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sympy/utilities/lambdify.py/_EvaluatorPrinter/_preprocess
class _EvaluatorPrinter: def _preprocess(self, args, expr):
"""Preprocess args, expr to replace arguments that do not map
to valid Python identifiers.
Returns string form of args, and updated expr.
"""
from sympy import Dummy, Symbol, Function, flatten
from sympy.matrices import DeferredVector
dummify = self._dummify
# Args of type Dummy can cause name collisions with args
# of type Symbol. Force dummify of everything in this
# situation.
if not dummify:
dummify = any(isinstance(arg, Dummy) for arg in flatten(args))
argstrs = []
for arg in args:
if iterable(arg):
nested_argstrs, expr = self._preprocess(arg, expr)
argstrs.append(nested_argstrs)
elif isinstance(arg, DeferredVector):
argstrs.append(str(arg))
elif isinstance(arg, Symbol):
argrep = self._argrepr(arg)
if dummify or not self._is_safe_ident(argrep):
dummy = Dummy()
argstrs.append(self._argrepr(dummy))
expr = self._subexpr(expr, {arg: dummy})
else:
argstrs.append(argrep)
elif isinstance(arg, Function):
dummy = Dummy()
argstrs.append(self._argrepr(dummy))
expr = self._subexpr(expr, {arg: dummy})
else:
argstrs.append(str(arg))
return argstrs, expr
|
apositive_train_query0_00000
|
|
examples/all.py/__import__
def __import__(name, globals=None, locals=None, fromlist=None):
"""An alternative to the import function so that we can import
modules defined as strings.
This code was taken from: http://docs.python.org/lib/examples-imp.html
"""
# Fast path: see if the module has already been imported.
try:
return sys.modules[name]
except KeyError:
pass
# If any of the following calls raises an exception,
# there's a problem we can't handle -- let the caller handle it.
module_name = name.split('.')[-1]
module_path = os.path.join(EXAMPLE_DIR, *name.split('.')[:-1])
fp, pathname, description = imp.find_module(module_name, [module_path])
try:
return imp.load_module(module_name, fp, pathname, description)
finally:
# Since we may exit via an exception, close fp explicitly.
if fp:
fp.close()
|
negative_train_query0_00000
|
|
examples/all.py/load_example_module
def load_example_module(example):
"""Loads modules based upon the given package name"""
mod = __import__(example)
return mod
|
negative_train_query0_00001
|
|
examples/all.py/run_examples
def run_examples(windowed=False, quiet=False, summary=True):
"""Run all examples in the list of modules.
Returns a boolean value indicating whether all the examples were
successful.
"""
successes = []
failures = []
examples = TERMINAL_EXAMPLES
if windowed:
examples += WINDOWED_EXAMPLES
if quiet:
from sympy.utilities.runtests import PyTestReporter
reporter = PyTestReporter()
reporter.write("Testing Examples\n")
reporter.write("-" * reporter.terminal_width)
else:
reporter = None
for example in examples:
if run_example(example, reporter=reporter):
successes.append(example)
else:
failures.append(example)
if summary:
show_summary(successes, failures, reporter=reporter)
return len(failures) == 0
|
negative_train_query0_00002
|
|
examples/all.py/run_example
def run_example(example, reporter=None):
"""Run a specific example.
Returns a boolean value indicating whether the example was successful.
"""
if reporter:
reporter.write(example)
else:
print("=" * 79)
print("Running: ", example)
try:
mod = load_example_module(example)
if reporter:
suppress_output(mod.main)
reporter.write("[PASS]", "Green", align="right")
else:
mod.main()
return True
except KeyboardInterrupt as e:
raise e
except:
if reporter:
reporter.write("[FAIL]", "Red", align="right")
traceback.print_exc()
return False
|
negative_train_query0_00003
|
|
examples/all.py/suppress_output
def suppress_output(fn):
"""Suppresses the output of fn on sys.stdout."""
save_stdout = sys.stdout
try:
sys.stdout = DummyFile()
fn()
finally:
sys.stdout = save_stdout
|
negative_train_query0_00004
|
|
examples/all.py/show_summary
def show_summary(successes, failures, reporter=None):
"""Shows a summary detailing which examples were successful and which failed."""
if reporter:
reporter.write("-" * reporter.terminal_width)
if failures:
reporter.write("FAILED:\n", "Red")
for example in failures:
reporter.write(" %s\n" % example)
else:
reporter.write("ALL EXAMPLES PASSED\n", "Green")
else:
if successes:
print("SUCCESSFUL: ", file=sys.stderr)
for example in successes:
print(" -", example, file=sys.stderr)
else:
print("NO SUCCESSFUL EXAMPLES", file=sys.stderr)
if failures:
print("FAILED: ", file=sys.stderr)
for example in failures:
print(" -", example, file=sys.stderr)
else:
print("NO FAILED EXAMPLES", file=sys.stderr)
|
negative_train_query0_00005
|
|
examples/all.py/main
def main(*args, **kws):
"""Main script runner"""
parser = optparse.OptionParser()
parser.add_option('-w', '--windowed', action="store_true", dest="windowed",
help="also run examples requiring windowed environment")
parser.add_option('-q', '--quiet', action="store_true", dest="quiet",
help="runs examples in 'quiet mode' suppressing example output and \
showing simple status messages.")
parser.add_option('--no-summary', action="store_true", dest="no_summary",
help="hides the summary at the end of testing the examples")
(options, _) = parser.parse_args()
return 0 if run_examples(windowed=options.windowed, quiet=options.quiet,
summary=not options.no_summary) else 1
|
negative_train_query0_00006
|
|
examples/all.py/DummyFile/write
class DummyFile: def write(self, x):
pass
|
negative_train_query0_00007
|
|
examples/intermediate/vandermonde.py/symbol_gen
def symbol_gen(sym_str):
"""Symbol generator
Generates sym_str_n where n is the number of times the generator
has been called.
"""
n = 0
while True:
yield Symbol("%s_%d" % (sym_str, n))
n += 1
|
negative_train_query0_00008
|
|
examples/intermediate/vandermonde.py/comb_w_rep
def comb_w_rep(n, k):
"""Combinations with repetition
Returns the list of k combinations with repetition from n objects.
"""
if k == 0:
return [[]]
combs = [[i] for i in range(n)]
for i in range(k - 1):
curr = []
for p in combs:
for m in range(p[-1], n):
curr.append(p + [m])
combs = curr
return combs
|
negative_train_query0_00009
|
|
examples/intermediate/vandermonde.py/vandermonde
def vandermonde(order, dim=1, syms='a b c d'):
"""Computes a Vandermonde matrix of given order and dimension.
Define syms to give beginning strings for temporary variables.
Returns the Matrix, the temporary variables, and the terms for the
polynomials.
"""
syms = syms.split()
n = len(syms)
if n < dim:
new_syms = []
for i in range(dim - n):
j, rem = divmod(i, n)
new_syms.append(syms[rem] + str(j))
syms.extend(new_syms)
terms = []
for i in range(order + 1):
terms.extend(comb_w_rep(dim, i))
rank = len(terms)
V = zeros(rank)
generators = [symbol_gen(syms[i]) for i in range(dim)]
all_syms = []
for i in range(rank):
row_syms = [next(g) for g in generators]
all_syms.append(row_syms)
for j, term in enumerate(terms):
v_entry = 1
for k in term:
v_entry *= row_syms[k]
V[i*rank + j] = v_entry
return V, all_syms, terms
|
negative_train_query0_00010
|
|
examples/intermediate/vandermonde.py/gen_poly
def gen_poly(points, order, syms):
"""Generates a polynomial using a Vandermonde system"""
num_pts = len(points)
if num_pts == 0:
raise ValueError("Must provide points")
dim = len(points[0]) - 1
if dim > len(syms):
raise ValueError("Must provide at least %d symbols for the polynomial" % dim)
V, tmp_syms, terms = vandermonde(order, dim)
if num_pts < V.shape[0]:
raise ValueError(
"Must provide %d points for order %d, dimension "
"%d polynomial, given %d points" %
(V.shape[0], order, dim, num_pts))
elif num_pts > V.shape[0]:
print("gen_poly given %d points but only requires %d, "\
"continuing using the first %d points" % \
(num_pts, V.shape[0], V.shape[0]))
num_pts = V.shape[0]
subs_dict = {}
for j in range(dim):
for i in range(num_pts):
subs_dict[tmp_syms[i][j]] = points[i][j]
V_pts = V.subs(subs_dict)
V_inv = V_pts.inv()
coeffs = V_inv.multiply(Matrix([points[i][-1] for i in range(num_pts)]))
f = 0
for j, term in enumerate(terms):
t = 1
for k in term:
t *= syms[k]
f += coeffs[j]*t
return f
|
negative_train_query0_00011
|
|
examples/intermediate/vandermonde.py/main
def main():
order = 2
V, tmp_syms, _ = vandermonde(order)
print("Vandermonde matrix of order 2 in 1 dimension")
pprint(V)
print('-'*79)
print("Computing the determinant and comparing to \sum_{0<i<j<=3}(a_j - a_i)")
det_sum = 1
for j in range(order + 1):
for i in range(j):
det_sum *= (tmp_syms[j][0] - tmp_syms[i][0])
print("""
det(V) = %(det)s
\sum = %(sum)s
= %(sum_expand)s
""" % {"det": V.det(),
"sum": det_sum,
"sum_expand": det_sum.expand(),
})
print('-'*79)
print("Polynomial fitting with a Vandermonde Matrix:")
x, y, z = symbols('x,y,z')
points = [(0, 3), (1, 2), (2, 3)]
print("""
Quadratic function, represented by 3 points:
points = %(pts)s
f = %(f)s
""" % {"pts": points,
"f": gen_poly(points, 2, [x]),
})
points = [(0, 1, 1), (1, 0, 0), (1, 1, 0), (Rational(1, 2), 0, 0),
(0, Rational(1, 2), 0), (Rational(1, 2), Rational(1, 2), 0)]
print("""
2D Quadratic function, represented by 6 points:
points = %(pts)s
f = %(f)s
""" % {"pts": points,
"f": gen_poly(points, 2, [x, y]),
})
points = [(0, 1, 1, 1), (1, 1, 0, 0), (1, 0, 1, 0), (1, 1, 1, 1)]
print("""
3D linear function, represented by 4 points:
points = %(pts)s
f = %(f)s
""" % {"pts": points,
"f": gen_poly(points, 1, [x, y, z]),
})
|
negative_train_query0_00012
|
|
examples/intermediate/infinite_1d_box.py/X_n
def X_n(n, a, x):
"""
Returns the wavefunction X_{n} for an infinite 1D box
``n``
the "principal" quantum number. Corresponds to the number of nodes in
the wavefunction. n >= 0
``a``
width of the well. a > 0
``x``
x coordinate.
"""
n, a, x = map(S, [n, a, x])
C = sqrt(2 / a)
return C * sin(pi * n * x / a)
|
negative_train_query0_00013
|
|
examples/intermediate/infinite_1d_box.py/E_n
def E_n(n, a, mass):
"""
Returns the Energy psi_{n} for a 1d potential hole with infinity borders
``n``
the "principal" quantum number. Corresponds to the number of nodes in
the wavefunction. n >= 0
``a``
width of the well. a > 0
``mass``
mass.
"""
return ((n * pi / a)**2) / mass
|
negative_train_query0_00014
|
|
examples/intermediate/infinite_1d_box.py/energy_corrections
def energy_corrections(perturbation, n, a=10, mass=0.5):
"""
Calculating first two order corrections due to perturbation theory and
returns tuple where zero element is unperturbated energy, and two second
is corrections
``n``
the "nodal" quantum number. Corresponds to the number of nodes in the
wavefunction. n >= 0
``a``
width of the well. a > 0
``mass``
mass.
"""
x, _a = var("x _a")
Vnm = lambda n, m, a: Integral(X_n(n, a, x) * X_n(m, a, x)
* perturbation.subs({_a: a}), (x, 0, a)).n()
# As we know from theory for V0*r/a we will just V(n, n-1) and V(n, n+1)
# wouldn't equals zero
return (E_n(n, a, mass).evalf(),
Vnm(n, n, a).evalf(),
(Vnm(n, n - 1, a)**2/(E_n(n, a, mass) - E_n(n - 1, a, mass))
+ Vnm(n, n + 1, a)**2/(E_n(n, a, mass) - E_n(n + 1, a, mass))).evalf())
|
negative_train_query0_00015
|
|
examples/intermediate/infinite_1d_box.py/main
def main():
print()
print("Applying perturbation theory to calculate the ground state energy")
print("of the infinite 1D box of width ``a`` with a perturbation")
print("which is linear in ``x``, up to second order in perturbation.")
print()
x, _a = var("x _a")
perturbation = .1 * x / _a
E1 = energy_corrections(perturbation, 1)
print("Energy for first term (n=1):")
print("E_1^{(0)} = ", E1[0])
print("E_1^{(1)} = ", E1[1])
print("E_1^{(2)} = ", E1[2])
print()
E2 = energy_corrections(perturbation, 2)
print("Energy for second term (n=2):")
print("E_2^{(0)} = ", E2[0])
print("E_2^{(1)} = ", E2[1])
print("E_2^{(2)} = ", E2[2])
print()
E3 = energy_corrections(perturbation, 3)
print("Energy for third term (n=3):")
print("E_3^{(0)} = ", E3[0])
print("E_3^{(1)} = ", E3[1])
print("E_3^{(2)} = ", E3[2])
print()
|
negative_train_query0_00016
|
|
examples/intermediate/trees.py/T
def T(x):
return x + x**2 + 2*x**3 + 4*x**4 + 9*x**5 + 20*x**6 + 48 * x**7 + \
115*x**8 + 286*x**9 + 719*x**10
|
negative_train_query0_00017
|
|
examples/intermediate/trees.py/A
def A(x):
return 1 + T(x) - T(x)**2/2 + T(x**2)/2
|
negative_train_query0_00018
|
|
examples/intermediate/trees.py/main
def main():
x = Symbol("x")
s = Poly(A(x), x)
num = list(reversed(s.coeffs()))[:11]
print(s.as_expr())
print(num)
|
negative_train_query0_00019
|
|
examples/intermediate/differential_equations.py/main
def main():
x = Symbol("x")
f = Function("f")
eq = Eq(f(x).diff(x), f(x))
print("Solution for ", eq, " : ", dsolve(eq, f(x)))
eq = Eq(f(x).diff(x, 2), -f(x))
print("Solution for ", eq, " : ", dsolve(eq, f(x)))
eq = Eq(x**2*f(x).diff(x), -3*x*f(x) + sin(x)/x)
print("Solution for ", eq, " : ", dsolve(eq, f(x)))
|
negative_train_query0_00020
|
|
examples/intermediate/print_gtk.py/main
def main():
x = Symbol('x')
example_limit = Limit(sin(x)/x, x, 0)
print_gtk(example_limit)
example_integral = Integral(x, (x, 0, 1))
print_gtk(example_integral)
|
negative_train_query0_00021
|
|
examples/intermediate/coupled_cluster.py/get_CC_operators
def get_CC_operators():
"""
Returns a tuple (T1,T2) of unique operators.
"""
i = symbols('i', below_fermi=True, cls=Dummy)
a = symbols('a', above_fermi=True, cls=Dummy)
t_ai = AntiSymmetricTensor('t', (a,), (i,))
ai = NO(Fd(a)*F(i))
i, j = symbols('i,j', below_fermi=True, cls=Dummy)
a, b = symbols('a,b', above_fermi=True, cls=Dummy)
t_abij = AntiSymmetricTensor('t', (a, b), (i, j))
abji = NO(Fd(a)*Fd(b)*F(j)*F(i))
T1 = t_ai*ai
T2 = Rational(1, 4)*t_abij*abji
return (T1, T2)
|
negative_train_query0_00022
|
|
examples/intermediate/coupled_cluster.py/main
def main():
print()
print("Calculates the Coupled-Cluster energy- and amplitude equations")
print("See 'An Introduction to Coupled Cluster Theory' by")
print("T. Daniel Crawford and Henry F. Schaefer III")
print("Reference to a Lecture Series: http://vergil.chemistry.gatech.edu/notes/sahan-cc-2010.pdf")
print()
# setup hamiltonian
p, q, r, s = symbols('p,q,r,s', cls=Dummy)
f = AntiSymmetricTensor('f', (p,), (q,))
pr = NO((Fd(p)*F(q)))
v = AntiSymmetricTensor('v', (p, q), (r, s))
pqsr = NO(Fd(p)*Fd(q)*F(s)*F(r))
H = f*pr + Rational(1, 4)*v*pqsr
print("Using the hamiltonian:", latex(H))
print("Calculating 4 nested commutators")
C = Commutator
T1, T2 = get_CC_operators()
T = T1 + T2
print("commutator 1...")
comm1 = wicks(C(H, T))
comm1 = evaluate_deltas(comm1)
comm1 = substitute_dummies(comm1)
T1, T2 = get_CC_operators()
T = T1 + T2
print("commutator 2...")
comm2 = wicks(C(comm1, T))
comm2 = evaluate_deltas(comm2)
comm2 = substitute_dummies(comm2)
T1, T2 = get_CC_operators()
T = T1 + T2
print("commutator 3...")
comm3 = wicks(C(comm2, T))
comm3 = evaluate_deltas(comm3)
comm3 = substitute_dummies(comm3)
T1, T2 = get_CC_operators()
T = T1 + T2
print("commutator 4...")
comm4 = wicks(C(comm3, T))
comm4 = evaluate_deltas(comm4)
comm4 = substitute_dummies(comm4)
print("construct Hausdorff expansion...")
eq = H + comm1 + comm2/2 + comm3/6 + comm4/24
eq = eq.expand()
eq = evaluate_deltas(eq)
eq = substitute_dummies(eq, new_indices=True,
pretty_indices=pretty_dummies_dict)
print("*********************")
print()
print("extracting CC equations from full Hbar")
i, j, k, l = symbols('i,j,k,l', below_fermi=True)
a, b, c, d = symbols('a,b,c,d', above_fermi=True)
print()
print("CC Energy:")
print(latex(wicks(eq, simplify_dummies=True,
keep_only_fully_contracted=True)))
print()
print("CC T1:")
eqT1 = wicks(NO(Fd(i)*F(a))*eq, simplify_kronecker_deltas=True, keep_only_fully_contracted=True)
eqT1 = substitute_dummies(eqT1)
print(latex(eqT1))
print()
print("CC T2:")
eqT2 = wicks(NO(Fd(i)*Fd(j)*F(b)*F(a))*eq, simplify_dummies=True, keep_only_fully_contracted=True, simplify_kronecker_deltas=True)
P = PermutationOperator
eqT2 = simplify_index_permutations(eqT2, [P(a, b), P(i, j)])
print(latex(eqT2))
|
negative_train_query0_00023
|
|
examples/intermediate/partial_differential_eqs.py/main
def main():
r, phi, theta = symbols("r,phi,theta")
Xi = Function('Xi')
R, Phi, Theta, u = map(Function, ['R', 'Phi', 'Theta', 'u'])
C1, C2 = symbols('C1,C2')
pprint("Separation of variables in Laplace equation in spherical coordinates")
pprint("Laplace equation in spherical coordinates:")
eq = Eq(D(Xi(r, phi, theta), r, 2) + 2/r * D(Xi(r, phi, theta), r) +
1/(r**2 * sin(phi)**2) * D(Xi(r, phi, theta), theta, 2) +
cos(phi)/(r**2 * sin(phi)) * D(Xi(r, phi, theta), phi) +
1/r**2 * D(Xi(r, phi, theta), phi, 2))
pprint(eq)
pprint("We can either separate this equation in regards with variable r:")
res_r = pde_separate(eq, Xi(r, phi, theta), [R(r), u(phi, theta)])
pprint(res_r)
pprint("Or separate it in regards of theta:")
res_theta = pde_separate(eq, Xi(r, phi, theta), [Theta(theta), u(r, phi)])
pprint(res_theta)
res_phi = pde_separate(eq, Xi(r, phi, theta), [Phi(phi), u(r, theta)])
pprint("But we cannot separate it in regards of variable phi: ")
pprint("Result: %s" % res_phi)
pprint("\n\nSo let's make theta dependent part equal with -C1:")
eq_theta = Eq(res_theta[0], -C1)
pprint(eq_theta)
pprint("\nThis also means that second part is also equal to -C1:")
eq_left = Eq(res_theta[1], -C1)
pprint(eq_left)
pprint("\nLets try to separate phi again :)")
res_theta = pde_separate(eq_left, u(r, phi), [Phi(phi), R(r)])
pprint("\nThis time it is successful:")
pprint(res_theta)
pprint("\n\nSo our final equations with separated variables are:")
pprint(eq_theta)
pprint(Eq(res_theta[0], C2))
pprint(Eq(res_theta[1], C2))
|
negative_train_query0_00024
|
|
examples/intermediate/sample.py/sample2d
def sample2d(f, x_args):
"""
Samples a 2d function f over specified intervals and returns two
arrays (X, Y) suitable for plotting with matlab (matplotlib)
syntax. See examples\mplot2d.py.
f is a function of one variable, such as x**2.
x_args is an interval given in the form (var, min, max, n)
"""
try:
f = sympify(f)
except SympifyError:
raise ValueError("f could not be interpretted as a SymPy function")
try:
x, x_min, x_max, x_n = x_args
except AttributeError:
raise ValueError("x_args must be a tuple of the form (var, min, max, n)")
x_l = float(x_max - x_min)
x_d = x_l/float(x_n)
X = np.arange(float(x_min), float(x_max) + x_d, x_d)
Y = np.empty(len(X))
for i in range(len(X)):
try:
Y[i] = float(f.subs(x, X[i]))
except TypeError:
Y[i] = None
return X, Y
|
negative_train_query0_00025
|
|
examples/intermediate/sample.py/sample3d
def sample3d(f, x_args, y_args):
"""
Samples a 3d function f over specified intervals and returns three
2d arrays (X, Y, Z) suitable for plotting with matlab (matplotlib)
syntax. See examples\mplot3d.py.
f is a function of two variables, such as x**2 + y**2.
x_args and y_args are intervals given in the form (var, min, max, n)
"""
x, x_min, x_max, x_n = None, None, None, None
y, y_min, y_max, y_n = None, None, None, None
try:
f = sympify(f)
except SympifyError:
raise ValueError("f could not be interpreted as a SymPy function")
try:
x, x_min, x_max, x_n = x_args
y, y_min, y_max, y_n = y_args
except AttributeError:
raise ValueError("x_args and y_args must be tuples of the form (var, min, max, intervals)")
x_l = float(x_max - x_min)
x_d = x_l/float(x_n)
x_a = np.arange(float(x_min), float(x_max) + x_d, x_d)
y_l = float(y_max - y_min)
y_d = y_l/float(y_n)
y_a = np.arange(float(y_min), float(y_max) + y_d, y_d)
def meshgrid(x, y):
"""
Taken from matplotlib.mlab.meshgrid.
"""
x = np.array(x)
y = np.array(y)
numRows, numCols = len(y), len(x)
x.shape = 1, numCols
X = np.repeat(x, numRows, 0)
y.shape = numRows, 1
Y = np.repeat(y, numCols, 1)
return X, Y
X, Y = np.meshgrid(x_a, y_a)
Z = np.ndarray((len(X), len(X[0])))
for j in range(len(X)):
for k in range(len(X[0])):
try:
Z[j][k] = float(f.subs(x, X[j][k]).subs(y, Y[j][k]))
except (TypeError, NotImplementedError):
Z[j][k] = 0
return X, Y, Z
|
negative_train_query0_00026
|
|
examples/intermediate/sample.py/sample
def sample(f, *var_args):
"""
Samples a 2d or 3d function over specified intervals and returns
a dataset suitable for plotting with matlab (matplotlib) syntax.
Wrapper for sample2d and sample3d.
f is a function of one or two variables, such as x**2.
var_args are intervals for each variable given in the form (var, min, max, n)
"""
if len(var_args) == 1:
return sample2d(f, var_args[0])
elif len(var_args) == 2:
return sample3d(f, var_args[0], var_args[1])
else:
raise ValueError("Only 2d and 3d sampling are supported at this time.")
|
negative_train_query0_00027
|
|
examples/intermediate/sample.py/meshgrid
def meshgrid(x, y):
"""
Taken from matplotlib.mlab.meshgrid.
"""
x = np.array(x)
y = np.array(y)
numRows, numCols = len(y), len(x)
x.shape = 1, numCols
X = np.repeat(x, numRows, 0)
y.shape = numRows, 1
Y = np.repeat(y, numCols, 1)
return X, Y
|
negative_train_query0_00028
|
|
examples/intermediate/mplot3d.py/mplot3d
def mplot3d(f, var1, var2, show=True):
"""
Plot a 3d function using matplotlib/Tk.
"""
import warnings
warnings.filterwarnings("ignore", "Could not match \S")
p = import_module('pylab')
# Try newer version first
p3 = import_module('mpl_toolkits.mplot3d',
__import__kwargs={'fromlist': ['something']}) or import_module('matplotlib.axes3d')
if not p or not p3:
sys.exit("Matplotlib is required to use mplot3d.")
x, y, z = sample(f, var1, var2)
fig = p.figure()
ax = p3.Axes3D(fig)
# ax.plot_surface(x, y, z, rstride=2, cstride=2)
ax.plot_wireframe(x, y, z)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
if show:
p.show()
|
negative_train_query0_00029
|
|
examples/intermediate/mplot3d.py/main
def main():
x = Symbol('x')
y = Symbol('y')
mplot3d(x**2 - y**2, (x, -10.0, 10.0, 20), (y, -10.0, 10.0, 20))
|
negative_train_query0_00030
|
|
examples/intermediate/mplot2d.py/mplot2d
def mplot2d(f, var, show=True):
"""
Plot a 2d function using matplotlib/Tk.
"""
import warnings
warnings.filterwarnings("ignore", "Could not match \S")
p = import_module('pylab')
if not p:
sys.exit("Matplotlib is required to use mplot2d.")
if not is_sequence(f):
f = [f, ]
for f_i in f:
x, y = sample(f_i, var)
p.plot(x, y)
p.draw()
if show:
p.show()
|
negative_train_query0_00031
|
|
examples/intermediate/mplot2d.py/main
def main():
x = Symbol('x')
# mplot2d(log(x), (x, 0, 2, 100))
# mplot2d([sin(x), -sin(x)], (x, float(-2*pi), float(2*pi), 50))
mplot2d([sqrt(x), -sqrt(x), sqrt(-x), -sqrt(-x)], (x, -40.0, 40.0, 80))
|
negative_train_query0_00032
|
|
examples/beginner/limits_examples.py/sqrt3
def sqrt3(x):
return x**Rational(1, 3)
|
negative_train_query0_00033
|
|
examples/beginner/limits_examples.py/show
def show(computed, correct):
print("computed:", computed, "correct:", correct)
|
negative_train_query0_00034
|
|
examples/beginner/limits_examples.py/main
def main():
x = Symbol("x")
show( limit(sqrt(x**2 - 5*x + 6) - x, x, oo), -Rational(5)/2 )
show( limit(x*(sqrt(x**2 + 1) - x), x, oo), Rational(1)/2 )
show( limit(x - sqrt(x**3 - 1), x, oo), Rational(0) )
show( limit(log(1 + exp(x))/x, x, -oo), Rational(0) )
show( limit(log(1 + exp(x))/x, x, oo), Rational(1) )
show( limit(sin(3*x)/x, x, 0), Rational(3) )
show( limit(sin(5*x)/sin(2*x), x, 0), Rational(5)/2 )
show( limit(((x - 1)/(x + 1))**x, x, oo), exp(-2))
|
negative_train_query0_00035
|
|
examples/beginner/series.py/main
def main():
x = Symbol('x')
e = 1/cos(x)
print('')
print("Series for sec(x):")
print('')
pprint(e.series(x, 0, 10))
print("\n")
e = 1/sin(x)
print("Series for csc(x):")
print('')
pprint(e.series(x, 0, 4))
print('')
|
negative_train_query0_00036
|
|
examples/beginner/precision.py/main
def main():
x = Pow(2, 50, evaluate=False)
y = Pow(10, -50, evaluate=False)
# A large, unevaluated expression
m = Mul(x, y, evaluate=False)
# Evaluating the expression
e = S(2)**50/S(10)**50
print("%s == %s" % (m, e))
|
negative_train_query0_00037
|
|
examples/beginner/plotting_nice_plot.py/main
def main():
fun1 = cos(x)*sin(y)
fun2 = sin(x)*sin(y)
fun3 = cos(y) + log(tan(y/2)) + 0.2*x
PygletPlot(fun1, fun2, fun3, [x, -0.00, 12.4, 40], [y, 0.1, 2, 40])
|
negative_train_query0_00038
|
|
examples/beginner/print_pretty.py/main
def main():
x = Symbol("x")
y = Symbol("y")
pprint( x**x )
print('\n') # separate with two blank likes
pprint(x**2 + y + x)
print('\n')
pprint(sin(x)**x)
print('\n')
pprint( sin(x)**cos(x) )
print('\n')
pprint( sin(x)/(cos(x)**2 * x**x + (2*y)) )
print('\n')
pprint( sin(x**2 + exp(x)) )
print('\n')
pprint( sqrt(exp(x)) )
print('\n')
pprint( sqrt(sqrt(exp(x))) )
print('\n')
pprint( (1/cos(x)).series(x, 0, 10) )
print('\n')
|
negative_train_query0_00039
|
|
examples/beginner/expansion.py/main
def main():
a = Symbol('a')
b = Symbol('b')
e = (a + b)**5
print("\nExpression:")
pprint(e)
print('\nExpansion of the above expression:')
pprint(e.expand())
print()
|
negative_train_query0_00040
|
|
examples/beginner/differentiation.py/main
def main():
a = Symbol('a')
b = Symbol('b')
e = (a + 2*b)**5
print("\nExpression : ")
print()
pprint(e)
print("\n\nDifferentiating w.r.t. a:")
print()
pprint(e.diff(a))
print("\n\nDifferentiating w.r.t. b:")
print()
pprint(e.diff(b))
print("\n\nSecond derivative of the above result w.r.t. a:")
print()
pprint(e.diff(b).diff(a, 2))
print("\n\nExpanding the above result:")
print()
pprint(e.expand().diff(b).diff(a, 2))
print()
|
negative_train_query0_00041
|
|
examples/beginner/functions.py/main
def main():
a = Symbol('a')
b = Symbol('b')
e = log((a + b)**5)
print()
pprint(e)
print('\n')
e = exp(e)
pprint(e)
print('\n')
e = log(exp((a + b)**5))
pprint(e)
print
|
negative_train_query0_00042
|
|
examples/beginner/substitution.py/main
def main():
x = sympy.Symbol('x')
y = sympy.Symbol('y')
e = 1/sympy.cos(x)
print()
pprint(e)
print('\n')
pprint(e.subs(sympy.cos(x), y))
print('\n')
pprint(e.subs(sympy.cos(x), y).subs(y, x**2))
e = 1/sympy.log(x)
e = e.subs(x, sympy.Float("2.71828"))
print('\n')
pprint(e)
print('\n')
pprint(e.evalf())
print()
a = sympy.Symbol('a')
b = sympy.Symbol('b')
e = a*2 + a**b/a
print('\n')
pprint(e)
a = 2
print('\n')
pprint(e.subs(a,8))
print()
|
negative_train_query0_00043
|
|
examples/beginner/basic.py/main
def main():
a = Symbol('a')
b = Symbol('b')
c = Symbol('c')
e = ( a*b*b + 2*b*a*b )**c
print('')
pprint(e)
print('')
|
negative_train_query0_00044
|
|
examples/advanced/grover_example.py/demo_vgate_app
def demo_vgate_app(v):
for i in range(2**v.nqubits):
print('qapply(v*IntQubit(%i, %r))' % (i, v.nqubits))
pprint(qapply(v*IntQubit(i, v.nqubits)))
qapply(v*IntQubit(i, v.nqubits))
|
negative_train_query0_00045
|
|
examples/advanced/grover_example.py/black_box
def black_box(qubits):
return True if qubits == IntQubit(1, qubits.nqubits) else False
|
negative_train_query0_00046
|
|
examples/advanced/grover_example.py/main
def main():
print()
print('Demonstration of Grover\'s Algorithm')
print('The OracleGate or V Gate carries the unknown function f(x)')
print('> V|x> = ((-1)^f(x))|x> where f(x) = 1 when x = a (True in our case)')
print('> and 0 (False in our case) otherwise')
print()
nqubits = 2
print('nqubits = ', nqubits)
v = OracleGate(nqubits, black_box)
print('Oracle or v = OracleGate(%r, black_box)' % nqubits)
print()
psi = superposition_basis(nqubits)
print('psi:')
pprint(psi)
demo_vgate_app(v)
print('qapply(v*psi)')
pprint(qapply(v*psi))
print()
w = WGate(nqubits)
print('WGate or w = WGate(%r)' % nqubits)
print('On a 2 Qubit system like psi, 1 iteration is enough to yield |1>')
print('qapply(w*v*psi)')
pprint(qapply(w*v*psi))
print()
nqubits = 3
print('On a 3 Qubit system, it requires 2 iterations to achieve')
print('|1> with high enough probability')
psi = superposition_basis(nqubits)
print('psi:')
pprint(psi)
v = OracleGate(nqubits, black_box)
print('Oracle or v = OracleGate(%r, black_box)' % nqubits)
print()
print('iter1 = grover.grover_iteration(psi, v)')
iter1 = qapply(grover_iteration(psi, v))
pprint(iter1)
print()
print('iter2 = grover.grover_iteration(iter1, v)')
iter2 = qapply(grover_iteration(iter1, v))
pprint(iter2)
print()
|
negative_train_query0_00047
|
|
examples/advanced/autowrap_integrators.py/main
def main():
print(__doc__)
# arrays are represented with IndexedBase, indices with Idx
m = Symbol('m', integer=True)
i = Idx('i', m)
A = IndexedBase('A')
B = IndexedBase('B')
x = Symbol('x')
print("Compiling ufuncs for radial harmonic oscillator solutions")
# setup a basis of ho-solutions (for l=0)
basis_ho = {}
for n in range(basis_dimension):
# Setup the radial ho solution for this n
expr = R_nl(n, orbital_momentum_l, omega2, x)
# Reduce the number of operations in the expression by eval to float
expr = expr.evalf(15)
print("The h.o. wave function with l = %i and n = %i is" % (
orbital_momentum_l, n))
pprint(expr)
# implement, compile and wrap it as a ufunc
basis_ho[n] = ufuncify(x, expr)
# now let's see if we can express a hydrogen radial wave in terms of
# the ho basis. Here's the solution we will approximate:
H_ufunc = ufuncify(x, hydro_nl(hydrogen_n, orbital_momentum_l, 1, x))
# The transformation to a different basis can be written like this,
#
# psi(r) = sum_i c(i) phi_i(r)
#
# where psi(r) is the hydrogen solution, phi_i(r) are the H.O. solutions
# and c(i) are scalar coefficients.
#
# So in order to express a hydrogen solution in terms of the H.O. basis, we
# need to determine the coefficients c(i). In position space, it means
# that we need to evaluate an integral:
#
# psi(r) = sum_i Integral(R**2*conj(phi(R))*psi(R), (R, 0, oo)) phi_i(r)
#
# To calculate the integral with autowrap, we notice that it contains an
# element-wise sum over all vectors. Using the Indexed class, it is
# possible to generate autowrapped functions that perform summations in
# the low-level code. (In fact, summations are very easy to create, and as
# we will see it is often necessary to take extra steps in order to avoid
# them.)
# we need one integration ufunc for each wave function in the h.o. basis
binary_integrator = {}
for n in range(basis_dimension):
#
# setup basis wave functions
#
# To get inline expressions in the low level code, we attach the
# wave function expressions to a regular SymPy function using the
# implemented_function utility. This is an extra step needed to avoid
# erroneous summations in the wave function expressions.
#
# Such function objects carry around the expression they represent,
# but the expression is not exposed unless explicit measures are taken.
# The benefit is that the routines that searches for repeated indices
# in order to make contractions will not search through the wave
# function expression.
psi_ho = implemented_function('psi_ho',
Lambda(x, R_nl(n, orbital_momentum_l, omega2, x)))
# We represent the hydrogen function by an array which will be an input
# argument to the binary routine. This will let the integrators find
# h.o. basis coefficients for any wave function we throw at them.
psi = IndexedBase('psi')
#
# setup expression for the integration
#
step = Symbol('step') # use symbolic stepsize for flexibility
# let i represent an index of the grid array, and let A represent the
# grid array. Then we can approximate the integral by a sum over the
# following expression (simplified rectangular rule, ignoring end point
# corrections):
expr = A[i]**2*psi_ho(A[i])*psi[i]*step
if n == 0:
print("Setting up binary integrators for the integral:")
pprint(Integral(x**2*psi_ho(x)*Function('psi')(x), (x, 0, oo)))
# Autowrap it. For functions that take more than one argument, it is
# a good idea to use the 'args' keyword so that you know the signature
# of the wrapped function. (The dimension m will be an optional
# argument, but it must be present in the args list.)
binary_integrator[n] = autowrap(expr, args=[A.label, psi.label, step, m])
# Lets see how it converges with the grid dimension
print("Checking convergence of integrator for n = %i" % n)
for g in range(3, 8):
grid, step = np.linspace(0, rmax, 2**g, retstep=True)
print("grid dimension %5i, integral = %e" % (2**g,
binary_integrator[n](grid, H_ufunc(grid), step)))
print("A binary integrator has been set up for each basis state")
print("We will now use them to reconstruct a hydrogen solution.")
# Note: We didn't need to specify grid or use gridsize before now
grid, stepsize = np.linspace(0, rmax, gridsize, retstep=True)
print("Calculating coefficients with gridsize = %i and stepsize %f" % (
len(grid), stepsize))
coeffs = {}
for n in range(basis_dimension):
coeffs[n] = binary_integrator[n](grid, H_ufunc(grid), stepsize)
print("c(%i) = %e" % (n, coeffs[n]))
print("Constructing the approximate hydrogen wave")
hydro_approx = 0
all_steps = {}
for n in range(basis_dimension):
hydro_approx += basis_ho[n](grid)*coeffs[n]
all_steps[n] = hydro_approx.copy()
if pylab:
line = pylab.plot(grid, all_steps[n], ':', label='max n = %i' % n)
# check error numerically
diff = np.max(np.abs(hydro_approx - H_ufunc(grid)))
print("Error estimate: the element with largest deviation misses by %f" % diff)
if diff > 0.01:
print("This is much, try to increase the basis size or adjust omega")
else:
print("Ah, that's a pretty good approximation!")
# Check visually
if pylab:
print("Here's a plot showing the contribution for each n")
line[0].set_linestyle('-')
pylab.plot(grid, H_ufunc(grid), 'r-', label='exact')
pylab.legend()
pylab.show()
print("""Note:
These binary integrators were specialized to find coefficients for a
harmonic oscillator basis, but they can process any wave function as long
as it is available as a vector and defined on a grid with equidistant
points. That is, on any grid you get from numpy.linspace.
To make the integrators even more flexible, you can setup the harmonic
oscillator solutions with symbolic parameters omega and l. Then the
autowrapped binary routine will take these scalar variables as arguments,
so that the integrators can find coefficients for *any* isotropic harmonic
oscillator basis.
""")
|
negative_train_query0_00048
|
|
examples/advanced/fem.py/bernstein_space
def bernstein_space(order, nsd):
if nsd > 3:
raise RuntimeError("Bernstein only implemented in 1D, 2D, and 3D")
sum = 0
basis = []
coeff = []
if nsd == 1:
b1, b2 = x, 1 - x
for o1 in range(0, order + 1):
for o2 in range(0, order + 1):
if o1 + o2 == order:
aij = Symbol("a_%d_%d" % (o1, o2))
sum += aij*binomial(order, o1)*pow(b1, o1)*pow(b2, o2)
basis.append(binomial(order, o1)*pow(b1, o1)*pow(b2, o2))
coeff.append(aij)
if nsd == 2:
b1, b2, b3 = x, y, 1 - x - y
for o1 in range(0, order + 1):
for o2 in range(0, order + 1):
for o3 in range(0, order + 1):
if o1 + o2 + o3 == order:
aij = Symbol("a_%d_%d_%d" % (o1, o2, o3))
fac = factorial(order) / (factorial(o1)*factorial(o2)*factorial(o3))
sum += aij*fac*pow(b1, o1)*pow(b2, o2)*pow(b3, o3)
basis.append(fac*pow(b1, o1)*pow(b2, o2)*pow(b3, o3))
coeff.append(aij)
if nsd == 3:
b1, b2, b3, b4 = x, y, z, 1 - x - y - z
for o1 in range(0, order + 1):
for o2 in range(0, order + 1):
for o3 in range(0, order + 1):
for o4 in range(0, order + 1):
if o1 + o2 + o3 + o4 == order:
aij = Symbol("a_%d_%d_%d_%d" % (o1, o2, o3, o4))
fac = factorial(order)/(factorial(o1)*factorial(o2)*factorial(o3)*factorial(o4))
sum += aij*fac*pow(b1, o1)*pow(b2, o2)*pow(b3, o3)*pow(b4, o4)
basis.append(fac*pow(b1, o1)*pow(b2, o2)*pow(b3, o3)*pow(b4, o4))
coeff.append(aij)
return sum, coeff, basis
|
negative_train_query0_00049
|
|
examples/advanced/fem.py/create_point_set
def create_point_set(order, nsd):
h = Rational(1, order)
set = []
if nsd == 1:
for i in range(0, order + 1):
x = i*h
if x <= 1:
set.append((x, y))
if nsd == 2:
for i in range(0, order + 1):
x = i*h
for j in range(0, order + 1):
y = j*h
if x + y <= 1:
set.append((x, y))
if nsd == 3:
for i in range(0, order + 1):
x = i*h
for j in range(0, order + 1):
y = j*h
for k in range(0, order + 1):
z = j*h
if x + y + z <= 1:
set.append((x, y, z))
return set
|
negative_train_query0_00050
|
|
examples/advanced/fem.py/create_matrix
def create_matrix(equations, coeffs):
A = zeros(len(equations))
i = 0
j = 0
for j in range(0, len(coeffs)):
c = coeffs[j]
for i in range(0, len(equations)):
e = equations[i]
d, _ = reduced(e, [c])
A[i, j] = d[0]
return A
|
negative_train_query0_00051
|
|
examples/advanced/fem.py/main
def main():
t = ReferenceSimplex(2)
fe = Lagrange(2, 2)
u = 0
# compute u = sum_i u_i N_i
us = []
for i in range(0, fe.nbf()):
ui = Symbol("u_%d" % i)
us.append(ui)
u += ui*fe.N[i]
J = zeros(fe.nbf())
for i in range(0, fe.nbf()):
Fi = u*fe.N[i]
print(Fi)
for j in range(0, fe.nbf()):
uj = us[j]
integrands = diff(Fi, uj)
print(integrands)
J[j, i] = t.integrate(integrands)
pprint(J)
|
negative_train_query0_00052
|
|
examples/advanced/fem.py/ReferenceSimplex/__init__
class ReferenceSimplex: def __init__(self, nsd):
self.nsd = nsd
if nsd <= 3:
coords = symbols('x,y,z')[:nsd]
else:
coords = [Symbol("x_%d" % d) for d in range(nsd)]
self.coords = coords
|
negative_train_query0_00053
|
|
examples/advanced/fem.py/ReferenceSimplex/integrate
class ReferenceSimplex: def integrate(self, f):
coords = self.coords
nsd = self.nsd
limit = 1
for p in coords:
limit -= p
intf = f
for d in range(0, nsd):
p = coords[d]
limit += p
intf = integrate(intf, (p, 0, limit))
return intf
|
negative_train_query0_00054
|
|
examples/advanced/fem.py/Lagrange/__init__
class Lagrange: def __init__(self, nsd, order):
self.nsd = nsd
self.order = order
self.compute_basis()
|
negative_train_query0_00055
|
|
examples/advanced/fem.py/Lagrange/nbf
class Lagrange: def nbf(self):
return len(self.N)
|
negative_train_query0_00056
|
|
examples/advanced/fem.py/Lagrange/compute_basis
class Lagrange: def compute_basis(self):
order = self.order
nsd = self.nsd
N = []
pol, coeffs, basis = bernstein_space(order, nsd)
points = create_point_set(order, nsd)
equations = []
for p in points:
ex = pol.subs(x, p[0])
if nsd > 1:
ex = ex.subs(y, p[1])
if nsd > 2:
ex = ex.subs(z, p[2])
equations.append(ex)
A = create_matrix(equations, coeffs)
Ainv = A.inv()
b = eye(len(equations))
xx = Ainv*b
for i in range(0, len(equations)):
Ni = pol
for j in range(0, len(coeffs)):
Ni = Ni.subs(coeffs[j], xx[j, i])
N.append(Ni)
self.N = N
|
negative_train_query0_00057
|
|
examples/advanced/autowrap_ufuncify.py/main
def main():
print(__doc__)
x = symbols('x')
# a numpy array we can apply the ufuncs to
grid = np.linspace(-1, 1, 1000)
# set mpmath precision to 20 significant numbers for verification
mpmath.mp.dps = 20
print("Compiling legendre ufuncs and checking results:")
# Let's also plot the ufunc's we generate
for n in range(6):
# Setup the SymPy expression to ufuncify
expr = legendre(n, x)
print("The polynomial of degree %i is" % n)
pprint(expr)
# This is where the magic happens:
binary_poly = ufuncify(x, expr)
# It's now ready for use with numpy arrays
polyvector = binary_poly(grid)
# let's check the values against mpmath's legendre function
maxdiff = 0
for j in range(len(grid)):
precise_val = mpmath.legendre(n, grid[j])
diff = abs(polyvector[j] - precise_val)
if diff > maxdiff:
maxdiff = diff
print("The largest error in applied ufunc was %e" % maxdiff)
assert maxdiff < 1e-14
# We can also attach the autowrapped legendre polynomial to a sympy
# function and plot values as they are calculated by the binary function
plot1 = plt.pyplot.plot(grid, polyvector, hold=True)
print("Here's a plot with values calculated by the wrapped binary functions")
plt.pyplot.show()
|
negative_train_query0_00058
|
|
examples/advanced/dense_coding_example.py/main
def main():
psi = superposition_basis(2)
psi
# Dense coding demo:
# Assume Alice has the left QBit in psi
print("An even superposition of 2 qubits. Assume Alice has the left QBit.")
pprint(psi)
# The corresponding gates applied to Alice's QBit are:
# Identity Gate (1), Not Gate (X), Z Gate (Z), Z Gate and Not Gate (ZX)
# Then there's the controlled not gate (with Alice's as control):CNOT(1, 0)
# And the Hadamard gate applied to Alice's Qbit: H(1)
# To Send Bob the message |0>|0>
print("To Send Bob the message |00>.")
circuit = H(1)*CNOT(1, 0)
result = qapply(circuit*psi)
result
pprint(result)
# To send Bob the message |0>|1>
print("To Send Bob the message |01>.")
circuit = H(1)*CNOT(1, 0)*X(1)
result = qapply(circuit*psi)
result
pprint(result)
# To send Bob the message |1>|0>
print("To Send Bob the message |10>.")
circuit = H(1)*CNOT(1, 0)*Z(1)
result = qapply(circuit*psi)
result
pprint(result)
# To send Bob the message |1>|1>
print("To Send Bob the message |11>.")
circuit = H(1)*CNOT(1, 0)*Z(1)*X(1)
result = qapply(circuit*psi)
result
pprint(result)
|
negative_train_query0_00059
|
|
examples/advanced/qft.py/u
def u(p, r):
""" p = (p1, p2, p3); r = 0,1 """
if r not in [1, 2]:
raise ValueError("Value of r should lie between 1 and 2")
p1, p2, p3 = p
if r == 1:
ksi = Matrix([[1], [0]])
else:
ksi = Matrix([[0], [1]])
a = (sigma1*p1 + sigma2*p2 + sigma3*p3) / (E + m)*ksi
if a == 0:
a = zeros(2, 1)
return sqrt(E + m) *\
Matrix([[ksi[0, 0]], [ksi[1, 0]], [a[0, 0]], [a[1, 0]]])
|
negative_train_query0_00060
|
|
examples/advanced/qft.py/v
def v(p, r):
""" p = (p1, p2, p3); r = 0,1 """
if r not in [1, 2]:
raise ValueError("Value of r should lie between 1 and 2")
p1, p2, p3 = p
if r == 1:
ksi = Matrix([[1], [0]])
else:
ksi = -Matrix([[0], [1]])
a = (sigma1*p1 + sigma2*p2 + sigma3*p3) / (E + m)*ksi
if a == 0:
a = zeros(2, 1)
return sqrt(E + m) *\
Matrix([[a[0, 0]], [a[1, 0]], [ksi[0, 0]], [ksi[1, 0]]])
|
negative_train_query0_00061
|
|
examples/advanced/qft.py/pslash
def pslash(p):
p1, p2, p3 = p
p0 = sqrt(m**2 + p1**2 + p2**2 + p3**2)
return gamma0*p0 - gamma1*p1 - gamma2*p2 - gamma3*p3
|
negative_train_query0_00062
|
|
examples/advanced/qft.py/Tr
def Tr(M):
return M.trace()
|
negative_train_query0_00063
|
|
examples/advanced/qft.py/xprint
def xprint(lhs, rhs):
pprint(Eq(sympify(lhs), rhs))
|
negative_train_query0_00064
|
|
examples/advanced/qft.py/main
def main():
a = Symbol("a", real=True)
b = Symbol("b", real=True)
c = Symbol("c", real=True)
p = (a, b, c)
assert u(p, 1).D*u(p, 2) == Matrix(1, 1, [0])
assert u(p, 2).D*u(p, 1) == Matrix(1, 1, [0])
p1, p2, p3 = [Symbol(x, real=True) for x in ["p1", "p2", "p3"]]
pp1, pp2, pp3 = [Symbol(x, real=True) for x in ["pp1", "pp2", "pp3"]]
k1, k2, k3 = [Symbol(x, real=True) for x in ["k1", "k2", "k3"]]
kp1, kp2, kp3 = [Symbol(x, real=True) for x in ["kp1", "kp2", "kp3"]]
p = (p1, p2, p3)
pp = (pp1, pp2, pp3)
k = (k1, k2, k3)
kp = (kp1, kp2, kp3)
mu = Symbol("mu")
e = (pslash(p) + m*ones(4))*(pslash(k) - m*ones(4))
f = pslash(p) + m*ones(4)
g = pslash(p) - m*ones(4)
xprint('Tr(f*g)', Tr(f*g))
M0 = [(v(pp, 1).D*mgamma(mu)*u(p, 1))*(u(k, 1).D*mgamma(mu, True) *
v(kp, 1)) for mu in range(4)]
M = M0[0] + M0[1] + M0[2] + M0[3]
M = M[0]
if not isinstance(M, Basic):
raise TypeError("Invalid type of variable")
d = Symbol("d", real=True) # d=E+m
xprint('M', M)
print("-"*40)
M = ((M.subs(E, d - m)).expand()*d**2).expand()
xprint('M2', 1 / (E + m)**2*M)
print("-"*40)
x, y = M.as_real_imag()
xprint('Re(M)', x)
xprint('Im(M)', y)
e = x**2 + y**2
xprint('abs(M)**2', e)
print("-"*40)
xprint('Expand(abs(M)**2)', e.expand())
|
negative_train_query0_00065
|
|
examples/advanced/pidigits.py/display_fraction
def display_fraction(digits, skip=0, colwidth=10, columns=5):
"""Pretty printer for first n digits of a fraction"""
perline = colwidth * columns
printed = 0
for linecount in range((len(digits) - skip) // (colwidth * columns)):
line = digits[skip + linecount*perline:skip + (linecount + 1)*perline]
for i in range(columns):
print(line[i*colwidth: (i + 1)*colwidth],)
print(":", (linecount + 1)*perline)
if (linecount + 1) % 10 == 0:
print
printed += colwidth*columns
rem = (len(digits) - skip) % (colwidth * columns)
if rem:
buf = digits[-rem:]
s = ""
for i in range(columns):
s += buf[:colwidth].ljust(colwidth + 1, " ")
buf = buf[colwidth:]
print(s + ":", printed + colwidth*columns)
|
negative_train_query0_00066
|
|
examples/advanced/pidigits.py/calculateit
def calculateit(func, base, n, tofile):
"""Writes first n base-digits of a mpmath function to file"""
prec = 100
intpart = libmp.numeral(3, base)
if intpart == 0:
skip = 0
else:
skip = len(intpart)
print("Step 1 of 2: calculating binary value...")
prec = int(n*math.log(base, 2)) + 10
t = clock()
a = func(prec)
step1_time = clock() - t
print("Step 2 of 2: converting to specified base...")
t = clock()
d = libmp.bin_to_radix(a.man, -a.exp, base, n)
d = libmp.numeral(d, base, n)
step2_time = clock() - t
print("\nWriting output...\n")
if tofile:
out_ = sys.stdout
sys.stdout = tofile
print("%i base-%i digits of pi:\n" % (n, base))
print(intpart, ".\n")
display_fraction(d, skip, colwidth=10, columns=5)
if tofile:
sys.stdout = out_
print("\nFinished in %f seconds (%f calc, %f convert)" % \
((step1_time + step2_time), step1_time, step2_time))
|
negative_train_query0_00067
|
|
examples/advanced/pidigits.py/interactive
def interactive():
"""Simple function to interact with user"""
print("Compute digits of pi with SymPy\n")
base = input("Which base? (2-36, 10 for decimal) \n> ")
digits = input("How many digits? (enter a big number, say, 10000)\n> ")
tofile = raw_input("Output to file? (enter a filename, or just press enter\nto print directly to the screen) \n> ")
if tofile:
tofile = open(tofile, "w")
calculateit(pi, base, digits, tofile)
|
negative_train_query0_00068
|
|
examples/advanced/pidigits.py/main
def main():
"""A non-interactive runner"""
base = 16
digits = 500
tofile = None
calculateit(pi, base, digits, tofile)
|
negative_train_query0_00069
|
|
examples/advanced/pyglet_plotting.py/main
def main():
x, y, z = symbols('x,y,z')
# toggle axes visibility with F5, colors with F6
axes_options = 'visible=false; colored=true; label_ticks=true; label_axes=true; overlay=true; stride=0.5'
# axes_options = 'colored=false; overlay=false; stride=(1.0, 0.5, 0.5)'
p = PygletPlot(
width=600,
height=500,
ortho=False,
invert_mouse_zoom=False,
axes=axes_options,
antialiasing=True)
examples = []
def example_wrapper(f):
examples.append(f)
return f
@example_wrapper
def mirrored_saddles():
p[5] = x**2 - y**2, [20], [20]
p[6] = y**2 - x**2, [20], [20]
@example_wrapper
def mirrored_saddles_saveimage():
p[5] = x**2 - y**2, [20], [20]
p[6] = y**2 - x**2, [20], [20]
p.wait_for_calculations()
# although the calculation is complete,
# we still need to wait for it to be
# rendered, so we'll sleep to be sure.
sleep(1)
p.saveimage("plot_example.png")
@example_wrapper
def mirrored_ellipsoids():
p[2] = x**2 + y**2, [40], [40], 'color=zfade'
p[3] = -x**2 - y**2, [40], [40], 'color=zfade'
@example_wrapper
def saddle_colored_by_derivative():
f = x**2 - y**2
p[1] = f, 'style=solid'
p[1].color = abs(f.diff(x)), abs(f.diff(x) + f.diff(y)), abs(f.diff(y))
@example_wrapper
def ding_dong_surface():
f = sqrt(1.0 - y)*y
p[1] = f, [x, 0, 2*pi,
40], [y, -
1, 4, 100], 'mode=cylindrical; style=solid; color=zfade4'
@example_wrapper
def polar_circle():
p[7] = 1, 'mode=polar'
@example_wrapper
def polar_flower():
p[8] = 1.5*sin(4*x), [160], 'mode=polar'
p[8].color = z, x, y, (0.5, 0.5, 0.5), (
0.8, 0.8, 0.8), (x, y, None, z) # z is used for t
@example_wrapper
def simple_cylinder():
p[9] = 1, 'mode=cylindrical'
@example_wrapper
def cylindrical_hyperbola():
# (note that polar is an alias for cylindrical)
p[10] = 1/y, 'mode=polar', [x], [y, -2, 2, 20]
@example_wrapper
def extruded_hyperbolas():
p[11] = 1/x, [x, -10, 10, 100], [1], 'style=solid'
p[12] = -1/x, [x, -10, 10, 100], [1], 'style=solid'
@example_wrapper
def torus():
a, b = 1, 0.5 # radius, thickness
p[13] = (a + b*cos(x))*cos(y), (a + b*cos(x)) *\
sin(y), b*sin(x), [x, 0, pi*2, 40], [y, 0, pi*2, 40]
@example_wrapper
def warped_torus():
a, b = 2, 1 # radius, thickness
p[13] = (a + b*cos(x))*cos(y), (a + b*cos(x))*sin(y), b *\
sin(x) + 0.5*sin(4*y), [x, 0, pi*2, 40], [y, 0, pi*2, 40]
@example_wrapper
def parametric_spiral():
p[14] = cos(y), sin(y), y / 10.0, [y, -4*pi, 4*pi, 100]
p[14].color = x, (0.1, 0.9), y, (0.1, 0.9), z, (0.1, 0.9)
@example_wrapper
def multistep_gradient():
p[1] = 1, 'mode=spherical', 'style=both'
# p[1] = exp(-x**2-y**2+(x*y)/4), [-1.7,1.7,100], [-1.7,1.7,100], 'style=solid'
# p[1] = 5*x*y*exp(-x**2-y**2), [-2,2,100], [-2,2,100]
gradient = [0.0, (0.3, 0.3, 1.0),
0.30, (0.3, 1.0, 0.3),
0.55, (0.95, 1.0, 0.2),
0.65, (1.0, 0.95, 0.2),
0.85, (1.0, 0.7, 0.2),
1.0, (1.0, 0.3, 0.2)]
p[1].color = z, [None, None, z], gradient
# p[1].color = 'zfade'
# p[1].color = 'zfade3'
@example_wrapper
def lambda_vs_sympy_evaluation():
start = clock()
p[4] = x**2 + y**2, [100], [100], 'style=solid'
p.wait_for_calculations()
print("lambda-based calculation took %s seconds." % (clock() - start))
start = clock()
p[4] = x**2 + y**2, [100], [100], 'style=solid; use_sympy_eval'
p.wait_for_calculations()
print(
"sympy substitution-based calculation took %s seconds." %
(clock() - start))
@example_wrapper
def gradient_vectors():
def gradient_vectors_inner(f, i):
from sympy import lambdify
from sympy.plotting.plot_interval import PlotInterval
from pyglet.gl import glBegin, glColor3f
from pyglet.gl import glVertex3f, glEnd, GL_LINES
def draw_gradient_vectors(f, iu, iv):
"""
Create a function which draws vectors
representing the gradient of f.
"""
dx, dy, dz = f.diff(x), f.diff(y), 0
FF = lambdify([x, y], [x, y, f])
FG = lambdify([x, y], [dx, dy, dz])
iu.v_steps /= 5
iv.v_steps /= 5
Gvl = list(list([FF(u, v), FG(u, v)]
for v in iv.frange())
for u in iu.frange())
def draw_arrow(p1, p2):
"""
Draw a single vector.
"""
glColor3f(0.4, 0.4, 0.9)
glVertex3f(*p1)
glColor3f(0.9, 0.4, 0.4)
glVertex3f(*p2)
def draw():
"""
Iterate through the calculated
vectors and draw them.
"""
glBegin(GL_LINES)
for u in Gvl:
for v in u:
point = [[v[0][0], v[0][1], v[0][2]],
[v[0][0] + v[1][0], v[0][1] + v[1][1], v[0][2] + v[1][2]]]
draw_arrow(point[0], point[1])
glEnd()
return draw
p[i] = f, [-0.5, 0.5, 25], [-0.5, 0.5, 25], 'style=solid'
iu = PlotInterval(p[i].intervals[0])
iv = PlotInterval(p[i].intervals[1])
p[i].postdraw.append(draw_gradient_vectors(f, iu, iv))
gradient_vectors_inner(x**2 + y**2, 1)
gradient_vectors_inner(-x**2 - y**2, 2)
def help_str():
s = ("\nPlot p has been created. Useful commands: \n"
" help(p), p[1] = x**2, print p, p.clear() \n\n"
"Available examples (see source in plotting.py):\n\n")
for i in range(len(examples)):
s += "(%i) %s\n" % (i, examples[i].__name__)
s += "\n"
s += "e.g. >>> example(2)\n"
s += " >>> ding_dong_surface()\n"
return s
def example(i):
if callable(i):
p.clear()
i()
elif i >= 0 and i < len(examples):
p.clear()
examples[i]()
else:
print("Not a valid example.\n")
print(p)
example(0) # 0 - 15 are defined above
print(help_str())
|
negative_train_query0_00070
|
|
examples/advanced/pyglet_plotting.py/example_wrapper
def example_wrapper(f):
examples.append(f)
return f
|
negative_train_query0_00071
|
|
examples/advanced/pyglet_plotting.py/mirrored_saddles
def mirrored_saddles():
p[5] = x**2 - y**2, [20], [20]
p[6] = y**2 - x**2, [20], [20]
|
negative_train_query0_00072
|
|
examples/advanced/pyglet_plotting.py/mirrored_saddles_saveimage
def mirrored_saddles_saveimage():
p[5] = x**2 - y**2, [20], [20]
p[6] = y**2 - x**2, [20], [20]
p.wait_for_calculations()
# although the calculation is complete,
# we still need to wait for it to be
# rendered, so we'll sleep to be sure.
sleep(1)
p.saveimage("plot_example.png")
|
negative_train_query0_00073
|
|
examples/advanced/pyglet_plotting.py/mirrored_ellipsoids
def mirrored_ellipsoids():
p[2] = x**2 + y**2, [40], [40], 'color=zfade'
p[3] = -x**2 - y**2, [40], [40], 'color=zfade'
|
negative_train_query0_00074
|
|
examples/advanced/pyglet_plotting.py/saddle_colored_by_derivative
def saddle_colored_by_derivative():
f = x**2 - y**2
p[1] = f, 'style=solid'
p[1].color = abs(f.diff(x)), abs(f.diff(x) + f.diff(y)), abs(f.diff(y))
|
negative_train_query0_00075
|
|
examples/advanced/pyglet_plotting.py/ding_dong_surface
def ding_dong_surface():
f = sqrt(1.0 - y)*y
p[1] = f, [x, 0, 2*pi,
40], [y, -
1, 4, 100], 'mode=cylindrical; style=solid; color=zfade4'
|
negative_train_query0_00076
|
|
examples/advanced/pyglet_plotting.py/polar_circle
def polar_circle():
p[7] = 1, 'mode=polar'
|
negative_train_query0_00077
|
|
examples/advanced/pyglet_plotting.py/polar_flower
def polar_flower():
p[8] = 1.5*sin(4*x), [160], 'mode=polar'
p[8].color = z, x, y, (0.5, 0.5, 0.5), (
0.8, 0.8, 0.8), (x, y, None, z) # z is used for t
|
negative_train_query0_00078
|
|
examples/advanced/pyglet_plotting.py/simple_cylinder
def simple_cylinder():
p[9] = 1, 'mode=cylindrical'
|
negative_train_query0_00079
|
|
examples/advanced/pyglet_plotting.py/cylindrical_hyperbola
def cylindrical_hyperbola():
# (note that polar is an alias for cylindrical)
p[10] = 1/y, 'mode=polar', [x], [y, -2, 2, 20]
|
negative_train_query0_00080
|
|
examples/advanced/pyglet_plotting.py/extruded_hyperbolas
def extruded_hyperbolas():
p[11] = 1/x, [x, -10, 10, 100], [1], 'style=solid'
p[12] = -1/x, [x, -10, 10, 100], [1], 'style=solid'
|
negative_train_query0_00081
|
|
examples/advanced/pyglet_plotting.py/torus
def torus():
a, b = 1, 0.5 # radius, thickness
p[13] = (a + b*cos(x))*cos(y), (a + b*cos(x)) *\
sin(y), b*sin(x), [x, 0, pi*2, 40], [y, 0, pi*2, 40]
|
negative_train_query0_00082
|
|
examples/advanced/pyglet_plotting.py/warped_torus
def warped_torus():
a, b = 2, 1 # radius, thickness
p[13] = (a + b*cos(x))*cos(y), (a + b*cos(x))*sin(y), b *\
sin(x) + 0.5*sin(4*y), [x, 0, pi*2, 40], [y, 0, pi*2, 40]
|
negative_train_query0_00083
|
|
examples/advanced/pyglet_plotting.py/parametric_spiral
def parametric_spiral():
p[14] = cos(y), sin(y), y / 10.0, [y, -4*pi, 4*pi, 100]
p[14].color = x, (0.1, 0.9), y, (0.1, 0.9), z, (0.1, 0.9)
|
negative_train_query0_00084
|
|
examples/advanced/pyglet_plotting.py/multistep_gradient
def multistep_gradient():
p[1] = 1, 'mode=spherical', 'style=both'
# p[1] = exp(-x**2-y**2+(x*y)/4), [-1.7,1.7,100], [-1.7,1.7,100], 'style=solid'
# p[1] = 5*x*y*exp(-x**2-y**2), [-2,2,100], [-2,2,100]
gradient = [0.0, (0.3, 0.3, 1.0),
0.30, (0.3, 1.0, 0.3),
0.55, (0.95, 1.0, 0.2),
0.65, (1.0, 0.95, 0.2),
0.85, (1.0, 0.7, 0.2),
1.0, (1.0, 0.3, 0.2)]
p[1].color = z, [None, None, z], gradient
|
negative_train_query0_00085
|
|
examples/advanced/pyglet_plotting.py/lambda_vs_sympy_evaluation
def lambda_vs_sympy_evaluation():
start = clock()
p[4] = x**2 + y**2, [100], [100], 'style=solid'
p.wait_for_calculations()
print("lambda-based calculation took %s seconds." % (clock() - start))
start = clock()
p[4] = x**2 + y**2, [100], [100], 'style=solid; use_sympy_eval'
p.wait_for_calculations()
print(
"sympy substitution-based calculation took %s seconds." %
(clock() - start))
|
negative_train_query0_00086
|
|
examples/advanced/pyglet_plotting.py/gradient_vectors
def gradient_vectors():
def gradient_vectors_inner(f, i):
from sympy import lambdify
from sympy.plotting.plot_interval import PlotInterval
from pyglet.gl import glBegin, glColor3f
from pyglet.gl import glVertex3f, glEnd, GL_LINES
def draw_gradient_vectors(f, iu, iv):
"""
Create a function which draws vectors
representing the gradient of f.
"""
dx, dy, dz = f.diff(x), f.diff(y), 0
FF = lambdify([x, y], [x, y, f])
FG = lambdify([x, y], [dx, dy, dz])
iu.v_steps /= 5
iv.v_steps /= 5
Gvl = list(list([FF(u, v), FG(u, v)]
for v in iv.frange())
for u in iu.frange())
def draw_arrow(p1, p2):
"""
Draw a single vector.
"""
glColor3f(0.4, 0.4, 0.9)
glVertex3f(*p1)
glColor3f(0.9, 0.4, 0.4)
glVertex3f(*p2)
def draw():
"""
Iterate through the calculated
vectors and draw them.
"""
glBegin(GL_LINES)
for u in Gvl:
for v in u:
point = [[v[0][0], v[0][1], v[0][2]],
[v[0][0] + v[1][0], v[0][1] + v[1][1], v[0][2] + v[1][2]]]
draw_arrow(point[0], point[1])
glEnd()
return draw
p[i] = f, [-0.5, 0.5, 25], [-0.5, 0.5, 25], 'style=solid'
iu = PlotInterval(p[i].intervals[0])
iv = PlotInterval(p[i].intervals[1])
p[i].postdraw.append(draw_gradient_vectors(f, iu, iv))
gradient_vectors_inner(x**2 + y**2, 1)
gradient_vectors_inner(-x**2 - y**2, 2)
|
negative_train_query0_00087
|
|
examples/advanced/pyglet_plotting.py/help_str
def help_str():
s = ("\nPlot p has been created. Useful commands: \n"
" help(p), p[1] = x**2, print p, p.clear() \n\n"
"Available examples (see source in plotting.py):\n\n")
for i in range(len(examples)):
s += "(%i) %s\n" % (i, examples[i].__name__)
s += "\n"
s += "e.g. >>> example(2)\n"
s += " >>> ding_dong_surface()\n"
return s
|
negative_train_query0_00088
|
|
examples/advanced/pyglet_plotting.py/example
def example(i):
if callable(i):
p.clear()
i()
elif i >= 0 and i < len(examples):
p.clear()
examples[i]()
else:
print("Not a valid example.\n")
print(p)
|
negative_train_query0_00089
|
|
examples/advanced/pyglet_plotting.py/gradient_vectors_inner
def gradient_vectors_inner(f, i):
from sympy import lambdify
from sympy.plotting.plot_interval import PlotInterval
from pyglet.gl import glBegin, glColor3f
from pyglet.gl import glVertex3f, glEnd, GL_LINES
def draw_gradient_vectors(f, iu, iv):
"""
Create a function which draws vectors
representing the gradient of f.
"""
dx, dy, dz = f.diff(x), f.diff(y), 0
FF = lambdify([x, y], [x, y, f])
FG = lambdify([x, y], [dx, dy, dz])
iu.v_steps /= 5
iv.v_steps /= 5
Gvl = list(list([FF(u, v), FG(u, v)]
for v in iv.frange())
for u in iu.frange())
def draw_arrow(p1, p2):
"""
Draw a single vector.
"""
glColor3f(0.4, 0.4, 0.9)
glVertex3f(*p1)
glColor3f(0.9, 0.4, 0.4)
glVertex3f(*p2)
def draw():
"""
Iterate through the calculated
vectors and draw them.
"""
glBegin(GL_LINES)
for u in Gvl:
for v in u:
point = [[v[0][0], v[0][1], v[0][2]],
[v[0][0] + v[1][0], v[0][1] + v[1][1], v[0][2] + v[1][2]]]
draw_arrow(point[0], point[1])
glEnd()
return draw
p[i] = f, [-0.5, 0.5, 25], [-0.5, 0.5, 25], 'style=solid'
iu = PlotInterval(p[i].intervals[0])
iv = PlotInterval(p[i].intervals[1])
p[i].postdraw.append(draw_gradient_vectors(f, iu, iv))
|
negative_train_query0_00090
|
|
examples/advanced/pyglet_plotting.py/draw_gradient_vectors
def draw_gradient_vectors(f, iu, iv):
"""
Create a function which draws vectors
representing the gradient of f.
"""
dx, dy, dz = f.diff(x), f.diff(y), 0
FF = lambdify([x, y], [x, y, f])
FG = lambdify([x, y], [dx, dy, dz])
iu.v_steps /= 5
iv.v_steps /= 5
Gvl = list(list([FF(u, v), FG(u, v)]
for v in iv.frange())
for u in iu.frange())
def draw_arrow(p1, p2):
"""
Draw a single vector.
"""
glColor3f(0.4, 0.4, 0.9)
glVertex3f(*p1)
glColor3f(0.9, 0.4, 0.4)
glVertex3f(*p2)
def draw():
"""
Iterate through the calculated
vectors and draw them.
"""
glBegin(GL_LINES)
for u in Gvl:
for v in u:
point = [[v[0][0], v[0][1], v[0][2]],
[v[0][0] + v[1][0], v[0][1] + v[1][1], v[0][2] + v[1][2]]]
draw_arrow(point[0], point[1])
glEnd()
return draw
|
negative_train_query0_00091
|
|
examples/advanced/pyglet_plotting.py/draw_arrow
def draw_arrow(p1, p2):
"""
Draw a single vector.
"""
glColor3f(0.4, 0.4, 0.9)
glVertex3f(*p1)
glColor3f(0.9, 0.4, 0.4)
glVertex3f(*p2)
|
negative_train_query0_00092
|
|
examples/advanced/pyglet_plotting.py/draw
def draw():
"""
Iterate through the calculated
vectors and draw them.
"""
glBegin(GL_LINES)
for u in Gvl:
for v in u:
point = [[v[0][0], v[0][1], v[0][2]],
[v[0][0] + v[1][0], v[0][1] + v[1][1], v[0][2] + v[1][2]]]
draw_arrow(point[0], point[1])
glEnd()
|
negative_train_query0_00093
|
|
examples/advanced/relativity.py/grad
def grad(f, X):
a = []
for x in X:
a.append(f.diff(x))
return a
|
negative_train_query0_00094
|
|
examples/advanced/relativity.py/d
def d(m, x):
return grad(m[0, 0], x)
|
negative_train_query0_00095
|
|
examples/advanced/relativity.py/curvature
def curvature(Rmn):
return Rmn.ud(0, 0) + Rmn.ud(1, 1) + Rmn.ud(2, 2) + Rmn.ud(3, 3)
|
negative_train_query0_00096
|
|
examples/advanced/relativity.py/pprint_Gamma_udd
def pprint_Gamma_udd(i, k, l):
pprint(Eq(Symbol('Gamma^%i_%i%i' % (i, k, l)), Gamma.udd(i, k, l)))
|
negative_train_query0_00097
|
|
examples/advanced/relativity.py/pprint_Rmn_dd
def pprint_Rmn_dd(i, j):
pprint(Eq(Symbol('R_%i%i' % (i, j)), Rmn.dd(i, j)))
|
negative_train_query0_00098
|
End of preview. Expand
in Data Studio
Software Issue Localization.
| Task category | t2t |
| Domains | Programming, Written |
| Reference | https://www.swebench.com/ |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("SWEbenchLiteRR")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repository.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@misc{jimenez2024swebenchlanguagemodelsresolve,
archiveprefix = {arXiv},
author = {Carlos E. Jimenez and John Yang and Alexander Wettig and Shunyu Yao and Kexin Pei and Ofir Press and Karthik Narasimhan},
eprint = {2310.06770},
primaryclass = {cs.CL},
title = {SWE-bench: Can Language Models Resolve Real-World GitHub Issues?},
url = {https://arxiv.org/abs/2310.06770},
year = {2024},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
Dataset Statistics
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("SWEbenchLiteRR")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB
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