining whether %s contains %s has not
been implemented.''' % (msgset, other)))
for soln in solns:
try:
if soln in self.base_set:
return S.true
except TypeError:
return self.base_set.contains(soln.evalf())
return S.false
def _form_frstar(self, bl):
"""Form the generalized inertia force."""
if not iterable(bl):
raise TypeError('Bodies must be supplied in an iterable.')
t = dynamicsymbols._t
N = self._inertial
# Dicts setting things to zero
udot_zero = dict((i, 0) for i in self._udot)
uaux_zero = dict((i, 0) for i in self._uaux)
uauxdot = [diff(i, t) for i in self._uaux]
uauxdot_zero = dict((i, 0) for i in uauxdot)
# Dictionary of q' and q'' to u and u'
q_ddot_u_map = dict((k.diff(t), v.diff(t)) for (k, v) in
self._qdot_u_map.items())
q_ddot_u_map.update(self._qdot_u_map)
# Fill up the list of partials: format is a list with num elements
# equal to number of entries in body list. Each of these elements is a
# list - either of length 1 for the translational components of
# particles or of length 2 for the translational and rotational
# components of rigid bodies. The inner most list is the list of
# partial velocities.
def get_partial_velocity(body):
if isinstance(body, RigidBody):
vlist = [body.masscenter.vel(N), body.frame.ang_vel_in(N)]
elif isinstance(body, Particle):
vlist = [body.point.vel(N),]
else:
raise TypeError('The body list may only contain either '
'RigidBody or Particle as list elements.')
v = [msubs(vel, self._qdot_u_map) for vel in vlist]
return partial_velocity(v, self.u, N)
partials = [get_partial_velocity(body) for body in bl]
# Compute fr_star in two components:
# fr_star = -(MM*u' + nonMM)
o = len(self.u)
MM = zeros(o, o)
nonMM = zeros(o, 1)
zero_uaux = lambda expr: msubs(expr, uaux_zero)
zero_udot_uaux = lambda expr: msubs(msubs(expr, udot_zero), uaux_zero)
for i, body in enumerate(bl):
if isinstance(body, RigidBody):
M = zero_uaux(body.mass)
I = zero_uaux(body.central_inertia)
vel = zero_uaux(body.masscenter.vel(N))
omega = zero_uaux(body.frame.ang_vel_in(N))
acc = zero_udot_uaux(body.masscenter.acc(N))
inertial_force = (M.diff(t) * vel + M * acc)
inertial_torque = zero_uaux((I.dt(body.frame) & omega) +
msubs(I & body.frame.ang_acc_in(N), udot_zero) +
(omega ^ (I & omega)))
for j in range(o):
tmp_vel = zero_uaux(partials[i][0][j])
tmp_ang = zero_uaux(I & partials[i][1][j])
for k in range(o):
# translational
MM[j, k] += M * (tmp_vel & partials[i][0][k])
# rotational
MM[j, k] += (tmp_ang & partials[i][1][k])
nonMM[j] += inertial_force & partials[i][0][j]
nonMM[j] += inertial_torque & partials[i][1][j]
else:
M = zero_uaux(body.mass)
vel = zero_uaux(body.point.vel(N))
acc = zero_udot_uaux(body.point.acc(N))
inertial_force = (M.diff(t) * vel + M * acc)
for j in range(o):
temp = zero_uaux(partials[i][0][j])
for k in range(o):
MM[j, k] += M * (temp & partials[i][0][k])
nonMM[j] += inertial_force & partials[i][0][j]
# Compose fr_star out of MM and nonMM
MM = zero_uaux(msubs(MM, q_ddot_u_map))
nonMM = msubs(msubs(nonMM, q_ddot_u_map),
udot_zero, uauxdot_zero, uaux_zero)
fr_star = -(MM * msubs(Matrix(self._udot), uauxdot_zero) + nonMM)
# If there are dependent speeds, we need to find fr_star_tilde
if self._udep:
p = o - len(self._udep)
fr_star_ind = fr_star[:p, 0]
fr_star_dep = fr_star[p:o, 0]
fr_star = fr_star_ind + (self._Ars.T * fr_star_dep)
# Apply the same to MM
MMi = MM[:p, :]
MMd = MM[p:o, :]
MM = MMi + (self._Ars.T * MMd)
self._bodylist = bl
self._frstar = fr_star
self._k_d = MM
self._f_d = -msubs(self._fr + self._frstar, udot_zero)
return fr_star
from sympy.utilities.iterables import iterable
def do_once(method):
"A decorator that runs a method only once."
attrname = "_%s_result" % id(method)
def decorated(self, *args, **kwargs):
try:
return getattr(self, attrname)
except AttributeError:
setattr(self, attrname, method(self, *args, **kwargs))
return getattr(self, attrname)
return decorated
iterate = lambda x: iter(x) if iterable(x) else iter([x])
def __init__(self, Lagrangian, qs, coneqs=None, forcelist=None,
frame=None, hol_coneqs=None, nonhol_coneqs=None):
"""Supply the following for the initialization of LagrangesMethod
Lagrangian : Sympifyable
qs: array_like
The generalized coordinates
hol_coneqs: array_like, optional
The holonomic constraint equations
nonhol_coneqs: array_like, optional
The nonholonomic constraint equations
forcelist : iterable, optional
Takes an iterable of (Point, Vector) or (ReferenceFrame, Vector)
tuples which represent the force at a point or torque on a frame.
This feature is primarily to account for the nonconservative forces
and/or moments.
frame : ReferenceFrame, optional
Supply the inertial frame. This is used to determine the
generalized forces due to non-conservative forces.
"""
self._L = Matrix([sympify(Lagrangian)])
self.eom = None
self._m_cd = Matrix() # Mass Matrix of differentiated coneqs
self._m_d = Matrix() # Mass Matrix of dynamic equations
self._f_cd = Matrix() # Forcing part of the diff coneqs
self._f_d = Matrix() # Forcing part of the dynamic equations
self.lam_coeffs = Matrix() # The coeffecients of the multipliers
forcelist = forcelist if forcelist else []
if not iterable(forcelist):
raise TypeError('Force pairs must be supplied in an iterable.')
self._forcelist = forcelist
if frame and not isinstance(frame, ReferenceFrame):
raise TypeError('frame must be a valid ReferenceFrame')
self.inertial = frame
self.lam_vec = Matrix()
self._term1 = Matrix()
self._term2 = Matrix()
self._term3 = Matrix()
self._term4 = Matrix()
# Creating the qs, qdots and qdoubledots
if not iterable(qs):
raise TypeError('Generalized coordinates must be an iterable')
self._q = Matrix(qs)
self._qdots = self.q.diff(dynamicsymbols._t)
self._qdoubledots = self._qdots.diff(dynamicsymbols._t)
# Deal with constraint equations
if coneqs:
SymPyDeprecationWarning("The `coneqs` kwarg is deprecated in "
"favor of `hol_coneqs` and `nonhol_coneqs`. Please "
"update your code").warn()
self.coneqs = coneqs
else:
mat_build = lambda x: Matrix(x) if x else Matrix()
hol_coneqs = mat_build(hol_coneqs)
nonhol_coneqs = mat_build(nonhol_coneqs)
self.coneqs = Matrix([hol_coneqs.diff(dynamicsymbols._t),
nonhol_coneqs])
self._hol_coneqs = hol_coneqs
