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reduce line length
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@alexbrown1995
alexbrown1995 committed Aug 29, 2023
1 parent 5a9a45c commit 66a59c8
Showing 1 changed file with 45 additions and 24 deletions.
69 changes: 45 additions & 24 deletions gusto/time_discretisation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,9 @@
import numpy as np


__all__ = ["ForwardEuler", "BackwardEuler", "ExplicitMultistage", "SSPRK3", "RK4", "Heun",
"ThetaMethod", "TrapeziumRule", "BDF2", "TR_BDF2", "Leapfrog", "AdamsMoulton", "AdamsBashforth"]
__all__ = ["ForwardEuler", "BackwardEuler", "ExplicitMultistage", "SSPRK3", "RK4",
"Heun", "ThetaMethod", "TrapeziumRule", "BDF2", "TR_BDF2", "Leapfrog",
"AdamsMoulton", "AdamsBashforth"]


def wrapper_apply(original_apply):
Expand Down Expand Up @@ -167,7 +168,8 @@ def setup(self, equation, apply_bcs=True, *active_labels):
self.bcs = None
elif self.wrapper is not None:
# Transfer boundary conditions onto test function space
self.bcs = [DirichletBC(self.fs, bc.function_arg, bc.sub_domain) for bc in bcs]
self.bcs = [DirichletBC(self.fs, bc.function_arg, bc.sub_domain)
for bc in bcs]
else:
self.bcs = bcs

Expand Down Expand Up @@ -301,7 +303,8 @@ def solver(self):
problem = NonlinearVariationalProblem(self.lhs - self.rhs, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__
# If snes_type not specified by user, set this to ksp only to avoid outer Newton iteration
return NonlinearVariationalSolver(problem, solver_parameters={'snes_type': 'ksponly'} | self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters={'snes_type': 'ksponly'}
| self.solver_parameters, options_prefix=solver_name)

@abstractmethod
def apply_cycle(self, x_out, x_in):
Expand Down Expand Up @@ -334,7 +337,8 @@ def apply(self, x_out, x_in):

class ExplicitMultistage(ExplicitTimeDiscretisation):
"""
A class for implementing general explicit multistage (Runge-Kutta) methods based on its Butcher tableau.
A class for implementing general explicit multistage (Runge-Kutta)
methods based on its Butcher tableau.
A Butcher tableau is formed in the following way for a s-th order explicit scheme:
Expand Down Expand Up @@ -368,7 +372,8 @@ class ExplicitMultistage(ExplicitTimeDiscretisation):
"""

def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None, limiter=None, options=None, butcher_matrix=None):
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None,
limiter=None, options=None, butcher_matrix=None):
super().__init__(domain, field_name=field_name, subcycles=subcycles,
solver_parameters=solver_parameters,
limiter=limiter, options=options)
Expand Down Expand Up @@ -458,7 +463,8 @@ class ForwardEuler(ExplicitMultistage):
k0 = F[y^n]
y^(n+1) = y^n + dt*k0
"""
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None, limiter=None, options=None, butcher_matrix=None):
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None,
limiter=None, options=None, butcher_matrix=None):
super().__init__(domain, field_name=field_name, subcycles=subcycles,
solver_parameters=solver_parameters,
limiter=limiter, options=options, butcher_matrix=butcher_matrix)
Expand All @@ -476,7 +482,8 @@ class SSPRK3(ExplicitMultistage):
k2 = F[y^n + (1/4)*dt*(k0+k1)]
y^(n+1) = y^n + (1/6)*dt*(k0 + k1 + 4*k2)
"""
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None, limiter=None, options=None, butcher_matrix=None):
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None,
limiter=None, options=None, butcher_matrix=None):
super().__init__(domain, field_name=field_name, subcycles=subcycles,
solver_parameters=solver_parameters,
limiter=limiter, options=options, butcher_matrix=butcher_matrix)
Expand All @@ -499,7 +506,8 @@ class RK4(ExplicitMultistage):
where superscripts indicate the time-level.
"""
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None, limiter=None, options=None, butcher_matrix=None):
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None,
limiter=None, options=None, butcher_matrix=None):
super().__init__(domain, field_name=field_name, subcycles=subcycles,
solver_parameters=solver_parameters,
limiter=limiter, options=options, butcher_matrix=butcher_matrix)
Expand All @@ -520,7 +528,8 @@ class Heun(ExplicitMultistage):
where superscripts indicate the time-level and subscripts indicate the stage
number.
"""
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None, limiter=None, options=None, butcher_matrix=None):
def __init__(self, domain, field_name=None, subcycles=None, solver_parameters=None,
limiter=None, options=None, butcher_matrix=None):
super().__init__(domain, field_name,
solver_parameters=solver_parameters,
limiter=limiter, options=options)
Expand Down Expand Up @@ -817,15 +826,17 @@ def solver0(self):
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs0-self.rhs0, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__+"0"
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

@property
def solver(self):
"""Set up the problem and the solver for BDF2 steps."""
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs-self.rhs, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

def apply(self, x_out, *x_in):
"""
Expand All @@ -849,8 +860,8 @@ def apply(self, x_out, *x_in):

class TR_BDF2(TimeDiscretisation):
"""
Implements the two stage implicit TR-BDF2 time stepping method, with a trapezoidal stage (TR) followed
by a second order backwards difference stage (BDF2).
Implements the two stage implicit TR-BDF2 time stepping method, with a
trapezoidal stage (TR) followed by a second order backwards difference stage (BDF2).
The TR-BDF2 time stepping method for operator F is written as
y^(n+g) = y^n + dt*g/2*F[y^n] + dt*g/2*F[y^(n+g)] (TR stage)
Expand Down Expand Up @@ -940,7 +951,8 @@ def rhs_bdf2(self):
map_if_true=replace_subject(self.xnpg, old_idx=self.idx),
map_if_false=drop)

r = (1.0/(self.gamma*(2.0-self.gamma)))*xnpg - ((1.0-self.gamma)**2/(self.gamma*(2.0-self.gamma))) * xn
r = (1.0/(self.gamma*(2.0-self.gamma)))*xnpg -

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gusto/time_discretisation.py:954:56: E999 SyntaxError: invalid syntax
((1.0-self.gamma)**2/(self.gamma*(2.0-self.gamma)))*xn

return r.form

Expand All @@ -950,15 +962,17 @@ def solver_tr(self):
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs-self.rhs, self.xnpg, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__+"_tr"
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

@cached_property
def solver_bdf2(self):
"""Set up the problem and the solver."""
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs_bdf2-self.rhs_bdf2, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__+"_bdf2"
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

def apply(self, x_out, x_in):
"""
Expand Down Expand Up @@ -1019,15 +1033,17 @@ def solver0(self):
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs-self.rhs0, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__+"0"
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

@property
def solver(self):
"""Set up the problem and the solver for leapfrog steps."""
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs-self.rhs, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

def apply(self, x_out, *x_in):
"""
Expand Down Expand Up @@ -1102,7 +1118,8 @@ def setup(self, equation, apply_bcs=True, *active_labels):
elif (self.order == 4):
self.b = [-(9.0)/(24.0), (37.0)/(24.0), -(59.0)/(24.0), (55.0)/(24.0)]
elif (self.order == 5):
self.b = [(251.0)/(720.0), -(1274.0)/(720.0), (2616.0)/(720.0), -(2774.0)/(720.0), (2901.0)/(720.0)]
self.b = [(251.0)/(720.0), -(1274.0)/(720.0), (2616.0)/(720.0),
-(2774.0)/(720.0), (2901.0)/(720.0)]

@property
def nlevels(self):
Expand Down Expand Up @@ -1146,15 +1163,17 @@ def solver0(self):
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs-self.rhs0, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__+"0"
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

@property
def solver(self):
"""Set up the problem and the solver for Adams Bashforth steps."""
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs-self.rhs, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

def apply(self, x_out, *x_in):
"""
Expand Down Expand Up @@ -1292,15 +1311,17 @@ def solver0(self):
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs0-self.rhs0, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__+"0"
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

@property
def solver(self):
"""Set up the problem and the solver for Adams Moulton steps."""
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs-self.rhs, self.x_out, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters,
options_prefix=solver_name)

def apply(self, x_out, *x_in):
"""
Expand Down

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