def evaluateExpression(self):
sympified = sp.sympify(self.expression)
valu = float(self.value)
print sympified
ans = sympified.subs([(self.var, valu)])
self.ui.evaluatedExpressionLabel.setText(QtCore.QString.number(ans))
python类sympify()的实例源码
def from_json(json_data):
if not isinstance(json_data["pos_list"], list):
raise ReGraphError("pos_list field should contain a list")
for el in json_data["pos_list"]:
if not test_number(el):
raise ReGraphError("{} is not a number".format(el))
return convert(AtFinSet(set(sympify(json_data["pos_list"]))))
def safe_sympify(value):
_check_input(value)
return sympify(value)
def _find_max_delay(delays):
if all(symengine.sympify(delay).is_Number for delay in delays):
return float(max(delays))
else:
raise ValueError("Delay depends on time or dynamics; cannot determine max_delay automatically. You have to pass it as an argument to jitcdde.")
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('degree', type=int)
parser.add_argument('prec', type=int, default=50)
parser.add_argument('data', help="""Data of matrix to compute exp of. Should
be in scipy sparse csr format.""")
parser.add_argument('time', type=float)
parser.add_argument('--expr', type=lambda e: sympify(e, locals=globals()), help="""Precomputed CRAM
expression. Should have the same prec as 'prec'. If not provided, will be
computed from scratch.""")
parser.add_argument('--log-level', default=None, choices=['debug', 'info',
'warning', 'error', 'critical'])
# TODO: Add options for arguments to pass to various functions as needed.
try:
import argcomplete
argcomplete.autocomplete(parser)
except ImportError:
pass
args = parser.parse_args()
if args.log_level:
logger.setLevel(getattr(logging, args.log_level.upper()))
res = run_transmute_test(args.data, args.degree, args.prec,
args.time,expr=args.expr, _print=True)
print("Column sums (min, max):")
errors = {}
colsums = {}
for r in sorted(res):
if res[r] is None:
print('Could not compute', r)
continue
colsums[r] = np.sum(res[r], axis=1)
errors[r] = np.max(colsums[r]) - np.min(colsums[r])
for r in sorted(errors, key=lambda i:errors[i], reverse=True):
print(r, np.min(colsums[r]), np.max(colsums[r]))
def get_sympy_expression(value):
"""
Return sympy expression from json string using sympify
mu is assumed to be positive but using sympify does not apply this
assumption"""
expression_value = sympify(value)
return expression_value.subs(mu_temp, mu)
def rv(symbol, cls, *args):
args = list(map(sympify, args))
dist = cls(*args)
dist.check(*args)
return SingleDiscretePSpace(symbol, dist).value
def test_meijerg_confluence():
def t(m, a, b):
from sympy import sympify, Piecewise
a, b = sympify([a, b])
m_ = m
m = hyperexpand(m)
if not m == Piecewise((a, abs(z) < 1), (b, abs(1/z) < 1), (m_, True)):
return False
if not (m.args[0].args[0] == a and m.args[1].args[0] == b):
return False
z0 = randcplx()/10
if abs(m.subs(z, z0).n() - a.subs(z, z0).n()).n() > 1e-10:
return False
if abs(m.subs(z, 1/z0).n() - b.subs(z, 1/z0).n()).n() > 1e-10:
return False
return True
assert t(meijerg([], [1, 1], [0, 0], [], z), -log(z), 0)
assert t(meijerg(
[], [3, 1], [0, 0], [], z), -z**2/4 + z - log(z)/2 - S(3)/4, 0)
assert t(meijerg([], [3, 1], [-1, 0], [], z),
z**2/12 - z/2 + log(z)/2 + S(1)/4 + 1/(6*z), 0)
assert t(meijerg([], [1, 1, 1, 1], [0, 0, 0, 0], [], z), -log(z)**3/6, 0)
assert t(meijerg([1, 1], [], [], [0, 0], z), 0, -log(1/z))
assert t(meijerg([1, 1], [2, 2], [1, 1], [0, 0], z),
-z*log(z) + 2*z, -log(1/z) + 2)
assert t(meijerg([S(1)/2], [1, 1], [0, 0], [S(3)/2], z), log(z)/2 - 1, 0)
def u(an, ap, bm, bq):
m = meijerg(an, ap, bm, bq, z)
m2 = hyperexpand(m, allow_hyper=True)
if m2.has(meijerg) and not (m2.is_Piecewise and len(m2.args) == 3):
return False
return tn(m, m2, z)
assert u([], [1], [0, 0], [])
assert u([1, 1], [], [], [0])
assert u([1, 1], [2, 2, 5], [1, 1, 6], [0, 0])
assert u([1, 1], [2, 2, 5], [1, 1, 6], [0])
def test_lt_gt():
from sympy import sympify as S
x, y = Symbol('x'), Symbol('y')
assert (x >= y) == GreaterThan(x, y)
assert (x >= 0) == GreaterThan(x, 0)
assert (x <= y) == LessThan(x, y)
assert (x <= 0) == LessThan(x, 0)
assert (0 <= x) == GreaterThan(x, 0)
assert (0 >= x) == LessThan(x, 0)
assert (S(0) >= x) == GreaterThan(0, x)
assert (S(0) <= x) == LessThan(0, x)
assert (x > y) == StrictGreaterThan(x, y)
assert (x > 0) == StrictGreaterThan(x, 0)
assert (x < y) == StrictLessThan(x, y)
assert (x < 0) == StrictLessThan(x, 0)
assert (0 < x) == StrictGreaterThan(x, 0)
assert (0 > x) == StrictLessThan(x, 0)
assert (S(0) > x) == StrictGreaterThan(0, x)
assert (S(0) < x) == StrictLessThan(0, x)
e = x**2 + 4*x + 1
assert (e >= 0) == GreaterThan(e, 0)
assert (0 <= e) == GreaterThan(e, 0)
assert (e > 0) == StrictGreaterThan(e, 0)
assert (0 < e) == StrictGreaterThan(e, 0)
assert (e <= 0) == LessThan(e, 0)
assert (0 >= e) == LessThan(e, 0)
assert (e < 0) == StrictLessThan(e, 0)
assert (0 > e) == StrictLessThan(e, 0)
assert (S(0) >= e) == GreaterThan(0, e)
assert (S(0) <= e) == LessThan(0, e)
assert (S(0) < e) == StrictLessThan(0, e)
assert (S(0) > e) == StrictGreaterThan(0, e)
def test_Wild_properties():
# these tests only include Atoms
x = Symbol("x")
y = Symbol("y")
p = Symbol("p", positive=True)
k = Symbol("k", integer=True)
n = Symbol("n", integer=True, positive=True)
given_patterns = [ x, y, p, k, -k, n, -n, sympify(-3), sympify(3),
pi, Rational(3, 2), I ]
integerp = lambda k: k.is_integer
positivep = lambda k: k.is_positive
symbolp = lambda k: k.is_Symbol
realp = lambda k: k.is_real
S = Wild("S", properties=[symbolp])
R = Wild("R", properties=[realp])
Y = Wild("Y", exclude=[x, p, k, n])
P = Wild("P", properties=[positivep])
K = Wild("K", properties=[integerp])
N = Wild("N", properties=[positivep, integerp])
given_wildcards = [ S, R, Y, P, K, N ]
goodmatch = {
S: (x, y, p, k, n),
R: (p, k, -k, n, -n, -3, 3, pi, Rational(3, 2)),
Y: (y, -3, 3, pi, Rational(3, 2), I ),
P: (p, n, 3, pi, Rational(3, 2)),
K: (k, -k, n, -n, -3, 3),
N: (n, 3)}
for A in given_wildcards:
for pat in given_patterns:
d = pat.match(A)
if pat in goodmatch[A]:
assert d[A] in goodmatch[A]
else:
assert d is None
def _is_scalar(e):
""" Helper method used in Tr"""
# sympify to set proper attributes
e = sympify(e)
if isinstance(e, Expr):
if (e.is_Integer or e.is_Float or
e.is_Rational or e.is_Number or
(e.is_Symbol and e.is_commutative)
):
return True
return False
def set_v_min(self, v_min):
if v_min is None:
self._v_min = None
return
try:
self._v_min = sympify(v_min)
float(self._v_min.evalf())
except TypeError:
raise ValueError("v_min could not be interpreted as a number.")
def set_v_max(self, v_max):
if v_max is None:
self._v_max = None
return
try:
self._v_max = sympify(v_max)
float(self._v_max.evalf())
except TypeError:
raise ValueError("v_max could not be interpreted as a number.")
def _interpret_args(args):
interval_wrong_order = "PlotInterval %s was given before any function(s)."
interpret_error = "Could not interpret %s as a function or interval."
functions, intervals = [], []
if isinstance(args[0], GeometryEntity):
for coords in list(args[0].arbitrary_point()):
functions.append(coords)
intervals.append(PlotInterval.try_parse(args[0].plot_interval()))
else:
for a in args:
i = PlotInterval.try_parse(a)
if i is not None:
if len(functions) == 0:
raise ValueError(interval_wrong_order % (str(i)))
else:
intervals.append(i)
else:
if is_sequence(a, include=str):
raise ValueError(interpret_error % (str(a)))
try:
f = sympify(a)
functions.append(f)
except TypeError:
raise ValueError(interpret_error % str(a))
return functions, intervals
def __init__(self, expr, var_start_end, **kwargs):
super(LineOver1DRangeSeries, self).__init__()
self.expr = sympify(expr)
self.label = str(self.expr)
self.var = sympify(var_start_end[0])
self.start = float(var_start_end[1])
self.end = float(var_start_end[2])
self.nb_of_points = kwargs.get('nb_of_points', 300)
self.adaptive = kwargs.get('adaptive', True)
self.depth = kwargs.get('depth', 12)
self.line_color = kwargs.get('line_color', None)
def __init__(self, expr_x, expr_y, expr_z, var_start_end, **kwargs):
super(Parametric3DLineSeries, self).__init__()
self.expr_x = sympify(expr_x)
self.expr_y = sympify(expr_y)
self.expr_z = sympify(expr_z)
self.label = "(%s, %s)" % (str(self.expr_x), str(self.expr_y))
self.var = sympify(var_start_end[0])
self.start = float(var_start_end[1])
self.end = float(var_start_end[2])
self.nb_of_points = kwargs.get('nb_of_points', 300)
self.line_color = kwargs.get('line_color', None)
def __init__(self, expr, var_start_end_x, var_start_end_y, **kwargs):
super(SurfaceOver2DRangeSeries, self).__init__()
self.expr = sympify(expr)
self.var_x = sympify(var_start_end_x[0])
self.start_x = float(var_start_end_x[1])
self.end_x = float(var_start_end_x[2])
self.var_y = sympify(var_start_end_y[0])
self.start_y = float(var_start_end_y[1])
self.end_y = float(var_start_end_y[2])
self.nb_of_points_x = kwargs.get('nb_of_points_x', 50)
self.nb_of_points_y = kwargs.get('nb_of_points_y', 50)
self.surface_color = kwargs.get('surface_color', None)
def __init__(self, expr, var_start_end_x, var_start_end_y):
super(ContourSeries, self).__init__()
self.nb_of_points_x = 50
self.nb_of_points_y = 50
self.expr = sympify(expr)
self.var_x = sympify(var_start_end_x[0])
self.start_x = float(var_start_end_x[1])
self.end_x = float(var_start_end_x[2])
self.var_y = sympify(var_start_end_y[0])
self.start_y = float(var_start_end_y[1])
self.end_y = float(var_start_end_y[2])
self.get_points = self.get_meshes
def _sympify_qubit_map(mapping):
new_map = {}
for key in mapping:
new_map[key] = sympify(mapping[key])
return new_map
def targets(self):
"""A tuple of target qubits."""
return sympify(tuple(range(self.args[0])))
#-------------------------------------------------------------------------
# Apply
#-------------------------------------------------------------------------
