def apply(self, f, xs, evaluation):
'SageIntegrate[f_, xs__]'
xs = xs.get_sequence()
vars = []
for x in xs:
if x.has_form('List', 3):
x, a, b = x.leaves
else:
a = b = None
if not x.get_name():
return evaluation.message('Integrate', 'ilim')
if a is None or b is None:
vars.append(x)
else:
vars.append((x, a, b))
(f, vars), subs = to_sage((f, vars), evaluation)
try:
result = sage.integrate(f, *vars)
except TypeError:
# SageIntegrate[f[x], x] raises TypeError because maxima can't handle unknown functions, obviously
return
if a is not None and b is not None:
prec_a = a.get_precision()
prec_b = b.get_precision()
if prec_a is not None and prec_b is not None:
#prec = min(prec_a, prec_b)
result = sage.n(result)
return from_sage(result, subs)
def apply(self, f, xs, evaluation):
'SageIntegrate[f_, xs__]'
xs = xs.get_sequence()
vars = []
for x in xs:
if x.has_form('List', 3):
x, a, b = x.leaves
else:
a = b = None
if not x.get_name():
return evaluation.message('Integrate', 'ilim')
if a is None or b is None:
vars.append(x)
else:
vars.append((x, a, b))
(f, vars), subs = to_sage((f, vars), evaluation)
try:
result = sage.integrate(f, *vars)
except TypeError:
# SageIntegrate[f[x], x] raises TypeError because maxima can't handle unknown functions, obviously
return
if a is not None and b is not None:
prec_a = a.get_precision()
prec_b = b.get_precision()
if prec_a is not None and prec_b is not None:
#prec = min(prec_a, prec_b)
result = sage.n(result)
return from_sage(result, subs)
def apply(self, c, G, h, evaluation):
"LinearProgramming[c_, G_, h_]"
(c, G, h), subs = to_sage((c, G, h), evaluation)
n = len(c)
c = vector(c)
G = matrix([[-item for item in row] for row in G] + [unit_vector(k, n, -1) for k in range(n)])
h = vector([-item for item in h] + [0] * n)
result = linear_program(c, G, h)
status = result["status"]
if status == "dual infeasible":
evaluation.message("LinearProgramming", "lpsub")
return Expression("List", *([Symbol("Indeterminate")] * n))
elif status == "primal infeasible":
return evaluation.message("LinearProgramming", "lpsnf")
# print result
x = result["x"]
x = [round(value, mpf("0.000001")) for value in x] # round result to 6 digits after comma
return from_sage(x, subs)
def apply(self, c, G, h, evaluation):
"LinearProgramming[c_, G_, h_]"
(c, G, h), subs = to_sage((c, G, h), evaluation)
n = len(c)
c = vector(c)
G = matrix([[-item for item in row]
for row in G] + [unit_vector(k, n, -1) for k in range(n)])
h = vector([-item for item in h] + [0] * n)
result = linear_program(c, G, h)
status = result['status']
if status == 'dual infeasible':
evaluation.message('LinearProgramming', 'lpsub')
return Expression('List', *([Symbol('Indeterminate')] * n))
elif status == 'primal infeasible':
return evaluation.message('LinearProgramming', 'lpsnf')
#print result
x = result['x']
x = [round(value, 6)
for value in x] # round result to 6 digits after comma
return from_sage(x, subs)
