def __init__(self, name, U, params, Ls=None):
self.name = name
self.U = U
self.U_grid = U[0].grid
self.U_grid_eo = gpt.grid(self.U_grid.gdimensions,
self.U_grid.precision, gpt.redblack)
if Ls is None:
self.F_grid = self.U_grid
self.F_grid_eo = self.U_grid_eo
else:
self.F_grid = gpt.grid([Ls] + self.U_grid.gdimensions,
self.U_grid.precision)
self.F_grid_eo = gpt.grid(self.F_grid.gdimensions,
self.U_grid.precision, gpt.redblack)
self.params = {
"U_grid": self.U_grid.obj,
"U_grid_rb": self.U_grid_eo.obj,
"F_grid": self.F_grid.obj,
"F_grid_rb": self.F_grid_eo.obj,
"U": [u.v_obj[0] for u in self.U]
}
for k in params:
assert (not k
in ["U_grid", "U_grid_rb", "F_grid", "F_grid_rb", "U"])
self.params[k] = params[k]
self.obj = cgpt.create_fermion_operator(name, self.U_grid.precision,
self.params)
# register matrix operations
gpt.qcd.fermion.register(self)
def __init__(self, name, U, params, otype=None):
super().__init__(name, U, params, otype, False)
self.obj = []
for i in range(len(U[0].v_obj)):
self.params["U"] = [a.v_obj[i] for a in self.U]
self.obj.append(
cgpt.create_fermion_operator(
self.name, self.U_grid.precision, self.params
)
)
def __init__(self, name, U, params, otype=None):
super().__init__(name, U, params, otype, True)
self.tmp = gpt.lattice(self.F_grid, otype)
self.tmp_eo = gpt.lattice(self.F_grid_eo, otype)
self.U_self_inv = gpt.matrix.inv(self.U[8])
self.obj = []
for i in range(len(U[0].v_obj)):
self.params["U"] = [a.v_obj[i] for a in self.U]
self.params["U_self_inv"] = self.U_self_inv.v_obj[i]
self.params["dag_factor"] = gpt.coarse.prefactor_dagger(
self.U[8], i)
self.obj.append(
cgpt.create_fermion_operator(self.name, self.U_grid.precision,
self.params))
def __init__(self, name, U, params, otype=None):
super().__init__(name, U, params, otype, True)
self.obj = cgpt.create_fermion_operator(name, self.U_grid.precision,
self.params)
def __init__(self, name, U, params, Ls=None, otype=None):
# keep constructor parameters
self.name = name
self.U = U
self.params_constructor = params
self.Ls = Ls
# derived objects
self.U_grid = U[0].grid
self.U_grid_eo = gpt.grid(
self.U_grid.gdimensions,
self.U_grid.precision,
gpt.redblack,
parent=self.U_grid.parent,
mpi=self.U_grid.mpi,
)
if Ls is None:
self.F_grid = self.U_grid
self.F_grid_eo = self.U_grid_eo
else:
self.F_grid = self.U_grid.inserted_dimension(0, Ls)
self.F_grid_eo = gpt.grid(
self.F_grid.gdimensions,
self.U_grid.precision,
gpt.redblack,
parent=self.F_grid.parent,
mpi=self.F_grid.mpi,
)
# parameter for create_fermion_operator
self.params = {
"U_grid": self.U_grid.obj,
"U_grid_rb": self.U_grid_eo.obj,
"F_grid": self.F_grid.obj,
"F_grid_rb": self.F_grid_eo.obj,
"U": [u.v_obj[0] for u in self.U],
}
for k in params:
assert k not in ["U_grid", "U_grid_rb", "F_grid", "F_grid_rb", "U"]
self.params[k] = params[k]
# create fermion operator
self.obj = cgpt.create_fermion_operator(
name, self.U_grid.precision, self.params
)
# register matrix operators
class registry:
pass
gpt.qcd.fermion.register(registry, self)
# map Grid matrix operations to clean matrix_operator structure
super().__init__(
mat=registry.M, adj_mat=registry.Mdag, otype=otype, grid=self.F_grid
)
self.Meooe = gpt.matrix_operator(
mat=registry.Meooe,
adj_mat=registry.MeooeDag,
otype=otype,
grid=self.F_grid_eo,
)
self.Mooee = gpt.matrix_operator(
mat=registry.Mooee,
adj_mat=registry.MooeeDag,
inv_mat=registry.MooeeInv,
adj_inv_mat=registry.MooeeInvDag,
otype=otype,
grid=self.F_grid_eo,
)
self.Mdiag = gpt.matrix_operator(registry.Mdiag, otype=otype, grid=self.F_grid)
self.Dminus = gpt.matrix_operator(
mat=registry.Dminus,
adj_mat=registry.DminusDag,
otype=otype,
grid=self.F_grid,
)
self.ImportPhysicalFermionSource = gpt.matrix_operator(
registry.ImportPhysicalFermionSource,
otype=otype,
grid=(self.F_grid, self.U_grid),
)
self.ImportUnphysicalFermion = gpt.matrix_operator(
registry.ImportUnphysicalFermion,
otype=otype,
grid=(self.F_grid, self.U_grid),
)
self.ExportPhysicalFermionSolution = gpt.matrix_operator(
registry.ExportPhysicalFermionSolution,
otype=otype,
grid=(self.U_grid, self.F_grid),
)
self.ExportPhysicalFermionSource = gpt.matrix_operator(
registry.ExportPhysicalFermionSource,
otype=otype,
grid=(self.U_grid, self.F_grid),
)
self.G5M = gpt.matrix_operator(
lambda dst, src: self._G5M(dst, src), otype=otype, grid=self.F_grid
)
def setup(self, name, grid, params):
self.obj = cgpt.create_fermion_operator(name, grid.precision, params)
