def test_Dict():
x, y, z = symbols('x y z')
d = Dict({x: 1, y: 2, z: 3})
assert d[x] == 1
assert d[y] == 2
raises(KeyError, lambda: d[2])
assert len(d) == 3
assert set(d.keys()) == set((x, y, z))
assert set(d.values()) == set((S(1), S(2), S(3)))
assert d.get(5, 'default') == 'default'
assert x in d and z in d and not 5 in d
assert d.has(x) and d.has(1) # SymPy Basic .has method
# Test input types
# input - a python dict
# input - items as args - SymPy style
assert (Dict({x: 1, y: 2, z: 3}) == Dict((x, 1), (y, 2), (z, 3)))
raises(TypeError, lambda: Dict(((x, 1), (y, 2), (z, 3))))
with raises(NotImplementedError):
d[5] = 6 # assert immutability
assert set(d.items()) == set((Tuple(x, S(1)), Tuple(y,
S(2)), Tuple(z, S(3))))
assert set(d) == set([x, y, z])
assert str(d) == '{x: 1, y: 2, z: 3}'
assert d.__repr__() == '{x: 1, y: 2, z: 3}'
def test_Dict():
assert str(Dict({1: 1 + x})) == sstr({1: 1 + x}) == "{1: x + 1}"
assert str(Dict({
1: x**2,
2: y * x
})) in ("{1: x**2, 2: x*y}", "{2: x*y, 1: x**2}")
assert sstr(Dict({1: x**2, 2: y * x})) == "{1: x**2, 2: x*y}"
def test_domains():
X, Y = Die('x', 6), Die('y', 6)
x, y = X.symbol, Y.symbol
# Domains
d = where(X > Y)
assert d.condition == (x > y)
d = where(And(X > Y, Y > 3))
assert d.as_boolean() == Or(And(Eq(x, 5), Eq(y,
4)), And(Eq(x, 6), Eq(y, 5)),
And(Eq(x, 6), Eq(y, 4)))
assert len(d.elements) == 3
assert len(pspace(X + Y).domain.elements) == 36
Z = Die('x', 4)
raises(ValueError,
lambda: P(X > Z)) # Two domains with same internal symbol
pspace(X + Y).domain.set == FiniteSet(1, 2, 3, 4, 5, 6)**2
assert where(X > 3).set == FiniteSet(4, 5, 6)
assert X.pspace.domain.dict == FiniteSet(
Dict({X.symbol: i}) for i in range(1, 7))
assert where(X > Y).dict == FiniteSet(
Dict({
X.symbol: i,
Y.symbol: j
}) for i in range(1, 7) for j in range(1, 7) if i > j)
def __new__(cls, iterable=None, shape=None, **kwargs):
from sympy.utilities.iterables import flatten
shape, flat_list = cls._handle_ndarray_creation_inputs(iterable, shape, **kwargs)
shape = Tuple(*map(_sympify, shape))
cls._check_special_bounds(flat_list, shape)
loop_size = functools.reduce(lambda x,y: x*y, shape) if shape else 0
# Sparse array:
if isinstance(flat_list, (dict, Dict)):
sparse_array = Dict(flat_list)
else:
sparse_array = {}
for i, el in enumerate(flatten(flat_list)):
if el != 0:
sparse_array[i] = _sympify(el)
sparse_array = Dict(sparse_array)
self = Basic.__new__(cls, sparse_array, shape, **kwargs)
self._shape = shape
self._rank = len(shape)
self._loop_size = loop_size
self._sparse_array = sparse_array
return self
def test_gcd_terms():
f = 2*(x + 1)*(x + 4)/(5*x**2 + 5) + (2*x + 2)*(x + 5)/(x**2 + 1)/5 + \
(2*x + 2)*(x + 6)/(5*x**2 + 5)
assert _gcd_terms(f) == ((Rational(6, 5)) * ((1 + x) / (1 + x**2)), 5 + x,
1)
assert _gcd_terms(Add.make_args(f)) == \
((Rational(6, 5))*((1 + x)/(1 + x**2)), 5 + x, 1)
newf = (Rational(6, 5)) * ((1 + x) * (5 + x) / (1 + x**2))
assert gcd_terms(f) == newf
args = Add.make_args(f)
# non-Basic sequences of terms treated as terms of Add
assert gcd_terms(list(args)) == newf
assert gcd_terms(tuple(args)) == newf
assert gcd_terms(set(args)) == newf
# but a Basic sequence is treated as a container
assert gcd_terms(Tuple(*args)) != newf
assert gcd_terms(Basic(Tuple(1, 3*y + 3*x*y), Tuple(1, 3))) == \
Basic((1, 3*y*(x + 1)), (1, 3))
# but we shouldn't change keys of a dictionary or some may be lost
assert gcd_terms(Dict((x*(1 + y), 2), (x + x*y, y + x*y))) == \
Dict({x*(y + 1): 2, x + x*y: y*(1 + x)})
assert gcd_terms((2 * x + 2)**3 +
(2 * x + 2)**2) == 4 * (x + 1)**2 * (2 * x + 3)
assert gcd_terms(0) == 0
assert gcd_terms(1) == 1
assert gcd_terms(x) == x
assert gcd_terms(2 + 2 * x) == Mul(2, 1 + x, evaluate=False)
arg = x * (2 * x + 4 * y)
garg = 2 * x * (x + 2 * y)
assert gcd_terms(arg) == garg
assert gcd_terms(sin(arg)) == sin(garg)
# issue 6139-like
alpha, alpha1, alpha2, alpha3 = symbols('alpha:4')
a = alpha**2 - alpha * x**2 + alpha + x**3 - x * (alpha + 1)
rep = (alpha,
(1 + sqrt(5)) / 2 + alpha1 * x + alpha2 * x**2 + alpha3 * x**3)
s = (a / (x - alpha)).subs(*rep).series(x, 0, 1)
assert simplify(collect(s, x)) == -sqrt(5) / 2 - Rational(3, 2) + O(x)
# issue 5917
assert _gcd_terms([S.Zero, S.Zero]) == (0, 0, 1)
assert _gcd_terms([2 * x + 4]) == (2, x + 2, 1)
eq = x / (x + 1 / x)
assert gcd_terms(eq, fraction=False) == eq
eq = x / 2 / y + 1 / x / y
assert gcd_terms(eq, fraction=True, clear=True) == \
(x**2 + 2)/(2*x*y)
assert gcd_terms(eq, fraction=True, clear=False) == \
(x**2/2 + 1)/(x*y)
assert gcd_terms(eq, fraction=False, clear=True) == \
(x + 2/x)/(2*y)
assert gcd_terms(eq, fraction=False, clear=False) == \
(x/2 + 1/x)/y
def _register_as_base_dim(self):
SymPyDeprecationWarning(deprecated_since_version="1.2",
issue=13336,
feature="do not call ._register_as_base_dim()",
useinstead="DimensionSystem").warn()
if not self.name.is_Symbol:
raise TypeError("Base dimensions need to have symbolic name")
name = self.name
if name in dimsys_default.dimensional_dependencies:
raise IndexError("already in dependencies dict")
# Horrible code:
d = dict(dimsys_default.dimensional_dependencies)
d[name] = Dict({name: 1})
dimsys_default._args = (dimsys_default.args[:2] + (Dict(d), ))
def test_latex_dict():
d = {Rational(1): 1, x**2: 2, x: 3, x**3: 4}
assert latex(
d
) == '\\begin{Bmatrix}1 : 1, & x : 3, & x^{2} : 2, & x^{3} : 4\\end{Bmatrix}'
D = Dict(d)
assert latex(
D
) == '\\begin{Bmatrix}1 : 1, & x : 3, & x^{2} : 2, & x^{3} : 4\\end{Bmatrix}'
def rv(name, cls, *args):
args = list(map(sympify, args))
i = 0
while i < len(args): # Converting to Dict since dict is not hashable
if isinstance(args[i], dict):
args[i] = Dict(args[i])
i += 1
dist = cls(*args)
dist.check(*args)
return SingleFinitePSpace(name, dist).value
def _new(cls, *args, **kwargs):
s = MutableSparseMatrix(*args)
rows = Integer(s.rows)
cols = Integer(s.cols)
mat = Dict(s._smat)
obj = Basic.__new__(cls, rows, cols, mat)
obj.rows = s.rows
obj.cols = s.cols
obj._smat = s._smat
return obj
def test_RoutineCall():
test = routine('test', (a, b, c), expr)
rcall = test(1, 2, 3)
assert rcall.routine == test
assert rcall.arguments == (1, 2, 3)
assert rcall.returns == ScalarRoutineCallResult(rcall, -1)
assert rcall.inplace == Dict()
# Multiple results
test = routine('test', (a, b, c, out), (expr, inp_expr))
rcall = test(1, 2, 3, out)
assert rcall.arguments == (1, 2, 3, out)
assert rcall.returns == ScalarRoutineCallResult(rcall, -1)
assert rcall.inplace == Dict({out: ScalarRoutineCallResult(rcall, out)})
# Matrix result
test = routine('test', (a, b, c, x), (expr, mat_expr))
rcall = test(1, 2, 3, x)
assert rcall.arguments == (1, 2, 3, x)
assert rcall.returns == ScalarRoutineCallResult(rcall, -1)
assert rcall.inplace == Dict({x: MatrixRoutineCallResult(rcall, x)})
def test_ndim_array_initiation():
arr_with_one_element = ImmutableDenseNDimArray([23])
assert len(arr_with_one_element) == 1
assert arr_with_one_element[0] == 23
assert arr_with_one_element[:] == [23]
assert arr_with_one_element.rank() == 1
arr_with_symbol_element = ImmutableDenseNDimArray([Symbol('x')])
assert len(arr_with_symbol_element) == 1
assert arr_with_symbol_element[0] == Symbol('x')
assert arr_with_symbol_element[:] == [Symbol('x')]
assert arr_with_symbol_element.rank() == 1
number5 = 5
vector = ImmutableDenseNDimArray.zeros(number5)
assert len(vector) == number5
assert vector.shape == (number5, )
assert vector.rank() == 1
vector = ImmutableSparseNDimArray.zeros(number5)
assert len(vector) == number5
assert vector.shape == (number5, )
assert vector._sparse_array == Dict()
assert vector.rank() == 1
n_dim_array = ImmutableDenseNDimArray(range(3**4), (
3,
3,
3,
3,
))
assert len(n_dim_array) == 3 * 3 * 3 * 3
assert n_dim_array.shape == (3, 3, 3, 3)
assert n_dim_array.rank() == 4
array_shape = (3, 3, 3, 3)
sparse_array = ImmutableSparseNDimArray.zeros(*array_shape)
assert len(sparse_array._sparse_array) == 0
assert len(sparse_array) == 3 * 3 * 3 * 3
assert n_dim_array.shape == array_shape
assert n_dim_array.rank() == 4
one_dim_array = ImmutableDenseNDimArray([2, 3, 1])
assert len(one_dim_array) == 3
assert one_dim_array.shape == (3, )
assert one_dim_array.rank() == 1
assert one_dim_array.tolist() == [2, 3, 1]
shape = (3, 3)
array_with_many_args = ImmutableSparseNDimArray.zeros(*shape)
assert len(array_with_many_args) == 3 * 3
assert array_with_many_args.shape == shape
assert array_with_many_args[0, 0] == 0
assert array_with_many_args.rank() == 2
def __new__(cls, density):
density = Dict(density)
for k in density.values():
k_sym = sympify(k)
if fuzzy_not(
fuzzy_and(
(k_sym.is_nonnegative, (k_sym - 1).is_nonpositive))):
raise ValueError(
"Probability at a point must be between 0 and 1.")
sum_sym = sum(density.values())
if sum_sym != 1:
raise ValueError("Total Probability must be equal to 1.")
return Basic.__new__(cls, density)
def _visit_Dict(self, stmt):
d = {}
for key, value in zip(stmt.keys, stmt.values):
key = self._visit(key)
value = self._visit(value)
# sympy does not allow keys to be strings
if isinstance(key, LiteralString):
errors.report(SYMPY_RESTRICTION_DICT_KEYS, severity='error')
d[key] = value
return Dict(d)
def __new__(cls, *args, **kwargs):
shape, flat_list = cls._handle_ndarray_creation_inputs(*args, **kwargs)
shape = Tuple(*map(_sympify, shape))
loop_size = functools.reduce(lambda x,y: x*y, shape) if shape else 0
# Sparse array:
if isinstance(flat_list, (dict, Dict)):
sparse_array = Dict(flat_list)
else:
sparse_array = {}
for i, el in enumerate(flatten(flat_list)):
if el != 0:
sparse_array[i] = _sympify(el)
sparse_array = Dict(sparse_array)
self = Basic.__new__(cls, sparse_array, shape, **kwargs)
self._shape = shape
self._rank = len(shape)
self._loop_size = loop_size
self._sparse_array = sparse_array
return self
def test_dict_set():
a, b, c = map(Wild, 'abc')
f = 3 * cos(4 * x)
r = f.match(a * cos(b * x))
assert r == {a: 3, b: 4}
e = a / b * sin(b * x)
assert e.subs(r) == r[a] / r[b] * sin(r[b] * x)
assert e.subs(r) == 3 * sin(4 * x) / 4
s = set(r.items())
assert e.subs(s) == r[a] / r[b] * sin(r[b] * x)
assert e.subs(s) == 3 * sin(4 * x) / 4
assert e.subs(r) == r[a] / r[b] * sin(r[b] * x)
assert e.subs(r) == 3 * sin(4 * x) / 4
assert x.subs(Dict((x, 1))) == 1
def _visit_DictNode(self, stmt):
d = {}
for i in stmt.value:
if not isinstance(i, DictitemNode):
raise PyccelSyntaxError('Expecting a DictitemNode')
key = self._visit(i.key)
value = self._visit(i.value)
# sympy does not allow keys to be strings
if isinstance(key, str):
errors.report(SYMPY_RESTRICTION_DICT_KEYS, severity='error')
d[key] = value
return Dict(d)
def test_Dict():
x, y, z = symbols('x y z')
d = Dict({x: 1, y: 2, z: 3})
assert d[x] == 1
assert d[y] == 2
raises(KeyError, lambda: d[2])
raises(KeyError, lambda: d['2'])
assert len(d) == 3
assert set(d.keys()) == {x, y, z}
assert set(d.values()) == {S.One, S(2), S(3)}
assert d.get(5, 'default') == 'default'
assert d.get('5', 'default') == 'default'
assert x in d and z in d and not 5 in d and not '5' in d
assert d.has(x) and d.has(1) # SymPy Basic .has method
# Test input types
# input - a python dict
# input - items as args - SymPy style
assert (Dict({x: 1, y: 2, z: 3}) == Dict((x, 1), (y, 2), (z, 3)))
raises(TypeError, lambda: Dict(((x, 1), (y, 2), (z, 3))))
with raises(NotImplementedError):
d[5] = 6 # assert immutability
assert set(d.items()) == {Tuple(x, S.One), Tuple(y, S(2)), Tuple(z, S(3))}
assert set(d) == {x, y, z}
assert str(d) == '{x: 1, y: 2, z: 3}'
assert d.__repr__() == '{x: 1, y: 2, z: 3}'
# Test creating a Dict from a Dict.
d = Dict({x: 1, y: 2, z: 3})
assert d == Dict(d)
# Test for supporting defaultdict
d = defaultdict(int)
assert d[x] == 0
assert d[y] == 0
assert d[z] == 0
assert Dict(d)
d = Dict(d)
assert len(d) == 3
assert set(d.keys()) == {x, y, z}
assert set(d.values()) == {S.Zero, S.Zero, S.Zero}
def parse_dict(d):
return Dict({parse_dim_name(i): j for i, j in d.items()})
def __new__(cls, base_dims, derived_dims=[], dimensional_dependencies={}, name=None, descr=None):
dimensional_dependencies = dict(dimensional_dependencies)
if (name is not None) or (descr is not None):
SymPyDeprecationWarning(
deprecated_since_version="1.2",
issue=13336,
useinstead="do not define a `name` or `descr`",
).warn()
def parse_dim(dim):
if isinstance(dim, string_types):
dim = Dimension(Symbol(dim))
elif isinstance(dim, Dimension):
pass
elif isinstance(dim, Symbol):
dim = Dimension(dim)
else:
raise TypeError("%s wrong type" % dim)
return dim
base_dims = [parse_dim(i) for i in base_dims]
derived_dims = [parse_dim(i) for i in derived_dims]
for dim in base_dims:
dim = dim.name
if (dim in dimensional_dependencies
and (len(dimensional_dependencies[dim]) != 1 or
dimensional_dependencies[dim].get(dim, None) != 1)):
raise IndexError("Repeated value in base dimensions")
dimensional_dependencies[dim] = Dict({dim: 1})
def parse_dim_name(dim):
if isinstance(dim, Dimension):
return dim.name
elif isinstance(dim, string_types):
return Symbol(dim)
elif isinstance(dim, Symbol):
return dim
else:
raise TypeError("unrecognized type %s for %s" % (type(dim), dim))
for dim in dimensional_dependencies.keys():
dim = parse_dim(dim)
if (dim not in derived_dims) and (dim not in base_dims):
derived_dims.append(dim)
def parse_dict(d):
return Dict({parse_dim_name(i): j for i, j in d.items()})
# Make sure everything is a SymPy type:
dimensional_dependencies = {parse_dim_name(i): parse_dict(j) for i, j in
dimensional_dependencies.items()}
for dim in derived_dims:
if dim in base_dims:
raise ValueError("Dimension %s both in base and derived" % dim)
if dim.name not in dimensional_dependencies:
# TODO: should this raise a warning?
dimensional_dependencies[dim] = Dict({dim.name: 1})
base_dims.sort(key=default_sort_key)
derived_dims.sort(key=default_sort_key)
base_dims = Tuple(*base_dims)
derived_dims = Tuple(*derived_dims)
dimensional_dependencies = Dict({i: Dict(j) for i, j in dimensional_dependencies.items()})
obj = Basic.__new__(cls, base_dims, derived_dims, dimensional_dependencies)
return obj
def test_factorint():
assert primefactors(123456) == [2, 3, 643]
assert factorint(0) == {0: 1}
assert factorint(1) == {}
assert factorint(-1) == {-1: 1}
assert factorint(-2) == {-1: 1, 2: 1}
assert factorint(-16) == {-1: 1, 2: 4}
assert factorint(2) == {2: 1}
assert factorint(126) == {2: 1, 3: 2, 7: 1}
assert factorint(123456) == {2: 6, 3: 1, 643: 1}
assert factorint(5951757) == {3: 1, 7: 1, 29: 2, 337: 1}
assert factorint(64015937) == {7993: 1, 8009: 1}
assert factorint(2**(2**6) + 1) == {274177: 1, 67280421310721: 1}
#issue 19683
assert factorint(10**38 - 1) == {3: 2, 11: 1, 909090909090909091: 1, 1111111111111111111: 1}
#issue 17676
assert factorint(28300421052393658575) == {3: 1, 5: 2, 11: 2, 43: 1, 2063: 2, 4127: 1, 4129: 1}
assert factorint(2063**2 * 4127**1 * 4129**1) == {2063: 2, 4127: 1, 4129: 1}
assert factorint(2347**2 * 7039**1 * 7043**1) == {2347: 2, 7039: 1, 7043: 1}
assert factorint(0, multiple=True) == [0]
assert factorint(1, multiple=True) == []
assert factorint(-1, multiple=True) == [-1]
assert factorint(-2, multiple=True) == [-1, 2]
assert factorint(-16, multiple=True) == [-1, 2, 2, 2, 2]
assert factorint(2, multiple=True) == [2]
assert factorint(24, multiple=True) == [2, 2, 2, 3]
assert factorint(126, multiple=True) == [2, 3, 3, 7]
assert factorint(123456, multiple=True) == [2, 2, 2, 2, 2, 2, 3, 643]
assert factorint(5951757, multiple=True) == [3, 7, 29, 29, 337]
assert factorint(64015937, multiple=True) == [7993, 8009]
assert factorint(2**(2**6) + 1, multiple=True) == [274177, 67280421310721]
assert factorint(fac(1, evaluate=False)) == {}
assert factorint(fac(7, evaluate=False)) == {2: 4, 3: 2, 5: 1, 7: 1}
assert factorint(fac(15, evaluate=False)) == \
{2: 11, 3: 6, 5: 3, 7: 2, 11: 1, 13: 1}
assert factorint(fac(20, evaluate=False)) == \
{2: 18, 3: 8, 5: 4, 7: 2, 11: 1, 13: 1, 17: 1, 19: 1}
assert factorint(fac(23, evaluate=False)) == \
{2: 19, 3: 9, 5: 4, 7: 3, 11: 2, 13: 1, 17: 1, 19: 1, 23: 1}
assert multiproduct(factorint(fac(200))) == fac(200)
assert multiproduct(factorint(fac(200, evaluate=False))) == fac(200)
for b, e in factorint(fac(150)).items():
assert e == fac_multiplicity(150, b)
for b, e in factorint(fac(150, evaluate=False)).items():
assert e == fac_multiplicity(150, b)
assert factorint(103005006059**7) == {103005006059: 7}
assert factorint(31337**191) == {31337: 191}
assert factorint(2**1000 * 3**500 * 257**127 * 383**60) == \
{2: 1000, 3: 500, 257: 127, 383: 60}
assert len(factorint(fac(10000))) == 1229
assert len(factorint(fac(10000, evaluate=False))) == 1229
assert factorint(12932983746293756928584532764589230) == \
{2: 1, 5: 1, 73: 1, 727719592270351: 1, 63564265087747: 1, 383: 1}
assert factorint(727719592270351) == {727719592270351: 1}
assert factorint(2**64 + 1, use_trial=False) == factorint(2**64 + 1)
for n in range(60000):
assert multiproduct(factorint(n)) == n
assert pollard_rho(2**64 + 1, seed=1) == 274177
assert pollard_rho(19, seed=1) is None
assert factorint(3, limit=2) == {3: 1}
assert factorint(12345) == {3: 1, 5: 1, 823: 1}
assert factorint(
12345, limit=3) == {4115: 1, 3: 1} # the 5 is greater than the limit
assert factorint(1, limit=1) == {}
assert factorint(0, 3) == {0: 1}
assert factorint(12, limit=1) == {12: 1}
assert factorint(30, limit=2) == {2: 1, 15: 1}
assert factorint(16, limit=2) == {2: 4}
assert factorint(124, limit=3) == {2: 2, 31: 1}
assert factorint(4*31**2, limit=3) == {2: 2, 31: 2}
p1 = nextprime(2**32)
p2 = nextprime(2**16)
p3 = nextprime(p2)
assert factorint(p1*p2*p3) == {p1: 1, p2: 1, p3: 1}
assert factorint(13*17*19, limit=15) == {13: 1, 17*19: 1}
assert factorint(1951*15013*15053, limit=2000) == {225990689: 1, 1951: 1}
assert factorint(primorial(17) + 1, use_pm1=0) == \
{int(19026377261): 1, 3467: 1, 277: 1, 105229: 1}
# when prime b is closer than approx sqrt(8*p) to prime p then they are
# "close" and have a trivial factorization
a = nextprime(2**2**8) # 78 digits
b = nextprime(a + 2**2**4)
assert 'Fermat' in capture(lambda: factorint(a*b, verbose=1))
raises(ValueError, lambda: pollard_rho(4))
raises(ValueError, lambda: pollard_pm1(3))
raises(ValueError, lambda: pollard_pm1(10, B=2))
# verbose coverage
n = nextprime(2**16)*nextprime(2**17)*nextprime(1901)
assert 'with primes' in capture(lambda: factorint(n, verbose=1))
capture(lambda: factorint(nextprime(2**16)*1012, verbose=1))
n = nextprime(2**17)
capture(lambda: factorint(n**3, verbose=1)) # perfect power termination
capture(lambda: factorint(2*n, verbose=1)) # factoring complete msg
# exceed 1st
n = nextprime(2**17)
n *= nextprime(n)
assert '1000' in capture(lambda: factorint(n, limit=1000, verbose=1))
n *= nextprime(n)
assert len(factorint(n)) == 3
assert len(factorint(n, limit=p1)) == 3
n *= nextprime(2*n)
# exceed 2nd
assert '2001' in capture(lambda: factorint(n, limit=2000, verbose=1))
assert capture(
lambda: factorint(n, limit=4000, verbose=1)).count('Pollard') == 2
# non-prime pm1 result
n = nextprime(8069)
n *= nextprime(2*n)*nextprime(2*n, 2)
capture(lambda: factorint(n, verbose=1)) # non-prime pm1 result
# factor fermat composite
p1 = nextprime(2**17)
p2 = nextprime(2*p1)
assert factorint((p1*p2**2)**3) == {p1: 3, p2: 6}
# Test for non integer input
raises(ValueError, lambda: factorint(4.5))
# test dict/Dict input
sans = '2**10*3**3'
n = {4: 2, 12: 3}
assert str(factorint(n)) == sans
assert str(factorint(Dict(n))) == sans
def test_ndim_array_initiation():
arr_with_no_elements = ImmutableDenseNDimArray([], shape=(0, ))
assert len(arr_with_no_elements) == 0
assert arr_with_no_elements.rank() == 1
raises(ValueError, lambda: ImmutableDenseNDimArray([0], shape=(0, )))
raises(ValueError, lambda: ImmutableDenseNDimArray([1, 2, 3], shape=(0, )))
raises(ValueError, lambda: ImmutableDenseNDimArray([], shape=()))
raises(ValueError, lambda: ImmutableSparseNDimArray([0], shape=(0, )))
raises(ValueError,
lambda: ImmutableSparseNDimArray([1, 2, 3], shape=(0, )))
raises(ValueError, lambda: ImmutableSparseNDimArray([], shape=()))
arr_with_one_element = ImmutableDenseNDimArray([23])
assert len(arr_with_one_element) == 1
assert arr_with_one_element[0] == 23
assert arr_with_one_element[:] == ImmutableDenseNDimArray([23])
assert arr_with_one_element.rank() == 1
arr_with_symbol_element = ImmutableDenseNDimArray([Symbol('x')])
assert len(arr_with_symbol_element) == 1
assert arr_with_symbol_element[0] == Symbol('x')
assert arr_with_symbol_element[:] == ImmutableDenseNDimArray([Symbol('x')])
assert arr_with_symbol_element.rank() == 1
number5 = 5
vector = ImmutableDenseNDimArray.zeros(number5)
assert len(vector) == number5
assert vector.shape == (number5, )
assert vector.rank() == 1
vector = ImmutableSparseNDimArray.zeros(number5)
assert len(vector) == number5
assert vector.shape == (number5, )
assert vector._sparse_array == Dict()
assert vector.rank() == 1
n_dim_array = ImmutableDenseNDimArray(range(3**4), (
3,
3,
3,
3,
))
assert len(n_dim_array) == 3 * 3 * 3 * 3
assert n_dim_array.shape == (3, 3, 3, 3)
assert n_dim_array.rank() == 4
array_shape = (3, 3, 3, 3)
sparse_array = ImmutableSparseNDimArray.zeros(*array_shape)
assert len(sparse_array._sparse_array) == 0
assert len(sparse_array) == 3 * 3 * 3 * 3
assert n_dim_array.shape == array_shape
assert n_dim_array.rank() == 4
one_dim_array = ImmutableDenseNDimArray([2, 3, 1])
assert len(one_dim_array) == 3
assert one_dim_array.shape == (3, )
assert one_dim_array.rank() == 1
assert one_dim_array.tolist() == [2, 3, 1]
shape = (3, 3)
array_with_many_args = ImmutableSparseNDimArray.zeros(*shape)
assert len(array_with_many_args) == 3 * 3
assert array_with_many_args.shape == shape
assert array_with_many_args[0, 0] == 0
assert array_with_many_args.rank() == 2
shape = (long(3), long(3))
array_with_long_shape = ImmutableSparseNDimArray.zeros(*shape)
assert len(array_with_long_shape) == 3 * 3
assert array_with_long_shape.shape == shape
assert array_with_long_shape[long(0), long(0)] == 0
assert array_with_long_shape.rank() == 2
vector_with_long_shape = ImmutableDenseNDimArray(range(5), long(5))
assert len(vector_with_long_shape) == 5
assert vector_with_long_shape.shape == (long(5), )
assert vector_with_long_shape.rank() == 1
raises(ValueError, lambda: vector_with_long_shape[long(5)])
from sympy.abc import x
for ArrayType in [ImmutableDenseNDimArray, ImmutableSparseNDimArray]:
rank_zero_array = ArrayType(x)
assert len(rank_zero_array) == 1
assert rank_zero_array.shape == ()
assert rank_zero_array.rank() == 0
assert rank_zero_array[()] == x
raises(ValueError, lambda: rank_zero_array[0])
def _new(cls, *args, **kwargs):
s = SparseMatrix(*args)
rows = Integer(s.rows)
cols = Integer(s.cols)
mat = Dict(s._smat)
return Basic.__new__(cls, rows, cols, mat)
def __new__(cls, density):
density = Dict(density)
return Basic.__new__(cls, density)
def to_sympy(stmt):
if isinstance(stmt, NamedAbstraction):
name = stmt.name
expr = to_sympy(stmt.abstraction)
return expr
elif isinstance(stmt, Abstraction):
args = [to_sympy(i) for i in stmt.args]
expr = to_sympy(stmt.expr)
func = Lambda(args, expr)
# add a name for the lambda expression
# TODO improve this by implementing a new class
# for Lambda in lampy
setattr(func, 'name', 'lambda_{}'.format(random_string(4)))
return func
elif isinstance(stmt, Application):
name = stmt.name
first = to_sympy(stmt.args[0])
if name in _internal_map_functors:
if not isinstance(first, (Lambda, BasicMap)):
func_name = str(stmt.args[0])
func = FunctionSymbol(func_name)
else:
func = first
arguments = stmt.args[1:]
arguments = [to_sympy(i) for i in arguments]
return _map_registery[name](func, arguments)
elif name == 'reduce':
if not (len(stmt.args) == 2):
raise ValueError('Wrong number of arguments for reduce')
op = stmt.args[0]
if not op in _internal_reduction_operators:
msg = "Only 'add' and 'mul' reduction operators are available"
raise ValueError(msg)
target = to_sympy(stmt.args[1])
if op == 'add':
return AddReduce(target)
elif op == 'mul':
return MulReduce(target)
elif name == 'partial':
if not isinstance(first, Lambda):
func_name = str(stmt.args[0])
func = FunctionSymbol(func_name)
else:
func = first
arguments = stmt.args[1:]
arguments = [to_sympy(i) for i in arguments]
assert (all([isinstance(i, Tuple) for i in arguments]))
# ...
d = {}
for i in arguments:
key = i[0]
value = i[1]
d[key] = value
arguments = Dict(d)
# ...
return PartialFunction(func, arguments)
else:
args = [to_sympy(i) for i in stmt.args]
return Function(name)(*args)
elif isinstance(stmt, (int, float)):
return stmt
elif isinstance(stmt, str):
if stmt == '_':
return _
else:
return sympify(stmt)
elif isinstance(stmt, ValuedItem):
key = to_sympy(stmt.name)
value = to_sympy(stmt.value)
return Tuple(key, value)
else:
raise TypeError('Not implemented for {}'.format(type(stmt)))
def test_diagram():
A = Object("A")
B = Object("B")
C = Object("C")
f = NamedMorphism(A, B, "f")
g = NamedMorphism(B, C, "g")
id_A = IdentityMorphism(A)
id_B = IdentityMorphism(B)
empty = EmptySet()
# Test the addition of identities.
d1 = Diagram([f])
assert d1.objects == FiniteSet(A, B)
assert d1.hom(A, B) == (FiniteSet(f), empty)
assert d1.hom(A, A) == (FiniteSet(id_A), empty)
assert d1.hom(B, B) == (FiniteSet(id_B), empty)
assert d1 == Diagram([id_A, f])
assert d1 == Diagram([f, f])
# Test the addition of composites.
d2 = Diagram([f, g])
homAC = d2.hom(A, C)[0]
assert d2.objects == FiniteSet(A, B, C)
assert g * f in d2.premises.keys()
assert homAC == FiniteSet(g * f)
# Test equality, inequality and hash.
d11 = Diagram([f])
assert d1 == d11
assert d1 != d2
assert hash(d1) == hash(d11)
d11 = Diagram({f: "unique"})
assert d1 != d11
# Make sure that (re-)adding composites (with new properties)
# works as expected.
d = Diagram([f, g], {g * f: "unique"})
assert d.conclusions == Dict({g * f: FiniteSet("unique")})
# Check the hom-sets when there are premises and conclusions.
assert d.hom(A, C) == (FiniteSet(g * f), FiniteSet(g * f))
d = Diagram([f, g], [g * f])
assert d.hom(A, C) == (FiniteSet(g * f), FiniteSet(g * f))
# Check how the properties of composite morphisms are computed.
d = Diagram({f: ["unique", "isomorphism"], g: "unique"})
assert d.premises[g * f] == FiniteSet("unique")
# Check that conclusion morphisms with new objects are not allowed.
d = Diagram([f], [g])
assert d.conclusions == Dict({})
# Test an empty diagram.
d = Diagram()
assert d.premises == Dict({})
assert d.conclusions == Dict({})
assert d.objects == empty
# Check a SymPy Dict object.
d = Diagram(Dict({f: FiniteSet("unique", "isomorphism"), g: "unique"}))
assert d.premises[g * f] == FiniteSet("unique")
# Check the addition of components of composite morphisms.
d = Diagram([g * f])
assert f in d.premises
assert g in d.premises
# Check subdiagrams.
d = Diagram([f, g], {g * f: "unique"})
d1 = Diagram([f])
assert d.is_subdiagram(d1)
assert not d1.is_subdiagram(d)
d = Diagram([NamedMorphism(B, A, "f'")])
assert not d.is_subdiagram(d1)
assert not d1.is_subdiagram(d)
d1 = Diagram([f, g], {g * f: ["unique", "something"]})
assert not d.is_subdiagram(d1)
assert not d1.is_subdiagram(d)
d = Diagram({f: "blooh"})
d1 = Diagram({f: "bleeh"})
assert not d.is_subdiagram(d1)
assert not d1.is_subdiagram(d)
d = Diagram([f, g], {f: "unique", g * f: "veryunique"})
d1 = d.subdiagram_from_objects(FiniteSet(A, B))
assert d1 == Diagram([f], {f: "unique"})
raises(ValueError, lambda: d.subdiagram_from_objects(FiniteSet(A,
Object("D"))))
raises(ValueError, lambda: Diagram({IdentityMorphism(A): "unique"}))
