from and_node import And
from warp_affine import WarpAffine
from dubbel_io_test import DubbelIoTest
#Only create node classes once, when module is first loaded
DEFAULT_DUMMY_NODE = BaseNode() #Used if name of node is not in the dictionary
NODE_DICTIONARY = {'HalfScaleGaussian' : HalfScaleGaussian(),
'Subtract' : Subtract(),
'Threshold' : Threshold(),
'Sobel3x3' : Sobel3x3(),
'AbsDiff' : AbsDiff(),
'ConvertDepth' : ConvertDepth(),
'Dilate3x3' : Dilate3x3(),
'Erode3x3' : Erode3x3(),
'Add' : Add(),
'Multiply' : Multiply(),
'ScaleImage' : ScaleImage(),
'Magnitude' : Magnitude(),
'TableLookup' : TableLookup(),
'Or' : Or(),
'And' : And(),
'WarpAffine' : WarpAffine(),
'DubbelIoTest' : DubbelIoTest(), #This is a dummy node used only for testing the parser.
'Dilate2x2' : Dilate2x2(),
'Erode2x2' : Erode2x2()
}
#This dict gives the first parameter index for the first input image in the vxCreateXXXNode function call
#Note that the vx_graph parameter is not counted when accessing node parameters
#Therefore the first index starts on 0, for the first parameter AFTER vx_graph.
def add(self):
self.add = Add()
self.add.show()
def as_real_imag(self, deep=True, **hints):
from sympy.core.symbol import symbols
from sympy.polys.polytools import poly
from sympy.core.function import expand_multinomial
if self.exp.is_Integer:
exp = self.exp
re, im = self.base.as_real_imag(deep=deep)
a, b = symbols('a b', cls=Dummy)
if exp >= 0:
if re.is_Number and im.is_Number:
# We can be more efficient in this case
expr = expand_multinomial(self.base**exp)
return expr.as_real_imag()
expr = poly(
(a + b)**exp) # a = re, b = im; expr = (a + b*I)**exp
else:
mag = re**2 + im**2
re, im = re / mag, -im / mag
if re.is_Number and im.is_Number:
# We can be more efficient in this case
expr = expand_multinomial(
(re + im * S.ImaginaryUnit)**-exp)
return expr.as_real_imag()
expr = poly((a + b)**-exp)
# Terms with even b powers will be real
r = [i for i in expr.terms() if not i[0][1] % 2]
re_part = Add(*[cc * a**aa * b**bb for (aa, bb), cc in r])
# Terms with odd b powers will be imaginary
r = [i for i in expr.terms() if i[0][1] % 4 == 1]
im_part1 = Add(*[cc * a**aa * b**bb for (aa, bb), cc in r])
r = [i for i in expr.terms() if i[0][1] % 4 == 3]
im_part3 = Add(*[cc * a**aa * b**bb for (aa, bb), cc in r])
return (re_part.subs({
a: re,
b: S.ImaginaryUnit * im
}), im_part1.subs({
a: re,
b: im
}) + im_part3.subs({
a: re,
b: -im
}))
elif self.exp.is_Rational:
# NOTE: This is not totally correct since for x**(p/q) with
# x being imaginary there are actually q roots, but
# only a single one is returned from here.
re, im = self.base.as_real_imag(deep=deep)
r = Pow(Pow(re, 2) + Pow(im, 2), S.Half)
t = C.atan2(im, re)
rp, tp = Pow(r, self.exp), t * self.exp
return (rp * C.cos(tp), rp * C.sin(tp))
else:
if deep:
hints['complex'] = False
return (C.re(self.expand(deep, complex=False)),
C.im(self.expand(deep, **hints)))
else:
return (C.re(self), C.im(self))
def _eval_nseries(self, x, n, logx):
# NOTE! This function is an important part of the gruntz algorithm
# for computing limits. It has to return a generalized power series
# with coefficients in C(log, log(x)). In more detail:
# It has to return an expression
# c_0*x**e_0 + c_1*x**e_1 + ... (finitely many terms)
# where e_i are numbers (not necessarily integers) and c_i are expression
# involving only numbers, the log function, and log(x).
from sympy import powsimp, collect, exp, log, O, ceiling
b, e = self.args
if e.is_Integer:
if e > 0:
# positive integer powers are easy to expand, e.g.:
# sin(x)**4 = (x-x**3/3+...)**4 = ...
return Pow(b._eval_nseries(x, n=n, logx=logx),
e)._eval_expand_multinomial(deep=False)
elif e is S.NegativeOne:
# this is also easy to expand using the formula:
# 1/(1 + x) = 1 - x + x**2 - x**3 ...
# so we need to rewrite base to the form "1+x"
b = b._eval_nseries(x, n=n, logx=logx)
prefactor = b.as_leading_term(x)
# express "rest" as: rest = 1 + k*x**l + ... + O(x**n)
rest = ((b - prefactor) / prefactor)._eval_expand_mul()
if rest == 0:
# if prefactor == w**4 + x**2*w**4 + 2*x*w**4, we need to
# factor the w**4 out using collect:
return 1 / collect(prefactor, x)
if rest.is_Order:
return 1 / prefactor + rest / prefactor
n2 = rest.getn()
if n2 is not None:
n = n2
# remove the O - powering this is slow
if logx is not None:
rest = rest.removeO()
k, l = rest.leadterm(x)
if l.is_Rational and l > 0:
pass
elif l.is_number and l > 0:
l = l.evalf()
else:
raise NotImplementedError()
terms = [1 / prefactor]
for m in xrange(1, ceiling(n / l)):
new_term = terms[-1] * (-rest)
if new_term.is_Pow:
new_term = new_term._eval_expand_multinomial(
deep=False)
else:
new_term = new_term._eval_expand_mul(deep=False)
terms.append(new_term)
# Append O(...), we know the order.
if n2 is None or logx is not None:
terms.append(O(x**n))
return powsimp(Add(*terms), deep=True, combine='exp')
else:
# negative powers are rewritten to the cases above, for example:
# sin(x)**(-4) = 1/( sin(x)**4) = ...
# and expand the denominator:
denominator = (b**(-e))._eval_nseries(x, n=n, logx=logx)
if 1 / denominator == self:
return self
# now we have a type 1/f(x), that we know how to expand
return (1 / denominator)._eval_nseries(x, n=n, logx=logx)
if e.has(Symbol):
return exp(e * log(b))._eval_nseries(x, n=n, logx=logx)
if b == x:
return powsimp(self, deep=True, combine='exp')
# work for b(x)**e where e is not an Integer and does not contain x
# and hopefully has no other symbols
def e2int(e):
"""return the integer value (if possible) of e and a
flag indicating whether it is bounded or not."""
n = e.limit(x, 0)
unbounded = n.is_unbounded
if not unbounded:
# XXX was int or floor intended? int used to behave like floor
# so int(-Rational(1, 2)) returned -1 rather than int's 0
try:
n = int(n)
except TypeError:
#well, the n is something more complicated (like 1+log(2))
try:
n = int(n.evalf()) + 1 # XXX why is 1 being added?
except TypeError:
pass # hope that base allows this to be resolved
n = _sympify(n)
return n, unbounded
order = O(x**n, x)
ei, unbounded = e2int(e)
b0 = b.limit(x, 0)
if unbounded and (b0 is S.One or b0.has(Symbol)):
# XXX what order
if b0 is S.One:
resid = (b - 1)
if resid.is_positive:
return S.Infinity
elif resid.is_negative:
return S.Zero
raise ValueError('cannot determine sign of %s' % resid)
return b0**ei
if (b0 is S.Zero or b0.is_unbounded):
if unbounded is not False:
return b0**e # XXX what order
if not ei.is_number: # if not, how will we proceed?
raise ValueError('expecting numerical exponent but got %s' %
ei)
nuse = n - ei
lt = b.compute_leading_term(x, logx=logx) # arg = sin(x); lt = x
# XXX o is not used -- was this to be used as o and o2 below to compute a new e?
o = order * lt**(1 - e)
bs = b._eval_nseries(x, n=nuse, logx=logx)
if bs.is_Add:
bs = bs.removeO()
if bs.is_Add:
# bs -> lt + rest -> lt*(1 + (bs/lt - 1))
return ((Pow(lt, e) * Pow((bs / lt).expand(), e).nseries(
x, n=nuse, logx=logx)).expand() + order)
return bs**e + order
# either b0 is bounded but neither 1 nor 0 or e is unbounded
# b -> b0 + (b-b0) -> b0 * (1 + (b/b0-1))
o2 = order * (b0**-e)
z = (b / b0 - 1)
o = O(z, x)
#r = self._compute_oseries3(z, o2, self.taylor_term)
if o is S.Zero or o2 is S.Zero:
unbounded = True
else:
if o.expr.is_number:
e2 = log(o2.expr * x) / log(x)
else:
e2 = log(o2.expr) / log(o.expr)
n, unbounded = e2int(e2)
if unbounded:
# requested accuracy gives infinite series,
# order is probably non-polynomial e.g. O(exp(-1/x), x).
r = 1 + z
else:
l = []
g = None
for i in xrange(n + 2):
g = self.taylor_term(i, z, g)
g = g.nseries(x, n=n, logx=logx)
l.append(g)
r = Add(*l)
return r * b0**e + order
def extract_multiplicatively(self, c):
"""Return None if it's not possible to make self in the form
c * something in a nice way, i.e. preserving the properties
of arguments of self.
>>> from sympy import symbols, Rational
>>> x, y = symbols('xy', real=True)
>>> ((x*y)**3).extract_multiplicatively(x**2 * y)
x*y**2
>>> ((x*y)**3).extract_multiplicatively(x**4 * y)
>>> (2*x).extract_multiplicatively(2)
x
>>> (2*x).extract_multiplicatively(3)
>>> (Rational(1,2)*x).extract_multiplicatively(3)
x/6
"""
c = sympify(c)
if c is S.One:
return self
elif c == self:
return S.One
elif c.is_Mul:
x = self.extract_multiplicatively(c.as_two_terms()[0])
if x != None:
return x.extract_multiplicatively(c.as_two_terms()[1])
quotient = self / c
if self.is_Number:
if self is S.Infinity:
if c.is_positive:
return S.Infinity
elif self is S.NegativeInfinity:
if c.is_negative:
return S.Infinity
elif c.is_positive:
return S.NegativeInfinity
elif self is S.ComplexInfinity:
if not c.is_zero:
return S.ComplexInfinity
elif self is S.NaN:
return S.NaN
elif self.is_Integer:
if not quotient.is_Integer:
return None
elif self.is_positive and quotient.is_negative:
return None
else:
return quotient
elif self.is_Rational:
if not quotient.is_Rational:
return None
elif self.is_positive and quotient.is_negative:
return None
else:
return quotient
elif self.is_Real:
if not quotient.is_Real:
return None
elif self.is_positive and quotient.is_negative:
return None
else:
return quotient
elif self.is_NumberSymbol or self.is_Symbol or self is S.ImaginaryUnit:
if quotient.is_Mul and len(quotient.args) == 2:
if quotient.args[0].is_Integer and quotient.args[0].is_positive and quotient.args[1] == self:
return quotient
elif quotient.is_Integer:
return quotient
elif self.is_Add:
newargs = []
for arg in self.args:
newarg = arg.extract_multiplicatively(c)
if newarg != None:
newargs.append(newarg)
else:
return None
return Add(*newargs)
elif self.is_Mul:
for i in xrange(len(self.args)):
newargs = list(self.args)
del(newargs[i])
tmp = self._new_rawargs(*newargs).extract_multiplicatively(c)
if tmp != None:
return tmp * self.args[i]
elif self.is_Pow:
if c.is_Pow and c.base == self.base:
new_exp = self.exp.extract_additively(c.exp)
if new_exp != None:
return self.base ** (new_exp)
elif c == self.base:
new_exp = self.exp.extract_additively(1)
if new_exp != None:
return self.base ** (new_exp)
def _eval_expand_multinomial(self, deep=True, **hints):
"""(a+b+..) ** n -> a**n + n*a**(n-1)*b + .., n is nonzero integer"""
if deep:
b = self.base.expand(deep=deep, **hints)
e = self.exp.expand(deep=deep, **hints)
else:
b = self.base
e = self.exp
if b is None:
base = self.base
else:
base = b
if e is None:
exp = self.exp
else:
exp = e
if e is not None or b is not None:
result = Pow(base, exp)
if result.is_Pow:
base, exp = result.base, result.exp
else:
return result
else:
result = None
if exp.is_Rational and exp.p > 0 and base.is_Add:
if not exp.is_Integer:
n = Integer(exp.p // exp.q)
if not n:
return Pow(base, exp)
else:
radical, result = Pow(base, exp - n), []
for term in Add.make_args(
Pow(base, n)._eval_expand_multinomial(deep=False)):
result.append(term * radical)
return Add(*result)
n = int(exp)
if base.is_commutative:
order_terms, other_terms = [], []
for order in base.args:
if order.is_Order:
order_terms.append(order)
else:
other_terms.append(order)
if order_terms:
# (f(x) + O(x^n))^m -> f(x)^m + m*f(x)^{m-1} *O(x^n)
f = Add(*other_terms)
g = (f**(n - 1)).expand()
return (f * g).expand() + n * g * Add(*order_terms)
if base.is_number:
# Efficiently expand expressions of the form (a + b*I)**n
# where 'a' and 'b' are real numbers and 'n' is integer.
a, b = base.as_real_imag()
if a.is_Rational and b.is_Rational:
if not a.is_Integer:
if not b.is_Integer:
k = Pow(a.q * b.q, n)
a, b = a.p * b.q, a.q * b.p
else:
k = Pow(a.q, n)
a, b = a.p, a.q * b
elif not b.is_Integer:
k = Pow(b.q, n)
a, b = a * b.q, b.p
else:
k = 1
a, b, c, d = int(a), int(b), 1, 0
while n:
if n & 1:
c, d = a * c - b * d, b * c + a * d
n -= 1
a, b = a * a - b * b, 2 * a * b
n //= 2
I = S.ImaginaryUnit
if k == 1:
return c + I * d
else:
return Integer(c) / k + I * d / k
p = other_terms
# (x+y)**3 -> x**3 + 3*x**2*y + 3*x*y**2 + y**3
# in this particular example:
# p = [x,y]; n = 3
# so now it's easy to get the correct result -- we get the
# coefficients first:
from sympy import multinomial_coefficients
expansion_dict = multinomial_coefficients(len(p), n)
# in our example: {(3, 0): 1, (1, 2): 3, (0, 3): 1, (2, 1): 3}
# and now construct the expression.
# An elegant way would be to use Poly, but unfortunately it is
# slower than the direct method below, so it is commented out:
#b = {}
#for k in expansion_dict:
# b[k] = Integer(expansion_dict[k])
#return Poly(b, *p).as_expr()
from sympy.polys.polyutils import basic_from_dict
result = basic_from_dict(expansion_dict, *p)
return result
else:
if n == 2:
return Add(*[f * g for f in base.args for g in base.args])
else:
multi = (base**(n -
1))._eval_expand_multinomial(deep=False)
if multi.is_Add:
return Add(
*[f * g for f in base.args for g in multi.args])
else:
return Add(*[f * multi for f in base.args])
elif exp.is_Rational and exp.p < 0 and base.is_Add and abs(
exp.p) > exp.q:
return 1 / Pow(base, -exp)._eval_expand_multinomial(deep=False)
elif exp.is_Add and base.is_Number:
# a + b a b
# n --> n n , where n, a, b are Numbers
coeff, tail = S.One, S.Zero
for term in exp.args:
if term.is_Number:
coeff *= Pow(base, term)
else:
tail += term
return coeff * Pow(base, tail)
else:
return result
def __rsub__(self, other):
return Add(other, -self)
def __sub__(self, other):
return Add(self, -other)
def __radd__(self, other):
return Add(other, self)
def __add__(self, other):
return Add(self, other)
def count_ops(expr, visual=False):
"""
Return a representation (integer or expression) of the operations in expr.
If `visual` is False (default) then the sum of the coefficients of the
visual expression will be returned.
If `visual` is True then the number of each type of operation is shown
with the core class types (or their virtual equivalent) multiplied by the
number of times they occur.
If expr is an iterable, the sum of the op counts of the
items will be returned.
Examples:
>>> from sympy.abc import a, b, x, y
>>> from sympy import sin, count_ops
Although there isn't a SUB object, minus signs are interpreted as
either negations or subtractions:
>>> (x - y).count_ops(visual=True)
SUB
>>> (-x).count_ops(visual=True)
NEG
Here, there are two Adds and a Pow:
>>> (1 + a + b**2).count_ops(visual=True)
POW + 2*ADD
In the following, an Add, Mul, Pow and two functions:
>>> (sin(x)*x + sin(x)**2).count_ops(visual=True)
ADD + MUL + POW + 2*SIN
for a total of 5:
>>> (sin(x)*x + sin(x)**2).count_ops(visual=False)
5
Note that "what you type" is not always what you get. The expression
1/x/y is translated by sympy into 1/(x*y) so it gives a DIV and MUL rather
than two DIVs:
>>> (1/x/y).count_ops(visual=True)
DIV + MUL
The visual option can be used to demonstrate the difference in
operations for expressions in different forms. Here, the Horner
representation is compared with the expanded form of a polynomial:
>>> eq=x*(1 + x*(2 + x*(3 + x)))
>>> count_ops(eq.expand(), visual=True) - count_ops(eq, visual=True)
-MUL + 3*POW
The count_ops function also handles iterables:
>>> count_ops([x, sin(x), None, True, x + 2], visual=False)
2
>>> count_ops([x, sin(x), None, True, x + 2], visual=True)
ADD + SIN
>>> count_ops({x: sin(x), x + 2: y + 1}, visual=True)
SIN + 2*ADD
"""
from sympy.simplify.simplify import fraction
expr = sympify(expr)
if isinstance(expr, Expr):
ops = []
args = [expr]
NEG = C.Symbol('NEG')
DIV = C.Symbol('DIV')
SUB = C.Symbol('SUB')
ADD = C.Symbol('ADD')
def isneg(a):
c = a.as_coeff_mul()[0]
return c.is_Number and c.is_negative
while args:
a = args.pop()
if a.is_Rational:
#-1/3 = NEG + DIV
if a is not S.One:
if a.p < 0:
ops.append(NEG)
if a.q != 1:
ops.append(DIV)
continue
elif a.is_Mul:
if isneg(a):
ops.append(NEG)
if a.args[0] is S.NegativeOne:
a = a.as_two_terms()[1]
else:
a = -a
n, d = fraction(a)
if n.is_Integer:
ops.append(DIV)
if n < 0:
ops.append(NEG)
args.append(d)
continue # won't be -Mul but could be Add
elif d is not S.One:
if not d.is_Integer:
args.append(d)
ops.append(DIV)
args.append(n)
continue # could be -Mul
elif a.is_Add:
aargs = list(a.args)
negs = 0
for i, ai in enumerate(aargs):
if isneg(ai):
negs += 1
args.append(-ai)
if i > 0:
ops.append(SUB)
else:
args.append(ai)
if i > 0:
ops.append(ADD)
if negs == len(aargs): # -x - y = NEG + SUB
ops.append(NEG)
elif isneg(aargs[0]
): # -x + y = SUB, but we already recorded an ADD
ops.append(SUB - ADD)
continue
if a.is_Pow and a.exp is S.NegativeOne:
ops.append(DIV)
args.append(a.base) # won't be -Mul but could be Add
continue
if (a.is_Mul or a.is_Pow or a.is_Function
or isinstance(a, Derivative) or isinstance(a, C.Integral)):
o = C.Symbol(a.func.__name__.upper())
# count the args
if (a.is_Mul or isinstance(a, C.LatticeOp)):
ops.append(o * (len(a.args) - 1))
else:
ops.append(o)
args.extend(a.args)
elif type(expr) is dict:
ops = [
count_ops(k, visual=visual) + count_ops(v, visual=visual)
for k, v in expr.iteritems()
]
elif hasattr(expr, '__iter__'):
ops = [count_ops(i, visual=visual) for i in expr]
elif not isinstance(expr, Basic):
ops = []
else: # it's Basic not isinstance(expr, Expr):
assert isinstance(expr, Basic)
ops = [count_ops(a, visual=visual) for a in expr.args]
if not ops:
if visual:
return S.Zero
return 0
ops = Add(*ops)
if visual:
return ops
if ops.is_Number:
return int(ops)
return sum(int((a.args or [1])[0]) for a in Add.make_args(ops))