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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/test_models.py
|
import sympy.physics.mechanics.models as models
from sympy.core.backend import (cos, sin, Matrix, symbols, zeros)
from sympy import simplify
from sympy.physics.mechanics import (dynamicsymbols)
def test_multi_mass_spring_damper_inputs():
c0, k0, m0 = symbols("c0 k0 m0")
g = symbols("g")
v0, x0, f0 = dynamicsymbols("v0 x0 f0")
kane1 = models.multi_mass_spring_damper(1)
massmatrix1 = Matrix([[m0]])
forcing1 = Matrix([[-c0*v0 - k0*x0]])
assert simplify(massmatrix1 - kane1.mass_matrix) == Matrix([0])
assert simplify(forcing1 - kane1.forcing) == Matrix([0])
kane2 = models.multi_mass_spring_damper(1, True)
massmatrix2 = Matrix([[m0]])
forcing2 = Matrix([[-c0*v0 + g*m0 - k0*x0]])
assert simplify(massmatrix2 - kane2.mass_matrix) == Matrix([0])
assert simplify(forcing2 - kane2.forcing) == Matrix([0])
kane3 = models.multi_mass_spring_damper(1, True, True)
massmatrix3 = Matrix([[m0]])
forcing3 = Matrix([[-c0*v0 + g*m0 - k0*x0 + f0]])
assert simplify(massmatrix3 - kane3.mass_matrix) == Matrix([0])
assert simplify(forcing3 - kane3.forcing) == Matrix([0])
kane4 = models.multi_mass_spring_damper(1, False, True)
massmatrix4 = Matrix([[m0]])
forcing4 = Matrix([[-c0*v0 - k0*x0 + f0]])
assert simplify(massmatrix4 - kane4.mass_matrix) == Matrix([0])
assert simplify(forcing4 - kane4.forcing) == Matrix([0])
def test_multi_mass_spring_damper_higher_order():
c0, k0, m0 = symbols("c0 k0 m0")
c1, k1, m1 = symbols("c1 k1 m1")
c2, k2, m2 = symbols("c2 k2 m2")
v0, x0 = dynamicsymbols("v0 x0")
v1, x1 = dynamicsymbols("v1 x1")
v2, x2 = dynamicsymbols("v2 x2")
kane1 = models.multi_mass_spring_damper(3)
massmatrix1 = Matrix([[m0 + m1 + m2, m1 + m2, m2],
[m1 + m2, m1 + m2, m2],
[m2, m2, m2]])
forcing1 = Matrix([[-c0*v0 - k0*x0],
[-c1*v1 - k1*x1],
[-c2*v2 - k2*x2]])
assert simplify(massmatrix1 - kane1.mass_matrix) == zeros(3)
assert simplify(forcing1 - kane1.forcing) == Matrix([0, 0, 0])
def test_n_link_pendulum_on_cart_inputs():
l0, m0 = symbols("l0 m0")
m1 = symbols("m1")
g = symbols("g")
q0, q1, F, T1 = dynamicsymbols("q0 q1 F T1")
u0, u1 = dynamicsymbols("u0 u1")
kane1 = models.n_link_pendulum_on_cart(1)
massmatrix1 = Matrix([[m0 + m1, -l0*m1*cos(q1)],
[-l0*m1*cos(q1), l0**2*m1]])
forcing1 = Matrix([[-l0*m1*u1**2*sin(q1) + F], [g*l0*m1*sin(q1)]])
assert simplify(massmatrix1 - kane1.mass_matrix) == zeros(2)
assert simplify(forcing1 - kane1.forcing) == Matrix([0, 0])
kane2 = models.n_link_pendulum_on_cart(1, False)
massmatrix2 = Matrix([[m0 + m1, -l0*m1*cos(q1)],
[-l0*m1*cos(q1), l0**2*m1]])
forcing2 = Matrix([[-l0*m1*u1**2*sin(q1)], [g*l0*m1*sin(q1)]])
assert simplify(massmatrix2 - kane2.mass_matrix) == zeros(2)
assert simplify(forcing2 - kane2.forcing) == Matrix([0, 0])
kane3 = models.n_link_pendulum_on_cart(1, False, True)
massmatrix3 = Matrix([[m0 + m1, -l0*m1*cos(q1)],
[-l0*m1*cos(q1), l0**2*m1]])
forcing3 = Matrix([[-l0*m1*u1**2*sin(q1)], [g*l0*m1*sin(q1) + T1]])
assert simplify(massmatrix3 - kane3.mass_matrix) == zeros(2)
assert simplify(forcing3 - kane3.forcing) == Matrix([0, 0])
kane4 = models.n_link_pendulum_on_cart(1, True, False)
massmatrix4 = Matrix([[m0 + m1, -l0*m1*cos(q1)],
[-l0*m1*cos(q1), l0**2*m1]])
forcing4 = Matrix([[-l0*m1*u1**2*sin(q1) + F], [g*l0*m1*sin(q1)]])
assert simplify(massmatrix4 - kane4.mass_matrix) == zeros(2)
assert simplify(forcing4 - kane4.forcing) == Matrix([0, 0])
def test_n_link_pendulum_on_cart_higher_order():
l0, m0 = symbols("l0 m0")
l1, m1 = symbols("l1 m1")
m2 = symbols("m2")
g = symbols("g")
q0, q1, q2 = dynamicsymbols("q0 q1 q2")
u0, u1, u2 = dynamicsymbols("u0 u1 u2")
F, T1 = dynamicsymbols("F T1")
kane1 = models.n_link_pendulum_on_cart(2)
massmatrix1 = Matrix([[m0 + m1 + m2, -l0*m1*cos(q1) - l0*m2*cos(q1),
-l1*m2*cos(q2)],
[-l0*m1*cos(q1) - l0*m2*cos(q1), l0**2*m1 + l0**2*m2,
l0*l1*m2*(sin(q1)*sin(q2) + cos(q1)*cos(q2))],
[-l1*m2*cos(q2),
l0*l1*m2*(sin(q1)*sin(q2) + cos(q1)*cos(q2)),
l1**2*m2]])
forcing1 = Matrix([[-l0*m1*u1**2*sin(q1) - l0*m2*u1**2*sin(q1) -
l1*m2*u2**2*sin(q2) + F],
[g*l0*m1*sin(q1) + g*l0*m2*sin(q1) -
l0*l1*m2*(sin(q1)*cos(q2) - sin(q2)*cos(q1))*u2**2],
[g*l1*m2*sin(q2) - l0*l1*m2*(-sin(q1)*cos(q2) +
sin(q2)*cos(q1))*u1**2]])
assert simplify(massmatrix1 - kane1.mass_matrix) == zeros(3)
assert simplify(forcing1 - kane1.forcing) == Matrix([0, 0, 0])
| 5,073 | 42 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/test_kane2.py
|
import warnings
from sympy.core.compatibility import range
from sympy.core.backend import cos, Matrix, sin, zeros, tan, pi, symbols
from sympy import trigsimp, simplify, solve
from sympy.physics.mechanics import (cross, dot, dynamicsymbols, KanesMethod,
inertia, inertia_of_point_mass,
Point, ReferenceFrame, RigidBody)
from sympy.utilities.exceptions import SymPyDeprecationWarning
def test_aux_dep():
# This test is about rolling disc dynamics, comparing the results found
# with KanesMethod to those found when deriving the equations "manually"
# with SymPy.
# The terms Fr, Fr*, and Fr*_steady are all compared between the two
# methods. Here, Fr*_steady refers to the generalized inertia forces for an
# equilibrium configuration.
# Note: comparing to the test of test_rolling_disc() in test_kane.py, this
# test also tests auxiliary speeds and configuration and motion constraints
#, seen in the generalized dependent coordinates q[3], and depend speeds
# u[3], u[4] and u[5].
# First, mannual derivation of Fr, Fr_star, Fr_star_steady.
# Symbols for time and constant parameters.
# Symbols for contact forces: Fx, Fy, Fz.
t, r, m, g, I, J = symbols('t r m g I J')
Fx, Fy, Fz = symbols('Fx Fy Fz')
# Configuration variables and their time derivatives:
# q[0] -- yaw
# q[1] -- lean
# q[2] -- spin
# q[3] -- dot(-r*B.z, A.z) -- distance from ground plane to disc center in
# A.z direction
# Generalized speeds and their time derivatives:
# u[0] -- disc angular velocity component, disc fixed x direction
# u[1] -- disc angular velocity component, disc fixed y direction
# u[2] -- disc angular velocity component, disc fixed z direction
# u[3] -- disc velocity component, A.x direction
# u[4] -- disc velocity component, A.y direction
# u[5] -- disc velocity component, A.z direction
# Auxiliary generalized speeds:
# ua[0] -- contact point auxiliary generalized speed, A.x direction
# ua[1] -- contact point auxiliary generalized speed, A.y direction
# ua[2] -- contact point auxiliary generalized speed, A.z direction
q = dynamicsymbols('q:4')
qd = [qi.diff(t) for qi in q]
u = dynamicsymbols('u:6')
ud = [ui.diff(t) for ui in u]
ud_zero = dict(zip(ud, [0.]*len(ud)))
ua = dynamicsymbols('ua:3')
ua_zero = dict(zip(ua, [0.]*len(ua)))
# Reference frames:
# Yaw intermediate frame: A.
# Lean intermediate frame: B.
# Disc fixed frame: C.
N = ReferenceFrame('N')
A = N.orientnew('A', 'Axis', [q[0], N.z])
B = A.orientnew('B', 'Axis', [q[1], A.x])
C = B.orientnew('C', 'Axis', [q[2], B.y])
# Angular velocity and angular acceleration of disc fixed frame
# u[0], u[1] and u[2] are generalized independent speeds.
C.set_ang_vel(N, u[0]*B.x + u[1]*B.y + u[2]*B.z)
C.set_ang_acc(N, C.ang_vel_in(N).diff(t, B)
+ cross(B.ang_vel_in(N), C.ang_vel_in(N)))
# Velocity and acceleration of points:
# Disc-ground contact point: P.
# Center of disc: O, defined from point P with depend coordinate: q[3]
# u[3], u[4] and u[5] are generalized dependent speeds.
P = Point('P')
P.set_vel(N, ua[0]*A.x + ua[1]*A.y + ua[2]*A.z)
O = P.locatenew('O', q[3]*A.z + r*sin(q[1])*A.y)
O.set_vel(N, u[3]*A.x + u[4]*A.y + u[5]*A.z)
O.set_acc(N, O.vel(N).diff(t, A) + cross(A.ang_vel_in(N), O.vel(N)))
# Kinematic differential equations:
# Two equalities: one is w_c_n_qd = C.ang_vel_in(N) in three coordinates
# directions of B, for qd0, qd1 and qd2.
# the other is v_o_n_qd = O.vel(N) in A.z direction for qd3.
# Then, solve for dq/dt's in terms of u's: qd_kd.
w_c_n_qd = qd[0]*A.z + qd[1]*B.x + qd[2]*B.y
v_o_n_qd = O.pos_from(P).diff(t, A) + cross(A.ang_vel_in(N), O.pos_from(P))
kindiffs = Matrix([dot(w_c_n_qd - C.ang_vel_in(N), uv) for uv in B] +
[dot(v_o_n_qd - O.vel(N), A.z)])
qd_kd = solve(kindiffs, qd)
# Values of generalized speeds during a steady turn for later substitution
# into the Fr_star_steady.
steady_conditions = solve(kindiffs.subs({qd[1] : 0, qd[3] : 0}), u)
steady_conditions.update({qd[1] : 0, qd[3] : 0})
# Partial angular velocities and velocities.
partial_w_C = [C.ang_vel_in(N).diff(ui, N) for ui in u + ua]
partial_v_O = [O.vel(N).diff(ui, N) for ui in u + ua]
partial_v_P = [P.vel(N).diff(ui, N) for ui in u + ua]
# Configuration constraint: f_c, the projection of radius r in A.z direction
# is q[3].
# Velocity constraints: f_v, for u3, u4 and u5.
# Acceleration constraints: f_a.
f_c = Matrix([dot(-r*B.z, A.z) - q[3]])
f_v = Matrix([dot(O.vel(N) - (P.vel(N) + cross(C.ang_vel_in(N),
O.pos_from(P))), ai).expand() for ai in A])
v_o_n = cross(C.ang_vel_in(N), O.pos_from(P))
a_o_n = v_o_n.diff(t, A) + cross(A.ang_vel_in(N), v_o_n)
f_a = Matrix([dot(O.acc(N) - a_o_n, ai) for ai in A])
# Solve for constraint equations in the form of
# u_dependent = A_rs * [u_i; u_aux].
# First, obtain constraint coefficient matrix: M_v * [u; ua] = 0;
# Second, taking u[0], u[1], u[2] as independent,
# taking u[3], u[4], u[5] as dependent,
# rearranging the matrix of M_v to be A_rs for u_dependent.
# Third, u_aux ==0 for u_dep, and resulting dictionary of u_dep_dict.
M_v = zeros(3, 9)
for i in range(3):
for j, ui in enumerate(u + ua):
M_v[i, j] = f_v[i].diff(ui)
M_v_i = M_v[:, :3]
M_v_d = M_v[:, 3:6]
M_v_aux = M_v[:, 6:]
M_v_i_aux = M_v_i.row_join(M_v_aux)
A_rs = - M_v_d.inv() * M_v_i_aux
u_dep = A_rs[:, :3] * Matrix(u[:3])
u_dep_dict = dict(zip(u[3:], u_dep))
# Active forces: F_O acting on point O; F_P acting on point P.
# Generalized active forces (unconstrained): Fr_u = F_point * pv_point.
F_O = m*g*A.z
F_P = Fx * A.x + Fy * A.y + Fz * A.z
Fr_u = Matrix([dot(F_O, pv_o) + dot(F_P, pv_p) for pv_o, pv_p in
zip(partial_v_O, partial_v_P)])
# Inertia force: R_star_O.
# Inertia of disc: I_C_O, where J is a inertia component about principal axis.
# Inertia torque: T_star_C.
# Generalized inertia forces (unconstrained): Fr_star_u.
R_star_O = -m*O.acc(N)
I_C_O = inertia(B, I, J, I)
T_star_C = -(dot(I_C_O, C.ang_acc_in(N)) \
+ cross(C.ang_vel_in(N), dot(I_C_O, C.ang_vel_in(N))))
Fr_star_u = Matrix([dot(R_star_O, pv) + dot(T_star_C, pav) for pv, pav in
zip(partial_v_O, partial_w_C)])
# Form nonholonomic Fr: Fr_c, and nonholonomic Fr_star: Fr_star_c.
# Also, nonholonomic Fr_star in steady turning condition: Fr_star_steady.
Fr_c = Fr_u[:3, :].col_join(Fr_u[6:, :]) + A_rs.T * Fr_u[3:6, :]
Fr_star_c = Fr_star_u[:3, :].col_join(Fr_star_u[6:, :])\
+ A_rs.T * Fr_star_u[3:6, :]
Fr_star_steady = Fr_star_c.subs(ud_zero).subs(u_dep_dict)\
.subs(steady_conditions).subs({q[3]: -r*cos(q[1])}).expand()
# Second, using KaneMethod in mechanics for fr, frstar and frstar_steady.
# Rigid Bodies: disc, with inertia I_C_O.
iner_tuple = (I_C_O, O)
disc = RigidBody('disc', O, C, m, iner_tuple)
bodyList = [disc]
# Generalized forces: Gravity: F_o; Auxiliary forces: F_p.
F_o = (O, F_O)
F_p = (P, F_P)
forceList = [F_o, F_p]
# KanesMethod.
kane = KanesMethod(
N, q_ind= q[:3], u_ind= u[:3], kd_eqs=kindiffs,
q_dependent=q[3:], configuration_constraints = f_c,
u_dependent=u[3:], velocity_constraints= f_v,
u_auxiliary=ua
)
# fr, frstar, frstar_steady and kdd(kinematic differential equations).
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
(fr, frstar)= kane.kanes_equations(forceList, bodyList)
frstar_steady = frstar.subs(ud_zero).subs(u_dep_dict).subs(steady_conditions)\
.subs({q[3]: -r*cos(q[1])}).expand()
kdd = kane.kindiffdict()
assert Matrix(Fr_c).expand() == fr.expand()
assert Matrix(Fr_star_c.subs(kdd)).expand() == frstar.expand()
assert (simplify(Matrix(Fr_star_steady).expand()) ==
simplify(frstar_steady.expand()))
def test_non_central_inertia():
# This tests that the calculation of Fr* does not depend the point
# about which the inertia of a rigid body is defined. This test solves
# exercises 8.12, 8.17 from Kane 1985.
# Declare symbols
q1, q2, q3 = dynamicsymbols('q1:4')
q1d, q2d, q3d = dynamicsymbols('q1:4', level=1)
u1, u2, u3, u4, u5 = dynamicsymbols('u1:6')
u_prime, R, M, g, e, f, theta = symbols('u\' R, M, g, e, f, theta')
a, b, mA, mB, IA, J, K, t = symbols('a b mA mB IA J K t')
Q1, Q2, Q3 = symbols('Q1, Q2 Q3')
IA22, IA23, IA33 = symbols('IA22 IA23 IA33')
# Reference Frames
F = ReferenceFrame('F')
P = F.orientnew('P', 'axis', [-theta, F.y])
A = P.orientnew('A', 'axis', [q1, P.x])
A.set_ang_vel(F, u1*A.x + u3*A.z)
# define frames for wheels
B = A.orientnew('B', 'axis', [q2, A.z])
C = A.orientnew('C', 'axis', [q3, A.z])
B.set_ang_vel(A, u4 * A.z)
C.set_ang_vel(A, u5 * A.z)
# define points D, S*, Q on frame A and their velocities
pD = Point('D')
pD.set_vel(A, 0)
# u3 will not change v_D_F since wheels are still assumed to roll without slip.
pD.set_vel(F, u2 * A.y)
pS_star = pD.locatenew('S*', e*A.y)
pQ = pD.locatenew('Q', f*A.y - R*A.x)
for p in [pS_star, pQ]:
p.v2pt_theory(pD, F, A)
# masscenters of bodies A, B, C
pA_star = pD.locatenew('A*', a*A.y)
pB_star = pD.locatenew('B*', b*A.z)
pC_star = pD.locatenew('C*', -b*A.z)
for p in [pA_star, pB_star, pC_star]:
p.v2pt_theory(pD, F, A)
# points of B, C touching the plane P
pB_hat = pB_star.locatenew('B^', -R*A.x)
pC_hat = pC_star.locatenew('C^', -R*A.x)
pB_hat.v2pt_theory(pB_star, F, B)
pC_hat.v2pt_theory(pC_star, F, C)
# the velocities of B^, C^ are zero since B, C are assumed to roll without slip
kde = [q1d - u1, q2d - u4, q3d - u5]
vc = [dot(p.vel(F), A.y) for p in [pB_hat, pC_hat]]
# inertias of bodies A, B, C
# IA22, IA23, IA33 are not specified in the problem statement, but are
# necessary to define an inertia object. Although the values of
# IA22, IA23, IA33 are not known in terms of the variables given in the
# problem statement, they do not appear in the general inertia terms.
inertia_A = inertia(A, IA, IA22, IA33, 0, IA23, 0)
inertia_B = inertia(B, K, K, J)
inertia_C = inertia(C, K, K, J)
# define the rigid bodies A, B, C
rbA = RigidBody('rbA', pA_star, A, mA, (inertia_A, pA_star))
rbB = RigidBody('rbB', pB_star, B, mB, (inertia_B, pB_star))
rbC = RigidBody('rbC', pC_star, C, mB, (inertia_C, pC_star))
km = KanesMethod(F, q_ind=[q1, q2, q3], u_ind=[u1, u2], kd_eqs=kde,
u_dependent=[u4, u5], velocity_constraints=vc,
u_auxiliary=[u3])
forces = [(pS_star, -M*g*F.x), (pQ, Q1*A.x + Q2*A.y + Q3*A.z)]
bodies = [rbA, rbB, rbC]
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
fr, fr_star = km.kanes_equations(forces, bodies)
vc_map = solve(vc, [u4, u5])
# KanesMethod returns the negative of Fr, Fr* as defined in Kane1985.
fr_star_expected = Matrix([
-(IA + 2*J*b**2/R**2 + 2*K +
mA*a**2 + 2*mB*b**2) * u1.diff(t) - mA*a*u1*u2,
-(mA + 2*mB +2*J/R**2) * u2.diff(t) + mA*a*u1**2,
0])
t = trigsimp(fr_star.subs(vc_map).subs({u3: 0})).doit().expand()
assert ((fr_star_expected - t).expand() == zeros(3, 1))
# define inertias of rigid bodies A, B, C about point D
# I_S/O = I_S/S* + I_S*/O
bodies2 = []
for rb, I_star in zip([rbA, rbB, rbC], [inertia_A, inertia_B, inertia_C]):
I = I_star + inertia_of_point_mass(rb.mass,
rb.masscenter.pos_from(pD),
rb.frame)
bodies2.append(RigidBody('', rb.masscenter, rb.frame, rb.mass,
(I, pD)))
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
fr2, fr_star2 = km.kanes_equations(forces, bodies2)
t = trigsimp(fr_star2.subs(vc_map).subs({u3: 0})).doit()
assert (fr_star_expected - t).expand() == zeros(3, 1)
def test_sub_qdot():
# This test solves exercises 8.12, 8.17 from Kane 1985 and defines
# some velocities in terms of q, qdot.
## --- Declare symbols ---
q1, q2, q3 = dynamicsymbols('q1:4')
q1d, q2d, q3d = dynamicsymbols('q1:4', level=1)
u1, u2, u3 = dynamicsymbols('u1:4')
u_prime, R, M, g, e, f, theta = symbols('u\' R, M, g, e, f, theta')
a, b, mA, mB, IA, J, K, t = symbols('a b mA mB IA J K t')
IA22, IA23, IA33 = symbols('IA22 IA23 IA33')
Q1, Q2, Q3 = symbols('Q1 Q2 Q3')
# --- Reference Frames ---
F = ReferenceFrame('F')
P = F.orientnew('P', 'axis', [-theta, F.y])
A = P.orientnew('A', 'axis', [q1, P.x])
A.set_ang_vel(F, u1*A.x + u3*A.z)
# define frames for wheels
B = A.orientnew('B', 'axis', [q2, A.z])
C = A.orientnew('C', 'axis', [q3, A.z])
## --- define points D, S*, Q on frame A and their velocities ---
pD = Point('D')
pD.set_vel(A, 0)
# u3 will not change v_D_F since wheels are still assumed to roll w/o slip
pD.set_vel(F, u2 * A.y)
pS_star = pD.locatenew('S*', e*A.y)
pQ = pD.locatenew('Q', f*A.y - R*A.x)
# masscenters of bodies A, B, C
pA_star = pD.locatenew('A*', a*A.y)
pB_star = pD.locatenew('B*', b*A.z)
pC_star = pD.locatenew('C*', -b*A.z)
for p in [pS_star, pQ, pA_star, pB_star, pC_star]:
p.v2pt_theory(pD, F, A)
# points of B, C touching the plane P
pB_hat = pB_star.locatenew('B^', -R*A.x)
pC_hat = pC_star.locatenew('C^', -R*A.x)
pB_hat.v2pt_theory(pB_star, F, B)
pC_hat.v2pt_theory(pC_star, F, C)
# --- relate qdot, u ---
# the velocities of B^, C^ are zero since B, C are assumed to roll w/o slip
kde = [dot(p.vel(F), A.y) for p in [pB_hat, pC_hat]]
kde += [u1 - q1d]
kde_map = solve(kde, [q1d, q2d, q3d])
for k, v in list(kde_map.items()):
kde_map[k.diff(t)] = v.diff(t)
# inertias of bodies A, B, C
# IA22, IA23, IA33 are not specified in the problem statement, but are
# necessary to define an inertia object. Although the values of
# IA22, IA23, IA33 are not known in terms of the variables given in the
# problem statement, they do not appear in the general inertia terms.
inertia_A = inertia(A, IA, IA22, IA33, 0, IA23, 0)
inertia_B = inertia(B, K, K, J)
inertia_C = inertia(C, K, K, J)
# define the rigid bodies A, B, C
rbA = RigidBody('rbA', pA_star, A, mA, (inertia_A, pA_star))
rbB = RigidBody('rbB', pB_star, B, mB, (inertia_B, pB_star))
rbC = RigidBody('rbC', pC_star, C, mB, (inertia_C, pC_star))
## --- use kanes method ---
km = KanesMethod(F, [q1, q2, q3], [u1, u2], kd_eqs=kde, u_auxiliary=[u3])
forces = [(pS_star, -M*g*F.x), (pQ, Q1*A.x + Q2*A.y + Q3*A.z)]
bodies = [rbA, rbB, rbC]
# Q2 = -u_prime * u2 * Q1 / sqrt(u2**2 + f**2 * u1**2)
# -u_prime * R * u2 / sqrt(u2**2 + f**2 * u1**2) = R / Q1 * Q2
fr_expected = Matrix([
f*Q3 + M*g*e*sin(theta)*cos(q1),
Q2 + M*g*sin(theta)*sin(q1),
e*M*g*cos(theta) - Q1*f - Q2*R])
#Q1 * (f - u_prime * R * u2 / sqrt(u2**2 + f**2 * u1**2)))])
fr_star_expected = Matrix([
-(IA + 2*J*b**2/R**2 + 2*K +
mA*a**2 + 2*mB*b**2) * u1.diff(t) - mA*a*u1*u2,
-(mA + 2*mB +2*J/R**2) * u2.diff(t) + mA*a*u1**2,
0])
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
fr, fr_star = km.kanes_equations(forces, bodies)
assert (fr.expand() == fr_expected.expand())
assert ((fr_star_expected - trigsimp(fr_star)).expand() == zeros(3, 1))
def test_sub_qdot2():
# This test solves exercises 8.3 from Kane 1985 and defines
# all velocities in terms of q, qdot. We check that the generalized active
# forces are correctly computed if u terms are only defined in the
# kinematic differential equations.
#
# This functionality was added in PR 8948. Without qdot/u substitution, the
# KanesMethod constructor will fail during the constraint initialization as
# the B matrix will be poorly formed and inversion of the dependent part
# will fail.
g, m, Px, Py, Pz, R, t = symbols('g m Px Py Pz R t')
q = dynamicsymbols('q:5')
qd = dynamicsymbols('q:5', level=1)
u = dynamicsymbols('u:5')
## Define inertial, intermediate, and rigid body reference frames
A = ReferenceFrame('A')
B_prime = A.orientnew('B_prime', 'Axis', [q[0], A.z])
B = B_prime.orientnew('B', 'Axis', [pi/2 - q[1], B_prime.x])
C = B.orientnew('C', 'Axis', [q[2], B.z])
## Define points of interest and their velocities
pO = Point('O')
pO.set_vel(A, 0)
# R is the point in plane H that comes into contact with disk C.
pR = pO.locatenew('R', q[3]*A.x + q[4]*A.y)
pR.set_vel(A, pR.pos_from(pO).diff(t, A))
pR.set_vel(B, 0)
# C^ is the point in disk C that comes into contact with plane H.
pC_hat = pR.locatenew('C^', 0)
pC_hat.set_vel(C, 0)
# C* is the point at the center of disk C.
pCs = pC_hat.locatenew('C*', R*B.y)
pCs.set_vel(C, 0)
pCs.set_vel(B, 0)
# calculate velocites of points C* and C^ in frame A
pCs.v2pt_theory(pR, A, B) # points C* and R are fixed in frame B
pC_hat.v2pt_theory(pCs, A, C) # points C* and C^ are fixed in frame C
## Define forces on each point of the system
R_C_hat = Px*A.x + Py*A.y + Pz*A.z
R_Cs = -m*g*A.z
forces = [(pC_hat, R_C_hat), (pCs, R_Cs)]
## Define kinematic differential equations
# let ui = omega_C_A & bi (i = 1, 2, 3)
# u4 = qd4, u5 = qd5
u_expr = [C.ang_vel_in(A) & uv for uv in B]
u_expr += qd[3:]
kde = [ui - e for ui, e in zip(u, u_expr)]
km1 = KanesMethod(A, q, u, kde)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
fr1, _ = km1.kanes_equations(forces, [])
## Calculate generalized active forces if we impose the condition that the
# disk C is rolling without slipping
u_indep = u[:3]
u_dep = list(set(u) - set(u_indep))
vc = [pC_hat.vel(A) & uv for uv in [A.x, A.y]]
km2 = KanesMethod(A, q, u_indep, kde,
u_dependent=u_dep, velocity_constraints=vc)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
fr2, _ = km2.kanes_equations(forces, [])
fr1_expected = Matrix([
-R*g*m*sin(q[1]),
-R*(Px*cos(q[0]) + Py*sin(q[0]))*tan(q[1]),
R*(Px*cos(q[0]) + Py*sin(q[0])),
Px,
Py])
fr2_expected = Matrix([
-R*g*m*sin(q[1]),
0,
0])
assert (trigsimp(fr1.expand()) == trigsimp(fr1_expected.expand()))
assert (trigsimp(fr2.expand()) == trigsimp(fr2_expected.expand()))
| 19,633 | 40.597458 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/test_system.py
|
from sympy.core.backend import symbols, Matrix, atan, zeros
from sympy import simplify
from sympy.physics.mechanics import (dynamicsymbols, Particle, Point,
ReferenceFrame, SymbolicSystem)
from sympy.utilities.pytest import raises
# This class is going to be tested using a simple pendulum set up in x and y
# coordinates
x, y, u, v, lam = dynamicsymbols('x y u v lambda')
m, l, g = symbols('m l g')
# Set up the different forms the equations can take
# [1] Explicit form where the kinematics and dynamics are combined
# x' = F(x, t, r, p)
#
# [2] Implicit form where the kinematics and dynamics are combined
# M(x, p) x' = F(x, t, r, p)
#
# [3] Implicit form where the kinematics and dynamics are separate
# M(q, p) u' = F(q, u, t, r, p)
# q' = G(q, u, t, r, p)
dyn_implicit_mat = Matrix([[1, 0, -x/m],
[0, 1, -y/m],
[0, 0, l**2/m]])
dyn_implicit_rhs = Matrix([0, 0, u**2 + v**2 - g*y])
comb_implicit_mat = Matrix([[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, -x/m],
[0, 0, 0, 1, -y/m],
[0, 0, 0, 0, l**2/m]])
comb_implicit_rhs = Matrix([u, v, 0, 0, u**2 + v**2 - g*y])
kin_explicit_rhs = Matrix([u, v])
comb_explicit_rhs = comb_implicit_mat.LUsolve(comb_implicit_rhs)
# Set up a body and load to pass into the system
theta = atan(x / y)
N = ReferenceFrame('N')
A = N.orientnew('A', 'Axis', [theta, N.z])
O = Point('O')
P = O.locatenew('P', l * A.x)
Pa = Particle('Pa', P, m)
bodies = [Pa]
loads = [(P, g * m * N.x)]
# Set up some output equations to be given to SymbolicSystem
# Change to make these fit the pendulum
PE = symbols("PE")
out_eqns = {PE: m*g*(l+y)}
# Set up remaining arguments that can be passed to SymbolicSystem
alg_con = [2]
alg_con_full = [4]
coordinates = (x, y, lam)
speeds = (u, v)
states = (x, y, u, v, lam)
coord_idxs = (0, 1)
speed_idxs = (2, 3)
def test_form_1():
symsystem1 = SymbolicSystem(states, comb_explicit_rhs,
alg_con=alg_con_full, output_eqns=out_eqns,
coord_idxs=coord_idxs, speed_idxs=speed_idxs,
bodies=bodies, loads=loads)
assert symsystem1.coordinates == Matrix([x, y])
assert symsystem1.speeds == Matrix([u, v])
assert symsystem1.states == Matrix([x, y, u, v, lam])
assert symsystem1.alg_con == [4]
inter = comb_explicit_rhs
assert simplify(symsystem1.comb_explicit_rhs - inter) == zeros(5, 1)
assert set(symsystem1.dynamic_symbols()) == set([y, v, lam, u, x])
assert type(symsystem1.dynamic_symbols()) == tuple
assert set(symsystem1.constant_symbols()) == set([l, g, m])
assert type(symsystem1.constant_symbols()) == tuple
assert symsystem1.output_eqns == out_eqns
assert symsystem1.bodies == (Pa,)
assert symsystem1.loads == ((P, g * m * N.x),)
def test_form_2():
symsystem2 = SymbolicSystem(coordinates, comb_implicit_rhs, speeds=speeds,
mass_matrix=comb_implicit_mat,
alg_con=alg_con_full, output_eqns=out_eqns,
bodies=bodies, loads=loads)
assert symsystem2.coordinates == Matrix([x, y, lam])
assert symsystem2.speeds == Matrix([u, v])
assert symsystem2.states == Matrix([x, y, lam, u, v])
assert symsystem2.alg_con == [4]
inter = comb_implicit_rhs
assert simplify(symsystem2.comb_implicit_rhs - inter) == zeros(5, 1)
assert simplify(symsystem2.comb_implicit_mat-comb_implicit_mat) == zeros(5)
assert set(symsystem2.dynamic_symbols()) == set([y, v, lam, u, x])
assert type(symsystem2.dynamic_symbols()) == tuple
assert set(symsystem2.constant_symbols()) == set([l, g, m])
assert type(symsystem2.constant_symbols()) == tuple
inter = comb_explicit_rhs
symsystem2.compute_explicit_form()
assert simplify(symsystem2.comb_explicit_rhs - inter) == zeros(5, 1)
assert symsystem2.output_eqns == out_eqns
assert symsystem2.bodies == (Pa,)
assert symsystem2.loads == ((P, g * m * N.x),)
def test_form_3():
symsystem3 = SymbolicSystem(states, dyn_implicit_rhs,
mass_matrix=dyn_implicit_mat,
coordinate_derivatives=kin_explicit_rhs,
alg_con=alg_con, coord_idxs=coord_idxs,
speed_idxs=speed_idxs, bodies=bodies,
loads=loads)
assert symsystem3.coordinates == Matrix([x, y])
assert symsystem3.speeds == Matrix([u, v])
assert symsystem3.states == Matrix([x, y, u, v, lam])
assert symsystem3.alg_con == [4]
inter1 = kin_explicit_rhs
inter2 = dyn_implicit_rhs
assert simplify(symsystem3.kin_explicit_rhs - inter1) == zeros(2, 1)
assert simplify(symsystem3.dyn_implicit_mat - dyn_implicit_mat) == zeros(3)
assert simplify(symsystem3.dyn_implicit_rhs - inter2) == zeros(3, 1)
inter = comb_implicit_rhs
assert simplify(symsystem3.comb_implicit_rhs - inter) == zeros(5, 1)
assert simplify(symsystem3.comb_implicit_mat-comb_implicit_mat) == zeros(5)
inter = comb_explicit_rhs
symsystem3.compute_explicit_form()
assert simplify(symsystem3.comb_explicit_rhs - inter) == zeros(5, 1)
assert set(symsystem3.dynamic_symbols()) == set([y, v, lam, u, x])
assert type(symsystem3.dynamic_symbols()) == tuple
assert set(symsystem3.constant_symbols()) == set([l, g, m])
assert type(symsystem3.constant_symbols()) == tuple
assert symsystem3.output_eqns == {}
assert symsystem3.bodies == (Pa,)
assert symsystem3.loads == ((P, g * m * N.x),)
def test_property_attributes():
symsystem = SymbolicSystem(states, comb_explicit_rhs,
alg_con=alg_con_full, output_eqns=out_eqns,
coord_idxs=coord_idxs, speed_idxs=speed_idxs,
bodies=bodies, loads=loads)
with raises(AttributeError):
symsystem.bodies = 42
with raises(AttributeError):
symsystem.coordinates = 42
with raises(AttributeError):
symsystem.dyn_implicit_rhs = 42
with raises(AttributeError):
symsystem.comb_implicit_rhs = 42
with raises(AttributeError):
symsystem.loads = 42
with raises(AttributeError):
symsystem.dyn_implicit_mat = 42
with raises(AttributeError):
symsystem.comb_implicit_mat = 42
with raises(AttributeError):
symsystem.kin_explicit_rhs = 42
with raises(AttributeError):
symsystem.comb_explicit_rhs = 42
with raises(AttributeError):
symsystem.speeds = 42
with raises(AttributeError):
symsystem.states = 42
with raises(AttributeError):
symsystem.alg_con = 42
def test_not_specified_errors():
"""This test will cover errors that arise from trying to access attributes
that were not specificed upon object creation or were specified on creation
and the user trys to recalculate them."""
# Trying to access form 2 when form 1 given
# Trying to access form 3 when form 2 given
symsystem1 = SymbolicSystem(states, comb_explicit_rhs)
with raises(AttributeError):
symsystem1.comb_implicit_mat
with raises(AttributeError):
symsystem1.comb_implicit_rhs
with raises(AttributeError):
symsystem1.dyn_implicit_mat
with raises(AttributeError):
symsystem1.dyn_implicit_rhs
with raises(AttributeError):
symsystem1.kin_explicit_rhs
with raises(AttributeError):
symsystem1.compute_explicit_form()
symsystem2 = SymbolicSystem(coordinates, comb_implicit_rhs, speeds=speeds,
mass_matrix=comb_implicit_mat)
with raises(AttributeError):
symsystem2.dyn_implicit_mat
with raises(AttributeError):
symsystem2.dyn_implicit_rhs
with raises(AttributeError):
symsystem2.kin_explicit_rhs
# Attribute error when trying to access coordinates and speeds when only the
# states were given.
with raises(AttributeError):
symsystem1.coordinates
with raises(AttributeError):
symsystem1.speeds
# Attribute error when trying to access bodies and loads when they are not
# given
with raises(AttributeError):
symsystem1.bodies
with raises(AttributeError):
symsystem1.loads
# Attribute error when trying to access comb_explicit_rhs before it was
# calculated
with raises(AttributeError):
symsystem2.comb_explicit_rhs
| 8,729 | 34.487805 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/test_body.py
|
from sympy.core.backend import Symbol, symbols
from sympy.physics.vector import Point, ReferenceFrame
from sympy.physics.mechanics import inertia, Body
from sympy.utilities.pytest import raises
def test_default():
body = Body('body')
assert body.name == 'body'
assert body.loads == []
point = Point('body_masscenter')
point.set_vel(body.frame, 0)
com = body.masscenter
frame = body.frame
assert com.vel(frame) == point.vel(frame)
assert body.mass == Symbol('body_mass')
ixx, iyy, izz = symbols('body_ixx body_iyy body_izz')
ixy, iyz, izx = symbols('body_ixy body_iyz body_izx')
assert body.inertia == (inertia(body.frame, ixx, iyy, izz, ixy, iyz, izx),
body.masscenter)
def test_custom_rigid_body():
# Body with RigidBody.
rigidbody_masscenter = Point('rigidbody_masscenter')
rigidbody_mass = Symbol('rigidbody_mass')
rigidbody_frame = ReferenceFrame('rigidbody_frame')
body_inertia = inertia(rigidbody_frame, 1, 0, 0)
rigid_body = Body('rigidbody_body', rigidbody_masscenter, rigidbody_mass,
rigidbody_frame, body_inertia)
com = rigid_body.masscenter
frame = rigid_body.frame
rigidbody_masscenter.set_vel(rigidbody_frame, 0)
assert com.vel(frame) == rigidbody_masscenter.vel(frame)
assert com.pos_from(com) == rigidbody_masscenter.pos_from(com)
assert rigid_body.mass == rigidbody_mass
assert rigid_body.inertia == (body_inertia, rigidbody_masscenter)
assert hasattr(rigid_body, 'masscenter')
assert hasattr(rigid_body, 'mass')
assert hasattr(rigid_body, 'frame')
assert hasattr(rigid_body, 'inertia')
def test_particle_body():
# Body with Particle
particle_masscenter = Point('particle_masscenter')
particle_mass = Symbol('particle_mass')
particle_frame = ReferenceFrame('particle_frame')
particle_body = Body('particle_body', particle_masscenter, particle_mass,
particle_frame)
com = particle_body.masscenter
frame = particle_body.frame
particle_masscenter.set_vel(particle_frame, 0)
assert com.vel(frame) == particle_masscenter.vel(frame)
assert com.pos_from(com) == particle_masscenter.pos_from(com)
assert particle_body.mass == particle_mass
assert not hasattr(particle_body, "_inertia")
assert hasattr(particle_body, 'frame')
assert hasattr(particle_body, 'masscenter')
assert hasattr(particle_body, 'mass')
def test_particle_body_add_force():
# Body with Particle
particle_masscenter = Point('particle_masscenter')
particle_mass = Symbol('particle_mass')
particle_frame = ReferenceFrame('particle_frame')
particle_body = Body('particle_body', particle_masscenter, particle_mass,
particle_frame)
a = Symbol('a')
force_vector = a * particle_body.frame.x
particle_body.apply_force(force_vector, particle_body.masscenter)
assert len(particle_body.loads) == 1
point = particle_body.masscenter.locatenew(
particle_body._name + '_point0', 0)
point.set_vel(particle_body.frame, 0)
force_point = particle_body.loads[0][0]
frame = particle_body.frame
assert force_point.vel(frame) == point.vel(frame)
assert force_point.pos_from(force_point) == point.pos_from(force_point)
assert particle_body.loads[0][1] == force_vector
def test_body_add_force():
# Body with RigidBody.
rigidbody_masscenter = Point('rigidbody_masscenter')
rigidbody_mass = Symbol('rigidbody_mass')
rigidbody_frame = ReferenceFrame('rigidbody_frame')
body_inertia = inertia(rigidbody_frame, 1, 0, 0)
rigid_body = Body('rigidbody_body', rigidbody_masscenter, rigidbody_mass,
rigidbody_frame, body_inertia)
l = Symbol('l')
Fa = Symbol('Fa')
point = rigid_body.masscenter.locatenew(
'rigidbody_body_point0',
l * rigid_body.frame.x)
point.set_vel(rigid_body.frame, 0)
force_vector = Fa * rigid_body.frame.z
# apply_force with point
rigid_body.apply_force(force_vector, point)
assert len(rigid_body.loads) == 1
force_point = rigid_body.loads[0][0]
frame = rigid_body.frame
assert force_point.vel(frame) == point.vel(frame)
assert force_point.pos_from(force_point) == point.pos_from(force_point)
assert rigid_body.loads[0][1] == force_vector
# apply_force without point
rigid_body.apply_force(force_vector)
assert len(rigid_body.loads) == 2
assert rigid_body.loads[1][1] == force_vector
# passing something else than point
raises(TypeError, lambda: rigid_body.apply_force(force_vector, 0))
raises(TypeError, lambda: rigid_body.apply_force(0))
def test_body_add_torque():
body = Body('body')
torque_vector = body.frame.x
body.apply_torque(torque_vector)
assert len(body.loads) == 1
assert body.loads[0] == (body.frame, torque_vector)
raises(TypeError, lambda: body.apply_torque(0))
| 4,976 | 37.581395 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/test_lagrange2.py
|
from sympy.core.backend import symbols
from sympy.physics.mechanics import dynamicsymbols
from sympy.physics.mechanics import ReferenceFrame, Point, Particle
from sympy.physics.mechanics import LagrangesMethod, Lagrangian
### This test asserts that a system with more than one external forces
### is acurately formed with Lagrange method (see issue #8626)
def test_lagrange_2forces():
### Equations for two damped springs in serie with two forces
### generalized coordinates
qs = q1, q2 = dynamicsymbols('q1, q2')
### generalized speeds
qds = q1d, q2d = dynamicsymbols('q1, q2', 1)
### Mass, spring strength, friction coefficient
m, k, nu = symbols('m, k, nu')
N = ReferenceFrame('N')
O = Point('O')
### Two points
P1 = O.locatenew('P1', q1 * N.x)
P1.set_vel(N, q1d * N.x)
P2 = O.locatenew('P1', q2 * N.x)
P2.set_vel(N, q2d * N.x)
pP1 = Particle('pP1', P1, m)
pP1.potential_energy = k * q1**2 / 2
pP2 = Particle('pP2', P2, m)
pP2.potential_energy = k * (q1 - q2)**2 / 2
#### Friction forces
forcelist = [(P1, - nu * q1d * N.x),
(P2, - nu * q2d * N.x)]
lag = Lagrangian(N, pP1, pP2)
l_method = LagrangesMethod(lag, (q1, q2), forcelist=forcelist, frame=N)
l_method.form_lagranges_equations()
eq1 = l_method.eom[0]
assert eq1.diff(q1d) == nu
eq2 = l_method.eom[1]
assert eq2.diff(q2d) == nu
| 1,424 | 29.319149 | 75 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/test_particle.py
|
from sympy import symbols
from sympy.physics.mechanics import Point, Particle, ReferenceFrame
def test_particle():
m, m2, v1, v2, v3, r, g, h = symbols('m m2 v1 v2 v3 r g h')
P = Point('P')
P2 = Point('P2')
p = Particle('pa', P, m)
assert p.mass == m
assert p.point == P
# Test the mass setter
p.mass = m2
assert p.mass == m2
# Test the point setter
p.point = P2
assert p.point == P2
# Test the linear momentum function
N = ReferenceFrame('N')
O = Point('O')
P2.set_pos(O, r * N.y)
P2.set_vel(N, v1 * N.x)
assert p.linear_momentum(N) == m2 * v1 * N.x
assert p.angular_momentum(O, N) == -m2 * r *v1 * N.z
P2.set_vel(N, v2 * N.y)
assert p.linear_momentum(N) == m2 * v2 * N.y
assert p.angular_momentum(O, N) == 0
P2.set_vel(N, v3 * N.z)
assert p.linear_momentum(N) == m2 * v3 * N.z
assert p.angular_momentum(O, N) == m2 * r * v3 * N.x
P2.set_vel(N, v1 * N.x + v2 * N.y + v3 * N.z)
assert p.linear_momentum(N) == m2 * (v1 * N.x + v2 * N.y + v3 * N.z)
assert p.angular_momentum(O, N) == m2 * r * (v3 * N.x - v1 * N.z)
p.potential_energy = m * g * h
assert p.potential_energy == m * g * h
# TODO make the result not be system-dependent
assert p.kinetic_energy(
N) in [m2*(v1**2 + v2**2 + v3**2)/2,
m2 * v1**2 / 2 + m2 * v2**2 / 2 + m2 * v3**2 / 2]
| 1,388 | 33.725 | 72 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/__init__.py
| 0 | 0 | 0 |
py
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/test_kane.py
|
import warnings
from sympy.core.backend import (cos, expand, Matrix, sin, symbols, tan, sqrt, S,
zeros)
from sympy import simplify
from sympy.utilities.exceptions import SymPyDeprecationWarning
from sympy.physics.mechanics import (dynamicsymbols, ReferenceFrame, Point,
RigidBody, KanesMethod, inertia, Particle,
dot)
def test_one_dof():
# This is for a 1 dof spring-mass-damper case.
# It is described in more detail in the KanesMethod docstring.
q, u = dynamicsymbols('q u')
qd, ud = dynamicsymbols('q u', 1)
m, c, k = symbols('m c k')
N = ReferenceFrame('N')
P = Point('P')
P.set_vel(N, u * N.x)
kd = [qd - u]
FL = [(P, (-k * q - c * u) * N.x)]
pa = Particle('pa', P, m)
BL = [pa]
KM = KanesMethod(N, [q], [u], kd)
# The old input format raises a deprecation warning, so catch it here so
# it doesn't cause py.test to fail.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
KM.kanes_equations(FL, BL)
MM = KM.mass_matrix
forcing = KM.forcing
rhs = MM.inv() * forcing
assert expand(rhs[0]) == expand(-(q * k + u * c) / m)
assert simplify(KM.rhs() -
KM.mass_matrix_full.LUsolve(KM.forcing_full)) == zeros(2, 1)
assert (KM.linearize(A_and_B=True, )[0] == Matrix([[0, 1], [-k/m, -c/m]]))
def test_two_dof():
# This is for a 2 d.o.f., 2 particle spring-mass-damper.
# The first coordinate is the displacement of the first particle, and the
# second is the relative displacement between the first and second
# particles. Speeds are defined as the time derivatives of the particles.
q1, q2, u1, u2 = dynamicsymbols('q1 q2 u1 u2')
q1d, q2d, u1d, u2d = dynamicsymbols('q1 q2 u1 u2', 1)
m, c1, c2, k1, k2 = symbols('m c1 c2 k1 k2')
N = ReferenceFrame('N')
P1 = Point('P1')
P2 = Point('P2')
P1.set_vel(N, u1 * N.x)
P2.set_vel(N, (u1 + u2) * N.x)
kd = [q1d - u1, q2d - u2]
# Now we create the list of forces, then assign properties to each
# particle, then create a list of all particles.
FL = [(P1, (-k1 * q1 - c1 * u1 + k2 * q2 + c2 * u2) * N.x), (P2, (-k2 *
q2 - c2 * u2) * N.x)]
pa1 = Particle('pa1', P1, m)
pa2 = Particle('pa2', P2, m)
BL = [pa1, pa2]
# Finally we create the KanesMethod object, specify the inertial frame,
# pass relevant information, and form Fr & Fr*. Then we calculate the mass
# matrix and forcing terms, and finally solve for the udots.
KM = KanesMethod(N, q_ind=[q1, q2], u_ind=[u1, u2], kd_eqs=kd)
# The old input format raises a deprecation warning, so catch it here so
# it doesn't cause py.test to fail.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
KM.kanes_equations(FL, BL)
MM = KM.mass_matrix
forcing = KM.forcing
rhs = MM.inv() * forcing
assert expand(rhs[0]) == expand((-k1 * q1 - c1 * u1 + k2 * q2 + c2 * u2)/m)
assert expand(rhs[1]) == expand((k1 * q1 + c1 * u1 - 2 * k2 * q2 - 2 *
c2 * u2) / m)
assert simplify(KM.rhs() -
KM.mass_matrix_full.LUsolve(KM.forcing_full)) == zeros(4, 1)
def test_pend():
q, u = dynamicsymbols('q u')
qd, ud = dynamicsymbols('q u', 1)
m, l, g = symbols('m l g')
N = ReferenceFrame('N')
P = Point('P')
P.set_vel(N, -l * u * sin(q) * N.x + l * u * cos(q) * N.y)
kd = [qd - u]
FL = [(P, m * g * N.x)]
pa = Particle('pa', P, m)
BL = [pa]
KM = KanesMethod(N, [q], [u], kd)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
KM.kanes_equations(FL, BL)
MM = KM.mass_matrix
forcing = KM.forcing
rhs = MM.inv() * forcing
rhs.simplify()
assert expand(rhs[0]) == expand(-g / l * sin(q))
assert simplify(KM.rhs() -
KM.mass_matrix_full.LUsolve(KM.forcing_full)) == zeros(2, 1)
def test_rolling_disc():
# Rolling Disc Example
# Here the rolling disc is formed from the contact point up, removing the
# need to introduce generalized speeds. Only 3 configuration and three
# speed variables are need to describe this system, along with the disc's
# mass and radius, and the local gravity (note that mass will drop out).
q1, q2, q3, u1, u2, u3 = dynamicsymbols('q1 q2 q3 u1 u2 u3')
q1d, q2d, q3d, u1d, u2d, u3d = dynamicsymbols('q1 q2 q3 u1 u2 u3', 1)
r, m, g = symbols('r m g')
# The kinematics are formed by a series of simple rotations. Each simple
# rotation creates a new frame, and the next rotation is defined by the new
# frame's basis vectors. This example uses a 3-1-2 series of rotations, or
# Z, X, Y series of rotations. Angular velocity for this is defined using
# the second frame's basis (the lean frame).
N = ReferenceFrame('N')
Y = N.orientnew('Y', 'Axis', [q1, N.z])
L = Y.orientnew('L', 'Axis', [q2, Y.x])
R = L.orientnew('R', 'Axis', [q3, L.y])
w_R_N_qd = R.ang_vel_in(N)
R.set_ang_vel(N, u1 * L.x + u2 * L.y + u3 * L.z)
# This is the translational kinematics. We create a point with no velocity
# in N; this is the contact point between the disc and ground. Next we form
# the position vector from the contact point to the disc's center of mass.
# Finally we form the velocity and acceleration of the disc.
C = Point('C')
C.set_vel(N, 0)
Dmc = C.locatenew('Dmc', r * L.z)
Dmc.v2pt_theory(C, N, R)
# This is a simple way to form the inertia dyadic.
I = inertia(L, m / 4 * r**2, m / 2 * r**2, m / 4 * r**2)
# Kinematic differential equations; how the generalized coordinate time
# derivatives relate to generalized speeds.
kd = [dot(R.ang_vel_in(N) - w_R_N_qd, uv) for uv in L]
# Creation of the force list; it is the gravitational force at the mass
# center of the disc. Then we create the disc by assigning a Point to the
# center of mass attribute, a ReferenceFrame to the frame attribute, and mass
# and inertia. Then we form the body list.
ForceList = [(Dmc, - m * g * Y.z)]
BodyD = RigidBody('BodyD', Dmc, R, m, (I, Dmc))
BodyList = [BodyD]
# Finally we form the equations of motion, using the same steps we did
# before. Specify inertial frame, supply generalized speeds, supply
# kinematic differential equation dictionary, compute Fr from the force
# list and Fr* from the body list, compute the mass matrix and forcing
# terms, then solve for the u dots (time derivatives of the generalized
# speeds).
KM = KanesMethod(N, q_ind=[q1, q2, q3], u_ind=[u1, u2, u3], kd_eqs=kd)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
KM.kanes_equations(ForceList, BodyList)
MM = KM.mass_matrix
forcing = KM.forcing
rhs = MM.inv() * forcing
kdd = KM.kindiffdict()
rhs = rhs.subs(kdd)
rhs.simplify()
assert rhs.expand() == Matrix([(6*u2*u3*r - u3**2*r*tan(q2) +
4*g*sin(q2))/(5*r), -2*u1*u3/3, u1*(-2*u2 + u3*tan(q2))]).expand()
assert simplify(KM.rhs() -
KM.mass_matrix_full.LUsolve(KM.forcing_full)) == zeros(6, 1)
# This code tests our output vs. benchmark values. When r=g=m=1, the
# critical speed (where all eigenvalues of the linearized equations are 0)
# is 1 / sqrt(3) for the upright case.
A = KM.linearize(A_and_B=True)[0]
A_upright = A.subs({r: 1, g: 1, m: 1}).subs({q1: 0, q2: 0, q3: 0, u1: 0, u3: 0})
import sympy
assert sympy.sympify(A_upright.subs({u2: 1 / sqrt(3)})).eigenvals() == {S(0): 6}
def test_aux():
# Same as above, except we have 2 auxiliary speeds for the ground contact
# point, which is known to be zero. In one case, we go through then
# substitute the aux. speeds in at the end (they are zero, as well as their
# derivative), in the other case, we use the built-in auxiliary speed part
# of KanesMethod. The equations from each should be the same.
q1, q2, q3, u1, u2, u3 = dynamicsymbols('q1 q2 q3 u1 u2 u3')
q1d, q2d, q3d, u1d, u2d, u3d = dynamicsymbols('q1 q2 q3 u1 u2 u3', 1)
u4, u5, f1, f2 = dynamicsymbols('u4, u5, f1, f2')
u4d, u5d = dynamicsymbols('u4, u5', 1)
r, m, g = symbols('r m g')
N = ReferenceFrame('N')
Y = N.orientnew('Y', 'Axis', [q1, N.z])
L = Y.orientnew('L', 'Axis', [q2, Y.x])
R = L.orientnew('R', 'Axis', [q3, L.y])
w_R_N_qd = R.ang_vel_in(N)
R.set_ang_vel(N, u1 * L.x + u2 * L.y + u3 * L.z)
C = Point('C')
C.set_vel(N, u4 * L.x + u5 * (Y.z ^ L.x))
Dmc = C.locatenew('Dmc', r * L.z)
Dmc.v2pt_theory(C, N, R)
Dmc.a2pt_theory(C, N, R)
I = inertia(L, m / 4 * r**2, m / 2 * r**2, m / 4 * r**2)
kd = [dot(R.ang_vel_in(N) - w_R_N_qd, uv) for uv in L]
ForceList = [(Dmc, - m * g * Y.z), (C, f1 * L.x + f2 * (Y.z ^ L.x))]
BodyD = RigidBody('BodyD', Dmc, R, m, (I, Dmc))
BodyList = [BodyD]
KM = KanesMethod(N, q_ind=[q1, q2, q3], u_ind=[u1, u2, u3, u4, u5],
kd_eqs=kd)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
(fr, frstar) = KM.kanes_equations(ForceList, BodyList)
fr = fr.subs({u4d: 0, u5d: 0}).subs({u4: 0, u5: 0})
frstar = frstar.subs({u4d: 0, u5d: 0}).subs({u4: 0, u5: 0})
KM2 = KanesMethod(N, q_ind=[q1, q2, q3], u_ind=[u1, u2, u3], kd_eqs=kd,
u_auxiliary=[u4, u5])
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
(fr2, frstar2) = KM2.kanes_equations(ForceList, BodyList)
fr2 = fr2.subs({u4d: 0, u5d: 0}).subs({u4: 0, u5: 0})
frstar2 = frstar2.subs({u4d: 0, u5d: 0}).subs({u4: 0, u5: 0})
frstar.simplify()
frstar2.simplify()
assert (fr - fr2).expand() == Matrix([0, 0, 0, 0, 0])
assert (frstar - frstar2).expand() == Matrix([0, 0, 0, 0, 0])
def test_parallel_axis():
# This is for a 2 dof inverted pendulum on a cart.
# This tests the parallel axis code in KanesMethod. The inertia of the
# pendulum is defined about the hinge, not about the center of mass.
# Defining the constants and knowns of the system
gravity = symbols('g')
k, ls = symbols('k ls')
a, mA, mC = symbols('a mA mC')
F = dynamicsymbols('F')
Ix, Iy, Iz = symbols('Ix Iy Iz')
# Declaring the Generalized coordinates and speeds
q1, q2 = dynamicsymbols('q1 q2')
q1d, q2d = dynamicsymbols('q1 q2', 1)
u1, u2 = dynamicsymbols('u1 u2')
u1d, u2d = dynamicsymbols('u1 u2', 1)
# Creating reference frames
N = ReferenceFrame('N')
A = ReferenceFrame('A')
A.orient(N, 'Axis', [-q2, N.z])
A.set_ang_vel(N, -u2 * N.z)
# Origin of Newtonian reference frame
O = Point('O')
# Creating and Locating the positions of the cart, C, and the
# center of mass of the pendulum, A
C = O.locatenew('C', q1 * N.x)
Ao = C.locatenew('Ao', a * A.y)
# Defining velocities of the points
O.set_vel(N, 0)
C.set_vel(N, u1 * N.x)
Ao.v2pt_theory(C, N, A)
Cart = Particle('Cart', C, mC)
Pendulum = RigidBody('Pendulum', Ao, A, mA, (inertia(A, Ix, Iy, Iz), C))
# kinematical differential equations
kindiffs = [q1d - u1, q2d - u2]
bodyList = [Cart, Pendulum]
forceList = [(Ao, -N.y * gravity * mA),
(C, -N.y * gravity * mC),
(C, -N.x * k * (q1 - ls)),
(C, N.x * F)]
km = KanesMethod(N, [q1, q2], [u1, u2], kindiffs)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
(fr, frstar) = km.kanes_equations(forceList, bodyList)
mm = km.mass_matrix_full
assert mm[3, 3] == Iz
def test_input_format():
# 1 dof problem from test_one_dof
q, u = dynamicsymbols('q u')
qd, ud = dynamicsymbols('q u', 1)
m, c, k = symbols('m c k')
N = ReferenceFrame('N')
P = Point('P')
P.set_vel(N, u * N.x)
kd = [qd - u]
FL = [(P, (-k * q - c * u) * N.x)]
pa = Particle('pa', P, m)
BL = [pa]
KM = KanesMethod(N, [q], [u], kd)
# test for input format kane.kanes_equations((body1, body2, particle1))
assert KM.kanes_equations(BL)[0] == Matrix([0])
# test for input format kane.kanes_equations(bodies=(body1, body 2), loads=(load1,load2))
assert KM.kanes_equations(bodies=BL, loads=None)[0] == Matrix([0])
# test for input format kane.kanes_equations(bodies=(body1, body 2), loads=None)
assert KM.kanes_equations(BL, loads=None)[0] == Matrix([0])
# test for input format kane.kanes_equations(bodies=(body1, body 2))
assert KM.kanes_equations(BL)[0] == Matrix([0])
# test for error raised when a wrong force list (in this case a string) is provided
from sympy.utilities.pytest import raises
raises(ValueError, lambda: KM._form_fr('bad input'))
# 2 dof problem from test_two_dof
q1, q2, u1, u2 = dynamicsymbols('q1 q2 u1 u2')
q1d, q2d, u1d, u2d = dynamicsymbols('q1 q2 u1 u2', 1)
m, c1, c2, k1, k2 = symbols('m c1 c2 k1 k2')
N = ReferenceFrame('N')
P1 = Point('P1')
P2 = Point('P2')
P1.set_vel(N, u1 * N.x)
P2.set_vel(N, (u1 + u2) * N.x)
kd = [q1d - u1, q2d - u2]
FL = ((P1, (-k1 * q1 - c1 * u1 + k2 * q2 + c2 * u2) * N.x), (P2, (-k2 *
q2 - c2 * u2) * N.x))
pa1 = Particle('pa1', P1, m)
pa2 = Particle('pa2', P2, m)
BL = (pa1, pa2)
KM = KanesMethod(N, q_ind=[q1, q2], u_ind=[u1, u2], kd_eqs=kd)
# test for input format
# kane.kanes_equations((body1, body2), (load1, load2))
KM.kanes_equations(BL, FL)
MM = KM.mass_matrix
forcing = KM.forcing
rhs = MM.inv() * forcing
assert expand(rhs[0]) == expand((-k1 * q1 - c1 * u1 + k2 * q2 + c2 * u2)/m)
assert expand(rhs[1]) == expand((k1 * q1 + c1 * u1 - 2 * k2 * q2 - 2 *
c2 * u2) / m)
| 14,170 | 38.583799 | 93 |
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/mechanics/tests/test_rigidbody.py
|
from sympy import symbols
from sympy.physics.mechanics import Point, ReferenceFrame, Dyadic, RigidBody
from sympy.physics.mechanics import dynamicsymbols, outer, inertia
from sympy.physics.mechanics import inertia_of_point_mass
from sympy.core.backend import expand
def test_rigidbody():
m, m2, v1, v2, v3, omega = symbols('m m2 v1 v2 v3 omega')
A = ReferenceFrame('A')
A2 = ReferenceFrame('A2')
P = Point('P')
P2 = Point('P2')
I = Dyadic(0)
I2 = Dyadic(0)
B = RigidBody('B', P, A, m, (I, P))
assert B.mass == m
assert B.frame == A
assert B.masscenter == P
assert B.inertia == (I, B.masscenter)
B.mass = m2
B.frame = A2
B.masscenter = P2
B.inertia = (I2, B.masscenter)
assert B.mass == m2
assert B.frame == A2
assert B.masscenter == P2
assert B.inertia == (I2, B.masscenter)
assert B.masscenter == P2
assert B.inertia == (I2, B.masscenter)
# Testing linear momentum function assuming A2 is the inertial frame
N = ReferenceFrame('N')
P2.set_vel(N, v1 * N.x + v2 * N.y + v3 * N.z)
assert B.linear_momentum(N) == m2 * (v1 * N.x + v2 * N.y + v3 * N.z)
def test_rigidbody2():
M, v, r, omega, g, h = dynamicsymbols('M v r omega g h')
N = ReferenceFrame('N')
b = ReferenceFrame('b')
b.set_ang_vel(N, omega * b.x)
P = Point('P')
I = outer(b.x, b.x)
Inertia_tuple = (I, P)
B = RigidBody('B', P, b, M, Inertia_tuple)
P.set_vel(N, v * b.x)
assert B.angular_momentum(P, N) == omega * b.x
O = Point('O')
O.set_vel(N, v * b.x)
P.set_pos(O, r * b.y)
assert B.angular_momentum(O, N) == omega * b.x - M*v*r*b.z
B.potential_energy = M * g * h
assert B.potential_energy == M * g * h
assert expand(2 * B.kinetic_energy(N)) == omega**2 + M * v**2
def test_rigidbody3():
q1, q2, q3, q4 = dynamicsymbols('q1:5')
p1, p2, p3 = symbols('p1:4')
m = symbols('m')
A = ReferenceFrame('A')
B = A.orientnew('B', 'axis', [q1, A.x])
O = Point('O')
O.set_vel(A, q2*A.x + q3*A.y + q4*A.z)
P = O.locatenew('P', p1*B.x + p2*B.y + p3*B.z)
P.v2pt_theory(O, A, B)
I = outer(B.x, B.x)
rb1 = RigidBody('rb1', P, B, m, (I, P))
# I_S/O = I_S/S* + I_S*/O
rb2 = RigidBody('rb2', P, B, m,
(I + inertia_of_point_mass(m, P.pos_from(O), B), O))
assert rb1.central_inertia == rb2.central_inertia
assert rb1.angular_momentum(O, A) == rb2.angular_momentum(O, A)
def test_pendulum_angular_momentum():
"""Consider a pendulum of length OA = 2a, of mass m as a rigid body of
center of mass G (OG = a) which turn around (O,z). The angle between the
reference frame R and the rod is q. The inertia of the body is I =
(G,0,ma^2/3,ma^2/3). """
m, a = symbols('m, a')
q = dynamicsymbols('q')
R = ReferenceFrame('R')
R1 = R.orientnew('R1', 'Axis', [q, R.z])
R1.set_ang_vel(R, q.diff() * R.z)
I = inertia(R1, 0, m * a**2 / 3, m * a**2 / 3)
O = Point('O')
A = O.locatenew('A', 2*a * R1.x)
G = O.locatenew('G', a * R1.x)
S = RigidBody('S', G, R1, m, (I, G))
O.set_vel(R, 0)
A.v2pt_theory(O, R, R1)
G.v2pt_theory(O, R, R1)
assert (4 * m * a**2 / 3 * q.diff() * R.z -
S.angular_momentum(O, R).express(R)) == 0
| 3,298 | 29.546296 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/continuum_mechanics/beam.py
|
"""
This module can be used to solve 2D beam bending problems with
singularity functions in mechanics.
"""
from __future__ import print_function, division
from sympy.core import S, Symbol, diff
from sympy.solvers import linsolve
from sympy.printing import sstr
from sympy.functions import SingularityFunction
from sympy.core import sympify
from sympy.integrals import integrate
from sympy.series import limit
class Beam(object):
"""
A Beam is a structural element that is capable of withstanding load
primarily by resisting against bending. Beams are characterized by
their cross sectional profile(Second moment of area), their length
and their material.
.. note::
While solving a beam bending problem, a user should choose its
own sign convention and should stick to it. The results will
automatically follow the chosen sign convention.
Examples
========
There is a beam of length 4 meters. A constant distributed load of 6 N/m
is applied from half of the beam till the end. There are two simple supports
below the beam, one at the starting point and another at the ending point
of the beam. The deflection of the beam at the end is resticted.
Using the sign convention of downwards forces being positive.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols, Piecewise
>>> E, I = symbols('E, I')
>>> R1, R2 = symbols('R1, R2')
>>> b = Beam(4, E, I)
>>> b.apply_load(R1, 0, -1)
>>> b.apply_load(6, 2, 0)
>>> b.apply_load(R2, 4, -1)
>>> b.bc_deflection = [(0, 0), (4, 0)]
>>> b.boundary_conditions
{'deflection': [(0, 0), (4, 0)], 'slope': []}
>>> b.load
R1*SingularityFunction(x, 0, -1) + R2*SingularityFunction(x, 4, -1) + 6*SingularityFunction(x, 2, 0)
>>> b.solve_for_reaction_loads(R1, R2)
>>> b.load
-3*SingularityFunction(x, 0, -1) + 6*SingularityFunction(x, 2, 0) - 9*SingularityFunction(x, 4, -1)
>>> b.shear_force()
-3*SingularityFunction(x, 0, 0) + 6*SingularityFunction(x, 2, 1) - 9*SingularityFunction(x, 4, 0)
>>> b.bending_moment()
-3*SingularityFunction(x, 0, 1) + 3*SingularityFunction(x, 2, 2) - 9*SingularityFunction(x, 4, 1)
>>> b.slope()
(-3*SingularityFunction(x, 0, 2)/2 + SingularityFunction(x, 2, 3) - 9*SingularityFunction(x, 4, 2)/2 + 7)/(E*I)
>>> b.deflection()
(7*x - SingularityFunction(x, 0, 3)/2 + SingularityFunction(x, 2, 4)/4 - 3*SingularityFunction(x, 4, 3)/2)/(E*I)
>>> b.deflection().rewrite(Piecewise)
(7*x - Piecewise((x**3, x > 0), (0, True))/2
- 3*Piecewise(((x - 4)**3, x - 4 > 0), (0, True))/2
+ Piecewise(((x - 2)**4, x - 2 > 0), (0, True))/4)/(E*I)
"""
def __init__(self, length, elastic_modulus, second_moment, variable=Symbol('x')):
"""Initializes the class.
Parameters
==========
length : Sympifyable
A Symbol or value representing the Beam's length.
elastic_modulus : Sympifyable
A SymPy expression representing the Beam's Modulus of Elasticity.
It is a measure of the stiffness of the Beam material.
second_moment : Sympifyable
A SymPy expression representing the Beam's Second moment of area.
It is a geometrical property of an area which reflects how its
points are distributed with respect to its neutral axis.
variable : Symbol, optional
A Symbol object that will be used as the variable along the beam
while representing the load, shear, moment, slope and deflection
curve. By default, it is set to ``Symbol('x')``.
"""
self.length = length
self.elastic_modulus = elastic_modulus
self.second_moment = second_moment
self.variable = variable
self._boundary_conditions = {'deflection': [], 'slope': []}
self._load = 0
self._reaction_loads = {}
def __str__(self):
str_sol = 'Beam({}, {}, {})'.format(sstr(self._length), sstr(self._elastic_modulus), sstr(self._second_moment))
return str_sol
@property
def reaction_loads(self):
""" Returns the reaction forces in a dictionary."""
return self._reaction_loads
@property
def length(self):
"""Length of the Beam."""
return self._length
@length.setter
def length(self, l):
self._length = sympify(l)
@property
def variable(self):
"""
A symbol that can be used as a variable along the length of the beam
while representing load distribution, shear force curve, bending
moment, slope curve and the deflection curve. By default, it is set
to ``Symbol('x')``, but this property is mutable.
Examples
========
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols
>>> E, I = symbols('E, I')
>>> x, y, z = symbols('x, y, z')
>>> b = Beam(4, E, I)
>>> b.variable
x
>>> b.variable = y
>>> b.variable
y
>>> b = Beam(4, E, I, z)
>>> b.variable
z
"""
return self._variable
@variable.setter
def variable(self, v):
if isinstance(v, Symbol):
self._variable = v
else:
raise TypeError("""The variable should be a Symbol object.""")
@property
def elastic_modulus(self):
"""Young's Modulus of the Beam. """
return self._elastic_modulus
@elastic_modulus.setter
def elastic_modulus(self, e):
self._elastic_modulus = sympify(e)
@property
def second_moment(self):
"""Second moment of area of the Beam. """
return self._second_moment
@second_moment.setter
def second_moment(self, i):
self._second_moment = sympify(i)
@property
def boundary_conditions(self):
"""
Returns a dictionary of boundary conditions applied on the beam.
The dictionary has three kewwords namely moment, slope and deflection.
The value of each keyword is a list of tuple, where each tuple
contains loaction and value of a boundary condition in the format
(location, value).
Examples
========
There is a beam of length 4 meters. The bending moment at 0 should be 4
and at 4 it should be 0. The slope of the beam should be 1 at 0. The
deflection should be 2 at 0.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols
>>> E, I = symbols('E, I')
>>> b = Beam(4, E, I)
>>> b.bc_deflection = [(0, 2)]
>>> b.bc_slope = [(0, 1)]
>>> b.boundary_conditions
{'deflection': [(0, 2)], 'slope': [(0, 1)]}
Here the deflection of the beam should be ``2`` at ``0``.
Similarly, the slope of the beam should be ``1`` at ``0``.
"""
return self._boundary_conditions
@property
def bc_slope(self):
return self._boundary_conditions['slope']
@bc_slope.setter
def bc_slope(self, s_bcs):
self._boundary_conditions['slope'] = s_bcs
@property
def bc_deflection(self):
return self._boundary_conditions['deflection']
@bc_deflection.setter
def bc_deflection(self, d_bcs):
self._boundary_conditions['deflection'] = d_bcs
def apply_load(self, value, start, order, end=None):
"""
This method adds up the loads given to a particular beam object.
Parameters
==========
value : Sympifyable
The magnitude of an applied load.
start : Sympifyable
The starting point of the applied load. For point moments and
point forces this is the location of application.
order : Integer
The order of the applied load.
- For moments, order= -2
- For point loads, order=-1
- For constant distributed load, order=0
- For ramp loads, order=1
- For parabolic ramp loads, order=2
- ... so on.
end : Sympifyable, optional
An optional argument that can be used if the load has an end point
within the length of the beam.
Examples
========
There is a beam of length 4 meters. A moment of magnitude 3 Nm is
applied in the clockwise direction at the starting point of the beam.
A pointload of magnitude 4 N is applied from the top of the beam at
2 meters from the starting point and a parabolic ramp load of magnitude
2 N/m is applied below the beam starting from 2 meters to 3 meters
away from the starting point of the beam.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols
>>> E, I = symbols('E, I')
>>> b = Beam(4, E, I)
>>> b.apply_load(-3, 0, -2)
>>> b.apply_load(4, 2, -1)
>>> b.apply_load(-2, 2, 2, end = 3)
>>> b.load
-3*SingularityFunction(x, 0, -2) + 4*SingularityFunction(x, 2, -1) - 2*SingularityFunction(x, 2, 2)
+ 2*SingularityFunction(x, 3, 0) + 2*SingularityFunction(x, 3, 2)
"""
x = self.variable
value = sympify(value)
start = sympify(start)
order = sympify(order)
self._load += value*SingularityFunction(x, start, order)
if end:
if order == 0:
self._load -= value*SingularityFunction(x, end, order)
elif order.is_positive:
self._load -= value*SingularityFunction(x, end, order) + value*SingularityFunction(x, end, 0)
else:
raise ValueError("""Order of the load should be positive.""")
@property
def load(self):
"""
Returns a Singularity Function expression which represents
the load distribution curve of the Beam object.
Examples
========
There is a beam of length 4 meters. A moment of magnitude 3 Nm is
applied in the clockwise direction at the starting point of the beam.
A pointload of magnitude 4 N is applied from the top of the beam at
2 meters from the starting point and a parabolic ramp load of magnitude
2 N/m is applied below the beam starting from 3 meters away from the
starting point of the beam.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols
>>> E, I = symbols('E, I')
>>> b = Beam(4, E, I)
>>> b.apply_load(-3, 0, -2)
>>> b.apply_load(4, 2, -1)
>>> b.apply_load(-2, 3, 2)
>>> b.load
-3*SingularityFunction(x, 0, -2) + 4*SingularityFunction(x, 2, -1) - 2*SingularityFunction(x, 3, 2)
"""
return self._load
def solve_for_reaction_loads(self, *reactions):
"""
Solves for the reaction forces.
Examples
========
There is a beam of length 30 meters. A moment of magnitude 120 Nm is
applied in the clockwise direction at the end of the beam. A pointload
of magnitude 8 N is applied from the top of the beam at the starting
point. There are two simple supports below the beam. One at the end
and another one at a distance of 10 meters from the start. The
deflection is restricted at both the supports.
Using the sign convention of upward forces and clockwise moment
being positive.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols, linsolve, limit
>>> E, I = symbols('E, I')
>>> R1, R2 = symbols('R1, R2')
>>> b = Beam(30, E, I)
>>> b.apply_load(-8, 0, -1)
>>> b.apply_load(R1, 10, -1) # Reaction force at x = 10
>>> b.apply_load(R2, 30, -1) # Reaction force at x = 30
>>> b.apply_load(120, 30, -2)
>>> b.bc_deflection = [(10, 0), (30, 0)]
>>> b.load
R1*SingularityFunction(x, 10, -1) + R2*SingularityFunction(x, 30, -1)
- 8*SingularityFunction(x, 0, -1) + 120*SingularityFunction(x, 30, -2)
>>> b.solve_for_reaction_loads(R1, R2)
>>> b.reaction_loads
{R1: 6, R2: 2}
>>> b.load
-8*SingularityFunction(x, 0, -1) + 6*SingularityFunction(x, 10, -1)
+ 120*SingularityFunction(x, 30, -2) + 2*SingularityFunction(x, 30, -1)
"""
x = self.variable
l = self.length
shear_curve = limit(self.shear_force(), x, l)
moment_curve = limit(self.bending_moment(), x, l)
reaction_values = linsolve([shear_curve, moment_curve], reactions).args
self._reaction_loads = dict(zip(reactions, reaction_values[0]))
self._load = self._load.subs(self._reaction_loads)
def shear_force(self):
"""
Returns a Singularity Function expression which represents
the shear force curve of the Beam object.
Examples
========
There is a beam of length 30 meters. A moment of magnitude 120 Nm is
applied in the clockwise direction at the end of the beam. A pointload
of magnitude 8 N is applied from the top of the beam at the starting
point. There are two simple supports below the beam. One at the end
and another one at a distance of 10 meters from the start. The
deflection is restricted at both the supports.
Using the sign convention of upward forces and clockwise moment
being positive.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols
>>> E, I = symbols('E, I')
>>> R1, R2 = symbols('R1, R2')
>>> b = Beam(30, E, I)
>>> b.apply_load(-8, 0, -1)
>>> b.apply_load(R1, 10, -1)
>>> b.apply_load(R2, 30, -1)
>>> b.apply_load(120, 30, -2)
>>> b.bc_deflection = [(10, 0), (30, 0)]
>>> b.solve_for_reaction_loads(R1, R2)
>>> b.shear_force()
-8*SingularityFunction(x, 0, 0) + 6*SingularityFunction(x, 10, 0) + 120*SingularityFunction(x, 30, -1) + 2*SingularityFunction(x, 30, 0)
"""
x = self.variable
return integrate(self.load, x)
def bending_moment(self):
"""
Returns a Singularity Function expression which represents
the bending moment curve of the Beam object.
Examples
========
There is a beam of length 30 meters. A moment of magnitude 120 Nm is
applied in the clockwise direction at the end of the beam. A pointload
of magnitude 8 N is applied from the top of the beam at the starting
point. There are two simple supports below the beam. One at the end
and another one at a distance of 10 meters from the start. The
deflection is restricted at both the supports.
Using the sign convention of upward forces and clockwise moment
being positive.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols
>>> E, I = symbols('E, I')
>>> R1, R2 = symbols('R1, R2')
>>> b = Beam(30, E, I)
>>> b.apply_load(-8, 0, -1)
>>> b.apply_load(R1, 10, -1)
>>> b.apply_load(R2, 30, -1)
>>> b.apply_load(120, 30, -2)
>>> b.bc_deflection = [(10, 0), (30, 0)]
>>> b.solve_for_reaction_loads(R1, R2)
>>> b.bending_moment()
-8*SingularityFunction(x, 0, 1) + 6*SingularityFunction(x, 10, 1) + 120*SingularityFunction(x, 30, 0) + 2*SingularityFunction(x, 30, 1)
"""
x = self.variable
return integrate(self.shear_force(), x)
def slope(self):
"""
Returns a Singularity Function expression which represents
the slope the elastic curve of the Beam object.
Examples
========
There is a beam of length 30 meters. A moment of magnitude 120 Nm is
applied in the clockwise direction at the end of the beam. A pointload
of magnitude 8 N is applied from the top of the beam at the starting
point. There are two simple supports below the beam. One at the end
and another one at a distance of 10 meters from the start. The
deflection is restricted at both the supports.
Using the sign convention of upward forces and clockwise moment
being positive.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols
>>> E, I = symbols('E, I')
>>> R1, R2 = symbols('R1, R2')
>>> b = Beam(30, E, I)
>>> b.apply_load(-8, 0, -1)
>>> b.apply_load(R1, 10, -1)
>>> b.apply_load(R2, 30, -1)
>>> b.apply_load(120, 30, -2)
>>> b.bc_deflection = [(10, 0), (30, 0)]
>>> b.solve_for_reaction_loads(R1, R2)
>>> b.slope()
(-4*SingularityFunction(x, 0, 2) + 3*SingularityFunction(x, 10, 2)
+ 120*SingularityFunction(x, 30, 1) + SingularityFunction(x, 30, 2) + 4000/3)/(E*I)
"""
x = self.variable
E = self.elastic_modulus
I = self.second_moment
if not self._boundary_conditions['slope']:
return diff(self.deflection(), x)
C3 = Symbol('C3')
slope_curve = integrate(self.bending_moment(), x) + C3
bc_eqs = []
for position, value in self._boundary_conditions['slope']:
eqs = slope_curve.subs(x, position) - value
bc_eqs.append(eqs)
constants = list(linsolve(bc_eqs, C3))
slope_curve = slope_curve.subs({C3: constants[0][0]})
return S(1)/(E*I)*slope_curve
def deflection(self):
"""
Returns a Singularity Function expression which represents
the elastic curve or deflection of the Beam object.
Examples
========
There is a beam of length 30 meters. A moment of magnitude 120 Nm is
applied in the clockwise direction at the end of the beam. A pointload
of magnitude 8 N is applied from the top of the beam at the starting
point. There are two simple supports below the beam. One at the end
and another one at a distance of 10 meters from the start. The
deflection is restricted at both the supports.
Using the sign convention of upward forces and clockwise moment
being positive.
>>> from sympy.physics.continuum_mechanics.beam import Beam
>>> from sympy import symbols
>>> E, I = symbols('E, I')
>>> R1, R2 = symbols('R1, R2')
>>> b = Beam(30, E, I)
>>> b.apply_load(-8, 0, -1)
>>> b.apply_load(R1, 10, -1)
>>> b.apply_load(R2, 30, -1)
>>> b.apply_load(120, 30, -2)
>>> b.bc_deflection = [(10, 0), (30, 0)]
>>> b.solve_for_reaction_loads(R1, R2)
>>> b.deflection()
(4000*x/3 - 4*SingularityFunction(x, 0, 3)/3 + SingularityFunction(x, 10, 3)
+ 60*SingularityFunction(x, 30, 2) + SingularityFunction(x, 30, 3)/3 - 12000)/(E*I)
"""
x = self.variable
E = self.elastic_modulus
I = self.second_moment
if not self._boundary_conditions['deflection'] and not self._boundary_conditions['slope']:
return S(1)/(E*I)*integrate(integrate(self.bending_moment(), x), x)
elif not self._boundary_conditions['deflection']:
return integrate(self.slope(), x)
elif not self._boundary_conditions['slope'] and self._boundary_conditions['deflection']:
C3 = Symbol('C3')
C4 = Symbol('C4')
slope_curve = integrate(self.bending_moment(), x) + C3
deflection_curve = integrate(slope_curve, x) + C4
bc_eqs = []
for position, value in self._boundary_conditions['deflection']:
eqs = deflection_curve.subs(x, position) - value
bc_eqs.append(eqs)
constants = list(linsolve(bc_eqs, (C3, C4)))
deflection_curve = deflection_curve.subs({C3: constants[0][0], C4: constants[0][1]})
return S(1)/(E*I)*deflection_curve
C4 = Symbol('C4')
deflection_curve = integrate((E*I)*self.slope(), x) + C4
bc_eqs = []
for position, value in self._boundary_conditions['deflection']:
eqs = deflection_curve.subs(x, position) - value
bc_eqs.append(eqs)
constants = list(linsolve(bc_eqs, C4))
deflection_curve = deflection_curve.subs({C4: constants[0][0]})
return S(1)/(E*I)*deflection_curve
| 20,692 | 38.71785 | 144 |
py
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/continuum_mechanics/__init__.py
|
from .beam import Beam
| 23 | 11 | 22 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/continuum_mechanics/tests/test_beam.py
|
from sympy import Symbol, symbols
from sympy.physics.continuum_mechanics.beam import Beam
from sympy.functions import SingularityFunction
from sympy.utilities.pytest import raises
x = Symbol('x')
y = Symbol('y')
R1, R2 = symbols('R1, R2')
def test_Beam():
E = Symbol('E')
E_1 = Symbol('E_1')
I = Symbol('I')
I_1 = Symbol('I_1')
b = Beam(1, E, I)
assert b.length == 1
assert b.elastic_modulus == E
assert b.second_moment == I
assert b.variable == x
# Test the length setter
b.length = 4
assert b.length == 4
# Test the E setter
b.elastic_modulus = E_1
assert b.elastic_modulus == E_1
# Test the I setter
b.second_moment = I_1
assert b.second_moment is I_1
# Test the variable setter
b.variable = y
assert b.variable is y
# Test for all boundary conditions.
b.bc_deflection = [(0, 2)]
b.bc_slope = [(0, 1)]
assert b.boundary_conditions == {'deflection': [(0, 2)], 'slope': [(0, 1)]}
# Test for slope boundary condition method
b.bc_slope.extend([(4, 3), (5, 0)])
s_bcs = b.bc_slope
assert s_bcs == [(0, 1), (4, 3), (5, 0)]
# Test for deflection boundary condition method
b.bc_deflection.extend([(4, 3), (5, 0)])
d_bcs = b.bc_deflection
assert d_bcs == [(0, 2), (4, 3), (5, 0)]
# Test for updated boundary conditions
bcs_new = b.boundary_conditions
assert bcs_new == {
'deflection': [(0, 2), (4, 3), (5, 0)],
'slope': [(0, 1), (4, 3), (5, 0)]}
b1 = Beam(30, E, I)
b1.apply_load(-8, 0, -1)
b1.apply_load(R1, 10, -1)
b1.apply_load(R2, 30, -1)
b1.apply_load(120, 30, -2)
b1.bc_deflection = [(10, 0), (30, 0)]
b1.solve_for_reaction_loads(R1, R2)
# Test for finding reaction forces
p = b1.reaction_loads
q = {R1: 6, R2: 2}
assert p == q
# Test for load distribution function.
p = b1.load
q = -8*SingularityFunction(x, 0, -1) + 6*SingularityFunction(x, 10, -1) + 120*SingularityFunction(x, 30, -2) + 2*SingularityFunction(x, 30, -1)
assert p == q
# Test for shear force distribution function
p = b1.shear_force()
q = -8*SingularityFunction(x, 0, 0) + 6*SingularityFunction(x, 10, 0) + 120*SingularityFunction(x, 30, -1) + 2*SingularityFunction(x, 30, 0)
assert p == q
# Test for bending moment distribution function
p = b1.bending_moment()
q = -8*SingularityFunction(x, 0, 1) + 6*SingularityFunction(x, 10, 1) + 120*SingularityFunction(x, 30, 0) + 2*SingularityFunction(x, 30, 1)
assert p == q
# Test for slope distribution function
p = b1.slope()
q = -4*SingularityFunction(x, 0, 2) + 3*SingularityFunction(x, 10, 2) + 120*SingularityFunction(x, 30, 1) + SingularityFunction(x, 30, 2) + 4000/3
assert p == q/(E*I)
# Test for deflection distribution function
p = b1.deflection()
q = 4000*x/3 - 4*SingularityFunction(x, 0, 3)/3 + SingularityFunction(x, 10, 3) + 60*SingularityFunction(x, 30, 2) + SingularityFunction(x, 30, 3)/3 - 12000
assert p == q/(E*I)
# Test using symbols
l = Symbol('l')
w0 = Symbol('w0')
w2 = Symbol('w2')
a1 = Symbol('a1')
c = Symbol('c')
c1 = Symbol('c1')
d = Symbol('d')
e = Symbol('e')
f = Symbol('f')
b2 = Beam(l, E, I)
b2.apply_load(w0, a1, 1)
b2.apply_load(w2, c1, -1)
b2.bc_deflection = [(c, d)]
b2.bc_slope = [(e, f)]
# Test for load distribution function.
p = b2.load
q = w0*SingularityFunction(x, a1, 1) + w2*SingularityFunction(x, c1, -1)
assert p == q
# Test for shear force distribution function
p = b2.shear_force()
q = w0*SingularityFunction(x, a1, 2)/2 + w2*SingularityFunction(x, c1, 0)
assert p == q
# Test for bending moment distribution function
p = b2.bending_moment()
q = w0*SingularityFunction(x, a1, 3)/6 + w2*SingularityFunction(x, c1, 1)
assert p == q
# Test for slope distribution function
p = b2.slope()
q = (f - w0*SingularityFunction(e, a1, 4)/24 + w0*SingularityFunction(x, a1, 4)/24 - w2*SingularityFunction(e, c1, 2)/2 + w2*SingularityFunction(x, c1, 2)/2)
assert p == q/(E*I)
# Test for deflection distribution function
p = b2.deflection()
q = (-c*f + c*w0*SingularityFunction(e, a1, 4)/24 + c*w2*SingularityFunction(e, c1, 2)/2 + d + f*x - w0*x*SingularityFunction(e, a1, 4)/24 - w0*SingularityFunction(c, a1, 5)/120 + w0*SingularityFunction(x, a1, 5)/120 - w2*x*SingularityFunction(e, c1, 2)/2 - w2*SingularityFunction(c, c1, 3)/6 + w2*SingularityFunction(x, c1, 3)/6)
assert p == q/(E*I)
b3 = Beam(9, E, I)
b3.apply_load(value=-2, start=2, order=2, end=3)
b3.bc_slope.append((0, 2))
p = b3.load
q = - 2*SingularityFunction(x, 2, 2) + 2*SingularityFunction(x, 3, 0) + 2*SingularityFunction(x, 3, 2)
assert p == q
p = b3.slope()
q = -SingularityFunction(x, 2, 5)/30 + SingularityFunction(x, 3, 3)/3 + SingularityFunction(x, 3, 5)/30 + 2
assert p == q/(E*I)
p = b3.deflection()
q = 2*x - SingularityFunction(x, 2, 6)/180 + SingularityFunction(x, 3, 4)/12 + SingularityFunction(x, 3, 6)/180
assert p == q/(E*I)
b4 = Beam(4, E, I)
b4.apply_load(-3, 0, 0, end=3)
p = b4.load
q = -3*SingularityFunction(x, 0, 0) + 3*SingularityFunction(x, 3, 0)
assert p == q
p = b4.slope()
q = -3*SingularityFunction(x, 0, 3)/6 + 3*SingularityFunction(x, 3, 3)/6
assert p == q/(E*I)
p = b4.deflection()
q = -3*SingularityFunction(x, 0, 4)/24 + 3*SingularityFunction(x, 3, 4)/24
assert p == q/(E*I)
raises(ValueError, lambda: b4.apply_load(-3, 0, -1, end=3))
with raises(TypeError):
b4.variable = 1
| 5,693 | 31.724138 | 334 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/continuum_mechanics/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/test_pring.py
|
from sympy.physics.pring import wavefunction, energy
from sympy.core.compatibility import range
from sympy import pi, integrate, sqrt, exp, simplify, I
from sympy.abc import m, x, r
from sympy.physics.quantum.constants import hbar
def test_wavefunction():
Psi = {
0: (1/sqrt(2 * pi)),
1: (1/sqrt(2 * pi)) * exp(I * x),
2: (1/sqrt(2 * pi)) * exp(2 * I * x),
3: (1/sqrt(2 * pi)) * exp(3 * I * x)
}
for n in Psi:
assert simplify(wavefunction(n, x) - Psi[n]) == 0
def test_norm(n=1):
# Maximum "n" which is tested:
for i in range(n + 1):
assert integrate(
wavefunction(i, x) * wavefunction(-i, x), (x, 0, 2 * pi)) == 1
def test_orthogonality(n=1):
# Maximum "n" which is tested:
for i in range(n + 1):
for j in range(i+1, n+1):
assert integrate(
wavefunction(i, x) * wavefunction(j, x), (x, 0, 2 * pi)) == 0
def test_energy(n=1):
# Maximum "n" which is tested:
for i in range(n+1):
assert simplify(
energy(i, m, r) - ((i**2 * hbar**2) / (2 * m * r**2))) == 0
| 1,115 | 27.615385 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/test_paulialgebra.py
|
from sympy import I, symbols
from sympy.physics.paulialgebra import Pauli
from sympy.utilities.pytest import XFAIL
from sympy.physics.quantum import TensorProduct
sigma1 = Pauli(1)
sigma2 = Pauli(2)
sigma3 = Pauli(3)
tau1 = symbols("tau1", commutative = False)
def test_Pauli():
assert sigma1 == sigma1
assert sigma1 != sigma2
assert sigma1*sigma2 == I*sigma3
assert sigma3*sigma1 == I*sigma2
assert sigma2*sigma3 == I*sigma1
assert sigma1*sigma1 == 1
assert sigma2*sigma2 == 1
assert sigma3*sigma3 == 1
assert sigma1**0 == 1
assert sigma1**1 == sigma1
assert sigma1**2 == 1
assert sigma1**3 == sigma1
assert sigma1**4 == 1
assert sigma3**2 == 1
assert sigma1*2*sigma1 == 2
def test_evaluate_pauli_product():
from sympy.physics.paulialgebra import evaluate_pauli_product
assert evaluate_pauli_product(I*sigma2*sigma3) == -sigma1
# Check issue 6471
assert evaluate_pauli_product(-I*4*sigma1*sigma2) == 4*sigma3
assert evaluate_pauli_product(
1 + I*sigma1*sigma2*sigma1*sigma2 + \
I*sigma1*sigma2*tau1*sigma1*sigma3 + \
((tau1**2).subs(tau1, I*sigma1)) + \
sigma3*((tau1**2).subs(tau1, I*sigma1)) + \
TensorProduct(I*sigma1*sigma2*sigma1*sigma2, 1)
) == 1 -I + I*sigma3*tau1*sigma2 - 1 - sigma3 - I*TensorProduct(1,1)
@XFAIL
def test_Pauli_should_work():
assert sigma1*sigma3*sigma1 == -sigma3
| 1,437 | 24.22807 | 72 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/test_clebsch_gordan.py
|
from sympy import S, sqrt, pi, Dummy, Sum, Ynm, symbols
from sympy.physics.wigner import (clebsch_gordan, wigner_9j, wigner_6j, gaunt,
racah, dot_rot_grad_Ynm, Wigner3j, wigner_3j)
from sympy.core.numbers import Rational
# for test cases, refer : https://en.wikipedia.org/wiki/Table_of_Clebsch%E2%80%93Gordan_coefficients
def test_clebsch_gordan_docs():
assert clebsch_gordan(S(3)/2, S(1)/2, 2, S(3)/2, S(1)/2, 2) == 1
assert clebsch_gordan(S(3)/2, S(1)/2, 1, S(3)/2, -S(1)/2, 1) == sqrt(3)/2
assert clebsch_gordan(S(3)/2, S(1)/2, 1, -S(1)/2, S(1)/2, 0) == -sqrt(2)/2
def test_clebsch_gordan1():
j_1 = S(1)/2
j_2 = S(1)/2
m = 1
j = 1
m_1 = S(1)/2
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1
j_1 = S(1)/2
j_2 = S(1)/2
m = -1
j = 1
m_1 = -S(1)/2
m_2 = -S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1
j_1 = S(1)/2
j_2 = S(1)/2
m = 0
j = 1
m_1 = S(1)/2
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 0
j_1 = S(1)/2
j_2 = S(1)/2
m = 0
j = 1
m_1 = S(1)/2
m_2 = -S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == sqrt(2)/2
j_1 = S(1)/2
j_2 = S(1)/2
m = 0
j = 0
m_1 = S(1)/2
m_2 = -S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == sqrt(2)/2
j_1 = S(1)/2
j_2 = S(1)/2
m = 0
j = 1
m_1 = -S(1)/2
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == sqrt(2)/2
j_1 = S(1)/2
j_2 = S(1)/2
m = 0
j = 0
m_1 = -S(1)/2
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == -sqrt(2)/2
def test_clebsch_gordan2():
j_1 = S(1)
j_2 = S(1)/2
m = S(3)/2
j = S(3)/2
m_1 = 1
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1
j_1 = S(1)
j_2 = S(1)/2
m = S(1)/2
j = S(3)/2
m_1 = 1
m_2 = -S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1/sqrt(3)
j_1 = S(1)
j_2 = S(1)/2
m = S(1)/2
j = S(1)/2
m_1 = 1
m_2 = -S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == sqrt(2)/sqrt(3)
j_1 = S(1)
j_2 = S(1)/2
m = S(1)/2
j = S(1)/2
m_1 = 0
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == -1/sqrt(3)
j_1 = S(1)
j_2 = S(1)/2
m = S(1)/2
j = S(3)/2
m_1 = 0
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == sqrt(2)/sqrt(3)
j_1 = S(1)
j_2 = S(1)
m = S(2)
j = S(2)
m_1 = 1
m_2 = 1
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1
j_1 = S(1)
j_2 = S(1)
m = 1
j = S(2)
m_1 = 1
m_2 = 0
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1/sqrt(2)
j_1 = S(1)
j_2 = S(1)
m = 1
j = S(2)
m_1 = 0
m_2 = 1
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1/sqrt(2)
j_1 = S(1)
j_2 = S(1)
m = 1
j = 1
m_1 = 1
m_2 = 0
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1/sqrt(2)
j_1 = S(1)
j_2 = S(1)
m = 1
j = 1
m_1 = 0
m_2 = 1
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == -1/sqrt(2)
def test_clebsch_gordan3():
j_1 = S(3)/2
j_2 = S(3)/2
m = S(3)
j = S(3)
m_1 = S(3)/2
m_2 = S(3)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1
j_1 = S(3)/2
j_2 = S(3)/2
m = S(2)
j = S(2)
m_1 = S(3)/2
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1/sqrt(2)
j_1 = S(3)/2
j_2 = S(3)/2
m = S(2)
j = S(3)
m_1 = S(3)/2
m_2 = S(1)/2
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1/sqrt(2)
def test_clebsch_gordan4():
j_1 = S(2)
j_2 = S(2)
m = S(4)
j = S(4)
m_1 = S(2)
m_2 = S(2)
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1
j_1 = S(2)
j_2 = S(2)
m = S(3)
j = S(3)
m_1 = S(2)
m_2 = 1
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1/sqrt(2)
j_1 = S(2)
j_2 = S(2)
m = S(2)
j = S(3)
m_1 = 1
m_2 = 1
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 0
def test_clebsch_gordan5():
j_1 = S(5)/2
j_2 = S(1)
m = S(7)/2
j = S(7)/2
m_1 = S(5)/2
m_2 = 1
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1
j_1 = S(5)/2
j_2 = S(1)
m = S(5)/2
j = S(5)/2
m_1 = S(5)/2
m_2 = 0
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == sqrt(5)/sqrt(7)
j_1 = S(5)/2
j_2 = S(1)
m = S(3)/2
j = S(3)/2
m_1 = S(1)/2
m_2 = 1
assert clebsch_gordan(j_1, j_2, j, m_1, m_2, m) == 1/sqrt(15)
def test_wigner():
def tn(a, b):
return (a - b).n(64) < S('1e-64')
assert tn(wigner_9j(1, 1, 1, 1, 1, 1, 1, 1, 0, prec=64), S(1)/18)
assert wigner_9j(3, 3, 2, 3, 3, 2, 3, 3, 2) == 3221*sqrt(
70)/(246960*sqrt(105)) - 365/(3528*sqrt(70)*sqrt(105))
assert wigner_6j(5, 5, 5, 5, 5, 5) == Rational(1, 52)
assert tn(wigner_6j(8, 8, 8, 8, 8, 8, prec=64), -S(12219)/965770)
def test_gaunt():
def tn(a, b):
return (a - b).n(64) < S('1e-64')
assert gaunt(1, 0, 1, 1, 0, -1) == -1/(2*sqrt(pi))
assert tn(gaunt(
10, 10, 12, 9, 3, -12, prec=64), (-S(98)/62031) * sqrt(6279)/sqrt(pi))
def gaunt_ref(l1, l2, l3, m1, m2, m3):
return (
sqrt((2 * l1 + 1) * (2 * l2 + 1) * (2 * l3 + 1) / (4 * pi)) *
wigner_3j(l1, l2, l3, 0, 0, 0) *
wigner_3j(l1, l2, l3, m1, m2, m3)
)
threshold = 1e-10
l_max = 3
l3_max = 24
for l1 in range(l_max + 1):
for l2 in range(l_max + 1):
for l3 in range(l3_max + 1):
for m1 in range(-l1, l1 + 1):
for m2 in range(-l2, l2 + 1):
for m3 in range(-l3, l3 + 1):
args = l1, l2, l3, m1, m2, m3
g = gaunt(*args)
g0 = gaunt_ref(*args)
assert abs(g - g0) < threshold
if m1 + m2 + m3 != 0:
assert abs(g) < threshold
if (l1 + l2 + l3) % 2:
assert abs(g) < threshold
def test_racah():
assert racah(3,3,3,3,3,3) == Rational(-1,14)
assert racah(2,2,2,2,2,2) == Rational(-3,70)
assert racah(7,8,7,1,7,7, prec=4).is_Float
assert racah(5.5,7.5,9.5,6.5,8,9) == -719*sqrt(598)/1158924
assert abs(racah(5.5,7.5,9.5,6.5,8,9, prec=4) - (-0.01517)) < S('1e-4')
def test_dot_rota_grad_SH():
theta, phi = symbols("theta phi")
assert dot_rot_grad_Ynm(1, 1, 1, 1, 1, 0) != \
sqrt(30)*Ynm(2, 2, 1, 0)/(10*sqrt(pi))
assert dot_rot_grad_Ynm(1, 1, 1, 1, 1, 0).doit() == \
sqrt(30)*Ynm(2, 2, 1, 0)/(10*sqrt(pi))
assert dot_rot_grad_Ynm(1, 5, 1, 1, 1, 2) != \
0
assert dot_rot_grad_Ynm(1, 5, 1, 1, 1, 2).doit() == \
0
assert dot_rot_grad_Ynm(3, 3, 3, 3, theta, phi).doit() == \
15*sqrt(3003)*Ynm(6, 6, theta, phi)/(143*sqrt(pi))
assert dot_rot_grad_Ynm(3, 3, 1, 1, theta, phi).doit() == \
sqrt(3)*Ynm(4, 4, theta, phi)/sqrt(pi)
assert dot_rot_grad_Ynm(3, 2, 2, 0, theta, phi).doit() == \
3*sqrt(55)*Ynm(5, 2, theta, phi)/(11*sqrt(pi))
assert dot_rot_grad_Ynm(3, 2, 3, 2, theta, phi).doit() == \
-sqrt(70)*Ynm(4, 4, theta, phi)/(11*sqrt(pi)) + \
45*sqrt(182)*Ynm(6, 4, theta, phi)/(143*sqrt(pi))
| 7,530 | 24.019934 | 100 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/test_sho.py
|
from sympy.core import symbols, Rational, Function, diff
from sympy.core.compatibility import range
from sympy.physics.sho import R_nl, E_nl
from sympy import simplify
def test_sho_R_nl():
omega, r = symbols('omega r')
l = symbols('l', integer=True)
u = Function('u')
# check that it obeys the Schrodinger equation
for n in range(5):
schreq = ( -diff(u(r), r, 2)/2 + ((l*(l + 1))/(2*r**2)
+ omega**2*r**2/2 - E_nl(n, l, omega))*u(r) )
result = schreq.subs(u(r), r*R_nl(n, l, omega/2, r))
assert simplify(result.doit()) == 0
def test_energy():
n, l, hw = symbols('n l hw')
assert simplify(E_nl(n, l, hw) - (2*n + l + Rational(3, 2))*hw) == 0
| 718 | 30.26087 | 72 |
py
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/test_hydrogen.py
|
from sympy import exp, integrate, oo, S, simplify, sqrt, symbols
from sympy.core.compatibility import range
from sympy.physics.hydrogen import R_nl, E_nl, E_nl_dirac
from sympy.utilities.pytest import raises
n, r, Z = symbols('n r Z')
def feq(a, b, max_relative_error=1e-12, max_absolute_error=1e-12):
a = float(a)
b = float(b)
# if the numbers are close enough (absolutely), then they are equal
if abs(a - b) < max_absolute_error:
return True
# if not, they can still be equal if their relative error is small
if abs(b) > abs(a):
relative_error = abs((a - b)/b)
else:
relative_error = abs((a - b)/a)
return relative_error <= max_relative_error
def test_wavefunction():
a = 1/Z
R = {
(1, 0): 2*sqrt(1/a**3) * exp(-r/a),
(2, 0): sqrt(1/(2*a**3)) * exp(-r/(2*a)) * (1 - r/(2*a)),
(2, 1): S(1)/2 * sqrt(1/(6*a**3)) * exp(-r/(2*a)) * r/a,
(3, 0): S(2)/3 * sqrt(1/(3*a**3)) * exp(-r/(3*a)) *
(1 - 2*r/(3*a) + S(2)/27 * (r/a)**2),
(3, 1): S(4)/27 * sqrt(2/(3*a**3)) * exp(-r/(3*a)) *
(1 - r/(6*a)) * r/a,
(3, 2): S(2)/81 * sqrt(2/(15*a**3)) * exp(-r/(3*a)) * (r/a)**2,
(4, 0): S(1)/4 * sqrt(1/a**3) * exp(-r/(4*a)) *
(1 - 3*r/(4*a) + S(1)/8 * (r/a)**2 - S(1)/192 * (r/a)**3),
(4, 1): S(1)/16 * sqrt(5/(3*a**3)) * exp(-r/(4*a)) *
(1 - r/(4*a) + S(1)/80 * (r/a)**2) * (r/a),
(4, 2): S(1)/64 * sqrt(1/(5*a**3)) * exp(-r/(4*a)) *
(1 - r/(12*a)) * (r/a)**2,
(4, 3): S(1)/768 * sqrt(1/(35*a**3)) * exp(-r/(4*a)) * (r/a)**3,
}
for n, l in R:
assert simplify(R_nl(n, l, r, Z) - R[(n, l)]) == 0
def test_norm():
# Maximum "n" which is tested:
n_max = 2 # it works, but is slow, for n_max > 2
for n in range(n_max + 1):
for l in range(n):
assert integrate(R_nl(n, l, r)**2 * r**2, (r, 0, oo)) == 1
def test_hydrogen_energies():
assert E_nl(n, Z) == -Z**2/(2*n**2)
assert E_nl(n) == -1/(2*n**2)
assert E_nl(1, 47) == -S(47)**2/(2*1**2)
assert E_nl(2, 47) == -S(47)**2/(2*2**2)
assert E_nl(1) == -S(1)/(2*1**2)
assert E_nl(2) == -S(1)/(2*2**2)
assert E_nl(3) == -S(1)/(2*3**2)
assert E_nl(4) == -S(1)/(2*4**2)
assert E_nl(100) == -S(1)/(2*100**2)
raises(ValueError, lambda: E_nl(0))
def test_hydrogen_energies_relat():
# First test exact formulas for small "c" so that we get nice expressions:
assert E_nl_dirac(2, 0, Z=1, c=1) == 1/sqrt(2) - 1
assert simplify(E_nl_dirac(2, 0, Z=1, c=2) - ( (8*sqrt(3) + 16)
/ sqrt(16*sqrt(3) + 32) - 4)) == 0
assert simplify(E_nl_dirac(2, 0, Z=1, c=3) - ( (54*sqrt(2) + 81)
/ sqrt(108*sqrt(2) + 162) - 9)) == 0
# Now test for almost the correct speed of light, without floating point
# numbers:
assert simplify(E_nl_dirac(2, 0, Z=1, c=137) - ( (352275361 + 10285412 *
sqrt(1173)) / sqrt(704550722 + 20570824 * sqrt(1173)) - 18769)) == 0
assert simplify(E_nl_dirac(2, 0, Z=82, c=137) - ( (352275361 + 2571353 *
sqrt(12045)) / sqrt(704550722 + 5142706*sqrt(12045)) - 18769)) == 0
# Test using exact speed of light, and compare against the nonrelativistic
# energies:
for n in range(1, 5):
for l in range(n):
assert feq(E_nl_dirac(n, l), E_nl(n), 1e-5, 1e-5)
if l > 0:
assert feq(E_nl_dirac(n, l, False), E_nl(n), 1e-5, 1e-5)
Z = 2
for n in range(1, 5):
for l in range(n):
assert feq(E_nl_dirac(n, l, Z=Z), E_nl(n, Z), 1e-4, 1e-4)
if l > 0:
assert feq(E_nl_dirac(n, l, False, Z), E_nl(n, Z), 1e-4, 1e-4)
Z = 3
for n in range(1, 5):
for l in range(n):
assert feq(E_nl_dirac(n, l, Z=Z), E_nl(n, Z), 1e-3, 1e-3)
if l > 0:
assert feq(E_nl_dirac(n, l, False, Z), E_nl(n, Z), 1e-3, 1e-3)
# Test the exceptions:
raises(ValueError, lambda: E_nl_dirac(0, 0))
raises(ValueError, lambda: E_nl_dirac(1, -1))
raises(ValueError, lambda: E_nl_dirac(1, 0, False))
| 4,122 | 36.144144 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/test_qho_1d.py
|
from sympy import exp, integrate, oo, Rational, pi, S, simplify, sqrt, Symbol
from sympy.core.compatibility import range
from sympy.abc import omega, m, x
from sympy.physics.qho_1d import psi_n, E_n, coherent_state
from sympy.physics.quantum.constants import hbar
nu = m * omega / hbar
def test_wavefunction():
Psi = {
0: (nu/pi)**(S(1)/4) * exp(-nu * x**2 /2),
1: (nu/pi)**(S(1)/4) * sqrt(2*nu) * x * exp(-nu * x**2 /2),
2: (nu/pi)**(S(1)/4) * (2 * nu * x**2 - 1)/sqrt(2) * exp(-nu * x**2 /2),
3: (nu/pi)**(S(1)/4) * sqrt(nu/3) * (2 * nu * x**3 - 3 * x) * exp(-nu * x**2 /2)
}
for n in Psi:
assert simplify(psi_n(n, x, m, omega) - Psi[n]) == 0
def test_norm(n=1):
# Maximum "n" which is tested:
for i in range(n + 1):
assert integrate(psi_n(i, x, 1, 1)**2, (x, -oo, oo)) == 1
def test_orthogonality(n=1):
# Maximum "n" which is tested:
for i in range(n + 1):
for j in range(i + 1, n + 1):
assert integrate(
psi_n(i, x, 1, 1)*psi_n(j, x, 1, 1), (x, -oo, oo)) == 0
def test_energies(n=1):
# Maximum "n" which is tested:
for i in range(n + 1):
assert E_n(i, omega) == hbar * omega * (i + Rational(1, 2))
def test_coherent_state(n=10):
# Maximum "n" which is tested:
# test whether coherent state is the eigenstate of annihilation operator
alpha = Symbol("alpha")
for i in range(n + 1):
assert simplify(sqrt(n + 1) * coherent_state(n + 1, alpha)) == simplify(alpha * coherent_state(n, alpha))
| 1,552 | 32.76087 | 113 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/test_secondquant.py
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from sympy.physics.secondquant import (
Dagger, Bd, VarBosonicBasis, BBra, B, BKet, FixedBosonicBasis,
matrix_rep, apply_operators, InnerProduct, Commutator, KroneckerDelta,
AnnihilateBoson, CreateBoson, BosonicOperator,
F, Fd, FKet, BosonState, CreateFermion, AnnihilateFermion,
evaluate_deltas, AntiSymmetricTensor, contraction, NO, wicks,
PermutationOperator, simplify_index_permutations,
_sort_anticommuting_fermions, _get_ordered_dummies,
substitute_dummies
)
from sympy import (Dummy, expand, Function, I, Rational, simplify, sqrt, Sum,
Symbol, symbols)
from sympy.core.compatibility import range
from sympy.utilities.pytest import XFAIL, slow
def test_PermutationOperator():
p, q, r, s = symbols('p,q,r,s')
f, g, h, i = map(Function, 'fghi')
P = PermutationOperator
assert P(p, q).get_permuted(f(p)*g(q)) == -f(q)*g(p)
assert P(p, q).get_permuted(f(p, q)) == -f(q, p)
assert P(p, q).get_permuted(f(p)) == f(p)
expr = (f(p)*g(q)*h(r)*i(s)
- f(q)*g(p)*h(r)*i(s)
- f(p)*g(q)*h(s)*i(r)
+ f(q)*g(p)*h(s)*i(r))
perms = [P(p, q), P(r, s)]
assert (simplify_index_permutations(expr, perms) ==
P(p, q)*P(r, s)*f(p)*g(q)*h(r)*i(s))
def test_index_permutations_with_dummies():
a, b, c, d = symbols('a b c d')
p, q, r, s = symbols('p q r s', cls=Dummy)
f, g = map(Function, 'fg')
P = PermutationOperator
# No dummy substitution necessary
expr = f(a, b, p, q) - f(b, a, p, q)
assert simplify_index_permutations(
expr, [P(a, b)]) == P(a, b)*f(a, b, p, q)
# Cases where dummy substitution is needed
expected = P(a, b)*substitute_dummies(f(a, b, p, q))
expr = f(a, b, p, q) - f(b, a, q, p)
result = simplify_index_permutations(expr, [P(a, b)])
assert expected == substitute_dummies(result)
expr = f(a, b, q, p) - f(b, a, p, q)
result = simplify_index_permutations(expr, [P(a, b)])
assert expected == substitute_dummies(result)
# A case where nothing can be done
expr = f(a, b, q, p) - g(b, a, p, q)
result = simplify_index_permutations(expr, [P(a, b)])
assert expr == result
def test_dagger():
i, j, n, m = symbols('i,j,n,m')
assert Dagger(1) == 1
assert Dagger(1.0) == 1.0
assert Dagger(2*I) == -2*I
assert Dagger(Rational(1, 2)*I/3.0) == -Rational(1, 2)*I/3.0
assert Dagger(BKet([n])) == BBra([n])
assert Dagger(B(0)) == Bd(0)
assert Dagger(Bd(0)) == B(0)
assert Dagger(B(n)) == Bd(n)
assert Dagger(Bd(n)) == B(n)
assert Dagger(B(0) + B(1)) == Bd(0) + Bd(1)
assert Dagger(n*m) == Dagger(n)*Dagger(m) # n, m commute
assert Dagger(B(n)*B(m)) == Bd(m)*Bd(n)
assert Dagger(B(n)**10) == Dagger(B(n))**10
def test_operator():
i, j = symbols('i,j')
o = BosonicOperator(i)
assert o.state == i
assert o.is_symbolic
o = BosonicOperator(1)
assert o.state == 1
assert not o.is_symbolic
def test_create():
i, j, n, m = symbols('i,j,n,m')
o = Bd(i)
assert isinstance(o, CreateBoson)
o = o.subs(i, j)
assert o.atoms(Symbol) == {j}
o = Bd(0)
assert o.apply_operator(BKet([n])) == sqrt(n + 1)*BKet([n + 1])
o = Bd(n)
assert o.apply_operator(BKet([n])) == o*BKet([n])
def test_annihilate():
i, j, n, m = symbols('i,j,n,m')
o = B(i)
assert isinstance(o, AnnihilateBoson)
o = o.subs(i, j)
assert o.atoms(Symbol) == {j}
o = B(0)
assert o.apply_operator(BKet([n])) == sqrt(n)*BKet([n - 1])
o = B(n)
assert o.apply_operator(BKet([n])) == o*BKet([n])
def test_basic_state():
i, j, n, m = symbols('i,j,n,m')
s = BosonState([0, 1, 2, 3, 4])
assert len(s) == 5
assert s.args[0] == tuple(range(5))
assert s.up(0) == BosonState([1, 1, 2, 3, 4])
assert s.down(4) == BosonState([0, 1, 2, 3, 3])
for i in range(5):
assert s.up(i).down(i) == s
assert s.down(0) == 0
for i in range(5):
assert s[i] == i
s = BosonState([n, m])
assert s.down(0) == BosonState([n - 1, m])
assert s.up(0) == BosonState([n + 1, m])
@XFAIL
def test_move1():
i, j = symbols('i,j')
A, C = symbols('A,C', cls=Function)
o = A(i)*C(j)
# This almost works, but has a minus sign wrong
assert move(o, 0, 1) == KroneckerDelta(i, j) + C(j)*A(i)
@XFAIL
def test_move2():
i, j = symbols('i,j')
A, C = symbols('A,C', cls=Function)
o = C(j)*A(i)
# This almost works, but has a minus sign wrong
assert move(o, 0, 1) == -KroneckerDelta(i, j) + A(i)*C(j)
def test_basic_apply():
n = symbols("n")
e = B(0)*BKet([n])
assert apply_operators(e) == sqrt(n)*BKet([n - 1])
e = Bd(0)*BKet([n])
assert apply_operators(e) == sqrt(n + 1)*BKet([n + 1])
def test_complex_apply():
n, m = symbols("n,m")
o = Bd(0)*B(0)*Bd(1)*B(0)
e = apply_operators(o*BKet([n, m]))
answer = sqrt(n)*sqrt(m + 1)*(-1 + n)*BKet([-1 + n, 1 + m])
assert expand(e) == expand(answer)
def test_number_operator():
n = symbols("n")
o = Bd(0)*B(0)
e = apply_operators(o*BKet([n]))
assert e == n*BKet([n])
def test_inner_product():
i, j, k, l = symbols('i,j,k,l')
s1 = BBra([0])
s2 = BKet([1])
assert InnerProduct(s1, Dagger(s1)) == 1
assert InnerProduct(s1, s2) == 0
s1 = BBra([i, j])
s2 = BKet([k, l])
r = InnerProduct(s1, s2)
assert r == KroneckerDelta(i, k)*KroneckerDelta(j, l)
def test_symbolic_matrix_elements():
n, m = symbols('n,m')
s1 = BBra([n])
s2 = BKet([m])
o = B(0)
e = apply_operators(s1*o*s2)
assert e == sqrt(m)*KroneckerDelta(n, m - 1)
def test_matrix_elements():
b = VarBosonicBasis(5)
o = B(0)
m = matrix_rep(o, b)
for i in range(4):
assert m[i, i + 1] == sqrt(i + 1)
o = Bd(0)
m = matrix_rep(o, b)
for i in range(4):
assert m[i + 1, i] == sqrt(i + 1)
@slow
def test_sho():
n, m = symbols('n,m')
h_n = Bd(n)*B(n)*(n + Rational(1, 2))
H = Sum(h_n, (n, 0, 5))
o = H.doit(deep=False)
b = FixedBosonicBasis(2, 6)
m = matrix_rep(o, b)
# We need to double check these energy values to make sure that they
# are correct and have the proper degeneracies!
diag = [1, 2, 3, 3, 4, 5, 4, 5, 6, 7, 5, 6, 7, 8, 9, 6, 7, 8, 9, 10, 11]
for i in range(len(diag)):
assert diag[i] == m[i, i]
def test_commutation():
n, m = symbols("n,m", above_fermi=True)
c = Commutator(B(0), Bd(0))
assert c == 1
c = Commutator(Bd(0), B(0))
assert c == -1
c = Commutator(B(n), Bd(0))
assert c == KroneckerDelta(n, 0)
c = Commutator(B(0), Bd(0))
e = simplify(apply_operators(c*BKet([n])))
assert e == BKet([n])
c = Commutator(B(0), B(1))
e = simplify(apply_operators(c*BKet([n, m])))
assert e == 0
c = Commutator(F(m), Fd(m))
assert c == +1 - 2*NO(Fd(m)*F(m))
c = Commutator(Fd(m), F(m))
assert c == -1 + 2*NO(Fd(m)*F(m))
C = Commutator
X, Y, Z = symbols('X,Y,Z', commutative=False)
assert C(C(X, Y), Z) != 0
assert C(C(X, Z), Y) != 0
assert C(Y, C(X, Z)) != 0
i, j, k, l = symbols('i,j,k,l', below_fermi=True)
a, b, c, d = symbols('a,b,c,d', above_fermi=True)
p, q, r, s = symbols('p,q,r,s')
D = KroneckerDelta
assert C(Fd(a), F(i)) == -2*NO(F(i)*Fd(a))
assert C(Fd(j), NO(Fd(a)*F(i))).doit(wicks=True) == -D(j, i)*Fd(a)
assert C(Fd(a)*F(i), Fd(b)*F(j)).doit(wicks=True) == 0
def test_create_f():
i, j, n, m = symbols('i,j,n,m')
o = Fd(i)
assert isinstance(o, CreateFermion)
o = o.subs(i, j)
assert o.atoms(Symbol) == {j}
o = Fd(1)
assert o.apply_operator(FKet([n])) == FKet([1, n])
assert o.apply_operator(FKet([n])) == -FKet([n, 1])
o = Fd(n)
assert o.apply_operator(FKet([])) == FKet([n])
vacuum = FKet([], fermi_level=4)
assert vacuum == FKet([], fermi_level=4)
i, j, k, l = symbols('i,j,k,l', below_fermi=True)
a, b, c, d = symbols('a,b,c,d', above_fermi=True)
p, q, r, s = symbols('p,q,r,s')
assert Fd(i).apply_operator(FKet([i, j, k], 4)) == FKet([j, k], 4)
assert Fd(a).apply_operator(FKet([i, b, k], 4)) == FKet([a, i, b, k], 4)
def test_annihilate_f():
i, j, n, m = symbols('i,j,n,m')
o = F(i)
assert isinstance(o, AnnihilateFermion)
o = o.subs(i, j)
assert o.atoms(Symbol) == {j}
o = F(1)
assert o.apply_operator(FKet([1, n])) == FKet([n])
assert o.apply_operator(FKet([n, 1])) == -FKet([n])
o = F(n)
assert o.apply_operator(FKet([n])) == FKet([])
i, j, k, l = symbols('i,j,k,l', below_fermi=True)
a, b, c, d = symbols('a,b,c,d', above_fermi=True)
p, q, r, s = symbols('p,q,r,s')
assert F(i).apply_operator(FKet([i, j, k], 4)) == 0
assert F(a).apply_operator(FKet([i, b, k], 4)) == 0
assert F(l).apply_operator(FKet([i, j, k], 3)) == 0
assert F(l).apply_operator(FKet([i, j, k], 4)) == FKet([l, i, j, k], 4)
def test_create_b():
i, j, n, m = symbols('i,j,n,m')
o = Bd(i)
assert isinstance(o, CreateBoson)
o = o.subs(i, j)
assert o.atoms(Symbol) == {j}
o = Bd(0)
assert o.apply_operator(BKet([n])) == sqrt(n + 1)*BKet([n + 1])
o = Bd(n)
assert o.apply_operator(BKet([n])) == o*BKet([n])
def test_annihilate_b():
i, j, n, m = symbols('i,j,n,m')
o = B(i)
assert isinstance(o, AnnihilateBoson)
o = o.subs(i, j)
assert o.atoms(Symbol) == {j}
o = B(0)
def test_wicks():
p, q, r, s = symbols('p,q,r,s', above_fermi=True)
# Testing for particles only
str = F(p)*Fd(q)
assert wicks(str) == NO(F(p)*Fd(q)) + KroneckerDelta(p, q)
str = Fd(p)*F(q)
assert wicks(str) == NO(Fd(p)*F(q))
str = F(p)*Fd(q)*F(r)*Fd(s)
nstr = wicks(str)
fasit = NO(
KroneckerDelta(p, q)*KroneckerDelta(r, s)
+ KroneckerDelta(p, q)*AnnihilateFermion(r)*CreateFermion(s)
+ KroneckerDelta(r, s)*AnnihilateFermion(p)*CreateFermion(q)
- KroneckerDelta(p, s)*AnnihilateFermion(r)*CreateFermion(q)
- AnnihilateFermion(p)*AnnihilateFermion(r)*CreateFermion(q)*CreateFermion(s))
assert nstr == fasit
assert (p*q*nstr).expand() == wicks(p*q*str)
assert (nstr*p*q*2).expand() == wicks(str*p*q*2)
# Testing CC equations particles and holes
i, j, k, l = symbols('i j k l', below_fermi=True, cls=Dummy)
a, b, c, d = symbols('a b c d', above_fermi=True, cls=Dummy)
p, q, r, s = symbols('p q r s', cls=Dummy)
assert (wicks(F(a)*NO(F(i)*F(j))*Fd(b)) ==
NO(F(a)*F(i)*F(j)*Fd(b)) +
KroneckerDelta(a, b)*NO(F(i)*F(j)))
assert (wicks(F(a)*NO(F(i)*F(j)*F(k))*Fd(b)) ==
NO(F(a)*F(i)*F(j)*F(k)*Fd(b)) -
KroneckerDelta(a, b)*NO(F(i)*F(j)*F(k)))
expr = wicks(Fd(i)*NO(Fd(j)*F(k))*F(l))
assert (expr ==
-KroneckerDelta(i, k)*NO(Fd(j)*F(l)) -
KroneckerDelta(j, l)*NO(Fd(i)*F(k)) -
KroneckerDelta(i, k)*KroneckerDelta(j, l) +
KroneckerDelta(i, l)*NO(Fd(j)*F(k)) +
NO(Fd(i)*Fd(j)*F(k)*F(l)))
expr = wicks(F(a)*NO(F(b)*Fd(c))*Fd(d))
assert (expr ==
-KroneckerDelta(a, c)*NO(F(b)*Fd(d)) -
KroneckerDelta(b, d)*NO(F(a)*Fd(c)) -
KroneckerDelta(a, c)*KroneckerDelta(b, d) +
KroneckerDelta(a, d)*NO(F(b)*Fd(c)) +
NO(F(a)*F(b)*Fd(c)*Fd(d)))
def test_NO():
i, j, k, l = symbols('i j k l', below_fermi=True)
a, b, c, d = symbols('a b c d', above_fermi=True)
p, q, r, s = symbols('p q r s', cls=Dummy)
assert (NO(Fd(p)*F(q) + Fd(a)*F(b)) ==
NO(Fd(p)*F(q)) + NO(Fd(a)*F(b)))
assert (NO(Fd(i)*NO(F(j)*Fd(a))) ==
NO(Fd(i)*F(j)*Fd(a)))
assert NO(1) == 1
assert NO(i) == i
assert (NO(Fd(a)*Fd(b)*(F(c) + F(d))) ==
NO(Fd(a)*Fd(b)*F(c)) +
NO(Fd(a)*Fd(b)*F(d)))
assert NO(Fd(a)*F(b))._remove_brackets() == Fd(a)*F(b)
assert NO(F(j)*Fd(i))._remove_brackets() == F(j)*Fd(i)
assert (NO(Fd(p)*F(q)).subs(Fd(p), Fd(a) + Fd(i)) ==
NO(Fd(a)*F(q)) + NO(Fd(i)*F(q)))
assert (NO(Fd(p)*F(q)).subs(F(q), F(a) + F(i)) ==
NO(Fd(p)*F(a)) + NO(Fd(p)*F(i)))
expr = NO(Fd(p)*F(q))._remove_brackets()
assert wicks(expr) == NO(expr)
assert NO(Fd(a)*F(b)) == - NO(F(b)*Fd(a))
no = NO(Fd(a)*F(i)*F(b)*Fd(j))
l1 = [ ind for ind in no.iter_q_creators() ]
assert l1 == [0, 1]
l2 = [ ind for ind in no.iter_q_annihilators() ]
assert l2 == [3, 2]
def test_sorting():
i, j = symbols('i,j', below_fermi=True)
a, b = symbols('a,b', above_fermi=True)
p, q = symbols('p,q')
# p, q
assert _sort_anticommuting_fermions([Fd(p), F(q)]) == ([Fd(p), F(q)], 0)
assert _sort_anticommuting_fermions([F(p), Fd(q)]) == ([Fd(q), F(p)], 1)
# i, p
assert _sort_anticommuting_fermions([F(p), Fd(i)]) == ([F(p), Fd(i)], 0)
assert _sort_anticommuting_fermions([Fd(i), F(p)]) == ([F(p), Fd(i)], 1)
assert _sort_anticommuting_fermions([Fd(p), Fd(i)]) == ([Fd(p), Fd(i)], 0)
assert _sort_anticommuting_fermions([Fd(i), Fd(p)]) == ([Fd(p), Fd(i)], 1)
assert _sort_anticommuting_fermions([F(p), F(i)]) == ([F(i), F(p)], 1)
assert _sort_anticommuting_fermions([F(i), F(p)]) == ([F(i), F(p)], 0)
assert _sort_anticommuting_fermions([Fd(p), F(i)]) == ([F(i), Fd(p)], 1)
assert _sort_anticommuting_fermions([F(i), Fd(p)]) == ([F(i), Fd(p)], 0)
# a, p
assert _sort_anticommuting_fermions([F(p), Fd(a)]) == ([Fd(a), F(p)], 1)
assert _sort_anticommuting_fermions([Fd(a), F(p)]) == ([Fd(a), F(p)], 0)
assert _sort_anticommuting_fermions([Fd(p), Fd(a)]) == ([Fd(a), Fd(p)], 1)
assert _sort_anticommuting_fermions([Fd(a), Fd(p)]) == ([Fd(a), Fd(p)], 0)
assert _sort_anticommuting_fermions([F(p), F(a)]) == ([F(p), F(a)], 0)
assert _sort_anticommuting_fermions([F(a), F(p)]) == ([F(p), F(a)], 1)
assert _sort_anticommuting_fermions([Fd(p), F(a)]) == ([Fd(p), F(a)], 0)
assert _sort_anticommuting_fermions([F(a), Fd(p)]) == ([Fd(p), F(a)], 1)
# i, a
assert _sort_anticommuting_fermions([F(i), Fd(j)]) == ([F(i), Fd(j)], 0)
assert _sort_anticommuting_fermions([Fd(j), F(i)]) == ([F(i), Fd(j)], 1)
assert _sort_anticommuting_fermions([Fd(a), Fd(i)]) == ([Fd(a), Fd(i)], 0)
assert _sort_anticommuting_fermions([Fd(i), Fd(a)]) == ([Fd(a), Fd(i)], 1)
assert _sort_anticommuting_fermions([F(a), F(i)]) == ([F(i), F(a)], 1)
assert _sort_anticommuting_fermions([F(i), F(a)]) == ([F(i), F(a)], 0)
def test_contraction():
i, j, k, l = symbols('i,j,k,l', below_fermi=True)
a, b, c, d = symbols('a,b,c,d', above_fermi=True)
p, q, r, s = symbols('p,q,r,s')
assert contraction(Fd(i), F(j)) == KroneckerDelta(i, j)
assert contraction(F(a), Fd(b)) == KroneckerDelta(a, b)
assert contraction(F(a), Fd(i)) == 0
assert contraction(Fd(a), F(i)) == 0
assert contraction(F(i), Fd(a)) == 0
assert contraction(Fd(i), F(a)) == 0
assert contraction(Fd(i), F(p)) == KroneckerDelta(i, p)
restr = evaluate_deltas(contraction(Fd(p), F(q)))
assert restr.is_only_below_fermi
restr = evaluate_deltas(contraction(F(p), Fd(q)))
assert restr.is_only_above_fermi
def test_evaluate_deltas():
i, j, k = symbols('i,j,k')
r = KroneckerDelta(i, j) * KroneckerDelta(j, k)
assert evaluate_deltas(r) == KroneckerDelta(i, k)
r = KroneckerDelta(i, 0) * KroneckerDelta(j, k)
assert evaluate_deltas(r) == KroneckerDelta(i, 0) * KroneckerDelta(j, k)
r = KroneckerDelta(1, j) * KroneckerDelta(j, k)
assert evaluate_deltas(r) == KroneckerDelta(1, k)
r = KroneckerDelta(j, 2) * KroneckerDelta(k, j)
assert evaluate_deltas(r) == KroneckerDelta(2, k)
r = KroneckerDelta(i, 0) * KroneckerDelta(i, j) * KroneckerDelta(j, 1)
assert evaluate_deltas(r) == 0
r = (KroneckerDelta(0, i) * KroneckerDelta(0, j)
* KroneckerDelta(1, j) * KroneckerDelta(1, j))
assert evaluate_deltas(r) == 0
def test_Tensors():
i, j, k, l = symbols('i j k l', below_fermi=True, cls=Dummy)
a, b, c, d = symbols('a b c d', above_fermi=True, cls=Dummy)
p, q, r, s = symbols('p q r s')
AT = AntiSymmetricTensor
assert AT('t', (a, b), (i, j)) == -AT('t', (b, a), (i, j))
assert AT('t', (a, b), (i, j)) == AT('t', (b, a), (j, i))
assert AT('t', (a, b), (i, j)) == -AT('t', (a, b), (j, i))
assert AT('t', (a, a), (i, j)) == 0
assert AT('t', (a, b), (i, i)) == 0
assert AT('t', (a, b, c), (i, j)) == -AT('t', (b, a, c), (i, j))
assert AT('t', (a, b, c), (i, j, k)) == AT('t', (b, a, c), (i, k, j))
tabij = AT('t', (a, b), (i, j))
assert tabij.has(a)
assert tabij.has(b)
assert tabij.has(i)
assert tabij.has(j)
assert tabij.subs(b, c) == AT('t', (a, c), (i, j))
assert (2*tabij).subs(i, c) == 2*AT('t', (a, b), (c, j))
assert AT('t', (a, a), (i, j)).subs(a, b) == AT('t', (b, b), (i, j))
assert AT('t', (a, i), (a, j)).subs(a, b) == AT('t', (b, i), (b, j))
def test_fully_contracted():
i, j, k, l = symbols('i j k l', below_fermi=True)
a, b, c, d = symbols('a b c d', above_fermi=True)
p, q, r, s = symbols('p q r s', cls=Dummy)
Fock = (AntiSymmetricTensor('f', (p,), (q,))*
NO(Fd(p)*F(q)))
V = (AntiSymmetricTensor('v', (p, q), (r, s))*
NO(Fd(p)*Fd(q)*F(s)*F(r)))/4
Fai = wicks(NO(Fd(i)*F(a))*Fock,
keep_only_fully_contracted=True,
simplify_kronecker_deltas=True)
assert Fai == AntiSymmetricTensor('f', (a,), (i,))
Vabij = wicks(NO(Fd(i)*Fd(j)*F(b)*F(a))*V,
keep_only_fully_contracted=True,
simplify_kronecker_deltas=True)
assert Vabij == AntiSymmetricTensor('v', (a, b), (i, j))
def test_substitute_dummies_without_dummies():
i, j = symbols('i,j')
assert substitute_dummies(att(i, j) + 2) == att(i, j) + 2
assert substitute_dummies(att(i, j) + 1) == att(i, j) + 1
def test_substitute_dummies_NO_operator():
i, j = symbols('i j', cls=Dummy)
assert substitute_dummies(att(i, j)*NO(Fd(i)*F(j))
- att(j, i)*NO(Fd(j)*F(i))) == 0
def test_substitute_dummies_SQ_operator():
i, j = symbols('i j', cls=Dummy)
assert substitute_dummies(att(i, j)*Fd(i)*F(j)
- att(j, i)*Fd(j)*F(i)) == 0
def test_substitute_dummies_new_indices():
i, j = symbols('i j', below_fermi=True, cls=Dummy)
a, b = symbols('a b', above_fermi=True, cls=Dummy)
p, q = symbols('p q', cls=Dummy)
f = Function('f')
assert substitute_dummies(f(i, a, p) - f(j, b, q), new_indices=True) == 0
def test_substitute_dummies_substitution_order():
i, j, k, l = symbols('i j k l', below_fermi=True, cls=Dummy)
f = Function('f')
from sympy.utilities.iterables import variations
for permut in variations([i, j, k, l], 4):
assert substitute_dummies(f(*permut) - f(i, j, k, l)) == 0
def test_dummy_order_inner_outer_lines_VT1T1T1():
ii = symbols('i', below_fermi=True)
aa = symbols('a', above_fermi=True)
k, l = symbols('k l', below_fermi=True, cls=Dummy)
c, d = symbols('c d', above_fermi=True, cls=Dummy)
v = Function('v')
t = Function('t')
dums = _get_ordered_dummies
# Coupled-Cluster T1 terms with V*T1*T1*T1
# t^{a}_{k} t^{c}_{i} t^{d}_{l} v^{lk}_{dc}
exprs = [
# permut v and t <=> swapping internal lines, equivalent
# irrespective of symmetries in v
v(k, l, c, d)*t(c, ii)*t(d, l)*t(aa, k),
v(l, k, c, d)*t(c, ii)*t(d, k)*t(aa, l),
v(k, l, d, c)*t(d, ii)*t(c, l)*t(aa, k),
v(l, k, d, c)*t(d, ii)*t(c, k)*t(aa, l),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_dummy_order_inner_outer_lines_VT1T1T1T1():
ii, jj = symbols('i j', below_fermi=True)
aa, bb = symbols('a b', above_fermi=True)
k, l = symbols('k l', below_fermi=True, cls=Dummy)
c, d = symbols('c d', above_fermi=True, cls=Dummy)
v = Function('v')
t = Function('t')
dums = _get_ordered_dummies
# Coupled-Cluster T2 terms with V*T1*T1*T1*T1
exprs = [
# permut t <=> swapping external lines, not equivalent
# except if v has certain symmetries.
v(k, l, c, d)*t(c, ii)*t(d, jj)*t(aa, k)*t(bb, l),
v(k, l, c, d)*t(c, jj)*t(d, ii)*t(aa, k)*t(bb, l),
v(k, l, c, d)*t(c, ii)*t(d, jj)*t(bb, k)*t(aa, l),
v(k, l, c, d)*t(c, jj)*t(d, ii)*t(bb, k)*t(aa, l),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [
# permut v <=> swapping external lines, not equivalent
# except if v has certain symmetries.
#
# Note that in contrast to above, these permutations have identical
# dummy order. That is because the proximity to external indices
# has higher influence on the canonical dummy ordering than the
# position of a dummy on the factors. In fact, the terms here are
# similar in structure as the result of the dummy substitions above.
v(k, l, c, d)*t(c, ii)*t(d, jj)*t(aa, k)*t(bb, l),
v(l, k, c, d)*t(c, ii)*t(d, jj)*t(aa, k)*t(bb, l),
v(k, l, d, c)*t(c, ii)*t(d, jj)*t(aa, k)*t(bb, l),
v(l, k, d, c)*t(c, ii)*t(d, jj)*t(aa, k)*t(bb, l),
]
for permut in exprs[1:]:
assert dums(exprs[0]) == dums(permut)
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [
# permut t and v <=> swapping internal lines, equivalent.
# Canonical dummy order is different, and a consistent
# substitution reveals the equivalence.
v(k, l, c, d)*t(c, ii)*t(d, jj)*t(aa, k)*t(bb, l),
v(k, l, d, c)*t(c, jj)*t(d, ii)*t(aa, k)*t(bb, l),
v(l, k, c, d)*t(c, ii)*t(d, jj)*t(bb, k)*t(aa, l),
v(l, k, d, c)*t(c, jj)*t(d, ii)*t(bb, k)*t(aa, l),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_equivalent_internal_lines_VT1T1():
i, j, k, l = symbols('i j k l', below_fermi=True, cls=Dummy)
a, b, c, d = symbols('a b c d', above_fermi=True, cls=Dummy)
v = Function('v')
t = Function('t')
dums = _get_ordered_dummies
exprs = [ # permute v. Different dummy order. Not equivalent.
v(i, j, a, b)*t(a, i)*t(b, j),
v(j, i, a, b)*t(a, i)*t(b, j),
v(i, j, b, a)*t(a, i)*t(b, j),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [ # permute v. Different dummy order. Equivalent
v(i, j, a, b)*t(a, i)*t(b, j),
v(j, i, b, a)*t(a, i)*t(b, j),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
exprs = [ # permute t. Same dummy order, not equivalent.
v(i, j, a, b)*t(a, i)*t(b, j),
v(i, j, a, b)*t(b, i)*t(a, j),
]
for permut in exprs[1:]:
assert dums(exprs[0]) == dums(permut)
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [ # permute v and t. Different dummy order, equivalent
v(i, j, a, b)*t(a, i)*t(b, j),
v(j, i, a, b)*t(a, j)*t(b, i),
v(i, j, b, a)*t(b, i)*t(a, j),
v(j, i, b, a)*t(b, j)*t(a, i),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_equivalent_internal_lines_VT2conjT2():
# this diagram requires special handling in TCE
i, j, k, l, m, n = symbols('i j k l m n', below_fermi=True, cls=Dummy)
a, b, c, d, e, f = symbols('a b c d e f', above_fermi=True, cls=Dummy)
p1, p2, p3, p4 = symbols('p1 p2 p3 p4', above_fermi=True, cls=Dummy)
h1, h2, h3, h4 = symbols('h1 h2 h3 h4', below_fermi=True, cls=Dummy)
from sympy.utilities.iterables import variations
v = Function('v')
t = Function('t')
dums = _get_ordered_dummies
# v(abcd)t(abij)t(ijcd)
template = v(p1, p2, p3, p4)*t(p1, p2, i, j)*t(i, j, p3, p4)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert dums(base) != dums(expr)
assert substitute_dummies(expr) == substitute_dummies(base)
template = v(p1, p2, p3, p4)*t(p1, p2, j, i)*t(j, i, p3, p4)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert dums(base) != dums(expr)
assert substitute_dummies(expr) == substitute_dummies(base)
# v(abcd)t(abij)t(jicd)
template = v(p1, p2, p3, p4)*t(p1, p2, i, j)*t(j, i, p3, p4)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert dums(base) != dums(expr)
assert substitute_dummies(expr) == substitute_dummies(base)
template = v(p1, p2, p3, p4)*t(p1, p2, j, i)*t(i, j, p3, p4)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert dums(base) != dums(expr)
assert substitute_dummies(expr) == substitute_dummies(base)
def test_equivalent_internal_lines_VT2conjT2_ambiguous_order():
# These diagrams invokes _determine_ambiguous() because the
# dummies can not be ordered unambiguously by the key alone
i, j, k, l, m, n = symbols('i j k l m n', below_fermi=True, cls=Dummy)
a, b, c, d, e, f = symbols('a b c d e f', above_fermi=True, cls=Dummy)
p1, p2, p3, p4 = symbols('p1 p2 p3 p4', above_fermi=True, cls=Dummy)
h1, h2, h3, h4 = symbols('h1 h2 h3 h4', below_fermi=True, cls=Dummy)
from sympy.utilities.iterables import variations
v = Function('v')
t = Function('t')
dums = _get_ordered_dummies
# v(abcd)t(abij)t(cdij)
template = v(p1, p2, p3, p4)*t(p1, p2, i, j)*t(p3, p4, i, j)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert dums(base) != dums(expr)
assert substitute_dummies(expr) == substitute_dummies(base)
template = v(p1, p2, p3, p4)*t(p1, p2, j, i)*t(p3, p4, i, j)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert dums(base) != dums(expr)
assert substitute_dummies(expr) == substitute_dummies(base)
def test_equivalent_internal_lines_VT2():
i, j, k, l = symbols('i j k l', below_fermi=True, cls=Dummy)
a, b, c, d = symbols('a b c d', above_fermi=True, cls=Dummy)
v = Function('v')
t = Function('t')
dums = _get_ordered_dummies
exprs = [
# permute v. Same dummy order, not equivalent.
#
# This test show that the dummy order may not be sensitive to all
# index permutations. The following expressions have identical
# structure as the resulting terms from of the dummy subsitutions
# in the test above. Here, all expressions have the same dummy
# order, so they cannot be simplified by means of dummy
# substitution. In order to simplify further, it is necessary to
# exploit symmetries in the objects, for instance if t or v is
# antisymmetric.
v(i, j, a, b)*t(a, b, i, j),
v(j, i, a, b)*t(a, b, i, j),
v(i, j, b, a)*t(a, b, i, j),
v(j, i, b, a)*t(a, b, i, j),
]
for permut in exprs[1:]:
assert dums(exprs[0]) == dums(permut)
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [
# permute t.
v(i, j, a, b)*t(a, b, i, j),
v(i, j, a, b)*t(b, a, i, j),
v(i, j, a, b)*t(a, b, j, i),
v(i, j, a, b)*t(b, a, j, i),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [ # permute v and t. Relabelling of dummies should be equivalent.
v(i, j, a, b)*t(a, b, i, j),
v(j, i, a, b)*t(a, b, j, i),
v(i, j, b, a)*t(b, a, i, j),
v(j, i, b, a)*t(b, a, j, i),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_internal_external_VT2T2():
ii, jj = symbols('i j', below_fermi=True)
aa, bb = symbols('a b', above_fermi=True)
k, l = symbols('k l', below_fermi=True, cls=Dummy)
c, d = symbols('c d', above_fermi=True, cls=Dummy)
v = Function('v')
t = Function('t')
dums = _get_ordered_dummies
exprs = [
v(k, l, c, d)*t(aa, c, ii, k)*t(bb, d, jj, l),
v(l, k, c, d)*t(aa, c, ii, l)*t(bb, d, jj, k),
v(k, l, d, c)*t(aa, d, ii, k)*t(bb, c, jj, l),
v(l, k, d, c)*t(aa, d, ii, l)*t(bb, c, jj, k),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
exprs = [
v(k, l, c, d)*t(aa, c, ii, k)*t(d, bb, jj, l),
v(l, k, c, d)*t(aa, c, ii, l)*t(d, bb, jj, k),
v(k, l, d, c)*t(aa, d, ii, k)*t(c, bb, jj, l),
v(l, k, d, c)*t(aa, d, ii, l)*t(c, bb, jj, k),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
exprs = [
v(k, l, c, d)*t(c, aa, ii, k)*t(bb, d, jj, l),
v(l, k, c, d)*t(c, aa, ii, l)*t(bb, d, jj, k),
v(k, l, d, c)*t(d, aa, ii, k)*t(bb, c, jj, l),
v(l, k, d, c)*t(d, aa, ii, l)*t(bb, c, jj, k),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_internal_external_pqrs():
ii, jj = symbols('i j')
aa, bb = symbols('a b')
k, l = symbols('k l', cls=Dummy)
c, d = symbols('c d', cls=Dummy)
v = Function('v')
t = Function('t')
dums = _get_ordered_dummies
exprs = [
v(k, l, c, d)*t(aa, c, ii, k)*t(bb, d, jj, l),
v(l, k, c, d)*t(aa, c, ii, l)*t(bb, d, jj, k),
v(k, l, d, c)*t(aa, d, ii, k)*t(bb, c, jj, l),
v(l, k, d, c)*t(aa, d, ii, l)*t(bb, c, jj, k),
]
for permut in exprs[1:]:
assert dums(exprs[0]) != dums(permut)
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_dummy_order_well_defined():
aa, bb = symbols('a b', above_fermi=True)
k, l, m = symbols('k l m', below_fermi=True, cls=Dummy)
c, d = symbols('c d', above_fermi=True, cls=Dummy)
p, q = symbols('p q', cls=Dummy)
A = Function('A')
B = Function('B')
C = Function('C')
dums = _get_ordered_dummies
# We go through all key components in the order of increasing priority,
# and consider only fully orderable expressions. Non-orderable expressions
# are tested elsewhere.
# pos in first factor determines sort order
assert dums(A(k, l)*B(l, k)) == [k, l]
assert dums(A(l, k)*B(l, k)) == [l, k]
assert dums(A(k, l)*B(k, l)) == [k, l]
assert dums(A(l, k)*B(k, l)) == [l, k]
# factors involving the index
assert dums(A(k, l)*B(l, m)*C(k, m)) == [l, k, m]
assert dums(A(k, l)*B(l, m)*C(m, k)) == [l, k, m]
assert dums(A(l, k)*B(l, m)*C(k, m)) == [l, k, m]
assert dums(A(l, k)*B(l, m)*C(m, k)) == [l, k, m]
assert dums(A(k, l)*B(m, l)*C(k, m)) == [l, k, m]
assert dums(A(k, l)*B(m, l)*C(m, k)) == [l, k, m]
assert dums(A(l, k)*B(m, l)*C(k, m)) == [l, k, m]
assert dums(A(l, k)*B(m, l)*C(m, k)) == [l, k, m]
# same, but with factor order determined by non-dummies
assert dums(A(k, aa, l)*A(l, bb, m)*A(bb, k, m)) == [l, k, m]
assert dums(A(k, aa, l)*A(l, bb, m)*A(bb, m, k)) == [l, k, m]
assert dums(A(k, aa, l)*A(m, bb, l)*A(bb, k, m)) == [l, k, m]
assert dums(A(k, aa, l)*A(m, bb, l)*A(bb, m, k)) == [l, k, m]
assert dums(A(l, aa, k)*A(l, bb, m)*A(bb, k, m)) == [l, k, m]
assert dums(A(l, aa, k)*A(l, bb, m)*A(bb, m, k)) == [l, k, m]
assert dums(A(l, aa, k)*A(m, bb, l)*A(bb, k, m)) == [l, k, m]
assert dums(A(l, aa, k)*A(m, bb, l)*A(bb, m, k)) == [l, k, m]
# index range
assert dums(A(p, c, k)*B(p, c, k)) == [k, c, p]
assert dums(A(p, k, c)*B(p, c, k)) == [k, c, p]
assert dums(A(c, k, p)*B(p, c, k)) == [k, c, p]
assert dums(A(c, p, k)*B(p, c, k)) == [k, c, p]
assert dums(A(k, c, p)*B(p, c, k)) == [k, c, p]
assert dums(A(k, p, c)*B(p, c, k)) == [k, c, p]
assert dums(B(p, c, k)*A(p, c, k)) == [k, c, p]
assert dums(B(p, k, c)*A(p, c, k)) == [k, c, p]
assert dums(B(c, k, p)*A(p, c, k)) == [k, c, p]
assert dums(B(c, p, k)*A(p, c, k)) == [k, c, p]
assert dums(B(k, c, p)*A(p, c, k)) == [k, c, p]
assert dums(B(k, p, c)*A(p, c, k)) == [k, c, p]
def test_dummy_order_ambiguous():
aa, bb = symbols('a b', above_fermi=True)
i, j, k, l, m = symbols('i j k l m', below_fermi=True, cls=Dummy)
a, b, c, d, e = symbols('a b c d e', above_fermi=True, cls=Dummy)
p, q = symbols('p q', cls=Dummy)
p1, p2, p3, p4 = symbols('p1 p2 p3 p4', above_fermi=True, cls=Dummy)
p5, p6, p7, p8 = symbols('p5 p6 p7 p8', above_fermi=True, cls=Dummy)
h1, h2, h3, h4 = symbols('h1 h2 h3 h4', below_fermi=True, cls=Dummy)
h5, h6, h7, h8 = symbols('h5 h6 h7 h8', below_fermi=True, cls=Dummy)
A = Function('A')
B = Function('B')
from sympy.utilities.iterables import variations
# A*A*A*A*B -- ordering of p5 and p4 is used to figure out the rest
template = A(p1, p2)*A(p4, p1)*A(p2, p3)*A(p3, p5)*B(p5, p4)
permutator = variations([a, b, c, d, e], 5)
base = template.subs(zip([p1, p2, p3, p4, p5], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4, p5], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
# A*A*A*A*A -- an arbitrary index is assigned and the rest are figured out
template = A(p1, p2)*A(p4, p1)*A(p2, p3)*A(p3, p5)*A(p5, p4)
permutator = variations([a, b, c, d, e], 5)
base = template.subs(zip([p1, p2, p3, p4, p5], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4, p5], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
# A*A*A -- ordering of p5 and p4 is used to figure out the rest
template = A(p1, p2, p4, p1)*A(p2, p3, p3, p5)*A(p5, p4)
permutator = variations([a, b, c, d, e], 5)
base = template.subs(zip([p1, p2, p3, p4, p5], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4, p5], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
def atv(*args):
return AntiSymmetricTensor('v', args[:2], args[2:] )
def att(*args):
if len(args) == 4:
return AntiSymmetricTensor('t', args[:2], args[2:] )
elif len(args) == 2:
return AntiSymmetricTensor('t', (args[0],), (args[1],))
def test_dummy_order_inner_outer_lines_VT1T1T1_AT():
ii = symbols('i', below_fermi=True)
aa = symbols('a', above_fermi=True)
k, l = symbols('k l', below_fermi=True, cls=Dummy)
c, d = symbols('c d', above_fermi=True, cls=Dummy)
# Coupled-Cluster T1 terms with V*T1*T1*T1
# t^{a}_{k} t^{c}_{i} t^{d}_{l} v^{lk}_{dc}
exprs = [
# permut v and t <=> swapping internal lines, equivalent
# irrespective of symmetries in v
atv(k, l, c, d)*att(c, ii)*att(d, l)*att(aa, k),
atv(l, k, c, d)*att(c, ii)*att(d, k)*att(aa, l),
atv(k, l, d, c)*att(d, ii)*att(c, l)*att(aa, k),
atv(l, k, d, c)*att(d, ii)*att(c, k)*att(aa, l),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_dummy_order_inner_outer_lines_VT1T1T1T1_AT():
ii, jj = symbols('i j', below_fermi=True)
aa, bb = symbols('a b', above_fermi=True)
k, l = symbols('k l', below_fermi=True, cls=Dummy)
c, d = symbols('c d', above_fermi=True, cls=Dummy)
# Coupled-Cluster T2 terms with V*T1*T1*T1*T1
# non-equivalent substitutions (change of sign)
exprs = [
# permut t <=> swapping external lines
atv(k, l, c, d)*att(c, ii)*att(d, jj)*att(aa, k)*att(bb, l),
atv(k, l, c, d)*att(c, jj)*att(d, ii)*att(aa, k)*att(bb, l),
atv(k, l, c, d)*att(c, ii)*att(d, jj)*att(bb, k)*att(aa, l),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == -substitute_dummies(permut)
# equivalent substitutions
exprs = [
atv(k, l, c, d)*att(c, ii)*att(d, jj)*att(aa, k)*att(bb, l),
# permut t <=> swapping external lines
atv(k, l, c, d)*att(c, jj)*att(d, ii)*att(bb, k)*att(aa, l),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_equivalent_internal_lines_VT1T1_AT():
i, j, k, l = symbols('i j k l', below_fermi=True, cls=Dummy)
a, b, c, d = symbols('a b c d', above_fermi=True, cls=Dummy)
exprs = [ # permute v. Different dummy order. Not equivalent.
atv(i, j, a, b)*att(a, i)*att(b, j),
atv(j, i, a, b)*att(a, i)*att(b, j),
atv(i, j, b, a)*att(a, i)*att(b, j),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [ # permute v. Different dummy order. Equivalent
atv(i, j, a, b)*att(a, i)*att(b, j),
atv(j, i, b, a)*att(a, i)*att(b, j),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
exprs = [ # permute t. Same dummy order, not equivalent.
atv(i, j, a, b)*att(a, i)*att(b, j),
atv(i, j, a, b)*att(b, i)*att(a, j),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [ # permute v and t. Different dummy order, equivalent
atv(i, j, a, b)*att(a, i)*att(b, j),
atv(j, i, a, b)*att(a, j)*att(b, i),
atv(i, j, b, a)*att(b, i)*att(a, j),
atv(j, i, b, a)*att(b, j)*att(a, i),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_equivalent_internal_lines_VT2conjT2_AT():
# this diagram requires special handling in TCE
i, j, k, l, m, n = symbols('i j k l m n', below_fermi=True, cls=Dummy)
a, b, c, d, e, f = symbols('a b c d e f', above_fermi=True, cls=Dummy)
p1, p2, p3, p4 = symbols('p1 p2 p3 p4', above_fermi=True, cls=Dummy)
h1, h2, h3, h4 = symbols('h1 h2 h3 h4', below_fermi=True, cls=Dummy)
from sympy.utilities.iterables import variations
# atv(abcd)att(abij)att(ijcd)
template = atv(p1, p2, p3, p4)*att(p1, p2, i, j)*att(i, j, p3, p4)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
template = atv(p1, p2, p3, p4)*att(p1, p2, j, i)*att(j, i, p3, p4)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
# atv(abcd)att(abij)att(jicd)
template = atv(p1, p2, p3, p4)*att(p1, p2, i, j)*att(j, i, p3, p4)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
template = atv(p1, p2, p3, p4)*att(p1, p2, j, i)*att(i, j, p3, p4)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
def test_equivalent_internal_lines_VT2conjT2_ambiguous_order_AT():
# These diagrams invokes _determine_ambiguous() because the
# dummies can not be ordered unambiguously by the key alone
i, j, k, l, m, n = symbols('i j k l m n', below_fermi=True, cls=Dummy)
a, b, c, d, e, f = symbols('a b c d e f', above_fermi=True, cls=Dummy)
p1, p2, p3, p4 = symbols('p1 p2 p3 p4', above_fermi=True, cls=Dummy)
h1, h2, h3, h4 = symbols('h1 h2 h3 h4', below_fermi=True, cls=Dummy)
from sympy.utilities.iterables import variations
# atv(abcd)att(abij)att(cdij)
template = atv(p1, p2, p3, p4)*att(p1, p2, i, j)*att(p3, p4, i, j)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
template = atv(p1, p2, p3, p4)*att(p1, p2, j, i)*att(p3, p4, i, j)
permutator = variations([a, b, c, d], 4)
base = template.subs(zip([p1, p2, p3, p4], next(permutator)))
for permut in permutator:
subslist = zip([p1, p2, p3, p4], permut)
expr = template.subs(subslist)
assert substitute_dummies(expr) == substitute_dummies(base)
def test_equivalent_internal_lines_VT2_AT():
i, j, k, l = symbols('i j k l', below_fermi=True, cls=Dummy)
a, b, c, d = symbols('a b c d', above_fermi=True, cls=Dummy)
exprs = [
# permute v. Same dummy order, not equivalent.
atv(i, j, a, b)*att(a, b, i, j),
atv(j, i, a, b)*att(a, b, i, j),
atv(i, j, b, a)*att(a, b, i, j),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [
# permute t.
atv(i, j, a, b)*att(a, b, i, j),
atv(i, j, a, b)*att(b, a, i, j),
atv(i, j, a, b)*att(a, b, j, i),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) != substitute_dummies(permut)
exprs = [ # permute v and t. Relabelling of dummies should be equivalent.
atv(i, j, a, b)*att(a, b, i, j),
atv(j, i, a, b)*att(a, b, j, i),
atv(i, j, b, a)*att(b, a, i, j),
atv(j, i, b, a)*att(b, a, j, i),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_internal_external_VT2T2_AT():
ii, jj = symbols('i j', below_fermi=True)
aa, bb = symbols('a b', above_fermi=True)
k, l = symbols('k l', below_fermi=True, cls=Dummy)
c, d = symbols('c d', above_fermi=True, cls=Dummy)
exprs = [
atv(k, l, c, d)*att(aa, c, ii, k)*att(bb, d, jj, l),
atv(l, k, c, d)*att(aa, c, ii, l)*att(bb, d, jj, k),
atv(k, l, d, c)*att(aa, d, ii, k)*att(bb, c, jj, l),
atv(l, k, d, c)*att(aa, d, ii, l)*att(bb, c, jj, k),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
exprs = [
atv(k, l, c, d)*att(aa, c, ii, k)*att(d, bb, jj, l),
atv(l, k, c, d)*att(aa, c, ii, l)*att(d, bb, jj, k),
atv(k, l, d, c)*att(aa, d, ii, k)*att(c, bb, jj, l),
atv(l, k, d, c)*att(aa, d, ii, l)*att(c, bb, jj, k),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
exprs = [
atv(k, l, c, d)*att(c, aa, ii, k)*att(bb, d, jj, l),
atv(l, k, c, d)*att(c, aa, ii, l)*att(bb, d, jj, k),
atv(k, l, d, c)*att(d, aa, ii, k)*att(bb, c, jj, l),
atv(l, k, d, c)*att(d, aa, ii, l)*att(bb, c, jj, k),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
def test_internal_external_pqrs_AT():
ii, jj = symbols('i j')
aa, bb = symbols('a b')
k, l = symbols('k l', cls=Dummy)
c, d = symbols('c d', cls=Dummy)
exprs = [
atv(k, l, c, d)*att(aa, c, ii, k)*att(bb, d, jj, l),
atv(l, k, c, d)*att(aa, c, ii, l)*att(bb, d, jj, k),
atv(k, l, d, c)*att(aa, d, ii, k)*att(bb, c, jj, l),
atv(l, k, d, c)*att(aa, d, ii, l)*att(bb, c, jj, k),
]
for permut in exprs[1:]:
assert substitute_dummies(exprs[0]) == substitute_dummies(permut)
| 45,657 | 36.455291 | 86 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/tests/test_physics_matrices.py
|
from sympy.physics.matrices import msigma, mgamma, minkowski_tensor, pat_matrix, mdft
from sympy import zeros, eye, I, Matrix, sqrt, Rational
def test_parallel_axis_theorem():
# This tests the parallel axis theorem matrix by comparing to test
# matrices.
# First case, 1 in all directions.
mat1 = Matrix(((2, -1, -1), (-1, 2, -1), (-1, -1, 2)))
assert pat_matrix(1, 1, 1, 1) == mat1
assert pat_matrix(2, 1, 1, 1) == 2*mat1
# Second case, 1 in x, 0 in all others
mat2 = Matrix(((0, 0, 0), (0, 1, 0), (0, 0, 1)))
assert pat_matrix(1, 1, 0, 0) == mat2
assert pat_matrix(2, 1, 0, 0) == 2*mat2
# Third case, 1 in y, 0 in all others
mat3 = Matrix(((1, 0, 0), (0, 0, 0), (0, 0, 1)))
assert pat_matrix(1, 0, 1, 0) == mat3
assert pat_matrix(2, 0, 1, 0) == 2*mat3
# Fourth case, 1 in z, 0 in all others
mat4 = Matrix(((1, 0, 0), (0, 1, 0), (0, 0, 0)))
assert pat_matrix(1, 0, 0, 1) == mat4
assert pat_matrix(2, 0, 0, 1) == 2*mat4
def test_Pauli():
#this and the following test are testing both Pauli and Dirac matrices
#and also that the general Matrix class works correctly in a real world
#situation
sigma1 = msigma(1)
sigma2 = msigma(2)
sigma3 = msigma(3)
assert sigma1 == sigma1
assert sigma1 != sigma2
# sigma*I -> I*sigma (see #354)
assert sigma1*sigma2 == sigma3*I
assert sigma3*sigma1 == sigma2*I
assert sigma2*sigma3 == sigma1*I
assert sigma1*sigma1 == eye(2)
assert sigma2*sigma2 == eye(2)
assert sigma3*sigma3 == eye(2)
assert sigma1*2*sigma1 == 2*eye(2)
assert sigma1*sigma3*sigma1 == -sigma3
def test_Dirac():
gamma0 = mgamma(0)
gamma1 = mgamma(1)
gamma2 = mgamma(2)
gamma3 = mgamma(3)
gamma5 = mgamma(5)
# gamma*I -> I*gamma (see #354)
assert gamma5 == gamma0 * gamma1 * gamma2 * gamma3 * I
assert gamma1 * gamma2 + gamma2 * gamma1 == zeros(4)
assert gamma0 * gamma0 == eye(4) * minkowski_tensor[0, 0]
assert gamma2 * gamma2 != eye(4) * minkowski_tensor[0, 0]
assert gamma2 * gamma2 == eye(4) * minkowski_tensor[2, 2]
assert mgamma(5, True) == \
mgamma(0, True)*mgamma(1, True)*mgamma(2, True)*mgamma(3, True)*I
def test_mdft():
assert mdft(1) == Matrix([[1]])
assert mdft(2) == 1/sqrt(2)*Matrix([[1,1],[1,-1]])
assert mdft(4) == Matrix([[Rational(1,2), Rational(1,2), Rational(1,2),\
Rational(1,2)],[Rational(1,2), -I/2, Rational(-1,2), I/2\
],[Rational(1,2), Rational(-1,2), Rational(1,2), Rational(-1,2)],\
[Rational(1,2), I/2, Rational(-1,2), -I/2]])
| 2,619 | 32.589744 | 85 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/dimensions.py
|
# -*- coding:utf-8 -*-
"""
Definition of physical dimensions.
Unit systems will be constructed on top of these dimensions.
Most of the examples in the doc use MKS system and are presented from the
computer point of view: from a human point, adding length to time is not legal
in MKS but it is in natural system; for a computer in natural system there is
no time dimension (but a velocity dimension instead) - in the basis - so the
question of adding time to length has no meaning.
"""
from __future__ import division
import collections
from sympy.core.compatibility import reduce, string_types
from sympy import sympify, Integer, Matrix, Symbol, S, Abs
from sympy.core.expr import Expr
class Dimension(Expr):
"""
This class represent the dimension of a physical quantities.
The ``Dimension`` constructor takes as parameters a name and an optional
symbol.
For example, in classical mechanics we know that time is different from
temperature and dimensions make this difference (but they do not provide
any measure of these quantites.
>>> from sympy.physics.units import Dimension
>>> length = Dimension('length')
>>> length
Dimension(length)
>>> time = Dimension('time')
>>> time
Dimension(time)
Dimensions can be composed using multiplication, division and
exponentiation (by a number) to give new dimensions. Addition and
subtraction is defined only when the two objects are the same dimension.
>>> velocity = length / time
>>> velocity
Dimension(length/time)
>>> velocity.get_dimensional_dependencies()
{'length': 1, 'time': -1}
>>> length + length
Dimension(length)
>>> l2 = length**2
>>> l2
Dimension(length**2)
>>> l2.get_dimensional_dependencies()
{'length': 2}
"""
_op_priority = 13.0
_dimensional_dependencies = dict()
is_commutative = True
is_number = False
# make sqrt(M**2) --> M
is_positive = True
is_real = True
def __new__(cls, name, symbol=None):
if isinstance(name, string_types):
name = Symbol(name)
else:
name = sympify(name)
if not isinstance(name, Expr):
raise TypeError("Dimension name needs to be a valid math expression")
if isinstance(symbol, string_types):
symbol = Symbol(symbol)
elif symbol is not None:
assert isinstance(symbol, Symbol)
if symbol is not None:
obj = Expr.__new__(cls, name, symbol)
else:
obj = Expr.__new__(cls, name)
obj._name = name
obj._symbol = symbol
return obj
@property
def name(self):
return self._name
@property
def symbol(self):
return self._symbol
def __hash__(self):
return Expr.__hash__(self)
def __eq__(self, other):
if isinstance(other, Dimension):
return self.get_dimensional_dependencies() == other.get_dimensional_dependencies()
return False
def __str__(self):
"""
Display the string representation of the dimension.
"""
if self.symbol is None:
return "Dimension(%s)" % (self.name)
else:
return "Dimension(%s, %s)" % (self.name, self.symbol)
def __repr__(self):
return self.__str__()
def __neg__(self):
return self
def _register_as_base_dim(self):
if self.name in self._dimensional_dependencies:
raise IndexError("already in dependecies dict")
if not self.name.is_Symbol:
raise TypeError("Base dimensions need to have symbolic name")
self._dimensional_dependencies[self.name] = {self.name: 1}
def __add__(self, other):
"""
Define the addition for Dimension.
Addition of dimension has a sense only if the second object is the same
dimension (we don't add length to time).
"""
if not isinstance(other, Dimension):
raise TypeError("Only dimension can be added; '%s' is not valid"
% type(other))
elif isinstance(other, Dimension) and self != other:
raise ValueError("Only dimension which are equal can be added; "
"'%s' and '%s' are different" % (self, other))
return self
def __sub__(self, other):
# there is no notion of ordering (or magnitude) among dimension,
# subtraction is equivalent to addition when the operation is legal
return self + other
def __pow__(self, other):
return self._eval_power(other)
def _eval_power(self, other):
other = sympify(other)
return Dimension(self.name**other)
def __mul__(self, other):
if not isinstance(other, Dimension):
return self
return Dimension(self.name*other.name)
def __div__(self, other):
if not isinstance(other, Dimension):
return self
return Dimension(self.name/other.name)
def __rdiv__(self, other):
return other * pow(self, -1)
__truediv__ = __div__
__rtruediv__ = __rdiv__
def get_dimensional_dependencies(self, mark_dimensionless=False):
name = self.name
dimdep = self._get_dimensional_dependencies_for_name(name)
if mark_dimensionless and dimdep == {}:
return {'dimensionless': 1}
return dimdep
@classmethod
def _from_dimensional_dependencies(cls, dependencies):
return reduce(lambda x, y: x * y, (
Dimension(d)**e for d, e in dependencies.items()
))
@classmethod
def _get_dimensional_dependencies_for_name(cls, name):
if name.is_Symbol:
if name.name in Dimension._dimensional_dependencies:
return Dimension._dimensional_dependencies[name.name]
else:
return {}
if name.is_Number:
return {}
if name.is_Mul:
ret = collections.defaultdict(int)
dicts = [Dimension._get_dimensional_dependencies_for_name(i) for i in name.args]
for d in dicts:
for k, v in d.items():
ret[k] += v
return {k: v for (k, v) in ret.items() if v != 0}
if name.is_Pow:
if name.exp == 0:
return {}
dim = Dimension._get_dimensional_dependencies_for_name(name.base)
return {k: v*name.exp for (k, v) in dim.items()}
if name.is_Function:
args = (Dimension._from_dimensional_dependencies(
Dimension._get_dimensional_dependencies_for_name(arg)
) for arg in name.args)
result = name.func(*args)
if isinstance(result, cls):
return result.get_dimensional_dependencies()
# TODO shall we consider a result that is not a dimension?
# return Dimension._get_dimensional_dependencies_for_name(result)
@property
def is_dimensionless(self):
"""
Check if the dimension object really has a dimension.
A dimension should have at least one component with non-zero power.
"""
dimensional_dependencies = self.get_dimensional_dependencies()
return dimensional_dependencies == {}
@property
def has_integer_powers(self):
"""
Check if the dimension object has only integer powers.
All the dimension powers should be integers, but rational powers may
appear in intermediate steps. This method may be used to check that the
final result is well-defined.
"""
for dpow in self.get_dimensional_dependencies().values():
if not isinstance(dpow, (int, Integer)):
return False
else:
return True
# base dimensions (MKS)
length = Dimension(name="length", symbol="L")
mass = Dimension(name="mass", symbol="M")
time = Dimension(name="time", symbol="T")
# base dimensions (MKSA not in MKS)
current = Dimension(name='current', symbol='I')
# other base dimensions:
temperature = Dimension("temperature", "T")
amount_of_substance = Dimension("amount_of_substance")
luminous_intensity = Dimension("luminous_intensity")
# derived dimensions (MKS)
velocity = Dimension(name="velocity")
acceleration = Dimension(name="acceleration")
momentum = Dimension(name="momentum")
force = Dimension(name="force", symbol="F")
energy = Dimension(name="energy", symbol="E")
power = Dimension(name="power")
pressure = Dimension(name="pressure")
frequency = Dimension(name="frequency", symbol="f")
action = Dimension(name="action", symbol="A")
volume = Dimension("volume")
# derived dimensions (MKSA not in MKS)
voltage = Dimension(name='voltage', symbol='U')
impedance = Dimension(name='impedance', symbol='Z')
conductance = Dimension(name='conductance', symbol='G')
capacitance = Dimension(name='capacitance')
inductance = Dimension(name='inductance')
charge = Dimension(name='charge', symbol='Q')
magnetic_density = Dimension(name='magnetic_density', symbol='B')
magnetic_flux = Dimension(name='magnetic_flux')
# Create dimensions according the the base units in MKSA.
# For other unit systems, they can be derived by transforming the base
# dimensional dependency dictionary.
# Dimensional dependencies for MKS base dimensions
Dimension._dimensional_dependencies["length"] = dict(length=1)
Dimension._dimensional_dependencies["mass"] = dict(mass=1)
Dimension._dimensional_dependencies["time"] = dict(time=1)
# Dimensional dependencies for base dimensions (MKSA not in MKS)
Dimension._dimensional_dependencies["current"] = dict(current=1)
# Dimensional dependencies for other base dimensions:
Dimension._dimensional_dependencies["temperature"] = dict(temperature=1)
Dimension._dimensional_dependencies["amount_of_substance"] = dict(amount_of_substance=1)
Dimension._dimensional_dependencies["luminous_intensity"] = dict(luminous_intensity=1)
# Dimensional dependencies for derived dimensions
Dimension._dimensional_dependencies["velocity"] = dict(length=1, time=-1)
Dimension._dimensional_dependencies["acceleration"] = dict(length=1, time=-2)
Dimension._dimensional_dependencies["momentum"] = dict(mass=1, length=1, time=-1)
Dimension._dimensional_dependencies["force"] = dict(mass=1, length=1, time=-2)
Dimension._dimensional_dependencies["energy"] = dict(mass=1, length=2, time=-2)
Dimension._dimensional_dependencies["power"] = dict(length=2, mass=1, time=-3)
Dimension._dimensional_dependencies["pressure"] = dict(mass=1, length=-1, time=-2)
Dimension._dimensional_dependencies["frequency"] = dict(time=-1)
Dimension._dimensional_dependencies["action"] = dict(length=2, mass=1, time=-1)
Dimension._dimensional_dependencies["volume"] = dict(length=3)
# Dimensional dependencies for derived dimensions
Dimension._dimensional_dependencies["voltage"] = dict(mass=1, length=2, current=-1, time=-3)
Dimension._dimensional_dependencies["impedance"] = dict(mass=1, length=2, current=-2, time=-3)
Dimension._dimensional_dependencies["conductance"] = dict(mass=-1, length=-2, current=2, time=3)
Dimension._dimensional_dependencies["capacitance"] = dict(mass=-1, length=-2, current=2, time=4)
Dimension._dimensional_dependencies["inductance"] = dict(mass=1, length=2, current=-2, time=-2)
Dimension._dimensional_dependencies["charge"] = dict(current=1, time=1)
Dimension._dimensional_dependencies["magnetic_density"] = dict(mass=1, current=-1, time=-2)
Dimension._dimensional_dependencies["magnetic_flux"] = dict(length=2, mass=1, current=-1, time=-2)
class DimensionSystem(object):
"""
DimensionSystem represents a coherent set of dimensions.
In a system dimensions are of three types:
- base dimensions;
- derived dimensions: these are defined in terms of the base dimensions
(for example velocity is defined from the division of length by time);
- canonical dimensions: these are used to define systems because one has
to start somewhere: we can not build ex nihilo a system (see the
discussion in the documentation for more details).
All intermediate computations will use the canonical basis, but at the end
one can choose to print result in some other basis.
In a system dimensions can be represented as a vector, where the components
represent the powers associated to each base dimension.
"""
def __init__(self, base, dims=(), name="", descr=""):
"""
Initialize the dimension system.
It is important that base units have a name or a symbol such that
one can sort them in a unique way to define the vector basis.
"""
self.name = name
self.descr = descr
if (None, None) in [(d.name, d.symbol) for d in base]:
raise ValueError("Base dimensions must have a symbol or a name")
self._base_dims = self.sort_dims(base)
# base is first such that named dimension are keeped
self._dims = tuple(set(base) | set(dims))
if self.is_consistent is False:
raise ValueError("The system with basis '%s' is not consistent"
% str(self._base_dims))
def __str__(self):
"""
Return the name of the system.
If it does not exist, then it makes a list of symbols (or names) of
the base dimensions.
"""
if self.name != "":
return self.name
else:
return "DimensionSystem(%s)" % ", ".join(str(d) for d in self._base_dims)
def __repr__(self):
return "<DimensionSystem: %s>" % repr(self._base_dims)
def __getitem__(self, key):
"""
Shortcut to the get_dim method, using key access.
"""
d = self.get_dim(key)
#TODO: really want to raise an error?
if d is None:
raise KeyError(key)
return d
def __call__(self, unit):
"""
Wrapper to the method print_dim_base
"""
return self.print_dim_base(unit)
def get_dim(self, dim):
"""
Find a specific dimension which is part of the system.
dim can be a string or a dimension object. If no dimension is found,
then return None.
"""
#TODO: if the argument is a list, return a list of all matching dims
found_dim = None
#TODO: use copy instead of direct assignment for found_dim?
if isinstance(dim, string_types):
dim = Symbol(dim)
if dim.is_Symbol:
for d in self._dims:
if dim in (d.name, d.symbol):
found_dim = d
break
elif isinstance(dim, Dimension):
for i, idim in enumerate(self._dims):
if dim.get_dimensional_dependencies() == idim.get_dimensional_dependencies():
return idim
return found_dim
def extend(self, base, dims=(), name='', description=''):
"""
Extend the current system into a new one.
Take the base and normal units of the current system to merge
them to the base and normal units given in argument.
If not provided, name and description are overriden by empty strings.
"""
base = self._base_dims + tuple(base)
dims = self._dims + tuple(dims)
return DimensionSystem(base, dims, name, description)
@staticmethod
def sort_dims(dims):
"""
Sort dimensions given in argument using their str function.
This function will ensure that we get always the same tuple for a given
set of dimensions.
"""
return tuple(sorted(dims, key=str))
@property
def list_can_dims(self):
"""
List all canonical dimension names.
"""
dimset = set([])
for i in self._base_dims:
dimset.update(set(i.get_dimensional_dependencies().keys()))
return tuple(sorted(dimset))
@property
def inv_can_transf_matrix(self):
"""
Compute the inverse transformation matrix from the base to the
canonical dimension basis.
It corresponds to the matrix where columns are the vector of base
dimensions in canonical basis.
This matrix will almost never be used because dimensions are always
define with respect to the canonical basis, so no work has to be done
to get them in this basis. Nonetheless if this matrix is not square
(or not invertible) it means that we have chosen a bad basis.
"""
matrix = reduce(lambda x, y: x.row_join(y),
[self.dim_can_vector(d) for d in self._base_dims])
return matrix
#@cacheit
@property
def can_transf_matrix(self):
"""
Return the canonical transformation matrix from the canonical to the
base dimension basis.
It is the inverse of the matrix computed with inv_can_transf_matrix().
"""
#TODO: the inversion will fail if the system is inconsistent, for
# example if the matrix is not a square
return reduce(lambda x, y: x.row_join(y),
[self.dim_can_vector(d) for d in self._base_dims]
).inv()
def dim_can_vector(self, dim):
"""
Dimensional representation in terms of the canonical base dimensions.
"""
vec = []
for d in self.list_can_dims:
vec.append(dim.get_dimensional_dependencies().get(d, 0))
return Matrix(vec)
def dim_vector(self, dim):
"""
Vector representation in terms of the base dimensions.
"""
return self.can_transf_matrix * Matrix(self.dim_can_vector(dim))
def print_dim_base(self, dim):
"""
Give the string expression of a dimension in term of the basis symbols.
"""
dims = self.dim_vector(dim)
symbols = [i.symbol if i.symbol is not None else i.name for i in self._base_dims]
res = S.One
for (s, p) in zip(symbols, dims):
res *= s**p
return res
@property
def dim(self):
"""
Give the dimension of the system.
That is return the number of dimensions forming the basis.
"""
return len(self._base_dims)
@property
def is_consistent(self):
"""
Check if the system is well defined.
"""
# not enough or too many base dimensions compared to independent
# dimensions
# in vector language: the set of vectors do not form a basis
if self.inv_can_transf_matrix.is_square is False:
return False
return True
| 18,785 | 32.486631 | 98 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/definitions.py
|
from sympy import pi, Rational, sqrt
from sympy.physics.units import Quantity
from sympy.physics.units.dimensions import length, mass, force, energy, power, pressure, frequency, time, velocity, \
impedance, voltage, conductance, capacitance, inductance, charge, magnetic_density, magnetic_flux, current, action, \
amount_of_substance, luminous_intensity, temperature, acceleration
from sympy.physics.units.prefixes import kilo, milli, micro, nano, pico, deci, centi
#### UNITS ####
# Dimensionless:
percent = percents = Quantity("percent", 1, Rational(1, 100))
permille = Quantity("permille", 1, Rational(1, 1000))
# Angular units (dimensionless)
rad = radian = radians = Quantity("radian", 1, 1)
deg = degree = degrees = Quantity("degree", 1, pi/180, "deg")
sr = steradian = steradians = Quantity("steradian", 1, 1, "sr")
mil = angular_mil = angular_mils = Quantity("angular_mil", 1, 2*pi/6400, "mil")
# Base units:
m = meter = meters = Quantity("meter", length, 1, abbrev="m")
kg = kilogram = kilograms = Quantity("kilogram", mass, kilo, abbrev="g")
s = second = seconds = Quantity("second", time, 1, abbrev="s")
A = ampere = amperes = Quantity("ampere", current, 1, abbrev='A')
K = kelvin = kelvins = Quantity('kelvin', temperature, 1, 'K')
mol = mole = moles = Quantity("mole", amount_of_substance, 1, "mol")
cd = candela = candelas = Quantity("candela", luminous_intensity, 1, "cd")
# gram; used to define its prefixed units
g = gram = grams = Quantity("gram", mass, 1, "g")
mg = milligram = milligrams = Quantity("milligram", mass, milli*gram, "mg")
ug = microgram = micrograms = Quantity("microgram", mass, micro*gram, "ug")
# derived units
newton = newtons = N = Quantity("newton", force, kilogram*meter/second**2, "N")
joule = joules = J = Quantity("joule", energy, newton*meter, "J")
watt = watts = W = Quantity("watt", power, joule/second, "W")
pascal = pascals = Pa = pa = Quantity("pascal", pressure, newton/meter**2, "Pa")
hertz = hz = Hz = Quantity("hertz", frequency, 1, "Hz")
# MKSA extension to MKS: derived units
coulomb = coulombs = C = Quantity("coulomb", charge, 1, abbrev='C')
volt = volts = v = V = Quantity("volt", voltage, joule/coulomb, abbrev='V')
ohm = ohms = Quantity("ohm", impedance, volt/ampere, abbrev='ohm')
siemens = S = mho = mhos = Quantity("siemens", conductance, ampere/volt, abbrev='S')
farad = farads = F = Quantity("farad", capacitance, coulomb/volt, abbrev='F')
henry = henrys = H = Quantity("henry", inductance, volt*second/ampere, abbrev='H')
tesla = teslas = T = Quantity("tesla", magnetic_density, volt*second/meter**2, abbrev='T')
weber = webers = Wb = wb = Quantity("weber", magnetic_flux, joule/ampere, abbrev='Wb')
# Other derived units:
optical_power = dioptre = D = Quantity("dioptre", 1/length, 1/meter)
lux = lx = Quantity("lux", luminous_intensity/length**2, steradian*candela/meter**2)
# Common length units
km = kilometer = kilometers = Quantity("kilometer", length, kilo*meter, "km")
dm = decimeter = decimeters = Quantity("decimeter", length, deci*meter, "dm")
cm = centimeter = centimeters = Quantity("centimeter", length, centi*meter, "cm")
mm = millimeter = millimeters = Quantity("millimeter", length, milli*meter, "mm")
um = micrometer = micrometers = micron = microns = Quantity("micrometer", length, micro*meter, "um")
nm = nanometer = nanometers = Quantity("nanometer", length, nano*meter, "nn")
pm = picometer = picometers = Quantity("picometer", length, pico*meter, "pm")
ft = foot = feet = Quantity("foot", length, Rational(3048, 10000)*meter, "ft")
inch = inches = Quantity("inch", length, foot/12)
yd = yard = yards = Quantity("yard", length, 3*feet, "yd")
mi = mile = miles = Quantity("mile", length, 5280*feet)
nmi = nautical_mile = nautical_miles = Quantity("nautical_mile", length, 6076*feet)
# Common volume and area units
l = liter = liters = Quantity("liter", length**3, meter**3 / 1000)
dl = deciliter = deciliters = Quantity("deciliter", length**3, liter / 10)
cl = centiliter = centiliters = Quantity("centiliter", length**3, liter / 100)
ml = milliliter = milliliters = Quantity("milliliter", length**3, liter / 1000)
# Common time units
ms = millisecond = milliseconds = Quantity("millisecond", time, milli*second, "ms")
us = microsecond = microseconds = Quantity("microsecond", time, micro*second, "us")
ns = nanosecond = nanoseconds = Quantity("nanosecond", time, nano*second, "ns")
ps = picosecond = picoseconds = Quantity("picosecond", time, pico*second, "ps")
minute = minutes = Quantity("minute", time, 60*second)
h = hour = hours = Quantity("hour", time, 60*minute)
day = days = Quantity("day", time, 24*hour)
anomalistic_year = anomalistic_years = Quantity("anomalistic_year", time, 365.259636*day)
sidereal_year = sidereal_years = Quantity("sidereal_year", time, 31558149.540)
tropical_year = tropical_years = Quantity("tropical_year", time, 365.24219*day)
common_year = common_years = Quantity("common_year", time, 365*day)
julian_year = julian_years = Quantity("julian_year", time, 365.25*day)
draconic_year = draconic_years = Quantity("draconic_year", time, 346.62*day)
gaussian_year = gaussian_years = Quantity("gaussian_year", time, 365.2568983*day)
full_moon_cycle = full_moon_cycles = Quantity("full_moon_cycle", time, 411.78443029*day)
year = years = tropical_year
#### CONSTANTS ####
# Newton constant
G = gravitational_constant = Quantity("gravitational_constant", length**3*mass**-1*time**-2, 6.67408e-11*m**3/(kg*s**2), abbrev="G")
# speed of light
c = speed_of_light = Quantity("speed_of_light", velocity, 299792458*meter/second, abbrev="c")
# Wave impedance of free space
Z0 = Quantity("WaveImpedence", impedance, 119.9169832*pi, abbrev='Z_0')
# Reduced Planck constant
hbar = Quantity("hbar", action, 1.05457266e-34*joule*second, abbrev="hbar")
# Planck constant
planck = Quantity("planck", action, 2*pi*hbar, abbrev="h")
# Electronvolt
eV = electronvolt = electronvolts = Quantity("electronvolt", energy, 1.60219e-19*joule, abbrev="eV")
# Avogadro number
avogadro_number = Quantity("avogadro_number", 1, 6.022140857e23)
# Avogadro constant
avogadro = avogadro_constant = Quantity("avogadro_constant", amount_of_substance**-1, avogadro_number / mol)
# Boltzmann constant
boltzmann = boltzmann_constant = Quantity("boltzmann_constant", energy/temperature, 1.38064852e-23*joule/kelvin)
# Atomic mass
amu = amus = atomic_mass_unit = atomic_mass_constant = Quantity("atomic_mass_constant", mass, 1.660539040e-24*gram)
# Molar gas constant
R = molar_gas_constant = Quantity("molar_gas_constant", energy/(temperature * amount_of_substance),
8.3144598*joule/kelvin/mol, abbrev="R")
# Faraday constant
faraday_constant = Quantity("faraday_constant", charge/amount_of_substance, 96485.33289*C/mol)
# Josephson constant
josephson_constant = Quantity("josephson_constant", frequency/voltage, 483597.8525e9*hertz/V, abbrev="K_j")
# Von Klitzing constant
von_klitzing_constant = Quantity("von_klitzing_constant", voltage/current, 25812.8074555*ohm, abbrev="R_k")
# Acceleration due to gravity (on the Earth surface)
gee = gees = acceleration_due_to_gravity = Quantity("acceleration_due_to_gravity", acceleration, 9.80665*meter/second**2, "g")
# magnetic constant:
u0 = magnetic_constant = Quantity("magnetic_constant", force/current**2, 4*pi/10**7 * newton/ampere**2)
# electric constat:
e0 = electric_constant = vacuum_permittivity = Quantity("vacuum_permittivity", capacitance/length, 1/(u0 * c**2))
# vacuum impedance:
Z0 = vacuum_impedance = Quantity("vacuum_impedance", impedance, u0 * c)
# Coulomb's constant:
coulomb_constant = electric_force_constant = Quantity("coulomb_constant", force*length**2/charge**2, 1/(4*pi*vacuum_permittivity), "k_e")
atmosphere = atmospheres = atm = Quantity("atmosphere", pressure, 101325 * pascal, "atm")
kPa = kilopascal = Quantity("kilopascal", pressure, kilo*Pa, "kPa")
bar = bars = Quantity("bar", pressure, 100*kPa, "bar")
pound = pounds = Quantity("pound", mass, 0.45359237 * kg) # exact
psi = Quantity("psi", pressure, pound * gee / inch ** 2)
dHg0 = 13.5951 # approx value at 0 C
mmHg = torr = Quantity("mmHg", pressure, dHg0 * acceleration_due_to_gravity * kilogram / meter**2)
mmu = mmus = milli_mass_unit = Quantity("milli_mass_unit", mass, atomic_mass_unit/1000)
quart = quarts = Quantity("quart", length**3, Rational(231, 4) * inch**3)
# Other convenient units and magnitudes
ly = lightyear = lightyears = Quantity("lightyear", length, speed_of_light*julian_year, "ly")
au = astronomical_unit = astronomical_units = Quantity("astronomical_unit", length, 149597870691*meter, "AU")
# Planck units:
planck_mass = Quantity("planck_mass", mass, sqrt(hbar*speed_of_light/G), "m_P")
planck_time = Quantity("planck_time", time, sqrt(hbar*G/speed_of_light**5), "t_P")
planck_temperature = Quantity("planck_temperature", temperature, sqrt(hbar*speed_of_light**5/G/boltzmann**2), "T_P")
planck_length = Quantity("planck_length", length, sqrt(hbar*G/speed_of_light**3), "l_P")
# TODO: add more from https://en.wikipedia.org/wiki/Planck_units
| 9,054 | 52.264706 | 137 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/quantities.py
|
# -*- coding: utf-8 -*-
"""
Physical quantities.
"""
from __future__ import division
from sympy.core.compatibility import string_types
from sympy import Abs, sympify, Mul, Pow, S, Symbol, Add, AtomicExpr, Basic, Function
from sympy.physics.units import Dimension
from sympy.physics.units import dimensions
from sympy.physics.units.prefixes import Prefix
class Quantity(AtomicExpr):
"""
Physical quantity.
"""
is_commutative = True
is_real = True
is_number = False
is_nonzero = True
_diff_wrt = True
def __new__(cls, name, dimension, scale_factor=S.One, abbrev=None, **assumptions):
if not isinstance(name, Symbol):
name = Symbol(name)
if not isinstance(dimension, dimensions.Dimension):
if dimension == 1:
dimension = Dimension(1)
else:
raise ValueError("expected dimension or 1")
scale_factor = sympify(scale_factor)
dimex = Quantity.get_dimensional_expr(scale_factor)
if dimex != 1:
if dimension != Dimension(dimex):
raise ValueError("quantity value and dimension mismatch")
# replace all prefixes by their ratio to canonical units:
scale_factor = scale_factor.replace(lambda x: isinstance(x, Prefix), lambda x: x.scale_factor)
# replace all quantities by their ratio to canonical units:
scale_factor = scale_factor.replace(lambda x: isinstance(x, Quantity), lambda x: x.scale_factor)
if abbrev is None:
abbrev = name
elif isinstance(abbrev, string_types):
abbrev = Symbol(abbrev)
obj = AtomicExpr.__new__(cls, name, dimension, scale_factor, abbrev)
obj._name = name
obj._dimension = dimension
obj._scale_factor = scale_factor
obj._abbrev = abbrev
return obj
@property
def name(self):
return self._name
@property
def dimension(self):
return self._dimension
@property
def abbrev(self):
"""
Symbol representing the unit name.
Prepend the abbreviation with the prefix symbol if it is defines.
"""
return self._abbrev
@property
def scale_factor(self):
"""
Overall magnitude of the quantity as compared to the canonical units.
"""
return self._scale_factor
def _eval_is_positive(self):
return self.scale_factor.is_positive
def _eval_is_constant(self):
return self.scale_factor.is_constant()
def _eval_Abs(self):
# FIXME prefer usage of self.__class__ or type(self) instead
return self.func(self.name, self.dimension, Abs(self.scale_factor),
self.abbrev)
@staticmethod
def get_dimensional_expr(expr):
if isinstance(expr, Mul):
return Mul(*[Quantity.get_dimensional_expr(i) for i in expr.args])
elif isinstance(expr, Pow):
return Quantity.get_dimensional_expr(expr.base) ** expr.exp
elif isinstance(expr, Add):
return Quantity.get_dimensional_expr(expr.args[0])
elif isinstance(expr, Function):
fds = [Quantity.get_dimensional_expr(arg) for arg in expr.args]
return expr.func(*fds)
elif isinstance(expr, Quantity):
return expr.dimension.name
return 1
@staticmethod
def _collect_factor_and_dimension(expr):
if isinstance(expr, Quantity):
return expr.scale_factor, expr.dimension
elif isinstance(expr, Mul):
factor = 1
dimension = 1
for arg in expr.args:
arg_factor, arg_dim = Quantity._collect_factor_and_dimension(arg)
factor *= arg_factor
dimension *= arg_dim
return factor, dimension
elif isinstance(expr, Pow):
factor, dim = Quantity._collect_factor_and_dimension(expr.base)
return factor ** expr.exp, dim ** expr.exp
elif isinstance(expr, Add):
raise NotImplementedError
else:
return 1, 1
def convert_to(self, other):
"""
Convert the quantity to another quantity of same dimensions.
Examples
========
>>> from sympy.physics.units import speed_of_light, meter, second
>>> speed_of_light
speed_of_light
>>> speed_of_light.convert_to(meter/second)
299792458*meter/second
>>> from sympy.physics.units import liter
>>> liter.convert_to(meter**3)
meter**3/1000
"""
from .util import convert_to
return convert_to(self, other)
@property
def free_symbols(self):
return set([])
def _Quantity_constructor_postprocessor_Add(expr):
# Construction postprocessor for the addition,
# checks for dimension mismatches of the addends, thus preventing
# expressions like `meter + second` to be created.
deset = {
tuple(sorted(Dimension(
Quantity.get_dimensional_expr(i) if not i.is_number else 1
).get_dimensional_dependencies().items()))
for i in expr.args
if i.free_symbols == set() # do not raise if there are symbols
# (free symbols could contain the units corrections)
}
# If `deset` has more than one element, then some dimensions do not
# match in the sum:
if len(deset) > 1:
raise ValueError("summation of quantities of incompatible dimensions")
return expr
Basic._constructor_postprocessor_mapping[Quantity] = {
"Add" : [_Quantity_constructor_postprocessor_Add],
}
| 5,651 | 30.575419 | 104 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/util.py
|
# -*- coding: utf-8 -*-
"""
Several methods to simplify expressions involving unit objects.
"""
from __future__ import division
import collections
from sympy.physics.units.quantities import Quantity
from sympy import Add, Mul, Pow, Function, Rational, Tuple, sympify
from sympy.core.compatibility import reduce
from sympy.physics.units.dimensions import Dimension
def dim_simplify(expr):
"""
NOTE: this function could be deprecated in the future.
Simplify expression by recursively evaluating the dimension arguments.
This function proceeds to a very rough dimensional analysis. It tries to
simplify expression with dimensions, and it deletes all what multiplies a
dimension without being a dimension. This is necessary to avoid strange
behavior when Add(L, L) be transformed into Mul(2, L).
"""
if isinstance(expr, Dimension):
return expr
if isinstance(expr, Pow):
return dim_simplify(expr.base)**dim_simplify(expr.exp)
elif isinstance(expr, Function):
return dim_simplify(expr.args[0])
elif isinstance(expr, Add):
if (all(isinstance(arg, Dimension) for arg in expr.args) or
all(arg.is_dimensionless for arg in expr.args if isinstance(arg, Dimension))):
return reduce(lambda x, y: x.add(y), expr.args)
else:
raise ValueError("Dimensions cannot be added: %s" % expr)
elif isinstance(expr, Mul):
return Dimension(Mul(*[dim_simplify(i).name for i in expr.args if isinstance(i, Dimension)]))
raise ValueError("Cannot be simplifed: %s", expr)
def _get_conversion_matrix_for_expr(expr, target_units):
from sympy import Matrix
expr_dim = Dimension(Quantity.get_dimensional_expr(expr))
dim_dependencies = expr_dim.get_dimensional_dependencies(mark_dimensionless=True)
target_dims = [Dimension(Quantity.get_dimensional_expr(x)) for x in target_units]
canon_dim_units = {i for x in target_dims for i in x.get_dimensional_dependencies(mark_dimensionless=True)}
canon_expr_units = {i for i in dim_dependencies}
if not canon_expr_units.issubset(canon_dim_units):
return None
canon_dim_units = sorted(canon_dim_units)
camat = Matrix([[i.get_dimensional_dependencies(mark_dimensionless=True).get(j, 0) for i in target_dims] for j in canon_dim_units])
exprmat = Matrix([dim_dependencies.get(k, 0) for k in canon_dim_units])
res_exponents = camat.solve_least_squares(exprmat, method=None)
return res_exponents
def convert_to(expr, target_units):
"""
Convert ``expr`` to the same expression with all of its units and quantities
represented as factors of ``target_units``, whenever the dimension is compatible.
``target_units`` may be a single unit/quantity, or a collection of
units/quantities.
Examples
========
>>> from sympy.physics.units import speed_of_light, meter, gram, second, day
>>> from sympy.physics.units import mile, newton, kilogram, atomic_mass_constant
>>> from sympy.physics.units import kilometer, centimeter
>>> from sympy.physics.units import convert_to
>>> convert_to(mile, kilometer)
25146*kilometer/15625
>>> convert_to(mile, kilometer).n()
1.609344*kilometer
>>> convert_to(speed_of_light, meter/second)
299792458*meter/second
>>> convert_to(day, second)
86400*second
>>> 3*newton
3*newton
>>> convert_to(3*newton, kilogram*meter/second**2)
3*kilogram*meter/second**2
>>> convert_to(atomic_mass_constant, gram)
1.66053904e-24*gram
Conversion to multiple units:
>>> convert_to(speed_of_light, [meter, second])
299792458*meter/second
>>> convert_to(3*newton, [centimeter, gram, second])
300000*centimeter*gram/second**2
Conversion to Planck units:
>>> from sympy.physics.units import gravitational_constant, hbar
>>> convert_to(atomic_mass_constant, [gravitational_constant, speed_of_light, hbar]).n()
7.62950196312651e-20*gravitational_constant**(-0.5)*hbar**0.5*speed_of_light**0.5
"""
if not isinstance(target_units, (collections.Iterable, Tuple)):
target_units = [target_units]
if isinstance(expr, Add):
return Add.fromiter(convert_to(i, target_units) for i in expr.args)
expr = sympify(expr)
if not isinstance(expr, Quantity) and expr.has(Quantity):
expr = expr.replace(lambda x: isinstance(x, Quantity), lambda x: x.convert_to(target_units))
def get_total_scale_factor(expr):
if isinstance(expr, Mul):
return reduce(lambda x, y: x * y, [get_total_scale_factor(i) for i in expr.args])
elif isinstance(expr, Pow):
return get_total_scale_factor(expr.base) ** expr.exp
elif isinstance(expr, Quantity):
return expr.scale_factor
return expr
depmat = _get_conversion_matrix_for_expr(expr, target_units)
if depmat is None:
return expr
expr_scale_factor = get_total_scale_factor(expr)
return expr_scale_factor * Mul.fromiter((1/get_total_scale_factor(u) * u) ** p for u, p in zip(target_units, depmat))
| 5,114 | 35.798561 | 136 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/prefixes.py
|
# -*- coding: utf-8 -*-
"""
Module defining unit prefixe class and some constants.
Constant dict for SI and binary prefixes are defined as PREFIXES and
BIN_PREFIXES.
"""
from sympy import sympify, Expr
class Prefix(Expr):
"""
This class represent prefixes, with their name, symbol and factor.
Prefixes are used to create derived units from a given unit. They should
always be encapsulated into units.
The factor is constructed from a base (default is 10) to some power, and
it gives the total multiple or fraction. For example the kilometer km
is constructed from the meter (factor 1) and the kilo (10 to the power 3,
i.e. 1000). The base can be changed to allow e.g. binary prefixes.
A prefix multiplied by something will always return the product of this
other object times the factor, except if the other object:
- is a prefix and they can be combined into a new prefix;
- defines multiplication with prefixes (which is the case for the Unit
class).
"""
_op_priority = 13.0
def __new__(cls, name, abbrev, exponent, base=sympify(10)):
name = sympify(name)
abbrev = sympify(abbrev)
exponent = sympify(exponent)
base = sympify(base)
obj = Expr.__new__(cls, name, abbrev, exponent, base)
obj._name = name
obj._abbrev = abbrev
obj._scale_factor = base**exponent
obj._exponent = exponent
obj._base = base
return obj
@property
def name(self):
return self._name
@property
def abbrev(self):
return self._abbrev
@property
def scale_factor(self):
return self._scale_factor
@property
def base(self):
return self._base
def __str__(self):
# TODO: add proper printers and tests:
if self.base == 10:
return "Prefix(%s, %s, %s)" % (self.name, self.abbrev, self._exponent)
else:
return "Prefix(%s, %s, %s, %s)" % (self.name, self.abbrev, self._exponent, self.base)
__repr__ = __str__
def __mul__(self, other):
fact = self.scale_factor * other.scale_factor
if fact == 1:
return 1
elif isinstance(other, Prefix):
# simplify prefix
for p in PREFIXES:
if PREFIXES[p].scale_factor == fact:
return PREFIXES[p]
return fact
return self.scale_factor * other
def __div__(self, other):
fact = self.scale_factor / other.scale_factor
if fact == 1:
return 1
elif isinstance(other, Prefix):
for p in PREFIXES:
if PREFIXES[p].scale_factor == fact:
return PREFIXES[p]
return fact
return self.scale_factor / other
__truediv__ = __div__
def __rdiv__(self, other):
if other == 1:
for p in PREFIXES:
if PREFIXES[p].scale_factor == 1 / self.scale_factor:
return PREFIXES[p]
return other / self.scale_factor
__rtruediv__ = __rdiv__
def prefix_unit(unit, prefixes):
"""
Return a list of all units formed by unit and the given prefixes.
You can use the predefined PREFIXES or BIN_PREFIXES, but you can also
pass as argument a subdict of them if you don't want all prefixed units.
>>> from sympy.physics.units.prefixes import (PREFIXES,
... prefix_unit)
>>> from sympy.physics.units.systems import MKS
>>> from sympy.physics.units import m
>>> pref = {"m": PREFIXES["m"], "c": PREFIXES["c"], "d": PREFIXES["d"]}
>>> prefix_unit(m, pref) #doctest: +SKIP
[cm, dm, mm]
"""
from sympy.physics.units.quantities import Quantity
prefixed_units = []
for prefix_abbr, prefix in prefixes.items():
prefixed_units.append(Quantity("%s%s" % (prefix.name, unit.name), unit.dimension, unit.scale_factor * prefix,
"%s%s" % (prefix.abbrev, unit.abbrev)))
return prefixed_units
yotta = Prefix('yotta', 'Y', 24)
zetta = Prefix('zetta', 'Z', 21)
exa = Prefix('exa', 'E', 18)
peta = Prefix('peta', 'P', 15)
tera = Prefix('tera', 'T', 12)
giga = Prefix('giga', 'G', 9)
mega = Prefix('mega', 'M', 6)
kilo = Prefix('kilo', 'k', 3)
hecto = Prefix('hecto', 'h', 2)
deca = Prefix('deca', 'da', 1)
deci = Prefix('deci', 'd', -1)
centi = Prefix('centi', 'c', -2)
milli = Prefix('milli', 'm', -3)
micro = Prefix('micro', 'mu', -6)
nano = Prefix('nano', 'n', -9)
pico = Prefix('pico', 'p', -12)
femto = Prefix('femto', 'f', -15)
atto = Prefix('atto', 'a', -18)
zepto = Prefix('zepto', 'z', -21)
yocto = Prefix('yocto', 'y', -24)
# http://physics.nist.gov/cuu/Units/prefixes.html
PREFIXES = {
'Y': yotta,
'Z': zetta,
'E': exa,
'P': peta,
'T': tera,
'G': giga,
'M': mega,
'k': kilo,
'h': hecto,
'da': deca,
'd': deci,
'c': centi,
'm': milli,
'mu': micro,
'n': nano,
'p': pico,
'f': femto,
'a': atto,
'z': zepto,
'y': yocto,
}
kibi = Prefix('kibi', 'Y', 10, 2)
mebi = Prefix('mebi', 'Y', 20, 2)
gibi = Prefix('gibi', 'Y', 30, 2)
tebi = Prefix('tebi', 'Y', 40, 2)
pebi = Prefix('pebi', 'Y', 50, 2)
exbi = Prefix('exbi', 'Y', 60, 2)
# http://physics.nist.gov/cuu/Units/binary.html
BIN_PREFIXES = {
'Ki': kibi,
'Mi': mebi,
'Gi': gibi,
'Ti': tebi,
'Pi': pebi,
'Ei': exbi,
}
| 5,506 | 26.128079 | 117 |
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/__init__.py
|
# -*- coding: utf-8 -*-
"""
Dimensional analysis and unit systems.
This module defines dimension/unit systems and physical quantities. It is
based on a group-theoretical construction where dimensions are represented as
vectors (coefficients being the exponents), and units are defined as a dimension
to which we added a scale.
Quantities are built from a factor and a unit, and are the basic objects that
one will use when doing computations.
All objects except systems and prefixes can be used in sympy expressions.
Note that as part of a CAS, various objects do not combine automatically
under operations.
Details about the implementation can be found in the documentation, and we
will not repeat all the explanations we gave there concerning our approach.
Ideas about future developments can be found on the `Github wiki
<https://github.com/sympy/sympy/wiki/Unit-systems>`_, and you should consult
this page if you are willing to help.
Useful functions:
- ``find_unit``: easily lookup pre-defined units.
- ``convert_to(expr, newunit)``: converts an expression into the same
expression expressed in another unit.
"""
from sympy.core.compatibility import string_types
from .dimensions import Dimension, DimensionSystem
from .unitsystem import UnitSystem
from .util import convert_to
from .quantities import Quantity
from .dimensions import (
amount_of_substance, acceleration, action,
capacitance, charge, conductance, current, energy,
force, frequency, impedance, inductance, length,
luminous_intensity, magnetic_density,
magnetic_flux, mass, momentum, power, pressure, temperature, time,
velocity, voltage, volume,
)
Unit = Quantity
speed = velocity
luminosity = luminous_intensity
magnetic_flux_density = magnetic_density
amount = amount_of_substance
from .prefixes import (
# 10-power based:
yotta,
zetta,
exa,
peta,
tera,
giga,
mega,
kilo,
hecto,
deca,
deci,
centi,
milli,
micro,
nano,
pico,
femto,
atto,
zepto,
yocto,
# 2-power based:
kibi,
mebi,
gibi,
tebi,
pebi,
exbi,
)
from .definitions import (
percent, percents,
permille,
rad, radian, radians,
deg, degree, degrees,
sr, steradian, steradians,
mil, angular_mil, angular_mils,
m, meter, meters,
kg, kilogram, kilograms,
s, second, seconds,
A, ampere, amperes,
K, kelvin, kelvins,
mol, mole, moles,
cd, candela, candelas,
g, gram, grams,
mg, milligram, milligrams,
ug, microgram, micrograms,
newton, newtons, N,
joule, joules, J,
watt, watts, W,
pascal, pascals, Pa, pa,
hertz, hz, Hz,
coulomb, coulombs, C,
volt, volts, v, V,
ohm, ohms,
siemens, S, mho, mhos,
farad, farads, F,
henry, henrys, H,
tesla, teslas, T,
weber, webers, Wb, wb,
optical_power, dioptre, D,
lux, lx,
km, kilometer, kilometers,
dm, decimeter, decimeters,
cm, centimeter, centimeters,
mm, millimeter, millimeters,
um, micrometer, micrometers, micron, microns,
nm, nanometer, nanometers,
pm, picometer, picometers,
ft, foot, feet,
inch, inches,
yd, yard, yards,
mi, mile, miles,
nmi, nautical_mile, nautical_miles,
l, liter, liters,
dl, deciliter, deciliters,
cl, centiliter, centiliters,
ml, milliliter, milliliters,
ms, millisecond, milliseconds,
us, microsecond, microseconds,
ns, nanosecond, nanoseconds,
ps, picosecond, picoseconds,
minute, minutes,
h, hour, hours,
day, days,
anomalistic_year, anomalistic_years,
sidereal_year, sidereal_years,
tropical_year, tropical_years,
common_year, common_years,
julian_year, julian_years,
draconic_year, draconic_years,
gaussian_year, gaussian_years,
full_moon_cycle, full_moon_cycles,
year, years, tropical_year,
G, gravitational_constant,
c, speed_of_light,
Z0,
hbar,
planck,
eV, electronvolt, electronvolts,
avogadro_number,
avogadro, avogadro_constant,
boltzmann, boltzmann_constant,
R, molar_gas_constant,
faraday_constant,
josephson_constant,
von_klitzing_constant,
amu, amus, atomic_mass_unit, atomic_mass_constant,
gee, gees, acceleration_due_to_gravity,
u0, magnetic_constant,
e0, electric_constant, vacuum_permittivity,
Z0, vacuum_impedance,
coulomb_constant, electric_force_constant,
atmosphere, atmospheres, atm,
kPa,
bar, bars,
pound, pounds,
psi,
dHg0,
mmHg, torr,
mmu, mmus, milli_mass_unit,
quart, quarts,
ly, lightyear, lightyears,
au, astronomical_unit, astronomical_units,
planck_mass,
planck_time,
planck_temperature,
planck_length,
)
def find_unit(quantity):
"""
Return a list of matching units or dimension names.
- If ``quantity`` is a string -- units/dimensions containing the string
`quantity`.
- If ``quantity`` is a unit or dimension -- units having matching base
units or dimensions.
Examples
========
>>> from sympy.physics import units as u
>>> u.find_unit('charge')
['C', 'coulomb', 'coulombs']
>>> u.find_unit(u.charge)
['C', 'coulomb', 'coulombs']
>>> u.find_unit("ampere")
['ampere', 'amperes']
>>> u.find_unit('volt')
['volt', 'volts', 'electronvolt', 'electronvolts']
>>> u.find_unit(u.inch**3)[:5]
['l', 'cl', 'dl', 'ml', 'liter']
"""
import sympy.physics.units as u
rv = []
if isinstance(quantity, string_types):
rv = [i for i in dir(u) if quantity in i and isinstance(getattr(u, i), Quantity)]
dim = getattr(u, quantity)
if isinstance(dim, Dimension):
rv.extend(find_unit(dim))
else:
for i in sorted(dir(u)):
other = getattr(u, i)
if not isinstance(other, Quantity):
continue
if isinstance(quantity, Quantity):
if quantity.dimension == other.dimension:
rv.append(str(i))
elif isinstance(quantity, Dimension):
if other.dimension == quantity:
rv.append(str(i))
elif other.dimension == Dimension(Quantity.get_dimensional_expr(quantity)):
rv.append(str(i))
return sorted(rv, key=len)
# NOTE: the old units module had additional variables:
# 'density', 'illuminance', 'resistance'.
# They were not dimensions, but units (old Unit class).
| 6,507 | 26.576271 | 89 |
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/unitsystem.py
|
# -*- coding: utf-8 -*-
"""
Unit system for physical quantities; include definition of constants.
"""
from __future__ import division
from sympy.core.decorators import deprecated
from sympy.physics.units.quantities import Quantity
from sympy import S
from .dimensions import DimensionSystem
class UnitSystem(object):
"""
UnitSystem represents a coherent set of units.
A unit system is basically a dimension system with notions of scales. Many
of the methods are defined in the same way.
It is much better if all base units have a symbol.
"""
def __init__(self, base, units=(), name="", descr=""):
self.name = name
self.descr = descr
# construct the associated dimension system
self._system = DimensionSystem([u.dimension for u in base],
[u.dimension for u in units])
if self.is_consistent is False:
raise ValueError("The system with basis '%s' is not consistent"
% str(self._base_units))
self._units = tuple(set(base) | set(units))
# create a dict linkin
# this is possible since we have already verified that the base units
# form a coherent system
base_dict = dict((u.dimension, u) for u in base)
# order the base units in the same order than the dimensions in the
# associated system, in order to ensure that we get always the same
self._base_units = tuple(base_dict[d] for d in self._system._base_dims)
def __str__(self):
"""
Return the name of the system.
If it does not exist, then it makes a list of symbols (or names) of
the base dimensions.
"""
if self.name != "":
return self.name
else:
return "UnitSystem((%s))" % ", ".join(str(d) for d in self._base_units)
def __repr__(self):
return '<UnitSystem: %s>' % repr(self._base_units)
def __getitem__(self, key):
"""
Shortcut to the get_unit method, using key access.
"""
u = self.get_unit(key)
#TODO: really want to raise an error?
if u is None:
raise KeyError(key)
return u
def extend(self, base, units=(), name="", description=""):
"""
Extend the current system into a new one.
Take the base and normal units of the current system to merge
them to the base and normal units given in argument.
If not provided, name and description are overriden by empty strings.
"""
base = self._base_units + tuple(base)
units = self._units + tuple(units)
return UnitSystem(base, units, name, description)
def print_unit_base(self, unit):
"""
Give the string expression of a unit in term of the basis.
Units are displayed by decreasing power.
"""
res = S.One
factor = unit.scale_factor
vec = self._system.dim_vector(unit.dimension)
for (u, p) in sorted(zip(self._base_units, vec), key=lambda x: x[1],
reverse=True):
factor /= u.scale_factor ** p
if p == 0:
continue
elif p == 1:
res *= u
else:
res *= u**p
return factor * res
@property
def dim(self):
"""
Give the dimension of the system.
That is return the number of units forming the basis.
"""
return self._system.dim
@property
def is_consistent(self):
"""
Check if the underlying dimension system is consistent.
"""
return self._system.is_consistent
| 3,727 | 27.242424 | 83 |
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|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/tests/test_util.py
|
# -*- coding: utf-8 -*-
from __future__ import division
from sympy import sstr, pi
from sympy.physics.units import G
from sympy import Add, Pow, Mul, sin, Tuple, sqrt, sympify
from sympy.physics.units import coulomb
from sympy.physics.units import hbar
from sympy.physics.units import joule
from sympy.physics.units import kelvin
from sympy.physics.units import mile, speed_of_light, meter, second, minute, hour, day
from sympy.physics.units import centimeter
from sympy.physics.units import inch
from sympy.physics.units import kilogram
from sympy.physics.units import kilometer
from sympy.physics.units import length
from sympy.physics.units import newton
from sympy.physics.units import planck
from sympy.physics.units import planck_length
from sympy.physics.units import planck_mass
from sympy.physics.units import planck_temperature
from sympy.physics.units import planck_time
from sympy.physics.units import radians, degree
from sympy.physics.units import steradian
from sympy.physics.units import time, gram
from sympy.physics.units.util import dim_simplify, convert_to
def NS(e, n=15, **options):
return sstr(sympify(e).evalf(n, **options), full_prec=True)
L = length
T = time
def test_dim_simplify_add():
assert dim_simplify(Add(L, L)) == L
assert dim_simplify(L + L) == L
def test_dim_simplify_mul():
assert dim_simplify(L*T) == L*T
assert dim_simplify(L * T) == L*T
def test_dim_simplify_pow():
assert dim_simplify(Pow(L, 2)) == L**2
assert dim_simplify(L**2) == L**2
def test_dim_simplify_rec():
assert dim_simplify(Mul(Add(L, L), T)) == L*T
assert dim_simplify((L + L) * T) == L*T
def test_dim_simplify_dimless():
# TODO: this should be somehow simplified on its own,
# without the need of calling `dim_simplify`:
assert dim_simplify(sin(L*L**-1)**2*L).get_dimensional_dependencies() == L.get_dimensional_dependencies()
assert dim_simplify(sin(L * L**(-1))**2 * L).get_dimensional_dependencies() == L.get_dimensional_dependencies()
def test_convert_to_quantities():
assert convert_to(3, meter) == 3
assert convert_to(mile, kilometer) == 1.609344*kilometer
assert convert_to(meter/second, speed_of_light) == speed_of_light/299792458
assert convert_to(299792458*meter/second, speed_of_light) == speed_of_light
assert convert_to(2*299792458*meter/second, speed_of_light) == 2*speed_of_light
assert convert_to(speed_of_light, meter/second) == 299792458*meter/second
assert convert_to(2*speed_of_light, meter/second) == 599584916*meter/second
assert convert_to(day, second) == 86400*second
assert convert_to(2*hour, minute) == 120*minute
assert convert_to(mile, meter) == 1609.344*meter
assert convert_to(mile/hour, kilometer/hour) == 25146*kilometer/(15625*hour)
assert convert_to(3*newton, meter/second) == 3*newton
assert convert_to(3*newton, kilogram*meter/second**2) == 3*meter*kilogram/second**2
assert convert_to(kilometer + mile, meter) == 2609.344*meter
assert convert_to(2*kilometer + 3*mile, meter) == 6828.032*meter
assert convert_to(inch**2, meter**2) == 16129*meter**2/25000000
assert convert_to(3*inch**2, meter) == 48387*meter**2/25000000
assert convert_to(2*kilometer/hour + 3*mile/hour, meter/second) == 53344*meter/(28125*second)
assert convert_to(2*kilometer/hour + 3*mile/hour, centimeter/second) == 213376*centimeter/(1125*second)
assert convert_to(kilometer * (mile + kilometer), meter) == 2609344 * meter ** 2
assert convert_to(steradian, coulomb) == steradian
assert convert_to(radians, degree) == 180*degree/pi
assert convert_to(radians, [meter, degree]) == 180*degree/pi
assert convert_to(pi*radians, degree) == 180*degree
assert convert_to(pi, degree) == 180*degree
def test_convert_to_tuples_of_quantities():
assert convert_to(speed_of_light, [meter, second]) == 299792458 * meter / second
assert convert_to(speed_of_light, (meter, second)) == 299792458 * meter / second
assert convert_to(speed_of_light, Tuple(meter, second)) == 299792458 * meter / second
assert convert_to(joule, [meter, kilogram, second]) == kilogram*meter**2/second**2
assert convert_to(joule, [centimeter, gram, second]) == 10000000*centimeter**2*gram/second**2
assert convert_to(299792458*meter/second, [speed_of_light]) == speed_of_light
assert convert_to(speed_of_light / 2, [meter, second, kilogram]) == meter/second*299792458 / 2
# This doesn't make physically sense, but let's keep it as a conversion test:
assert convert_to(2 * speed_of_light, [meter, second, kilogram]) == 2 * 299792458 * meter / second
assert convert_to(G, [G, speed_of_light, planck]) == 1.0*G
assert NS(convert_to(meter, [G, speed_of_light, hbar]), n=7) == '6.187242e+34*gravitational_constant**0.5000000*hbar**0.5000000*speed_of_light**(-1.500000)'
assert NS(convert_to(planck_mass, kilogram), n=7) == '2.176471e-8*kilogram'
assert NS(convert_to(planck_length, meter), n=7) == '1.616229e-35*meter'
assert NS(convert_to(planck_time, second), n=6) == '5.39116e-44*second'
assert NS(convert_to(planck_temperature, kelvin), n=7) == '1.416809e+32*kelvin'
assert NS(convert_to(convert_to(meter, [G, speed_of_light, planck]), meter), n=10) == '1.000000000*meter'
| 5,284 | 45.359649 | 160 |
py
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/tests/test_dimensionsystem.py
|
# -*- coding: utf-8 -*-
from sympy import Matrix, eye, symbols
from sympy.physics.units.dimensions import Dimension, DimensionSystem, length, time, velocity, mass, current, \
action, charge
from sympy.utilities.pytest import raises
def test_definition():
base = (length, time)
ms = DimensionSystem(base, (velocity,), "MS", "MS system")
assert ms._base_dims == DimensionSystem.sort_dims(base)
assert set(ms._dims) == set(base + (velocity,))
assert ms.name == "MS"
assert ms.descr == "MS system"
def test_error_definition():
raises(ValueError, lambda: DimensionSystem((current, charge, time)))
raises(ValueError, lambda: DimensionSystem((length, time, velocity)))
def test_str_repr():
assert str(DimensionSystem((length, time), name="MS")) == "MS"
dimsys = DimensionSystem((length, time))
assert str(dimsys) == 'DimensionSystem(Dimension(length, L), Dimension(time, T))'
assert (repr(DimensionSystem((length, time), name="MS"))
== '<DimensionSystem: (Dimension(length, L), Dimension(time, T))>')
def test_call():
mksa = DimensionSystem((length, time, mass, current), (action,))
assert mksa(action) == mksa.print_dim_base(action)
def test_get_dim():
ms = DimensionSystem((length, time), (velocity,))
assert ms.get_dim("L") == length
assert ms.get_dim("length") == length
assert ms.get_dim(length) == length
assert ms["L"] == ms.get_dim("L")
raises(KeyError, lambda: ms["M"])
def test_extend():
ms = DimensionSystem((length, time), (velocity,))
mks = ms.extend((mass,), (action,))
res = DimensionSystem((length, time, mass), (velocity, action))
assert mks._base_dims == res._base_dims
assert set(mks._dims) == set(res._dims)
def test_sort_dims():
assert (DimensionSystem.sort_dims((length, velocity, time))
== (length, time, velocity))
def test_list_dims():
dimsys = DimensionSystem((length, time, mass))
assert dimsys.list_can_dims == ("length", "mass", "time")
def test_dim_can_vector():
dimsys = DimensionSystem((length, mass, time), (velocity, action))
assert dimsys.dim_can_vector(length) == Matrix([1, 0, 0])
assert dimsys.dim_can_vector(velocity) == Matrix([1, 0, -1])
dimsys = DimensionSystem((length, velocity, action), (mass, time))
assert dimsys.dim_can_vector(length) == Matrix([1, 0, 0])
assert dimsys.dim_can_vector(velocity) == Matrix([1, 0, -1])
def test_dim_vector():
dimsys = DimensionSystem((length, mass, time), (velocity, action))
assert dimsys.dim_vector(length) == Matrix([1, 0, 0])
assert dimsys.dim_vector(velocity) == Matrix([1, 0, -1])
dimsys = DimensionSystem((length, velocity, action), (mass, time))
assert dimsys.dim_vector(length) == Matrix([0, 1, 0])
assert dimsys.dim_vector(velocity) == Matrix([0, 0, 1])
assert dimsys.dim_vector(time) == Matrix([0, 1, -1])
def test_inv_can_transf_matrix():
dimsys = DimensionSystem((length, mass, time))
assert dimsys.inv_can_transf_matrix == eye(3)
dimsys = DimensionSystem((length, velocity, action))
assert dimsys.inv_can_transf_matrix == Matrix([[2, 1, 1], [1, 0, 0], [-1, 0, -1]])
def test_can_transf_matrix():
dimsys = DimensionSystem((length, mass, time))
assert dimsys.can_transf_matrix == eye(3)
dimsys = DimensionSystem((length, velocity, action))
assert dimsys.can_transf_matrix == Matrix([[0, 1, 0], [1, -1, 1], [0, -1, -1]])
def test_is_consistent():
assert DimensionSystem((length, time)).is_consistent is True
#assert DimensionSystem((length, time, velocity)).is_consistent is False
def test_print_dim_base():
mksa = DimensionSystem((length, time, mass, current), (action,))
L, M, T = symbols("L M T")
assert mksa.print_dim_base(action) == L**2*M/T
def test_dim():
dimsys = DimensionSystem((length, mass, time), (velocity, action))
assert dimsys.dim == 3
| 3,964 | 29.736434 | 111 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/tests/test_unitsystem.py
|
# -*- coding: utf-8 -*-
from sympy import Rational
from sympy.physics.units.definitions import (m, s, c, kg)
from sympy.physics.units.dimensions import Dimension, DimensionSystem, length, time, mass, velocity, current, \
action
from sympy.physics.units.unitsystem import UnitSystem
from sympy.physics.units.quantities import Quantity
from sympy.utilities.pytest import raises
def test_definition():
# want to test if the system can have several units of the same dimension
dm = Quantity("dm", length, Rational(1, 10))
base = (m, s)
base_dim = (m.dimension, s.dimension)
ms = UnitSystem(base, (c, dm), "MS", "MS system")
assert set(ms._base_units) == set(base)
assert set(ms._units) == set((m, s, c, dm))
#assert ms._units == DimensionSystem._sort_dims(base + (velocity,))
assert ms.name == "MS"
assert ms.descr == "MS system"
assert ms._system._base_dims == DimensionSystem.sort_dims(base_dim)
assert set(ms._system._dims) == set(base_dim + (velocity,))
def test_error_definition():
raises(ValueError, lambda: UnitSystem((m, s, c)))
def test_str_repr():
assert str(UnitSystem((m, s), name="MS")) == "MS"
assert str(UnitSystem((m, s))) == "UnitSystem((meter, second))"
assert repr(UnitSystem((m, s))) == "<UnitSystem: (%s, %s)>" % (m, s)
def test_print_unit_base():
A = Quantity("A", current, 1)
Js = Quantity("Js", action, 1)
mksa = UnitSystem((m, kg, s, A), (Js,))
assert mksa.print_unit_base(Js) == m**2*kg*s**-1/1000
def test_extend():
ms = UnitSystem((m, s), (c,))
Js = Quantity("Js", action, 1)
mks = ms.extend((kg,), (Js,))
res = UnitSystem((m, s, kg), (c, Js))
assert set(mks._base_units) == set(res._base_units)
assert set(mks._units) == set(res._units)
def test_dim():
dimsys = UnitSystem((m, kg, s), (c,))
assert dimsys.dim == 3
def test_is_consistent():
assert UnitSystem((m, s)).is_consistent is True
| 1,953 | 29.061538 | 111 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/tests/test_prefixes.py
|
# -*- coding: utf-8 -*-
from sympy import symbols
from sympy.physics.units.prefixes import PREFIXES, prefix_unit
def test_prefix_operations():
m = PREFIXES['m']
k = PREFIXES['k']
M = PREFIXES['M']
assert m * k == 1
assert k * k == M
assert 1 / m == k
assert k / m == M
def test_prefix_unit():
from sympy.physics.units import Quantity, Dimension
length = Dimension("length")
m = Quantity("meter", length, 1, abbrev="m")
pref = {"m": PREFIXES["m"], "c": PREFIXES["c"], "d": PREFIXES["d"]}
res = [Quantity("millimeter", length, PREFIXES["m"], "mm"),
Quantity("centimeter", length, PREFIXES["c"], "cm"),
Quantity("decimeter", length, PREFIXES["d"], "dm")]
prefs = prefix_unit(m, pref)
assert set(prefs) == set(res)
assert set(map(lambda x: x.abbrev, prefs)) == set(symbols("mm,cm,dm"))
| 873 | 26.3125 | 74 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/tests/test_dimensions.py
|
# -*- coding: utf-8 -*-
from sympy import sympify, Symbol, S, sqrt
from sympy.physics.units.dimensions import Dimension
from sympy.physics.units.dimensions import length, time
from sympy.utilities.pytest import raises
def test_definition():
assert length.get_dimensional_dependencies() == {"length": 1}
assert length.name == Symbol("length")
assert length.symbol == Symbol("L")
halflength = sqrt(length)
assert halflength.get_dimensional_dependencies() == {'length': S.Half}
def test_error_definition():
# tuple with more or less than two entries
raises(TypeError, lambda: Dimension(("length", 1, 2)))
raises(TypeError, lambda: Dimension(["length"]))
# non-number power
raises(TypeError, lambda: Dimension({"length": "a"}))
# non-number with named argument
raises(TypeError, lambda: Dimension({"length": (1, 2)}))
def test_str():
assert str(Dimension("length")) == "Dimension(length)"
assert str(Dimension("length", "L")) == "Dimension(length, L)"
def test_properties():
assert length.is_dimensionless is False
assert (length/length).is_dimensionless is True
assert Dimension("undefined").is_dimensionless is True
assert length.has_integer_powers is True
assert (length**(-1)).has_integer_powers is True
assert (length**1.5).has_integer_powers is False
def test_add_sub():
assert length + length == length
assert length - length == length
assert -length == length
raises(TypeError, lambda: length + 1)
raises(TypeError, lambda: length - 1)
raises(ValueError, lambda: length + time)
raises(ValueError, lambda: length - time)
def test_mul_div_exp():
velo = length / time
assert (length * length) == length ** 2
assert (length * length).get_dimensional_dependencies() == {"length": 2}
assert (length ** 2).get_dimensional_dependencies() == {"length": 2}
assert (length * time).get_dimensional_dependencies() == { "length": 1, "time": 1}
assert velo.get_dimensional_dependencies() == { "length": 1, "time": -1}
assert (velo ** 2).get_dimensional_dependencies() == {"length": 2, "time": -2}
assert (length / length).get_dimensional_dependencies() == {}
assert (velo / length * time).get_dimensional_dependencies() == {}
assert (length ** -1).get_dimensional_dependencies() == {"length": -1}
assert (velo ** -1.5).get_dimensional_dependencies() == {"length": -1.5, "time": 1.5}
length_a = length**"a"
assert length_a.get_dimensional_dependencies() == {"length": Symbol("a")}
assert length != 1
assert length / length != 1
| 2,607 | 32.87013 | 89 |
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/tests/test_quantities.py
|
# -*- coding: utf-8 -*-
from __future__ import division
from sympy import Symbol, Add, Number, S, integrate, sqrt, Rational, Abs, diff, symbols, Basic
from sympy.physics.units import convert_to, find_unit
from sympy.physics.units.definitions import s, m, kg, speed_of_light, day, minute, km, foot, meter, grams, amu, au, \
quart, inch, coulomb, millimeter, steradian, second, mile, centimeter, hour, kilogram, pressure, temperature, energy
from sympy.physics.units.dimensions import Dimension, length, time, charge, mass
from sympy.physics.units.quantities import Quantity
from sympy.physics.units.prefixes import PREFIXES, kilo
from sympy.utilities.pytest import raises
k = PREFIXES["k"]
def test_str_repr():
assert str(kg) == "kilogram"
def test_eq():
# simple test
assert 10*m == 10*m
assert 10*m != 10*s
def test_convert_to():
q = Quantity("q1", length, 5000)
assert q.convert_to(m) == 5000*m
assert speed_of_light.convert_to(m / s) == 299792458 * m / s
# TODO: eventually support this kind of conversion:
# assert (2*speed_of_light).convert_to(m / s) == 2 * 299792458 * m / s
assert day.convert_to(s) == 86400*s
# Wrong dimension to convert:
assert q.convert_to(s) == q
assert speed_of_light.convert_to(m) == speed_of_light
def test_Quantity_definition():
q = Quantity("s10", time, 10, "sabbr")
assert q.scale_factor == 10
assert q.dimension == time
assert q.abbrev == Symbol("sabbr")
u = Quantity("u", length, 10, abbrev="dam")
assert u.dimension == length
assert u.scale_factor == 10
assert u.abbrev == Symbol("dam")
km = Quantity("km", length, kilo)
assert km.scale_factor == 1000
assert km.func(*km.args) == km
assert km.func(*km.args).args == km.args
v = Quantity("u", length, 5*kilo)
assert v.dimension == length
assert v.scale_factor == 5 * 1000
def test_abbrev():
u = Quantity("u", length, 1)
assert u.name == Symbol("u")
assert u.abbrev == Symbol("u")
u = Quantity("u", length, 2, "om")
assert u.name == Symbol("u")
assert u.abbrev == Symbol("om")
assert u.scale_factor == 2
assert isinstance(u.scale_factor, Number)
u = Quantity("u", length, 3*kilo, "ikm")
assert u.abbrev == Symbol("ikm")
assert u.scale_factor == 3000
def test_print():
u = Quantity("unitname", length, 10, "dam")
assert repr(u) == "unitname"
assert str(u) == "unitname"
def test_Quantity_eq():
u = Quantity("u", length, 10, "dam")
v = Quantity("v1", length, 10)
assert u != v
v = Quantity("v2", time, 10, "ds")
assert u != v
v = Quantity("v3", length, 1, "dm")
assert u != v
def test_add_sub():
u = Quantity("u", length, 10)
v = Quantity("v", length, 5)
w = Quantity("w", time, 2)
assert isinstance(u + v, Add)
assert (u + v.convert_to(u)) == (1 + S.Half)*u
# TODO: eventually add this:
# assert (u + v).convert_to(u) == (1 + S.Half)*u
assert isinstance(u - v, Add)
assert (u - v.convert_to(u)) == S.Half*u
# TODO: eventually add this:
# assert (u - v).convert_to(u) == S.Half*u
def test_abs():
v_w1 = Quantity('v_w1', length/time, meter/second)
v_w2 = Quantity('v_w2', length/time, meter/second)
v_w3 = Quantity('v_w3', length/time, meter/second)
expr = v_w3 - Abs(v_w1 - v_w2)
Dq = Dimension(Quantity.get_dimensional_expr(expr))
assert Dimension.get_dimensional_dependencies(Dq) == {
'length': 1,
'time': -1,
}
assert meter == sqrt(meter**2)
def test_check_unit_consistency():
return # TODO remove
u = Quantity("u", length, 10)
v = Quantity("v", length, 5)
w = Quantity("w", time, 2)
# TODO: no way of checking unit consistency:
#raises(ValueError, lambda: check_unit_consistency(u + w))
#raises(ValueError, lambda: check_unit_consistency(u - w))
#raises(TypeError, lambda: check_unit_consistency(u + 1))
#raises(TypeError, lambda: check_unit_consistency(u - 1))
def test_mul_div():
u = Quantity("u", length, 10)
assert 1 / u == u**(-1)
assert u / 1 == u
v1 = u / Quantity("t", time, 2)
v2 = Quantity("v", length / time, 5)
# Pow only supports structural equality:
assert v1 != v2
assert v1 == v2.convert_to(v1)
# TODO: decide whether to allow such expression in the future
# (requires somehow manipulating the core).
#assert u / Quantity(length, 2) == 5
assert u * 1 == u
ut1 = u * Quantity("t", time, 2)
ut2 = Quantity("ut", length*time, 20)
# Mul only supports structural equality:
assert ut1 != ut2
assert ut1 == ut2.convert_to(ut1)
# Mul only supports structural equality:
assert u * Quantity("lp1", length**-1, 2) != 20
assert u**0 == 1
assert u**1 == u
# TODO: Pow only support structural equality:
assert u ** 2 != Quantity("u2", length ** 2, 100)
assert u ** -1 != Quantity("u3", length ** -1, 0.1)
assert u ** 2 == Quantity("u2", length ** 2, 100).convert_to(u)
assert u ** -1 == Quantity("u3", length ** -1, S.One/10).convert_to(u)
def test_units():
assert convert_to((5*m/s * day) / km, 1) == 432
assert convert_to(foot / meter, meter) == Rational('0.3048')
# amu is a pure mass so mass/mass gives a number, not an amount (mol)
# TODO: need better simplification routine:
assert str(convert_to(grams/amu, grams).n(2)) == '6.0e+23'
# Light from the sun needs about 8.3 minutes to reach earth
t = (1*au / speed_of_light) / minute
# TODO: need a better way to simplify expressions containing units:
t = convert_to(convert_to(t, meter / minute), meter)
assert t == 49865956897/5995849160
# TODO: fix this, it should give `m` without `Abs`
assert sqrt(m**2) == Abs(m)
assert (sqrt(m))**2 == m
t = Symbol('t')
assert integrate(t*m/s, (t, 1*s, 5*s)) == 12*m*s
assert (t * m/s).integrate((t, 1*s, 5*s)) == 12*m*s
def test_issue_quart():
assert convert_to(4 * quart / inch ** 3, meter) == 231
assert convert_to(4 * quart / inch ** 3, millimeter) == 231
def test_issue_5565():
raises(ValueError, lambda: m < s)
assert (m < km).is_Relational
def test_find_unit():
assert find_unit('coulomb') == ['coulomb', 'coulombs', 'coulomb_constant']
assert find_unit(coulomb) == ['C', 'coulomb', 'coulombs']
assert find_unit(charge) == ['C', 'coulomb', 'coulombs']
assert find_unit(inch) == [
'm', 'au', 'cm', 'dm', 'ft', 'km', 'ly', 'mi', 'mm', 'nm', 'pm', 'um',
'yd', 'nmi', 'feet', 'foot', 'inch', 'mile', 'yard', 'meter', 'miles',
'yards', 'inches', 'meters', 'micron', 'microns', 'decimeter',
'kilometer', 'lightyear', 'nanometer', 'picometer', 'centimeter',
'decimeters', 'kilometers', 'lightyears', 'micrometer', 'millimeter',
'nanometers', 'picometers', 'centimeters', 'micrometers',
'millimeters', 'nautical_mile', 'planck_length', 'nautical_miles', 'astronomical_unit',
'astronomical_units']
assert find_unit(inch**-1) == ['D', 'dioptre', 'optical_power']
assert find_unit(length**-1) == ['D', 'dioptre', 'optical_power']
assert find_unit(inch ** 3) == [
'l', 'cl', 'dl', 'ml', 'liter', 'quart', 'liters', 'quarts',
'deciliter', 'centiliter', 'deciliters', 'milliliter',
'centiliters', 'milliliters']
assert find_unit('voltage') == ['V', 'v', 'volt', 'volts']
def test_Quantity_derivative():
x = symbols("x")
assert diff(x*meter, x) == meter
assert diff(x**3*meter**2, x) == 3*x**2*meter**2
assert diff(meter, meter) == 1
assert diff(meter**2, meter) == 2*meter
def test_sum_of_incompatible_quantities():
raises(ValueError, lambda: meter + 1)
raises(ValueError, lambda: meter + second)
raises(ValueError, lambda: 2 * meter + second)
raises(ValueError, lambda: 2 * meter + 3 * second)
raises(ValueError, lambda: 1 / second + 1 / meter)
raises(ValueError, lambda: 2 * meter*(mile + centimeter) + km)
expr = 2 * (mile + centimeter)/second + km/hour
assert expr in Basic._constructor_postprocessor_mapping
for i in expr.args:
assert i in Basic._constructor_postprocessor_mapping
def test_quantity_postprocessing():
q1 = Quantity('q1', length*pressure**2*temperature/time)
q2 = Quantity('q2', energy*pressure*temperature/(length**2*time))
assert q1 + q2
q = q1 + q2
Dq = Dimension(Quantity.get_dimensional_expr(q))
assert Dimension.get_dimensional_dependencies(Dq) == {
'length': -1,
'mass': 2,
'temperature': 1,
'time': -5,
}
| 8,632 | 31.333333 | 120 |
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/tests/__init__.py
|
# -*- coding: utf-8 -*-
| 24 | 11.5 | 23 |
py
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/systems/natural.py
|
# -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
"""
Naturalunit system.
The natural system comes from "setting c = 1, hbar = 1". From the computer
point of view it means that we use velocity and action instead of length and
time. Moreover instead of mass we use energy.
"""
from __future__ import division
from sympy.physics.units.definitions import eV, hbar, c
from sympy.physics.units.dimensions import DimensionSystem
from sympy.physics.units.dimensions import length, mass, time, momentum,\
force, energy, power, frequency, action, velocity
from sympy.physics.units.unitsystem import UnitSystem
from sympy.physics.units.prefixes import PREFIXES, prefix_unit
dims = (length, mass, time, momentum, force, energy, power, frequency)
# dimension system
_natural_dim = DimensionSystem(base=(action, energy, velocity), dims=dims,
name="Natural system")
units = prefix_unit(eV, PREFIXES)
# unit system
natural = UnitSystem(base=(hbar, eV, c), units=units, name="Natural system")
| 1,018 | 30.84375 | 76 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/systems/mks.py
|
# -*- coding: utf-8 -*-
"""
MKS unit system.
MKS stands for "meter, kilogram, second".
"""
from __future__ import division
from sympy.physics.units.definitions import (m, kg, s, J, N, W, Pa, Hz, g, G, c)
from sympy.physics.units.dimensions import (velocity, acceleration, momentum, force, energy, power, pressure,
frequency, action, length, mass, time)
from sympy.physics.units import DimensionSystem, UnitSystem
from sympy.physics.units.prefixes import PREFIXES, prefix_unit
dims = (velocity, acceleration, momentum, force, energy, power, pressure,
frequency, action)
# dimension system
_mks_dim = DimensionSystem(base=(length, mass, time), dims=dims, name="MKS")
units = [m, g, s, J, N, W, Pa, Hz]
all_units = []
# Prefixes of units like g, J, N etc get added using `prefix_unit`
# in the for loop, but the actual units have to be added manually.
all_units.extend([g, J, N, W, Pa, Hz])
for u in units:
all_units.extend(prefix_unit(u, PREFIXES))
all_units.extend([G, c])
# unit system
MKS = UnitSystem(base=(m, kg, s), units=all_units, name="MKS")
| 1,116 | 29.189189 | 109 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/systems/mksa.py
|
# -*- coding: utf-8 -*-
"""
MKS unit system.
MKS stands for "meter, kilogram, second, ampere".
"""
from __future__ import division
from sympy.physics.units.definitions import A, V, C, S, ohm, F, H, Z0, Wb, T
from sympy.physics.units.dimensions import (voltage, impedance,
conductance, capacitance, inductance, charge,
magnetic_density, magnetic_flux, current)
from sympy.physics.units.prefixes import PREFIXES, prefix_unit
from sympy.physics.units.systems.mks import MKS, _mks_dim
dims = (voltage, impedance, conductance, capacitance, inductance, charge,
magnetic_density, magnetic_flux)
# dimension system
_mksa_dim = _mks_dim.extend(base=(current,), dims=dims, name='MKSA')
units = [A, V, ohm, S, F, H, C, T, Wb]
all_units = []
for u in units:
all_units.extend(prefix_unit(u, PREFIXES))
all_units.extend([Z0])
MKSA = MKS.extend(base=(A,), units=all_units, name='MKSA')
| 981 | 28.757576 | 89 |
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/units/systems/__init__.py
|
# -*- coding: utf-8 -*-
from sympy.physics.units.systems.mks import _mks_dim, MKS
from sympy.physics.units.systems.mksa import _mksa_dim, MKSA
from sympy.physics.units.systems.natural import _natural_dim, natural
| 214 | 34.833333 | 69 |
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/dyadic.py
|
from sympy.core.backend import sympify, Add, ImmutableMatrix as Matrix
from sympy.core.compatibility import unicode
from .printing import (VectorLatexPrinter, VectorPrettyPrinter,
VectorStrPrinter)
__all__ = ['Dyadic']
class Dyadic(object):
"""A Dyadic object.
See:
http://en.wikipedia.org/wiki/Dyadic_tensor
Kane, T., Levinson, D. Dynamics Theory and Applications. 1985 McGraw-Hill
A more powerful way to represent a rigid body's inertia. While it is more
complex, by choosing Dyadic components to be in body fixed basis vectors,
the resulting matrix is equivalent to the inertia tensor.
"""
def __init__(self, inlist):
"""
Just like Vector's init, you shouldn't call this unless creating a
zero dyadic.
zd = Dyadic(0)
Stores a Dyadic as a list of lists; the inner list has the measure
number and the two unit vectors; the outerlist holds each unique
unit vector pair.
"""
self.args = []
if inlist == 0:
inlist = []
while len(inlist) != 0:
added = 0
for i, v in enumerate(self.args):
if ((str(inlist[0][1]) == str(self.args[i][1])) and
(str(inlist[0][2]) == str(self.args[i][2]))):
self.args[i] = (self.args[i][0] + inlist[0][0],
inlist[0][1], inlist[0][2])
inlist.remove(inlist[0])
added = 1
break
if added != 1:
self.args.append(inlist[0])
inlist.remove(inlist[0])
i = 0
# This code is to remove empty parts from the list
while i < len(self.args):
if ((self.args[i][0] == 0) | (self.args[i][1] == 0) |
(self.args[i][2] == 0)):
self.args.remove(self.args[i])
i -= 1
i += 1
def __add__(self, other):
"""The add operator for Dyadic. """
other = _check_dyadic(other)
return Dyadic(self.args + other.args)
def __and__(self, other):
"""The inner product operator for a Dyadic and a Dyadic or Vector.
Parameters
==========
other : Dyadic or Vector
The other Dyadic or Vector to take the inner product with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer
>>> N = ReferenceFrame('N')
>>> D1 = outer(N.x, N.y)
>>> D2 = outer(N.y, N.y)
>>> D1.dot(D2)
(N.x|N.y)
>>> D1.dot(N.y)
N.x
"""
from sympy.physics.vector.vector import Vector, _check_vector
if isinstance(other, Dyadic):
other = _check_dyadic(other)
ol = Dyadic(0)
for i, v in enumerate(self.args):
for i2, v2 in enumerate(other.args):
ol += v[0] * v2[0] * (v[2] & v2[1]) * (v[1] | v2[2])
else:
other = _check_vector(other)
ol = Vector(0)
for i, v in enumerate(self.args):
ol += v[0] * v[1] * (v[2] & other)
return ol
def __div__(self, other):
"""Divides the Dyadic by a sympifyable expression. """
return self.__mul__(1 / other)
__truediv__ = __div__
def __eq__(self, other):
"""Tests for equality.
Is currently weak; needs stronger comparison testing
"""
if other == 0:
other = Dyadic(0)
other = _check_dyadic(other)
if (self.args == []) and (other.args == []):
return True
elif (self.args == []) or (other.args == []):
return False
return set(self.args) == set(other.args)
def __mul__(self, other):
"""Multiplies the Dyadic by a sympifyable expression.
Parameters
==========
other : Sympafiable
The scalar to multiply this Dyadic with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer
>>> N = ReferenceFrame('N')
>>> d = outer(N.x, N.x)
>>> 5 * d
5*(N.x|N.x)
"""
newlist = [v for v in self.args]
for i, v in enumerate(newlist):
newlist[i] = (sympify(other) * newlist[i][0], newlist[i][1],
newlist[i][2])
return Dyadic(newlist)
def __ne__(self, other):
return not self.__eq__(other)
def __neg__(self):
return self * -1
def _latex(self, printer=None):
ar = self.args # just to shorten things
if len(ar) == 0:
return str(0)
ol = [] # output list, to be concatenated to a string
mlp = VectorLatexPrinter()
for i, v in enumerate(ar):
# if the coef of the dyadic is 1, we skip the 1
if ar[i][0] == 1:
ol.append(' + ' + mlp.doprint(ar[i][1]) + r"\otimes " +
mlp.doprint(ar[i][2]))
# if the coef of the dyadic is -1, we skip the 1
elif ar[i][0] == -1:
ol.append(' - ' +
mlp.doprint(ar[i][1]) +
r"\otimes " +
mlp.doprint(ar[i][2]))
# If the coefficient of the dyadic is not 1 or -1,
# we might wrap it in parentheses, for readability.
elif ar[i][0] != 0:
arg_str = mlp.doprint(ar[i][0])
if isinstance(ar[i][0], Add):
arg_str = '(%s)' % arg_str
if arg_str.startswith('-'):
arg_str = arg_str[1:]
str_start = ' - '
else:
str_start = ' + '
ol.append(str_start + arg_str + mlp.doprint(ar[i][1]) +
r"\otimes " + mlp.doprint(ar[i][2]))
outstr = ''.join(ol)
if outstr.startswith(' + '):
outstr = outstr[3:]
elif outstr.startswith(' '):
outstr = outstr[1:]
return outstr
def _pretty(self, printer=None):
e = self
class Fake(object):
baseline = 0
def render(self, *args, **kwargs):
ar = e.args # just to shorten things
settings = printer._settings if printer else {}
if printer:
use_unicode = printer._use_unicode
else:
from sympy.printing.pretty.pretty_symbology import (
pretty_use_unicode)
use_unicode = pretty_use_unicode()
mpp = printer if printer else VectorPrettyPrinter(settings)
if len(ar) == 0:
return unicode(0)
bar = u"\N{CIRCLED TIMES}" if use_unicode else "|"
ol = [] # output list, to be concatenated to a string
for i, v in enumerate(ar):
# if the coef of the dyadic is 1, we skip the 1
if ar[i][0] == 1:
ol.extend([u" + ",
mpp.doprint(ar[i][1]),
bar,
mpp.doprint(ar[i][2])])
# if the coef of the dyadic is -1, we skip the 1
elif ar[i][0] == -1:
ol.extend([u" - ",
mpp.doprint(ar[i][1]),
bar,
mpp.doprint(ar[i][2])])
# If the coefficient of the dyadic is not 1 or -1,
# we might wrap it in parentheses, for readability.
elif ar[i][0] != 0:
if isinstance(ar[i][0], Add):
arg_str = mpp._print(
ar[i][0]).parens()[0]
else:
arg_str = mpp.doprint(ar[i][0])
if arg_str.startswith(u"-"):
arg_str = arg_str[1:]
str_start = u" - "
else:
str_start = u" + "
ol.extend([str_start, arg_str, u" ",
mpp.doprint(ar[i][1]),
bar,
mpp.doprint(ar[i][2])])
outstr = u"".join(ol)
if outstr.startswith(u" + "):
outstr = outstr[3:]
elif outstr.startswith(" "):
outstr = outstr[1:]
return outstr
return Fake()
def __rand__(self, other):
"""The inner product operator for a Vector or Dyadic, and a Dyadic
This is for: Vector dot Dyadic
Parameters
==========
other : Vector
The vector we are dotting with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, dot, outer
>>> N = ReferenceFrame('N')
>>> d = outer(N.x, N.x)
>>> dot(N.x, d)
N.x
"""
from sympy.physics.vector.vector import Vector, _check_vector
other = _check_vector(other)
ol = Vector(0)
for i, v in enumerate(self.args):
ol += v[0] * v[2] * (v[1] & other)
return ol
def __rsub__(self, other):
return (-1 * self) + other
def __rxor__(self, other):
"""For a cross product in the form: Vector x Dyadic
Parameters
==========
other : Vector
The Vector that we are crossing this Dyadic with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer, cross
>>> N = ReferenceFrame('N')
>>> d = outer(N.x, N.x)
>>> cross(N.y, d)
- (N.z|N.x)
"""
from sympy.physics.vector.vector import _check_vector
other = _check_vector(other)
ol = Dyadic(0)
for i, v in enumerate(self.args):
ol += v[0] * ((other ^ v[1]) | v[2])
return ol
def __str__(self, printer=None):
"""Printing method. """
ar = self.args # just to shorten things
if len(ar) == 0:
return str(0)
ol = [] # output list, to be concatenated to a string
for i, v in enumerate(ar):
# if the coef of the dyadic is 1, we skip the 1
if ar[i][0] == 1:
ol.append(' + (' + str(ar[i][1]) + '|' + str(ar[i][2]) + ')')
# if the coef of the dyadic is -1, we skip the 1
elif ar[i][0] == -1:
ol.append(' - (' + str(ar[i][1]) + '|' + str(ar[i][2]) + ')')
# If the coefficient of the dyadic is not 1 or -1,
# we might wrap it in parentheses, for readability.
elif ar[i][0] != 0:
arg_str = VectorStrPrinter().doprint(ar[i][0])
if isinstance(ar[i][0], Add):
arg_str = "(%s)" % arg_str
if arg_str[0] == '-':
arg_str = arg_str[1:]
str_start = ' - '
else:
str_start = ' + '
ol.append(str_start + arg_str + '*(' + str(ar[i][1]) +
'|' + str(ar[i][2]) + ')')
outstr = ''.join(ol)
if outstr.startswith(' + '):
outstr = outstr[3:]
elif outstr.startswith(' '):
outstr = outstr[1:]
return outstr
def __sub__(self, other):
"""The subtraction operator. """
return self.__add__(other * -1)
def __xor__(self, other):
"""For a cross product in the form: Dyadic x Vector.
Parameters
==========
other : Vector
The Vector that we are crossing this Dyadic with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer, cross
>>> N = ReferenceFrame('N')
>>> d = outer(N.x, N.x)
>>> cross(d, N.y)
(N.x|N.z)
"""
from sympy.physics.vector.vector import _check_vector
other = _check_vector(other)
ol = Dyadic(0)
for i, v in enumerate(self.args):
ol += v[0] * (v[1] | (v[2] ^ other))
return ol
_sympystr = __str__
_sympyrepr = _sympystr
__repr__ = __str__
__radd__ = __add__
__rmul__ = __mul__
def express(self, frame1, frame2=None):
"""Expresses this Dyadic in alternate frame(s)
The first frame is the list side expression, the second frame is the
right side; if Dyadic is in form A.x|B.y, you can express it in two
different frames. If no second frame is given, the Dyadic is
expressed in only one frame.
Calls the global express function
Parameters
==========
frame1 : ReferenceFrame
The frame to express the left side of the Dyadic in
frame2 : ReferenceFrame
If provided, the frame to express the right side of the Dyadic in
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer, dynamicsymbols
>>> N = ReferenceFrame('N')
>>> q = dynamicsymbols('q')
>>> B = N.orientnew('B', 'Axis', [q, N.z])
>>> d = outer(N.x, N.x)
>>> d.express(B, N)
cos(q)*(B.x|N.x) - sin(q)*(B.y|N.x)
"""
from sympy.physics.vector.functions import express
return express(self, frame1, frame2)
def to_matrix(self, reference_frame, second_reference_frame=None):
"""Returns the matrix form of the dyadic with respect to one or two
reference frames.
Parameters
----------
reference_frame : ReferenceFrame
The reference frame that the rows and columns of the matrix
correspond to. If a second reference frame is provided, this
only corresponds to the rows of the matrix.
second_reference_frame : ReferenceFrame, optional, default=None
The reference frame that the columns of the matrix correspond
to.
Returns
-------
matrix : ImmutableMatrix, shape(3,3)
The matrix that gives the 2D tensor form.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> Vector.simp = True
>>> from sympy.physics.mechanics import inertia
>>> Ixx, Iyy, Izz, Ixy, Iyz, Ixz = symbols('Ixx, Iyy, Izz, Ixy, Iyz, Ixz')
>>> N = ReferenceFrame('N')
>>> inertia_dyadic = inertia(N, Ixx, Iyy, Izz, Ixy, Iyz, Ixz)
>>> inertia_dyadic.to_matrix(N)
Matrix([
[Ixx, Ixy, Ixz],
[Ixy, Iyy, Iyz],
[Ixz, Iyz, Izz]])
>>> beta = symbols('beta')
>>> A = N.orientnew('A', 'Axis', (beta, N.x))
>>> inertia_dyadic.to_matrix(A)
Matrix([
[ Ixx, Ixy*cos(beta) + Ixz*sin(beta), -Ixy*sin(beta) + Ixz*cos(beta)],
[ Ixy*cos(beta) + Ixz*sin(beta), Iyy*cos(2*beta)/2 + Iyy/2 + Iyz*sin(2*beta) - Izz*cos(2*beta)/2 + Izz/2, -Iyy*sin(2*beta)/2 + Iyz*cos(2*beta) + Izz*sin(2*beta)/2],
[-Ixy*sin(beta) + Ixz*cos(beta), -Iyy*sin(2*beta)/2 + Iyz*cos(2*beta) + Izz*sin(2*beta)/2, -Iyy*cos(2*beta)/2 + Iyy/2 - Iyz*sin(2*beta) + Izz*cos(2*beta)/2 + Izz/2]])
"""
if second_reference_frame is None:
second_reference_frame = reference_frame
return Matrix([i.dot(self).dot(j) for i in reference_frame for j in
second_reference_frame]).reshape(3, 3)
def doit(self, **hints):
"""Calls .doit() on each term in the Dyadic"""
return sum([Dyadic([(v[0].doit(**hints), v[1], v[2])])
for v in self.args], Dyadic(0))
def dt(self, frame):
"""Take the time derivative of this Dyadic in a frame.
This function calls the global time_derivative method
Parameters
==========
frame : ReferenceFrame
The frame to take the time derivative in
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer, dynamicsymbols
>>> N = ReferenceFrame('N')
>>> q = dynamicsymbols('q')
>>> B = N.orientnew('B', 'Axis', [q, N.z])
>>> d = outer(N.x, N.x)
>>> d.dt(B)
- q'*(N.y|N.x) - q'*(N.x|N.y)
"""
from sympy.physics.vector.functions import time_derivative
return time_derivative(self, frame)
def simplify(self):
"""Returns a simplified Dyadic."""
out = Dyadic(0)
for v in self.args:
out += Dyadic([(v[0].simplify(), v[1], v[2])])
return out
def subs(self, *args, **kwargs):
"""Substituion on the Dyadic.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy import Symbol
>>> N = ReferenceFrame('N')
>>> s = Symbol('s')
>>> a = s * (N.x|N.x)
>>> a.subs({s: 2})
2*(N.x|N.x)
"""
return sum([Dyadic([(v[0].subs(*args, **kwargs), v[1], v[2])])
for v in self.args], Dyadic(0))
def applyfunc(self, f):
"""Apply a function to each component of a Dyadic."""
if not callable(f):
raise TypeError("`f` must be callable.")
out = Dyadic(0)
for a, b, c in self.args:
out += f(a) * (b|c)
return out
dot = __and__
cross = __xor__
def _check_dyadic(other):
if not isinstance(other, Dyadic):
raise TypeError('A Dyadic must be supplied')
return other
| 18,016 | 32.364815 | 189 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/frame.py
|
from sympy.core.backend import (diff, expand, sin, cos, sympify,
eye, symbols, ImmutableMatrix as Matrix, MatrixBase)
from sympy import (trigsimp, solve, Symbol, Dummy)
from sympy.core.compatibility import string_types, range
from sympy.physics.vector.vector import Vector, _check_vector
__all__ = ['CoordinateSym', 'ReferenceFrame']
class CoordinateSym(Symbol):
"""
A coordinate symbol/base scalar associated wrt a Reference Frame.
Ideally, users should not instantiate this class. Instances of
this class must only be accessed through the corresponding frame
as 'frame[index]'.
CoordinateSyms having the same frame and index parameters are equal
(even though they may be instantiated separately).
Parameters
==========
name : string
The display name of the CoordinateSym
frame : ReferenceFrame
The reference frame this base scalar belongs to
index : 0, 1 or 2
The index of the dimension denoted by this coordinate variable
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, CoordinateSym
>>> A = ReferenceFrame('A')
>>> A[1]
A_y
>>> type(A[0])
<class 'sympy.physics.vector.frame.CoordinateSym'>
>>> a_y = CoordinateSym('a_y', A, 1)
>>> a_y == A[1]
True
"""
def __new__(cls, name, frame, index):
# We can't use the cached Symbol.__new__ because this class depends on
# frame and index, which are not passed to Symbol.__xnew__.
assumptions = {}
super(CoordinateSym, cls)._sanitize(assumptions, cls)
obj = super(CoordinateSym, cls).__xnew__(cls, name, **assumptions)
_check_frame(frame)
if index not in range(0, 3):
raise ValueError("Invalid index specified")
obj._id = (frame, index)
return obj
@property
def frame(self):
return self._id[0]
def __eq__(self, other):
#Check if the other object is a CoordinateSym of the same frame
#and same index
if isinstance(other, CoordinateSym):
if other._id == self._id:
return True
return False
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
return tuple((self._id[0].__hash__(), self._id[1])).__hash__()
class ReferenceFrame(object):
"""A reference frame in classical mechanics.
ReferenceFrame is a class used to represent a reference frame in classical
mechanics. It has a standard basis of three unit vectors in the frame's
x, y, and z directions.
It also can have a rotation relative to a parent frame; this rotation is
defined by a direction cosine matrix relating this frame's basis vectors to
the parent frame's basis vectors. It can also have an angular velocity
vector, defined in another frame.
"""
_count = 0
def __init__(self, name, indices=None, latexs=None, variables=None):
"""ReferenceFrame initialization method.
A ReferenceFrame has a set of orthonormal basis vectors, along with
orientations relative to other ReferenceFrames and angular velocities
relative to other ReferenceFrames.
Parameters
==========
indices : list (of strings)
If custom indices are desired for console, pretty, and LaTeX
printing, supply three as a list. The basis vectors can then be
accessed with the get_item method.
latexs : list (of strings)
If custom names are desired for LaTeX printing of each basis
vector, supply the names here in a list.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, vlatex
>>> N = ReferenceFrame('N')
>>> N.x
N.x
>>> O = ReferenceFrame('O', indices=('1', '2', '3'))
>>> O.x
O['1']
>>> O['1']
O['1']
>>> P = ReferenceFrame('P', latexs=('A1', 'A2', 'A3'))
>>> vlatex(P.x)
'A1'
"""
if not isinstance(name, string_types):
raise TypeError('Need to supply a valid name')
# The if statements below are for custom printing of basis-vectors for
# each frame.
# First case, when custom indices are supplied
if indices is not None:
if not isinstance(indices, (tuple, list)):
raise TypeError('Supply the indices as a list')
if len(indices) != 3:
raise ValueError('Supply 3 indices')
for i in indices:
if not isinstance(i, string_types):
raise TypeError('Indices must be strings')
self.str_vecs = [(name + '[\'' + indices[0] + '\']'),
(name + '[\'' + indices[1] + '\']'),
(name + '[\'' + indices[2] + '\']')]
self.pretty_vecs = [(name.lower() + u"_" + indices[0]),
(name.lower() + u"_" + indices[1]),
(name.lower() + u"_" + indices[2])]
self.latex_vecs = [(r"\mathbf{\hat{%s}_{%s}}" % (name.lower(),
indices[0])), (r"\mathbf{\hat{%s}_{%s}}" %
(name.lower(), indices[1])),
(r"\mathbf{\hat{%s}_{%s}}" % (name.lower(),
indices[2]))]
self.indices = indices
# Second case, when no custom indices are supplied
else:
self.str_vecs = [(name + '.x'), (name + '.y'), (name + '.z')]
self.pretty_vecs = [name.lower() + u"_x",
name.lower() + u"_y",
name.lower() + u"_z"]
self.latex_vecs = [(r"\mathbf{\hat{%s}_x}" % name.lower()),
(r"\mathbf{\hat{%s}_y}" % name.lower()),
(r"\mathbf{\hat{%s}_z}" % name.lower())]
self.indices = ['x', 'y', 'z']
# Different step, for custom latex basis vectors
if latexs is not None:
if not isinstance(latexs, (tuple, list)):
raise TypeError('Supply the indices as a list')
if len(latexs) != 3:
raise ValueError('Supply 3 indices')
for i in latexs:
if not isinstance(i, string_types):
raise TypeError('Latex entries must be strings')
self.latex_vecs = latexs
self.name = name
self._var_dict = {}
#The _dcm_dict dictionary will only store the dcms of parent-child
#relationships. The _dcm_cache dictionary will work as the dcm
#cache.
self._dcm_dict = {}
self._dcm_cache = {}
self._ang_vel_dict = {}
self._ang_acc_dict = {}
self._dlist = [self._dcm_dict, self._ang_vel_dict, self._ang_acc_dict]
self._cur = 0
self._x = Vector([(Matrix([1, 0, 0]), self)])
self._y = Vector([(Matrix([0, 1, 0]), self)])
self._z = Vector([(Matrix([0, 0, 1]), self)])
#Associate coordinate symbols wrt this frame
if variables is not None:
if not isinstance(variables, (tuple, list)):
raise TypeError('Supply the variable names as a list/tuple')
if len(variables) != 3:
raise ValueError('Supply 3 variable names')
for i in variables:
if not isinstance(i, string_types):
raise TypeError('Variable names must be strings')
else:
variables = [name + '_x', name + '_y', name + '_z']
self.varlist = (CoordinateSym(variables[0], self, 0), \
CoordinateSym(variables[1], self, 1), \
CoordinateSym(variables[2], self, 2))
ReferenceFrame._count += 1
self.index = ReferenceFrame._count
def __getitem__(self, ind):
"""
Returns basis vector for the provided index, if the index is a string.
If the index is a number, returns the coordinate variable correspon-
-ding to that index.
"""
if not isinstance(ind, str):
if ind < 3:
return self.varlist[ind]
else:
raise ValueError("Invalid index provided")
if self.indices[0] == ind:
return self.x
if self.indices[1] == ind:
return self.y
if self.indices[2] == ind:
return self.z
else:
raise ValueError('Not a defined index')
def __iter__(self):
return iter([self.x, self.y, self.z])
def __str__(self):
"""Returns the name of the frame. """
return self.name
__repr__ = __str__
def _dict_list(self, other, num):
"""Creates a list from self to other using _dcm_dict. """
outlist = [[self]]
oldlist = [[]]
while outlist != oldlist:
oldlist = outlist[:]
for i, v in enumerate(outlist):
templist = v[-1]._dlist[num].keys()
for i2, v2 in enumerate(templist):
if not v.__contains__(v2):
littletemplist = v + [v2]
if not outlist.__contains__(littletemplist):
outlist.append(littletemplist)
for i, v in enumerate(oldlist):
if v[-1] != other:
outlist.remove(v)
outlist.sort(key=len)
if len(outlist) != 0:
return outlist[0]
raise ValueError('No Connecting Path found between ' + self.name +
' and ' + other.name)
def _w_diff_dcm(self, otherframe):
"""Angular velocity from time differentiating the DCM. """
from sympy.physics.vector.functions import dynamicsymbols
dcm2diff = self.dcm(otherframe)
diffed = dcm2diff.diff(dynamicsymbols._t)
angvelmat = diffed * dcm2diff.T
w1 = trigsimp(expand(angvelmat[7]), recursive=True)
w2 = trigsimp(expand(angvelmat[2]), recursive=True)
w3 = trigsimp(expand(angvelmat[3]), recursive=True)
return -Vector([(Matrix([w1, w2, w3]), self)])
def variable_map(self, otherframe):
"""
Returns a dictionary which expresses the coordinate variables
of this frame in terms of the variables of otherframe.
If Vector.simp is True, returns a simplified version of the mapped
values. Else, returns them without simplification.
Simplification of the expressions may take time.
Parameters
==========
otherframe : ReferenceFrame
The other frame to map the variables to
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, dynamicsymbols
>>> A = ReferenceFrame('A')
>>> q = dynamicsymbols('q')
>>> B = A.orientnew('B', 'Axis', [q, A.z])
>>> A.variable_map(B)
{A_x: B_x*cos(q(t)) - B_y*sin(q(t)), A_y: B_x*sin(q(t)) + B_y*cos(q(t)), A_z: B_z}
"""
_check_frame(otherframe)
if (otherframe, Vector.simp) in self._var_dict:
return self._var_dict[(otherframe, Vector.simp)]
else:
vars_matrix = self.dcm(otherframe) * Matrix(otherframe.varlist)
mapping = {}
for i, x in enumerate(self):
if Vector.simp:
mapping[self.varlist[i]] = trigsimp(vars_matrix[i], method='fu')
else:
mapping[self.varlist[i]] = vars_matrix[i]
self._var_dict[(otherframe, Vector.simp)] = mapping
return mapping
def ang_acc_in(self, otherframe):
"""Returns the angular acceleration Vector of the ReferenceFrame.
Effectively returns the Vector:
^N alpha ^B
which represent the angular acceleration of B in N, where B is self, and
N is otherframe.
Parameters
==========
otherframe : ReferenceFrame
The ReferenceFrame which the angular acceleration is returned in.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> N = ReferenceFrame('N')
>>> A = ReferenceFrame('A')
>>> V = 10 * N.x
>>> A.set_ang_acc(N, V)
>>> A.ang_acc_in(N)
10*N.x
"""
_check_frame(otherframe)
if otherframe in self._ang_acc_dict:
return self._ang_acc_dict[otherframe]
else:
return self.ang_vel_in(otherframe).dt(otherframe)
def ang_vel_in(self, otherframe):
"""Returns the angular velocity Vector of the ReferenceFrame.
Effectively returns the Vector:
^N omega ^B
which represent the angular velocity of B in N, where B is self, and
N is otherframe.
Parameters
==========
otherframe : ReferenceFrame
The ReferenceFrame which the angular velocity is returned in.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> N = ReferenceFrame('N')
>>> A = ReferenceFrame('A')
>>> V = 10 * N.x
>>> A.set_ang_vel(N, V)
>>> A.ang_vel_in(N)
10*N.x
"""
_check_frame(otherframe)
flist = self._dict_list(otherframe, 1)
outvec = Vector(0)
for i in range(len(flist) - 1):
outvec += flist[i]._ang_vel_dict[flist[i + 1]]
return outvec
def dcm(self, otherframe):
"""The direction cosine matrix between frames.
This gives the DCM between this frame and the otherframe.
The format is N.xyz = N.dcm(B) * B.xyz
A SymPy Matrix is returned.
Parameters
==========
otherframe : ReferenceFrame
The otherframe which the DCM is generated to.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> from sympy import symbols
>>> q1 = symbols('q1')
>>> N = ReferenceFrame('N')
>>> A = N.orientnew('A', 'Axis', [q1, N.x])
>>> N.dcm(A)
Matrix([
[1, 0, 0],
[0, cos(q1), -sin(q1)],
[0, sin(q1), cos(q1)]])
"""
_check_frame(otherframe)
#Check if the dcm wrt that frame has already been calculated
if otherframe in self._dcm_cache:
return self._dcm_cache[otherframe]
flist = self._dict_list(otherframe, 0)
outdcm = eye(3)
for i in range(len(flist) - 1):
outdcm = outdcm * flist[i]._dcm_dict[flist[i + 1]]
#After calculation, store the dcm in dcm cache for faster
#future retrieval
self._dcm_cache[otherframe] = outdcm
otherframe._dcm_cache[self] = outdcm.T
return outdcm
def orient(self, parent, rot_type, amounts, rot_order=''):
"""Defines the orientation of this frame relative to a parent frame.
Parameters
==========
parent : ReferenceFrame
The frame that this ReferenceFrame will have its orientation matrix
defined in relation to.
rot_type : str
The type of orientation matrix that is being created. Supported
types are 'Body', 'Space', 'Quaternion', 'Axis', and 'DCM'.
See examples for correct usage.
amounts : list OR value
The quantities that the orientation matrix will be defined by.
In case of rot_type='DCM', value must be a
sympy.matrices.MatrixBase object (or subclasses of it).
rot_order : str
If applicable, the order of a series of rotations.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> from sympy import symbols, eye, ImmutableMatrix
>>> q0, q1, q2, q3 = symbols('q0 q1 q2 q3')
>>> N = ReferenceFrame('N')
>>> B = ReferenceFrame('B')
Now we have a choice of how to implement the orientation. First is
Body. Body orientation takes this reference frame through three
successive simple rotations. Acceptable rotation orders are of length
3, expressed in XYZ or 123, and cannot have a rotation about about an
axis twice in a row.
>>> B.orient(N, 'Body', [q1, q2, q3], '123')
>>> B.orient(N, 'Body', [q1, q2, 0], 'ZXZ')
>>> B.orient(N, 'Body', [0, 0, 0], 'XYX')
Next is Space. Space is like Body, but the rotations are applied in the
opposite order.
>>> B.orient(N, 'Space', [q1, q2, q3], '312')
Next is Quaternion. This orients the new ReferenceFrame with
Quaternions, defined as a finite rotation about lambda, a unit vector,
by some amount theta.
This orientation is described by four parameters:
q0 = cos(theta/2)
q1 = lambda_x sin(theta/2)
q2 = lambda_y sin(theta/2)
q3 = lambda_z sin(theta/2)
Quaternion does not take in a rotation order.
>>> B.orient(N, 'Quaternion', [q0, q1, q2, q3])
Next is Axis. This is a rotation about an arbitrary, non-time-varying
axis by some angle. The axis is supplied as a Vector. This is how
simple rotations are defined.
>>> B.orient(N, 'Axis', [q1, N.x + 2 * N.y])
Last is DCM (Direction Cosine Matrix). This is a rotation matrix
given manually.
>>> B.orient(N, 'DCM', eye(3))
>>> B.orient(N, 'DCM', ImmutableMatrix([[0, 1, 0], [0, 0, -1], [-1, 0, 0]]))
"""
from sympy.physics.vector.functions import dynamicsymbols
_check_frame(parent)
# Allow passing a rotation matrix manually.
if rot_type == 'DCM':
# When rot_type == 'DCM', then amounts must be a Matrix type object
# (e.g. sympy.matrices.dense.MutableDenseMatrix).
if not isinstance(amounts, MatrixBase):
raise TypeError("Amounts must be a sympy Matrix type object.")
else:
amounts = list(amounts)
for i, v in enumerate(amounts):
if not isinstance(v, Vector):
amounts[i] = sympify(v)
def _rot(axis, angle):
"""DCM for simple axis 1,2,or 3 rotations. """
if axis == 1:
return Matrix([[1, 0, 0],
[0, cos(angle), -sin(angle)],
[0, sin(angle), cos(angle)]])
elif axis == 2:
return Matrix([[cos(angle), 0, sin(angle)],
[0, 1, 0],
[-sin(angle), 0, cos(angle)]])
elif axis == 3:
return Matrix([[cos(angle), -sin(angle), 0],
[sin(angle), cos(angle), 0],
[0, 0, 1]])
approved_orders = ('123', '231', '312', '132', '213', '321', '121',
'131', '212', '232', '313', '323', '')
rot_order = str(
rot_order).upper() # Now we need to make sure XYZ = 123
rot_type = rot_type.upper()
rot_order = [i.replace('X', '1') for i in rot_order]
rot_order = [i.replace('Y', '2') for i in rot_order]
rot_order = [i.replace('Z', '3') for i in rot_order]
rot_order = ''.join(rot_order)
if not rot_order in approved_orders:
raise TypeError('The supplied order is not an approved type')
parent_orient = []
if rot_type == 'AXIS':
if not rot_order == '':
raise TypeError('Axis orientation takes no rotation order')
if not (isinstance(amounts, (list, tuple)) & (len(amounts) == 2)):
raise TypeError('Amounts are a list or tuple of length 2')
theta = amounts[0]
axis = amounts[1]
axis = _check_vector(axis)
if not axis.dt(parent) == 0:
raise ValueError('Axis cannot be time-varying')
axis = axis.express(parent).normalize()
axis = axis.args[0][0]
parent_orient = ((eye(3) - axis * axis.T) * cos(theta) +
Matrix([[0, -axis[2], axis[1]], [axis[2], 0, -axis[0]],
[-axis[1], axis[0], 0]]) * sin(theta) + axis * axis.T)
elif rot_type == 'QUATERNION':
if not rot_order == '':
raise TypeError(
'Quaternion orientation takes no rotation order')
if not (isinstance(amounts, (list, tuple)) & (len(amounts) == 4)):
raise TypeError('Amounts are a list or tuple of length 4')
q0, q1, q2, q3 = amounts
parent_orient = (Matrix([[q0 ** 2 + q1 ** 2 - q2 ** 2 - q3 **
2, 2 * (q1 * q2 - q0 * q3), 2 * (q0 * q2 + q1 * q3)],
[2 * (q1 * q2 + q0 * q3), q0 ** 2 - q1 ** 2 + q2 ** 2 - q3 ** 2,
2 * (q2 * q3 - q0 * q1)], [2 * (q1 * q3 - q0 * q2), 2 * (q0 *
q1 + q2 * q3), q0 ** 2 - q1 ** 2 - q2 ** 2 + q3 ** 2]]))
elif rot_type == 'BODY':
if not (len(amounts) == 3 & len(rot_order) == 3):
raise TypeError('Body orientation takes 3 values & 3 orders')
a1 = int(rot_order[0])
a2 = int(rot_order[1])
a3 = int(rot_order[2])
parent_orient = (_rot(a1, amounts[0]) * _rot(a2, amounts[1])
* _rot(a3, amounts[2]))
elif rot_type == 'SPACE':
if not (len(amounts) == 3 & len(rot_order) == 3):
raise TypeError('Space orientation takes 3 values & 3 orders')
a1 = int(rot_order[0])
a2 = int(rot_order[1])
a3 = int(rot_order[2])
parent_orient = (_rot(a3, amounts[2]) * _rot(a2, amounts[1])
* _rot(a1, amounts[0]))
elif rot_type == 'DCM':
parent_orient = amounts
else:
raise NotImplementedError('That is not an implemented rotation')
#Reset the _dcm_cache of this frame, and remove it from the _dcm_caches
#of the frames it is linked to. Also remove it from the _dcm_dict of
#its parent
frames = self._dcm_cache.keys()
dcm_dict_del = []
dcm_cache_del = []
for frame in frames:
if frame in self._dcm_dict:
dcm_dict_del += [frame]
dcm_cache_del += [frame]
for frame in dcm_dict_del:
del frame._dcm_dict[self]
for frame in dcm_cache_del:
del frame._dcm_cache[self]
#Add the dcm relationship to _dcm_dict
self._dcm_dict = self._dlist[0] = {}
self._dcm_dict.update({parent: parent_orient.T})
parent._dcm_dict.update({self: parent_orient})
#Also update the dcm cache after resetting it
self._dcm_cache = {}
self._dcm_cache.update({parent: parent_orient.T})
parent._dcm_cache.update({self: parent_orient})
if rot_type == 'QUATERNION':
t = dynamicsymbols._t
q0, q1, q2, q3 = amounts
q0d = diff(q0, t)
q1d = diff(q1, t)
q2d = diff(q2, t)
q3d = diff(q3, t)
w1 = 2 * (q1d * q0 + q2d * q3 - q3d * q2 - q0d * q1)
w2 = 2 * (q2d * q0 + q3d * q1 - q1d * q3 - q0d * q2)
w3 = 2 * (q3d * q0 + q1d * q2 - q2d * q1 - q0d * q3)
wvec = Vector([(Matrix([w1, w2, w3]), self)])
elif rot_type == 'AXIS':
thetad = (amounts[0]).diff(dynamicsymbols._t)
wvec = thetad * amounts[1].express(parent).normalize()
elif rot_type == 'DCM':
wvec = self._w_diff_dcm(parent)
else:
try:
from sympy.polys.polyerrors import CoercionFailed
from sympy.physics.vector.functions import kinematic_equations
q1, q2, q3 = amounts
u1, u2, u3 = symbols('u1, u2, u3', cls=Dummy)
templist = kinematic_equations([u1, u2, u3], [q1, q2, q3],
rot_type, rot_order)
templist = [expand(i) for i in templist]
td = solve(templist, [u1, u2, u3])
u1 = expand(td[u1])
u2 = expand(td[u2])
u3 = expand(td[u3])
wvec = u1 * self.x + u2 * self.y + u3 * self.z
except (CoercionFailed, AssertionError):
wvec = self._w_diff_dcm(parent)
self._ang_vel_dict.update({parent: wvec})
parent._ang_vel_dict.update({self: -wvec})
self._var_dict = {}
def orientnew(self, newname, rot_type, amounts, rot_order='',
variables=None, indices=None, latexs=None):
"""Creates a new ReferenceFrame oriented with respect to this Frame.
See ReferenceFrame.orient() for acceptable rotation types, amounts,
and orders. Parent is going to be self.
Parameters
==========
newname : str
The name for the new ReferenceFrame
rot_type : str
The type of orientation matrix that is being created.
amounts : list OR value
The quantities that the orientation matrix will be defined by.
rot_order : str
If applicable, the order of a series of rotations.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> from sympy import symbols
>>> q0, q1, q2, q3 = symbols('q0 q1 q2 q3')
>>> N = ReferenceFrame('N')
Now we have a choice of how to implement the orientation. First is
Body. Body orientation takes this reference frame through three
successive simple rotations. Acceptable rotation orders are of length
3, expressed in XYZ or 123, and cannot have a rotation about about an
axis twice in a row.
>>> A = N.orientnew('A', 'Body', [q1, q2, q3], '123')
>>> A = N.orientnew('A', 'Body', [q1, q2, 0], 'ZXZ')
>>> A = N.orientnew('A', 'Body', [0, 0, 0], 'XYX')
Next is Space. Space is like Body, but the rotations are applied in the
opposite order.
>>> A = N.orientnew('A', 'Space', [q1, q2, q3], '312')
Next is Quaternion. This orients the new ReferenceFrame with
Quaternions, defined as a finite rotation about lambda, a unit vector,
by some amount theta.
This orientation is described by four parameters:
q0 = cos(theta/2)
q1 = lambda_x sin(theta/2)
q2 = lambda_y sin(theta/2)
q3 = lambda_z sin(theta/2)
Quaternion does not take in a rotation order.
>>> A = N.orientnew('A', 'Quaternion', [q0, q1, q2, q3])
Last is Axis. This is a rotation about an arbitrary, non-time-varying
axis by some angle. The axis is supplied as a Vector. This is how
simple rotations are defined.
>>> A = N.orientnew('A', 'Axis', [q1, N.x])
"""
newframe = self.__class__(newname, variables, indices, latexs)
newframe.orient(self, rot_type, amounts, rot_order)
return newframe
def set_ang_acc(self, otherframe, value):
"""Define the angular acceleration Vector in a ReferenceFrame.
Defines the angular acceleration of this ReferenceFrame, in another.
Angular acceleration can be defined with respect to multiple different
ReferenceFrames. Care must be taken to not create loops which are
inconsistent.
Parameters
==========
otherframe : ReferenceFrame
A ReferenceFrame to define the angular acceleration in
value : Vector
The Vector representing angular acceleration
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> N = ReferenceFrame('N')
>>> A = ReferenceFrame('A')
>>> V = 10 * N.x
>>> A.set_ang_acc(N, V)
>>> A.ang_acc_in(N)
10*N.x
"""
if value == 0:
value = Vector(0)
value = _check_vector(value)
_check_frame(otherframe)
self._ang_acc_dict.update({otherframe: value})
otherframe._ang_acc_dict.update({self: -value})
def set_ang_vel(self, otherframe, value):
"""Define the angular velocity vector in a ReferenceFrame.
Defines the angular velocity of this ReferenceFrame, in another.
Angular velocity can be defined with respect to multiple different
ReferenceFrames. Care must be taken to not create loops which are
inconsistent.
Parameters
==========
otherframe : ReferenceFrame
A ReferenceFrame to define the angular velocity in
value : Vector
The Vector representing angular velocity
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> N = ReferenceFrame('N')
>>> A = ReferenceFrame('A')
>>> V = 10 * N.x
>>> A.set_ang_vel(N, V)
>>> A.ang_vel_in(N)
10*N.x
"""
if value == 0:
value = Vector(0)
value = _check_vector(value)
_check_frame(otherframe)
self._ang_vel_dict.update({otherframe: value})
otherframe._ang_vel_dict.update({self: -value})
@property
def x(self):
"""The basis Vector for the ReferenceFrame, in the x direction. """
return self._x
@property
def y(self):
"""The basis Vector for the ReferenceFrame, in the y direction. """
return self._y
@property
def z(self):
"""The basis Vector for the ReferenceFrame, in the z direction. """
return self._z
def partial_velocity(self, frame, *gen_speeds):
"""Returns the partial angular velocities of this frame in the given
frame with respect to one or more provided generalized speeds.
Parameters
==========
frame : ReferenceFrame
The frame with which the angular velocity is defined in.
gen_speeds : functions of time
The generalized speeds.
Returns
=======
partial_velocities : tuple of Vector
The partial angular velocity vectors corresponding to the provided
generalized speeds.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, dynamicsymbols
>>> N = ReferenceFrame('N')
>>> A = ReferenceFrame('A')
>>> u1, u2 = dynamicsymbols('u1, u2')
>>> A.set_ang_vel(N, u1 * A.x + u2 * N.y)
>>> A.partial_velocity(N, u1)
A.x
>>> A.partial_velocity(N, u1, u2)
(A.x, N.y)
"""
partials = [self.ang_vel_in(frame).diff(speed, frame, var_in_dcm=False)
for speed in gen_speeds]
if len(partials) == 1:
return partials[0]
else:
return tuple(partials)
def _check_frame(other):
from .vector import VectorTypeError
if not isinstance(other, ReferenceFrame):
raise VectorTypeError(other, ReferenceFrame('A'))
| 31,258 | 36.391148 | 90 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/point.py
|
from __future__ import print_function, division
from sympy.core.compatibility import range
from .vector import Vector, _check_vector
from .frame import _check_frame
__all__ = ['Point']
class Point(object):
"""This object represents a point in a dynamic system.
It stores the: position, velocity, and acceleration of a point.
The position is a vector defined as the vector distance from a parent
point to this point.
"""
def __init__(self, name):
"""Initialization of a Point object. """
self.name = name
self._pos_dict = {}
self._vel_dict = {}
self._acc_dict = {}
self._pdlist = [self._pos_dict, self._vel_dict, self._acc_dict]
def __str__(self):
return self.name
__repr__ = __str__
def _check_point(self, other):
if not isinstance(other, Point):
raise TypeError('A Point must be supplied')
def _pdict_list(self, other, num):
"""Creates a list from self to other using _dcm_dict. """
outlist = [[self]]
oldlist = [[]]
while outlist != oldlist:
oldlist = outlist[:]
for i, v in enumerate(outlist):
templist = v[-1]._pdlist[num].keys()
for i2, v2 in enumerate(templist):
if not v.__contains__(v2):
littletemplist = v + [v2]
if not outlist.__contains__(littletemplist):
outlist.append(littletemplist)
for i, v in enumerate(oldlist):
if v[-1] != other:
outlist.remove(v)
outlist.sort(key=len)
if len(outlist) != 0:
return outlist[0]
raise ValueError('No Connecting Path found between ' + other.name +
' and ' + self.name)
def a1pt_theory(self, otherpoint, outframe, interframe):
"""Sets the acceleration of this point with the 1-point theory.
The 1-point theory for point acceleration looks like this:
^N a^P = ^B a^P + ^N a^O + ^N alpha^B x r^OP + ^N omega^B x (^N omega^B
x r^OP) + 2 ^N omega^B x ^B v^P
where O is a point fixed in B, P is a point moving in B, and B is
rotating in frame N.
Parameters
==========
otherpoint : Point
The first point of the 1-point theory (O)
outframe : ReferenceFrame
The frame we want this point's acceleration defined in (N)
fixedframe : ReferenceFrame
The intermediate frame in this calculation (B)
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> from sympy.physics.vector import Vector, dynamicsymbols
>>> q = dynamicsymbols('q')
>>> q2 = dynamicsymbols('q2')
>>> qd = dynamicsymbols('q', 1)
>>> q2d = dynamicsymbols('q2', 1)
>>> N = ReferenceFrame('N')
>>> B = ReferenceFrame('B')
>>> B.set_ang_vel(N, 5 * B.y)
>>> O = Point('O')
>>> P = O.locatenew('P', q * B.x)
>>> P.set_vel(B, qd * B.x + q2d * B.y)
>>> O.set_vel(N, 0)
>>> P.a1pt_theory(O, N, B)
(-25*q + q'')*B.x + q2''*B.y - 10*q'*B.z
"""
_check_frame(outframe)
_check_frame(interframe)
self._check_point(otherpoint)
dist = self.pos_from(otherpoint)
v = self.vel(interframe)
a1 = otherpoint.acc(outframe)
a2 = self.acc(interframe)
omega = interframe.ang_vel_in(outframe)
alpha = interframe.ang_acc_in(outframe)
self.set_acc(outframe, a2 + 2 * (omega ^ v) + a1 + (alpha ^ dist) +
(omega ^ (omega ^ dist)))
return self.acc(outframe)
def a2pt_theory(self, otherpoint, outframe, fixedframe):
"""Sets the acceleration of this point with the 2-point theory.
The 2-point theory for point acceleration looks like this:
^N a^P = ^N a^O + ^N alpha^B x r^OP + ^N omega^B x (^N omega^B x r^OP)
where O and P are both points fixed in frame B, which is rotating in
frame N.
Parameters
==========
otherpoint : Point
The first point of the 2-point theory (O)
outframe : ReferenceFrame
The frame we want this point's acceleration defined in (N)
fixedframe : ReferenceFrame
The frame in which both points are fixed (B)
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame, dynamicsymbols
>>> q = dynamicsymbols('q')
>>> qd = dynamicsymbols('q', 1)
>>> N = ReferenceFrame('N')
>>> B = N.orientnew('B', 'Axis', [q, N.z])
>>> O = Point('O')
>>> P = O.locatenew('P', 10 * B.x)
>>> O.set_vel(N, 5 * N.x)
>>> P.a2pt_theory(O, N, B)
- 10*q'**2*B.x + 10*q''*B.y
"""
_check_frame(outframe)
_check_frame(fixedframe)
self._check_point(otherpoint)
dist = self.pos_from(otherpoint)
a = otherpoint.acc(outframe)
omega = fixedframe.ang_vel_in(outframe)
alpha = fixedframe.ang_acc_in(outframe)
self.set_acc(outframe, a + (alpha ^ dist) + (omega ^ (omega ^ dist)))
return self.acc(outframe)
def acc(self, frame):
"""The acceleration Vector of this Point in a ReferenceFrame.
Parameters
==========
frame : ReferenceFrame
The frame in which the returned acceleration vector will be defined in
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> N = ReferenceFrame('N')
>>> p1 = Point('p1')
>>> p1.set_acc(N, 10 * N.x)
>>> p1.acc(N)
10*N.x
"""
_check_frame(frame)
if not (frame in self._acc_dict):
if self._vel_dict[frame] != 0:
return (self._vel_dict[frame]).dt(frame)
else:
return Vector(0)
return self._acc_dict[frame]
def locatenew(self, name, value):
"""Creates a new point with a position defined from this point.
Parameters
==========
name : str
The name for the new point
value : Vector
The position of the new point relative to this point
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Point
>>> N = ReferenceFrame('N')
>>> P1 = Point('P1')
>>> P2 = P1.locatenew('P2', 10 * N.x)
"""
if not isinstance(name, str):
raise TypeError('Must supply a valid name')
if value == 0:
value = Vector(0)
value = _check_vector(value)
p = Point(name)
p.set_pos(self, value)
self.set_pos(p, -value)
return p
def pos_from(self, otherpoint):
"""Returns a Vector distance between this Point and the other Point.
Parameters
==========
otherpoint : Point
The otherpoint we are locating this one relative to
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> N = ReferenceFrame('N')
>>> p1 = Point('p1')
>>> p2 = Point('p2')
>>> p1.set_pos(p2, 10 * N.x)
>>> p1.pos_from(p2)
10*N.x
"""
outvec = Vector(0)
plist = self._pdict_list(otherpoint, 0)
for i in range(len(plist) - 1):
outvec += plist[i]._pos_dict[plist[i + 1]]
return outvec
def set_acc(self, frame, value):
"""Used to set the acceleration of this Point in a ReferenceFrame.
Parameters
==========
frame : ReferenceFrame
The frame in which this point's acceleration is defined
value : Vector
The vector value of this point's acceleration in the frame
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> N = ReferenceFrame('N')
>>> p1 = Point('p1')
>>> p1.set_acc(N, 10 * N.x)
>>> p1.acc(N)
10*N.x
"""
if value == 0:
value = Vector(0)
value = _check_vector(value)
_check_frame(frame)
self._acc_dict.update({frame: value})
def set_pos(self, otherpoint, value):
"""Used to set the position of this point w.r.t. another point.
Parameters
==========
otherpoint : Point
The other point which this point's location is defined relative to
value : Vector
The vector which defines the location of this point
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> N = ReferenceFrame('N')
>>> p1 = Point('p1')
>>> p2 = Point('p2')
>>> p1.set_pos(p2, 10 * N.x)
>>> p1.pos_from(p2)
10*N.x
"""
if value == 0:
value = Vector(0)
value = _check_vector(value)
self._check_point(otherpoint)
self._pos_dict.update({otherpoint: value})
otherpoint._pos_dict.update({self: -value})
def set_vel(self, frame, value):
"""Sets the velocity Vector of this Point in a ReferenceFrame.
Parameters
==========
frame : ReferenceFrame
The frame in which this point's velocity is defined
value : Vector
The vector value of this point's velocity in the frame
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> N = ReferenceFrame('N')
>>> p1 = Point('p1')
>>> p1.set_vel(N, 10 * N.x)
>>> p1.vel(N)
10*N.x
"""
if value == 0:
value = Vector(0)
value = _check_vector(value)
_check_frame(frame)
self._vel_dict.update({frame: value})
def v1pt_theory(self, otherpoint, outframe, interframe):
"""Sets the velocity of this point with the 1-point theory.
The 1-point theory for point velocity looks like this:
^N v^P = ^B v^P + ^N v^O + ^N omega^B x r^OP
where O is a point fixed in B, P is a point moving in B, and B is
rotating in frame N.
Parameters
==========
otherpoint : Point
The first point of the 2-point theory (O)
outframe : ReferenceFrame
The frame we want this point's velocity defined in (N)
interframe : ReferenceFrame
The intermediate frame in this calculation (B)
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> from sympy.physics.vector import Vector, dynamicsymbols
>>> q = dynamicsymbols('q')
>>> q2 = dynamicsymbols('q2')
>>> qd = dynamicsymbols('q', 1)
>>> q2d = dynamicsymbols('q2', 1)
>>> N = ReferenceFrame('N')
>>> B = ReferenceFrame('B')
>>> B.set_ang_vel(N, 5 * B.y)
>>> O = Point('O')
>>> P = O.locatenew('P', q * B.x)
>>> P.set_vel(B, qd * B.x + q2d * B.y)
>>> O.set_vel(N, 0)
>>> P.v1pt_theory(O, N, B)
q'*B.x + q2'*B.y - 5*q*B.z
"""
_check_frame(outframe)
_check_frame(interframe)
self._check_point(otherpoint)
dist = self.pos_from(otherpoint)
v1 = self.vel(interframe)
v2 = otherpoint.vel(outframe)
omega = interframe.ang_vel_in(outframe)
self.set_vel(outframe, v1 + v2 + (omega ^ dist))
return self.vel(outframe)
def v2pt_theory(self, otherpoint, outframe, fixedframe):
"""Sets the velocity of this point with the 2-point theory.
The 2-point theory for point velocity looks like this:
^N v^P = ^N v^O + ^N omega^B x r^OP
where O and P are both points fixed in frame B, which is rotating in
frame N.
Parameters
==========
otherpoint : Point
The first point of the 2-point theory (O)
outframe : ReferenceFrame
The frame we want this point's velocity defined in (N)
fixedframe : ReferenceFrame
The frame in which both points are fixed (B)
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame, dynamicsymbols
>>> q = dynamicsymbols('q')
>>> qd = dynamicsymbols('q', 1)
>>> N = ReferenceFrame('N')
>>> B = N.orientnew('B', 'Axis', [q, N.z])
>>> O = Point('O')
>>> P = O.locatenew('P', 10 * B.x)
>>> O.set_vel(N, 5 * N.x)
>>> P.v2pt_theory(O, N, B)
5*N.x + 10*q'*B.y
"""
_check_frame(outframe)
_check_frame(fixedframe)
self._check_point(otherpoint)
dist = self.pos_from(otherpoint)
v = otherpoint.vel(outframe)
omega = fixedframe.ang_vel_in(outframe)
self.set_vel(outframe, v + (omega ^ dist))
return self.vel(outframe)
def vel(self, frame):
"""The velocity Vector of this Point in the ReferenceFrame.
Parameters
==========
frame : ReferenceFrame
The frame in which the returned velocity vector will be defined in
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> N = ReferenceFrame('N')
>>> p1 = Point('p1')
>>> p1.set_vel(N, 10 * N.x)
>>> p1.vel(N)
10*N.x
"""
_check_frame(frame)
if not (frame in self._vel_dict):
raise ValueError('Velocity of point ' + self.name + ' has not been'
' defined in ReferenceFrame ' + frame.name)
return self._vel_dict[frame]
def partial_velocity(self, frame, *gen_speeds):
"""Returns the partial velocities of the linear velocity vector of this
point in the given frame with respect to one or more provided
generalized speeds.
Parameters
==========
frame : ReferenceFrame
The frame with which the velocity is defined in.
gen_speeds : functions of time
The generalized speeds.
Returns
=======
partial_velocities : tuple of Vector
The partial velocity vectors corresponding to the provided
generalized speeds.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Point
>>> from sympy.physics.vector import dynamicsymbols
>>> N = ReferenceFrame('N')
>>> A = ReferenceFrame('A')
>>> p = Point('p')
>>> u1, u2 = dynamicsymbols('u1, u2')
>>> p.set_vel(N, u1 * N.x + u2 * A.y)
>>> p.partial_velocity(N, u1)
N.x
>>> p.partial_velocity(N, u1, u2)
(N.x, A.y)
"""
partials = [self.vel(frame).diff(speed, frame, var_in_dcm=False) for
speed in gen_speeds]
if len(partials) == 1:
return partials[0]
else:
return tuple(partials)
| 15,193 | 29.448898 | 82 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/fieldfunctions.py
|
from sympy import diff, integrate, S
from sympy.physics.vector import Vector, express
from sympy.physics.vector.frame import _check_frame
from sympy.physics.vector.vector import _check_vector
__all__ = ['curl', 'divergence', 'gradient', 'is_conservative',
'is_solenoidal', 'scalar_potential',
'scalar_potential_difference']
def curl(vect, frame):
"""
Returns the curl of a vector field computed wrt the coordinate
symbols of the given frame.
Parameters
==========
vect : Vector
The vector operand
frame : ReferenceFrame
The reference frame to calculate the curl in
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy.physics.vector import curl
>>> R = ReferenceFrame('R')
>>> v1 = R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z
>>> curl(v1, R)
0
>>> v2 = R[0]*R[1]*R[2]*R.x
>>> curl(v2, R)
R_x*R_y*R.y - R_x*R_z*R.z
"""
_check_vector(vect)
if vect == 0:
return Vector(0)
vect = express(vect, frame, variables=True)
#A mechanical approach to avoid looping overheads
vectx = vect.dot(frame.x)
vecty = vect.dot(frame.y)
vectz = vect.dot(frame.z)
outvec = Vector(0)
outvec += (diff(vectz, frame[1]) - diff(vecty, frame[2])) * frame.x
outvec += (diff(vectx, frame[2]) - diff(vectz, frame[0])) * frame.y
outvec += (diff(vecty, frame[0]) - diff(vectx, frame[1])) * frame.z
return outvec
def divergence(vect, frame):
"""
Returns the divergence of a vector field computed wrt the coordinate
symbols of the given frame.
Parameters
==========
vect : Vector
The vector operand
frame : ReferenceFrame
The reference frame to calculate the divergence in
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy.physics.vector import divergence
>>> R = ReferenceFrame('R')
>>> v1 = R[0]*R[1]*R[2] * (R.x+R.y+R.z)
>>> divergence(v1, R)
R_x*R_y + R_x*R_z + R_y*R_z
>>> v2 = 2*R[1]*R[2]*R.y
>>> divergence(v2, R)
2*R_z
"""
_check_vector(vect)
if vect == 0:
return S(0)
vect = express(vect, frame, variables=True)
vectx = vect.dot(frame.x)
vecty = vect.dot(frame.y)
vectz = vect.dot(frame.z)
out = S(0)
out += diff(vectx, frame[0])
out += diff(vecty, frame[1])
out += diff(vectz, frame[2])
return out
def gradient(scalar, frame):
"""
Returns the vector gradient of a scalar field computed wrt the
coordinate symbols of the given frame.
Parameters
==========
scalar : sympifiable
The scalar field to take the gradient of
frame : ReferenceFrame
The frame to calculate the gradient in
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy.physics.vector import gradient
>>> R = ReferenceFrame('R')
>>> s1 = R[0]*R[1]*R[2]
>>> gradient(s1, R)
R_y*R_z*R.x + R_x*R_z*R.y + R_x*R_y*R.z
>>> s2 = 5*R[0]**2*R[2]
>>> gradient(s2, R)
10*R_x*R_z*R.x + 5*R_x**2*R.z
"""
_check_frame(frame)
outvec = Vector(0)
scalar = express(scalar, frame, variables=True)
for i, x in enumerate(frame):
outvec += diff(scalar, frame[i]) * x
return outvec
def is_conservative(field):
"""
Checks if a field is conservative.
Paramaters
==========
field : Vector
The field to check for conservative property
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy.physics.vector import is_conservative
>>> R = ReferenceFrame('R')
>>> is_conservative(R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z)
True
>>> is_conservative(R[2] * R.y)
False
"""
#Field is conservative irrespective of frame
#Take the first frame in the result of the
#separate method of Vector
if field == Vector(0):
return True
frame = list(field.separate())[0]
return curl(field, frame).simplify() == Vector(0)
def is_solenoidal(field):
"""
Checks if a field is solenoidal.
Paramaters
==========
field : Vector
The field to check for solenoidal property
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy.physics.vector import is_solenoidal
>>> R = ReferenceFrame('R')
>>> is_solenoidal(R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z)
True
>>> is_solenoidal(R[1] * R.y)
False
"""
#Field is solenoidal irrespective of frame
#Take the first frame in the result of the
#separate method in Vector
if field == Vector(0):
return True
frame = list(field.separate())[0]
return divergence(field, frame).simplify() == S(0)
def scalar_potential(field, frame):
"""
Returns the scalar potential function of a field in a given frame
(without the added integration constant).
Parameters
==========
field : Vector
The vector field whose scalar potential function is to be
calculated
frame : ReferenceFrame
The frame to do the calculation in
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy.physics.vector import scalar_potential, gradient
>>> R = ReferenceFrame('R')
>>> scalar_potential(R.z, R) == R[2]
True
>>> scalar_field = 2*R[0]**2*R[1]*R[2]
>>> grad_field = gradient(scalar_field, R)
>>> scalar_potential(grad_field, R)
2*R_x**2*R_y*R_z
"""
#Check whether field is conservative
if not is_conservative(field):
raise ValueError("Field is not conservative")
if field == Vector(0):
return S(0)
#Express the field exntirely in frame
#Subsitute coordinate variables also
_check_frame(frame)
field = express(field, frame, variables=True)
#Make a list of dimensions of the frame
dimensions = [x for x in frame]
#Calculate scalar potential function
temp_function = integrate(field.dot(dimensions[0]), frame[0])
for i, dim in enumerate(dimensions[1:]):
partial_diff = diff(temp_function, frame[i + 1])
partial_diff = field.dot(dim) - partial_diff
temp_function += integrate(partial_diff, frame[i + 1])
return temp_function
def scalar_potential_difference(field, frame, point1, point2, origin):
"""
Returns the scalar potential difference between two points in a
certain frame, wrt a given field.
If a scalar field is provided, its values at the two points are
considered. If a conservative vector field is provided, the values
of its scalar potential function at the two points are used.
Returns (potential at position 2) - (potential at position 1)
Parameters
==========
field : Vector/sympyfiable
The field to calculate wrt
frame : ReferenceFrame
The frame to do the calculations in
point1 : Point
The initial Point in given frame
position2 : Point
The second Point in the given frame
origin : Point
The Point to use as reference point for position vector
calculation
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Point
>>> from sympy.physics.vector import scalar_potential_difference
>>> R = ReferenceFrame('R')
>>> O = Point('O')
>>> P = O.locatenew('P', R[0]*R.x + R[1]*R.y + R[2]*R.z)
>>> vectfield = 4*R[0]*R[1]*R.x + 2*R[0]**2*R.y
>>> scalar_potential_difference(vectfield, R, O, P, O)
2*R_x**2*R_y
>>> Q = O.locatenew('O', 3*R.x + R.y + 2*R.z)
>>> scalar_potential_difference(vectfield, R, P, Q, O)
-2*R_x**2*R_y + 18
"""
_check_frame(frame)
if isinstance(field, Vector):
#Get the scalar potential function
scalar_fn = scalar_potential(field, frame)
else:
#Field is a scalar
scalar_fn = field
#Express positions in required frame
position1 = express(point1.pos_from(origin), frame, variables=True)
position2 = express(point2.pos_from(origin), frame, variables=True)
#Get the two positions as substitution dicts for coordinate variables
subs_dict1 = {}
subs_dict2 = {}
for i, x in enumerate(frame):
subs_dict1[frame[i]] = x.dot(position1)
subs_dict2[frame[i]] = x.dot(position2)
return scalar_fn.subs(subs_dict2) - scalar_fn.subs(subs_dict1)
| 8,504 | 26.085987 | 73 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/functions.py
|
from __future__ import print_function, division
from sympy.core.backend import (sympify, diff, sin, cos, Matrix, symbols,
Function, S, Symbol)
from sympy import integrate, trigsimp
from sympy.core.compatibility import reduce
from .vector import Vector, _check_vector
from .frame import CoordinateSym, _check_frame
from .dyadic import Dyadic
from .printing import vprint, vsprint, vpprint, vlatex, init_vprinting
from sympy.utilities.iterables import iterable
__all__ = ['cross', 'dot', 'express', 'time_derivative', 'outer',
'kinematic_equations', 'get_motion_params', 'partial_velocity',
'dynamicsymbols', 'vprint', 'vsprint', 'vpprint', 'vlatex',
'init_vprinting']
def cross(vec1, vec2):
"""Cross product convenience wrapper for Vector.cross(): \n"""
if not isinstance(vec1, (Vector, Dyadic)):
raise TypeError('Cross product is between two vectors')
return vec1 ^ vec2
cross.__doc__ += Vector.cross.__doc__
def dot(vec1, vec2):
"""Dot product convenience wrapper for Vector.dot(): \n"""
if not isinstance(vec1, (Vector, Dyadic)):
raise TypeError('Dot product is between two vectors')
return vec1 & vec2
dot.__doc__ += Vector.dot.__doc__
def express(expr, frame, frame2=None, variables=False):
"""
Global function for 'express' functionality.
Re-expresses a Vector, scalar(sympyfiable) or Dyadic in given frame.
Refer to the local methods of Vector and Dyadic for details.
If 'variables' is True, then the coordinate variables (CoordinateSym
instances) of other frames present in the vector/scalar field or
dyadic expression are also substituted in terms of the base scalars of
this frame.
Parameters
==========
expr : Vector/Dyadic/scalar(sympyfiable)
The expression to re-express in ReferenceFrame 'frame'
frame: ReferenceFrame
The reference frame to express expr in
frame2 : ReferenceFrame
The other frame required for re-expression(only for Dyadic expr)
variables : boolean
Specifies whether to substitute the coordinate variables present
in expr, in terms of those of frame
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer, dynamicsymbols
>>> N = ReferenceFrame('N')
>>> q = dynamicsymbols('q')
>>> B = N.orientnew('B', 'Axis', [q, N.z])
>>> d = outer(N.x, N.x)
>>> from sympy.physics.vector import express
>>> express(d, B, N)
cos(q)*(B.x|N.x) - sin(q)*(B.y|N.x)
>>> express(B.x, N)
cos(q)*N.x + sin(q)*N.y
>>> express(N[0], B, variables=True)
B_x*cos(q(t)) - B_y*sin(q(t))
"""
_check_frame(frame)
if expr == 0:
return expr
if isinstance(expr, Vector):
#Given expr is a Vector
if variables:
#If variables attribute is True, substitute
#the coordinate variables in the Vector
frame_list = [x[-1] for x in expr.args]
subs_dict = {}
for f in frame_list:
subs_dict.update(f.variable_map(frame))
expr = expr.subs(subs_dict)
#Re-express in this frame
outvec = Vector([])
for i, v in enumerate(expr.args):
if v[1] != frame:
temp = frame.dcm(v[1]) * v[0]
if Vector.simp:
temp = temp.applyfunc(lambda x:
trigsimp(x, method='fu'))
outvec += Vector([(temp, frame)])
else:
outvec += Vector([v])
return outvec
if isinstance(expr, Dyadic):
if frame2 is None:
frame2 = frame
_check_frame(frame2)
ol = Dyadic(0)
for i, v in enumerate(expr.args):
ol += express(v[0], frame, variables=variables) * \
(express(v[1], frame, variables=variables) |
express(v[2], frame2, variables=variables))
return ol
else:
if variables:
#Given expr is a scalar field
frame_set = set([])
expr = sympify(expr)
#Subsitute all the coordinate variables
for x in expr.free_symbols:
if isinstance(x, CoordinateSym)and x.frame != frame:
frame_set.add(x.frame)
subs_dict = {}
for f in frame_set:
subs_dict.update(f.variable_map(frame))
return expr.subs(subs_dict)
return expr
def time_derivative(expr, frame, order=1):
"""
Calculate the time derivative of a vector/scalar field function
or dyadic expression in given frame.
References
==========
http://en.wikipedia.org/wiki/Rotating_reference_frame#Time_derivatives_in_the_two_frames
Parameters
==========
expr : Vector/Dyadic/sympifyable
The expression whose time derivative is to be calculated
frame : ReferenceFrame
The reference frame to calculate the time derivative in
order : integer
The order of the derivative to be calculated
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, dynamicsymbols
>>> from sympy import Symbol
>>> q1 = Symbol('q1')
>>> u1 = dynamicsymbols('u1')
>>> N = ReferenceFrame('N')
>>> A = N.orientnew('A', 'Axis', [q1, N.x])
>>> v = u1 * N.x
>>> A.set_ang_vel(N, 10*A.x)
>>> from sympy.physics.vector import time_derivative
>>> time_derivative(v, N)
u1'*N.x
>>> time_derivative(u1*A[0], N)
N_x*Derivative(u1(t), t)
>>> B = N.orientnew('B', 'Axis', [u1, N.z])
>>> from sympy.physics.vector import outer
>>> d = outer(N.x, N.x)
>>> time_derivative(d, B)
- u1'*(N.y|N.x) - u1'*(N.x|N.y)
"""
t = dynamicsymbols._t
_check_frame(frame)
if order == 0:
return expr
if order % 1 != 0 or order < 0:
raise ValueError("Unsupported value of order entered")
if isinstance(expr, Vector):
outlist = []
for i, v in enumerate(expr.args):
if v[1] == frame:
outlist += [(express(v[0], frame,
variables=True).diff(t), frame)]
else:
outlist += (time_derivative(Vector([v]), v[1]) + \
(v[1].ang_vel_in(frame) ^ Vector([v]))).args
outvec = Vector(outlist)
return time_derivative(outvec, frame, order - 1)
if isinstance(expr, Dyadic):
ol = Dyadic(0)
for i, v in enumerate(expr.args):
ol += (v[0].diff(t) * (v[1] | v[2]))
ol += (v[0] * (time_derivative(v[1], frame) | v[2]))
ol += (v[0] * (v[1] | time_derivative(v[2], frame)))
return time_derivative(ol, frame, order - 1)
else:
return diff(express(expr, frame, variables=True), t, order)
def outer(vec1, vec2):
"""Outer product convenience wrapper for Vector.outer():\n"""
if not isinstance(vec1, Vector):
raise TypeError('Outer product is between two Vectors')
return vec1 | vec2
outer.__doc__ += Vector.outer.__doc__
def kinematic_equations(speeds, coords, rot_type, rot_order=''):
"""Gives equations relating the qdot's to u's for a rotation type.
Supply rotation type and order as in orient. Speeds are assumed to be
body-fixed; if we are defining the orientation of B in A using by rot_type,
the angular velocity of B in A is assumed to be in the form: speed[0]*B.x +
speed[1]*B.y + speed[2]*B.z
Parameters
==========
speeds : list of length 3
The body fixed angular velocity measure numbers.
coords : list of length 3 or 4
The coordinates used to define the orientation of the two frames.
rot_type : str
The type of rotation used to create the equations. Body, Space, or
Quaternion only
rot_order : str
If applicable, the order of a series of rotations.
Examples
========
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy.physics.vector import kinematic_equations, vprint
>>> u1, u2, u3 = dynamicsymbols('u1 u2 u3')
>>> q1, q2, q3 = dynamicsymbols('q1 q2 q3')
>>> vprint(kinematic_equations([u1,u2,u3], [q1,q2,q3], 'body', '313'),
... order=None)
[-(u1*sin(q3) + u2*cos(q3))/sin(q2) + q1', -u1*cos(q3) + u2*sin(q3) + q2', (u1*sin(q3) + u2*cos(q3))*cos(q2)/sin(q2) - u3 + q3']
"""
# Code below is checking and sanitizing input
approved_orders = ('123', '231', '312', '132', '213', '321', '121', '131',
'212', '232', '313', '323', '1', '2', '3', '')
rot_order = str(rot_order).upper() # Now we need to make sure XYZ = 123
rot_type = rot_type.upper()
rot_order = [i.replace('X', '1') for i in rot_order]
rot_order = [i.replace('Y', '2') for i in rot_order]
rot_order = [i.replace('Z', '3') for i in rot_order]
rot_order = ''.join(rot_order)
if not isinstance(speeds, (list, tuple)):
raise TypeError('Need to supply speeds in a list')
if len(speeds) != 3:
raise TypeError('Need to supply 3 body-fixed speeds')
if not isinstance(coords, (list, tuple)):
raise TypeError('Need to supply coordinates in a list')
if rot_type.lower() in ['body', 'space']:
if rot_order not in approved_orders:
raise ValueError('Not an acceptable rotation order')
if len(coords) != 3:
raise ValueError('Need 3 coordinates for body or space')
# Actual hard-coded kinematic differential equations
q1, q2, q3 = coords
q1d, q2d, q3d = [diff(i, dynamicsymbols._t) for i in coords]
w1, w2, w3 = speeds
s1, s2, s3 = [sin(q1), sin(q2), sin(q3)]
c1, c2, c3 = [cos(q1), cos(q2), cos(q3)]
if rot_type.lower() == 'body':
if rot_order == '123':
return [q1d - (w1 * c3 - w2 * s3) / c2, q2d - w1 * s3 - w2 *
c3, q3d - (-w1 * c3 + w2 * s3) * s2 / c2 - w3]
if rot_order == '231':
return [q1d - (w2 * c3 - w3 * s3) / c2, q2d - w2 * s3 - w3 *
c3, q3d - w1 - (- w2 * c3 + w3 * s3) * s2 / c2]
if rot_order == '312':
return [q1d - (-w1 * s3 + w3 * c3) / c2, q2d - w1 * c3 - w3 *
s3, q3d - (w1 * s3 - w3 * c3) * s2 / c2 - w2]
if rot_order == '132':
return [q1d - (w1 * c3 + w3 * s3) / c2, q2d + w1 * s3 - w3 *
c3, q3d - (w1 * c3 + w3 * s3) * s2 / c2 - w2]
if rot_order == '213':
return [q1d - (w1 * s3 + w2 * c3) / c2, q2d - w1 * c3 + w2 *
s3, q3d - (w1 * s3 + w2 * c3) * s2 / c2 - w3]
if rot_order == '321':
return [q1d - (w2 * s3 + w3 * c3) / c2, q2d - w2 * c3 + w3 *
s3, q3d - w1 - (w2 * s3 + w3 * c3) * s2 / c2]
if rot_order == '121':
return [q1d - (w2 * s3 + w3 * c3) / s2, q2d - w2 * c3 + w3 *
s3, q3d - w1 + (w2 * s3 + w3 * c3) * c2 / s2]
if rot_order == '131':
return [q1d - (-w2 * c3 + w3 * s3) / s2, q2d - w2 * s3 - w3 *
c3, q3d - w1 - (w2 * c3 - w3 * s3) * c2 / s2]
if rot_order == '212':
return [q1d - (w1 * s3 - w3 * c3) / s2, q2d - w1 * c3 - w3 *
s3, q3d - (-w1 * s3 + w3 * c3) * c2 / s2 - w2]
if rot_order == '232':
return [q1d - (w1 * c3 + w3 * s3) / s2, q2d + w1 * s3 - w3 *
c3, q3d + (w1 * c3 + w3 * s3) * c2 / s2 - w2]
if rot_order == '313':
return [q1d - (w1 * s3 + w2 * c3) / s2, q2d - w1 * c3 + w2 *
s3, q3d + (w1 * s3 + w2 * c3) * c2 / s2 - w3]
if rot_order == '323':
return [q1d - (-w1 * c3 + w2 * s3) / s2, q2d - w1 * s3 - w2 *
c3, q3d - (w1 * c3 - w2 * s3) * c2 / s2 - w3]
if rot_type.lower() == 'space':
if rot_order == '123':
return [q1d - w1 - (w2 * s1 + w3 * c1) * s2 / c2, q2d - w2 *
c1 + w3 * s1, q3d - (w2 * s1 + w3 * c1) / c2]
if rot_order == '231':
return [q1d - (w1 * c1 + w3 * s1) * s2 / c2 - w2, q2d + w1 *
s1 - w3 * c1, q3d - (w1 * c1 + w3 * s1) / c2]
if rot_order == '312':
return [q1d - (w1 * s1 + w2 * c1) * s2 / c2 - w3, q2d - w1 *
c1 + w2 * s1, q3d - (w1 * s1 + w2 * c1) / c2]
if rot_order == '132':
return [q1d - w1 - (-w2 * c1 + w3 * s1) * s2 / c2, q2d - w2 *
s1 - w3 * c1, q3d - (w2 * c1 - w3 * s1) / c2]
if rot_order == '213':
return [q1d - (w1 * s1 - w3 * c1) * s2 / c2 - w2, q2d - w1 *
c1 - w3 * s1, q3d - (-w1 * s1 + w3 * c1) / c2]
if rot_order == '321':
return [q1d - (-w1 * c1 + w2 * s1) * s2 / c2 - w3, q2d - w1 *
s1 - w2 * c1, q3d - (w1 * c1 - w2 * s1) / c2]
if rot_order == '121':
return [q1d - w1 + (w2 * s1 + w3 * c1) * c2 / s2, q2d - w2 *
c1 + w3 * s1, q3d - (w2 * s1 + w3 * c1) / s2]
if rot_order == '131':
return [q1d - w1 - (w2 * c1 - w3 * s1) * c2 / s2, q2d - w2 *
s1 - w3 * c1, q3d - (-w2 * c1 + w3 * s1) / s2]
if rot_order == '212':
return [q1d - (-w1 * s1 + w3 * c1) * c2 / s2 - w2, q2d - w1 *
c1 - w3 * s1, q3d - (w1 * s1 - w3 * c1) / s2]
if rot_order == '232':
return [q1d + (w1 * c1 + w3 * s1) * c2 / s2 - w2, q2d + w1 *
s1 - w3 * c1, q3d - (w1 * c1 + w3 * s1) / s2]
if rot_order == '313':
return [q1d + (w1 * s1 + w2 * c1) * c2 / s2 - w3, q2d - w1 *
c1 + w2 * s1, q3d - (w1 * s1 + w2 * c1) / s2]
if rot_order == '323':
return [q1d - (w1 * c1 - w2 * s1) * c2 / s2 - w3, q2d - w1 *
s1 - w2 * c1, q3d - (-w1 * c1 + w2 * s1) / s2]
elif rot_type.lower() == 'quaternion':
if rot_order != '':
raise ValueError('Cannot have rotation order for quaternion')
if len(coords) != 4:
raise ValueError('Need 4 coordinates for quaternion')
# Actual hard-coded kinematic differential equations
e0, e1, e2, e3 = coords
w = Matrix(speeds + [0])
E = Matrix([[e0, -e3, e2, e1], [e3, e0, -e1, e2], [-e2, e1, e0, e3],
[-e1, -e2, -e3, e0]])
edots = Matrix([diff(i, dynamicsymbols._t) for i in [e1, e2, e3, e0]])
return list(edots.T - 0.5 * w.T * E.T)
else:
raise ValueError('Not an approved rotation type for this function')
def get_motion_params(frame, **kwargs):
"""
Returns the three motion parameters - (acceleration, velocity, and
position) as vectorial functions of time in the given frame.
If a higher order differential function is provided, the lower order
functions are used as boundary conditions. For example, given the
acceleration, the velocity and position parameters are taken as
boundary conditions.
The values of time at which the boundary conditions are specified
are taken from timevalue1(for position boundary condition) and
timevalue2(for velocity boundary condition).
If any of the boundary conditions are not provided, they are taken
to be zero by default (zero vectors, in case of vectorial inputs). If
the boundary conditions are also functions of time, they are converted
to constants by substituting the time values in the dynamicsymbols._t
time Symbol.
This function can also be used for calculating rotational motion
parameters. Have a look at the Parameters and Examples for more clarity.
Parameters
==========
frame : ReferenceFrame
The frame to express the motion parameters in
acceleration : Vector
Acceleration of the object/frame as a function of time
velocity : Vector
Velocity as function of time or as boundary condition
of velocity at time = timevalue1
position : Vector
Velocity as function of time or as boundary condition
of velocity at time = timevalue1
timevalue1 : sympyfiable
Value of time for position boundary condition
timevalue2 : sympyfiable
Value of time for velocity boundary condition
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, get_motion_params, dynamicsymbols
>>> from sympy import symbols
>>> R = ReferenceFrame('R')
>>> v1, v2, v3 = dynamicsymbols('v1 v2 v3')
>>> v = v1*R.x + v2*R.y + v3*R.z
>>> get_motion_params(R, position = v)
(v1''*R.x + v2''*R.y + v3''*R.z, v1'*R.x + v2'*R.y + v3'*R.z, v1*R.x + v2*R.y + v3*R.z)
>>> a, b, c = symbols('a b c')
>>> v = a*R.x + b*R.y + c*R.z
>>> get_motion_params(R, velocity = v)
(0, a*R.x + b*R.y + c*R.z, a*t*R.x + b*t*R.y + c*t*R.z)
>>> parameters = get_motion_params(R, acceleration = v)
>>> parameters[1]
a*t*R.x + b*t*R.y + c*t*R.z
>>> parameters[2]
a*t**2/2*R.x + b*t**2/2*R.y + c*t**2/2*R.z
"""
##Helper functions
def _process_vector_differential(vectdiff, condition, \
variable, ordinate, frame):
"""
Helper function for get_motion methods. Finds derivative of vectdiff wrt
variable, and its integral using the specified boundary condition at
value of variable = ordinate.
Returns a tuple of - (derivative, function and integral) wrt vectdiff
"""
#Make sure boundary condition is independent of 'variable'
if condition != 0:
condition = express(condition, frame, variables=True)
#Special case of vectdiff == 0
if vectdiff == Vector(0):
return (0, 0, condition)
#Express vectdiff completely in condition's frame to give vectdiff1
vectdiff1 = express(vectdiff, frame)
#Find derivative of vectdiff
vectdiff2 = time_derivative(vectdiff, frame)
#Integrate and use boundary condition
vectdiff0 = Vector(0)
lims = (variable, ordinate, variable)
for dim in frame:
function1 = vectdiff1.dot(dim)
abscissa = dim.dot(condition).subs({variable : ordinate})
# Indefinite integral of 'function1' wrt 'variable', using
# the given initial condition (ordinate, abscissa).
vectdiff0 += (integrate(function1, lims) + abscissa) * dim
#Return tuple
return (vectdiff2, vectdiff, vectdiff0)
##Function body
_check_frame(frame)
#Decide mode of operation based on user's input
if 'acceleration' in kwargs:
mode = 2
elif 'velocity' in kwargs:
mode = 1
else:
mode = 0
#All the possible parameters in kwargs
#Not all are required for every case
#If not specified, set to default values(may or may not be used in
#calculations)
conditions = ['acceleration', 'velocity', 'position',
'timevalue', 'timevalue1', 'timevalue2']
for i, x in enumerate(conditions):
if x not in kwargs:
if i < 3:
kwargs[x] = Vector(0)
else:
kwargs[x] = S(0)
elif i < 3:
_check_vector(kwargs[x])
else:
kwargs[x] = sympify(kwargs[x])
if mode == 2:
vel = _process_vector_differential(kwargs['acceleration'],
kwargs['velocity'],
dynamicsymbols._t,
kwargs['timevalue2'], frame)[2]
pos = _process_vector_differential(vel, kwargs['position'],
dynamicsymbols._t,
kwargs['timevalue1'], frame)[2]
return (kwargs['acceleration'], vel, pos)
elif mode == 1:
return _process_vector_differential(kwargs['velocity'],
kwargs['position'],
dynamicsymbols._t,
kwargs['timevalue1'], frame)
else:
vel = time_derivative(kwargs['position'], frame)
acc = time_derivative(vel, frame)
return (acc, vel, kwargs['position'])
def partial_velocity(vel_vecs, gen_speeds, frame):
"""Returns a list of partial velocities with respect to the provided
generalized speeds in the given reference frame for each of the supplied
velocity vectors.
The output is a list of lists. The outer list has a number of elements
equal to the number of supplied velocity vectors. The inner lists are, for
each velocity vector, the partial derivatives of that velocity vector with
respect to the generalized speeds supplied.
Parameters
==========
vel_vecs : iterable
An iterable of velocity vectors (angular or linear).
gen_speeds : iterable
An iterable of generalized speeds.
frame : ReferenceFrame
The reference frame that the partial derivatives are going to be taken
in.
Examples
========
>>> from sympy.physics.vector import Point, ReferenceFrame
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy.physics.vector import partial_velocity
>>> u = dynamicsymbols('u')
>>> N = ReferenceFrame('N')
>>> P = Point('P')
>>> P.set_vel(N, u * N.x)
>>> vel_vecs = [P.vel(N)]
>>> gen_speeds = [u]
>>> partial_velocity(vel_vecs, gen_speeds, N)
[[N.x]]
"""
if not iterable(vel_vecs):
raise TypeError('Velocity vectors must be contained in an iterable.')
if not iterable(gen_speeds):
raise TypeError('Generalized speeds must be contained in an iterable')
vec_partials = []
for vec in vel_vecs:
partials = []
for speed in gen_speeds:
partials.append(vec.diff(speed, frame, var_in_dcm=False))
vec_partials.append(partials)
return vec_partials
def dynamicsymbols(names, level=0):
"""Uses symbols and Function for functions of time.
Creates a SymPy UndefinedFunction, which is then initialized as a function
of a variable, the default being Symbol('t').
Parameters
==========
names : str
Names of the dynamic symbols you want to create; works the same way as
inputs to symbols
level : int
Level of differentiation of the returned function; d/dt once of t,
twice of t, etc.
Examples
========
>>> from sympy.physics.vector import dynamicsymbols
>>> from sympy import diff, Symbol
>>> q1 = dynamicsymbols('q1')
>>> q1
q1(t)
>>> diff(q1, Symbol('t'))
Derivative(q1(t), t)
"""
esses = symbols(names, cls=Function)
t = dynamicsymbols._t
if iterable(esses):
esses = [reduce(diff, [t] * level, e(t)) for e in esses]
return esses
else:
return reduce(diff, [t] * level, esses(t))
dynamicsymbols._t = Symbol('t')
dynamicsymbols._str = '\''
| 23,442 | 37.180782 | 132 |
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|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/printing.py
|
# -*- coding: utf-8 -*-
from sympy import Derivative
from sympy.core.function import UndefinedFunction
from sympy.core.symbol import Symbol
from sympy.interactive.printing import init_printing
from sympy.printing.conventions import split_super_sub
from sympy.printing.latex import LatexPrinter, translate
from sympy.printing.pretty.pretty import PrettyPrinter
from sympy.printing.str import StrPrinter
__all__ = ['vprint', 'vsstrrepr', 'vsprint', 'vpprint', 'vlatex',
'init_vprinting']
class VectorStrPrinter(StrPrinter):
"""String Printer for vector expressions. """
def _print_Derivative(self, e):
from sympy.physics.vector.functions import dynamicsymbols
t = dynamicsymbols._t
if (bool(sum([i == t for i in e.variables])) &
isinstance(type(e.args[0]), UndefinedFunction)):
ol = str(e.args[0].func)
for i, v in enumerate(e.variables):
ol += dynamicsymbols._str
return ol
else:
return StrPrinter().doprint(e)
def _print_Function(self, e):
from sympy.physics.vector.functions import dynamicsymbols
t = dynamicsymbols._t
if isinstance(type(e), UndefinedFunction):
return StrPrinter().doprint(e).replace("(%s)" % t, '')
return e.func.__name__ + "(%s)" % self.stringify(e.args, ", ")
class VectorStrReprPrinter(VectorStrPrinter):
"""String repr printer for vector expressions."""
def _print_str(self, s):
return repr(s)
class VectorLatexPrinter(LatexPrinter):
"""Latex Printer for vector expressions. """
def _print_Function(self, expr, exp=None):
from sympy.physics.vector.functions import dynamicsymbols
func = expr.func.__name__
t = dynamicsymbols._t
if hasattr(self, '_print_' + func):
return getattr(self, '_print_' + func)(expr, exp)
elif isinstance(type(expr), UndefinedFunction) and (expr.args == (t,)):
name, supers, subs = split_super_sub(func)
name = translate(name)
supers = [translate(sup) for sup in supers]
subs = [translate(sub) for sub in subs]
if len(supers) != 0:
supers = r"^{%s}" % "".join(supers)
else:
supers = r""
if len(subs) != 0:
subs = r"_{%s}" % "".join(subs)
else:
subs = r""
if exp:
supers += r"^{%s}" % self._print(exp)
return r"%s" % (name + supers + subs)
else:
args = [str(self._print(arg)) for arg in expr.args]
# How inverse trig functions should be displayed, formats are:
# abbreviated: asin, full: arcsin, power: sin^-1
inv_trig_style = self._settings['inv_trig_style']
# If we are dealing with a power-style inverse trig function
inv_trig_power_case = False
# If it is applicable to fold the argument brackets
can_fold_brackets = self._settings['fold_func_brackets'] and \
len(args) == 1 and \
not self._needs_function_brackets(expr.args[0])
inv_trig_table = ["asin", "acos", "atan", "acot"]
# If the function is an inverse trig function, handle the style
if func in inv_trig_table:
if inv_trig_style == "abbreviated":
func = func
elif inv_trig_style == "full":
func = "arc" + func[1:]
elif inv_trig_style == "power":
func = func[1:]
inv_trig_power_case = True
# Can never fold brackets if we're raised to a power
if exp is not None:
can_fold_brackets = False
if inv_trig_power_case:
name = r"\operatorname{%s}^{-1}" % func
elif exp is not None:
name = r"\operatorname{%s}^{%s}" % (func, exp)
else:
name = r"\operatorname{%s}" % func
if can_fold_brackets:
name += r"%s"
else:
name += r"\left(%s\right)"
if inv_trig_power_case and exp is not None:
name += r"^{%s}" % exp
return name % ",".join(args)
def _print_Derivative(self, der_expr):
from sympy.physics.vector.functions import dynamicsymbols
# make sure it is an the right form
der_expr = der_expr.doit()
if not isinstance(der_expr, Derivative):
return self.doprint(der_expr)
# check if expr is a dynamicsymbol
from sympy.core.function import AppliedUndef
t = dynamicsymbols._t
expr = der_expr.expr
red = expr.atoms(AppliedUndef)
syms = der_expr.variables
test1 = not all([True for i in red if i.free_symbols == {t}])
test2 = not all([(t == i) for i in syms])
if test1 or test2:
return LatexPrinter().doprint(der_expr)
# done checking
dots = len(syms)
base = self._print_Function(expr)
base_split = base.split('_', 1)
base = base_split[0]
if dots == 1:
base = r"\dot{%s}" % base
elif dots == 2:
base = r"\ddot{%s}" % base
elif dots == 3:
base = r"\dddot{%s}" % base
if len(base_split) is not 1:
base += '_' + base_split[1]
return base
def parenthesize(self, item, level, strict=False):
item_latex = self._print(item)
if item_latex.startswith(r"\dot") or item_latex.startswith(r"\ddot") or item_latex.startswith(r"\dddot"):
return self._print(item)
else:
return LatexPrinter.parenthesize(self, item, level, strict)
class VectorPrettyPrinter(PrettyPrinter):
"""Pretty Printer for vectorialexpressions. """
def _print_Derivative(self, deriv):
from sympy.physics.vector.functions import dynamicsymbols
# XXX use U('PARTIAL DIFFERENTIAL') here ?
t = dynamicsymbols._t
dot_i = 0
can_break = True
syms = list(reversed(deriv.variables))
x = None
while len(syms) > 0:
if syms[-1] == t:
syms.pop()
dot_i += 1
else:
return super(VectorPrettyPrinter, self)._print_Derivative(deriv)
if not (isinstance(type(deriv.expr), UndefinedFunction)
and (deriv.expr.args == (t,))):
return super(VectorPrettyPrinter, self)._print_Derivative(deriv)
else:
pform = self._print_Function(deriv.expr)
# the following condition would happen with some sort of non-standard
# dynamic symbol I guess, so we'll just print the SymPy way
if len(pform.picture) > 1:
return super(VectorPrettyPrinter, self)._print_Derivative(deriv)
dots = {0 : u"",
1 : u"\N{COMBINING DOT ABOVE}",
2 : u"\N{COMBINING DIAERESIS}",
3 : u"\N{COMBINING THREE DOTS ABOVE}",
4 : u"\N{COMBINING FOUR DOTS ABOVE}"}
d = pform.__dict__
pic = d['picture'][0]
uni = d['unicode']
lp = len(pic) // 2 + 1
lu = len(uni) // 2 + 1
pic_split = [pic[:lp], pic[lp:]]
uni_split = [uni[:lu], uni[lu:]]
d['picture'] = [pic_split[0] + dots[dot_i] + pic_split[1]]
d['unicode'] = uni_split[0] + dots[dot_i] + uni_split[1]
return pform
def _print_Function(self, e):
from sympy.physics.vector.functions import dynamicsymbols
t = dynamicsymbols._t
# XXX works only for applied functions
func = e.func
args = e.args
func_name = func.__name__
pform = self._print_Symbol(Symbol(func_name))
# If this function is an Undefined function of t, it is probably a
# dynamic symbol, so we'll skip the (t). The rest of the code is
# identical to the normal PrettyPrinter code
if not (isinstance(func, UndefinedFunction) and (args == (t,))):
return super(VectorPrettyPrinter, self)._print_Function(e)
return pform
def vprint(expr, **settings):
r"""Function for printing of expressions generated in the
sympy.physics vector package.
Extends SymPy's StrPrinter, takes the same setting accepted by SymPy's
`sstr()`, and is equivalent to `print(sstr(foo))`.
Parameters
==========
expr : valid SymPy object
SymPy expression to print.
settings : args
Same as the settings accepted by SymPy's sstr().
Examples
========
>>> from sympy.physics.vector import vprint, dynamicsymbols
>>> u1 = dynamicsymbols('u1')
>>> print(u1)
u1(t)
>>> vprint(u1)
u1
"""
outstr = vsprint(expr, **settings)
from sympy.core.compatibility import builtins
if (outstr != 'None'):
builtins._ = outstr
print(outstr)
def vsstrrepr(expr, **settings):
"""Function for displaying expression representation's with vector
printing enabled.
Parameters
==========
expr : valid SymPy object
SymPy expression to print.
settings : args
Same as the settings accepted by SymPy's sstrrepr().
"""
p = VectorStrReprPrinter(settings)
return p.doprint(expr)
def vsprint(expr, **settings):
r"""Function for displaying expressions generated in the
sympy.physics vector package.
Returns the output of vprint() as a string.
Parameters
==========
expr : valid SymPy object
SymPy expression to print
settings : args
Same as the settings accepted by SymPy's sstr().
Examples
========
>>> from sympy.physics.vector import vsprint, dynamicsymbols
>>> u1, u2 = dynamicsymbols('u1 u2')
>>> u2d = dynamicsymbols('u2', level=1)
>>> print("%s = %s" % (u1, u2 + u2d))
u1(t) = u2(t) + Derivative(u2(t), t)
>>> print("%s = %s" % (vsprint(u1), vsprint(u2 + u2d)))
u1 = u2 + u2'
"""
string_printer = VectorStrPrinter(settings)
return string_printer.doprint(expr)
def vpprint(expr, **settings):
r"""Function for pretty printing of expressions generated in the
sympy.physics vector package.
Mainly used for expressions not inside a vector; the output of running
scripts and generating equations of motion. Takes the same options as
SymPy's pretty_print(); see that function for more information.
Parameters
==========
expr : valid SymPy object
SymPy expression to pretty print
settings : args
Same as those accepted by SymPy's pretty_print.
"""
pp = VectorPrettyPrinter(settings)
# Note that this is copied from sympy.printing.pretty.pretty_print:
# XXX: this is an ugly hack, but at least it works
use_unicode = pp._settings['use_unicode']
from sympy.printing.pretty.pretty_symbology import pretty_use_unicode
uflag = pretty_use_unicode(use_unicode)
try:
return pp.doprint(expr)
finally:
pretty_use_unicode(uflag)
def vlatex(expr, **settings):
r"""Function for printing latex representation of sympy.physics.vector
objects.
For latex representation of Vectors, Dyadics, and dynamicsymbols. Takes the
same options as SymPy's latex(); see that function for more information;
Parameters
==========
expr : valid SymPy object
SymPy expression to represent in LaTeX form
settings : args
Same as latex()
Examples
========
>>> from sympy.physics.vector import vlatex, ReferenceFrame, dynamicsymbols
>>> N = ReferenceFrame('N')
>>> q1, q2 = dynamicsymbols('q1 q2')
>>> q1d, q2d = dynamicsymbols('q1 q2', 1)
>>> q1dd, q2dd = dynamicsymbols('q1 q2', 2)
>>> vlatex(N.x + N.y)
'\\mathbf{\\hat{n}_x} + \\mathbf{\\hat{n}_y}'
>>> vlatex(q1 + q2)
'q_{1} + q_{2}'
>>> vlatex(q1d)
'\\dot{q}_{1}'
>>> vlatex(q1 * q2d)
'q_{1} \\dot{q}_{2}'
>>> vlatex(q1dd * q1 / q1d)
'\\frac{q_{1} \\ddot{q}_{1}}{\\dot{q}_{1}}'
"""
latex_printer = VectorLatexPrinter(settings)
return latex_printer.doprint(expr)
def init_vprinting(**kwargs):
"""Initializes time derivative printing for all SymPy objects, i.e. any
functions of time will be displayed in a more compact notation. The main
benefit of this is for printing of time derivatives; instead of
displaying as ``Derivative(f(t),t)``, it will display ``f'``. This is
only actually needed for when derivatives are present and are not in a
physics.vector.Vector or physics.vector.Dyadic object. This function is a
light wrapper to `sympy.interactive.init_printing`. Any keyword
arguments for it are valid here.
{0}
Examples
========
>>> from sympy import Function, symbols
>>> from sympy.physics.vector import init_vprinting
>>> t, x = symbols('t, x')
>>> omega = Function('omega')
>>> omega(x).diff()
Derivative(omega(x), x)
>>> omega(t).diff()
Derivative(omega(t), t)
Now use the string printer:
>>> init_vprinting(pretty_print=False)
>>> omega(x).diff()
Derivative(omega(x), x)
>>> omega(t).diff()
omega'
"""
kwargs['str_printer'] = vsstrrepr
kwargs['pretty_printer'] = vpprint
kwargs['latex_printer'] = vlatex
init_printing(**kwargs)
params = init_printing.__doc__.split('Examples\n ========')[0]
init_vprinting.__doc__ = init_vprinting.__doc__.format(params)
| 13,617 | 31.193853 | 113 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/__init__.py
|
__all__ = []
# The following pattern is used below for importing sub-modules:
#
# 1. "from foo import *". This imports all the names from foo.__all__ into
# this module. But, this does not put those names into the __all__ of
# this module. This enables "from sympy.physics.vector import ReferenceFrame" to
# work.
# 2. "import foo; __all__.extend(foo.__all__)". This adds all the names in
# foo.__all__ to the __all__ of this module. The names in __all__
# determine which names are imported when
# "from sympy.physics.vector import *" is done.
from . import frame
from .frame import *
__all__.extend(frame.__all__)
from . import dyadic
from .dyadic import *
__all__.extend(dyadic.__all__)
from . import vector
from .vector import *
__all__.extend(vector.__all__)
from . import point
from .point import *
__all__.extend(point.__all__)
from . import functions
from .functions import *
__all__.extend(functions.__all__)
from . import printing
from .printing import *
__all__.extend(printing.__all__)
from . import fieldfunctions
from .fieldfunctions import *
__all__.extend(fieldfunctions.__all__)
| 1,123 | 26.414634 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/vector.py
|
from sympy.core.backend import (S, sympify, expand, sqrt, Add, zeros,
ImmutableMatrix as Matrix)
from sympy import trigsimp
from sympy.core.compatibility import unicode
from sympy.utilities.misc import filldedent
__all__ = ['Vector']
class Vector(object):
"""The class used to define vectors.
It along with ReferenceFrame are the building blocks of describing a
classical mechanics system in PyDy and sympy.physics.vector.
Attributes
==========
simp : Boolean
Let certain methods use trigsimp on their outputs
"""
simp = False
def __init__(self, inlist):
"""This is the constructor for the Vector class. You shouldn't be
calling this, it should only be used by other functions. You should be
treating Vectors like you would with if you were doing the math by
hand, and getting the first 3 from the standard basis vectors from a
ReferenceFrame.
The only exception is to create a zero vector:
zv = Vector(0)
"""
self.args = []
if inlist == 0:
inlist = []
if isinstance(inlist, dict):
d = inlist
else:
d = {}
for inp in inlist:
if inp[1] in d:
d[inp[1]] += inp[0]
else:
d[inp[1]] = inp[0]
for k, v in d.items():
if v != Matrix([0, 0, 0]):
self.args.append((v, k))
def __hash__(self):
return hash(tuple(self.args))
def __add__(self, other):
"""The add operator for Vector. """
other = _check_vector(other)
return Vector(self.args + other.args)
def __and__(self, other):
"""Dot product of two vectors.
Returns a scalar, the dot product of the two Vectors
Parameters
==========
other : Vector
The Vector which we are dotting with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, dot
>>> from sympy import symbols
>>> q1 = symbols('q1')
>>> N = ReferenceFrame('N')
>>> dot(N.x, N.x)
1
>>> dot(N.x, N.y)
0
>>> A = N.orientnew('A', 'Axis', [q1, N.x])
>>> dot(N.y, A.y)
cos(q1)
"""
from sympy.physics.vector.dyadic import Dyadic
if isinstance(other, Dyadic):
return NotImplemented
other = _check_vector(other)
out = S(0)
for i, v1 in enumerate(self.args):
for j, v2 in enumerate(other.args):
out += ((v2[0].T)
* (v2[1].dcm(v1[1]))
* (v1[0]))[0]
if Vector.simp:
return trigsimp(sympify(out), recursive=True)
else:
return sympify(out)
def __div__(self, other):
"""This uses mul and inputs self and 1 divided by other. """
return self.__mul__(sympify(1) / other)
__truediv__ = __div__
def __eq__(self, other):
"""Tests for equality.
It is very import to note that this is only as good as the SymPy
equality test; False does not always mean they are not equivalent
Vectors.
If other is 0, and self is empty, returns True.
If other is 0 and self is not empty, returns False.
If none of the above, only accepts other as a Vector.
"""
if other == 0:
other = Vector(0)
try:
other = _check_vector(other)
except TypeError:
return False
if (self.args == []) and (other.args == []):
return True
elif (self.args == []) or (other.args == []):
return False
frame = self.args[0][1]
for v in frame:
if expand((self - other) & v) != 0:
return False
return True
def __mul__(self, other):
"""Multiplies the Vector by a sympifyable expression.
Parameters
==========
other : Sympifyable
The scalar to multiply this Vector with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy import Symbol
>>> N = ReferenceFrame('N')
>>> b = Symbol('b')
>>> V = 10 * b * N.x
>>> print(V)
10*b*N.x
"""
newlist = [v for v in self.args]
for i, v in enumerate(newlist):
newlist[i] = (sympify(other) * newlist[i][0], newlist[i][1])
return Vector(newlist)
def __ne__(self, other):
return not self.__eq__(other)
def __neg__(self):
return self * -1
def __or__(self, other):
"""Outer product between two Vectors.
A rank increasing operation, which returns a Dyadic from two Vectors
Parameters
==========
other : Vector
The Vector to take the outer product with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer
>>> N = ReferenceFrame('N')
>>> outer(N.x, N.x)
(N.x|N.x)
"""
from sympy.physics.vector.dyadic import Dyadic
other = _check_vector(other)
ol = Dyadic(0)
for i, v in enumerate(self.args):
for i2, v2 in enumerate(other.args):
# it looks this way because if we are in the same frame and
# use the enumerate function on the same frame in a nested
# fashion, then bad things happen
ol += Dyadic([(v[0][0] * v2[0][0], v[1].x, v2[1].x)])
ol += Dyadic([(v[0][0] * v2[0][1], v[1].x, v2[1].y)])
ol += Dyadic([(v[0][0] * v2[0][2], v[1].x, v2[1].z)])
ol += Dyadic([(v[0][1] * v2[0][0], v[1].y, v2[1].x)])
ol += Dyadic([(v[0][1] * v2[0][1], v[1].y, v2[1].y)])
ol += Dyadic([(v[0][1] * v2[0][2], v[1].y, v2[1].z)])
ol += Dyadic([(v[0][2] * v2[0][0], v[1].z, v2[1].x)])
ol += Dyadic([(v[0][2] * v2[0][1], v[1].z, v2[1].y)])
ol += Dyadic([(v[0][2] * v2[0][2], v[1].z, v2[1].z)])
return ol
def _latex(self, printer=None):
"""Latex Printing method. """
from sympy.physics.vector.printing import VectorLatexPrinter
ar = self.args # just to shorten things
if len(ar) == 0:
return str(0)
ol = [] # output list, to be concatenated to a string
for i, v in enumerate(ar):
for j in 0, 1, 2:
# if the coef of the basis vector is 1, we skip the 1
if ar[i][0][j] == 1:
ol.append(' + ' + ar[i][1].latex_vecs[j])
# if the coef of the basis vector is -1, we skip the 1
elif ar[i][0][j] == -1:
ol.append(' - ' + ar[i][1].latex_vecs[j])
elif ar[i][0][j] != 0:
# If the coefficient of the basis vector is not 1 or -1;
# also, we might wrap it in parentheses, for readability.
arg_str = VectorLatexPrinter().doprint(ar[i][0][j])
if isinstance(ar[i][0][j], Add):
arg_str = "(%s)" % arg_str
if arg_str[0] == '-':
arg_str = arg_str[1:]
str_start = ' - '
else:
str_start = ' + '
ol.append(str_start + arg_str + ar[i][1].latex_vecs[j])
outstr = ''.join(ol)
if outstr.startswith(' + '):
outstr = outstr[3:]
elif outstr.startswith(' '):
outstr = outstr[1:]
return outstr
def _pretty(self, printer=None):
"""Pretty Printing method. """
from sympy.physics.vector.printing import VectorPrettyPrinter
from sympy.printing.pretty.stringpict import prettyForm
e = self
class Fake(object):
def render(self, *args, **kwargs):
ar = e.args # just to shorten things
if len(ar) == 0:
return unicode(0)
settings = printer._settings if printer else {}
vp = printer if printer else VectorPrettyPrinter(settings)
pforms = [] # output list, to be concatenated to a string
for i, v in enumerate(ar):
for j in 0, 1, 2:
# if the coef of the basis vector is 1, we skip the 1
if ar[i][0][j] == 1:
pform = vp._print(ar[i][1].pretty_vecs[j])
# if the coef of the basis vector is -1, we skip the 1
elif ar[i][0][j] == -1:
pform = vp._print(ar[i][1].pretty_vecs[j])
pform= prettyForm(*pform.left(" - "))
bin = prettyForm.NEG
pform = prettyForm(binding=bin, *pform)
elif ar[i][0][j] != 0:
# If the basis vector coeff is not 1 or -1,
# we might wrap it in parentheses, for readability.
if isinstance(ar[i][0][j], Add):
pform = vp._print(
ar[i][0][j]).parens()
else:
pform = vp._print(
ar[i][0][j])
pform = prettyForm(*pform.right(" ",
ar[i][1].pretty_vecs[j]))
else:
continue
pforms.append(pform)
pform = prettyForm.__add__(*pforms)
kwargs["wrap_line"] = kwargs.get("wrap_line")
kwargs["num_columns"] = kwargs.get("num_columns")
out_str = pform.render(*args, **kwargs)
mlines = [line.rstrip() for line in out_str.split("\n")]
return "\n".join(mlines)
return Fake()
def __ror__(self, other):
"""Outer product between two Vectors.
A rank increasing operation, which returns a Dyadic from two Vectors
Parameters
==========
other : Vector
The Vector to take the outer product with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, outer
>>> N = ReferenceFrame('N')
>>> outer(N.x, N.x)
(N.x|N.x)
"""
from sympy.physics.vector.dyadic import Dyadic
other = _check_vector(other)
ol = Dyadic(0)
for i, v in enumerate(other.args):
for i2, v2 in enumerate(self.args):
# it looks this way because if we are in the same frame and
# use the enumerate function on the same frame in a nested
# fashion, then bad things happen
ol += Dyadic([(v[0][0] * v2[0][0], v[1].x, v2[1].x)])
ol += Dyadic([(v[0][0] * v2[0][1], v[1].x, v2[1].y)])
ol += Dyadic([(v[0][0] * v2[0][2], v[1].x, v2[1].z)])
ol += Dyadic([(v[0][1] * v2[0][0], v[1].y, v2[1].x)])
ol += Dyadic([(v[0][1] * v2[0][1], v[1].y, v2[1].y)])
ol += Dyadic([(v[0][1] * v2[0][2], v[1].y, v2[1].z)])
ol += Dyadic([(v[0][2] * v2[0][0], v[1].z, v2[1].x)])
ol += Dyadic([(v[0][2] * v2[0][1], v[1].z, v2[1].y)])
ol += Dyadic([(v[0][2] * v2[0][2], v[1].z, v2[1].z)])
return ol
def __rsub__(self, other):
return (-1 * self) + other
def __str__(self, printer=None, order=True):
"""Printing method. """
from sympy.physics.vector.printing import VectorStrPrinter
if not order or len(self.args) == 1:
ar = list(self.args)
elif len(self.args) == 0:
return str(0)
else:
d = {v[1]: v[0] for v in self.args}
keys = sorted(d.keys(), key=lambda x: x.index)
ar = []
for key in keys:
ar.append((d[key], key))
ol = [] # output list, to be concatenated to a string
for i, v in enumerate(ar):
for j in 0, 1, 2:
# if the coef of the basis vector is 1, we skip the 1
if ar[i][0][j] == 1:
ol.append(' + ' + ar[i][1].str_vecs[j])
# if the coef of the basis vector is -1, we skip the 1
elif ar[i][0][j] == -1:
ol.append(' - ' + ar[i][1].str_vecs[j])
elif ar[i][0][j] != 0:
# If the coefficient of the basis vector is not 1 or -1;
# also, we might wrap it in parentheses, for readability.
arg_str = VectorStrPrinter().doprint(ar[i][0][j])
if isinstance(ar[i][0][j], Add):
arg_str = "(%s)" % arg_str
if arg_str[0] == '-':
arg_str = arg_str[1:]
str_start = ' - '
else:
str_start = ' + '
ol.append(str_start + arg_str + '*' + ar[i][1].str_vecs[j])
outstr = ''.join(ol)
if outstr.startswith(' + '):
outstr = outstr[3:]
elif outstr.startswith(' '):
outstr = outstr[1:]
return outstr
def __sub__(self, other):
"""The subraction operator. """
return self.__add__(other * -1)
def __xor__(self, other):
"""The cross product operator for two Vectors.
Returns a Vector, expressed in the same ReferenceFrames as self.
Parameters
==========
other : Vector
The Vector which we are crossing with
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector
>>> from sympy import symbols
>>> q1 = symbols('q1')
>>> N = ReferenceFrame('N')
>>> N.x ^ N.y
N.z
>>> A = N.orientnew('A', 'Axis', [q1, N.x])
>>> A.x ^ N.y
N.z
>>> N.y ^ A.x
- sin(q1)*A.y - cos(q1)*A.z
"""
from sympy.physics.vector.dyadic import Dyadic
if isinstance(other, Dyadic):
return NotImplemented
other = _check_vector(other)
if other.args == []:
return Vector(0)
def _det(mat):
"""This is needed as a little method for to find the determinant
of a list in python; needs to work for a 3x3 list.
SymPy's Matrix won't take in Vector, so need a custom function.
You shouldn't be calling this.
"""
return (mat[0][0] * (mat[1][1] * mat[2][2] - mat[1][2] * mat[2][1])
+ mat[0][1] * (mat[1][2] * mat[2][0] - mat[1][0] *
mat[2][2]) + mat[0][2] * (mat[1][0] * mat[2][1] -
mat[1][1] * mat[2][0]))
outlist = []
ar = other.args # For brevity
for i, v in enumerate(ar):
tempx = v[1].x
tempy = v[1].y
tempz = v[1].z
tempm = ([[tempx, tempy, tempz], [self & tempx, self & tempy,
self & tempz], [Vector([ar[i]]) & tempx,
Vector([ar[i]]) & tempy, Vector([ar[i]]) & tempz]])
outlist += _det(tempm).args
return Vector(outlist)
_sympystr = __str__
_sympyrepr = _sympystr
__repr__ = __str__
__radd__ = __add__
__rand__ = __and__
__rmul__ = __mul__
def separate(self):
"""
The constituents of this vector in different reference frames,
as per its definition.
Returns a dict mapping each ReferenceFrame to the corresponding
constituent Vector.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> R1 = ReferenceFrame('R1')
>>> R2 = ReferenceFrame('R2')
>>> v = R1.x + R2.x
>>> v.separate() == {R1: R1.x, R2: R2.x}
True
"""
components = {}
for x in self.args:
components[x[1]] = Vector([x])
return components
def dot(self, other):
return self & other
dot.__doc__ = __and__.__doc__
def cross(self, other):
return self ^ other
cross.__doc__ = __xor__.__doc__
def outer(self, other):
return self | other
outer.__doc__ = __or__.__doc__
def diff(self, var, frame, var_in_dcm=True):
"""Returns the partial derivative of the vector with respect to a
variable in the provided reference frame.
Parameters
==========
var : Symbol
What the partial derivative is taken with respect to.
frame : ReferenceFrame
The reference frame that the partial derivative is taken in.
var_in_dcm : boolean
If true, the differentiation algorithm assumes that the variable
may be present in any of the direction cosine matrices that relate
the frame to the frames of any component of the vector. But if it
is known that the variable is not present in the direction cosine
matrices, false can be set to skip full reexpression in the desired
frame.
Examples
========
>>> from sympy import Symbol
>>> from sympy.physics.vector import dynamicsymbols, ReferenceFrame
>>> from sympy.physics.vector import Vector
>>> Vector.simp = True
>>> t = Symbol('t')
>>> q1 = dynamicsymbols('q1')
>>> N = ReferenceFrame('N')
>>> A = N.orientnew('A', 'Axis', [q1, N.y])
>>> A.x.diff(t, N)
- q1'*A.z
>>> B = ReferenceFrame('B')
>>> u1, u2 = dynamicsymbols('u1, u2')
>>> v = u1 * A.x + u2 * B.y
>>> v.diff(u2, N, var_in_dcm=False)
B.y
"""
from sympy.physics.vector.frame import _check_frame
var = sympify(var)
_check_frame(frame)
inlist = []
for vector_component in self.args:
measure_number = vector_component[0]
component_frame = vector_component[1]
if component_frame == frame:
inlist += [(measure_number.diff(var), frame)]
else:
# If the direction cosine matrix relating the component frame
# with the derivative frame does not contain the variable.
if not var_in_dcm or (frame.dcm(component_frame).diff(var) ==
zeros(3, 3)):
inlist += [(measure_number.diff(var),
component_frame)]
else: # else express in the frame
reexp_vec_comp = Vector([vector_component]).express(frame)
deriv = reexp_vec_comp.args[0][0].diff(var)
inlist += Vector([(deriv, frame)]).express(component_frame).args
return Vector(inlist)
def express(self, otherframe, variables=False):
"""
Returns a Vector equivalent to this one, expressed in otherframe.
Uses the global express method.
Parameters
==========
otherframe : ReferenceFrame
The frame for this Vector to be described in
variables : boolean
If True, the coordinate symbols(if present) in this Vector
are re-expressed in terms otherframe
Examples
========
>>> from sympy.physics.vector import ReferenceFrame, Vector, dynamicsymbols
>>> q1 = dynamicsymbols('q1')
>>> N = ReferenceFrame('N')
>>> A = N.orientnew('A', 'Axis', [q1, N.y])
>>> A.x.express(N)
cos(q1)*N.x - sin(q1)*N.z
"""
from sympy.physics.vector import express
return express(self, otherframe, variables=variables)
def to_matrix(self, reference_frame):
"""Returns the matrix form of the vector with respect to the given
frame.
Parameters
----------
reference_frame : ReferenceFrame
The reference frame that the rows of the matrix correspond to.
Returns
-------
matrix : ImmutableMatrix, shape(3,1)
The matrix that gives the 1D vector.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy.physics.mechanics.functions import inertia
>>> a, b, c = symbols('a, b, c')
>>> N = ReferenceFrame('N')
>>> vector = a * N.x + b * N.y + c * N.z
>>> vector.to_matrix(N)
Matrix([
[a],
[b],
[c]])
>>> beta = symbols('beta')
>>> A = N.orientnew('A', 'Axis', (beta, N.x))
>>> vector.to_matrix(A)
Matrix([
[ a],
[ b*cos(beta) + c*sin(beta)],
[-b*sin(beta) + c*cos(beta)]])
"""
return Matrix([self.dot(unit_vec) for unit_vec in
reference_frame]).reshape(3, 1)
def doit(self, **hints):
"""Calls .doit() on each term in the Vector"""
d = {}
for v in self.args:
d[v[1]] = v[0].applyfunc(lambda x: x.doit(**hints))
return Vector(d)
def dt(self, otherframe):
"""
Returns a Vector which is the time derivative of
the self Vector, taken in frame otherframe.
Calls the global time_derivative method
Parameters
==========
otherframe : ReferenceFrame
The frame to calculate the time derivative in
"""
from sympy.physics.vector import time_derivative
return time_derivative(self, otherframe)
def simplify(self):
"""Returns a simplified Vector."""
d = {}
for v in self.args:
d[v[1]] = v[0].simplify()
return Vector(d)
def subs(self, *args, **kwargs):
"""Substituion on the Vector.
Examples
========
>>> from sympy.physics.vector import ReferenceFrame
>>> from sympy import Symbol
>>> N = ReferenceFrame('N')
>>> s = Symbol('s')
>>> a = N.x * s
>>> a.subs({s: 2})
2*N.x
"""
d = {}
for v in self.args:
d[v[1]] = v[0].subs(*args, **kwargs)
return Vector(d)
def magnitude(self):
"""Returns the magnitude (Euclidean norm) of self."""
return sqrt(self & self)
def normalize(self):
"""Returns a Vector of magnitude 1, codirectional with self."""
return Vector(self.args + []) / self.magnitude()
def applyfunc(self, f):
"""Apply a function to each component of a vector."""
if not callable(f):
raise TypeError("`f` must be callable.")
d = {}
for v in self.args:
d[v[1]] = v[0].applyfunc(f)
return Vector(d)
class VectorTypeError(TypeError):
def __init__(self, other, want):
msg = filldedent("Expected an instance of %s, but received object "
"'%s' of %s." % (type(want), other, type(other)))
super(VectorTypeError, self).__init__(msg)
def _check_vector(other):
if not isinstance(other, Vector):
raise TypeError('A Vector must be supplied')
return other
| 23,627 | 32.232068 | 84 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/test_functions.py
|
from sympy import S, Integral, sin, cos, pi, sqrt, symbols
from sympy.physics.vector import Dyadic, Point, ReferenceFrame, Vector
from sympy.physics.vector.functions import (cross, dot, express,
time_derivative,
kinematic_equations, outer,
partial_velocity,
get_motion_params, dynamicsymbols)
from sympy.utilities.pytest import raises
Vector.simp = True
q1, q2, q3, q4, q5 = symbols('q1 q2 q3 q4 q5')
N = ReferenceFrame('N')
A = N.orientnew('A', 'Axis', [q1, N.z])
B = A.orientnew('B', 'Axis', [q2, A.x])
C = B.orientnew('C', 'Axis', [q3, B.y])
def test_dot():
assert dot(A.x, A.x) == 1
assert dot(A.x, A.y) == 0
assert dot(A.x, A.z) == 0
assert dot(A.y, A.x) == 0
assert dot(A.y, A.y) == 1
assert dot(A.y, A.z) == 0
assert dot(A.z, A.x) == 0
assert dot(A.z, A.y) == 0
assert dot(A.z, A.z) == 1
def test_dot_different_frames():
assert dot(N.x, A.x) == cos(q1)
assert dot(N.x, A.y) == -sin(q1)
assert dot(N.x, A.z) == 0
assert dot(N.y, A.x) == sin(q1)
assert dot(N.y, A.y) == cos(q1)
assert dot(N.y, A.z) == 0
assert dot(N.z, A.x) == 0
assert dot(N.z, A.y) == 0
assert dot(N.z, A.z) == 1
assert dot(N.x, A.x + A.y) == sqrt(2)*cos(q1 + pi/4) == dot(A.x + A.y, N.x)
assert dot(A.x, C.x) == cos(q3)
assert dot(A.x, C.y) == 0
assert dot(A.x, C.z) == sin(q3)
assert dot(A.y, C.x) == sin(q2)*sin(q3)
assert dot(A.y, C.y) == cos(q2)
assert dot(A.y, C.z) == -sin(q2)*cos(q3)
assert dot(A.z, C.x) == -cos(q2)*sin(q3)
assert dot(A.z, C.y) == sin(q2)
assert dot(A.z, C.z) == cos(q2)*cos(q3)
def test_cross():
assert cross(A.x, A.x) == 0
assert cross(A.x, A.y) == A.z
assert cross(A.x, A.z) == -A.y
assert cross(A.y, A.x) == -A.z
assert cross(A.y, A.y) == 0
assert cross(A.y, A.z) == A.x
assert cross(A.z, A.x) == A.y
assert cross(A.z, A.y) == -A.x
assert cross(A.z, A.z) == 0
def test_cross_different_frames():
assert cross(N.x, A.x) == sin(q1)*A.z
assert cross(N.x, A.y) == cos(q1)*A.z
assert cross(N.x, A.z) == -sin(q1)*A.x - cos(q1)*A.y
assert cross(N.y, A.x) == -cos(q1)*A.z
assert cross(N.y, A.y) == sin(q1)*A.z
assert cross(N.y, A.z) == cos(q1)*A.x - sin(q1)*A.y
assert cross(N.z, A.x) == A.y
assert cross(N.z, A.y) == -A.x
assert cross(N.z, A.z) == 0
assert cross(N.x, A.x) == sin(q1)*A.z
assert cross(N.x, A.y) == cos(q1)*A.z
assert cross(N.x, A.x + A.y) == sin(q1)*A.z + cos(q1)*A.z
assert cross(A.x + A.y, N.x) == -sin(q1)*A.z - cos(q1)*A.z
assert cross(A.x, C.x) == sin(q3)*C.y
assert cross(A.x, C.y) == -sin(q3)*C.x + cos(q3)*C.z
assert cross(A.x, C.z) == -cos(q3)*C.y
assert cross(C.x, A.x) == -sin(q3)*C.y
assert cross(C.y, A.x) == sin(q3)*C.x - cos(q3)*C.z
assert cross(C.z, A.x) == cos(q3)*C.y
def test_operator_match():
"""Test that the output of dot, cross, outer functions match
operator behavior.
"""
A = ReferenceFrame('A')
v = A.x + A.y
d = v | v
zerov = Vector(0)
zerod = Dyadic(0)
# dot products
assert d & d == dot(d, d)
assert d & zerod == dot(d, zerod)
assert zerod & d == dot(zerod, d)
assert d & v == dot(d, v)
assert v & d == dot(v, d)
assert d & zerov == dot(d, zerov)
assert zerov & d == dot(zerov, d)
raises(TypeError, lambda: dot(d, S(0)))
raises(TypeError, lambda: dot(S(0), d))
raises(TypeError, lambda: dot(d, 0))
raises(TypeError, lambda: dot(0, d))
assert v & v == dot(v, v)
assert v & zerov == dot(v, zerov)
assert zerov & v == dot(zerov, v)
raises(TypeError, lambda: dot(v, S(0)))
raises(TypeError, lambda: dot(S(0), v))
raises(TypeError, lambda: dot(v, 0))
raises(TypeError, lambda: dot(0, v))
# cross products
raises(TypeError, lambda: cross(d, d))
raises(TypeError, lambda: cross(d, zerod))
raises(TypeError, lambda: cross(zerod, d))
assert d ^ v == cross(d, v)
assert v ^ d == cross(v, d)
assert d ^ zerov == cross(d, zerov)
assert zerov ^ d == cross(zerov, d)
assert zerov ^ d == cross(zerov, d)
raises(TypeError, lambda: cross(d, S(0)))
raises(TypeError, lambda: cross(S(0), d))
raises(TypeError, lambda: cross(d, 0))
raises(TypeError, lambda: cross(0, d))
assert v ^ v == cross(v, v)
assert v ^ zerov == cross(v, zerov)
assert zerov ^ v == cross(zerov, v)
raises(TypeError, lambda: cross(v, S(0)))
raises(TypeError, lambda: cross(S(0), v))
raises(TypeError, lambda: cross(v, 0))
raises(TypeError, lambda: cross(0, v))
# outer products
raises(TypeError, lambda: outer(d, d))
raises(TypeError, lambda: outer(d, zerod))
raises(TypeError, lambda: outer(zerod, d))
raises(TypeError, lambda: outer(d, v))
raises(TypeError, lambda: outer(v, d))
raises(TypeError, lambda: outer(d, zerov))
raises(TypeError, lambda: outer(zerov, d))
raises(TypeError, lambda: outer(zerov, d))
raises(TypeError, lambda: outer(d, S(0)))
raises(TypeError, lambda: outer(S(0), d))
raises(TypeError, lambda: outer(d, 0))
raises(TypeError, lambda: outer(0, d))
assert v | v == outer(v, v)
assert v | zerov == outer(v, zerov)
assert zerov | v == outer(zerov, v)
raises(TypeError, lambda: outer(v, S(0)))
raises(TypeError, lambda: outer(S(0), v))
raises(TypeError, lambda: outer(v, 0))
raises(TypeError, lambda: outer(0, v))
def test_express():
assert express(Vector(0), N) == Vector(0)
assert express(S(0), N) == S(0)
assert express(A.x, C) == cos(q3)*C.x + sin(q3)*C.z
assert express(A.y, C) == sin(q2)*sin(q3)*C.x + cos(q2)*C.y - \
sin(q2)*cos(q3)*C.z
assert express(A.z, C) == -sin(q3)*cos(q2)*C.x + sin(q2)*C.y + \
cos(q2)*cos(q3)*C.z
assert express(A.x, N) == cos(q1)*N.x + sin(q1)*N.y
assert express(A.y, N) == -sin(q1)*N.x + cos(q1)*N.y
assert express(A.z, N) == N.z
assert express(A.x, A) == A.x
assert express(A.y, A) == A.y
assert express(A.z, A) == A.z
assert express(A.x, B) == B.x
assert express(A.y, B) == cos(q2)*B.y - sin(q2)*B.z
assert express(A.z, B) == sin(q2)*B.y + cos(q2)*B.z
assert express(A.x, C) == cos(q3)*C.x + sin(q3)*C.z
assert express(A.y, C) == sin(q2)*sin(q3)*C.x + cos(q2)*C.y - \
sin(q2)*cos(q3)*C.z
assert express(A.z, C) == -sin(q3)*cos(q2)*C.x + sin(q2)*C.y + \
cos(q2)*cos(q3)*C.z
# Check to make sure UnitVectors get converted properly
assert express(N.x, N) == N.x
assert express(N.y, N) == N.y
assert express(N.z, N) == N.z
assert express(N.x, A) == (cos(q1)*A.x - sin(q1)*A.y)
assert express(N.y, A) == (sin(q1)*A.x + cos(q1)*A.y)
assert express(N.z, A) == A.z
assert express(N.x, B) == (cos(q1)*B.x - sin(q1)*cos(q2)*B.y +
sin(q1)*sin(q2)*B.z)
assert express(N.y, B) == (sin(q1)*B.x + cos(q1)*cos(q2)*B.y -
sin(q2)*cos(q1)*B.z)
assert express(N.z, B) == (sin(q2)*B.y + cos(q2)*B.z)
assert express(N.x, C) == (
(cos(q1)*cos(q3) - sin(q1)*sin(q2)*sin(q3))*C.x -
sin(q1)*cos(q2)*C.y +
(sin(q3)*cos(q1) + sin(q1)*sin(q2)*cos(q3))*C.z)
assert express(N.y, C) == (
(sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1))*C.x +
cos(q1)*cos(q2)*C.y +
(sin(q1)*sin(q3) - sin(q2)*cos(q1)*cos(q3))*C.z)
assert express(N.z, C) == (-sin(q3)*cos(q2)*C.x + sin(q2)*C.y +
cos(q2)*cos(q3)*C.z)
assert express(A.x, N) == (cos(q1)*N.x + sin(q1)*N.y)
assert express(A.y, N) == (-sin(q1)*N.x + cos(q1)*N.y)
assert express(A.z, N) == N.z
assert express(A.x, A) == A.x
assert express(A.y, A) == A.y
assert express(A.z, A) == A.z
assert express(A.x, B) == B.x
assert express(A.y, B) == (cos(q2)*B.y - sin(q2)*B.z)
assert express(A.z, B) == (sin(q2)*B.y + cos(q2)*B.z)
assert express(A.x, C) == (cos(q3)*C.x + sin(q3)*C.z)
assert express(A.y, C) == (sin(q2)*sin(q3)*C.x + cos(q2)*C.y -
sin(q2)*cos(q3)*C.z)
assert express(A.z, C) == (-sin(q3)*cos(q2)*C.x + sin(q2)*C.y +
cos(q2)*cos(q3)*C.z)
assert express(B.x, N) == (cos(q1)*N.x + sin(q1)*N.y)
assert express(B.y, N) == (-sin(q1)*cos(q2)*N.x +
cos(q1)*cos(q2)*N.y + sin(q2)*N.z)
assert express(B.z, N) == (sin(q1)*sin(q2)*N.x -
sin(q2)*cos(q1)*N.y + cos(q2)*N.z)
assert express(B.x, A) == A.x
assert express(B.y, A) == (cos(q2)*A.y + sin(q2)*A.z)
assert express(B.z, A) == (-sin(q2)*A.y + cos(q2)*A.z)
assert express(B.x, B) == B.x
assert express(B.y, B) == B.y
assert express(B.z, B) == B.z
assert express(B.x, C) == (cos(q3)*C.x + sin(q3)*C.z)
assert express(B.y, C) == C.y
assert express(B.z, C) == (-sin(q3)*C.x + cos(q3)*C.z)
assert express(C.x, N) == (
(cos(q1)*cos(q3) - sin(q1)*sin(q2)*sin(q3))*N.x +
(sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1))*N.y -
sin(q3)*cos(q2)*N.z)
assert express(C.y, N) == (
-sin(q1)*cos(q2)*N.x + cos(q1)*cos(q2)*N.y + sin(q2)*N.z)
assert express(C.z, N) == (
(sin(q3)*cos(q1) + sin(q1)*sin(q2)*cos(q3))*N.x +
(sin(q1)*sin(q3) - sin(q2)*cos(q1)*cos(q3))*N.y +
cos(q2)*cos(q3)*N.z)
assert express(C.x, A) == (cos(q3)*A.x + sin(q2)*sin(q3)*A.y -
sin(q3)*cos(q2)*A.z)
assert express(C.y, A) == (cos(q2)*A.y + sin(q2)*A.z)
assert express(C.z, A) == (sin(q3)*A.x - sin(q2)*cos(q3)*A.y +
cos(q2)*cos(q3)*A.z)
assert express(C.x, B) == (cos(q3)*B.x - sin(q3)*B.z)
assert express(C.y, B) == B.y
assert express(C.z, B) == (sin(q3)*B.x + cos(q3)*B.z)
assert express(C.x, C) == C.x
assert express(C.y, C) == C.y
assert express(C.z, C) == C.z == (C.z)
# Check to make sure Vectors get converted back to UnitVectors
assert N.x == express((cos(q1)*A.x - sin(q1)*A.y), N)
assert N.y == express((sin(q1)*A.x + cos(q1)*A.y), N)
assert N.x == express((cos(q1)*B.x - sin(q1)*cos(q2)*B.y +
sin(q1)*sin(q2)*B.z), N)
assert N.y == express((sin(q1)*B.x + cos(q1)*cos(q2)*B.y -
sin(q2)*cos(q1)*B.z), N)
assert N.z == express((sin(q2)*B.y + cos(q2)*B.z), N)
"""
These don't really test our code, they instead test the auto simplification
(or lack thereof) of SymPy.
assert N.x == express((
(cos(q1)*cos(q3)-sin(q1)*sin(q2)*sin(q3))*C.x -
sin(q1)*cos(q2)*C.y +
(sin(q3)*cos(q1)+sin(q1)*sin(q2)*cos(q3))*C.z), N)
assert N.y == express((
(sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1))*C.x +
cos(q1)*cos(q2)*C.y +
(sin(q1)*sin(q3) - sin(q2)*cos(q1)*cos(q3))*C.z), N)
assert N.z == express((-sin(q3)*cos(q2)*C.x + sin(q2)*C.y +
cos(q2)*cos(q3)*C.z), N)
"""
assert A.x == express((cos(q1)*N.x + sin(q1)*N.y), A)
assert A.y == express((-sin(q1)*N.x + cos(q1)*N.y), A)
assert A.y == express((cos(q2)*B.y - sin(q2)*B.z), A)
assert A.z == express((sin(q2)*B.y + cos(q2)*B.z), A)
assert A.x == express((cos(q3)*C.x + sin(q3)*C.z), A)
# Tripsimp messes up here too.
#print express((sin(q2)*sin(q3)*C.x + cos(q2)*C.y -
# sin(q2)*cos(q3)*C.z), A)
assert A.y == express((sin(q2)*sin(q3)*C.x + cos(q2)*C.y -
sin(q2)*cos(q3)*C.z), A)
assert A.z == express((-sin(q3)*cos(q2)*C.x + sin(q2)*C.y +
cos(q2)*cos(q3)*C.z), A)
assert B.x == express((cos(q1)*N.x + sin(q1)*N.y), B)
assert B.y == express((-sin(q1)*cos(q2)*N.x +
cos(q1)*cos(q2)*N.y + sin(q2)*N.z), B)
assert B.z == express((sin(q1)*sin(q2)*N.x -
sin(q2)*cos(q1)*N.y + cos(q2)*N.z), B)
assert B.y == express((cos(q2)*A.y + sin(q2)*A.z), B)
assert B.z == express((-sin(q2)*A.y + cos(q2)*A.z), B)
assert B.x == express((cos(q3)*C.x + sin(q3)*C.z), B)
assert B.z == express((-sin(q3)*C.x + cos(q3)*C.z), B)
"""
assert C.x == express((
(cos(q1)*cos(q3)-sin(q1)*sin(q2)*sin(q3))*N.x +
(sin(q1)*cos(q3)+sin(q2)*sin(q3)*cos(q1))*N.y -
sin(q3)*cos(q2)*N.z), C)
assert C.y == express((
-sin(q1)*cos(q2)*N.x + cos(q1)*cos(q2)*N.y + sin(q2)*N.z), C)
assert C.z == express((
(sin(q3)*cos(q1)+sin(q1)*sin(q2)*cos(q3))*N.x +
(sin(q1)*sin(q3)-sin(q2)*cos(q1)*cos(q3))*N.y +
cos(q2)*cos(q3)*N.z), C)
"""
assert C.x == express((cos(q3)*A.x + sin(q2)*sin(q3)*A.y -
sin(q3)*cos(q2)*A.z), C)
assert C.y == express((cos(q2)*A.y + sin(q2)*A.z), C)
assert C.z == express((sin(q3)*A.x - sin(q2)*cos(q3)*A.y +
cos(q2)*cos(q3)*A.z), C)
assert C.x == express((cos(q3)*B.x - sin(q3)*B.z), C)
assert C.z == express((sin(q3)*B.x + cos(q3)*B.z), C)
def test_time_derivative():
#The use of time_derivative for calculations pertaining to scalar
#fields has been tested in test_coordinate_vars in test_essential.py
A = ReferenceFrame('A')
q = dynamicsymbols('q')
qd = dynamicsymbols('q', 1)
B = A.orientnew('B', 'Axis', [q, A.z])
d = A.x | A.x
assert time_derivative(d, B) == (-qd) * (A.y | A.x) + \
(-qd) * (A.x | A.y)
d1 = A.x | B.y
assert time_derivative(d1, A) == - qd*(A.x|B.x)
assert time_derivative(d1, B) == - qd*(A.y|B.y)
d2 = A.x | B.x
assert time_derivative(d2, A) == qd*(A.x|B.y)
assert time_derivative(d2, B) == - qd*(A.y|B.x)
d3 = A.x | B.z
assert time_derivative(d3, A) == 0
assert time_derivative(d3, B) == - qd*(A.y|B.z)
q1, q2, q3, q4 = dynamicsymbols('q1 q2 q3 q4')
q1d, q2d, q3d, q4d = dynamicsymbols('q1 q2 q3 q4', 1)
q1dd, q2dd, q3dd, q4dd = dynamicsymbols('q1 q2 q3 q4', 2)
C = B.orientnew('C', 'Axis', [q4, B.x])
v1 = q1 * A.z
v2 = q2*A.x + q3*B.y
v3 = q1*A.x + q2*A.y + q3*A.z
assert time_derivative(B.x, C) == 0
assert time_derivative(B.y, C) == - q4d*B.z
assert time_derivative(B.z, C) == q4d*B.y
assert time_derivative(v1, B) == q1d*A.z
assert time_derivative(v1, C) == - q1*sin(q)*q4d*A.x + \
q1*cos(q)*q4d*A.y + q1d*A.z
assert time_derivative(v2, A) == q2d*A.x - q3*qd*B.x + q3d*B.y
assert time_derivative(v2, C) == q2d*A.x - q2*qd*A.y + \
q2*sin(q)*q4d*A.z + q3d*B.y - q3*q4d*B.z
assert time_derivative(v3, B) == (q2*qd + q1d)*A.x + \
(-q1*qd + q2d)*A.y + q3d*A.z
assert time_derivative(d, C) == - qd*(A.y|A.x) + \
sin(q)*q4d*(A.z|A.x) - qd*(A.x|A.y) + sin(q)*q4d*(A.x|A.z)
def test_get_motion_methods():
#Initialization
t = dynamicsymbols._t
s1, s2, s3 = symbols('s1 s2 s3')
S1, S2, S3 = symbols('S1 S2 S3')
S4, S5, S6 = symbols('S4 S5 S6')
t1, t2 = symbols('t1 t2')
a, b, c = dynamicsymbols('a b c')
ad, bd, cd = dynamicsymbols('a b c', 1)
a2d, b2d, c2d = dynamicsymbols('a b c', 2)
v0 = S1*N.x + S2*N.y + S3*N.z
v01 = S4*N.x + S5*N.y + S6*N.z
v1 = s1*N.x + s2*N.y + s3*N.z
v2 = a*N.x + b*N.y + c*N.z
v2d = ad*N.x + bd*N.y + cd*N.z
v2dd = a2d*N.x + b2d*N.y + c2d*N.z
#Test position parameter
assert get_motion_params(frame = N) == (0, 0, 0)
assert get_motion_params(N, position=v1) == (0, 0, v1)
assert get_motion_params(N, position=v2) == (v2dd, v2d, v2)
#Test velocity parameter
assert get_motion_params(N, velocity=v1) == (0, v1, v1 * t)
assert get_motion_params(N, velocity=v1, position=v0, timevalue1=t1) == \
(0, v1, v0 + v1*(t - t1))
answer = get_motion_params(N, velocity=v1, position=v2, timevalue1=t1)
answer_expected = (0, v1, v1*t - v1*t1 + v2.subs(t, t1))
assert answer == answer_expected
answer = get_motion_params(N, velocity=v2, position=v0, timevalue1=t1)
integral_vector = Integral(a, (t, t1, t))*N.x + Integral(b, (t, t1, t))*N.y \
+ Integral(c, (t, t1, t))*N.z
answer_expected = (v2d, v2, v0 + integral_vector)
assert answer == answer_expected
#Test acceleration parameter
assert get_motion_params(N, acceleration=v1) == \
(v1, v1 * t, v1 * t**2/2)
assert get_motion_params(N, acceleration=v1, velocity=v0,
position=v2, timevalue1=t1, timevalue2=t2) == \
(v1, (v0 + v1*t - v1*t2),
-v0*t1 + v1*t**2/2 + v1*t2*t1 - \
v1*t1**2/2 + t*(v0 - v1*t2) + \
v2.subs(t, t1))
assert get_motion_params(N, acceleration=v1, velocity=v0,
position=v01, timevalue1=t1, timevalue2=t2) == \
(v1, v0 + v1*t - v1*t2,
-v0*t1 + v01 + v1*t**2/2 + \
v1*t2*t1 - v1*t1**2/2 + \
t*(v0 - v1*t2))
answer = get_motion_params(N, acceleration=a*N.x, velocity=S1*N.x,
position=S2*N.x, timevalue1=t1, timevalue2=t2)
i1 = Integral(a, (t, t2, t))
answer_expected = (a*N.x, (S1 + i1)*N.x, \
(S2 + Integral(S1 + i1, (t, t1, t)))*N.x)
assert answer == answer_expected
def test_kin_eqs():
q0, q1, q2, q3 = dynamicsymbols('q0 q1 q2 q3')
q0d, q1d, q2d, q3d = dynamicsymbols('q0 q1 q2 q3', 1)
u1, u2, u3 = dynamicsymbols('u1 u2 u3')
kds = kinematic_equations([u1, u2, u3], [q0, q1, q2, q3], 'quaternion')
assert kds == [-0.5 * q0 * u1 - 0.5 * q2 * u3 + 0.5 * q3 * u2 + q1d,
-0.5 * q0 * u2 + 0.5 * q1 * u3 - 0.5 * q3 * u1 + q2d,
-0.5 * q0 * u3 - 0.5 * q1 * u2 + 0.5 * q2 * u1 + q3d,
0.5 * q1 * u1 + 0.5 * q2 * u2 + 0.5 * q3 * u3 + q0d]
def test_partial_velocity():
q1, q2, q3, u1, u2, u3 = dynamicsymbols('q1 q2 q3 u1 u2 u3')
u4, u5 = dynamicsymbols('u4, u5')
r = symbols('r')
N = ReferenceFrame('N')
Y = N.orientnew('Y', 'Axis', [q1, N.z])
L = Y.orientnew('L', 'Axis', [q2, Y.x])
R = L.orientnew('R', 'Axis', [q3, L.y])
R.set_ang_vel(N, u1 * L.x + u2 * L.y + u3 * L.z)
C = Point('C')
C.set_vel(N, u4 * L.x + u5 * (Y.z ^ L.x))
Dmc = C.locatenew('Dmc', r * L.z)
Dmc.v2pt_theory(C, N, R)
vel_list = [Dmc.vel(N), C.vel(N), R.ang_vel_in(N)]
u_list = [u1, u2, u3, u4, u5]
assert (partial_velocity(vel_list, u_list, N) ==
[[- r*L.y, r*L.x, 0, L.x, cos(q2)*L.y - sin(q2)*L.z],
[0, 0, 0, L.x, cos(q2)*L.y - sin(q2)*L.z],
[L.x, L.y, L.z, 0, 0]])
# Make sure that partial velocities can be computed regardless if the
# orientation between frames is defined or not.
A = ReferenceFrame('A')
B = ReferenceFrame('B')
v = u4 * A.x + u5 * B.y
assert partial_velocity((v, ), (u4, u5), A) == [[A.x, B.y]]
| 18,751 | 38.561181 | 81 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/test_frame.py
|
from sympy import sin, cos, pi, zeros, eye, ImmutableMatrix as Matrix
from sympy.physics.vector import (ReferenceFrame, Vector, CoordinateSym,
dynamicsymbols, time_derivative, express)
Vector.simp = True
def test_coordinate_vars():
"""Tests the coordinate variables functionality"""
A = ReferenceFrame('A')
assert CoordinateSym('Ax', A, 0) == A[0]
assert CoordinateSym('Ax', A, 1) == A[1]
assert CoordinateSym('Ax', A, 2) == A[2]
q = dynamicsymbols('q')
qd = dynamicsymbols('q', 1)
assert isinstance(A[0], CoordinateSym) and \
isinstance(A[0], CoordinateSym) and \
isinstance(A[0], CoordinateSym)
assert A.variable_map(A) == {A[0]:A[0], A[1]:A[1], A[2]:A[2]}
assert A[0].frame == A
B = A.orientnew('B', 'Axis', [q, A.z])
assert B.variable_map(A) == {B[2]: A[2], B[1]: -A[0]*sin(q) + A[1]*cos(q),
B[0]: A[0]*cos(q) + A[1]*sin(q)}
assert A.variable_map(B) == {A[0]: B[0]*cos(q) - B[1]*sin(q),
A[1]: B[0]*sin(q) + B[1]*cos(q), A[2]: B[2]}
assert time_derivative(B[0], A) == -A[0]*sin(q)*qd + A[1]*cos(q)*qd
assert time_derivative(B[1], A) == -A[0]*cos(q)*qd - A[1]*sin(q)*qd
assert time_derivative(B[2], A) == 0
assert express(B[0], A, variables=True) == A[0]*cos(q) + A[1]*sin(q)
assert express(B[1], A, variables=True) == -A[0]*sin(q) + A[1]*cos(q)
assert express(B[2], A, variables=True) == A[2]
assert time_derivative(A[0]*A.x + A[1]*A.y + A[2]*A.z, B) == A[1]*qd*A.x - A[0]*qd*A.y
assert time_derivative(B[0]*B.x + B[1]*B.y + B[2]*B.z, A) == - B[1]*qd*B.x + B[0]*qd*B.y
assert express(B[0]*B[1]*B[2], A, variables=True) == \
A[2]*(-A[0]*sin(q) + A[1]*cos(q))*(A[0]*cos(q) + A[1]*sin(q))
assert (time_derivative(B[0]*B[1]*B[2], A) -
(A[2]*(-A[0]**2*cos(2*q) -
2*A[0]*A[1]*sin(2*q) +
A[1]**2*cos(2*q))*qd)).trigsimp() == 0
assert express(B[0]*B.x + B[1]*B.y + B[2]*B.z, A) == \
(B[0]*cos(q) - B[1]*sin(q))*A.x + (B[0]*sin(q) + \
B[1]*cos(q))*A.y + B[2]*A.z
assert express(B[0]*B.x + B[1]*B.y + B[2]*B.z, A, variables=True) == \
A[0]*A.x + A[1]*A.y + A[2]*A.z
assert express(A[0]*A.x + A[1]*A.y + A[2]*A.z, B) == \
(A[0]*cos(q) + A[1]*sin(q))*B.x + \
(-A[0]*sin(q) + A[1]*cos(q))*B.y + A[2]*B.z
assert express(A[0]*A.x + A[1]*A.y + A[2]*A.z, B, variables=True) == \
B[0]*B.x + B[1]*B.y + B[2]*B.z
N = B.orientnew('N', 'Axis', [-q, B.z])
assert N.variable_map(A) == {N[0]: A[0], N[2]: A[2], N[1]: A[1]}
C = A.orientnew('C', 'Axis', [q, A.x + A.y + A.z])
mapping = A.variable_map(C)
assert mapping[A[0]] == 2*C[0]*cos(q)/3 + C[0]/3 - 2*C[1]*sin(q + pi/6)/3 +\
C[1]/3 - 2*C[2]*cos(q + pi/3)/3 + C[2]/3
assert mapping[A[1]] == -2*C[0]*cos(q + pi/3)/3 + \
C[0]/3 + 2*C[1]*cos(q)/3 + C[1]/3 - 2*C[2]*sin(q + pi/6)/3 + C[2]/3
assert mapping[A[2]] == -2*C[0]*sin(q + pi/6)/3 + C[0]/3 - \
2*C[1]*cos(q + pi/3)/3 + C[1]/3 + 2*C[2]*cos(q)/3 + C[2]/3
def test_ang_vel():
q1, q2, q3, q4 = dynamicsymbols('q1 q2 q3 q4')
q1d, q2d, q3d, q4d = dynamicsymbols('q1 q2 q3 q4', 1)
N = ReferenceFrame('N')
A = N.orientnew('A', 'Axis', [q1, N.z])
B = A.orientnew('B', 'Axis', [q2, A.x])
C = B.orientnew('C', 'Axis', [q3, B.y])
D = N.orientnew('D', 'Axis', [q4, N.y])
u1, u2, u3 = dynamicsymbols('u1 u2 u3')
assert A.ang_vel_in(N) == (q1d)*A.z
assert B.ang_vel_in(N) == (q2d)*B.x + (q1d)*A.z
assert C.ang_vel_in(N) == (q3d)*C.y + (q2d)*B.x + (q1d)*A.z
A2 = N.orientnew('A2', 'Axis', [q4, N.y])
assert N.ang_vel_in(N) == 0
assert N.ang_vel_in(A) == -q1d*N.z
assert N.ang_vel_in(B) == -q1d*A.z - q2d*B.x
assert N.ang_vel_in(C) == -q1d*A.z - q2d*B.x - q3d*B.y
assert N.ang_vel_in(A2) == -q4d*N.y
assert A.ang_vel_in(N) == q1d*N.z
assert A.ang_vel_in(A) == 0
assert A.ang_vel_in(B) == - q2d*B.x
assert A.ang_vel_in(C) == - q2d*B.x - q3d*B.y
assert A.ang_vel_in(A2) == q1d*N.z - q4d*N.y
assert B.ang_vel_in(N) == q1d*A.z + q2d*A.x
assert B.ang_vel_in(A) == q2d*A.x
assert B.ang_vel_in(B) == 0
assert B.ang_vel_in(C) == -q3d*B.y
assert B.ang_vel_in(A2) == q1d*A.z + q2d*A.x - q4d*N.y
assert C.ang_vel_in(N) == q1d*A.z + q2d*A.x + q3d*B.y
assert C.ang_vel_in(A) == q2d*A.x + q3d*C.y
assert C.ang_vel_in(B) == q3d*B.y
assert C.ang_vel_in(C) == 0
assert C.ang_vel_in(A2) == q1d*A.z + q2d*A.x + q3d*B.y - q4d*N.y
assert A2.ang_vel_in(N) == q4d*A2.y
assert A2.ang_vel_in(A) == q4d*A2.y - q1d*N.z
assert A2.ang_vel_in(B) == q4d*N.y - q1d*A.z - q2d*A.x
assert A2.ang_vel_in(C) == q4d*N.y - q1d*A.z - q2d*A.x - q3d*B.y
assert A2.ang_vel_in(A2) == 0
C.set_ang_vel(N, u1*C.x + u2*C.y + u3*C.z)
assert C.ang_vel_in(N) == (u1)*C.x + (u2)*C.y + (u3)*C.z
assert N.ang_vel_in(C) == (-u1)*C.x + (-u2)*C.y + (-u3)*C.z
assert C.ang_vel_in(D) == (u1)*C.x + (u2)*C.y + (u3)*C.z + (-q4d)*D.y
assert D.ang_vel_in(C) == (-u1)*C.x + (-u2)*C.y + (-u3)*C.z + (q4d)*D.y
q0 = dynamicsymbols('q0')
q0d = dynamicsymbols('q0', 1)
E = N.orientnew('E', 'Quaternion', (q0, q1, q2, q3))
assert E.ang_vel_in(N) == (
2 * (q1d * q0 + q2d * q3 - q3d * q2 - q0d * q1) * E.x +
2 * (q2d * q0 + q3d * q1 - q1d * q3 - q0d * q2) * E.y +
2 * (q3d * q0 + q1d * q2 - q2d * q1 - q0d * q3) * E.z)
F = N.orientnew('F', 'Body', (q1, q2, q3), '313')
assert F.ang_vel_in(N) == ((sin(q2)*sin(q3)*q1d + cos(q3)*q2d)*F.x +
(sin(q2)*cos(q3)*q1d - sin(q3)*q2d)*F.y + (cos(q2)*q1d + q3d)*F.z)
G = N.orientnew('G', 'Axis', (q1, N.x + N.y))
assert G.ang_vel_in(N) == q1d * (N.x + N.y).normalize()
assert N.ang_vel_in(G) == -q1d * (N.x + N.y).normalize()
def test_dcm():
q1, q2, q3, q4 = dynamicsymbols('q1 q2 q3 q4')
N = ReferenceFrame('N')
A = N.orientnew('A', 'Axis', [q1, N.z])
B = A.orientnew('B', 'Axis', [q2, A.x])
C = B.orientnew('C', 'Axis', [q3, B.y])
D = N.orientnew('D', 'Axis', [q4, N.y])
E = N.orientnew('E', 'Space', [q1, q2, q3], '123')
assert N.dcm(C) == Matrix([
[- sin(q1) * sin(q2) * sin(q3) + cos(q1) * cos(q3), - sin(q1) *
cos(q2), sin(q1) * sin(q2) * cos(q3) + sin(q3) * cos(q1)], [sin(q1) *
cos(q3) + sin(q2) * sin(q3) * cos(q1), cos(q1) * cos(q2), sin(q1) *
sin(q3) - sin(q2) * cos(q1) * cos(q3)], [- sin(q3) * cos(q2), sin(q2),
cos(q2) * cos(q3)]])
# This is a little touchy. Is it ok to use simplify in assert?
test_mat = D.dcm(C) - Matrix(
[[cos(q1) * cos(q3) * cos(q4) - sin(q3) * (- sin(q4) * cos(q2) +
sin(q1) * sin(q2) * cos(q4)), - sin(q2) * sin(q4) - sin(q1) *
cos(q2) * cos(q4), sin(q3) * cos(q1) * cos(q4) + cos(q3) * (- sin(q4) *
cos(q2) + sin(q1) * sin(q2) * cos(q4))], [sin(q1) * cos(q3) +
sin(q2) * sin(q3) * cos(q1), cos(q1) * cos(q2), sin(q1) * sin(q3) -
sin(q2) * cos(q1) * cos(q3)], [sin(q4) * cos(q1) * cos(q3) -
sin(q3) * (cos(q2) * cos(q4) + sin(q1) * sin(q2) * sin(q4)), sin(q2) *
cos(q4) - sin(q1) * sin(q4) * cos(q2), sin(q3) * sin(q4) * cos(q1) +
cos(q3) * (cos(q2) * cos(q4) + sin(q1) * sin(q2) * sin(q4))]])
assert test_mat.expand() == zeros(3, 3)
assert E.dcm(N) == Matrix(
[[cos(q2)*cos(q3), sin(q3)*cos(q2), -sin(q2)],
[sin(q1)*sin(q2)*cos(q3) - sin(q3)*cos(q1), sin(q1)*sin(q2)*sin(q3) +
cos(q1)*cos(q3), sin(q1)*cos(q2)], [sin(q1)*sin(q3) +
sin(q2)*cos(q1)*cos(q3), - sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1),
cos(q1)*cos(q2)]])
def test_orientnew_respects_parent_class():
class MyReferenceFrame(ReferenceFrame):
pass
B = MyReferenceFrame('B')
C = B.orientnew('C', 'Axis', [0, B.x])
assert isinstance(C, MyReferenceFrame)
def test_issue_10348():
u = dynamicsymbols('u:3')
I = ReferenceFrame('I')
A = I.orientnew('A', 'space', u, 'XYZ')
def test_issue_11503():
A = ReferenceFrame("A")
B = A.orientnew("B", "Axis", [35, A.y])
C = ReferenceFrame("C")
A.orient(C, "Axis", [70, C.z])
def test_partial_velocity():
N = ReferenceFrame('N')
A = ReferenceFrame('A')
u1, u2 = dynamicsymbols('u1, u2')
A.set_ang_vel(N, u1 * A.x + u2 * N.y)
assert N.partial_velocity(A, u1) == -A.x
assert N.partial_velocity(A, u1, u2) == (-A.x, -N.y)
assert A.partial_velocity(N, u1) == A.x
assert A.partial_velocity(N, u1, u2) == (A.x, N.y)
assert N.partial_velocity(N, u1) == 0
assert A.partial_velocity(A, u1) == 0
def test_issue_11498():
A = ReferenceFrame('A')
B = ReferenceFrame('B')
# Identity transformation
A.orient(B, 'DCM', eye(3))
assert A.dcm(B) == Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
assert B.dcm(A) == Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
# x -> y
# y -> -z
# z -> -x
A.orient(B, 'DCM', Matrix([[0, 1, 0], [0, 0, -1], [-1, 0, 0]]))
assert B.dcm(A) == Matrix([[0, 1, 0], [0, 0, -1], [-1, 0, 0]])
assert A.dcm(B) == Matrix([[0, 0, -1], [1, 0, 0], [0, -1, 0]])
assert B.dcm(A).T == A.dcm(B)
| 9,298 | 41.852535 | 92 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/test_point.py
|
from sympy.physics.vector import dynamicsymbols, Point, ReferenceFrame
from sympy.utilities.pytest import raises
def test_point_v1pt_theorys():
q, q2 = dynamicsymbols('q q2')
qd, q2d = dynamicsymbols('q q2', 1)
qdd, q2dd = dynamicsymbols('q q2', 2)
N = ReferenceFrame('N')
B = ReferenceFrame('B')
B.set_ang_vel(N, qd * B.z)
O = Point('O')
P = O.locatenew('P', B.x)
P.set_vel(B, 0)
O.set_vel(N, 0)
assert P.v1pt_theory(O, N, B) == qd * B.y
O.set_vel(N, N.x)
assert P.v1pt_theory(O, N, B) == N.x + qd * B.y
P.set_vel(B, B.z)
assert P.v1pt_theory(O, N, B) == B.z + N.x + qd * B.y
def test_point_a1pt_theorys():
q, q2 = dynamicsymbols('q q2')
qd, q2d = dynamicsymbols('q q2', 1)
qdd, q2dd = dynamicsymbols('q q2', 2)
N = ReferenceFrame('N')
B = ReferenceFrame('B')
B.set_ang_vel(N, qd * B.z)
O = Point('O')
P = O.locatenew('P', B.x)
P.set_vel(B, 0)
O.set_vel(N, 0)
assert P.a1pt_theory(O, N, B) == -(qd**2) * B.x + qdd * B.y
P.set_vel(B, q2d * B.z)
assert P.a1pt_theory(O, N, B) == -(qd**2) * B.x + qdd * B.y + q2dd * B.z
O.set_vel(N, q2d * B.x)
assert P.a1pt_theory(O, N, B) == ((q2dd - qd**2) * B.x + (q2d * qd + qdd) * B.y +
q2dd * B.z)
def test_point_v2pt_theorys():
q = dynamicsymbols('q')
qd = dynamicsymbols('q', 1)
N = ReferenceFrame('N')
B = N.orientnew('B', 'Axis', [q, N.z])
O = Point('O')
P = O.locatenew('P', 0)
O.set_vel(N, 0)
assert P.v2pt_theory(O, N, B) == 0
P = O.locatenew('P', B.x)
assert P.v2pt_theory(O, N, B) == (qd * B.z ^ B.x)
O.set_vel(N, N.x)
assert P.v2pt_theory(O, N, B) == N.x + qd * B.y
def test_point_a2pt_theorys():
q = dynamicsymbols('q')
qd = dynamicsymbols('q', 1)
qdd = dynamicsymbols('q', 2)
N = ReferenceFrame('N')
B = N.orientnew('B', 'Axis', [q, N.z])
O = Point('O')
P = O.locatenew('P', 0)
O.set_vel(N, 0)
assert P.a2pt_theory(O, N, B) == 0
P.set_pos(O, B.x)
assert P.a2pt_theory(O, N, B) == (-qd**2) * B.x + (qdd) * B.y
def test_point_funcs():
q, q2 = dynamicsymbols('q q2')
qd, q2d = dynamicsymbols('q q2', 1)
qdd, q2dd = dynamicsymbols('q q2', 2)
N = ReferenceFrame('N')
B = ReferenceFrame('B')
B.set_ang_vel(N, 5 * B.y)
O = Point('O')
P = O.locatenew('P', q * B.x)
assert P.pos_from(O) == q * B.x
P.set_vel(B, qd * B.x + q2d * B.y)
assert P.vel(B) == qd * B.x + q2d * B.y
O.set_vel(N, 0)
assert O.vel(N) == 0
assert P.a1pt_theory(O, N, B) == ((-25 * q + qdd) * B.x + (q2dd) * B.y +
(-10 * qd) * B.z)
B = N.orientnew('B', 'Axis', [q, N.z])
O = Point('O')
P = O.locatenew('P', 10 * B.x)
O.set_vel(N, 5 * N.x)
assert O.vel(N) == 5 * N.x
assert P.a2pt_theory(O, N, B) == (-10 * qd**2) * B.x + (10 * qdd) * B.y
B.set_ang_vel(N, 5 * B.y)
O = Point('O')
P = O.locatenew('P', q * B.x)
P.set_vel(B, qd * B.x + q2d * B.y)
O.set_vel(N, 0)
assert P.v1pt_theory(O, N, B) == qd * B.x + q2d * B.y - 5 * q * B.z
def test_point_pos():
q = dynamicsymbols('q')
N = ReferenceFrame('N')
B = N.orientnew('B', 'Axis', [q, N.z])
O = Point('O')
P = O.locatenew('P', 10 * N.x + 5 * B.x)
assert P.pos_from(O) == 10 * N.x + 5 * B.x
Q = P.locatenew('Q', 10 * N.y + 5 * B.y)
assert Q.pos_from(P) == 10 * N.y + 5 * B.y
assert Q.pos_from(O) == 10 * N.x + 10 * N.y + 5 * B.x + 5 * B.y
assert O.pos_from(Q) == -10 * N.x - 10 * N.y - 5 * B.x - 5 * B.y
def test_point_partial_velocity():
N = ReferenceFrame('N')
A = ReferenceFrame('A')
p = Point('p')
u1, u2 = dynamicsymbols('u1, u2')
p.set_vel(N, u1 * A.x + u2 * N.y)
assert p.partial_velocity(N, u1) == A.x
assert p.partial_velocity(N, u1, u2) == (A.x, N.y)
raises(ValueError, lambda: p.partial_velocity(A, u1))
| 3,940 | 29.550388 | 85 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/test_output.py
|
from sympy import S
from sympy.physics.vector import Vector, ReferenceFrame, Dyadic
from sympy.utilities.pytest import raises
Vector.simp = True
A = ReferenceFrame('A')
def test_output_type():
A = ReferenceFrame('A')
v = A.x + A.y
d = v | v
zerov = Vector(0)
zerod = Dyadic(0)
# dot products
assert isinstance(d & d, Dyadic)
assert isinstance(d & zerod, Dyadic)
assert isinstance(zerod & d, Dyadic)
assert isinstance(d & v, Vector)
assert isinstance(v & d, Vector)
assert isinstance(d & zerov, Vector)
assert isinstance(zerov & d, Vector)
raises(TypeError, lambda: d & S(0))
raises(TypeError, lambda: S(0) & d)
raises(TypeError, lambda: d & 0)
raises(TypeError, lambda: 0 & d)
assert not isinstance(v & v, (Vector, Dyadic))
assert not isinstance(v & zerov, (Vector, Dyadic))
assert not isinstance(zerov & v, (Vector, Dyadic))
raises(TypeError, lambda: v & S(0))
raises(TypeError, lambda: S(0) & v)
raises(TypeError, lambda: v & 0)
raises(TypeError, lambda: 0 & v)
# cross products
raises(TypeError, lambda: d ^ d)
raises(TypeError, lambda: d ^ zerod)
raises(TypeError, lambda: zerod ^ d)
assert isinstance(d ^ v, Dyadic)
assert isinstance(v ^ d, Dyadic)
assert isinstance(d ^ zerov, Dyadic)
assert isinstance(zerov ^ d, Dyadic)
assert isinstance(zerov ^ d, Dyadic)
raises(TypeError, lambda: d ^ S(0))
raises(TypeError, lambda: S(0) ^ d)
raises(TypeError, lambda: d ^ 0)
raises(TypeError, lambda: 0 ^ d)
assert isinstance(v ^ v, Vector)
assert isinstance(v ^ zerov, Vector)
assert isinstance(zerov ^ v, Vector)
raises(TypeError, lambda: v ^ S(0))
raises(TypeError, lambda: S(0) ^ v)
raises(TypeError, lambda: v ^ 0)
raises(TypeError, lambda: 0 ^ v)
# outer products
raises(TypeError, lambda: d | d)
raises(TypeError, lambda: d | zerod)
raises(TypeError, lambda: zerod | d)
raises(TypeError, lambda: d | v)
raises(TypeError, lambda: v | d)
raises(TypeError, lambda: d | zerov)
raises(TypeError, lambda: zerov | d)
raises(TypeError, lambda: zerov | d)
raises(TypeError, lambda: d | S(0))
raises(TypeError, lambda: S(0) | d)
raises(TypeError, lambda: d | 0)
raises(TypeError, lambda: 0 | d)
assert isinstance(v | v, Dyadic)
assert isinstance(v | zerov, Dyadic)
assert isinstance(zerov | v, Dyadic)
raises(TypeError, lambda: v | S(0))
raises(TypeError, lambda: S(0) | v)
raises(TypeError, lambda: v | 0)
raises(TypeError, lambda: 0 | v)
| 2,594 | 32.701299 | 63 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/test_printing.py
|
# -*- coding: utf-8 -*-
from sympy import symbols, sin, cos, sqrt, Function
from sympy.core.compatibility import u_decode as u
from sympy.physics.vector import ReferenceFrame, dynamicsymbols
from sympy.physics.vector.printing import (VectorLatexPrinter, vpprint)
# TODO : Figure out how to make the pretty printing tests readable like the
# ones in sympy.printing.pretty.tests.test_printing.
a, b, c = symbols('a, b, c')
alpha, omega, beta = dynamicsymbols('alpha, omega, beta')
A = ReferenceFrame('A')
N = ReferenceFrame('N')
v = a ** 2 * N.x + b * N.y + c * sin(alpha) * N.z
w = alpha * N.x + sin(omega) * N.y + alpha * beta * N.z
o = a/b * N.x + (c+b)/a * N.y + c**2/b * N.z
y = a ** 2 * (N.x | N.y) + b * (N.y | N.y) + c * sin(alpha) * (N.z | N.y)
x = alpha * (N.x | N.x) + sin(omega) * (N.y | N.z) + alpha * beta * (N.z | N.x)
def ascii_vpretty(expr):
return vpprint(expr, use_unicode=False, wrap_line=False)
def unicode_vpretty(expr):
return vpprint(expr, use_unicode=True, wrap_line=False)
def test_latex_printer():
r = Function('r')('t')
assert VectorLatexPrinter().doprint(r ** 2) == "r^{2}"
def test_vector_pretty_print():
# TODO : The unit vectors should print with subscripts but they just
# print as `n_x` instead of making `x` a subscript with unicode.
# TODO : The pretty print division does not print correctly here:
# w = alpha * N.x + sin(omega) * N.y + alpha / beta * N.z
expected = """\
2
a n_x + b n_y + c*sin(alpha) n_z\
"""
uexpected = u("""\
2
a n_x + b n_y + c⋅sin(α) n_z\
""")
assert ascii_vpretty(v) == expected
assert unicode_vpretty(v) == uexpected
expected = u('alpha n_x + sin(omega) n_y + alpha*beta n_z')
uexpected = u('α n_x + sin(ω) n_y + α⋅β n_z')
assert ascii_vpretty(w) == expected
assert unicode_vpretty(w) == uexpected
expected = """\
2
a b + c c
- n_x + ----- n_y + -- n_z
b a b\
"""
uexpected = u("""\
2
a b + c c
─ n_x + ───── n_y + ── n_z
b a b\
""")
assert ascii_vpretty(o) == expected
assert unicode_vpretty(o) == uexpected
def test_vector_latex():
a, b, c, d, omega = symbols('a, b, c, d, omega')
v = (a ** 2 + b / c) * A.x + sqrt(d) * A.y + cos(omega) * A.z
assert v._latex() == (r'(a^{2} + \frac{b}{c})\mathbf{\hat{a}_x} + '
r'\sqrt{d}\mathbf{\hat{a}_y} + '
r'\operatorname{cos}\left(\omega\right)'
r'\mathbf{\hat{a}_z}')
theta, omega, alpha, q = dynamicsymbols('theta, omega, alpha, q')
v = theta * A.x + omega * omega * A.y + (q * alpha) * A.z
assert v._latex() == (r'\theta\mathbf{\hat{a}_x} + '
r'\omega^{2}\mathbf{\hat{a}_y} + '
r'\alpha q\mathbf{\hat{a}_z}')
phi1, phi2, phi3 = dynamicsymbols('phi1, phi2, phi3')
theta1, theta2, theta3 = symbols('theta1, theta2, theta3')
v = (sin(theta1) * A.x +
cos(phi1) * cos(phi2) * A.y +
cos(theta1 + phi3) * A.z)
assert v._latex() == (r'\operatorname{sin}\left(\theta_{1}\right)'
r'\mathbf{\hat{a}_x} + \operatorname{cos}'
r'\left(\phi_{1}\right) \operatorname{cos}'
r'\left(\phi_{2}\right)\mathbf{\hat{a}_y} + '
r'\operatorname{cos}\left(\theta_{1} + '
r'\phi_{3}\right)\mathbf{\hat{a}_z}')
N = ReferenceFrame('N')
a, b, c, d, omega = symbols('a, b, c, d, omega')
v = (a ** 2 + b / c) * N.x + sqrt(d) * N.y + cos(omega) * N.z
expected = (r'(a^{2} + \frac{b}{c})\mathbf{\hat{n}_x} + '
r'\sqrt{d}\mathbf{\hat{n}_y} + '
r'\operatorname{cos}\left(\omega\right)'
r'\mathbf{\hat{n}_z}')
assert v._latex() == expected
lp = VectorLatexPrinter()
assert lp.doprint(v) == expected
# Try custom unit vectors.
N = ReferenceFrame('N', latexs=(r'\hat{i}', r'\hat{j}', r'\hat{k}'))
v = (a ** 2 + b / c) * N.x + sqrt(d) * N.y + cos(omega) * N.z
expected = (r'(a^{2} + \frac{b}{c})\hat{i} + '
r'\sqrt{d}\hat{j} + '
r'\operatorname{cos}\left(\omega\right)\hat{k}')
assert v._latex() == expected
def test_vector_latex_with_functions():
N = ReferenceFrame('N')
omega, alpha = dynamicsymbols('omega, alpha')
v = omega.diff() * N.x
assert v._latex() == r'\dot{\omega}\mathbf{\hat{n}_x}'
v = omega.diff() ** alpha * N.x
assert v._latex() == (r'\dot{\omega}^{\alpha}'
r'\mathbf{\hat{n}_x}')
def test_dyadic_pretty_print():
expected = """\
2
a n_x|n_y + b n_y|n_y + c*sin(alpha) n_z|n_y\
"""
uexpected = u("""\
2
a n_x⊗n_y + b n_y⊗n_y + c⋅sin(α) n_z⊗n_y\
""")
assert ascii_vpretty(y) == expected
assert unicode_vpretty(y) == uexpected
expected = u('alpha n_x|n_x + sin(omega) n_y|n_z + alpha*beta n_z|n_x')
uexpected = u('α n_x⊗n_x + sin(ω) n_y⊗n_z + α⋅β n_z⊗n_x')
assert ascii_vpretty(x) == expected
assert unicode_vpretty(x) == uexpected
def test_dyadic_latex():
expected = (r'a^{2}\mathbf{\hat{n}_x}\otimes \mathbf{\hat{n}_y} + '
r'b\mathbf{\hat{n}_y}\otimes \mathbf{\hat{n}_y} + '
r'c \operatorname{sin}\left(\alpha\right)'
r'\mathbf{\hat{n}_z}\otimes \mathbf{\hat{n}_y}')
assert y._latex() == expected
expected = (r'\alpha\mathbf{\hat{n}_x}\otimes \mathbf{\hat{n}_x} + '
r'\operatorname{sin}\left(\omega\right)\mathbf{\hat{n}_y}'
r'\otimes \mathbf{\hat{n}_z} + '
r'\alpha \beta\mathbf{\hat{n}_z}\otimes \mathbf{\hat{n}_x}')
assert x._latex() == expected
| 5,811 | 30.080214 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/test_fieldfunctions.py
|
from sympy import S, Symbol, sin, cos
from sympy.physics.vector import ReferenceFrame, Vector, Point, \
dynamicsymbols
from sympy.physics.vector.fieldfunctions import divergence, \
gradient, curl, is_conservative, is_solenoidal, \
scalar_potential, scalar_potential_difference
from sympy.utilities.pytest import raises
R = ReferenceFrame('R')
q = dynamicsymbols('q')
P = R.orientnew('P', 'Axis', [q, R.z])
def test_curl():
assert curl(Vector(0), R) == Vector(0)
assert curl(R.x, R) == Vector(0)
assert curl(2*R[1]**2*R.y, R) == Vector(0)
assert curl(R[0]*R[1]*R.z, R) == R[0]*R.x - R[1]*R.y
assert curl(R[0]*R[1]*R[2] * (R.x+R.y+R.z), R) == \
(-R[0]*R[1] + R[0]*R[2])*R.x + (R[0]*R[1] - R[1]*R[2])*R.y + \
(-R[0]*R[2] + R[1]*R[2])*R.z
assert curl(2*R[0]**2*R.y, R) == 4*R[0]*R.z
assert curl(P[0]**2*R.x + P.y, R) == \
- 2*(R[0]*cos(q) + R[1]*sin(q))*sin(q)*R.z
assert curl(P[0]*R.y, P) == cos(q)*P.z
def test_divergence():
assert divergence(Vector(0), R) == S(0)
assert divergence(R.x, R) == S(0)
assert divergence(R[0]**2*R.x, R) == 2*R[0]
assert divergence(R[0]*R[1]*R[2] * (R.x+R.y+R.z), R) == \
R[0]*R[1] + R[0]*R[2] + R[1]*R[2]
assert divergence((1/(R[0]*R[1]*R[2])) * (R.x+R.y+R.z), R) == \
-1/(R[0]*R[1]*R[2]**2) - 1/(R[0]*R[1]**2*R[2]) - \
1/(R[0]**2*R[1]*R[2])
v = P[0]*P.x + P[1]*P.y + P[2]*P.z
assert divergence(v, P) == 3
assert divergence(v, R).simplify() == 3
assert divergence(P[0]*R.x + R[0]*P.x, R) == 2*cos(q)
def test_gradient():
a = Symbol('a')
assert gradient(0, R) == Vector(0)
assert gradient(R[0], R) == R.x
assert gradient(R[0]*R[1]*R[2], R) == \
R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z
assert gradient(2*R[0]**2, R) == 4*R[0]*R.x
assert gradient(a*sin(R[1])/R[0], R) == \
- a*sin(R[1])/R[0]**2*R.x + a*cos(R[1])/R[0]*R.y
assert gradient(P[0]*P[1], R) == \
(-R[0]*sin(2*q) + R[1]*cos(2*q))*R.x + \
(R[0]*cos(2*q) + R[1]*sin(2*q))*R.y
assert gradient(P[0]*R[2], P) == P[2]*P.x + P[0]*P.z
scalar_field = 2*R[0]**2*R[1]*R[2]
grad_field = gradient(scalar_field, R)
vector_field = R[1]**2*R.x + 3*R[0]*R.y + 5*R[1]*R[2]*R.z
curl_field = curl(vector_field, R)
def test_conservative():
assert is_conservative(0) is True
assert is_conservative(R.x) is True
assert is_conservative(2 * R.x + 3 * R.y + 4 * R.z) is True
assert is_conservative(R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z) is \
True
assert is_conservative(R[0] * R.y) is False
assert is_conservative(grad_field) is True
assert is_conservative(curl_field) is False
assert is_conservative(4*R[0]*R[1]*R[2]*R.x + 2*R[0]**2*R[2]*R.y) is \
False
assert is_conservative(R[2]*P.x + P[0]*R.z) is True
def test_solenoidal():
assert is_solenoidal(0) is True
assert is_solenoidal(R.x) is True
assert is_solenoidal(2 * R.x + 3 * R.y + 4 * R.z) is True
assert is_solenoidal(R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z) is \
True
assert is_solenoidal(R[1] * R.y) is False
assert is_solenoidal(grad_field) is False
assert is_solenoidal(curl_field) is True
assert is_solenoidal((-2*R[1] + 3)*R.z) is True
assert is_solenoidal(cos(q)*R.x + sin(q)*R.y + cos(q)*P.z) is True
assert is_solenoidal(R[2]*P.x + P[0]*R.z) is True
def test_scalar_potential():
assert scalar_potential(0, R) == 0
assert scalar_potential(R.x, R) == R[0]
assert scalar_potential(R.y, R) == R[1]
assert scalar_potential(R.z, R) == R[2]
assert scalar_potential(R[1]*R[2]*R.x + R[0]*R[2]*R.y + \
R[0]*R[1]*R.z, R) == R[0]*R[1]*R[2]
assert scalar_potential(grad_field, R) == scalar_field
assert scalar_potential(R[2]*P.x + P[0]*R.z, R) == \
R[0]*R[2]*cos(q) + R[1]*R[2]*sin(q)
assert scalar_potential(R[2]*P.x + P[0]*R.z, P) == P[0]*P[2]
raises(ValueError, lambda: scalar_potential(R[0] * R.y, R))
def test_scalar_potential_difference():
origin = Point('O')
point1 = origin.locatenew('P1', 1*R.x + 2*R.y + 3*R.z)
point2 = origin.locatenew('P2', 4*R.x + 5*R.y + 6*R.z)
genericpointR = origin.locatenew('RP', R[0]*R.x + R[1]*R.y + R[2]*R.z)
genericpointP = origin.locatenew('PP', P[0]*P.x + P[1]*P.y + P[2]*P.z)
assert scalar_potential_difference(S(0), R, point1, point2, \
origin) == 0
assert scalar_potential_difference(scalar_field, R, origin, \
genericpointR, origin) == \
scalar_field
assert scalar_potential_difference(grad_field, R, origin, \
genericpointR, origin) == \
scalar_field
assert scalar_potential_difference(grad_field, R, point1, point2,
origin) == 948
assert scalar_potential_difference(R[1]*R[2]*R.x + R[0]*R[2]*R.y + \
R[0]*R[1]*R.z, R, point1,
genericpointR, origin) == \
R[0]*R[1]*R[2] - 6
potential_diff_P = 2*P[2]*(P[0]*sin(q) + P[1]*cos(q))*\
(P[0]*cos(q) - P[1]*sin(q))**2
assert scalar_potential_difference(grad_field, P, origin, \
genericpointP, \
origin).simplify() == \
potential_diff_P
| 5,638 | 41.719697 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/test_vector.py
|
from sympy import symbols, pi, sin, cos, ImmutableMatrix as Matrix
from sympy.physics.vector import ReferenceFrame, Vector, dynamicsymbols, dot
from sympy.abc import x, y, z
from sympy.utilities.pytest import raises
Vector.simp = True
A = ReferenceFrame('A')
def test_Vector():
assert A.x != A.y
assert A.y != A.z
assert A.z != A.x
v1 = x*A.x + y*A.y + z*A.z
v2 = x**2*A.x + y**2*A.y + z**2*A.z
v3 = v1 + v2
v4 = v1 - v2
assert isinstance(v1, Vector)
assert dot(v1, A.x) == x
assert dot(v1, A.y) == y
assert dot(v1, A.z) == z
assert isinstance(v2, Vector)
assert dot(v2, A.x) == x**2
assert dot(v2, A.y) == y**2
assert dot(v2, A.z) == z**2
assert isinstance(v3, Vector)
# We probably shouldn't be using simplify in dot...
assert dot(v3, A.x) == x**2 + x
assert dot(v3, A.y) == y**2 + y
assert dot(v3, A.z) == z**2 + z
assert isinstance(v4, Vector)
# We probably shouldn't be using simplify in dot...
assert dot(v4, A.x) == x - x**2
assert dot(v4, A.y) == y - y**2
assert dot(v4, A.z) == z - z**2
assert v1.to_matrix(A) == Matrix([[x], [y], [z]])
q = symbols('q')
B = A.orientnew('B', 'Axis', (q, A.x))
assert v1.to_matrix(B) == Matrix([[x],
[ y * cos(q) + z * sin(q)],
[-y * sin(q) + z * cos(q)]])
#Test the separate method
B = ReferenceFrame('B')
v5 = x*A.x + y*A.y + z*B.z
assert Vector(0).separate() == {}
assert v1.separate() == {A: v1}
assert v5.separate() == {A: x*A.x + y*A.y, B: z*B.z}
def test_Vector_diffs():
q1, q2, q3, q4 = dynamicsymbols('q1 q2 q3 q4')
q1d, q2d, q3d, q4d = dynamicsymbols('q1 q2 q3 q4', 1)
q1dd, q2dd, q3dd, q4dd = dynamicsymbols('q1 q2 q3 q4', 2)
N = ReferenceFrame('N')
A = N.orientnew('A', 'Axis', [q3, N.z])
B = A.orientnew('B', 'Axis', [q2, A.x])
v1 = q2 * A.x + q3 * N.y
v2 = q3 * B.x + v1
v3 = v1.dt(B)
v4 = v2.dt(B)
v5 = q1*A.x + q2*A.y + q3*A.z
assert v1.dt(N) == q2d * A.x + q2 * q3d * A.y + q3d * N.y
assert v1.dt(A) == q2d * A.x + q3 * q3d * N.x + q3d * N.y
assert v1.dt(B) == (q2d * A.x + q3 * q3d * N.x + q3d *\
N.y - q3 * cos(q3) * q2d * N.z)
assert v2.dt(N) == (q2d * A.x + (q2 + q3) * q3d * A.y + q3d * B.x + q3d *
N.y)
assert v2.dt(A) == q2d * A.x + q3d * B.x + q3 * q3d * N.x + q3d * N.y
assert v2.dt(B) == (q2d * A.x + q3d * B.x + q3 * q3d * N.x + q3d * N.y -
q3 * cos(q3) * q2d * N.z)
assert v3.dt(N) == (q2dd * A.x + q2d * q3d * A.y + (q3d**2 + q3 * q3dd) *
N.x + q3dd * N.y + (q3 * sin(q3) * q2d * q3d -
cos(q3) * q2d * q3d - q3 * cos(q3) * q2dd) * N.z)
assert v3.dt(A) == (q2dd * A.x + (2 * q3d**2 + q3 * q3dd) * N.x + (q3dd -
q3 * q3d**2) * N.y + (q3 * sin(q3) * q2d * q3d -
cos(q3) * q2d * q3d - q3 * cos(q3) * q2dd) * N.z)
assert v3.dt(B) == (q2dd * A.x - q3 * cos(q3) * q2d**2 * A.y + (2 *
q3d**2 + q3 * q3dd) * N.x + (q3dd - q3 * q3d**2) *
N.y + (2 * q3 * sin(q3) * q2d * q3d - 2 * cos(q3) *
q2d * q3d - q3 * cos(q3) * q2dd) * N.z)
assert v4.dt(N) == (q2dd * A.x + q3d * (q2d + q3d) * A.y + q3dd * B.x +
(q3d**2 + q3 * q3dd) * N.x + q3dd * N.y + (q3 *
sin(q3) * q2d * q3d - cos(q3) * q2d * q3d - q3 *
cos(q3) * q2dd) * N.z)
assert v4.dt(A) == (q2dd * A.x + q3dd * B.x + (2 * q3d**2 + q3 * q3dd) *
N.x + (q3dd - q3 * q3d**2) * N.y + (q3 * sin(q3) *
q2d * q3d - cos(q3) * q2d * q3d - q3 * cos(q3) *
q2dd) * N.z)
assert v4.dt(B) == (q2dd * A.x - q3 * cos(q3) * q2d**2 * A.y + q3dd * B.x +
(2 * q3d**2 + q3 * q3dd) * N.x + (q3dd - q3 * q3d**2) *
N.y + (2 * q3 * sin(q3) * q2d * q3d - 2 * cos(q3) *
q2d * q3d - q3 * cos(q3) * q2dd) * N.z)
assert v5.dt(B) == q1d*A.x + (q3*q2d + q2d)*A.y + (-q2*q2d + q3d)*A.z
assert v5.dt(A) == q1d*A.x + q2d*A.y + q3d*A.z
assert v5.dt(N) == (-q2*q3d + q1d)*A.x + (q1*q3d + q2d)*A.y + q3d*A.z
assert v3.diff(q1d, N) == 0
assert v3.diff(q2d, N) == A.x - q3 * cos(q3) * N.z
assert v3.diff(q3d, N) == q3 * N.x + N.y
assert v3.diff(q1d, A) == 0
assert v3.diff(q2d, A) == A.x - q3 * cos(q3) * N.z
assert v3.diff(q3d, A) == q3 * N.x + N.y
assert v3.diff(q1d, B) == 0
assert v3.diff(q2d, B) == A.x - q3 * cos(q3) * N.z
assert v3.diff(q3d, B) == q3 * N.x + N.y
assert v4.diff(q1d, N) == 0
assert v4.diff(q2d, N) == A.x - q3 * cos(q3) * N.z
assert v4.diff(q3d, N) == B.x + q3 * N.x + N.y
assert v4.diff(q1d, A) == 0
assert v4.diff(q2d, A) == A.x - q3 * cos(q3) * N.z
assert v4.diff(q3d, A) == B.x + q3 * N.x + N.y
assert v4.diff(q1d, B) == 0
assert v4.diff(q2d, B) == A.x - q3 * cos(q3) * N.z
assert v4.diff(q3d, B) == B.x + q3 * N.x + N.y
def test_vector_var_in_dcm():
N = ReferenceFrame('N')
A = ReferenceFrame('A')
B = ReferenceFrame('B')
u1, u2, u3, u4 = dynamicsymbols('u1 u2 u3 u4')
v = u1 * u2 * A.x + u3 * N.y + u4**2 * N.z
assert v.diff(u1, N, var_in_dcm=False) == u2 * A.x
assert v.diff(u1, A, var_in_dcm=False) == u2 * A.x
assert v.diff(u3, N, var_in_dcm=False) == N.y
assert v.diff(u3, A, var_in_dcm=False) == N.y
assert v.diff(u3, B, var_in_dcm=False) == N.y
assert v.diff(u4, N, var_in_dcm=False) == 2 * u4 * N.z
raises(ValueError, lambda: v.diff(u1, N))
def test_vector_simplify():
x, y, z, k, n, m, w, f, s, A = symbols('x, y, z, k, n, m, w, f, s, A')
N = ReferenceFrame('N')
test1 = (1 / x + 1 / y) * N.x
assert (test1 & N.x) != (x + y) / (x * y)
test1 = test1.simplify()
assert (test1 & N.x) == (x + y) / (x * y)
test2 = (A**2 * s**4 / (4 * pi * k * m**3)) * N.x
test2 = test2.simplify()
assert (test2 & N.x) == (A**2 * s**4 / (4 * pi * k * m**3))
test3 = ((4 + 4 * x - 2 * (2 + 2 * x)) / (2 + 2 * x)) * N.x
test3 = test3.simplify()
assert (test3 & N.x) == 0
test4 = ((-4 * x * y**2 - 2 * y**3 - 2 * x**2 * y) / (x + y)**2) * N.x
test4 = test4.simplify()
assert (test4 & N.x) == -2 * y
| 6,464 | 38.420732 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/vector/tests/test_dyadic.py
|
from sympy import sin, cos, symbols, pi, ImmutableMatrix as Matrix
from sympy.physics.vector import ReferenceFrame, Vector, dynamicsymbols
Vector.simp = True
A = ReferenceFrame('A')
def test_dyadic():
d1 = A.x | A.x
d2 = A.y | A.y
d3 = A.x | A.y
assert d1 * 0 == 0
assert d1 != 0
assert d1 * 2 == 2 * A.x | A.x
assert d1 / 2. == 0.5 * d1
assert d1 & (0 * d1) == 0
assert d1 & d2 == 0
assert d1 & A.x == A.x
assert d1 ^ A.x == 0
assert d1 ^ A.y == A.x | A.z
assert d1 ^ A.z == - A.x | A.y
assert d2 ^ A.x == - A.y | A.z
assert A.x ^ d1 == 0
assert A.y ^ d1 == - A.z | A.x
assert A.z ^ d1 == A.y | A.x
assert A.x & d1 == A.x
assert A.y & d1 == 0
assert A.y & d2 == A.y
assert d1 & d3 == A.x | A.y
assert d3 & d1 == 0
assert d1.dt(A) == 0
q = dynamicsymbols('q')
qd = dynamicsymbols('q', 1)
B = A.orientnew('B', 'Axis', [q, A.z])
assert d1.express(B) == d1.express(B, B)
assert d1.express(B) == ((cos(q)**2) * (B.x | B.x) + (-sin(q) * cos(q)) *
(B.x | B.y) + (-sin(q) * cos(q)) * (B.y | B.x) + (sin(q)**2) *
(B.y | B.y))
assert d1.express(B, A) == (cos(q)) * (B.x | A.x) + (-sin(q)) * (B.y | A.x)
assert d1.express(A, B) == (cos(q)) * (A.x | B.x) + (-sin(q)) * (A.x | B.y)
assert d1.dt(B) == (-qd) * (A.y | A.x) + (-qd) * (A.x | A.y)
assert d1.to_matrix(A) == Matrix([[1, 0, 0], [0, 0, 0], [0, 0, 0]])
assert d1.to_matrix(A, B) == Matrix([[cos(q), -sin(q), 0],
[0, 0, 0],
[0, 0, 0]])
assert d3.to_matrix(A) == Matrix([[0, 1, 0], [0, 0, 0], [0, 0, 0]])
a, b, c, d, e, f = symbols('a, b, c, d, e, f')
v1 = a * A.x + b * A.y + c * A.z
v2 = d * A.x + e * A.y + f * A.z
d4 = v1.outer(v2)
assert d4.to_matrix(A) == Matrix([[a * d, a * e, a * f],
[b * d, b * e, b * f],
[c * d, c * e, c * f]])
d5 = v1.outer(v1)
C = A.orientnew('C', 'Axis', [q, A.x])
for expected, actual in zip(C.dcm(A) * d5.to_matrix(A) * C.dcm(A).T,
d5.to_matrix(C)):
assert (expected - actual).simplify() == 0
def test_dyadic_simplify():
x, y, z, k, n, m, w, f, s, A = symbols('x, y, z, k, n, m, w, f, s, A')
N = ReferenceFrame('N')
dy = N.x | N.x
test1 = (1 / x + 1 / y) * dy
assert (N.x & test1 & N.x) != (x + y) / (x * y)
test1 = test1.simplify()
assert (N.x & test1 & N.x) == (x + y) / (x * y)
test2 = (A**2 * s**4 / (4 * pi * k * m**3)) * dy
test2 = test2.simplify()
assert (N.x & test2 & N.x) == (A**2 * s**4 / (4 * pi * k * m**3))
test3 = ((4 + 4 * x - 2 * (2 + 2 * x)) / (2 + 2 * x)) * dy
test3 = test3.simplify()
assert (N.x & test3 & N.x) == 0
test4 = ((-4 * x * y**2 - 2 * y**3 - 2 * x**2 * y) / (x + y)**2) * dy
test4 = test4.simplify()
assert (N.x & test4 & N.x) == -2 * y
| 3,012 | 34.869048 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/gaussopt.py
|
# -*- encoding: utf-8 -*-
"""
Gaussian optics.
The module implements:
- Ray transfer matrices for geometrical and gaussian optics.
See RayTransferMatrix, GeometricRay and BeamParameter
- Conjugation relations for geometrical and gaussian optics.
See geometric_conj*, gauss_conj and conjugate_gauss_beams
The conventions for the distances are as follows:
focal distance
positive for convergent lenses
object distance
positive for real objects
image distance
positive for real images
"""
from __future__ import print_function, division
__all__ = [
'RayTransferMatrix',
'FreeSpace',
'FlatRefraction',
'CurvedRefraction',
'FlatMirror',
'CurvedMirror',
'ThinLens',
'GeometricRay',
'BeamParameter',
'waist2rayleigh',
'rayleigh2waist',
'geometric_conj_ab',
'geometric_conj_af',
'geometric_conj_bf',
'gaussian_conj',
'conjugate_gauss_beams',
]
from sympy import (atan2, Expr, I, im, Matrix, oo, pi, re, sqrt, sympify,
together)
from sympy.utilities.misc import filldedent
###
# A, B, C, D matrices
###
class RayTransferMatrix(Matrix):
"""
Base class for a Ray Transfer Matrix.
It should be used if there isn't already a more specific subclass mentioned
in See Also.
Parameters
==========
parameters : A, B, C and D or 2x2 matrix (Matrix(2, 2, [A, B, C, D]))
Examples
========
>>> from sympy.physics.optics import RayTransferMatrix, ThinLens
>>> from sympy import Symbol, Matrix
>>> mat = RayTransferMatrix(1, 2, 3, 4)
>>> mat
Matrix([
[1, 2],
[3, 4]])
>>> RayTransferMatrix(Matrix([[1, 2], [3, 4]]))
Matrix([
[1, 2],
[3, 4]])
>>> mat.A
1
>>> f = Symbol('f')
>>> lens = ThinLens(f)
>>> lens
Matrix([
[ 1, 0],
[-1/f, 1]])
>>> lens.C
-1/f
See Also
========
GeometricRay, BeamParameter,
FreeSpace, FlatRefraction, CurvedRefraction,
FlatMirror, CurvedMirror, ThinLens
References
==========
.. [1] http://en.wikipedia.org/wiki/Ray_transfer_matrix_analysis
"""
def __new__(cls, *args):
if len(args) == 4:
temp = ((args[0], args[1]), (args[2], args[3]))
elif len(args) == 1 \
and isinstance(args[0], Matrix) \
and args[0].shape == (2, 2):
temp = args[0]
else:
raise ValueError(filldedent('''
Expecting 2x2 Matrix or the 4 elements of
the Matrix but got %s''' % str(args)))
return Matrix.__new__(cls, temp)
def __mul__(self, other):
if isinstance(other, RayTransferMatrix):
return RayTransferMatrix(Matrix.__mul__(self, other))
elif isinstance(other, GeometricRay):
return GeometricRay(Matrix.__mul__(self, other))
elif isinstance(other, BeamParameter):
temp = self*Matrix(((other.q,), (1,)))
q = (temp[0]/temp[1]).expand(complex=True)
return BeamParameter(other.wavelen,
together(re(q)),
z_r=together(im(q)))
else:
return Matrix.__mul__(self, other)
@property
def A(self):
"""
The A parameter of the Matrix.
Examples
========
>>> from sympy.physics.optics import RayTransferMatrix
>>> mat = RayTransferMatrix(1, 2, 3, 4)
>>> mat.A
1
"""
return self[0, 0]
@property
def B(self):
"""
The B parameter of the Matrix.
Examples
========
>>> from sympy.physics.optics import RayTransferMatrix
>>> mat = RayTransferMatrix(1, 2, 3, 4)
>>> mat.B
2
"""
return self[0, 1]
@property
def C(self):
"""
The C parameter of the Matrix.
Examples
========
>>> from sympy.physics.optics import RayTransferMatrix
>>> mat = RayTransferMatrix(1, 2, 3, 4)
>>> mat.C
3
"""
return self[1, 0]
@property
def D(self):
"""
The D parameter of the Matrix.
Examples
========
>>> from sympy.physics.optics import RayTransferMatrix
>>> mat = RayTransferMatrix(1, 2, 3, 4)
>>> mat.D
4
"""
return self[1, 1]
class FreeSpace(RayTransferMatrix):
"""
Ray Transfer Matrix for free space.
Parameters
==========
distance
See Also
========
RayTransferMatrix
Examples
========
>>> from sympy.physics.optics import FreeSpace
>>> from sympy import symbols
>>> d = symbols('d')
>>> FreeSpace(d)
Matrix([
[1, d],
[0, 1]])
"""
def __new__(cls, d):
return RayTransferMatrix.__new__(cls, 1, d, 0, 1)
class FlatRefraction(RayTransferMatrix):
"""
Ray Transfer Matrix for refraction.
Parameters
==========
n1 : refractive index of one medium
n2 : refractive index of other medium
See Also
========
RayTransferMatrix
Examples
========
>>> from sympy.physics.optics import FlatRefraction
>>> from sympy import symbols
>>> n1, n2 = symbols('n1 n2')
>>> FlatRefraction(n1, n2)
Matrix([
[1, 0],
[0, n1/n2]])
"""
def __new__(cls, n1, n2):
n1, n2 = map(sympify, (n1, n2))
return RayTransferMatrix.__new__(cls, 1, 0, 0, n1/n2)
class CurvedRefraction(RayTransferMatrix):
"""
Ray Transfer Matrix for refraction on curved interface.
Parameters
==========
R : radius of curvature (positive for concave)
n1 : refractive index of one medium
n2 : refractive index of other medium
See Also
========
RayTransferMatrix
Examples
========
>>> from sympy.physics.optics import CurvedRefraction
>>> from sympy import symbols
>>> R, n1, n2 = symbols('R n1 n2')
>>> CurvedRefraction(R, n1, n2)
Matrix([
[ 1, 0],
[(n1 - n2)/(R*n2), n1/n2]])
"""
def __new__(cls, R, n1, n2):
R, n1, n2 = map(sympify, (R, n1, n2))
return RayTransferMatrix.__new__(cls, 1, 0, (n1 - n2)/R/n2, n1/n2)
class FlatMirror(RayTransferMatrix):
"""
Ray Transfer Matrix for reflection.
See Also
========
RayTransferMatrix
Examples
========
>>> from sympy.physics.optics import FlatMirror
>>> FlatMirror()
Matrix([
[1, 0],
[0, 1]])
"""
def __new__(cls):
return RayTransferMatrix.__new__(cls, 1, 0, 0, 1)
class CurvedMirror(RayTransferMatrix):
"""
Ray Transfer Matrix for reflection from curved surface.
Parameters
==========
R : radius of curvature (positive for concave)
See Also
========
RayTransferMatrix
Examples
========
>>> from sympy.physics.optics import CurvedMirror
>>> from sympy import symbols
>>> R = symbols('R')
>>> CurvedMirror(R)
Matrix([
[ 1, 0],
[-2/R, 1]])
"""
def __new__(cls, R):
R = sympify(R)
return RayTransferMatrix.__new__(cls, 1, 0, -2/R, 1)
class ThinLens(RayTransferMatrix):
"""
Ray Transfer Matrix for a thin lens.
Parameters
==========
f : the focal distance
See Also
========
RayTransferMatrix
Examples
========
>>> from sympy.physics.optics import ThinLens
>>> from sympy import symbols
>>> f = symbols('f')
>>> ThinLens(f)
Matrix([
[ 1, 0],
[-1/f, 1]])
"""
def __new__(cls, f):
f = sympify(f)
return RayTransferMatrix.__new__(cls, 1, 0, -1/f, 1)
###
# Representation for geometric ray
###
class GeometricRay(Matrix):
"""
Representation for a geometric ray in the Ray Transfer Matrix formalism.
Parameters
==========
h : height, and
angle : angle, or
matrix : a 2x1 matrix (Matrix(2, 1, [height, angle]))
Examples
========
>>> from sympy.physics.optics import GeometricRay, FreeSpace
>>> from sympy import symbols, Matrix
>>> d, h, angle = symbols('d, h, angle')
>>> GeometricRay(h, angle)
Matrix([
[ h],
[angle]])
>>> FreeSpace(d)*GeometricRay(h, angle)
Matrix([
[angle*d + h],
[ angle]])
>>> GeometricRay( Matrix( ((h,), (angle,)) ) )
Matrix([
[ h],
[angle]])
See Also
========
RayTransferMatrix
"""
def __new__(cls, *args):
if len(args) == 1 and isinstance(args[0], Matrix) \
and args[0].shape == (2, 1):
temp = args[0]
elif len(args) == 2:
temp = ((args[0],), (args[1],))
else:
raise ValueError(filldedent('''
Expecting 2x1 Matrix or the 2 elements of
the Matrix but got %s''' % str(args)))
return Matrix.__new__(cls, temp)
@property
def height(self):
"""
The distance from the optical axis.
Examples
========
>>> from sympy.physics.optics import GeometricRay
>>> from sympy import symbols
>>> h, angle = symbols('h, angle')
>>> gRay = GeometricRay(h, angle)
>>> gRay.height
h
"""
return self[0]
@property
def angle(self):
"""
The angle with the optical axis.
Examples
========
>>> from sympy.physics.optics import GeometricRay
>>> from sympy import symbols
>>> h, angle = symbols('h, angle')
>>> gRay = GeometricRay(h, angle)
>>> gRay.angle
angle
"""
return self[1]
###
# Representation for gauss beam
###
class BeamParameter(Expr):
"""
Representation for a gaussian ray in the Ray Transfer Matrix formalism.
Parameters
==========
wavelen : the wavelength,
z : the distance to waist, and
w : the waist, or
z_r : the rayleigh range
Examples
========
>>> from sympy.physics.optics import BeamParameter
>>> p = BeamParameter(530e-9, 1, w=1e-3)
>>> p.q
1 + 1.88679245283019*I*pi
>>> p.q.n()
1.0 + 5.92753330865999*I
>>> p.w_0.n()
0.00100000000000000
>>> p.z_r.n()
5.92753330865999
>>> from sympy.physics.optics import FreeSpace
>>> fs = FreeSpace(10)
>>> p1 = fs*p
>>> p.w.n()
0.00101413072159615
>>> p1.w.n()
0.00210803120913829
See Also
========
RayTransferMatrix
References
==========
.. [1] http://en.wikipedia.org/wiki/Complex_beam_parameter
.. [2] http://en.wikipedia.org/wiki/Gaussian_beam
"""
#TODO A class Complex may be implemented. The BeamParameter may
# subclass it. See:
# https://groups.google.com/d/topic/sympy/7XkU07NRBEs/discussion
__slots__ = ['z', 'z_r', 'wavelen']
def __new__(cls, wavelen, z, **kwargs):
wavelen, z = map(sympify, (wavelen, z))
inst = Expr.__new__(cls, wavelen, z)
inst.wavelen = wavelen
inst.z = z
if len(kwargs) != 1:
raise ValueError('Constructor expects exactly one named argument.')
elif 'z_r' in kwargs:
inst.z_r = sympify(kwargs['z_r'])
elif 'w' in kwargs:
inst.z_r = waist2rayleigh(sympify(kwargs['w']), wavelen)
else:
raise ValueError('The constructor needs named argument w or z_r')
return inst
@property
def q(self):
"""
The complex parameter representing the beam.
Examples
========
>>> from sympy.physics.optics import BeamParameter
>>> p = BeamParameter(530e-9, 1, w=1e-3)
>>> p.q
1 + 1.88679245283019*I*pi
"""
return self.z + I*self.z_r
@property
def radius(self):
"""
The radius of curvature of the phase front.
Examples
========
>>> from sympy.physics.optics import BeamParameter
>>> p = BeamParameter(530e-9, 1, w=1e-3)
>>> p.radius
1 + 3.55998576005696*pi**2
"""
return self.z*(1 + (self.z_r/self.z)**2)
@property
def w(self):
"""
The beam radius at `1/e^2` intensity.
See Also
========
w_0 : the minimal radius of beam
Examples
========
>>> from sympy.physics.optics import BeamParameter
>>> p = BeamParameter(530e-9, 1, w=1e-3)
>>> p.w
0.001*sqrt(0.2809/pi**2 + 1)
"""
return self.w_0*sqrt(1 + (self.z/self.z_r)**2)
@property
def w_0(self):
"""
The beam waist (minimal radius).
See Also
========
w : the beam radius at `1/e^2` intensity
Examples
========
>>> from sympy.physics.optics import BeamParameter
>>> p = BeamParameter(530e-9, 1, w=1e-3)
>>> p.w_0
0.00100000000000000
"""
return sqrt(self.z_r/pi*self.wavelen)
@property
def divergence(self):
"""
Half of the total angular spread.
Examples
========
>>> from sympy.physics.optics import BeamParameter
>>> p = BeamParameter(530e-9, 1, w=1e-3)
>>> p.divergence
0.00053/pi
"""
return self.wavelen/pi/self.w_0
@property
def gouy(self):
"""
The Gouy phase.
Examples
========
>>> from sympy.physics.optics import BeamParameter
>>> p = BeamParameter(530e-9, 1, w=1e-3)
>>> p.gouy
atan(0.53/pi)
"""
return atan2(self.z, self.z_r)
@property
def waist_approximation_limit(self):
"""
The minimal waist for which the gauss beam approximation is valid.
The gauss beam is a solution to the paraxial equation. For curvatures
that are too great it is not a valid approximation.
Examples
========
>>> from sympy.physics.optics import BeamParameter
>>> p = BeamParameter(530e-9, 1, w=1e-3)
>>> p.waist_approximation_limit
1.06e-6/pi
"""
return 2*self.wavelen/pi
###
# Utilities
###
def waist2rayleigh(w, wavelen):
"""
Calculate the rayleigh range from the waist of a gaussian beam.
See Also
========
rayleigh2waist, BeamParameter
Examples
========
>>> from sympy.physics.optics import waist2rayleigh
>>> from sympy import symbols
>>> w, wavelen = symbols('w wavelen')
>>> waist2rayleigh(w, wavelen)
pi*w**2/wavelen
"""
w, wavelen = map(sympify, (w, wavelen))
return w**2*pi/wavelen
def rayleigh2waist(z_r, wavelen):
"""Calculate the waist from the rayleigh range of a gaussian beam.
See Also
========
waist2rayleigh, BeamParameter
Examples
========
>>> from sympy.physics.optics import rayleigh2waist
>>> from sympy import symbols
>>> z_r, wavelen = symbols('z_r wavelen')
>>> rayleigh2waist(z_r, wavelen)
sqrt(wavelen*z_r)/sqrt(pi)
"""
z_r, wavelen = map(sympify, (z_r, wavelen))
return sqrt(z_r/pi*wavelen)
def geometric_conj_ab(a, b):
"""
Conjugation relation for geometrical beams under paraxial conditions.
Takes the distances to the optical element and returns the needed
focal distance.
See Also
========
geometric_conj_af, geometric_conj_bf
Examples
========
>>> from sympy.physics.optics import geometric_conj_ab
>>> from sympy import symbols
>>> a, b = symbols('a b')
>>> geometric_conj_ab(a, b)
a*b/(a + b)
"""
a, b = map(sympify, (a, b))
if abs(a) == oo or abs(b) == oo:
return a if abs(b) == oo else b
else:
return a*b/(a + b)
def geometric_conj_af(a, f):
"""
Conjugation relation for geometrical beams under paraxial conditions.
Takes the object distance (for geometric_conj_af) or the image distance
(for geometric_conj_bf) to the optical element and the focal distance.
Then it returns the other distance needed for conjugation.
See Also
========
geometric_conj_ab
Examples
========
>>> from sympy.physics.optics.gaussopt import geometric_conj_af, geometric_conj_bf
>>> from sympy import symbols
>>> a, b, f = symbols('a b f')
>>> geometric_conj_af(a, f)
a*f/(a - f)
>>> geometric_conj_bf(b, f)
b*f/(b - f)
"""
a, f = map(sympify, (a, f))
return -geometric_conj_ab(a, -f)
geometric_conj_bf = geometric_conj_af
def gaussian_conj(s_in, z_r_in, f):
"""
Conjugation relation for gaussian beams.
Parameters
==========
s_in : the distance to optical element from the waist
z_r_in : the rayleigh range of the incident beam
f : the focal length of the optical element
Returns
=======
a tuple containing (s_out, z_r_out, m)
s_out : the distance between the new waist and the optical element
z_r_out : the rayleigh range of the emergent beam
m : the ration between the new and the old waists
Examples
========
>>> from sympy.physics.optics import gaussian_conj
>>> from sympy import symbols
>>> s_in, z_r_in, f = symbols('s_in z_r_in f')
>>> gaussian_conj(s_in, z_r_in, f)[0]
1/(-1/(s_in + z_r_in**2/(-f + s_in)) + 1/f)
>>> gaussian_conj(s_in, z_r_in, f)[1]
z_r_in/(1 - s_in**2/f**2 + z_r_in**2/f**2)
>>> gaussian_conj(s_in, z_r_in, f)[2]
1/sqrt(1 - s_in**2/f**2 + z_r_in**2/f**2)
"""
s_in, z_r_in, f = map(sympify, (s_in, z_r_in, f))
s_out = 1 / ( -1/(s_in + z_r_in**2/(s_in - f)) + 1/f )
m = 1/sqrt((1 - (s_in/f)**2) + (z_r_in/f)**2)
z_r_out = z_r_in / ((1 - (s_in/f)**2) + (z_r_in/f)**2)
return (s_out, z_r_out, m)
def conjugate_gauss_beams(wavelen, waist_in, waist_out, **kwargs):
"""
Find the optical setup conjugating the object/image waists.
Parameters
==========
wavelen : the wavelength of the beam
waist_in and waist_out : the waists to be conjugated
f : the focal distance of the element used in the conjugation
Returns
=======
a tuple containing (s_in, s_out, f)
s_in : the distance before the optical element
s_out : the distance after the optical element
f : the focal distance of the optical element
Examples
========
>>> from sympy.physics.optics import conjugate_gauss_beams
>>> from sympy import symbols, factor
>>> l, w_i, w_o, f = symbols('l w_i w_o f')
>>> conjugate_gauss_beams(l, w_i, w_o, f=f)[0]
f*(-sqrt(w_i**2/w_o**2 - pi**2*w_i**4/(f**2*l**2)) + 1)
>>> factor(conjugate_gauss_beams(l, w_i, w_o, f=f)[1])
f*w_o**2*(w_i**2/w_o**2 - sqrt(w_i**2/w_o**2 -
pi**2*w_i**4/(f**2*l**2)))/w_i**2
>>> conjugate_gauss_beams(l, w_i, w_o, f=f)[2]
f
"""
#TODO add the other possible arguments
wavelen, waist_in, waist_out = map(sympify, (wavelen, waist_in, waist_out))
m = waist_out / waist_in
z = waist2rayleigh(waist_in, wavelen)
if len(kwargs) != 1:
raise ValueError("The function expects only one named argument")
elif 'dist' in kwargs:
raise NotImplementedError(filldedent('''
Currently only focal length is supported as a parameter'''))
elif 'f' in kwargs:
f = sympify(kwargs['f'])
s_in = f * (1 - sqrt(1/m**2 - z**2/f**2))
s_out = gaussian_conj(s_in, z, f)[0]
elif 's_in' in kwargs:
raise NotImplementedError(filldedent('''
Currently only focal length is supported as a parameter'''))
else:
raise ValueError(filldedent('''
The functions expects the focal length as a named argument'''))
return (s_in, s_out, f)
#TODO
#def plot_beam():
# """Plot the beam radius as it propagates in space."""
# pass
#TODO
#def plot_beam_conjugation():
# """
# Plot the intersection of two beams.
#
# Represents the conjugation relation.
#
# See Also
# ========
#
# conjugate_gauss_beams
# """
# pass
| 20,059 | 21.873432 | 86 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/waves.py
|
"""
This module has all the classes and functions related to waves in optics.
**Contains**
* TWave
"""
from __future__ import print_function, division
__all__ = ['TWave']
from sympy import (sympify, pi, sin, cos, sqrt, Symbol, S,
symbols, Derivative, atan2)
from sympy.core.expr import Expr
from sympy.physics.units import speed_of_light, meter, second
c = speed_of_light.convert_to(meter/second)
class TWave(Expr):
r"""
This is a simple transverse sine wave travelling in a one dimensional space.
Basic properties are required at the time of creation of the object but
they can be changed later with respective methods provided.
It has been represented as :math:`A \times cos(k*x - \omega \times t + \phi )`
where :math:`A` is amplitude, :math:`\omega` is angular velocity, :math:`k`is
wavenumber, :math:`x` is a spatial variable to represent the position on the
dimension on which the wave propagates and :math:`\phi` is phase angle of the wave.
Arguments
=========
amplitude : Sympifyable
Amplitude of the wave.
frequency : Sympifyable
Frequency of the wave.
phase : Sympifyable
Phase angle of the wave.
time_period : Sympifyable
Time period of the wave.
n : Sympifyable
Refractive index of the medium.
Raises
=======
ValueError : When neither frequency nor time period is provided
or they are not consistent.
TypeError : When anyting other than TWave objects is added.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A1, phi1, A2, phi2, f = symbols('A1, phi1, A2, phi2, f')
>>> w1 = TWave(A1, f, phi1)
>>> w2 = TWave(A2, f, phi2)
>>> w3 = w1 + w2 # Superposition of two waves
>>> w3
TWave(sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2) + A2**2), f,
atan2(A1*cos(phi1) + A2*cos(phi2), A1*sin(phi1) + A2*sin(phi2)))
>>> w3.amplitude
sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2) + A2**2)
>>> w3.phase
atan2(A1*cos(phi1) + A2*cos(phi2), A1*sin(phi1) + A2*sin(phi2))
>>> w3.speed
299792458*meter/(second*n)
>>> w3.angular_velocity
2*pi*f
"""
def __init__(
self,
amplitude,
frequency=None,
phase=S.Zero,
time_period=None,
n=Symbol('n')):
frequency = sympify(frequency)
amplitude = sympify(amplitude)
phase = sympify(phase)
time_period = sympify(time_period)
n = sympify(n)
self._frequency = frequency
self._amplitude = amplitude
self._phase = phase
self._time_period = time_period
self._n = n
if time_period is not None:
self._frequency = 1/self._time_period
if frequency is not None:
self._time_period = 1/self._frequency
if time_period is not None:
if frequency != 1/time_period:
raise ValueError("frequency and time_period should be consistent.")
if frequency is None and time_period is None:
raise ValueError("Either frequency or time period is needed.")
@property
def frequency(self):
"""
Returns the frequency of the wave.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A, phi, f = symbols('A, phi, f')
>>> w = TWave(A, f, phi)
>>> w.frequency
f
"""
return self._frequency
@property
def time_period(self):
"""
Returns the time period of the wave.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A, phi, f = symbols('A, phi, f')
>>> w = TWave(A, f, phi)
>>> w.time_period
1/f
"""
return self._time_period
@property
def wavelength(self):
"""
Returns wavelength of the wave.
It depends on the medium of the wave.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A, phi, f = symbols('A, phi, f')
>>> w = TWave(A, f, phi)
>>> w.wavelength
299792458*meter/(second*f*n)
"""
return c/(self._frequency*self._n)
@property
def amplitude(self):
"""
Returns the amplitude of the wave.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A, phi, f = symbols('A, phi, f')
>>> w = TWave(A, f, phi)
>>> w.amplitude
A
"""
return self._amplitude
@property
def phase(self):
"""
Returns the phase angle of the wave.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A, phi, f = symbols('A, phi, f')
>>> w = TWave(A, f, phi)
>>> w.phase
phi
"""
return self._phase
@property
def speed(self):
"""
Returns the speed of travelling wave.
It is medium dependent.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A, phi, f = symbols('A, phi, f')
>>> w = TWave(A, f, phi)
>>> w.speed
299792458*meter/(second*n)
"""
return self.wavelength*self._frequency
@property
def angular_velocity(self):
"""
Returns angular velocity of the wave.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A, phi, f = symbols('A, phi, f')
>>> w = TWave(A, f, phi)
>>> w.angular_velocity
2*pi*f
"""
return 2*pi*self._frequency
@property
def wavenumber(self):
"""
Returns wavenumber of the wave.
Examples
========
>>> from sympy import symbols
>>> from sympy.physics.optics import TWave
>>> A, phi, f = symbols('A, phi, f')
>>> w = TWave(A, f, phi)
>>> w.wavenumber
pi*second*f*n/(149896229*meter)
"""
return 2*pi/self.wavelength
def __str__(self):
"""String representation of a TWave."""
from sympy.printing import sstr
return type(self).__name__ + sstr(self.args)
__repr__ = __str__
def __add__(self, other):
"""
Addition of two waves will result in their superposition.
The type of interference will depend on their phase angles.
"""
if isinstance(other, TWave):
if self._frequency == other._frequency and self.wavelength == other.wavelength:
return TWave(sqrt(self._amplitude**2 + other._amplitude**2 + 2 *
self.amplitude*other.amplitude*cos(
self._phase - other.phase)),
self.frequency,
atan2(self._amplitude*cos(self._phase)
+other._amplitude*cos(other._phase),
self._amplitude*sin(self._phase)
+other._amplitude*sin(other._phase))
)
else:
raise NotImplementedError("Interference of waves with different frequencies"
" has not been implemented.")
else:
raise TypeError(type(other).__name__ + " and TWave objects can't be added.")
def _eval_rewrite_as_sin(self, *args):
return self._amplitude*sin(self.wavenumber*Symbol('x')
- self.angular_velocity*Symbol('t') + self._phase + pi/2, evaluate=False)
def _eval_rewrite_as_cos(self, *args):
return self._amplitude*cos(self.wavenumber*Symbol('x')
- self.angular_velocity*Symbol('t') + self._phase)
def _eval_rewrite_as_pde(self, *args):
from sympy import Function
mu, epsilon, x, t = symbols('mu, epsilon, x, t')
E = Function('E')
return Derivative(E(x, t), x, 2) + mu*epsilon*Derivative(E(x, t), t, 2)
def _eval_rewrite_as_exp(self, *args):
from sympy import exp, I
return self._amplitude*exp(I*(self.wavenumber*Symbol('x')
- self.angular_velocity*Symbol('t') + self._phase))
| 8,540 | 28.25 | 92 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/utils.py
|
"""
**Contains**
* refraction_angle
* deviation
* lens_makers_formula
* mirror_formula
* lens_formula
* hyperfocal_distance
"""
from __future__ import division
__all__ = ['refraction_angle',
'deviation',
'lens_makers_formula',
'mirror_formula',
'lens_formula',
'hyperfocal_distance'
]
from sympy import Symbol, sympify, sqrt, Matrix, acos, oo, Limit
from sympy.core.compatibility import is_sequence
from sympy.geometry.line import Ray3D
from sympy.geometry.util import intersection
from sympy.geometry.plane import Plane
from .medium import Medium
def refraction_angle(incident, medium1, medium2, normal=None, plane=None):
"""
This function calculates transmitted vector after refraction at planar
surface. `medium1` and `medium2` can be `Medium` or any sympifiable object.
If `incident` is an object of `Ray3D`, `normal` also has to be an instance
of `Ray3D` in order to get the output as a `Ray3D`. Please note that if
plane of separation is not provided and normal is an instance of `Ray3D`,
normal will be assumed to be intersecting incident ray at the plane of
separation. This will not be the case when `normal` is a `Matrix` or
any other sequence.
If `incident` is an instance of `Ray3D` and `plane` has not been provided
and `normal` is not `Ray3D`, output will be a `Matrix`.
Parameters
==========
incident : Matrix, Ray3D, or sequence
Incident vector
medium1 : sympy.physics.optics.medium.Medium or sympifiable
Medium 1 or its refractive index
medium2 : sympy.physics.optics.medium.Medium or sympifiable
Medium 2 or its refractive index
normal : Matrix, Ray3D, or sequence
Normal vector
plane : Plane
Plane of separation of the two media.
Examples
========
>>> from sympy.physics.optics import refraction_angle
>>> from sympy.geometry import Point3D, Ray3D, Plane
>>> from sympy.matrices import Matrix
>>> from sympy import symbols
>>> n = Matrix([0, 0, 1])
>>> P = Plane(Point3D(0, 0, 0), normal_vector=[0, 0, 1])
>>> r1 = Ray3D(Point3D(-1, -1, 1), Point3D(0, 0, 0))
>>> refraction_angle(r1, 1, 1, n)
Matrix([
[ 1],
[ 1],
[-1]])
>>> refraction_angle(r1, 1, 1, plane=P)
Ray3D(Point3D(0, 0, 0), Point3D(1, 1, -1))
With different index of refraction of the two media
>>> n1, n2 = symbols('n1, n2')
>>> refraction_angle(r1, n1, n2, n)
Matrix([
[ n1/n2],
[ n1/n2],
[-sqrt(3)*sqrt(-2*n1**2/(3*n2**2) + 1)]])
>>> refraction_angle(r1, n1, n2, plane=P)
Ray3D(Point3D(0, 0, 0), Point3D(n1/n2, n1/n2, -sqrt(3)*sqrt(-2*n1**2/(3*n2**2) + 1)))
"""
# A flag to check whether to return Ray3D or not
return_ray = False
if plane is not None and normal is not None:
raise ValueError("Either plane or normal is acceptable.")
if not isinstance(incident, Matrix):
if is_sequence(incident):
_incident = Matrix(incident)
elif isinstance(incident, Ray3D):
_incident = Matrix(incident.direction_ratio)
else:
raise TypeError(
"incident should be a Matrix, Ray3D, or sequence")
else:
_incident = incident
# If plane is provided, get direction ratios of the normal
# to the plane from the plane else go with `normal` param.
if plane is not None:
if not isinstance(plane, Plane):
raise TypeError("plane should be an instance of geometry.plane.Plane")
# If we have the plane, we can get the intersection
# point of incident ray and the plane and thus return
# an instance of Ray3D.
if isinstance(incident, Ray3D):
return_ray = True
intersection_pt = plane.intersection(incident)[0]
_normal = Matrix(plane.normal_vector)
else:
if not isinstance(normal, Matrix):
if is_sequence(normal):
_normal = Matrix(normal)
elif isinstance(normal, Ray3D):
_normal = Matrix(normal.direction_ratio)
if isinstance(incident, Ray3D):
intersection_pt = intersection(incident, normal)
if len(intersection_pt) == 0:
raise ValueError(
"Normal isn't concurrent with the incident ray.")
else:
return_ray = True
intersection_pt = intersection_pt[0]
else:
raise TypeError(
"Normal should be a Matrix, Ray3D, or sequence")
else:
_normal = normal
n1, n2 = None, None
if isinstance(medium1, Medium):
n1 = medium1.refractive_index
else:
n1 = sympify(medium1)
if isinstance(medium2, Medium):
n2 = medium2.refractive_index
else:
n2 = sympify(medium2)
eta = n1/n2 # Relative index of refraction
# Calculating magnitude of the vectors
mag_incident = sqrt(sum([i**2 for i in _incident]))
mag_normal = sqrt(sum([i**2 for i in _normal]))
# Converting vectors to unit vectors by dividing
# them with their magnitudes
_incident /= mag_incident
_normal /= mag_normal
c1 = -_incident.dot(_normal) # cos(angle_of_incidence)
cs2 = 1 - eta**2*(1 - c1**2) # cos(angle_of_refraction)**2
if cs2.is_negative: # This is the case of total internal reflection(TIR).
return 0
drs = eta*_incident + (eta*c1 - sqrt(cs2))*_normal
# Multiplying unit vector by its magnitude
drs = drs*mag_incident
if not return_ray:
return drs
else:
return Ray3D(intersection_pt, direction_ratio=drs)
def deviation(incident, medium1, medium2, normal=None, plane=None):
"""
This function calculates the angle of deviation of a ray
due to refraction at planar surface.
Parameters
==========
incident : Matrix, Ray3D, or sequence
Incident vector
medium1 : sympy.physics.optics.medium.Medium or sympifiable
Medium 1 or its refractive index
medium2 : sympy.physics.optics.medium.Medium or sympifiable
Medium 2 or its refractive index
normal : Matrix, Ray3D, or sequence
Normal vector
plane : Plane
Plane of separation of the two media.
Examples
========
>>> from sympy.physics.optics import deviation
>>> from sympy.geometry import Point3D, Ray3D, Plane
>>> from sympy.matrices import Matrix
>>> from sympy import symbols
>>> n1, n2 = symbols('n1, n2')
>>> n = Matrix([0, 0, 1])
>>> P = Plane(Point3D(0, 0, 0), normal_vector=[0, 0, 1])
>>> r1 = Ray3D(Point3D(-1, -1, 1), Point3D(0, 0, 0))
>>> deviation(r1, 1, 1, n)
0
>>> deviation(r1, n1, n2, plane=P)
-acos(-sqrt(-2*n1**2/(3*n2**2) + 1)) + acos(-sqrt(3)/3)
"""
refracted = refraction_angle(incident,
medium1,
medium2,
normal=normal,
plane=plane)
if refracted != 0:
if isinstance(refracted, Ray3D):
refracted = Matrix(refracted.direction_ratio)
if not isinstance(incident, Matrix):
if is_sequence(incident):
_incident = Matrix(incident)
elif isinstance(incident, Ray3D):
_incident = Matrix(incident.direction_ratio)
else:
raise TypeError(
"incident should be a Matrix, Ray3D, or sequence")
else:
_incident = incident
if plane is None:
if not isinstance(normal, Matrix):
if is_sequence(normal):
_normal = Matrix(normal)
elif isinstance(normal, Ray3D):
_normal = Matrix(normal.direction_ratio)
else:
raise TypeError(
"normal should be a Matrix, Ray3D, or sequence")
else:
_normal = normal
else:
_normal = Matrix(plane.normal_vector)
mag_incident = sqrt(sum([i**2 for i in _incident]))
mag_normal = sqrt(sum([i**2 for i in _normal]))
mag_refracted = sqrt(sum([i**2 for i in refracted]))
_incident /= mag_incident
_normal /= mag_normal
refracted /= mag_refracted
i = acos(_incident.dot(_normal))
r = acos(refracted.dot(_normal))
return i - r
def lens_makers_formula(n_lens, n_surr, r1, r2):
"""
This function calculates focal length of a thin lens.
It follows cartesian sign convention.
Parameters
==========
n_lens : Medium or sympifiable
Index of refraction of lens.
n_surr : Medium or sympifiable
Index of reflection of surrounding.
r1 : sympifiable
Radius of curvature of first surface.
r2 : sympifiable
Radius of curvature of second surface.
Examples
========
>>> from sympy.physics.optics import lens_makers_formula
>>> lens_makers_formula(1.33, 1, 10, -10)
15.1515151515151
"""
if isinstance(n_lens, Medium):
n_lens = n_lens.refractive_index
else:
n_lens = sympify(n_lens)
if isinstance(n_surr, Medium):
n_surr = n_surr.refractive_index
else:
n_surr = sympify(n_surr)
r1 = sympify(r1)
r2 = sympify(r2)
return 1/((n_lens - n_surr)/n_surr*(1/r1 - 1/r2))
def mirror_formula(focal_length=None, u=None, v=None):
"""
This function provides one of the three parameters
when two of them are supplied.
This is valid only for paraxial rays.
Parameters
==========
focal_length : sympifiable
Focal length of the mirror.
u : sympifiable
Distance of object from the pole on
the principal axis.
v : sympifiable
Distance of the image from the pole
on the principal axis.
Examples
========
>>> from sympy.physics.optics import mirror_formula
>>> from sympy.abc import f, u, v
>>> mirror_formula(focal_length=f, u=u)
f*u/(-f + u)
>>> mirror_formula(focal_length=f, v=v)
f*v/(-f + v)
>>> mirror_formula(u=u, v=v)
u*v/(u + v)
"""
if focal_length and u and v:
raise ValueError("Please provide only two parameters")
focal_length = sympify(focal_length)
u = sympify(u)
v = sympify(v)
if u == oo:
_u = Symbol('u')
if v == oo:
_v = Symbol('v')
if focal_length == oo:
_f = Symbol('f')
if focal_length is None:
if u == oo and v == oo:
return Limit(Limit(_v*_u/(_v + _u), _u, oo), _v, oo).doit()
if u == oo:
return Limit(v*_u/(v + _u), _u, oo).doit()
if v == oo:
return Limit(_v*u/(_v + u), _v, oo).doit()
return v*u/(v + u)
if u is None:
if v == oo and focal_length == oo:
return Limit(Limit(_v*_f/(_v - _f), _v, oo), _f, oo).doit()
if v == oo:
return Limit(_v*focal_length/(_v - focal_length), _v, oo).doit()
if focal_length == oo:
return Limit(v*_f/(v - _f), _f, oo).doit()
return v*focal_length/(v - focal_length)
if v is None:
if u == oo and focal_length == oo:
return Limit(Limit(_u*_f/(_u - _f), _u, oo), _f, oo).doit()
if u == oo:
return Limit(_u*focal_length/(_u - focal_length), _u, oo).doit()
if focal_length == oo:
return Limit(u*_f/(u - _f), _f, oo).doit()
return u*focal_length/(u - focal_length)
def lens_formula(focal_length=None, u=None, v=None):
"""
This function provides one of the three parameters
when two of them are supplied.
This is valid only for paraxial rays.
Parameters
==========
focal_length : sympifiable
Focal length of the mirror.
u : sympifiable
Distance of object from the optical center on
the principal axis.
v : sympifiable
Distance of the image from the optical center
on the principal axis.
Examples
========
>>> from sympy.physics.optics import lens_formula
>>> from sympy.abc import f, u, v
>>> lens_formula(focal_length=f, u=u)
f*u/(f + u)
>>> lens_formula(focal_length=f, v=v)
f*v/(f - v)
>>> lens_formula(u=u, v=v)
u*v/(u - v)
"""
if focal_length and u and v:
raise ValueError("Please provide only two parameters")
focal_length = sympify(focal_length)
u = sympify(u)
v = sympify(v)
if u == oo:
_u = Symbol('u')
if v == oo:
_v = Symbol('v')
if focal_length == oo:
_f = Symbol('f')
if focal_length is None:
if u == oo and v == oo:
return Limit(Limit(_v*_u/(_u - _v), _u, oo), _v, oo).doit()
if u == oo:
return Limit(v*_u/(_u - v), _u, oo).doit()
if v == oo:
return Limit(_v*u/(u - _v), _v, oo).doit()
return v*u/(u - v)
if u is None:
if v == oo and focal_length == oo:
return Limit(Limit(_v*_f/(_f - _v), _v, oo), _f, oo).doit()
if v == oo:
return Limit(_v*focal_length/(focal_length - _v), _v, oo).doit()
if focal_length == oo:
return Limit(v*_f/(_f - v), _f, oo).doit()
return v*focal_length/(focal_length - v)
if v is None:
if u == oo and focal_length == oo:
return Limit(Limit(_u*_f/(_u + _f), _u, oo), _f, oo).doit()
if u == oo:
return Limit(_u*focal_length/(_u + focal_length), _u, oo).doit()
if focal_length == oo:
return Limit(u*_f/(u + _f), _f, oo).doit()
return u*focal_length/(u + focal_length)
def hyperfocal_distance(f, N, c):
"""
Parameters
==========
f: sympifiable
Focal length of a given lens
N: sympifiable
F-number of a given lens
c: sympifiable
Circle of Confusion (CoC) of a given image format
Example
=======
>>> from sympy.physics.optics import hyperfocal_distance
>>> from sympy.abc import f, N, c
>>> round(hyperfocal_distance(f = 0.5, N = 8, c = 0.0033), 2)
9.47
"""
f = sympify(f)
N = sympify(N)
c = sympify(c)
return (1/(N * c))*(f**2)
| 14,434 | 30.725275 | 89 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/__init__.py
|
__all__ = []
# The following pattern is used below for importing sub-modules:
#
# 1. "from foo import *". This imports all the names from foo.__all__ into
# this module. But, this does not put those names into the __all__ of
# this module. This enables "from sympy.physics.optics import TWave" to
# work.
# 2. "import foo; __all__.extend(foo.__all__)". This adds all the names in
# foo.__all__ to the __all__ of this module. The names in __all__
# determine which names are imported when
# "from sympy.physics.optics import *" is done.
from . import waves
from .waves import TWave
__all__.extend(waves.__all__)
from . import gaussopt
from .gaussopt import (RayTransferMatrix, FreeSpace, FlatRefraction,
CurvedRefraction, FlatMirror, CurvedMirror, ThinLens, GeometricRay,
BeamParameter, waist2rayleigh, rayleigh2waist, geometric_conj_ab,
geometric_conj_af, geometric_conj_bf, gaussian_conj, conjugate_gauss_beams)
__all__.extend(gaussopt.__all__)
from . import medium
from .medium import Medium
__all__.extend(medium.__all__)
from . import utils
from .utils import (refraction_angle, deviation, lens_makers_formula,
mirror_formula, lens_formula, hyperfocal_distance)
__all__.extend(utils.__all__)
| 1,239 | 33.444444 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/medium.py
|
"""
**Contains**
* Medium
"""
from __future__ import division
from sympy.physics.units import second, meter, kilogram, ampere
__all__ = ['Medium']
from sympy import Symbol, sympify, sqrt
from sympy.physics.units import speed_of_light, u0, e0
c = speed_of_light.convert_to(meter/second)
_e0mksa = e0.convert_to(ampere**2*second**4/(kilogram*meter**3))
_u0mksa = u0.convert_to(meter*kilogram/(ampere**2*second**2))
class Medium(Symbol):
"""
This class represents an optical medium. The prime reason to implement this is
to facilitate refraction, Fermat's priciple, etc.
An optical medium is a material through which electromagnetic waves propagate.
The permittivity and permeability of the medium define how electromagnetic
waves propagate in it.
Parameters
==========
name: string
The display name of the Medium.
permittivity: Sympifyable
Electric permittivity of the space.
permeability: Sympifyable
Magnetic permeability of the space.
n: Sympifyable
Index of refraction of the medium.
Examples
========
>>> from sympy.abc import epsilon, mu
>>> from sympy.physics.optics import Medium
>>> m1 = Medium('m1')
>>> m2 = Medium('m2', epsilon, mu)
>>> m1.intrinsic_impedance
149896229*pi*kilogram*meter**2/(1250000*ampere**2*second**3)
>>> m2.refractive_index
299792458*meter*sqrt(epsilon*mu)/second
References
==========
.. [1] http://en.wikipedia.org/wiki/Optical_medium
"""
def __new__(cls, name, permittivity=None, permeability=None, n=None):
obj = super(Medium, cls).__new__(cls, name)
obj._permittivity = sympify(permittivity)
obj._permeability = sympify(permeability)
obj._n = sympify(n)
if n is not None:
if permittivity != None and permeability == None:
obj._permeability = n**2/(c**2*obj._permittivity)
if permeability != None and permittivity == None:
obj._permittivity = n**2/(c**2*obj._permeability)
if permittivity != None and permittivity != None:
if abs(n - c*sqrt(obj._permittivity*obj._permeability)) > 1e-6:
raise ValueError("Values are not consistent.")
elif permittivity is not None and permeability is not None:
obj._n = c*sqrt(permittivity*permeability)
elif permittivity is None and permeability is None:
obj._permittivity = _e0mksa
obj._permeability = _u0mksa
return obj
@property
def intrinsic_impedance(self):
"""
Returns intrinsic impedance of the medium.
The intrinsic impedance of a medium is the ratio of the
transverse components of the electric and magnetic fields
of the electromagnetic wave travelling in the medium.
In a region with no electrical conductivity it simplifies
to the square root of ratio of magnetic permeability to
electric permittivity.
Examples
========
>>> from sympy.physics.optics import Medium
>>> m = Medium('m')
>>> m.intrinsic_impedance
149896229*pi*kilogram*meter**2/(1250000*ampere**2*second**3)
"""
return sqrt(self._permeability/self._permittivity)
@property
def speed(self):
"""
Returns speed of the electromagnetic wave travelling in the medium.
Examples
========
>>> from sympy.physics.optics import Medium
>>> m = Medium('m')
>>> m.speed
299792458*meter/second
"""
return 1/sqrt(self._permittivity*self._permeability)
@property
def refractive_index(self):
"""
Returns refractive index of the medium.
Examples
========
>>> from sympy.physics.optics import Medium
>>> m = Medium('m')
>>> m.refractive_index
1
"""
return (c/self.speed)
@property
def permittivity(self):
"""
Returns electric permittivity of the medium.
Examples
========
>>> from sympy.physics.optics import Medium
>>> m = Medium('m')
>>> m.permittivity
625000*ampere**2*second**4/(22468879468420441*pi*kilogram*meter**3)
"""
return self._permittivity
@property
def permeability(self):
"""
Returns magnetic permeability of the medium.
Examples
========
>>> from sympy.physics.optics import Medium
>>> m = Medium('m')
>>> m.permeability
pi*kilogram*meter/(2500000*ampere**2*second**2)
"""
return self._permeability
def __str__(self):
from sympy.printing import sstr
return type(self).__name__ + sstr(self.args)
def __lt__(self, other):
"""
Compares based on refractive index of the medium.
"""
return self.refractive_index < other.refractive_index
def __gt__(self, other):
return not self.__lt__(other)
def __eq__(self, other):
return self.refractive_index == other.refractive_index
def __ne__(self, other):
return not self.__eq__(other)
| 5,236 | 26.134715 | 82 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/tests/test_waves.py
|
from sympy import (symbols, Symbol, pi, sqrt, cos, sin, Derivative,
Function, simplify, I, atan2)
from sympy.abc import epsilon, mu
from sympy.functions.elementary.exponential import exp
from sympy.physics.units import speed_of_light, m, s
from sympy.physics.optics import TWave
c = speed_of_light.convert_to(m/s)
def test_twave():
A1, phi1, A2, phi2, f = symbols('A1, phi1, A2, phi2, f')
n = Symbol('n') # Refractive index
t = Symbol('t') # Time
x = Symbol('x') # Spatial varaible
k = Symbol('k') # Wave number
E = Function('E')
w1 = TWave(A1, f, phi1)
w2 = TWave(A2, f, phi2)
assert w1.amplitude == A1
assert w1.frequency == f
assert w1.phase == phi1
assert w1.wavelength == c/(f*n)
assert w1.time_period == 1/f
w3 = w1 + w2
assert w3.amplitude == sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2) + A2**2)
assert w3.frequency == f
assert w3.wavelength == c/(f*n)
assert w3.time_period == 1/f
assert w3.angular_velocity == 2*pi*f
assert w3.wavenumber == 2*pi*f*n/c
assert simplify(w3.rewrite('sin') - sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2)
+ A2**2)*sin(pi*f*n*x*s/(149896229*m) - 2*pi*f*t + atan2(A1*cos(phi1)
+ A2*cos(phi2), A1*sin(phi1) + A2*sin(phi2)) + pi/2)) == 0
assert w3.rewrite('pde') == epsilon*mu*Derivative(E(x, t), t, t) + Derivative(E(x, t), x, x)
assert w3.rewrite(cos) == sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2)
+ A2**2)*cos(pi*f*n*x*s/(149896229*m) - 2*pi*f*t + atan2(A1*cos(phi1)
+ A2*cos(phi2), A1*sin(phi1) + A2*sin(phi2)))
assert w3.rewrite('exp') == sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2)
+ A2**2)*exp(I*(pi*f*n*x*s/(149896229*m) - 2*pi*f*t
+ atan2(A1*cos(phi1) + A2*cos(phi2), A1*sin(phi1) + A2*sin(phi2))))
| 1,749 | 39.697674 | 96 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/tests/test_gaussopt.py
|
from sympy import atan2, factor, Float, I, Matrix, N, oo, pi, sqrt, symbols
from sympy.physics.optics import (BeamParameter, CurvedMirror,
CurvedRefraction, FlatMirror, FlatRefraction, FreeSpace, GeometricRay,
RayTransferMatrix, ThinLens, conjugate_gauss_beams,
gaussian_conj, geometric_conj_ab, geometric_conj_af, geometric_conj_bf,
rayleigh2waist, waist2rayleigh)
def streq(a, b):
return str(a) == str(b)
def test_gauss_opt():
mat = RayTransferMatrix(1, 2, 3, 4)
assert mat == Matrix([[1, 2], [3, 4]])
assert mat == RayTransferMatrix( Matrix([[1, 2], [3, 4]]) )
assert [mat.A, mat.B, mat.C, mat.D] == [1, 2, 3, 4]
d, f, h, n1, n2, R = symbols('d f h n1 n2 R')
lens = ThinLens(f)
assert lens == Matrix([[ 1, 0], [-1/f, 1]])
assert lens.C == -1/f
assert FreeSpace(d) == Matrix([[ 1, d], [0, 1]])
assert FlatRefraction(n1, n2) == Matrix([[1, 0], [0, n1/n2]])
assert CurvedRefraction(
R, n1, n2) == Matrix([[1, 0], [(n1 - n2)/(R*n2), n1/n2]])
assert FlatMirror() == Matrix([[1, 0], [0, 1]])
assert CurvedMirror(R) == Matrix([[ 1, 0], [-2/R, 1]])
assert ThinLens(f) == Matrix([[ 1, 0], [-1/f, 1]])
mul = CurvedMirror(R)*FreeSpace(d)
mul_mat = Matrix([[ 1, 0], [-2/R, 1]])*Matrix([[ 1, d], [0, 1]])
assert mul.A == mul_mat[0, 0]
assert mul.B == mul_mat[0, 1]
assert mul.C == mul_mat[1, 0]
assert mul.D == mul_mat[1, 1]
angle = symbols('angle')
assert GeometricRay(h, angle) == Matrix([[ h], [angle]])
assert FreeSpace(
d)*GeometricRay(h, angle) == Matrix([[angle*d + h], [angle]])
assert GeometricRay( Matrix( ((h,), (angle,)) ) ) == Matrix([[h], [angle]])
assert (FreeSpace(d)*GeometricRay(h, angle)).height == angle*d + h
assert (FreeSpace(d)*GeometricRay(h, angle)).angle == angle
p = BeamParameter(530e-9, 1, w=1e-3)
assert streq(p.q, 1 + 1.88679245283019*I*pi)
assert streq(N(p.q), 1.0 + 5.92753330865999*I)
assert streq(N(p.w_0), Float(0.00100000000000000))
assert streq(N(p.z_r), Float(5.92753330865999))
fs = FreeSpace(10)
p1 = fs*p
assert streq(N(p.w), Float(0.00101413072159615))
assert streq(N(p1.w), Float(0.00210803120913829))
w, wavelen = symbols('w wavelen')
assert waist2rayleigh(w, wavelen) == pi*w**2/wavelen
z_r, wavelen = symbols('z_r wavelen')
assert rayleigh2waist(z_r, wavelen) == sqrt(wavelen*z_r)/sqrt(pi)
a, b, f = symbols('a b f')
assert geometric_conj_ab(a, b) == a*b/(a + b)
assert geometric_conj_af(a, f) == a*f/(a - f)
assert geometric_conj_bf(b, f) == b*f/(b - f)
assert geometric_conj_ab(oo, b) == b
assert geometric_conj_ab(a, oo) == a
s_in, z_r_in, f = symbols('s_in z_r_in f')
assert gaussian_conj(
s_in, z_r_in, f)[0] == 1/(-1/(s_in + z_r_in**2/(-f + s_in)) + 1/f)
assert gaussian_conj(
s_in, z_r_in, f)[1] == z_r_in/(1 - s_in**2/f**2 + z_r_in**2/f**2)
assert gaussian_conj(
s_in, z_r_in, f)[2] == 1/sqrt(1 - s_in**2/f**2 + z_r_in**2/f**2)
l, w_i, w_o, f = symbols('l w_i w_o f')
assert conjugate_gauss_beams(l, w_i, w_o, f=f)[0] == f*(
-sqrt(w_i**2/w_o**2 - pi**2*w_i**4/(f**2*l**2)) + 1)
assert factor(conjugate_gauss_beams(l, w_i, w_o, f=f)[1]) == f*w_o**2*(
w_i**2/w_o**2 - sqrt(w_i**2/w_o**2 - pi**2*w_i**4/(f**2*l**2)))/w_i**2
assert conjugate_gauss_beams(l, w_i, w_o, f=f)[2] == f
z, l, w = symbols('z l r', positive=True)
p = BeamParameter(l, z, w=w)
assert p.radius == z*(pi**2*w**4/(l**2*z**2) + 1)
assert p.w == w*sqrt(l**2*z**2/(pi**2*w**4) + 1)
assert p.w_0 == w
assert p.divergence == l/(pi*w)
assert p.gouy == atan2(z, pi*w**2/l)
assert p.waist_approximation_limit == 2*l/pi
| 3,760 | 39.880435 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/tests/test_medium.py
|
from __future__ import division
from sympy import sqrt, simplify
from sympy.physics.optics import Medium
from sympy.abc import epsilon, mu
from sympy.physics.units import speed_of_light, u0, e0, m, kg, s, A
c = speed_of_light.convert_to(m/s)
e0 = e0.convert_to(A**2*s**4/(kg*m**3))
u0 = u0.convert_to(m*kg/(A**2*s**2))
def test_medium():
m1 = Medium('m1')
assert m1.intrinsic_impedance == sqrt(u0/e0)
assert m1.speed == 1/sqrt(e0*u0)
assert m1.refractive_index == c*sqrt(e0*u0)
assert m1.permittivity == e0
assert m1.permeability == u0
m2 = Medium('m2', epsilon, mu)
assert m2.intrinsic_impedance == sqrt(mu/epsilon)
assert m2.speed == 1/sqrt(epsilon*mu)
assert m2.refractive_index == c*sqrt(epsilon*mu)
assert m2.permittivity == epsilon
assert m2.permeability == mu
# Increasing electric permittivity and magnetic permeability
# by small amount from its value in vacuum.
m3 = Medium('m3', 9.0*10**(-12)*s**4*A**2/(m**3*kg), 1.45*10**(-6)*kg*m/(A**2*s**2))
assert m3.refractive_index > m1.refractive_index
assert m3 > m1
# Decreasing electric permittivity and magnetic permeability
# by small amount from its value in vacuum.
m4 = Medium('m4', 7.0*10**(-12)*s**4*A**2/(m**3*kg), 1.15*10**(-6)*kg*m/(A**2*s**2))
assert m4.refractive_index < m1.refractive_index
assert m4 < m1
m5 = Medium('m5', permittivity=710*10**(-12)*s**4*A**2/(m**3*kg), n=1.33)
assert abs(m5.intrinsic_impedance - 6.24845417765552*kg*m**2/(A**2*s**3)) \
< 1e-12*kg*m**2/(A**2*s**3)
assert abs(m5.speed - 225407863.157895*m/s) < 1e-6*m/s
assert abs(m5.refractive_index - 1.33000000000000) < 1e-12
assert abs(m5.permittivity - 7.1e-10*A**2*s**4/(kg*m**3)) \
< 1e-20*A**2*s**4/(kg*m**3)
assert abs(m5.permeability - 2.77206575232851e-8*kg*m/(A**2*s**2)) \
< 1e-20*kg*m/(A**2*s**2)
| 1,914 | 40.630435 | 88 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/optics/tests/test_utils.py
|
from __future__ import division
from sympy.physics.optics.utils import (refraction_angle, deviation,
lens_makers_formula, mirror_formula, lens_formula,
hyperfocal_distance)
from sympy.physics.optics.medium import Medium
from sympy.physics.units import e0
from sympy import symbols, sqrt, Matrix, oo
from sympy.geometry.point import Point3D
from sympy.geometry.line import Ray3D
from sympy.geometry.plane import Plane
def test_refraction_angle():
n1, n2 = symbols('n1, n2')
m1 = Medium('m1')
m2 = Medium('m2')
r1 = Ray3D(Point3D(-1, -1, 1), Point3D(0, 0, 0))
i = Matrix([1, 1, 1])
n = Matrix([0, 0, 1])
normal_ray = Ray3D(Point3D(0, 0, 0), Point3D(0, 0, 1))
P = Plane(Point3D(0, 0, 0), normal_vector=[0, 0, 1])
assert refraction_angle(r1, 1, 1, n) == Matrix([
[ 1],
[ 1],
[-1]])
assert refraction_angle([1, 1, 1], 1, 1, n) == Matrix([
[ 1],
[ 1],
[-1]])
assert refraction_angle((1, 1, 1), 1, 1, n) == Matrix([
[ 1],
[ 1],
[-1]])
assert refraction_angle(i, 1, 1, [0, 0, 1]) == Matrix([
[ 1],
[ 1],
[-1]])
assert refraction_angle(i, 1, 1, (0, 0, 1)) == Matrix([
[ 1],
[ 1],
[-1]])
assert refraction_angle(i, 1, 1, normal_ray) == Matrix([
[ 1],
[ 1],
[-1]])
assert refraction_angle(i, 1, 1, plane=P) == Matrix([
[ 1],
[ 1],
[-1]])
assert refraction_angle(r1, 1, 1, plane=P) == \
Ray3D(Point3D(0, 0, 0), Point3D(1, 1, -1))
assert refraction_angle(r1, m1, 1.33, plane=P) == \
Ray3D(Point3D(0, 0, 0), Point3D(100/133, 100/133, -789378201649271*sqrt(3)/1000000000000000))
assert refraction_angle(r1, 1, m2, plane=P) == \
Ray3D(Point3D(0, 0, 0), Point3D(1, 1, -1))
assert refraction_angle(r1, n1, n2, plane=P) == \
Ray3D(Point3D(0, 0, 0), Point3D(n1/n2, n1/n2, -sqrt(3)*sqrt(-2*n1**2/(3*n2**2) + 1)))
assert refraction_angle(r1, 1.33, 1, plane=P) == 0 # TIR
assert refraction_angle(r1, 1, 1, normal_ray) == \
Ray3D(Point3D(0, 0, 0), direction_ratio=[1, 1, -1])
def test_deviation():
n1, n2 = symbols('n1, n2')
m1 = Medium('m1')
m2 = Medium('m2')
r1 = Ray3D(Point3D(-1, -1, 1), Point3D(0, 0, 0))
n = Matrix([0, 0, 1])
i = Matrix([-1, -1, -1])
normal_ray = Ray3D(Point3D(0, 0, 0), Point3D(0, 0, 1))
P = Plane(Point3D(0, 0, 0), normal_vector=[0, 0, 1])
assert deviation(r1, 1, 1, normal=n) == 0
assert deviation(r1, 1, 1, plane=P) == 0
assert deviation(r1, 1, 1.1, plane=P).evalf(3) + 0.119 < 1e-3
assert deviation(i, 1, 1.1, normal=normal_ray).evalf(3) + 0.119 < 1e-3
assert deviation(r1, 1.33, 1, plane=P) is None # TIR
assert deviation(r1, 1, 1, normal=[0, 0, 1]) == 0
assert deviation([-1, -1, -1], 1, 1, normal=[0, 0, 1]) == 0
def test_lens_makers_formula():
n1, n2 = symbols('n1, n2')
m1 = Medium('m1', permittivity=e0, n=1)
m2 = Medium('m2', permittivity=e0, n=1.33)
assert lens_makers_formula(n1, n2, 10, -10) == 5*n2/(n1 - n2)
assert round(lens_makers_formula(m1, m2, 10, -10), 2) == -20.15
assert round(lens_makers_formula(1.33, 1, 10, -10), 2) == 15.15
def test_mirror_formula():
u, v, f = symbols('u, v, f')
assert mirror_formula(focal_length=f, u=u) == f*u/(-f + u)
assert mirror_formula(focal_length=f, v=v) == f*v/(-f + v)
assert mirror_formula(u=u, v=v) == u*v/(u + v)
assert mirror_formula(u=oo, v=v) == v
assert mirror_formula(u=oo, v=oo) == oo
def test_lens_formula():
u, v, f = symbols('u, v, f')
assert lens_formula(focal_length=f, u=u) == f*u/(f + u)
assert lens_formula(focal_length=f, v=v) == f*v/(f - v)
assert lens_formula(u=u, v=v) == u*v/(u - v)
assert lens_formula(u=oo, v=v) == v
assert lens_formula(u=oo, v=oo) == oo
def test_hyperfocal_distance():
f, N, c = symbols('f, N, c')
assert hyperfocal_distance(f=f, N=N, c=c) == f**2/(N*c)
assert round(hyperfocal_distance(f=0.5, N=8, c=0.0033), 2) == 9.47
| 4,835 | 41.79646 | 101 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/hep/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/hep/gamma_matrices.py
|
"""
Module to handle gamma matrices expressed as tensor objects.
Examples
========
>>> from sympy.physics.hep.gamma_matrices import GammaMatrix as G, LorentzIndex
>>> from sympy.tensor.tensor import tensor_indices
>>> i = tensor_indices('i', LorentzIndex)
>>> G(i)
GammaMatrix(i)
Note that there is already an instance of GammaMatrixHead in four dimensions:
GammaMatrix, which is simply declare as
>>> from sympy.physics.hep.gamma_matrices import GammaMatrix
>>> from sympy.tensor.tensor import tensor_indices
>>> i = tensor_indices('i', LorentzIndex)
>>> GammaMatrix(i)
GammaMatrix(i)
To access the metric tensor
>>> LorentzIndex.metric
metric(LorentzIndex,LorentzIndex)
"""
from sympy import S, Mul, eye, trace
from sympy.tensor.tensor import TensorIndexType, TensorIndex,\
TensMul, TensAdd, tensor_mul, Tensor, tensorhead
from sympy.core.compatibility import range
# DiracSpinorIndex = TensorIndexType('DiracSpinorIndex', dim=4, dummy_fmt="S")
LorentzIndex = TensorIndexType('LorentzIndex', dim=4, dummy_fmt="L")
GammaMatrix = tensorhead("GammaMatrix", [LorentzIndex], [[1]], comm=None)
def extract_type_tens(expression, component):
"""
Extract from a ``TensExpr`` all tensors with `component`.
Returns two tensor expressions:
* the first contains all ``Tensor`` of having `component`.
* the second contains all remaining.
"""
if isinstance(expression, Tensor):
sp = [expression]
elif isinstance(expression, TensMul):
sp = expression.args
else:
raise ValueError('wrong type')
# Collect all gamma matrices of the same dimension
new_expr = S.One
residual_expr = S.One
for i in sp:
if isinstance(i, Tensor) and i.component == component:
new_expr *= i
else:
residual_expr *= i
return new_expr, residual_expr
def simplify_gamma_expression(expression):
extracted_expr, residual_expr = extract_type_tens(expression, GammaMatrix)
res_expr = _simplify_single_line(extracted_expr)
return res_expr * residual_expr
def simplify_gpgp(ex, sort=True):
"""
simplify products ``G(i)*p(-i)*G(j)*p(-j) -> p(i)*p(-i)``
Examples
========
>>> from sympy.physics.hep.gamma_matrices import GammaMatrix as G, \
LorentzIndex, simplify_gpgp
>>> from sympy.tensor.tensor import tensor_indices, tensorhead
>>> p, q = tensorhead('p, q', [LorentzIndex], [[1]])
>>> i0,i1,i2,i3,i4,i5 = tensor_indices('i0:6', LorentzIndex)
>>> ps = p(i0)*G(-i0)
>>> qs = q(i0)*G(-i0)
>>> simplify_gpgp(ps*qs*qs)
GammaMatrix(-L_0)*p(L_0)*q(L_1)*q(-L_1)
"""
def _simplify_gpgp(ex):
components = ex.components
a = []
comp_map = []
for i, comp in enumerate(components):
comp_map.extend([i]*comp.rank)
dum = [(i[0], i[1], comp_map[i[0]], comp_map[i[1]]) for i in ex.dum]
for i in range(len(components)):
if components[i] != GammaMatrix:
continue
for dx in dum:
if dx[2] == i:
p_pos1 = dx[3]
elif dx[3] == i:
p_pos1 = dx[2]
else:
continue
comp1 = components[p_pos1]
if comp1.comm == 0 and comp1.rank == 1:
a.append((i, p_pos1))
if not a:
return ex
elim = set()
tv = []
hit = True
coeff = S.One
ta = None
while hit:
hit = False
for i, ai in enumerate(a[:-1]):
if ai[0] in elim:
continue
if ai[0] != a[i + 1][0] - 1:
continue
if components[ai[1]] != components[a[i + 1][1]]:
continue
elim.add(ai[0])
elim.add(ai[1])
elim.add(a[i + 1][0])
elim.add(a[i + 1][1])
if not ta:
ta = ex.split()
mu = TensorIndex('mu', LorentzIndex)
hit = True
if i == 0:
coeff = ex.coeff
tx = components[ai[1]](mu)*components[ai[1]](-mu)
if len(a) == 2:
tx *= 4 # eye(4)
tv.append(tx)
break
if tv:
a = [x for j, x in enumerate(ta) if j not in elim]
a.extend(tv)
t = tensor_mul(*a)*coeff
# t = t.replace(lambda x: x.is_Matrix, lambda x: 1)
return t
else:
return ex
if sort:
ex = ex.sorted_components()
# this would be better off with pattern matching
while 1:
t = _simplify_gpgp(ex)
if t != ex:
ex = t
else:
return t
def gamma_trace(t):
"""
trace of a single line of gamma matrices
Examples
========
>>> from sympy.physics.hep.gamma_matrices import GammaMatrix as G, \
gamma_trace, LorentzIndex
>>> from sympy.tensor.tensor import tensor_indices, tensorhead
>>> p, q = tensorhead('p, q', [LorentzIndex], [[1]])
>>> i0,i1,i2,i3,i4,i5 = tensor_indices('i0:6', LorentzIndex)
>>> ps = p(i0)*G(-i0)
>>> qs = q(i0)*G(-i0)
>>> gamma_trace(G(i0)*G(i1))
4*metric(i0, i1)
>>> gamma_trace(ps*ps) - 4*p(i0)*p(-i0)
0
>>> gamma_trace(ps*qs + ps*ps) - 4*p(i0)*p(-i0) - 4*p(i0)*q(-i0)
0
"""
if isinstance(t, TensAdd):
res = TensAdd(*[_trace_single_line(x) for x in t.args])
return res
t = _simplify_single_line(t)
res = _trace_single_line(t)
return res
def _simplify_single_line(expression):
"""
Simplify single-line product of gamma matrices.
Examples
========
>>> from sympy.physics.hep.gamma_matrices import GammaMatrix as G, \
LorentzIndex, _simplify_single_line
>>> from sympy.tensor.tensor import tensor_indices, tensorhead
>>> p = tensorhead('p', [LorentzIndex], [[1]])
>>> i0,i1 = tensor_indices('i0:2', LorentzIndex)
>>> _simplify_single_line(G(i0)*G(i1)*p(-i1)*G(-i0)) + 2*G(i0)*p(-i0)
0
"""
t1, t2 = extract_type_tens(expression, GammaMatrix)
if t1 != 1:
t1 = kahane_simplify(t1)
res = t1*t2
return res
def _trace_single_line(t):
"""
Evaluate the trace of a single gamma matrix line inside a ``TensExpr``.
Notes
=====
If there are ``DiracSpinorIndex.auto_left`` and ``DiracSpinorIndex.auto_right``
indices trace over them; otherwise traces are not implied (explain)
Examples
========
>>> from sympy.physics.hep.gamma_matrices import GammaMatrix as G, \
LorentzIndex, _trace_single_line
>>> from sympy.tensor.tensor import tensor_indices, tensorhead
>>> p = tensorhead('p', [LorentzIndex], [[1]])
>>> i0,i1,i2,i3,i4,i5 = tensor_indices('i0:6', LorentzIndex)
>>> _trace_single_line(G(i0)*G(i1))
4*metric(i0, i1)
>>> _trace_single_line(G(i0)*p(-i0)*G(i1)*p(-i1)) - 4*p(i0)*p(-i0)
0
"""
def _trace_single_line1(t):
t = t.sorted_components()
components = t.components
ncomps = len(components)
g = LorentzIndex.metric
# gamma matirices are in a[i:j]
hit = 0
for i in range(ncomps):
if components[i] == GammaMatrix:
hit = 1
break
for j in range(i + hit, ncomps):
if components[j] != GammaMatrix:
break
else:
j = ncomps
numG = j - i
if numG == 0:
tcoeff = t.coeff
return t.nocoeff if tcoeff else t
if numG % 2 == 1:
return TensMul.from_data(S.Zero, [], [], [])
elif numG > 4:
# find the open matrix indices and connect them:
a = t.split()
ind1 = a[i].get_indices()[0]
ind2 = a[i + 1].get_indices()[0]
aa = a[:i] + a[i + 2:]
t1 = tensor_mul(*aa)*g(ind1, ind2)
t1 = t1.contract_metric(g)
args = [t1]
sign = 1
for k in range(i + 2, j):
sign = -sign
ind2 = a[k].get_indices()[0]
aa = a[:i] + a[i + 1:k] + a[k + 1:]
t2 = sign*tensor_mul(*aa)*g(ind1, ind2)
t2 = t2.contract_metric(g)
t2 = simplify_gpgp(t2, False)
args.append(t2)
t3 = TensAdd(*args)
t3 = _trace_single_line(t3)
return t3
else:
a = t.split()
t1 = _gamma_trace1(*a[i:j])
a2 = a[:i] + a[j:]
t2 = tensor_mul(*a2)
t3 = t1*t2
if not t3:
return t3
t3 = t3.contract_metric(g)
return t3
if isinstance(t, TensAdd):
a = [_trace_single_line1(x)*x.coeff for x in t.args]
return TensAdd(*a)
elif isinstance(t, (Tensor, TensMul)):
r = t.coeff*_trace_single_line1(t)
return r
else:
return trace(t)
def _gamma_trace1(*a):
gctr = 4 # FIXME specific for d=4
g = LorentzIndex.metric
if not a:
return gctr
n = len(a)
if n%2 == 1:
#return TensMul.from_data(S.Zero, [], [], [])
return S.Zero
if n == 2:
ind0 = a[0].get_indices()[0]
ind1 = a[1].get_indices()[0]
return gctr*g(ind0, ind1)
if n == 4:
ind0 = a[0].get_indices()[0]
ind1 = a[1].get_indices()[0]
ind2 = a[2].get_indices()[0]
ind3 = a[3].get_indices()[0]
return gctr*(g(ind0, ind1)*g(ind2, ind3) - \
g(ind0, ind2)*g(ind1, ind3) + g(ind0, ind3)*g(ind1, ind2))
def kahane_simplify(expression):
r"""
This function cancels contracted elements in a product of four
dimensional gamma matrices, resulting in an expression equal to the given
one, without the contracted gamma matrices.
Parameters
==========
`expression` the tensor expression containing the gamma matrices to simplify.
Notes
=====
If spinor indices are given, the matrices must be given in
the order given in the product.
Algorithm
=========
The idea behind the algorithm is to use some well-known identities,
i.e., for contractions enclosing an even number of `\gamma` matrices
`\gamma^\mu \gamma_{a_1} \cdots \gamma_{a_{2N}} \gamma_\mu = 2 (\gamma_{a_{2N}} \gamma_{a_1} \cdots \gamma_{a_{2N-1}} + \gamma_{a_{2N-1}} \cdots \gamma_{a_1} \gamma_{a_{2N}} )`
for an odd number of `\gamma` matrices
`\gamma^\mu \gamma_{a_1} \cdots \gamma_{a_{2N+1}} \gamma_\mu = -2 \gamma_{a_{2N+1}} \gamma_{a_{2N}} \cdots \gamma_{a_{1}}`
Instead of repeatedly applying these identities to cancel out all contracted indices,
it is possible to recognize the links that would result from such an operation,
the problem is thus reduced to a simple rearrangement of free gamma matrices.
Examples
========
When using, always remember that the original expression coefficient
has to be handled separately
>>> from sympy.physics.hep.gamma_matrices import GammaMatrix as G, LorentzIndex
>>> from sympy.physics.hep.gamma_matrices import kahane_simplify
>>> from sympy.tensor.tensor import tensor_indices
>>> i0, i1, i2 = tensor_indices('i0:3', LorentzIndex)
>>> ta = G(i0)*G(-i0)
>>> kahane_simplify(ta)
Matrix([
[4, 0, 0, 0],
[0, 4, 0, 0],
[0, 0, 4, 0],
[0, 0, 0, 4]])
>>> tb = G(i0)*G(i1)*G(-i0)
>>> kahane_simplify(tb)
-2*GammaMatrix(i1)
>>> t = G(i0)*G(-i0)
>>> kahane_simplify(t)
Matrix([
[4, 0, 0, 0],
[0, 4, 0, 0],
[0, 0, 4, 0],
[0, 0, 0, 4]])
>>> t = G(i0)*G(-i0)
>>> kahane_simplify(t)
Matrix([
[4, 0, 0, 0],
[0, 4, 0, 0],
[0, 0, 4, 0],
[0, 0, 0, 4]])
If there are no contractions, the same expression is returned
>>> tc = G(i0)*G(i1)
>>> kahane_simplify(tc)
GammaMatrix(i0)*GammaMatrix(i1)
References
==========
[1] Algorithm for Reducing Contracted Products of gamma Matrices,
Joseph Kahane, Journal of Mathematical Physics, Vol. 9, No. 10, October 1968.
"""
if isinstance(expression, Mul):
return expression
if isinstance(expression, TensAdd):
return TensAdd(*[kahane_simplify(arg) for arg in expression.args])
if isinstance(expression, Tensor):
return expression
assert isinstance(expression, TensMul)
gammas = expression.args
for gamma in gammas:
assert gamma.component == GammaMatrix
free = expression.free
# spinor_free = [_ for _ in expression.free_in_args if _[1] != 0]
# if len(spinor_free) == 2:
# spinor_free.sort(key=lambda x: x[2])
# assert spinor_free[0][1] == 1 and spinor_free[-1][1] == 2
# assert spinor_free[0][2] == 0
# elif spinor_free:
# raise ValueError('spinor indices do not match')
dum = []
for dum_pair in expression.dum:
if expression.index_types[dum_pair[0]] == LorentzIndex:
dum.append((dum_pair[0], dum_pair[1]))
dum = sorted(dum)
if len(dum) == 0: # or GammaMatrixHead:
# no contractions in `expression`, just return it.
return expression
# find the `first_dum_pos`, i.e. the position of the first contracted
# gamma matrix, Kahane's algorithm as described in his paper requires the
# gamma matrix expression to start with a contracted gamma matrix, this is
# a workaround which ignores possible initial free indices, and re-adds
# them later.
first_dum_pos = min(map(min, dum))
# for p1, p2, a1, a2 in expression.dum_in_args:
# if p1 != 0 or p2 != 0:
# # only Lorentz indices, skip Dirac indices:
# continue
# first_dum_pos = min(p1, p2)
# break
total_number = len(free) + len(dum)*2
number_of_contractions = len(dum)
free_pos = [None]*total_number
for i in free:
free_pos[i[1]] = i[0]
# `index_is_free` is a list of booleans, to identify index position
# and whether that index is free or dummy.
index_is_free = [False]*total_number
for i, indx in enumerate(free):
index_is_free[indx[1]] = True
# `links` is a dictionary containing the graph described in Kahane's paper,
# to every key correspond one or two values, representing the linked indices.
# All values in `links` are integers, negative numbers are used in the case
# where it is necessary to insert gamma matrices between free indices, in
# order to make Kahane's algorithm work (see paper).
links = dict()
for i in range(first_dum_pos, total_number):
links[i] = []
# `cum_sign` is a step variable to mark the sign of every index, see paper.
cum_sign = -1
# `cum_sign_list` keeps storage for all `cum_sign` (every index).
cum_sign_list = [None]*total_number
block_free_count = 0
# multiply `resulting_coeff` by the coefficient parameter, the rest
# of the algorithm ignores a scalar coefficient.
resulting_coeff = S.One
# initialize a list of lists of indices. The outer list will contain all
# additive tensor expressions, while the inner list will contain the
# free indices (rearranged according to the algorithm).
resulting_indices = [[]]
# start to count the `connected_components`, which together with the number
# of contractions, determines a -1 or +1 factor to be multiplied.
connected_components = 1
# First loop: here we fill `cum_sign_list`, and draw the links
# among consecutive indices (they are stored in `links`). Links among
# non-consecutive indices will be drawn later.
for i, is_free in enumerate(index_is_free):
# if `expression` starts with free indices, they are ignored here;
# they are later added as they are to the beginning of all
# `resulting_indices` list of lists of indices.
if i < first_dum_pos:
continue
if is_free:
block_free_count += 1
# if previous index was free as well, draw an arch in `links`.
if block_free_count > 1:
links[i - 1].append(i)
links[i].append(i - 1)
else:
# Change the sign of the index (`cum_sign`) if the number of free
# indices preceding it is even.
cum_sign *= 1 if (block_free_count % 2) else -1
if block_free_count == 0 and i != first_dum_pos:
# check if there are two consecutive dummy indices:
# in this case create virtual indices with negative position,
# these "virtual" indices represent the insertion of two
# gamma^0 matrices to separate consecutive dummy indices, as
# Kahane's algorithm requires dummy indices to be separated by
# free indices. The product of two gamma^0 matrices is unity,
# so the new expression being examined is the same as the
# original one.
if cum_sign == -1:
links[-1-i] = [-1-i+1]
links[-1-i+1] = [-1-i]
if (i - cum_sign) in links:
if i != first_dum_pos:
links[i].append(i - cum_sign)
if block_free_count != 0:
if i - cum_sign < len(index_is_free):
if index_is_free[i - cum_sign]:
links[i - cum_sign].append(i)
block_free_count = 0
cum_sign_list[i] = cum_sign
# The previous loop has only created links between consecutive free indices,
# it is necessary to properly create links among dummy (contracted) indices,
# according to the rules described in Kahane's paper. There is only one exception
# to Kahane's rules: the negative indices, which handle the case of some
# consecutive free indices (Kahane's paper just describes dummy indices
# separated by free indices, hinting that free indices can be added without
# altering the expression result).
for i in dum:
# get the positions of the two contracted indices:
pos1 = i[0]
pos2 = i[1]
# create Kahane's upper links, i.e. the upper arcs between dummy
# (i.e. contracted) indices:
links[pos1].append(pos2)
links[pos2].append(pos1)
# create Kahane's lower links, this corresponds to the arcs below
# the line described in the paper:
# first we move `pos1` and `pos2` according to the sign of the indices:
linkpos1 = pos1 + cum_sign_list[pos1]
linkpos2 = pos2 + cum_sign_list[pos2]
# otherwise, perform some checks before creating the lower arcs:
# make sure we are not exceeding the total number of indices:
if linkpos1 >= total_number:
continue
if linkpos2 >= total_number:
continue
# make sure we are not below the first dummy index in `expression`:
if linkpos1 < first_dum_pos:
continue
if linkpos2 < first_dum_pos:
continue
# check if the previous loop created "virtual" indices between dummy
# indices, in such a case relink `linkpos1` and `linkpos2`:
if (-1-linkpos1) in links:
linkpos1 = -1-linkpos1
if (-1-linkpos2) in links:
linkpos2 = -1-linkpos2
# move only if not next to free index:
if linkpos1 >= 0 and not index_is_free[linkpos1]:
linkpos1 = pos1
if linkpos2 >=0 and not index_is_free[linkpos2]:
linkpos2 = pos2
# create the lower arcs:
if linkpos2 not in links[linkpos1]:
links[linkpos1].append(linkpos2)
if linkpos1 not in links[linkpos2]:
links[linkpos2].append(linkpos1)
# This loop starts from the `first_dum_pos` index (first dummy index)
# walks through the graph deleting the visited indices from `links`,
# it adds a gamma matrix for every free index in encounters, while it
# completely ignores dummy indices and virtual indices.
pointer = first_dum_pos
previous_pointer = 0
while True:
if pointer in links:
next_ones = links.pop(pointer)
else:
break
if previous_pointer in next_ones:
next_ones.remove(previous_pointer)
previous_pointer = pointer
if next_ones:
pointer = next_ones[0]
else:
break
if pointer == previous_pointer:
break
if pointer >=0 and free_pos[pointer] is not None:
for ri in resulting_indices:
ri.append(free_pos[pointer])
# The following loop removes the remaining connected components in `links`.
# If there are free indices inside a connected component, it gives a
# contribution to the resulting expression given by the factor
# `gamma_a gamma_b ... gamma_z + gamma_z ... gamma_b gamma_a`, in Kahanes's
# paper represented as {gamma_a, gamma_b, ... , gamma_z},
# virtual indices are ignored. The variable `connected_components` is
# increased by one for every connected component this loop encounters.
# If the connected component has virtual and dummy indices only
# (no free indices), it contributes to `resulting_indices` by a factor of two.
# The multiplication by two is a result of the
# factor {gamma^0, gamma^0} = 2 I, as it appears in Kahane's paper.
# Note: curly brackets are meant as in the paper, as a generalized
# multi-element anticommutator!
while links:
connected_components += 1
pointer = min(links.keys())
previous_pointer = pointer
# the inner loop erases the visited indices from `links`, and it adds
# all free indices to `prepend_indices` list, virtual indices are
# ignored.
prepend_indices = []
while True:
if pointer in links:
next_ones = links.pop(pointer)
else:
break
if previous_pointer in next_ones:
if len(next_ones) > 1:
next_ones.remove(previous_pointer)
previous_pointer = pointer
if next_ones:
pointer = next_ones[0]
if pointer >= first_dum_pos and free_pos[pointer] is not None:
prepend_indices.insert(0, free_pos[pointer])
# if `prepend_indices` is void, it means there are no free indices
# in the loop (and it can be shown that there must be a virtual index),
# loops of virtual indices only contribute by a factor of two:
if len(prepend_indices) == 0:
resulting_coeff *= 2
# otherwise, add the free indices in `prepend_indices` to
# the `resulting_indices`:
else:
expr1 = prepend_indices
expr2 = list(reversed(prepend_indices))
resulting_indices = [expri + ri for ri in resulting_indices for expri in (expr1, expr2)]
# sign correction, as described in Kahane's paper:
resulting_coeff *= -1 if (number_of_contractions - connected_components + 1) % 2 else 1
# power of two factor, as described in Kahane's paper:
resulting_coeff *= 2**(number_of_contractions)
# If `first_dum_pos` is not zero, it means that there are trailing free gamma
# matrices in front of `expression`, so multiply by them:
for i in range(0, first_dum_pos):
[ri.insert(0, free_pos[i]) for ri in resulting_indices]
resulting_expr = S.Zero
for i in resulting_indices:
temp_expr = S.One
for j in i:
temp_expr *= GammaMatrix(j)
resulting_expr += temp_expr
t = resulting_coeff * resulting_expr
t1 = None
if isinstance(t, TensAdd):
t1 = t.args[0]
elif isinstance(t, TensMul):
t1 = t
if t1:
pass
else:
t = eye(4)*t
return t
| 24,161 | 32.74581 | 180 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/hep/tests/test_gamma_matrices.py
|
from sympy import Matrix
from sympy.tensor.tensor import tensor_indices, tensorhead, TensExpr
from sympy import eye
from sympy.physics.hep.gamma_matrices import GammaMatrix as G, LorentzIndex, \
kahane_simplify, gamma_trace, _simplify_single_line, simplify_gamma_expression
def _is_tensor_eq(arg1, arg2):
if isinstance(arg1, TensExpr):
return arg1.equals(arg2)
elif isinstance(arg2, TensExpr):
return arg2.equals(arg1)
return arg1 == arg2
def execute_gamma_simplify_tests_for_function(tfunc, D):
"""
Perform tests to check if sfunc is able to simplify gamma matrix expressions.
Parameters
==========
`sfunc` a function to simplify a `TIDS`, shall return the simplified `TIDS`.
`D` the number of dimension (in most cases `D=4`).
"""
mu, nu, rho, sigma = tensor_indices("mu, nu, rho, sigma", LorentzIndex)
a1, a2, a3, a4, a5, a6 = tensor_indices("a1:7", LorentzIndex)
mu11, mu12, mu21, mu31, mu32, mu41, mu51, mu52 = tensor_indices("mu11, mu12, mu21, mu31, mu32, mu41, mu51, mu52", LorentzIndex)
mu61, mu71, mu72 = tensor_indices("mu61, mu71, mu72", LorentzIndex)
m0, m1, m2, m3, m4, m5, m6 = tensor_indices("m0:7", LorentzIndex)
def g(xx, yy):
return (G(xx)*G(yy) + G(yy)*G(xx))/2
# Some examples taken from Kahane's paper, 4 dim only:
if D == 4:
t = (G(a1)*G(mu11)*G(a2)*G(mu21)*G(-a1)*G(mu31)*G(-a2))
assert _is_tensor_eq(tfunc(t), -4*G(mu11)*G(mu31)*G(mu21) - 4*G(mu31)*G(mu11)*G(mu21))
t = (G(a1)*G(mu11)*G(mu12)*\
G(a2)*G(mu21)*\
G(a3)*G(mu31)*G(mu32)*\
G(a4)*G(mu41)*\
G(-a2)*G(mu51)*G(mu52)*\
G(-a1)*G(mu61)*\
G(-a3)*G(mu71)*G(mu72)*\
G(-a4))
assert _is_tensor_eq(tfunc(t), \
16*G(mu31)*G(mu32)*G(mu72)*G(mu71)*G(mu11)*G(mu52)*G(mu51)*G(mu12)*G(mu61)*G(mu21)*G(mu41) + 16*G(mu31)*G(mu32)*G(mu72)*G(mu71)*G(mu12)*G(mu51)*G(mu52)*G(mu11)*G(mu61)*G(mu21)*G(mu41) + 16*G(mu71)*G(mu72)*G(mu32)*G(mu31)*G(mu11)*G(mu52)*G(mu51)*G(mu12)*G(mu61)*G(mu21)*G(mu41) + 16*G(mu71)*G(mu72)*G(mu32)*G(mu31)*G(mu12)*G(mu51)*G(mu52)*G(mu11)*G(mu61)*G(mu21)*G(mu41))
# Fully Lorentz-contracted expressions, these return scalars:
def add_delta(ne):
return ne * eye(4) # DiracSpinorIndex.delta(DiracSpinorIndex.auto_left, -DiracSpinorIndex.auto_right)
t = (G(mu)*G(-mu))
ts = add_delta(D)
assert _is_tensor_eq(tfunc(t), ts)
t = (G(mu)*G(nu)*G(-mu)*G(-nu))
ts = add_delta(2*D - D**2) # -8
assert _is_tensor_eq(tfunc(t), ts)
t = (G(mu)*G(nu)*G(-nu)*G(-mu))
ts = add_delta(D**2) # 16
assert _is_tensor_eq(tfunc(t), ts)
t = (G(mu)*G(nu)*G(-rho)*G(-nu)*G(-mu)*G(rho))
ts = add_delta(4*D - 4*D**2 + D**3) # 16
assert _is_tensor_eq(tfunc(t), ts)
t = (G(mu)*G(nu)*G(rho)*G(-rho)*G(-nu)*G(-mu))
ts = add_delta(D**3) # 64
assert _is_tensor_eq(tfunc(t), ts)
t = (G(a1)*G(a2)*G(a3)*G(a4)*G(-a3)*G(-a1)*G(-a2)*G(-a4))
ts = add_delta(-8*D + 16*D**2 - 8*D**3 + D**4) # -32
assert _is_tensor_eq(tfunc(t), ts)
t = (G(-mu)*G(-nu)*G(-rho)*G(-sigma)*G(nu)*G(mu)*G(sigma)*G(rho))
ts = add_delta(-16*D + 24*D**2 - 8*D**3 + D**4) # 64
assert _is_tensor_eq(tfunc(t), ts)
t = (G(-mu)*G(nu)*G(-rho)*G(sigma)*G(rho)*G(-nu)*G(mu)*G(-sigma))
ts = add_delta(8*D - 12*D**2 + 6*D**3 - D**4) # -32
assert _is_tensor_eq(tfunc(t), ts)
t = (G(a1)*G(a2)*G(a3)*G(a4)*G(a5)*G(-a3)*G(-a2)*G(-a1)*G(-a5)*G(-a4))
ts = add_delta(64*D - 112*D**2 + 60*D**3 - 12*D**4 + D**5) # 256
assert _is_tensor_eq(tfunc(t), ts)
t = (G(a1)*G(a2)*G(a3)*G(a4)*G(a5)*G(-a3)*G(-a1)*G(-a2)*G(-a4)*G(-a5))
ts = add_delta(64*D - 120*D**2 + 72*D**3 - 16*D**4 + D**5) # -128
assert _is_tensor_eq(tfunc(t), ts)
t = (G(a1)*G(a2)*G(a3)*G(a4)*G(a5)*G(a6)*G(-a3)*G(-a2)*G(-a1)*G(-a6)*G(-a5)*G(-a4))
ts = add_delta(416*D - 816*D**2 + 528*D**3 - 144*D**4 + 18*D**5 - D**6) # -128
assert _is_tensor_eq(tfunc(t), ts)
t = (G(a1)*G(a2)*G(a3)*G(a4)*G(a5)*G(a6)*G(-a2)*G(-a3)*G(-a1)*G(-a6)*G(-a4)*G(-a5))
ts = add_delta(416*D - 848*D**2 + 584*D**3 - 172*D**4 + 22*D**5 - D**6) # -128
assert _is_tensor_eq(tfunc(t), ts)
# Expressions with free indices:
t = (G(mu)*G(nu)*G(rho)*G(sigma)*G(-mu))
assert _is_tensor_eq(tfunc(t), (-2*G(sigma)*G(rho)*G(nu) + (4-D)*G(nu)*G(rho)*G(sigma)))
t = (G(mu)*G(nu)*G(-mu))
assert _is_tensor_eq(tfunc(t), (2-D)*G(nu))
t = (G(mu)*G(nu)*G(rho)*G(-mu))
assert _is_tensor_eq(tfunc(t), 2*G(nu)*G(rho) + 2*G(rho)*G(nu) - (4-D)*G(nu)*G(rho))
t = 2*G(m2)*G(m0)*G(m1)*G(-m0)*G(-m1)
st = tfunc(t)
assert _is_tensor_eq(st, (D*(-2*D + 4))*G(m2))
t = G(m2)*G(m0)*G(m1)*G(-m0)*G(-m2)
st = tfunc(t)
assert _is_tensor_eq(st, ((-D + 2)**2)*G(m1))
t = G(m0)*G(m1)*G(m2)*G(m3)*G(-m1)
st = tfunc(t)
assert _is_tensor_eq(st, (D - 4)*G(m0)*G(m2)*G(m3) + 4*G(m0)*g(m2, m3))
t = G(m0)*G(m1)*G(m2)*G(m3)*G(-m1)*G(-m0)
st = tfunc(t)
assert _is_tensor_eq(st, ((D - 4)**2)*G(m2)*G(m3) + (8*D - 16)*g(m2, m3))
t = G(m2)*G(m0)*G(m1)*G(-m2)*G(-m0)
st = tfunc(t)
assert _is_tensor_eq(st, ((-D + 2)*(D - 4) + 4)*G(m1))
t = G(m3)*G(m1)*G(m0)*G(m2)*G(-m3)*G(-m0)*G(-m2)
st = tfunc(t)
assert _is_tensor_eq(st, (-4*D + (-D + 2)**2*(D - 4) + 8)*G(m1))
t = 2*G(m0)*G(m1)*G(m2)*G(m3)*G(-m0)
st = tfunc(t)
assert _is_tensor_eq(st, ((-2*D + 8)*G(m1)*G(m2)*G(m3) - 4*G(m3)*G(m2)*G(m1)))
t = G(m5)*G(m0)*G(m1)*G(m4)*G(m2)*G(-m4)*G(m3)*G(-m0)
st = tfunc(t)
assert _is_tensor_eq(st, (((-D + 2)*(-D + 4))*G(m5)*G(m1)*G(m2)*G(m3) + (2*D - 4)*G(m5)*G(m3)*G(m2)*G(m1)))
t = -G(m0)*G(m1)*G(m2)*G(m3)*G(-m0)*G(m4)
st = tfunc(t)
assert _is_tensor_eq(st, ((D - 4)*G(m1)*G(m2)*G(m3)*G(m4) + 2*G(m3)*G(m2)*G(m1)*G(m4)))
t = G(-m5)*G(m0)*G(m1)*G(m2)*G(m3)*G(m4)*G(-m0)*G(m5)
st = tfunc(t)
result1 = ((-D + 4)**2 + 4)*G(m1)*G(m2)*G(m3)*G(m4) +\
(4*D - 16)*G(m3)*G(m2)*G(m1)*G(m4) + (4*D - 16)*G(m4)*G(m1)*G(m2)*G(m3)\
+ 4*G(m2)*G(m1)*G(m4)*G(m3) + 4*G(m3)*G(m4)*G(m1)*G(m2) +\
4*G(m4)*G(m3)*G(m2)*G(m1)
# Kahane's algorithm yields this result, which is equivalent to `result1`
# in four dimensions, but is not automatically recognized as equal:
result2 = 8*G(m1)*G(m2)*G(m3)*G(m4) + 8*G(m4)*G(m3)*G(m2)*G(m1)
if D == 4:
assert _is_tensor_eq(st, (result1)) or _is_tensor_eq(st, (result2))
else:
assert _is_tensor_eq(st, (result1))
# and a few very simple cases, with no contracted indices:
t = G(m0)
st = tfunc(t)
assert _is_tensor_eq(st, t)
t = -7*G(m0)
st = tfunc(t)
assert _is_tensor_eq(st, t)
t = 224*G(m0)*G(m1)*G(-m2)*G(m3)
st = tfunc(t)
assert _is_tensor_eq(st, t)
def test_kahane_algorithm():
# Wrap this function to convert to and from TIDS:
def tfunc(e):
return _simplify_single_line(e)
execute_gamma_simplify_tests_for_function(tfunc, D=4)
def test_kahane_simplify1():
i0,i1,i2,i3,i4,i5,i6,i7,i8,i9,i10,i11,i12,i13,i14,i15 = tensor_indices('i0:16', LorentzIndex)
mu, nu, rho, sigma = tensor_indices("mu, nu, rho, sigma", LorentzIndex)
D = 4
t = G(i0)*G(i1)
r = kahane_simplify(t)
assert r.equals(t)
t = G(i0)*G(i1)*G(-i0)
r = kahane_simplify(t)
assert r.equals(-2*G(i1))
t = G(i0)*G(i1)*G(-i0)
r = kahane_simplify(t)
assert r.equals(-2*G(i1))
t = G(i0)*G(i1)
r = kahane_simplify(t)
assert r.equals(t)
t = G(i0)*G(i1)
r = kahane_simplify(t)
assert r.equals(t)
t = G(i0)*G(-i0)
r = kahane_simplify(t)
assert r.equals(4*eye(4))
t = G(i0)*G(-i0)
r = kahane_simplify(t)
assert r.equals(4*eye(4))
t = G(i0)*G(-i0)
r = kahane_simplify(t)
assert r.equals(4*eye(4))
t = G(i0)*G(i1)*G(-i0)
r = kahane_simplify(t)
assert r.equals(-2*G(i1))
t = G(i0)*G(i1)*G(-i0)*G(-i1)
r = kahane_simplify(t)
assert r.equals((2*D - D**2)*eye(4))
t = G(i0)*G(i1)*G(-i0)*G(-i1)
r = kahane_simplify(t)
assert r.equals((2*D - D**2)*eye(4))
t = G(i0)*G(-i0)*G(i1)*G(-i1)
r = kahane_simplify(t)
assert r.equals(16*eye(4))
t = (G(mu)*G(nu)*G(-nu)*G(-mu))
r = kahane_simplify(t)
assert r.equals(D**2*eye(4))
t = (G(mu)*G(nu)*G(-nu)*G(-mu))
r = kahane_simplify(t)
assert r.equals(D**2*eye(4))
t = (G(mu)*G(nu)*G(-nu)*G(-mu))
r = kahane_simplify(t)
assert r.equals(D**2*eye(4))
t = (G(mu)*G(nu)*G(-rho)*G(-nu)*G(-mu)*G(rho))
r = kahane_simplify(t)
assert r.equals((4*D - 4*D**2 + D**3)*eye(4))
t = (G(-mu)*G(-nu)*G(-rho)*G(-sigma)*G(nu)*G(mu)*G(sigma)*G(rho))
r = kahane_simplify(t)
assert r.equals((-16*D + 24*D**2 - 8*D**3 + D**4)*eye(4))
t = (G(-mu)*G(nu)*G(-rho)*G(sigma)*G(rho)*G(-nu)*G(mu)*G(-sigma))
r = kahane_simplify(t)
assert r.equals((8*D - 12*D**2 + 6*D**3 - D**4)*eye(4))
# Expressions with free indices:
t = (G(mu)*G(nu)*G(rho)*G(sigma)*G(-mu))
r = kahane_simplify(t)
assert r.equals(-2*G(sigma)*G(rho)*G(nu))
t = (G(mu)*G(nu)*G(rho)*G(sigma)*G(-mu))
r = kahane_simplify(t)
assert r.equals(-2*G(sigma)*G(rho)*G(nu))
def test_gamma_matrix_class():
i, j, k = tensor_indices('i,j,k', LorentzIndex)
# define another type of TensorHead to see if exprs are correctly handled:
A = tensorhead('A', [LorentzIndex], [[1]])
t = A(k)*G(i)*G(-i)
ts = simplify_gamma_expression(t)
assert _is_tensor_eq(ts, Matrix([
[4, 0, 0, 0],
[0, 4, 0, 0],
[0, 0, 4, 0],
[0, 0, 0, 4]])*A(k))
t = G(i)*A(k)*G(j)
ts = simplify_gamma_expression(t)
assert _is_tensor_eq(ts, A(k)*G(i)*G(j))
execute_gamma_simplify_tests_for_function(simplify_gamma_expression, D=4)
def test_gamma_matrix_trace():
g = LorentzIndex.metric
m0, m1, m2, m3, m4, m5, m6 = tensor_indices('m0:7', LorentzIndex)
n0, n1, n2, n3, n4, n5 = tensor_indices('n0:6', LorentzIndex)
# working in D=4 dimensions
D = 4
# traces of odd number of gamma matrices are zero:
t = G(m0)
t1 = gamma_trace(t)
assert t1.equals(0)
t = G(m0)*G(m1)*G(m2)
t1 = gamma_trace(t)
assert t1.equals(0)
t = G(m0)*G(m1)*G(-m0)
t1 = gamma_trace(t)
assert t1.equals(0)
t = G(m0)*G(m1)*G(m2)*G(m3)*G(m4)
t1 = gamma_trace(t)
assert t1.equals(0)
# traces without internal contractions:
t = G(m0)*G(m1)
t1 = gamma_trace(t)
assert _is_tensor_eq(t1, 4*g(m0, m1))
t = G(m0)*G(m1)*G(m2)*G(m3)
t1 = gamma_trace(t)
t2 = -4*g(m0, m2)*g(m1, m3) + 4*g(m0, m1)*g(m2, m3) + 4*g(m0, m3)*g(m1, m2)
st2 = str(t2)
assert _is_tensor_eq(t1, t2)
t = G(m0)*G(m1)*G(m2)*G(m3)*G(m4)*G(m5)
t1 = gamma_trace(t)
t2 = t1*g(-m0, -m5)
t2 = t2.contract_metric(g)
assert _is_tensor_eq(t2, D*gamma_trace(G(m1)*G(m2)*G(m3)*G(m4)))
# traces of expressions with internal contractions:
t = G(m0)*G(-m0)
t1 = gamma_trace(t)
assert t1.equals(4*D)
t = G(m0)*G(m1)*G(-m0)*G(-m1)
t1 = gamma_trace(t)
assert t1.equals(8*D - 4*D**2)
t = G(m0)*G(m1)*G(m2)*G(m3)*G(m4)*G(-m0)
t1 = gamma_trace(t)
t2 = (-4*D)*g(m1, m3)*g(m2, m4) + (4*D)*g(m1, m2)*g(m3, m4) + \
(4*D)*g(m1, m4)*g(m2, m3)
assert t1.equals(t2)
t = G(-m5)*G(m0)*G(m1)*G(m2)*G(m3)*G(m4)*G(-m0)*G(m5)
t1 = gamma_trace(t)
t2 = (32*D + 4*(-D + 4)**2 - 64)*(g(m1, m2)*g(m3, m4) - \
g(m1, m3)*g(m2, m4) + g(m1, m4)*g(m2, m3))
assert t1.equals(t2)
t = G(m0)*G(m1)*G(-m0)*G(m3)
t1 = gamma_trace(t)
assert t1.equals((-4*D + 8)*g(m1, m3))
# p, q = S1('p,q')
# ps = p(m0)*G(-m0)
# qs = q(m0)*G(-m0)
# t = ps*qs*ps*qs
# t1 = gamma_trace(t)
# assert t1 == 8*p(m0)*q(-m0)*p(m1)*q(-m1) - 4*p(m0)*p(-m0)*q(m1)*q(-m1)
t = G(m0)*G(m1)*G(m2)*G(m3)*G(m4)*G(m5)*G(-m0)*G(-m1)*G(-m2)*G(-m3)*G(-m4)*G(-m5)
t1 = gamma_trace(t)
assert t1.equals(-4*D**6 + 120*D**5 - 1040*D**4 + 3360*D**3 - 4480*D**2 + 2048*D)
t = G(m0)*G(m1)*G(n1)*G(m2)*G(n2)*G(m3)*G(m4)*G(-n2)*G(-n1)*G(-m0)*G(-m1)*G(-m2)*G(-m3)*G(-m4)
t1 = gamma_trace(t)
tresu = -7168*D + 16768*D**2 - 14400*D**3 + 5920*D**4 - 1232*D**5 + 120*D**6 - 4*D**7
assert t1.equals(tresu)
# checked with Mathematica
# In[1]:= <<Tracer.m
# In[2]:= Spur[l];
# In[3]:= GammaTrace[l, {m0},{m1},{n1},{m2},{n2},{m3},{m4},{n3},{n4},{m0},{m1},{m2},{m3},{m4}]
t = G(m0)*G(m1)*G(n1)*G(m2)*G(n2)*G(m3)*G(m4)*G(n3)*G(n4)*G(-m0)*G(-m1)*G(-m2)*G(-m3)*G(-m4)
t1 = gamma_trace(t)
# t1 = t1.expand_coeff()
c1 = -4*D**5 + 120*D**4 - 1200*D**3 + 5280*D**2 - 10560*D + 7808
c2 = -4*D**5 + 88*D**4 - 560*D**3 + 1440*D**2 - 1600*D + 640
assert _is_tensor_eq(t1, c1*g(n1, n4)*g(n2, n3) + c2*g(n1, n2)*g(n3, n4) + \
(-c1)*g(n1, n3)*g(n2, n4))
p, q = tensorhead('p,q', [LorentzIndex], [[1]])
ps = p(m0)*G(-m0)
qs = q(m0)*G(-m0)
p2 = p(m0)*p(-m0)
q2 = q(m0)*q(-m0)
pq = p(m0)*q(-m0)
t = ps*qs*ps*qs
r = gamma_trace(t)
assert _is_tensor_eq(r, 8*pq*pq - 4*p2*q2)
t = ps*qs*ps*qs*ps*qs
r = gamma_trace(t)
assert r.equals(-12*p2*pq*q2 + 16*pq*pq*pq)
t = ps*qs*ps*qs*ps*qs*ps*qs
r = gamma_trace(t)
assert r.equals(-32*pq*pq*p2*q2 + 32*pq*pq*pq*pq + 4*p2*p2*q2*q2)
t = 4*p(m1)*p(m0)*p(-m0)*q(-m1)*q(m2)*q(-m2)
assert _is_tensor_eq(gamma_trace(t), t)
t = ps*ps*ps*ps*ps*ps*ps*ps
r = gamma_trace(t)
assert r.equals(4*p2*p2*p2*p2)
| 13,728 | 33.151741 | 382 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/physics/hep/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/multidimensional.py
|
"""
Provides functionality for multidimensional usage of scalar-functions.
Read the vectorize docstring for more details.
"""
from __future__ import print_function, division
from sympy.core.decorators import wraps
from sympy.core.compatibility import range
def apply_on_element(f, args, kwargs, n):
"""
Returns a structure with the same dimension as the specified argument,
where each basic element is replaced by the function f applied on it. All
other arguments stay the same.
"""
# Get the specified argument.
if isinstance(n, int):
structure = args[n]
is_arg = True
elif isinstance(n, str):
structure = kwargs[n]
is_arg = False
# Define reduced function that is only dependend of the specified argument.
def f_reduced(x):
if hasattr(x, "__iter__"):
return list(map(f_reduced, x))
else:
if is_arg:
args[n] = x
else:
kwargs[n] = x
return f(*args, **kwargs)
# f_reduced will call itself recursively so that in the end f is applied to
# all basic elements.
return list(map(f_reduced, structure))
def iter_copy(structure):
"""
Returns a copy of an iterable object (also copying all embedded iterables).
"""
l = []
for i in structure:
if hasattr(i, "__iter__"):
l.append(iter_copy(i))
else:
l.append(i)
return l
def structure_copy(structure):
"""
Returns a copy of the given structure (numpy-array, list, iterable, ..).
"""
if hasattr(structure, "copy"):
return structure.copy()
return iter_copy(structure)
class vectorize:
"""
Generalizes a function taking scalars to accept multidimensional arguments.
For example
>>> from sympy import diff, sin, symbols, Function
>>> from sympy.core.multidimensional import vectorize
>>> x, y, z = symbols('x y z')
>>> f, g, h = list(map(Function, 'fgh'))
>>> @vectorize(0)
... def vsin(x):
... return sin(x)
>>> vsin([1, x, y])
[sin(1), sin(x), sin(y)]
>>> @vectorize(0, 1)
... def vdiff(f, y):
... return diff(f, y)
>>> vdiff([f(x, y, z), g(x, y, z), h(x, y, z)], [x, y, z])
[[Derivative(f(x, y, z), x), Derivative(f(x, y, z), y), Derivative(f(x, y, z), z)], [Derivative(g(x, y, z), x), Derivative(g(x, y, z), y), Derivative(g(x, y, z), z)], [Derivative(h(x, y, z), x), Derivative(h(x, y, z), y), Derivative(h(x, y, z), z)]]
"""
def __init__(self, *mdargs):
"""
The given numbers and strings characterize the arguments that will be
treated as data structures, where the decorated function will be applied
to every single element.
If no argument is given, everything is treated multidimensional.
"""
for a in mdargs:
if not isinstance(a, (int, str)):
raise TypeError("a is of invalid type")
self.mdargs = mdargs
def __call__(self, f):
"""
Returns a wrapper for the one-dimensional function that can handle
multidimensional arguments.
"""
@wraps(f)
def wrapper(*args, **kwargs):
# Get arguments that should be treated multidimensional
if self.mdargs:
mdargs = self.mdargs
else:
mdargs = range(len(args)) + kwargs.keys()
arglength = len(args)
for n in mdargs:
if isinstance(n, int):
if n >= arglength:
continue
entry = args[n]
is_arg = True
elif isinstance(n, str):
try:
entry = kwargs[n]
except KeyError:
continue
is_arg = False
if hasattr(entry, "__iter__"):
# Create now a copy of the given array and manipulate then
# the entries directly.
if is_arg:
args = list(args)
args[n] = structure_copy(entry)
else:
kwargs[n] = structure_copy(entry)
result = apply_on_element(wrapper, args, kwargs, n)
return result
return f(*args, **kwargs)
return wrapper
| 4,451 | 30.8 | 253 |
py
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/compatibility.py
|
"""
Reimplementations of constructs introduced in later versions of Python than
we support. Also some functions that are needed SymPy-wide and are located
here for easy import.
"""
from __future__ import print_function, division
import operator
from collections import defaultdict
from sympy.external import import_module
"""
Python 2 and Python 3 compatible imports
String and Unicode compatible changes:
* `unicode()` removed in Python 3, import `unicode` for Python 2/3
compatible function
* `unichr()` removed in Python 3, import `unichr` for Python 2/3 compatible
function
* Use `u()` for escaped unicode sequences (e.g. u'\u2020' -> u('\u2020'))
* Use `u_decode()` to decode utf-8 formatted unicode strings
* `string_types` gives str in Python 3, unicode and str in Python 2,
equivalent to basestring
Integer related changes:
* `long()` removed in Python 3, import `long` for Python 2/3 compatible
function
* `integer_types` gives int in Python 3, int and long in Python 2
Types related changes:
* `class_types` gives type in Python 3, type and ClassType in Python 2
Renamed function attributes:
* Python 2 `.func_code`, Python 3 `.__func__`, access with
`get_function_code()`
* Python 2 `.func_globals`, Python 3 `.__globals__`, access with
`get_function_globals()`
* Python 2 `.func_name`, Python 3 `.__name__`, access with
`get_function_name()`
Moved modules:
* `reduce()`
* `StringIO()`
* `cStringIO()` (same as `StingIO()` in Python 3)
* Python 2 `__builtins__`, access with Python 3 name, `builtins`
Iterator/list changes:
* `xrange` removed in Python 3, import `xrange` for Python 2/3 compatible
iterator version of range
exec:
* Use `exec_()`, with parameters `exec_(code, globs=None, locs=None)`
Metaclasses:
* Use `with_metaclass()`, examples below
* Define class `Foo` with metaclass `Meta`, and no parent:
class Foo(with_metaclass(Meta)):
pass
* Define class `Foo` with metaclass `Meta` and parent class `Bar`:
class Foo(with_metaclass(Meta, Bar)):
pass
"""
import sys
PY3 = sys.version_info[0] > 2
if PY3:
class_types = type,
integer_types = (int,)
string_types = (str,)
long = int
int_info = sys.int_info
# String / unicode compatibility
unicode = str
unichr = chr
def u_decode(x):
return x
Iterator = object
# Moved definitions
get_function_code = operator.attrgetter("__code__")
get_function_globals = operator.attrgetter("__globals__")
get_function_name = operator.attrgetter("__name__")
import builtins
from functools import reduce
from io import StringIO
cStringIO = StringIO
exec_=getattr(builtins, "exec")
range=range
else:
import codecs
import types
class_types = (type, types.ClassType)
integer_types = (int, long)
string_types = (str, unicode)
long = long
int_info = sys.long_info
# String / unicode compatibility
unicode = unicode
unichr = unichr
def u_decode(x):
return x.decode('utf-8')
class Iterator(object):
def next(self):
return type(self).__next__(self)
# Moved definitions
get_function_code = operator.attrgetter("func_code")
get_function_globals = operator.attrgetter("func_globals")
get_function_name = operator.attrgetter("func_name")
import __builtin__ as builtins
reduce = reduce
from StringIO import StringIO
from cStringIO import StringIO as cStringIO
def exec_(_code_, _globs_=None, _locs_=None):
"""Execute code in a namespace."""
if _globs_ is None:
frame = sys._getframe(1)
_globs_ = frame.f_globals
if _locs_ is None:
_locs_ = frame.f_locals
del frame
elif _locs_ is None:
_locs_ = _globs_
exec("exec _code_ in _globs_, _locs_")
range=xrange
def with_metaclass(meta, *bases):
"""
Create a base class with a metaclass.
For example, if you have the metaclass
>>> class Meta(type):
... pass
Use this as the metaclass by doing
>>> from sympy.core.compatibility import with_metaclass
>>> class MyClass(with_metaclass(Meta, object)):
... pass
This is equivalent to the Python 2::
class MyClass(object):
__metaclass__ = Meta
or Python 3::
class MyClass(object, metaclass=Meta):
pass
That is, the first argument is the metaclass, and the remaining arguments
are the base classes. Note that if the base class is just ``object``, you
may omit it.
>>> MyClass.__mro__
(<class 'MyClass'>, <... 'object'>)
>>> type(MyClass)
<class 'Meta'>
"""
# This requires a bit of explanation: the basic idea is to make a dummy
# metaclass for one level of class instantiation that replaces itself with
# the actual metaclass.
# Code copied from the 'six' library.
class metaclass(meta):
def __new__(cls, name, this_bases, d):
return meta(name, bases, d)
return type.__new__(metaclass, "NewBase", (), {})
# These are in here because telling if something is an iterable just by calling
# hasattr(obj, "__iter__") behaves differently in Python 2 and Python 3. In
# particular, hasattr(str, "__iter__") is False in Python 2 and True in Python 3.
# I think putting them here also makes it easier to use them in the core.
class NotIterable:
"""
Use this as mixin when creating a class which is not supposed to return
true when iterable() is called on its instances. I.e. avoid infinite loop
when calling e.g. list() on the instance
"""
pass
def iterable(i, exclude=(string_types, dict, NotIterable)):
"""
Return a boolean indicating whether ``i`` is SymPy iterable.
True also indicates that the iterator is finite, i.e. you e.g.
call list(...) on the instance.
When SymPy is working with iterables, it is almost always assuming
that the iterable is not a string or a mapping, so those are excluded
by default. If you want a pure Python definition, make exclude=None. To
exclude multiple items, pass them as a tuple.
You can also set the _iterable attribute to True or False on your class,
which will override the checks here, including the exclude test.
As a rule of thumb, some SymPy functions use this to check if they should
recursively map over an object. If an object is technically iterable in
the Python sense but does not desire this behavior (e.g., because its
iteration is not finite, or because iteration might induce an unwanted
computation), it should disable it by setting the _iterable attribute to False.
See also: is_sequence
Examples
========
>>> from sympy.utilities.iterables import iterable
>>> from sympy import Tuple
>>> things = [[1], (1,), set([1]), Tuple(1), (j for j in [1, 2]), {1:2}, '1', 1]
>>> for i in things:
... print('%s %s' % (iterable(i), type(i)))
True <... 'list'>
True <... 'tuple'>
True <... 'set'>
True <class 'sympy.core.containers.Tuple'>
True <... 'generator'>
False <... 'dict'>
False <... 'str'>
False <... 'int'>
>>> iterable({}, exclude=None)
True
>>> iterable({}, exclude=str)
True
>>> iterable("no", exclude=str)
False
"""
if hasattr(i, '_iterable'):
return i._iterable
try:
iter(i)
except TypeError:
return False
if exclude:
return not isinstance(i, exclude)
return True
def is_sequence(i, include=None):
"""
Return a boolean indicating whether ``i`` is a sequence in the SymPy
sense. If anything that fails the test below should be included as
being a sequence for your application, set 'include' to that object's
type; multiple types should be passed as a tuple of types.
Note: although generators can generate a sequence, they often need special
handling to make sure their elements are captured before the generator is
exhausted, so these are not included by default in the definition of a
sequence.
See also: iterable
Examples
========
>>> from sympy.utilities.iterables import is_sequence
>>> from types import GeneratorType
>>> is_sequence([])
True
>>> is_sequence(set())
False
>>> is_sequence('abc')
False
>>> is_sequence('abc', include=str)
True
>>> generator = (c for c in 'abc')
>>> is_sequence(generator)
False
>>> is_sequence(generator, include=(str, GeneratorType))
True
"""
return (hasattr(i, '__getitem__') and
iterable(i) or
bool(include) and
isinstance(i, include))
try:
from itertools import zip_longest
except ImportError: # <= Python 2.7
from itertools import izip_longest as zip_longest
try:
from string import maketrans
except ImportError:
maketrans = str.maketrans
def as_int(n):
"""
Convert the argument to a builtin integer.
The return value is guaranteed to be equal to the input. ValueError is
raised if the input has a non-integral value.
Examples
========
>>> from sympy.core.compatibility import as_int
>>> from sympy import sqrt
>>> 3.0
3.0
>>> as_int(3.0) # convert to int and test for equality
3
>>> int(sqrt(10))
3
>>> as_int(sqrt(10))
Traceback (most recent call last):
...
ValueError: ... is not an integer
"""
try:
result = int(n)
if result != n:
raise TypeError
except TypeError:
raise ValueError('%s is not an integer' % (n,))
return result
def default_sort_key(item, order=None):
"""Return a key that can be used for sorting.
The key has the structure:
(class_key, (len(args), args), exponent.sort_key(), coefficient)
This key is supplied by the sort_key routine of Basic objects when
``item`` is a Basic object or an object (other than a string) that
sympifies to a Basic object. Otherwise, this function produces the
key.
The ``order`` argument is passed along to the sort_key routine and is
used to determine how the terms *within* an expression are ordered.
(See examples below) ``order`` options are: 'lex', 'grlex', 'grevlex',
and reversed values of the same (e.g. 'rev-lex'). The default order
value is None (which translates to 'lex').
Examples
========
>>> from sympy import S, I, default_sort_key, sin, cos, sqrt
>>> from sympy.core.function import UndefinedFunction
>>> from sympy.abc import x
The following are equivalent ways of getting the key for an object:
>>> x.sort_key() == default_sort_key(x)
True
Here are some examples of the key that is produced:
>>> default_sort_key(UndefinedFunction('f'))
((0, 0, 'UndefinedFunction'), (1, ('f',)), ((1, 0, 'Number'),
(0, ()), (), 1), 1)
>>> default_sort_key('1')
((0, 0, 'str'), (1, ('1',)), ((1, 0, 'Number'), (0, ()), (), 1), 1)
>>> default_sort_key(S.One)
((1, 0, 'Number'), (0, ()), (), 1)
>>> default_sort_key(2)
((1, 0, 'Number'), (0, ()), (), 2)
While sort_key is a method only defined for SymPy objects,
default_sort_key will accept anything as an argument so it is
more robust as a sorting key. For the following, using key=
lambda i: i.sort_key() would fail because 2 doesn't have a sort_key
method; that's why default_sort_key is used. Note, that it also
handles sympification of non-string items likes ints:
>>> a = [2, I, -I]
>>> sorted(a, key=default_sort_key)
[2, -I, I]
The returned key can be used anywhere that a key can be specified for
a function, e.g. sort, min, max, etc...:
>>> a.sort(key=default_sort_key); a[0]
2
>>> min(a, key=default_sort_key)
2
Note
----
The key returned is useful for getting items into a canonical order
that will be the same across platforms. It is not directly useful for
sorting lists of expressions:
>>> a, b = x, 1/x
Since ``a`` has only 1 term, its value of sort_key is unaffected by
``order``:
>>> a.sort_key() == a.sort_key('rev-lex')
True
If ``a`` and ``b`` are combined then the key will differ because there
are terms that can be ordered:
>>> eq = a + b
>>> eq.sort_key() == eq.sort_key('rev-lex')
False
>>> eq.as_ordered_terms()
[x, 1/x]
>>> eq.as_ordered_terms('rev-lex')
[1/x, x]
But since the keys for each of these terms are independent of ``order``'s
value, they don't sort differently when they appear separately in a list:
>>> sorted(eq.args, key=default_sort_key)
[1/x, x]
>>> sorted(eq.args, key=lambda i: default_sort_key(i, order='rev-lex'))
[1/x, x]
The order of terms obtained when using these keys is the order that would
be obtained if those terms were *factors* in a product.
Although it is useful for quickly putting expressions in canonical order,
it does not sort expressions based on their complexity defined by the
number of operations, power of variables and others:
>>> sorted([sin(x)*cos(x), sin(x)], key=default_sort_key)
[sin(x)*cos(x), sin(x)]
>>> sorted([x, x**2, sqrt(x), x**3], key=default_sort_key)
[sqrt(x), x, x**2, x**3]
See Also
========
ordered, sympy.core.expr.as_ordered_factors, sympy.core.expr.as_ordered_terms
"""
from .singleton import S
from .basic import Basic
from .sympify import sympify, SympifyError
from .compatibility import iterable
if isinstance(item, Basic):
return item.sort_key(order=order)
if iterable(item, exclude=string_types):
if isinstance(item, dict):
args = item.items()
unordered = True
elif isinstance(item, set):
args = item
unordered = True
else:
# e.g. tuple, list
args = list(item)
unordered = False
args = [default_sort_key(arg, order=order) for arg in args]
if unordered:
# e.g. dict, set
args = sorted(args)
cls_index, args = 10, (len(args), tuple(args))
else:
if not isinstance(item, string_types):
try:
item = sympify(item)
except SympifyError:
# e.g. lambda x: x
pass
else:
if isinstance(item, Basic):
# e.g int -> Integer
return default_sort_key(item)
# e.g. UndefinedFunction
# e.g. str
cls_index, args = 0, (1, (str(item),))
return (cls_index, 0, item.__class__.__name__
), args, S.One.sort_key(), S.One
def _nodes(e):
"""
A helper for ordered() which returns the node count of ``e`` which
for Basic objects is the number of Basic nodes in the expression tree
but for other objects is 1 (unless the object is an iterable or dict
for which the sum of nodes is returned).
"""
from .basic import Basic
if isinstance(e, Basic):
return e.count(Basic)
elif iterable(e):
return 1 + sum(_nodes(ei) for ei in e)
elif isinstance(e, dict):
return 1 + sum(_nodes(k) + _nodes(v) for k, v in e.items())
else:
return 1
def ordered(seq, keys=None, default=True, warn=False):
"""Return an iterator of the seq where keys are used to break ties in
a conservative fashion: if, after applying a key, there are no ties
then no other keys will be computed.
Two default keys will be applied if 1) keys are not provided or 2) the
given keys don't resolve all ties (but only if `default` is True). The
two keys are `_nodes` (which places smaller expressions before large) and
`default_sort_key` which (if the `sort_key` for an object is defined
properly) should resolve any ties.
If ``warn`` is True then an error will be raised if there were no
keys remaining to break ties. This can be used if it was expected that
there should be no ties between items that are not identical.
Examples
========
>>> from sympy.utilities.iterables import ordered
>>> from sympy import count_ops
>>> from sympy.abc import x, y
The count_ops is not sufficient to break ties in this list and the first
two items appear in their original order (i.e. the sorting is stable):
>>> list(ordered([y + 2, x + 2, x**2 + y + 3],
... count_ops, default=False, warn=False))
...
[y + 2, x + 2, x**2 + y + 3]
The default_sort_key allows the tie to be broken:
>>> list(ordered([y + 2, x + 2, x**2 + y + 3]))
...
[x + 2, y + 2, x**2 + y + 3]
Here, sequences are sorted by length, then sum:
>>> seq, keys = [[[1, 2, 1], [0, 3, 1], [1, 1, 3], [2], [1]], [
... lambda x: len(x),
... lambda x: sum(x)]]
...
>>> list(ordered(seq, keys, default=False, warn=False))
[[1], [2], [1, 2, 1], [0, 3, 1], [1, 1, 3]]
If ``warn`` is True, an error will be raised if there were not
enough keys to break ties:
>>> list(ordered(seq, keys, default=False, warn=True))
Traceback (most recent call last):
...
ValueError: not enough keys to break ties
Notes
=====
The decorated sort is one of the fastest ways to sort a sequence for
which special item comparison is desired: the sequence is decorated,
sorted on the basis of the decoration (e.g. making all letters lower
case) and then undecorated. If one wants to break ties for items that
have the same decorated value, a second key can be used. But if the
second key is expensive to compute then it is inefficient to decorate
all items with both keys: only those items having identical first key
values need to be decorated. This function applies keys successively
only when needed to break ties. By yielding an iterator, use of the
tie-breaker is delayed as long as possible.
This function is best used in cases when use of the first key is
expected to be a good hashing function; if there are no unique hashes
from application of a key then that key should not have been used. The
exception, however, is that even if there are many collisions, if the
first group is small and one does not need to process all items in the
list then time will not be wasted sorting what one was not interested
in. For example, if one were looking for the minimum in a list and
there were several criteria used to define the sort order, then this
function would be good at returning that quickly if the first group
of candidates is small relative to the number of items being processed.
"""
d = defaultdict(list)
if keys:
if not isinstance(keys, (list, tuple)):
keys = [keys]
keys = list(keys)
f = keys.pop(0)
for a in seq:
d[f(a)].append(a)
else:
if not default:
raise ValueError('if default=False then keys must be provided')
d[None].extend(seq)
for k in sorted(d.keys()):
if len(d[k]) > 1:
if keys:
d[k] = ordered(d[k], keys, default, warn)
elif default:
d[k] = ordered(d[k], (_nodes, default_sort_key,),
default=False, warn=warn)
elif warn:
from sympy.utilities.iterables import uniq
u = list(uniq(d[k]))
if len(u) > 1:
raise ValueError(
'not enough keys to break ties: %s' % u)
for v in d[k]:
yield v
d.pop(k)
# If HAS_GMPY is 0, no supported version of gmpy is available. Otherwise,
# HAS_GMPY contains the major version number of gmpy; i.e. 1 for gmpy, and
# 2 for gmpy2.
# Versions of gmpy prior to 1.03 do not work correctly with int(largempz)
# For example, int(gmpy.mpz(2**256)) would raise OverflowError.
# See issue 4980.
# Minimum version of gmpy changed to 1.13 to allow a single code base to also
# work with gmpy2.
def _getenv(key, default=None):
from os import getenv
return getenv(key, default)
GROUND_TYPES = _getenv('SYMPY_GROUND_TYPES', 'auto').lower()
HAS_GMPY = 0
if GROUND_TYPES != 'python':
# Don't try to import gmpy2 if ground types is set to gmpy1. This is
# primarily intended for testing.
if GROUND_TYPES != 'gmpy1':
gmpy = import_module('gmpy2', min_module_version='2.0.0',
module_version_attr='version', module_version_attr_call_args=())
if gmpy:
HAS_GMPY = 2
else:
GROUND_TYPES = 'gmpy'
if not HAS_GMPY:
gmpy = import_module('gmpy', min_module_version='1.13',
module_version_attr='version', module_version_attr_call_args=())
if gmpy:
HAS_GMPY = 1
if GROUND_TYPES == 'auto':
if HAS_GMPY:
GROUND_TYPES = 'gmpy'
else:
GROUND_TYPES = 'python'
if GROUND_TYPES == 'gmpy' and not HAS_GMPY:
from warnings import warn
warn("gmpy library is not installed, switching to 'python' ground types")
GROUND_TYPES = 'python'
# SYMPY_INTS is a tuple containing the base types for valid integer types.
SYMPY_INTS = integer_types
if GROUND_TYPES == 'gmpy':
SYMPY_INTS += (type(gmpy.mpz(0)),)
# lru_cache compatible with py2.6->py3.2 copied directly from
# http://code.activestate.com/
# recipes/578078-py26-and-py30-backport-of-python-33s-lru-cache/
from collections import namedtuple
from functools import update_wrapper
from threading import RLock
_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
class _HashedSeq(list):
__slots__ = 'hashvalue'
def __init__(self, tup, hash=hash):
self[:] = tup
self.hashvalue = hash(tup)
def __hash__(self):
return self.hashvalue
def _make_key(args, kwds, typed,
kwd_mark = (object(),),
fasttypes = set((int, str, frozenset, type(None))),
sorted=sorted, tuple=tuple, type=type, len=len):
'Make a cache key from optionally typed positional and keyword arguments'
key = args
if kwds:
sorted_items = sorted(kwds.items())
key += kwd_mark
for item in sorted_items:
key += item
if typed:
key += tuple(type(v) for v in args)
if kwds:
key += tuple(type(v) for k, v in sorted_items)
elif len(key) == 1 and type(key[0]) in fasttypes:
return key[0]
return _HashedSeq(key)
def lru_cache(maxsize=100, typed=False):
"""Least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
If *typed* is True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
distinct results.
Arguments to the cached function must be hashable.
View the cache statistics named tuple (hits, misses, maxsize, currsize) with
f.cache_info(). Clear the cache and statistics with f.cache_clear().
Access the underlying function with f.__wrapped__.
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
# Users should only access the lru_cache through its public API:
# cache_info, cache_clear, and f.__wrapped__
# The internals of the lru_cache are encapsulated for thread safety and
# to allow the implementation to change (including a possible C version).
def decorating_function(user_function):
cache = dict()
stats = [0, 0] # make statistics updateable non-locally
HITS, MISSES = 0, 1 # names for the stats fields
make_key = _make_key
cache_get = cache.get # bound method to lookup key or return None
_len = len # localize the global len() function
lock = RLock() # because linkedlist updates aren't threadsafe
root = [] # root of the circular doubly linked list
root[:] = [root, root, None, None] # initialize by pointing to self
nonlocal_root = [root] # make updateable non-locally
PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
if maxsize == 0:
def wrapper(*args, **kwds):
# no caching, just do a statistics update after a successful call
result = user_function(*args, **kwds)
stats[MISSES] += 1
return result
elif maxsize is None:
def wrapper(*args, **kwds):
# simple caching without ordering or size limit
key = make_key(args, kwds, typed)
result = cache_get(key, root) # root used here as a unique not-found sentinel
if result is not root:
stats[HITS] += 1
return result
result = user_function(*args, **kwds)
cache[key] = result
stats[MISSES] += 1
return result
else:
def wrapper(*args, **kwds):
# size limited caching that tracks accesses by recency
try:
key = make_key(args, kwds, typed) if kwds or typed else args
except TypeError:
stats[MISSES] += 1
return user_function(*args, **kwds)
with lock:
link = cache_get(key)
if link is not None:
# record recent use of the key by moving it to the front of the list
root, = nonlocal_root
link_prev, link_next, key, result = link
link_prev[NEXT] = link_next
link_next[PREV] = link_prev
last = root[PREV]
last[NEXT] = root[PREV] = link
link[PREV] = last
link[NEXT] = root
stats[HITS] += 1
return result
result = user_function(*args, **kwds)
with lock:
root, = nonlocal_root
if key in cache:
# getting here means that this same key was added to the
# cache while the lock was released. since the link
# update is already done, we need only return the
# computed result and update the count of misses.
pass
elif _len(cache) >= maxsize:
# use the old root to store the new key and result
oldroot = root
oldroot[KEY] = key
oldroot[RESULT] = result
# empty the oldest link and make it the new root
root = nonlocal_root[0] = oldroot[NEXT]
oldkey = root[KEY]
oldvalue = root[RESULT]
root[KEY] = root[RESULT] = None
# now update the cache dictionary for the new links
del cache[oldkey]
cache[key] = oldroot
else:
# put result in a new link at the front of the list
last = root[PREV]
link = [last, root, key, result]
last[NEXT] = root[PREV] = cache[key] = link
stats[MISSES] += 1
return result
def cache_info():
"""Report cache statistics"""
with lock:
return _CacheInfo(stats[HITS], stats[MISSES], maxsize, len(cache))
def cache_clear():
"""Clear the cache and cache statistics"""
with lock:
cache.clear()
root = nonlocal_root[0]
root[:] = [root, root, None, None]
stats[:] = [0, 0]
wrapper.__wrapped__ = user_function
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return update_wrapper(wrapper, user_function)
return decorating_function
### End of backported lru_cache
if sys.version_info[:2] >= (3, 3):
# 3.2 has an lru_cache with an incompatible API
from functools import lru_cache
| 28,770 | 32.338355 | 95 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/mul.py
|
from __future__ import print_function, division
from collections import defaultdict
from functools import cmp_to_key
import operator
from .sympify import sympify
from .basic import Basic
from .singleton import S
from .operations import AssocOp
from .cache import cacheit
from .logic import fuzzy_not, _fuzzy_group
from .compatibility import reduce, range
from .expr import Expr
# internal marker to indicate:
# "there are still non-commutative objects -- don't forget to process them"
class NC_Marker:
is_Order = False
is_Mul = False
is_Number = False
is_Poly = False
is_commutative = False
# Key for sorting commutative args in canonical order
_args_sortkey = cmp_to_key(Basic.compare)
def _mulsort(args):
# in-place sorting of args
args.sort(key=_args_sortkey)
def _unevaluated_Mul(*args):
"""Return a well-formed unevaluated Mul: Numbers are collected and
put in slot 0, any arguments that are Muls will be flattened, and args
are sorted. Use this when args have changed but you still want to return
an unevaluated Mul.
Examples
========
>>> from sympy.core.mul import _unevaluated_Mul as uMul
>>> from sympy import S, sqrt, Mul
>>> from sympy.abc import x
>>> a = uMul(*[S(3.0), x, S(2)])
>>> a.args[0]
6.00000000000000
>>> a.args[1]
x
Two unevaluated Muls with the same arguments will
always compare as equal during testing:
>>> m = uMul(sqrt(2), sqrt(3))
>>> m == uMul(sqrt(3), sqrt(2))
True
>>> u = Mul(sqrt(3), sqrt(2), evaluate=False)
>>> m == uMul(u)
True
>>> m == Mul(*m.args)
False
"""
args = list(args)
newargs = []
ncargs = []
co = S.One
while args:
a = args.pop()
if a.is_Mul:
c, nc = a.args_cnc()
args.extend(c)
if nc:
ncargs.append(Mul._from_args(nc))
elif a.is_Number:
co *= a
else:
newargs.append(a)
_mulsort(newargs)
if co is not S.One:
newargs.insert(0, co)
if ncargs:
newargs.append(Mul._from_args(ncargs))
return Mul._from_args(newargs)
class Mul(Expr, AssocOp):
__slots__ = []
is_Mul = True
@classmethod
def flatten(cls, seq):
"""Return commutative, noncommutative and order arguments by
combining related terms.
Notes
=====
* In an expression like ``a*b*c``, python process this through sympy
as ``Mul(Mul(a, b), c)``. This can have undesirable consequences.
- Sometimes terms are not combined as one would like:
{c.f. https://github.com/sympy/sympy/issues/4596}
>>> from sympy import Mul, sqrt
>>> from sympy.abc import x, y, z
>>> 2*(x + 1) # this is the 2-arg Mul behavior
2*x + 2
>>> y*(x + 1)*2
2*y*(x + 1)
>>> 2*(x + 1)*y # 2-arg result will be obtained first
y*(2*x + 2)
>>> Mul(2, x + 1, y) # all 3 args simultaneously processed
2*y*(x + 1)
>>> 2*((x + 1)*y) # parentheses can control this behavior
2*y*(x + 1)
Powers with compound bases may not find a single base to
combine with unless all arguments are processed at once.
Post-processing may be necessary in such cases.
{c.f. https://github.com/sympy/sympy/issues/5728}
>>> a = sqrt(x*sqrt(y))
>>> a**3
(x*sqrt(y))**(3/2)
>>> Mul(a,a,a)
(x*sqrt(y))**(3/2)
>>> a*a*a
x*sqrt(y)*sqrt(x*sqrt(y))
>>> _.subs(a.base, z).subs(z, a.base)
(x*sqrt(y))**(3/2)
- If more than two terms are being multiplied then all the
previous terms will be re-processed for each new argument.
So if each of ``a``, ``b`` and ``c`` were :class:`Mul`
expression, then ``a*b*c`` (or building up the product
with ``*=``) will process all the arguments of ``a`` and
``b`` twice: once when ``a*b`` is computed and again when
``c`` is multiplied.
Using ``Mul(a, b, c)`` will process all arguments once.
* The results of Mul are cached according to arguments, so flatten
will only be called once for ``Mul(a, b, c)``. If you can
structure a calculation so the arguments are most likely to be
repeats then this can save time in computing the answer. For
example, say you had a Mul, M, that you wished to divide by ``d[i]``
and multiply by ``n[i]`` and you suspect there are many repeats
in ``n``. It would be better to compute ``M*n[i]/d[i]`` rather
than ``M/d[i]*n[i]`` since every time n[i] is a repeat, the
product, ``M*n[i]`` will be returned without flattening -- the
cached value will be returned. If you divide by the ``d[i]``
first (and those are more unique than the ``n[i]``) then that will
create a new Mul, ``M/d[i]`` the args of which will be traversed
again when it is multiplied by ``n[i]``.
{c.f. https://github.com/sympy/sympy/issues/5706}
This consideration is moot if the cache is turned off.
NB
--
The validity of the above notes depends on the implementation
details of Mul and flatten which may change at any time. Therefore,
you should only consider them when your code is highly performance
sensitive.
Removal of 1 from the sequence is already handled by AssocOp.__new__.
"""
from sympy.calculus.util import AccumBounds
rv = None
if len(seq) == 2:
a, b = seq
if b.is_Rational:
a, b = b, a
assert not a is S.One
if not a.is_zero and a.is_Rational:
r, b = b.as_coeff_Mul()
if b.is_Add:
if r is not S.One: # 2-arg hack
# leave the Mul as a Mul
rv = [cls(a*r, b, evaluate=False)], [], None
elif b.is_commutative:
if a is S.One:
rv = [b], [], None
else:
r, b = b.as_coeff_Add()
bargs = [_keep_coeff(a, bi) for bi in Add.make_args(b)]
_addsort(bargs)
ar = a*r
if ar:
bargs.insert(0, ar)
bargs = [Add._from_args(bargs)]
rv = bargs, [], None
if rv:
return rv
# apply associativity, separate commutative part of seq
c_part = [] # out: commutative factors
nc_part = [] # out: non-commutative factors
nc_seq = []
coeff = S.One # standalone term
# e.g. 3 * ...
c_powers = [] # (base,exp) n
# e.g. (x,n) for x
num_exp = [] # (num-base, exp) y
# e.g. (3, y) for ... * 3 * ...
neg1e = S.Zero # exponent on -1 extracted from Number-based Pow and I
pnum_rat = {} # (num-base, Rat-exp) 1/2
# e.g. (3, 1/2) for ... * 3 * ...
order_symbols = None
# --- PART 1 ---
#
# "collect powers and coeff":
#
# o coeff
# o c_powers
# o num_exp
# o neg1e
# o pnum_rat
#
# NOTE: this is optimized for all-objects-are-commutative case
for o in seq:
# O(x)
if o.is_Order:
o, order_symbols = o.as_expr_variables(order_symbols)
# Mul([...])
if o.is_Mul:
if o.is_commutative:
seq.extend(o.args) # XXX zerocopy?
else:
# NCMul can have commutative parts as well
for q in o.args:
if q.is_commutative:
seq.append(q)
else:
nc_seq.append(q)
# append non-commutative marker, so we don't forget to
# process scheduled non-commutative objects
seq.append(NC_Marker)
continue
# 3
elif o.is_Number:
if o is S.NaN or coeff is S.ComplexInfinity and o is S.Zero:
# we know for sure the result will be nan
return [S.NaN], [], None
elif coeff.is_Number: # it could be zoo
coeff *= o
if coeff is S.NaN:
# we know for sure the result will be nan
return [S.NaN], [], None
continue
elif isinstance(o, AccumBounds):
coeff = o.__mul__(coeff)
continue
elif o is S.ComplexInfinity:
if not coeff:
# 0 * zoo = NaN
return [S.NaN], [], None
if coeff is S.ComplexInfinity:
# zoo * zoo = zoo
return [S.ComplexInfinity], [], None
coeff = S.ComplexInfinity
continue
elif o is S.ImaginaryUnit:
neg1e += S.Half
continue
elif o.is_commutative:
# e
# o = b
b, e = o.as_base_exp()
# y
# 3
if o.is_Pow:
if b.is_Number:
# get all the factors with numeric base so they can be
# combined below, but don't combine negatives unless
# the exponent is an integer
if e.is_Rational:
if e.is_Integer:
coeff *= Pow(b, e) # it is an unevaluated power
continue
elif e.is_negative: # also a sign of an unevaluated power
seq.append(Pow(b, e))
continue
elif b.is_negative:
neg1e += e
b = -b
if b is not S.One:
pnum_rat.setdefault(b, []).append(e)
continue
elif b.is_positive or e.is_integer:
num_exp.append((b, e))
continue
elif b is S.ImaginaryUnit and e.is_Rational:
neg1e += e/2
continue
c_powers.append((b, e))
# NON-COMMUTATIVE
# TODO: Make non-commutative exponents not combine automatically
else:
if o is not NC_Marker:
nc_seq.append(o)
# process nc_seq (if any)
while nc_seq:
o = nc_seq.pop(0)
if not nc_part:
nc_part.append(o)
continue
# b c b+c
# try to combine last terms: a * a -> a
o1 = nc_part.pop()
b1, e1 = o1.as_base_exp()
b2, e2 = o.as_base_exp()
new_exp = e1 + e2
# Only allow powers to combine if the new exponent is
# not an Add. This allow things like a**2*b**3 == a**5
# if a.is_commutative == False, but prohibits
# a**x*a**y and x**a*x**b from combining (x,y commute).
if b1 == b2 and (not new_exp.is_Add):
o12 = b1 ** new_exp
# now o12 could be a commutative object
if o12.is_commutative:
seq.append(o12)
continue
else:
nc_seq.insert(0, o12)
else:
nc_part.append(o1)
nc_part.append(o)
# We do want a combined exponent if it would not be an Add, such as
# y 2y 3y
# x * x -> x
# We determine if two exponents have the same term by using
# as_coeff_Mul.
#
# Unfortunately, this isn't smart enough to consider combining into
# exponents that might already be adds, so things like:
# z - y y
# x * x will be left alone. This is because checking every possible
# combination can slow things down.
# gather exponents of common bases...
def _gather(c_powers):
common_b = {} # b:e
for b, e in c_powers:
co = e.as_coeff_Mul()
common_b.setdefault(b, {}).setdefault(
co[1], []).append(co[0])
for b, d in common_b.items():
for di, li in d.items():
d[di] = Add(*li)
new_c_powers = []
for b, e in common_b.items():
new_c_powers.extend([(b, c*t) for t, c in e.items()])
return new_c_powers
# in c_powers
c_powers = _gather(c_powers)
# and in num_exp
num_exp = _gather(num_exp)
# --- PART 2 ---
#
# o process collected powers (x**0 -> 1; x**1 -> x; otherwise Pow)
# o combine collected powers (2**x * 3**x -> 6**x)
# with numeric base
# ................................
# now we have:
# - coeff:
# - c_powers: (b, e)
# - num_exp: (2, e)
# - pnum_rat: {(1/3, [1/3, 2/3, 1/4])}
# 0 1
# x -> 1 x -> x
# this should only need to run twice; if it fails because
# it needs to be run more times, perhaps this should be
# changed to a "while True" loop -- the only reason it
# isn't such now is to allow a less-than-perfect result to
# be obtained rather than raising an error or entering an
# infinite loop
for i in range(2):
new_c_powers = []
changed = False
for b, e in c_powers:
if e.is_zero:
continue
if e is S.One:
if b.is_Number:
coeff *= b
continue
p = b
if e is not S.One:
p = Pow(b, e)
# check to make sure that the base doesn't change
# after exponentiation; to allow for unevaluated
# Pow, we only do so if b is not already a Pow
if p.is_Pow and not b.is_Pow:
bi = b
b, e = p.as_base_exp()
if b != bi:
changed = True
c_part.append(p)
new_c_powers.append((b, e))
# there might have been a change, but unless the base
# matches some other base, there is nothing to do
if changed and len(set(
b for b, e in new_c_powers)) != len(new_c_powers):
# start over again
c_part = []
c_powers = _gather(new_c_powers)
else:
break
# x x x
# 2 * 3 -> 6
inv_exp_dict = {} # exp:Mul(num-bases) x x
# e.g. x:6 for ... * 2 * 3 * ...
for b, e in num_exp:
inv_exp_dict.setdefault(e, []).append(b)
for e, b in inv_exp_dict.items():
inv_exp_dict[e] = cls(*b)
c_part.extend([Pow(b, e) for e, b in inv_exp_dict.items() if e])
# b, e -> e' = sum(e), b
# {(1/5, [1/3]), (1/2, [1/12, 1/4]} -> {(1/3, [1/5, 1/2])}
comb_e = {}
for b, e in pnum_rat.items():
comb_e.setdefault(Add(*e), []).append(b)
del pnum_rat
# process them, reducing exponents to values less than 1
# and updating coeff if necessary else adding them to
# num_rat for further processing
num_rat = []
for e, b in comb_e.items():
b = cls(*b)
if e.q == 1:
coeff *= Pow(b, e)
continue
if e.p > e.q:
e_i, ep = divmod(e.p, e.q)
coeff *= Pow(b, e_i)
e = Rational(ep, e.q)
num_rat.append((b, e))
del comb_e
# extract gcd of bases in num_rat
# 2**(1/3)*6**(1/4) -> 2**(1/3+1/4)*3**(1/4)
pnew = defaultdict(list)
i = 0 # steps through num_rat which may grow
while i < len(num_rat):
bi, ei = num_rat[i]
grow = []
for j in range(i + 1, len(num_rat)):
bj, ej = num_rat[j]
g = bi.gcd(bj)
if g is not S.One:
# 4**r1*6**r2 -> 2**(r1+r2) * 2**r1 * 3**r2
# this might have a gcd with something else
e = ei + ej
if e.q == 1:
coeff *= Pow(g, e)
else:
if e.p > e.q:
e_i, ep = divmod(e.p, e.q) # change e in place
coeff *= Pow(g, e_i)
e = Rational(ep, e.q)
grow.append((g, e))
# update the jth item
num_rat[j] = (bj/g, ej)
# update bi that we are checking with
bi = bi/g
if bi is S.One:
break
if bi is not S.One:
obj = Pow(bi, ei)
if obj.is_Number:
coeff *= obj
else:
# changes like sqrt(12) -> 2*sqrt(3)
for obj in Mul.make_args(obj):
if obj.is_Number:
coeff *= obj
else:
assert obj.is_Pow
bi, ei = obj.args
pnew[ei].append(bi)
num_rat.extend(grow)
i += 1
# combine bases of the new powers
for e, b in pnew.items():
pnew[e] = cls(*b)
# handle -1 and I
if neg1e:
# treat I as (-1)**(1/2) and compute -1's total exponent
p, q = neg1e.as_numer_denom()
# if the integer part is odd, extract -1
n, p = divmod(p, q)
if n % 2:
coeff = -coeff
# if it's a multiple of 1/2 extract I
if q == 2:
c_part.append(S.ImaginaryUnit)
elif p:
# see if there is any positive base this power of
# -1 can join
neg1e = Rational(p, q)
for e, b in pnew.items():
if e == neg1e and b.is_positive:
pnew[e] = -b
break
else:
# keep it separate; we've already evaluated it as
# much as possible so evaluate=False
c_part.append(Pow(S.NegativeOne, neg1e, evaluate=False))
# add all the pnew powers
c_part.extend([Pow(b, e) for e, b in pnew.items()])
# oo, -oo
if (coeff is S.Infinity) or (coeff is S.NegativeInfinity):
def _handle_for_oo(c_part, coeff_sign):
new_c_part = []
for t in c_part:
if t.is_positive:
continue
if t.is_negative:
coeff_sign *= -1
continue
new_c_part.append(t)
return new_c_part, coeff_sign
c_part, coeff_sign = _handle_for_oo(c_part, 1)
nc_part, coeff_sign = _handle_for_oo(nc_part, coeff_sign)
coeff *= coeff_sign
# zoo
if coeff is S.ComplexInfinity:
# zoo might be
# infinite_real + bounded_im
# bounded_real + infinite_im
# infinite_real + infinite_im
# and non-zero real or imaginary will not change that status.
c_part = [c for c in c_part if not (fuzzy_not(c.is_zero) and
c.is_real is not None)]
nc_part = [c for c in nc_part if not (fuzzy_not(c.is_zero) and
c.is_real is not None)]
# 0
elif coeff is S.Zero:
# we know for sure the result will be 0 except the multiplicand
# is infinity
if any(c.is_finite == False for c in c_part):
return [S.NaN], [], order_symbols
return [coeff], [], order_symbols
# check for straggling Numbers that were produced
_new = []
for i in c_part:
if i.is_Number:
coeff *= i
else:
_new.append(i)
c_part = _new
# order commutative part canonically
_mulsort(c_part)
# current code expects coeff to be always in slot-0
if coeff is not S.One:
c_part.insert(0, coeff)
# we are done
if (not nc_part and len(c_part) == 2 and c_part[0].is_Number and
c_part[1].is_Add):
# 2*(1+a) -> 2 + 2 * a
coeff = c_part[0]
c_part = [Add(*[coeff*f for f in c_part[1].args])]
return c_part, nc_part, order_symbols
def _eval_power(b, e):
# don't break up NC terms: (A*B)**3 != A**3*B**3, it is A*B*A*B*A*B
cargs, nc = b.args_cnc(split_1=False)
if e.is_Integer:
return Mul(*[Pow(b, e, evaluate=False) for b in cargs]) * \
Pow(Mul._from_args(nc), e, evaluate=False)
if e.is_Rational and e.q == 2:
from sympy.core.power import integer_nthroot
from sympy.functions.elementary.complexes import sign
if b.is_imaginary:
a = b.as_real_imag()[1]
if a.is_Rational:
n, d = abs(a/2).as_numer_denom()
n, t = integer_nthroot(n, 2)
if t:
d, t = integer_nthroot(d, 2)
if t:
r = sympify(n)/d
return _unevaluated_Mul(r**e.p, (1 + sign(a)*S.ImaginaryUnit)**e.p)
p = Pow(b, e, evaluate=False)
if e.is_Rational or e.is_Float:
return p._eval_expand_power_base()
return p
@classmethod
def class_key(cls):
return 3, 0, cls.__name__
def _eval_evalf(self, prec):
c, m = self.as_coeff_Mul()
if c is S.NegativeOne:
if m.is_Mul:
rv = -AssocOp._eval_evalf(m, prec)
else:
mnew = m._eval_evalf(prec)
if mnew is not None:
m = mnew
rv = -m
else:
rv = AssocOp._eval_evalf(self, prec)
if rv.is_number:
return rv.expand()
return rv
@property
def _mpc_(self):
"""
Convert self to an mpmath mpc if possible
"""
from sympy.core.numbers import I, Float
im_part, imag_unit = self.as_coeff_Mul()
if not imag_unit == I:
# ValueError may seem more reasonable but since it's a @property,
# we need to use AttributeError to keep from confusing things like
# hasattr.
raise AttributeError("Cannot convert Mul to mpc. Must be of the form Number*I")
return (Float(0)._mpf_, Float(im_part)._mpf_)
@cacheit
def as_two_terms(self):
"""Return head and tail of self.
This is the most efficient way to get the head and tail of an
expression.
- if you want only the head, use self.args[0];
- if you want to process the arguments of the tail then use
self.as_coef_mul() which gives the head and a tuple containing
the arguments of the tail when treated as a Mul.
- if you want the coefficient when self is treated as an Add
then use self.as_coeff_add()[0]
>>> from sympy.abc import x, y
>>> (3*x*y).as_two_terms()
(3, x*y)
"""
args = self.args
if len(args) == 1:
return S.One, self
elif len(args) == 2:
return args
else:
return args[0], self._new_rawargs(*args[1:])
@cacheit
def as_coefficients_dict(self):
"""Return a dictionary mapping terms to their coefficient.
Since the dictionary is a defaultdict, inquiries about terms which
were not present will return a coefficient of 0. The dictionary
is considered to have a single term.
Examples
========
>>> from sympy.abc import a, x
>>> (3*a*x).as_coefficients_dict()
{a*x: 3}
>>> _[a]
0
"""
d = defaultdict(int)
args = self.args
if len(args) == 1 or not args[0].is_Number:
d[self] = S.One
else:
d[self._new_rawargs(*args[1:])] = args[0]
return d
@cacheit
def as_coeff_mul(self, *deps, **kwargs):
rational = kwargs.pop('rational', True)
if deps:
l1 = []
l2 = []
for f in self.args:
if f.has(*deps):
l2.append(f)
else:
l1.append(f)
return self._new_rawargs(*l1), tuple(l2)
args = self.args
if args[0].is_Number:
if not rational or args[0].is_Rational:
return args[0], args[1:]
elif args[0].is_negative:
return S.NegativeOne, (-args[0],) + args[1:]
return S.One, args
def as_coeff_Mul(self, rational=False):
"""Efficiently extract the coefficient of a product. """
coeff, args = self.args[0], self.args[1:]
if coeff.is_Number:
if not rational or coeff.is_Rational:
if len(args) == 1:
return coeff, args[0]
else:
return coeff, self._new_rawargs(*args)
elif coeff.is_negative:
return S.NegativeOne, self._new_rawargs(*((-coeff,) + args))
return S.One, self
def as_real_imag(self, deep=True, **hints):
from sympy import Abs, expand_mul, im, re
other = []
coeffr = []
coeffi = []
addterms = S.One
for a in self.args:
r, i = a.as_real_imag()
if i.is_zero:
coeffr.append(r)
elif r.is_zero:
coeffi.append(i*S.ImaginaryUnit)
elif a.is_commutative:
# search for complex conjugate pairs:
for i, x in enumerate(other):
if x == a.conjugate():
coeffr.append(Abs(x)**2)
del other[i]
break
else:
if a.is_Add:
addterms *= a
else:
other.append(a)
else:
other.append(a)
m = self.func(*other)
if hints.get('ignore') == m:
return
if len(coeffi) % 2:
imco = im(coeffi.pop(0))
# all other pairs make a real factor; they will be
# put into reco below
else:
imco = S.Zero
reco = self.func(*(coeffr + coeffi))
r, i = (reco*re(m), reco*im(m))
if addterms == 1:
if m == 1:
if imco is S.Zero:
return (reco, S.Zero)
else:
return (S.Zero, reco*imco)
if imco is S.Zero:
return (r, i)
return (-imco*i, imco*r)
addre, addim = expand_mul(addterms, deep=False).as_real_imag()
if imco is S.Zero:
return (r*addre - i*addim, i*addre + r*addim)
else:
r, i = -imco*i, imco*r
return (r*addre - i*addim, r*addim + i*addre)
@staticmethod
def _expandsums(sums):
"""
Helper function for _eval_expand_mul.
sums must be a list of instances of Basic.
"""
L = len(sums)
if L == 1:
return sums[0].args
terms = []
left = Mul._expandsums(sums[:L//2])
right = Mul._expandsums(sums[L//2:])
terms = [Mul(a, b) for a in left for b in right]
added = Add(*terms)
return Add.make_args(added) # it may have collapsed down to one term
def _eval_expand_mul(self, **hints):
from sympy import fraction
# Handle things like 1/(x*(x + 1)), which are automatically converted
# to 1/x*1/(x + 1)
expr = self
n, d = fraction(expr)
if d.is_Mul:
n, d = [i._eval_expand_mul(**hints) if i.is_Mul else i
for i in (n, d)]
expr = n/d
if not expr.is_Mul:
return expr
plain, sums, rewrite = [], [], False
for factor in expr.args:
if factor.is_Add:
sums.append(factor)
rewrite = True
else:
if factor.is_commutative:
plain.append(factor)
else:
sums.append(Basic(factor)) # Wrapper
if not rewrite:
return expr
else:
plain = self.func(*plain)
if sums:
terms = self.func._expandsums(sums)
args = []
for term in terms:
t = self.func(plain, term)
if t.is_Mul and any(a.is_Add for a in t.args):
t = t._eval_expand_mul()
args.append(t)
return Add(*args)
else:
return plain
@cacheit
def _eval_derivative(self, s):
args = list(self.args)
terms = []
for i in range(len(args)):
d = args[i].diff(s)
if d:
terms.append(self.func(*(args[:i] + [d] + args[i + 1:])))
return Add(*terms)
def _eval_difference_delta(self, n, step):
from sympy.series.limitseq import difference_delta as dd
arg0 = self.args[0]
rest = Mul(*self.args[1:])
return (arg0.subs(n, n + step) * dd(rest, n, step) + dd(arg0, n, step) *
rest)
def _matches_simple(self, expr, repl_dict):
# handle (w*3).matches('x*5') -> {w: x*5/3}
coeff, terms = self.as_coeff_Mul()
terms = Mul.make_args(terms)
if len(terms) == 1:
newexpr = self.__class__._combine_inverse(expr, coeff)
return terms[0].matches(newexpr, repl_dict)
return
def matches(self, expr, repl_dict={}, old=False):
expr = sympify(expr)
if self.is_commutative and expr.is_commutative:
return AssocOp._matches_commutative(self, expr, repl_dict, old)
elif self.is_commutative is not expr.is_commutative:
return None
c1, nc1 = self.args_cnc()
c2, nc2 = expr.args_cnc()
repl_dict = repl_dict.copy()
if c1:
if not c2:
c2 = [1]
a = self.func(*c1)
if isinstance(a, AssocOp):
repl_dict = a._matches_commutative(self.func(*c2), repl_dict, old)
else:
repl_dict = a.matches(self.func(*c2), repl_dict)
if repl_dict:
a = self.func(*nc1)
if isinstance(a, self.func):
repl_dict = a._matches(self.func(*nc2), repl_dict)
else:
repl_dict = a.matches(self.func(*nc2), repl_dict)
return repl_dict or None
def _matches(self, expr, repl_dict={}):
# weed out negative one prefixes#
from sympy import Wild
sign = 1
a, b = self.as_two_terms()
if a is S.NegativeOne:
if b.is_Mul:
sign = -sign
else:
# the remainder, b, is not a Mul anymore
return b.matches(-expr, repl_dict)
expr = sympify(expr)
if expr.is_Mul and expr.args[0] is S.NegativeOne:
expr = -expr
sign = -sign
if not expr.is_Mul:
# expr can only match if it matches b and a matches +/- 1
if len(self.args) == 2:
# quickly test for equality
if b == expr:
return a.matches(Rational(sign), repl_dict)
# do more expensive match
dd = b.matches(expr, repl_dict)
if dd is None:
return None
dd = a.matches(Rational(sign), dd)
return dd
return None
d = repl_dict.copy()
# weed out identical terms
pp = list(self.args)
ee = list(expr.args)
for p in self.args:
if p in expr.args:
ee.remove(p)
pp.remove(p)
# only one symbol left in pattern -> match the remaining expression
if len(pp) == 1 and isinstance(pp[0], Wild):
if len(ee) == 1:
d[pp[0]] = sign * ee[0]
else:
d[pp[0]] = sign * expr.func(*ee)
return d
if len(ee) != len(pp):
return None
for p, e in zip(pp, ee):
d = p.xreplace(d).matches(e, d)
if d is None:
return None
return d
@staticmethod
def _combine_inverse(lhs, rhs):
"""
Returns lhs/rhs, but treats arguments like symbols, so things like
oo/oo return 1, instead of a nan.
"""
if lhs == rhs:
return S.One
def check(l, r):
if l.is_Float and r.is_comparable:
# if both objects are added to 0 they will share the same "normalization"
# and are more likely to compare the same. Since Add(foo, 0) will not allow
# the 0 to pass, we use __add__ directly.
return l.__add__(0) == r.evalf().__add__(0)
return False
if check(lhs, rhs) or check(rhs, lhs):
return S.One
if lhs.is_Mul and rhs.is_Mul:
a = list(lhs.args)
b = [1]
for x in rhs.args:
if x in a:
a.remove(x)
elif -x in a:
a.remove(-x)
b.append(-1)
else:
b.append(x)
return lhs.func(*a)/rhs.func(*b)
return lhs/rhs
def as_powers_dict(self):
d = defaultdict(int)
for term in self.args:
b, e = term.as_base_exp()
d[b] += e
return d
def as_numer_denom(self):
# don't use _from_args to rebuild the numerators and denominators
# as the order is not guaranteed to be the same once they have
# been separated from each other
numers, denoms = list(zip(*[f.as_numer_denom() for f in self.args]))
return self.func(*numers), self.func(*denoms)
def as_base_exp(self):
e1 = None
bases = []
nc = 0
for m in self.args:
b, e = m.as_base_exp()
if not b.is_commutative:
nc += 1
if e1 is None:
e1 = e
elif e != e1 or nc > 1:
return self, S.One
bases.append(b)
return self.func(*bases), e1
def _eval_is_polynomial(self, syms):
return all(term._eval_is_polynomial(syms) for term in self.args)
def _eval_is_rational_function(self, syms):
return all(term._eval_is_rational_function(syms) for term in self.args)
def _eval_is_algebraic_expr(self, syms):
return all(term._eval_is_algebraic_expr(syms) for term in self.args)
_eval_is_finite = lambda self: _fuzzy_group(
a.is_finite for a in self.args)
_eval_is_commutative = lambda self: _fuzzy_group(
a.is_commutative for a in self.args)
_eval_is_complex = lambda self: _fuzzy_group(
(a.is_complex for a in self.args), quick_exit=True)
def _eval_is_infinite(self):
if any(a.is_infinite for a in self.args):
if any(a.is_zero for a in self.args):
return S.NaN.is_infinite
if any(a.is_zero is None for a in self.args):
return None
return True
def _eval_is_rational(self):
r = _fuzzy_group((a.is_rational for a in self.args), quick_exit=True)
if r:
return r
elif r is False:
return self.is_zero
def _eval_is_algebraic(self):
r = _fuzzy_group((a.is_algebraic for a in self.args), quick_exit=True)
if r:
return r
elif r is False:
return self.is_zero
def _eval_is_zero(self):
zero = infinite = False
for a in self.args:
z = a.is_zero
if z:
if infinite:
return # 0*oo is nan and nan.is_zero is None
zero = True
else:
if not a.is_finite:
if zero:
return # 0*oo is nan and nan.is_zero is None
infinite = True
if zero is False and z is None: # trap None
zero = None
return zero
def _eval_is_integer(self):
is_rational = self.is_rational
if is_rational:
n, d = self.as_numer_denom()
if d is S.One:
return True
elif d is S(2):
return n.is_even
elif is_rational is False:
return False
def _eval_is_polar(self):
has_polar = any(arg.is_polar for arg in self.args)
return has_polar and \
all(arg.is_polar or arg.is_positive for arg in self.args)
def _eval_is_real(self):
return self._eval_real_imag(True)
def _eval_real_imag(self, real):
zero = False
t_not_re_im = None
for t in self.args:
if not t.is_complex:
return t.is_complex
elif t.is_imaginary: # I
real = not real
elif t.is_real: # 2
if not zero:
z = t.is_zero
if not z and zero is False:
zero = z
elif z:
if all(a.is_finite for a in self.args):
return True
return
elif t.is_real is False:
# symbolic or literal like `2 + I` or symbolic imaginary
if t_not_re_im:
return # complex terms might cancel
t_not_re_im = t
elif t.is_imaginary is False: # symbolic like `2` or `2 + I`
if t_not_re_im:
return # complex terms might cancel
t_not_re_im = t
else:
return
if t_not_re_im:
if t_not_re_im.is_real is False:
if real: # like 3
return zero # 3*(smthng like 2 + I or i) is not real
if t_not_re_im.is_imaginary is False: # symbolic 2 or 2 + I
if not real: # like I
return zero # I*(smthng like 2 or 2 + I) is not real
elif zero is False:
return real # can't be trumped by 0
elif real:
return real # doesn't matter what zero is
def _eval_is_imaginary(self):
z = self.is_zero
if z:
return False
elif z is False:
return self._eval_real_imag(False)
def _eval_is_hermitian(self):
return self._eval_herm_antiherm(True)
def _eval_herm_antiherm(self, real):
one_nc = zero = one_neither = False
for t in self.args:
if not t.is_commutative:
if one_nc:
return
one_nc = True
if t.is_antihermitian:
real = not real
elif t.is_hermitian:
if not zero:
z = t.is_zero
if not z and zero is False:
zero = z
elif z:
if all(a.is_finite for a in self.args):
return True
return
elif t.is_hermitian is False:
if one_neither:
return
one_neither = True
else:
return
if one_neither:
if real:
return zero
elif zero is False or real:
return real
def _eval_is_antihermitian(self):
z = self.is_zero
if z:
return False
elif z is False:
return self._eval_herm_antiherm(False)
def _eval_is_irrational(self):
for t in self.args:
a = t.is_irrational
if a:
others = list(self.args)
others.remove(t)
if all((x.is_rational and fuzzy_not(x.is_zero)) is True for x in others):
return True
return
if a is None:
return
return False
def _eval_is_positive(self):
"""Return True if self is positive, False if not, and None if it
cannot be determined.
This algorithm is non-recursive and works by keeping track of the
sign which changes when a negative or nonpositive is encountered.
Whether a nonpositive or nonnegative is seen is also tracked since
the presence of these makes it impossible to return True, but
possible to return False if the end result is nonpositive. e.g.
pos * neg * nonpositive -> pos or zero -> None is returned
pos * neg * nonnegative -> neg or zero -> False is returned
"""
return self._eval_pos_neg(1)
def _eval_pos_neg(self, sign):
saw_NON = saw_NOT = False
for t in self.args:
if t.is_positive:
continue
elif t.is_negative:
sign = -sign
elif t.is_zero:
if all(a.is_finite for a in self.args):
return False
return
elif t.is_nonpositive:
sign = -sign
saw_NON = True
elif t.is_nonnegative:
saw_NON = True
elif t.is_positive is False:
sign = -sign
if saw_NOT:
return
saw_NOT = True
elif t.is_negative is False:
if saw_NOT:
return
saw_NOT = True
else:
return
if sign == 1 and saw_NON is False and saw_NOT is False:
return True
if sign < 0:
return False
def _eval_is_negative(self):
if self.args[0] == -1:
return (-self).is_positive # remove -1
return self._eval_pos_neg(-1)
def _eval_is_odd(self):
is_integer = self.is_integer
if is_integer:
r, acc = True, 1
for t in self.args:
if not t.is_integer:
return None
elif t.is_even:
r = False
elif t.is_integer:
if r is False:
pass
elif acc != 1 and (acc + t).is_odd:
r = False
elif t.is_odd is None:
r = None
acc = t
return r
# !integer -> !odd
elif is_integer is False:
return False
def _eval_is_even(self):
is_integer = self.is_integer
if is_integer:
return fuzzy_not(self.is_odd)
elif is_integer is False:
return False
def _eval_is_prime(self):
"""
If product is a positive integer, multiplication
will never result in a prime number.
"""
if self.is_number:
"""
If input is a number that is not completely simplified.
e.g. Mul(sqrt(3), sqrt(3), evaluate=False)
So we manually evaluate it and return whether that is prime or not.
"""
# Note: `doit()` was not used due to test failing (Infinite Recursion)
r = S.One
for arg in self.args:
r *= arg
return r.is_prime
if self.is_integer and self.is_positive:
"""
Here we count the number of arguments that have a minimum value
greater than two.
If there are more than one of such a symbol then the result is not prime.
Else, the result cannot be determined.
"""
number_of_args = 0 # count of symbols with minimum value greater than one
for arg in self.args:
if (arg-1).is_positive:
number_of_args += 1
if number_of_args > 1:
return False
def _eval_subs(self, old, new):
from sympy.functions.elementary.complexes import sign
from sympy.ntheory.factor_ import multiplicity
from sympy.simplify.powsimp import powdenest
from sympy.simplify.radsimp import fraction
if not old.is_Mul:
return None
# try keep replacement literal so -2*x doesn't replace 4*x
if old.args[0].is_Number and old.args[0] < 0:
if self.args[0].is_Number:
if self.args[0] < 0:
return self._subs(-old, -new)
return None
def base_exp(a):
# if I and -1 are in a Mul, they get both end up with
# a -1 base (see issue 6421); all we want here are the
# true Pow or exp separated into base and exponent
from sympy import exp
if a.is_Pow or a.func is exp:
return a.as_base_exp()
return a, S.One
def breakup(eq):
"""break up powers of eq when treated as a Mul:
b**(Rational*e) -> b**e, Rational
commutatives come back as a dictionary {b**e: Rational}
noncommutatives come back as a list [(b**e, Rational)]
"""
(c, nc) = (defaultdict(int), list())
for a in Mul.make_args(eq):
a = powdenest(a)
(b, e) = base_exp(a)
if e is not S.One:
(co, _) = e.as_coeff_mul()
b = Pow(b, e/co)
e = co
if a.is_commutative:
c[b] += e
else:
nc.append([b, e])
return (c, nc)
def rejoin(b, co):
"""
Put rational back with exponent; in general this is not ok, but
since we took it from the exponent for analysis, it's ok to put
it back.
"""
(b, e) = base_exp(b)
return Pow(b, e*co)
def ndiv(a, b):
"""if b divides a in an extractive way (like 1/4 divides 1/2
but not vice versa, and 2/5 does not divide 1/3) then return
the integer number of times it divides, else return 0.
"""
if not b.q % a.q or not a.q % b.q:
return int(a/b)
return 0
# give Muls in the denominator a chance to be changed (see issue 5651)
# rv will be the default return value
rv = None
n, d = fraction(self)
self2 = self
if d is not S.One:
self2 = n._subs(old, new)/d._subs(old, new)
if not self2.is_Mul:
return self2._subs(old, new)
if self2 != self:
rv = self2
# Now continue with regular substitution.
# handle the leading coefficient and use it to decide if anything
# should even be started; we always know where to find the Rational
# so it's a quick test
co_self = self2.args[0]
co_old = old.args[0]
co_xmul = None
if co_old.is_Rational and co_self.is_Rational:
# if coeffs are the same there will be no updating to do
# below after breakup() step; so skip (and keep co_xmul=None)
if co_old != co_self:
co_xmul = co_self.extract_multiplicatively(co_old)
elif co_old.is_Rational:
return rv
# break self and old into factors
(c, nc) = breakup(self2)
(old_c, old_nc) = breakup(old)
# update the coefficients if we had an extraction
# e.g. if co_self were 2*(3/35*x)**2 and co_old = 3/5
# then co_self in c is replaced by (3/5)**2 and co_residual
# is 2*(1/7)**2
if co_xmul and co_xmul.is_Rational and abs(co_old) != 1:
mult = S(multiplicity(abs(co_old), co_self))
c.pop(co_self)
if co_old in c:
c[co_old] += mult
else:
c[co_old] = mult
co_residual = co_self/co_old**mult
else:
co_residual = 1
# do quick tests to see if we can't succeed
ok = True
if len(old_nc) > len(nc):
# more non-commutative terms
ok = False
elif len(old_c) > len(c):
# more commutative terms
ok = False
elif set(i[0] for i in old_nc).difference(set(i[0] for i in nc)):
# unmatched non-commutative bases
ok = False
elif set(old_c).difference(set(c)):
# unmatched commutative terms
ok = False
elif any(sign(c[b]) != sign(old_c[b]) for b in old_c):
# differences in sign
ok = False
if not ok:
return rv
if not old_c:
cdid = None
else:
rat = []
for (b, old_e) in old_c.items():
c_e = c[b]
rat.append(ndiv(c_e, old_e))
if not rat[-1]:
return rv
cdid = min(rat)
if not old_nc:
ncdid = None
for i in range(len(nc)):
nc[i] = rejoin(*nc[i])
else:
ncdid = 0 # number of nc replacements we did
take = len(old_nc) # how much to look at each time
limit = cdid or S.Infinity # max number that we can take
failed = [] # failed terms will need subs if other terms pass
i = 0
while limit and i + take <= len(nc):
hit = False
# the bases must be equivalent in succession, and
# the powers must be extractively compatible on the
# first and last factor but equal inbetween.
rat = []
for j in range(take):
if nc[i + j][0] != old_nc[j][0]:
break
elif j == 0:
rat.append(ndiv(nc[i + j][1], old_nc[j][1]))
elif j == take - 1:
rat.append(ndiv(nc[i + j][1], old_nc[j][1]))
elif nc[i + j][1] != old_nc[j][1]:
break
else:
rat.append(1)
j += 1
else:
ndo = min(rat)
if ndo:
if take == 1:
if cdid:
ndo = min(cdid, ndo)
nc[i] = Pow(new, ndo)*rejoin(nc[i][0],
nc[i][1] - ndo*old_nc[0][1])
else:
ndo = 1
# the left residual
l = rejoin(nc[i][0], nc[i][1] - ndo*
old_nc[0][1])
# eliminate all middle terms
mid = new
# the right residual (which may be the same as the middle if take == 2)
ir = i + take - 1
r = (nc[ir][0], nc[ir][1] - ndo*
old_nc[-1][1])
if r[1]:
if i + take < len(nc):
nc[i:i + take] = [l*mid, r]
else:
r = rejoin(*r)
nc[i:i + take] = [l*mid*r]
else:
# there was nothing left on the right
nc[i:i + take] = [l*mid]
limit -= ndo
ncdid += ndo
hit = True
if not hit:
# do the subs on this failing factor
failed.append(i)
i += 1
else:
if not ncdid:
return rv
# although we didn't fail, certain nc terms may have
# failed so we rebuild them after attempting a partial
# subs on them
failed.extend(range(i, len(nc)))
for i in failed:
nc[i] = rejoin(*nc[i]).subs(old, new)
# rebuild the expression
if cdid is None:
do = ncdid
elif ncdid is None:
do = cdid
else:
do = min(ncdid, cdid)
margs = []
for b in c:
if b in old_c:
# calculate the new exponent
e = c[b] - old_c[b]*do
margs.append(rejoin(b, e))
else:
margs.append(rejoin(b.subs(old, new), c[b]))
if cdid and not ncdid:
# in case we are replacing commutative with non-commutative,
# we want the new term to come at the front just like the
# rest of this routine
margs = [Pow(new, cdid)] + margs
return co_residual*self2.func(*margs)*self2.func(*nc)
def _eval_nseries(self, x, n, logx):
from sympy import Order, powsimp
terms = [t.nseries(x, n=n, logx=logx) for t in self.args]
res = powsimp(self.func(*terms).expand(), combine='exp', deep=True)
if res.has(Order):
res += Order(x**n, x)
return res
def _eval_as_leading_term(self, x):
return self.func(*[t.as_leading_term(x) for t in self.args])
def _eval_conjugate(self):
return self.func(*[t.conjugate() for t in self.args])
def _eval_transpose(self):
return self.func(*[t.transpose() for t in self.args[::-1]])
def _eval_adjoint(self):
return self.func(*[t.adjoint() for t in self.args[::-1]])
def _sage_(self):
s = 1
for x in self.args:
s *= x._sage_()
return s
def as_content_primitive(self, radical=False, clear=True):
"""Return the tuple (R, self/R) where R is the positive Rational
extracted from self.
Examples
========
>>> from sympy import sqrt
>>> (-3*sqrt(2)*(2 - 2*sqrt(2))).as_content_primitive()
(6, -sqrt(2)*(-sqrt(2) + 1))
See docstring of Expr.as_content_primitive for more examples.
"""
coef = S.One
args = []
for i, a in enumerate(self.args):
c, p = a.as_content_primitive(radical=radical, clear=clear)
coef *= c
if p is not S.One:
args.append(p)
# don't use self._from_args here to reconstruct args
# since there may be identical args now that should be combined
# e.g. (2+2*x)*(3+3*x) should be (6, (1 + x)**2) not (6, (1+x)*(1+x))
return coef, self.func(*args)
def as_ordered_factors(self, order=None):
"""Transform an expression into an ordered list of factors.
Examples
========
>>> from sympy import sin, cos
>>> from sympy.abc import x, y
>>> (2*x*y*sin(x)*cos(x)).as_ordered_factors()
[2, x, y, sin(x), cos(x)]
"""
cpart, ncpart = self.args_cnc()
cpart.sort(key=lambda expr: expr.sort_key(order=order))
return cpart + ncpart
@property
def _sorted_args(self):
return tuple(self.as_ordered_factors())
def prod(a, start=1):
"""Return product of elements of a. Start with int 1 so if only
ints are included then an int result is returned.
Examples
========
>>> from sympy import prod, S
>>> prod(range(3))
0
>>> type(_) is int
True
>>> prod([S(2), 3])
6
>>> _.is_Integer
True
You can start the product at something other than 1:
>>> prod([1, 2], 3)
6
"""
return reduce(operator.mul, a, start)
def _keep_coeff(coeff, factors, clear=True, sign=False):
"""Return ``coeff*factors`` unevaluated if necessary.
If ``clear`` is False, do not keep the coefficient as a factor
if it can be distributed on a single factor such that one or
more terms will still have integer coefficients.
If ``sign`` is True, allow a coefficient of -1 to remain factored out.
Examples
========
>>> from sympy.core.mul import _keep_coeff
>>> from sympy.abc import x, y
>>> from sympy import S
>>> _keep_coeff(S.Half, x + 2)
(x + 2)/2
>>> _keep_coeff(S.Half, x + 2, clear=False)
x/2 + 1
>>> _keep_coeff(S.Half, (x + 2)*y, clear=False)
y*(x + 2)/2
>>> _keep_coeff(S(-1), x + y)
-x - y
>>> _keep_coeff(S(-1), x + y, sign=True)
-(x + y)
"""
if not coeff.is_Number:
if factors.is_Number:
factors, coeff = coeff, factors
else:
return coeff*factors
if coeff is S.One:
return factors
elif coeff is S.NegativeOne and not sign:
return -factors
elif factors.is_Add:
if not clear and coeff.is_Rational and coeff.q != 1:
q = S(coeff.q)
for i in factors.args:
c, t = i.as_coeff_Mul()
r = c/q
if r == int(r):
return coeff*factors
return Mul._from_args((coeff, factors))
elif factors.is_Mul:
margs = list(factors.args)
if margs[0].is_Number:
margs[0] *= coeff
if margs[0] == 1:
margs.pop(0)
else:
margs.insert(0, coeff)
return Mul._from_args(margs)
else:
return coeff*factors
def expand_2arg(e):
from sympy.simplify.simplify import bottom_up
def do(e):
if e.is_Mul:
c, r = e.as_coeff_Mul()
if c.is_Number and r.is_Add:
return _unevaluated_Add(*[c*ri for ri in r.args])
return e
return bottom_up(e, do)
from .numbers import Rational
from .power import Pow
from .add import Add, _addsort, _unevaluated_Add
| 60,510 | 32.823924 | 99 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/power.py
|
from __future__ import print_function, division
from math import log as _log
from .sympify import _sympify
from .cache import cacheit
from .singleton import S
from .expr import Expr
from .evalf import PrecisionExhausted
from .function import (_coeff_isneg, expand_complex, expand_multinomial,
expand_mul)
from .logic import fuzzy_bool, fuzzy_not
from .compatibility import as_int, range
from .evaluate import global_evaluate
from sympy.utilities.iterables import sift
from mpmath.libmp import sqrtrem as mpmath_sqrtrem
from math import sqrt as _sqrt
def isqrt(n):
"""Return the largest integer less than or equal to sqrt(n)."""
if n < 17984395633462800708566937239552:
return int(_sqrt(n))
return integer_nthroot(int(n), 2)[0]
def integer_nthroot(y, n):
"""
Return a tuple containing x = floor(y**(1/n))
and a boolean indicating whether the result is exact (that is,
whether x**n == y).
Examples
========
>>> from sympy import integer_nthroot
>>> integer_nthroot(16, 2)
(4, True)
>>> integer_nthroot(26, 2)
(5, False)
To simply determine if a number is a perfect square, the is_square
function should be used:
>>> from sympy.ntheory.primetest import is_square
>>> is_square(26)
False
See Also
========
sympy.ntheory.primetest.is_square
"""
y, n = as_int(y), as_int(n)
if y < 0:
raise ValueError("y must be nonnegative")
if n < 1:
raise ValueError("n must be positive")
if y in (0, 1):
return y, True
if n == 1:
return y, True
if n == 2:
x, rem = mpmath_sqrtrem(y)
return int(x), not rem
if n > y:
return 1, False
# Get initial estimate for Newton's method. Care must be taken to
# avoid overflow
try:
guess = int(y**(1./n) + 0.5)
except OverflowError:
exp = _log(y, 2)/n
if exp > 53:
shift = int(exp - 53)
guess = int(2.0**(exp - shift) + 1) << shift
else:
guess = int(2.0**exp)
if guess > 2**50:
# Newton iteration
xprev, x = -1, guess
while 1:
t = x**(n - 1)
xprev, x = x, ((n - 1)*x + y//t)//n
if abs(x - xprev) < 2:
break
else:
x = guess
# Compensate
t = x**n
while t < y:
x += 1
t = x**n
while t > y:
x -= 1
t = x**n
return int(x), t == y # int converts long to int if possible
class Pow(Expr):
"""
Defines the expression x**y as "x raised to a power y"
Singleton definitions involving (0, 1, -1, oo, -oo, I, -I):
+--------------+---------+-----------------------------------------------+
| expr | value | reason |
+==============+=========+===============================================+
| z**0 | 1 | Although arguments over 0**0 exist, see [2]. |
+--------------+---------+-----------------------------------------------+
| z**1 | z | |
+--------------+---------+-----------------------------------------------+
| (-oo)**(-1) | 0 | |
+--------------+---------+-----------------------------------------------+
| (-1)**-1 | -1 | |
+--------------+---------+-----------------------------------------------+
| S.Zero**-1 | zoo | This is not strictly true, as 0**-1 may be |
| | | undefined, but is convenient in some contexts |
| | | where the base is assumed to be positive. |
+--------------+---------+-----------------------------------------------+
| 1**-1 | 1 | |
+--------------+---------+-----------------------------------------------+
| oo**-1 | 0 | |
+--------------+---------+-----------------------------------------------+
| 0**oo | 0 | Because for all complex numbers z near |
| | | 0, z**oo -> 0. |
+--------------+---------+-----------------------------------------------+
| 0**-oo | zoo | This is not strictly true, as 0**oo may be |
| | | oscillating between positive and negative |
| | | values or rotating in the complex plane. |
| | | It is convenient, however, when the base |
| | | is positive. |
+--------------+---------+-----------------------------------------------+
| 1**oo | nan | Because there are various cases where |
| 1**-oo | | lim(x(t),t)=1, lim(y(t),t)=oo (or -oo), |
| 1**zoo | | but lim( x(t)**y(t), t) != 1. See [3]. |
+--------------+---------+-----------------------------------------------+
| (-1)**oo | nan | Because of oscillations in the limit. |
| (-1)**(-oo) | | |
+--------------+---------+-----------------------------------------------+
| oo**oo | oo | |
+--------------+---------+-----------------------------------------------+
| oo**-oo | 0 | |
+--------------+---------+-----------------------------------------------+
| (-oo)**oo | nan | |
| (-oo)**-oo | | |
+--------------+---------+-----------------------------------------------+
| oo**I | nan | oo**e could probably be best thought of as |
| (-oo)**I | | the limit of x**e for real x as x tends to |
| | | oo. If e is I, then the limit does not exist |
| | | and nan is used to indicate that. |
+--------------+---------+-----------------------------------------------+
| oo**(1+I) | zoo | If the real part of e is positive, then the |
| (-oo)**(1+I) | | limit of abs(x**e) is oo. So the limit value |
| | | is zoo. |
+--------------+---------+-----------------------------------------------+
| oo**(-1+I) | 0 | If the real part of e is negative, then the |
| -oo**(-1+I) | | limit is 0. |
+--------------+---------+-----------------------------------------------+
Because symbolic computations are more flexible that floating point
calculations and we prefer to never return an incorrect answer,
we choose not to conform to all IEEE 754 conventions. This helps
us avoid extra test-case code in the calculation of limits.
See Also
========
sympy.core.numbers.Infinity
sympy.core.numbers.NegativeInfinity
sympy.core.numbers.NaN
References
==========
.. [1] http://en.wikipedia.org/wiki/Exponentiation
.. [2] http://en.wikipedia.org/wiki/Exponentiation#Zero_to_the_power_of_zero
.. [3] http://en.wikipedia.org/wiki/Indeterminate_forms
"""
is_Pow = True
__slots__ = ['is_commutative']
@cacheit
def __new__(cls, b, e, evaluate=None):
if evaluate is None:
evaluate = global_evaluate[0]
from sympy.functions.elementary.exponential import exp_polar
b = _sympify(b)
e = _sympify(e)
if evaluate:
if e is S.Zero:
return S.One
elif e is S.One:
return b
# Only perform autosimplification if exponent or base is a Symbol or number
elif (b.is_Symbol or b.is_number) and (e.is_Symbol or e.is_number) and\
e.is_integer and _coeff_isneg(b):
if e.is_even:
b = -b
elif e.is_odd:
return -Pow(-b, e)
if S.NaN in (b, e): # XXX S.NaN**x -> S.NaN under assumption that x != 0
return S.NaN
elif b is S.One:
if abs(e).is_infinite:
return S.NaN
return S.One
else:
# recognize base as E
if not e.is_Atom and b is not S.Exp1 and b.func is not exp_polar:
from sympy import numer, denom, log, sign, im, factor_terms
c, ex = factor_terms(e, sign=False).as_coeff_Mul()
den = denom(ex)
if den.func is log and den.args[0] == b:
return S.Exp1**(c*numer(ex))
elif den.is_Add:
s = sign(im(b))
if s.is_Number and s and den == \
log(-factor_terms(b, sign=False)) + s*S.ImaginaryUnit*S.Pi:
return S.Exp1**(c*numer(ex))
obj = b._eval_power(e)
if obj is not None:
return obj
obj = Expr.__new__(cls, b, e)
obj = cls._exec_constructor_postprocessors(obj)
if not isinstance(obj, Pow):
return obj
obj.is_commutative = (b.is_commutative and e.is_commutative)
return obj
@property
def base(self):
return self._args[0]
@property
def exp(self):
return self._args[1]
@classmethod
def class_key(cls):
return 3, 2, cls.__name__
def _eval_refine(self, assumptions):
from sympy.assumptions.ask import ask, Q
b, e = self.as_base_exp()
if ask(Q.integer(e), assumptions) and _coeff_isneg(b):
if ask(Q.even(e), assumptions):
return Pow(-b, e)
elif ask(Q.odd(e), assumptions):
return -Pow(-b, e)
def _eval_power(self, other):
from sympy import Abs, arg, exp, floor, im, log, re, sign
b, e = self.as_base_exp()
if b is S.NaN:
return (b**e)**other # let __new__ handle it
s = None
if other.is_integer:
s = 1
elif b.is_polar: # e.g. exp_polar, besselj, var('p', polar=True)...
s = 1
elif e.is_real is not None:
# helper functions ===========================
def _half(e):
"""Return True if the exponent has a literal 2 as the
denominator, else None."""
if getattr(e, 'q', None) == 2:
return True
n, d = e.as_numer_denom()
if n.is_integer and d == 2:
return True
def _n2(e):
"""Return ``e`` evaluated to a Number with 2 significant
digits, else None."""
try:
rv = e.evalf(2, strict=True)
if rv.is_Number:
return rv
except PrecisionExhausted:
pass
# ===================================================
if e.is_real:
# we need _half(other) with constant floor or
# floor(S.Half - e*arg(b)/2/pi) == 0
# handle -1 as special case
if e == -1:
# floor arg. is 1/2 + arg(b)/2/pi
if _half(other):
if b.is_negative is True:
return S.NegativeOne**other*Pow(-b, e*other)
if b.is_real is False:
return Pow(b.conjugate()/Abs(b)**2, other)
elif e.is_even:
if b.is_real:
b = abs(b)
if b.is_imaginary:
b = abs(im(b))*S.ImaginaryUnit
if (abs(e) < 1) == True or e == 1:
s = 1 # floor = 0
elif b.is_nonnegative:
s = 1 # floor = 0
elif re(b).is_nonnegative and (abs(e) < 2) == True:
s = 1 # floor = 0
elif fuzzy_not(im(b).is_zero) and abs(e) == 2:
s = 1 # floor = 0
elif _half(other):
s = exp(2*S.Pi*S.ImaginaryUnit*other*floor(
S.Half - e*arg(b)/(2*S.Pi)))
if s.is_real and _n2(sign(s) - s) == 0:
s = sign(s)
else:
s = None
else:
# e.is_real is False requires:
# _half(other) with constant floor or
# floor(S.Half - im(e*log(b))/2/pi) == 0
try:
s = exp(2*S.ImaginaryUnit*S.Pi*other*
floor(S.Half - im(e*log(b))/2/S.Pi))
# be careful to test that s is -1 or 1 b/c sign(I) == I:
# so check that s is real
if s.is_real and _n2(sign(s) - s) == 0:
s = sign(s)
else:
s = None
except PrecisionExhausted:
s = None
if s is not None:
return s*Pow(b, e*other)
def _eval_is_even(self):
if self.exp.is_integer and self.exp.is_positive:
return self.base.is_even
def _eval_is_positive(self):
from sympy import log
if self.base == self.exp:
if self.base.is_nonnegative:
return True
elif self.base.is_positive:
if self.exp.is_real:
return True
elif self.base.is_negative:
if self.exp.is_even:
return True
if self.exp.is_odd:
return False
elif self.base.is_nonpositive:
if self.exp.is_odd:
return False
elif self.base.is_imaginary:
if self.exp.is_integer:
m = self.exp % 4
if m.is_zero:
return True
if m.is_integer and m.is_zero is False:
return False
if self.exp.is_imaginary:
return log(self.base).is_imaginary
def _eval_is_negative(self):
if self.base.is_negative:
if self.exp.is_odd:
return True
if self.exp.is_even:
return False
elif self.base.is_positive:
if self.exp.is_real:
return False
elif self.base.is_nonnegative:
if self.exp.is_nonnegative:
return False
elif self.base.is_nonpositive:
if self.exp.is_even:
return False
elif self.base.is_real:
if self.exp.is_even:
return False
def _eval_is_zero(self):
if self.base.is_zero:
if self.exp.is_positive:
return True
elif self.exp.is_nonpositive:
return False
elif self.base.is_zero is False:
if self.exp.is_finite:
return False
elif self.exp.is_infinite:
if (1 - abs(self.base)).is_positive:
return self.exp.is_positive
elif (1 - abs(self.base)).is_negative:
return self.exp.is_negative
else:
# when self.base.is_zero is None
return None
def _eval_is_integer(self):
b, e = self.args
if b.is_rational:
if b.is_integer is False and e.is_positive:
return False # rat**nonneg
if b.is_integer and e.is_integer:
if b is S.NegativeOne:
return True
if e.is_nonnegative or e.is_positive:
return True
if b.is_integer and e.is_negative and (e.is_finite or e.is_integer):
if fuzzy_not((b - 1).is_zero) and fuzzy_not((b + 1).is_zero):
return False
if b.is_Number and e.is_Number:
check = self.func(*self.args)
return check.is_Integer
def _eval_is_real(self):
from sympy import arg, exp, log, Mul
real_b = self.base.is_real
if real_b is None:
if self.base.func == exp and self.base.args[0].is_imaginary:
return self.exp.is_imaginary
return
real_e = self.exp.is_real
if real_e is None:
return
if real_b and real_e:
if self.base.is_positive:
return True
elif self.base.is_nonnegative:
if self.exp.is_nonnegative:
return True
else:
if self.exp.is_integer:
return True
elif self.base.is_negative:
if self.exp.is_Rational:
return False
if real_e and self.exp.is_negative:
return Pow(self.base, -self.exp).is_real
im_b = self.base.is_imaginary
im_e = self.exp.is_imaginary
if im_b:
if self.exp.is_integer:
if self.exp.is_even:
return True
elif self.exp.is_odd:
return False
elif im_e and log(self.base).is_imaginary:
return True
elif self.exp.is_Add:
c, a = self.exp.as_coeff_Add()
if c and c.is_Integer:
return Mul(
self.base**c, self.base**a, evaluate=False).is_real
elif self.base in (-S.ImaginaryUnit, S.ImaginaryUnit):
if (self.exp/2).is_integer is False:
return False
if real_b and im_e:
if self.base is S.NegativeOne:
return True
c = self.exp.coeff(S.ImaginaryUnit)
if c:
ok = (c*log(self.base)/S.Pi).is_Integer
if ok is not None:
return ok
if real_b is False: # we already know it's not imag
i = arg(self.base)*self.exp/S.Pi
return i.is_integer
def _eval_is_complex(self):
if all(a.is_complex for a in self.args):
return True
def _eval_is_imaginary(self):
from sympy import arg, log
if self.base.is_imaginary:
if self.exp.is_integer:
odd = self.exp.is_odd
if odd is not None:
return odd
return
if self.exp.is_imaginary:
imlog = log(self.base).is_imaginary
if imlog is not None:
return False # I**i -> real; (2*I)**i -> complex ==> not imaginary
if self.base.is_real and self.exp.is_real:
if self.base.is_positive:
return False
else:
rat = self.exp.is_rational
if not rat:
return rat
if self.exp.is_integer:
return False
else:
half = (2*self.exp).is_integer
if half:
return self.base.is_negative
return half
if self.base.is_real is False: # we already know it's not imag
i = arg(self.base)*self.exp/S.Pi
isodd = (2*i).is_odd
if isodd is not None:
return isodd
if self.exp.is_negative:
return (1/self).is_imaginary
def _eval_is_odd(self):
if self.exp.is_integer:
if self.exp.is_positive:
return self.base.is_odd
elif self.exp.is_nonnegative and self.base.is_odd:
return True
elif self.base is S.NegativeOne:
return True
def _eval_is_finite(self):
if self.exp.is_negative:
if self.base.is_zero:
return False
if self.base.is_infinite:
return True
c1 = self.base.is_finite
if c1 is None:
return
c2 = self.exp.is_finite
if c2 is None:
return
if c1 and c2:
if self.exp.is_nonnegative or fuzzy_not(self.base.is_zero):
return True
def _eval_is_prime(self):
if self.exp == S.One:
return self.base.is_prime
if self.is_number:
return self.doit().is_prime
if self.is_integer and self.is_positive:
"""
a Power will be non-prime only if both base and exponent
are greater than 1
"""
if (self.base-1).is_positive or (self.exp-1).is_positive:
return False
def _eval_is_polar(self):
return self.base.is_polar
def _eval_subs(self, old, new):
from sympy import exp, log, Symbol
def _check(ct1, ct2, old):
"""Return bool, pow where, if bool is True, then the exponent of
Pow `old` will combine with `pow` so the substitution is valid,
otherwise bool will be False,
cti are the coefficient and terms of an exponent of self or old
In this _eval_subs routine a change like (b**(2*x)).subs(b**x, y)
will give y**2 since (b**x)**2 == b**(2*x); if that equality does
not hold then the substitution should not occur so `bool` will be
False.
"""
coeff1, terms1 = ct1
coeff2, terms2 = ct2
if terms1 == terms2:
pow = coeff1/coeff2
try:
pow = as_int(pow)
combines = True
except ValueError:
combines = Pow._eval_power(
Pow(*old.as_base_exp(), evaluate=False),
pow) is not None
return combines, pow
return False, None
if old == self.base:
return new**self.exp._subs(old, new)
# issue 10829: (4**x - 3*y + 2).subs(2**x, y) -> y**2 - 3*y + 2
if old.func is self.func and self.exp == old.exp:
l = log(self.base, old.base)
if l.is_Number:
return Pow(new, l)
if old.func is self.func and self.base == old.base:
if self.exp.is_Add is False:
ct1 = self.exp.as_independent(Symbol, as_Add=False)
ct2 = old.exp.as_independent(Symbol, as_Add=False)
ok, pow = _check(ct1, ct2, old)
if ok:
# issue 5180: (x**(6*y)).subs(x**(3*y),z)->z**2
return self.func(new, pow)
else: # b**(6*x+a).subs(b**(3*x), y) -> y**2 * b**a
# exp(exp(x) + exp(x**2)).subs(exp(exp(x)), w) -> w * exp(exp(x**2))
oarg = old.exp
new_l = []
o_al = []
ct2 = oarg.as_coeff_mul()
for a in self.exp.args:
newa = a._subs(old, new)
ct1 = newa.as_coeff_mul()
ok, pow = _check(ct1, ct2, old)
if ok:
new_l.append(new**pow)
continue
o_al.append(newa)
if new_l:
new_l.append(Pow(self.base, Add(*o_al), evaluate=False))
return Mul(*new_l)
if old.func is exp and self.exp.is_real and self.base.is_positive:
ct1 = old.args[0].as_independent(Symbol, as_Add=False)
ct2 = (self.exp*log(self.base)).as_independent(
Symbol, as_Add=False)
ok, pow = _check(ct1, ct2, old)
if ok:
return self.func(new, pow) # (2**x).subs(exp(x*log(2)), z) -> z
def as_base_exp(self):
"""Return base and exp of self.
If base is 1/Integer, then return Integer, -exp. If this extra
processing is not needed, the base and exp properties will
give the raw arguments
Examples
========
>>> from sympy import Pow, S
>>> p = Pow(S.Half, 2, evaluate=False)
>>> p.as_base_exp()
(2, -2)
>>> p.args
(1/2, 2)
"""
b, e = self.args
if b.is_Rational and b.p == 1 and b.q != 1:
return Integer(b.q), -e
return b, e
def _eval_adjoint(self):
from sympy.functions.elementary.complexes import adjoint
i, p = self.exp.is_integer, self.base.is_positive
if i:
return adjoint(self.base)**self.exp
if p:
return self.base**adjoint(self.exp)
if i is False and p is False:
expanded = expand_complex(self)
if expanded != self:
return adjoint(expanded)
def _eval_conjugate(self):
from sympy.functions.elementary.complexes import conjugate as c
i, p = self.exp.is_integer, self.base.is_positive
if i:
return c(self.base)**self.exp
if p:
return self.base**c(self.exp)
if i is False and p is False:
expanded = expand_complex(self)
if expanded != self:
return c(expanded)
if self.is_real:
return self
def _eval_transpose(self):
from sympy.functions.elementary.complexes import transpose
i, p = self.exp.is_integer, self.base.is_complex
if p:
return self.base**self.exp
if i:
return transpose(self.base)**self.exp
if i is False and p is False:
expanded = expand_complex(self)
if expanded != self:
return transpose(expanded)
def _eval_expand_power_exp(self, **hints):
"""a**(n+m) -> a**n*a**m"""
b = self.base
e = self.exp
if e.is_Add and e.is_commutative:
expr = []
for x in e.args:
expr.append(self.func(self.base, x))
return Mul(*expr)
return self.func(b, e)
def _eval_expand_power_base(self, **hints):
"""(a*b)**n -> a**n * b**n"""
force = hints.get('force', False)
b = self.base
e = self.exp
if not b.is_Mul:
return self
cargs, nc = b.args_cnc(split_1=False)
# expand each term - this is top-level-only
# expansion but we have to watch out for things
# that don't have an _eval_expand method
if nc:
nc = [i._eval_expand_power_base(**hints)
if hasattr(i, '_eval_expand_power_base') else i
for i in nc]
if e.is_Integer:
if e.is_positive:
rv = Mul(*nc*e)
else:
rv = 1/Mul(*nc*-e)
if cargs:
rv *= Mul(*cargs)**e
return rv
if not cargs:
return self.func(Mul(*nc), e, evaluate=False)
nc = [Mul(*nc)]
# sift the commutative bases
sifted = sift(cargs, lambda x: x.is_real)
maybe_real = sifted[True] + sifted[None]
other = sifted[False]
def pred(x):
if x is S.ImaginaryUnit:
return S.ImaginaryUnit
polar = x.is_polar
if polar:
return True
if polar is None:
return fuzzy_bool(x.is_nonnegative)
sifted = sift(maybe_real, pred)
nonneg = sifted[True]
other += sifted[None]
neg = sifted[False]
imag = sifted[S.ImaginaryUnit]
if imag:
I = S.ImaginaryUnit
i = len(imag) % 4
if i == 0:
pass
elif i == 1:
other.append(I)
elif i == 2:
if neg:
nonn = -neg.pop()
if nonn is not S.One:
nonneg.append(nonn)
else:
neg.append(S.NegativeOne)
else:
if neg:
nonn = -neg.pop()
if nonn is not S.One:
nonneg.append(nonn)
else:
neg.append(S.NegativeOne)
other.append(I)
del imag
# bring out the bases that can be separated from the base
if force or e.is_integer:
# treat all commutatives the same and put nc in other
cargs = nonneg + neg + other
other = nc
else:
# this is just like what is happening automatically, except
# that now we are doing it for an arbitrary exponent for which
# no automatic expansion is done
assert not e.is_Integer
# handle negatives by making them all positive and putting
# the residual -1 in other
if len(neg) > 1:
o = S.One
if not other and neg[0].is_Number:
o *= neg.pop(0)
if len(neg) % 2:
o = -o
for n in neg:
nonneg.append(-n)
if o is not S.One:
other.append(o)
elif neg and other:
if neg[0].is_Number and neg[0] is not S.NegativeOne:
other.append(S.NegativeOne)
nonneg.append(-neg[0])
else:
other.extend(neg)
else:
other.extend(neg)
del neg
cargs = nonneg
other += nc
rv = S.One
if cargs:
rv *= Mul(*[self.func(b, e, evaluate=False) for b in cargs])
if other:
rv *= self.func(Mul(*other), e, evaluate=False)
return rv
def _eval_expand_multinomial(self, **hints):
"""(a+b+..) ** n -> a**n + n*a**(n-1)*b + .., n is nonzero integer"""
base, exp = self.args
result = self
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 result
else:
radical, result = self.func(base, exp - n), []
expanded_base_n = self.func(base, n)
if expanded_base_n.is_Pow:
expanded_base_n = \
expanded_base_n._eval_expand_multinomial()
for term in Add.make_args(expanded_base_n):
result.append(term*radical)
return Add(*result)
n = int(exp)
if base.is_commutative:
order_terms, other_terms = [], []
for b in base.args:
if b.is_Order:
order_terms.append(b)
else:
other_terms.append(b)
if order_terms:
# (f(x) + O(x^n))^m -> f(x)^m + m*f(x)^{m-1} *O(x^n)
f = Add(*other_terms)
o = Add(*order_terms)
if n == 2:
return expand_multinomial(f**n, deep=False) + n*f*o
else:
g = expand_multinomial(f**(n - 1), deep=False)
return expand_mul(f*g, deep=False) + n*g*o
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 = self.func(a.q * b.q, n)
a, b = a.p*b.q, a.q*b.p
else:
k = self.func(a.q, n)
a, b = a.p, a.q*b
elif not b.is_Integer:
k = self.func(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
from sympy.polys.polyutils import basic_from_dict
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.
return basic_from_dict(expansion_dict, *p)
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()
if multi.is_Add:
return Add(*[f*g for f in base.args
for g in multi.args])
else:
# XXX can this ever happen if base was an Add?
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 / self.func(base, -exp)._eval_expand_multinomial()
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 *= self.func(base, term)
else:
tail += term
return coeff * self.func(base, tail)
else:
return result
def as_real_imag(self, deep=True, **hints):
from sympy import atan2, cos, im, re, sin
from sympy.polys.polytools import poly
if self.exp.is_Integer:
exp = self.exp
re, im = self.base.as_real_imag(deep=deep)
if not im:
return self, S.Zero
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:
re, im = self.base.as_real_imag(deep=deep)
if im.is_zero and self.exp is S.Half:
if re.is_nonnegative:
return self, S.Zero
if re.is_nonpositive:
return S.Zero, (-self.base)**self.exp
# XXX: 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.
r = self.func(self.func(re, 2) + self.func(im, 2), S.Half)
t = atan2(im, re)
rp, tp = self.func(r, self.exp), t*self.exp
return (rp*cos(tp), rp*sin(tp))
else:
if deep:
hints['complex'] = False
expanded = self.expand(deep, **hints)
if hints.get('ignore') == expanded:
return None
else:
return (re(expanded), im(expanded))
else:
return (re(self), im(self))
def _eval_derivative(self, s):
from sympy import log
dbase = self.base.diff(s)
dexp = self.exp.diff(s)
return self * (dexp * log(self.base) + dbase * self.exp/self.base)
def _eval_evalf(self, prec):
base, exp = self.as_base_exp()
base = base._evalf(prec)
if not exp.is_Integer:
exp = exp._evalf(prec)
if exp.is_negative and base.is_number and base.is_real is False:
base = base.conjugate() / (base * base.conjugate())._evalf(prec)
exp = -exp
return self.func(base, exp).expand()
return self.func(base, exp)
def _eval_is_polynomial(self, syms):
if self.exp.has(*syms):
return False
if self.base.has(*syms):
return bool(self.base._eval_is_polynomial(syms) and
self.exp.is_Integer and (self.exp >= 0))
else:
return True
def _eval_is_rational(self):
p = self.func(*self.as_base_exp()) # in case it's unevaluated
if not p.is_Pow:
return p.is_rational
b, e = p.as_base_exp()
if e.is_Rational and b.is_Rational:
# we didn't check that e is not an Integer
# because Rational**Integer autosimplifies
return False
if e.is_integer:
if b.is_rational:
if fuzzy_not(b.is_zero) or e.is_nonnegative:
return True
if b == e: # always rational, even for 0**0
return True
elif b.is_irrational:
return e.is_zero
def _eval_is_algebraic(self):
def _is_one(expr):
try:
return (expr - 1).is_zero
except ValueError:
# when the operation is not allowed
return False
if self.base.is_zero or _is_one(self.base):
return True
elif self.exp.is_rational:
if self.base.is_algebraic is False:
return self.exp.is_nonzero
return self.base.is_algebraic
elif self.base.is_algebraic and self.exp.is_algebraic:
if ((fuzzy_not(self.base.is_zero)
and fuzzy_not(_is_one(self.base)))
or self.base.is_integer is False
or self.base.is_irrational):
return self.exp.is_rational
def _eval_is_rational_function(self, syms):
if self.exp.has(*syms):
return False
if self.base.has(*syms):
return self.base._eval_is_rational_function(syms) and \
self.exp.is_Integer
else:
return True
def _eval_is_algebraic_expr(self, syms):
if self.exp.has(*syms):
return False
if self.base.has(*syms):
return self.base._eval_is_algebraic_expr(syms) and \
self.exp.is_Rational
else:
return True
def as_numer_denom(self):
if not self.is_commutative:
return self, S.One
base, exp = self.as_base_exp()
n, d = base.as_numer_denom()
# this should be the same as ExpBase.as_numer_denom wrt
# exponent handling
neg_exp = exp.is_negative
if not neg_exp and not (-exp).is_negative:
neg_exp = _coeff_isneg(exp)
int_exp = exp.is_integer
# the denominator cannot be separated from the numerator if
# its sign is unknown unless the exponent is an integer, e.g.
# sqrt(a/b) != sqrt(a)/sqrt(b) when a=1 and b=-1. But if the
# denominator is negative the numerator and denominator can
# be negated and the denominator (now positive) separated.
if not (d.is_real or int_exp):
n = base
d = S.One
dnonpos = d.is_nonpositive
if dnonpos:
n, d = -n, -d
elif dnonpos is None and not int_exp:
n = base
d = S.One
if neg_exp:
n, d = d, n
exp = -exp
return self.func(n, exp), self.func(d, exp)
def matches(self, expr, repl_dict={}, old=False):
expr = _sympify(expr)
# special case, pattern = 1 and expr.exp can match to 0
if expr is S.One:
d = repl_dict.copy()
d = self.exp.matches(S.Zero, d)
if d is not None:
return d
# make sure the expression to be matched is an Expr
if not isinstance(expr, Expr):
return None
b, e = expr.as_base_exp()
# special case number
sb, se = self.as_base_exp()
if sb.is_Symbol and se.is_Integer and expr:
if e.is_rational:
return sb.matches(b**(e/se), repl_dict)
return sb.matches(expr**(1/se), repl_dict)
d = repl_dict.copy()
d = self.base.matches(b, d)
if d is None:
return None
d = self.exp.xreplace(d).matches(e, d)
if d is None:
return Expr.matches(self, expr, repl_dict)
return d
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
# expressions involving only numbers, the log function, and log(x).
from sympy import ceiling, collect, exp, log, O, Order, powsimp
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 expand_multinomial(self.func(b._eval_nseries(x, n=n,
logx=logx), e), 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"
nuse = n
cf = 1
try:
ord = b.as_leading_term(x)
cf = Order(ord, x).getn()
if cf and cf.is_Number:
nuse = n + 2*ceiling(cf)
else:
cf = 1
except NotImplementedError:
pass
b_orig, prefactor = b, O(1, x)
while prefactor.is_Order:
nuse += 1
b = b_orig._eval_nseries(x, n=nuse, logx=logx)
prefactor = b.as_leading_term(x)
# express "rest" as: rest = 1 + k*x**l + ... + O(x**n)
rest = expand_mul((b - prefactor)/prefactor)
if rest.is_Order:
return 1/prefactor + rest/prefactor + O(x**n, x)
k, l = rest.leadterm(x)
if l.is_Rational and l > 0:
pass
elif l.is_number and l > 0:
l = l.evalf()
elif l == 0:
k = k.simplify()
if k == 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)
else:
raise NotImplementedError()
else:
raise NotImplementedError()
if cf < 0:
cf = S.One/abs(cf)
try:
dn = Order(1/prefactor, x).getn()
if dn and dn < 0:
pass
else:
dn = 0
except NotImplementedError:
dn = 0
terms = [1/prefactor]
for m in range(1, ceiling((n - dn + 1)/l*cf)):
new_term = terms[-1]*(-rest)
if new_term.is_Pow:
new_term = new_term._eval_expand_multinomial(
deep=False)
else:
new_term = expand_mul(new_term, deep=False)
terms.append(new_term)
terms.append(O(x**n, x))
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:
nuse, denominator = n, O(1, x)
while denominator.is_Order:
denominator = (b**(-e))._eval_nseries(x, n=nuse, logx=logx)
nuse += 1
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)
# see if the base is as simple as possible
bx = b
while bx.is_Pow and bx.exp.is_Rational:
bx = bx.base
if bx == x:
return self
# 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)
infinite = n.is_infinite
if not infinite:
# 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, infinite
order = O(x**n, x)
ei, infinite = e2int(e)
b0 = b.limit(x, 0)
if infinite 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_infinite):
if infinite 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
if e.is_real and e.is_positive:
lt = b.as_leading_term(x)
# Try to correct nuse (= m) guess from:
# (lt + rest + O(x**m))**e =
# lt**e*(1 + rest/lt + O(x**m)/lt)**e =
# lt**e + ... + O(x**m)*lt**(e - 1) = ... + O(x**n)
try:
cf = Order(lt, x).getn()
nuse = ceiling(n - cf*(e - 1))
except NotImplementedError:
pass
bs = b._eval_nseries(x, n=nuse, logx=logx)
terms = bs.removeO()
if terms.is_Add:
bs = terms
lt = terms.as_leading_term(x)
# bs -> lt + rest -> lt*(1 + (bs/lt - 1))
return ((self.func(lt, e) * self.func((bs/lt).expand(), e).nseries(
x, n=nuse, logx=logx)).expand() + order)
if bs.is_Add:
from sympy import O
# So, bs + O() == terms
c = Dummy('c')
res = []
for arg in bs.args:
if arg.is_Order:
arg = c*arg.expr
res.append(arg)
bs = Add(*res)
rv = (bs**e).series(x).subs(c, O(1, x))
rv += order
return rv
rv = bs**e
if terms != bs:
rv += order
return rv
# either b0 is bounded but neither 1 nor 0 or e is infinite
# b -> b0 + (b-b0) -> b0 * (1 + (b/b0-1))
o2 = order*(b0**-e)
z = (b/b0 - 1)
o = O(z, x)
if o is S.Zero or o2 is S.Zero:
infinite = True
else:
if o.expr.is_number:
e2 = log(o2.expr*x)/log(x)
else:
e2 = log(o2.expr)/log(o.expr)
n, infinite = e2int(e2)
if infinite:
# 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 range(n + 2):
g = self._taylor_term(i, z, g)
g = g.nseries(x, n=n, logx=logx)
l.append(g)
r = Add(*l)
return expand_mul(r*b0**e) + order
def _eval_as_leading_term(self, x):
from sympy import exp, log
if not self.exp.has(x):
return self.func(self.base.as_leading_term(x), self.exp)
return exp(self.exp * log(self.base)).as_leading_term(x)
@cacheit
def _taylor_term(self, n, x, *previous_terms): # of (1+x)**e
from sympy import binomial
return binomial(self.exp, n) * self.func(x, n)
def _sage_(self):
return self.args[0]._sage_()**self.args[1]._sage_()
def as_content_primitive(self, radical=False, clear=True):
"""Return the tuple (R, self/R) where R is the positive Rational
extracted from self.
Examples
========
>>> from sympy import sqrt
>>> sqrt(4 + 4*sqrt(2)).as_content_primitive()
(2, sqrt(1 + sqrt(2)))
>>> sqrt(3 + 3*sqrt(2)).as_content_primitive()
(1, sqrt(3)*sqrt(1 + sqrt(2)))
>>> from sympy import expand_power_base, powsimp, Mul
>>> from sympy.abc import x, y
>>> ((2*x + 2)**2).as_content_primitive()
(4, (x + 1)**2)
>>> (4**((1 + y)/2)).as_content_primitive()
(2, 4**(y/2))
>>> (3**((1 + y)/2)).as_content_primitive()
(1, 3**((y + 1)/2))
>>> (3**((5 + y)/2)).as_content_primitive()
(9, 3**((y + 1)/2))
>>> eq = 3**(2 + 2*x)
>>> powsimp(eq) == eq
True
>>> eq.as_content_primitive()
(9, 3**(2*x))
>>> powsimp(Mul(*_))
3**(2*x + 2)
>>> eq = (2 + 2*x)**y
>>> s = expand_power_base(eq); s.is_Mul, s
(False, (2*x + 2)**y)
>>> eq.as_content_primitive()
(1, (2*(x + 1))**y)
>>> s = expand_power_base(_[1]); s.is_Mul, s
(True, 2**y*(x + 1)**y)
See docstring of Expr.as_content_primitive for more examples.
"""
b, e = self.as_base_exp()
b = _keep_coeff(*b.as_content_primitive(radical=radical, clear=clear))
ce, pe = e.as_content_primitive(radical=radical, clear=clear)
if b.is_Rational:
#e
#= ce*pe
#= ce*(h + t)
#= ce*h + ce*t
#=> self
#= b**(ce*h)*b**(ce*t)
#= b**(cehp/cehq)*b**(ce*t)
#= b**(iceh+r/cehq)*b**(ce*t)
#= b**(iceh)*b**(r/cehq)*b**(ce*t)
#= b**(iceh)*b**(ce*t + r/cehq)
h, t = pe.as_coeff_Add()
if h.is_Rational:
ceh = ce*h
c = self.func(b, ceh)
r = S.Zero
if not c.is_Rational:
iceh, r = divmod(ceh.p, ceh.q)
c = self.func(b, iceh)
return c, self.func(b, _keep_coeff(ce, t + r/ce/ceh.q))
e = _keep_coeff(ce, pe)
# b**e = (h*t)**e = h**e*t**e = c*m*t**e
if e.is_Rational and b.is_Mul:
h, t = b.as_content_primitive(radical=radical, clear=clear) # h is positive
c, m = self.func(h, e).as_coeff_Mul() # so c is positive
m, me = m.as_base_exp()
if m is S.One or me == e: # probably always true
# return the following, not return c, m*Pow(t, e)
# which would change Pow into Mul; we let sympy
# decide what to do by using the unevaluated Mul, e.g
# should it stay as sqrt(2 + 2*sqrt(5)) or become
# sqrt(2)*sqrt(1 + sqrt(5))
return c, self.func(_keep_coeff(m, t), e)
return S.One, self.func(b, e)
def is_constant(self, *wrt, **flags):
expr = self
if flags.get('simplify', True):
expr = expr.simplify()
b, e = expr.as_base_exp()
bz = b.equals(0)
if bz: # recalculate with assumptions in case it's unevaluated
new = b**e
if new != expr:
return new.is_constant()
econ = e.is_constant(*wrt)
bcon = b.is_constant(*wrt)
if bcon:
if econ:
return True
bz = b.equals(0)
if bz is False:
return False
elif bcon is None:
return None
return e.equals(0)
def _eval_difference_delta(self, n, step):
b, e = self.args
if e.has(n) and not b.has(n):
new_e = e.subs(n, n + step)
return (b**(new_e - e) - 1) * self
from .add import Add
from .numbers import Integer
from .mul import Mul, _keep_coeff
from .symbol import Symbol, Dummy, symbols
| 55,912 | 35.736531 | 91 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/exprtools.py
|
"""Tools for manipulating of large commutative expressions. """
from __future__ import print_function, division
from sympy.core.add import Add
from sympy.core.compatibility import iterable, is_sequence, SYMPY_INTS, range
from sympy.core.mul import Mul, _keep_coeff
from sympy.core.power import Pow
from sympy.core.basic import Basic, preorder_traversal
from sympy.core.expr import Expr
from sympy.core.sympify import sympify
from sympy.core.numbers import Rational, Integer, Number, I
from sympy.core.singleton import S
from sympy.core.symbol import Dummy
from sympy.core.coreerrors import NonCommutativeExpression
from sympy.core.containers import Tuple, Dict
from sympy.utilities import default_sort_key
from sympy.utilities.iterables import (common_prefix, common_suffix,
variations, ordered)
from collections import defaultdict
_eps = Dummy(positive=True)
def _isnumber(i):
return isinstance(i, (SYMPY_INTS, float)) or i.is_Number
def _monotonic_sign(self):
"""Return the value closest to 0 that ``self`` may have if all symbols
are signed and the result is uniformly the same sign for all values of symbols.
If a symbol is only signed but not known to be an
integer or the result is 0 then a symbol representative of the sign of self
will be returned. Otherwise, None is returned if a) the sign could be positive
or negative or b) self is not in one of the following forms:
- L(x, y, ...) + A: a function linear in all symbols x, y, ... with an
additive constant; if A is zero then the function can be a monomial whose
sign is monotonic over the range of the variables, e.g. (x + 1)**3 if x is
nonnegative.
- A/L(x, y, ...) + B: the inverse of a function linear in all symbols x, y, ...
that does not have a sign change from positive to negative for any set
of values for the variables.
- M(x, y, ...) + A: a monomial M whose factors are all signed and a constant, A.
- A/M(x, y, ...) + B: the inverse of a monomial and constants A and B.
- P(x): a univariate polynomial
Examples
========
>>> from sympy.core.exprtools import _monotonic_sign as F
>>> from sympy import Dummy, S
>>> nn = Dummy(integer=True, nonnegative=True)
>>> p = Dummy(integer=True, positive=True)
>>> p2 = Dummy(integer=True, positive=True)
>>> F(nn + 1)
1
>>> F(p - 1)
_nneg
>>> F(nn*p + 1)
1
>>> F(p2*p + 1)
2
>>> F(nn - 1) # could be negative, zero or positive
"""
if not self.is_real:
return
if (-self).is_Symbol:
rv = _monotonic_sign(-self)
return rv if rv is None else -rv
if not self.is_Add and self.as_numer_denom()[1].is_number:
s = self
if s.is_prime:
if s.is_odd:
return S(3)
else:
return S(2)
elif s.is_positive:
if s.is_even:
return S(2)
elif s.is_integer:
return S.One
else:
return _eps
elif s.is_negative:
if s.is_even:
return S(-2)
elif s.is_integer:
return S.NegativeOne
else:
return -_eps
if s.is_zero or s.is_nonpositive or s.is_nonnegative:
return S.Zero
return None
# univariate polynomial
free = self.free_symbols
if len(free) == 1:
if self.is_polynomial():
from sympy.polys.polytools import real_roots
from sympy.polys.polyroots import roots
from sympy.polys.polyerrors import PolynomialError
x = free.pop()
x0 = _monotonic_sign(x)
if x0 == _eps or x0 == -_eps:
x0 = S.Zero
if x0 is not None:
d = self.diff(x)
if d.is_number:
roots = []
else:
try:
roots = real_roots(d)
except (PolynomialError, NotImplementedError):
roots = [r for r in roots(d, x) if r.is_real]
y = self.subs(x, x0)
if x.is_nonnegative and all(r <= x0 for r in roots):
if y.is_nonnegative and d.is_positive:
if y:
return y if y.is_positive else Dummy('pos', positive=True)
else:
return Dummy('nneg', nonnegative=True)
if y.is_nonpositive and d.is_negative:
if y:
return y if y.is_negative else Dummy('neg', negative=True)
else:
return Dummy('npos', nonpositive=True)
elif x.is_nonpositive and all(r >= x0 for r in roots):
if y.is_nonnegative and d.is_negative:
if y:
return Dummy('pos', positive=True)
else:
return Dummy('nneg', nonnegative=True)
if y.is_nonpositive and d.is_positive:
if y:
return Dummy('neg', negative=True)
else:
return Dummy('npos', nonpositive=True)
else:
n, d = self.as_numer_denom()
den = None
if n.is_number:
den = _monotonic_sign(d)
elif not d.is_number:
if _monotonic_sign(n) is not None:
den = _monotonic_sign(d)
if den is not None and (den.is_positive or den.is_negative):
v = n*den
if v.is_positive:
return Dummy('pos', positive=True)
elif v.is_nonnegative:
return Dummy('nneg', nonnegative=True)
elif v.is_negative:
return Dummy('neg', negative=True)
elif v.is_nonpositive:
return Dummy('npos', nonpositive=True)
return None
# multivariate
c, a = self.as_coeff_Add()
v = None
if not a.is_polynomial():
# F/A or A/F where A is a number and F is a signed, rational monomial
n, d = a.as_numer_denom()
if not (n.is_number or d.is_number):
return
if (
a.is_Mul or a.is_Pow) and \
a.is_rational and \
all(p.exp.is_Integer for p in a.atoms(Pow) if p.is_Pow) and \
(a.is_positive or a.is_negative):
v = S(1)
for ai in Mul.make_args(a):
if ai.is_number:
v *= ai
continue
reps = {}
for x in ai.free_symbols:
reps[x] = _monotonic_sign(x)
if reps[x] is None:
return
v *= ai.subs(reps)
elif c:
# signed linear expression
if not any(p for p in a.atoms(Pow) if not p.is_number) and (a.is_nonpositive or a.is_nonnegative):
free = list(a.free_symbols)
p = {}
for i in free:
v = _monotonic_sign(i)
if v is None:
return
p[i] = v or (_eps if i.is_nonnegative else -_eps)
v = a.xreplace(p)
if v is not None:
rv = v + c
if v.is_nonnegative and rv.is_positive:
return rv.subs(_eps, 0)
if v.is_nonpositive and rv.is_negative:
return rv.subs(_eps, 0)
def decompose_power(expr):
"""
Decompose power into symbolic base and integer exponent.
This is strictly only valid if the exponent from which
the integer is extracted is itself an integer or the
base is positive. These conditions are assumed and not
checked here.
Examples
========
>>> from sympy.core.exprtools import decompose_power
>>> from sympy.abc import x, y
>>> decompose_power(x)
(x, 1)
>>> decompose_power(x**2)
(x, 2)
>>> decompose_power(x**(2*y))
(x**y, 2)
>>> decompose_power(x**(2*y/3))
(x**(y/3), 2)
"""
base, exp = expr.as_base_exp()
if exp.is_Number:
if exp.is_Rational:
if not exp.is_Integer:
base = Pow(base, Rational(1, exp.q))
exp = exp.p
else:
base, exp = expr, 1
else:
exp, tail = exp.as_coeff_Mul(rational=True)
if exp is S.NegativeOne:
base, exp = Pow(base, tail), -1
elif exp is not S.One:
tail = _keep_coeff(Rational(1, exp.q), tail)
base, exp = Pow(base, tail), exp.p
else:
base, exp = expr, 1
return base, exp
def decompose_power_rat(expr):
"""
Decompose power into symbolic base and rational exponent.
"""
base, exp = expr.as_base_exp()
if exp.is_Number:
if not exp.is_Rational:
base, exp = expr, 1
else:
exp, tail = exp.as_coeff_Mul(rational=True)
if exp is S.NegativeOne:
base, exp = Pow(base, tail), -1
elif exp is not S.One:
tail = _keep_coeff(Rational(1, exp.q), tail)
base, exp = Pow(base, tail), exp.p
else:
base, exp = expr, 1
return base, exp
class Factors(object):
"""Efficient representation of ``f_1*f_2*...*f_n``."""
__slots__ = ['factors', 'gens']
def __init__(self, factors=None): # Factors
"""Initialize Factors from dict or expr.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x
>>> from sympy import I
>>> e = 2*x**3
>>> Factors(e)
Factors({2: 1, x: 3})
>>> Factors(e.as_powers_dict())
Factors({2: 1, x: 3})
>>> f = _
>>> f.factors # underlying dictionary
{2: 1, x: 3}
>>> f.gens # base of each factor
frozenset({2, x})
>>> Factors(0)
Factors({0: 1})
>>> Factors(I)
Factors({I: 1})
Notes
=====
Although a dictionary can be passed, only minimal checking is
performed: powers of -1 and I are made canonical.
"""
if isinstance(factors, (SYMPY_INTS, float)):
factors = S(factors)
if isinstance(factors, Factors):
factors = factors.factors.copy()
elif factors is None or factors is S.One:
factors = {}
elif factors is S.Zero or factors == 0:
factors = {S.Zero: S.One}
elif isinstance(factors, Number):
n = factors
factors = {}
if n < 0:
factors[S.NegativeOne] = S.One
n = -n
if n is not S.One:
if n.is_Float or n.is_Integer or n is S.Infinity:
factors[n] = S.One
elif n.is_Rational:
# since we're processing Numbers, the denominator is
# stored with a negative exponent; all other factors
# are left .
if n.p != 1:
factors[Integer(n.p)] = S.One
factors[Integer(n.q)] = S.NegativeOne
else:
raise ValueError('Expected Float|Rational|Integer, not %s' % n)
elif isinstance(factors, Basic) and not factors.args:
factors = {factors: S.One}
elif isinstance(factors, Expr):
c, nc = factors.args_cnc()
i = c.count(I)
for _ in range(i):
c.remove(I)
factors = dict(Mul._from_args(c).as_powers_dict())
if i:
factors[I] = S.One*i
if nc:
factors[Mul(*nc, evaluate=False)] = S.One
else:
factors = factors.copy() # /!\ should be dict-like
# tidy up -/+1 and I exponents if Rational
handle = []
for k in factors:
if k is I or k in (-1, 1):
handle.append(k)
if handle:
i1 = S.One
for k in handle:
if not _isnumber(factors[k]):
continue
i1 *= k**factors.pop(k)
if i1 is not S.One:
for a in i1.args if i1.is_Mul else [i1]: # at worst, -1.0*I*(-1)**e
if a is S.NegativeOne:
factors[a] = S.One
elif a is I:
factors[I] = S.One
elif a.is_Pow:
if S.NegativeOne not in factors:
factors[S.NegativeOne] = S.Zero
factors[S.NegativeOne] += a.exp
elif a == 1:
factors[a] = S.One
elif a == -1:
factors[-a] = S.One
factors[S.NegativeOne] = S.One
else:
raise ValueError('unexpected factor in i1: %s' % a)
self.factors = factors
try:
self.gens = frozenset(factors.keys())
except AttributeError:
raise TypeError('expecting Expr or dictionary')
def __hash__(self): # Factors
keys = tuple(ordered(self.factors.keys()))
values = [self.factors[k] for k in keys]
return hash((keys, values))
def __repr__(self): # Factors
return "Factors({%s})" % ', '.join(
['%s: %s' % (k, v) for k, v in ordered(self.factors.items())])
@property
def is_zero(self): # Factors
"""
>>> from sympy.core.exprtools import Factors
>>> Factors(0).is_zero
True
"""
f = self.factors
return len(f) == 1 and S.Zero in f
@property
def is_one(self): # Factors
"""
>>> from sympy.core.exprtools import Factors
>>> Factors(1).is_one
True
"""
return not self.factors
def as_expr(self): # Factors
"""Return the underlying expression.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y
>>> Factors((x*y**2).as_powers_dict()).as_expr()
x*y**2
"""
args = []
for factor, exp in self.factors.items():
if exp != 1:
b, e = factor.as_base_exp()
if isinstance(exp, int):
e = _keep_coeff(Integer(exp), e)
elif isinstance(exp, Rational):
e = _keep_coeff(exp, e)
else:
e *= exp
args.append(b**e)
else:
args.append(factor)
return Mul(*args)
def mul(self, other): # Factors
"""Return Factors of ``self * other``.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y, z
>>> a = Factors((x*y**2).as_powers_dict())
>>> b = Factors((x*y/z).as_powers_dict())
>>> a.mul(b)
Factors({x: 2, y: 3, z: -1})
>>> a*b
Factors({x: 2, y: 3, z: -1})
"""
if not isinstance(other, Factors):
other = Factors(other)
if any(f.is_zero for f in (self, other)):
return Factors(S.Zero)
factors = dict(self.factors)
for factor, exp in other.factors.items():
if factor in factors:
exp = factors[factor] + exp
if not exp:
del factors[factor]
continue
factors[factor] = exp
return Factors(factors)
def normal(self, other):
"""Return ``self`` and ``other`` with ``gcd`` removed from each.
The only differences between this and method ``div`` is that this
is 1) optimized for the case when there are few factors in common and
2) this does not raise an error if ``other`` is zero.
See Also
========
div
"""
if not isinstance(other, Factors):
other = Factors(other)
if other.is_zero:
return (Factors(), Factors(S.Zero))
if self.is_zero:
return (Factors(S.Zero), Factors())
self_factors = dict(self.factors)
other_factors = dict(other.factors)
for factor, self_exp in self.factors.items():
try:
other_exp = other.factors[factor]
except KeyError:
continue
exp = self_exp - other_exp
if not exp:
del self_factors[factor]
del other_factors[factor]
elif _isnumber(exp):
if exp > 0:
self_factors[factor] = exp
del other_factors[factor]
else:
del self_factors[factor]
other_factors[factor] = -exp
else:
r = self_exp.extract_additively(other_exp)
if r is not None:
if r:
self_factors[factor] = r
del other_factors[factor]
else: # should be handled already
del self_factors[factor]
del other_factors[factor]
else:
sc, sa = self_exp.as_coeff_Add()
if sc:
oc, oa = other_exp.as_coeff_Add()
diff = sc - oc
if diff > 0:
self_factors[factor] -= oc
other_exp = oa
elif diff < 0:
self_factors[factor] -= sc
other_factors[factor] -= sc
other_exp = oa - diff
else:
self_factors[factor] = sa
other_exp = oa
if other_exp:
other_factors[factor] = other_exp
else:
del other_factors[factor]
return Factors(self_factors), Factors(other_factors)
def div(self, other): # Factors
"""Return ``self`` and ``other`` with ``gcd`` removed from each.
This is optimized for the case when there are many factors in common.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y, z
>>> from sympy import S
>>> a = Factors((x*y**2).as_powers_dict())
>>> a.div(a)
(Factors({}), Factors({}))
>>> a.div(x*z)
(Factors({y: 2}), Factors({z: 1}))
The ``/`` operator only gives ``quo``:
>>> a/x
Factors({y: 2})
Factors treats its factors as though they are all in the numerator, so
if you violate this assumption the results will be correct but will
not strictly correspond to the numerator and denominator of the ratio:
>>> a.div(x/z)
(Factors({y: 2}), Factors({z: -1}))
Factors is also naive about bases: it does not attempt any denesting
of Rational-base terms, for example the following does not become
2**(2*x)/2.
>>> Factors(2**(2*x + 2)).div(S(8))
(Factors({2: 2*x + 2}), Factors({8: 1}))
factor_terms can clean up such Rational-bases powers:
>>> from sympy.core.exprtools import factor_terms
>>> n, d = Factors(2**(2*x + 2)).div(S(8))
>>> n.as_expr()/d.as_expr()
2**(2*x + 2)/8
>>> factor_terms(_)
2**(2*x)/2
"""
quo, rem = dict(self.factors), {}
if not isinstance(other, Factors):
other = Factors(other)
if other.is_zero:
raise ZeroDivisionError
if self.is_zero:
return (Factors(S.Zero), Factors())
for factor, exp in other.factors.items():
if factor in quo:
d = quo[factor] - exp
if _isnumber(d):
if d <= 0:
del quo[factor]
if d >= 0:
if d:
quo[factor] = d
continue
exp = -d
else:
r = quo[factor].extract_additively(exp)
if r is not None:
if r:
quo[factor] = r
else: # should be handled already
del quo[factor]
else:
other_exp = exp
sc, sa = quo[factor].as_coeff_Add()
if sc:
oc, oa = other_exp.as_coeff_Add()
diff = sc - oc
if diff > 0:
quo[factor] -= oc
other_exp = oa
elif diff < 0:
quo[factor] -= sc
other_exp = oa - diff
else:
quo[factor] = sa
other_exp = oa
if other_exp:
rem[factor] = other_exp
else:
assert factor not in rem
continue
rem[factor] = exp
return Factors(quo), Factors(rem)
def quo(self, other): # Factors
"""Return numerator Factor of ``self / other``.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y, z
>>> a = Factors((x*y**2).as_powers_dict())
>>> b = Factors((x*y/z).as_powers_dict())
>>> a.quo(b) # same as a/b
Factors({y: 1})
"""
return self.div(other)[0]
def rem(self, other): # Factors
"""Return denominator Factors of ``self / other``.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y, z
>>> a = Factors((x*y**2).as_powers_dict())
>>> b = Factors((x*y/z).as_powers_dict())
>>> a.rem(b)
Factors({z: -1})
>>> a.rem(a)
Factors({})
"""
return self.div(other)[1]
def pow(self, other): # Factors
"""Return self raised to a non-negative integer power.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y
>>> a = Factors((x*y**2).as_powers_dict())
>>> a**2
Factors({x: 2, y: 4})
"""
if isinstance(other, Factors):
other = other.as_expr()
if other.is_Integer:
other = int(other)
if isinstance(other, SYMPY_INTS) and other >= 0:
factors = {}
if other:
for factor, exp in self.factors.items():
factors[factor] = exp*other
return Factors(factors)
else:
raise ValueError("expected non-negative integer, got %s" % other)
def gcd(self, other): # Factors
"""Return Factors of ``gcd(self, other)``. The keys are
the intersection of factors with the minimum exponent for
each factor.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y, z
>>> a = Factors((x*y**2).as_powers_dict())
>>> b = Factors((x*y/z).as_powers_dict())
>>> a.gcd(b)
Factors({x: 1, y: 1})
"""
if not isinstance(other, Factors):
other = Factors(other)
if other.is_zero:
return Factors(self.factors)
factors = {}
for factor, exp in self.factors.items():
factor, exp = sympify(factor), sympify(exp)
if factor in other.factors:
lt = (exp - other.factors[factor]).is_negative
if lt == True:
factors[factor] = exp
elif lt == False:
factors[factor] = other.factors[factor]
return Factors(factors)
def lcm(self, other): # Factors
"""Return Factors of ``lcm(self, other)`` which are
the union of factors with the maximum exponent for
each factor.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y, z
>>> a = Factors((x*y**2).as_powers_dict())
>>> b = Factors((x*y/z).as_powers_dict())
>>> a.lcm(b)
Factors({x: 1, y: 2, z: -1})
"""
if not isinstance(other, Factors):
other = Factors(other)
if any(f.is_zero for f in (self, other)):
return Factors(S.Zero)
factors = dict(self.factors)
for factor, exp in other.factors.items():
if factor in factors:
exp = max(exp, factors[factor])
factors[factor] = exp
return Factors(factors)
def __mul__(self, other): # Factors
return self.mul(other)
def __divmod__(self, other): # Factors
return self.div(other)
def __div__(self, other): # Factors
return self.quo(other)
__truediv__ = __div__
def __mod__(self, other): # Factors
return self.rem(other)
def __pow__(self, other): # Factors
return self.pow(other)
def __eq__(self, other): # Factors
if not isinstance(other, Factors):
other = Factors(other)
return self.factors == other.factors
def __ne__(self, other): # Factors
return not self.__eq__(other)
class Term(object):
"""Efficient representation of ``coeff*(numer/denom)``. """
__slots__ = ['coeff', 'numer', 'denom']
def __init__(self, term, numer=None, denom=None): # Term
if numer is None and denom is None:
if not term.is_commutative:
raise NonCommutativeExpression(
'commutative expression expected')
coeff, factors = term.as_coeff_mul()
numer, denom = defaultdict(int), defaultdict(int)
for factor in factors:
base, exp = decompose_power(factor)
if base.is_Add:
cont, base = base.primitive()
coeff *= cont**exp
if exp > 0:
numer[base] += exp
else:
denom[base] += -exp
numer = Factors(numer)
denom = Factors(denom)
else:
coeff = term
if numer is None:
numer = Factors()
if denom is None:
denom = Factors()
self.coeff = coeff
self.numer = numer
self.denom = denom
def __hash__(self): # Term
return hash((self.coeff, self.numer, self.denom))
def __repr__(self): # Term
return "Term(%s, %s, %s)" % (self.coeff, self.numer, self.denom)
def as_expr(self): # Term
return self.coeff*(self.numer.as_expr()/self.denom.as_expr())
def mul(self, other): # Term
coeff = self.coeff*other.coeff
numer = self.numer.mul(other.numer)
denom = self.denom.mul(other.denom)
numer, denom = numer.normal(denom)
return Term(coeff, numer, denom)
def inv(self): # Term
return Term(1/self.coeff, self.denom, self.numer)
def quo(self, other): # Term
return self.mul(other.inv())
def pow(self, other): # Term
if other < 0:
return self.inv().pow(-other)
else:
return Term(self.coeff ** other,
self.numer.pow(other),
self.denom.pow(other))
def gcd(self, other): # Term
return Term(self.coeff.gcd(other.coeff),
self.numer.gcd(other.numer),
self.denom.gcd(other.denom))
def lcm(self, other): # Term
return Term(self.coeff.lcm(other.coeff),
self.numer.lcm(other.numer),
self.denom.lcm(other.denom))
def __mul__(self, other): # Term
if isinstance(other, Term):
return self.mul(other)
else:
return NotImplemented
def __div__(self, other): # Term
if isinstance(other, Term):
return self.quo(other)
else:
return NotImplemented
__truediv__ = __div__
def __pow__(self, other): # Term
if isinstance(other, SYMPY_INTS):
return self.pow(other)
else:
return NotImplemented
def __eq__(self, other): # Term
return (self.coeff == other.coeff and
self.numer == other.numer and
self.denom == other.denom)
def __ne__(self, other): # Term
return not self.__eq__(other)
def _gcd_terms(terms, isprimitive=False, fraction=True):
"""Helper function for :func:`gcd_terms`.
If ``isprimitive`` is True then the call to primitive
for an Add will be skipped. This is useful when the
content has already been extrated.
If ``fraction`` is True then the expression will appear over a common
denominator, the lcm of all term denominators.
"""
if isinstance(terms, Basic) and not isinstance(terms, Tuple):
terms = Add.make_args(terms)
terms = list(map(Term, [t for t in terms if t]))
# there is some simplification that may happen if we leave this
# here rather than duplicate it before the mapping of Term onto
# the terms
if len(terms) == 0:
return S.Zero, S.Zero, S.One
if len(terms) == 1:
cont = terms[0].coeff
numer = terms[0].numer.as_expr()
denom = terms[0].denom.as_expr()
else:
cont = terms[0]
for term in terms[1:]:
cont = cont.gcd(term)
for i, term in enumerate(terms):
terms[i] = term.quo(cont)
if fraction:
denom = terms[0].denom
for term in terms[1:]:
denom = denom.lcm(term.denom)
numers = []
for term in terms:
numer = term.numer.mul(denom.quo(term.denom))
numers.append(term.coeff*numer.as_expr())
else:
numers = [t.as_expr() for t in terms]
denom = Term(S(1)).numer
cont = cont.as_expr()
numer = Add(*numers)
denom = denom.as_expr()
if not isprimitive and numer.is_Add:
_cont, numer = numer.primitive()
cont *= _cont
return cont, numer, denom
def gcd_terms(terms, isprimitive=False, clear=True, fraction=True):
"""Compute the GCD of ``terms`` and put them together.
``terms`` can be an expression or a non-Basic sequence of expressions
which will be handled as though they are terms from a sum.
If ``isprimitive`` is True the _gcd_terms will not run the primitive
method on the terms.
``clear`` controls the removal of integers from the denominator of an Add
expression. When True (default), all numerical denominator will be cleared;
when False the denominators will be cleared only if all terms had numerical
denominators other than 1.
``fraction``, when True (default), will put the expression over a common
denominator.
Examples
========
>>> from sympy.core import gcd_terms
>>> from sympy.abc import x, y
>>> gcd_terms((x + 1)**2*y + (x + 1)*y**2)
y*(x + 1)*(x + y + 1)
>>> gcd_terms(x/2 + 1)
(x + 2)/2
>>> gcd_terms(x/2 + 1, clear=False)
x/2 + 1
>>> gcd_terms(x/2 + y/2, clear=False)
(x + y)/2
>>> gcd_terms(x/2 + 1/x)
(x**2 + 2)/(2*x)
>>> gcd_terms(x/2 + 1/x, fraction=False)
(x + 2/x)/2
>>> gcd_terms(x/2 + 1/x, fraction=False, clear=False)
x/2 + 1/x
>>> gcd_terms(x/2/y + 1/x/y)
(x**2 + 2)/(2*x*y)
>>> gcd_terms(x/2/y + 1/x/y, clear=False)
(x**2/2 + 1)/(x*y)
>>> gcd_terms(x/2/y + 1/x/y, clear=False, fraction=False)
(x/2 + 1/x)/y
The ``clear`` flag was ignored in this case because the returned
expression was a rational expression, not a simple sum.
See Also
========
factor_terms, sympy.polys.polytools.terms_gcd
"""
def mask(terms):
"""replace nc portions of each term with a unique Dummy symbols
and return the replacements to restore them"""
args = [(a, []) if a.is_commutative else a.args_cnc() for a in terms]
reps = []
for i, (c, nc) in enumerate(args):
if nc:
nc = Mul._from_args(nc)
d = Dummy()
reps.append((d, nc))
c.append(d)
args[i] = Mul._from_args(c)
else:
args[i] = c
return args, dict(reps)
isadd = isinstance(terms, Add)
addlike = isadd or not isinstance(terms, Basic) and \
is_sequence(terms, include=set) and \
not isinstance(terms, Dict)
if addlike:
if isadd: # i.e. an Add
terms = list(terms.args)
else:
terms = sympify(terms)
terms, reps = mask(terms)
cont, numer, denom = _gcd_terms(terms, isprimitive, fraction)
numer = numer.xreplace(reps)
coeff, factors = cont.as_coeff_Mul()
if not clear:
c, _coeff = coeff.as_coeff_Mul()
if not c.is_Integer and not clear and numer.is_Add:
n, d = c.as_numer_denom()
_numer = numer/d
if any(a.as_coeff_Mul()[0].is_Integer
for a in _numer.args):
numer = _numer
coeff = n*_coeff
return _keep_coeff(coeff, factors*numer/denom, clear=clear)
if not isinstance(terms, Basic):
return terms
if terms.is_Atom:
return terms
if terms.is_Mul:
c, args = terms.as_coeff_mul()
return _keep_coeff(c, Mul(*[gcd_terms(i, isprimitive, clear, fraction)
for i in args]), clear=clear)
def handle(a):
# don't treat internal args like terms of an Add
if not isinstance(a, Expr):
if isinstance(a, Basic):
return a.func(*[handle(i) for i in a.args])
return type(a)([handle(i) for i in a])
return gcd_terms(a, isprimitive, clear, fraction)
if isinstance(terms, Dict):
return Dict(*[(k, handle(v)) for k, v in terms.args])
return terms.func(*[handle(i) for i in terms.args])
def factor_terms(expr, radical=False, clear=False, fraction=False, sign=True):
"""Remove common factors from terms in all arguments without
changing the underlying structure of the expr. No expansion or
simplification (and no processing of non-commutatives) is performed.
If radical=True then a radical common to all terms will be factored
out of any Add sub-expressions of the expr.
If clear=False (default) then coefficients will not be separated
from a single Add if they can be distributed to leave one or more
terms with integer coefficients.
If fraction=True (default is False) then a common denominator will be
constructed for the expression.
If sign=True (default) then even if the only factor in common is a -1,
it will be factored out of the expression.
Examples
========
>>> from sympy import factor_terms, Symbol
>>> from sympy.abc import x, y
>>> factor_terms(x + x*(2 + 4*y)**3)
x*(8*(2*y + 1)**3 + 1)
>>> A = Symbol('A', commutative=False)
>>> factor_terms(x*A + x*A + x*y*A)
x*(y*A + 2*A)
When ``clear`` is False, a rational will only be factored out of an
Add expression if all terms of the Add have coefficients that are
fractions:
>>> factor_terms(x/2 + 1, clear=False)
x/2 + 1
>>> factor_terms(x/2 + 1, clear=True)
(x + 2)/2
If a -1 is all that can be factored out, to *not* factor it out, the
flag ``sign`` must be False:
>>> factor_terms(-x - y)
-(x + y)
>>> factor_terms(-x - y, sign=False)
-x - y
>>> factor_terms(-2*x - 2*y, sign=False)
-2*(x + y)
See Also
========
gcd_terms, sympy.polys.polytools.terms_gcd
"""
def do(expr):
from sympy.concrete.summations import Sum
from sympy.simplify.simplify import factor_sum
is_iterable = iterable(expr)
if not isinstance(expr, Basic) or expr.is_Atom:
if is_iterable:
return type(expr)([do(i) for i in expr])
return expr
if expr.is_Pow or expr.is_Function or \
is_iterable or not hasattr(expr, 'args_cnc'):
args = expr.args
newargs = tuple([do(i) for i in args])
if newargs == args:
return expr
return expr.func(*newargs)
if isinstance(expr, Sum):
return factor_sum(expr, radical=radical, clear=clear, fraction=fraction, sign=sign)
cont, p = expr.as_content_primitive(radical=radical, clear=clear)
if p.is_Add:
list_args = [do(a) for a in Add.make_args(p)]
# get a common negative (if there) which gcd_terms does not remove
if all(a.as_coeff_Mul()[0] < 0 for a in list_args):
cont = -cont
list_args = [-a for a in list_args]
# watch out for exp(-(x+2)) which gcd_terms will change to exp(-x-2)
special = {}
for i, a in enumerate(list_args):
b, e = a.as_base_exp()
if e.is_Mul and e != Mul(*e.args):
list_args[i] = Dummy()
special[list_args[i]] = a
# rebuild p not worrying about the order which gcd_terms will fix
p = Add._from_args(list_args)
p = gcd_terms(p,
isprimitive=True,
clear=clear,
fraction=fraction).xreplace(special)
elif p.args:
p = p.func(
*[do(a) for a in p.args])
rv = _keep_coeff(cont, p, clear=clear, sign=sign)
return rv
expr = sympify(expr)
return do(expr)
def _mask_nc(eq, name=None):
"""
Return ``eq`` with non-commutative objects replaced with Dummy
symbols. A dictionary that can be used to restore the original
values is returned: if it is None, the expression is noncommutative
and cannot be made commutative. The third value returned is a list
of any non-commutative symbols that appear in the returned equation.
``name``, if given, is the name that will be used with numered Dummy
variables that will replace the non-commutative objects and is mainly
used for doctesting purposes.
Notes
=====
All non-commutative objects other than Symbols are replaced with
a non-commutative Symbol. Identical objects will be identified
by identical symbols.
If there is only 1 non-commutative object in an expression it will
be replaced with a commutative symbol. Otherwise, the non-commutative
entities are retained and the calling routine should handle
replacements in this case since some care must be taken to keep
track of the ordering of symbols when they occur within Muls.
Examples
========
>>> from sympy.physics.secondquant import Commutator, NO, F, Fd
>>> from sympy import symbols, Mul
>>> from sympy.core.exprtools import _mask_nc
>>> from sympy.abc import x, y
>>> A, B, C = symbols('A,B,C', commutative=False)
One nc-symbol:
>>> _mask_nc(A**2 - x**2, 'd')
(_d0**2 - x**2, {_d0: A}, [])
Multiple nc-symbols:
>>> _mask_nc(A**2 - B**2, 'd')
(A**2 - B**2, None, [A, B])
An nc-object with nc-symbols but no others outside of it:
>>> _mask_nc(1 + x*Commutator(A, B), 'd')
(_d0*x + 1, {_d0: Commutator(A, B)}, [])
>>> _mask_nc(NO(Fd(x)*F(y)), 'd')
(_d0, {_d0: NO(CreateFermion(x)*AnnihilateFermion(y))}, [])
Multiple nc-objects:
>>> eq = x*Commutator(A, B) + x*Commutator(A, C)*Commutator(A, B)
>>> _mask_nc(eq, 'd')
(x*_d0 + x*_d1*_d0, {_d0: Commutator(A, B), _d1: Commutator(A, C)}, [_d0, _d1])
Multiple nc-objects and nc-symbols:
>>> eq = A*Commutator(A, B) + B*Commutator(A, C)
>>> _mask_nc(eq, 'd')
(A*_d0 + B*_d1, {_d0: Commutator(A, B), _d1: Commutator(A, C)}, [_d0, _d1, A, B])
If there is an object that:
- doesn't contain nc-symbols
- but has arguments which derive from Basic, not Expr
- and doesn't define an _eval_is_commutative routine
then it will give False (or None?) for the is_commutative test. Such
objects are also removed by this routine:
>>> from sympy import Basic
>>> eq = (1 + Mul(Basic(), Basic(), evaluate=False))
>>> eq.is_commutative
False
>>> _mask_nc(eq, 'd')
(_d0**2 + 1, {_d0: Basic()}, [])
"""
name = name or 'mask'
# Make Dummy() append sequential numbers to the name
def numbered_names():
i = 0
while True:
yield name + str(i)
i += 1
names = numbered_names()
def Dummy(*args, **kwargs):
from sympy import Dummy
return Dummy(next(names), *args, **kwargs)
expr = eq
if expr.is_commutative:
return eq, {}, []
# identify nc-objects; symbols and other
rep = []
nc_obj = set()
nc_syms = set()
pot = preorder_traversal(expr, keys=default_sort_key)
for i, a in enumerate(pot):
if any(a == r[0] for r in rep):
pot.skip()
elif not a.is_commutative:
if a.is_Symbol:
nc_syms.add(a)
elif not (a.is_Add or a.is_Mul or a.is_Pow):
if all(s.is_commutative for s in a.free_symbols):
rep.append((a, Dummy()))
else:
nc_obj.add(a)
pot.skip()
# If there is only one nc symbol or object, it can be factored regularly
# but polys is going to complain, so replace it with a Dummy.
if len(nc_obj) == 1 and not nc_syms:
rep.append((nc_obj.pop(), Dummy()))
elif len(nc_syms) == 1 and not nc_obj:
rep.append((nc_syms.pop(), Dummy()))
# Any remaining nc-objects will be replaced with an nc-Dummy and
# identified as an nc-Symbol to watch out for
nc_obj = sorted(nc_obj, key=default_sort_key)
for n in nc_obj:
nc = Dummy(commutative=False)
rep.append((n, nc))
nc_syms.add(nc)
expr = expr.subs(rep)
nc_syms = list(nc_syms)
nc_syms.sort(key=default_sort_key)
return expr, {v: k for k, v in rep} or None, nc_syms
def factor_nc(expr):
"""Return the factored form of ``expr`` while handling non-commutative
expressions.
Examples
========
>>> from sympy.core.exprtools import factor_nc
>>> from sympy import Symbol
>>> from sympy.abc import x
>>> A = Symbol('A', commutative=False)
>>> B = Symbol('B', commutative=False)
>>> factor_nc((x**2 + 2*A*x + A**2).expand())
(x + A)**2
>>> factor_nc(((x + A)*(x + B)).expand())
(x + A)*(x + B)
"""
from sympy.simplify.simplify import powsimp
from sympy.polys import gcd, factor
def _pemexpand(expr):
"Expand with the minimal set of hints necessary to check the result."
return expr.expand(deep=True, mul=True, power_exp=True,
power_base=False, basic=False, multinomial=True, log=False)
expr = sympify(expr)
if not isinstance(expr, Expr) or not expr.args:
return expr
if not expr.is_Add:
return expr.func(*[factor_nc(a) for a in expr.args])
expr, rep, nc_symbols = _mask_nc(expr)
if rep:
return factor(expr).subs(rep)
else:
args = [a.args_cnc() for a in Add.make_args(expr)]
c = g = l = r = S.One
hit = False
# find any commutative gcd term
for i, a in enumerate(args):
if i == 0:
c = Mul._from_args(a[0])
elif a[0]:
c = gcd(c, Mul._from_args(a[0]))
else:
c = S.One
if c is not S.One:
hit = True
c, g = c.as_coeff_Mul()
if g is not S.One:
for i, (cc, _) in enumerate(args):
cc = list(Mul.make_args(Mul._from_args(list(cc))/g))
args[i][0] = cc
for i, (cc, _) in enumerate(args):
cc[0] = cc[0]/c
args[i][0] = cc
# find any noncommutative common prefix
for i, a in enumerate(args):
if i == 0:
n = a[1][:]
else:
n = common_prefix(n, a[1])
if not n:
# is there a power that can be extracted?
if not args[0][1]:
break
b, e = args[0][1][0].as_base_exp()
ok = False
if e.is_Integer:
for t in args:
if not t[1]:
break
bt, et = t[1][0].as_base_exp()
if et.is_Integer and bt == b:
e = min(e, et)
else:
break
else:
ok = hit = True
l = b**e
il = b**-e
for i, a in enumerate(args):
args[i][1][0] = il*args[i][1][0]
break
if not ok:
break
else:
hit = True
lenn = len(n)
l = Mul(*n)
for i, a in enumerate(args):
args[i][1] = args[i][1][lenn:]
# find any noncommutative common suffix
for i, a in enumerate(args):
if i == 0:
n = a[1][:]
else:
n = common_suffix(n, a[1])
if not n:
# is there a power that can be extracted?
if not args[0][1]:
break
b, e = args[0][1][-1].as_base_exp()
ok = False
if e.is_Integer:
for t in args:
if not t[1]:
break
bt, et = t[1][-1].as_base_exp()
if et.is_Integer and bt == b:
e = min(e, et)
else:
break
else:
ok = hit = True
r = b**e
il = b**-e
for i, a in enumerate(args):
args[i][1][-1] = args[i][1][-1]*il
break
if not ok:
break
else:
hit = True
lenn = len(n)
r = Mul(*n)
for i, a in enumerate(args):
args[i][1] = a[1][:len(a[1]) - lenn]
if hit:
mid = Add(*[Mul(*cc)*Mul(*nc) for cc, nc in args])
else:
mid = expr
# sort the symbols so the Dummys would appear in the same
# order as the original symbols, otherwise you may introduce
# a factor of -1, e.g. A**2 - B**2) -- {A:y, B:x} --> y**2 - x**2
# and the former factors into two terms, (A - B)*(A + B) while the
# latter factors into 3 terms, (-1)*(x - y)*(x + y)
rep1 = [(n, Dummy()) for n in sorted(nc_symbols, key=default_sort_key)]
unrep1 = [(v, k) for k, v in rep1]
unrep1.reverse()
new_mid, r2, _ = _mask_nc(mid.subs(rep1))
new_mid = powsimp(factor(new_mid))
new_mid = new_mid.subs(r2).subs(unrep1)
if new_mid.is_Pow:
return _keep_coeff(c, g*l*new_mid*r)
if new_mid.is_Mul:
# XXX TODO there should be a way to inspect what order the terms
# must be in and just select the plausible ordering without
# checking permutations
cfac = []
ncfac = []
for f in new_mid.args:
if f.is_commutative:
cfac.append(f)
else:
b, e = f.as_base_exp()
if e.is_Integer:
ncfac.extend([b]*e)
else:
ncfac.append(f)
pre_mid = g*Mul(*cfac)*l
target = _pemexpand(expr/c)
for s in variations(ncfac, len(ncfac)):
ok = pre_mid*Mul(*s)*r
if _pemexpand(ok) == target:
return _keep_coeff(c, ok)
# mid was an Add that didn't factor successfully
return _keep_coeff(c, g*l*mid*r)
| 49,411 | 31.810093 | 106 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/core.py
|
""" The core's core. """
from __future__ import print_function, division
# used for canonical ordering of symbolic sequences
# via __cmp__ method:
# FIXME this is *so* irrelevant and outdated!
ordering_of_classes = [
# singleton numbers
'Zero', 'One', 'Half', 'Infinity', 'NaN', 'NegativeOne', 'NegativeInfinity',
# numbers
'Integer', 'Rational', 'Float',
# singleton symbols
'Exp1', 'Pi', 'ImaginaryUnit',
# symbols
'Symbol', 'Wild', 'Temporary',
# arithmetic operations
'Pow', 'Mul', 'Add',
# function values
'Derivative', 'Integral',
# defined singleton functions
'Abs', 'Sign', 'Sqrt',
'Floor', 'Ceiling',
'Re', 'Im', 'Arg',
'Conjugate',
'Exp', 'Log',
'Sin', 'Cos', 'Tan', 'Cot', 'ASin', 'ACos', 'ATan', 'ACot',
'Sinh', 'Cosh', 'Tanh', 'Coth', 'ASinh', 'ACosh', 'ATanh', 'ACoth',
'RisingFactorial', 'FallingFactorial',
'factorial', 'binomial',
'Gamma', 'LowerGamma', 'UpperGamma', 'PolyGamma',
'Erf',
# special polynomials
'Chebyshev', 'Chebyshev2',
# undefined functions
'Function', 'WildFunction',
# anonymous functions
'Lambda',
# Landau O symbol
'Order',
# relational operations
'Equality', 'Unequality', 'StrictGreaterThan', 'StrictLessThan',
'GreaterThan', 'LessThan',
]
class Registry(object):
"""
Base class for registry objects.
Registries map a name to an object using attribute notation. Registry
classes behave singletonically: all their instances share the same state,
which is stored in the class object.
All subclasses should set `__slots__ = []`.
"""
__slots__ = []
def __setattr__(self, name, obj):
setattr(self.__class__, name, obj)
def __delattr__(self, name):
delattr(self.__class__, name)
#A set containing all sympy class objects
all_classes = set()
class BasicMeta(type):
def __init__(cls, *args, **kws):
all_classes.add(cls)
def __cmp__(cls, other):
# If the other object is not a Basic subclass, then we are not equal to
# it.
if not isinstance(other, BasicMeta):
return -1
n1 = cls.__name__
n2 = other.__name__
if n1 == n2:
return 0
UNKNOWN = len(ordering_of_classes) + 1
try:
i1 = ordering_of_classes.index(n1)
except ValueError:
i1 = UNKNOWN
try:
i2 = ordering_of_classes.index(n2)
except ValueError:
i2 = UNKNOWN
if i1 == UNKNOWN and i2 == UNKNOWN:
return (n1 > n2) - (n1 < n2)
return (i1 > i2) - (i1 < i2)
def __lt__(cls, other):
if cls.__cmp__(other) == -1:
return True
return False
def __gt__(cls, other):
if cls.__cmp__(other) == 1:
return True
return False
| 2,874 | 26.380952 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/evaluate.py
|
from .cache import clear_cache
from contextlib import contextmanager
class _global_evaluate(list):
""" The cache must be cleared whenever global_evaluate is changed. """
def __setitem__(self, key, value):
clear_cache()
super(_global_evaluate, self).__setitem__(key, value)
global_evaluate = _global_evaluate([True])
@contextmanager
def evaluate(x):
""" Control automatic evaluation
This context managers controls whether or not all SymPy functions evaluate
by default.
Note that much of SymPy expects evaluated expressions. This functionality
is experimental and is unlikely to function as intended on large
expressions.
Examples
========
>>> from sympy.abc import x
>>> from sympy.core.evaluate import evaluate
>>> print(x + x)
2*x
>>> with evaluate(False):
... print(x + x)
x + x
"""
old = global_evaluate[0]
global_evaluate[0] = x
yield
global_evaluate[0] = old
| 986 | 21.953488 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/add.py
|
from __future__ import print_function, division
from collections import defaultdict
from functools import cmp_to_key
from .basic import Basic
from .compatibility import reduce, is_sequence, range
from .logic import _fuzzy_group, fuzzy_or, fuzzy_not
from .singleton import S
from .operations import AssocOp
from .cache import cacheit
from .numbers import ilcm, igcd
from .expr import Expr
# Key for sorting commutative args in canonical order
_args_sortkey = cmp_to_key(Basic.compare)
def _addsort(args):
# in-place sorting of args
args.sort(key=_args_sortkey)
def _unevaluated_Add(*args):
"""Return a well-formed unevaluated Add: Numbers are collected and
put in slot 0 and args are sorted. Use this when args have changed
but you still want to return an unevaluated Add.
Examples
========
>>> from sympy.core.add import _unevaluated_Add as uAdd
>>> from sympy import S, Add
>>> from sympy.abc import x, y
>>> a = uAdd(*[S(1.0), x, S(2)])
>>> a.args[0]
3.00000000000000
>>> a.args[1]
x
Beyond the Number being in slot 0, there is no other assurance of
order for the arguments since they are hash sorted. So, for testing
purposes, output produced by this in some other function can only
be tested against the output of this function or as one of several
options:
>>> opts = (Add(x, y, evaluated=False), Add(y, x, evaluated=False))
>>> a = uAdd(x, y)
>>> assert a in opts and a == uAdd(x, y)
"""
args = list(args)
newargs = []
co = S.Zero
while args:
a = args.pop()
if a.is_Add:
# this will keep nesting from building up
# so that x + (x + 1) -> x + x + 1 (3 args)
args.extend(a.args)
elif a.is_Number:
co += a
else:
newargs.append(a)
_addsort(newargs)
if co:
newargs.insert(0, co)
return Add._from_args(newargs)
class Add(Expr, AssocOp):
__slots__ = []
is_Add = True
@classmethod
def flatten(cls, seq):
"""
Takes the sequence "seq" of nested Adds and returns a flatten list.
Returns: (commutative_part, noncommutative_part, order_symbols)
Applies associativity, all terms are commutable with respect to
addition.
NB: the removal of 0 is already handled by AssocOp.__new__
See also
========
sympy.core.mul.Mul.flatten
"""
from sympy.calculus.util import AccumBounds
from sympy.matrices.expressions import MatrixExpr
rv = None
if len(seq) == 2:
a, b = seq
if b.is_Rational:
a, b = b, a
if a.is_Rational:
if b.is_Mul:
rv = [a, b], [], None
if rv:
if all(s.is_commutative for s in rv[0]):
return rv
return [], rv[0], None
terms = {} # term -> coeff
# e.g. x**2 -> 5 for ... + 5*x**2 + ...
coeff = S.Zero # coefficient (Number or zoo) to always be in slot 0
# e.g. 3 + ...
order_factors = []
for o in seq:
# O(x)
if o.is_Order:
for o1 in order_factors:
if o1.contains(o):
o = None
break
if o is None:
continue
order_factors = [o] + [
o1 for o1 in order_factors if not o.contains(o1)]
continue
# 3 or NaN
elif o.is_Number:
if (o is S.NaN or coeff is S.ComplexInfinity and
o.is_finite is False):
# we know for sure the result will be nan
return [S.NaN], [], None
if coeff.is_Number:
coeff += o
if coeff is S.NaN:
# we know for sure the result will be nan
return [S.NaN], [], None
continue
elif isinstance(o, AccumBounds):
coeff = o.__add__(coeff)
continue
elif isinstance(o, MatrixExpr):
coeff = o.__add__(coeff)
continue
elif o is S.ComplexInfinity:
if coeff.is_finite is False:
# we know for sure the result will be nan
return [S.NaN], [], None
coeff = S.ComplexInfinity
continue
# Add([...])
elif o.is_Add:
# NB: here we assume Add is always commutative
seq.extend(o.args) # TODO zerocopy?
continue
# Mul([...])
elif o.is_Mul:
c, s = o.as_coeff_Mul()
# check for unevaluated Pow, e.g. 2**3 or 2**(-1/2)
elif o.is_Pow:
b, e = o.as_base_exp()
if b.is_Number and (e.is_Integer or
(e.is_Rational and e.is_negative)):
seq.append(b**e)
continue
c, s = S.One, o
else:
# everything else
c = S.One
s = o
# now we have:
# o = c*s, where
#
# c is a Number
# s is an expression with number factor extracted
# let's collect terms with the same s, so e.g.
# 2*x**2 + 3*x**2 -> 5*x**2
if s in terms:
terms[s] += c
if terms[s] is S.NaN:
# we know for sure the result will be nan
return [S.NaN], [], None
else:
terms[s] = c
# now let's construct new args:
# [2*x**2, x**3, 7*x**4, pi, ...]
newseq = []
noncommutative = False
for s, c in terms.items():
# 0*s
if c is S.Zero:
continue
# 1*s
elif c is S.One:
newseq.append(s)
# c*s
else:
if s.is_Mul:
# Mul, already keeps its arguments in perfect order.
# so we can simply put c in slot0 and go the fast way.
cs = s._new_rawargs(*((c,) + s.args))
newseq.append(cs)
elif s.is_Add:
# we just re-create the unevaluated Mul
newseq.append(Mul(c, s, evaluate=False))
else:
# alternatively we have to call all Mul's machinery (slow)
newseq.append(Mul(c, s))
noncommutative = noncommutative or not s.is_commutative
# oo, -oo
if coeff is S.Infinity:
newseq = [f for f in newseq if not
(f.is_nonnegative or f.is_real and f.is_finite)]
elif coeff is S.NegativeInfinity:
newseq = [f for f in newseq if not
(f.is_nonpositive or f.is_real and f.is_finite)]
if coeff is S.ComplexInfinity:
# zoo might be
# infinite_real + finite_im
# finite_real + infinite_im
# infinite_real + infinite_im
# addition of a finite real or imaginary number won't be able to
# change the zoo nature; adding an infinite qualtity would result
# in a NaN condition if it had sign opposite of the infinite
# portion of zoo, e.g., infinite_real - infinite_real.
newseq = [c for c in newseq if not (c.is_finite and
c.is_real is not None)]
# process O(x)
if order_factors:
newseq2 = []
for t in newseq:
for o in order_factors:
# x + O(x) -> O(x)
if o.contains(t):
t = None
break
# x + O(x**2) -> x + O(x**2)
if t is not None:
newseq2.append(t)
newseq = newseq2 + order_factors
# 1 + O(1) -> O(1)
for o in order_factors:
if o.contains(coeff):
coeff = S.Zero
break
# order args canonically
_addsort(newseq)
# current code expects coeff to be first
if coeff is not S.Zero:
newseq.insert(0, coeff)
# we are done
if noncommutative:
return [], newseq, None
else:
return newseq, [], None
@classmethod
def class_key(cls):
"""Nice order of classes"""
return 3, 1, cls.__name__
def as_coefficients_dict(a):
"""Return a dictionary mapping terms to their Rational coefficient.
Since the dictionary is a defaultdict, inquiries about terms which
were not present will return a coefficient of 0. If an expression is
not an Add it is considered to have a single term.
Examples
========
>>> from sympy.abc import a, x
>>> (3*x + a*x + 4).as_coefficients_dict()
{1: 4, x: 3, a*x: 1}
>>> _[a]
0
>>> (3*a*x).as_coefficients_dict()
{a*x: 3}
"""
d = defaultdict(list)
for ai in a.args:
c, m = ai.as_coeff_Mul()
d[m].append(c)
for k, v in d.items():
if len(v) == 1:
d[k] = v[0]
else:
d[k] = Add(*v)
di = defaultdict(int)
di.update(d)
return di
@cacheit
def as_coeff_add(self, *deps):
"""
Returns a tuple (coeff, args) where self is treated as an Add and coeff
is the Number term and args is a tuple of all other terms.
Examples
========
>>> from sympy.abc import x
>>> (7 + 3*x).as_coeff_add()
(7, (3*x,))
>>> (7*x).as_coeff_add()
(0, (7*x,))
"""
if deps:
l1 = []
l2 = []
for f in self.args:
if f.has(*deps):
l2.append(f)
else:
l1.append(f)
return self._new_rawargs(*l1), tuple(l2)
coeff, notrat = self.args[0].as_coeff_add()
if coeff is not S.Zero:
return coeff, notrat + self.args[1:]
return S.Zero, self.args
def as_coeff_Add(self, rational=False):
"""Efficiently extract the coefficient of a summation. """
coeff, args = self.args[0], self.args[1:]
if coeff.is_Number and not rational or coeff.is_Rational:
return coeff, self._new_rawargs(*args)
return S.Zero, self
# Note, we intentionally do not implement Add.as_coeff_mul(). Rather, we
# let Expr.as_coeff_mul() just always return (S.One, self) for an Add. See
# issue 5524.
def _eval_power(self, e):
if e.is_Rational and self.is_number:
from sympy.core.evalf import pure_complex
from sympy.core.mul import _unevaluated_Mul
from sympy.core.exprtools import factor_terms
from sympy.core.function import expand_multinomial
from sympy.functions.elementary.complexes import sign
from sympy.functions.elementary.miscellaneous import sqrt
ri = pure_complex(self)
if ri:
r, i = ri
if e.q == 2:
D = sqrt(r**2 + i**2)
if D.is_Rational:
# (r, i, D) is a Pythagorean triple
root = sqrt(factor_terms((D - r)/2))**e.p
return root*expand_multinomial((
# principle value
(D + r)/abs(i) + sign(i)*S.ImaginaryUnit)**e.p)
elif e == -1:
return _unevaluated_Mul(
r - i*S.ImaginaryUnit,
1/(r**2 + i**2))
@cacheit
def _eval_derivative(self, s):
return self.func(*[a.diff(s) for a in self.args])
def _eval_nseries(self, x, n, logx):
terms = [t.nseries(x, n=n, logx=logx) for t in self.args]
return self.func(*terms)
def _matches_simple(self, expr, repl_dict):
# handle (w+3).matches('x+5') -> {w: x+2}
coeff, terms = self.as_coeff_add()
if len(terms) == 1:
return terms[0].matches(expr - coeff, repl_dict)
return
def matches(self, expr, repl_dict={}, old=False):
return AssocOp._matches_commutative(self, expr, repl_dict, old)
@staticmethod
def _combine_inverse(lhs, rhs):
"""
Returns lhs - rhs, but treats arguments like symbols, so things like
oo - oo return 0, instead of a nan.
"""
from sympy import oo, I, expand_mul
if lhs == oo and rhs == oo or lhs == oo*I and rhs == oo*I:
return S.Zero
return expand_mul(lhs - rhs)
@cacheit
def as_two_terms(self):
"""Return head and tail of self.
This is the most efficient way to get the head and tail of an
expression.
- if you want only the head, use self.args[0];
- if you want to process the arguments of the tail then use
self.as_coef_add() which gives the head and a tuple containing
the arguments of the tail when treated as an Add.
- if you want the coefficient when self is treated as a Mul
then use self.as_coeff_mul()[0]
>>> from sympy.abc import x, y
>>> (3*x*y).as_two_terms()
(3, x*y)
"""
if len(self.args) == 1:
return S.Zero, self
return self.args[0], self._new_rawargs(*self.args[1:])
def as_numer_denom(self):
# clear rational denominator
content, expr = self.primitive()
ncon, dcon = content.as_numer_denom()
# collect numerators and denominators of the terms
nd = defaultdict(list)
for f in expr.args:
ni, di = f.as_numer_denom()
nd[di].append(ni)
# put infinity in the numerator
if S.Zero in nd:
n = nd.pop(S.Zero)
assert len(n) == 1
n = n[0]
nd[S.One].append(n/S.Zero)
# check for quick exit
if len(nd) == 1:
d, n = nd.popitem()
return self.func(
*[_keep_coeff(ncon, ni) for ni in n]), _keep_coeff(dcon, d)
# sum up the terms having a common denominator
for d, n in nd.items():
if len(n) == 1:
nd[d] = n[0]
else:
nd[d] = self.func(*n)
# assemble single numerator and denominator
denoms, numers = [list(i) for i in zip(*iter(nd.items()))]
n, d = self.func(*[Mul(*(denoms[:i] + [numers[i]] + denoms[i + 1:]))
for i in range(len(numers))]), Mul(*denoms)
return _keep_coeff(ncon, n), _keep_coeff(dcon, d)
def _eval_is_polynomial(self, syms):
return all(term._eval_is_polynomial(syms) for term in self.args)
def _eval_is_rational_function(self, syms):
return all(term._eval_is_rational_function(syms) for term in self.args)
def _eval_is_algebraic_expr(self, syms):
return all(term._eval_is_algebraic_expr(syms) for term in self.args)
# assumption methods
_eval_is_real = lambda self: _fuzzy_group(
(a.is_real for a in self.args), quick_exit=True)
_eval_is_complex = lambda self: _fuzzy_group(
(a.is_complex for a in self.args), quick_exit=True)
_eval_is_antihermitian = lambda self: _fuzzy_group(
(a.is_antihermitian for a in self.args), quick_exit=True)
_eval_is_finite = lambda self: _fuzzy_group(
(a.is_finite for a in self.args), quick_exit=True)
_eval_is_hermitian = lambda self: _fuzzy_group(
(a.is_hermitian for a in self.args), quick_exit=True)
_eval_is_integer = lambda self: _fuzzy_group(
(a.is_integer for a in self.args), quick_exit=True)
_eval_is_rational = lambda self: _fuzzy_group(
(a.is_rational for a in self.args), quick_exit=True)
_eval_is_algebraic = lambda self: _fuzzy_group(
(a.is_algebraic for a in self.args), quick_exit=True)
_eval_is_commutative = lambda self: _fuzzy_group(
a.is_commutative for a in self.args)
def _eval_is_imaginary(self):
nz = []
im_I = []
for a in self.args:
if a.is_real:
if a.is_zero:
pass
elif a.is_zero is False:
nz.append(a)
else:
return
elif a.is_imaginary:
im_I.append(a*S.ImaginaryUnit)
elif (S.ImaginaryUnit*a).is_real:
im_I.append(a*S.ImaginaryUnit)
else:
return
b = self.func(*nz)
if b.is_zero:
return fuzzy_not(self.func(*im_I).is_zero)
elif b.is_zero is False:
return False
def _eval_is_zero(self):
if self.is_commutative is False:
# issue 10528: there is no way to know if a nc symbol
# is zero or not
return
nz = []
z = 0
im_or_z = False
im = False
for a in self.args:
if a.is_real:
if a.is_zero:
z += 1
elif a.is_zero is False:
nz.append(a)
else:
return
elif a.is_imaginary:
im = True
elif (S.ImaginaryUnit*a).is_real:
im_or_z = True
else:
return
if z == len(self.args):
return True
if len(nz) == len(self.args):
return None
b = self.func(*nz)
if b.is_zero:
if not im_or_z and not im:
return True
if im and not im_or_z:
return False
if b.is_zero is False:
return False
def _eval_is_odd(self):
l = [f for f in self.args if not (f.is_even is True)]
if not l:
return False
if l[0].is_odd:
return self._new_rawargs(*l[1:]).is_even
def _eval_is_irrational(self):
for t in self.args:
a = t.is_irrational
if a:
others = list(self.args)
others.remove(t)
if all(x.is_rational is True for x in others):
return True
return None
if a is None:
return
return False
def _eval_is_positive(self):
from sympy.core.exprtools import _monotonic_sign
if self.is_number:
return super(Add, self)._eval_is_positive()
c, a = self.as_coeff_Add()
if not c.is_zero:
v = _monotonic_sign(a)
if v is not None:
s = v + c
if s != self and s.is_positive and a.is_nonnegative:
return True
if len(self.free_symbols) == 1:
v = _monotonic_sign(self)
if v is not None and v != self and v.is_positive:
return True
pos = nonneg = nonpos = unknown_sign = False
saw_INF = set()
args = [a for a in self.args if not a.is_zero]
if not args:
return False
for a in args:
ispos = a.is_positive
infinite = a.is_infinite
if infinite:
saw_INF.add(fuzzy_or((ispos, a.is_nonnegative)))
if True in saw_INF and False in saw_INF:
return
if ispos:
pos = True
continue
elif a.is_nonnegative:
nonneg = True
continue
elif a.is_nonpositive:
nonpos = True
continue
if infinite is None:
return
unknown_sign = True
if saw_INF:
if len(saw_INF) > 1:
return
return saw_INF.pop()
elif unknown_sign:
return
elif not nonpos and not nonneg and pos:
return True
elif not nonpos and pos:
return True
elif not pos and not nonneg:
return False
def _eval_is_nonnegative(self):
from sympy.core.exprtools import _monotonic_sign
if not self.is_number:
c, a = self.as_coeff_Add()
if not c.is_zero and a.is_nonnegative:
v = _monotonic_sign(a)
if v is not None:
s = v + c
if s != self and s.is_nonnegative:
return True
if len(self.free_symbols) == 1:
v = _monotonic_sign(self)
if v is not None and v != self and v.is_nonnegative:
return True
def _eval_is_nonpositive(self):
from sympy.core.exprtools import _monotonic_sign
if not self.is_number:
c, a = self.as_coeff_Add()
if not c.is_zero and a.is_nonpositive:
v = _monotonic_sign(a)
if v is not None:
s = v + c
if s != self and s.is_nonpositive:
return True
if len(self.free_symbols) == 1:
v = _monotonic_sign(self)
if v is not None and v != self and v.is_nonpositive:
return True
def _eval_is_negative(self):
from sympy.core.exprtools import _monotonic_sign
if self.is_number:
return super(Add, self)._eval_is_negative()
c, a = self.as_coeff_Add()
if not c.is_zero:
v = _monotonic_sign(a)
if v is not None:
s = v + c
if s != self and s.is_negative and a.is_nonpositive:
return True
if len(self.free_symbols) == 1:
v = _monotonic_sign(self)
if v is not None and v != self and v.is_negative:
return True
neg = nonpos = nonneg = unknown_sign = False
saw_INF = set()
args = [a for a in self.args if not a.is_zero]
if not args:
return False
for a in args:
isneg = a.is_negative
infinite = a.is_infinite
if infinite:
saw_INF.add(fuzzy_or((isneg, a.is_nonpositive)))
if True in saw_INF and False in saw_INF:
return
if isneg:
neg = True
continue
elif a.is_nonpositive:
nonpos = True
continue
elif a.is_nonnegative:
nonneg = True
continue
if infinite is None:
return
unknown_sign = True
if saw_INF:
if len(saw_INF) > 1:
return
return saw_INF.pop()
elif unknown_sign:
return
elif not nonneg and not nonpos and neg:
return True
elif not nonneg and neg:
return True
elif not neg and not nonpos:
return False
def _eval_subs(self, old, new):
if not old.is_Add:
if old is S.Infinity and -old in self.args:
# foo - oo is foo + (-oo) internally
return self.xreplace({-old: -new})
return None
coeff_self, terms_self = self.as_coeff_Add()
coeff_old, terms_old = old.as_coeff_Add()
if coeff_self.is_Rational and coeff_old.is_Rational:
if terms_self == terms_old: # (2 + a).subs( 3 + a, y) -> -1 + y
return self.func(new, coeff_self, -coeff_old)
if terms_self == -terms_old: # (2 + a).subs(-3 - a, y) -> -1 - y
return self.func(-new, coeff_self, coeff_old)
if coeff_self.is_Rational and coeff_old.is_Rational \
or coeff_self == coeff_old:
args_old, args_self = self.func.make_args(
terms_old), self.func.make_args(terms_self)
if len(args_old) < len(args_self): # (a+b+c).subs(b+c,x) -> a+x
self_set = set(args_self)
old_set = set(args_old)
if old_set < self_set:
ret_set = self_set - old_set
return self.func(new, coeff_self, -coeff_old,
*[s._subs(old, new) for s in ret_set])
args_old = self.func.make_args(
-terms_old) # (a+b+c+d).subs(-b-c,x) -> a-x+d
old_set = set(args_old)
if old_set < self_set:
ret_set = self_set - old_set
return self.func(-new, coeff_self, coeff_old,
*[s._subs(old, new) for s in ret_set])
def removeO(self):
args = [a for a in self.args if not a.is_Order]
return self._new_rawargs(*args)
def getO(self):
args = [a for a in self.args if a.is_Order]
if args:
return self._new_rawargs(*args)
@cacheit
def extract_leading_order(self, symbols, point=None):
"""
Returns the leading term and its order.
Examples
========
>>> from sympy.abc import x
>>> (x + 1 + 1/x**5).extract_leading_order(x)
((x**(-5), O(x**(-5))),)
>>> (1 + x).extract_leading_order(x)
((1, O(1)),)
>>> (x + x**2).extract_leading_order(x)
((x, O(x)),)
"""
from sympy import Order
lst = []
symbols = list(symbols if is_sequence(symbols) else [symbols])
if not point:
point = [0]*len(symbols)
seq = [(f, Order(f, *zip(symbols, point))) for f in self.args]
for ef, of in seq:
for e, o in lst:
if o.contains(of) and o != of:
of = None
break
if of is None:
continue
new_lst = [(ef, of)]
for e, o in lst:
if of.contains(o) and o != of:
continue
new_lst.append((e, o))
lst = new_lst
return tuple(lst)
def as_real_imag(self, deep=True, **hints):
"""
returns a tuple representing a complex number
Examples
========
>>> from sympy import I
>>> (7 + 9*I).as_real_imag()
(7, 9)
>>> ((1 + I)/(1 - I)).as_real_imag()
(0, 1)
>>> ((1 + 2*I)*(1 + 3*I)).as_real_imag()
(-5, 5)
"""
sargs, terms = self.args, []
re_part, im_part = [], []
for term in sargs:
re, im = term.as_real_imag(deep=deep)
re_part.append(re)
im_part.append(im)
return (self.func(*re_part), self.func(*im_part))
def _eval_as_leading_term(self, x):
from sympy import expand_mul, factor_terms
old = self
expr = expand_mul(self)
if not expr.is_Add:
return expr.as_leading_term(x)
infinite = [t for t in expr.args if t.is_infinite]
expr = expr.func(*[t.as_leading_term(x) for t in expr.args]).removeO()
if not expr:
# simple leading term analysis gave us 0 but we have to send
# back a term, so compute the leading term (via series)
return old.compute_leading_term(x)
elif expr is S.NaN:
return old.func._from_args(infinite)
elif not expr.is_Add:
return expr
else:
plain = expr.func(*[s for s, _ in expr.extract_leading_order(x)])
rv = factor_terms(plain, fraction=False)
rv_simplify = rv.simplify()
# if it simplifies to an x-free expression, return that;
# tests don't fail if we don't but it seems nicer to do this
if x not in rv_simplify.free_symbols:
if rv_simplify.is_zero and plain.is_zero is not True:
return (expr - plain)._eval_as_leading_term(x)
return rv_simplify
return rv
def _eval_adjoint(self):
return self.func(*[t.adjoint() for t in self.args])
def _eval_conjugate(self):
return self.func(*[t.conjugate() for t in self.args])
def _eval_transpose(self):
return self.func(*[t.transpose() for t in self.args])
def __neg__(self):
return self.func(*[-t for t in self.args])
def _sage_(self):
s = 0
for x in self.args:
s += x._sage_()
return s
def primitive(self):
"""
Return ``(R, self/R)`` where ``R``` is the Rational GCD of ``self```.
``R`` is collected only from the leading coefficient of each term.
Examples
========
>>> from sympy.abc import x, y
>>> (2*x + 4*y).primitive()
(2, x + 2*y)
>>> (2*x/3 + 4*y/9).primitive()
(2/9, 3*x + 2*y)
>>> (2*x/3 + 4.2*y).primitive()
(1/3, 2*x + 12.6*y)
No subprocessing of term factors is performed:
>>> ((2 + 2*x)*x + 2).primitive()
(1, x*(2*x + 2) + 2)
Recursive subprocessing can be done with the as_content_primitive()
method:
>>> ((2 + 2*x)*x + 2).as_content_primitive()
(2, x*(x + 1) + 1)
See also: primitive() function in polytools.py
"""
terms = []
inf = False
for a in self.args:
c, m = a.as_coeff_Mul()
if not c.is_Rational:
c = S.One
m = a
inf = inf or m is S.ComplexInfinity
terms.append((c.p, c.q, m))
if not inf:
ngcd = reduce(igcd, [t[0] for t in terms], 0)
dlcm = reduce(ilcm, [t[1] for t in terms], 1)
else:
ngcd = reduce(igcd, [t[0] for t in terms if t[1]], 0)
dlcm = reduce(ilcm, [t[1] for t in terms if t[1]], 1)
if ngcd == dlcm == 1:
return S.One, self
if not inf:
for i, (p, q, term) in enumerate(terms):
terms[i] = _keep_coeff(Rational((p//ngcd)*(dlcm//q)), term)
else:
for i, (p, q, term) in enumerate(terms):
if q:
terms[i] = _keep_coeff(Rational((p//ngcd)*(dlcm//q)), term)
else:
terms[i] = _keep_coeff(Rational(p, q), term)
# we don't need a complete re-flattening since no new terms will join
# so we just use the same sort as is used in Add.flatten. When the
# coefficient changes, the ordering of terms may change, e.g.
# (3*x, 6*y) -> (2*y, x)
#
# We do need to make sure that term[0] stays in position 0, however.
#
if terms[0].is_Number or terms[0] is S.ComplexInfinity:
c = terms.pop(0)
else:
c = None
_addsort(terms)
if c:
terms.insert(0, c)
return Rational(ngcd, dlcm), self._new_rawargs(*terms)
def as_content_primitive(self, radical=False, clear=True):
"""Return the tuple (R, self/R) where R is the positive Rational
extracted from self. If radical is True (default is False) then
common radicals will be removed and included as a factor of the
primitive expression.
Examples
========
>>> from sympy import sqrt
>>> (3 + 3*sqrt(2)).as_content_primitive()
(3, 1 + sqrt(2))
Radical content can also be factored out of the primitive:
>>> (2*sqrt(2) + 4*sqrt(10)).as_content_primitive(radical=True)
(2, sqrt(2)*(1 + 2*sqrt(5)))
See docstring of Expr.as_content_primitive for more examples.
"""
con, prim = self.func(*[_keep_coeff(*a.as_content_primitive(
radical=radical, clear=clear)) for a in self.args]).primitive()
if not clear and not con.is_Integer and prim.is_Add:
con, d = con.as_numer_denom()
_p = prim/d
if any(a.as_coeff_Mul()[0].is_Integer for a in _p.args):
prim = _p
else:
con /= d
if radical and prim.is_Add:
# look for common radicals that can be removed
args = prim.args
rads = []
common_q = None
for m in args:
term_rads = defaultdict(list)
for ai in Mul.make_args(m):
if ai.is_Pow:
b, e = ai.as_base_exp()
if e.is_Rational and b.is_Integer:
term_rads[e.q].append(abs(int(b))**e.p)
if not term_rads:
break
if common_q is None:
common_q = set(term_rads.keys())
else:
common_q = common_q & set(term_rads.keys())
if not common_q:
break
rads.append(term_rads)
else:
# process rads
# keep only those in common_q
for r in rads:
for q in list(r.keys()):
if q not in common_q:
r.pop(q)
for q in r:
r[q] = prod(r[q])
# find the gcd of bases for each q
G = []
for q in common_q:
g = reduce(igcd, [r[q] for r in rads], 0)
if g != 1:
G.append(g**Rational(1, q))
if G:
G = Mul(*G)
args = [ai/G for ai in args]
prim = G*prim.func(*args)
return con, prim
@property
def _sorted_args(self):
from sympy.core.compatibility import default_sort_key
return tuple(sorted(self.args, key=lambda w: default_sort_key(w)))
def _eval_difference_delta(self, n, step):
from sympy.series.limitseq import difference_delta as dd
return self.func(*[dd(a, n, step) for a in self.args])
@property
def _mpc_(self):
"""
Convert self to an mpmath mpc if possible
"""
from sympy.core.numbers import I, Float
re_part, rest = self.as_coeff_Add()
im_part, imag_unit = rest.as_coeff_Mul()
if not imag_unit == I:
# ValueError may seem more reasonable but since it's a @property,
# we need to use AttributeError to keep from confusing things like
# hasattr.
raise AttributeError("Cannot convert Add to mpc. Must be of the form Number + Number*I")
return (Float(re_part)._mpf_, Float(im_part)._mpf_)
from .mul import Mul, _keep_coeff, prod
from sympy.core.numbers import Rational
| 35,333 | 32.715649 | 100 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/mod.py
|
from __future__ import print_function, division
from sympy.core.numbers import nan
from .function import Function
class Mod(Function):
"""Represents a modulo operation on symbolic expressions.
Receives two arguments, dividend p and divisor q.
The convention used is the same as Python's: the remainder always has the
same sign as the divisor.
Examples
========
>>> from sympy.abc import x, y
>>> x**2 % y
Mod(x**2, y)
>>> _.subs({x: 5, y: 6})
1
"""
@classmethod
def eval(cls, p, q):
from sympy.core.add import Add
from sympy.core.mul import Mul
from sympy.core.singleton import S
from sympy.core.exprtools import gcd_terms
from sympy.polys.polytools import gcd
def doit(p, q):
"""Try to return p % q if both are numbers or +/-p is known
to be less than or equal q.
"""
if p.is_infinite or q.is_infinite or p is nan or q is nan:
return nan
if (p == q or p == -q or
p.is_Pow and p.exp.is_Integer and p.base == q or
p.is_integer and q == 1):
return S.Zero
if q.is_Number:
if p.is_Number:
return (p % q)
if q == 2:
if p.is_even:
return S.Zero
elif p.is_odd:
return S.One
# by ratio
r = p/q
try:
d = int(r)
except TypeError:
pass
else:
if type(d) is int:
rv = p - d*q
if (rv*q < 0) == True:
rv += q
return rv
# by difference
d = p - q
if d.is_negative:
if q.is_negative:
return d
elif q.is_positive:
return p
rv = doit(p, q)
if rv is not None:
return rv
# denest
if p.func is cls:
# easy
qinner = p.args[1]
if qinner == q:
return p
# XXX other possibilities?
# extract gcd; any further simplification should be done by the user
G = gcd(p, q)
if G != 1:
p, q = [
gcd_terms(i/G, clear=False, fraction=False) for i in (p, q)]
pwas, qwas = p, q
# simplify terms
# (x + y + 2) % x -> Mod(y + 2, x)
if p.is_Add:
args = []
for i in p.args:
a = cls(i, q)
if a.count(cls) > i.count(cls):
args.append(i)
else:
args.append(a)
if args != list(p.args):
p = Add(*args)
else:
# handle coefficients if they are not Rational
# since those are not handled by factor_terms
# e.g. Mod(.6*x, .3*y) -> 0.3*Mod(2*x, y)
cp, p = p.as_coeff_Mul()
cq, q = q.as_coeff_Mul()
ok = False
if not cp.is_Rational or not cq.is_Rational:
r = cp % cq
if r == 0:
G *= cq
p *= int(cp/cq)
ok = True
if not ok:
p = cp*p
q = cq*q
# simple -1 extraction
if p.could_extract_minus_sign() and q.could_extract_minus_sign():
G, p, q = [-i for i in (G, p, q)]
# check again to see if p and q can now be handled as numbers
rv = doit(p, q)
if rv is not None:
return rv*G
# put 1.0 from G on inside
if G.is_Float and G == 1:
p *= G
return cls(p, q, evaluate=False)
elif G.is_Mul and G.args[0].is_Float and G.args[0] == 1:
p = G.args[0]*p
G = Mul._from_args(G.args[1:])
return G*cls(p, q, evaluate=(p, q) != (pwas, qwas))
def _eval_is_integer(self):
from sympy.core.logic import fuzzy_and, fuzzy_not
p, q = self.args
if fuzzy_and([p.is_integer, q.is_integer, fuzzy_not(q.is_zero)]):
return True
def _eval_is_nonnegative(self):
if self.args[1].is_positive:
return True
def _eval_is_nonpositive(self):
if self.args[1].is_negative:
return True
| 4,488 | 27.775641 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/alphabets.py
|
from __future__ import print_function, division
greeks = ('alpha', 'beta', 'gamma', 'delta', 'epsilon', 'zeta',
'eta', 'theta', 'iota', 'kappa', 'lambda', 'mu', 'nu',
'xi', 'omicron', 'pi', 'rho', 'sigma', 'tau', 'upsilon',
'phi', 'chi', 'psi', 'omega')
| 315 | 44.142857 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/symbol.py
|
from __future__ import print_function, division
from sympy.core.assumptions import StdFactKB
from sympy.core.compatibility import string_types, range
from .basic import Basic
from .sympify import sympify
from .singleton import S
from .expr import Expr, AtomicExpr
from .cache import cacheit
from .function import FunctionClass
from sympy.core.logic import fuzzy_bool
from sympy.logic.boolalg import Boolean
from sympy.utilities.iterables import cartes
import string
import re as _re
import random
class Symbol(AtomicExpr, Boolean):
"""
Assumptions:
commutative = True
You can override the default assumptions in the constructor:
>>> from sympy import symbols
>>> A,B = symbols('A,B', commutative = False)
>>> bool(A*B != B*A)
True
>>> bool(A*B*2 == 2*A*B) == True # multiplication by scalars is commutative
True
"""
is_comparable = False
__slots__ = ['name']
is_Symbol = True
is_symbol = True
@property
def _diff_wrt(self):
"""Allow derivatives wrt Symbols.
Examples
========
>>> from sympy import Symbol
>>> x = Symbol('x')
>>> x._diff_wrt
True
"""
return True
@staticmethod
def _sanitize(assumptions, obj=None):
"""Remove None, covert values to bool, check commutativity *in place*.
"""
# be strict about commutativity: cannot be None
is_commutative = fuzzy_bool(assumptions.get('commutative', True))
if is_commutative is None:
whose = '%s ' % obj.__name__ if obj else ''
raise ValueError(
'%scommutativity must be True or False.' % whose)
# sanitize other assumptions so 1 -> True and 0 -> False
for key in list(assumptions.keys()):
from collections import defaultdict
from sympy.utilities.exceptions import SymPyDeprecationWarning
keymap = defaultdict(lambda: None)
keymap.update({'bounded': 'finite', 'unbounded': 'infinite', 'infinitesimal': 'zero'})
if keymap[key]:
SymPyDeprecationWarning(
feature="%s assumption" % key,
useinstead="%s" % keymap[key],
issue=8071,
deprecated_since_version="0.7.6").warn()
assumptions[keymap[key]] = assumptions[key]
assumptions.pop(key)
key = keymap[key]
v = assumptions[key]
if v is None:
assumptions.pop(key)
continue
assumptions[key] = bool(v)
def __new__(cls, name, **assumptions):
"""Symbols are identified by name and assumptions::
>>> from sympy import Symbol
>>> Symbol("x") == Symbol("x")
True
>>> Symbol("x", real=True) == Symbol("x", real=False)
False
"""
cls._sanitize(assumptions, cls)
return Symbol.__xnew_cached_(cls, name, **assumptions)
def __new_stage2__(cls, name, **assumptions):
if not isinstance(name, string_types):
raise TypeError("name should be a string, not %s" % repr(type(name)))
obj = Expr.__new__(cls)
obj.name = name
# TODO: Issue #8873: Forcing the commutative assumption here means
# later code such as ``srepr()`` cannot tell whether the user
# specified ``commutative=True`` or omitted it. To workaround this,
# we keep a copy of the assumptions dict, then create the StdFactKB,
# and finally overwrite its ``._generator`` with the dict copy. This
# is a bit of a hack because we assume StdFactKB merely copies the
# given dict as ``._generator``, but future modification might, e.g.,
# compute a minimal equivalent assumption set.
tmp_asm_copy = assumptions.copy()
# be strict about commutativity
is_commutative = fuzzy_bool(assumptions.get('commutative', True))
assumptions['commutative'] = is_commutative
obj._assumptions = StdFactKB(assumptions)
obj._assumptions._generator = tmp_asm_copy # Issue #8873
return obj
__xnew__ = staticmethod(
__new_stage2__) # never cached (e.g. dummy)
__xnew_cached_ = staticmethod(
cacheit(__new_stage2__)) # symbols are always cached
def __getnewargs__(self):
return (self.name,)
def __getstate__(self):
return {'_assumptions': self._assumptions}
def _hashable_content(self):
# Note: user-specified assumptions not hashed, just derived ones
return (self.name,) + tuple(sorted(self.assumptions0.items()))
@property
def assumptions0(self):
return dict((key, value) for key, value
in self._assumptions.items() if value is not None)
@cacheit
def sort_key(self, order=None):
return self.class_key(), (1, (str(self),)), S.One.sort_key(), S.One
def as_dummy(self):
"""Return a Dummy having the same name and same assumptions as self."""
return Dummy(self.name, **self._assumptions.generator)
def __call__(self, *args):
from .function import Function
return Function(self.name)(*args)
def as_real_imag(self, deep=True, **hints):
from sympy import im, re
if hints.get('ignore') == self:
return None
else:
return (re(self), im(self))
def _sage_(self):
import sage.all as sage
return sage.var(self.name)
def is_constant(self, *wrt, **flags):
if not wrt:
return False
return not self in wrt
@property
def free_symbols(self):
return {self}
class Dummy(Symbol):
"""Dummy symbols are each unique, even if they have the same name:
>>> from sympy import Dummy
>>> Dummy("x") == Dummy("x")
False
If a name is not supplied then a string value of an internal count will be
used. This is useful when a temporary variable is needed and the name
of the variable used in the expression is not important.
>>> Dummy() #doctest: +SKIP
_Dummy_10
"""
# In the rare event that a Dummy object needs to be recreated, both the
# `name` and `dummy_index` should be passed. This is used by `srepr` for
# example:
# >>> d1 = Dummy()
# >>> d2 = eval(srepr(d1))
# >>> d2 == d1
# True
#
# If a new session is started between `srepr` and `eval`, there is a very
# small chance that `d2` will be equal to a previously-created Dummy.
_count = 0
_prng = random.Random()
_base_dummy_index = _prng.randint(10**6, 9*10**6)
__slots__ = ['dummy_index']
is_Dummy = True
def __new__(cls, name=None, dummy_index=None, **assumptions):
if dummy_index is not None:
assert name is not None, "If you specify a dummy_index, you must also provide a name"
if name is None:
name = "Dummy_" + str(Dummy._count)
if dummy_index is None:
dummy_index = Dummy._base_dummy_index + Dummy._count
Dummy._count += 1
cls._sanitize(assumptions, cls)
obj = Symbol.__xnew__(cls, name, **assumptions)
obj.dummy_index = dummy_index
return obj
def __getstate__(self):
return {'_assumptions': self._assumptions, 'dummy_index': self.dummy_index}
@cacheit
def sort_key(self, order=None):
return self.class_key(), (
2, (str(self), self.dummy_index)), S.One.sort_key(), S.One
def _hashable_content(self):
return Symbol._hashable_content(self) + (self.dummy_index,)
class Wild(Symbol):
"""
A Wild symbol matches anything, or anything
without whatever is explicitly excluded.
Examples
========
>>> from sympy import Wild, WildFunction, cos, pi
>>> from sympy.abc import x, y, z
>>> a = Wild('a')
>>> x.match(a)
{a_: x}
>>> pi.match(a)
{a_: pi}
>>> (3*x**2).match(a*x)
{a_: 3*x}
>>> cos(x).match(a)
{a_: cos(x)}
>>> b = Wild('b', exclude=[x])
>>> (3*x**2).match(b*x)
>>> b.match(a)
{a_: b_}
>>> A = WildFunction('A')
>>> A.match(a)
{a_: A_}
Tips
====
When using Wild, be sure to use the exclude
keyword to make the pattern more precise.
Without the exclude pattern, you may get matches
that are technically correct, but not what you
wanted. For example, using the above without
exclude:
>>> from sympy import symbols
>>> a, b = symbols('a b', cls=Wild)
>>> (2 + 3*y).match(a*x + b*y)
{a_: 2/x, b_: 3}
This is technically correct, because
(2/x)*x + 3*y == 2 + 3*y, but you probably
wanted it to not match at all. The issue is that
you really didn't want a and b to include x and y,
and the exclude parameter lets you specify exactly
this. With the exclude parameter, the pattern will
not match.
>>> a = Wild('a', exclude=[x, y])
>>> b = Wild('b', exclude=[x, y])
>>> (2 + 3*y).match(a*x + b*y)
Exclude also helps remove ambiguity from matches.
>>> E = 2*x**3*y*z
>>> a, b = symbols('a b', cls=Wild)
>>> E.match(a*b)
{a_: 2*y*z, b_: x**3}
>>> a = Wild('a', exclude=[x, y])
>>> E.match(a*b)
{a_: z, b_: 2*x**3*y}
>>> a = Wild('a', exclude=[x, y, z])
>>> E.match(a*b)
{a_: 2, b_: x**3*y*z}
"""
is_Wild = True
__slots__ = ['exclude', 'properties']
def __new__(cls, name, exclude=(), properties=(), **assumptions):
exclude = tuple([sympify(x) for x in exclude])
properties = tuple(properties)
cls._sanitize(assumptions, cls)
return Wild.__xnew__(cls, name, exclude, properties, **assumptions)
def __getnewargs__(self):
return (self.name, self.exclude, self.properties)
@staticmethod
@cacheit
def __xnew__(cls, name, exclude, properties, **assumptions):
obj = Symbol.__xnew__(cls, name, **assumptions)
obj.exclude = exclude
obj.properties = properties
return obj
def _hashable_content(self):
return super(Wild, self)._hashable_content() + (self.exclude, self.properties)
# TODO add check against another Wild
def matches(self, expr, repl_dict={}, old=False):
if any(expr.has(x) for x in self.exclude):
return None
if any(not f(expr) for f in self.properties):
return None
repl_dict = repl_dict.copy()
repl_dict[self] = expr
return repl_dict
def __call__(self, *args, **kwargs):
raise TypeError("'%s' object is not callable" % type(self).__name__)
_range = _re.compile('([0-9]*:[0-9]+|[a-zA-Z]?:[a-zA-Z])')
def symbols(names, **args):
r"""
Transform strings into instances of :class:`Symbol` class.
:func:`symbols` function returns a sequence of symbols with names taken
from ``names`` argument, which can be a comma or whitespace delimited
string, or a sequence of strings::
>>> from sympy import symbols, Function
>>> x, y, z = symbols('x,y,z')
>>> a, b, c = symbols('a b c')
The type of output is dependent on the properties of input arguments::
>>> symbols('x')
x
>>> symbols('x,')
(x,)
>>> symbols('x,y')
(x, y)
>>> symbols(('a', 'b', 'c'))
(a, b, c)
>>> symbols(['a', 'b', 'c'])
[a, b, c]
>>> symbols({'a', 'b', 'c'})
{a, b, c}
If an iterable container is needed for a single symbol, set the ``seq``
argument to ``True`` or terminate the symbol name with a comma::
>>> symbols('x', seq=True)
(x,)
To reduce typing, range syntax is supported to create indexed symbols.
Ranges are indicated by a colon and the type of range is determined by
the character to the right of the colon. If the character is a digit
then all contiguous digits to the left are taken as the nonnegative
starting value (or 0 if there is no digit left of the colon) and all
contiguous digits to the right are taken as 1 greater than the ending
value::
>>> symbols('x:10')
(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9)
>>> symbols('x5:10')
(x5, x6, x7, x8, x9)
>>> symbols('x5(:2)')
(x50, x51)
>>> symbols('x5:10,y:5')
(x5, x6, x7, x8, x9, y0, y1, y2, y3, y4)
>>> symbols(('x5:10', 'y:5'))
((x5, x6, x7, x8, x9), (y0, y1, y2, y3, y4))
If the character to the right of the colon is a letter, then the single
letter to the left (or 'a' if there is none) is taken as the start
and all characters in the lexicographic range *through* the letter to
the right are used as the range::
>>> symbols('x:z')
(x, y, z)
>>> symbols('x:c') # null range
()
>>> symbols('x(:c)')
(xa, xb, xc)
>>> symbols(':c')
(a, b, c)
>>> symbols('a:d, x:z')
(a, b, c, d, x, y, z)
>>> symbols(('a:d', 'x:z'))
((a, b, c, d), (x, y, z))
Multiple ranges are supported; contiguous numerical ranges should be
separated by parentheses to disambiguate the ending number of one
range from the starting number of the next::
>>> symbols('x:2(1:3)')
(x01, x02, x11, x12)
>>> symbols(':3:2') # parsing is from left to right
(00, 01, 10, 11, 20, 21)
Only one pair of parentheses surrounding ranges are removed, so to
include parentheses around ranges, double them. And to include spaces,
commas, or colons, escape them with a backslash::
>>> symbols('x((a:b))')
(x(a), x(b))
>>> symbols(r'x(:1\,:2)') # or r'x((:1)\,(:2))'
(x(0,0), x(0,1))
All newly created symbols have assumptions set according to ``args``::
>>> a = symbols('a', integer=True)
>>> a.is_integer
True
>>> x, y, z = symbols('x,y,z', real=True)
>>> x.is_real and y.is_real and z.is_real
True
Despite its name, :func:`symbols` can create symbol-like objects like
instances of Function or Wild classes. To achieve this, set ``cls``
keyword argument to the desired type::
>>> symbols('f,g,h', cls=Function)
(f, g, h)
>>> type(_[0])
<class 'sympy.core.function.UndefinedFunction'>
"""
result = []
if isinstance(names, string_types):
marker = 0
literals = [r'\,', r'\:', r'\ ']
for i in range(len(literals)):
lit = literals.pop(0)
if lit in names:
while chr(marker) in names:
marker += 1
lit_char = chr(marker)
marker += 1
names = names.replace(lit, lit_char)
literals.append((lit_char, lit[1:]))
def literal(s):
if literals:
for c, l in literals:
s = s.replace(c, l)
return s
names = names.strip()
as_seq = names.endswith(',')
if as_seq:
names = names[:-1].rstrip()
if not names:
raise ValueError('no symbols given')
# split on commas
names = [n.strip() for n in names.split(',')]
if not all(n for n in names):
raise ValueError('missing symbol between commas')
# split on spaces
for i in range(len(names) - 1, -1, -1):
names[i: i + 1] = names[i].split()
cls = args.pop('cls', Symbol)
seq = args.pop('seq', as_seq)
for name in names:
if not name:
raise ValueError('missing symbol')
if ':' not in name:
symbol = cls(literal(name), **args)
result.append(symbol)
continue
split = _range.split(name)
# remove 1 layer of bounding parentheses around ranges
for i in range(len(split) - 1):
if i and ':' in split[i] and split[i] != ':' and \
split[i - 1].endswith('(') and \
split[i + 1].startswith(')'):
split[i - 1] = split[i - 1][:-1]
split[i + 1] = split[i + 1][1:]
for i, s in enumerate(split):
if ':' in s:
if s[-1].endswith(':'):
raise ValueError('missing end range')
a, b = s.split(':')
if b[-1] in string.digits:
a = 0 if not a else int(a)
b = int(b)
split[i] = [str(c) for c in range(a, b)]
else:
a = a or 'a'
split[i] = [string.ascii_letters[c] for c in range(
string.ascii_letters.index(a),
string.ascii_letters.index(b) + 1)] # inclusive
if not split[i]:
break
else:
split[i] = [s]
else:
seq = True
if len(split) == 1:
names = split[0]
else:
names = [''.join(s) for s in cartes(*split)]
if literals:
result.extend([cls(literal(s), **args) for s in names])
else:
result.extend([cls(s, **args) for s in names])
if not seq and len(result) <= 1:
if not result:
return ()
return result[0]
return tuple(result)
else:
for name in names:
result.append(symbols(name, **args))
return type(names)(result)
def var(names, **args):
"""
Create symbols and inject them into the global namespace.
This calls :func:`symbols` with the same arguments and puts the results
into the *global* namespace. It's recommended not to use :func:`var` in
library code, where :func:`symbols` has to be used::
Examples
========
>>> from sympy import var
>>> var('x')
x
>>> x
x
>>> var('a,ab,abc')
(a, ab, abc)
>>> abc
abc
>>> var('x,y', real=True)
(x, y)
>>> x.is_real and y.is_real
True
See :func:`symbol` documentation for more details on what kinds of
arguments can be passed to :func:`var`.
"""
def traverse(symbols, frame):
"""Recursively inject symbols to the global namespace. """
for symbol in symbols:
if isinstance(symbol, Basic):
frame.f_globals[symbol.name] = symbol
elif isinstance(symbol, FunctionClass):
frame.f_globals[symbol.__name__] = symbol
else:
traverse(symbol, frame)
from inspect import currentframe
frame = currentframe().f_back
try:
syms = symbols(names, **args)
if syms is not None:
if isinstance(syms, Basic):
frame.f_globals[syms.name] = syms
elif isinstance(syms, FunctionClass):
frame.f_globals[syms.__name__] = syms
else:
traverse(syms, frame)
finally:
del frame # break cyclic dependencies as stated in inspect docs
return syms
| 19,367 | 29.939297 | 98 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/singleton.py
|
"""Singleton mechanism"""
from __future__ import print_function, division
from .core import Registry
from .assumptions import ManagedProperties
from .sympify import sympify
class SingletonRegistry(Registry):
"""
The registry for the singleton classes (accessible as ``S``).
This class serves as two separate things.
The first thing it is is the ``SingletonRegistry``. Several classes in
SymPy appear so often that they are singletonized, that is, using some
metaprogramming they are made so that they can only be instantiated once
(see the :class:`sympy.core.singleton.Singleton` class for details). For
instance, every time you create ``Integer(0)``, this will return the same
instance, :class:`sympy.core.numbers.Zero`. All singleton instances are
attributes of the ``S`` object, so ``Integer(0)`` can also be accessed as
``S.Zero``.
Singletonization offers two advantages: it saves memory, and it allows
fast comparison. It saves memory because no matter how many times the
singletonized objects appear in expressions in memory, they all point to
the same single instance in memory. The fast comparison comes from the
fact that you can use ``is`` to compare exact instances in Python
(usually, you need to use ``==`` to compare things). ``is`` compares
objects by memory address, and is very fast. For instance
>>> from sympy import S, Integer
>>> a = Integer(0)
>>> a is S.Zero
True
For the most part, the fact that certain objects are singletonized is an
implementation detail that users shouldn't need to worry about. In SymPy
library code, ``is`` comparison is often used for performance purposes
The primary advantage of ``S`` for end users is the convenient access to
certain instances that are otherwise difficult to type, like ``S.Half``
(instead of ``Rational(1, 2)``).
When using ``is`` comparison, make sure the argument is sympified. For
instance,
>>> 0 is S.Zero
False
This problem is not an issue when using ``==``, which is recommended for
most use-cases:
>>> 0 == S.Zero
True
The second thing ``S`` is is a shortcut for
:func:`sympy.core.sympify.sympify`. :func:`sympy.core.sympify.sympify` is
the function that converts Python objects such as ``int(1)`` into SymPy
objects such as ``Integer(1)``. It also converts the string form of an
expression into a SymPy expression, like ``sympify("x**2")`` ->
``Symbol("x")**2``. ``S(1)`` is the same thing as ``sympify(1)``
(basically, ``S.__call__`` has been defined to call ``sympify``).
This is for convenience, since ``S`` is a single letter. It's mostly
useful for defining rational numbers. Consider an expression like ``x +
1/2``. If you enter this directly in Python, it will evaluate the ``1/2``
and give ``0.5`` (or just ``0`` in Python 2, because of integer division),
because both arguments are ints (see also
:ref:`tutorial-gotchas-final-notes`). However, in SymPy, you usually want
the quotient of two integers to give an exact rational number. The way
Python's evaluation works, at least one side of an operator needs to be a
SymPy object for the SymPy evaluation to take over. You could write this
as ``x + Rational(1, 2)``, but this is a lot more typing. A shorter
version is ``x + S(1)/2``. Since ``S(1)`` returns ``Integer(1)``, the
division will return a ``Rational`` type, since it will call
``Integer.__div__``, which knows how to return a ``Rational``.
"""
__slots__ = []
# Also allow things like S(5)
__call__ = staticmethod(sympify)
def __init__(self):
self._classes_to_install = {}
# Dict of classes that have been registered, but that have not have been
# installed as an attribute of this SingletonRegistry.
# Installation automatically happens at the first attempt to access the
# attribute.
# The purpose of this is to allow registration during class
# initialization during import, but not trigger object creation until
# actual use (which should not happen until after all imports are
# finished).
def register(self, cls):
self._classes_to_install[cls.__name__] = cls
def __getattr__(self, name):
"""Python calls __getattr__ if no attribute of that name was installed
yet.
This __getattr__ checks whether a class with the requested name was
already registered but not installed; if no, raises an AttributeError.
Otherwise, retrieves the class, calculates its singleton value, installs
it as an attribute of the given name, and unregisters the class."""
if name not in self._classes_to_install:
raise AttributeError(
"Attribute '%s' was not installed on SymPy registry %s" % (
name, self))
class_to_install = self._classes_to_install[name]
value_to_install = class_to_install()
self.__setattr__(name, value_to_install)
del self._classes_to_install[name]
return value_to_install
def __repr__(self):
return "S"
S = SingletonRegistry()
class Singleton(ManagedProperties):
"""
Metaclass for singleton classes.
A singleton class has only one instance which is returned every time the
class is instantiated. Additionally, this instance can be accessed through
the global registry object S as S.<class_name>.
Examples
========
>>> from sympy import S, Basic
>>> from sympy.core.singleton import Singleton
>>> from sympy.core.compatibility import with_metaclass
>>> class MySingleton(with_metaclass(Singleton, Basic)):
... pass
>>> Basic() is Basic()
False
>>> MySingleton() is MySingleton()
True
>>> S.MySingleton is MySingleton()
True
Notes
=====
Instance creation is delayed until the first time the value is accessed.
(SymPy versions before 1.0 would create the instance during class
creation time, which would be prone to import cycles.)
This metaclass is a subclass of ManagedProperties because that is the
metaclass of many classes that need to be Singletons (Python does not allow
subclasses to have a different metaclass than the superclass, except the
subclass may use a subclassed metaclass).
"""
_instances = {}
"Maps singleton classes to their instances."
def __new__(cls, *args, **kwargs):
result = super(Singleton, cls).__new__(cls, *args, **kwargs)
S.register(result)
return result
def __call__(self, *args, **kwargs):
# Called when application code says SomeClass(), where SomeClass is a
# class of which Singleton is the metaclas.
# __call__ is invoked first, before __new__() and __init__().
if self not in Singleton._instances:
Singleton._instances[self] = \
super(Singleton, self).__call__(*args, **kwargs)
# Invokes the standard constructor of SomeClass.
return Singleton._instances[self]
# Inject pickling support.
def __getnewargs__(self):
return ()
self.__getnewargs__ = __getnewargs__
| 7,334 | 39.524862 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/assumptions.py
|
"""
This module contains the machinery handling assumptions.
All symbolic objects have assumption attributes that can be accessed via
.is_<assumption name> attribute.
Assumptions determine certain properties of symbolic objects and can
have 3 possible values: True, False, None. True is returned if the
object has the property and False is returned if it doesn't or can't
(i.e. doesn't make sense):
>>> from sympy import I
>>> I.is_algebraic
True
>>> I.is_real
False
>>> I.is_prime
False
When the property cannot be determined (or when a method is not
implemented) None will be returned, e.g. a generic symbol, x, may or
may not be positive so a value of None is returned for x.is_positive.
By default, all symbolic values are in the largest set in the given context
without specifying the property. For example, a symbol that has a property
being integer, is also real, complex, etc.
Here follows a list of possible assumption names:
.. glossary::
commutative
object commutes with any other object with
respect to multiplication operation.
complex
object can have only values from the set
of complex numbers.
imaginary
object value is a number that can be written as a real
number multiplied by the imaginary unit ``I``. See
[3]_. Please note, that ``0`` is not considered to be an
imaginary number, see
`issue #7649 <https://github.com/sympy/sympy/issues/7649>`_.
real
object can have only values from the set
of real numbers.
integer
object can have only values from the set
of integers.
odd
even
object can have only values from the set of
odd (even) integers [2]_.
prime
object is a natural number greater than ``1`` that has
no positive divisors other than ``1`` and itself. See [6]_.
composite
object is a positive integer that has at least one positive
divisor other than ``1`` or the number itself. See [4]_.
zero
object has the value of ``0``.
nonzero
object is a real number that is not zero.
rational
object can have only values from the set
of rationals.
algebraic
object can have only values from the set
of algebraic numbers [11]_.
transcendental
object can have only values from the set
of transcendental numbers [10]_.
irrational
object value cannot be represented exactly by Rational, see [5]_.
finite
infinite
object absolute value is bounded (arbitrarily large).
See [7]_, [8]_, [9]_.
negative
nonnegative
object can have only negative (nonnegative)
values [1]_.
positive
nonpositive
object can have only positive (only
nonpositive) values.
hermitian
antihermitian
object belongs to the field of hermitian
(antihermitian) operators.
Examples
========
>>> from sympy import Symbol
>>> x = Symbol('x', real=True); x
x
>>> x.is_real
True
>>> x.is_complex
True
See Also
========
.. seealso::
:py:class:`sympy.core.numbers.ImaginaryUnit`
:py:class:`sympy.core.numbers.Zero`
:py:class:`sympy.core.numbers.One`
Notes
=====
Assumption values are stored in obj._assumptions dictionary or
are returned by getter methods (with property decorators) or are
attributes of objects/classes.
References
==========
.. [1] http://en.wikipedia.org/wiki/Negative_number
.. [2] http://en.wikipedia.org/wiki/Parity_%28mathematics%29
.. [3] http://en.wikipedia.org/wiki/Imaginary_number
.. [4] http://en.wikipedia.org/wiki/Composite_number
.. [5] http://en.wikipedia.org/wiki/Irrational_number
.. [6] http://en.wikipedia.org/wiki/Prime_number
.. [7] http://en.wikipedia.org/wiki/Finite
.. [8] https://docs.python.org/3/library/math.html#math.isfinite
.. [9] http://docs.scipy.org/doc/numpy/reference/generated/numpy.isfinite.html
.. [10] http://en.wikipedia.org/wiki/Transcendental_number
.. [11] http://en.wikipedia.org/wiki/Algebraic_number
"""
from __future__ import print_function, division
from sympy.core.facts import FactRules, FactKB
from sympy.core.core import BasicMeta
from sympy.core.compatibility import integer_types
from random import shuffle
_assume_rules = FactRules([
'integer -> rational',
'rational -> real',
'rational -> algebraic',
'algebraic -> complex',
'real -> complex',
'real -> hermitian',
'imaginary -> complex',
'imaginary -> antihermitian',
'complex -> commutative',
'odd == integer & !even',
'even == integer & !odd',
'real == negative | zero | positive',
'transcendental == complex & !algebraic',
'negative == nonpositive & nonzero',
'positive == nonnegative & nonzero',
'zero == nonnegative & nonpositive',
'nonpositive == real & !positive',
'nonnegative == real & !negative',
'zero -> even & finite',
'prime -> integer & positive',
'composite -> integer & positive & !prime',
'irrational == real & !rational',
'imaginary -> !real',
'infinite -> !finite',
'noninteger == real & !integer',
'nonzero == real & !zero',
])
_assume_defined = _assume_rules.defined_facts.copy()
_assume_defined.add('polar')
_assume_defined = frozenset(_assume_defined)
class StdFactKB(FactKB):
"""A FactKB specialised for the built-in rules
This is the only kind of FactKB that Basic objects should use.
"""
rules = _assume_rules
def __init__(self, facts=None):
# save a copy of the facts dict
if not facts:
self._generator = {}
elif not isinstance(facts, FactKB):
self._generator = facts.copy()
else:
self._generator = facts.generator
if facts:
self.deduce_all_facts(facts)
def copy(self):
return self.__class__(self)
@property
def generator(self):
return self._generator.copy()
def as_property(fact):
"""Convert a fact name to the name of the corresponding property"""
return 'is_%s' % fact
def make_property(fact):
"""Create the automagic property corresponding to a fact."""
def getit(self):
try:
return self._assumptions[fact]
except KeyError:
if self._assumptions is self.default_assumptions:
self._assumptions = self.default_assumptions.copy()
return _ask(fact, self)
getit.func_name = as_property(fact)
return property(getit)
def _ask(fact, obj):
"""
Find the truth value for a property of an object.
This function is called when a request is made to see what a fact
value is.
For this we use several techniques:
First, the fact-evaluation function is tried, if it exists (for
example _eval_is_integer). Then we try related facts. For example
rational --> integer
another example is joined rule:
integer & !odd --> even
so in the latter case if we are looking at what 'even' value is,
'integer' and 'odd' facts will be asked.
In all cases, when we settle on some fact value, its implications are
deduced, and the result is cached in ._assumptions.
"""
assumptions = obj._assumptions
handler_map = obj._prop_handler
# Store None into the assumptions so that recursive attempts at
# evaluating the same fact don't trigger infinite recursion.
assumptions._tell(fact, None)
# First try the assumption evaluation function if it exists
try:
evaluate = handler_map[fact]
except KeyError:
pass
else:
a = evaluate(obj)
if a is not None:
assumptions.deduce_all_facts(((fact, a),))
return a
# Try assumption's prerequisites
prereq = list(_assume_rules.prereq[fact])
shuffle(prereq)
for pk in prereq:
if pk in assumptions:
continue
if pk in handler_map:
_ask(pk, obj)
# we might have found the value of fact
ret_val = assumptions.get(fact)
if ret_val is not None:
return ret_val
# Note: the result has already been cached
return None
class ManagedProperties(BasicMeta):
"""Metaclass for classes with old-style assumptions"""
def __init__(cls, *args, **kws):
BasicMeta.__init__(cls, *args, **kws)
local_defs = {}
for k in _assume_defined:
attrname = as_property(k)
v = cls.__dict__.get(attrname, '')
if isinstance(v, (bool, integer_types, type(None))):
if v is not None:
v = bool(v)
local_defs[k] = v
defs = {}
for base in reversed(cls.__bases__):
try:
defs.update(base._explicit_class_assumptions)
except AttributeError:
pass
defs.update(local_defs)
cls._explicit_class_assumptions = defs
cls.default_assumptions = StdFactKB(defs)
cls._prop_handler = {}
for k in _assume_defined:
try:
cls._prop_handler[k] = getattr(cls, '_eval_is_%s' % k)
except AttributeError:
pass
# Put definite results directly into the class dict, for speed
for k, v in cls.default_assumptions.items():
setattr(cls, as_property(k), v)
# protection e.g. for Integer.is_even=F <- (Rational.is_integer=F)
derived_from_bases = set()
for base in cls.__bases__:
try:
derived_from_bases |= set(base.default_assumptions)
except AttributeError:
continue # not an assumption-aware class
for fact in derived_from_bases - set(cls.default_assumptions):
pname = as_property(fact)
if pname not in cls.__dict__:
setattr(cls, pname, make_property(fact))
# Finally, add any missing automagic property (e.g. for Basic)
for fact in _assume_defined:
pname = as_property(fact)
if not hasattr(cls, pname):
setattr(cls, pname, make_property(fact))
| 10,451 | 27.557377 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/rules.py
|
"""
Replacement rules.
"""
from __future__ import print_function, division
class Transform(object):
"""
Immutable mapping that can be used as a generic transformation rule.
Parameters
----------
transform : callable
Computes the value corresponding to any key.
filter : callable, optional
If supplied, specifies which objects are in the mapping.
Examples
========
>>> from sympy.core.rules import Transform
>>> from sympy.abc import x
This Transform will return, as a value, one more than the key:
>>> add1 = Transform(lambda x: x + 1)
>>> add1[1]
2
>>> add1[x]
x + 1
By default, all values are considered to be in the dictionary. If a filter
is supplied, only the objects for which it returns True are considered as
being in the dictionary:
>>> add1_odd = Transform(lambda x: x + 1, lambda x: x%2 == 1)
>>> 2 in add1_odd
False
>>> add1_odd.get(2, 0)
0
>>> 3 in add1_odd
True
>>> add1_odd[3]
4
>>> add1_odd.get(3, 0)
4
"""
def __init__(self, transform, filter=lambda x: True):
self._transform = transform
self._filter = filter
def __contains__(self, item):
return self._filter(item)
def __getitem__(self, key):
if self._filter(key):
return self._transform(key)
else:
raise KeyError(key)
def get(self, item, default=None):
if item in self:
return self[item]
else:
return default
| 1,552 | 21.838235 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/cache.py
|
""" Caching facility for SymPy """
from __future__ import print_function, division
from distutils.version import LooseVersion as V
class _cache(list):
""" List of cached functions """
def print_cache(self):
"""print cache info"""
for item in self:
name = item.__name__
myfunc = item
while hasattr(myfunc, '__wrapped__'):
if hasattr(myfunc, 'cache_info'):
info = myfunc.cache_info()
break
else:
myfunc = myfunc.__wrapped__
else:
info = None
print(name, info)
def clear_cache(self):
"""clear cache content"""
for item in self:
myfunc = item
while hasattr(myfunc, '__wrapped__'):
if hasattr(myfunc, 'cache_clear'):
myfunc.cache_clear()
break
else:
myfunc = myfunc.__wrapped__
# global cache registry:
CACHE = _cache()
# make clear and print methods available
print_cache = CACHE.print_cache
clear_cache = CACHE.clear_cache
from sympy.core.compatibility import lru_cache
from functools import update_wrapper
try:
import fastcache
from warnings import warn
# the version attribute __version__ is not present for all versions
if not hasattr(fastcache, '__version__'):
warn("fastcache version >= 0.4.0 required", UserWarning)
raise ImportError
# ensure minimum required version of fastcache is present
if V(fastcache.__version__) < '0.4.0':
warn("fastcache version >= 0.4.0 required, detected {}"\
.format(fastcache.__version__), UserWarning)
raise ImportError
# Do not use fastcache if running under pypy
import platform
if platform.python_implementation() == 'PyPy':
raise ImportError
except ImportError:
def __cacheit(maxsize):
"""caching decorator.
important: the result of cached function must be *immutable*
Examples
========
>>> from sympy.core.cache import cacheit
>>> @cacheit
... def f(a, b):
... return a+b
>>> @cacheit
... def f(a, b):
... return [a, b] # <-- WRONG, returns mutable object
to force cacheit to check returned results mutability and consistency,
set environment variable SYMPY_USE_CACHE to 'debug'
"""
def func_wrapper(func):
cfunc = lru_cache(maxsize, typed=True)(func)
# wraps here does not propagate all the necessary info
# for py2.7, use update_wrapper below
def wrapper(*args, **kwargs):
try:
retval = cfunc(*args, **kwargs)
except TypeError:
retval = func(*args, **kwargs)
return retval
wrapper.cache_info = cfunc.cache_info
wrapper.cache_clear = cfunc.cache_clear
# Some versions of update_wrapper erroneously assign the final
# function of the wrapper chain to __wrapped__, see
# https://bugs.python.org/issue17482 .
# To work around this, we need to call update_wrapper first, then
# assign to wrapper.__wrapped__.
update_wrapper(wrapper, func)
wrapper.__wrapped__ = cfunc.__wrapped__
CACHE.append(wrapper)
return wrapper
return func_wrapper
else:
def __cacheit(maxsize):
"""caching decorator.
important: the result of cached function must be *immutable*
Examples
========
>>> from sympy.core.cache import cacheit
>>> @cacheit
... def f(a, b):
... return a+b
>>> @cacheit
... def f(a, b):
... return [a, b] # <-- WRONG, returns mutable object
to force cacheit to check returned results mutability and consistency,
set environment variable SYMPY_USE_CACHE to 'debug'
"""
def func_wrapper(func):
cfunc = fastcache.clru_cache(maxsize, typed=True, unhashable='ignore')(func)
CACHE.append(cfunc)
return cfunc
return func_wrapper
########################################
def __cacheit_nocache(func):
return func
def __cacheit_debug(maxsize):
"""cacheit + code to check cache consistency"""
def func_wrapper(func):
from .decorators import wraps
cfunc = __cacheit(maxsize)(func)
@wraps(func)
def wrapper(*args, **kw_args):
# always call function itself and compare it with cached version
r1 = func(*args, **kw_args)
r2 = cfunc(*args, **kw_args)
# try to see if the result is immutable
#
# this works because:
#
# hash([1,2,3]) -> raise TypeError
# hash({'a':1, 'b':2}) -> raise TypeError
# hash((1,[2,3])) -> raise TypeError
#
# hash((1,2,3)) -> just computes the hash
hash(r1), hash(r2)
# also see if returned values are the same
if r1 != r2:
raise RuntimeError("Returned values are not the same")
return r1
return wrapper
return func_wrapper
def _getenv(key, default=None):
from os import getenv
return getenv(key, default)
# SYMPY_USE_CACHE=yes/no/debug
USE_CACHE = _getenv('SYMPY_USE_CACHE', 'yes').lower()
# SYMPY_CACHE_SIZE=some_integer/None
# special cases :
# SYMPY_CACHE_SIZE=0 -> No caching
# SYMPY_CACHE_SIZE=None -> Unbounded caching
scs = _getenv('SYMPY_CACHE_SIZE', '1000')
if scs.lower() == 'none':
SYMPY_CACHE_SIZE = None
else:
try:
SYMPY_CACHE_SIZE = int(scs)
except ValueError:
raise RuntimeError(
'SYMPY_CACHE_SIZE must be a valid integer or None. ' + \
'Got: %s' % SYMPY_CACHE_SIZE)
if USE_CACHE == 'no':
cacheit = __cacheit_nocache
elif USE_CACHE == 'yes':
cacheit = __cacheit(SYMPY_CACHE_SIZE)
elif USE_CACHE == 'debug':
cacheit = __cacheit_debug(SYMPY_CACHE_SIZE) # a lot slower
else:
raise RuntimeError(
'unrecognized value for SYMPY_USE_CACHE: %s' % USE_CACHE)
| 6,402 | 29.20283 | 88 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/numbers.py
|
from __future__ import print_function, division
import decimal
import fractions
import math
import warnings
import re as regex
from collections import defaultdict
from .containers import Tuple
from .sympify import converter, sympify, _sympify, SympifyError
from .singleton import S, Singleton
from .expr import Expr, AtomicExpr
from .decorators import _sympifyit
from .cache import cacheit, clear_cache
from .logic import fuzzy_not
from sympy.core.compatibility import (
as_int, integer_types, long, string_types, with_metaclass, HAS_GMPY,
SYMPY_INTS, int_info)
import mpmath
import mpmath.libmp as mlib
from mpmath.libmp import mpf_pow, mpf_pi, mpf_e, phi_fixed
from mpmath.ctx_mp import mpnumeric
from mpmath.libmp.libmpf import (
finf as _mpf_inf, fninf as _mpf_ninf,
fnan as _mpf_nan, fzero as _mpf_zero, _normalize as mpf_normalize,
prec_to_dps)
from sympy.utilities.misc import debug, filldedent
from .evaluate import global_evaluate
from sympy.utilities.exceptions import SymPyDeprecationWarning
rnd = mlib.round_nearest
_LOG2 = math.log(2)
def comp(z1, z2, tol=None):
"""Return a bool indicating whether the error between z1 and z2 is <= tol.
If ``tol`` is None then True will be returned if there is a significant
difference between the numbers: ``abs(z1 - z2)*10**p <= 1/2`` where ``p``
is the lower of the precisions of the values. A comparison of strings will
be made if ``z1`` is a Number and a) ``z2`` is a string or b) ``tol`` is ''
and ``z2`` is a Number.
When ``tol`` is a nonzero value, if z2 is non-zero and ``|z1| > 1``
the error is normalized by ``|z1|``, so if you want to see if the
absolute error between ``z1`` and ``z2`` is <= ``tol`` then call this
as ``comp(z1 - z2, 0, tol)``.
"""
if type(z2) is str:
if not isinstance(z1, Number):
raise ValueError('when z2 is a str z1 must be a Number')
return str(z1) == z2
if not z1:
z1, z2 = z2, z1
if not z1:
return True
if not tol:
if tol is None:
if type(z2) is str and getattr(z1, 'is_Number', False):
return str(z1) == z2
a, b = Float(z1), Float(z2)
return int(abs(a - b)*10**prec_to_dps(
min(a._prec, b._prec)))*2 <= 1
elif all(getattr(i, 'is_Number', False) for i in (z1, z2)):
return z1._prec == z2._prec and str(z1) == str(z2)
raise ValueError('exact comparison requires two Numbers')
diff = abs(z1 - z2)
az1 = abs(z1)
if z2 and az1 > 1:
return diff/az1 <= tol
else:
return diff <= tol
def mpf_norm(mpf, prec):
"""Return the mpf tuple normalized appropriately for the indicated
precision after doing a check to see if zero should be returned or
not when the mantissa is 0. ``mpf_normlize`` always assumes that this
is zero, but it may not be since the mantissa for mpf's values "+inf",
"-inf" and "nan" have a mantissa of zero, too.
Note: this is not intended to validate a given mpf tuple, so sending
mpf tuples that were not created by mpmath may produce bad results. This
is only a wrapper to ``mpf_normalize`` which provides the check for non-
zero mpfs that have a 0 for the mantissa.
"""
sign, man, expt, bc = mpf
if not man:
# hack for mpf_normalize which does not do this;
# it assumes that if man is zero the result is 0
# (see issue 6639)
if not bc:
return _mpf_zero
else:
# don't change anything; this should already
# be a well formed mpf tuple
return mpf
# Necessary if mpmath is using the gmpy backend
from mpmath.libmp.backend import MPZ
rv = mpf_normalize(sign, MPZ(man), expt, bc, prec, rnd)
return rv
# TODO: we should use the warnings module
_errdict = {"divide": False}
def seterr(divide=False):
"""
Should sympy raise an exception on 0/0 or return a nan?
divide == True .... raise an exception
divide == False ... return nan
"""
if _errdict["divide"] != divide:
clear_cache()
_errdict["divide"] = divide
def _as_integer_ratio(p):
neg_pow, man, expt, bc = getattr(p, '_mpf_', mpmath.mpf(p)._mpf_)
p = [1, -1][neg_pow % 2]*man
if expt < 0:
q = 2**-expt
else:
q = 1
p *= 2**expt
return int(p), int(q)
def _decimal_to_Rational_prec(dec):
"""Convert an ordinary decimal instance to a Rational."""
if not dec.is_finite():
raise TypeError("dec must be finite, got %s." % dec)
s, d, e = dec.as_tuple()
prec = len(d)
if e >= 0: # it's an integer
rv = Integer(int(dec))
else:
s = (-1)**s
d = sum([di*10**i for i, di in enumerate(reversed(d))])
rv = Rational(s*d, 10**-e)
return rv, prec
def _literal_float(f):
"""Return True if n can be interpreted as a floating point number."""
pat = r"[-+]?((\d*\.\d+)|(\d+\.?))(eE[-+]?\d+)?"
return bool(regex.match(pat, f))
# (a,b) -> gcd(a,b)
_gcdcache = {}
# TODO caching with decorator, but not to degrade performance
def igcd(*args):
"""Computes nonnegative integer greatest common divisor.
The algorithm is based on the well known Euclid's algorithm. To
improve speed, igcd() has its own caching mechanism implemented.
Examples
========
>>> from sympy.core.numbers import igcd
>>> igcd(2, 4)
2
>>> igcd(5, 10, 15)
5
"""
if len(args) < 2:
raise TypeError(
'igcd() takes at least 2 arguments (%s given)' % len(args))
if 1 in args:
a = 1
k = 0
else:
a = abs(as_int(args[0]))
k = 1
if a != 1:
while k < len(args):
b = args[k]
k += 1
try:
a = _gcdcache[(a, b)]
except KeyError:
b = as_int(b)
if not b:
continue
if b == 1:
a = 1
break
if b < 0:
b = -b
t = a, b
a = igcd2(a, b)
_gcdcache[t] = _gcdcache[t[1], t[0]] = a
while k < len(args):
ok = as_int(args[k])
k += 1
return a
try:
from math import gcd as igcd2
except ImportError:
def igcd2(a, b):
"""Compute gcd of two Python integers a and b."""
if (a.bit_length() > BIGBITS and
b.bit_length() > BIGBITS):
return igcd_lehmer(a, b)
a, b = abs(a), abs(b)
while b:
a, b = b, a % b
return a
# Use Lehmer's algorithm only for very large numbers.
# The limit could be different on Python 2.7 and 3.x.
# If so, then this could be defined in compatibility.py.
BIGBITS = 5000
def igcd_lehmer(a, b):
"""Computes greatest common divisor of two integers.
Euclid's algorithm for the computation of the greatest
common divisor gcd(a, b) of two (positive) integers
a and b is based on the division identity
a = q*b + r,
where the quotient q and the remainder r are integers
and 0 <= r < b. Then each common divisor of a and b
divides r, and it follows that gcd(a, b) == gcd(b, r).
The algorithm works by constructing the sequence
r0, r1, r2, ..., where r0 = a, r1 = b, and each rn
is the remainder from the division of the two preceding
elements.
In Python, q = a // b and r = a % b are obtained by the
floor division and the remainder operations, respectively.
These are the most expensive arithmetic operations, especially
for large a and b.
Lehmer's algorithm is based on the observation that the quotients
qn = r(n-1) // rn are in general small integers even
when a and b are very large. Hence the quotients can be
usually determined from a relatively small number of most
significant bits.
The efficiency of the algorithm is further enhanced by not
computing each long remainder in Euclid's sequence. The remainders
are linear combinations of a and b with integer coefficients
derived from the quotients. The coefficients can be computed
as far as the quotients can be determined from the chosen
most significant parts of a and b. Only then a new pair of
consecutive remainders is computed and the algorithm starts
anew with this pair.
References
==========
.. [1] https://en.wikipedia.org/wiki/Lehmer%27s_GCD_algorithm
"""
a, b = abs(as_int(a)), abs(as_int(b))
if a < b:
a, b = b, a
# The algorithm works by using one or two digit division
# whenever possible. The outer loop will replace the
# pair (a, b) with a pair of shorter consecutive elements
# of the Euclidean gcd sequence until a and b
# fit into two Python (long) int digits.
nbits = 2*int_info.bits_per_digit
while a.bit_length() > nbits and b != 0:
# Quotients are mostly small integers that can
# be determined from most significant bits.
n = a.bit_length() - nbits
x, y = int(a >> n), int(b >> n) # most significant bits
# Elements of the Euclidean gcd sequence are linear
# combinations of a and b with integer coefficients.
# Compute the coefficients of consequtive pairs
# a' = A*a + B*b, b' = C*a + D*b
# using small integer arithmetic as far as possible.
A, B, C, D = 1, 0, 0, 1 # initial values
while True:
# The coefficients alternate in sign while looping.
# The inner loop combines two steps to keep track
# of the signs.
# At this point we have
# A > 0, B <= 0, C <= 0, D > 0,
# x' = x + B <= x < x" = x + A,
# y' = y + C <= y < y" = y + D,
# and
# x'*N <= a' < x"*N, y'*N <= b' < y"*N,
# where N = 2**n.
# Now, if y' > 0, and x"//y' and x'//y" agree,
# then their common value is equal to q = a'//b'.
# In addition,
# x'%y" = x' - q*y" < x" - q*y' = x"%y',
# and
# (x'%y")*N < a'%b' < (x"%y')*N.
# On the other hand, we also have x//y == q,
# and therefore
# x'%y" = x + B - q*(y + D) = x%y + B',
# x"%y' = x + A - q*(y + C) = x%y + A',
# where
# B' = B - q*D < 0, A' = A - q*C > 0.
if y + C <= 0:
break
q = (x + A) // (y + C)
# Now x'//y" <= q, and equality holds if
# x' - q*y" = (x - q*y) + (B - q*D) >= 0.
# This is a minor optimization to avoid division.
x_qy, B_qD = x - q*y, B - q*D
if x_qy + B_qD < 0:
break
# Next step in the Euclidean sequence.
x, y = y, x_qy
A, B, C, D = C, D, A - q*C, B_qD
# At this point the signs of the coefficients
# change and their roles are interchanged.
# A <= 0, B > 0, C > 0, D < 0,
# x' = x + A <= x < x" = x + B,
# y' = y + D < y < y" = y + C.
if y + D <= 0:
break
q = (x + B) // (y + D)
x_qy, A_qC = x - q*y, A - q*C
if x_qy + A_qC < 0:
break
x, y = y, x_qy
A, B, C, D = C, D, A_qC, B - q*D
# Now the conditions on top of the loop
# are again satisfied.
# A > 0, B < 0, C < 0, D > 0.
if B == 0:
# This can only happen when y == 0 in the beginning
# and the inner loop does nothing.
# Long division is forced.
a, b = b, a % b
continue
# Compute new long arguments using the coefficients.
a, b = A*a + B*b, C*a + D*b
# Small divisors. Finish with the standard algorithm.
while b:
a, b = b, a % b
return a
def ilcm(*args):
"""Computes integer least common multiple.
Examples
========
>>> from sympy.core.numbers import ilcm
>>> ilcm(5, 10)
10
>>> ilcm(7, 3)
21
>>> ilcm(5, 10, 15)
30
"""
if len(args) < 2:
raise TypeError(
'ilcm() takes at least 2 arguments (%s given)' % len(args))
if 0 in args:
return 0
a = args[0]
for b in args[1:]:
a = a*b // igcd(a, b)
return a
def igcdex(a, b):
"""Returns x, y, g such that g = x*a + y*b = gcd(a, b).
>>> from sympy.core.numbers import igcdex
>>> igcdex(2, 3)
(-1, 1, 1)
>>> igcdex(10, 12)
(-1, 1, 2)
>>> x, y, g = igcdex(100, 2004)
>>> x, y, g
(-20, 1, 4)
>>> x*100 + y*2004
4
"""
if (not a) and (not b):
return (0, 1, 0)
if not a:
return (0, b//abs(b), abs(b))
if not b:
return (a//abs(a), 0, abs(a))
if a < 0:
a, x_sign = -a, -1
else:
x_sign = 1
if b < 0:
b, y_sign = -b, -1
else:
y_sign = 1
x, y, r, s = 1, 0, 0, 1
while b:
(c, q) = (a % b, a // b)
(a, b, r, s, x, y) = (b, c, x - q*r, y - q*s, r, s)
return (x*x_sign, y*y_sign, a)
def mod_inverse(a, m):
"""
Return the number c such that, ( a * c ) % m == 1 where
c has the same sign as a. If no such value exists, a
ValueError is raised.
Examples
========
>>> from sympy import S
>>> from sympy.core.numbers import mod_inverse
Suppose we wish to find multiplicative inverse x of
3 modulo 11. This is the same as finding x such
that 3 * x = 1 (mod 11). One value of x that satisfies
this congruence is 4. Because 3 * 4 = 12 and 12 = 1 mod(11).
This is the value return by mod_inverse:
>>> mod_inverse(3, 11)
4
>>> mod_inverse(-3, 11)
-4
When there is a common factor between the numerators of
``a`` and ``m`` the inverse does not exist:
>>> mod_inverse(2, 4)
Traceback (most recent call last):
...
ValueError: inverse of 2 mod 4 does not exist
>>> mod_inverse(S(2)/7, S(5)/2)
7/2
References
==========
- https://en.wikipedia.org/wiki/Modular_multiplicative_inverse
- https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm
"""
c = None
try:
a, m = as_int(a), as_int(m)
if m > 1:
x, y, g = igcdex(a, m)
if g == 1:
c = x % m
if a < 0:
c -= m
except ValueError:
a, m = sympify(a), sympify(m)
if not (a.is_number and m.is_number):
raise TypeError(filldedent('''
Expected numbers for arguments; symbolic `mod_inverse`
is not implemented
but symbolic expressions can be handled with the
similar function,
sympy.polys.polytools.invert'''))
big = (m > 1)
if not (big is S.true or big is S.false):
raise ValueError('m > 1 did not evaluate; try to simplify %s' % m)
elif big:
c = 1/a
if c is None:
raise ValueError('inverse of %s (mod %s) does not exist' % (a, m))
return c
class Number(AtomicExpr):
"""
Represents any kind of number in sympy.
Floating point numbers are represented by the Float class.
Integer numbers (of any size), together with rational numbers (again,
there is no limit on their size) are represented by the Rational class.
If you want to represent, for example, ``1+sqrt(2)``, then you need to do::
Rational(1) + sqrt(Rational(2))
"""
is_commutative = True
is_number = True
is_Number = True
__slots__ = []
# Used to make max(x._prec, y._prec) return x._prec when only x is a float
_prec = -1
def __new__(cls, *obj):
if len(obj) == 1:
obj = obj[0]
if isinstance(obj, Number):
return obj
if isinstance(obj, SYMPY_INTS):
return Integer(obj)
if isinstance(obj, tuple) and len(obj) == 2:
return Rational(*obj)
if isinstance(obj, (float, mpmath.mpf, decimal.Decimal)):
return Float(obj)
if isinstance(obj, string_types):
val = sympify(obj)
if isinstance(val, Number):
return val
else:
raise ValueError('String "%s" does not denote a Number' % obj)
msg = "expected str|int|long|float|Decimal|Number object but got %r"
raise TypeError(msg % type(obj).__name__)
def invert(self, other, *gens, **args):
from sympy.polys.polytools import invert
if getattr(other, 'is_number', True):
return mod_inverse(self, other)
return invert(self, other, *gens, **args)
def __divmod__(self, other):
from .containers import Tuple
from sympy.functions.elementary.complexes import sign
try:
other = Number(other)
except TypeError:
msg = "unsupported operand type(s) for divmod(): '%s' and '%s'"
raise TypeError(msg % (type(self).__name__, type(other).__name__))
if not other:
raise ZeroDivisionError('modulo by zero')
if self.is_Integer and other.is_Integer:
return Tuple(*divmod(self.p, other.p))
else:
rat = self/other
w = sign(rat)*int(abs(rat)) # = rat.floor()
r = self - other*w
return Tuple(w, r)
def __rdivmod__(self, other):
try:
other = Number(other)
except TypeError:
msg = "unsupported operand type(s) for divmod(): '%s' and '%s'"
raise TypeError(msg % (type(other).__name__, type(self).__name__))
return divmod(other, self)
def __round__(self, *args):
return round(float(self), *args)
def _as_mpf_val(self, prec):
"""Evaluation of mpf tuple accurate to at least prec bits."""
raise NotImplementedError('%s needs ._as_mpf_val() method' %
(self.__class__.__name__))
def _eval_evalf(self, prec):
return Float._new(self._as_mpf_val(prec), prec)
def _as_mpf_op(self, prec):
prec = max(prec, self._prec)
return self._as_mpf_val(prec), prec
def __float__(self):
return mlib.to_float(self._as_mpf_val(53))
def floor(self):
raise NotImplementedError('%s needs .floor() method' %
(self.__class__.__name__))
def ceiling(self):
raise NotImplementedError('%s needs .ceiling() method' %
(self.__class__.__name__))
def _eval_conjugate(self):
return self
def _eval_order(self, *symbols):
from sympy import Order
# Order(5, x, y) -> Order(1,x,y)
return Order(S.One, *symbols)
def _eval_subs(self, old, new):
if old == -self:
return -new
return self # there is no other possibility
def _eval_is_finite(self):
return True
@classmethod
def class_key(cls):
return 1, 0, 'Number'
@cacheit
def sort_key(self, order=None):
return self.class_key(), (0, ()), (), self
@_sympifyit('other', NotImplemented)
def __add__(self, other):
if isinstance(other, Number) and global_evaluate[0]:
if other is S.NaN:
return S.NaN
elif other is S.Infinity:
return S.Infinity
elif other is S.NegativeInfinity:
return S.NegativeInfinity
return AtomicExpr.__add__(self, other)
@_sympifyit('other', NotImplemented)
def __sub__(self, other):
if isinstance(other, Number) and global_evaluate[0]:
if other is S.NaN:
return S.NaN
elif other is S.Infinity:
return S.NegativeInfinity
elif other is S.NegativeInfinity:
return S.Infinity
return AtomicExpr.__sub__(self, other)
@_sympifyit('other', NotImplemented)
def __mul__(self, other):
if isinstance(other, Number) and global_evaluate[0]:
if other is S.NaN:
return S.NaN
elif other is S.Infinity:
if self.is_zero:
return S.NaN
elif self.is_positive:
return S.Infinity
else:
return S.NegativeInfinity
elif other is S.NegativeInfinity:
if self.is_zero:
return S.NaN
elif self.is_positive:
return S.NegativeInfinity
else:
return S.Infinity
elif isinstance(other, Tuple):
return NotImplemented
return AtomicExpr.__mul__(self, other)
@_sympifyit('other', NotImplemented)
def __div__(self, other):
if isinstance(other, Number) and global_evaluate[0]:
if other is S.NaN:
return S.NaN
elif other is S.Infinity or other is S.NegativeInfinity:
return S.Zero
return AtomicExpr.__div__(self, other)
__truediv__ = __div__
def __eq__(self, other):
raise NotImplementedError('%s needs .__eq__() method' %
(self.__class__.__name__))
def __ne__(self, other):
raise NotImplementedError('%s needs .__ne__() method' %
(self.__class__.__name__))
def __lt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s < %s" % (self, other))
raise NotImplementedError('%s needs .__lt__() method' %
(self.__class__.__name__))
def __le__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s <= %s" % (self, other))
raise NotImplementedError('%s needs .__le__() method' %
(self.__class__.__name__))
def __gt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s > %s" % (self, other))
return _sympify(other).__lt__(self)
def __ge__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s >= %s" % (self, other))
return _sympify(other).__le__(self)
def __hash__(self):
return super(Number, self).__hash__()
def is_constant(self, *wrt, **flags):
return True
def as_coeff_mul(self, *deps, **kwargs):
# a -> c*t
if self.is_Rational or not kwargs.pop('rational', True):
return self, tuple()
elif self.is_negative:
return S.NegativeOne, (-self,)
return S.One, (self,)
def as_coeff_add(self, *deps):
# a -> c + t
if self.is_Rational:
return self, tuple()
return S.Zero, (self,)
def as_coeff_Mul(self, rational=False):
"""Efficiently extract the coefficient of a product. """
if rational and not self.is_Rational:
return S.One, self
return (self, S.One) if self else (S.One, self)
def as_coeff_Add(self, rational=False):
"""Efficiently extract the coefficient of a summation. """
if not rational:
return self, S.Zero
return S.Zero, self
def gcd(self, other):
"""Compute GCD of `self` and `other`. """
from sympy.polys import gcd
return gcd(self, other)
def lcm(self, other):
"""Compute LCM of `self` and `other`. """
from sympy.polys import lcm
return lcm(self, other)
def cofactors(self, other):
"""Compute GCD and cofactors of `self` and `other`. """
from sympy.polys import cofactors
return cofactors(self, other)
class Float(Number):
"""Represent a floating-point number of arbitrary precision.
Examples
========
>>> from sympy import Float
>>> Float(3.5)
3.50000000000000
>>> Float(3)
3.00000000000000
Creating Floats from strings (and Python ``int`` and ``long``
types) will give a minimum precision of 15 digits, but the
precision will automatically increase to capture all digits
entered.
>>> Float(1)
1.00000000000000
>>> Float(10**20)
100000000000000000000.
>>> Float('1e20')
100000000000000000000.
However, *floating-point* numbers (Python ``float`` types) retain
only 15 digits of precision:
>>> Float(1e20)
1.00000000000000e+20
>>> Float(1.23456789123456789)
1.23456789123457
It may be preferable to enter high-precision decimal numbers
as strings:
Float('1.23456789123456789')
1.23456789123456789
The desired number of digits can also be specified:
>>> Float('1e-3', 3)
0.00100
>>> Float(100, 4)
100.0
Float can automatically count significant figures if a null string
is sent for the precision; space are also allowed in the string. (Auto-
counting is only allowed for strings, ints and longs).
>>> Float('123 456 789 . 123 456', '')
123456789.123456
>>> Float('12e-3', '')
0.012
>>> Float(3, '')
3.
If a number is written in scientific notation, only the digits before the
exponent are considered significant if a decimal appears, otherwise the
"e" signifies only how to move the decimal:
>>> Float('60.e2', '') # 2 digits significant
6.0e+3
>>> Float('60e2', '') # 4 digits significant
6000.
>>> Float('600e-2', '') # 3 digits significant
6.00
Notes
=====
Floats are inexact by their nature unless their value is a binary-exact
value.
>>> approx, exact = Float(.1, 1), Float(.125, 1)
For calculation purposes, evalf needs to be able to change the precision
but this will not increase the accuracy of the inexact value. The
following is the most accurate 5-digit approximation of a value of 0.1
that had only 1 digit of precision:
>>> approx.evalf(5)
0.099609
By contrast, 0.125 is exact in binary (as it is in base 10) and so it
can be passed to Float or evalf to obtain an arbitrary precision with
matching accuracy:
>>> Float(exact, 5)
0.12500
>>> exact.evalf(20)
0.12500000000000000000
Trying to make a high-precision Float from a float is not disallowed,
but one must keep in mind that the *underlying float* (not the apparent
decimal value) is being obtained with high precision. For example, 0.3
does not have a finite binary representation. The closest rational is
the fraction 5404319552844595/2**54. So if you try to obtain a Float of
0.3 to 20 digits of precision you will not see the same thing as 0.3
followed by 19 zeros:
>>> Float(0.3, 20)
0.29999999999999998890
If you want a 20-digit value of the decimal 0.3 (not the floating point
approximation of 0.3) you should send the 0.3 as a string. The underlying
representation is still binary but a higher precision than Python's float
is used:
>>> Float('0.3', 20)
0.30000000000000000000
Although you can increase the precision of an existing Float using Float
it will not increase the accuracy -- the underlying value is not changed:
>>> def show(f): # binary rep of Float
... from sympy import Mul, Pow
... s, m, e, b = f._mpf_
... v = Mul(int(m), Pow(2, int(e), evaluate=False), evaluate=False)
... print('%s at prec=%s' % (v, f._prec))
...
>>> t = Float('0.3', 3)
>>> show(t)
4915/2**14 at prec=13
>>> show(Float(t, 20)) # higher prec, not higher accuracy
4915/2**14 at prec=70
>>> show(Float(t, 2)) # lower prec
307/2**10 at prec=10
The same thing happens when evalf is used on a Float:
>>> show(t.evalf(20))
4915/2**14 at prec=70
>>> show(t.evalf(2))
307/2**10 at prec=10
Finally, Floats can be instantiated with an mpf tuple (n, c, p) to
produce the number (-1)**n*c*2**p:
>>> n, c, p = 1, 5, 0
>>> (-1)**n*c*2**p
-5
>>> Float((1, 5, 0))
-5.00000000000000
An actual mpf tuple also contains the number of bits in c as the last
element of the tuple:
>>> _._mpf_
(1, 5, 0, 3)
This is not needed for instantiation and is not the same thing as the
precision. The mpf tuple and the precision are two separate quantities
that Float tracks.
"""
__slots__ = ['_mpf_', '_prec']
# A Float represents many real numbers,
# both rational and irrational.
is_rational = None
is_irrational = None
is_number = True
is_real = True
is_Float = True
def __new__(cls, num, dps=None, prec=None, precision=None):
if prec is not None:
SymPyDeprecationWarning(
feature="Using 'prec=XX' to denote decimal precision",
useinstead="'dps=XX' for decimal precision and 'precision=XX' "\
"for binary precision",
issue=12820,
deprecated_since_version="1.1").warn()
dps = prec
if dps is not None and precision is not None:
raise ValueError('Both decimal and binary precision supplied. '
'Supply only one. ')
if isinstance(num, string_types):
num = num.replace(' ', '')
if num.startswith('.') and len(num) > 1:
num = '0' + num
elif num.startswith('-.') and len(num) > 2:
num = '-0.' + num[2:]
elif isinstance(num, float) and num == 0:
num = '0'
elif isinstance(num, (SYMPY_INTS, Integer)):
num = str(num) # faster than mlib.from_int
elif num is S.Infinity:
num = '+inf'
elif num is S.NegativeInfinity:
num = '-inf'
elif isinstance(num, mpmath.mpf):
if precision is None:
if dps is None:
precision = num.context.prec
num = num._mpf_
if dps is None and precision is None:
dps = 15
if isinstance(num, Float):
return num
if isinstance(num, string_types) and _literal_float(num):
try:
Num = decimal.Decimal(num)
except decimal.InvalidOperation:
pass
else:
isint = '.' not in num
num, dps = _decimal_to_Rational_prec(Num)
if num.is_Integer and isint:
dps = max(dps, len(str(num).lstrip('-')))
dps = max(15, dps)
precision = mlib.libmpf.dps_to_prec(dps)
elif precision == '' and dps is None or precision is None and dps == '':
if not isinstance(num, string_types):
raise ValueError('The null string can only be used when '
'the number to Float is passed as a string or an integer.')
ok = None
if _literal_float(num):
try:
Num = decimal.Decimal(num)
except decimal.InvalidOperation:
pass
else:
isint = '.' not in num
num, dps = _decimal_to_Rational_prec(Num)
if num.is_Integer and isint:
dps = max(dps, len(str(num).lstrip('-')))
precision = mlib.libmpf.dps_to_prec(dps)
ok = True
if ok is None:
raise ValueError('string-float not recognized: %s' % num)
# decimal precision(dps) is set and maybe binary precision(precision)
# as well.From here on binary precision is used to compute the Float.
# Hence, if supplied use binary precision else translate from decimal
# precision.
if precision is None or precision == '':
precision = mlib.libmpf.dps_to_prec(dps)
if isinstance(num, float):
_mpf_ = mlib.from_float(num, precision, rnd)
elif isinstance(num, string_types):
_mpf_ = mlib.from_str(num, precision, rnd)
elif isinstance(num, decimal.Decimal):
if num.is_finite():
_mpf_ = mlib.from_str(str(num), precision, rnd)
elif num.is_nan():
_mpf_ = _mpf_nan
elif num.is_infinite():
if num > 0:
_mpf_ = _mpf_inf
else:
_mpf_ = _mpf_ninf
else:
raise ValueError("unexpected decimal value %s" % str(num))
elif isinstance(num, Rational):
_mpf_ = mlib.from_rational(num.p, num.q, precision, rnd)
elif isinstance(num, tuple) and len(num) in (3, 4):
if type(num[1]) is str:
# it's a hexadecimal (coming from a pickled object)
# assume that it is in standard form
num = list(num)
num[1] = long(num[1], 16)
_mpf_ = tuple(num)
else:
if len(num) == 4:
# handle normalization hack
return Float._new(num, precision)
else:
return (S.NegativeOne**num[0]*num[1]*S(2)**num[2]).evalf(precision)
elif isinstance(num, Float):
_mpf_ = num._mpf_
if precision < num._prec:
_mpf_ = mpf_norm(_mpf_, precision)
else:
_mpf_ = mpmath.mpf(num, prec=prec)._mpf_
# special cases
if _mpf_ == _mpf_zero:
pass # we want a Float
elif _mpf_ == _mpf_nan:
return S.NaN
obj = Expr.__new__(cls)
obj._mpf_ = _mpf_
obj._prec = precision
return obj
@classmethod
def _new(cls, _mpf_, _prec):
# special cases
if _mpf_ == _mpf_zero:
return S.Zero # XXX this is different from Float which gives 0.0
elif _mpf_ == _mpf_nan:
return S.NaN
obj = Expr.__new__(cls)
obj._mpf_ = mpf_norm(_mpf_, _prec)
# XXX: Should this be obj._prec = obj._mpf_[3]?
obj._prec = _prec
return obj
# mpz can't be pickled
def __getnewargs__(self):
return (mlib.to_pickable(self._mpf_),)
def __getstate__(self):
return {'_prec': self._prec}
def _hashable_content(self):
return (self._mpf_, self._prec)
def floor(self):
return Integer(int(mlib.to_int(
mlib.mpf_floor(self._mpf_, self._prec))))
def ceiling(self):
return Integer(int(mlib.to_int(
mlib.mpf_ceil(self._mpf_, self._prec))))
@property
def num(self):
return mpmath.mpf(self._mpf_)
def _as_mpf_val(self, prec):
rv = mpf_norm(self._mpf_, prec)
if rv != self._mpf_ and self._prec == prec:
debug(self._mpf_, rv)
return rv
def _as_mpf_op(self, prec):
return self._mpf_, max(prec, self._prec)
def _eval_is_finite(self):
if self._mpf_ in (_mpf_inf, _mpf_ninf):
return False
return True
def _eval_is_infinite(self):
if self._mpf_ in (_mpf_inf, _mpf_ninf):
return True
return False
def _eval_is_integer(self):
return self._mpf_ == _mpf_zero
def _eval_is_negative(self):
if self._mpf_ == _mpf_ninf:
return True
if self._mpf_ == _mpf_inf:
return False
return self.num < 0
def _eval_is_positive(self):
if self._mpf_ == _mpf_inf:
return True
if self._mpf_ == _mpf_ninf:
return False
return self.num > 0
def _eval_is_zero(self):
return self._mpf_ == _mpf_zero
def __nonzero__(self):
return self._mpf_ != _mpf_zero
__bool__ = __nonzero__
def __neg__(self):
return Float._new(mlib.mpf_neg(self._mpf_), self._prec)
@_sympifyit('other', NotImplemented)
def __add__(self, other):
if isinstance(other, Number) and global_evaluate[0]:
rhs, prec = other._as_mpf_op(self._prec)
return Float._new(mlib.mpf_add(self._mpf_, rhs, prec, rnd), prec)
return Number.__add__(self, other)
@_sympifyit('other', NotImplemented)
def __sub__(self, other):
if isinstance(other, Number) and global_evaluate[0]:
rhs, prec = other._as_mpf_op(self._prec)
return Float._new(mlib.mpf_sub(self._mpf_, rhs, prec, rnd), prec)
return Number.__sub__(self, other)
@_sympifyit('other', NotImplemented)
def __mul__(self, other):
if isinstance(other, Number) and global_evaluate[0]:
rhs, prec = other._as_mpf_op(self._prec)
return Float._new(mlib.mpf_mul(self._mpf_, rhs, prec, rnd), prec)
return Number.__mul__(self, other)
@_sympifyit('other', NotImplemented)
def __div__(self, other):
if isinstance(other, Number) and other != 0 and global_evaluate[0]:
rhs, prec = other._as_mpf_op(self._prec)
return Float._new(mlib.mpf_div(self._mpf_, rhs, prec, rnd), prec)
return Number.__div__(self, other)
__truediv__ = __div__
@_sympifyit('other', NotImplemented)
def __mod__(self, other):
if isinstance(other, Rational) and other.q != 1 and global_evaluate[0]:
# calculate mod with Rationals, *then* round the result
return Float(Rational.__mod__(Rational(self), other),
prec_to_dps(self._prec))
if isinstance(other, Float) and global_evaluate[0]:
r = self/other
if r == int(r):
prec = max([prec_to_dps(i)
for i in (self._prec, other._prec)])
return Float(0, prec)
if isinstance(other, Number) and global_evaluate[0]:
rhs, prec = other._as_mpf_op(self._prec)
return Float._new(mlib.mpf_mod(self._mpf_, rhs, prec, rnd), prec)
return Number.__mod__(self, other)
@_sympifyit('other', NotImplemented)
def __rmod__(self, other):
if isinstance(other, Float) and global_evaluate[0]:
return other.__mod__(self)
if isinstance(other, Number) and global_evaluate[0]:
rhs, prec = other._as_mpf_op(self._prec)
return Float._new(mlib.mpf_mod(rhs, self._mpf_, prec, rnd), prec)
return Number.__rmod__(self, other)
def _eval_power(self, expt):
"""
expt is symbolic object but not equal to 0, 1
(-p)**r -> exp(r*log(-p)) -> exp(r*(log(p) + I*Pi)) ->
-> p**r*(sin(Pi*r) + cos(Pi*r)*I)
"""
if self == 0:
if expt.is_positive:
return S.Zero
if expt.is_negative:
return Float('inf')
if isinstance(expt, Number):
if isinstance(expt, Integer):
prec = self._prec
return Float._new(
mlib.mpf_pow_int(self._mpf_, expt.p, prec, rnd), prec)
elif isinstance(expt, Rational) and \
expt.p == 1 and expt.q % 2 and self.is_negative:
return Pow(S.NegativeOne, expt, evaluate=False)*(
-self)._eval_power(expt)
expt, prec = expt._as_mpf_op(self._prec)
mpfself = self._mpf_
try:
y = mpf_pow(mpfself, expt, prec, rnd)
return Float._new(y, prec)
except mlib.ComplexResult:
re, im = mlib.mpc_pow(
(mpfself, _mpf_zero), (expt, _mpf_zero), prec, rnd)
return Float._new(re, prec) + \
Float._new(im, prec)*S.ImaginaryUnit
def __abs__(self):
return Float._new(mlib.mpf_abs(self._mpf_), self._prec)
def __int__(self):
if self._mpf_ == _mpf_zero:
return 0
return int(mlib.to_int(self._mpf_)) # uses round_fast = round_down
__long__ = __int__
def __eq__(self, other):
if isinstance(other, float):
# coerce to Float at same precision
o = Float(other)
try:
ompf = o._as_mpf_val(self._prec)
except ValueError:
return False
return bool(mlib.mpf_eq(self._mpf_, ompf))
try:
other = _sympify(other)
except SympifyError:
return False # sympy != other --> not ==
if isinstance(other, NumberSymbol):
if other.is_irrational:
return False
return other.__eq__(self)
if isinstance(other, Float):
return bool(mlib.mpf_eq(self._mpf_, other._mpf_))
if isinstance(other, Number):
# numbers should compare at the same precision;
# all _as_mpf_val routines should be sure to abide
# by the request to change the prec if necessary; if
# they don't, the equality test will fail since it compares
# the mpf tuples
ompf = other._as_mpf_val(self._prec)
return bool(mlib.mpf_eq(self._mpf_, ompf))
return False # Float != non-Number
def __ne__(self, other):
return not self.__eq__(other)
def __gt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s > %s" % (self, other))
if isinstance(other, NumberSymbol):
return other.__le__(self)
if other.is_comparable:
other = other.evalf()
if isinstance(other, Number) and other is not S.NaN:
return _sympify(bool(
mlib.mpf_gt(self._mpf_, other._as_mpf_val(self._prec))))
return Expr.__gt__(self, other)
def __ge__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s >= %s" % (self, other))
if isinstance(other, NumberSymbol):
return other.__lt__(self)
if other.is_comparable:
other = other.evalf()
if isinstance(other, Number) and other is not S.NaN:
return _sympify(bool(
mlib.mpf_ge(self._mpf_, other._as_mpf_val(self._prec))))
return Expr.__ge__(self, other)
def __lt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s < %s" % (self, other))
if isinstance(other, NumberSymbol):
return other.__ge__(self)
if other.is_real and other.is_number:
other = other.evalf()
if isinstance(other, Number) and other is not S.NaN:
return _sympify(bool(
mlib.mpf_lt(self._mpf_, other._as_mpf_val(self._prec))))
return Expr.__lt__(self, other)
def __le__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s <= %s" % (self, other))
if isinstance(other, NumberSymbol):
return other.__gt__(self)
if other.is_real and other.is_number:
other = other.evalf()
if isinstance(other, Number) and other is not S.NaN:
return _sympify(bool(
mlib.mpf_le(self._mpf_, other._as_mpf_val(self._prec))))
return Expr.__le__(self, other)
def __hash__(self):
return super(Float, self).__hash__()
def epsilon_eq(self, other, epsilon="1e-15"):
return abs(self - other) < Float(epsilon)
def _sage_(self):
import sage.all as sage
return sage.RealNumber(str(self))
def __format__(self, format_spec):
return format(decimal.Decimal(str(self)), format_spec)
# Add sympify converters
converter[float] = converter[decimal.Decimal] = Float
# this is here to work nicely in Sage
RealNumber = Float
class Rational(Number):
"""Represents integers and rational numbers (p/q) of any size.
Examples
========
>>> from sympy import Rational, nsimplify, S, pi
>>> Rational(3)
3
>>> Rational(1, 2)
1/2
Rational is unprejudiced in accepting input. If a float is passed, the
underlying value of the binary representation will be returned:
>>> Rational(.5)
1/2
>>> Rational(.2)
3602879701896397/18014398509481984
If the simpler representation of the float is desired then consider
limiting the denominator to the desired value or convert the float to
a string (which is roughly equivalent to limiting the denominator to
10**12):
>>> Rational(str(.2))
1/5
>>> Rational(.2).limit_denominator(10**12)
1/5
An arbitrarily precise Rational is obtained when a string literal is
passed:
>>> Rational("1.23")
123/100
>>> Rational('1e-2')
1/100
>>> Rational(".1")
1/10
>>> Rational('1e-2/3.2')
1/320
The conversion of other types of strings can be handled by
the sympify() function, and conversion of floats to expressions
or simple fractions can be handled with nsimplify:
>>> S('.[3]') # repeating digits in brackets
1/3
>>> S('3**2/10') # general expressions
9/10
>>> nsimplify(.3) # numbers that have a simple form
3/10
But if the input does not reduce to a literal Rational, an error will
be raised:
>>> Rational(pi)
Traceback (most recent call last):
...
TypeError: invalid input: pi
Low-level
---------
Access numerator and denominator as .p and .q:
>>> r = Rational(3, 4)
>>> r
3/4
>>> r.p
3
>>> r.q
4
Note that p and q return integers (not SymPy Integers) so some care
is needed when using them in expressions:
>>> r.p/r.q
0.75
See Also
========
sympify, sympy.simplify.simplify.nsimplify
"""
is_real = True
is_integer = False
is_rational = True
is_number = True
__slots__ = ['p', 'q']
is_Rational = True
@cacheit
def __new__(cls, p, q=None, gcd=None):
if q is None:
if isinstance(p, Rational):
return p
if isinstance(p, string_types):
if p.count('/') > 1:
raise TypeError('invalid input: %s' % p)
pq = p.rsplit('/', 1)
if len(pq) == 2:
p, q = pq
fp = fractions.Fraction(p)
fq = fractions.Fraction(q)
f = fp/fq
return Rational(f.numerator, f.denominator, 1)
p = p.replace(' ', '')
try:
p = fractions.Fraction(p)
except ValueError:
pass # error will raise below
if not isinstance(p, string_types):
try:
if isinstance(p, fractions.Fraction):
return Rational(p.numerator, p.denominator, 1)
except NameError:
pass # error will raise below
if isinstance(p, (float, Float)):
return Rational(*_as_integer_ratio(p))
if not isinstance(p, SYMPY_INTS + (Rational,)):
raise TypeError('invalid input: %s' % p)
q = q or S.One
gcd = 1
else:
p = Rational(p)
q = Rational(q)
if isinstance(q, Rational):
p *= q.q
q = q.p
if isinstance(p, Rational):
q *= p.q
p = p.p
# p and q are now integers
if q == 0:
if p == 0:
if _errdict["divide"]:
raise ValueError("Indeterminate 0/0")
else:
return S.NaN
return S.ComplexInfinity
if q < 0:
q = -q
p = -p
if not gcd:
gcd = igcd(abs(p), q)
if gcd > 1:
p //= gcd
q //= gcd
if q == 1:
return Integer(p)
if p == 1 and q == 2:
return S.Half
obj = Expr.__new__(cls)
obj.p = p
obj.q = q
return obj
def limit_denominator(self, max_denominator=1000000):
"""Closest Rational to self with denominator at most max_denominator.
>>> from sympy import Rational
>>> Rational('3.141592653589793').limit_denominator(10)
22/7
>>> Rational('3.141592653589793').limit_denominator(100)
311/99
"""
f = fractions.Fraction(self.p, self.q)
return Rational(f.limit_denominator(fractions.Fraction(int(max_denominator))))
def __getnewargs__(self):
return (self.p, self.q)
def _hashable_content(self):
return (self.p, self.q)
def _eval_is_positive(self):
return self.p > 0
def _eval_is_zero(self):
return self.p == 0
def __neg__(self):
return Rational(-self.p, self.q)
@_sympifyit('other', NotImplemented)
def __add__(self, other):
if global_evaluate[0]:
if isinstance(other, Integer):
return Rational(self.p + self.q*other.p, self.q, 1)
elif isinstance(other, Rational):
#TODO: this can probably be optimized more
return Rational(self.p*other.q + self.q*other.p, self.q*other.q)
elif isinstance(other, Float):
return other + self
else:
return Number.__add__(self, other)
return Number.__add__(self, other)
__radd__ = __add__
@_sympifyit('other', NotImplemented)
def __sub__(self, other):
if global_evaluate[0]:
if isinstance(other, Integer):
return Rational(self.p - self.q*other.p, self.q, 1)
elif isinstance(other, Rational):
return Rational(self.p*other.q - self.q*other.p, self.q*other.q)
elif isinstance(other, Float):
return -other + self
else:
return Number.__sub__(self, other)
return Number.__sub__(self, other)
@_sympifyit('other', NotImplemented)
def __rsub__(self, other):
if global_evaluate[0]:
if isinstance(other, Integer):
return Rational(self.q*other.p - self.p, self.q, 1)
elif isinstance(other, Rational):
return Rational(self.q*other.p - self.p*other.q, self.q*other.q)
elif isinstance(other, Float):
return -self + other
else:
return Number.__rsub__(self, other)
return Number.__rsub__(self, other)
@_sympifyit('other', NotImplemented)
def __mul__(self, other):
if global_evaluate[0]:
if isinstance(other, Integer):
return Rational(self.p*other.p, self.q, igcd(other.p, self.q))
elif isinstance(other, Rational):
return Rational(self.p*other.p, self.q*other.q, igcd(self.p, other.q)*igcd(self.q, other.p))
elif isinstance(other, Float):
return other*self
else:
return Number.__mul__(self, other)
return Number.__mul__(self, other)
__rmul__ = __mul__
@_sympifyit('other', NotImplemented)
def __div__(self, other):
if global_evaluate[0]:
if isinstance(other, Integer):
if self.p and other.p == S.Zero:
return S.ComplexInfinity
else:
return Rational(self.p, self.q*other.p, igcd(self.p, other.p))
elif isinstance(other, Rational):
return Rational(self.p*other.q, self.q*other.p, igcd(self.p, other.p)*igcd(self.q, other.q))
elif isinstance(other, Float):
return self*(1/other)
else:
return Number.__div__(self, other)
return Number.__div__(self, other)
@_sympifyit('other', NotImplemented)
def __rdiv__(self, other):
if global_evaluate[0]:
if isinstance(other, Integer):
return Rational(other.p*self.q, self.p, igcd(self.p, other.p))
elif isinstance(other, Rational):
return Rational(other.p*self.q, other.q*self.p, igcd(self.p, other.p)*igcd(self.q, other.q))
elif isinstance(other, Float):
return other*(1/self)
else:
return Number.__rdiv__(self, other)
return Number.__rdiv__(self, other)
__truediv__ = __div__
@_sympifyit('other', NotImplemented)
def __mod__(self, other):
if global_evaluate[0]:
if isinstance(other, Rational):
n = (self.p*other.q) // (other.p*self.q)
return Rational(self.p*other.q - n*other.p*self.q, self.q*other.q)
if isinstance(other, Float):
# calculate mod with Rationals, *then* round the answer
return Float(self.__mod__(Rational(other)),
prec_to_dps(other._prec))
return Number.__mod__(self, other)
return Number.__mod__(self, other)
@_sympifyit('other', NotImplemented)
def __rmod__(self, other):
if isinstance(other, Rational):
return Rational.__mod__(other, self)
return Number.__rmod__(self, other)
def _eval_power(self, expt):
if isinstance(expt, Number):
if isinstance(expt, Float):
return self._eval_evalf(expt._prec)**expt
if expt.is_negative:
# (3/4)**-2 -> (4/3)**2
ne = -expt
if (ne is S.One):
return Rational(self.q, self.p)
if self.is_negative:
if expt.q != 1:
return -(S.NegativeOne)**((expt.p % expt.q) /
S(expt.q))*Rational(self.q, -self.p)**ne
else:
return S.NegativeOne**ne*Rational(self.q, -self.p)**ne
else:
return Rational(self.q, self.p)**ne
if expt is S.Infinity: # -oo already caught by test for negative
if self.p > self.q:
# (3/2)**oo -> oo
return S.Infinity
if self.p < -self.q:
# (-3/2)**oo -> oo + I*oo
return S.Infinity + S.Infinity*S.ImaginaryUnit
return S.Zero
if isinstance(expt, Integer):
# (4/3)**2 -> 4**2 / 3**2
return Rational(self.p**expt.p, self.q**expt.p, 1)
if isinstance(expt, Rational):
if self.p != 1:
# (4/3)**(5/6) -> 4**(5/6)*3**(-5/6)
return Integer(self.p)**expt*Integer(self.q)**(-expt)
# as the above caught negative self.p, now self is positive
return Integer(self.q)**Rational(
expt.p*(expt.q - 1), expt.q) / \
Integer(self.q)**Integer(expt.p)
if self.is_negative and expt.is_even:
return (-self)**expt
return
def _as_mpf_val(self, prec):
return mlib.from_rational(self.p, self.q, prec, rnd)
def _mpmath_(self, prec, rnd):
return mpmath.make_mpf(mlib.from_rational(self.p, self.q, prec, rnd))
def __abs__(self):
return Rational(abs(self.p), self.q)
def __int__(self):
p, q = self.p, self.q
if p < 0:
return -int(-p//q)
return int(p//q)
__long__ = __int__
def floor(self):
return Integer(self.p // self.q)
def ceiling(self):
return -Integer(-self.p // self.q)
def __eq__(self, other):
try:
other = _sympify(other)
except SympifyError:
return False # sympy != other --> not ==
if isinstance(other, NumberSymbol):
if other.is_irrational:
return False
return other.__eq__(self)
if isinstance(other, Number):
if isinstance(other, Rational):
# a Rational is always in reduced form so will never be 2/4
# so we can just check equivalence of args
return self.p == other.p and self.q == other.q
if isinstance(other, Float):
return mlib.mpf_eq(self._as_mpf_val(other._prec), other._mpf_)
return False
def __ne__(self, other):
return not self.__eq__(other)
def __gt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s > %s" % (self, other))
if isinstance(other, NumberSymbol):
return other.__le__(self)
expr = self
if isinstance(other, Number):
if isinstance(other, Rational):
return _sympify(bool(self.p*other.q > self.q*other.p))
if isinstance(other, Float):
return _sympify(bool(mlib.mpf_gt(
self._as_mpf_val(other._prec), other._mpf_)))
elif other.is_number and other.is_real:
expr, other = Integer(self.p), self.q*other
return Expr.__gt__(expr, other)
def __ge__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s >= %s" % (self, other))
if isinstance(other, NumberSymbol):
return other.__lt__(self)
expr = self
if isinstance(other, Number):
if isinstance(other, Rational):
return _sympify(bool(self.p*other.q >= self.q*other.p))
if isinstance(other, Float):
return _sympify(bool(mlib.mpf_ge(
self._as_mpf_val(other._prec), other._mpf_)))
elif other.is_number and other.is_real:
expr, other = Integer(self.p), self.q*other
return Expr.__ge__(expr, other)
def __lt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s < %s" % (self, other))
if isinstance(other, NumberSymbol):
return other.__ge__(self)
expr = self
if isinstance(other, Number):
if isinstance(other, Rational):
return _sympify(bool(self.p*other.q < self.q*other.p))
if isinstance(other, Float):
return _sympify(bool(mlib.mpf_lt(
self._as_mpf_val(other._prec), other._mpf_)))
elif other.is_number and other.is_real:
expr, other = Integer(self.p), self.q*other
return Expr.__lt__(expr, other)
def __le__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s <= %s" % (self, other))
expr = self
if isinstance(other, NumberSymbol):
return other.__gt__(self)
elif isinstance(other, Number):
if isinstance(other, Rational):
return _sympify(bool(self.p*other.q <= self.q*other.p))
if isinstance(other, Float):
return _sympify(bool(mlib.mpf_le(
self._as_mpf_val(other._prec), other._mpf_)))
elif other.is_number and other.is_real:
expr, other = Integer(self.p), self.q*other
return Expr.__le__(expr, other)
def __hash__(self):
return super(Rational, self).__hash__()
def factors(self, limit=None, use_trial=True, use_rho=False,
use_pm1=False, verbose=False, visual=False):
"""A wrapper to factorint which return factors of self that are
smaller than limit (or cheap to compute). Special methods of
factoring are disabled by default so that only trial division is used.
"""
from sympy.ntheory import factorrat
return factorrat(self, limit=limit, use_trial=use_trial,
use_rho=use_rho, use_pm1=use_pm1,
verbose=verbose).copy()
@_sympifyit('other', NotImplemented)
def gcd(self, other):
if isinstance(other, Rational):
if other is S.Zero:
return other
return Rational(
Integer(igcd(self.p, other.p)),
Integer(ilcm(self.q, other.q)))
return Number.gcd(self, other)
@_sympifyit('other', NotImplemented)
def lcm(self, other):
if isinstance(other, Rational):
return Rational(
self.p*other.p//igcd(self.p, other.p),
igcd(self.q, other.q))
return Number.lcm(self, other)
def as_numer_denom(self):
return Integer(self.p), Integer(self.q)
def _sage_(self):
import sage.all as sage
return sage.Integer(self.p)/sage.Integer(self.q)
def as_content_primitive(self, radical=False, clear=True):
"""Return the tuple (R, self/R) where R is the positive Rational
extracted from self.
Examples
========
>>> from sympy import S
>>> (S(-3)/2).as_content_primitive()
(3/2, -1)
See docstring of Expr.as_content_primitive for more examples.
"""
if self:
if self.is_positive:
return self, S.One
return -self, S.NegativeOne
return S.One, self
def as_coeff_Mul(self, rational=False):
"""Efficiently extract the coefficient of a product. """
return self, S.One
def as_coeff_Add(self, rational=False):
"""Efficiently extract the coefficient of a summation. """
return self, S.Zero
# int -> Integer
_intcache = {}
# TODO move this tracing facility to sympy/core/trace.py ?
def _intcache_printinfo():
ints = sorted(_intcache.keys())
nhit = _intcache_hits
nmiss = _intcache_misses
if nhit == 0 and nmiss == 0:
print()
print('Integer cache statistic was not collected')
return
miss_ratio = float(nmiss) / (nhit + nmiss)
print()
print('Integer cache statistic')
print('-----------------------')
print()
print('#items: %i' % len(ints))
print()
print(' #hit #miss #total')
print()
print('%5i %5i (%7.5f %%) %5i' % (
nhit, nmiss, miss_ratio*100, nhit + nmiss)
)
print()
print(ints)
_intcache_hits = 0
_intcache_misses = 0
def int_trace(f):
import os
if os.getenv('SYMPY_TRACE_INT', 'no').lower() != 'yes':
return f
def Integer_tracer(cls, i):
global _intcache_hits, _intcache_misses
try:
_intcache_hits += 1
return _intcache[i]
except KeyError:
_intcache_hits -= 1
_intcache_misses += 1
return f(cls, i)
# also we want to hook our _intcache_printinfo into sys.atexit
import atexit
atexit.register(_intcache_printinfo)
return Integer_tracer
class Integer(Rational):
q = 1
is_integer = True
is_number = True
is_Integer = True
__slots__ = ['p']
def _as_mpf_val(self, prec):
return mlib.from_int(self.p, prec)
def _mpmath_(self, prec, rnd):
return mpmath.make_mpf(self._as_mpf_val(prec))
# TODO caching with decorator, but not to degrade performance
@int_trace
def __new__(cls, i):
if isinstance(i, string_types):
i = i.replace(' ', '')
# whereas we cannot, in general, make a Rational from an
# arbitrary expression, we can make an Integer unambiguously
# (except when a non-integer expression happens to round to
# an integer). So we proceed by taking int() of the input and
# let the int routines determine whether the expression can
# be made into an int or whether an error should be raised.
try:
ival = int(i)
except TypeError:
raise TypeError(
'Integer can only work with integer expressions.')
try:
return _intcache[ival]
except KeyError:
# We only work with well-behaved integer types. This converts, for
# example, numpy.int32 instances.
obj = Expr.__new__(cls)
obj.p = ival
_intcache[ival] = obj
return obj
def __getnewargs__(self):
return (self.p,)
# Arithmetic operations are here for efficiency
def __int__(self):
return self.p
__long__ = __int__
def floor(self):
return Integer(self.p)
def ceiling(self):
return Integer(self.p)
def __neg__(self):
return Integer(-self.p)
def __abs__(self):
if self.p >= 0:
return self
else:
return Integer(-self.p)
def __divmod__(self, other):
from .containers import Tuple
if isinstance(other, Integer) and global_evaluate[0]:
return Tuple(*(divmod(self.p, other.p)))
else:
return Number.__divmod__(self, other)
def __rdivmod__(self, other):
from .containers import Tuple
if isinstance(other, integer_types) and global_evaluate[0]:
return Tuple(*(divmod(other, self.p)))
else:
try:
other = Number(other)
except TypeError:
msg = "unsupported operand type(s) for divmod(): '%s' and '%s'"
oname = type(other).__name__
sname = type(self).__name__
raise TypeError(msg % (oname, sname))
return Number.__divmod__(other, self)
# TODO make it decorator + bytecodehacks?
def __add__(self, other):
if global_evaluate[0]:
if isinstance(other, integer_types):
return Integer(self.p + other)
elif isinstance(other, Integer):
return Integer(self.p + other.p)
elif isinstance(other, Rational):
return Rational(self.p*other.q + other.p, other.q, 1)
return Rational.__add__(self, other)
else:
return Add(self, other)
def __radd__(self, other):
if global_evaluate[0]:
if isinstance(other, integer_types):
return Integer(other + self.p)
elif isinstance(other, Rational):
return Rational(other.p + self.p*other.q, other.q, 1)
return Rational.__radd__(self, other)
return Rational.__radd__(self, other)
def __sub__(self, other):
if global_evaluate[0]:
if isinstance(other, integer_types):
return Integer(self.p - other)
elif isinstance(other, Integer):
return Integer(self.p - other.p)
elif isinstance(other, Rational):
return Rational(self.p*other.q - other.p, other.q, 1)
return Rational.__sub__(self, other)
return Rational.__sub__(self, other)
def __rsub__(self, other):
if global_evaluate[0]:
if isinstance(other, integer_types):
return Integer(other - self.p)
elif isinstance(other, Rational):
return Rational(other.p - self.p*other.q, other.q, 1)
return Rational.__rsub__(self, other)
return Rational.__rsub__(self, other)
def __mul__(self, other):
if global_evaluate[0]:
if isinstance(other, integer_types):
return Integer(self.p*other)
elif isinstance(other, Integer):
return Integer(self.p*other.p)
elif isinstance(other, Rational):
return Rational(self.p*other.p, other.q, igcd(self.p, other.q))
return Rational.__mul__(self, other)
return Rational.__mul__(self, other)
def __rmul__(self, other):
if global_evaluate[0]:
if isinstance(other, integer_types):
return Integer(other*self.p)
elif isinstance(other, Rational):
return Rational(other.p*self.p, other.q, igcd(self.p, other.q))
return Rational.__rmul__(self, other)
return Rational.__rmul__(self, other)
def __mod__(self, other):
if global_evaluate[0]:
if isinstance(other, integer_types):
return Integer(self.p % other)
elif isinstance(other, Integer):
return Integer(self.p % other.p)
return Rational.__mod__(self, other)
return Rational.__mod__(self, other)
def __rmod__(self, other):
if global_evaluate[0]:
if isinstance(other, integer_types):
return Integer(other % self.p)
elif isinstance(other, Integer):
return Integer(other.p % self.p)
return Rational.__rmod__(self, other)
return Rational.__rmod__(self, other)
def __eq__(self, other):
if isinstance(other, integer_types):
return (self.p == other)
elif isinstance(other, Integer):
return (self.p == other.p)
return Rational.__eq__(self, other)
def __ne__(self, other):
return not self.__eq__(other)
def __gt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s > %s" % (self, other))
if isinstance(other, Integer):
return _sympify(self.p > other.p)
return Rational.__gt__(self, other)
def __lt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s < %s" % (self, other))
if isinstance(other, Integer):
return _sympify(self.p < other.p)
return Rational.__lt__(self, other)
def __ge__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s >= %s" % (self, other))
if isinstance(other, Integer):
return _sympify(self.p >= other.p)
return Rational.__ge__(self, other)
def __le__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s <= %s" % (self, other))
if isinstance(other, Integer):
return _sympify(self.p <= other.p)
return Rational.__le__(self, other)
def __hash__(self):
return hash(self.p)
def __index__(self):
return self.p
########################################
def _eval_is_odd(self):
return bool(self.p % 2)
def _eval_power(self, expt):
"""
Tries to do some simplifications on self**expt
Returns None if no further simplifications can be done
When exponent is a fraction (so we have for example a square root),
we try to find a simpler representation by factoring the argument
up to factors of 2**15, e.g.
- sqrt(4) becomes 2
- sqrt(-4) becomes 2*I
- (2**(3+7)*3**(6+7))**Rational(1,7) becomes 6*18**(3/7)
Further simplification would require a special call to factorint on
the argument which is not done here for sake of speed.
"""
from sympy import perfect_power
if expt is S.Infinity:
if self.p > S.One:
return S.Infinity
# cases -1, 0, 1 are done in their respective classes
return S.Infinity + S.ImaginaryUnit*S.Infinity
if expt is S.NegativeInfinity:
return Rational(1, self)**S.Infinity
if not isinstance(expt, Number):
# simplify when expt is even
# (-2)**k --> 2**k
if self.is_negative and expt.is_even:
return (-self)**expt
if isinstance(expt, Float):
# Rational knows how to exponentiate by a Float
return super(Integer, self)._eval_power(expt)
if not isinstance(expt, Rational):
return
if expt is S.Half and self.is_negative:
# we extract I for this special case since everyone is doing so
return S.ImaginaryUnit*Pow(-self, expt)
if expt.is_negative:
# invert base and change sign on exponent
ne = -expt
if self.is_negative:
if expt.q != 1:
return -(S.NegativeOne)**((expt.p % expt.q) /
S(expt.q))*Rational(1, -self)**ne
else:
return (S.NegativeOne)**ne*Rational(1, -self)**ne
else:
return Rational(1, self.p)**ne
# see if base is a perfect root, sqrt(4) --> 2
x, xexact = integer_nthroot(abs(self.p), expt.q)
if xexact:
# if it's a perfect root we've finished
result = Integer(x**abs(expt.p))
if self.is_negative:
result *= S.NegativeOne**expt
return result
# The following is an algorithm where we collect perfect roots
# from the factors of base.
# if it's not an nth root, it still might be a perfect power
b_pos = int(abs(self.p))
p = perfect_power(b_pos)
if p is not False:
dict = {p[0]: p[1]}
else:
dict = Integer(self).factors(limit=2**15)
# now process the dict of factors
if self.is_negative:
dict[-1] = 1
out_int = 1 # integer part
out_rad = 1 # extracted radicals
sqr_int = 1
sqr_gcd = 0
sqr_dict = {}
for prime, exponent in dict.items():
exponent *= expt.p
# remove multiples of expt.q: (2**12)**(1/10) -> 2*(2**2)**(1/10)
div_e, div_m = divmod(exponent, expt.q)
if div_e > 0:
out_int *= prime**div_e
if div_m > 0:
# see if the reduced exponent shares a gcd with e.q
# (2**2)**(1/10) -> 2**(1/5)
g = igcd(div_m, expt.q)
if g != 1:
out_rad *= Pow(prime, Rational(div_m//g, expt.q//g))
else:
sqr_dict[prime] = div_m
# identify gcd of remaining powers
for p, ex in sqr_dict.items():
if sqr_gcd == 0:
sqr_gcd = ex
else:
sqr_gcd = igcd(sqr_gcd, ex)
if sqr_gcd == 1:
break
for k, v in sqr_dict.items():
sqr_int *= k**(v//sqr_gcd)
if sqr_int == self and out_int == 1 and out_rad == 1:
result = None
else:
result = out_int*out_rad*Pow(sqr_int, Rational(sqr_gcd, expt.q))
return result
def _eval_is_prime(self):
from sympy.ntheory import isprime
return isprime(self)
def _eval_is_composite(self):
if self > 1:
return fuzzy_not(self.is_prime)
else:
return False
def as_numer_denom(self):
return self, S.One
def __floordiv__(self, other):
return Integer(self.p // Integer(other).p)
def __rfloordiv__(self, other):
return Integer(Integer(other).p // self.p)
# Add sympify converters
for i_type in integer_types:
converter[i_type] = Integer
class AlgebraicNumber(Expr):
"""Class for representing algebraic numbers in SymPy. """
__slots__ = ['rep', 'root', 'alias', 'minpoly']
is_AlgebraicNumber = True
is_algebraic = True
is_number = True
def __new__(cls, expr, coeffs=None, alias=None, **args):
"""Construct a new algebraic number. """
from sympy import Poly
from sympy.polys.polyclasses import ANP, DMP
from sympy.polys.numberfields import minimal_polynomial
from sympy.core.symbol import Symbol
expr = sympify(expr)
if isinstance(expr, (tuple, Tuple)):
minpoly, root = expr
if not minpoly.is_Poly:
minpoly = Poly(minpoly)
elif expr.is_AlgebraicNumber:
minpoly, root = expr.minpoly, expr.root
else:
minpoly, root = minimal_polynomial(
expr, args.get('gen'), polys=True), expr
dom = minpoly.get_domain()
if coeffs is not None:
if not isinstance(coeffs, ANP):
rep = DMP.from_sympy_list(sympify(coeffs), 0, dom)
scoeffs = Tuple(*coeffs)
else:
rep = DMP.from_list(coeffs.to_list(), 0, dom)
scoeffs = Tuple(*coeffs.to_list())
if rep.degree() >= minpoly.degree():
rep = rep.rem(minpoly.rep)
else:
rep = DMP.from_list([1, 0], 0, dom)
scoeffs = Tuple(1, 0)
if root.is_negative:
rep = -rep
scoeffs = Tuple(-1, 0)
sargs = (root, scoeffs)
if alias is not None:
if not isinstance(alias, Symbol):
alias = Symbol(alias)
sargs = sargs + (alias,)
obj = Expr.__new__(cls, *sargs)
obj.rep = rep
obj.root = root
obj.alias = alias
obj.minpoly = minpoly
return obj
def __hash__(self):
return super(AlgebraicNumber, self).__hash__()
def _eval_evalf(self, prec):
return self.as_expr()._evalf(prec)
@property
def is_aliased(self):
"""Returns ``True`` if ``alias`` was set. """
return self.alias is not None
def as_poly(self, x=None):
"""Create a Poly instance from ``self``. """
from sympy import Dummy, Poly, PurePoly
if x is not None:
return Poly.new(self.rep, x)
else:
if self.alias is not None:
return Poly.new(self.rep, self.alias)
else:
return PurePoly.new(self.rep, Dummy('x'))
def as_expr(self, x=None):
"""Create a Basic expression from ``self``. """
return self.as_poly(x or self.root).as_expr().expand()
def coeffs(self):
"""Returns all SymPy coefficients of an algebraic number. """
return [ self.rep.dom.to_sympy(c) for c in self.rep.all_coeffs() ]
def native_coeffs(self):
"""Returns all native coefficients of an algebraic number. """
return self.rep.all_coeffs()
def to_algebraic_integer(self):
"""Convert ``self`` to an algebraic integer. """
from sympy import Poly
f = self.minpoly
if f.LC() == 1:
return self
coeff = f.LC()**(f.degree() - 1)
poly = f.compose(Poly(f.gen/f.LC()))
minpoly = poly*coeff
root = f.LC()*self.root
return AlgebraicNumber((minpoly, root), self.coeffs())
def _eval_simplify(self, ratio, measure):
from sympy.polys import CRootOf, minpoly
for r in [r for r in self.minpoly.all_roots() if r.func != CRootOf]:
if minpoly(self.root - r).is_Symbol:
# use the matching root if it's simpler
if measure(r) < ratio*measure(self.root):
return AlgebraicNumber(r)
return self
class RationalConstant(Rational):
"""
Abstract base class for rationals with specific behaviors
Derived classes must define class attributes p and q and should probably all
be singletons.
"""
__slots__ = []
def __new__(cls):
return AtomicExpr.__new__(cls)
class IntegerConstant(Integer):
__slots__ = []
def __new__(cls):
return AtomicExpr.__new__(cls)
class Zero(with_metaclass(Singleton, IntegerConstant)):
"""The number zero.
Zero is a singleton, and can be accessed by ``S.Zero``
Examples
========
>>> from sympy import S, Integer, zoo
>>> Integer(0) is S.Zero
True
>>> 1/S.Zero
zoo
References
==========
.. [1] http://en.wikipedia.org/wiki/Zero
"""
p = 0
q = 1
is_positive = False
is_negative = False
is_zero = True
is_number = True
__slots__ = []
@staticmethod
def __abs__():
return S.Zero
@staticmethod
def __neg__():
return S.Zero
def _eval_power(self, expt):
if expt.is_positive:
return self
if expt.is_negative:
return S.ComplexInfinity
if expt.is_real is False:
return S.NaN
# infinities are already handled with pos and neg
# tests above; now throw away leading numbers on Mul
# exponent
coeff, terms = expt.as_coeff_Mul()
if coeff.is_negative:
return S.ComplexInfinity**terms
if coeff is not S.One: # there is a Number to discard
return self**terms
def _eval_order(self, *symbols):
# Order(0,x) -> 0
return self
def __nonzero__(self):
return False
__bool__ = __nonzero__
def as_coeff_Mul(self, rational=False): # XXX this routine should be deleted
"""Efficiently extract the coefficient of a summation. """
return S.One, self
class One(with_metaclass(Singleton, IntegerConstant)):
"""The number one.
One is a singleton, and can be accessed by ``S.One``.
Examples
========
>>> from sympy import S, Integer
>>> Integer(1) is S.One
True
References
==========
.. [1] http://en.wikipedia.org/wiki/1_%28number%29
"""
is_number = True
p = 1
q = 1
__slots__ = []
@staticmethod
def __abs__():
return S.One
@staticmethod
def __neg__():
return S.NegativeOne
def _eval_power(self, expt):
return self
def _eval_order(self, *symbols):
return
@staticmethod
def factors(limit=None, use_trial=True, use_rho=False, use_pm1=False,
verbose=False, visual=False):
if visual:
return S.One
else:
return {}
class NegativeOne(with_metaclass(Singleton, IntegerConstant)):
"""The number negative one.
NegativeOne is a singleton, and can be accessed by ``S.NegativeOne``.
Examples
========
>>> from sympy import S, Integer
>>> Integer(-1) is S.NegativeOne
True
See Also
========
One
References
==========
.. [1] http://en.wikipedia.org/wiki/%E2%88%921_%28number%29
"""
is_number = True
p = -1
q = 1
__slots__ = []
@staticmethod
def __abs__():
return S.One
@staticmethod
def __neg__():
return S.One
def _eval_power(self, expt):
if expt.is_odd:
return S.NegativeOne
if expt.is_even:
return S.One
if isinstance(expt, Number):
if isinstance(expt, Float):
return Float(-1.0)**expt
if expt is S.NaN:
return S.NaN
if expt is S.Infinity or expt is S.NegativeInfinity:
return S.NaN
if expt is S.Half:
return S.ImaginaryUnit
if isinstance(expt, Rational):
if expt.q == 2:
return S.ImaginaryUnit**Integer(expt.p)
i, r = divmod(expt.p, expt.q)
if i:
return self**i*self**Rational(r, expt.q)
return
class Half(with_metaclass(Singleton, RationalConstant)):
"""The rational number 1/2.
Half is a singleton, and can be accessed by ``S.Half``.
Examples
========
>>> from sympy import S, Rational
>>> Rational(1, 2) is S.Half
True
References
==========
.. [1] http://en.wikipedia.org/wiki/One_half
"""
is_number = True
p = 1
q = 2
__slots__ = []
@staticmethod
def __abs__():
return S.Half
class Infinity(with_metaclass(Singleton, Number)):
r"""Positive infinite quantity.
In real analysis the symbol `\infty` denotes an unbounded
limit: `x\to\infty` means that `x` grows without bound.
Infinity is often used not only to define a limit but as a value
in the affinely extended real number system. Points labeled `+\infty`
and `-\infty` can be added to the topological space of the real numbers,
producing the two-point compactification of the real numbers. Adding
algebraic properties to this gives us the extended real numbers.
Infinity is a singleton, and can be accessed by ``S.Infinity``,
or can be imported as ``oo``.
Examples
========
>>> from sympy import oo, exp, limit, Symbol
>>> 1 + oo
oo
>>> 42/oo
0
>>> x = Symbol('x')
>>> limit(exp(x), x, oo)
oo
See Also
========
NegativeInfinity, NaN
References
==========
.. [1] http://en.wikipedia.org/wiki/Infinity
"""
is_commutative = True
is_positive = True
is_infinite = True
is_number = True
is_prime = False
__slots__ = []
def __new__(cls):
return AtomicExpr.__new__(cls)
def _latex(self, printer):
return r"\infty"
def _eval_subs(self, old, new):
if self == old:
return new
@_sympifyit('other', NotImplemented)
def __add__(self, other):
if isinstance(other, Number):
if other is S.NegativeInfinity or other is S.NaN:
return S.NaN
elif other.is_Float:
if other == Float('-inf'):
return S.NaN
else:
return Float('inf')
else:
return S.Infinity
return NotImplemented
__radd__ = __add__
@_sympifyit('other', NotImplemented)
def __sub__(self, other):
if isinstance(other, Number):
if other is S.Infinity or other is S.NaN:
return S.NaN
elif other.is_Float:
if other == Float('inf'):
return S.NaN
else:
return Float('inf')
else:
return S.Infinity
return NotImplemented
@_sympifyit('other', NotImplemented)
def __mul__(self, other):
if isinstance(other, Number):
if other is S.Zero or other is S.NaN:
return S.NaN
elif other.is_Float:
if other == 0:
return S.NaN
if other > 0:
return Float('inf')
else:
return Float('-inf')
else:
if other > 0:
return S.Infinity
else:
return S.NegativeInfinity
return NotImplemented
__rmul__ = __mul__
@_sympifyit('other', NotImplemented)
def __div__(self, other):
if isinstance(other, Number):
if other is S.Infinity or \
other is S.NegativeInfinity or \
other is S.NaN:
return S.NaN
elif other.is_Float:
if other == Float('-inf') or \
other == Float('inf'):
return S.NaN
elif other.is_nonnegative:
return Float('inf')
else:
return Float('-inf')
else:
if other >= 0:
return S.Infinity
else:
return S.NegativeInfinity
return NotImplemented
__truediv__ = __div__
def __abs__(self):
return S.Infinity
def __neg__(self):
return S.NegativeInfinity
def _eval_power(self, expt):
"""
``expt`` is symbolic object but not equal to 0 or 1.
================ ======= ==============================
Expression Result Notes
================ ======= ==============================
``oo ** nan`` ``nan``
``oo ** -p`` ``0`` ``p`` is number, ``oo``
================ ======= ==============================
See Also
========
Pow
NaN
NegativeInfinity
"""
from sympy.functions import re
if expt.is_positive:
return S.Infinity
if expt.is_negative:
return S.Zero
if expt is S.NaN:
return S.NaN
if expt is S.ComplexInfinity:
return S.NaN
if expt.is_real is False and expt.is_number:
expt_real = re(expt)
if expt_real.is_positive:
return S.ComplexInfinity
if expt_real.is_negative:
return S.Zero
if expt_real.is_zero:
return S.NaN
return self**expt.evalf()
def _as_mpf_val(self, prec):
return mlib.finf
def _sage_(self):
import sage.all as sage
return sage.oo
def __hash__(self):
return super(Infinity, self).__hash__()
def __eq__(self, other):
return other is S.Infinity
def __ne__(self, other):
return other is not S.Infinity
def __lt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s < %s" % (self, other))
if other.is_real:
return S.false
return Expr.__lt__(self, other)
def __le__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s <= %s" % (self, other))
if other.is_real:
if other.is_finite or other is S.NegativeInfinity:
return S.false
elif other.is_nonpositive:
return S.false
elif other.is_infinite and other.is_positive:
return S.true
return Expr.__le__(self, other)
def __gt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s > %s" % (self, other))
if other.is_real:
if other.is_finite or other is S.NegativeInfinity:
return S.true
elif other.is_nonpositive:
return S.true
elif other.is_infinite and other.is_positive:
return S.false
return Expr.__gt__(self, other)
def __ge__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s >= %s" % (self, other))
if other.is_real:
return S.true
return Expr.__ge__(self, other)
def __mod__(self, other):
return S.NaN
__rmod__ = __mod__
def floor(self):
return self
def ceiling(self):
return self
oo = S.Infinity
class NegativeInfinity(with_metaclass(Singleton, Number)):
"""Negative infinite quantity.
NegativeInfinity is a singleton, and can be accessed
by ``S.NegativeInfinity``.
See Also
========
Infinity
"""
is_commutative = True
is_negative = True
is_infinite = True
is_number = True
__slots__ = []
def __new__(cls):
return AtomicExpr.__new__(cls)
def _latex(self, printer):
return r"-\infty"
def _eval_subs(self, old, new):
if self == old:
return new
@_sympifyit('other', NotImplemented)
def __add__(self, other):
if isinstance(other, Number):
if other is S.Infinity or other is S.NaN:
return S.NaN
elif other.is_Float:
if other == Float('inf'):
return Float('nan')
else:
return Float('-inf')
else:
return S.NegativeInfinity
return NotImplemented
__radd__ = __add__
@_sympifyit('other', NotImplemented)
def __sub__(self, other):
if isinstance(other, Number):
if other is S.NegativeInfinity or other is S.NaN:
return S.NaN
elif other.is_Float:
if other == Float('-inf'):
return Float('nan')
else:
return Float('-inf')
else:
return S.NegativeInfinity
return NotImplemented
@_sympifyit('other', NotImplemented)
def __mul__(self, other):
if isinstance(other, Number):
if other is S.Zero or other is S.NaN:
return S.NaN
elif other.is_Float:
if other is S.NaN or other.is_zero:
return S.NaN
elif other.is_positive:
return Float('-inf')
else:
return Float('inf')
else:
if other.is_positive:
return S.NegativeInfinity
else:
return S.Infinity
return NotImplemented
__rmul__ = __mul__
@_sympifyit('other', NotImplemented)
def __div__(self, other):
if isinstance(other, Number):
if other is S.Infinity or \
other is S.NegativeInfinity or \
other is S.NaN:
return S.NaN
elif other.is_Float:
if other == Float('-inf') or \
other == Float('inf') or \
other is S.NaN:
return S.NaN
elif other.is_nonnegative:
return Float('-inf')
else:
return Float('inf')
else:
if other >= 0:
return S.NegativeInfinity
else:
return S.Infinity
return NotImplemented
__truediv__ = __div__
def __abs__(self):
return S.Infinity
def __neg__(self):
return S.Infinity
def _eval_power(self, expt):
"""
``expt`` is symbolic object but not equal to 0 or 1.
================ ======= ==============================
Expression Result Notes
================ ======= ==============================
``(-oo) ** nan`` ``nan``
``(-oo) ** oo`` ``nan``
``(-oo) ** -oo`` ``nan``
``(-oo) ** e`` ``oo`` ``e`` is positive even integer
``(-oo) ** o`` ``-oo`` ``o`` is positive odd integer
================ ======= ==============================
See Also
========
Infinity
Pow
NaN
"""
if expt.is_number:
if expt is S.NaN or \
expt is S.Infinity or \
expt is S.NegativeInfinity:
return S.NaN
if isinstance(expt, Integer) and expt.is_positive:
if expt.is_odd:
return S.NegativeInfinity
else:
return S.Infinity
return S.NegativeOne**expt*S.Infinity**expt
def _as_mpf_val(self, prec):
return mlib.fninf
def _sage_(self):
import sage.all as sage
return -(sage.oo)
def __hash__(self):
return super(NegativeInfinity, self).__hash__()
def __eq__(self, other):
return other is S.NegativeInfinity
def __ne__(self, other):
return other is not S.NegativeInfinity
def __lt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s < %s" % (self, other))
if other.is_real:
if other.is_finite or other is S.Infinity:
return S.true
elif other.is_nonnegative:
return S.true
elif other.is_infinite and other.is_negative:
return S.false
return Expr.__lt__(self, other)
def __le__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s <= %s" % (self, other))
if other.is_real:
return S.true
return Expr.__le__(self, other)
def __gt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s > %s" % (self, other))
if other.is_real:
return S.false
return Expr.__gt__(self, other)
def __ge__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s >= %s" % (self, other))
if other.is_real:
if other.is_finite or other is S.Infinity:
return S.false
elif other.is_nonnegative:
return S.false
elif other.is_infinite and other.is_negative:
return S.true
return Expr.__ge__(self, other)
def __mod__(self, other):
return S.NaN
__rmod__ = __mod__
def floor(self):
return self
def ceiling(self):
return self
class NaN(with_metaclass(Singleton, Number)):
"""
Not a Number.
This serves as a place holder for numeric values that are indeterminate.
Most operations on NaN, produce another NaN. Most indeterminate forms,
such as ``0/0`` or ``oo - oo` produce NaN. Two exceptions are ``0**0``
and ``oo**0``, which all produce ``1`` (this is consistent with Python's
float).
NaN is loosely related to floating point nan, which is defined in the
IEEE 754 floating point standard, and corresponds to the Python
``float('nan')``. Differences are noted below.
NaN is mathematically not equal to anything else, even NaN itself. This
explains the initially counter-intuitive results with ``Eq`` and ``==`` in
the examples below.
NaN is not comparable so inequalities raise a TypeError. This is in
constrast with floating point nan where all inequalities are false.
NaN is a singleton, and can be accessed by ``S.NaN``, or can be imported
as ``nan``.
Examples
========
>>> from sympy import nan, S, oo, Eq
>>> nan is S.NaN
True
>>> oo - oo
nan
>>> nan + 1
nan
>>> Eq(nan, nan) # mathematical equality
False
>>> nan == nan # structural equality
True
References
==========
.. [1] http://en.wikipedia.org/wiki/NaN
"""
is_commutative = True
is_real = None
is_rational = None
is_algebraic = None
is_transcendental = None
is_integer = None
is_comparable = False
is_finite = None
is_zero = None
is_prime = None
is_positive = None
is_negative = None
is_number = True
__slots__ = []
def __new__(cls):
return AtomicExpr.__new__(cls)
def _latex(self, printer):
return r"\mathrm{NaN}"
@_sympifyit('other', NotImplemented)
def __add__(self, other):
return self
@_sympifyit('other', NotImplemented)
def __sub__(self, other):
return self
@_sympifyit('other', NotImplemented)
def __mul__(self, other):
return self
@_sympifyit('other', NotImplemented)
def __div__(self, other):
return self
__truediv__ = __div__
def floor(self):
return self
def ceiling(self):
return self
def _as_mpf_val(self, prec):
return _mpf_nan
def _sage_(self):
import sage.all as sage
return sage.NaN
def __hash__(self):
return super(NaN, self).__hash__()
def __eq__(self, other):
# NaN is structurally equal to another NaN
return other is S.NaN
def __ne__(self, other):
return other is not S.NaN
def _eval_Eq(self, other):
# NaN is not mathematically equal to anything, even NaN
return S.false
# Expr will _sympify and raise TypeError
__gt__ = Expr.__gt__
__ge__ = Expr.__ge__
__lt__ = Expr.__lt__
__le__ = Expr.__le__
nan = S.NaN
class ComplexInfinity(with_metaclass(Singleton, AtomicExpr)):
r"""Complex infinity.
In complex analysis the symbol `\tilde\infty`, called "complex
infinity", represents a quantity with infinite magnitude, but
undetermined complex phase.
ComplexInfinity is a singleton, and can be accessed by
``S.ComplexInfinity``, or can be imported as ``zoo``.
Examples
========
>>> from sympy import zoo, oo
>>> zoo + 42
zoo
>>> 42/zoo
0
>>> zoo + zoo
nan
>>> zoo*zoo
zoo
See Also
========
Infinity
"""
is_commutative = True
is_infinite = True
is_number = True
is_prime = False
__slots__ = []
def __new__(cls):
return AtomicExpr.__new__(cls)
def _latex(self, printer):
return r"\tilde{\infty}"
@staticmethod
def __abs__():
return S.Infinity
def floor(self):
return self
def ceiling(self):
return self
@staticmethod
def __neg__():
return S.ComplexInfinity
def _eval_power(self, expt):
if expt is S.ComplexInfinity:
return S.NaN
if isinstance(expt, Number):
if expt is S.Zero:
return S.NaN
else:
if expt.is_positive:
return S.ComplexInfinity
else:
return S.Zero
def _sage_(self):
import sage.all as sage
return sage.UnsignedInfinityRing.gen()
zoo = S.ComplexInfinity
class NumberSymbol(AtomicExpr):
is_commutative = True
is_finite = True
is_number = True
__slots__ = []
is_NumberSymbol = True
def __new__(cls):
return AtomicExpr.__new__(cls)
def approximation(self, number_cls):
""" Return an interval with number_cls endpoints
that contains the value of NumberSymbol.
If not implemented, then return None.
"""
def _eval_evalf(self, prec):
return Float._new(self._as_mpf_val(prec), prec)
def __eq__(self, other):
try:
other = _sympify(other)
except SympifyError:
return False # sympy != other --> not ==
if self is other:
return True
if isinstance(other, Number) and self.is_irrational:
return False
return False # NumberSymbol != non-(Number|self)
def __ne__(self, other):
return not self.__eq__(other)
def __lt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s < %s" % (self, other))
if self is other:
return S.false
if isinstance(other, Number):
approx = self.approximation_interval(other.__class__)
if approx is not None:
l, u = approx
if other < l:
return S.false
if other > u:
return S.true
return _sympify(self.evalf() < other)
if other.is_real and other.is_number:
other = other.evalf()
return _sympify(self.evalf() < other)
return Expr.__lt__(self, other)
def __le__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s <= %s" % (self, other))
if self is other:
return S.true
if other.is_real and other.is_number:
other = other.evalf()
if isinstance(other, Number):
return _sympify(self.evalf() <= other)
return Expr.__le__(self, other)
def __gt__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s > %s" % (self, other))
r = _sympify((-self) < (-other))
if r in (S.true, S.false):
return r
else:
return Expr.__gt__(self, other)
def __ge__(self, other):
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s >= %s" % (self, other))
r = _sympify((-self) <= (-other))
if r in (S.true, S.false):
return r
else:
return Expr.__ge__(self, other)
def __int__(self):
# subclass with appropriate return value
raise NotImplementedError
def __long__(self):
return self.__int__()
def __hash__(self):
return super(NumberSymbol, self).__hash__()
class Exp1(with_metaclass(Singleton, NumberSymbol)):
r"""The `e` constant.
The transcendental number `e = 2.718281828\ldots` is the base of the
natural logarithm and of the exponential function, `e = \exp(1)`.
Sometimes called Euler's number or Napier's constant.
Exp1 is a singleton, and can be accessed by ``S.Exp1``,
or can be imported as ``E``.
Examples
========
>>> from sympy import exp, log, E
>>> E is exp(1)
True
>>> log(E)
1
References
==========
.. [1] http://en.wikipedia.org/wiki/E_%28mathematical_constant%29
"""
is_real = True
is_positive = True
is_negative = False # XXX Forces is_negative/is_nonnegative
is_irrational = True
is_number = True
is_algebraic = False
is_transcendental = True
__slots__ = []
def _latex(self, printer):
return r"e"
@staticmethod
def __abs__():
return S.Exp1
def __int__(self):
return 2
def _as_mpf_val(self, prec):
return mpf_e(prec)
def approximation_interval(self, number_cls):
if issubclass(number_cls, Integer):
return (Integer(2), Integer(3))
elif issubclass(number_cls, Rational):
pass
def _eval_power(self, expt):
from sympy import exp
return exp(expt)
def _eval_rewrite_as_sin(self):
from sympy import sin
I = S.ImaginaryUnit
return sin(I + S.Pi/2) - I*sin(I)
def _eval_rewrite_as_cos(self):
from sympy import cos
I = S.ImaginaryUnit
return cos(I) + I*cos(I + S.Pi/2)
def _sage_(self):
import sage.all as sage
return sage.e
E = S.Exp1
class Pi(with_metaclass(Singleton, NumberSymbol)):
r"""The `\pi` constant.
The transcendental number `\pi = 3.141592654\ldots` represents the ratio
of a circle's circumference to its diameter, the area of the unit circle,
the half-period of trigonometric functions, and many other things
in mathematics.
Pi is a singleton, and can be accessed by ``S.Pi``, or can
be imported as ``pi``.
Examples
========
>>> from sympy import S, pi, oo, sin, exp, integrate, Symbol
>>> S.Pi
pi
>>> pi > 3
True
>>> pi.is_irrational
True
>>> x = Symbol('x')
>>> sin(x + 2*pi)
sin(x)
>>> integrate(exp(-x**2), (x, -oo, oo))
sqrt(pi)
References
==========
.. [1] http://en.wikipedia.org/wiki/Pi
"""
is_real = True
is_positive = True
is_negative = False
is_irrational = True
is_number = True
is_algebraic = False
is_transcendental = True
__slots__ = []
def _latex(self, printer):
return r"\pi"
@staticmethod
def __abs__():
return S.Pi
def __int__(self):
return 3
def _as_mpf_val(self, prec):
return mpf_pi(prec)
def approximation_interval(self, number_cls):
if issubclass(number_cls, Integer):
return (Integer(3), Integer(4))
elif issubclass(number_cls, Rational):
return (Rational(223, 71), Rational(22, 7))
def _sage_(self):
import sage.all as sage
return sage.pi
pi = S.Pi
class GoldenRatio(with_metaclass(Singleton, NumberSymbol)):
r"""The golden ratio, `\phi`.
`\phi = \frac{1 + \sqrt{5}}{2}` is algebraic number. Two quantities
are in the golden ratio if their ratio is the same as the ratio of
their sum to the larger of the two quantities, i.e. their maximum.
GoldenRatio is a singleton, and can be accessed by ``S.GoldenRatio``.
Examples
========
>>> from sympy import S
>>> S.GoldenRatio > 1
True
>>> S.GoldenRatio.expand(func=True)
1/2 + sqrt(5)/2
>>> S.GoldenRatio.is_irrational
True
References
==========
.. [1] http://en.wikipedia.org/wiki/Golden_ratio
"""
is_real = True
is_positive = True
is_negative = False
is_irrational = True
is_number = True
is_algebraic = True
is_transcendental = False
__slots__ = []
def _latex(self, printer):
return r"\phi"
def __int__(self):
return 1
def _as_mpf_val(self, prec):
# XXX track down why this has to be increased
rv = mlib.from_man_exp(phi_fixed(prec + 10), -prec - 10)
return mpf_norm(rv, prec)
def _eval_expand_func(self, **hints):
from sympy import sqrt
return S.Half + S.Half*sqrt(5)
def approximation_interval(self, number_cls):
if issubclass(number_cls, Integer):
return (S.One, Rational(2))
elif issubclass(number_cls, Rational):
pass
def _sage_(self):
import sage.all as sage
return sage.golden_ratio
_eval_rewrite_as_sqrt = _eval_expand_func
class EulerGamma(with_metaclass(Singleton, NumberSymbol)):
r"""The Euler-Mascheroni constant.
`\gamma = 0.5772157\ldots` (also called Euler's constant) is a mathematical
constant recurring in analysis and number theory. It is defined as the
limiting difference between the harmonic series and the
natural logarithm:
.. math:: \gamma = \lim\limits_{n\to\infty}
\left(\sum\limits_{k=1}^n\frac{1}{k} - \ln n\right)
EulerGamma is a singleton, and can be accessed by ``S.EulerGamma``.
Examples
========
>>> from sympy import S
>>> S.EulerGamma.is_irrational
>>> S.EulerGamma > 0
True
>>> S.EulerGamma > 1
False
References
==========
.. [1] http://en.wikipedia.org/wiki/Euler%E2%80%93Mascheroni_constant
"""
is_real = True
is_positive = True
is_negative = False
is_irrational = None
is_number = True
__slots__ = []
def _latex(self, printer):
return r"\gamma"
def __int__(self):
return 0
def _as_mpf_val(self, prec):
# XXX track down why this has to be increased
v = mlib.libhyper.euler_fixed(prec + 10)
rv = mlib.from_man_exp(v, -prec - 10)
return mpf_norm(rv, prec)
def approximation_interval(self, number_cls):
if issubclass(number_cls, Integer):
return (S.Zero, S.One)
elif issubclass(number_cls, Rational):
return (S.Half, Rational(3, 5))
def _sage_(self):
import sage.all as sage
return sage.euler_gamma
class Catalan(with_metaclass(Singleton, NumberSymbol)):
r"""Catalan's constant.
`K = 0.91596559\ldots` is given by the infinite series
.. math:: K = \sum_{k=0}^{\infty} \frac{(-1)^k}{(2k+1)^2}
Catalan is a singleton, and can be accessed by ``S.Catalan``.
Examples
========
>>> from sympy import S
>>> S.Catalan.is_irrational
>>> S.Catalan > 0
True
>>> S.Catalan > 1
False
References
==========
.. [1] http://en.wikipedia.org/wiki/Catalan%27s_constant
"""
is_real = True
is_positive = True
is_negative = False
is_irrational = None
is_number = True
__slots__ = []
def __int__(self):
return 0
def _as_mpf_val(self, prec):
# XXX track down why this has to be increased
v = mlib.catalan_fixed(prec + 10)
rv = mlib.from_man_exp(v, -prec - 10)
return mpf_norm(rv, prec)
def approximation_interval(self, number_cls):
if issubclass(number_cls, Integer):
return (S.Zero, S.One)
elif issubclass(number_cls, Rational):
return (Rational(9, 10), S.One)
def _sage_(self):
import sage.all as sage
return sage.catalan
class ImaginaryUnit(with_metaclass(Singleton, AtomicExpr)):
r"""The imaginary unit, `i = \sqrt{-1}`.
I is a singleton, and can be accessed by ``S.I``, or can be
imported as ``I``.
Examples
========
>>> from sympy import I, sqrt
>>> sqrt(-1)
I
>>> I*I
-1
>>> 1/I
-I
References
==========
.. [1] http://en.wikipedia.org/wiki/Imaginary_unit
"""
is_commutative = True
is_imaginary = True
is_finite = True
is_number = True
is_algebraic = True
is_transcendental = False
__slots__ = []
def _latex(self, printer):
return r"i"
@staticmethod
def __abs__():
return S.One
def _eval_evalf(self, prec):
return self
def _eval_conjugate(self):
return -S.ImaginaryUnit
def _eval_power(self, expt):
"""
b is I = sqrt(-1)
e is symbolic object but not equal to 0, 1
I**r -> (-1)**(r/2) -> exp(r/2*Pi*I) -> sin(Pi*r/2) + cos(Pi*r/2)*I, r is decimal
I**0 mod 4 -> 1
I**1 mod 4 -> I
I**2 mod 4 -> -1
I**3 mod 4 -> -I
"""
if isinstance(expt, Number):
if isinstance(expt, Integer):
expt = expt.p % 4
if expt == 0:
return S.One
if expt == 1:
return S.ImaginaryUnit
if expt == 2:
return -S.One
return -S.ImaginaryUnit
return (S.NegativeOne)**(expt*S.Half)
return
def as_base_exp(self):
return S.NegativeOne, S.Half
def _sage_(self):
import sage.all as sage
return sage.I
@property
def _mpc_(self):
return (Float(0)._mpf_, Float(1)._mpf_)
I = S.ImaginaryUnit
def sympify_fractions(f):
return Rational(f.numerator, f.denominator)
converter[fractions.Fraction] = sympify_fractions
try:
if HAS_GMPY == 2:
import gmpy2 as gmpy
elif HAS_GMPY == 1:
import gmpy
else:
raise ImportError
def sympify_mpz(x):
return Integer(long(x))
def sympify_mpq(x):
return Rational(long(x.numerator), long(x.denominator))
converter[type(gmpy.mpz(1))] = sympify_mpz
converter[type(gmpy.mpq(1, 2))] = sympify_mpq
except ImportError:
pass
def sympify_mpmath(x):
return Expr._from_mpmath(x, x.context.prec)
converter[mpnumeric] = sympify_mpmath
def sympify_complex(a):
real, imag = list(map(sympify, (a.real, a.imag)))
return real + S.ImaginaryUnit*imag
converter[complex] = sympify_complex
_intcache[0] = S.Zero
_intcache[1] = S.One
_intcache[-1] = S.NegativeOne
from .power import Pow, integer_nthroot
from .mul import Mul
Mul.identity = One()
from .add import Add
Add.identity = Zero()
| 115,376 | 28.813178 | 108 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/function.py
|
"""
There are three types of functions implemented in SymPy:
1) defined functions (in the sense that they can be evaluated) like
exp or sin; they have a name and a body:
f = exp
2) undefined function which have a name but no body. Undefined
functions can be defined using a Function class as follows:
f = Function('f')
(the result will be a Function instance)
3) anonymous function (or lambda function) which have a body (defined
with dummy variables) but have no name:
f = Lambda(x, exp(x)*x)
f = Lambda((x, y), exp(x)*y)
The fourth type of functions are composites, like (sin + cos)(x); these work in
SymPy core, but are not yet part of SymPy.
Examples
========
>>> import sympy
>>> f = sympy.Function("f")
>>> from sympy.abc import x
>>> f(x)
f(x)
>>> print(sympy.srepr(f(x).func))
Function('f')
>>> f(x).args
(x,)
"""
from __future__ import print_function, division
from .add import Add
from .assumptions import ManagedProperties
from .basic import Basic
from .cache import cacheit
from .compatibility import iterable, is_sequence, as_int, ordered
from .decorators import _sympifyit
from .expr import Expr, AtomicExpr
from .numbers import Rational, Float
from .operations import LatticeOp
from .rules import Transform
from .singleton import S
from .sympify import sympify
from sympy.core.containers import Tuple, Dict
from sympy.core.logic import fuzzy_and
from sympy.core.compatibility import string_types, with_metaclass, range
from sympy.utilities import default_sort_key
from sympy.utilities.misc import filldedent
from sympy.utilities.iterables import uniq
from sympy.core.evaluate import global_evaluate
import sys
import mpmath
import mpmath.libmp as mlib
import inspect
import collections
def _coeff_isneg(a):
"""Return True if the leading Number is negative.
Examples
========
>>> from sympy.core.function import _coeff_isneg
>>> from sympy import S, Symbol, oo, pi
>>> _coeff_isneg(-3*pi)
True
>>> _coeff_isneg(S(3))
False
>>> _coeff_isneg(-oo)
True
>>> _coeff_isneg(Symbol('n', negative=True)) # coeff is 1
False
"""
if a.is_Mul:
a = a.args[0]
return a.is_Number and a.is_negative
class PoleError(Exception):
pass
class ArgumentIndexError(ValueError):
def __str__(self):
return ("Invalid operation with argument number %s for Function %s" %
(self.args[1], self.args[0]))
def _getnargs(cls):
if hasattr(cls, 'eval'):
if sys.version_info < (3, ):
return _getnargs_old(cls.eval)
else:
return _getnargs_new(cls.eval)
else:
return None
def _getnargs_old(eval_):
evalargspec = inspect.getargspec(eval_)
if evalargspec.varargs:
return None
else:
evalargs = len(evalargspec.args) - 1 # subtract 1 for cls
if evalargspec.defaults:
# if there are default args then they are optional; the
# fewest args will occur when all defaults are used and
# the most when none are used (i.e. all args are given)
return tuple(range(
evalargs - len(evalargspec.defaults), evalargs + 1))
return evalargs
def _getnargs_new(eval_):
parameters = inspect.signature(eval_).parameters.items()
if [p for n,p in parameters if p.kind == p.VAR_POSITIONAL]:
return None
else:
p_or_k = [p for n,p in parameters if p.kind == p.POSITIONAL_OR_KEYWORD]
num_no_default = len(list(filter(lambda p:p.default == p.empty, p_or_k)))
num_with_default = len(list(filter(lambda p:p.default != p.empty, p_or_k)))
if not num_with_default:
return num_no_default
return tuple(range(num_no_default, num_no_default+num_with_default+1))
class FunctionClass(ManagedProperties):
"""
Base class for function classes. FunctionClass is a subclass of type.
Use Function('<function name>' [ , signature ]) to create
undefined function classes.
"""
_new = type.__new__
def __init__(cls, *args, **kwargs):
# honor kwarg value or class-defined value before using
# the number of arguments in the eval function (if present)
nargs = kwargs.pop('nargs', cls.__dict__.get('nargs', _getnargs(cls)))
super(FunctionClass, cls).__init__(args, kwargs)
# Canonicalize nargs here; change to set in nargs.
if is_sequence(nargs):
if not nargs:
raise ValueError(filldedent('''
Incorrectly specified nargs as %s:
if there are no arguments, it should be
`nargs = 0`;
if there are any number of arguments,
it should be
`nargs = None`''' % str(nargs)))
nargs = tuple(ordered(set(nargs)))
elif nargs is not None:
nargs = (as_int(nargs),)
cls._nargs = nargs
@property
def __signature__(self):
"""
Allow Python 3's inspect.signature to give a useful signature for
Function subclasses.
"""
# Python 3 only, but backports (like the one in IPython) still might
# call this.
try:
from inspect import signature
except ImportError:
return None
# TODO: Look at nargs
return signature(self.eval)
@property
def nargs(self):
"""Return a set of the allowed number of arguments for the function.
Examples
========
>>> from sympy.core.function import Function
>>> from sympy.abc import x, y
>>> f = Function('f')
If the function can take any number of arguments, the set of whole
numbers is returned:
>>> Function('f').nargs
S.Naturals0
If the function was initialized to accept one or more arguments, a
corresponding set will be returned:
>>> Function('f', nargs=1).nargs
{1}
>>> Function('f', nargs=(2, 1)).nargs
{1, 2}
The undefined function, after application, also has the nargs
attribute; the actual number of arguments is always available by
checking the ``args`` attribute:
>>> f = Function('f')
>>> f(1).nargs
S.Naturals0
>>> len(f(1).args)
1
"""
from sympy.sets.sets import FiniteSet
# XXX it would be nice to handle this in __init__ but there are import
# problems with trying to import FiniteSet there
return FiniteSet(*self._nargs) if self._nargs else S.Naturals0
def __repr__(cls):
return cls.__name__
class Application(with_metaclass(FunctionClass, Basic)):
"""
Base class for applied functions.
Instances of Application represent the result of applying an application of
any type to any object.
"""
is_Function = True
@cacheit
def __new__(cls, *args, **options):
from sympy.sets.fancysets import Naturals0
from sympy.sets.sets import FiniteSet
args = list(map(sympify, args))
evaluate = options.pop('evaluate', global_evaluate[0])
# WildFunction (and anything else like it) may have nargs defined
# and we throw that value away here
options.pop('nargs', None)
if options:
raise ValueError("Unknown options: %s" % options)
if evaluate:
evaluated = cls.eval(*args)
if evaluated is not None:
return evaluated
obj = super(Application, cls).__new__(cls, *args, **options)
# make nargs uniform here
try:
# things passing through here:
# - functions subclassed from Function (e.g. myfunc(1).nargs)
# - functions like cos(1).nargs
# - AppliedUndef with given nargs like Function('f', nargs=1)(1).nargs
# Canonicalize nargs here
if is_sequence(obj.nargs):
nargs = tuple(ordered(set(obj.nargs)))
elif obj.nargs is not None:
nargs = (as_int(obj.nargs),)
else:
nargs = None
except AttributeError:
# things passing through here:
# - WildFunction('f').nargs
# - AppliedUndef with no nargs like Function('f')(1).nargs
nargs = obj._nargs # note the underscore here
# convert to FiniteSet
obj.nargs = FiniteSet(*nargs) if nargs else Naturals0()
return obj
@classmethod
def eval(cls, *args):
"""
Returns a canonical form of cls applied to arguments args.
The eval() method is called when the class cls is about to be
instantiated and it should return either some simplified instance
(possible of some other class), or if the class cls should be
unmodified, return None.
Examples of eval() for the function "sign"
---------------------------------------------
@classmethod
def eval(cls, arg):
if arg is S.NaN:
return S.NaN
if arg is S.Zero: return S.Zero
if arg.is_positive: return S.One
if arg.is_negative: return S.NegativeOne
if isinstance(arg, Mul):
coeff, terms = arg.as_coeff_Mul(rational=True)
if coeff is not S.One:
return cls(coeff) * cls(terms)
"""
return
@property
def func(self):
return self.__class__
def _eval_subs(self, old, new):
if (old.is_Function and new.is_Function and
callable(old) and callable(new) and
old == self.func and len(self.args) in new.nargs):
return new(*self.args)
class Function(Application, Expr):
"""Base class for applied mathematical functions.
It also serves as a constructor for undefined function classes.
Examples
========
First example shows how to use Function as a constructor for undefined
function classes:
>>> from sympy import Function, Symbol
>>> x = Symbol('x')
>>> f = Function('f')
>>> g = Function('g')(x)
>>> f
f
>>> f(x)
f(x)
>>> g
g(x)
>>> f(x).diff(x)
Derivative(f(x), x)
>>> g.diff(x)
Derivative(g(x), x)
In the following example Function is used as a base class for
``my_func`` that represents a mathematical function *my_func*. Suppose
that it is well known, that *my_func(0)* is *1* and *my_func* at infinity
goes to *0*, so we want those two simplifications to occur automatically.
Suppose also that *my_func(x)* is real exactly when *x* is real. Here is
an implementation that honours those requirements:
>>> from sympy import Function, S, oo, I, sin
>>> class my_func(Function):
...
... @classmethod
... def eval(cls, x):
... if x.is_Number:
... if x is S.Zero:
... return S.One
... elif x is S.Infinity:
... return S.Zero
...
... def _eval_is_real(self):
... return self.args[0].is_real
...
>>> x = S('x')
>>> my_func(0) + sin(0)
1
>>> my_func(oo)
0
>>> my_func(3.54).n() # Not yet implemented for my_func.
my_func(3.54)
>>> my_func(I).is_real
False
In order for ``my_func`` to become useful, several other methods would
need to be implemented. See source code of some of the already
implemented functions for more complete examples.
Also, if the function can take more than one argument, then ``nargs``
must be defined, e.g. if ``my_func`` can take one or two arguments
then,
>>> class my_func(Function):
... nargs = (1, 2)
...
>>>
"""
@property
def _diff_wrt(self):
"""Allow derivatives wrt functions.
Examples
========
>>> from sympy import Function, Symbol
>>> f = Function('f')
>>> x = Symbol('x')
>>> f(x)._diff_wrt
True
"""
return True
@cacheit
def __new__(cls, *args, **options):
# Handle calls like Function('f')
if cls is Function:
return UndefinedFunction(*args, **options)
n = len(args)
if n not in cls.nargs:
# XXX: exception message must be in exactly this format to
# make it work with NumPy's functions like vectorize(). See,
# for example, https://github.com/numpy/numpy/issues/1697.
# The ideal solution would be just to attach metadata to
# the exception and change NumPy to take advantage of this.
temp = ('%(name)s takes %(qual)s %(args)s '
'argument%(plural)s (%(given)s given)')
raise TypeError(temp % {
'name': cls,
'qual': 'exactly' if len(cls.nargs) == 1 else 'at least',
'args': min(cls.nargs),
'plural': 's'*(min(cls.nargs) != 1),
'given': n})
evaluate = options.get('evaluate', global_evaluate[0])
result = super(Function, cls).__new__(cls, *args, **options)
if not evaluate or not isinstance(result, cls):
return result
pr = max(cls._should_evalf(a) for a in result.args)
pr2 = min(cls._should_evalf(a) for a in result.args)
if pr2 > 0:
return result.evalf(mlib.libmpf.prec_to_dps(pr))
return result
@classmethod
def _should_evalf(cls, arg):
"""
Decide if the function should automatically evalf().
By default (in this implementation), this happens if (and only if) the
ARG is a floating point number.
This function is used by __new__.
Returns the precision to evalf to, or -1 if it shouldn't evalf.
"""
from sympy.core.symbol import Wild
if arg.is_Float:
return arg._prec
if not arg.is_Add:
return -1
# Don't use as_real_imag() here, that's too much work
a, b = Wild('a'), Wild('b')
m = arg.match(a + b*S.ImaginaryUnit)
if not m or not (m[a].is_Float or m[b].is_Float):
return -1
l = [m[i]._prec for i in m if m[i].is_Float]
l.append(-1)
return max(l)
@classmethod
def class_key(cls):
from sympy.sets.fancysets import Naturals0
funcs = {
'exp': 10,
'log': 11,
'sin': 20,
'cos': 21,
'tan': 22,
'cot': 23,
'sinh': 30,
'cosh': 31,
'tanh': 32,
'coth': 33,
'conjugate': 40,
're': 41,
'im': 42,
'arg': 43,
}
name = cls.__name__
try:
i = funcs[name]
except KeyError:
i = 0 if isinstance(cls.nargs, Naturals0) else 10000
return 4, i, name
@property
def is_commutative(self):
"""
Returns whether the functon is commutative.
"""
if all(getattr(t, 'is_commutative') for t in self.args):
return True
else:
return False
def _eval_evalf(self, prec):
# Lookup mpmath function based on name
fname = self.func.__name__
try:
if not hasattr(mpmath, fname):
from sympy.utilities.lambdify import MPMATH_TRANSLATIONS
fname = MPMATH_TRANSLATIONS[fname]
func = getattr(mpmath, fname)
except (AttributeError, KeyError):
try:
return Float(self._imp_(*[i.evalf(prec) for i in self.args]), prec)
except (AttributeError, TypeError, ValueError):
return
# Convert all args to mpf or mpc
# Convert the arguments to *higher* precision than requested for the
# final result.
# XXX + 5 is a guess, it is similar to what is used in evalf.py. Should
# we be more intelligent about it?
try:
args = [arg._to_mpmath(prec + 5) for arg in self.args]
def bad(m):
from mpmath import mpf, mpc
# the precision of an mpf value is the last element
# if that is 1 (and m[1] is not 1 which would indicate a
# power of 2), then the eval failed; so check that none of
# the arguments failed to compute to a finite precision.
# Note: An mpc value has two parts, the re and imag tuple;
# check each of those parts, too. Anything else is allowed to
# pass
if isinstance(m, mpf):
m = m._mpf_
return m[1] !=1 and m[-1] == 1
elif isinstance(m, mpc):
m, n = m._mpc_
return m[1] !=1 and m[-1] == 1 and \
n[1] !=1 and n[-1] == 1
else:
return False
if any(bad(a) for a in args):
raise ValueError # one or more args failed to compute with significance
except ValueError:
return
with mpmath.workprec(prec):
v = func(*args)
return Expr._from_mpmath(v, prec)
def _eval_derivative(self, s):
# f(x).diff(s) -> x.diff(s) * f.fdiff(1)(s)
i = 0
l = []
for a in self.args:
i += 1
da = a.diff(s)
if da is S.Zero:
continue
try:
df = self.fdiff(i)
except ArgumentIndexError:
df = Function.fdiff(self, i)
l.append(df * da)
return Add(*l)
def _eval_is_commutative(self):
return fuzzy_and(a.is_commutative for a in self.args)
def _eval_is_complex(self):
return fuzzy_and(a.is_complex for a in self.args)
def as_base_exp(self):
"""
Returns the method as the 2-tuple (base, exponent).
"""
return self, S.One
def _eval_aseries(self, n, args0, x, logx):
"""
Compute an asymptotic expansion around args0, in terms of self.args.
This function is only used internally by _eval_nseries and should not
be called directly; derived classes can overwrite this to implement
asymptotic expansions.
"""
from sympy.utilities.misc import filldedent
raise PoleError(filldedent('''
Asymptotic expansion of %s around %s is
not implemented.''' % (type(self), args0)))
def _eval_nseries(self, x, n, logx):
"""
This function does compute series for multivariate functions,
but the expansion is always in terms of *one* variable.
Examples
========
>>> from sympy import atan2
>>> from sympy.abc import x, y
>>> atan2(x, y).series(x, n=2)
atan2(0, y) + x/y + O(x**2)
>>> atan2(x, y).series(y, n=2)
-y/x + atan2(x, 0) + O(y**2)
This function also computes asymptotic expansions, if necessary
and possible:
>>> from sympy import loggamma
>>> loggamma(1/x)._eval_nseries(x,0,None)
-1/x - log(x)/x + log(x)/2 + O(1)
"""
from sympy import Order
from sympy.sets.sets import FiniteSet
args = self.args
args0 = [t.limit(x, 0) for t in args]
if any(t.is_finite is False for t in args0):
from sympy import oo, zoo, nan
# XXX could use t.as_leading_term(x) here but it's a little
# slower
a = [t.compute_leading_term(x, logx=logx) for t in args]
a0 = [t.limit(x, 0) for t in a]
if any([t.has(oo, -oo, zoo, nan) for t in a0]):
return self._eval_aseries(n, args0, x, logx)
# Careful: the argument goes to oo, but only logarithmically so. We
# are supposed to do a power series expansion "around the
# logarithmic term". e.g.
# f(1+x+log(x))
# -> f(1+logx) + x*f'(1+logx) + O(x**2)
# where 'logx' is given in the argument
a = [t._eval_nseries(x, n, logx) for t in args]
z = [r - r0 for (r, r0) in zip(a, a0)]
p = [Dummy() for t in z]
q = []
v = None
for ai, zi, pi in zip(a0, z, p):
if zi.has(x):
if v is not None:
raise NotImplementedError
q.append(ai + pi)
v = pi
else:
q.append(ai)
e1 = self.func(*q)
if v is None:
return e1
s = e1._eval_nseries(v, n, logx)
o = s.getO()
s = s.removeO()
s = s.subs(v, zi).expand() + Order(o.expr.subs(v, zi), x)
return s
if (self.func.nargs is S.Naturals0
or (self.func.nargs == FiniteSet(1) and args0[0])
or any(c > 1 for c in self.func.nargs)):
e = self
e1 = e.expand()
if e == e1:
#for example when e = sin(x+1) or e = sin(cos(x))
#let's try the general algorithm
term = e.subs(x, S.Zero)
if term.is_finite is False or term is S.NaN:
raise PoleError("Cannot expand %s around 0" % (self))
series = term
fact = S.One
_x = Dummy('x')
e = e.subs(x, _x)
for i in range(n - 1):
i += 1
fact *= Rational(i)
e = e.diff(_x)
subs = e.subs(_x, S.Zero)
if subs is S.NaN:
# try to evaluate a limit if we have to
subs = e.limit(_x, S.Zero)
if subs.is_finite is False:
raise PoleError("Cannot expand %s around 0" % (self))
term = subs*(x**i)/fact
term = term.expand()
series += term
return series + Order(x**n, x)
return e1.nseries(x, n=n, logx=logx)
arg = self.args[0]
l = []
g = None
# try to predict a number of terms needed
nterms = n + 2
cf = Order(arg.as_leading_term(x), x).getn()
if cf != 0:
nterms = int(nterms / cf)
for i in range(nterms):
g = self.taylor_term(i, arg, g)
g = g.nseries(x, n=n, logx=logx)
l.append(g)
return Add(*l) + Order(x**n, x)
def fdiff(self, argindex=1):
"""
Returns the first derivative of the function.
"""
if not (1 <= argindex <= len(self.args)):
raise ArgumentIndexError(self, argindex)
if self.args[argindex - 1].is_Symbol:
for i in range(len(self.args)):
if i == argindex - 1:
continue
# See issue 8510
if self.args[argindex - 1] in self.args[i].free_symbols:
break
else:
return Derivative(self, self.args[argindex - 1], evaluate=False)
# See issue 4624 and issue 4719 and issue 5600
arg_dummy = Dummy('xi_%i' % argindex, dummy_index=hash(self.args[argindex - 1]))
new_args = [arg for arg in self.args]
new_args[argindex-1] = arg_dummy
return Subs(Derivative(self.func(*new_args), arg_dummy),
arg_dummy, self.args[argindex - 1])
def _eval_as_leading_term(self, x):
"""Stub that should be overridden by new Functions to return
the first non-zero term in a series if ever an x-dependent
argument whose leading term vanishes as x -> 0 might be encountered.
See, for example, cos._eval_as_leading_term.
"""
from sympy import Order
args = [a.as_leading_term(x) for a in self.args]
o = Order(1, x)
if any(x in a.free_symbols and o.contains(a) for a in args):
# Whereas x and any finite number are contained in O(1, x),
# expressions like 1/x are not. If any arg simplified to a
# vanishing expression as x -> 0 (like x or x**2, but not
# 3, 1/x, etc...) then the _eval_as_leading_term is needed
# to supply the first non-zero term of the series,
#
# e.g. expression leading term
# ---------- ------------
# cos(1/x) cos(1/x)
# cos(cos(x)) cos(1)
# cos(x) 1 <- _eval_as_leading_term needed
# sin(x) x <- _eval_as_leading_term needed
#
raise NotImplementedError(
'%s has no _eval_as_leading_term routine' % self.func)
else:
return self.func(*args)
def _sage_(self):
import sage.all as sage
fname = self.func.__name__
func = getattr(sage, fname)
args = [arg._sage_() for arg in self.args]
return func(*args)
class AppliedUndef(Function):
"""
Base class for expressions resulting from the application of an undefined
function.
"""
def __new__(cls, *args, **options):
args = list(map(sympify, args))
obj = super(AppliedUndef, cls).__new__(cls, *args, **options)
return obj
def _eval_as_leading_term(self, x):
return self
def _sage_(self):
import sage.all as sage
fname = str(self.func)
args = [arg._sage_() for arg in self.args]
func = sage.function(fname)(*args)
return func
class UndefinedFunction(FunctionClass):
"""
The (meta)class of undefined functions.
"""
def __new__(mcl, name, bases=(AppliedUndef,), __dict__=None, **kwargs):
__dict__ = __dict__ or {}
__dict__.update(kwargs)
__dict__['__module__'] = None # For pickling
ret = super(UndefinedFunction, mcl).__new__(mcl, name, bases, __dict__)
return ret
def __instancecheck__(cls, instance):
return cls in type(instance).__mro__
UndefinedFunction.__eq__ = lambda s, o: (isinstance(o, s.__class__) and
(s.class_key() == o.class_key()))
class WildFunction(Function, AtomicExpr):
"""
A WildFunction function matches any function (with its arguments).
Examples
========
>>> from sympy import WildFunction, Function, cos
>>> from sympy.abc import x, y
>>> F = WildFunction('F')
>>> f = Function('f')
>>> F.nargs
S.Naturals0
>>> x.match(F)
>>> F.match(F)
{F_: F_}
>>> f(x).match(F)
{F_: f(x)}
>>> cos(x).match(F)
{F_: cos(x)}
>>> f(x, y).match(F)
{F_: f(x, y)}
To match functions with a given number of arguments, set ``nargs`` to the
desired value at instantiation:
>>> F = WildFunction('F', nargs=2)
>>> F.nargs
{2}
>>> f(x).match(F)
>>> f(x, y).match(F)
{F_: f(x, y)}
To match functions with a range of arguments, set ``nargs`` to a tuple
containing the desired number of arguments, e.g. if ``nargs = (1, 2)``
then functions with 1 or 2 arguments will be matched.
>>> F = WildFunction('F', nargs=(1, 2))
>>> F.nargs
{1, 2}
>>> f(x).match(F)
{F_: f(x)}
>>> f(x, y).match(F)
{F_: f(x, y)}
>>> f(x, y, 1).match(F)
"""
include = set()
def __init__(cls, name, **assumptions):
from sympy.sets.sets import Set, FiniteSet
cls.name = name
nargs = assumptions.pop('nargs', S.Naturals0)
if not isinstance(nargs, Set):
# Canonicalize nargs here. See also FunctionClass.
if is_sequence(nargs):
nargs = tuple(ordered(set(nargs)))
elif nargs is not None:
nargs = (as_int(nargs),)
nargs = FiniteSet(*nargs)
cls.nargs = nargs
def matches(self, expr, repl_dict={}, old=False):
if not isinstance(expr, (AppliedUndef, Function)):
return None
if len(expr.args) not in self.nargs:
return None
repl_dict = repl_dict.copy()
repl_dict[self] = expr
return repl_dict
class Derivative(Expr):
"""
Carries out differentiation of the given expression with respect to symbols.
expr must define ._eval_derivative(symbol) method that returns
the differentiation result. This function only needs to consider the
non-trivial case where expr contains symbol and it should call the diff()
method internally (not _eval_derivative); Derivative should be the only
one to call _eval_derivative.
Simplification of high-order derivatives:
Because there can be a significant amount of simplification that can be
done when multiple differentiations are performed, results will be
automatically simplified in a fairly conservative fashion unless the
keyword ``simplify`` is set to False.
>>> from sympy import sqrt, diff
>>> from sympy.abc import x
>>> e = sqrt((x + 1)**2 + x)
>>> diff(e, x, 5, simplify=False).count_ops()
136
>>> diff(e, x, 5).count_ops()
30
Ordering of variables:
If evaluate is set to True and the expression can not be evaluated, the
list of differentiation symbols will be sorted, that is, the expression is
assumed to have continuous derivatives up to the order asked. This sorting
assumes that derivatives wrt Symbols commute, derivatives wrt non-Symbols
commute, but Symbol and non-Symbol derivatives don't commute with each
other.
Derivative wrt non-Symbols:
This class also allows derivatives wrt non-Symbols that have _diff_wrt
set to True, such as Function and Derivative. When a derivative wrt a non-
Symbol is attempted, the non-Symbol is temporarily converted to a Symbol
while the differentiation is performed.
Note that this may seem strange, that Derivative allows things like
f(g(x)).diff(g(x)), or even f(cos(x)).diff(cos(x)). The motivation for
allowing this syntax is to make it easier to work with variational calculus
(i.e., the Euler-Lagrange method). The best way to understand this is that
the action of derivative with respect to a non-Symbol is defined by the
above description: the object is substituted for a Symbol and the
derivative is taken with respect to that. This action is only allowed for
objects for which this can be done unambiguously, for example Function and
Derivative objects. Note that this leads to what may appear to be
mathematically inconsistent results. For example::
>>> from sympy import cos, sin, sqrt
>>> from sympy.abc import x
>>> (2*cos(x)).diff(cos(x))
2
>>> (2*sqrt(1 - sin(x)**2)).diff(cos(x))
0
This appears wrong because in fact 2*cos(x) and 2*sqrt(1 - sin(x)**2) are
identically equal. However this is the wrong way to think of this. Think
of it instead as if we have something like this::
>>> from sympy.abc import c, s
>>> def F(u):
... return 2*u
...
>>> def G(u):
... return 2*sqrt(1 - u**2)
...
>>> F(cos(x))
2*cos(x)
>>> G(sin(x))
2*sqrt(-sin(x)**2 + 1)
>>> F(c).diff(c)
2
>>> F(c).diff(c)
2
>>> G(s).diff(c)
0
>>> G(sin(x)).diff(cos(x))
0
Here, the Symbols c and s act just like the functions cos(x) and sin(x),
respectively. Think of 2*cos(x) as f(c).subs(c, cos(x)) (or f(c) *at*
c = cos(x)) and 2*sqrt(1 - sin(x)**2) as g(s).subs(s, sin(x)) (or g(s) *at*
s = sin(x)), where f(u) == 2*u and g(u) == 2*sqrt(1 - u**2). Here, we
define the function first and evaluate it at the function, but we can
actually unambiguously do this in reverse in SymPy, because
expr.subs(Function, Symbol) is well-defined: just structurally replace the
function everywhere it appears in the expression.
This is the same notational convenience used in the Euler-Lagrange method
when one says F(t, f(t), f'(t)).diff(f(t)). What is actually meant is
that the expression in question is represented by some F(t, u, v) at u =
f(t) and v = f'(t), and F(t, f(t), f'(t)).diff(f(t)) simply means F(t, u,
v).diff(u) at u = f(t).
We do not allow derivatives to be taken with respect to expressions where this
is not so well defined. For example, we do not allow expr.diff(x*y)
because there are multiple ways of structurally defining where x*y appears
in an expression, some of which may surprise the reader (for example, a
very strict definition would have that (x*y*z).diff(x*y) == 0).
>>> from sympy.abc import x, y, z
>>> (x*y*z).diff(x*y)
Traceback (most recent call last):
...
ValueError: Can't differentiate wrt the variable: x*y, 1
Note that this definition also fits in nicely with the definition of the
chain rule. Note how the chain rule in SymPy is defined using unevaluated
Subs objects::
>>> from sympy import symbols, Function
>>> f, g = symbols('f g', cls=Function)
>>> f(2*g(x)).diff(x)
2*Derivative(g(x), x)*Subs(Derivative(f(_xi_1), _xi_1),
(_xi_1,), (2*g(x),))
>>> f(g(x)).diff(x)
Derivative(g(x), x)*Subs(Derivative(f(_xi_1), _xi_1),
(_xi_1,), (g(x),))
Finally, note that, to be consistent with variational calculus, and to
ensure that the definition of substituting a Function for a Symbol in an
expression is well-defined, derivatives of functions are assumed to not be
related to the function. In other words, we have::
>>> from sympy import diff
>>> diff(f(x), x).diff(f(x))
0
The same is true for derivatives of different orders::
>>> diff(f(x), x, 2).diff(diff(f(x), x, 1))
0
>>> diff(f(x), x, 1).diff(diff(f(x), x, 2))
0
Note, any class can allow derivatives to be taken with respect to itself.
See the docstring of Expr._diff_wrt.
Examples
========
Some basic examples:
>>> from sympy import Derivative, Symbol, Function
>>> f = Function('f')
>>> g = Function('g')
>>> x = Symbol('x')
>>> y = Symbol('y')
>>> Derivative(x**2, x, evaluate=True)
2*x
>>> Derivative(Derivative(f(x,y), x), y)
Derivative(f(x, y), x, y)
>>> Derivative(f(x), x, 3)
Derivative(f(x), x, x, x)
>>> Derivative(f(x, y), y, x, evaluate=True)
Derivative(f(x, y), x, y)
Now some derivatives wrt functions:
>>> Derivative(f(x)**2, f(x), evaluate=True)
2*f(x)
>>> Derivative(f(g(x)), x, evaluate=True)
Derivative(g(x), x)*Subs(Derivative(f(_xi_1), _xi_1),
(_xi_1,), (g(x),))
"""
is_Derivative = True
@property
def _diff_wrt(self):
"""Allow derivatives wrt Derivatives if it contains a function.
Examples
========
>>> from sympy import Function, Symbol, Derivative
>>> f = Function('f')
>>> x = Symbol('x')
>>> Derivative(f(x),x)._diff_wrt
True
>>> Derivative(x**2,x)._diff_wrt
False
"""
if self.expr.is_Function:
return True
else:
return False
def __new__(cls, expr, *variables, **assumptions):
expr = sympify(expr)
# There are no variables, we differentiate wrt all of the free symbols
# in expr.
if not variables:
variables = expr.free_symbols
if len(variables) != 1:
if expr.is_number:
return S.Zero
from sympy.utilities.misc import filldedent
if len(variables) == 0:
raise ValueError(filldedent('''
Since there are no variables in the expression,
the variable(s) of differentiation must be supplied
to differentiate %s''' % expr))
else:
raise ValueError(filldedent('''
Since there is more than one variable in the
expression, the variable(s) of differentiation
must be supplied to differentiate %s''' % expr))
# Standardize the variables by sympifying them and making appending a
# count of 1 if there is only one variable: diff(e,x)->diff(e,x,1).
variables = list(sympify(variables))
if not variables[-1].is_Integer or len(variables) == 1:
variables.append(S.One)
# Split the list of variables into a list of the variables we are diff
# wrt, where each element of the list has the form (s, count) where
# s is the entity to diff wrt and count is the order of the
# derivative.
variable_count = []
all_zero = True
i = 0
while i < len(variables) - 1: # process up to final Integer
v, count = variables[i: i + 2]
iwas = i
if v._diff_wrt:
# We need to test the more specific case of count being an
# Integer first.
if count.is_Integer:
count = int(count)
i += 2
elif count._diff_wrt:
count = 1
i += 1
if i == iwas: # didn't get an update because of bad input
from sympy.utilities.misc import filldedent
last_digit = int(str(count)[-1])
ordinal = 'st' if last_digit == 1 else 'nd' if last_digit == 2 else 'rd' if last_digit == 3 else 'th'
raise ValueError(filldedent('''
Can\'t calculate %s%s derivative wrt %s.''' % (count, ordinal, v)))
if all_zero and not count == 0:
all_zero = False
if count:
variable_count.append((v, count))
# We make a special case for 0th derivative, because there is no
# good way to unambiguously print this.
if all_zero:
return expr
# Pop evaluate because it is not really an assumption and we will need
# to track it carefully below.
evaluate = assumptions.pop('evaluate', False)
# Look for a quick exit if there are symbols that don't appear in
# expression at all. Note, this cannnot check non-symbols like
# functions and Derivatives as those can be created by intermediate
# derivatives.
if evaluate and all(isinstance(sc[0], Symbol) for sc in variable_count):
symbol_set = set(sc[0] for sc in variable_count)
if symbol_set.difference(expr.free_symbols):
return S.Zero
# We make a generator so as to only generate a variable when necessary.
# If a high order of derivative is requested and the expr becomes 0
# after a few differentiations, then we won't need the other variables.
variablegen = (v for v, count in variable_count for i in range(count))
# If we can't compute the derivative of expr (but we wanted to) and
# expr is itself not a Derivative, finish building an unevaluated
# derivative class by calling Expr.__new__.
if (not (hasattr(expr, '_eval_derivative') and evaluate) and
(not isinstance(expr, Derivative))):
variables = list(variablegen)
# If we wanted to evaluate, we sort the variables into standard
# order for later comparisons. This is too aggressive if evaluate
# is False, so we don't do it in that case.
if evaluate:
#TODO: check if assumption of discontinuous derivatives exist
variables = cls._sort_variables(variables)
# Here we *don't* need to reinject evaluate into assumptions
# because we are done with it and it is not an assumption that
# Expr knows about.
obj = Expr.__new__(cls, expr, *variables, **assumptions)
return obj
# Compute the derivative now by repeatedly calling the
# _eval_derivative method of expr for each variable. When this method
# returns None, the derivative couldn't be computed wrt that variable
# and we save the variable for later.
unhandled_variables = []
# Once we encouter a non_symbol that is unhandled, we stop taking
# derivatives entirely. This is because derivatives wrt functions
# don't commute with derivatives wrt symbols and we can't safely
# continue.
unhandled_non_symbol = False
nderivs = 0 # how many derivatives were performed
for v in variablegen:
is_symbol = v.is_symbol
if unhandled_non_symbol:
obj = None
else:
if not is_symbol:
new_v = Dummy('xi_%i' % i, dummy_index=hash(v))
expr = expr.xreplace({v: new_v})
old_v = v
v = new_v
obj = expr._eval_derivative(v)
nderivs += 1
if not is_symbol:
if obj is not None:
if not old_v.is_symbol and obj.is_Derivative:
# Derivative evaluated at a point that is not a
# symbol
obj = Subs(obj, v, old_v)
else:
obj = obj.xreplace({v: old_v})
v = old_v
if obj is None:
unhandled_variables.append(v)
if not is_symbol:
unhandled_non_symbol = True
elif obj is S.Zero:
return S.Zero
else:
expr = obj
if unhandled_variables:
unhandled_variables = cls._sort_variables(unhandled_variables)
expr = Expr.__new__(cls, expr, *unhandled_variables, **assumptions)
else:
# We got a Derivative at the end of it all, and we rebuild it by
# sorting its variables.
if isinstance(expr, Derivative):
expr = cls(
expr.args[0], *cls._sort_variables(expr.args[1:])
)
if nderivs > 1 and assumptions.get('simplify', True):
from sympy.core.exprtools import factor_terms
from sympy.simplify.simplify import signsimp
expr = factor_terms(signsimp(expr))
return expr
@classmethod
def _sort_variables(cls, vars):
"""Sort variables, but disallow sorting of non-symbols.
When taking derivatives, the following rules usually hold:
* Derivative wrt different symbols commute.
* Derivative wrt different non-symbols commute.
* Derivatives wrt symbols and non-symbols don't commute.
Examples
========
>>> from sympy import Derivative, Function, symbols
>>> vsort = Derivative._sort_variables
>>> x, y, z = symbols('x y z')
>>> f, g, h = symbols('f g h', cls=Function)
>>> vsort((x,y,z))
[x, y, z]
>>> vsort((h(x),g(x),f(x)))
[f(x), g(x), h(x)]
>>> vsort((z,y,x,h(x),g(x),f(x)))
[x, y, z, f(x), g(x), h(x)]
>>> vsort((x,f(x),y,f(y)))
[x, f(x), y, f(y)]
>>> vsort((y,x,g(x),f(x),z,h(x),y,x))
[x, y, f(x), g(x), z, h(x), x, y]
>>> vsort((z,y,f(x),x,f(x),g(x)))
[y, z, f(x), x, f(x), g(x)]
>>> vsort((z,y,f(x),x,f(x),g(x),z,z,y,x))
[y, z, f(x), x, f(x), g(x), x, y, z, z]
"""
sorted_vars = []
symbol_part = []
non_symbol_part = []
for v in vars:
if not v.is_symbol:
if len(symbol_part) > 0:
sorted_vars.extend(sorted(symbol_part,
key=default_sort_key))
symbol_part = []
non_symbol_part.append(v)
else:
if len(non_symbol_part) > 0:
sorted_vars.extend(sorted(non_symbol_part,
key=default_sort_key))
non_symbol_part = []
symbol_part.append(v)
if len(non_symbol_part) > 0:
sorted_vars.extend(sorted(non_symbol_part,
key=default_sort_key))
if len(symbol_part) > 0:
sorted_vars.extend(sorted(symbol_part,
key=default_sort_key))
return sorted_vars
def _eval_is_commutative(self):
return self.expr.is_commutative
def _eval_derivative(self, v):
# If the variable s we are diff wrt is not in self.variables, we
# assume that we might be able to take the derivative.
if v not in self.variables:
obj = self.expr.diff(v)
if obj is S.Zero:
return S.Zero
if isinstance(obj, Derivative):
return obj.func(obj.expr, *(self.variables + obj.variables))
# The derivative wrt s could have simplified things such that the
# derivative wrt things in self.variables can now be done. Thus,
# we set evaluate=True to see if there are any other derivatives
# that can be done. The most common case is when obj is a simple
# number so that the derivative wrt anything else will vanish.
return self.func(obj, *self.variables, evaluate=True)
# In this case s was in self.variables so the derivatve wrt s has
# already been attempted and was not computed, either because it
# couldn't be or evaluate=False originally.
return self.func(self.expr, *(self.variables + (v, )), evaluate=False)
def doit(self, **hints):
expr = self.expr
if hints.get('deep', True):
expr = expr.doit(**hints)
hints['evaluate'] = True
return self.func(expr, *self.variables, **hints)
@_sympifyit('z0', NotImplementedError)
def doit_numerically(self, z0):
"""
Evaluate the derivative at z numerically.
When we can represent derivatives at a point, this should be folded
into the normal evalf. For now, we need a special method.
"""
import mpmath
from sympy.core.expr import Expr
if len(self.free_symbols) != 1 or len(self.variables) != 1:
raise NotImplementedError('partials and higher order derivatives')
z = list(self.free_symbols)[0]
def eval(x):
f0 = self.expr.subs(z, Expr._from_mpmath(x, prec=mpmath.mp.prec))
f0 = f0.evalf(mlib.libmpf.prec_to_dps(mpmath.mp.prec))
return f0._to_mpmath(mpmath.mp.prec)
return Expr._from_mpmath(mpmath.diff(eval,
z0._to_mpmath(mpmath.mp.prec)),
mpmath.mp.prec)
@property
def expr(self):
return self._args[0]
@property
def variables(self):
return self._args[1:]
@property
def free_symbols(self):
return self.expr.free_symbols
def _eval_subs(self, old, new):
if old in self.variables and not new._diff_wrt:
# issue 4719
return Subs(self, old, new)
# If both are Derivatives with the same expr, check if old is
# equivalent to self or if old is a subderivative of self.
if old.is_Derivative and old.expr == self.expr:
# Check if canonnical order of variables is equal.
old_vars = collections.Counter(old.variables)
self_vars = collections.Counter(self.variables)
if old_vars == self_vars:
return new
# collections.Counter doesn't have __le__
def _subset(a, b):
return all(a[i] <= b[i] for i in a)
if _subset(old_vars, self_vars):
return Derivative(new, *(self_vars - old_vars).elements())
return Derivative(*(x._subs(old, new) for x in self.args))
def _eval_lseries(self, x, logx):
dx = self.variables
for term in self.expr.lseries(x, logx=logx):
yield self.func(term, *dx)
def _eval_nseries(self, x, n, logx):
arg = self.expr.nseries(x, n=n, logx=logx)
o = arg.getO()
dx = self.variables
rv = [self.func(a, *dx) for a in Add.make_args(arg.removeO())]
if o:
rv.append(o/x)
return Add(*rv)
def _eval_as_leading_term(self, x):
series_gen = self.expr.lseries(x)
d = S.Zero
for leading_term in series_gen:
d = diff(leading_term, *self.variables)
if d != 0:
break
return d
def _sage_(self):
import sage.all as sage
args = [arg._sage_() for arg in self.args]
return sage.derivative(*args)
def as_finite_difference(self, points=1, x0=None, wrt=None):
""" Expresses a Derivative instance as a finite difference.
Parameters
==========
points : sequence or coefficient, optional
If sequence: discrete values (length >= order+1) of the
independent variable used for generating the finite
difference weights.
If it is a coefficient, it will be used as the step-size
for generating an equidistant sequence of length order+1
centered around ``x0``. Default: 1 (step-size 1)
x0 : number or Symbol, optional
the value of the independent variable (``wrt``) at which the
derivative is to be approximated. Default: same as ``wrt``.
wrt : Symbol, optional
"with respect to" the variable for which the (partial)
derivative is to be approximated for. If not provided it
is required that the derivative is ordinary. Default: ``None``.
Examples
========
>>> from sympy import symbols, Function, exp, sqrt, Symbol
>>> x, h = symbols('x h')
>>> f = Function('f')
>>> f(x).diff(x).as_finite_difference()
-f(x - 1/2) + f(x + 1/2)
The default step size and number of points are 1 and
``order + 1`` respectively. We can change the step size by
passing a symbol as a parameter:
>>> f(x).diff(x).as_finite_difference(h)
-f(-h/2 + x)/h + f(h/2 + x)/h
We can also specify the discretized values to be used in a
sequence:
>>> f(x).diff(x).as_finite_difference([x, x+h, x+2*h])
-3*f(x)/(2*h) + 2*f(h + x)/h - f(2*h + x)/(2*h)
The algorithm is not restricted to use equidistant spacing, nor
do we need to make the approximation around ``x0``, but we can get
an expression estimating the derivative at an offset:
>>> e, sq2 = exp(1), sqrt(2)
>>> xl = [x-h, x+h, x+e*h]
>>> f(x).diff(x, 1).as_finite_difference(xl, x+h*sq2) # doctest: +ELLIPSIS
2*h*((h + sqrt(2)*h)/(2*h) - (-sqrt(2)*h + h)/(2*h))*f(E*h + x)/...
Partial derivatives are also supported:
>>> y = Symbol('y')
>>> d2fdxdy=f(x,y).diff(x,y)
>>> d2fdxdy.as_finite_difference(wrt=x)
-Derivative(f(x - 1/2, y), y) + Derivative(f(x + 1/2, y), y)
We can apply ``as_finite_difference`` to ``Derivative`` instances in
compound expressions using ``replace``:
>>> (1 + 42**f(x).diff(x)).replace(lambda arg: arg.is_Derivative,
... lambda arg: arg.as_finite_difference())
42**(-f(x - 1/2) + f(x + 1/2)) + 1
See also
========
sympy.calculus.finite_diff.apply_finite_diff
sympy.calculus.finite_diff.differentiate_finite
sympy.calculus.finite_diff.finite_diff_weights
"""
from ..calculus.finite_diff import _as_finite_diff
return _as_finite_diff(self, points, x0, wrt)
class Lambda(Expr):
"""
Lambda(x, expr) represents a lambda function similar to Python's
'lambda x: expr'. A function of several variables is written as
Lambda((x, y, ...), expr).
A simple example:
>>> from sympy import Lambda
>>> from sympy.abc import x
>>> f = Lambda(x, x**2)
>>> f(4)
16
For multivariate functions, use:
>>> from sympy.abc import y, z, t
>>> f2 = Lambda((x, y, z, t), x + y**z + t**z)
>>> f2(1, 2, 3, 4)
73
A handy shortcut for lots of arguments:
>>> p = x, y, z
>>> f = Lambda(p, x + y*z)
>>> f(*p)
x + y*z
"""
is_Function = True
def __new__(cls, variables, expr):
from sympy.sets.sets import FiniteSet
v = list(variables) if iterable(variables) else [variables]
for i in v:
if not getattr(i, 'is_Symbol', False):
raise TypeError('variable is not a symbol: %s' % i)
if len(v) == 1 and v[0] == expr:
return S.IdentityFunction
obj = Expr.__new__(cls, Tuple(*v), sympify(expr))
obj.nargs = FiniteSet(len(v))
return obj
@property
def variables(self):
"""The variables used in the internal representation of the function"""
return self._args[0]
@property
def expr(self):
"""The return value of the function"""
return self._args[1]
@property
def free_symbols(self):
return self.expr.free_symbols - set(self.variables)
def __call__(self, *args):
n = len(args)
if n not in self.nargs: # Lambda only ever has 1 value in nargs
# XXX: exception message must be in exactly this format to
# make it work with NumPy's functions like vectorize(). See,
# for example, https://github.com/numpy/numpy/issues/1697.
# The ideal solution would be just to attach metadata to
# the exception and change NumPy to take advantage of this.
## XXX does this apply to Lambda? If not, remove this comment.
temp = ('%(name)s takes exactly %(args)s '
'argument%(plural)s (%(given)s given)')
raise TypeError(temp % {
'name': self,
'args': list(self.nargs)[0],
'plural': 's'*(list(self.nargs)[0] != 1),
'given': n})
return self.expr.xreplace(dict(list(zip(self.variables, args))))
def __eq__(self, other):
if not isinstance(other, Lambda):
return False
if self.nargs != other.nargs:
return False
selfexpr = self.args[1]
otherexpr = other.args[1]
otherexpr = otherexpr.xreplace(dict(list(zip(other.args[0], self.args[0]))))
return selfexpr == otherexpr
def __ne__(self, other):
return not(self == other)
def __hash__(self):
return super(Lambda, self).__hash__()
def _hashable_content(self):
return (self.expr.xreplace(self.canonical_variables),)
@property
def is_identity(self):
"""Return ``True`` if this ``Lambda`` is an identity function. """
if len(self.args) == 2:
return self.args[0] == self.args[1]
else:
return None
class Subs(Expr):
"""
Represents unevaluated substitutions of an expression.
``Subs(expr, x, x0)`` receives 3 arguments: an expression, a variable or
list of distinct variables and a point or list of evaluation points
corresponding to those variables.
``Subs`` objects are generally useful to represent unevaluated derivatives
calculated at a point.
The variables may be expressions, but they are subjected to the limitations
of subs(), so it is usually a good practice to use only symbols for
variables, since in that case there can be no ambiguity.
There's no automatic expansion - use the method .doit() to effect all
possible substitutions of the object and also of objects inside the
expression.
When evaluating derivatives at a point that is not a symbol, a Subs object
is returned. One is also able to calculate derivatives of Subs objects - in
this case the expression is always expanded (for the unevaluated form, use
Derivative()).
A simple example:
>>> from sympy import Subs, Function, sin
>>> from sympy.abc import x, y, z
>>> f = Function('f')
>>> e = Subs(f(x).diff(x), x, y)
>>> e.subs(y, 0)
Subs(Derivative(f(x), x), (x,), (0,))
>>> e.subs(f, sin).doit()
cos(y)
An example with several variables:
>>> Subs(f(x)*sin(y) + z, (x, y), (0, 1))
Subs(z + f(x)*sin(y), (x, y), (0, 1))
>>> _.doit()
z + f(0)*sin(1)
"""
def __new__(cls, expr, variables, point, **assumptions):
from sympy import Symbol
if not is_sequence(variables, Tuple):
variables = [variables]
variables = list(sympify(variables))
if list(uniq(variables)) != variables:
repeated = [ v for v in set(variables) if variables.count(v) > 1 ]
raise ValueError('cannot substitute expressions %s more than '
'once.' % repeated)
point = Tuple(*(point if is_sequence(point, Tuple) else [point]))
if len(point) != len(variables):
raise ValueError('Number of point values must be the same as '
'the number of variables.')
expr = sympify(expr)
# use symbols with names equal to the point value (with preppended _)
# to give a variable-independent expression
pre = "_"
pts = sorted(set(point), key=default_sort_key)
from sympy.printing import StrPrinter
class CustomStrPrinter(StrPrinter):
def _print_Dummy(self, expr):
return str(expr) + str(expr.dummy_index)
def mystr(expr, **settings):
p = CustomStrPrinter(settings)
return p.doprint(expr)
while 1:
s_pts = {p: Symbol(pre + mystr(p)) for p in pts}
reps = [(v, s_pts[p])
for v, p in zip(variables, point)]
# if any underscore-preppended symbol is already a free symbol
# and is a variable with a different point value, then there
# is a clash, e.g. _0 clashes in Subs(_0 + _1, (_0, _1), (1, 0))
# because the new symbol that would be created is _1 but _1
# is already mapped to 0 so __0 and __1 are used for the new
# symbols
if any(r in expr.free_symbols and
r in variables and
Symbol(pre + mystr(point[variables.index(r)])) != r
for _, r in reps):
pre += "_"
continue
break
obj = Expr.__new__(cls, expr, Tuple(*variables), point)
obj._expr = expr.subs(reps)
return obj
def _eval_is_commutative(self):
return self.expr.is_commutative
def doit(self):
return self.expr.doit().subs(list(zip(self.variables, self.point)))
def evalf(self, prec=None, **options):
return self.doit().evalf(prec, **options)
n = evalf
@property
def variables(self):
"""The variables to be evaluated"""
return self._args[1]
@property
def expr(self):
"""The expression on which the substitution operates"""
return self._args[0]
@property
def point(self):
"""The values for which the variables are to be substituted"""
return self._args[2]
@property
def free_symbols(self):
return (self.expr.free_symbols - set(self.variables) |
set(self.point.free_symbols))
def _has(self, pattern):
if pattern in self.variables and pattern not in self.point:
return False
return super(Subs, self)._has(pattern)
def __eq__(self, other):
if not isinstance(other, Subs):
return False
return self._expr == other._expr
def __ne__(self, other):
return not(self == other)
def __hash__(self):
return super(Subs, self).__hash__()
def _hashable_content(self):
return (self._expr.xreplace(self.canonical_variables),)
def _eval_subs(self, old, new):
if old in self.variables:
if old in self.point:
newpoint = tuple(new if i == old else i for i in self.point)
return self.func(self.expr, self.variables, newpoint)
return self
def _eval_derivative(self, s):
if s not in self.free_symbols:
return S.Zero
return Add((Subs(self.expr.diff(s), self.variables, self.point).doit()
if s not in self.variables else S.Zero),
*[p.diff(s) * Subs(self.expr.diff(v), self.variables,
self.point).doit() for v, p in zip(self.variables, self.point)])
def _eval_nseries(self, x, n, logx):
if x in self.point:
# x is the variable being substituted into
apos = self.point.index(x)
other = self.variables[apos]
arg = self.expr.nseries(other, n=n, logx=logx)
o = arg.getO()
subs_args = [self.func(a, *self.args[1:]) for a in arg.removeO().args]
return Add(*subs_args) + o.subs(other, x)
arg = self.expr.nseries(x, n=n, logx=logx)
o = arg.getO()
subs_args = [self.func(a, *self.args[1:]) for a in arg.removeO().args]
return Add(*subs_args) + o
def _eval_as_leading_term(self, x):
if x in self.point:
ipos = self.point.index(x)
xvar = self.variables[ipos]
return self.expr.as_leading_term(xvar)
if x in self.variables:
# if `x` is a dummy variable, it means it won't exist after the
# substitution has been performed:
return self
# The variable is independent of the substitution:
return self.expr.as_leading_term(x)
def diff(f, *symbols, **kwargs):
"""
Differentiate f with respect to symbols.
This is just a wrapper to unify .diff() and the Derivative class; its
interface is similar to that of integrate(). You can use the same
shortcuts for multiple variables as with Derivative. For example,
diff(f(x), x, x, x) and diff(f(x), x, 3) both return the third derivative
of f(x).
You can pass evaluate=False to get an unevaluated Derivative class. Note
that if there are 0 symbols (such as diff(f(x), x, 0), then the result will
be the function (the zeroth derivative), even if evaluate=False.
Examples
========
>>> from sympy import sin, cos, Function, diff
>>> from sympy.abc import x, y
>>> f = Function('f')
>>> diff(sin(x), x)
cos(x)
>>> diff(f(x), x, x, x)
Derivative(f(x), x, x, x)
>>> diff(f(x), x, 3)
Derivative(f(x), x, x, x)
>>> diff(sin(x)*cos(y), x, 2, y, 2)
sin(x)*cos(y)
>>> type(diff(sin(x), x))
cos
>>> type(diff(sin(x), x, evaluate=False))
<class 'sympy.core.function.Derivative'>
>>> type(diff(sin(x), x, 0))
sin
>>> type(diff(sin(x), x, 0, evaluate=False))
sin
>>> diff(sin(x))
cos(x)
>>> diff(sin(x*y))
Traceback (most recent call last):
...
ValueError: specify differentiation variables to differentiate sin(x*y)
Note that ``diff(sin(x))`` syntax is meant only for convenience
in interactive sessions and should be avoided in library code.
References
==========
http://reference.wolfram.com/legacy/v5_2/Built-inFunctions/AlgebraicComputation/Calculus/D.html
See Also
========
Derivative
sympy.geometry.util.idiff: computes the derivative implicitly
"""
kwargs.setdefault('evaluate', True)
try:
return f._eval_diff(*symbols, **kwargs)
except AttributeError:
pass
return Derivative(f, *symbols, **kwargs)
def expand(e, deep=True, modulus=None, power_base=True, power_exp=True,
mul=True, log=True, multinomial=True, basic=True, **hints):
r"""
Expand an expression using methods given as hints.
Hints evaluated unless explicitly set to False are: ``basic``, ``log``,
``multinomial``, ``mul``, ``power_base``, and ``power_exp`` The following
hints are supported but not applied unless set to True: ``complex``,
``func``, and ``trig``. In addition, the following meta-hints are
supported by some or all of the other hints: ``frac``, ``numer``,
``denom``, ``modulus``, and ``force``. ``deep`` is supported by all
hints. Additionally, subclasses of Expr may define their own hints or
meta-hints.
The ``basic`` hint is used for any special rewriting of an object that
should be done automatically (along with the other hints like ``mul``)
when expand is called. This is a catch-all hint to handle any sort of
expansion that may not be described by the existing hint names. To use
this hint an object should override the ``_eval_expand_basic`` method.
Objects may also define their own expand methods, which are not run by
default. See the API section below.
If ``deep`` is set to ``True`` (the default), things like arguments of
functions are recursively expanded. Use ``deep=False`` to only expand on
the top level.
If the ``force`` hint is used, assumptions about variables will be ignored
in making the expansion.
Hints
=====
These hints are run by default
mul
---
Distributes multiplication over addition:
>>> from sympy import cos, exp, sin
>>> from sympy.abc import x, y, z
>>> (y*(x + z)).expand(mul=True)
x*y + y*z
multinomial
-----------
Expand (x + y + ...)**n where n is a positive integer.
>>> ((x + y + z)**2).expand(multinomial=True)
x**2 + 2*x*y + 2*x*z + y**2 + 2*y*z + z**2
power_exp
---------
Expand addition in exponents into multiplied bases.
>>> exp(x + y).expand(power_exp=True)
exp(x)*exp(y)
>>> (2**(x + y)).expand(power_exp=True)
2**x*2**y
power_base
----------
Split powers of multiplied bases.
This only happens by default if assumptions allow, or if the
``force`` meta-hint is used:
>>> ((x*y)**z).expand(power_base=True)
(x*y)**z
>>> ((x*y)**z).expand(power_base=True, force=True)
x**z*y**z
>>> ((2*y)**z).expand(power_base=True)
2**z*y**z
Note that in some cases where this expansion always holds, SymPy performs
it automatically:
>>> (x*y)**2
x**2*y**2
log
---
Pull out power of an argument as a coefficient and split logs products
into sums of logs.
Note that these only work if the arguments of the log function have the
proper assumptions--the arguments must be positive and the exponents must
be real--or else the ``force`` hint must be True:
>>> from sympy import log, symbols
>>> log(x**2*y).expand(log=True)
log(x**2*y)
>>> log(x**2*y).expand(log=True, force=True)
2*log(x) + log(y)
>>> x, y = symbols('x,y', positive=True)
>>> log(x**2*y).expand(log=True)
2*log(x) + log(y)
basic
-----
This hint is intended primarily as a way for custom subclasses to enable
expansion by default.
These hints are not run by default:
complex
-------
Split an expression into real and imaginary parts.
>>> x, y = symbols('x,y')
>>> (x + y).expand(complex=True)
re(x) + re(y) + I*im(x) + I*im(y)
>>> cos(x).expand(complex=True)
-I*sin(re(x))*sinh(im(x)) + cos(re(x))*cosh(im(x))
Note that this is just a wrapper around ``as_real_imag()``. Most objects
that wish to redefine ``_eval_expand_complex()`` should consider
redefining ``as_real_imag()`` instead.
func
----
Expand other functions.
>>> from sympy import gamma
>>> gamma(x + 1).expand(func=True)
x*gamma(x)
trig
----
Do trigonometric expansions.
>>> cos(x + y).expand(trig=True)
-sin(x)*sin(y) + cos(x)*cos(y)
>>> sin(2*x).expand(trig=True)
2*sin(x)*cos(x)
Note that the forms of ``sin(n*x)`` and ``cos(n*x)`` in terms of ``sin(x)``
and ``cos(x)`` are not unique, due to the identity `\sin^2(x) + \cos^2(x)
= 1`. The current implementation uses the form obtained from Chebyshev
polynomials, but this may change. See `this MathWorld article
<http://mathworld.wolfram.com/Multiple-AngleFormulas.html>`_ for more
information.
Notes
=====
- You can shut off unwanted methods::
>>> (exp(x + y)*(x + y)).expand()
x*exp(x)*exp(y) + y*exp(x)*exp(y)
>>> (exp(x + y)*(x + y)).expand(power_exp=False)
x*exp(x + y) + y*exp(x + y)
>>> (exp(x + y)*(x + y)).expand(mul=False)
(x + y)*exp(x)*exp(y)
- Use deep=False to only expand on the top level::
>>> exp(x + exp(x + y)).expand()
exp(x)*exp(exp(x)*exp(y))
>>> exp(x + exp(x + y)).expand(deep=False)
exp(x)*exp(exp(x + y))
- Hints are applied in an arbitrary, but consistent order (in the current
implementation, they are applied in alphabetical order, except
multinomial comes before mul, but this may change). Because of this,
some hints may prevent expansion by other hints if they are applied
first. For example, ``mul`` may distribute multiplications and prevent
``log`` and ``power_base`` from expanding them. Also, if ``mul`` is
applied before ``multinomial`, the expression might not be fully
distributed. The solution is to use the various ``expand_hint`` helper
functions or to use ``hint=False`` to this function to finely control
which hints are applied. Here are some examples::
>>> from sympy import expand, expand_mul, expand_power_base
>>> x, y, z = symbols('x,y,z', positive=True)
>>> expand(log(x*(y + z)))
log(x) + log(y + z)
Here, we see that ``log`` was applied before ``mul``. To get the mul
expanded form, either of the following will work::
>>> expand_mul(log(x*(y + z)))
log(x*y + x*z)
>>> expand(log(x*(y + z)), log=False)
log(x*y + x*z)
A similar thing can happen with the ``power_base`` hint::
>>> expand((x*(y + z))**x)
(x*y + x*z)**x
To get the ``power_base`` expanded form, either of the following will
work::
>>> expand((x*(y + z))**x, mul=False)
x**x*(y + z)**x
>>> expand_power_base((x*(y + z))**x)
x**x*(y + z)**x
>>> expand((x + y)*y/x)
y + y**2/x
The parts of a rational expression can be targeted::
>>> expand((x + y)*y/x/(x + 1), frac=True)
(x*y + y**2)/(x**2 + x)
>>> expand((x + y)*y/x/(x + 1), numer=True)
(x*y + y**2)/(x*(x + 1))
>>> expand((x + y)*y/x/(x + 1), denom=True)
y*(x + y)/(x**2 + x)
- The ``modulus`` meta-hint can be used to reduce the coefficients of an
expression post-expansion::
>>> expand((3*x + 1)**2)
9*x**2 + 6*x + 1
>>> expand((3*x + 1)**2, modulus=5)
4*x**2 + x + 1
- Either ``expand()`` the function or ``.expand()`` the method can be
used. Both are equivalent::
>>> expand((x + 1)**2)
x**2 + 2*x + 1
>>> ((x + 1)**2).expand()
x**2 + 2*x + 1
API
===
Objects can define their own expand hints by defining
``_eval_expand_hint()``. The function should take the form::
def _eval_expand_hint(self, **hints):
# Only apply the method to the top-level expression
...
See also the example below. Objects should define ``_eval_expand_hint()``
methods only if ``hint`` applies to that specific object. The generic
``_eval_expand_hint()`` method defined in Expr will handle the no-op case.
Each hint should be responsible for expanding that hint only.
Furthermore, the expansion should be applied to the top-level expression
only. ``expand()`` takes care of the recursion that happens when
``deep=True``.
You should only call ``_eval_expand_hint()`` methods directly if you are
100% sure that the object has the method, as otherwise you are liable to
get unexpected ``AttributeError``s. Note, again, that you do not need to
recursively apply the hint to args of your object: this is handled
automatically by ``expand()``. ``_eval_expand_hint()`` should
generally not be used at all outside of an ``_eval_expand_hint()`` method.
If you want to apply a specific expansion from within another method, use
the public ``expand()`` function, method, or ``expand_hint()`` functions.
In order for expand to work, objects must be rebuildable by their args,
i.e., ``obj.func(*obj.args) == obj`` must hold.
Expand methods are passed ``**hints`` so that expand hints may use
'metahints'--hints that control how different expand methods are applied.
For example, the ``force=True`` hint described above that causes
``expand(log=True)`` to ignore assumptions is such a metahint. The
``deep`` meta-hint is handled exclusively by ``expand()`` and is not
passed to ``_eval_expand_hint()`` methods.
Note that expansion hints should generally be methods that perform some
kind of 'expansion'. For hints that simply rewrite an expression, use the
.rewrite() API.
Examples
========
>>> from sympy import Expr, sympify
>>> class MyClass(Expr):
... def __new__(cls, *args):
... args = sympify(args)
... return Expr.__new__(cls, *args)
...
... def _eval_expand_double(self, **hints):
... '''
... Doubles the args of MyClass.
...
... If there more than four args, doubling is not performed,
... unless force=True is also used (False by default).
... '''
... force = hints.pop('force', False)
... if not force and len(self.args) > 4:
... return self
... return self.func(*(self.args + self.args))
...
>>> a = MyClass(1, 2, MyClass(3, 4))
>>> a
MyClass(1, 2, MyClass(3, 4))
>>> a.expand(double=True)
MyClass(1, 2, MyClass(3, 4, 3, 4), 1, 2, MyClass(3, 4, 3, 4))
>>> a.expand(double=True, deep=False)
MyClass(1, 2, MyClass(3, 4), 1, 2, MyClass(3, 4))
>>> b = MyClass(1, 2, 3, 4, 5)
>>> b.expand(double=True)
MyClass(1, 2, 3, 4, 5)
>>> b.expand(double=True, force=True)
MyClass(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)
See Also
========
expand_log, expand_mul, expand_multinomial, expand_complex, expand_trig,
expand_power_base, expand_power_exp, expand_func, hyperexpand
"""
# don't modify this; modify the Expr.expand method
hints['power_base'] = power_base
hints['power_exp'] = power_exp
hints['mul'] = mul
hints['log'] = log
hints['multinomial'] = multinomial
hints['basic'] = basic
return sympify(e).expand(deep=deep, modulus=modulus, **hints)
# This is a special application of two hints
def _mexpand(expr, recursive=False):
# expand multinomials and then expand products; this may not always
# be sufficient to give a fully expanded expression (see
# test_issue_8247_8354 in test_arit)
if expr is None:
return
was = None
while was != expr:
was, expr = expr, expand_mul(expand_multinomial(expr))
if not recursive:
break
return expr
# These are simple wrappers around single hints.
def expand_mul(expr, deep=True):
"""
Wrapper around expand that only uses the mul hint. See the expand
docstring for more information.
Examples
========
>>> from sympy import symbols, expand_mul, exp, log
>>> x, y = symbols('x,y', positive=True)
>>> expand_mul(exp(x+y)*(x+y)*log(x*y**2))
x*exp(x + y)*log(x*y**2) + y*exp(x + y)*log(x*y**2)
"""
return sympify(expr).expand(deep=deep, mul=True, power_exp=False,
power_base=False, basic=False, multinomial=False, log=False)
def expand_multinomial(expr, deep=True):
"""
Wrapper around expand that only uses the multinomial hint. See the expand
docstring for more information.
Examples
========
>>> from sympy import symbols, expand_multinomial, exp
>>> x, y = symbols('x y', positive=True)
>>> expand_multinomial((x + exp(x + 1))**2)
x**2 + 2*x*exp(x + 1) + exp(2*x + 2)
"""
return sympify(expr).expand(deep=deep, mul=False, power_exp=False,
power_base=False, basic=False, multinomial=True, log=False)
def expand_log(expr, deep=True, force=False):
"""
Wrapper around expand that only uses the log hint. See the expand
docstring for more information.
Examples
========
>>> from sympy import symbols, expand_log, exp, log
>>> x, y = symbols('x,y', positive=True)
>>> expand_log(exp(x+y)*(x+y)*log(x*y**2))
(x + y)*(log(x) + 2*log(y))*exp(x + y)
"""
return sympify(expr).expand(deep=deep, log=True, mul=False,
power_exp=False, power_base=False, multinomial=False,
basic=False, force=force)
def expand_func(expr, deep=True):
"""
Wrapper around expand that only uses the func hint. See the expand
docstring for more information.
Examples
========
>>> from sympy import expand_func, gamma
>>> from sympy.abc import x
>>> expand_func(gamma(x + 2))
x*(x + 1)*gamma(x)
"""
return sympify(expr).expand(deep=deep, func=True, basic=False,
log=False, mul=False, power_exp=False, power_base=False, multinomial=False)
def expand_trig(expr, deep=True):
"""
Wrapper around expand that only uses the trig hint. See the expand
docstring for more information.
Examples
========
>>> from sympy import expand_trig, sin
>>> from sympy.abc import x, y
>>> expand_trig(sin(x+y)*(x+y))
(x + y)*(sin(x)*cos(y) + sin(y)*cos(x))
"""
return sympify(expr).expand(deep=deep, trig=True, basic=False,
log=False, mul=False, power_exp=False, power_base=False, multinomial=False)
def expand_complex(expr, deep=True):
"""
Wrapper around expand that only uses the complex hint. See the expand
docstring for more information.
Examples
========
>>> from sympy import expand_complex, exp, sqrt, I
>>> from sympy.abc import z
>>> expand_complex(exp(z))
I*exp(re(z))*sin(im(z)) + exp(re(z))*cos(im(z))
>>> expand_complex(sqrt(I))
sqrt(2)/2 + sqrt(2)*I/2
See Also
========
Expr.as_real_imag
"""
return sympify(expr).expand(deep=deep, complex=True, basic=False,
log=False, mul=False, power_exp=False, power_base=False, multinomial=False)
def expand_power_base(expr, deep=True, force=False):
"""
Wrapper around expand that only uses the power_base hint.
See the expand docstring for more information.
A wrapper to expand(power_base=True) which separates a power with a base
that is a Mul into a product of powers, without performing any other
expansions, provided that assumptions about the power's base and exponent
allow.
deep=False (default is True) will only apply to the top-level expression.
force=True (default is False) will cause the expansion to ignore
assumptions about the base and exponent. When False, the expansion will
only happen if the base is non-negative or the exponent is an integer.
>>> from sympy.abc import x, y, z
>>> from sympy import expand_power_base, sin, cos, exp
>>> (x*y)**2
x**2*y**2
>>> (2*x)**y
(2*x)**y
>>> expand_power_base(_)
2**y*x**y
>>> expand_power_base((x*y)**z)
(x*y)**z
>>> expand_power_base((x*y)**z, force=True)
x**z*y**z
>>> expand_power_base(sin((x*y)**z), deep=False)
sin((x*y)**z)
>>> expand_power_base(sin((x*y)**z), force=True)
sin(x**z*y**z)
>>> expand_power_base((2*sin(x))**y + (2*cos(x))**y)
2**y*sin(x)**y + 2**y*cos(x)**y
>>> expand_power_base((2*exp(y))**x)
2**x*exp(y)**x
>>> expand_power_base((2*cos(x))**y)
2**y*cos(x)**y
Notice that sums are left untouched. If this is not the desired behavior,
apply full ``expand()`` to the expression:
>>> expand_power_base(((x+y)*z)**2)
z**2*(x + y)**2
>>> (((x+y)*z)**2).expand()
x**2*z**2 + 2*x*y*z**2 + y**2*z**2
>>> expand_power_base((2*y)**(1+z))
2**(z + 1)*y**(z + 1)
>>> ((2*y)**(1+z)).expand()
2*2**z*y*y**z
"""
return sympify(expr).expand(deep=deep, log=False, mul=False,
power_exp=False, power_base=True, multinomial=False,
basic=False, force=force)
def expand_power_exp(expr, deep=True):
"""
Wrapper around expand that only uses the power_exp hint.
See the expand docstring for more information.
Examples
========
>>> from sympy import expand_power_exp
>>> from sympy.abc import x, y
>>> expand_power_exp(x**(y + 2))
x**2*x**y
"""
return sympify(expr).expand(deep=deep, complex=False, basic=False,
log=False, mul=False, power_exp=True, power_base=False, multinomial=False)
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)
2*ADD + POW
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)
2*ADD + SIN
"""
from sympy import Integral, Symbol
from sympy.simplify.radsimp import fraction
from sympy.logic.boolalg import BooleanFunction
expr = sympify(expr)
if isinstance(expr, Expr):
ops = []
args = [expr]
NEG = Symbol('NEG')
DIV = Symbol('DIV')
SUB = Symbol('SUB')
ADD = Symbol('ADD')
while args:
a = args.pop()
# XXX: This is a hack to support non-Basic args
if isinstance(a, string_types):
continue
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 _coeff_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 _coeff_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 _coeff_isneg(aargs[0]): # -x + y = SUB, but already recorded 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, Integral)):
o = Symbol(a.func.__name__.upper())
# count the args
if (a.is_Mul or isinstance(a, LatticeOp)):
ops.append(o*(len(a.args) - 1))
else:
ops.append(o)
if not a.is_Symbol:
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.items()]
elif iterable(expr):
ops = [count_ops(i, visual=visual) for i in expr]
elif isinstance(expr, BooleanFunction):
ops = []
for arg in expr.args:
ops.append(count_ops(arg, visual=True))
o = Symbol(expr.func.__name__.upper())
ops.append(o)
elif not isinstance(expr, Basic):
ops = []
else: # it's Basic not isinstance(expr, Expr):
if not isinstance(expr, Basic):
raise TypeError("Invalid type of expr")
else:
ops = []
args = [expr]
while args:
a = args.pop()
# XXX: This is a hack to support non-Basic args
if isinstance(a, string_types):
continue
if a.args:
o = Symbol(a.func.__name__.upper())
if a.is_Boolean:
ops.append(o*(len(a.args)-1))
else:
ops.append(o)
args.extend(a.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))
def nfloat(expr, n=15, exponent=False):
"""Make all Rationals in expr Floats except those in exponents
(unless the exponents flag is set to True).
Examples
========
>>> from sympy.core.function import nfloat
>>> from sympy.abc import x, y
>>> from sympy import cos, pi, sqrt
>>> nfloat(x**4 + x/2 + cos(pi/3) + 1 + sqrt(y))
x**4 + 0.5*x + sqrt(y) + 1.5
>>> nfloat(x**4 + sqrt(y), exponent=True)
x**4.0 + y**0.5
"""
from sympy.core.power import Pow
from sympy.polys.rootoftools import RootOf
if iterable(expr, exclude=string_types):
if isinstance(expr, (dict, Dict)):
return type(expr)([(k, nfloat(v, n, exponent)) for k, v in
list(expr.items())])
return type(expr)([nfloat(a, n, exponent) for a in expr])
rv = sympify(expr)
if rv.is_Number:
return Float(rv, n)
elif rv.is_number:
# evalf doesn't always set the precision
rv = rv.n(n)
if rv.is_Number:
rv = Float(rv.n(n), n)
else:
pass # pure_complex(rv) is likely True
return rv
# watch out for RootOf instances that don't like to have
# their exponents replaced with Dummies and also sometimes have
# problems with evaluating at low precision (issue 6393)
rv = rv.xreplace({ro: ro.n(n) for ro in rv.atoms(RootOf)})
if not exponent:
reps = [(p, Pow(p.base, Dummy())) for p in rv.atoms(Pow)]
rv = rv.xreplace(dict(reps))
rv = rv.n(n)
if not exponent:
rv = rv.xreplace({d.exp: p.exp for p, d in reps})
else:
# Pow._eval_evalf special cases Integer exponents so if
# exponent is suppose to be handled we have to do so here
rv = rv.xreplace(Transform(
lambda x: Pow(x.base, Float(x.exp, n)),
lambda x: x.is_Pow and x.exp.is_Integer))
return rv.xreplace(Transform(
lambda x: x.func(*nfloat(x.args, n, exponent)),
lambda x: isinstance(x, Function)))
from sympy.core.symbol import Dummy, Symbol
| 90,447 | 33.144205 | 117 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/expr.py
|
from __future__ import print_function, division
from .sympify import sympify, _sympify, SympifyError
from .basic import Basic, Atom
from .singleton import S
from .evalf import EvalfMixin, pure_complex
from .decorators import _sympifyit, call_highest_priority
from .cache import cacheit
from .compatibility import reduce, as_int, default_sort_key, range
from mpmath.libmp import mpf_log, prec_to_dps
from collections import defaultdict
class Expr(Basic, EvalfMixin):
"""
Base class for algebraic expressions.
Everything that requires arithmetic operations to be defined
should subclass this class, instead of Basic (which should be
used only for argument storage and expression manipulation, i.e.
pattern matching, substitutions, etc).
See Also
========
sympy.core.basic.Basic
"""
__slots__ = []
@property
def _diff_wrt(self):
"""Is it allowed to take derivative wrt to this instance.
This determines if it is allowed to take derivatives wrt this object.
Subclasses such as Symbol, Function and Derivative should return True
to enable derivatives wrt them. The implementation in Derivative
separates the Symbol and non-Symbol _diff_wrt=True variables and
temporarily converts the non-Symbol vars in Symbols when performing
the differentiation.
Note, see the docstring of Derivative for how this should work
mathematically. In particular, note that expr.subs(yourclass, Symbol)
should be well-defined on a structural level, or this will lead to
inconsistent results.
Examples
========
>>> from sympy import Expr
>>> e = Expr()
>>> e._diff_wrt
False
>>> class MyClass(Expr):
... _diff_wrt = True
...
>>> (2*MyClass()).diff(MyClass())
2
"""
return False
@cacheit
def sort_key(self, order=None):
coeff, expr = self.as_coeff_Mul()
if expr.is_Pow:
expr, exp = expr.args
else:
expr, exp = expr, S.One
if expr.is_Dummy:
args = (expr.sort_key(),)
elif expr.is_Atom:
args = (str(expr),)
else:
if expr.is_Add:
args = expr.as_ordered_terms(order=order)
elif expr.is_Mul:
args = expr.as_ordered_factors(order=order)
else:
args = expr.args
args = tuple(
[ default_sort_key(arg, order=order) for arg in args ])
args = (len(args), tuple(args))
exp = exp.sort_key(order=order)
return expr.class_key(), args, exp, coeff
# ***************
# * Arithmetics *
# ***************
# Expr and its sublcasses use _op_priority to determine which object
# passed to a binary special method (__mul__, etc.) will handle the
# operation. In general, the 'call_highest_priority' decorator will choose
# the object with the highest _op_priority to handle the call.
# Custom subclasses that want to define their own binary special methods
# should set an _op_priority value that is higher than the default.
#
# **NOTE**:
# This is a temporary fix, and will eventually be replaced with
# something better and more powerful. See issue 5510.
_op_priority = 10.0
def __pos__(self):
return self
def __neg__(self):
return Mul(S.NegativeOne, self)
def __abs__(self):
from sympy import Abs
return Abs(self)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__radd__')
def __add__(self, other):
return Add(self, other)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__add__')
def __radd__(self, other):
return Add(other, self)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__rsub__')
def __sub__(self, other):
return Add(self, -other)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__sub__')
def __rsub__(self, other):
return Add(other, -self)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__rmul__')
def __mul__(self, other):
return Mul(self, other)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__mul__')
def __rmul__(self, other):
return Mul(other, self)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__rpow__')
def __pow__(self, other):
return Pow(self, other)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__pow__')
def __rpow__(self, other):
return Pow(other, self)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__rdiv__')
def __div__(self, other):
return Mul(self, Pow(other, S.NegativeOne))
@_sympifyit('other', NotImplemented)
@call_highest_priority('__div__')
def __rdiv__(self, other):
return Mul(other, Pow(self, S.NegativeOne))
__truediv__ = __div__
__rtruediv__ = __rdiv__
@_sympifyit('other', NotImplemented)
@call_highest_priority('__rmod__')
def __mod__(self, other):
return Mod(self, other)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__mod__')
def __rmod__(self, other):
return Mod(other, self)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__rfloordiv__')
def __floordiv__(self, other):
from sympy.functions.elementary.integers import floor
return floor(self / other)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__floordiv__')
def __rfloordiv__(self, other):
from sympy.functions.elementary.integers import floor
return floor(self / other)
def __int__(self):
# Although we only need to round to the units position, we'll
# get one more digit so the extra testing below can be avoided
# unless the rounded value rounded to an integer, e.g. if an
# expression were equal to 1.9 and we rounded to the unit position
# we would get a 2 and would not know if this rounded up or not
# without doing a test (as done below). But if we keep an extra
# digit we know that 1.9 is not the same as 1 and there is no
# need for further testing: our int value is correct. If the value
# were 1.99, however, this would round to 2.0 and our int value is
# off by one. So...if our round value is the same as the int value
# (regardless of how much extra work we do to calculate extra decimal
# places) we need to test whether we are off by one.
from sympy import Dummy
if not self.is_number:
raise TypeError("can't convert symbols to int")
r = self.round(2)
if not r.is_Number:
raise TypeError("can't convert complex to int")
if r in (S.NaN, S.Infinity, S.NegativeInfinity):
raise TypeError("can't convert %s to int" % r)
i = int(r)
if not i:
return 0
# off-by-one check
if i == r and not (self - i).equals(0):
isign = 1 if i > 0 else -1
x = Dummy()
# in the following (self - i).evalf(2) will not always work while
# (self - r).evalf(2) and the use of subs does; if the test that
# was added when this comment was added passes, it might be safe
# to simply use sign to compute this rather than doing this by hand:
diff_sign = 1 if (self - x).evalf(2, subs={x: i}) > 0 else -1
if diff_sign != isign:
i -= isign
return i
__long__ = __int__
def __float__(self):
# Don't bother testing if it's a number; if it's not this is going
# to fail, and if it is we still need to check that it evalf'ed to
# a number.
result = self.evalf()
if result.is_Number:
return float(result)
if result.is_number and result.as_real_imag()[1]:
raise TypeError("can't convert complex to float")
raise TypeError("can't convert expression to float")
def __complex__(self):
result = self.evalf()
re, im = result.as_real_imag()
return complex(float(re), float(im))
def __ge__(self, other):
from sympy import GreaterThan
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s >= %s" % (self, other))
for me in (self, other):
if (me.is_complex and me.is_real is False) or \
me.has(S.ComplexInfinity):
raise TypeError("Invalid comparison of complex %s" % me)
if me is S.NaN:
raise TypeError("Invalid NaN comparison")
n2 = _n2(self, other)
if n2 is not None:
return _sympify(n2 >= 0)
if self.is_real or other.is_real:
dif = self - other
if dif.is_nonnegative is not None and \
dif.is_nonnegative is not dif.is_negative:
return sympify(dif.is_nonnegative)
return GreaterThan(self, other, evaluate=False)
def __le__(self, other):
from sympy import LessThan
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s <= %s" % (self, other))
for me in (self, other):
if (me.is_complex and me.is_real is False) or \
me.has(S.ComplexInfinity):
raise TypeError("Invalid comparison of complex %s" % me)
if me is S.NaN:
raise TypeError("Invalid NaN comparison")
n2 = _n2(self, other)
if n2 is not None:
return _sympify(n2 <= 0)
if self.is_real or other.is_real:
dif = self - other
if dif.is_nonpositive is not None and \
dif.is_nonpositive is not dif.is_positive:
return sympify(dif.is_nonpositive)
return LessThan(self, other, evaluate=False)
def __gt__(self, other):
from sympy import StrictGreaterThan
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s > %s" % (self, other))
for me in (self, other):
if (me.is_complex and me.is_real is False) or \
me.has(S.ComplexInfinity):
raise TypeError("Invalid comparison of complex %s" % me)
if me is S.NaN:
raise TypeError("Invalid NaN comparison")
n2 = _n2(self, other)
if n2 is not None:
return _sympify(n2 > 0)
if self.is_real or other.is_real:
dif = self - other
if dif.is_positive is not None and \
dif.is_positive is not dif.is_nonpositive:
return sympify(dif.is_positive)
return StrictGreaterThan(self, other, evaluate=False)
def __lt__(self, other):
from sympy import StrictLessThan
try:
other = _sympify(other)
except SympifyError:
raise TypeError("Invalid comparison %s < %s" % (self, other))
for me in (self, other):
if (me.is_complex and me.is_real is False) or \
me.has(S.ComplexInfinity):
raise TypeError("Invalid comparison of complex %s" % me)
if me is S.NaN:
raise TypeError("Invalid NaN comparison")
n2 = _n2(self, other)
if n2 is not None:
return _sympify(n2 < 0)
if self.is_real or other.is_real:
dif = self - other
if dif.is_negative is not None and \
dif.is_negative is not dif.is_nonnegative:
return sympify(dif.is_negative)
return StrictLessThan(self, other, evaluate=False)
@staticmethod
def _from_mpmath(x, prec):
from sympy import Float
if hasattr(x, "_mpf_"):
return Float._new(x._mpf_, prec)
elif hasattr(x, "_mpc_"):
re, im = x._mpc_
re = Float._new(re, prec)
im = Float._new(im, prec)*S.ImaginaryUnit
return re + im
else:
raise TypeError("expected mpmath number (mpf or mpc)")
@property
def is_number(self):
"""Returns True if ``self`` has no free symbols.
It will be faster than ``if not self.free_symbols``, however, since
``is_number`` will fail as soon as it hits a free symbol.
Examples
========
>>> from sympy import log, Integral
>>> from sympy.abc import x
>>> x.is_number
False
>>> (2*x).is_number
False
>>> (2 + log(2)).is_number
True
>>> (2 + Integral(2, x)).is_number
False
>>> (2 + Integral(2, (x, 1, 2))).is_number
True
"""
return all(obj.is_number for obj in self.args)
def _random(self, n=None, re_min=-1, im_min=-1, re_max=1, im_max=1):
"""Return self evaluated, if possible, replacing free symbols with
random complex values, if necessary.
The random complex value for each free symbol is generated
by the random_complex_number routine giving real and imaginary
parts in the range given by the re_min, re_max, im_min, and im_max
values. The returned value is evaluated to a precision of n
(if given) else the maximum of 15 and the precision needed
to get more than 1 digit of precision. If the expression
could not be evaluated to a number, or could not be evaluated
to more than 1 digit of precision, then None is returned.
Examples
========
>>> from sympy import sqrt
>>> from sympy.abc import x, y
>>> x._random() # doctest: +SKIP
0.0392918155679172 + 0.916050214307199*I
>>> x._random(2) # doctest: +SKIP
-0.77 - 0.87*I
>>> (x + y/2)._random(2) # doctest: +SKIP
-0.57 + 0.16*I
>>> sqrt(2)._random(2)
1.4
See Also
========
sympy.utilities.randtest.random_complex_number
"""
free = self.free_symbols
prec = 1
if free:
from sympy.utilities.randtest import random_complex_number
a, c, b, d = re_min, re_max, im_min, im_max
reps = dict(list(zip(free, [random_complex_number(a, b, c, d, rational=True)
for zi in free])))
try:
nmag = abs(self.evalf(2, subs=reps))
except (ValueError, TypeError):
# if an out of range value resulted in evalf problems
# then return None -- XXX is there a way to know how to
# select a good random number for a given expression?
# e.g. when calculating n! negative values for n should not
# be used
return None
else:
reps = {}
nmag = abs(self.evalf(2))
if not hasattr(nmag, '_prec'):
# e.g. exp_polar(2*I*pi) doesn't evaluate but is_number is True
return None
if nmag._prec == 1:
# increase the precision up to the default maximum
# precision to see if we can get any significance
from mpmath.libmp.libintmath import giant_steps
from sympy.core.evalf import DEFAULT_MAXPREC as target
# evaluate
for prec in giant_steps(2, target):
nmag = abs(self.evalf(prec, subs=reps))
if nmag._prec != 1:
break
if nmag._prec != 1:
if n is None:
n = max(prec, 15)
return self.evalf(n, subs=reps)
# never got any significance
return None
def is_constant(self, *wrt, **flags):
"""Return True if self is constant, False if not, or None if
the constancy could not be determined conclusively.
If an expression has no free symbols then it is a constant. If
there are free symbols it is possible that the expression is a
constant, perhaps (but not necessarily) zero. To test such
expressions, two strategies are tried:
1) numerical evaluation at two random points. If two such evaluations
give two different values and the values have a precision greater than
1 then self is not constant. If the evaluations agree or could not be
obtained with any precision, no decision is made. The numerical testing
is done only if ``wrt`` is different than the free symbols.
2) differentiation with respect to variables in 'wrt' (or all free
symbols if omitted) to see if the expression is constant or not. This
will not always lead to an expression that is zero even though an
expression is constant (see added test in test_expr.py). If
all derivatives are zero then self is constant with respect to the
given symbols.
If neither evaluation nor differentiation can prove the expression is
constant, None is returned unless two numerical values happened to be
the same and the flag ``failing_number`` is True -- in that case the
numerical value will be returned.
If flag simplify=False is passed, self will not be simplified;
the default is True since self should be simplified before testing.
Examples
========
>>> from sympy import cos, sin, Sum, S, pi
>>> from sympy.abc import a, n, x, y
>>> x.is_constant()
False
>>> S(2).is_constant()
True
>>> Sum(x, (x, 1, 10)).is_constant()
True
>>> Sum(x, (x, 1, n)).is_constant()
False
>>> Sum(x, (x, 1, n)).is_constant(y)
True
>>> Sum(x, (x, 1, n)).is_constant(n)
False
>>> Sum(x, (x, 1, n)).is_constant(x)
True
>>> eq = a*cos(x)**2 + a*sin(x)**2 - a
>>> eq.is_constant()
True
>>> eq.subs({x: pi, a: 2}) == eq.subs({x: pi, a: 3}) == 0
True
>>> (0**x).is_constant()
False
>>> x.is_constant()
False
>>> (x**x).is_constant()
False
>>> one = cos(x)**2 + sin(x)**2
>>> one.is_constant()
True
>>> ((one - 1)**(x + 1)).is_constant() in (True, False) # could be 0 or 1
True
"""
simplify = flags.get('simplify', True)
# Except for expressions that contain units, only one of these should
# be necessary since if something is
# known to be a number it should also know that there are no
# free symbols. But is_number quits as soon as it hits a non-number
# whereas free_symbols goes until all free symbols have been collected,
# thus is_number should be faster. But a double check on free symbols
# is made just in case there is a discrepancy between the two.
free = self.free_symbols
if self.is_number or not free:
# if the following assertion fails then that object's free_symbols
# method needs attention: if an expression is a number it cannot
# have free symbols
assert not free
return True
# if we are only interested in some symbols and they are not in the
# free symbols then this expression is constant wrt those symbols
wrt = set(wrt)
if wrt and not wrt & free:
return True
wrt = wrt or free
# simplify unless this has already been done
expr = self
if simplify:
expr = expr.simplify()
# is_zero should be a quick assumptions check; it can be wrong for
# numbers (see test_is_not_constant test), giving False when it
# shouldn't, but hopefully it will never give True unless it is sure.
if expr.is_zero:
return True
# try numerical evaluation to see if we get two different values
failing_number = None
if wrt == free:
# try 0 (for a) and 1 (for b)
try:
a = expr.subs(list(zip(free, [0]*len(free))),
simultaneous=True)
if a is S.NaN:
# evaluation may succeed when substitution fails
a = expr._random(None, 0, 0, 0, 0)
except ZeroDivisionError:
a = None
if a is not None and a is not S.NaN:
try:
b = expr.subs(list(zip(free, [1]*len(free))),
simultaneous=True)
if b is S.NaN:
# evaluation may succeed when substitution fails
b = expr._random(None, 1, 0, 1, 0)
except ZeroDivisionError:
b = None
if b is not None and b is not S.NaN and b.equals(a) is False:
return False
# try random real
b = expr._random(None, -1, 0, 1, 0)
if b is not None and b is not S.NaN and b.equals(a) is False:
return False
# try random complex
b = expr._random()
if b is not None and b is not S.NaN:
if b.equals(a) is False:
return False
failing_number = a if a.is_number else b
# now we will test each wrt symbol (or all free symbols) to see if the
# expression depends on them or not using differentiation. This is
# not sufficient for all expressions, however, so we don't return
# False if we get a derivative other than 0 with free symbols.
for w in wrt:
deriv = expr.diff(w)
if simplify:
deriv = deriv.simplify()
if deriv != 0:
if not (pure_complex(deriv, or_real=True)):
if flags.get('failing_number', False):
return failing_number
elif deriv.free_symbols:
# dead line provided _random returns None in such cases
return None
return False
return True
def equals(self, other, failing_expression=False):
"""Return True if self == other, False if it doesn't, or None. If
failing_expression is True then the expression which did not simplify
to a 0 will be returned instead of None.
If ``self`` is a Number (or complex number) that is not zero, then
the result is False.
If ``self`` is a number and has not evaluated to zero, evalf will be
used to test whether the expression evaluates to zero. If it does so
and the result has significance (i.e. the precision is either -1, for
a Rational result, or is greater than 1) then the evalf value will be
used to return True or False.
"""
from sympy.simplify.simplify import nsimplify, simplify
from sympy.solvers.solveset import solveset
from sympy.polys.polyerrors import NotAlgebraic
from sympy.polys.numberfields import minimal_polynomial
other = sympify(other)
if self == other:
return True
# they aren't the same so see if we can make the difference 0;
# don't worry about doing simplification steps one at a time
# because if the expression ever goes to 0 then the subsequent
# simplification steps that are done will be very fast.
diff = factor_terms(simplify(self - other), radical=True)
if not diff:
return True
if not diff.has(Add, Mod):
# if there is no expanding to be done after simplifying
# then this can't be a zero
return False
constant = diff.is_constant(simplify=False, failing_number=True)
if constant is False:
return False
if constant is None and (diff.free_symbols or not diff.is_number):
# e.g. unless the right simplification is done, a symbolic
# zero is possible (see expression of issue 6829: without
# simplification constant will be None).
return
if constant is True:
ndiff = diff._random()
if ndiff:
return False
# sometimes we can use a simplified result to give a clue as to
# what the expression should be; if the expression is *not* zero
# then we should have been able to compute that and so now
# we can just consider the cases where the approximation appears
# to be zero -- we try to prove it via minimal_polynomial.
if diff.is_number:
approx = diff.nsimplify()
if not approx:
# try to prove via self-consistency
surds = [s for s in diff.atoms(Pow) if s.args[0].is_Integer]
# it seems to work better to try big ones first
surds.sort(key=lambda x: -x.args[0])
for s in surds:
try:
# simplify is False here -- this expression has already
# been identified as being hard to identify as zero;
# we will handle the checking ourselves using nsimplify
# to see if we are in the right ballpark or not and if so
# *then* the simplification will be attempted.
if s.is_Symbol:
sol = list(solveset(diff, s))
else:
sol = [s]
if sol:
if s in sol:
return True
if s.is_real:
if any(nsimplify(si, [s]) == s and simplify(si) == s
for si in sol):
return True
except NotImplementedError:
pass
# try to prove with minimal_polynomial but know when
# *not* to use this or else it can take a long time. e.g. issue 8354
if True: # change True to condition that assures non-hang
try:
mp = minimal_polynomial(diff)
if mp.is_Symbol:
return True
return False
except (NotAlgebraic, NotImplementedError):
pass
# diff has not simplified to zero; constant is either None, True
# or the number with significance (prec != 1) that was randomly
# calculated twice as the same value.
if constant not in (True, None) and constant != 0:
return False
if failing_expression:
return diff
return None
def _eval_is_positive(self):
from sympy.polys.numberfields import minimal_polynomial
from sympy.polys.polyerrors import NotAlgebraic
if self.is_number:
if self.is_real is False:
return False
try:
# check to see that we can get a value
n2 = self._eval_evalf(2)
if n2 is None:
raise AttributeError
if n2._prec == 1: # no significance
raise AttributeError
if n2 == S.NaN:
raise AttributeError
except (AttributeError, ValueError):
return None
n, i = self.evalf(2).as_real_imag()
if not i.is_Number or not n.is_Number:
return False
if n._prec != 1 and i._prec != 1:
return bool(not i and n > 0)
elif n._prec == 1 and (not i or i._prec == 1) and \
self.is_algebraic and not self.has(Function):
try:
if minimal_polynomial(self).is_Symbol:
return False
except (NotAlgebraic, NotImplementedError):
pass
def _eval_is_negative(self):
from sympy.polys.numberfields import minimal_polynomial
from sympy.polys.polyerrors import NotAlgebraic
if self.is_number:
if self.is_real is False:
return False
try:
# check to see that we can get a value
n2 = self._eval_evalf(2)
if n2 is None:
raise AttributeError
if n2._prec == 1: # no significance
raise AttributeError
if n2 == S.NaN:
raise AttributeError
except (AttributeError, ValueError):
return None
n, i = self.evalf(2).as_real_imag()
if not i.is_Number or not n.is_Number:
return False
if n._prec != 1 and i._prec != 1:
return bool(not i and n < 0)
elif n._prec == 1 and (not i or i._prec == 1) and \
self.is_algebraic and not self.has(Function):
try:
if minimal_polynomial(self).is_Symbol:
return False
except (NotAlgebraic, NotImplementedError):
pass
def _eval_interval(self, x, a, b):
"""
Returns evaluation over an interval. For most functions this is:
self.subs(x, b) - self.subs(x, a),
possibly using limit() if NaN is returned from subs.
If b or a is None, it only evaluates -self.subs(x, a) or self.subs(b, x),
respectively.
"""
from sympy.series import limit, Limit
from sympy.solvers.solveset import solveset
from sympy.sets.sets import Interval
if (a is None and b is None):
raise ValueError('Both interval ends cannot be None.')
if a == b:
return 0
if a is None:
A = 0
else:
A = self.subs(x, a)
if A.has(S.NaN, S.Infinity, S.NegativeInfinity, S.ComplexInfinity):
if (a < b) != False:
A = limit(self, x, a,"+")
else:
A = limit(self, x, a,"-")
if A is S.NaN:
return A
if isinstance(A, Limit):
raise NotImplementedError("Could not compute limit")
if b is None:
B = 0
else:
B = self.subs(x, b)
if B.has(S.NaN, S.Infinity, S.NegativeInfinity, S.ComplexInfinity):
if (a < b) != False:
B = limit(self, x, b,"-")
else:
B = limit(self, x, b,"+")
if isinstance(B, Limit):
raise NotImplementedError("Could not compute limit")
if (a and b) is None:
return B - A
value = B - A
if a.is_comparable and b.is_comparable:
if a < b:
domain = Interval(a, b)
else:
domain = Interval(b, a)
singularities = list(solveset(self.cancel().as_numer_denom()[1], x, domain = domain))
for s in singularities:
if a < s < b:
value += -limit(self, x, s, "+") + limit(self, x, s, "-")
elif b < s < a:
value += limit(self, x, s, "+") - limit(self, x, s, "-")
return value
def _eval_power(self, other):
# subclass to compute self**other for cases when
# other is not NaN, 0, or 1
return None
def _eval_conjugate(self):
if self.is_real:
return self
elif self.is_imaginary:
return -self
def conjugate(self):
from sympy.functions.elementary.complexes import conjugate as c
return c(self)
def _eval_transpose(self):
from sympy.functions.elementary.complexes import conjugate
if self.is_complex:
return self
elif self.is_hermitian:
return conjugate(self)
elif self.is_antihermitian:
return -conjugate(self)
def transpose(self):
from sympy.functions.elementary.complexes import transpose
return transpose(self)
def _eval_adjoint(self):
from sympy.functions.elementary.complexes import conjugate, transpose
if self.is_hermitian:
return self
elif self.is_antihermitian:
return -self
obj = self._eval_conjugate()
if obj is not None:
return transpose(obj)
obj = self._eval_transpose()
if obj is not None:
return conjugate(obj)
def adjoint(self):
from sympy.functions.elementary.complexes import adjoint
return adjoint(self)
@classmethod
def _parse_order(cls, order):
"""Parse and configure the ordering of terms. """
from sympy.polys.orderings import monomial_key
try:
reverse = order.startswith('rev-')
except AttributeError:
reverse = False
else:
if reverse:
order = order[4:]
monom_key = monomial_key(order)
def neg(monom):
result = []
for m in monom:
if isinstance(m, tuple):
result.append(neg(m))
else:
result.append(-m)
return tuple(result)
def key(term):
_, ((re, im), monom, ncpart) = term
monom = neg(monom_key(monom))
ncpart = tuple([e.sort_key(order=order) for e in ncpart])
coeff = ((bool(im), im), (re, im))
return monom, ncpart, coeff
return key, reverse
def as_ordered_factors(self, order=None):
"""Return list of ordered factors (if Mul) else [self]."""
return [self]
def as_ordered_terms(self, order=None, data=False):
"""
Transform an expression to an ordered list of terms.
Examples
========
>>> from sympy import sin, cos
>>> from sympy.abc import x
>>> (sin(x)**2*cos(x) + sin(x)**2 + 1).as_ordered_terms()
[sin(x)**2*cos(x), sin(x)**2, 1]
"""
key, reverse = self._parse_order(order)
terms, gens = self.as_terms()
if not any(term.is_Order for term, _ in terms):
ordered = sorted(terms, key=key, reverse=reverse)
else:
_terms, _order = [], []
for term, repr in terms:
if not term.is_Order:
_terms.append((term, repr))
else:
_order.append((term, repr))
ordered = sorted(_terms, key=key, reverse=True) \
+ sorted(_order, key=key, reverse=True)
if data:
return ordered, gens
else:
return [term for term, _ in ordered]
def as_terms(self):
"""Transform an expression to a list of terms. """
from .add import Add
from .mul import Mul
from .exprtools import decompose_power
gens, terms = set([]), []
for term in Add.make_args(self):
coeff, _term = term.as_coeff_Mul()
coeff = complex(coeff)
cpart, ncpart = {}, []
if _term is not S.One:
for factor in Mul.make_args(_term):
if factor.is_number:
try:
coeff *= complex(factor)
except TypeError:
pass
else:
continue
if factor.is_commutative:
base, exp = decompose_power(factor)
cpart[base] = exp
gens.add(base)
else:
ncpart.append(factor)
coeff = coeff.real, coeff.imag
ncpart = tuple(ncpart)
terms.append((term, (coeff, cpart, ncpart)))
gens = sorted(gens, key=default_sort_key)
k, indices = len(gens), {}
for i, g in enumerate(gens):
indices[g] = i
result = []
for term, (coeff, cpart, ncpart) in terms:
monom = [0]*k
for base, exp in cpart.items():
monom[indices[base]] = exp
result.append((term, (coeff, tuple(monom), ncpart)))
return result, gens
def removeO(self):
"""Removes the additive O(..) symbol if there is one"""
return self
def getO(self):
"""Returns the additive O(..) symbol if there is one, else None."""
return None
def getn(self):
"""
Returns the order of the expression.
The order is determined either from the O(...) term. If there
is no O(...) term, it returns None.
Examples
========
>>> from sympy import O
>>> from sympy.abc import x
>>> (1 + x + O(x**2)).getn()
2
>>> (1 + x).getn()
"""
from sympy import Dummy, Symbol
o = self.getO()
if o is None:
return None
elif o.is_Order:
o = o.expr
if o is S.One:
return S.Zero
if o.is_Symbol:
return S.One
if o.is_Pow:
return o.args[1]
if o.is_Mul: # x**n*log(x)**n or x**n/log(x)**n
for oi in o.args:
if oi.is_Symbol:
return S.One
if oi.is_Pow:
syms = oi.atoms(Symbol)
if len(syms) == 1:
x = syms.pop()
oi = oi.subs(x, Dummy('x', positive=True))
if oi.base.is_Symbol and oi.exp.is_Rational:
return abs(oi.exp)
raise NotImplementedError('not sure of order of %s' % o)
def count_ops(self, visual=None):
"""wrapper for count_ops that returns the operation count."""
from .function import count_ops
return count_ops(self, visual)
def args_cnc(self, cset=False, warn=True, split_1=True):
"""Return [commutative factors, non-commutative factors] of self.
self is treated as a Mul and the ordering of the factors is maintained.
If ``cset`` is True the commutative factors will be returned in a set.
If there were repeated factors (as may happen with an unevaluated Mul)
then an error will be raised unless it is explicitly supressed by
setting ``warn`` to False.
Note: -1 is always separated from a Number unless split_1 is False.
>>> from sympy import symbols, oo
>>> A, B = symbols('A B', commutative=0)
>>> x, y = symbols('x y')
>>> (-2*x*y).args_cnc()
[[-1, 2, x, y], []]
>>> (-2.5*x).args_cnc()
[[-1, 2.5, x], []]
>>> (-2*x*A*B*y).args_cnc()
[[-1, 2, x, y], [A, B]]
>>> (-2*x*A*B*y).args_cnc(split_1=False)
[[-2, x, y], [A, B]]
>>> (-2*x*y).args_cnc(cset=True)
[{-1, 2, x, y}, []]
The arg is always treated as a Mul:
>>> (-2 + x + A).args_cnc()
[[], [x - 2 + A]]
>>> (-oo).args_cnc() # -oo is a singleton
[[-1, oo], []]
"""
if self.is_Mul:
args = list(self.args)
else:
args = [self]
for i, mi in enumerate(args):
if not mi.is_commutative:
c = args[:i]
nc = args[i:]
break
else:
c = args
nc = []
if c and split_1 and (
c[0].is_Number and
c[0].is_negative and
c[0] is not S.NegativeOne):
c[:1] = [S.NegativeOne, -c[0]]
if cset:
clen = len(c)
c = set(c)
if clen and warn and len(c) != clen:
raise ValueError('repeated commutative arguments: %s' %
[ci for ci in c if list(self.args).count(ci) > 1])
return [c, nc]
def coeff(self, x, n=1, right=False):
"""
Returns the coefficient from the term(s) containing ``x**n``. If ``n``
is zero then all terms independent of ``x`` will be returned.
When ``x`` is noncommutative, the coefficient to the left (default) or
right of ``x`` can be returned. The keyword 'right' is ignored when
``x`` is commutative.
See Also
========
as_coefficient: separate the expression into a coefficient and factor
as_coeff_Add: separate the additive constant from an expression
as_coeff_Mul: separate the multiplicative constant from an expression
as_independent: separate x-dependent terms/factors from others
sympy.polys.polytools.coeff_monomial: efficiently find the single coefficient of a monomial in Poly
sympy.polys.polytools.nth: like coeff_monomial but powers of monomial terms are used
Examples
========
>>> from sympy import symbols
>>> from sympy.abc import x, y, z
You can select terms that have an explicit negative in front of them:
>>> (-x + 2*y).coeff(-1)
x
>>> (x - 2*y).coeff(-1)
2*y
You can select terms with no Rational coefficient:
>>> (x + 2*y).coeff(1)
x
>>> (3 + 2*x + 4*x**2).coeff(1)
0
You can select terms independent of x by making n=0; in this case
expr.as_independent(x)[0] is returned (and 0 will be returned instead
of None):
>>> (3 + 2*x + 4*x**2).coeff(x, 0)
3
>>> eq = ((x + 1)**3).expand() + 1
>>> eq
x**3 + 3*x**2 + 3*x + 2
>>> [eq.coeff(x, i) for i in reversed(range(4))]
[1, 3, 3, 2]
>>> eq -= 2
>>> [eq.coeff(x, i) for i in reversed(range(4))]
[1, 3, 3, 0]
You can select terms that have a numerical term in front of them:
>>> (-x - 2*y).coeff(2)
-y
>>> from sympy import sqrt
>>> (x + sqrt(2)*x).coeff(sqrt(2))
x
The matching is exact:
>>> (3 + 2*x + 4*x**2).coeff(x)
2
>>> (3 + 2*x + 4*x**2).coeff(x**2)
4
>>> (3 + 2*x + 4*x**2).coeff(x**3)
0
>>> (z*(x + y)**2).coeff((x + y)**2)
z
>>> (z*(x + y)**2).coeff(x + y)
0
In addition, no factoring is done, so 1 + z*(1 + y) is not obtained
from the following:
>>> (x + z*(x + x*y)).coeff(x)
1
If such factoring is desired, factor_terms can be used first:
>>> from sympy import factor_terms
>>> factor_terms(x + z*(x + x*y)).coeff(x)
z*(y + 1) + 1
>>> n, m, o = symbols('n m o', commutative=False)
>>> n.coeff(n)
1
>>> (3*n).coeff(n)
3
>>> (n*m + m*n*m).coeff(n) # = (1 + m)*n*m
1 + m
>>> (n*m + m*n*m).coeff(n, right=True) # = (1 + m)*n*m
m
If there is more than one possible coefficient 0 is returned:
>>> (n*m + m*n).coeff(n)
0
If there is only one possible coefficient, it is returned:
>>> (n*m + x*m*n).coeff(m*n)
x
>>> (n*m + x*m*n).coeff(m*n, right=1)
1
"""
x = sympify(x)
if not isinstance(x, Basic):
return S.Zero
n = as_int(n)
if not x:
return S.Zero
if x == self:
if n == 1:
return S.One
return S.Zero
if x is S.One:
co = [a for a in Add.make_args(self)
if a.as_coeff_Mul()[0] is S.One]
if not co:
return S.Zero
return Add(*co)
if n == 0:
if x.is_Add and self.is_Add:
c = self.coeff(x, right=right)
if not c:
return S.Zero
if not right:
return self - Add(*[a*x for a in Add.make_args(c)])
return self - Add(*[x*a for a in Add.make_args(c)])
return self.as_independent(x, as_Add=True)[0]
# continue with the full method, looking for this power of x:
x = x**n
def incommon(l1, l2):
if not l1 or not l2:
return []
n = min(len(l1), len(l2))
for i in range(n):
if l1[i] != l2[i]:
return l1[:i]
return l1[:]
def find(l, sub, first=True):
""" Find where list sub appears in list l. When ``first`` is True
the first occurance from the left is returned, else the last
occurance is returned. Return None if sub is not in l.
>> l = range(5)*2
>> find(l, [2, 3])
2
>> find(l, [2, 3], first=0)
7
>> find(l, [2, 4])
None
"""
if not sub or not l or len(sub) > len(l):
return None
n = len(sub)
if not first:
l.reverse()
sub.reverse()
for i in range(0, len(l) - n + 1):
if all(l[i + j] == sub[j] for j in range(n)):
break
else:
i = None
if not first:
l.reverse()
sub.reverse()
if i is not None and not first:
i = len(l) - (i + n)
return i
co = []
args = Add.make_args(self)
self_c = self.is_commutative
x_c = x.is_commutative
if self_c and not x_c:
return S.Zero
if self_c:
xargs = x.args_cnc(cset=True, warn=False)[0]
for a in args:
margs = a.args_cnc(cset=True, warn=False)[0]
if len(xargs) > len(margs):
continue
resid = margs.difference(xargs)
if len(resid) + len(xargs) == len(margs):
co.append(Mul(*resid))
if co == []:
return S.Zero
elif co:
return Add(*co)
elif x_c:
xargs = x.args_cnc(cset=True, warn=False)[0]
for a in args:
margs, nc = a.args_cnc(cset=True)
if len(xargs) > len(margs):
continue
resid = margs.difference(xargs)
if len(resid) + len(xargs) == len(margs):
co.append(Mul(*(list(resid) + nc)))
if co == []:
return S.Zero
elif co:
return Add(*co)
else: # both nc
xargs, nx = x.args_cnc(cset=True)
# find the parts that pass the commutative terms
for a in args:
margs, nc = a.args_cnc(cset=True)
if len(xargs) > len(margs):
continue
resid = margs.difference(xargs)
if len(resid) + len(xargs) == len(margs):
co.append((resid, nc))
# now check the non-comm parts
if not co:
return S.Zero
if all(n == co[0][1] for r, n in co):
ii = find(co[0][1], nx, right)
if ii is not None:
if not right:
return Mul(Add(*[Mul(*r) for r, c in co]), Mul(*co[0][1][:ii]))
else:
return Mul(*co[0][1][ii + len(nx):])
beg = reduce(incommon, (n[1] for n in co))
if beg:
ii = find(beg, nx, right)
if ii is not None:
if not right:
gcdc = co[0][0]
for i in range(1, len(co)):
gcdc = gcdc.intersection(co[i][0])
if not gcdc:
break
return Mul(*(list(gcdc) + beg[:ii]))
else:
m = ii + len(nx)
return Add(*[Mul(*(list(r) + n[m:])) for r, n in co])
end = list(reversed(
reduce(incommon, (list(reversed(n[1])) for n in co))))
if end:
ii = find(end, nx, right)
if ii is not None:
if not right:
return Add(*[Mul(*(list(r) + n[:-len(end) + ii])) for r, n in co])
else:
return Mul(*end[ii + len(nx):])
# look for single match
hit = None
for i, (r, n) in enumerate(co):
ii = find(n, nx, right)
if ii is not None:
if not hit:
hit = ii, r, n
else:
break
else:
if hit:
ii, r, n = hit
if not right:
return Mul(*(list(r) + n[:ii]))
else:
return Mul(*n[ii + len(nx):])
return S.Zero
def as_expr(self, *gens):
"""
Convert a polynomial to a SymPy expression.
Examples
========
>>> from sympy import sin
>>> from sympy.abc import x, y
>>> f = (x**2 + x*y).as_poly(x, y)
>>> f.as_expr()
x**2 + x*y
>>> sin(x).as_expr()
sin(x)
"""
return self
def as_coefficient(self, expr):
"""
Extracts symbolic coefficient at the given expression. In
other words, this functions separates 'self' into the product
of 'expr' and 'expr'-free coefficient. If such separation
is not possible it will return None.
Examples
========
>>> from sympy import E, pi, sin, I, Poly
>>> from sympy.abc import x
>>> E.as_coefficient(E)
1
>>> (2*E).as_coefficient(E)
2
>>> (2*sin(E)*E).as_coefficient(E)
Two terms have E in them so a sum is returned. (If one were
desiring the coefficient of the term exactly matching E then
the constant from the returned expression could be selected.
Or, for greater precision, a method of Poly can be used to
indicate the desired term from which the coefficient is
desired.)
>>> (2*E + x*E).as_coefficient(E)
x + 2
>>> _.args[0] # just want the exact match
2
>>> p = Poly(2*E + x*E); p
Poly(x*E + 2*E, x, E, domain='ZZ')
>>> p.coeff_monomial(E)
2
>>> p.nth(0, 1)
2
Since the following cannot be written as a product containing
E as a factor, None is returned. (If the coefficient ``2*x`` is
desired then the ``coeff`` method should be used.)
>>> (2*E*x + x).as_coefficient(E)
>>> (2*E*x + x).coeff(E)
2*x
>>> (E*(x + 1) + x).as_coefficient(E)
>>> (2*pi*I).as_coefficient(pi*I)
2
>>> (2*I).as_coefficient(pi*I)
See Also
========
coeff: return sum of terms have a given factor
as_coeff_Add: separate the additive constant from an expression
as_coeff_Mul: separate the multiplicative constant from an expression
as_independent: separate x-dependent terms/factors from others
sympy.polys.polytools.coeff_monomial: efficiently find the single coefficient of a monomial in Poly
sympy.polys.polytools.nth: like coeff_monomial but powers of monomial terms are used
"""
r = self.extract_multiplicatively(expr)
if r and not r.has(expr):
return r
def as_independent(self, *deps, **hint):
"""
A mostly naive separation of a Mul or Add into arguments that are not
are dependent on deps. To obtain as complete a separation of variables
as possible, use a separation method first, e.g.:
* separatevars() to change Mul, Add and Pow (including exp) into Mul
* .expand(mul=True) to change Add or Mul into Add
* .expand(log=True) to change log expr into an Add
The only non-naive thing that is done here is to respect noncommutative
ordering of variables and to always return (0, 0) for `self` of zero
regardless of hints.
For nonzero `self`, the returned tuple (i, d) has the
following interpretation:
* i will has no variable that appears in deps
* d will be 1 or else have terms that contain variables that are in deps
* if self is an Add then self = i + d
* if self is a Mul then self = i*d
* otherwise (self, S.One) or (S.One, self) is returned.
To force the expression to be treated as an Add, use the hint as_Add=True
Examples
========
-- self is an Add
>>> from sympy import sin, cos, exp
>>> from sympy.abc import x, y, z
>>> (x + x*y).as_independent(x)
(0, x*y + x)
>>> (x + x*y).as_independent(y)
(x, x*y)
>>> (2*x*sin(x) + y + x + z).as_independent(x)
(y + z, 2*x*sin(x) + x)
>>> (2*x*sin(x) + y + x + z).as_independent(x, y)
(z, 2*x*sin(x) + x + y)
-- self is a Mul
>>> (x*sin(x)*cos(y)).as_independent(x)
(cos(y), x*sin(x))
non-commutative terms cannot always be separated out when self is a Mul
>>> from sympy import symbols
>>> n1, n2, n3 = symbols('n1 n2 n3', commutative=False)
>>> (n1 + n1*n2).as_independent(n2)
(n1, n1*n2)
>>> (n2*n1 + n1*n2).as_independent(n2)
(0, n1*n2 + n2*n1)
>>> (n1*n2*n3).as_independent(n1)
(1, n1*n2*n3)
>>> (n1*n2*n3).as_independent(n2)
(n1, n2*n3)
>>> ((x-n1)*(x-y)).as_independent(x)
(1, (x - y)*(x - n1))
-- self is anything else:
>>> (sin(x)).as_independent(x)
(1, sin(x))
>>> (sin(x)).as_independent(y)
(sin(x), 1)
>>> exp(x+y).as_independent(x)
(1, exp(x + y))
-- force self to be treated as an Add:
>>> (3*x).as_independent(x, as_Add=True)
(0, 3*x)
-- force self to be treated as a Mul:
>>> (3+x).as_independent(x, as_Add=False)
(1, x + 3)
>>> (-3+x).as_independent(x, as_Add=False)
(1, x - 3)
Note how the below differs from the above in making the
constant on the dep term positive.
>>> (y*(-3+x)).as_independent(x)
(y, x - 3)
-- use .as_independent() for true independence testing instead
of .has(). The former considers only symbols in the free
symbols while the latter considers all symbols
>>> from sympy import Integral
>>> I = Integral(x, (x, 1, 2))
>>> I.has(x)
True
>>> x in I.free_symbols
False
>>> I.as_independent(x) == (I, 1)
True
>>> (I + x).as_independent(x) == (I, x)
True
Note: when trying to get independent terms, a separation method
might need to be used first. In this case, it is important to keep
track of what you send to this routine so you know how to interpret
the returned values
>>> from sympy import separatevars, log
>>> separatevars(exp(x+y)).as_independent(x)
(exp(y), exp(x))
>>> (x + x*y).as_independent(y)
(x, x*y)
>>> separatevars(x + x*y).as_independent(y)
(x, y + 1)
>>> (x*(1 + y)).as_independent(y)
(x, y + 1)
>>> (x*(1 + y)).expand(mul=True).as_independent(y)
(x, x*y)
>>> a, b=symbols('a b', positive=True)
>>> (log(a*b).expand(log=True)).as_independent(b)
(log(a), log(b))
See Also
========
.separatevars(), .expand(log=True), Add.as_two_terms(),
Mul.as_two_terms(), .as_coeff_add(), .as_coeff_mul()
"""
from .symbol import Symbol
from .add import _unevaluated_Add
from .mul import _unevaluated_Mul
from sympy.utilities.iterables import sift
if self.is_zero:
return S.Zero, S.Zero
func = self.func
if hint.get('as_Add', func is Add):
want = Add
else:
want = Mul
# sift out deps into symbolic and other and ignore
# all symbols but those that are in the free symbols
sym = set()
other = []
for d in deps:
if isinstance(d, Symbol): # Symbol.is_Symbol is True
sym.add(d)
else:
other.append(d)
def has(e):
"""return the standard has() if there are no literal symbols, else
check to see that symbol-deps are in the free symbols."""
has_other = e.has(*other)
if not sym:
return has_other
return has_other or e.has(*(e.free_symbols & sym))
if (want is not func or
func is not Add and func is not Mul):
if has(self):
return (want.identity, self)
else:
return (self, want.identity)
else:
if func is Add:
args = list(self.args)
else:
args, nc = self.args_cnc()
d = sift(args, lambda x: has(x))
depend = d[True]
indep = d[False]
if func is Add: # all terms were treated as commutative
return (Add(*indep), _unevaluated_Add(*depend))
else: # handle noncommutative by stopping at first dependent term
for i, n in enumerate(nc):
if has(n):
depend.extend(nc[i:])
break
indep.append(n)
return Mul(*indep), (
Mul(*depend, evaluate=False) if nc else
_unevaluated_Mul(*depend))
def as_real_imag(self, deep=True, **hints):
"""Performs complex expansion on 'self' and returns a tuple
containing collected both real and imaginary parts. This
method can't be confused with re() and im() functions,
which does not perform complex expansion at evaluation.
However it is possible to expand both re() and im()
functions and get exactly the same results as with
a single call to this function.
>>> from sympy import symbols, I
>>> x, y = symbols('x,y', real=True)
>>> (x + y*I).as_real_imag()
(x, y)
>>> from sympy.abc import z, w
>>> (z + w*I).as_real_imag()
(re(z) - im(w), re(w) + im(z))
"""
from sympy import im, re
if hints.get('ignore') == self:
return None
else:
return (re(self), im(self))
def as_powers_dict(self):
"""Return self as a dictionary of factors with each factor being
treated as a power. The keys are the bases of the factors and the
values, the corresponding exponents. The resulting dictionary should
be used with caution if the expression is a Mul and contains non-
commutative factors since the order that they appeared will be lost in
the dictionary."""
d = defaultdict(int)
d.update(dict([self.as_base_exp()]))
return d
def as_coefficients_dict(self):
"""Return a dictionary mapping terms to their Rational coefficient.
Since the dictionary is a defaultdict, inquiries about terms which
were not present will return a coefficient of 0. If an expression is
not an Add it is considered to have a single term.
Examples
========
>>> from sympy.abc import a, x
>>> (3*x + a*x + 4).as_coefficients_dict()
{1: 4, x: 3, a*x: 1}
>>> _[a]
0
>>> (3*a*x).as_coefficients_dict()
{a*x: 3}
"""
c, m = self.as_coeff_Mul()
if not c.is_Rational:
c = S.One
m = self
d = defaultdict(int)
d.update({m: c})
return d
def as_base_exp(self):
# a -> b ** e
return self, S.One
def as_coeff_mul(self, *deps, **kwargs):
"""Return the tuple (c, args) where self is written as a Mul, ``m``.
c should be a Rational multiplied by any factors of the Mul that are
independent of deps.
args should be a tuple of all other factors of m; args is empty
if self is a Number or if self is independent of deps (when given).
This should be used when you don't know if self is a Mul or not but
you want to treat self as a Mul or if you want to process the
individual arguments of the tail of self as a Mul.
- if you know self is a Mul and want only the head, use self.args[0];
- if you don't want to process the arguments of the tail but need the
tail then use self.as_two_terms() which gives the head and tail;
- if you want to split self into an independent and dependent parts
use ``self.as_independent(*deps)``
>>> from sympy import S
>>> from sympy.abc import x, y
>>> (S(3)).as_coeff_mul()
(3, ())
>>> (3*x*y).as_coeff_mul()
(3, (x, y))
>>> (3*x*y).as_coeff_mul(x)
(3*y, (x,))
>>> (3*y).as_coeff_mul(x)
(3*y, ())
"""
if deps:
if not self.has(*deps):
return self, tuple()
return S.One, (self,)
def as_coeff_add(self, *deps):
"""Return the tuple (c, args) where self is written as an Add, ``a``.
c should be a Rational added to any terms of the Add that are
independent of deps.
args should be a tuple of all other terms of ``a``; args is empty
if self is a Number or if self is independent of deps (when given).
This should be used when you don't know if self is an Add or not but
you want to treat self as an Add or if you want to process the
individual arguments of the tail of self as an Add.
- if you know self is an Add and want only the head, use self.args[0];
- if you don't want to process the arguments of the tail but need the
tail then use self.as_two_terms() which gives the head and tail.
- if you want to split self into an independent and dependent parts
use ``self.as_independent(*deps)``
>>> from sympy import S
>>> from sympy.abc import x, y
>>> (S(3)).as_coeff_add()
(3, ())
>>> (3 + x).as_coeff_add()
(3, (x,))
>>> (3 + x + y).as_coeff_add(x)
(y + 3, (x,))
>>> (3 + y).as_coeff_add(x)
(y + 3, ())
"""
if deps:
if not self.has(*deps):
return self, tuple()
return S.Zero, (self,)
def primitive(self):
"""Return the positive Rational that can be extracted non-recursively
from every term of self (i.e., self is treated like an Add). This is
like the as_coeff_Mul() method but primitive always extracts a positive
Rational (never a negative or a Float).
Examples
========
>>> from sympy.abc import x
>>> (3*(x + 1)**2).primitive()
(3, (x + 1)**2)
>>> a = (6*x + 2); a.primitive()
(2, 3*x + 1)
>>> b = (x/2 + 3); b.primitive()
(1/2, x + 6)
>>> (a*b).primitive() == (1, a*b)
True
"""
if not self:
return S.One, S.Zero
c, r = self.as_coeff_Mul(rational=True)
if c.is_negative:
c, r = -c, -r
return c, r
def as_content_primitive(self, radical=False, clear=True):
"""This method should recursively remove a Rational from all arguments
and return that (content) and the new self (primitive). The content
should always be positive and ``Mul(*foo.as_content_primitive()) == foo``.
The primitive need no be in canonical form and should try to preserve
the underlying structure if possible (i.e. expand_mul should not be
applied to self).
Examples
========
>>> from sympy import sqrt
>>> from sympy.abc import x, y, z
>>> eq = 2 + 2*x + 2*y*(3 + 3*y)
The as_content_primitive function is recursive and retains structure:
>>> eq.as_content_primitive()
(2, x + 3*y*(y + 1) + 1)
Integer powers will have Rationals extracted from the base:
>>> ((2 + 6*x)**2).as_content_primitive()
(4, (3*x + 1)**2)
>>> ((2 + 6*x)**(2*y)).as_content_primitive()
(1, (2*(3*x + 1))**(2*y))
Terms may end up joining once their as_content_primitives are added:
>>> ((5*(x*(1 + y)) + 2*x*(3 + 3*y))).as_content_primitive()
(11, x*(y + 1))
>>> ((3*(x*(1 + y)) + 2*x*(3 + 3*y))).as_content_primitive()
(9, x*(y + 1))
>>> ((3*(z*(1 + y)) + 2.0*x*(3 + 3*y))).as_content_primitive()
(1, 6.0*x*(y + 1) + 3*z*(y + 1))
>>> ((5*(x*(1 + y)) + 2*x*(3 + 3*y))**2).as_content_primitive()
(121, x**2*(y + 1)**2)
>>> ((5*(x*(1 + y)) + 2.0*x*(3 + 3*y))**2).as_content_primitive()
(1, 121.0*x**2*(y + 1)**2)
Radical content can also be factored out of the primitive:
>>> (2*sqrt(2) + 4*sqrt(10)).as_content_primitive(radical=True)
(2, sqrt(2)*(1 + 2*sqrt(5)))
If clear=False (default is True) then content will not be removed
from an Add if it can be distributed to leave one or more
terms with integer coefficients.
>>> (x/2 + y).as_content_primitive()
(1/2, x + 2*y)
>>> (x/2 + y).as_content_primitive(clear=False)
(1, x/2 + y)
"""
return S.One, self
def as_numer_denom(self):
""" expression -> a/b -> a, b
This is just a stub that should be defined by
an object's class methods to get anything else.
See Also
========
normal: return a/b instead of a, b
"""
return self, S.One
def normal(self):
from .mul import _unevaluated_Mul
n, d = self.as_numer_denom()
if d is S.One:
return n
if d.is_Number:
if d is S.One:
return n
else:
return _unevaluated_Mul(n, 1/d)
else:
return n/d
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('x,y', 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
"""
from .function import _coeff_isneg
c = sympify(c)
if self is S.NaN:
return None
if c is S.One:
return self
elif c == self:
return S.One
if c.is_Add:
cc, pc = c.primitive()
if cc is not S.One:
c = Mul(cc, pc, evaluate=False)
if c.is_Mul:
a, b = c.as_two_terms()
x = self.extract_multiplicatively(a)
if x is not None:
return x.extract_multiplicatively(b)
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_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_Float:
if not quotient.is_Float:
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 and c.is_Number:
return quotient
elif self.is_Add:
cs, ps = self.primitive()
# assert cs >= 1
if c.is_Number and c is not S.NegativeOne:
# assert c != 1 (handled at top)
if cs is not S.One:
if c.is_negative:
xc = -(cs.extract_multiplicatively(-c))
else:
xc = cs.extract_multiplicatively(c)
if xc is not None:
return xc*ps # rely on 2-arg Mul to restore Add
return # |c| != 1 can only be extracted from cs
if c == ps:
return cs
# check args of ps
newargs = []
for arg in ps.args:
newarg = arg.extract_multiplicatively(c)
if newarg is None:
return # all or nothing
newargs.append(newarg)
# args should be in same order so use unevaluated return
if cs is not S.One:
return Add._from_args([cs*t for t in newargs])
else:
return Add._from_args(newargs)
elif self.is_Mul:
args = list(self.args)
for i, arg in enumerate(args):
newarg = arg.extract_multiplicatively(c)
if newarg is not None:
args[i] = newarg
return Mul(*args)
elif self.is_Pow:
if c.is_Pow and c.base == self.base:
new_exp = self.exp.extract_additively(c.exp)
if new_exp is not None:
return self.base ** (new_exp)
elif c == self.base:
new_exp = self.exp.extract_additively(1)
if new_exp is not None:
return self.base ** (new_exp)
def extract_additively(self, c):
"""Return self - c if it's possible to subtract c from self and
make all matching coefficients move towards zero, else return None.
Examples
========
>>> from sympy.abc import x, y
>>> e = 2*x + 3
>>> e.extract_additively(x + 1)
x + 2
>>> e.extract_additively(3*x)
>>> e.extract_additively(4)
>>> (y*(x + 1)).extract_additively(x + 1)
>>> ((x + 1)*(x + 2*y + 1) + 3).extract_additively(x + 1)
(x + 1)*(x + 2*y) + 3
Sometimes auto-expansion will return a less simplified result
than desired; gcd_terms might be used in such cases:
>>> from sympy import gcd_terms
>>> (4*x*(y + 1) + y).extract_additively(x)
4*x*(y + 1) + x*(4*y + 3) - x*(4*y + 4) + y
>>> gcd_terms(_)
x*(4*y + 3) + y
See Also
========
extract_multiplicatively
coeff
as_coefficient
"""
c = sympify(c)
if self is S.NaN:
return None
if c is S.Zero:
return self
elif c == self:
return S.Zero
elif self is S.Zero:
return None
if self.is_Number:
if not c.is_Number:
return None
co = self
diff = co - c
# XXX should we match types? i.e should 3 - .1 succeed?
if (co > 0 and diff > 0 and diff < co or
co < 0 and diff < 0 and diff > co):
return diff
return None
if c.is_Number:
co, t = self.as_coeff_Add()
xa = co.extract_additively(c)
if xa is None:
return None
return xa + t
# handle the args[0].is_Number case separately
# since we will have trouble looking for the coeff of
# a number.
if c.is_Add and c.args[0].is_Number:
# whole term as a term factor
co = self.coeff(c)
xa0 = (co.extract_additively(1) or 0)*c
if xa0:
diff = self - co*c
return (xa0 + (diff.extract_additively(c) or diff)) or None
# term-wise
h, t = c.as_coeff_Add()
sh, st = self.as_coeff_Add()
xa = sh.extract_additively(h)
if xa is None:
return None
xa2 = st.extract_additively(t)
if xa2 is None:
return None
return xa + xa2
# whole term as a term factor
co = self.coeff(c)
xa0 = (co.extract_additively(1) or 0)*c
if xa0:
diff = self - co*c
return (xa0 + (diff.extract_additively(c) or diff)) or None
# term-wise
coeffs = []
for a in Add.make_args(c):
ac, at = a.as_coeff_Mul()
co = self.coeff(at)
if not co:
return None
coc, cot = co.as_coeff_Add()
xa = coc.extract_additively(ac)
if xa is None:
return None
self -= co*at
coeffs.append((cot + xa)*at)
coeffs.append(self)
return Add(*coeffs)
def could_extract_minus_sign(self):
"""Canonical way to choose an element in the set {e, -e} where
e is any expression. If the canonical element is e, we have
e.could_extract_minus_sign() == True, else
e.could_extract_minus_sign() == False.
For any expression, the set ``{e.could_extract_minus_sign(),
(-e).could_extract_minus_sign()}`` must be ``{True, False}``.
>>> from sympy.abc import x, y
>>> (x-y).could_extract_minus_sign() != (y-x).could_extract_minus_sign()
True
"""
negative_self = -self
self_has_minus = (self.extract_multiplicatively(-1) is not None)
negative_self_has_minus = (
(negative_self).extract_multiplicatively(-1) is not None)
if self_has_minus != negative_self_has_minus:
return self_has_minus
else:
if self.is_Add:
# We choose the one with less arguments with minus signs
all_args = len(self.args)
negative_args = len([False for arg in self.args if arg.could_extract_minus_sign()])
positive_args = all_args - negative_args
if positive_args > negative_args:
return False
elif positive_args < negative_args:
return True
elif self.is_Mul:
# We choose the one with an odd number of minus signs
num, den = self.as_numer_denom()
args = Mul.make_args(num) + Mul.make_args(den)
arg_signs = [arg.could_extract_minus_sign() for arg in args]
negative_args = list(filter(None, arg_signs))
return len(negative_args) % 2 == 1
# As a last resort, we choose the one with greater value of .sort_key()
return bool(self.sort_key() < negative_self.sort_key())
def extract_branch_factor(self, allow_half=False):
"""
Try to write self as ``exp_polar(2*pi*I*n)*z`` in a nice way.
Return (z, n).
>>> from sympy import exp_polar, I, pi
>>> from sympy.abc import x, y
>>> exp_polar(I*pi).extract_branch_factor()
(exp_polar(I*pi), 0)
>>> exp_polar(2*I*pi).extract_branch_factor()
(1, 1)
>>> exp_polar(-pi*I).extract_branch_factor()
(exp_polar(I*pi), -1)
>>> exp_polar(3*pi*I + x).extract_branch_factor()
(exp_polar(x + I*pi), 1)
>>> (y*exp_polar(-5*pi*I)*exp_polar(3*pi*I + 2*pi*x)).extract_branch_factor()
(y*exp_polar(2*pi*x), -1)
>>> exp_polar(-I*pi/2).extract_branch_factor()
(exp_polar(-I*pi/2), 0)
If allow_half is True, also extract exp_polar(I*pi):
>>> exp_polar(I*pi).extract_branch_factor(allow_half=True)
(1, 1/2)
>>> exp_polar(2*I*pi).extract_branch_factor(allow_half=True)
(1, 1)
>>> exp_polar(3*I*pi).extract_branch_factor(allow_half=True)
(1, 3/2)
>>> exp_polar(-I*pi).extract_branch_factor(allow_half=True)
(1, -1/2)
"""
from sympy import exp_polar, pi, I, ceiling, Add
n = S(0)
res = S(1)
args = Mul.make_args(self)
exps = []
for arg in args:
if arg.func is exp_polar:
exps += [arg.exp]
else:
res *= arg
piimult = S(0)
extras = []
while exps:
exp = exps.pop()
if exp.is_Add:
exps += exp.args
continue
if exp.is_Mul:
coeff = exp.as_coefficient(pi*I)
if coeff is not None:
piimult += coeff
continue
extras += [exp]
if not piimult.free_symbols:
coeff = piimult
tail = ()
else:
coeff, tail = piimult.as_coeff_add(*piimult.free_symbols)
# round down to nearest multiple of 2
branchfact = ceiling(coeff/2 - S(1)/2)*2
n += branchfact/2
c = coeff - branchfact
if allow_half:
nc = c.extract_additively(1)
if nc is not None:
n += S(1)/2
c = nc
newexp = pi*I*Add(*((c, ) + tail)) + Add(*extras)
if newexp != 0:
res *= exp_polar(newexp)
return res, n
def _eval_is_polynomial(self, syms):
if self.free_symbols.intersection(syms) == set([]):
return True
return False
def is_polynomial(self, *syms):
r"""
Return True if self is a polynomial in syms and False otherwise.
This checks if self is an exact polynomial in syms. This function
returns False for expressions that are "polynomials" with symbolic
exponents. Thus, you should be able to apply polynomial algorithms to
expressions for which this returns True, and Poly(expr, \*syms) should
work if and only if expr.is_polynomial(\*syms) returns True. The
polynomial does not have to be in expanded form. If no symbols are
given, all free symbols in the expression will be used.
This is not part of the assumptions system. You cannot do
Symbol('z', polynomial=True).
Examples
========
>>> from sympy import Symbol
>>> x = Symbol('x')
>>> ((x**2 + 1)**4).is_polynomial(x)
True
>>> ((x**2 + 1)**4).is_polynomial()
True
>>> (2**x + 1).is_polynomial(x)
False
>>> n = Symbol('n', nonnegative=True, integer=True)
>>> (x**n + 1).is_polynomial(x)
False
This function does not attempt any nontrivial simplifications that may
result in an expression that does not appear to be a polynomial to
become one.
>>> from sympy import sqrt, factor, cancel
>>> y = Symbol('y', positive=True)
>>> a = sqrt(y**2 + 2*y + 1)
>>> a.is_polynomial(y)
False
>>> factor(a)
y + 1
>>> factor(a).is_polynomial(y)
True
>>> b = (y**2 + 2*y + 1)/(y + 1)
>>> b.is_polynomial(y)
False
>>> cancel(b)
y + 1
>>> cancel(b).is_polynomial(y)
True
See also .is_rational_function()
"""
if syms:
syms = set(map(sympify, syms))
else:
syms = self.free_symbols
if syms.intersection(self.free_symbols) == set([]):
# constant polynomial
return True
else:
return self._eval_is_polynomial(syms)
def _eval_is_rational_function(self, syms):
if self.free_symbols.intersection(syms) == set([]):
return True
return False
def is_rational_function(self, *syms):
"""
Test whether function is a ratio of two polynomials in the given
symbols, syms. When syms is not given, all free symbols will be used.
The rational function does not have to be in expanded or in any kind of
canonical form.
This function returns False for expressions that are "rational
functions" with symbolic exponents. Thus, you should be able to call
.as_numer_denom() and apply polynomial algorithms to the result for
expressions for which this returns True.
This is not part of the assumptions system. You cannot do
Symbol('z', rational_function=True).
Examples
========
>>> from sympy import Symbol, sin
>>> from sympy.abc import x, y
>>> (x/y).is_rational_function()
True
>>> (x**2).is_rational_function()
True
>>> (x/sin(y)).is_rational_function(y)
False
>>> n = Symbol('n', integer=True)
>>> (x**n + 1).is_rational_function(x)
False
This function does not attempt any nontrivial simplifications that may
result in an expression that does not appear to be a rational function
to become one.
>>> from sympy import sqrt, factor
>>> y = Symbol('y', positive=True)
>>> a = sqrt(y**2 + 2*y + 1)/y
>>> a.is_rational_function(y)
False
>>> factor(a)
(y + 1)/y
>>> factor(a).is_rational_function(y)
True
See also is_algebraic_expr().
"""
if self in [S.NaN, S.Infinity, -S.Infinity, S.ComplexInfinity]:
return False
if syms:
syms = set(map(sympify, syms))
else:
syms = self.free_symbols
if syms.intersection(self.free_symbols) == set([]):
# constant rational function
return True
else:
return self._eval_is_rational_function(syms)
def _eval_is_algebraic_expr(self, syms):
if self.free_symbols.intersection(syms) == set([]):
return True
return False
def is_algebraic_expr(self, *syms):
"""
This tests whether a given expression is algebraic or not, in the
given symbols, syms. When syms is not given, all free symbols
will be used. The rational function does not have to be in expanded
or in any kind of canonical form.
This function returns False for expressions that are "algebraic
expressions" with symbolic exponents. This is a simple extension to the
is_rational_function, including rational exponentiation.
Examples
========
>>> from sympy import Symbol, sqrt
>>> x = Symbol('x', real=True)
>>> sqrt(1 + x).is_rational_function()
False
>>> sqrt(1 + x).is_algebraic_expr()
True
This function does not attempt any nontrivial simplifications that may
result in an expression that does not appear to be an algebraic
expression to become one.
>>> from sympy import exp, factor
>>> a = sqrt(exp(x)**2 + 2*exp(x) + 1)/(exp(x) + 1)
>>> a.is_algebraic_expr(x)
False
>>> factor(a).is_algebraic_expr()
True
See Also
========
is_rational_function()
References
==========
- http://en.wikipedia.org/wiki/Algebraic_expression
"""
if syms:
syms = set(map(sympify, syms))
else:
syms = self.free_symbols
if syms.intersection(self.free_symbols) == set([]):
# constant algebraic expression
return True
else:
return self._eval_is_algebraic_expr(syms)
###################################################################################
##################### SERIES, LEADING TERM, LIMIT, ORDER METHODS ##################
###################################################################################
def series(self, x=None, x0=0, n=6, dir="+", logx=None):
"""
Series expansion of "self" around ``x = x0`` yielding either terms of
the series one by one (the lazy series given when n=None), else
all the terms at once when n != None.
Returns the series expansion of "self" around the point ``x = x0``
with respect to ``x`` up to ``O((x - x0)**n, x, x0)`` (default n is 6).
If ``x=None`` and ``self`` is univariate, the univariate symbol will
be supplied, otherwise an error will be raised.
>>> from sympy import cos, exp
>>> from sympy.abc import x, y
>>> cos(x).series()
1 - x**2/2 + x**4/24 + O(x**6)
>>> cos(x).series(n=4)
1 - x**2/2 + O(x**4)
>>> cos(x).series(x, x0=1, n=2)
cos(1) - (x - 1)*sin(1) + O((x - 1)**2, (x, 1))
>>> e = cos(x + exp(y))
>>> e.series(y, n=2)
cos(x + 1) - y*sin(x + 1) + O(y**2)
>>> e.series(x, n=2)
cos(exp(y)) - x*sin(exp(y)) + O(x**2)
If ``n=None`` then a generator of the series terms will be returned.
>>> term=cos(x).series(n=None)
>>> [next(term) for i in range(2)]
[1, -x**2/2]
For ``dir=+`` (default) the series is calculated from the right and
for ``dir=-`` the series from the left. For smooth functions this
flag will not alter the results.
>>> abs(x).series(dir="+")
x
>>> abs(x).series(dir="-")
-x
"""
from sympy import collect, Dummy, Order, Rational, Symbol
if x is None:
syms = self.atoms(Symbol)
if not syms:
return self
elif len(syms) > 1:
raise ValueError('x must be given for multivariate functions.')
x = syms.pop()
if not self.has(x):
if n is None:
return (s for s in [self])
else:
return self
if len(dir) != 1 or dir not in '+-':
raise ValueError("Dir must be '+' or '-'")
if x0 in [S.Infinity, S.NegativeInfinity]:
dir = {S.Infinity: '+', S.NegativeInfinity: '-'}[x0]
s = self.subs(x, 1/x).series(x, n=n, dir=dir)
if n is None:
return (si.subs(x, 1/x) for si in s)
return s.subs(x, 1/x)
# use rep to shift origin to x0 and change sign (if dir is negative)
# and undo the process with rep2
if x0 or dir == '-':
if dir == '-':
rep = -x + x0
rep2 = -x
rep2b = x0
else:
rep = x + x0
rep2 = x
rep2b = -x0
s = self.subs(x, rep).series(x, x0=0, n=n, dir='+', logx=logx)
if n is None: # lseries...
return (si.subs(x, rep2 + rep2b) for si in s)
return s.subs(x, rep2 + rep2b)
# from here on it's x0=0 and dir='+' handling
if x.is_positive is x.is_negative is None or x.is_Symbol is not True:
# replace x with an x that has a positive assumption
xpos = Dummy('x', positive=True, finite=True)
rv = self.subs(x, xpos).series(xpos, x0, n, dir, logx=logx)
if n is None:
return (s.subs(xpos, x) for s in rv)
else:
return rv.subs(xpos, x)
if n is not None: # nseries handling
s1 = self._eval_nseries(x, n=n, logx=logx)
o = s1.getO() or S.Zero
if o:
# make sure the requested order is returned
ngot = o.getn()
if ngot > n:
# leave o in its current form (e.g. with x*log(x)) so
# it eats terms properly, then replace it below
if n != 0:
s1 += o.subs(x, x**Rational(n, ngot))
else:
s1 += Order(1, x)
elif ngot < n:
# increase the requested number of terms to get the desired
# number keep increasing (up to 9) until the received order
# is different than the original order and then predict how
# many additional terms are needed
for more in range(1, 9):
s1 = self._eval_nseries(x, n=n + more, logx=logx)
newn = s1.getn()
if newn != ngot:
ndo = n + (n - ngot)*more/(newn - ngot)
s1 = self._eval_nseries(x, n=ndo, logx=logx)
while s1.getn() < n:
s1 = self._eval_nseries(x, n=ndo, logx=logx)
ndo += 1
break
else:
raise ValueError('Could not calculate %s terms for %s'
% (str(n), self))
s1 += Order(x**n, x)
o = s1.getO()
s1 = s1.removeO()
else:
o = Order(x**n, x)
s1done = s1.doit()
if (s1done + o).removeO() == s1done:
o = S.Zero
try:
return collect(s1, x) + o
except NotImplementedError:
return s1 + o
else: # lseries handling
def yield_lseries(s):
"""Return terms of lseries one at a time."""
for si in s:
if not si.is_Add:
yield si
continue
# yield terms 1 at a time if possible
# by increasing order until all the
# terms have been returned
yielded = 0
o = Order(si, x)*x
ndid = 0
ndo = len(si.args)
while 1:
do = (si - yielded + o).removeO()
o *= x
if not do or do.is_Order:
continue
if do.is_Add:
ndid += len(do.args)
else:
ndid += 1
yield do
if ndid == ndo:
break
yielded += do
return yield_lseries(self.removeO()._eval_lseries(x, logx=logx))
def taylor_term(self, n, x, *previous_terms):
"""General method for the taylor term.
This method is slow, because it differentiates n-times. Subclasses can
redefine it to make it faster by using the "previous_terms".
"""
from sympy import Dummy, factorial
x = sympify(x)
_x = Dummy('x')
return self.subs(x, _x).diff(_x, n).subs(_x, x).subs(x, 0) * x**n / factorial(n)
def lseries(self, x=None, x0=0, dir='+', logx=None):
"""
Wrapper for series yielding an iterator of the terms of the series.
Note: an infinite series will yield an infinite iterator. The following,
for exaxmple, will never terminate. It will just keep printing terms
of the sin(x) series::
for term in sin(x).lseries(x):
print term
The advantage of lseries() over nseries() is that many times you are
just interested in the next term in the series (i.e. the first term for
example), but you don't know how many you should ask for in nseries()
using the "n" parameter.
See also nseries().
"""
return self.series(x, x0, n=None, dir=dir, logx=logx)
def _eval_lseries(self, x, logx=None):
# default implementation of lseries is using nseries(), and adaptively
# increasing the "n". As you can see, it is not very efficient, because
# we are calculating the series over and over again. Subclasses should
# override this method and implement much more efficient yielding of
# terms.
n = 0
series = self._eval_nseries(x, n=n, logx=logx)
if not series.is_Order:
if series.is_Add:
yield series.removeO()
else:
yield series
return
while series.is_Order:
n += 1
series = self._eval_nseries(x, n=n, logx=logx)
e = series.removeO()
yield e
while 1:
while 1:
n += 1
series = self._eval_nseries(x, n=n, logx=logx).removeO()
if e != series:
break
yield series - e
e = series
def nseries(self, x=None, x0=0, n=6, dir='+', logx=None):
"""
Wrapper to _eval_nseries if assumptions allow, else to series.
If x is given, x0 is 0, dir='+', and self has x, then _eval_nseries is
called. This calculates "n" terms in the innermost expressions and
then builds up the final series just by "cross-multiplying" everything
out.
The optional ``logx`` parameter can be used to replace any log(x) in the
returned series with a symbolic value to avoid evaluating log(x) at 0. A
symbol to use in place of log(x) should be provided.
Advantage -- it's fast, because we don't have to determine how many
terms we need to calculate in advance.
Disadvantage -- you may end up with less terms than you may have
expected, but the O(x**n) term appended will always be correct and
so the result, though perhaps shorter, will also be correct.
If any of those assumptions is not met, this is treated like a
wrapper to series which will try harder to return the correct
number of terms.
See also lseries().
Examples
========
>>> from sympy import sin, log, Symbol
>>> from sympy.abc import x, y
>>> sin(x).nseries(x, 0, 6)
x - x**3/6 + x**5/120 + O(x**6)
>>> log(x+1).nseries(x, 0, 5)
x - x**2/2 + x**3/3 - x**4/4 + O(x**5)
Handling of the ``logx`` parameter --- in the following example the
expansion fails since ``sin`` does not have an asymptotic expansion
at -oo (the limit of log(x) as x approaches 0):
>>> e = sin(log(x))
>>> e.nseries(x, 0, 6)
Traceback (most recent call last):
...
PoleError: ...
...
>>> logx = Symbol('logx')
>>> e.nseries(x, 0, 6, logx=logx)
sin(logx)
In the following example, the expansion works but gives only an Order term
unless the ``logx`` parameter is used:
>>> e = x**y
>>> e.nseries(x, 0, 2)
O(log(x)**2)
>>> e.nseries(x, 0, 2, logx=logx)
exp(logx*y)
"""
if x and not x in self.free_symbols:
return self
if x is None or x0 or dir != '+': # {see XPOS above} or (x.is_positive == x.is_negative == None):
return self.series(x, x0, n, dir)
else:
return self._eval_nseries(x, n=n, logx=logx)
def _eval_nseries(self, x, n, logx):
"""
Return terms of series for self up to O(x**n) at x=0
from the positive direction.
This is a method that should be overridden in subclasses. Users should
never call this method directly (use .nseries() instead), so you don't
have to write docstrings for _eval_nseries().
"""
from sympy.utilities.misc import filldedent
raise NotImplementedError(filldedent("""
The _eval_nseries method should be added to
%s to give terms up to O(x**n) at x=0
from the positive direction so it is available when
nseries calls it.""" % self.func)
)
def limit(self, x, xlim, dir='+'):
""" Compute limit x->xlim.
"""
from sympy.series.limits import limit
return limit(self, x, xlim, dir)
def compute_leading_term(self, x, logx=None):
"""
as_leading_term is only allowed for results of .series()
This is a wrapper to compute a series first.
"""
from sympy import Dummy, log
from sympy.series.gruntz import calculate_series
if self.removeO() == 0:
return self
if logx is None:
d = Dummy('logx')
s = calculate_series(self, x, d).subs(d, log(x))
else:
s = calculate_series(self, x, logx)
return s.as_leading_term(x)
@cacheit
def as_leading_term(self, *symbols):
"""
Returns the leading (nonzero) term of the series expansion of self.
The _eval_as_leading_term routines are used to do this, and they must
always return a non-zero value.
Examples
========
>>> from sympy.abc import x
>>> (1 + x + x**2).as_leading_term(x)
1
>>> (1/x**2 + x + x**2).as_leading_term(x)
x**(-2)
"""
from sympy import powsimp
if len(symbols) > 1:
c = self
for x in symbols:
c = c.as_leading_term(x)
return c
elif not symbols:
return self
x = sympify(symbols[0])
if not x.is_Symbol:
raise ValueError('expecting a Symbol but got %s' % x)
if x not in self.free_symbols:
return self
obj = self._eval_as_leading_term(x)
if obj is not None:
return powsimp(obj, deep=True, combine='exp')
raise NotImplementedError('as_leading_term(%s, %s)' % (self, x))
def _eval_as_leading_term(self, x):
return self
def as_coeff_exponent(self, x):
""" ``c*x**e -> c,e`` where x can be any symbolic expression.
"""
from sympy import collect
s = collect(self, x)
c, p = s.as_coeff_mul(x)
if len(p) == 1:
b, e = p[0].as_base_exp()
if b == x:
return c, e
return s, S.Zero
def leadterm(self, x):
"""
Returns the leading term a*x**b as a tuple (a, b).
Examples
========
>>> from sympy.abc import x
>>> (1+x+x**2).leadterm(x)
(1, 0)
>>> (1/x**2+x+x**2).leadterm(x)
(1, -2)
"""
from sympy import Dummy, log
l = self.as_leading_term(x)
d = Dummy('logx')
if l.has(log(x)):
l = l.subs(log(x), d)
c, e = l.as_coeff_exponent(x)
if x in c.free_symbols:
from sympy.utilities.misc import filldedent
raise ValueError(filldedent("""
cannot compute leadterm(%s, %s). The coefficient
should have been free of x but got %s""" % (self, x, c)))
c = c.subs(d, log(x))
return c, e
def as_coeff_Mul(self, rational=False):
"""Efficiently extract the coefficient of a product. """
return S.One, self
def as_coeff_Add(self, rational=False):
"""Efficiently extract the coefficient of a summation. """
return S.Zero, self
def fps(self, x=None, x0=0, dir=1, hyper=True, order=4, rational=True,
full=False):
"""
Compute formal power power series of self.
See the docstring of the :func:`fps` function in sympy.series.formal for
more information.
"""
from sympy.series.formal import fps
return fps(self, x, x0, dir, hyper, order, rational, full)
def fourier_series(self, limits=None):
"""Compute fourier sine/cosine series of self.
See the docstring of the :func:`fourier_series` in sympy.series.fourier
for more information.
"""
from sympy.series.fourier import fourier_series
return fourier_series(self, limits)
###################################################################################
##################### DERIVATIVE, INTEGRAL, FUNCTIONAL METHODS ####################
###################################################################################
def diff(self, *symbols, **assumptions):
new_symbols = list(map(sympify, symbols)) # e.g. x, 2, y, z
assumptions.setdefault("evaluate", True)
return Derivative(self, *new_symbols, **assumptions)
###########################################################################
###################### EXPRESSION EXPANSION METHODS #######################
###########################################################################
# Relevant subclasses should override _eval_expand_hint() methods. See
# the docstring of expand() for more info.
def _eval_expand_complex(self, **hints):
real, imag = self.as_real_imag(**hints)
return real + S.ImaginaryUnit*imag
@staticmethod
def _expand_hint(expr, hint, deep=True, **hints):
"""
Helper for ``expand()``. Recursively calls ``expr._eval_expand_hint()``.
Returns ``(expr, hit)``, where expr is the (possibly) expanded
``expr`` and ``hit`` is ``True`` if ``expr`` was truly expanded and
``False`` otherwise.
"""
hit = False
# XXX: Hack to support non-Basic args
# |
# V
if deep and getattr(expr, 'args', ()) and not expr.is_Atom:
sargs = []
for arg in expr.args:
arg, arghit = Expr._expand_hint(arg, hint, **hints)
hit |= arghit
sargs.append(arg)
if hit:
expr = expr.func(*sargs)
if hasattr(expr, hint):
newexpr = getattr(expr, hint)(**hints)
if newexpr != expr:
return (newexpr, True)
return (expr, hit)
@cacheit
def expand(self, deep=True, modulus=None, power_base=True, power_exp=True,
mul=True, log=True, multinomial=True, basic=True, **hints):
"""
Expand an expression using hints.
See the docstring of the expand() function in sympy.core.function for
more information.
"""
from sympy.simplify.radsimp import fraction
hints.update(power_base=power_base, power_exp=power_exp, mul=mul,
log=log, multinomial=multinomial, basic=basic)
expr = self
if hints.pop('frac', False):
n, d = [a.expand(deep=deep, modulus=modulus, **hints)
for a in fraction(self)]
return n/d
elif hints.pop('denom', False):
n, d = fraction(self)
return n/d.expand(deep=deep, modulus=modulus, **hints)
elif hints.pop('numer', False):
n, d = fraction(self)
return n.expand(deep=deep, modulus=modulus, **hints)/d
# Although the hints are sorted here, an earlier hint may get applied
# at a given node in the expression tree before another because of how
# the hints are applied. e.g. expand(log(x*(y + z))) -> log(x*y +
# x*z) because while applying log at the top level, log and mul are
# applied at the deeper level in the tree so that when the log at the
# upper level gets applied, the mul has already been applied at the
# lower level.
# Additionally, because hints are only applied once, the expression
# may not be expanded all the way. For example, if mul is applied
# before multinomial, x*(x + 1)**2 won't be expanded all the way. For
# now, we just use a special case to make multinomial run before mul,
# so that at least polynomials will be expanded all the way. In the
# future, smarter heuristics should be applied.
# TODO: Smarter heuristics
def _expand_hint_key(hint):
"""Make multinomial come before mul"""
if hint == 'mul':
return 'mulz'
return hint
for hint in sorted(hints.keys(), key=_expand_hint_key):
use_hint = hints[hint]
if use_hint:
hint = '_eval_expand_' + hint
expr, hit = Expr._expand_hint(expr, hint, deep=deep, **hints)
while True:
was = expr
if hints.get('multinomial', False):
expr, _ = Expr._expand_hint(
expr, '_eval_expand_multinomial', deep=deep, **hints)
if hints.get('mul', False):
expr, _ = Expr._expand_hint(
expr, '_eval_expand_mul', deep=deep, **hints)
if hints.get('log', False):
expr, _ = Expr._expand_hint(
expr, '_eval_expand_log', deep=deep, **hints)
if expr == was:
break
if modulus is not None:
modulus = sympify(modulus)
if not modulus.is_Integer or modulus <= 0:
raise ValueError(
"modulus must be a positive integer, got %s" % modulus)
terms = []
for term in Add.make_args(expr):
coeff, tail = term.as_coeff_Mul(rational=True)
coeff %= modulus
if coeff:
terms.append(coeff*tail)
expr = Add(*terms)
return expr
###########################################################################
################### GLOBAL ACTION VERB WRAPPER METHODS ####################
###########################################################################
def integrate(self, *args, **kwargs):
"""See the integrate function in sympy.integrals"""
from sympy.integrals import integrate
return integrate(self, *args, **kwargs)
def simplify(self, ratio=1.7, measure=None):
"""See the simplify function in sympy.simplify"""
from sympy.simplify import simplify
from sympy.core.function import count_ops
measure = measure or count_ops
return simplify(self, ratio, measure)
def nsimplify(self, constants=[], tolerance=None, full=False):
"""See the nsimplify function in sympy.simplify"""
from sympy.simplify import nsimplify
return nsimplify(self, constants, tolerance, full)
def separate(self, deep=False, force=False):
"""See the separate function in sympy.simplify"""
from sympy.core.function import expand_power_base
return expand_power_base(self, deep=deep, force=force)
def collect(self, syms, func=None, evaluate=True, exact=False, distribute_order_term=True):
"""See the collect function in sympy.simplify"""
from sympy.simplify import collect
return collect(self, syms, func, evaluate, exact, distribute_order_term)
def together(self, *args, **kwargs):
"""See the together function in sympy.polys"""
from sympy.polys import together
return together(self, *args, **kwargs)
def apart(self, x=None, **args):
"""See the apart function in sympy.polys"""
from sympy.polys import apart
return apart(self, x, **args)
def ratsimp(self):
"""See the ratsimp function in sympy.simplify"""
from sympy.simplify import ratsimp
return ratsimp(self)
def trigsimp(self, **args):
"""See the trigsimp function in sympy.simplify"""
from sympy.simplify import trigsimp
return trigsimp(self, **args)
def radsimp(self, **kwargs):
"""See the radsimp function in sympy.simplify"""
from sympy.simplify import radsimp
return radsimp(self, **kwargs)
def powsimp(self, *args, **kwargs):
"""See the powsimp function in sympy.simplify"""
from sympy.simplify import powsimp
return powsimp(self, *args, **kwargs)
def combsimp(self):
"""See the combsimp function in sympy.simplify"""
from sympy.simplify import combsimp
return combsimp(self)
def factor(self, *gens, **args):
"""See the factor() function in sympy.polys.polytools"""
from sympy.polys import factor
return factor(self, *gens, **args)
def refine(self, assumption=True):
"""See the refine function in sympy.assumptions"""
from sympy.assumptions import refine
return refine(self, assumption)
def cancel(self, *gens, **args):
"""See the cancel function in sympy.polys"""
from sympy.polys import cancel
return cancel(self, *gens, **args)
def invert(self, g, *gens, **args):
"""Return the multiplicative inverse of ``self`` mod ``g``
where ``self`` (and ``g``) may be symbolic expressions).
See Also
========
sympy.core.numbers.mod_inverse, sympy.polys.polytools.invert
"""
from sympy.polys.polytools import invert
from sympy.core.numbers import mod_inverse
if self.is_number and getattr(g, 'is_number', True):
return mod_inverse(self, g)
return invert(self, g, *gens, **args)
def round(self, p=0):
"""Return x rounded to the given decimal place.
If a complex number would results, apply round to the real
and imaginary components of the number.
Examples
========
>>> from sympy import pi, E, I, S, Add, Mul, Number
>>> S(10.5).round()
11.
>>> pi.round()
3.
>>> pi.round(2)
3.14
>>> (2*pi + E*I).round()
6. + 3.*I
The round method has a chopping effect:
>>> (2*pi + I/10).round()
6.
>>> (pi/10 + 2*I).round()
2.*I
>>> (pi/10 + E*I).round(2)
0.31 + 2.72*I
Notes
=====
Do not confuse the Python builtin function, round, with the
SymPy method of the same name. The former always returns a float
(or raises an error if applied to a complex value) while the
latter returns either a Number or a complex number:
>>> isinstance(round(S(123), -2), Number)
False
>>> isinstance(S(123).round(-2), Number)
True
>>> isinstance((3*I).round(), Mul)
True
>>> isinstance((1 + 3*I).round(), Add)
True
"""
from sympy import Float
x = self
if not x.is_number:
raise TypeError("can't round symbolic expression")
if not x.is_Atom:
xn = x.n(2)
if not pure_complex(xn, or_real=True):
raise TypeError('Expected a number but got %s:' %
getattr(getattr(x,'func', x), '__name__', type(x)))
elif x in (S.NaN, S.Infinity, S.NegativeInfinity, S.ComplexInfinity):
return x
if not x.is_real:
i, r = x.as_real_imag()
return i.round(p) + S.ImaginaryUnit*r.round(p)
if not x:
return x
p = int(p)
precs = [f._prec for f in x.atoms(Float)]
dps = prec_to_dps(max(precs)) if precs else None
mag_first_dig = _mag(x)
allow = digits_needed = mag_first_dig + p
if dps is not None and allow > dps:
allow = dps
mag = Pow(10, p) # magnitude needed to bring digit p to units place
xwas = x
x += 1/(2*mag) # add the half for rounding
i10 = 10*mag*x.n((dps if dps is not None else digits_needed) + 1)
if i10.is_negative:
x = xwas - 1/(2*mag) # should have gone the other way
i10 = 10*mag*x.n((dps if dps is not None else digits_needed) + 1)
rv = -(Integer(-i10)//10)
else:
rv = Integer(i10)//10
q = 1
if p > 0:
q = mag
elif p < 0:
rv /= mag
rv = Rational(rv, q)
if rv.is_Integer:
# use str or else it won't be a float
return Float(str(rv), digits_needed)
else:
if not allow and rv > self:
allow += 1
return Float(rv, allow)
class AtomicExpr(Atom, Expr):
"""
A parent class for object which are both atoms and Exprs.
For example: Symbol, Number, Rational, Integer, ...
But not: Add, Mul, Pow, ...
"""
is_number = False
is_Atom = True
__slots__ = []
def _eval_derivative(self, s):
if self == s:
return S.One
return S.Zero
def _eval_is_polynomial(self, syms):
return True
def _eval_is_rational_function(self, syms):
return True
def _eval_is_algebraic_expr(self, syms):
return True
def _eval_nseries(self, x, n, logx):
return self
def _mag(x):
"""Return integer ``i`` such that .1 <= x/10**i < 1
Examples
========
>>> from sympy.core.expr import _mag
>>> from sympy import Float
>>> _mag(Float(.1))
0
>>> _mag(Float(.01))
-1
>>> _mag(Float(1234))
4
"""
from math import log10, ceil, log
from sympy import Float
xpos = abs(x.n())
if not xpos:
return S.Zero
try:
mag_first_dig = int(ceil(log10(xpos)))
except (ValueError, OverflowError):
mag_first_dig = int(ceil(Float(mpf_log(xpos._mpf_, 53))/log(10)))
# check that we aren't off by 1
if (xpos/10**mag_first_dig) >= 1:
assert 1 <= (xpos/10**mag_first_dig) < 10
mag_first_dig += 1
return mag_first_dig
class UnevaluatedExpr(Expr):
"""
Expression that is not evaluated unless released.
Examples
========
>>> from sympy import UnevaluatedExpr
>>> from sympy.abc import a, b, x, y
>>> x*(1/x)
1
>>> x*UnevaluatedExpr(1/x)
x*1/x
"""
def __new__(cls, arg, **kwargs):
arg = _sympify(arg)
obj = Expr.__new__(cls, arg, **kwargs)
return obj
def doit(self, *args, **kwargs):
if kwargs.get("deep", True):
return self.args[0].doit(*args, **kwargs)
else:
return self.args[0]
def _n2(a, b):
"""Return (a - b).evalf(2) if it, a and b are comparable, else None.
This should only be used when a and b are already sympified.
"""
if not all(i.is_number for i in (a, b)):
return
# /!\ if is very important (see issue 8245) not to
# use a re-evaluated number in the calculation of dif
if a.is_comparable and b.is_comparable:
dif = (a - b).evalf(2)
if dif.is_comparable:
return dif
from .mul import Mul
from .add import Add
from .power import Pow
from .function import Derivative, Function
from .mod import Mod
from .exprtools import factor_terms
from .numbers import Integer, Rational
| 116,889 | 33.552173 | 109 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/sympify.py
|
"""sympify -- convert objects SymPy internal format"""
from __future__ import print_function, division
from inspect import getmro
from .core import all_classes as sympy_classes
from .compatibility import iterable, string_types, range
from .evaluate import global_evaluate
class SympifyError(ValueError):
def __init__(self, expr, base_exc=None):
self.expr = expr
self.base_exc = base_exc
def __str__(self):
if self.base_exc is None:
return "SympifyError: %r" % (self.expr,)
return ("Sympify of expression '%s' failed, because of exception being "
"raised:\n%s: %s" % (self.expr, self.base_exc.__class__.__name__,
str(self.base_exc)))
converter = {} # See sympify docstring.
class CantSympify(object):
"""
Mix in this trait to a class to disallow sympification of its instances.
Examples
========
>>> from sympy.core.sympify import sympify, CantSympify
>>> class Something(dict):
... pass
...
>>> sympify(Something())
{}
>>> class Something(dict, CantSympify):
... pass
...
>>> sympify(Something())
Traceback (most recent call last):
...
SympifyError: SympifyError: {}
"""
pass
def sympify(a, locals=None, convert_xor=True, strict=False, rational=False,
evaluate=None):
"""Converts an arbitrary expression to a type that can be used inside SymPy.
For example, it will convert Python ints into instance of sympy.Rational,
floats into instances of sympy.Float, etc. It is also able to coerce symbolic
expressions which inherit from Basic. This can be useful in cooperation
with SAGE.
It currently accepts as arguments:
- any object defined in sympy
- standard numeric python types: int, long, float, Decimal
- strings (like "0.09" or "2e-19")
- booleans, including ``None`` (will leave ``None`` unchanged)
- lists, sets or tuples containing any of the above
.. warning::
Note that this function uses ``eval``, and thus shouldn't be used on
unsanitized input.
If the argument is already a type that SymPy understands, it will do
nothing but return that value. This can be used at the beginning of a
function to ensure you are working with the correct type.
>>> from sympy import sympify
>>> sympify(2).is_integer
True
>>> sympify(2).is_real
True
>>> sympify(2.0).is_real
True
>>> sympify("2.0").is_real
True
>>> sympify("2e-45").is_real
True
If the expression could not be converted, a SympifyError is raised.
>>> sympify("x***2")
Traceback (most recent call last):
...
SympifyError: SympifyError: "could not parse u'x***2'"
Locals
------
The sympification happens with access to everything that is loaded
by ``from sympy import *``; anything used in a string that is not
defined by that import will be converted to a symbol. In the following,
the ``bitcount`` function is treated as a symbol and the ``O`` is
interpreted as the Order object (used with series) and it raises
an error when used improperly:
>>> s = 'bitcount(42)'
>>> sympify(s)
bitcount(42)
>>> sympify("O(x)")
O(x)
>>> sympify("O + 1")
Traceback (most recent call last):
...
TypeError: unbound method...
In order to have ``bitcount`` be recognized it can be imported into a
namespace dictionary and passed as locals:
>>> from sympy.core.compatibility import exec_
>>> ns = {}
>>> exec_('from sympy.core.evalf import bitcount', ns)
>>> sympify(s, locals=ns)
6
In order to have the ``O`` interpreted as a Symbol, identify it as such
in the namespace dictionary. This can be done in a variety of ways; all
three of the following are possibilities:
>>> from sympy import Symbol
>>> ns["O"] = Symbol("O") # method 1
>>> exec_('from sympy.abc import O', ns) # method 2
>>> ns.update(dict(O=Symbol("O"))) # method 3
>>> sympify("O + 1", locals=ns)
O + 1
If you want *all* single-letter and Greek-letter variables to be symbols
then you can use the clashing-symbols dictionaries that have been defined
there as private variables: _clash1 (single-letter variables), _clash2
(the multi-letter Greek names) or _clash (both single and multi-letter
names that are defined in abc).
>>> from sympy.abc import _clash1
>>> _clash1
{'C': C, 'E': E, 'I': I, 'N': N, 'O': O, 'Q': Q, 'S': S}
>>> sympify('I & Q', _clash1)
I & Q
Strict
------
If the option ``strict`` is set to ``True``, only the types for which an
explicit conversion has been defined are converted. In the other
cases, a SympifyError is raised.
>>> print(sympify(None))
None
>>> sympify(None, strict=True)
Traceback (most recent call last):
...
SympifyError: SympifyError: None
Evaluation
----------
If the option ``evaluate`` is set to ``False``, then arithmetic and
operators will be converted into their SymPy equivalents and the
``evaluate=False`` option will be added. Nested ``Add`` or ``Mul`` will
be denested first. This is done via an AST transformation that replaces
operators with their SymPy equivalents, so if an operand redefines any
of those operations, the redefined operators will not be used.
>>> sympify('2**2 / 3 + 5')
19/3
>>> sympify('2**2 / 3 + 5', evaluate=False)
2**2/3 + 5
Extending
---------
To extend ``sympify`` to convert custom objects (not derived from ``Basic``),
just define a ``_sympy_`` method to your class. You can do that even to
classes that you do not own by subclassing or adding the method at runtime.
>>> from sympy import Matrix
>>> class MyList1(object):
... def __iter__(self):
... yield 1
... yield 2
... return
... def __getitem__(self, i): return list(self)[i]
... def _sympy_(self): return Matrix(self)
>>> sympify(MyList1())
Matrix([
[1],
[2]])
If you do not have control over the class definition you could also use the
``converter`` global dictionary. The key is the class and the value is a
function that takes a single argument and returns the desired SymPy
object, e.g. ``converter[MyList] = lambda x: Matrix(x)``.
>>> class MyList2(object): # XXX Do not do this if you control the class!
... def __iter__(self): # Use _sympy_!
... yield 1
... yield 2
... return
... def __getitem__(self, i): return list(self)[i]
>>> from sympy.core.sympify import converter
>>> converter[MyList2] = lambda x: Matrix(x)
>>> sympify(MyList2())
Matrix([
[1],
[2]])
Notes
=====
Sometimes autosimplification during sympification results in expressions
that are very different in structure than what was entered. Until such
autosimplification is no longer done, the ``kernS`` function might be of
some use. In the example below you can see how an expression reduces to
-1 by autosimplification, but does not do so when ``kernS`` is used.
>>> from sympy.core.sympify import kernS
>>> from sympy.abc import x
>>> -2*(-(-x + 1/x)/(x*(x - 1/x)**2) - 1/(x*(x - 1/x))) - 1
-1
>>> s = '-2*(-(-x + 1/x)/(x*(x - 1/x)**2) - 1/(x*(x - 1/x))) - 1'
>>> sympify(s)
-1
>>> kernS(s)
-2*(-(-x + 1/x)/(x*(x - 1/x)**2) - 1/(x*(x - 1/x))) - 1
"""
if evaluate is None:
if global_evaluate[0] is False:
evaluate = global_evaluate[0]
else:
evaluate = True
try:
if a in sympy_classes:
return a
except TypeError: # Type of a is unhashable
pass
try:
cls = a.__class__
except AttributeError: # a is probably an old-style class object
cls = type(a)
if cls in sympy_classes:
return a
if cls is type(None):
if strict:
raise SympifyError(a)
else:
return a
# Support for basic numpy datatypes
if type(a).__module__ == 'numpy':
import numpy as np
if np.isscalar(a):
if not isinstance(a, np.floating):
func = converter[complex] if np.iscomplex(a) else sympify
return func(np.asscalar(a))
else:
try:
from sympy.core.numbers import Float
prec = np.finfo(a).nmant
a = str(list(np.reshape(np.asarray(a),
(1, np.size(a)))[0]))[1:-1]
return Float(a, precision=prec)
except NotImplementedError:
raise SympifyError('Translation for numpy float : %s '
'is not implemented' % a)
try:
return converter[cls](a)
except KeyError:
for superclass in getmro(cls):
try:
return converter[superclass](a)
except KeyError:
continue
if isinstance(a, CantSympify):
raise SympifyError(a)
try:
return a._sympy_()
except AttributeError:
pass
if not isinstance(a, string_types):
for coerce in (float, int):
try:
return sympify(coerce(a))
except (TypeError, ValueError, AttributeError, SympifyError):
continue
if strict:
raise SympifyError(a)
try:
from ..tensor.array import Array
return Array(a.flat, a.shape) # works with e.g. NumPy arrays
except AttributeError:
pass
if iterable(a):
try:
return type(a)([sympify(x, locals=locals, convert_xor=convert_xor,
rational=rational) for x in a])
except TypeError:
# Not all iterables are rebuildable with their type.
pass
if isinstance(a, dict):
try:
return type(a)([sympify(x, locals=locals, convert_xor=convert_xor,
rational=rational) for x in a.items()])
except TypeError:
# Not all iterables are rebuildable with their type.
pass
# At this point we were given an arbitrary expression
# which does not inherit from Basic and doesn't implement
# _sympy_ (which is a canonical and robust way to convert
# anything to SymPy expression).
#
# As a last chance, we try to take "a"'s normal form via unicode()
# and try to parse it. If it fails, then we have no luck and
# return an exception
try:
from .compatibility import unicode
a = unicode(a)
except Exception as exc:
raise SympifyError(a, exc)
from sympy.parsing.sympy_parser import (parse_expr, TokenError,
standard_transformations)
from sympy.parsing.sympy_parser import convert_xor as t_convert_xor
from sympy.parsing.sympy_parser import rationalize as t_rationalize
transformations = standard_transformations
if rational:
transformations += (t_rationalize,)
if convert_xor:
transformations += (t_convert_xor,)
try:
a = a.replace('\n', '')
expr = parse_expr(a, local_dict=locals, transformations=transformations, evaluate=evaluate)
except (TokenError, SyntaxError) as exc:
raise SympifyError('could not parse %r' % a, exc)
return expr
def _sympify(a):
"""
Short version of sympify for internal usage for __add__ and __eq__ methods
where it is ok to allow some things (like Python integers and floats) in
the expression. This excludes things (like strings) that are unwise to
allow into such an expression.
>>> from sympy import Integer
>>> Integer(1) == 1
True
>>> Integer(1) == '1'
False
>>> from sympy.abc import x
>>> x + 1
x + 1
>>> x + '1'
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for +: 'Symbol' and 'str'
see: sympify
"""
return sympify(a, strict=True)
def kernS(s):
"""Use a hack to try keep autosimplification from joining Integer or
minus sign into an Add of a Mul; this modification doesn't
prevent the 2-arg Mul from becoming an Add, however.
Examples
========
>>> from sympy.core.sympify import kernS
>>> from sympy.abc import x, y, z
The 2-arg Mul allows a leading Integer to be distributed but kernS will
prevent that:
>>> 2*(x + y)
2*x + 2*y
>>> kernS('2*(x + y)')
2*(x + y)
If use of the hack fails, the un-hacked string will be passed to sympify...
and you get what you get.
XXX This hack should not be necessary once issue 4596 has been resolved.
"""
import re
from sympy.core.symbol import Symbol
hit = False
if '(' in s:
if s.count('(') != s.count(")"):
raise SympifyError('unmatched left parenthesis')
kern = '_kern'
while kern in s:
kern += "_"
olds = s
# digits*( -> digits*kern*(
s = re.sub(r'(\d+)( *\* *)\(', r'\1*%s\2(' % kern, s)
# negated parenthetical
kern2 = kern + "2"
while kern2 in s:
kern2 += "_"
# step 1: -(...) --> kern-kern*(...)
target = r'%s-%s*(' % (kern, kern)
s = re.sub(r'- *\(', target, s)
# step 2: double the matching closing parenthesis
# kern-kern*(...) --> kern-kern*(...)kern2
i = nest = 0
while True:
j = s.find(target, i)
if j == -1:
break
j = s.find('(')
for j in range(j, len(s)):
if s[j] == "(":
nest += 1
elif s[j] == ")":
nest -= 1
if nest == 0:
break
s = s[:j] + kern2 + s[j:]
i = j
# step 3: put in the parentheses
# kern-kern*(...)kern2 --> (-kern*(...))
s = s.replace(target, target.replace(kern, "(", 1))
s = s.replace(kern2, ')')
hit = kern in s
for i in range(2):
try:
expr = sympify(s)
break
except: # the kern might cause unknown errors, so use bare except
if hit:
s = olds # maybe it didn't like the kern; use un-kerned s
hit = False
continue
expr = sympify(s) # let original error raise
if not hit:
return expr
rep = {Symbol(kern): 1}
def _clear(expr):
if isinstance(expr, (list, tuple, set)):
return type(expr)([_clear(e) for e in expr])
if hasattr(expr, 'subs'):
return expr.subs(rep, hack2=True)
return expr
expr = _clear(expr)
# hope that kern is not there anymore
return expr
| 15,002 | 30.126556 | 99 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/backend.py
|
import os
USE_SYMENGINE = os.getenv('USE_SYMENGINE', '0')
USE_SYMENGINE = USE_SYMENGINE.lower() in ('1', 't', 'true')
if USE_SYMENGINE:
from symengine import (Symbol, Integer, sympify, S,
SympifyError, exp, log, gamma, sqrt, I, E, pi, Matrix,
sin, cos, tan, cot, csc, sec, asin, acos, atan, acot, acsc, asec,
sinh, cosh, tanh, coth, asinh, acosh, atanh, acoth,
lambdify, symarray, diff, zeros, eye, diag, ones, zeros,
expand, Function, symbols, var, Add, Mul, Derivative,
ImmutableMatrix, MatrixBase)
from symengine import AppliedUndef
else:
from sympy import (Symbol, Integer, sympify, S,
SympifyError, exp, log, gamma, sqrt, I, E, pi, Matrix,
sin, cos, tan, cot, csc, sec, asin, acos, atan, acot, acsc, asec,
sinh, cosh, tanh, coth, asinh, acosh, atanh, acoth,
lambdify, symarray, diff, zeros, eye, diag, ones, zeros,
expand, Function, symbols, var, Add, Mul, Derivative,
ImmutableMatrix, MatrixBase)
from sympy.core.function import AppliedUndef
| 1,061 | 45.173913 | 73 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/relational.py
|
from __future__ import print_function, division
from .basic import S
from .compatibility import ordered
from .expr import Expr
from .evalf import EvalfMixin
from .function import _coeff_isneg
from .sympify import _sympify
from .evaluate import global_evaluate
from sympy.logic.boolalg import Boolean, BooleanAtom
__all__ = (
'Rel', 'Eq', 'Ne', 'Lt', 'Le', 'Gt', 'Ge',
'Relational', 'Equality', 'Unequality', 'StrictLessThan', 'LessThan',
'StrictGreaterThan', 'GreaterThan',
)
# Note, see issue 4986. Ideally, we wouldn't want to subclass both Boolean
# and Expr.
class Relational(Boolean, Expr, EvalfMixin):
"""Base class for all relation types.
Subclasses of Relational should generally be instantiated directly, but
Relational can be instantiated with a valid `rop` value to dispatch to
the appropriate subclass.
Parameters
==========
rop : str or None
Indicates what subclass to instantiate. Valid values can be found
in the keys of Relational.ValidRelationalOperator.
Examples
========
>>> from sympy import Rel
>>> from sympy.abc import x, y
>>> Rel(y, x+x**2, '==')
Eq(y, x**2 + x)
"""
__slots__ = []
is_Relational = True
# ValidRelationOperator - Defined below, because the necessary classes
# have not yet been defined
def __new__(cls, lhs, rhs, rop=None, **assumptions):
# If called by a subclass, do nothing special and pass on to Expr.
if cls is not Relational:
return Expr.__new__(cls, lhs, rhs, **assumptions)
# If called directly with an operator, look up the subclass
# corresponding to that operator and delegate to it
try:
cls = cls.ValidRelationOperator[rop]
return cls(lhs, rhs, **assumptions)
except KeyError:
raise ValueError("Invalid relational operator symbol: %r" % rop)
@property
def lhs(self):
"""The left-hand side of the relation."""
return self._args[0]
@property
def rhs(self):
"""The right-hand side of the relation."""
return self._args[1]
@property
def reversed(self):
"""Return the relationship with sides (and sign) reversed.
Examples
========
>>> from sympy import Eq
>>> from sympy.abc import x
>>> Eq(x, 1)
Eq(x, 1)
>>> _.reversed
Eq(1, x)
>>> x < 1
x < 1
>>> _.reversed
1 > x
"""
ops = {Gt: Lt, Ge: Le, Lt: Gt, Le: Ge}
a, b = self.args
return ops.get(self.func, self.func)(b, a, evaluate=False)
def _eval_evalf(self, prec):
return self.func(*[s._evalf(prec) for s in self.args])
@property
def canonical(self):
"""Return a canonical form of the relational.
The rules for the canonical form, in order of decreasing priority are:
1) Number on right if left is not a Number;
2) Symbol on the left;
3) Gt/Ge changed to Lt/Le;
4) Lt/Le are unchanged;
5) Eq and Ne get ordered args.
"""
r = self
if r.func in (Ge, Gt):
r = r.reversed
elif r.func in (Lt, Le):
pass
elif r.func in (Eq, Ne):
r = r.func(*ordered(r.args), evaluate=False)
else:
raise NotImplementedError
if r.lhs.is_Number and not r.rhs.is_Number:
r = r.reversed
elif r.rhs.is_Symbol and not r.lhs.is_Symbol:
r = r.reversed
if _coeff_isneg(r.lhs):
r = r.reversed.func(-r.lhs, -r.rhs, evaluate=False)
return r
def equals(self, other, failing_expression=False):
"""Return True if the sides of the relationship are mathematically
identical and the type of relationship is the same.
If failing_expression is True, return the expression whose truth value
was unknown."""
if isinstance(other, Relational):
if self == other or self.reversed == other:
return True
a, b = self, other
if a.func in (Eq, Ne) or b.func in (Eq, Ne):
if a.func != b.func:
return False
l, r = [i.equals(j, failing_expression=failing_expression)
for i, j in zip(a.args, b.args)]
if l is True:
return r
if r is True:
return l
lr, rl = [i.equals(j, failing_expression=failing_expression)
for i, j in zip(a.args, b.reversed.args)]
if lr is True:
return rl
if rl is True:
return lr
e = (l, r, lr, rl)
if all(i is False for i in e):
return False
for i in e:
if i not in (True, False):
return i
else:
if b.func != a.func:
b = b.reversed
if a.func != b.func:
return False
l = a.lhs.equals(b.lhs, failing_expression=failing_expression)
if l is False:
return False
r = a.rhs.equals(b.rhs, failing_expression=failing_expression)
if r is False:
return False
if l is True:
return r
return l
def _eval_simplify(self, ratio, measure):
r = self
r = r.func(*[i.simplify(ratio=ratio, measure=measure)
for i in r.args])
if r.is_Relational:
dif = r.lhs - r.rhs
# replace dif with a valid Number that will
# allow a definitive comparison with 0
v = None
if dif.is_comparable:
v = dif.n(2)
elif dif.equals(0): # XXX this is expensive
v = S.Zero
if v is not None:
r = r.func._eval_relation(v, S.Zero)
r = r.canonical
if measure(r) < ratio*measure(self):
return r
else:
return self
def __nonzero__(self):
raise TypeError("cannot determine truth value of Relational")
__bool__ = __nonzero__
def as_set(self):
"""
Rewrites univariate inequality in terms of real sets
Examples
========
>>> from sympy import Symbol, Eq
>>> x = Symbol('x', real=True)
>>> (x > 0).as_set()
Interval.open(0, oo)
>>> Eq(x, 0).as_set()
{0}
"""
from sympy.solvers.inequalities import solve_univariate_inequality
syms = self.free_symbols
if len(syms) == 1:
sym = syms.pop()
else:
raise NotImplementedError("Sorry, Relational.as_set procedure"
" is not yet implemented for"
" multivariate expressions")
return solve_univariate_inequality(self, sym, relational=False)
Rel = Relational
class Equality(Relational):
"""An equal relation between two objects.
Represents that two objects are equal. If they can be easily shown
to be definitively equal (or unequal), this will reduce to True (or
False). Otherwise, the relation is maintained as an unevaluated
Equality object. Use the ``simplify`` function on this object for
more nontrivial evaluation of the equality relation.
As usual, the keyword argument ``evaluate=False`` can be used to
prevent any evaluation.
Examples
========
>>> from sympy import Eq, simplify, exp, cos
>>> from sympy.abc import x, y
>>> Eq(y, x + x**2)
Eq(y, x**2 + x)
>>> Eq(2, 5)
False
>>> Eq(2, 5, evaluate=False)
Eq(2, 5)
>>> _.doit()
False
>>> Eq(exp(x), exp(x).rewrite(cos))
Eq(exp(x), sinh(x) + cosh(x))
>>> simplify(_)
True
See Also
========
sympy.logic.boolalg.Equivalent : for representing equality between two
boolean expressions
Notes
=====
This class is not the same as the == operator. The == operator tests
for exact structural equality between two expressions; this class
compares expressions mathematically.
If either object defines an `_eval_Eq` method, it can be used in place of
the default algorithm. If `lhs._eval_Eq(rhs)` or `rhs._eval_Eq(lhs)`
returns anything other than None, that return value will be substituted for
the Equality. If None is returned by `_eval_Eq`, an Equality object will
be created as usual.
"""
rel_op = '=='
__slots__ = []
is_Equality = True
def __new__(cls, lhs, rhs=0, **options):
from sympy.core.add import Add
from sympy.core.logic import fuzzy_bool
from sympy.core.expr import _n2
from sympy.simplify.simplify import clear_coefficients
lhs = _sympify(lhs)
rhs = _sympify(rhs)
evaluate = options.pop('evaluate', global_evaluate[0])
if evaluate:
# If one expression has an _eval_Eq, return its results.
if hasattr(lhs, '_eval_Eq'):
r = lhs._eval_Eq(rhs)
if r is not None:
return r
if hasattr(rhs, '_eval_Eq'):
r = rhs._eval_Eq(lhs)
if r is not None:
return r
# If expressions have the same structure, they must be equal.
if lhs == rhs:
return S.true
elif all(isinstance(i, BooleanAtom) for i in (rhs, lhs)):
return S.false
# check finiteness
fin = L, R = [i.is_finite for i in (lhs, rhs)]
if None not in fin:
if L != R:
return S.false
if L is False:
if lhs == -rhs: # Eq(oo, -oo)
return S.false
return S.true
elif None in fin and False in fin:
return Relational.__new__(cls, lhs, rhs, **options)
if all(isinstance(i, Expr) for i in (lhs, rhs)):
# see if the difference evaluates
dif = lhs - rhs
z = dif.is_zero
if z is not None:
if z is False and dif.is_commutative: # issue 10728
return S.false
if z:
return S.true
# evaluate numerically if possible
n2 = _n2(lhs, rhs)
if n2 is not None:
return _sympify(n2 == 0)
# see if the ratio evaluates
n, d = dif.as_numer_denom()
rv = None
if n.is_zero:
rv = d.is_nonzero
elif n.is_finite:
if d.is_infinite:
rv = S.true
elif n.is_zero is False:
rv = d.is_infinite
if rv is None:
# if the condition that makes the denominator infinite does not
# make the original expression True then False can be returned
l, r = clear_coefficients(d, S.Infinity)
args = [_.subs(l, r) for _ in (lhs, rhs)]
if args != [lhs, rhs]:
rv = fuzzy_bool(Eq(*args))
if rv is True:
rv = None
elif any(a.is_infinite for a in Add.make_args(n)): # (inf or nan)/x != 0
rv = S.false
if rv is not None:
return _sympify(rv)
return Relational.__new__(cls, lhs, rhs, **options)
@classmethod
def _eval_relation(cls, lhs, rhs):
return _sympify(lhs == rhs)
Eq = Equality
class Unequality(Relational):
"""An unequal relation between two objects.
Represents that two objects are not equal. If they can be shown to be
definitively equal, this will reduce to False; if definitively unequal,
this will reduce to True. Otherwise, the relation is maintained as an
Unequality object.
Examples
========
>>> from sympy import Ne
>>> from sympy.abc import x, y
>>> Ne(y, x+x**2)
Ne(y, x**2 + x)
See Also
========
Equality
Notes
=====
This class is not the same as the != operator. The != operator tests
for exact structural equality between two expressions; this class
compares expressions mathematically.
This class is effectively the inverse of Equality. As such, it uses the
same algorithms, including any available `_eval_Eq` methods.
"""
rel_op = '!='
__slots__ = []
def __new__(cls, lhs, rhs, **options):
lhs = _sympify(lhs)
rhs = _sympify(rhs)
evaluate = options.pop('evaluate', global_evaluate[0])
if evaluate:
is_equal = Equality(lhs, rhs)
if isinstance(is_equal, BooleanAtom):
return ~is_equal
return Relational.__new__(cls, lhs, rhs, **options)
@classmethod
def _eval_relation(cls, lhs, rhs):
return _sympify(lhs != rhs)
Ne = Unequality
class _Inequality(Relational):
"""Internal base class for all *Than types.
Each subclass must implement _eval_relation to provide the method for
comparing two real numbers.
"""
__slots__ = []
def __new__(cls, lhs, rhs, **options):
lhs = _sympify(lhs)
rhs = _sympify(rhs)
evaluate = options.pop('evaluate', global_evaluate[0])
if evaluate:
# First we invoke the appropriate inequality method of `lhs`
# (e.g., `lhs.__lt__`). That method will try to reduce to
# boolean or raise an exception. It may keep calling
# superclasses until it reaches `Expr` (e.g., `Expr.__lt__`).
# In some cases, `Expr` will just invoke us again (if neither it
# nor a subclass was able to reduce to boolean or raise an
# exception). In that case, it must call us with
# `evaluate=False` to prevent infinite recursion.
r = cls._eval_relation(lhs, rhs)
if r is not None:
return r
# Note: not sure r could be None, perhaps we never take this
# path? In principle, could use this to shortcut out if a
# class realizes the inequality cannot be evaluated further.
# make a "non-evaluated" Expr for the inequality
return Relational.__new__(cls, lhs, rhs, **options)
class _Greater(_Inequality):
"""Not intended for general use
_Greater is only used so that GreaterThan and StrictGreaterThan may subclass
it for the .gts and .lts properties.
"""
__slots__ = ()
@property
def gts(self):
return self._args[0]
@property
def lts(self):
return self._args[1]
class _Less(_Inequality):
"""Not intended for general use.
_Less is only used so that LessThan and StrictLessThan may subclass it for
the .gts and .lts properties.
"""
__slots__ = ()
@property
def gts(self):
return self._args[1]
@property
def lts(self):
return self._args[0]
class GreaterThan(_Greater):
"""Class representations of inequalities.
Extended Summary
================
The ``*Than`` classes represent inequal relationships, where the left-hand
side is generally bigger or smaller than the right-hand side. For example,
the GreaterThan class represents an inequal relationship where the
left-hand side is at least as big as the right side, if not bigger. In
mathematical notation:
lhs >= rhs
In total, there are four ``*Than`` classes, to represent the four
inequalities:
+-----------------+--------+
|Class Name | Symbol |
+=================+========+
|GreaterThan | (>=) |
+-----------------+--------+
|LessThan | (<=) |
+-----------------+--------+
|StrictGreaterThan| (>) |
+-----------------+--------+
|StrictLessThan | (<) |
+-----------------+--------+
All classes take two arguments, lhs and rhs.
+----------------------------+-----------------+
|Signature Example | Math equivalent |
+============================+=================+
|GreaterThan(lhs, rhs) | lhs >= rhs |
+----------------------------+-----------------+
|LessThan(lhs, rhs) | lhs <= rhs |
+----------------------------+-----------------+
|StrictGreaterThan(lhs, rhs) | lhs > rhs |
+----------------------------+-----------------+
|StrictLessThan(lhs, rhs) | lhs < rhs |
+----------------------------+-----------------+
In addition to the normal .lhs and .rhs of Relations, ``*Than`` inequality
objects also have the .lts and .gts properties, which represent the "less
than side" and "greater than side" of the operator. Use of .lts and .gts
in an algorithm rather than .lhs and .rhs as an assumption of inequality
direction will make more explicit the intent of a certain section of code,
and will make it similarly more robust to client code changes:
>>> from sympy import GreaterThan, StrictGreaterThan
>>> from sympy import LessThan, StrictLessThan
>>> from sympy import And, Ge, Gt, Le, Lt, Rel, S
>>> from sympy.abc import x, y, z
>>> from sympy.core.relational import Relational
>>> e = GreaterThan(x, 1)
>>> e
x >= 1
>>> '%s >= %s is the same as %s <= %s' % (e.gts, e.lts, e.lts, e.gts)
'x >= 1 is the same as 1 <= x'
Examples
========
One generally does not instantiate these classes directly, but uses various
convenience methods:
>>> e1 = Ge( x, 2 ) # Ge is a convenience wrapper
>>> print(e1)
x >= 2
>>> rels = Ge( x, 2 ), Gt( x, 2 ), Le( x, 2 ), Lt( x, 2 )
>>> print('%s\\n%s\\n%s\\n%s' % rels)
x >= 2
x > 2
x <= 2
x < 2
Another option is to use the Python inequality operators (>=, >, <=, <)
directly. Their main advantage over the Ge, Gt, Le, and Lt counterparts, is
that one can write a more "mathematical looking" statement rather than
littering the math with oddball function calls. However there are certain
(minor) caveats of which to be aware (search for 'gotcha', below).
>>> e2 = x >= 2
>>> print(e2)
x >= 2
>>> print("e1: %s, e2: %s" % (e1, e2))
e1: x >= 2, e2: x >= 2
>>> e1 == e2
True
However, it is also perfectly valid to instantiate a ``*Than`` class less
succinctly and less conveniently:
>>> rels = Rel(x, 1, '>='), Relational(x, 1, '>='), GreaterThan(x, 1)
>>> print('%s\\n%s\\n%s' % rels)
x >= 1
x >= 1
x >= 1
>>> rels = Rel(x, 1, '>'), Relational(x, 1, '>'), StrictGreaterThan(x, 1)
>>> print('%s\\n%s\\n%s' % rels)
x > 1
x > 1
x > 1
>>> rels = Rel(x, 1, '<='), Relational(x, 1, '<='), LessThan(x, 1)
>>> print("%s\\n%s\\n%s" % rels)
x <= 1
x <= 1
x <= 1
>>> rels = Rel(x, 1, '<'), Relational(x, 1, '<'), StrictLessThan(x, 1)
>>> print('%s\\n%s\\n%s' % rels)
x < 1
x < 1
x < 1
Notes
=====
There are a couple of "gotchas" when using Python's operators.
The first enters the mix when comparing against a literal number as the lhs
argument. Due to the order that Python decides to parse a statement, it may
not immediately find two objects comparable. For example, to evaluate the
statement (1 < x), Python will first recognize the number 1 as a native
number, and then that x is *not* a native number. At this point, because a
native Python number does not know how to compare itself with a SymPy object
Python will try the reflective operation, (x > 1). Unfortunately, there is
no way available to SymPy to recognize this has happened, so the statement
(1 < x) will turn silently into (x > 1).
>>> e1 = x > 1
>>> e2 = x >= 1
>>> e3 = x < 1
>>> e4 = x <= 1
>>> e5 = 1 > x
>>> e6 = 1 >= x
>>> e7 = 1 < x
>>> e8 = 1 <= x
>>> print("%s %s\\n"*4 % (e1, e2, e3, e4, e5, e6, e7, e8))
x > 1 x >= 1
x < 1 x <= 1
x < 1 x <= 1
x > 1 x >= 1
If the order of the statement is important (for visual output to the
console, perhaps), one can work around this annoyance in a couple ways: (1)
"sympify" the literal before comparison, (2) use one of the wrappers, or (3)
use the less succinct methods described above:
>>> e1 = S(1) > x
>>> e2 = S(1) >= x
>>> e3 = S(1) < x
>>> e4 = S(1) <= x
>>> e5 = Gt(1, x)
>>> e6 = Ge(1, x)
>>> e7 = Lt(1, x)
>>> e8 = Le(1, x)
>>> print("%s %s\\n"*4 % (e1, e2, e3, e4, e5, e6, e7, e8))
1 > x 1 >= x
1 < x 1 <= x
1 > x 1 >= x
1 < x 1 <= x
The other gotcha is with chained inequalities. Occasionally, one may be
tempted to write statements like:
>>> e = x < y < z
Traceback (most recent call last):
...
TypeError: symbolic boolean expression has no truth value.
Due to an implementation detail or decision of Python [1]_, there is no way
for SymPy to reliably create that as a chained inequality. To create a
chained inequality, the only method currently available is to make use of
And:
>>> e = And(x < y, y < z)
>>> type( e )
And
>>> e
(x < y) & (y < z)
Note that this is different than chaining an equality directly via use of
parenthesis (this is currently an open bug in SymPy [2]_):
>>> e = (x < y) < z
>>> type( e )
<class 'sympy.core.relational.StrictLessThan'>
>>> e
(x < y) < z
Any code that explicitly relies on this latter functionality will not be
robust as this behaviour is completely wrong and will be corrected at some
point. For the time being (circa Jan 2012), use And to create chained
inequalities.
.. [1] This implementation detail is that Python provides no reliable
method to determine that a chained inequality is being built. Chained
comparison operators are evaluated pairwise, using "and" logic (see
http://docs.python.org/2/reference/expressions.html#notin). This is done
in an efficient way, so that each object being compared is only
evaluated once and the comparison can short-circuit. For example, ``1
> 2 > 3`` is evaluated by Python as ``(1 > 2) and (2 > 3)``. The
``and`` operator coerces each side into a bool, returning the object
itself when it short-circuits. The bool of the --Than operators
will raise TypeError on purpose, because SymPy cannot determine the
mathematical ordering of symbolic expressions. Thus, if we were to
compute ``x > y > z``, with ``x``, ``y``, and ``z`` being Symbols,
Python converts the statement (roughly) into these steps:
(1) x > y > z
(2) (x > y) and (y > z)
(3) (GreaterThanObject) and (y > z)
(4) (GreaterThanObject.__nonzero__()) and (y > z)
(5) TypeError
Because of the "and" added at step 2, the statement gets turned into a
weak ternary statement, and the first object's __nonzero__ method will
raise TypeError. Thus, creating a chained inequality is not possible.
In Python, there is no way to override the ``and`` operator, or to
control how it short circuits, so it is impossible to make something
like ``x > y > z`` work. There was a PEP to change this,
:pep:`335`, but it was officially closed in March, 2012.
.. [2] For more information, see these two bug reports:
"Separate boolean and symbolic relationals"
`Issue 4986 <https://github.com/sympy/sympy/issues/4986>`_
"It right 0 < x < 1 ?"
`Issue 6059 <https://github.com/sympy/sympy/issues/6059>`_
"""
__slots__ = ()
rel_op = '>='
@classmethod
def _eval_relation(cls, lhs, rhs):
# We don't use the op symbol here: workaround issue #7951
return _sympify(lhs.__ge__(rhs))
Ge = GreaterThan
class LessThan(_Less):
__doc__ = GreaterThan.__doc__
__slots__ = ()
rel_op = '<='
@classmethod
def _eval_relation(cls, lhs, rhs):
# We don't use the op symbol here: workaround issue #7951
return _sympify(lhs.__le__(rhs))
Le = LessThan
class StrictGreaterThan(_Greater):
__doc__ = GreaterThan.__doc__
__slots__ = ()
rel_op = '>'
@classmethod
def _eval_relation(cls, lhs, rhs):
# We don't use the op symbol here: workaround issue #7951
return _sympify(lhs.__gt__(rhs))
Gt = StrictGreaterThan
class StrictLessThan(_Less):
__doc__ = GreaterThan.__doc__
__slots__ = ()
rel_op = '<'
@classmethod
def _eval_relation(cls, lhs, rhs):
# We don't use the op symbol here: workaround issue #7951
return _sympify(lhs.__lt__(rhs))
Lt = StrictLessThan
# A class-specific (not object-specific) data item used for a minor speedup. It
# is defined here, rather than directly in the class, because the classes that
# it references have not been defined until now (e.g. StrictLessThan).
Relational.ValidRelationOperator = {
None: Equality,
'==': Equality,
'eq': Equality,
'!=': Unequality,
'<>': Unequality,
'ne': Unequality,
'>=': GreaterThan,
'ge': GreaterThan,
'<=': LessThan,
'le': LessThan,
'>': StrictGreaterThan,
'gt': StrictGreaterThan,
'<': StrictLessThan,
'lt': StrictLessThan,
}
| 25,967 | 31.138614 | 91 |
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|
cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/__init__.py
|
"""Core module. Provides the basic operations needed in sympy.
"""
from .sympify import sympify, SympifyError
from .cache import cacheit
from .basic import Basic, Atom, preorder_traversal
from .singleton import S
from .expr import Expr, AtomicExpr, UnevaluatedExpr
from .symbol import Symbol, Wild, Dummy, symbols, var
from .numbers import Number, Float, Rational, Integer, NumberSymbol, \
RealNumber, igcd, ilcm, seterr, E, I, nan, oo, pi, zoo, \
AlgebraicNumber, comp, mod_inverse
from .power import Pow, integer_nthroot
from .mul import Mul, prod
from .add import Add
from .mod import Mod
from .relational import ( Rel, Eq, Ne, Lt, Le, Gt, Ge,
Equality, GreaterThan, LessThan, Unequality, StrictGreaterThan,
StrictLessThan )
from .multidimensional import vectorize
from .function import Lambda, WildFunction, Derivative, diff, FunctionClass, \
Function, Subs, expand, PoleError, count_ops, \
expand_mul, expand_log, expand_func, \
expand_trig, expand_complex, expand_multinomial, nfloat, \
expand_power_base, expand_power_exp
from .evalf import PrecisionExhausted, N
from .containers import Tuple, Dict
from .exprtools import gcd_terms, factor_terms, factor_nc
from .evaluate import evaluate
# expose singletons
Catalan = S.Catalan
EulerGamma = S.EulerGamma
GoldenRatio = S.GoldenRatio
| 1,323 | 36.828571 | 78 |
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|
cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/coreerrors.py
|
"""Definitions of common exceptions for :mod:`sympy.core` module. """
from __future__ import print_function, division
class BaseCoreError(Exception):
"""Base class for core related exceptions. """
class NonCommutativeExpression(BaseCoreError):
"""Raised when expression didn't have commutative property. """
| 321 | 25.833333 | 69 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/containers.py
|
"""Module for SymPy containers
(SymPy objects that store other SymPy objects)
The containers implemented in this module are subclassed to Basic.
They are supposed to work seamlessly within the SymPy framework.
"""
from __future__ import print_function, division
from sympy.core.basic import Basic
from sympy.core.compatibility import as_int, range
from sympy.core.sympify import sympify, converter
from sympy.utilities.iterables import iterable
class Tuple(Basic):
"""
Wrapper around the builtin tuple object
The Tuple is a subclass of Basic, so that it works well in the
SymPy framework. The wrapped tuple is available as self.args, but
you can also access elements or slices with [:] syntax.
Parameters
==========
sympify : bool
If ``False``, ``sympify`` is not called on ``args``. This
can be used for speedups for very large tuples where the
elements are known to already be sympy objects.
Example
=======
>>> from sympy import symbols
>>> from sympy.core.containers import Tuple
>>> a, b, c, d = symbols('a b c d')
>>> Tuple(a, b, c)[1:]
(b, c)
>>> Tuple(a, b, c).subs(a, d)
(d, b, c)
"""
def __new__(cls, *args, **kwargs):
if kwargs.get('sympify', True):
args = ( sympify(arg) for arg in args )
obj = Basic.__new__(cls, *args)
return obj
def __getitem__(self, i):
if isinstance(i, slice):
indices = i.indices(len(self))
return Tuple(*(self.args[j] for j in range(*indices)))
return self.args[i]
def __len__(self):
return len(self.args)
def __contains__(self, item):
return item in self.args
def __iter__(self):
return iter(self.args)
def __add__(self, other):
if isinstance(other, Tuple):
return Tuple(*(self.args + other.args))
elif isinstance(other, tuple):
return Tuple(*(self.args + other))
else:
return NotImplemented
def __radd__(self, other):
if isinstance(other, Tuple):
return Tuple(*(other.args + self.args))
elif isinstance(other, tuple):
return Tuple(*(other + self.args))
else:
return NotImplemented
def __mul__(self, other):
try:
n = as_int(other)
except ValueError:
raise TypeError("Can't multiply sequence by non-integer of type '%s'" % type(other))
return self.func(*(self.args*n))
__rmul__ = __mul__
def __eq__(self, other):
if isinstance(other, Basic):
return super(Tuple, self).__eq__(other)
return self.args == other
def __ne__(self, other):
if isinstance(other, Basic):
return super(Tuple, self).__ne__(other)
return self.args != other
def __hash__(self):
return hash(self.args)
def _to_mpmath(self, prec):
return tuple(a._to_mpmath(prec) for a in self.args)
def __lt__(self, other):
return sympify(self.args < other.args)
def __le__(self, other):
return sympify(self.args <= other.args)
# XXX: Basic defines count() as something different, so we can't
# redefine it here. Originally this lead to cse() test failure.
def tuple_count(self, value):
"""T.count(value) -> integer -- return number of occurrences of value"""
return self.args.count(value)
def index(self, value, start=None, stop=None):
"""T.index(value, [start, [stop]]) -> integer -- return first index of value.
Raises ValueError if the value is not present."""
# XXX: One would expect:
#
# return self.args.index(value, start, stop)
#
# here. Any trouble with that? Yes:
#
# >>> (1,).index(1, None, None)
# Traceback (most recent call last):
# File "<stdin>", line 1, in <module>
# TypeError: slice indices must be integers or None or have an __index__ method
#
# See: http://bugs.python.org/issue13340
if start is None and stop is None:
return self.args.index(value)
elif stop is None:
return self.args.index(value, start)
else:
return self.args.index(value, start, stop)
converter[tuple] = lambda tup: Tuple(*tup)
def tuple_wrapper(method):
"""
Decorator that converts any tuple in the function arguments into a Tuple.
The motivation for this is to provide simple user interfaces. The user can
call a function with regular tuples in the argument, and the wrapper will
convert them to Tuples before handing them to the function.
>>> from sympy.core.containers import tuple_wrapper
>>> def f(*args):
... return args
>>> g = tuple_wrapper(f)
The decorated function g sees only the Tuple argument:
>>> g(0, (1, 2), 3)
(0, (1, 2), 3)
"""
def wrap_tuples(*args, **kw_args):
newargs = []
for arg in args:
if type(arg) is tuple:
newargs.append(Tuple(*arg))
else:
newargs.append(arg)
return method(*newargs, **kw_args)
return wrap_tuples
class Dict(Basic):
"""
Wrapper around the builtin dict object
The Dict is a subclass of Basic, so that it works well in the
SymPy framework. Because it is immutable, it may be included
in sets, but its values must all be given at instantiation and
cannot be changed afterwards. Otherwise it behaves identically
to the Python dict.
>>> from sympy.core.containers import Dict
>>> D = Dict({1: 'one', 2: 'two'})
>>> for key in D:
... if key == 1:
... print('%s %s' % (key, D[key]))
1 one
The args are sympified so the 1 and 2 are Integers and the values
are Symbols. Queries automatically sympify args so the following work:
>>> 1 in D
True
>>> D.has('one') # searches keys and values
True
>>> 'one' in D # not in the keys
False
>>> D[1]
one
"""
def __new__(cls, *args):
if len(args) == 1 and isinstance(args[0], (dict, Dict)):
items = [Tuple(k, v) for k, v in args[0].items()]
elif iterable(args) and all(len(arg) == 2 for arg in args):
items = [Tuple(k, v) for k, v in args]
else:
raise TypeError('Pass Dict args as Dict((k1, v1), ...) or Dict({k1: v1, ...})')
elements = frozenset(items)
obj = Basic.__new__(cls, elements)
obj.elements = elements
obj._dict = dict(items) # In case Tuple decides it wants to sympify
return obj
def __getitem__(self, key):
"""x.__getitem__(y) <==> x[y]"""
return self._dict[sympify(key)]
def __setitem__(self, key, value):
raise NotImplementedError("SymPy Dicts are Immutable")
@property
def args(self):
return tuple(self.elements)
def items(self):
'''D.items() -> list of D's (key, value) pairs, as 2-tuples'''
return self._dict.items()
def keys(self):
'''D.keys() -> list of D's keys'''
return self._dict.keys()
def values(self):
'''D.values() -> list of D's values'''
return self._dict.values()
def __iter__(self):
'''x.__iter__() <==> iter(x)'''
return iter(self._dict)
def __len__(self):
'''x.__len__() <==> len(x)'''
return self._dict.__len__()
def get(self, key, default=None):
'''D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.'''
return self._dict.get(sympify(key), default)
def __contains__(self, key):
'''D.__contains__(k) -> True if D has a key k, else False'''
return sympify(key) in self._dict
def __lt__(self, other):
return sympify(self.args < other.args)
@property
def _sorted_args(self):
from sympy.utilities import default_sort_key
return tuple(sorted(self.args, key=default_sort_key))
| 8,046 | 29.138577 | 96 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/trace.py
|
from __future__ import print_function, division
from sympy import Expr, Add, Mul, Pow, sympify, Matrix, Tuple
from sympy.core.compatibility import range
from sympy.utilities import default_sort_key
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 _cycle_permute(l):
""" Cyclic permutations based on canonical ordering
This method does the sort based ascii values while
a better approach would be to used lexicographic sort.
TODO: Handle condition such as symbols have subscripts/superscripts
in case of lexicographic sort
"""
if len(l) == 1:
return l
min_item = min(l, key=default_sort_key)
indices = [i for i, x in enumerate(l) if x == min_item]
le = list(l)
le.extend(l) # duplicate and extend string for easy processing
# adding the first min_item index back for easier looping
indices.append(len(l) + indices[0])
# create sublist of items with first item as min_item and last_item
# in each of the sublist is item just before the next occurence of
# minitem in the cycle formed.
sublist = [[le[indices[i]:indices[i + 1]]] for i in
range(len(indices) - 1)]
# we do comparison of strings by comparing elements
# in each sublist
idx = sublist.index(min(sublist))
ordered_l = le[indices[idx]:indices[idx] + len(l)]
return ordered_l
def _rearrange_args(l):
""" this just moves the last arg to first position
to enable expansion of args
A,B,A ==> A**2,B
"""
if len(l) == 1:
return l
x = list(l[-1:])
x.extend(l[0:-1])
return Mul(*x).args
class Tr(Expr):
""" Generic Trace operation than can trace over:
a) sympy matrix
b) operators
c) outer products
Parameters
==========
o : operator, matrix, expr
i : tuple/list indices (optional)
Examples
========
# TODO: Need to handle printing
a) Trace(A+B) = Tr(A) + Tr(B)
b) Trace(scalar*Operator) = scalar*Trace(Operator)
>>> from sympy.core.trace import Tr
>>> from sympy import symbols, Matrix
>>> a, b = symbols('a b', commutative=True)
>>> A, B = symbols('A B', commutative=False)
>>> Tr(a*A,[2])
a*Tr(A)
>>> m = Matrix([[1,2],[1,1]])
>>> Tr(m)
2
"""
def __new__(cls, *args):
""" Construct a Trace object.
Parameters
==========
args = sympy expression
indices = tuple/list if indices, optional
"""
# expect no indices,int or a tuple/list/Tuple
if (len(args) == 2):
if not isinstance(args[1], (list, Tuple, tuple)):
indices = Tuple(args[1])
else:
indices = Tuple(*args[1])
expr = args[0]
elif (len(args) == 1):
indices = Tuple()
expr = args[0]
else:
raise ValueError("Arguments to Tr should be of form "
"(expr[, [indices]])")
if isinstance(expr, Matrix):
return expr.trace()
elif hasattr(expr, 'trace') and callable(expr.trace):
#for any objects that have trace() defined e.g numpy
return expr.trace()
elif isinstance(expr, Add):
return Add(*[Tr(arg, indices) for arg in expr.args])
elif isinstance(expr, Mul):
c_part, nc_part = expr.args_cnc()
if len(nc_part) == 0:
return Mul(*c_part)
else:
obj = Expr.__new__(cls, Mul(*nc_part), indices )
#this check is needed to prevent cached instances
#being returned even if len(c_part)==0
return Mul(*c_part)*obj if len(c_part) > 0 else obj
elif isinstance(expr, Pow):
if (_is_scalar(expr.args[0]) and
_is_scalar(expr.args[1])):
return expr
else:
return Expr.__new__(cls, expr, indices)
else:
if (_is_scalar(expr)):
return expr
return Expr.__new__(cls, expr, indices)
def doit(self, **kwargs):
""" Perform the trace operation.
#TODO: Current version ignores the indices set for partial trace.
>>> from sympy.core.trace import Tr
>>> from sympy.physics.quantum.operator import OuterProduct
>>> from sympy.physics.quantum.spin import JzKet, JzBra
>>> t = Tr(OuterProduct(JzKet(1,1), JzBra(1,1)))
>>> t.doit()
1
"""
if hasattr(self.args[0], '_eval_trace'):
return self.args[0]._eval_trace(indices=self.args[1])
return self
@property
def is_number(self):
# TODO : improve this implementation
return True
#TODO: Review if the permute method is needed
# and if it needs to return a new instance
def permute(self, pos):
""" Permute the arguments cyclically.
Parameters
==========
pos : integer, if positive, shift-right, else shift-left
Examples
========
>>> from sympy.core.trace import Tr
>>> from sympy import symbols
>>> A, B, C, D = symbols('A B C D', commutative=False)
>>> t = Tr(A*B*C*D)
>>> t.permute(2)
Tr(C*D*A*B)
>>> t.permute(-2)
Tr(C*D*A*B)
"""
if pos > 0:
pos = pos % len(self.args[0].args)
else:
pos = -(abs(pos) % len(self.args[0].args))
args = list(self.args[0].args[-pos:] + self.args[0].args[0:-pos])
return Tr(Mul(*(args)))
def _hashable_content(self):
if isinstance(self.args[0], Mul):
args = _cycle_permute(_rearrange_args(self.args[0].args))
else:
args = [self.args[0]]
return tuple(args) + (self.args[1], )
| 6,108 | 27.152074 | 73 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/evalf.py
|
"""
Adaptive numerical evaluation of SymPy expressions, using mpmath
for mathematical functions.
"""
from __future__ import print_function, division
import math
import mpmath.libmp as libmp
from mpmath import (
make_mpc, make_mpf, mp, mpc, mpf, nsum, quadts, quadosc, workprec)
from mpmath import inf as mpmath_inf
from mpmath.libmp import (from_int, from_man_exp, from_rational, fhalf,
fnan, fnone, fone, fzero, mpf_abs, mpf_add,
mpf_atan, mpf_atan2, mpf_cmp, mpf_cos, mpf_e, mpf_exp, mpf_log, mpf_lt,
mpf_mul, mpf_neg, mpf_pi, mpf_pow, mpf_pow_int, mpf_shift, mpf_sin,
mpf_sqrt, normalize, round_nearest, to_int, to_str)
from mpmath.libmp import bitcount as mpmath_bitcount
from mpmath.libmp.backend import MPZ
from mpmath.libmp.libmpc import _infs_nan
from mpmath.libmp.libmpf import dps_to_prec, prec_to_dps
from mpmath.libmp.gammazeta import mpf_bernoulli
from .compatibility import SYMPY_INTS, range
from .sympify import sympify
from .singleton import S
from sympy.utilities.iterables import is_sequence
LG10 = math.log(10, 2)
rnd = round_nearest
def bitcount(n):
return mpmath_bitcount(int(n))
# Used in a few places as placeholder values to denote exponents and
# precision levels, e.g. of exact numbers. Must be careful to avoid
# passing these to mpmath functions or returning them in final results.
INF = float(mpmath_inf)
MINUS_INF = float(-mpmath_inf)
# ~= 100 digits. Real men set this to INF.
DEFAULT_MAXPREC = 333
class PrecisionExhausted(ArithmeticError):
pass
#----------------------------------------------------------------------------#
# #
# Helper functions for arithmetic and complex parts #
# #
#----------------------------------------------------------------------------#
"""
An mpf value tuple is a tuple of integers (sign, man, exp, bc)
representing a floating-point number: [1, -1][sign]*man*2**exp where
sign is 0 or 1 and bc should correspond to the number of bits used to
represent the mantissa (man) in binary notation, e.g.
>>> from sympy.core.evalf import bitcount
>>> sign, man, exp, bc = 0, 5, 1, 3
>>> n = [1, -1][sign]*man*2**exp
>>> n, bitcount(man)
(10, 3)
A temporary result is a tuple (re, im, re_acc, im_acc) where
re and im are nonzero mpf value tuples representing approximate
numbers, or None to denote exact zeros.
re_acc, im_acc are integers denoting log2(e) where e is the estimated
relative accuracy of the respective complex part, but may be anything
if the corresponding complex part is None.
"""
def fastlog(x):
"""Fast approximation of log2(x) for an mpf value tuple x.
Notes: Calculated as exponent + width of mantissa. This is an
approximation for two reasons: 1) it gives the ceil(log2(abs(x)))
value and 2) it is too high by 1 in the case that x is an exact
power of 2. Although this is easy to remedy by testing to see if
the odd mpf mantissa is 1 (indicating that one was dealing with
an exact power of 2) that would decrease the speed and is not
necessary as this is only being used as an approximation for the
number of bits in x. The correct return value could be written as
"x[2] + (x[3] if x[1] != 1 else 0)".
Since mpf tuples always have an odd mantissa, no check is done
to see if the mantissa is a multiple of 2 (in which case the
result would be too large by 1).
Examples
========
>>> from sympy import log
>>> from sympy.core.evalf import fastlog, bitcount
>>> s, m, e = 0, 5, 1
>>> bc = bitcount(m)
>>> n = [1, -1][s]*m*2**e
>>> n, (log(n)/log(2)).evalf(2), fastlog((s, m, e, bc))
(10, 3.3, 4)
"""
if not x or x == fzero:
return MINUS_INF
return x[2] + x[3]
def pure_complex(v, or_real=False):
"""Return a and b if v matches a + I*b where b is not zero and
a and b are Numbers, else None. If `or_real` is True then 0 will
be returned for `b` if `v` is a real number.
>>> from sympy.core.evalf import pure_complex
>>> from sympy import sqrt, I, S
>>> a, b, surd = S(2), S(3), sqrt(2)
>>> pure_complex(a)
>>> pure_complex(a, or_real=True)
(2, 0)
>>> pure_complex(surd)
>>> pure_complex(a + b*I)
(2, 3)
>>> pure_complex(I)
(0, 1)
"""
h, t = v.as_coeff_Add()
if not t:
if or_real:
return h, t
return
c, i = t.as_coeff_Mul()
if i is S.ImaginaryUnit:
return h, c
def scaled_zero(mag, sign=1):
"""Return an mpf representing a power of two with magnitude ``mag``
and -1 for precision. Or, if ``mag`` is a scaled_zero tuple, then just
remove the sign from within the list that it was initially wrapped
in.
Examples
========
>>> from sympy.core.evalf import scaled_zero
>>> from sympy import Float
>>> z, p = scaled_zero(100)
>>> z, p
(([0], 1, 100, 1), -1)
>>> ok = scaled_zero(z)
>>> ok
(0, 1, 100, 1)
>>> Float(ok)
1.26765060022823e+30
>>> Float(ok, p)
0.e+30
>>> ok, p = scaled_zero(100, -1)
>>> Float(scaled_zero(ok), p)
-0.e+30
"""
if type(mag) is tuple and len(mag) == 4 and iszero(mag, scaled=True):
return (mag[0][0],) + mag[1:]
elif isinstance(mag, SYMPY_INTS):
if sign not in [-1, 1]:
raise ValueError('sign must be +/-1')
rv, p = mpf_shift(fone, mag), -1
s = 0 if sign == 1 else 1
rv = ([s],) + rv[1:]
return rv, p
else:
raise ValueError('scaled zero expects int or scaled_zero tuple.')
def iszero(mpf, scaled=False):
if not scaled:
return not mpf or not mpf[1] and not mpf[-1]
return mpf and type(mpf[0]) is list and mpf[1] == mpf[-1] == 1
def complex_accuracy(result):
"""
Returns relative accuracy of a complex number with given accuracies
for the real and imaginary parts. The relative accuracy is defined
in the complex norm sense as ||z|+|error|| / |z| where error
is equal to (real absolute error) + (imag absolute error)*i.
The full expression for the (logarithmic) error can be approximated
easily by using the max norm to approximate the complex norm.
In the worst case (re and im equal), this is wrong by a factor
sqrt(2), or by log2(sqrt(2)) = 0.5 bit.
"""
re, im, re_acc, im_acc = result
if not im:
if not re:
return INF
return re_acc
if not re:
return im_acc
re_size = fastlog(re)
im_size = fastlog(im)
absolute_error = max(re_size - re_acc, im_size - im_acc)
relative_error = absolute_error - max(re_size, im_size)
return -relative_error
def get_abs(expr, prec, options):
re, im, re_acc, im_acc = evalf(expr, prec + 2, options)
if not re:
re, re_acc, im, im_acc = im, im_acc, re, re_acc
if im:
if expr.is_number:
abs_expr, _, acc, _ = evalf(abs(N(expr, prec + 2)),
prec + 2, options)
return abs_expr, None, acc, None
else:
if 'subs' in options:
return libmp.mpc_abs((re, im), prec), None, re_acc, None
return abs(expr), None, prec, None
elif re:
return mpf_abs(re), None, re_acc, None
else:
return None, None, None, None
def get_complex_part(expr, no, prec, options):
"""no = 0 for real part, no = 1 for imaginary part"""
workprec = prec
i = 0
while 1:
res = evalf(expr, workprec, options)
value, accuracy = res[no::2]
# XXX is the last one correct? Consider re((1+I)**2).n()
if (not value) or accuracy >= prec or -value[2] > prec:
return value, None, accuracy, None
workprec += max(30, 2**i)
i += 1
def evalf_abs(expr, prec, options):
return get_abs(expr.args[0], prec, options)
def evalf_re(expr, prec, options):
return get_complex_part(expr.args[0], 0, prec, options)
def evalf_im(expr, prec, options):
return get_complex_part(expr.args[0], 1, prec, options)
def finalize_complex(re, im, prec):
if re == fzero and im == fzero:
raise ValueError("got complex zero with unknown accuracy")
elif re == fzero:
return None, im, None, prec
elif im == fzero:
return re, None, prec, None
size_re = fastlog(re)
size_im = fastlog(im)
if size_re > size_im:
re_acc = prec
im_acc = prec + min(-(size_re - size_im), 0)
else:
im_acc = prec
re_acc = prec + min(-(size_im - size_re), 0)
return re, im, re_acc, im_acc
def chop_parts(value, prec):
"""
Chop off tiny real or complex parts.
"""
re, im, re_acc, im_acc = value
# Method 1: chop based on absolute value
if re and re not in _infs_nan and (fastlog(re) < -prec + 4):
re, re_acc = None, None
if im and im not in _infs_nan and (fastlog(im) < -prec + 4):
im, im_acc = None, None
# Method 2: chop if inaccurate and relatively small
if re and im:
delta = fastlog(re) - fastlog(im)
if re_acc < 2 and (delta - re_acc <= -prec + 4):
re, re_acc = None, None
if im_acc < 2 and (delta - im_acc >= prec - 4):
im, im_acc = None, None
return re, im, re_acc, im_acc
def check_target(expr, result, prec):
a = complex_accuracy(result)
if a < prec:
raise PrecisionExhausted("Failed to distinguish the expression: \n\n%s\n\n"
"from zero. Try simplifying the input, using chop=True, or providing "
"a higher maxn for evalf" % (expr))
def get_integer_part(expr, no, options, return_ints=False):
"""
With no = 1, computes ceiling(expr)
With no = -1, computes floor(expr)
Note: this function either gives the exact result or signals failure.
"""
from sympy.functions.elementary.complexes import re, im
# The expression is likely less than 2^30 or so
assumed_size = 30
ire, iim, ire_acc, iim_acc = evalf(expr, assumed_size, options)
# We now know the size, so we can calculate how much extra precision
# (if any) is needed to get within the nearest integer
if ire and iim:
gap = max(fastlog(ire) - ire_acc, fastlog(iim) - iim_acc)
elif ire:
gap = fastlog(ire) - ire_acc
elif iim:
gap = fastlog(iim) - iim_acc
else:
# ... or maybe the expression was exactly zero
return None, None, None, None
margin = 10
if gap >= -margin:
ire, iim, ire_acc, iim_acc = \
evalf(expr, margin + assumed_size + gap, options)
# We can now easily find the nearest integer, but to find floor/ceil, we
# must also calculate whether the difference to the nearest integer is
# positive or negative (which may fail if very close).
def calc_part(expr, nexpr):
from sympy.core.add import Add
nint = int(to_int(nexpr, rnd))
n, c, p, b = nexpr
is_int = (p == 0)
if not is_int:
# if there are subs and they all contain integer re/im parts
# then we can (hopefully) safely substitute them into the
# expression
s = options.get('subs', False)
if s:
doit = True
from sympy.core.compatibility import as_int
for v in s.values():
try:
as_int(v)
except ValueError:
try:
[as_int(i) for i in v.as_real_imag()]
continue
except (ValueError, AttributeError):
doit = False
break
if doit:
expr = expr.subs(s)
expr = Add(expr, -nint, evaluate=False)
x, _, x_acc, _ = evalf(expr, 10, options)
try:
check_target(expr, (x, None, x_acc, None), 3)
except PrecisionExhausted:
if not expr.equals(0):
raise PrecisionExhausted
x = fzero
nint += int(no*(mpf_cmp(x or fzero, fzero) == no))
nint = from_int(nint)
return nint, fastlog(nint) + 10
re_, im_, re_acc, im_acc = None, None, None, None
if ire:
re_, re_acc = calc_part(re(expr, evaluate=False), ire)
if iim:
im_, im_acc = calc_part(im(expr, evaluate=False), iim)
if return_ints:
return int(to_int(re_ or fzero)), int(to_int(im_ or fzero))
return re_, im_, re_acc, im_acc
def evalf_ceiling(expr, prec, options):
return get_integer_part(expr.args[0], 1, options)
def evalf_floor(expr, prec, options):
return get_integer_part(expr.args[0], -1, options)
#----------------------------------------------------------------------------#
# #
# Arithmetic operations #
# #
#----------------------------------------------------------------------------#
def add_terms(terms, prec, target_prec):
"""
Helper for evalf_add. Adds a list of (mpfval, accuracy) terms.
Returns
-------
- None, None if there are no non-zero terms;
- terms[0] if there is only 1 term;
- scaled_zero if the sum of the terms produces a zero by cancellation
e.g. mpfs representing 1 and -1 would produce a scaled zero which need
special handling since they are not actually zero and they are purposely
malformed to ensure that they can't be used in anything but accuracy
calculations;
- a tuple that is scaled to target_prec that corresponds to the
sum of the terms.
The returned mpf tuple will be normalized to target_prec; the input
prec is used to define the working precision.
XXX explain why this is needed and why one can't just loop using mpf_add
"""
terms = [t for t in terms if not iszero(t)]
if not terms:
return None, None
elif len(terms) == 1:
return terms[0]
# see if any argument is NaN or oo and thus warrants a special return
special = []
from sympy.core.numbers import Float
for t in terms:
arg = Float._new(t[0], 1)
if arg is S.NaN or arg.is_infinite:
special.append(arg)
if special:
from sympy.core.add import Add
rv = evalf(Add(*special), prec + 4, {})
return rv[0], rv[2]
working_prec = 2*prec
sum_man, sum_exp, absolute_error = 0, 0, MINUS_INF
for x, accuracy in terms:
sign, man, exp, bc = x
if sign:
man = -man
absolute_error = max(absolute_error, bc + exp - accuracy)
delta = exp - sum_exp
if exp >= sum_exp:
# x much larger than existing sum?
# first: quick test
if ((delta > working_prec) and
((not sum_man) or
delta - bitcount(abs(sum_man)) > working_prec)):
sum_man = man
sum_exp = exp
else:
sum_man += (man << delta)
else:
delta = -delta
# x much smaller than existing sum?
if delta - bc > working_prec:
if not sum_man:
sum_man, sum_exp = man, exp
else:
sum_man = (sum_man << delta) + man
sum_exp = exp
if not sum_man:
return scaled_zero(absolute_error)
if sum_man < 0:
sum_sign = 1
sum_man = -sum_man
else:
sum_sign = 0
sum_bc = bitcount(sum_man)
sum_accuracy = sum_exp + sum_bc - absolute_error
r = normalize(sum_sign, sum_man, sum_exp, sum_bc, target_prec,
rnd), sum_accuracy
return r
def evalf_add(v, prec, options):
res = pure_complex(v)
if res:
h, c = res
re, _, re_acc, _ = evalf(h, prec, options)
im, _, im_acc, _ = evalf(c, prec, options)
return re, im, re_acc, im_acc
oldmaxprec = options.get('maxprec', DEFAULT_MAXPREC)
i = 0
target_prec = prec
while 1:
options['maxprec'] = min(oldmaxprec, 2*prec)
terms = [evalf(arg, prec + 10, options) for arg in v.args]
re, re_acc = add_terms(
[a[0::2] for a in terms if a[0]], prec, target_prec)
im, im_acc = add_terms(
[a[1::2] for a in terms if a[1]], prec, target_prec)
acc = complex_accuracy((re, im, re_acc, im_acc))
if acc >= target_prec:
if options.get('verbose'):
print("ADD: wanted", target_prec, "accurate bits, got", re_acc, im_acc)
break
else:
if (prec - target_prec) > options['maxprec']:
break
prec = prec + max(10 + 2**i, target_prec - acc)
i += 1
if options.get('verbose'):
print("ADD: restarting with prec", prec)
options['maxprec'] = oldmaxprec
if iszero(re, scaled=True):
re = scaled_zero(re)
if iszero(im, scaled=True):
im = scaled_zero(im)
return re, im, re_acc, im_acc
def evalf_mul(v, prec, options):
res = pure_complex(v)
if res:
# the only pure complex that is a mul is h*I
_, h = res
im, _, im_acc, _ = evalf(h, prec, options)
return None, im, None, im_acc
args = list(v.args)
# see if any argument is NaN or oo and thus warrants a special return
special = []
from sympy.core.numbers import Float
for arg in args:
arg = evalf(arg, prec, options)
if arg[0] is None:
continue
arg = Float._new(arg[0], 1)
if arg is S.NaN or arg.is_infinite:
special.append(arg)
if special:
from sympy.core.mul import Mul
special = Mul(*special)
return evalf(special, prec + 4, {})
# With guard digits, multiplication in the real case does not destroy
# accuracy. This is also true in the complex case when considering the
# total accuracy; however accuracy for the real or imaginary parts
# separately may be lower.
acc = prec
# XXX: big overestimate
working_prec = prec + len(args) + 5
# Empty product is 1
start = man, exp, bc = MPZ(1), 0, 1
# First, we multiply all pure real or pure imaginary numbers.
# direction tells us that the result should be multiplied by
# I**direction; all other numbers get put into complex_factors
# to be multiplied out after the first phase.
last = len(args)
direction = 0
args.append(S.One)
complex_factors = []
for i, arg in enumerate(args):
if i != last and pure_complex(arg):
args[-1] = (args[-1]*arg).expand()
continue
elif i == last and arg is S.One:
continue
re, im, re_acc, im_acc = evalf(arg, working_prec, options)
if re and im:
complex_factors.append((re, im, re_acc, im_acc))
continue
elif re:
(s, m, e, b), w_acc = re, re_acc
elif im:
(s, m, e, b), w_acc = im, im_acc
direction += 1
else:
return None, None, None, None
direction += 2*s
man *= m
exp += e
bc += b
if bc > 3*working_prec:
man >>= working_prec
exp += working_prec
acc = min(acc, w_acc)
sign = (direction & 2) >> 1
if not complex_factors:
v = normalize(sign, man, exp, bitcount(man), prec, rnd)
# multiply by i
if direction & 1:
return None, v, None, acc
else:
return v, None, acc, None
else:
# initialize with the first term
if (man, exp, bc) != start:
# there was a real part; give it an imaginary part
re, im = (sign, man, exp, bitcount(man)), (0, MPZ(0), 0, 0)
i0 = 0
else:
# there is no real part to start (other than the starting 1)
wre, wim, wre_acc, wim_acc = complex_factors[0]
acc = min(acc,
complex_accuracy((wre, wim, wre_acc, wim_acc)))
re = wre
im = wim
i0 = 1
for wre, wim, wre_acc, wim_acc in complex_factors[i0:]:
# acc is the overall accuracy of the product; we aren't
# computing exact accuracies of the product.
acc = min(acc,
complex_accuracy((wre, wim, wre_acc, wim_acc)))
use_prec = working_prec
A = mpf_mul(re, wre, use_prec)
B = mpf_mul(mpf_neg(im), wim, use_prec)
C = mpf_mul(re, wim, use_prec)
D = mpf_mul(im, wre, use_prec)
re = mpf_add(A, B, use_prec)
im = mpf_add(C, D, use_prec)
if options.get('verbose'):
print("MUL: wanted", prec, "accurate bits, got", acc)
# multiply by I
if direction & 1:
re, im = mpf_neg(im), re
return re, im, acc, acc
def evalf_pow(v, prec, options):
target_prec = prec
base, exp = v.args
# We handle x**n separately. This has two purposes: 1) it is much
# faster, because we avoid calling evalf on the exponent, and 2) it
# allows better handling of real/imaginary parts that are exactly zero
if exp.is_Integer:
p = exp.p
# Exact
if not p:
return fone, None, prec, None
# Exponentiation by p magnifies relative error by |p|, so the
# base must be evaluated with increased precision if p is large
prec += int(math.log(abs(p), 2))
re, im, re_acc, im_acc = evalf(base, prec + 5, options)
# Real to integer power
if re and not im:
return mpf_pow_int(re, p, target_prec), None, target_prec, None
# (x*I)**n = I**n * x**n
if im and not re:
z = mpf_pow_int(im, p, target_prec)
case = p % 4
if case == 0:
return z, None, target_prec, None
if case == 1:
return None, z, None, target_prec
if case == 2:
return mpf_neg(z), None, target_prec, None
if case == 3:
return None, mpf_neg(z), None, target_prec
# Zero raised to an integer power
if not re:
return None, None, None, None
# General complex number to arbitrary integer power
re, im = libmp.mpc_pow_int((re, im), p, prec)
# Assumes full accuracy in input
return finalize_complex(re, im, target_prec)
# Pure square root
if exp is S.Half:
xre, xim, _, _ = evalf(base, prec + 5, options)
# General complex square root
if xim:
re, im = libmp.mpc_sqrt((xre or fzero, xim), prec)
return finalize_complex(re, im, prec)
if not xre:
return None, None, None, None
# Square root of a negative real number
if mpf_lt(xre, fzero):
return None, mpf_sqrt(mpf_neg(xre), prec), None, prec
# Positive square root
return mpf_sqrt(xre, prec), None, prec, None
# We first evaluate the exponent to find its magnitude
# This determines the working precision that must be used
prec += 10
yre, yim, _, _ = evalf(exp, prec, options)
# Special cases: x**0
if not (yre or yim):
return fone, None, prec, None
ysize = fastlog(yre)
# Restart if too big
# XXX: prec + ysize might exceed maxprec
if ysize > 5:
prec += ysize
yre, yim, _, _ = evalf(exp, prec, options)
# Pure exponential function; no need to evalf the base
if base is S.Exp1:
if yim:
re, im = libmp.mpc_exp((yre or fzero, yim), prec)
return finalize_complex(re, im, target_prec)
return mpf_exp(yre, target_prec), None, target_prec, None
xre, xim, _, _ = evalf(base, prec + 5, options)
# 0**y
if not (xre or xim):
return None, None, None, None
# (real ** complex) or (complex ** complex)
if yim:
re, im = libmp.mpc_pow(
(xre or fzero, xim or fzero), (yre or fzero, yim),
target_prec)
return finalize_complex(re, im, target_prec)
# complex ** real
if xim:
re, im = libmp.mpc_pow_mpf((xre or fzero, xim), yre, target_prec)
return finalize_complex(re, im, target_prec)
# negative ** real
elif mpf_lt(xre, fzero):
re, im = libmp.mpc_pow_mpf((xre, fzero), yre, target_prec)
return finalize_complex(re, im, target_prec)
# positive ** real
else:
return mpf_pow(xre, yre, target_prec), None, target_prec, None
#----------------------------------------------------------------------------#
# #
# Special functions #
# #
#----------------------------------------------------------------------------#
def evalf_trig(v, prec, options):
"""
This function handles sin and cos of complex arguments.
TODO: should also handle tan of complex arguments.
"""
from sympy import cos, sin
if v.func is cos:
func = mpf_cos
elif v.func is sin:
func = mpf_sin
else:
raise NotImplementedError
arg = v.args[0]
# 20 extra bits is possibly overkill. It does make the need
# to restart very unlikely
xprec = prec + 20
re, im, re_acc, im_acc = evalf(arg, xprec, options)
if im:
if 'subs' in options:
v = v.subs(options['subs'])
return evalf(v._eval_evalf(prec), prec, options)
if not re:
if v.func is cos:
return fone, None, prec, None
elif v.func is sin:
return None, None, None, None
else:
raise NotImplementedError
# For trigonometric functions, we are interested in the
# fixed-point (absolute) accuracy of the argument.
xsize = fastlog(re)
# Magnitude <= 1.0. OK to compute directly, because there is no
# danger of hitting the first root of cos (with sin, magnitude
# <= 2.0 would actually be ok)
if xsize < 1:
return func(re, prec, rnd), None, prec, None
# Very large
if xsize >= 10:
xprec = prec + xsize
re, im, re_acc, im_acc = evalf(arg, xprec, options)
# Need to repeat in case the argument is very close to a
# multiple of pi (or pi/2), hitting close to a root
while 1:
y = func(re, prec, rnd)
ysize = fastlog(y)
gap = -ysize
accuracy = (xprec - xsize) - gap
if accuracy < prec:
if options.get('verbose'):
print("SIN/COS", accuracy, "wanted", prec, "gap", gap)
print(to_str(y, 10))
if xprec > options.get('maxprec', DEFAULT_MAXPREC):
return y, None, accuracy, None
xprec += gap
re, im, re_acc, im_acc = evalf(arg, xprec, options)
continue
else:
return y, None, prec, None
def evalf_log(expr, prec, options):
from sympy import Abs, Add, log
if len(expr.args)>1:
expr = expr.doit()
return evalf(expr, prec, options)
arg = expr.args[0]
workprec = prec + 10
xre, xim, xacc, _ = evalf(arg, workprec, options)
if xim:
# XXX: use get_abs etc instead
re = evalf_log(
log(Abs(arg, evaluate=False), evaluate=False), prec, options)
im = mpf_atan2(xim, xre or fzero, prec)
return re[0], im, re[2], prec
imaginary_term = (mpf_cmp(xre, fzero) < 0)
re = mpf_log(mpf_abs(xre), prec, rnd)
size = fastlog(re)
if prec - size > workprec and re != fzero:
# We actually need to compute 1+x accurately, not x
arg = Add(S.NegativeOne, arg, evaluate=False)
xre, xim, _, _ = evalf_add(arg, prec, options)
prec2 = workprec - fastlog(xre)
# xre is now x - 1 so we add 1 back here to calculate x
re = mpf_log(mpf_abs(mpf_add(xre, fone, prec2)), prec, rnd)
re_acc = prec
if imaginary_term:
return re, mpf_pi(prec), re_acc, prec
else:
return re, None, re_acc, None
def evalf_atan(v, prec, options):
arg = v.args[0]
xre, xim, reacc, imacc = evalf(arg, prec + 5, options)
if xre is xim is None:
return (None,)*4
if xim:
raise NotImplementedError
return mpf_atan(xre, prec, rnd), None, prec, None
def evalf_subs(prec, subs):
""" Change all Float entries in `subs` to have precision prec. """
newsubs = {}
for a, b in subs.items():
b = S(b)
if b.is_Float:
b = b._eval_evalf(prec)
newsubs[a] = b
return newsubs
def evalf_piecewise(expr, prec, options):
from sympy import Float, Integer
if 'subs' in options:
expr = expr.subs(evalf_subs(prec, options['subs']))
newopts = options.copy()
del newopts['subs']
if hasattr(expr, 'func'):
return evalf(expr, prec, newopts)
if type(expr) == float:
return evalf(Float(expr), prec, newopts)
if type(expr) == int:
return evalf(Integer(expr), prec, newopts)
# We still have undefined symbols
raise NotImplementedError
def evalf_bernoulli(expr, prec, options):
arg = expr.args[0]
if not arg.is_Integer:
raise ValueError("Bernoulli number index must be an integer")
n = int(arg)
b = mpf_bernoulli(n, prec, rnd)
if b == fzero:
return None, None, None, None
return b, None, prec, None
#----------------------------------------------------------------------------#
# #
# High-level operations #
# #
#----------------------------------------------------------------------------#
def as_mpmath(x, prec, options):
from sympy.core.numbers import Infinity, NegativeInfinity, Zero
x = sympify(x)
if isinstance(x, Zero) or x == 0:
return mpf(0)
if isinstance(x, Infinity):
return mpf('inf')
if isinstance(x, NegativeInfinity):
return mpf('-inf')
# XXX
re, im, _, _ = evalf(x, prec, options)
if im:
return mpc(re or fzero, im)
return mpf(re)
def do_integral(expr, prec, options):
func = expr.args[0]
x, xlow, xhigh = expr.args[1]
if xlow == xhigh:
xlow = xhigh = 0
elif x not in func.free_symbols:
# only the difference in limits matters in this case
# so if there is a symbol in common that will cancel
# out when taking the difference, then use that
# difference
if xhigh.free_symbols & xlow.free_symbols:
diff = xhigh - xlow
if not diff.free_symbols:
xlow, xhigh = 0, diff
oldmaxprec = options.get('maxprec', DEFAULT_MAXPREC)
options['maxprec'] = min(oldmaxprec, 2*prec)
with workprec(prec + 5):
xlow = as_mpmath(xlow, prec + 15, options)
xhigh = as_mpmath(xhigh, prec + 15, options)
# Integration is like summation, and we can phone home from
# the integrand function to update accuracy summation style
# Note that this accuracy is inaccurate, since it fails
# to account for the variable quadrature weights,
# but it is better than nothing
from sympy import cos, sin, Wild
have_part = [False, False]
max_real_term = [MINUS_INF]
max_imag_term = [MINUS_INF]
def f(t):
re, im, re_acc, im_acc = evalf(func, mp.prec, {'subs': {x: t}})
have_part[0] = re or have_part[0]
have_part[1] = im or have_part[1]
max_real_term[0] = max(max_real_term[0], fastlog(re))
max_imag_term[0] = max(max_imag_term[0], fastlog(im))
if im:
return mpc(re or fzero, im)
return mpf(re or fzero)
if options.get('quad') == 'osc':
A = Wild('A', exclude=[x])
B = Wild('B', exclude=[x])
D = Wild('D')
m = func.match(cos(A*x + B)*D)
if not m:
m = func.match(sin(A*x + B)*D)
if not m:
raise ValueError("An integrand of the form sin(A*x+B)*f(x) "
"or cos(A*x+B)*f(x) is required for oscillatory quadrature")
period = as_mpmath(2*S.Pi/m[A], prec + 15, options)
result = quadosc(f, [xlow, xhigh], period=period)
# XXX: quadosc does not do error detection yet
quadrature_error = MINUS_INF
else:
result, quadrature_error = quadts(f, [xlow, xhigh], error=1)
quadrature_error = fastlog(quadrature_error._mpf_)
options['maxprec'] = oldmaxprec
if have_part[0]:
re = result.real._mpf_
if re == fzero:
re, re_acc = scaled_zero(
min(-prec, -max_real_term[0], -quadrature_error))
re = scaled_zero(re) # handled ok in evalf_integral
else:
re_acc = -max(max_real_term[0] - fastlog(re) -
prec, quadrature_error)
else:
re, re_acc = None, None
if have_part[1]:
im = result.imag._mpf_
if im == fzero:
im, im_acc = scaled_zero(
min(-prec, -max_imag_term[0], -quadrature_error))
im = scaled_zero(im) # handled ok in evalf_integral
else:
im_acc = -max(max_imag_term[0] - fastlog(im) -
prec, quadrature_error)
else:
im, im_acc = None, None
result = re, im, re_acc, im_acc
return result
def evalf_integral(expr, prec, options):
limits = expr.limits
if len(limits) != 1 or len(limits[0]) != 3:
raise NotImplementedError
workprec = prec
i = 0
maxprec = options.get('maxprec', INF)
while 1:
result = do_integral(expr, workprec, options)
accuracy = complex_accuracy(result)
if accuracy >= prec: # achieved desired precision
break
if workprec >= maxprec: # can't increase accuracy any more
break
if accuracy == -1:
# maybe the answer really is zero and maybe we just haven't increased
# the precision enough. So increase by doubling to not take too long
# to get to maxprec.
workprec *= 2
else:
workprec += max(prec, 2**i)
workprec = min(workprec, maxprec)
i += 1
return result
def check_convergence(numer, denom, n):
"""
Returns (h, g, p) where
-- h is:
> 0 for convergence of rate 1/factorial(n)**h
< 0 for divergence of rate factorial(n)**(-h)
= 0 for geometric or polynomial convergence or divergence
-- abs(g) is:
> 1 for geometric convergence of rate 1/h**n
< 1 for geometric divergence of rate h**n
= 1 for polynomial convergence or divergence
(g < 0 indicates an alternating series)
-- p is:
> 1 for polynomial convergence of rate 1/n**h
<= 1 for polynomial divergence of rate n**(-h)
"""
from sympy import Poly
npol = Poly(numer, n)
dpol = Poly(denom, n)
p = npol.degree()
q = dpol.degree()
rate = q - p
if rate:
return rate, None, None
constant = dpol.LC() / npol.LC()
if abs(constant) != 1:
return rate, constant, None
if npol.degree() == dpol.degree() == 0:
return rate, constant, 0
pc = npol.all_coeffs()[1]
qc = dpol.all_coeffs()[1]
return rate, constant, (qc - pc)/dpol.LC()
def hypsum(expr, n, start, prec):
"""
Sum a rapidly convergent infinite hypergeometric series with
given general term, e.g. e = hypsum(1/factorial(n), n). The
quotient between successive terms must be a quotient of integer
polynomials.
"""
from sympy import Float, hypersimp, lambdify
if prec == float('inf'):
raise NotImplementedError('does not support inf prec')
if start:
expr = expr.subs(n, n + start)
hs = hypersimp(expr, n)
if hs is None:
raise NotImplementedError("a hypergeometric series is required")
num, den = hs.as_numer_denom()
func1 = lambdify(n, num)
func2 = lambdify(n, den)
h, g, p = check_convergence(num, den, n)
if h < 0:
raise ValueError("Sum diverges like (n!)^%i" % (-h))
term = expr.subs(n, 0)
if not term.is_Rational:
raise NotImplementedError("Non rational term functionality is not implemented.")
# Direct summation if geometric or faster
if h > 0 or (h == 0 and abs(g) > 1):
term = (MPZ(term.p) << prec) // term.q
s = term
k = 1
while abs(term) > 5:
term *= MPZ(func1(k - 1))
term //= MPZ(func2(k - 1))
s += term
k += 1
return from_man_exp(s, -prec)
else:
alt = g < 0
if abs(g) < 1:
raise ValueError("Sum diverges like (%i)^n" % abs(1/g))
if p < 1 or (p == 1 and not alt):
raise ValueError("Sum diverges like n^%i" % (-p))
# We have polynomial convergence: use Richardson extrapolation
vold = None
ndig = prec_to_dps(prec)
while True:
# Need to use at least quad precision because a lot of cancellation
# might occur in the extrapolation process; we check the answer to
# make sure that the desired precision has been reached, too.
prec2 = 4*prec
term0 = (MPZ(term.p) << prec2) // term.q
def summand(k, _term=[term0]):
if k:
k = int(k)
_term[0] *= MPZ(func1(k - 1))
_term[0] //= MPZ(func2(k - 1))
return make_mpf(from_man_exp(_term[0], -prec2))
with workprec(prec):
v = nsum(summand, [0, mpmath_inf], method='richardson')
vf = Float(v, ndig)
if vold is not None and vold == vf:
break
prec += prec # double precision each time
vold = vf
return v._mpf_
def evalf_prod(expr, prec, options):
from sympy import Sum
if all((l[1] - l[2]).is_Integer for l in expr.limits):
re, im, re_acc, im_acc = evalf(expr.doit(), prec=prec, options=options)
else:
re, im, re_acc, im_acc = evalf(expr.rewrite(Sum), prec=prec, options=options)
return re, im, re_acc, im_acc
def evalf_sum(expr, prec, options):
from sympy import Float
if 'subs' in options:
expr = expr.subs(options['subs'])
func = expr.function
limits = expr.limits
if len(limits) != 1 or len(limits[0]) != 3:
raise NotImplementedError
if func is S.Zero:
return mpf(0), None, None, None
prec2 = prec + 10
try:
n, a, b = limits[0]
if b != S.Infinity or a != int(a):
raise NotImplementedError
# Use fast hypergeometric summation if possible
v = hypsum(func, n, int(a), prec2)
delta = prec - fastlog(v)
if fastlog(v) < -10:
v = hypsum(func, n, int(a), delta)
return v, None, min(prec, delta), None
except NotImplementedError:
# Euler-Maclaurin summation for general series
eps = Float(2.0)**(-prec)
for i in range(1, 5):
m = n = 2**i * prec
s, err = expr.euler_maclaurin(m=m, n=n, eps=eps,
eval_integral=False)
err = err.evalf()
if err <= eps:
break
err = fastlog(evalf(abs(err), 20, options)[0])
re, im, re_acc, im_acc = evalf(s, prec2, options)
if re_acc is None:
re_acc = -err
if im_acc is None:
im_acc = -err
return re, im, re_acc, im_acc
#----------------------------------------------------------------------------#
# #
# Symbolic interface #
# #
#----------------------------------------------------------------------------#
def evalf_symbol(x, prec, options):
val = options['subs'][x]
if isinstance(val, mpf):
if not val:
return None, None, None, None
return val._mpf_, None, prec, None
else:
if not '_cache' in options:
options['_cache'] = {}
cache = options['_cache']
cached, cached_prec = cache.get(x, (None, MINUS_INF))
if cached_prec >= prec:
return cached
v = evalf(sympify(val), prec, options)
cache[x] = (v, prec)
return v
evalf_table = None
def _create_evalf_table():
global evalf_table
from sympy.functions.combinatorial.numbers import bernoulli
from sympy.concrete.products import Product
from sympy.concrete.summations import Sum
from sympy.core.add import Add
from sympy.core.mul import Mul
from sympy.core.numbers import Exp1, Float, Half, ImaginaryUnit, Integer, NaN, NegativeOne, One, Pi, Rational, Zero
from sympy.core.power import Pow
from sympy.core.symbol import Dummy, Symbol
from sympy.functions.elementary.complexes import Abs, im, re
from sympy.functions.elementary.exponential import exp, log
from sympy.functions.elementary.integers import ceiling, floor
from sympy.functions.elementary.piecewise import Piecewise
from sympy.functions.elementary.trigonometric import atan, cos, sin
from sympy.integrals.integrals import Integral
evalf_table = {
Symbol: evalf_symbol,
Dummy: evalf_symbol,
Float: lambda x, prec, options: (x._mpf_, None, prec, None),
Rational: lambda x, prec, options: (from_rational(x.p, x.q, prec), None, prec, None),
Integer: lambda x, prec, options: (from_int(x.p, prec), None, prec, None),
Zero: lambda x, prec, options: (None, None, prec, None),
One: lambda x, prec, options: (fone, None, prec, None),
Half: lambda x, prec, options: (fhalf, None, prec, None),
Pi: lambda x, prec, options: (mpf_pi(prec), None, prec, None),
Exp1: lambda x, prec, options: (mpf_e(prec), None, prec, None),
ImaginaryUnit: lambda x, prec, options: (None, fone, None, prec),
NegativeOne: lambda x, prec, options: (fnone, None, prec, None),
NaN: lambda x, prec, options: (fnan, None, prec, None),
exp: lambda x, prec, options: evalf_pow(
Pow(S.Exp1, x.args[0], evaluate=False), prec, options),
cos: evalf_trig,
sin: evalf_trig,
Add: evalf_add,
Mul: evalf_mul,
Pow: evalf_pow,
log: evalf_log,
atan: evalf_atan,
Abs: evalf_abs,
re: evalf_re,
im: evalf_im,
floor: evalf_floor,
ceiling: evalf_ceiling,
Integral: evalf_integral,
Sum: evalf_sum,
Product: evalf_prod,
Piecewise: evalf_piecewise,
bernoulli: evalf_bernoulli,
}
def evalf(x, prec, options):
from sympy import re as re_, im as im_
try:
rf = evalf_table[x.func]
r = rf(x, prec, options)
except KeyError:
try:
# Fall back to ordinary evalf if possible
if 'subs' in options:
x = x.subs(evalf_subs(prec, options['subs']))
xe = x._eval_evalf(prec)
re, im = xe.as_real_imag()
if re.has(re_) or im.has(im_):
raise NotImplementedError
if re == 0:
re = None
reprec = None
elif re.is_number:
re = re._to_mpmath(prec, allow_ints=False)._mpf_
reprec = prec
if im == 0:
im = None
imprec = None
elif im.is_number:
im = im._to_mpmath(prec, allow_ints=False)._mpf_
imprec = prec
r = re, im, reprec, imprec
except AttributeError:
raise NotImplementedError
if options.get("verbose"):
print("### input", x)
print("### output", to_str(r[0] or fzero, 50))
print("### raw", r) # r[0], r[2]
print()
chop = options.get('chop', False)
if chop:
if chop is True:
chop_prec = prec
else:
# convert (approximately) from given tolerance;
# the formula here will will make 1e-i rounds to 0 for
# i in the range +/-27 while 2e-i will not be chopped
chop_prec = int(round(-3.321*math.log10(chop) + 2.5))
if chop_prec == 3:
chop_prec -= 1
r = chop_parts(r, chop_prec)
if options.get("strict"):
check_target(x, r, prec)
return r
class EvalfMixin(object):
"""Mixin class adding evalf capabililty."""
__slots__ = []
def evalf(self, n=15, subs=None, maxn=100, chop=False, strict=False, quad=None, verbose=False):
"""
Evaluate the given formula to an accuracy of n digits.
Optional keyword arguments:
subs=<dict>
Substitute numerical values for symbols, e.g.
subs={x:3, y:1+pi}. The substitutions must be given as a
dictionary.
maxn=<integer>
Allow a maximum temporary working precision of maxn digits
(default=100)
chop=<bool>
Replace tiny real or imaginary parts in subresults
by exact zeros (default=False)
strict=<bool>
Raise PrecisionExhausted if any subresult fails to evaluate
to full accuracy, given the available maxprec
(default=False)
quad=<str>
Choose algorithm for numerical quadrature. By default,
tanh-sinh quadrature is used. For oscillatory
integrals on an infinite interval, try quad='osc'.
verbose=<bool>
Print debug information (default=False)
"""
from sympy import Float, Number
n = n if n is not None else 15
if subs and is_sequence(subs):
raise TypeError('subs must be given as a dictionary')
# for sake of sage that doesn't like evalf(1)
if n == 1 and isinstance(self, Number):
from sympy.core.expr import _mag
rv = self.evalf(2, subs, maxn, chop, strict, quad, verbose)
m = _mag(rv)
rv = rv.round(1 - m)
return rv
if not evalf_table:
_create_evalf_table()
prec = dps_to_prec(n)
options = {'maxprec': max(prec, int(maxn*LG10)), 'chop': chop,
'strict': strict, 'verbose': verbose}
if subs is not None:
options['subs'] = subs
if quad is not None:
options['quad'] = quad
try:
result = evalf(self, prec + 4, options)
except NotImplementedError:
# Fall back to the ordinary evalf
v = self._eval_evalf(prec)
if v is None:
return self
try:
# If the result is numerical, normalize it
result = evalf(v, prec, options)
except NotImplementedError:
# Probably contains symbols or unknown functions
return v
re, im, re_acc, im_acc = result
if re:
p = max(min(prec, re_acc), 1)
re = Float._new(re, p)
else:
re = S.Zero
if im:
p = max(min(prec, im_acc), 1)
im = Float._new(im, p)
return re + im*S.ImaginaryUnit
else:
return re
n = evalf
def _evalf(self, prec):
"""Helper for evalf. Does the same thing but takes binary precision"""
r = self._eval_evalf(prec)
if r is None:
r = self
return r
def _eval_evalf(self, prec):
return
def _to_mpmath(self, prec, allow_ints=True):
# mpmath functions accept ints as input
errmsg = "cannot convert to mpmath number"
if allow_ints and self.is_Integer:
return self.p
if hasattr(self, '_as_mpf_val'):
return make_mpf(self._as_mpf_val(prec))
try:
re, im, _, _ = evalf(self, prec, {})
if im:
if not re:
re = fzero
return make_mpc((re, im))
elif re:
return make_mpf(re)
else:
return make_mpf(fzero)
except NotImplementedError:
v = self._eval_evalf(prec)
if v is None:
raise ValueError(errmsg)
if v.is_Float:
return make_mpf(v._mpf_)
# Number + Number*I is also fine
re, im = v.as_real_imag()
if allow_ints and re.is_Integer:
re = from_int(re.p)
elif re.is_Float:
re = re._mpf_
else:
raise ValueError(errmsg)
if allow_ints and im.is_Integer:
im = from_int(im.p)
elif im.is_Float:
im = im._mpf_
else:
raise ValueError(errmsg)
return make_mpc((re, im))
def N(x, n=15, **options):
r"""
Calls x.evalf(n, \*\*options).
Both .n() and N() are equivalent to .evalf(); use the one that you like better.
See also the docstring of .evalf() for information on the options.
Examples
========
>>> from sympy import Sum, oo, N
>>> from sympy.abc import k
>>> Sum(1/k**k, (k, 1, oo))
Sum(k**(-k), (k, 1, oo))
>>> N(_, 4)
1.291
"""
return sympify(x).evalf(n, **options)
| 50,319 | 32.771812 | 119 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/facts.py
|
# -*- coding: utf-8 -*-
r"""This is rule-based deduction system for SymPy
The whole thing is split into two parts
- rules compilation and preparation of tables
- runtime inference
For rule-based inference engines, the classical work is RETE algorithm [1],
[2] Although we are not implementing it in full (or even significantly)
it's still still worth a read to understand the underlying ideas.
In short, every rule in a system of rules is one of two forms:
- atom -> ... (alpha rule)
- And(atom1, atom2, ...) -> ... (beta rule)
The major complexity is in efficient beta-rules processing and usually for an
expert system a lot of effort goes into code that operates on beta-rules.
Here we take minimalistic approach to get something usable first.
- (preparation) of alpha- and beta- networks, everything except
- (runtime) FactRules.deduce_all_facts
_____________________________________
( Kirr: I've never thought that doing )
( logic stuff is that difficult... )
-------------------------------------
o ^__^
o (oo)\_______
(__)\ )\/\
||----w |
|| ||
Some references on the topic
----------------------------
[1] http://en.wikipedia.org/wiki/Rete_algorithm
[2] http://reports-archive.adm.cs.cmu.edu/anon/1995/CMU-CS-95-113.pdf
http://en.wikipedia.org/wiki/Propositional_formula
http://en.wikipedia.org/wiki/Inference_rule
http://en.wikipedia.org/wiki/List_of_rules_of_inference
"""
from __future__ import print_function, division
from collections import defaultdict
from .logic import Logic, And, Or, Not
from sympy.core.compatibility import string_types, range
def _base_fact(atom):
"""Return the literal fact of an atom.
Effectively, this merely strips the Not around a fact.
"""
if isinstance(atom, Not):
return atom.arg
else:
return atom
def _as_pair(atom):
if isinstance(atom, Not):
return (atom.arg, False)
else:
return (atom, True)
# XXX this prepares forward-chaining rules for alpha-network
def transitive_closure(implications):
"""
Computes the transitive closure of a list of implications
Uses Warshall's algorithm, as described at
http://www.cs.hope.edu/~cusack/Notes/Notes/DiscreteMath/Warshall.pdf.
"""
full_implications = set(implications)
literals = set().union(*map(set, full_implications))
for k in literals:
for i in literals:
if (i, k) in full_implications:
for j in literals:
if (k, j) in full_implications:
full_implications.add((i, j))
return full_implications
def deduce_alpha_implications(implications):
"""deduce all implications
Description by example
----------------------
given set of logic rules:
a -> b
b -> c
we deduce all possible rules:
a -> b, c
b -> c
implications: [] of (a,b)
return: {} of a -> set([b, c, ...])
"""
implications = implications + [(Not(j), Not(i)) for (i, j) in implications]
res = defaultdict(set)
full_implications = transitive_closure(implications)
for a, b in full_implications:
if a == b:
continue # skip a->a cyclic input
res[a].add(b)
# Clean up tautologies and check consistency
for a, impl in res.items():
impl.discard(a)
na = Not(a)
if na in impl:
raise ValueError(
'implications are inconsistent: %s -> %s %s' % (a, na, impl))
return res
def apply_beta_to_alpha_route(alpha_implications, beta_rules):
"""apply additional beta-rules (And conditions) to already-built
alpha implication tables
TODO: write about
- static extension of alpha-chains
- attaching refs to beta-nodes to alpha chains
e.g.
alpha_implications:
a -> [b, !c, d]
b -> [d]
...
beta_rules:
&(b,d) -> e
then we'll extend a's rule to the following
a -> [b, !c, d, e]
"""
x_impl = {}
for x in alpha_implications.keys():
x_impl[x] = (set(alpha_implications[x]), [])
for bcond, bimpl in beta_rules:
for bk in bcond.args:
if bk in x_impl:
continue
x_impl[bk] = (set(), [])
# static extensions to alpha rules:
# A: x -> a,b B: &(a,b) -> c ==> A: x -> a,b,c
seen_static_extension = True
while seen_static_extension:
seen_static_extension = False
for bcond, bimpl in beta_rules:
if not isinstance(bcond, And):
raise TypeError("Cond is not And")
bargs = set(bcond.args)
for x, (ximpls, bb) in x_impl.items():
x_all = ximpls | {x}
# A: ... -> a B: &(...) -> a is non-informative
if bimpl not in x_all and bargs.issubset(x_all):
ximpls.add(bimpl)
# we introduced new implication - now we have to restore
# completeness of the whole set.
bimpl_impl = x_impl.get(bimpl)
if bimpl_impl is not None:
ximpls |= bimpl_impl[0]
seen_static_extension = True
# attach beta-nodes which can be possibly triggered by an alpha-chain
for bidx, (bcond, bimpl) in enumerate(beta_rules):
bargs = set(bcond.args)
for x, (ximpls, bb) in x_impl.items():
x_all = ximpls | {x}
# A: ... -> a B: &(...) -> a (non-informative)
if bimpl in x_all:
continue
# A: x -> a... B: &(!a,...) -> ... (will never trigger)
# A: x -> a... B: &(...) -> !a (will never trigger)
if any(Not(xi) in bargs or Not(xi) == bimpl for xi in x_all):
continue
if bargs & x_all:
bb.append(bidx)
return x_impl
def rules_2prereq(rules):
"""build prerequisites table from rules
Description by example
----------------------
given set of logic rules:
a -> b, c
b -> c
we build prerequisites (from what points something can be deduced):
b <- a
c <- a, b
rules: {} of a -> [b, c, ...]
return: {} of c <- [a, b, ...]
Note however, that this prerequisites may be *not* enough to prove a
fact. An example is 'a -> b' rule, where prereq(a) is b, and prereq(b)
is a. That's because a=T -> b=T, and b=F -> a=F, but a=F -> b=?
"""
prereq = defaultdict(set)
for (a, _), impl in rules.items():
if isinstance(a, Not):
a = a.args[0]
for (i, _) in impl:
if isinstance(i, Not):
i = i.args[0]
prereq[i].add(a)
return prereq
################
# RULES PROVER #
################
class TautologyDetected(Exception):
"""(internal) Prover uses it for reporting detected tautology"""
pass
class Prover(object):
"""ai - prover of logic rules
given a set of initial rules, Prover tries to prove all possible rules
which follow from given premises.
As a result proved_rules are always either in one of two forms: alpha or
beta:
Alpha rules
-----------
This are rules of the form::
a -> b & c & d & ...
Beta rules
----------
This are rules of the form::
&(a,b,...) -> c & d & ...
i.e. beta rules are join conditions that say that something follows when
*several* facts are true at the same time.
"""
def __init__(self):
self.proved_rules = []
self._rules_seen = set()
def split_alpha_beta(self):
"""split proved rules into alpha and beta chains"""
rules_alpha = [] # a -> b
rules_beta = [] # &(...) -> b
for a, b in self.proved_rules:
if isinstance(a, And):
rules_beta.append((a, b))
else:
rules_alpha.append((a, b))
return rules_alpha, rules_beta
@property
def rules_alpha(self):
return self.split_alpha_beta()[0]
@property
def rules_beta(self):
return self.split_alpha_beta()[1]
def process_rule(self, a, b):
"""process a -> b rule""" # TODO write more?
if (not a) or isinstance(b, bool):
return
if isinstance(a, bool):
return
if (a, b) in self._rules_seen:
return
else:
self._rules_seen.add((a, b))
# this is the core of processing
try:
self._process_rule(a, b)
except TautologyDetected:
pass
def _process_rule(self, a, b):
# right part first
# a -> b & c --> a -> b ; a -> c
# (?) FIXME this is only correct when b & c != null !
if isinstance(b, And):
for barg in b.args:
self.process_rule(a, barg)
# a -> b | c --> !b & !c -> !a
# --> a & !b -> c
# --> a & !c -> b
elif isinstance(b, Or):
# detect tautology first
if not isinstance(a, Logic): # Atom
# tautology: a -> a|c|...
if a in b.args:
raise TautologyDetected(a, b, 'a -> a|c|...')
self.process_rule(And(*[Not(barg) for barg in b.args]), Not(a))
for bidx in range(len(b.args)):
barg = b.args[bidx]
brest = b.args[:bidx] + b.args[bidx + 1:]
self.process_rule(And(a, Not(barg)), Or(*brest))
# left part
# a & b -> c --> IRREDUCIBLE CASE -- WE STORE IT AS IS
# (this will be the basis of beta-network)
elif isinstance(a, And):
if b in a.args:
raise TautologyDetected(a, b, 'a & b -> a')
self.proved_rules.append((a, b))
# XXX NOTE at present we ignore !c -> !a | !b
elif isinstance(a, Or):
if b in a.args:
raise TautologyDetected(a, b, 'a | b -> a')
for aarg in a.args:
self.process_rule(aarg, b)
else:
# both `a` and `b` are atoms
self.proved_rules.append((a, b)) # a -> b
self.proved_rules.append((Not(b), Not(a))) # !b -> !a
########################################
class FactRules(object):
"""Rules that describe how to deduce facts in logic space
When defined, these rules allow implications to quickly be determined
for a set of facts. For this precomputed deduction tables are used.
see `deduce_all_facts` (forward-chaining)
Also it is possible to gather prerequisites for a fact, which is tried
to be proven. (backward-chaining)
Definition Syntax
-----------------
a -> b -- a=T -> b=T (and automatically b=F -> a=F)
a -> !b -- a=T -> b=F
a == b -- a -> b & b -> a
a -> b & c -- a=T -> b=T & c=T
# TODO b | c
Internals
---------
.full_implications[k, v]: all the implications of fact k=v
.beta_triggers[k, v]: beta rules that might be triggered when k=v
.prereq -- {} k <- [] of k's prerequisites
.defined_facts -- set of defined fact names
"""
def __init__(self, rules):
"""Compile rules into internal lookup tables"""
if isinstance(rules, string_types):
rules = rules.splitlines()
# --- parse and process rules ---
P = Prover()
for rule in rules:
# XXX `a` is hardcoded to be always atom
a, op, b = rule.split(None, 2)
a = Logic.fromstring(a)
b = Logic.fromstring(b)
if op == '->':
P.process_rule(a, b)
elif op == '==':
P.process_rule(a, b)
P.process_rule(b, a)
else:
raise ValueError('unknown op %r' % op)
# --- build deduction networks ---
self.beta_rules = []
for bcond, bimpl in P.rules_beta:
self.beta_rules.append(
(set(_as_pair(a) for a in bcond.args), _as_pair(bimpl)))
# deduce alpha implications
impl_a = deduce_alpha_implications(P.rules_alpha)
# now:
# - apply beta rules to alpha chains (static extension), and
# - further associate beta rules to alpha chain (for inference
# at runtime)
impl_ab = apply_beta_to_alpha_route(impl_a, P.rules_beta)
# extract defined fact names
self.defined_facts = set(_base_fact(k) for k in impl_ab.keys())
# build rels (forward chains)
full_implications = defaultdict(set)
beta_triggers = defaultdict(set)
for k, (impl, betaidxs) in impl_ab.items():
full_implications[_as_pair(k)] = set(_as_pair(i) for i in impl)
beta_triggers[_as_pair(k)] = betaidxs
self.full_implications = full_implications
self.beta_triggers = beta_triggers
# build prereq (backward chains)
prereq = defaultdict(set)
rel_prereq = rules_2prereq(full_implications)
for k, pitems in rel_prereq.items():
prereq[k] |= pitems
self.prereq = prereq
class InconsistentAssumptions(ValueError):
def __str__(self):
kb, fact, value = self.args
return "%s, %s=%s" % (kb, fact, value)
class FactKB(dict):
"""
A simple propositional knowledge base relying on compiled inference rules.
"""
def __str__(self):
return '{\n%s}' % ',\n'.join(
["\t%s: %s" % i for i in sorted(self.items())])
def __init__(self, rules):
self.rules = rules
def _tell(self, k, v):
"""Add fact k=v to the knowledge base.
Returns True if the KB has actually been updated, False otherwise.
"""
if k in self and self[k] is not None:
if self[k] == v:
return False
else:
raise InconsistentAssumptions(self, k, v)
else:
self[k] = v
return True
# *********************************************
# * This is the workhorse, so keep it *fast*. *
# *********************************************
def deduce_all_facts(self, facts):
"""
Update the KB with all the implications of a list of facts.
Facts can be specified as a dictionary or as a list of (key, value)
pairs.
"""
# keep frequently used attributes locally, so we'll avoid extra
# attribute access overhead
full_implications = self.rules.full_implications
beta_triggers = self.rules.beta_triggers
beta_rules = self.rules.beta_rules
if isinstance(facts, dict):
facts = facts.items()
while facts:
beta_maytrigger = set()
# --- alpha chains ---
for k, v in facts:
if not self._tell(k, v) or v is None:
continue
# lookup routing tables
for key, value in full_implications[k, v]:
self._tell(key, value)
beta_maytrigger.update(beta_triggers[k, v])
# --- beta chains ---
facts = []
for bidx in beta_maytrigger:
bcond, bimpl = beta_rules[bidx]
if all(self.get(k) is v for k, v in bcond):
facts.append(bimpl)
| 15,895 | 28.491651 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/logic.py
|
"""Logic expressions handling
NOTE
----
at present this is mainly needed for facts.py , feel free however to improve
this stuff for general purpose.
"""
from __future__ import print_function, division
from sympy.core.compatibility import range
def _torf(args):
"""Return True if all args are True, False if they
are all False, else None.
>>> from sympy.core.logic import _torf
>>> _torf((True, True))
True
>>> _torf((False, False))
False
>>> _torf((True, False))
"""
sawT = sawF = False
for a in args:
if a is True:
if sawF:
return
sawT = True
elif a is False:
if sawT:
return
sawF = True
else:
return
return sawT
def _fuzzy_group(args, quick_exit=False):
"""Return True if all args are True, None if there is any None else False
unless ``quick_exit`` is True (then return None as soon as a second False
is seen.
``_fuzzy_group`` is like ``fuzzy_and`` except that it is more
conservative in returning a False, waiting to make sure that all
arguments are True or False and returning None if any arguments are
None. It also has the capability of permiting only a single False and
returning None if more than one is seen. For example, the presence of a
single transcendental amongst rationals would indicate that the group is
no longer rational; but a second transcendental in the group would make the
determination impossible.
Examples
========
>>> from sympy.core.logic import _fuzzy_group
By default, multiple Falses mean the group is broken:
>>> _fuzzy_group([False, False, True])
False
If multiple Falses mean the group status is unknown then set
`quick_exit` to True so None can be returned when the 2nd False is seen:
>>> _fuzzy_group([False, False, True], quick_exit=True)
But if only a single False is seen then the group is known to
be broken:
>>> _fuzzy_group([False, True, True], quick_exit=True)
False
"""
saw_other = False
for a in args:
if a is True:
continue
if a is None:
return
if quick_exit and saw_other:
return
saw_other = True
return not saw_other
def fuzzy_bool(x):
"""Return True, False or None according to x.
Whereas bool(x) returns True or False, fuzzy_bool allows
for the None value and non-false values (which become None), too.
Examples
========
>>> from sympy.core.logic import fuzzy_bool
>>> from sympy.abc import x
>>> fuzzy_bool(x), fuzzy_bool(None)
(None, None)
>>> bool(x), bool(None)
(True, False)
"""
if x is None:
return None
if x in (True, False):
return bool(x)
def fuzzy_and(args):
"""Return True (all True), False (any False) or None.
Examples
========
>>> from sympy.core.logic import fuzzy_and
>>> from sympy import Dummy
If you had a list of objects to test the commutivity of
and you want the fuzzy_and logic applied, passing an
iterator will allow the commutativity to only be computed
as many times as necessary. With this list, False can be
returned after analyzing the first symbol:
>>> syms = [Dummy(commutative=False), Dummy()]
>>> fuzzy_and(s.is_commutative for s in syms)
False
That False would require less work than if a list of pre-computed
items was sent:
>>> fuzzy_and([s.is_commutative for s in syms])
False
"""
rv = True
for ai in args:
ai = fuzzy_bool(ai)
if ai is False:
return False
if rv: # this will stop updating if a None is ever trapped
rv = ai
return rv
def fuzzy_not(v):
"""
Not in fuzzy logic
Return None if `v` is None else `not v`.
Examples
========
>>> from sympy.core.logic import fuzzy_not
>>> fuzzy_not(True)
False
>>> fuzzy_not(None)
>>> fuzzy_not(False)
True
"""
if v is None:
return v
else:
return not v
def fuzzy_or(args):
"""
Or in fuzzy logic. Returns True (any True), False (all False), or None
See the docstrings of fuzzy_and and fuzzy_not for more info. fuzzy_or is
related to the two by the standard De Morgan's law.
>>> from sympy.core.logic import fuzzy_or
>>> fuzzy_or([True, False])
True
>>> fuzzy_or([True, None])
True
>>> fuzzy_or([False, False])
False
>>> print(fuzzy_or([False, None]))
None
"""
return fuzzy_not(fuzzy_and(fuzzy_not(i) for i in args))
class Logic(object):
"""Logical expression"""
# {} 'op' -> LogicClass
op_2class = {}
def __new__(cls, *args):
obj = object.__new__(cls)
obj.args = args
return obj
def __getnewargs__(self):
return self.args
def __hash__(self):
return hash( (type(self).__name__,) + tuple(self.args) )
def __eq__(a, b):
if not isinstance(b, type(a)):
return False
else:
return a.args == b.args
def __ne__(a, b):
if not isinstance(b, type(a)):
return True
else:
return a.args != b.args
def __lt__(self, other):
if self.__cmp__(other) == -1:
return True
return False
def __cmp__(self, other):
if type(self) is not type(other):
a = str(type(self))
b = str(type(other))
else:
a = self.args
b = other.args
return (a > b) - (a < b)
def __str__(self):
return '%s(%s)' % (self.__class__.__name__, ', '.join(str(a) for a in self.args))
__repr__ = __str__
@staticmethod
def fromstring(text):
"""Logic from string with space around & and | but none after !.
e.g.
!a & b | c
"""
lexpr = None # current logical expression
schedop = None # scheduled operation
for term in text.split():
# operation symbol
if term in '&|':
if schedop is not None:
raise ValueError(
'double op forbidden: "%s %s"' % (term, schedop))
if lexpr is None:
raise ValueError(
'%s cannot be in the beginning of expression' % term)
schedop = term
continue
if '&' in term or '|' in term:
raise ValueError('& and | must have space around them')
if term[0] == '!':
if len(term) == 1:
raise ValueError('do not include space after "!"')
term = Not(term[1:])
# already scheduled operation, e.g. '&'
if schedop:
lexpr = Logic.op_2class[schedop](lexpr, term)
schedop = None
continue
# this should be atom
if lexpr is not None:
raise ValueError(
'missing op between "%s" and "%s"' % (lexpr, term))
lexpr = term
# let's check that we ended up in correct state
if schedop is not None:
raise ValueError('premature end-of-expression in "%s"' % text)
if lexpr is None:
raise ValueError('"%s" is empty' % text)
# everything looks good now
return lexpr
class AndOr_Base(Logic):
def __new__(cls, *args):
bargs = []
for a in args:
if a == cls.op_x_notx:
return a
elif a == (not cls.op_x_notx):
continue # skip this argument
bargs.append(a)
args = sorted(set(cls.flatten(bargs)), key=hash)
for a in args:
if Not(a) in args:
return cls.op_x_notx
if len(args) == 1:
return args.pop()
elif len(args) == 0:
return not cls.op_x_notx
return Logic.__new__(cls, *args)
@classmethod
def flatten(cls, args):
# quick-n-dirty flattening for And and Or
args_queue = list(args)
res = []
while True:
try:
arg = args_queue.pop(0)
except IndexError:
break
if isinstance(arg, Logic):
if isinstance(arg, cls):
args_queue.extend(arg.args)
continue
res.append(arg)
args = tuple(res)
return args
class And(AndOr_Base):
op_x_notx = False
def _eval_propagate_not(self):
# !(a&b&c ...) == !a | !b | !c ...
return Or( *[Not(a) for a in self.args] )
# (a|b|...) & c == (a&c) | (b&c) | ...
def expand(self):
# first locate Or
for i in range(len(self.args)):
arg = self.args[i]
if isinstance(arg, Or):
arest = self.args[:i] + self.args[i + 1:]
orterms = [And( *(arest + (a,)) ) for a in arg.args]
for j in range(len(orterms)):
if isinstance(orterms[j], Logic):
orterms[j] = orterms[j].expand()
res = Or(*orterms)
return res
else:
return self
class Or(AndOr_Base):
op_x_notx = True
def _eval_propagate_not(self):
# !(a|b|c ...) == !a & !b & !c ...
return And( *[Not(a) for a in self.args] )
class Not(Logic):
def __new__(cls, arg):
if isinstance(arg, str):
return Logic.__new__(cls, arg)
elif isinstance(arg, bool):
return not arg
elif isinstance(arg, Not):
return arg.args[0]
elif isinstance(arg, Logic):
# XXX this is a hack to expand right from the beginning
arg = arg._eval_propagate_not()
return arg
else:
raise ValueError('Not: unknown argument %r' % (arg,))
@property
def arg(self):
return self.args[0]
Logic.op_2class['&'] = And
Logic.op_2class['|'] = Or
Logic.op_2class['!'] = Not
| 10,185 | 24.592965 | 89 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/core/operations.py
|
from __future__ import print_function, division
from sympy.core.sympify import _sympify, sympify
from sympy.core.basic import Basic, _aresame
from sympy.core.cache import cacheit
from sympy.core.compatibility import ordered, range
from sympy.core.logic import fuzzy_and
from sympy.core.evaluate import global_evaluate
class AssocOp(Basic):
""" Associative operations, can separate noncommutative and
commutative parts.
(a op b) op c == a op (b op c) == a op b op c.
Base class for Add and Mul.
This is an abstract base class, concrete derived classes must define
the attribute `identity`.
"""
# for performance reason, we don't let is_commutative go to assumptions,
# and keep it right here
__slots__ = ['is_commutative']
@cacheit
def __new__(cls, *args, **options):
from sympy import Order
args = list(map(_sympify, args))
args = [a for a in args if a is not cls.identity]
if not options.pop('evaluate', global_evaluate[0]):
return cls._from_args(args)
if len(args) == 0:
return cls.identity
if len(args) == 1:
return args[0]
c_part, nc_part, order_symbols = cls.flatten(args)
is_commutative = not nc_part
obj = cls._from_args(c_part + nc_part, is_commutative)
obj = cls._exec_constructor_postprocessors(obj)
if order_symbols is not None:
return Order(obj, *order_symbols)
return obj
@classmethod
def _from_args(cls, args, is_commutative=None):
"""Create new instance with already-processed args"""
if len(args) == 0:
return cls.identity
elif len(args) == 1:
return args[0]
obj = super(AssocOp, cls).__new__(cls, *args)
if is_commutative is None:
is_commutative = fuzzy_and(a.is_commutative for a in args)
obj.is_commutative = is_commutative
return obj
def _new_rawargs(self, *args, **kwargs):
"""Create new instance of own class with args exactly as provided by
caller but returning the self class identity if args is empty.
This is handy when we want to optimize things, e.g.
>>> from sympy import Mul, S
>>> from sympy.abc import x, y
>>> e = Mul(3, x, y)
>>> e.args
(3, x, y)
>>> Mul(*e.args[1:])
x*y
>>> e._new_rawargs(*e.args[1:]) # the same as above, but faster
x*y
Note: use this with caution. There is no checking of arguments at
all. This is best used when you are rebuilding an Add or Mul after
simply removing one or more terms. If modification which result,
for example, in extra 1s being inserted (as when collecting an
expression's numerators and denominators) they will not show up in
the result but a Mul will be returned nonetheless:
>>> m = (x*y)._new_rawargs(S.One, x); m
x
>>> m == x
False
>>> m.is_Mul
True
Another issue to be aware of is that the commutativity of the result
is based on the commutativity of self. If you are rebuilding the
terms that came from a commutative object then there will be no
problem, but if self was non-commutative then what you are
rebuilding may now be commutative.
Although this routine tries to do as little as possible with the
input, getting the commutativity right is important, so this level
of safety is enforced: commutativity will always be recomputed if
self is non-commutative and kwarg `reeval=False` has not been
passed.
"""
if kwargs.pop('reeval', True) and self.is_commutative is False:
is_commutative = None
else:
is_commutative = self.is_commutative
return self._from_args(args, is_commutative)
@classmethod
def flatten(cls, seq):
"""Return seq so that none of the elements are of type `cls`. This is
the vanilla routine that will be used if a class derived from AssocOp
does not define its own flatten routine."""
# apply associativity, no commutativity property is used
new_seq = []
while seq:
o = seq.pop()
if o.__class__ is cls: # classes must match exactly
seq.extend(o.args)
else:
new_seq.append(o)
# c_part, nc_part, order_symbols
return [], new_seq, None
def _matches_commutative(self, expr, repl_dict={}, old=False):
"""
Matches Add/Mul "pattern" to an expression "expr".
repl_dict ... a dictionary of (wild: expression) pairs, that get
returned with the results
This function is the main workhorse for Add/Mul.
For instance:
>>> from sympy import symbols, Wild, sin
>>> a = Wild("a")
>>> b = Wild("b")
>>> c = Wild("c")
>>> x, y, z = symbols("x y z")
>>> (a+sin(b)*c)._matches_commutative(x+sin(y)*z)
{a_: x, b_: y, c_: z}
In the example above, "a+sin(b)*c" is the pattern, and "x+sin(y)*z" is
the expression.
The repl_dict contains parts that were already matched. For example
here:
>>> (x+sin(b)*c)._matches_commutative(x+sin(y)*z, repl_dict={a: x})
{a_: x, b_: y, c_: z}
the only function of the repl_dict is to return it in the
result, e.g. if you omit it:
>>> (x+sin(b)*c)._matches_commutative(x+sin(y)*z)
{b_: y, c_: z}
the "a: x" is not returned in the result, but otherwise it is
equivalent.
"""
# make sure expr is Expr if pattern is Expr
from .expr import Add, Expr
from sympy import Mul
if isinstance(self, Expr) and not isinstance(expr, Expr):
return None
# handle simple patterns
if self == expr:
return repl_dict
d = self._matches_simple(expr, repl_dict)
if d is not None:
return d
# eliminate exact part from pattern: (2+a+w1+w2).matches(expr) -> (w1+w2).matches(expr-a-2)
from .function import WildFunction
from .symbol import Wild
wild_part = []
exact_part = []
for p in ordered(self.args):
if p.has(Wild, WildFunction) and (not expr.has(p)):
# not all Wild should stay Wilds, for example:
# (w2+w3).matches(w1) -> (w1+w3).matches(w1) -> w3.matches(0)
wild_part.append(p)
else:
exact_part.append(p)
if exact_part:
exact = self.func(*exact_part)
free = expr.free_symbols
if free and (exact.free_symbols - free):
# there are symbols in the exact part that are not
# in the expr; but if there are no free symbols, let
# the matching continue
return None
newpattern = self.func(*wild_part)
newexpr = self._combine_inverse(expr, exact)
if not old and (expr.is_Add or expr.is_Mul):
if newexpr.count_ops() > expr.count_ops():
return None
return newpattern.matches(newexpr, repl_dict)
# now to real work ;)
i = 0
saw = set()
while expr not in saw:
saw.add(expr)
expr_list = (self.identity,) + tuple(ordered(self.make_args(expr)))
for last_op in reversed(expr_list):
for w in reversed(wild_part):
d1 = w.matches(last_op, repl_dict)
if d1 is not None:
d2 = self.xreplace(d1).matches(expr, d1)
if d2 is not None:
return d2
if i == 0:
if self.is_Mul:
# make e**i look like Mul
if expr.is_Pow and expr.exp.is_Integer:
if expr.exp > 0:
expr = Mul(*[expr.base, expr.base**(expr.exp - 1)], evaluate=False)
else:
expr = Mul(*[1/expr.base, expr.base**(expr.exp + 1)], evaluate=False)
i += 1
continue
elif self.is_Add:
# make i*e look like Add
c, e = expr.as_coeff_Mul()
if abs(c) > 1:
if c > 0:
expr = Add(*[e, (c - 1)*e], evaluate=False)
else:
expr = Add(*[-e, (c + 1)*e], evaluate=False)
i += 1
continue
# try collection on non-Wild symbols
from sympy.simplify.radsimp import collect
was = expr
did = set()
for w in reversed(wild_part):
c, w = w.as_coeff_mul(Wild)
free = c.free_symbols - did
if free:
did.update(free)
expr = collect(expr, free)
if expr != was:
i += 0
continue
break # if we didn't continue, there is nothing more to do
return
def _has_matcher(self):
"""Helper for .has()"""
def _ncsplit(expr):
# this is not the same as args_cnc because here
# we don't assume expr is a Mul -- hence deal with args --
# and always return a set.
cpart, ncpart = [], []
for arg in expr.args:
if arg.is_commutative:
cpart.append(arg)
else:
ncpart.append(arg)
return set(cpart), ncpart
c, nc = _ncsplit(self)
cls = self.__class__
def is_in(expr):
if expr == self:
return True
elif not isinstance(expr, Basic):
return False
elif isinstance(expr, cls):
_c, _nc = _ncsplit(expr)
if (c & _c) == c:
if not nc:
return True
elif len(nc) <= len(_nc):
for i in range(len(_nc) - len(nc)):
if _nc[i:i + len(nc)] == nc:
return True
return False
return is_in
def _eval_evalf(self, prec):
"""
Evaluate the parts of self that are numbers; if the whole thing
was a number with no functions it would have been evaluated, but
it wasn't so we must judiciously extract the numbers and reconstruct
the object. This is *not* simply replacing numbers with evaluated
numbers. Nunmbers should be handled in the largest pure-number
expression as possible. So the code below separates ``self`` into
number and non-number parts and evaluates the number parts and
walks the args of the non-number part recursively (doing the same
thing).
"""
from .add import Add
from .mul import Mul
from .symbol import Symbol
from .function import AppliedUndef
if isinstance(self, (Mul, Add)):
x, tail = self.as_independent(Symbol, AppliedUndef)
# if x is an AssocOp Function then the _evalf below will
# call _eval_evalf (here) so we must break the recursion
if not (tail is self.identity or
isinstance(x, AssocOp) and x.is_Function or
x is self.identity and isinstance(tail, AssocOp)):
# here, we have a number so we just call to _evalf with prec;
# prec is not the same as n, it is the binary precision so
# that's why we don't call to evalf.
x = x._evalf(prec) if x is not self.identity else self.identity
args = []
tail_args = tuple(self.func.make_args(tail))
for a in tail_args:
# here we call to _eval_evalf since we don't know what we
# are dealing with and all other _eval_evalf routines should
# be doing the same thing (i.e. taking binary prec and
# finding the evalf-able args)
newa = a._eval_evalf(prec)
if newa is None:
args.append(a)
else:
args.append(newa)
if not _aresame(tuple(args), tail_args):
tail = self.func(*args)
return self.func(x, tail)
# this is the same as above, but there were no pure-number args to
# deal with
args = []
for a in self.args:
newa = a._eval_evalf(prec)
if newa is None:
args.append(a)
else:
args.append(newa)
if not _aresame(tuple(args), self.args):
return self.func(*args)
return self
@classmethod
def make_args(cls, expr):
"""
Return a sequence of elements `args` such that cls(*args) == expr
>>> from sympy import Symbol, Mul, Add
>>> x, y = map(Symbol, 'xy')
>>> Mul.make_args(x*y)
(x, y)
>>> Add.make_args(x*y)
(x*y,)
>>> set(Add.make_args(x*y + y)) == set([y, x*y])
True
"""
if isinstance(expr, cls):
return expr.args
else:
return (sympify(expr),)
class ShortCircuit(Exception):
pass
class LatticeOp(AssocOp):
"""
Join/meet operations of an algebraic lattice[1].
These binary operations are associative (op(op(a, b), c) = op(a, op(b, c))),
commutative (op(a, b) = op(b, a)) and idempotent (op(a, a) = op(a) = a).
Common examples are AND, OR, Union, Intersection, max or min. They have an
identity element (op(identity, a) = a) and an absorbing element
conventionally called zero (op(zero, a) = zero).
This is an abstract base class, concrete derived classes must declare
attributes zero and identity. All defining properties are then respected.
>>> from sympy import Integer
>>> from sympy.core.operations import LatticeOp
>>> class my_join(LatticeOp):
... zero = Integer(0)
... identity = Integer(1)
>>> my_join(2, 3) == my_join(3, 2)
True
>>> my_join(2, my_join(3, 4)) == my_join(2, 3, 4)
True
>>> my_join(0, 1, 4, 2, 3, 4)
0
>>> my_join(1, 2)
2
References:
[1] - http://en.wikipedia.org/wiki/Lattice_%28order%29
"""
is_commutative = True
def __new__(cls, *args, **options):
args = (_sympify(arg) for arg in args)
try:
_args = frozenset(cls._new_args_filter(args))
except ShortCircuit:
return sympify(cls.zero)
if not _args:
return sympify(cls.identity)
elif len(_args) == 1:
return set(_args).pop()
else:
# XXX in almost every other case for __new__, *_args is
# passed along, but the expectation here is for _args
obj = super(AssocOp, cls).__new__(cls, _args)
obj._argset = _args
return obj
@classmethod
def _new_args_filter(cls, arg_sequence, call_cls=None):
"""Generator filtering args"""
ncls = call_cls or cls
for arg in arg_sequence:
if arg == ncls.zero:
raise ShortCircuit(arg)
elif arg == ncls.identity:
continue
elif arg.func == ncls:
for x in arg.args:
yield x
else:
yield arg
@classmethod
def make_args(cls, expr):
"""
Return a set of args such that cls(*arg_set) == expr.
"""
if isinstance(expr, cls):
return expr._argset
else:
return frozenset([sympify(expr)])
@property
@cacheit
def args(self):
return tuple(ordered(self._argset))
@staticmethod
def _compare_pretty(a, b):
return (str(a) > str(b)) - (str(a) < str(b))
| 16,577 | 34.883117 | 99 |
py
|
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