def test_solve_qps():
for i0 in xrange(100):
with numpy_seed(i0):
qps = QPSolver(*get_random_problem())
for i1 in xrange(10):
if i1 == 0:
free = np.ones(qps.nx, dtype=bool)
else:
with numpy_seed(i0*100 + i1):
free = get_random_free(qps)
x = qps.solve(free)
qps.check_feasible(x, free)
def test_esp_cost_solve():
for irep in xrange(nrep):
with numpy_seed(irep):
A = np.random.uniform(-1, 1, (11, 11))
B = np.random.uniform(-1, 1, 11)
C = np.random.uniform(-1, 1, ())
A = np.dot(A, A.T)
cost = ESPCost(A, B, C, 10)
# test without constraint
x = cost.solve()
assert abs(cost.gradient(x)).max() < 1e-8
# test with constraint 0.0
x = cost.solve(qtot=0.0)
charges = x[:10]
assert abs(charges.sum()) < 1e-8
gradient = cost.gradient(x)
assert abs(gradient[:10] - gradient[:10].mean()).max() < 1e-9
assert abs(gradient[10:]).max() < 1e-9
# test with constraint 1.0
x = cost.solve(qtot=1.0)
charges = x[:10]
assert abs(charges.sum()-1) < 1e-9
gradient = cost.gradient(x)
assert abs(gradient[:10] - gradient[:10].mean()).max() < 1e-9
assert abs(gradient[10:]).max() < 1e-9
def translate_random_rvecs(coordinates, rvecs, seed):
"""Translate nuclei by a random integer linear combination of cell vectors."""
result = coordinates.copy()
with numpy_seed(seed):
for j in xrange(len(coordinates)):
result[j] += np.dot(np.random.randint(-3, 4, 3), rvecs)
return result
def test_esp_cost_solve_regularized():
for irep in xrange(nrep):
with numpy_seed(irep):
A = np.random.uniform(-1, 1, (11, 11))
B = np.random.uniform(-1, 1, 11)
C = np.random.uniform(-1, 1, ())
A = np.dot(A, A.T)
cost = ESPCost(A, B, C, 10)
# test without constraint
ridge = 1e-6
x = cost.solve(ridge=ridge)
l = ridge*np.diag(cost._A)[:cost.natom].mean()
ls = np.ones(cost._B.shape)*l
ls[-1] = 0
assert abs(cost.gradient(x) + 2*ls*x).max() < 1e-9
# test with constraint 0.0
x = cost.solve(qtot=0.0, ridge=ridge)
charges = x[:10]
assert abs(charges.sum()) < 1e-9
gradient = cost.gradient(x) + 2*ls*x
assert abs(gradient[:10] - gradient[:10].mean()).max() < 1e-10
assert abs(gradient[10:]).max() < 1e-10
# test with constraint 1.0
x = cost.solve(qtot=1.0, ridge=ridge)
charges = x[:10]
assert abs(charges.sum()-1) < 1e-9
gradient = cost.gradient(x) + 2*ls*x
assert abs(gradient[:10] - gradient[:10].mean()).max() < 1e-10
assert abs(gradient[10:]).max() < 1e-10
def test_value_charges2():
for irep in xrange(nrep):
cost = get_random_esp_cost_cube3d(irep)
for ix in xrange(nrep):
with numpy_seed(irep*10 + ix):
x = np.random.normal(0, 1, len(cost._A))
x -= x.sum()
assert cost.value(x) >= cost.value_charges(x[:-1])
def test_orbitals_from_fock():
with numpy_seed(1):
a = np.random.normal(0, 1, (5, 5))
fock = a+a.T
a = np.random.normal(0, 1, (5, 5))
olp = np.dot(a, a.T)
orb = Orbitals(5)
orb.from_fock(fock, olp)
assert orb.error_eigen(fock, olp) < 1e-5
def get_random_esp_cost_cube0d_args(seed=1):
"""Generate random cube data for an aperiodic system."""
shape = np.array([5, 5, 5])
pbc = np.array([0, 0, 0])
with numpy_seed(seed):
coordinates = np.random.normal(0, 1, (5, 3))
origin = np.random.uniform(-3, 3, 3)
grid_rvecs = np.diag(np.random.uniform(2.0, 3.0, 3))
grid_rvecs += np.random.uniform(-0.1, 0.1, (3, 3))
vref = np.random.normal(0, 1, shape)
weights = np.random.uniform(0, 1, shape)
return coordinates, origin, grid_rvecs, shape, pbc, vref, weights
def test_orbitals_error_eigen():
with numpy_seed(1):
orb = Orbitals(5)
a = np.random.normal(0, 1, (5, 5))
fock = a+a.T
evals, evecs = np.linalg.eigh(fock)
orb.coeffs[:] = evecs
orb.energies[:] = evals
olp = np.identity(5)
assert orb.error_eigen(fock, olp) < 1e-10
orb.coeffs[:] += np.random.normal(0, 1e-3, (5, 5))
assert orb.error_eigen(fock, olp) > 1e-10
def test_esp_cost_cube3d_gradient():
for irep in xrange(nrep):
# Some parameters
coordinates, origin, grid_rvecs, shape, pbc, vref, weights = \
get_random_esp_cost_cube3d_args(irep)
grid = UniformGrid(origin, grid_rvecs, shape, pbc)
cost = ESPCost.from_grid_data(coordinates, grid, vref, weights)
with numpy_seed(irep):
x0 = np.random.uniform(-0.5, 0.5, len(coordinates)+1)
dxs = np.random.uniform(-1e-5, 1e-5, (100, len(coordinates)+1))
check_delta(cost.value, cost.gradient, x0, dxs)
def test_esp_cost_cube3d_invariance_rcut():
for irep in xrange(3): # Repeating this only 3 times because it is so slow.
# Some parameters
coordinates, origin, grid_rvecs, shape, pbc, vref, weights = \
get_random_esp_cost_cube3d_args(irep)
grid = UniformGrid(origin, grid_rvecs, shape, pbc)
# Generate costs with displaced origin
costs = []
for icut in xrange(nrep):
with numpy_seed(irep*10 + icut):
rcut = np.random.uniform(10, 30)
alpha = 4.5/rcut
gcut = 1.5*alpha
cost = ESPCost.from_grid_data(coordinates, grid, vref, weights, rcut=rcut,
alpha=alpha, gcut=gcut)
costs.append(cost)
# Compare the cost functions
check_costs(costs, eps1=1e-8)
def test_tridiagsym_solve():
N = 10
A = np.zeros((N, N), float)
for irep in xrange(100):
with numpy_seed(irep): # Make random numbers reproducible
# randomize the diagonal
A.ravel()[::N + 1] = np.random.uniform(1, 2, N)
# randomize the upper diagonal
A.ravel()[1::N + 1] = np.random.uniform(-1, 0, N - 1)
# clone the lower diagonal
A.ravel()[N::N + 1] = A.ravel()[1::N + 1]
# test the inverse for all possible basis vectors
Ainv = np.linalg.inv(A)
for i in xrange(N):
right = np.zeros(N, float)
right[i] = 1.0
solution = np.zeros(N, float)
tridiagsym_solve(A.ravel()[::N + 1].copy(),
A.ravel()[N::N + 1].copy(), right, solution)
error = abs(solution - Ainv[:, i]).max()
assert (error < 1e-9)
def test_eval_spline_grid_add_random():
npoint = 10
cs = get_cosine_spline()
for irep in xrange(10):
with numpy_seed(irep):
cell = get_random_cell(1.0, irep % 4)
points = np.random.normal(-2, 3, (npoint, 3))
g = IntGrid(points, np.random.normal(0, 1.0, npoint))
center1 = np.random.uniform(-2, 2, 3)
center2 = np.random.uniform(-2, 2, 3)
output1 = np.zeros(npoint)
g.eval_spline(cs, center1, output1, cell)
output2 = np.zeros(npoint)
g.eval_spline(cs, center2, output2, cell)
output3 = np.zeros(npoint)
g.eval_spline(cs, center1, output3, cell)
g.eval_spline(cs, center2, output3, cell)
assert abs(output1 + output2 - output3).max() < 1e-10
def test_eval_spline_grid_add_random():
npoint = 10
cs = get_cosine_spline()
for irep in xrange(10):
with numpy_seed(irep):
cell = get_random_cell(1.0, irep % 4)
points = np.random.normal(-2, 3, (npoint,3))
g = IntGrid(points, np.random.normal(0, 1.0, npoint))
center1 = np.random.uniform(-2, 2, 3)
center2 = np.random.uniform(-2, 2, 3)
output1 = np.zeros(npoint)
g.eval_spline(cs, center1, output1, cell)
output2 = np.zeros(npoint)
g.eval_spline(cs, center2, output2, cell)
output3 = np.zeros(npoint)
g.eval_spline(cs, center1, output3, cell)
g.eval_spline(cs, center2, output3, cell)
assert abs(output1 + output2 - output3).max() < 1e-10
def test_consistent():
for irep in xrange(nrep):
with numpy_seed(irep):
# random system
natom = 5
coordinates = np.random.uniform(0, 10, (natom, 3))
charges = np.random.normal(0, 1, natom)
charges -= charges.mean()
# random grid
origin = np.random.uniform(-3, 3, 3)
size = 10
grid_rvecs = np.diag(np.random.uniform(8.0, 10.0, 3)) / size
grid_rvecs += np.random.uniform(-1.0, 1.0, (3, 3)) / size
shape = np.array([size, size, size])
pbc = np.array([1, 1, 1])
ugrid = UniformGrid(origin, grid_rvecs, shape, pbc)
# compute the ESP
rcut = 20.0
alpha = 3.0 / 10.0
gcut = 1.1 * alpha
esp = np.zeros(shape)
compute_esp_grid_cube(ugrid, esp, coordinates, charges, rcut, alpha,
gcut)
# Set up weights
weights = np.ones(shape)
for i in xrange(natom):
multiply_near_mask(coordinates[i], ugrid, 1.0, 0.5, weights)
# Fit the charges and test
cost = ESPCost.from_grid_data(coordinates, ugrid, esp, weights)
x = cost.solve()
assert cost.value_charges(charges) < 1e-7
assert cost.value(x) < 1e-7
assert abs(charges - x[:-1]).max() < 1e-4
def test_consistent():
for irep in xrange(nrep):
with numpy_seed(irep):
# random system
natom = 5
coordinates = np.random.uniform(0, 10, (natom, 3))
charges = np.random.normal(0, 1, natom)
charges -= charges.mean()
# random grid
origin = np.random.uniform(-3, 3, 3)
size = 10
grid_rvecs = np.diag(np.random.uniform(8.0, 10.0, 3))/size
grid_rvecs += np.random.uniform(-1.0, 1.0, (3, 3))/size
shape = np.array([size, size, size])
pbc = np.array([1, 1, 1])
ugrid = UniformGrid(origin, grid_rvecs, shape, pbc)
# compute the ESP
rcut = 20.0
alpha = 3.0/10.0
gcut = 1.1*alpha
esp = np.zeros(shape)
compute_esp_grid_cube(ugrid, esp, coordinates, charges, rcut, alpha, gcut)
# Set up weights
weights = np.ones(shape)
for i in xrange(natom):
multiply_near_mask(coordinates[i], ugrid, 1.0, 0.5, weights)
# Fit the charges and test
cost = ESPCost.from_grid_data(coordinates, ugrid, esp, weights)
x = cost.solve()
assert cost.value_charges(charges) < 1e-7
assert cost.value(x) < 1e-7
assert abs(charges - x[:-1]).max() < 1e-4
def test_tridiagsym_solve():
N = 10
A = np.zeros((N,N), float)
for irep in xrange(100):
with numpy_seed(irep): # Make random numbers reproducible
# randomize the diagonal
A.ravel()[::N+1] = np.random.uniform(1,2,N)
# randomize the upper diagonal
A.ravel()[1::N+1] = np.random.uniform(-1,0,N-1)
# clone the lower diagonal
A.ravel()[N::N+1] = A.ravel()[1::N+1]
# test the inverse for all possible basis vectors
Ainv = np.linalg.inv(A)
for i in xrange(N):
right = np.zeros(N, float)
right[i] = 1.0
solution = np.zeros(N, float)
tridiagsym_solve(
A.ravel()[::N+1].copy(),
A.ravel()[N::N+1].copy(),
right, solution
)
error = abs(solution - Ainv[:,i]).max()
assert(error < 1e-9)
def main():
"""Main program."""
# Get command-line args
example_paths, do_update, do_force, do_verbose = parse_args()
# Silence the logger
if not do_verbose:
log.set_level(log.silent)
# Exit status becomes 1 when some test results have changed beyond the thresholds, or
# when previous or reference values are missing.
exit_status = 0
# Run all examples.
for example_path in example_paths:
print example_path
# Sanity checks
if not example_path.endswith(".py"):
raise RegressionTestError('Example path should end with ".py": {}.'
.format(example_path))
with open(example_path) as f:
example_lines = f.readlines()
try:
example_lines.index(comment_regtest)
except ValueError:
raise RegressionTestError('Missing regression-testing comment in example {}.'
.format(example_path))
# Run example and collect results.
example_globals = {}
with numpy_seed():
print ' First random number:', np.random.rand()
with open(example_path) as fh, numpy_seed():
exec fh in example_globals # pylint: disable=exec-used
# Check that rt_results is defined in the example.
if 'rt_results' not in example_globals:
raise RegressionTestError('No rt_results defined in example {}.'
.format(example_path))
# Extract relevant variables from example.
rt_results = example_globals['rt_results']
rt_atols = example_globals.get('rt_atols', {})
rt_rtols = example_globals.get('rt_rtols', {})
rt_references = example_globals.get('rt_references', {})
rt_previous = example_globals.get('rt_previous', {})
known_keys = ['rt_results', 'rt_atols', 'rt_rtols', 'rt_references', 'rt_previous']
for key in example_globals:
if key.startswith('rt_') and key not in known_keys:
print 'Unrecognized variable starting with rt_: {}.' % key
# Compare results with expected or previous values, if previous values are present.
rt_status = {}
for key, result in rt_results.iteritems():
target = rt_references.get(key, rt_previous.get(key))
if target is None:
rt_status[key] = 'PREVIOUS OR REFERENCE MISSING'
else:
atol = rt_atols.get(key, default_atol)
rtol = rt_rtols.get(key, default_rtol)
if not isinstance(result, target.__class__):
rt_status[key] = 'WRONG TYPE'
elif isinstance(result, np.ndarray) and (result.shape != target.shape):
rt_status[key] = 'WRONG SHAPE'
elif np.allclose(result, target, atol=atol, rtol=rtol):
rt_status[key] = 'OK'
else:
rt_status[key] = 'BOUNDS EXCEEDED'
if do_update:
update_example(example_path, example_lines, rt_previous, rt_results,
rt_status, do_force)
else:
exit_status |= report_regressions(rt_results, rt_atols, rt_rtols, rt_references,
rt_previous, rt_status)
# Stop with proper exit code.
sys.exit(exit_status)
def get_random_unitary(seed=1):
"""Return a random 3x3 unitary transformation."""
with numpy_seed(seed):
A = np.random.normal(0, 1, (3, 3))
A = 0.5*(A+A.T)
return np.linalg.eigh(A)[1]
def get_random_shift(seed=1):
"""Generate a random displacement in 3D Cartesian coordinates."""
with numpy_seed(seed):
return np.random.uniform(-3, 3, 3)
def main():
"""Main program."""
# Get command-line args
example_paths, do_update, do_force, do_verbose = parse_args()
# Silence the logger
if not do_verbose:
log.set_level(log.silent)
# Exit status becomes 1 when some test results have changed beyond the thresholds, or
# when previous or reference values are missing.
exit_status = 0
# Run all examples.
for example_path in example_paths:
print example_path
# Sanity checks
if not example_path.endswith(".py"):
raise RegressionTestError('Example path should end with ".py": {}.'
.format(example_path))
with open(example_path) as f:
example_lines = f.readlines()
try:
example_lines.index(comment_regtest)
except ValueError:
raise RegressionTestError('Missing regression-testing comment in example {}.'
.format(example_path))
# Run example and collect results.
example_globals = {}
with numpy_seed():
print ' First random number:', np.random.rand()
with open(example_path) as fh, numpy_seed():
exec fh in example_globals # pylint: disable=exec-used
# Check that rt_results is defined in the example.
if 'rt_results' not in example_globals:
raise RegressionTestError('No rt_results defined in example {}.'
.format(example_path))
# Extract relevant variables from example.
rt_results = example_globals['rt_results']
rt_atols = example_globals.get('rt_atols', {})
rt_rtols = example_globals.get('rt_rtols', {})
rt_references = example_globals.get('rt_references', {})
rt_previous = example_globals.get('rt_previous', {})
known_keys = ['rt_results', 'rt_atols', 'rt_rtols', 'rt_references', 'rt_previous']
for key in example_globals:
if key.startswith('rt_') and key not in known_keys:
print 'Unrecognized variable starting with rt_: {}.' % key
# Compare results with expected or previous values, if previous values are present.
rt_status = {}
for key, result in rt_results.iteritems():
target = rt_references.get(key, rt_previous.get(key))
if target is None:
rt_status[key] = 'PREVIOUS OR REFERENCE MISSING'
else:
atol = rt_atols.get(key, default_atol)
rtol = rt_rtols.get(key, default_rtol)
if not isinstance(result, target.__class__):
rt_status[key] = 'WRONG TYPE'
elif isinstance(result, np.ndarray) and (result.shape != target.shape):
rt_status[key] = 'WRONG SHAPE'
elif np.allclose(result, target, atol=atol, rtol=rtol):
rt_status[key] = 'OK'
else:
rt_status[key] = 'BOUNDS EXCEEDED'
if do_update:
update_example(example_path, example_lines, rt_previous, rt_results,
rt_status, do_force)
else:
exit_status |= report_regressions(rt_results, rt_atols, rt_rtols, rt_references,
rt_previous, rt_status)
# Stop with proper exit code.
sys.exit(exit_status)
