def test_large_diffs():
# Test situations where free energy differences are very large
A = np.array([
[1.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 1.0, 0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
])
G = np.array([0.0, 40, 40, 40, -2000, -2050])
x0 = np.array([[1e-6, 1e-6, 1e-6, 1e-6, 0, 0]])
logx = eqtk.solve(c0=x0,
A=A,
G=G,
G_units=None,
units=None,
return_log=True)
assert eqtk.eqcheck(logx, x0, A=A, G=G, c_as_log=True)
A = np.array([
[1.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 1.0, 0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
])
G = np.array([0.0, 40, 40, 40, -2100, -2100])
x0 = np.array([[1e-6, 1e-6, 1e-6, 1.0001e-6, 0, 0]])
logx = eqtk.solve(c0=x0,
A=A,
G=G,
G_units=None,
units=None,
return_log=True)
assert eqtk.eqcheck(logx, x0, A=A, G=G, c_as_log=True)
A = np.array([
[1.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 1.0, 0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
])
G = np.array([0.0, 40, 150, 200, -4200, -4215])
x0 = np.array([[1e-6, 1e-6, 1e-6, 1.0000e-6, 0, 0]])
logx = eqtk.solve(c0=x0,
A=A,
G=G,
G_units=None,
units=None,
return_log=True)
assert eqtk.eqcheck(logx, x0, A=A, G=G, c_as_log=True)
def compute_eq(G, x0):
hypothesis.assume(not (np.isnan(x0).any() or np.isnan(G).any()))
x = eqtk.solve(x0, A=A, G=G)
eq_check, cons_check = eqtk.eqcheck(x, x0, A=A, G=G)
assert np.isclose(eq_check, 1.0).all(), "Equilibrium error"
assert np.isclose(cons_check, 1.0).all(), "Conservation of mass error"
def test_small_conc_failure():
A = np.array(
[
[1.0, 0.0, 1.0, 1.0, 0.0],
[1.0, 1.0, 0.0, 0.0, 2.0],
[1.0, 0.0, 0.0, 1.0, 2.0],
]
)
G = np.array(
[
-1.1323012373599138e02,
-2.7028447814426110e-01,
-2.3382656193096754e01,
-1.0088531260804201e02,
-5.7676558386243052e01,
]
)
x0 = np.array(
[
[
1.8134373707286439e-08,
3.5913242229740680e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
]
]
)
x = eqtk.solve(c0=x0, A=A, G=G)
assert eqtk.eqcheck(x, x0, A=A, G=G)
def test_past_failure_2():
N = np.array([[-2.0, 1.0, 0.0, 0.0], [-3.0, 0.0, 1.0, 0.0], [-4.0, 0.0, 0.0, 1.0]])
minus_log_K = np.array([-43.66660344, -68.14676841, -92.28023823])
x0 = np.array([[1.87852623e-06, 3.75705246e-06, 1.25235082e-06, 4.69631557e-07]])
K = np.exp(-minus_log_K)
x = eqtk.solve(x0, N, K)
assert eqtk.eqcheck(x, x0, N, K)
def check_test_case(tc):
A = np.array(tc["A"]).transpose()
G = -np.array(tc["g"])
x0 = np.array(tc["x0"])
# Remove infinities
infs = np.isinf(G)
A = A[:, ~infs]
G = G[~infs]
x0 = x0[~infs]
x = eqtk.solve(c0=x0, A=A, G=G)
assert eqtk.eqcheck(x, x0, A=A, G=G)
x = eqtk.solve(c0=x0, A=A, G=G, normal_A=False)
assert eqtk.eqcheck(x, x0, A=A, G=G)
def test_spontaneous_production_failure():
N = np.array(
[[1, 0, 1, 0, -1, 0], [1, 0, 0, 1, 0, -1], [1, 1, 1, 0, 0, 0]], dtype=float
)
A = np.array(
[[0, 0, 0, 1, 0, 1], [1, 0, -1, 0, 0, 1], [0, -1, 1, 0, 1, 0]], dtype=float
)
G = np.array([0, 1, 2, 3, 4, 5])
K = np.exp(-np.dot(N, G))
for x0_val in [
[1, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 0, 0],
[1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0],
]:
x0 = np.array(x0_val, dtype=float)
x_NK = eqtk.solve(c0=x0, N=N, K=K)
with pytest.raises(ValueError) as excinfo:
x_AG = eqtk.solve(c0=x0, A=A, G=G)
excinfo.match("`A` must have all nonnegative entries.")
assert eqtk.eqcheck(x_NK, x0, N=N, K=K)
def test_competition():
N = np.array([[1, 1, 0, -1, 0], [1, 0, 1, 0, -1]])
K = np.array([0.05, 0.01])
x0 = np.array([2.0, 0.05, 1.0, 0.0, 0.0])
x = eqtk.solve(c0=x0, N=N, K=K, units="µM")
assert eqtk.eqcheck(x, x0, N, K)
def test_binary_binding_3():
N = np.array([[-1, -1, 1]])
K = np.array([2e3])
A = np.array([[1, 0, 1], [0, 1, 1]])
G = np.array([0, 0, -np.log(K)])
x0 = np.array([3e-3, 5e-3, 0.0])
x = eqtk.solve(c0=x0, N=N, K=K, units="M")
assert eqtk.eqcheck(x, x0, N, K, units="M")
def test_random_cases(n_random_test_cases=200,
max_particles=4,
max_compound_size=5):
# Generate list of random test cases
random_test_cases = []
for i in range(n_random_test_cases):
n_parts = np.random.randint(1, max_particles)
max_cmp_size = np.random.randint(1, max_compound_size + 1)
random_test_cases.append(
eqtk.testcases.random_elemental_test_case(n_parts, max_cmp_size))
for tc in random_test_cases:
c_NK = eqtk.solve(c0=tc["c0"], N=tc["N"], K=tc["K"])
c_AG = eqtk.solve(c0=tc["c0"], A=tc["A"], G=tc["G"])
assert np.allclose(c_NK, tc["c"])
assert np.allclose(c_AG, tc["c"])
assert eqtk.eqcheck(c_NK, tc["c0"], tc["N"], tc["K"])
assert eqtk.eqcheck(c_AG, tc["c0"], A=tc["A"], G=tc["G"])
def test_example_4():
rxns = """
AB <=> A + B ; 0.015
AC <=> A + C ; 0.003
"""
N = eqtk.parse_rxns(rxns)
c0 = {"A": 1.0, "B": 0.5, "C": 0.25, "AB": 0, "AC": 0}
c = eqtk.solve(c0, N, units="mM")
assert eqtk.eqcheck(c, c0, N=N, units="mM")
def test_example_3():
N = np.array([
[-1, -1, -1, 1, 1, 0, 0],
[-1, 0, 0, 0, 0, -1, 1],
[-1, 1, 1, 0, -1, 0, 0],
[0, 1, -1, 0, -1, 1, 0],
])
K = np.array([1.0, 2.0, 3.0, 5.0])
x0 = np.array([1.0, 2.0, 3.0, 5.0, 7.0, 11.0, 13.0])
x = eqtk.solve(c0=x0, N=N, K=K, units="M")
assert eqtk.eqcheck(x, x0, N, K, units="M")
def test_binary_binding_5():
N = np.array([[-2, 0, 1, 0, 0], [-1, -1, 0, 1, 0], [0, -2, 0, 0, 1]])
K = np.array([2e4, 3e4, 8e3])
x0_1 = np.array([3e-4, 5e-5, 0, 0, 0])
x0_2 = np.array([0, 0, 1.5e-4, 0, 2.5e-5])
x0_3 = np.array([7e-5, 1e-5, 1.0e-4, 3e-5, 5e-6])
x0 = np.vstack((x0_1, x0_2, x0_3))
A = np.array([[1, 0, 2, 1, 0], [0, 1, 0, 1, 2]])
x = eqtk.solve(c0=x0, N=N, K=K, units="M")
assert eqtk.eqcheck(x, x0, N=N, K=K, units="M")
def test_exponential_chain():
n_rxns = 20
N = np.diag(np.ones(n_rxns), 1)[:-1, :]
np.fill_diagonal(N, -1)
N[0, 0] = -2
N[1:, 0] = -1
K = 100.0 * np.ones(n_rxns)
c0 = np.zeros((50, n_rxns + 1))
c0[:, 0] = np.linspace(0.0, 10.0, 50)
c = eqtk.solve(c0, N=N, K=K, units="M")
assert eqtk.eqcheck(c, c0, N=N, K=K, units="M")
def test_sequential_binding():
N = np.array([[-1, -1, 1, 0, 0], [0, -1, -1, 1, 0], [0, -1, 0, -1, 1]])
K = np.array([50.0, 10.0, 40.0])
c0 = np.array([0.006, 0.003, 0.0, 0.0, 0.0])
titrated_species = 1
c0_titrated = np.linspace(0.0, 0.01, 50)
c0 = np.array([c0 for _ in range(len(c0_titrated))])
c0[:, titrated_species] = c0_titrated
c = eqtk.solve(c0, N=N, K=K, units="M")
assert eqtk.eqcheck(c, c0, N=N, K=K)
def test_example_1():
N = np.array([
[-1, 0, 1, 0, 0, 0],
[-1, -1, 0, 1, 0, 0],
[0, -2, 0, 0, 1, 0],
[0, -1, -1, 0, 0, 1],
])
K = np.array([50.0, 10.0, 40.0, 100.0])
x0 = np.array([[0.001, 0.003, 0.0, 0.0, 0.0, 0.0],
[0.001, 0.0, 0.0, 0.0, 0.0, 0.0]])
x = eqtk.solve(c0=x0, N=N, K=K, units="M")
assert eqtk.eqcheck(x, x0, N, K, units="M")
def test_past_failure_1():
A = np.array([[1.0, 0.0, 2.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0, 2.0]])
G = np.array([0.0, 0.0, -16.76857677, -2.38430181, 1.22028775])
x0 = np.array(
[
[
1.65989040e-10,
1.07630096e-04,
1.65989040e-10,
1.65989040e-10,
5.38150479e-05,
]
]
)
x = eqtk.solve(x0, A=A, G=G)
assert eqtk.eqcheck(x, x0, A=A, G=G)
def test_scale_factor_failure():
A = np.array([[1.0, 0.0, 2.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0, 2.0]])
G = np.array([0.0, 0.0, 0.77428976, -5.64873697, -0.95863043])
x0 = np.array(
[
[
5.50293892e-05,
6.49273515e-08,
2.75796219e-05,
1.29854703e-07,
3.24636758e-08,
]
]
)
x = eqtk.solve(c0=x0, A=A, G=G)
assert eqtk.eqcheck(x, x0, A=A, G=G)
def test_phosphoric_acid_titration():
N = np.array([
[1, 0, -1, 1, 0, 0],
[1, 0, 0, -1, 1, 0],
[1, 0, 0, 0, -1, 1],
[1, 1, 0, 0, 0, 0],
])
K = np.array([10**(-2.15), 10**(-7.2), 10**(-12.15), 1e-14])
c0 = np.array([0.0, 0.0, 0.001, 0.0, 0.0, 0.0])
c0_titrant = np.array([0.0, 0.001, 0.0, 0.0, 0.0, 0.0])
vol_titrant = np.linspace(0.0, 4.0, 100)
c = eqtk.volumetric_titration(c0,
c0_titrant,
vol_titrant,
N=N,
K=K,
units="M")
new_c0 = eqtk.solvers._volumetric_to_c0(c0, c0_titrant, vol_titrant)
assert eqtk.eqcheck(c, new_c0, N=N, K=K, units="M")
def test_eqcheck_simple_binding_multiple_inputs(input_tuple):
cA0_0, cB0_0, cA0_1, cB0_1, cA0_2, cB0_2, Kd = input_tuple
tc_0 = eqtk.testcases.simple_binding(cA0_0, cB0_0, Kd)
tc_1 = eqtk.testcases.simple_binding(cA0_0, cB0_0, Kd)
tc_2 = eqtk.testcases.simple_binding(cA0_0, cB0_0, Kd)
c_df = pd.concat(
(
tc_0["c_series"].to_frame().transpose(),
tc_1["c_series"].to_frame().transpose(),
tc_2["c_series"].to_frame().transpose(),
)
)
c_df_log = pd.concat(
(
tc_0["c_series_log"].to_frame().transpose(),
tc_1["c_series_log"].to_frame().transpose(),
tc_2["c_series_log"].to_frame().transpose(),
)
)
c = np.vstack((tc_0["c"], tc_1["c"], tc_2["c"]))
c0 = np.vstack((tc_0["c0"], tc_1["c0"], tc_2["c0"]))
N = tc_0["N"]
K = tc_0["K"]
N_df = tc_0["N_df"]
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
c, c0, N, K, return_detailed=True
)
assert all_ok
assert eq_check.shape == (3, 1)
assert cons_check.shape == (3, 2)
assert eq_satisfied.shape == (3, 1)
assert cons_satisfied.shape == (3, 2)
assert np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert np.all(eq_satisfied)
assert np.all(cons_satisfied)
assert eqtk.eqcheck(c, c0, N, K)
erroneous_c = 1.0001 * c
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
erroneous_c, c0, N, K, return_detailed=True
)
assert not all_ok
assert eq_check.shape == (3, 1)
assert cons_check.shape == (3, 2)
assert eq_satisfied.shape == (3, 1)
assert cons_satisfied.shape == (3, 2)
assert not np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert not np.all(eq_satisfied)
assert not np.all(cons_satisfied)
assert not eqtk.eqcheck(erroneous_c, c0, N, K)
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
c=c_df, N=N_df, return_detailed=True
)
assert all_ok
assert eq_check.shape == (3, 1)
assert cons_check.shape == (3, 2)
assert eq_satisfied.shape == (3, 1)
assert cons_satisfied.shape == (3, 2)
assert np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert np.all(eq_satisfied)
assert np.all(cons_satisfied)
assert eqtk.eqcheck(c=c_df, N=N_df)
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
c_df_log, N=N_df, return_detailed=True
)
assert all_ok
assert eq_check.shape == (3, 1)
assert cons_check.shape == (3, 2)
assert eq_satisfied.shape == (3, 1)
assert cons_satisfied.shape == (3, 2)
assert np.allclose(eq_check, 1.0), "Error in equilibrium check with series."
assert np.all(eq_satisfied), "Error in equilibrium check with series."
assert np.all(cons_satisfied), "Error in conservation check with series."
assert eqtk.eqcheck(c_df_log, N=N_df)
c_df.loc[:, ~c_df.columns.str.contains("__0")] *= 1.0001
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
c_df, N=N_df, return_detailed=True
)
assert not all_ok
assert eq_check.shape == (3, 1)
assert cons_check.shape == (3, 2)
assert eq_satisfied.shape == (3, 1)
assert cons_satisfied.shape == (3, 2)
assert not np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert not np.all(eq_satisfied)
assert not np.all(cons_satisfied)
assert not eqtk.eqcheck(c=c_df, N=N_df)
def test_eqcheck_simple_binding(input_tuple):
cA0, cB0, Kd = input_tuple
tc = eqtk.testcases.simple_binding(cA0, cB0, Kd)
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
tc["c"], tc["c0"], tc["N"], tc["K"], return_detailed=True
)
assert all_ok
assert eq_check.shape == (1,)
assert cons_check.shape == (2,)
assert eq_satisfied.shape == (1,)
assert cons_satisfied.shape == (2,)
assert np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert np.all(eq_satisfied), "Error in conservation check."
assert np.all(cons_satisfied), "Error in conservation check."
assert eqtk.eqcheck(tc["c"], tc["c0"], tc["N"], tc["K"])
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
tc["c_log"], tc["c0"], tc["N"], tc["K"], c_as_log=True, return_detailed=True
)
assert all_ok
assert eq_check.shape == (1,)
assert cons_check.shape == (2,)
assert eq_satisfied.shape == (1,)
assert cons_satisfied.shape == (2,)
assert np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert np.all(eq_satisfied), "Error in conservation check."
assert np.all(cons_satisfied), "Error in conservation check."
assert eqtk.eqcheck(tc["c_log"], tc["c0"], tc["N"], tc["K"], c_as_log=True)
tc["erroneous_c"] = 1.0001 * tc["c"]
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
tc["erroneous_c"], tc["c0"], tc["N"], tc["K"], return_detailed=True
)
assert not all_ok
assert eq_check.shape == (1,)
assert cons_check.shape == (2,)
assert eq_satisfied.shape == (1,)
assert cons_satisfied.shape == (2,)
assert not np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert not np.all(eq_satisfied), "Error in conservation check."
assert not np.all(cons_satisfied), "Error in conservation check."
assert not eqtk.eqcheck(tc["erroneous_c"], tc["c0"], tc["N"], tc["K"])
tc["erroneous_c_log"] = tc["c_log"] - 0.001
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
tc["erroneous_c_log"],
tc["c0"],
tc["N"],
tc["K"],
c_as_log=True,
return_detailed=True,
)
assert not all_ok
assert eq_check.shape == (1,)
assert cons_check.shape == (2,)
assert eq_satisfied.shape == (1,)
assert cons_satisfied.shape == (2,)
assert not np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert not np.all(eq_satisfied), "Error in conservation check."
assert not np.all(cons_satisfied), "Error in conservation check."
assert not eqtk.eqcheck(
tc["erroneous_c_log"], tc["c0"], tc["N"], tc["K"], c_as_log=True
)
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
c=tc["c_series"], N=tc["N_df"], return_detailed=True
)
assert all_ok
assert eq_check.shape == (1,)
assert cons_check.shape == (2,)
assert eq_satisfied.shape == (1,)
assert cons_satisfied.shape == (2,)
assert np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert np.all(eq_satisfied), "Error in conservation check."
assert np.all(cons_satisfied), "Error in conservation check."
assert eqtk.eqcheck(c=tc["c_series"], N=tc["N_df"])
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
c=tc["c_series_log"], N=tc["N_df"], return_detailed=True
)
assert all_ok
assert eq_check.shape == (1,)
assert cons_check.shape == (2,)
assert eq_satisfied.shape == (1,)
assert cons_satisfied.shape == (2,)
assert np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert np.all(eq_satisfied), "Error in conservation check."
assert np.all(cons_satisfied), "Error in conservation check."
assert eqtk.eqcheck(c=tc["c_series_log"], N=tc["N_df"])
tc["c_series"][~tc["c_series"].index.str.contains("__0")] *= 1.0001
all_ok, eq_check, eq_satisfied, cons_check, cons_satisfied = eqtk.eqcheck(
tc["c_series"], N=tc["N_df"], return_detailed=True
)
assert not all_ok
assert eq_check.shape == (1,)
assert cons_check.shape == (2,)
assert eq_satisfied.shape == (1,)
assert cons_satisfied.shape == (2,)
assert not np.allclose(eq_check, 1.0), "Error in equilibrium check."
assert not np.all(eq_satisfied), "Error in conservation check."
assert not np.all(cons_satisfied), "Error in conservation check."
assert not eqtk.eqcheck(c=tc["c_series"], N=tc["N_df"])
def test_large():
# fmt: off
A = np.array([
[
1.0,
1.0,
0.0,
0.0,
0.0,
2.0,
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
3.0,
2.0,
2.0,
2.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
4.0,
3.0,
3.0,
3.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
2.0,
2.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
2.0,
1.0,
1.0,
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1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
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0.0,
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0.0,
0.0,
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[
1.0,
0.0,
1.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
2.0,
1.0,
1.0,
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0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
2.0,
1.0,
1.0,
1.0,
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0.0,
1.0,
0.0,
0.0,
3.0,
2.0,
2.0,
1.0,
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
2.0,
1.0,
1.0,
1.0,
0.0,
0.0,
1.0,
0.0,
0.0,
2.0,
1.0,
1.0,
3.0,
2.0,
2.0,
2.0,
1.0,
2.0,
1.0,
1.0,
0.0,
0.0,
2.0,
1.0,
1.0,
1.0,
0.0,
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1.0,
0.0,
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0.0,
2.0,
1.0,
1.0,
1.0,
0.0,
0.0,
1.0,
0.0,
0.0,
4.0,
3.0,
3.0,
2.0,
2.0,
2.0,
2.0,
2.0,
2.0,
1.0,
1.0,
1.0,
1.0,
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1.0,
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
[
1.0,
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0.0,
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1.0,
0.0,
0.0,
1.0,
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2.0,
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1.0,
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1.0,
2.0,
0.0,
1.0,
0.0,
2.0,
1.0,
1.0,
2.0,
1.0,
2.0,
3.0,
2.0,
1.0,
2.0,
1.0,
0.0,
0.0,
1.0,
0.0,
1.0,
2.0,
1.0,
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1.0,
0.0,
0.0,
1.0,
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2.0,
1.0,
1.0,
0.0,
2.0,
1.0,
3.0,
2.0,
2.0,
1.0,
0.0,
2.0,
1.0,
1.0,
0.0,
4.0,
3.0,
2.0,
2.0,
1.0,
0.0,
],
[
1.0,
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2.0,
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3.0,
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1.0,
1.0,
2.0,
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1.0,
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1.0,
2.0,
2.0,
1.0,
2.0,
2.0,
3.0,
0.0,
1.0,
2.0,
2.0,
3.0,
4.0,
],
])
# fmt: on
G = np.array([
-4.2015367534032657e02,
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-5.4817682865924418e01,
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-2.5691105508367849e02,
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-4.3284119923642481e02,
-2.4211326870787389e02,
-2.9484362717015421e02,
-2.8668170416631432e02,
-3.3775513541674047e02,
-6.9179245072223310e01,
-1.0595181053210460e02,
-1.6153764121002649e02,
-1.5335633483058461e02,
-2.0267567033456882e02,
-2.5467945122971807e02,
])
x0 = np.array([[
1.8134373707286439e-08,
3.5913242229740680e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
8.9783105574351699e-15,
1.1971080743246893e-14,
1.1971080743246893e-14,
1.1971080743246893e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
3.5913242229740680e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
8.9783105574351699e-15,
1.1971080743246893e-14,
1.1971080743246893e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
8.9783105574351699e-15,
1.1971080743246893e-14,
1.7956621114870340e-14,
1.7956621114870340e-14,
1.1971080743246893e-14,
8.9783105574351699e-15,
]])
x = eqtk.solve(c0=x0, A=A, G=G)
assert eqtk.eqcheck(x, x0, A=A, G=G)
