From 1d44160d58a66b10dffc0d8b648f82739edd35c1 Mon Sep 17 00:00:00 2001 From: sanromd Date: Tue, 2 Dec 2014 16:43:25 +0300 Subject: [PATCH 01/21] polynomial interpolation routines added --- src/pyclaw/sharpclaw/poly.f90 | 95 +++++++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 src/pyclaw/sharpclaw/poly.f90 diff --git a/src/pyclaw/sharpclaw/poly.f90 b/src/pyclaw/sharpclaw/poly.f90 new file mode 100644 index 000000000..cb095a7a3 --- /dev/null +++ b/src/pyclaw/sharpclaw/poly.f90 @@ -0,0 +1,95 @@ +module poly +contains + + ! =================================================================== + subroutine poly4(q,ql,qr,num_eqn,maxnx,num_ghost) + ! =================================================================== + + implicit none + + integer, intent(in) :: num_eqn, maxnx, num_ghost + double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) + double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) + + integer :: i,n + + do n = 1,num_eqn + do i = num_ghost-1,maxnx+num_ghost+1 + ql(n,i) = (-5.d0*q(n,-2+i)+60.d0*q(n,-1+i)+90.d0*q(n,i)-20.d0*q(n,1+i)+3.d0*q(n,2+i))/128.d0 + qr(n,i) = (3.d0*q(n,-2+i)-20.d0*q(n,-1+i)+90.d0*q(n,i)+60.d0*q(n,1+i)-5.d0*q(n,2+i))/128.d0 + end do + end do + + end subroutine poly4 + + ! =================================================================== + subroutine poly6(q,ql,qr,num_eqn,maxnx,num_ghost) + ! =================================================================== + + implicit none + + integer, intent(in) :: num_eqn, maxnx, num_ghost + double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) + double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) + + integer :: i,n + + do n = 1,num_eqn + do i = num_ghost-1,maxnx+num_ghost+1 + ql(n,i) = (7.d0*q(n,-3+i) - 70.d0*q(n,-2+i) + 525.d0*q(n,-1+i) + 700.d0*q(n,i) & + - 175.d0*q(n,1+i) + 42.d0*q(n,2+i) - 5.d0*q(n,3+i))/1024.d0 + qr(n,i) = (42.d0*q(n,-2+i) - 175.d0*q(n,-1+i) + 700.d0*q(n,i) & + + 525.d0*q(n,1+i) - 70.d0*q(n,2+i) + 7.d0*q(n,3+i) - 5.d0*q(n,-3+i))/1024.d0 + end do + end do + + end subroutine poly6 + + ! =================================================================== + subroutine poly8(q,ql,qr,num_eqn,maxnx,num_ghost) + ! =================================================================== + + implicit none + + integer, intent(in) :: num_eqn, maxnx, num_ghost + double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) + double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) + + integer :: i,n + + do n = 1,num_eqn + do i = num_ghost-1,maxnx+num_ghost+1 + ql(n,i) = (-45.d0*q(n,-4+i) + 504.d0*q(n,-3+i) - 2940.d0*q(n,-2+i) + 17640.d0*q(n,-1+i) & + + 22050.d0*q(n,i) - 5880.d0*q(n,1+i) + 1764.d0*q(n,2+i) - 360.d0*q(n,3+i) + 35.d0*q(n,4+i))/32768.d0 + qr(n,i) = (35.d0*q(n,-4+i) - 360.d0*q(n,-3+i) + 1764.d0*q(n,-2+i) - 5880.d0*q(n,-1+i) + 22050.d0*q(n,i) & + + 17640.d0*q(n,1+i) - 2940.d0*q(n,2+i) + 504.d0*q(n,3+i) - 45.d0*q(n,4+i))/32768.d0 + end do + end do + + end subroutine poly8 + + ! =================================================================== + subroutine poly10(q,ql,qr,num_eqn,maxnx,num_ghost) + ! =================================================================== + + implicit none + + integer, intent(in) :: num_eqn, maxnx, num_ghost + double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) + double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) + + integer :: i,n + + do n = 1,num_eqn + do i = num_ghost-1,maxnx+num_ghost+1 + ql(n,i) = (77.d0*q(n,-5+i) - 990.d0*q(n,-4+i) + 6237.d0*q(n,-3+i) - 27720.d0*q(n,-2+i) & + + 145530.d0*q(n,-1+i) + 174636.d0*q(n,i) - 48510.d0*q(n,1+i) + 16632.d0*q(n,2+i) - 4455.d0*q(n,3+i) & + + 770.d0*q(n,4+i) - 63.d0*q(n,5+i))/262144.d0 + qr(n,i) = (-63.d0*q(n,-5+i) + 770.d0*q(n,-4+i) - 4455.d0*q(n,-3+i) + 16632.d0*q(n,-2+i) & + - 48510.d0*q(n,-1+i) + 174636.d0*q(n,i) + 145530.d0*q(n,1+i) - 27720.d0*q(n,2+i) + 6237.d0*q(n,3+i) & + - 990.d0*q(n,4+i) + 77.d0*q(n,5+i))/262144.d0 + end do + end do + end subroutine poly10 + +end module poly \ No newline at end of file From 62ecd182e4688495d86f1ecd25d38ebb15fcf22c Mon Sep 17 00:00:00 2001 From: sanromd Date: Tue, 25 Jul 2017 16:10:04 +0300 Subject: [PATCH 02/21] solved merge conflicts by changing weno_order to interpolation_order --- examples/acoustics_1d_homogeneous/acoustics_1d.py | 4 ++-- examples/acoustics_1d_homogeneous/test_acoustics.py | 2 +- examples/advection_1d/advection_1d.py | 4 ++-- examples/advection_1d/test_advection.py | 2 +- .../variable_coefficient_advection.py | 2 +- examples/euler_2d/shock_bubble_interaction.py | 2 +- src/pyclaw/sharpclaw/ClawParams.f90 | 2 +- src/pyclaw/sharpclaw/reconstruct.f90 | 6 +++--- src/pyclaw/sharpclaw/solver.py | 10 +++++----- 9 files changed, 17 insertions(+), 17 deletions(-) diff --git a/examples/acoustics_1d_homogeneous/acoustics_1d.py b/examples/acoustics_1d_homogeneous/acoustics_1d.py index 72f1f0e39..2464ffa90 100644 --- a/examples/acoustics_1d_homogeneous/acoustics_1d.py +++ b/examples/acoustics_1d_homogeneous/acoustics_1d.py @@ -24,7 +24,7 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', - outdir='./_output', ptwise=False, weno_order=5, + outdir='./_output', ptwise=False, interpolation_order=5, time_integrator='SSP104', disable_output=False, output_style=1): if use_petsc: @@ -46,7 +46,7 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', solver.limiters = pyclaw.limiters.tvd.MC elif solver_type == 'sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.weno_order = weno_order + solver.interpolation_order = interpolation_order solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 4 diff --git a/examples/acoustics_1d_homogeneous/test_acoustics.py b/examples/acoustics_1d_homogeneous/test_acoustics.py index 83682a054..0c5e3bdef 100644 --- a/examples/acoustics_1d_homogeneous/test_acoustics.py +++ b/examples/acoustics_1d_homogeneous/test_acoustics.py @@ -59,7 +59,7 @@ def acoustics_verify(claw): weno_tests = gen_variants(acoustics_1d.setup, verify_expected(0.000153), kernel_languages=('Fortran',), solver_type='sharpclaw', time_integrator='SSP104', - weno_order=17, disable_output=True) + interpolation_order=17, disable_output=True) from itertools import chain for test in chain(classic_tests, time_step_test, ptwise_tests, diff --git a/examples/advection_1d/advection_1d.py b/examples/advection_1d/advection_1d.py index 533505165..c6d624b33 100755 --- a/examples/advection_1d/advection_1d.py +++ b/examples/advection_1d/advection_1d.py @@ -20,7 +20,7 @@ import numpy as np from clawpack import riemann -def setup(nx=100, kernel_language='Python', use_petsc=False, solver_type='classic', weno_order=5, +def setup(nx=100, kernel_language='Python', use_petsc=False, solver_type='classic', interpolation_order=5, time_integrator='SSP104', outdir='./_output'): if use_petsc: @@ -37,7 +37,7 @@ def setup(nx=100, kernel_language='Python', use_petsc=False, solver_type='classi solver = pyclaw.ClawSolver1D(riemann_solver) elif solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.weno_order = weno_order + solver.interpolation_order = interpolation_order solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 5 diff --git a/examples/advection_1d/test_advection.py b/examples/advection_1d/test_advection.py index edd54bb8a..8b5fe3d86 100644 --- a/examples/advection_1d/test_advection.py +++ b/examples/advection_1d/test_advection.py @@ -39,7 +39,7 @@ def advection_verify(claw): weno_tests = gen_variants(advection_1d.setup, verify_expected(7.489618e-06), kernel_languages=('Fortran',), solver_type='sharpclaw', - time_integrator='SSP104', weno_order=17, + time_integrator='SSP104', interpolation_order=17, outdir=None) from itertools import chain diff --git a/examples/advection_1d_variable/variable_coefficient_advection.py b/examples/advection_1d_variable/variable_coefficient_advection.py index 4dd01af25..89f80dc50 100755 --- a/examples/advection_1d_variable/variable_coefficient_advection.py +++ b/examples/advection_1d_variable/variable_coefficient_advection.py @@ -68,7 +68,7 @@ def setup(use_petsc=False,solver_type='classic',kernel_language='Python',outdir= solver = pyclaw.SharpClawSolver1D(riemann.vc_advection_1D) elif kernel_language=='Python': solver = pyclaw.SharpClawSolver1D(riemann.vc_advection_1D_py.vc_advection_1D) - solver.weno_order=weno_order + solver.interpolation_order=interpolation_order else: raise Exception('Unrecognized value of solver_type.') solver.kernel_language = kernel_language diff --git a/examples/euler_2d/shock_bubble_interaction.py b/examples/euler_2d/shock_bubble_interaction.py index 6d847bb3f..d97b72e8e 100755 --- a/examples/euler_2d/shock_bubble_interaction.py +++ b/examples/euler_2d/shock_bubble_interaction.py @@ -186,7 +186,7 @@ def setup(use_petsc=False,solver_type='classic', outdir='_output', kernel_langua if solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver2D(riemann.euler_5wave_2D) solver.dq_src = dq_Euler_radial - solver.weno_order = 5 + solver.interpolation_order = 5 solver.lim_type = 2 else: solver = pyclaw.ClawSolver2D(riemann.euler_5wave_2D) diff --git a/src/pyclaw/sharpclaw/ClawParams.f90 b/src/pyclaw/sharpclaw/ClawParams.f90 index b1e28357e..81d8a6764 100644 --- a/src/pyclaw/sharpclaw/ClawParams.f90 +++ b/src/pyclaw/sharpclaw/ClawParams.f90 @@ -9,7 +9,7 @@ module ClawParams integer :: num_dim, num_waves, index_capa ! Method-related parameters: - integer :: char_decomp, lim_type, weno_order + integer :: char_decomp, lim_type, interpolation_order integer, allocatable :: mthlim(:) logical :: fwave, tfluct_solver diff --git a/src/pyclaw/sharpclaw/reconstruct.f90 b/src/pyclaw/sharpclaw/reconstruct.f90 index 4db69532e..6502ec8dc 100644 --- a/src/pyclaw/sharpclaw/reconstruct.f90 +++ b/src/pyclaw/sharpclaw/reconstruct.f90 @@ -97,14 +97,14 @@ subroutine weno_comp(q,ql,qr,num_eqn,maxnx,num_ghost) ! It does no characteristic decomposition use weno - use clawparams, only: weno_order + use clawparams, only: interpolation_order implicit none integer, intent(in) :: num_eqn, maxnx, num_ghost double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) - select case(weno_order) + select case(interpolation_order) case (5) call weno5(q,ql,qr,num_eqn,maxnx,num_ghost) case (7) @@ -120,7 +120,7 @@ subroutine weno_comp(q,ql,qr,num_eqn,maxnx,num_ghost) case (17) call weno17(q,ql,qr,num_eqn,maxnx,num_ghost) case default - print *, 'ERROR: weno_order must be an odd number between 5 and 17 (inclusive).' + print *, 'ERROR: interpolation_order must be an odd number between 5 and 17 (inclusive).' stop end select diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index 1c5401765..4e96cfe79 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -45,7 +45,7 @@ class SharpClawSolver(Solver): ``Default = 2`` - .. attribute:: weno_order + .. attribute:: interpolation_order Order of the WENO reconstruction. From 1st to 17th order (PyWENO) @@ -161,7 +161,7 @@ def __init__(self,riemann_solver=None,claw_package=None): """ self.limiters = [1] self.lim_type = 2 - self.weno_order = 5 + self.interpolation_order = 5 self.time_integrator = 'SSP104' self.char_decomp = 0 self.tfluct_solver = False @@ -206,9 +206,9 @@ def setup(self,solution): Allocate RK stage arrays or previous step solutions and fortran routine work arrays. """ if self.lim_type == 2: - self.num_ghost = (self.weno_order+1)//2 + self.num_ghost = (self.interpolation_order+1)/2 - if self.lim_type == 2 and self.weno_order != 5 and self.kernel_language == 'Python': + if self.lim_type == 2 and self.interpolation_order != 5 and self.kernel_language == 'Python': raise Exception('Only 5th-order WENO reconstruction is implemented in Python kernels. \ Use Fortran for higher-order WENO.') # This is a hack to deal with the fact that petsc4py @@ -563,7 +563,7 @@ def _set_fortran_parameters(self,state,clawparams,workspace,reconstruct): grid = state.grid clawparams.num_dim = grid.num_dim clawparams.lim_type = self.lim_type - clawparams.weno_order = self.weno_order + clawparams.interpolation_order = self.interpolation_order clawparams.char_decomp = self.char_decomp clawparams.tfluct_solver = self.tfluct_solver clawparams.fwave = self.fwave From f9edcb104fd0dcff159fe447a3d22980a40d1802 Mon Sep 17 00:00:00 2001 From: sanromd Date: Wed, 3 Dec 2014 14:59:34 +0300 Subject: [PATCH 03/21] added calls to module poly in resonsctruct and set setup to compile poly --- src/pyclaw/sharpclaw/reconstruct.f90 | 29 ++++++++++++++++++++++++++++ src/pyclaw/sharpclaw/setup.py | 6 +++--- 2 files changed, 32 insertions(+), 3 deletions(-) diff --git a/src/pyclaw/sharpclaw/reconstruct.f90 b/src/pyclaw/sharpclaw/reconstruct.f90 index 6502ec8dc..91378cf74 100644 --- a/src/pyclaw/sharpclaw/reconstruct.f90 +++ b/src/pyclaw/sharpclaw/reconstruct.f90 @@ -821,4 +821,33 @@ subroutine tvd2_wave(q,ql,qr,wave,s,mthlim,num_eqn,num_ghost) return end subroutine tvd2_wave + ! =================================================================== + subroutine poly_comp(q,ql,qr,num_eqn,maxnx,num_ghost) + ! =================================================================== + + use poly + use clawparams, only: interpolation_order + implicit none + + integer, intent(in) :: num_eqn, maxnx, num_ghost + double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) + double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) + + select case(interpolation_order) + case (4) + call poly4(q,ql,qr,num_eqn,maxnx,num_ghost) + case (6) + call poly6(q,ql,qr,num_eqn,maxnx,num_ghost) + case (8) + call poly8(q,ql,qr,num_eqn,maxnx,num_ghost) + case(10) + call poly10(q,ql,qr,num_eqn,maxnx,num_ghost) + case default + print *, 'ERROR: poly_order must be an even number between 4 and 8.' + stop + end select + + return + end subroutine poly_comp + end module reconstruct diff --git a/src/pyclaw/sharpclaw/setup.py b/src/pyclaw/sharpclaw/setup.py index 38c076780..d96369e3a 100644 --- a/src/pyclaw/sharpclaw/setup.py +++ b/src/pyclaw/sharpclaw/setup.py @@ -6,16 +6,16 @@ def configuration(parent_package='',top_path=None): config = Configuration('sharpclaw', parent_package, top_path) config.add_extension('sharpclaw1', - ['ClawParams.f90','weno.f90','reconstruct.f90', + ['ClawParams.f90','weno.f90','poly.f90','reconstruct.f90', 'evec.f90','workspace.f90','flux1.f90']) config.add_extension('sharpclaw2', - ['ClawParams.f90','weno.f90','reconstruct.f90', + ['ClawParams.f90','weno.f90','poly.f90','reconstruct.f90', 'evec.f90','workspace.f90','flux2.f90', 'flux1.f90']) config.add_extension('sharpclaw3', - ['ClawParams.f90','weno.f90','reconstruct.f90', + ['ClawParams.f90','weno.f90','poly.f90','reconstruct.f90', 'evec.f90','workspace.f90','flux3.f90', 'flux1.f90']) From ffe513fff9ec309f5b27492c1d97b3106d604afc Mon Sep 17 00:00:00 2001 From: sanromd Date: Wed, 3 Dec 2014 17:25:51 +0300 Subject: [PATCH 04/21] added WARNING and ERROR messages to reconsctruct when reconstrcution order for Poly is >6 --- src/pyclaw/sharpclaw/reconstruct.f90 | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/pyclaw/sharpclaw/reconstruct.f90 b/src/pyclaw/sharpclaw/reconstruct.f90 index 91378cf74..5e1d5ae20 100644 --- a/src/pyclaw/sharpclaw/reconstruct.f90 +++ b/src/pyclaw/sharpclaw/reconstruct.f90 @@ -120,7 +120,7 @@ subroutine weno_comp(q,ql,qr,num_eqn,maxnx,num_ghost) case (17) call weno17(q,ql,qr,num_eqn,maxnx,num_ghost) case default - print *, 'ERROR: interpolation_order must be an odd number between 5 and 17 (inclusive).' + print *, 'ERROR: reconstruction_order in WENO must be an odd number between 5 and 17 (inclusive).' stop end select @@ -839,11 +839,13 @@ subroutine poly_comp(q,ql,qr,num_eqn,maxnx,num_ghost) case (6) call poly6(q,ql,qr,num_eqn,maxnx,num_ghost) case (8) + print *, 'Warning: 8th reconstruction order is not fully tested' call poly8(q,ql,qr,num_eqn,maxnx,num_ghost) case(10) + print *, 'Warning: 10th reconstruction order is not fully tested' call poly10(q,ql,qr,num_eqn,maxnx,num_ghost) case default - print *, 'ERROR: poly_order must be an even number between 4 and 8.' + print *, 'ERROR: reconstruction_order for polynomial must be either 4 or 6' stop end select From 9a648ee661dbe6cdcfe0a275d3aac4c90dc6743e Mon Sep 17 00:00:00 2001 From: sanromd Date: Wed, 3 Dec 2014 17:26:14 +0300 Subject: [PATCH 05/21] added poly to custom sharpclaw build in euler_1d --- examples/euler_1d/setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/euler_1d/setup.py b/examples/euler_1d/setup.py index 0bc16b985..6cdbbf5dd 100644 --- a/examples/euler_1d/setup.py +++ b/examples/euler_1d/setup.py @@ -22,6 +22,7 @@ def configuration(parent_package='',top_path=None): sharpclaw_srcs = [pjoin(sharpclaw_dir, src) for src in ['ClawParams.f90', 'workspace.f90', 'weno.f90', + 'poly.f90', 'reconstruct.f90','flux1.f90']] config.add_extension('sharpclaw1', From 1dbfabe909fcabad46e350d393808e7b5f3ace79 Mon Sep 17 00:00:00 2001 From: sanromd Date: Tue, 25 Jul 2017 16:12:29 +0300 Subject: [PATCH 06/21] solved merge conflict by changin weno_order to interpolation_order --- examples/acoustics_1d_homogeneous/acoustics_1d.py | 4 ++-- examples/acoustics_1d_homogeneous/test_acoustics.py | 2 +- examples/advection_1d/advection_1d.py | 4 ++-- examples/advection_1d/test_advection.py | 2 +- .../variable_coefficient_advection.py | 2 +- examples/euler_2d/shock_bubble_interaction.py | 2 +- src/pyclaw/sharpclaw/ClawParams.f90 | 2 +- src/pyclaw/sharpclaw/reconstruct.f90 | 8 ++++---- src/pyclaw/sharpclaw/solver.py | 10 +++++----- 9 files changed, 18 insertions(+), 18 deletions(-) diff --git a/examples/acoustics_1d_homogeneous/acoustics_1d.py b/examples/acoustics_1d_homogeneous/acoustics_1d.py index 2464ffa90..b06187783 100644 --- a/examples/acoustics_1d_homogeneous/acoustics_1d.py +++ b/examples/acoustics_1d_homogeneous/acoustics_1d.py @@ -24,7 +24,7 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', - outdir='./_output', ptwise=False, interpolation_order=5, + outdir='./_output', ptwise=False, reconstruction_order=5, time_integrator='SSP104', disable_output=False, output_style=1): if use_petsc: @@ -46,7 +46,7 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', solver.limiters = pyclaw.limiters.tvd.MC elif solver_type == 'sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.interpolation_order = interpolation_order + solver.reconstruction_order = reconstruction_order solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 4 diff --git a/examples/acoustics_1d_homogeneous/test_acoustics.py b/examples/acoustics_1d_homogeneous/test_acoustics.py index 0c5e3bdef..61e53245c 100644 --- a/examples/acoustics_1d_homogeneous/test_acoustics.py +++ b/examples/acoustics_1d_homogeneous/test_acoustics.py @@ -59,7 +59,7 @@ def acoustics_verify(claw): weno_tests = gen_variants(acoustics_1d.setup, verify_expected(0.000153), kernel_languages=('Fortran',), solver_type='sharpclaw', time_integrator='SSP104', - interpolation_order=17, disable_output=True) + reconstruction_order=17, disable_output=True) from itertools import chain for test in chain(classic_tests, time_step_test, ptwise_tests, diff --git a/examples/advection_1d/advection_1d.py b/examples/advection_1d/advection_1d.py index c6d624b33..2e68c1306 100755 --- a/examples/advection_1d/advection_1d.py +++ b/examples/advection_1d/advection_1d.py @@ -20,7 +20,7 @@ import numpy as np from clawpack import riemann -def setup(nx=100, kernel_language='Python', use_petsc=False, solver_type='classic', interpolation_order=5, +def setup(nx=100, kernel_language='Python', use_petsc=False, solver_type='classic', reconstruction_order=5, time_integrator='SSP104', outdir='./_output'): if use_petsc: @@ -37,7 +37,7 @@ def setup(nx=100, kernel_language='Python', use_petsc=False, solver_type='classi solver = pyclaw.ClawSolver1D(riemann_solver) elif solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.interpolation_order = interpolation_order + solver.reconstruction_order = reconstruction_order solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 5 diff --git a/examples/advection_1d/test_advection.py b/examples/advection_1d/test_advection.py index 8b5fe3d86..53c35f49d 100644 --- a/examples/advection_1d/test_advection.py +++ b/examples/advection_1d/test_advection.py @@ -39,7 +39,7 @@ def advection_verify(claw): weno_tests = gen_variants(advection_1d.setup, verify_expected(7.489618e-06), kernel_languages=('Fortran',), solver_type='sharpclaw', - time_integrator='SSP104', interpolation_order=17, + time_integrator='SSP104', reconstruction_order=17, outdir=None) from itertools import chain diff --git a/examples/advection_1d_variable/variable_coefficient_advection.py b/examples/advection_1d_variable/variable_coefficient_advection.py index 89f80dc50..347ba76df 100755 --- a/examples/advection_1d_variable/variable_coefficient_advection.py +++ b/examples/advection_1d_variable/variable_coefficient_advection.py @@ -68,7 +68,7 @@ def setup(use_petsc=False,solver_type='classic',kernel_language='Python',outdir= solver = pyclaw.SharpClawSolver1D(riemann.vc_advection_1D) elif kernel_language=='Python': solver = pyclaw.SharpClawSolver1D(riemann.vc_advection_1D_py.vc_advection_1D) - solver.interpolation_order=interpolation_order + solver.reconstruction_order=reconstruction_order else: raise Exception('Unrecognized value of solver_type.') solver.kernel_language = kernel_language diff --git a/examples/euler_2d/shock_bubble_interaction.py b/examples/euler_2d/shock_bubble_interaction.py index d97b72e8e..934cf2dc9 100755 --- a/examples/euler_2d/shock_bubble_interaction.py +++ b/examples/euler_2d/shock_bubble_interaction.py @@ -186,7 +186,7 @@ def setup(use_petsc=False,solver_type='classic', outdir='_output', kernel_langua if solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver2D(riemann.euler_5wave_2D) solver.dq_src = dq_Euler_radial - solver.interpolation_order = 5 + solver.reconstruction_order = 5 solver.lim_type = 2 else: solver = pyclaw.ClawSolver2D(riemann.euler_5wave_2D) diff --git a/src/pyclaw/sharpclaw/ClawParams.f90 b/src/pyclaw/sharpclaw/ClawParams.f90 index 81d8a6764..75f671711 100644 --- a/src/pyclaw/sharpclaw/ClawParams.f90 +++ b/src/pyclaw/sharpclaw/ClawParams.f90 @@ -9,7 +9,7 @@ module ClawParams integer :: num_dim, num_waves, index_capa ! Method-related parameters: - integer :: char_decomp, lim_type, interpolation_order + integer :: char_decomp, lim_type, reconstruction_order integer, allocatable :: mthlim(:) logical :: fwave, tfluct_solver diff --git a/src/pyclaw/sharpclaw/reconstruct.f90 b/src/pyclaw/sharpclaw/reconstruct.f90 index 5e1d5ae20..e9cf7df80 100644 --- a/src/pyclaw/sharpclaw/reconstruct.f90 +++ b/src/pyclaw/sharpclaw/reconstruct.f90 @@ -97,14 +97,14 @@ subroutine weno_comp(q,ql,qr,num_eqn,maxnx,num_ghost) ! It does no characteristic decomposition use weno - use clawparams, only: interpolation_order + use clawparams, only: reconstruction_order implicit none integer, intent(in) :: num_eqn, maxnx, num_ghost double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) - select case(interpolation_order) + select case(reconstruction_order) case (5) call weno5(q,ql,qr,num_eqn,maxnx,num_ghost) case (7) @@ -826,14 +826,14 @@ subroutine poly_comp(q,ql,qr,num_eqn,maxnx,num_ghost) ! =================================================================== use poly - use clawparams, only: interpolation_order + use clawparams, only: reconstruction_order implicit none integer, intent(in) :: num_eqn, maxnx, num_ghost double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) - select case(interpolation_order) + select case(reconstruction_order) case (4) call poly4(q,ql,qr,num_eqn,maxnx,num_ghost) case (6) diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index 4e96cfe79..c71a34919 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -45,7 +45,7 @@ class SharpClawSolver(Solver): ``Default = 2`` - .. attribute:: interpolation_order + .. attribute:: reconstruction_order Order of the WENO reconstruction. From 1st to 17th order (PyWENO) @@ -161,7 +161,7 @@ def __init__(self,riemann_solver=None,claw_package=None): """ self.limiters = [1] self.lim_type = 2 - self.interpolation_order = 5 + self.reconstruction_order = 5 self.time_integrator = 'SSP104' self.char_decomp = 0 self.tfluct_solver = False @@ -206,9 +206,9 @@ def setup(self,solution): Allocate RK stage arrays or previous step solutions and fortran routine work arrays. """ if self.lim_type == 2: - self.num_ghost = (self.interpolation_order+1)/2 + self.num_ghost = (self.reconstruction_order+1)/2 - if self.lim_type == 2 and self.interpolation_order != 5 and self.kernel_language == 'Python': + if self.lim_type == 2 and self.reconstruction_order != 5 and self.kernel_language == 'Python': raise Exception('Only 5th-order WENO reconstruction is implemented in Python kernels. \ Use Fortran for higher-order WENO.') # This is a hack to deal with the fact that petsc4py @@ -563,7 +563,7 @@ def _set_fortran_parameters(self,state,clawparams,workspace,reconstruct): grid = state.grid clawparams.num_dim = grid.num_dim clawparams.lim_type = self.lim_type - clawparams.interpolation_order = self.interpolation_order + clawparams.reconstruction_order = self.reconstruction_order clawparams.char_decomp = self.char_decomp clawparams.tfluct_solver = self.tfluct_solver clawparams.fwave = self.fwave From 5a53304020fe87cc542a46d9c13e7d43cb68f96c Mon Sep 17 00:00:00 2001 From: sanromd Date: Sun, 28 Dec 2014 14:13:36 +0300 Subject: [PATCH 07/21] added allocated, deallocate and call for polyinterpolation and WENO5 --- src/pyclaw/sharpclaw/flux1.f90 | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/pyclaw/sharpclaw/flux1.f90 b/src/pyclaw/sharpclaw/flux1.f90 index e631adaa5..7e9ccce36 100644 --- a/src/pyclaw/sharpclaw/flux1.f90 +++ b/src/pyclaw/sharpclaw/flux1.f90 @@ -115,9 +115,11 @@ subroutine flux1(q1d,dq1d,aux,dt,cfl,t,ixyz,num_aux,num_eqn,mx,num_ghost,maxnx,r end select case(3) call weno5(q1d,ql,qr,num_eqn,maxnx,num_ghost) + case(4) + call poly_comp(q1d,ql,qr,num_eqn,maxnx,num_ghost) case default write(*,*) 'ERROR: Unrecognized limiter type option' - write(*,*) 'You should set 1<=lim_type<=3' + write(*,*) 'You should set 1<=lim_type<=4' stop end select From 943c2f2c0bab850668d77651260ef92b2d59947f Mon Sep 17 00:00:00 2001 From: sanromd Date: Sun, 28 Dec 2014 14:13:56 +0300 Subject: [PATCH 08/21] allocate in reconstruct --- src/pyclaw/sharpclaw/reconstruct.f90 | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/src/pyclaw/sharpclaw/reconstruct.f90 b/src/pyclaw/sharpclaw/reconstruct.f90 index e9cf7df80..781b866fb 100644 --- a/src/pyclaw/sharpclaw/reconstruct.f90 +++ b/src/pyclaw/sharpclaw/reconstruct.f90 @@ -48,6 +48,9 @@ subroutine alloc_recon_workspace(maxnx,num_ghost,num_eqn,num_waves,lim_type,char case(3) allocate(uu(2,maxnx+2*num_ghost)) allocate( dq1m(maxnx+2*num_ghost)) + case(4) + allocate(uu(2,maxnx+2*num_ghost)) + allocate( dq1m(maxnx+2*num_ghost)) end select recon_alloc = .True. @@ -83,8 +86,15 @@ subroutine dealloc_recon_workspace(lim_type,char_decomp) deallocate(hh) deallocate(uh) end select - recon_alloc = .False. + case(3) + deallocate(uu) + deallocate(dq1m) + case(4) + deallocate(uu) + deallocate(dq1m) end select + recon_alloc = .False. + end subroutine dealloc_recon_workspace ! =================================================================== From f7f409f831f651556fd836ff8345a8215ee033ff Mon Sep 17 00:00:00 2001 From: sanromd Date: Sun, 28 Dec 2014 14:14:31 +0300 Subject: [PATCH 09/21] added documentation to solver for lim_type=4 (polynomial) --- src/pyclaw/sharpclaw/solver.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index c71a34919..cfcb4e278 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -42,13 +42,14 @@ class SharpClawSolver(Solver): - 0: No limiting. - 1: TVD reconstruction. - 2: WENO reconstruction. + - 3: Polynomial reconstruction. ``Default = 2`` .. attribute:: reconstruction_order - Order of the WENO reconstruction. From 1st to 17th order (PyWENO) - + Order of the reconstruction. From 1st to 17th order (PyWENO), and from + 4 to 10 (even) in Poly ``Default = 5`` .. attribute:: time_integrator @@ -208,6 +209,9 @@ def setup(self,solution): if self.lim_type == 2: self.num_ghost = (self.reconstruction_order+1)/2 + if self.lim_type == 3: + self.num_ghost = 1 + self.reconstruction_order/2 + if self.lim_type == 2 and self.reconstruction_order != 5 and self.kernel_language == 'Python': raise Exception('Only 5th-order WENO reconstruction is implemented in Python kernels. \ Use Fortran for higher-order WENO.') From e51b1d50639a09bdddd0944a8eabcc6f2558d631 Mon Sep 17 00:00:00 2001 From: damiansa Date: Tue, 20 Oct 2020 12:22:30 +0300 Subject: [PATCH 10/21] changed weno_order for reconstruction_order in function setup --- examples/advection_1d/advection_1d_nonunif.py | 2 +- examples/cubic_1d/cubic.py | 5 +++-- fvmbook/chap6/limiters.ipynb | 2 +- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/examples/advection_1d/advection_1d_nonunif.py b/examples/advection_1d/advection_1d_nonunif.py index 3df68c528..b8e014fb8 100644 --- a/examples/advection_1d/advection_1d_nonunif.py +++ b/examples/advection_1d/advection_1d_nonunif.py @@ -32,7 +32,7 @@ def mapc2p_nonunif(xc): return xp -def setup(nx=100, kernel_language='Fortran', use_petsc=False, solver_type='classic', weno_order=5, +def setup(nx=100, kernel_language='Fortran', use_petsc=False, solver_type='classic', reconstruction_order=5, time_integrator='SSP104', outdir='./_output'): if use_petsc: diff --git a/examples/cubic_1d/cubic.py b/examples/cubic_1d/cubic.py index 53e0eee12..ac03892c1 100755 --- a/examples/cubic_1d/cubic.py +++ b/examples/cubic_1d/cubic.py @@ -17,7 +17,7 @@ import numpy as np from clawpack import riemann -def setup(use_petsc=0, outdir='./_output', solver_type='classic', weno_order=5, N=1000): +def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction_order=5, lim_type=2, N=1000): if use_petsc: import clawpack.petclaw as pyclaw @@ -28,7 +28,8 @@ def setup(use_petsc=0, outdir='./_output', solver_type='classic', weno_order=5, if solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.weno_order = weno_order + solver.reconstruction_order = reconstruction_order + solver.lim_type = lim_type else: solver = pyclaw.ClawSolver1D(riemann_solver) solver.limiters = pyclaw.limiters.tvd.vanleer diff --git a/fvmbook/chap6/limiters.ipynb b/fvmbook/chap6/limiters.ipynb index 3832a9e74..472c6f9b0 100644 --- a/fvmbook/chap6/limiters.ipynb +++ b/fvmbook/chap6/limiters.ipynb @@ -55,7 +55,7 @@ " elif scheme == 'first-order':\n", " solver.order = 1\n", " elif 'weno' in scheme:\n", - " solver.weno_order = int(scheme[4:]) #weno5, weno7, ...\n", + " solver.reconstruction_order = int(scheme[4:]) #weno5, weno7, ...\n", " else:\n", " raise Exception('Unrecognized limiter')\n", "\n", From 385bc83a5e704b000f590bf2b39a886ab53bf317 Mon Sep 17 00:00:00 2001 From: damiansa Date: Tue, 20 Oct 2020 12:22:57 +0300 Subject: [PATCH 11/21] added lim_type to function setup --- examples/acoustics_1d_homogeneous/acoustics_1d.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/acoustics_1d_homogeneous/acoustics_1d.py b/examples/acoustics_1d_homogeneous/acoustics_1d.py index b06187783..c59e3e110 100644 --- a/examples/acoustics_1d_homogeneous/acoustics_1d.py +++ b/examples/acoustics_1d_homogeneous/acoustics_1d.py @@ -24,7 +24,7 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', - outdir='./_output', ptwise=False, reconstruction_order=5, + outdir='./_output', ptwise=False, reconstruction_order=5, lim_type=2, time_integrator='SSP104', disable_output=False, output_style=1): if use_petsc: @@ -48,6 +48,7 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', solver = pyclaw.SharpClawSolver1D(riemann_solver) solver.reconstruction_order = reconstruction_order solver.time_integrator = time_integrator + solver.lim_type = lim_type if time_integrator == 'SSPLMMk3': solver.lmm_steps = 4 else: From e5ac2fe44f602f4a28af5dee36e64dd5da378275 Mon Sep 17 00:00:00 2001 From: damiansa Date: Tue, 20 Oct 2020 12:23:43 +0300 Subject: [PATCH 12/21] declared self.num_ghosts as int --- src/pyclaw/sharpclaw/solver.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index cfcb4e278..991ef5cac 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -207,10 +207,10 @@ def setup(self,solution): Allocate RK stage arrays or previous step solutions and fortran routine work arrays. """ if self.lim_type == 2: - self.num_ghost = (self.reconstruction_order+1)/2 + self.num_ghost = int((self.reconstruction_order+1)/2) if self.lim_type == 3: - self.num_ghost = 1 + self.reconstruction_order/2 + self.num_ghost = int(1 + self.reconstruction_order/2) if self.lim_type == 2 and self.reconstruction_order != 5 and self.kernel_language == 'Python': raise Exception('Only 5th-order WENO reconstruction is implemented in Python kernels. \ From 4de6e91af93e7aaf43653460d784fd07f2a533e4 Mon Sep 17 00:00:00 2001 From: damiansa Date: Tue, 20 Oct 2020 14:55:34 +0300 Subject: [PATCH 13/21] removed case 4 for lim_type select in reconstruct. Polynomial reconstruction can be selected using lim_type=3 --- src/pyclaw/sharpclaw/reconstruct.f90 | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/pyclaw/sharpclaw/reconstruct.f90 b/src/pyclaw/sharpclaw/reconstruct.f90 index 781b866fb..27f46905a 100644 --- a/src/pyclaw/sharpclaw/reconstruct.f90 +++ b/src/pyclaw/sharpclaw/reconstruct.f90 @@ -48,9 +48,6 @@ subroutine alloc_recon_workspace(maxnx,num_ghost,num_eqn,num_waves,lim_type,char case(3) allocate(uu(2,maxnx+2*num_ghost)) allocate( dq1m(maxnx+2*num_ghost)) - case(4) - allocate(uu(2,maxnx+2*num_ghost)) - allocate( dq1m(maxnx+2*num_ghost)) end select recon_alloc = .True. @@ -89,9 +86,6 @@ subroutine dealloc_recon_workspace(lim_type,char_decomp) case(3) deallocate(uu) deallocate(dq1m) - case(4) - deallocate(uu) - deallocate(dq1m) end select recon_alloc = .False. From 0f939e8c9cd655875892008be2a70cfe061360da Mon Sep 17 00:00:00 2001 From: damiansa Date: Wed, 21 Oct 2020 08:12:27 +0300 Subject: [PATCH 14/21] corrected error message in poly routine; it now describes the reconstruction_orders included' --- src/pyclaw/sharpclaw/reconstruct.f90 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pyclaw/sharpclaw/reconstruct.f90 b/src/pyclaw/sharpclaw/reconstruct.f90 index 27f46905a..c783768b5 100644 --- a/src/pyclaw/sharpclaw/reconstruct.f90 +++ b/src/pyclaw/sharpclaw/reconstruct.f90 @@ -849,7 +849,7 @@ subroutine poly_comp(q,ql,qr,num_eqn,maxnx,num_ghost) print *, 'Warning: 10th reconstruction order is not fully tested' call poly10(q,ql,qr,num_eqn,maxnx,num_ghost) case default - print *, 'ERROR: reconstruction_order for polynomial must be either 4 or 6' + print *, 'ERROR: reconstruction_order for polynomial must be an odd number between 4 and 10 (inclusive)' stop end select From dd8d553ff5fb417153d423d2b7a0d0bcc4eec567 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 06:27:47 +0000 Subject: [PATCH 15/21] Rework SharpClaw polynomial reconstruction; add lim_type names The previous polynomial weights were point-value Lagrange interpolation weights. PyClaw stores cell averages, so every order converged at 2nd order, and with the default three ghost cells orders 8 and 10 read outside the array and blew up. poly.f90 now builds interface values from cell averages with exact weights, in three variants that all take an even reconstruction_order N in {4, 6, 8, 10}: lim_type 4, 'poly' N cells centred on the face; order N; single-valued at faces, so no numerical dissipation lim_type 5, 'poly_dissipative' same width shifted one cell upwind; order N, dissipative lim_type 6, 'poly_cell_centered' N+1 cells centred on the cell; order N+1, dissipative (WENO with ideal linear weights) solver.py sets num_ghost = N/2 + 1 for these, validates the order and kernel language in Python instead of a Fortran stop, and no longer applies the polynomial ghost-cell formula to lim_type 3, which again behaves as upstream. lim_type also accepts names ('godunov', 'tvd', 'weno', 'weno5', 'poly', 'poly_dissipative', 'poly_cell_centered') and is still stored as an integer. weno_order is kept as an alias of reconstruction_order that issues a FutureWarning; setting it raised TypeError on this branch before. WENO code paths are unchanged. Adds tests for reconstruction order, face single-valuedness, solver convergence, energy conservation, high-order stability, a 2D run, the lim_type names and the weno_order alias. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01ThxEJQ7zPNDJ2WipNvNBpT --- src/pyclaw/sharpclaw/flux1.f90 | 4 +- src/pyclaw/sharpclaw/poly.f90 | 217 +++++++++++++++--------- src/pyclaw/sharpclaw/reconstruct.f90 | 23 +-- src/pyclaw/sharpclaw/solver.py | 105 +++++++++++- src/pyclaw/tests/test_sharpclaw_poly.py | 208 +++++++++++++++++++++++ 5 files changed, 449 insertions(+), 108 deletions(-) create mode 100644 src/pyclaw/tests/test_sharpclaw_poly.py diff --git a/src/pyclaw/sharpclaw/flux1.f90 b/src/pyclaw/sharpclaw/flux1.f90 index 7e9ccce36..689ac85a4 100644 --- a/src/pyclaw/sharpclaw/flux1.f90 +++ b/src/pyclaw/sharpclaw/flux1.f90 @@ -115,11 +115,11 @@ subroutine flux1(q1d,dq1d,aux,dt,cfl,t,ixyz,num_aux,num_eqn,mx,num_ghost,maxnx,r end select case(3) call weno5(q1d,ql,qr,num_eqn,maxnx,num_ghost) - case(4) + case(4:6) ! Linear polynomial reconstruction (module poly) call poly_comp(q1d,ql,qr,num_eqn,maxnx,num_ghost) case default write(*,*) 'ERROR: Unrecognized limiter type option' - write(*,*) 'You should set 1<=lim_type<=4' + write(*,*) 'You should set 1<=lim_type<=6' stop end select diff --git a/src/pyclaw/sharpclaw/poly.f90 b/src/pyclaw/sharpclaw/poly.f90 index cb095a7a3..61dcdfcfe 100644 --- a/src/pyclaw/sharpclaw/poly.f90 +++ b/src/pyclaw/sharpclaw/poly.f90 @@ -1,95 +1,154 @@ +! =================================================================== module poly -contains - - ! =================================================================== - subroutine poly4(q,ql,qr,num_eqn,maxnx,num_ghost) - ! =================================================================== - - implicit none - - integer, intent(in) :: num_eqn, maxnx, num_ghost - double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) - double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) +! =================================================================== +! Linear (unlimited) polynomial reconstruction of interface values +! from cell averages, for the SharpClaw finite-volume solvers. +! +! q(:,i) holds the average of q over cell i. For each cell, ql(:,i) is +! the reconstructed value at the left face x_(i-1/2) and qr(:,i) the +! value at the right face x_(i+1/2). The weights are exact: they are +! obtained by differentiating the polynomial that interpolates the +! primitive function of q at the cell faces, so they are correct for +! cell averages (not point values). They are applied component-wise. +! +! lim_type selects the stencil; order = solver.reconstruction_order, +! an even number N in {4, 6, 8, 10}. With k = N/2: +! +! 4 ('poly') ql(i) uses cells i-k..i+k-1 (N cells, +! centred on the face). Order N. ql(i+1) +! equals qr(i), so the scheme adds no +! numerical dissipation. +! 5 ('poly_dissipative') ql(i) uses cells i-k+1..i+k (the central +! stencil shifted one cell upwind for each +! face state). Order N, dissipative. +! 6 ('poly_cell_centered') ql(i) uses cells i-k..i+k (N+1 cells, +! centred on the cell, polynomial degree N). +! Order N+1, dissipative; equal to WENO of +! order N+1 with its ideal linear weights. +! +! qr uses the mirror image of the ql stencil. Every variant reaches k +! cells from the cell being reconstructed, so num_ghost = k + 1. +! +! Coefficients generated with exact rational arithmetic. +! =================================================================== + + implicit none - integer :: i,n - - do n = 1,num_eqn - do i = num_ghost-1,maxnx+num_ghost+1 - ql(n,i) = (-5.d0*q(n,-2+i)+60.d0*q(n,-1+i)+90.d0*q(n,i)-20.d0*q(n,1+i)+3.d0*q(n,2+i))/128.d0 - qr(n,i) = (3.d0*q(n,-2+i)-20.d0*q(n,-1+i)+90.d0*q(n,i)+60.d0*q(n,1+i)-5.d0*q(n,2+i))/128.d0 - end do - end do - - end subroutine poly4 +contains ! =================================================================== - subroutine poly6(q,ql,qr,num_eqn,maxnx,num_ghost) + subroutine poly_weights(lim_type, order, lo, hi, w, ok) ! =================================================================== - - implicit none - - integer, intent(in) :: num_eqn, maxnx, num_ghost - double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) - double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) - - integer :: i,n - - do n = 1,num_eqn - do i = num_ghost-1,maxnx+num_ghost+1 - ql(n,i) = (7.d0*q(n,-3+i) - 70.d0*q(n,-2+i) + 525.d0*q(n,-1+i) + 700.d0*q(n,i) & - - 175.d0*q(n,1+i) + 42.d0*q(n,2+i) - 5.d0*q(n,3+i))/1024.d0 - qr(n,i) = (42.d0*q(n,-2+i) - 175.d0*q(n,-1+i) + 700.d0*q(n,i) & - + 525.d0*q(n,1+i) - 70.d0*q(n,2+i) + 7.d0*q(n,3+i) - 5.d0*q(n,-3+i))/1024.d0 - end do - end do - - end subroutine poly6 + ! Weights for the left-face value ql(i) = sum_j w(j) * q(i+j), + ! j = lo..hi. + + integer, intent(in) :: lim_type, order + integer, intent(out) :: lo, hi + double precision, intent(out) :: w(-5:5) + logical, intent(out) :: ok + + ok = .true. + lo = 0; hi = 0 + w = 0.d0 + + select case (lim_type) + case (4) ! central, non-dissipative + select case (order) + case (4) + lo = -2; hi = 1 + w(-2:1) = [-1.d0, 7.d0, 7.d0, -1.d0] / 12.d0 + case (6) + lo = -3; hi = 2 + w(-3:2) = [1.d0, -8.d0, 37.d0, 37.d0, -8.d0, 1.d0] / 60.d0 + case (8) + lo = -4; hi = 3 + w(-4:3) = [-3.d0, 29.d0, -139.d0, 533.d0, 533.d0, -139.d0, 29.d0, -3.d0] / 840.d0 + case (10) + lo = -5; hi = 4 + w(-5:4) = [2.d0, -23.d0, 127.d0, -473.d0, 1627.d0, & + 1627.d0, -473.d0, 127.d0, -23.d0, 2.d0] / 2520.d0 + case default + ok = .false. + end select + case (5) ! upwind-biased, dissipative + select case (order) + case (4) + lo = -1; hi = 2 + w(-1:2) = [3.d0, 13.d0, -5.d0, 1.d0] / 12.d0 + case (6) + lo = -2; hi = 3 + w(-2:3) = [-2.d0, 22.d0, 57.d0, -23.d0, 7.d0, -1.d0] / 60.d0 + case (8) + lo = -3; hi = 4 + w(-3:4) = [5.d0, -55.d0, 365.d0, 743.d0, -307.d0, 113.d0, -27.d0, 3.d0] / 840.d0 + case (10) + lo = -4; hi = 5 + w(-4:5) = [-3.d0, 37.d0, -233.d0, 1207.d0, 2131.d0, & + -893.d0, 367.d0, -113.d0, 22.d0, -2.d0] / 2520.d0 + case default + ok = .false. + end select + case (6) ! cell-centred, dissipative + select case (order) + case (4) + lo = -2; hi = 2 + w(-2:2) = [-3.d0, 27.d0, 47.d0, -13.d0, 2.d0] / 60.d0 + case (6) + lo = -3; hi = 3 + w(-3:3) = [4.d0, -38.d0, 214.d0, 319.d0, -101.d0, 25.d0, -3.d0] / 420.d0 + case (8) + lo = -4; hi = 4 + w(-4:4) = [-5.d0, 55.d0, -305.d0, 1375.d0, & + 1879.d0, -641.d0, 199.d0, -41.d0, 4.d0] / 2520.d0 + case (10) + lo = -5; hi = 5 + w(-5:5) = [12.d0, -153.d0, 947.d0, -4003.d0, 15797.d0, & + 20417.d0, -7303.d0, 2597.d0, -703.d0, 122.d0, -10.d0] / 27720.d0 + case default + ok = .false. + end select + case default + ok = .false. + end select + + end subroutine poly_weights ! =================================================================== - subroutine poly8(q,ql,qr,num_eqn,maxnx,num_ghost) + subroutine poly_recon(q,ql,qr,num_eqn,maxnx,num_ghost,lim_type,order) ! =================================================================== - implicit none - - integer, intent(in) :: num_eqn, maxnx, num_ghost + integer, intent(in) :: num_eqn, maxnx, num_ghost, lim_type, order double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) - integer :: i,n - - do n = 1,num_eqn - do i = num_ghost-1,maxnx+num_ghost+1 - ql(n,i) = (-45.d0*q(n,-4+i) + 504.d0*q(n,-3+i) - 2940.d0*q(n,-2+i) + 17640.d0*q(n,-1+i) & - + 22050.d0*q(n,i) - 5880.d0*q(n,1+i) + 1764.d0*q(n,2+i) - 360.d0*q(n,3+i) + 35.d0*q(n,4+i))/32768.d0 - qr(n,i) = (35.d0*q(n,-4+i) - 360.d0*q(n,-3+i) + 1764.d0*q(n,-2+i) - 5880.d0*q(n,-1+i) + 22050.d0*q(n,i) & - + 17640.d0*q(n,1+i) - 2940.d0*q(n,2+i) + 504.d0*q(n,3+i) - 45.d0*q(n,4+i))/32768.d0 + double precision :: w(-5:5) + integer :: lo, hi, reach, i, j, mx2 + logical :: ok + + call poly_weights(lim_type, order, lo, hi, w, ok) + if (.not. ok) then + print *, 'ERROR: polynomial reconstruction needs lim_type 4, 5 or 6' + print *, ' and an even reconstruction_order between 4 and 10.' + stop + endif + + mx2 = size(q,2) + reach = max(-lo, hi) + if (reach >= num_ghost) then + print *, 'ERROR: polynomial reconstruction needs num_ghost >= ', reach+1 + stop + endif + + ql = 0.d0 + qr = 0.d0 + do i = 1+reach, mx2-reach + do j = lo, hi + ql(:,i) = ql(:,i) + w(j)*q(:,i+j) + ! qr mirrors the ql stencil about cell i + qr(:,i) = qr(:,i) + w(j)*q(:,i-j) end do end do - - end subroutine poly8 - - ! =================================================================== - subroutine poly10(q,ql,qr,num_eqn,maxnx,num_ghost) - ! =================================================================== - implicit none - - integer, intent(in) :: num_eqn, maxnx, num_ghost - double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) - double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) - - integer :: i,n - - do n = 1,num_eqn - do i = num_ghost-1,maxnx+num_ghost+1 - ql(n,i) = (77.d0*q(n,-5+i) - 990.d0*q(n,-4+i) + 6237.d0*q(n,-3+i) - 27720.d0*q(n,-2+i) & - + 145530.d0*q(n,-1+i) + 174636.d0*q(n,i) - 48510.d0*q(n,1+i) + 16632.d0*q(n,2+i) - 4455.d0*q(n,3+i) & - + 770.d0*q(n,4+i) - 63.d0*q(n,5+i))/262144.d0 - qr(n,i) = (-63.d0*q(n,-5+i) + 770.d0*q(n,-4+i) - 4455.d0*q(n,-3+i) + 16632.d0*q(n,-2+i) & - - 48510.d0*q(n,-1+i) + 174636.d0*q(n,i) + 145530.d0*q(n,1+i) - 27720.d0*q(n,2+i) + 6237.d0*q(n,3+i) & - - 990.d0*q(n,4+i) + 77.d0*q(n,5+i))/262144.d0 - end do - end do - end subroutine poly10 + end subroutine poly_recon -end module poly \ No newline at end of file +end module poly diff --git a/src/pyclaw/sharpclaw/reconstruct.f90 b/src/pyclaw/sharpclaw/reconstruct.f90 index c783768b5..fb75d0997 100644 --- a/src/pyclaw/sharpclaw/reconstruct.f90 +++ b/src/pyclaw/sharpclaw/reconstruct.f90 @@ -828,32 +828,19 @@ end subroutine tvd2_wave ! =================================================================== subroutine poly_comp(q,ql,qr,num_eqn,maxnx,num_ghost) ! =================================================================== + ! Linear polynomial reconstruction; see module poly for the + ! meaning of lim_type 4, 5 and 6. - use poly - use clawparams, only: reconstruction_order + use poly, only: poly_recon + use clawparams, only: lim_type, reconstruction_order implicit none integer, intent(in) :: num_eqn, maxnx, num_ghost double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) - select case(reconstruction_order) - case (4) - call poly4(q,ql,qr,num_eqn,maxnx,num_ghost) - case (6) - call poly6(q,ql,qr,num_eqn,maxnx,num_ghost) - case (8) - print *, 'Warning: 8th reconstruction order is not fully tested' - call poly8(q,ql,qr,num_eqn,maxnx,num_ghost) - case(10) - print *, 'Warning: 10th reconstruction order is not fully tested' - call poly10(q,ql,qr,num_eqn,maxnx,num_ghost) - case default - print *, 'ERROR: reconstruction_order for polynomial must be an odd number between 4 and 10 (inclusive)' - stop - end select + call poly_recon(q,ql,qr,num_eqn,maxnx,num_ghost,lim_type,reconstruction_order) - return end subroutine poly_comp end module reconstruct diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index d39254e0b..9f1518efc 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -5,6 +5,8 @@ # Created: 2010-03-20 # Author: David Ketcheson """ +import warnings + # Solver superclass from clawpack.pyclaw.solver import Solver from clawpack.pyclaw.util import add_parent_doc @@ -36,18 +38,57 @@ class SharpClawSolver(Solver): .. attribute:: lim_type - Limiter(s) to be used. - - 0: No limiting. - - 1: TVD reconstruction. - - 2: WENO reconstruction. - - 3: Polynomial reconstruction. + Reconstruction used to compute interface values from cell + averages. Can be set with an integer or, equivalently, a name: + + - -1 or ``'godunov'``: first-order Godunov (Python kernels only). + - 0: no limiting (not implemented). + - 1 or ``'tvd'``: TVD reconstruction. + - 2 or ``'weno'``: WENO reconstruction (odd + ``reconstruction_order``, 5 to 17). + - 3 or ``'weno5'``: original fifth-order WENO routine. + - 4 or ``'poly'``: non-dissipative polynomial reconstruction. + - 5 or ``'poly_dissipative'``: dissipative (upwind-biased) + polynomial reconstruction. + - 6 or ``'poly_cell_centered'``: dissipative, cell-centred + polynomial reconstruction. + + The value is always stored, and read back, as an integer. + + The polynomial reconstructions (4, 5 and 6) are linear and + unlimited, so they suit smooth solutions, e.g. electromagnetic + waves in smooth media; at discontinuities they produce Gibbs + oscillations. They take an even ``reconstruction_order`` N in + {4, 6, 8, 10}, are available with Fortran kernels only, and act + component-wise (``char_decomp`` is ignored). With k = N/2: + + - ``'poly'`` uses the N cells centred on each face, so both + sides of a face get the same value: no numerical dissipation + is added (only the time integrator dissipates); for linear, + constant-coefficient problems the semi-discrete scheme + conserves the discrete L2 energy. Formal order N. Unresolved (grid-scale) + modes are not damped. + - ``'poly_dissipative'`` uses the same width, shifted one cell + upwind for each face state. Formal order N, with damping + that grows with wavenumber. + - ``'poly_cell_centered'`` uses the N+1 cells centred on each + cell (a polynomial of degree N). Formal order N+1, and less + dissipative than ``'poly_dissipative'``; it equals WENO of + order N+1 with its ideal linear weights. + + All three need k+1 ghost cells, which is set automatically. The + time integrator ``'SSP104'`` is fourth order, so with N > 4 the + time error dominates unless the CFL number is reduced. ``Default = 2`` .. attribute:: reconstruction_order - Order of the reconstruction. From 1st to 17th order (PyWENO), and from - 4 to 10 (even) in Poly + Order of the reconstruction: odd, from 5 to 17, for WENO + (``lim_type`` 2); even, from 4 to 10, for the polynomial + reconstructions (``lim_type`` 4, 5 and 6; see ``lim_type`` for + how N relates to the formal order). Replaces ``weno_order``, + which still works but issues a ``FutureWarning``. ``Default = 5`` .. attribute:: time_integrator @@ -151,6 +192,45 @@ class SharpClawSolver(Solver): 'SSPLMM53': [0.24, 0.25] } + _lim_type_names = { + 'godunov': -1, + 'tvd': 1, + 'weno': 2, + 'weno5': 3, + 'poly': 4, + 'poly_dissipative': 5, + 'poly_cell_centered': 6, + } + + _poly_lim_types = (4, 5, 6) + _poly_orders = (4, 6, 8, 10) + + @property + def lim_type(self): + return self._lim_type + + @lim_type.setter + def lim_type(self, value): + if isinstance(value, str): + try: + value = self._lim_type_names[value.lower()] + except KeyError: + raise ValueError("Unknown lim_type '%s'; valid names are %s" + % (value, ', '.join(self._lim_type_names))) + self._lim_type = value + + @property + def weno_order(self): + warnings.warn("solver.weno_order has been replaced by " + "solver.reconstruction_order", FutureWarning, stacklevel=2) + return self.reconstruction_order + + @weno_order.setter + def weno_order(self, value): + warnings.warn("solver.weno_order has been replaced by " + "solver.reconstruction_order", FutureWarning, stacklevel=2) + self.reconstruction_order = value + # ======================================================================== # Initialization routines # ======================================================================== @@ -207,8 +287,15 @@ def setup(self,solution): if self.lim_type == 2: self.num_ghost = int((self.reconstruction_order+1)/2) - if self.lim_type == 3: - self.num_ghost = int(1 + self.reconstruction_order/2) + if self.lim_type in self._poly_lim_types: + if self.reconstruction_order not in self._poly_orders: + raise ValueError('Polynomial reconstruction (lim_type %s) needs an even ' + 'reconstruction_order in %s, not %s.' + % (self.lim_type, self._poly_orders, self.reconstruction_order)) + if self.kernel_language != 'Fortran': + raise NotImplementedError('Polynomial reconstruction (lim_type %s) is only ' + 'implemented in Fortran kernels.' % self.lim_type) + self.num_ghost = self.reconstruction_order//2 + 1 if self.lim_type == 2 and self.reconstruction_order != 5 and self.kernel_language == 'Python': raise Exception('Only 5th-order WENO reconstruction is implemented in Python kernels. \ diff --git a/src/pyclaw/tests/test_sharpclaw_poly.py b/src/pyclaw/tests/test_sharpclaw_poly.py new file mode 100644 index 000000000..a67eb8b88 --- /dev/null +++ b/src/pyclaw/tests/test_sharpclaw_poly.py @@ -0,0 +1,208 @@ +""" +Tests for the SharpClaw polynomial reconstructions (lim_type 4, 5 and 6), +the lim_type names and the deprecated weno_order alias. +""" +import warnings + +import numpy as np +import pytest + +from clawpack import pyclaw, riemann + +sharpclaw1 = pytest.importorskip("clawpack.pyclaw.sharpclaw.sharpclaw1") + +POLY = {4: 'poly', 5: 'poly_dissipative', 6: 'poly_cell_centered'} +ORDERS = (4, 6, 8, 10) + + +def formal_order(lim_type, order): + """Formal order of accuracy of the interface values.""" + return order + 1 if lim_type == 6 else order + + +def sine_averages(xc, h, kwave=1): + """Exact cell averages of sin(2*pi*kwave*x).""" + w = 2*np.pi*kwave + return (np.cos(w*(xc - h/2)) - np.cos(w*(xc + h/2)))/(w*h) + + +def reconstruct(num_cells, lim_type, order): + """Reconstruct interface values of sin(2*pi*x) on a periodic grid; + return the max interface error and the max jump ql(i+1) - qr(i).""" + num_ghost = order//2 + 1 + h = 1./num_cells + xc = (np.arange(-num_ghost, num_cells + num_ghost) + 0.5)*h + q = sine_averages(xc, h)[None, :] + ql, qr = sharpclaw1.poly.poly_recon(q, num_cells, num_ghost, lim_type, order) + real = slice(num_ghost, num_cells + num_ghost) + err_l = np.abs(ql[0, real] - np.sin(2*np.pi*(xc[real] - h/2))).max() + err_r = np.abs(qr[0, real] - np.sin(2*np.pi*(xc[real] + h/2))).max() + jump = np.abs(ql[0, num_ghost+1:num_cells+num_ghost+1] + - qr[0, num_ghost:num_cells+num_ghost]).max() + return max(err_l, err_r), jump + + +def advect(num_cells, lim_type, order, kwave=1, cfl=0.4, kernel_language='Fortran'): + """Advect sin(2*pi*kwave*x) once around a periodic unit domain; + return the L1 error and the ratio of final to initial L2 norm.""" + solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + solver.kernel_language = kernel_language + solver.lim_type = lim_type + solver.reconstruction_order = order + solver.bc_lower[0] = pyclaw.BC.periodic + solver.bc_upper[0] = pyclaw.BC.periodic + solver.cfl_desired = cfl + solver.cfl_max = 1.05*cfl + + x = pyclaw.Dimension(0., 1., num_cells, name='x') + domain = pyclaw.Domain(x) + state = pyclaw.State(domain, 1) + state.problem_data['u'] = 1. + state.q[0, :] = sine_averages(x.centers, x.delta, kwave) + + claw = pyclaw.Controller() + claw.solution = pyclaw.Solution(state, domain) + claw.solver = solver + claw.tfinal = 1. + claw.num_output_times = 1 + claw.output_format = None + claw.keep_copy = True + claw.verbosity = 0 + claw.run() + + q0 = claw.frames[0].q[0] + qf = claw.frames[-1].q[0] + error = x.delta*np.abs(qf - q0).sum() + energy_ratio = np.linalg.norm(qf)/np.linalg.norm(q0) + return error, energy_ratio + + +class TestReconstruction: + + @pytest.mark.parametrize("lim_type", sorted(POLY)) + @pytest.mark.parametrize("order", ORDERS) + def test_order_of_accuracy(self, lim_type, order): + # Coarse grids for the high orders keep the errors above round-off + grids = (16, 32) if order < 8 else (8, 16) + coarse, fine = (reconstruct(n, lim_type, order)[0] for n in grids) + observed = np.log2(coarse/fine) + assert observed > formal_order(lim_type, order) - 0.4 + + @pytest.mark.parametrize("order", ORDERS) + def test_poly_is_single_valued_at_faces(self, order): + """lim_type 4 gives the same value on both sides of each face, + so the Riemann solver adds no dissipation.""" + assert reconstruct(32, 4, order)[1] < 1e-14 + + @pytest.mark.parametrize("lim_type", (5, 6)) + def test_dissipative_variants_jump_at_faces(self, lim_type): + assert reconstruct(32, lim_type, 4)[1] > 1e-6 + + +class TestSolver: + + @pytest.mark.parametrize("lim_type", sorted(POLY)) + def test_convergence(self, lim_type): + coarse, fine = (advect(n, lim_type, 4)[0] for n in (40, 80)) + assert np.log2(coarse/fine) > formal_order(lim_type, 4) - 0.3 + + @pytest.mark.parametrize("lim_type", sorted(POLY)) + @pytest.mark.parametrize("order", (8, 10)) + def test_high_orders_are_stable(self, lim_type, order): + error, energy_ratio = advect(40, lim_type, order) + assert error < 1e-5 + assert energy_ratio <= 1. + 1e-12 + + def test_poly_conserves_energy(self): + """An unresolved mode (4 cells per wavelength) keeps its energy + with 'poly' but is damped by the dissipative variants.""" + ratios = {name: advect(16, name, 6, kwave=4)[1] for name in POLY.values()} + assert ratios['poly'] > 0.999 + assert ratios['poly_dissipative'] < ratios['poly_cell_centered'] < 0.5 + + @pytest.mark.parametrize("order", (5, 7, 12)) + def test_rejects_invalid_order(self, order): + with pytest.raises(ValueError): + advect(20, 'poly', order) + + def test_rejects_python_kernels(self): + with pytest.raises(NotImplementedError): + advect(20, 'poly', 4, kernel_language='Python') + + +class TestLimTypeNames: + + @pytest.mark.parametrize("name, value", [ + ('godunov', -1), ('tvd', 1), ('weno', 2), ('weno5', 3), + ('poly', 4), ('poly_dissipative', 5), ('poly_cell_centered', 6)]) + def test_name_maps_to_integer(self, name, value): + solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + solver.lim_type = name + assert solver.lim_type == value + solver.lim_type = name.upper() + assert solver.lim_type == value + solver.lim_type = value + assert solver.lim_type == value + + def test_unknown_name_raises(self): + solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + with pytest.raises(ValueError): + solver.lim_type = 'spline' + + @pytest.mark.parametrize("name, value", [('weno', 2), ('weno5', 3), ('poly', 4)]) + def test_name_and_integer_give_same_result(self, name, value): + order = 4 if value == 4 else 5 + assert advect(20, name, order) == advect(20, value, order) + + +class TestWenoOrderAlias: + + def test_weno_order_maps_to_reconstruction_order(self): + solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + with pytest.warns(FutureWarning, match="reconstruction_order"): + solver.weno_order = 9 + assert solver.reconstruction_order == 9 + with pytest.warns(FutureWarning): + assert solver.weno_order == 9 + + def test_reconstruction_order_does_not_warn(self): + solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + with warnings.catch_warnings(): + warnings.simplefilter("error") + solver.reconstruction_order = 7 + + +def test_poly_2d(): + """The 2D solver reuses the 1D reconstruction along each direction.""" + solver = pyclaw.SharpClawSolver2D(riemann.advection_2D) + solver.lim_type = 'poly' + solver.reconstruction_order = 6 + solver.bc_lower = [pyclaw.BC.periodic]*2 + solver.bc_upper = [pyclaw.BC.periodic]*2 + solver.cfl_desired = 0.4 + solver.cfl_max = 0.42 + + x = pyclaw.Dimension(0., 1., 24, name='x') + y = pyclaw.Dimension(0., 1., 24, name='y') + domain = pyclaw.Domain([x, y]) + state = pyclaw.State(domain, 1) + state.problem_data['u'] = 1. + state.problem_data['v'] = 1. + X, Y = state.grid.p_centers + h = x.delta + state.q[0, ...] = sine_averages(X, h)*sine_averages(Y, h) + + claw = pyclaw.Controller() + claw.solution = pyclaw.Solution(state, domain) + claw.solver = solver + claw.tfinal = 1. + claw.num_output_times = 1 + claw.output_format = None + claw.keep_copy = True + claw.verbosity = 0 + claw.run() + + assert solver.num_ghost == 4 + q0 = claw.frames[0].q[0] + qf = claw.frames[-1].q[0] + assert np.abs(qf - q0).max() < 1e-4 From 151f674c9c559f185a854376a060181d87d767f6 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 07:37:31 +0000 Subject: [PATCH 16/21] Add solver.poly_order and accept weno_order/poly_order in setup() solver.weno_order and solver.poly_order are now permanent properties that read back the order of the active reconstruction family (None otherwise) and, when set, select that family: weno_order switches to lim_type 2 unless lim_type is already 2 or 3, and poly_order switches to lim_type 4 unless lim_type is already 4, 5 or 6. reconstruction_order stays the canonical attribute. weno_order no longer warns. SharpClawSolver.set_reconstruction() applies lim_type, reconstruction_order, weno_order and poly_order together and raises ValueError for contradictory combinations. The 1D examples that took an order argument (acoustics_1d, advection_1d, advection_1d_nonunif, variable_coefficient_advection, cubic) accept weno_order= and poly_order= again, so upstream-style calls such as setup(weno_order=11) run WENO11 on lim_type 2 as before. Tests cover the properties, the set_reconstruction rules, the example keywords (including the upstream WENO11 regression value), and check that poly.f90 matches its generator in development/ when present. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01ThxEJQ7zPNDJ2WipNvNBpT --- .../acoustics_1d_homogeneous/acoustics_1d.py | 8 +- examples/advection_1d/advection_1d.py | 8 +- examples/advection_1d/advection_1d_nonunif.py | 2 +- .../variable_coefficient_advection.py | 6 +- examples/cubic_1d/cubic.py | 7 +- src/pyclaw/sharpclaw/solver.py | 85 ++++++++- src/pyclaw/tests/test_sharpclaw_poly.py | 165 ++++++++++++++++-- 7 files changed, 248 insertions(+), 33 deletions(-) diff --git a/examples/acoustics_1d_homogeneous/acoustics_1d.py b/examples/acoustics_1d_homogeneous/acoustics_1d.py index 28ac9fe2d..c59123850 100644 --- a/examples/acoustics_1d_homogeneous/acoustics_1d.py +++ b/examples/acoustics_1d_homogeneous/acoustics_1d.py @@ -23,9 +23,9 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', - outdir='./_output', ptwise=False, reconstruction_order=5, lim_type=2, + outdir='./_output', ptwise=False, reconstruction_order=None, lim_type=None, time_integrator='SSP104', disable_output=False, output_style=1, - num_cells=100): + num_cells=100, weno_order=None, poly_order=None): if use_petsc: import clawpack.petclaw as pyclaw @@ -46,9 +46,9 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', solver.limiters = pyclaw.limiters.tvd.MC elif solver_type == 'sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.reconstruction_order = reconstruction_order + solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, + weno_order=weno_order, poly_order=poly_order) solver.time_integrator = time_integrator - solver.lim_type = lim_type if time_integrator == 'SSPLMMk3': solver.lmm_steps = 4 else: diff --git a/examples/advection_1d/advection_1d.py b/examples/advection_1d/advection_1d.py index 1b631e9bd..e87959313 100755 --- a/examples/advection_1d/advection_1d.py +++ b/examples/advection_1d/advection_1d.py @@ -19,8 +19,9 @@ import numpy as np from clawpack import riemann -def setup(nx=400, kernel_language='Python', use_petsc=False, solver_type='classic', reconstruction_order=5, - time_integrator='SSP104', outdir='./_output'): +def setup(nx=400, kernel_language='Python', use_petsc=False, solver_type='classic', reconstruction_order=None, + time_integrator='SSP104', outdir='./_output', lim_type=None, + weno_order=None, poly_order=None): if use_petsc: import clawpack.petclaw as pyclaw @@ -36,7 +37,8 @@ def setup(nx=400, kernel_language='Python', use_petsc=False, solver_type='classi solver = pyclaw.ClawSolver1D(riemann_solver) elif solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.reconstruction_order = reconstruction_order + solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, + weno_order=weno_order, poly_order=poly_order) solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 5 diff --git a/examples/advection_1d/advection_1d_nonunif.py b/examples/advection_1d/advection_1d_nonunif.py index 3ace6431a..787c7d901 100644 --- a/examples/advection_1d/advection_1d_nonunif.py +++ b/examples/advection_1d/advection_1d_nonunif.py @@ -32,7 +32,7 @@ def mapc2p_nonunif(xc): def setup(nx=100, kernel_language='Fortran', use_petsc=False, solver_type='classic', reconstruction_order=5, - time_integrator='SSP104', outdir='./_output'): + time_integrator='SSP104', outdir='./_output', weno_order=None, poly_order=None): if use_petsc: import clawpack.petclaw as pyclaw diff --git a/examples/advection_1d_variable/variable_coefficient_advection.py b/examples/advection_1d_variable/variable_coefficient_advection.py index 122c29f20..0b301ebe0 100755 --- a/examples/advection_1d_variable/variable_coefficient_advection.py +++ b/examples/advection_1d_variable/variable_coefficient_advection.py @@ -51,7 +51,8 @@ def auxinit(state): def setup(use_petsc=False,solver_type='classic',kernel_language='Python',outdir='./_output', - reconstruction_order=5,time_integrator='SSP104',rk_coeffs=None): + reconstruction_order=None,time_integrator='SSP104',rk_coeffs=None, + lim_type=None,weno_order=None,poly_order=None): from clawpack import riemann if use_petsc: @@ -69,7 +70,8 @@ def setup(use_petsc=False,solver_type='classic',kernel_language='Python',outdir= solver = pyclaw.SharpClawSolver1D(riemann.advection_color_1D) elif kernel_language=='Python': solver = pyclaw.SharpClawSolver1D(riemann.vc_advection_1D_py.vc_advection_1D) - solver.reconstruction_order = reconstruction_order + solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, + weno_order=weno_order, poly_order=poly_order) solver.time_integrator = time_integrator if time_integrator == 'RK': solver.a, solver.b, solver.c = rk_coeffs diff --git a/examples/cubic_1d/cubic.py b/examples/cubic_1d/cubic.py index 2f6c32ade..7dc4190f9 100755 --- a/examples/cubic_1d/cubic.py +++ b/examples/cubic_1d/cubic.py @@ -16,7 +16,8 @@ import numpy as np from clawpack import riemann -def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction_order=5, lim_type=2, N=1000): +def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction_order=None, lim_type=None, N=1000, + weno_order=None, poly_order=None): if use_petsc: import clawpack.petclaw as pyclaw @@ -27,8 +28,8 @@ def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction if solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.reconstruction_order = reconstruction_order - solver.lim_type = lim_type + solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, + weno_order=weno_order, poly_order=poly_order) else: solver = pyclaw.ClawSolver1D(riemann_solver) solver.limiters = pyclaw.limiters.tvd.vanleer diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index 9f1518efc..bbca0719e 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -5,8 +5,6 @@ # Created: 2010-03-20 # Author: David Ketcheson """ -import warnings - # Solver superclass from clawpack.pyclaw.solver import Solver from clawpack.pyclaw.util import add_parent_doc @@ -87,10 +85,25 @@ class SharpClawSolver(Solver): Order of the reconstruction: odd, from 5 to 17, for WENO (``lim_type`` 2); even, from 4 to 10, for the polynomial reconstructions (``lim_type`` 4, 5 and 6; see ``lim_type`` for - how N relates to the formal order). Replaces ``weno_order``, - which still works but issues a ``FutureWarning``. + how N relates to the formal order). This is the canonical + attribute; ``weno_order`` and ``poly_order`` are views of it that + also select the kind of reconstruction. ``Default = 5`` + .. attribute:: weno_order + + The WENO order: ``reconstruction_order`` when ``lim_type`` is 2, + 5 when it is 3 (fixed-order routine), otherwise None. Setting it + sets ``reconstruction_order`` and switches ``lim_type`` to 2 unless + it is already 2 or 3. + + .. attribute:: poly_order + + The polynomial reconstruction order: ``reconstruction_order`` when + ``lim_type`` is 4, 5 or 6, otherwise None. Setting it (to 4, 6, 8 + or 10) sets ``reconstruction_order`` and switches ``lim_type`` to 4 + unless it is already 4, 5 or 6. + .. attribute:: time_integrator Time integrator to be used. Currently implemented methods: @@ -219,18 +232,72 @@ def lim_type(self, value): % (value, ', '.join(self._lim_type_names))) self._lim_type = value + _weno_lim_types = (2, 3) + @property def weno_order(self): - warnings.warn("solver.weno_order has been replaced by " - "solver.reconstruction_order", FutureWarning, stacklevel=2) - return self.reconstruction_order + if self.lim_type == 2: + return self.reconstruction_order + if self.lim_type == 3: + return 5 # the original WENO5 routine has a fixed order + return None @weno_order.setter def weno_order(self, value): - warnings.warn("solver.weno_order has been replaced by " - "solver.reconstruction_order", FutureWarning, stacklevel=2) + if self.lim_type not in self._weno_lim_types: + self.lim_type = 2 self.reconstruction_order = value + @property + def poly_order(self): + if self.lim_type in self._poly_lim_types: + return self.reconstruction_order + return None + + @poly_order.setter + def poly_order(self, value): + if value not in self._poly_orders: + raise ValueError('poly_order must be one of %s, not %s.' % (self._poly_orders, value)) + if self.lim_type not in self._poly_lim_types: + self.lim_type = 4 + self.reconstruction_order = value + + def set_reconstruction(self, lim_type=None, reconstruction_order=None, + weno_order=None, poly_order=None): + r""" + Set the reconstruction from the arguments an example's ``setup()`` + receives. Arguments left as None keep the solver's current value. + + ``weno_order`` selects WENO (``lim_type`` 2 unless ``lim_type`` is + already 2 or 3) and ``poly_order`` selects a polynomial + reconstruction (``lim_type`` 4 unless ``lim_type`` is already 4, 5 + or 6). Contradictory arguments raise ``ValueError``: both + ``weno_order`` and ``poly_order``, either of them together with a + different ``reconstruction_order``, or either of them with an + explicit ``lim_type`` of the other kind. + """ + if weno_order is not None and poly_order is not None: + raise ValueError('Pass either weno_order or poly_order, not both.') + family_order = weno_order if weno_order is not None else poly_order + if (family_order is not None and reconstruction_order is not None + and reconstruction_order != family_order): + raise ValueError('reconstruction_order=%s contradicts %s=%s.' + % (reconstruction_order, + 'weno_order' if weno_order is not None else 'poly_order', + family_order)) + if lim_type is not None: + self.lim_type = lim_type + if weno_order is not None and self.lim_type not in self._weno_lim_types: + raise ValueError('weno_order cannot be used with lim_type %s.' % self.lim_type) + if poly_order is not None and self.lim_type not in self._poly_lim_types: + raise ValueError('poly_order cannot be used with lim_type %s.' % self.lim_type) + if weno_order is not None: + self.weno_order = weno_order + elif poly_order is not None: + self.poly_order = poly_order + elif reconstruction_order is not None: + self.reconstruction_order = reconstruction_order + # ======================================================================== # Initialization routines # ======================================================================== diff --git a/src/pyclaw/tests/test_sharpclaw_poly.py b/src/pyclaw/tests/test_sharpclaw_poly.py index a67eb8b88..a1e6db901 100644 --- a/src/pyclaw/tests/test_sharpclaw_poly.py +++ b/src/pyclaw/tests/test_sharpclaw_poly.py @@ -1,6 +1,7 @@ """ Tests for the SharpClaw polynomial reconstructions (lim_type 4, 5 and 6), -the lim_type names and the deprecated weno_order alias. +the lim_type names, the weno_order and poly_order properties and the +example setup() keywords. """ import warnings @@ -155,21 +156,132 @@ def test_name_and_integer_give_same_result(self, name, value): assert advect(20, name, order) == advect(20, value, order) -class TestWenoOrderAlias: +class TestOrderProperties: - def test_weno_order_maps_to_reconstruction_order(self): + def make_solver(self, **kwargs): solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) - with pytest.warns(FutureWarning, match="reconstruction_order"): - solver.weno_order = 9 - assert solver.reconstruction_order == 9 - with pytest.warns(FutureWarning): - assert solver.weno_order == 9 + for key, value in kwargs.items(): + setattr(solver, key, value) + return solver + + def test_weno_order_reads_by_lim_type(self): + assert self.make_solver(reconstruction_order=9).weno_order == 9 + assert self.make_solver(lim_type='weno5').weno_order == 5 + assert self.make_solver(lim_type='poly', reconstruction_order=6).weno_order is None + + def test_weno_order_selects_weno(self): + solver = self.make_solver(lim_type='poly', reconstruction_order=6) + solver.weno_order = 7 + assert (solver.lim_type, solver.reconstruction_order) == (2, 7) + solver = self.make_solver(lim_type='weno5') + solver.weno_order = 5 + assert solver.lim_type == 3 + + def test_poly_order_reads_by_lim_type(self): + assert self.make_solver().poly_order is None + for lim_type in POLY: + assert self.make_solver(lim_type=lim_type, reconstruction_order=8).poly_order == 8 + + def test_poly_order_selects_poly(self): + solver = self.make_solver() + solver.poly_order = 6 + assert (solver.lim_type, solver.reconstruction_order) == (4, 6) + solver = self.make_solver(lim_type='poly_cell_centered') + solver.poly_order = 10 + assert (solver.lim_type, solver.reconstruction_order) == (6, 10) + + @pytest.mark.parametrize("order", (5, 12)) + def test_poly_order_rejects_invalid_order(self, order): + with pytest.raises(ValueError): + self.make_solver().poly_order = order - def test_reconstruction_order_does_not_warn(self): - solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + def test_order_properties_do_not_warn(self): with warnings.catch_warnings(): warnings.simplefilter("error") - solver.reconstruction_order = 7 + solver = self.make_solver(weno_order=7) + solver.poly_order = 4 + solver.weno_order, solver.poly_order + + +class TestSetReconstruction: + + def configure(self, **kwargs): + solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + solver.set_reconstruction(**kwargs) + return solver.lim_type, solver.reconstruction_order + + @pytest.mark.parametrize("kwargs, expected", [ + ({}, (2, 5)), + ({'reconstruction_order': 7}, (2, 7)), + ({'weno_order': 11}, (2, 11)), + ({'weno_order': 9, 'reconstruction_order': 9}, (2, 9)), + ({'lim_type': 'weno5', 'weno_order': 5}, (3, 5)), + ({'poly_order': 6}, (4, 6)), + ({'lim_type': 'poly_dissipative', 'poly_order': 8}, (5, 8)), + ({'lim_type': 6, 'reconstruction_order': 4}, (6, 4)), + ]) + def test_valid(self, kwargs, expected): + assert self.configure(**kwargs) == expected + + @pytest.mark.parametrize("kwargs", [ + {'weno_order': 5, 'poly_order': 4}, + {'weno_order': 7, 'reconstruction_order': 5}, + {'poly_order': 6, 'reconstruction_order': 4}, + {'lim_type': 'poly', 'weno_order': 5}, + {'lim_type': 'weno', 'poly_order': 4}, + {'lim_type': 'tvd', 'poly_order': 4}, + ]) + def test_contradictions_raise(self, kwargs): + with pytest.raises(ValueError): + self.configure(**kwargs) + + +class TestExampleSetup: + """Example setup() functions accept weno_order and poly_order.""" + + def solver(self, **kwargs): + from examples.acoustics_1d_homogeneous import acoustics_1d + claw = acoustics_1d.setup(solver_type='sharpclaw', disable_output=True, **kwargs) + return claw.solver + + def test_weno_order(self): + solver = self.solver(weno_order=11) + assert (solver.lim_type, solver.reconstruction_order) == (2, 11) + + def test_poly_order(self): + solver = self.solver(poly_order=6) + assert (solver.lim_type, solver.reconstruction_order) == (4, 6) + solver = self.solver(poly_order=6, lim_type='poly_dissipative') + assert (solver.lim_type, solver.reconstruction_order) == (5, 6) + + def test_defaults_unchanged(self): + solver = self.solver() + assert (solver.lim_type, solver.reconstruction_order) == (2, 5) + + def test_weno_order_matches_upstream_result(self): + """setup(weno_order=11) reproduces the WENO11 regression value.""" + from examples.acoustics_1d_homogeneous.test_acoustics import error + assert abs(error(kernel_language='Fortran', solver_type='sharpclaw', + weno_order=11) - 0.000521) < 1e-5 + + @pytest.mark.parametrize("module, kwargs", [ + ('examples.advection_1d.advection_1d', {'kernel_language': 'Fortran'}), + ('examples.advection_1d_variable.variable_coefficient_advection', {'kernel_language': 'Fortran'}), + ('examples.cubic_1d.cubic', {'N': 50}), + ]) + @pytest.mark.parametrize("order_kwarg", [{'weno_order': 7}, {'poly_order': 6}]) + def test_other_examples(self, module, kwargs, order_kwarg): + import importlib + setup = importlib.import_module(module).setup + claw = setup(solver_type='sharpclaw', **kwargs, **order_kwarg) + expected_lim_type = 2 if 'weno_order' in order_kwarg else 4 + assert claw.solver.lim_type == expected_lim_type + assert claw.solver.reconstruction_order == list(order_kwarg.values())[0] + + def test_nonuniform_example_accepts_keywords(self): + from examples.advection_1d import advection_1d_nonunif + advection_1d_nonunif.setup(weno_order=5) + advection_1d_nonunif.setup(poly_order=4) def test_poly_2d(): @@ -206,3 +318,34 @@ def test_poly_2d(): q0 = claw.frames[0].q[0] qf = claw.frames[-1].q[0] assert np.abs(qf - q0).max() < 1e-4 + + +class TestGenerator: + """poly.f90 is generated by development/poly_reconstruction/poly_weights.py; + these tests stop the two from drifting apart.""" + + @pytest.fixture(scope='class') + def generator(self): + import importlib.util + import os + path = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'development', + 'poly_reconstruction', 'poly_weights.py') + if not os.path.exists(path): + pytest.skip('development/ is not available') + spec = importlib.util.spec_from_file_location('poly_weights', path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + def test_poly_f90_matches_generator(self, generator): + with open(generator.POLY_F90) as f: + assert f.read() == generator.fortran_source(), \ + 'poly.f90 is out of date: run development/poly_reconstruction/poly_weights.py --write' + + @pytest.mark.parametrize("lim_type", sorted(POLY)) + @pytest.mark.parametrize("order", ORDERS) + def test_compiled_weights_match_exact_weights(self, generator, lim_type, order): + lo, hi, w, ok = sharpclaw1.poly.poly_weights(lim_type, order) + cells, exact = generator.ql_weights(lim_type, order) + assert ok and (lo, hi) == (cells[0], cells[-1]) + np.testing.assert_allclose(w[lo+5:hi+6], [float(v) for v in exact], rtol=0, atol=1e-15) From 67e11672dc2572ddf3afcbd73b19295ea83f328c Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 07:37:32 +0000 Subject: [PATCH 17/21] Add polynomial reconstruction generator, toy example and write-up development/poly_reconstruction/ contains: - poly_weights.py: exact (rational) weights for lim_type 4, 5 and 6, their Fourier symbols, and the generator of src/pyclaw/sharpclaw/poly.f90 (--write, --check, --table). - sine_reconstruction.py: reconstructs sin(2 pi x) from N cell averages with every type and order; prints the error and observed order and, with --fortran, compares with the compiled code. - test_poly_reconstruction.py: toy tests (accuracy, polynomial exactness, stability and dissipation, the old point-value weights being second order, generator up to date). - make_figures.py and figures/*.png: figures made with NumPy and matplotlib only, run by hand; output is deterministic. - README.md: what the reconstructions compute, why averages must not be treated as point values, the primitive-function construction, the stencils, weight tables, accuracy, and dissipation/dispersion, including the exact identity that 'poly_cell_centered' of order N is 'poly' of order N+2 plus an upwind term proportional to the jump. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01ThxEJQ7zPNDJ2WipNvNBpT --- development/poly_reconstruction/README.md | 233 ++++++++++++ .../figures/averages_vs_points.png | Bin 0 -> 145491 bytes .../figures/convergence.png | Bin 0 -> 168522 bytes .../figures/face_states.png | Bin 0 -> 106493 bytes .../poly_reconstruction/figures/fourier.png | Bin 0 -> 259795 bytes .../figures/primitive_construction.png | Bin 0 -> 132934 bytes .../figures/sine_reconstruction.png | Bin 0 -> 129239 bytes .../poly_reconstruction/figures/stencils.png | Bin 0 -> 120490 bytes .../poly_reconstruction/make_figures.py | 320 +++++++++++++++++ .../poly_reconstruction/poly_weights.py | 331 ++++++++++++++++++ .../sine_reconstruction.py | 83 +++++ .../test_poly_reconstruction.py | 71 ++++ 12 files changed, 1038 insertions(+) create mode 100644 development/poly_reconstruction/README.md create mode 100644 development/poly_reconstruction/figures/averages_vs_points.png create mode 100644 development/poly_reconstruction/figures/convergence.png create mode 100644 development/poly_reconstruction/figures/face_states.png create mode 100644 development/poly_reconstruction/figures/fourier.png create mode 100644 development/poly_reconstruction/figures/primitive_construction.png create mode 100644 development/poly_reconstruction/figures/sine_reconstruction.png create mode 100644 development/poly_reconstruction/figures/stencils.png create mode 100644 development/poly_reconstruction/make_figures.py create mode 100644 development/poly_reconstruction/poly_weights.py create mode 100644 development/poly_reconstruction/sine_reconstruction.py create mode 100644 development/poly_reconstruction/test_poly_reconstruction.py diff --git a/development/poly_reconstruction/README.md b/development/poly_reconstruction/README.md new file mode 100644 index 000000000..1e84ce641 --- /dev/null +++ b/development/poly_reconstruction/README.md @@ -0,0 +1,233 @@ +# Polynomial reconstructions in SharpClaw + +SharpClaw offers three linear polynomial reconstructions alongside WENO, +selected with `solver.lim_type` (4, 5 or 6). They were written for +electromagnetic wave problems, where an option that adds no numerical +dissipation is valuable. This note explains what they compute, which +cells they use, how accurate they are and how they damp and disperse +waves. Everything shown here is regenerated by the scripts in this +directory. + +| `lim_type` | name | cells per face value | formal order | numerical dissipation | +|---|---|---|---|---| +| 4 | `'poly'` | N, centred on the face | N | none | +| 5 | `'poly_dissipative'` | N, shifted one cell upwind | N | strongest | +| 6 | `'poly_cell_centered'` | N+1, centred on the cell | N+1 | moderate | + +N is `solver.reconstruction_order` (or `solver.poly_order`), an even +number in {4, 6, 8, 10}. + +## 1. What a reconstruction computes + +PyClaw is a finite-volume code: `q[i]` holds the **average** of the +solution over cell $i$, + +$$\bar q_i = \frac{1}{h}\int_{x_{i-1/2}}^{x_{i+1/2}} q(x)\,dx .$$ + +At every step SharpClaw needs the solution **at the faces**. For each +cell the reconstruction returns two values: `ql(i)`, the value at its left +face $x_{i-1/2}$, and `qr(i)`, the value at its right face $x_{i+1/2}$. +Face $x_{i+1/2}$ therefore receives two values, `qr(i)` from the left and +`ql(i+1)` from the right, and the Riemann solver resolves the jump between +them. That jump is where the upwinding, and so the numerical dissipation, +comes from. Nothing computes values at cell centres. + +All three reconstructions are linear combinations of nearby averages with +fixed weights: + +$$\texttt{ql}(i) = \sum_j w_j\,\bar q_{i+j}, \qquad \texttt{qr}(i) = \sum_j w_j\,\bar q_{i-j}.$$ + +## 2. Why the averages must be treated as averages + +An average is not the value at the cell centre. For smooth $q$, +$\bar q_i = q(x_i) + \tfrac{h^2}{24}q''(x_i) + O(h^4)$. Weights that +interpolate point values (Lagrange interpolation through the centres) +inherit that $O(h^2)$ error, whatever the stencil width. The first +version of `poly.f90` did exactly this, and every order converged at +second order: + +![Cell averages vs centre values, and old vs new weights](figures/averages_vs_points.png) + +## 3. How the weights are built + +The primitive function $Q(x)=\int_{x_0}^{x} q\,dx$ is known **exactly** +at every face: it is a running sum of $h\,\bar q$. Interpolate $Q$ at the +faces of the stencil with a polynomial $P$, and the face value is the +slope $P'$ at that face. The weights do not depend on $q$, so this is +done once, in exact rational arithmetic, by `poly_weights.py`: + +![Primitive-function construction](figures/primitive_construction.png) + +## 4. Which cells each type uses + +With $k = N/2$, the cells used for `ql(i)` are + +| type | cells for `ql(i)` | cells for `qr(i)` | idea | +|---|---|---|---| +| `'poly'` | $i-k \dots i+k-1$ | $i-k+1 \dots i+k$ | N cells centred on the face | +| `'poly_dissipative'` | $i-k+1 \dots i+k$ | $i-k \dots i+k-1$ | the `'poly'` stencil shifted one cell upwind | +| `'poly_cell_centered'` | $i-k \dots i+k$ | $i-k \dots i+k$ | N+1 cells centred on cell $i$ | + +![Stencils for N = 4 and N = 6](figures/stencils.png) + +The difference that matters is what the two sides of one face see. With +`'poly'`, `qr(i)` and `ql(i+1)` use the same cells with the same weights, +so they are the same number: the Riemann solver sees no jump and adds no +dissipation. The other two types reconstruct each side from a different +stencil, so the states differ by $O(h^{N})$ (or $O(h^{N+1})$) and the +jump is damped. + +![The two states at one face](figures/face_states.png) + +Every type reaches $k$ cells from the cell being reconstructed, so +SharpClaw sets `num_ghost = N/2 + 1` automatically. + +### Weights + +Generated by `python poly_weights.py --table`; `qr(i)` uses the mirror +image ($w_j$ applied to $\bar q_{i-j}$). + +**lim_type = 4, `'poly'`** + +| N | order | cells for `ql(i)` | weights | +|---|---|---|---| +| 4 | 4 | i-2 … i+1 | (-1, 7, 7, -1) / 12 | +| 6 | 6 | i-3 … i+2 | (1, -8, 37, 37, -8, 1) / 60 | +| 8 | 8 | i-4 … i+3 | (-3, 29, -139, 533, 533, -139, 29, -3) / 840 | +| 10 | 10 | i-5 … i+4 | (2, -23, 127, -473, 1627, 1627, -473, 127, -23, 2) / 2520 | + +**lim_type = 5, `'poly_dissipative'`** + +| N | order | cells for `ql(i)` | weights | +|---|---|---|---| +| 4 | 4 | i-1 … i+2 | (3, 13, -5, 1) / 12 | +| 6 | 6 | i-2 … i+3 | (-2, 22, 57, -23, 7, -1) / 60 | +| 8 | 8 | i-3 … i+4 | (5, -55, 365, 743, -307, 113, -27, 3) / 840 | +| 10 | 10 | i-4 … i+5 | (-3, 37, -233, 1207, 2131, -893, 367, -113, 22, -2) / 2520 | + +**lim_type = 6, `'poly_cell_centered'`** + +| N | order | cells for `ql(i)` | weights | +|---|---|---|---| +| 4 | 5 | i-2 … i+2 | (-3, 27, 47, -13, 2) / 60 | +| 6 | 7 | i-3 … i+3 | (4, -38, 214, 319, -101, 25, -3) / 420 | +| 8 | 9 | i-4 … i+4 | (-5, 55, -305, 1375, 1879, -641, 199, -41, 4) / 2520 | +| 10 | 11 | i-5 … i+5 | (12, -153, 947, -4003, 15797, 20417, -7303, 2597, -703, 122, -10) / 27720 | + +Two checks on these numbers: `'poly'` with N = 4 is the classical +fourth-order face value (-1, 7, 7, -1)/12, and `'poly_cell_centered'` +with N = 4 gives exactly the ideal (linear) weights of WENO5. + +## 5. Accuracy + +`sine_reconstruction.py` reconstructs $\sin 2\pi x$ from its exact cell +averages and compares the face values with the exact ones. At a coarse +4 cells per wavelength the difference between the types is visible: all +three are close to the curve, but only `'poly'` gives each face a single +value. + +![Face values of sin 4πx from 8 cells](figures/sine_reconstruction.png) + +Errors fall at the formal order until they reach round-off: + +![Convergence of the interface error](figures/convergence.png) + +Measured with 16 → 32 cells (`python sine_reconstruction.py`): + +| type | N = 4 | N = 6 | N = 8 | N = 10 | +|---|---|---|---|---| +| `'poly'` | 3.98 | 5.97 | 7.95 | 9.94 | +| `'poly_dissipative'` | 3.98 | 5.97 | 7.95 | 9.94 | +| `'poly_cell_centered'` | 4.96 | 6.94 | 8.93 | 10.92 | + +In the full solver the observed order is also limited by the time +integrator: the default `'SSP104'` is fourth order, so for N > 4 the time +error dominates unless the CFL number is reduced. + +## 6. Dissipation and dispersion + +For the model problem $u_t + u_x = 0$ with the upwind flux, the +semi-discrete scheme evolves a Fourier mode $e^{ij\theta}$ ($\theta = kh$) +as $e^{\lambda t}$; the exact value is $\lambda = -i\theta/h$. +$\mathrm{Re}\,\lambda$ is the damping rate and $-\mathrm{Im}\,\lambda$ +the modified wavenumber (below the diagonal, waves travel too slowly). + +![Damping rate and modified wavenumber](figures/fourier.png) + +The same data at a few resolutions, as phase-speed ratio +$-\mathrm{Im}\,\lambda/\theta$ (1 is exact) and damping rate +$\mathrm{Re}\,\lambda$ (with $h = 1$): + +| points per wavelength | `'poly'` N=4 | `'poly'` N=8 | `'poly_dissipative'` N=4 | `'poly_dissipative'` N=8 | `'poly_cell_centered'` N=4 | `'poly_cell_centered'` N=8 | +|---|---|---|---|---|---|---| +| 16 | 0.9992 / 0 | 1.0000 / 0 | 1.0011 / -0.000 | 1.0000 / -0.000 | 1.0000 / -0.000 | 1.0000 / -0.000 | +| 8 | 0.9882 / 0 | 0.9998 / 0 | 1.0140 / -0.008 | 1.0002 / -0.000 | 0.9985 / -0.003 | 1.0000 / -0.000 | +| 6 | 0.9648 / 0 | 0.9983 / 0 | 1.0337 / -0.042 | 1.0013 / -0.002 | 0.9924 / -0.017 | 0.9996 / -0.001 | +| 4 | 0.8488 / 0 | 0.9701 / 0 | 1.0610 / -0.333 | 1.0065 / -0.057 | 0.9337 / -0.133 | 0.9863 / -0.025 | + +What this means in practice: + +- **`'poly'` does not damp anything.** $\mathrm{Re}\,\lambda = 0$ for + every wavenumber, so for linear, constant-coefficient problems the + semi-discrete scheme conserves the discrete $L^2$ energy; only the time + integrator dissipates (slightly). The flip side is that unresolved + modes, including grid-scale ($\theta = \pi$) noise, are never damped. + Waves lag (phase speed below 1). +- **`'poly_dissipative'` damps most** and its waves run slightly fast + (phase speed above 1). +- **`'poly_cell_centered'` damps less** and its waves lag. It has an exact + relation to `'poly'`: the average of its two face states is exactly the + `'poly'` face value of order N+2. In other words, `'poly_cell_centered'` + of order N **is** `'poly'` of order N+2 plus an upwind correction + proportional to the jump, which is why their dispersion curves coincide. + +## 7. Using them + +```python +solver = pyclaw.SharpClawSolver1D(riemann_solver) +solver.lim_type = 'poly' # or 4; 'poly_dissipative' (5), 'poly_cell_centered' (6) +solver.reconstruction_order = 6 # even: 4, 6, 8 or 10 +# or, equivalently, select the polynomial family and its order at once: +solver.poly_order = 6 # sets lim_type to 4 unless it is already 4, 5 or 6 +``` + +`solver.lim_type` also accepts `'godunov'` (-1), `'tvd'` (1), `'weno'` +(2) and `'weno5'` (3). `solver.weno_order` and `solver.poly_order` read +back the order of the active family (None otherwise). The example +`setup()` functions accept `weno_order=` or `poly_order=` as well as +`reconstruction_order=` and `lim_type=`. + +Limitations: + +- **No limiting.** At discontinuities the reconstructions produce Gibbs + oscillations; they are meant for smooth solutions. +- **Fortran kernels only**, applied component-wise (`char_decomp` is + ignored). They work in 1D, 2D and 3D, since the 2D and 3D solvers apply + the 1D reconstruction along each direction. +- **Energy conservation** of `'poly'` is shown here only for linear, + constant-coefficient problems; with variable coefficients (e.g. + material interfaces stored in `aux`) it is not guaranteed and should be + checked for the problem at hand. + +## 8. Files and how to regenerate everything + +| file | purpose | +|---|---| +| `poly_weights.py` | exact weights, Fourier symbols, and the generator of `src/pyclaw/sharpclaw/poly.f90` | +| `sine_reconstruction.py` | the sine example; `--cells N` sets the grid, `--fortran` compares with the compiled code | +| `test_poly_reconstruction.py` | toy tests (run with `python -m pytest` in this directory) | +| `make_figures.py` | regenerates `figures/*.png` (NumPy and matplotlib only) | + +```bash +cd development/poly_reconstruction +python poly_weights.py --check # is poly.f90 up to date? +python poly_weights.py --write # regenerate it after changing the stencils +python sine_reconstruction.py # error and observed-order table +python -m pytest # toy tests +python make_figures.py # rebuild the figures (run by hand) +``` + +CI does not run anything in `development/`, but +`src/pyclaw/tests/test_sharpclaw_poly.py` checks that `poly.f90` matches +this generator and that the compiled weights equal the 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z%28)33lRk}6sq#`2R|Qt_;zopyYNHpQOm+zK;{E6c?;J+)EyrUwBxfGcNBH=yOW>o$^Q8H^OI2G{jq)7 zozp*pKw|XL&SDhj zC6ow^!rz2PeqP@tQ&#l`sX=sxa+2x7uY0AhG?zPuh_XJ0@@bR;EvP*n!d2&(@*E+e zSW!zVr2FCR3r}&dR_^yG2gBQ><>ieY{6Zi?<=c96bWHufrjTJa)iirPBscPWOG`_c zVD(kG__F4;w0`e717D{UzThzfv|Uoxm`+FnaqFZ4b{bq>Jxb_WO`i?c^g`WD)H^|DhaY6k-(FggQcA&aj zzyJ2;4A-Oo&+GWS{Oj@m;}219?SLUd5%9k+;NPF?tY7?hv;O_`|MQ|awEv$kx%LVz Ys+nx;f^km|bttC9YZ;Lop$}jFFG', color=INK_2, lw=1, shrinkA=4, shrinkB=4)) + ax1.legend(loc='lower left', fontsize=8.5) + ax1.set_title('Average ≠ centre value: gap ≈ h²q″/24', loc='left') + + cells_n = [8, 16, 32, 64, 128] + for order in pw.ORDERS: + cells = list(pw.stencil(6, order)) + w_pt = [float(v) for v in pw.point_weights(cells, Fraction(-1, 2))] + errors = [] + for m in cells_n: + hh = 1. / m + xx = (np.arange(m) + 0.5) * hh + ql = sum(wj * np.roll(sine_averages(m), -j) for j, wj in zip(cells, w_pt)) + errors.append(np.abs(ql - np.sin(2*np.pi*(xx - hh/2))).max()) + ax2.loglog(cells_n, errors, color=QR, lw=1.5, alpha=0.9) + fv = np.array([interface_error(m, 6, order) for m in cells_n]) + keep = fv > 1e-13 + ax2.loglog(np.array(cells_n)[keep], fv[keep], color=ORDER_RAMP[order], marker='o', ms=4) + ax2.text(140, 2e-5, 'point-value weights\n(old), all N:\nslope 2', color=QR, fontsize=8.5, va='center') + for order in pw.ORDERS: + ax2.plot([], [], color=ORDER_RAMP[order], marker='o', ms=4, + label='cell-average, N=%d: slope %d' % (order, order + 1)) + ax2.legend(loc='center left', bbox_to_anchor=(1.0, 0.45), fontsize=8) + ax2.set_xlim(7, 600) + ax2.set_ylim(1e-14, 1) + ax2.set_xlabel('number of cells') + ax2.set_ylabel('max interface error') + ax2.set_title('Old vs new weights (2k+1-cell stencils)', loc='left') + save(fig, 'averages_vs_points.png') + + +# ---------------------------------------------------------------------------- +def fig_primitive_construction(): + """Interpolate the primitive at the faces, differentiate at the face.""" + cells = list(pw.stencil(6, 4)) # i-2 .. i+2 + q = lambda s: 1.0 + 0.5*np.sin(0.9*s + 0.4) + 0.08*s # noqa: E731 + big_q = lambda s: 1.0*s - 0.5/0.9*np.cos(0.9*s + 0.4) + 0.04*s**2 # noqa: E731 + faces = np.arange(cells[0] - 0.5, cells[-1] + 1.0) + avg = [big_q(j + 0.5) - big_q(j - 0.5) for j in cells] + prim = np.concatenate([[0], np.cumsum(avg)]) + target = -0.5 + xs = np.linspace(faces[0] - 0.3, faces[-1] + 0.3, 400) + + fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(7.5, 6.2), sharex=True, + gridspec_kw={'height_ratios': [1, 1.15]}) + for j, a in zip(cells, avg): + ax1.add_patch(Rectangle((j - 0.5, 0), 1, a, facecolor=QL_FILL, edgecolor=SURFACE, lw=2)) + ax1.text(j, a + 0.05, r'$\bar{q}_{i%s}$' % ('' if j == 0 else '%+d' % j), ha='center', fontsize=10, color=INK_2) + ax1.plot(xs, q(xs), color=MUTED, lw=1.5) + ax1.plot([target], [q(target)], 'o', color=QL, ms=8, zorder=5) + ax1.annotate('wanted: ql(i) = q(x$_{i-1/2}$)', (target, q(target)), (target - 2.3, 1.85), + color=QL, fontsize=9, arrowprops=dict(arrowstyle='->', color=QL, lw=1)) + ax1.set_ylim(0, 2.1) + ax1.set_ylabel('q') + ax1.set_title('1. Known: the cell averages over the stencil', loc='left') + + coeffs = np.polyfit(faces, prim, len(faces) - 1) + ax2.plot(xs, np.polyval(coeffs, xs), color=INK_2, lw=1.5, label='interpolating polynomial P(x)') + ax2.plot(faces, prim, 'o', color=INK, ms=6, label=r'Q at the faces = running sum of $h\,\bar{q}$') + slope = np.polyval(np.polyder(coeffs), target) + t = np.linspace(target - 1.1, target + 1.1, 2) + ax2.plot(t, np.polyval(coeffs, target) + slope*(t - target), color=QL, lw=2.5, + label="tangent at x$_{i-1/2}$: slope P′ = ql(i)") + ax2.set_ylabel('Q(x) = ∫ q dx') + ax2.set_title('2. Interpolate the primitive at the faces; its slope at the face is ql(i)', loc='left') + ax2.legend(loc='upper left', fontsize=8.5) + ax2.set_xticks(faces) + ax2.set_xticklabels(['x$_{i%s}$' % ('-1/2' if f == -0.5 else '%+g' % f).replace('+0.5', '+1/2') + .replace('-1.5', '-3/2').replace('+1.5', '+3/2').replace('-2.5', '-5/2') + .replace('+2.5', '+5/2') for f in faces]) + for ax in (ax1, ax2): + ax.axvline(target, color=QL, lw=0.8, ls=':') + fig.text(0.01, -0.02, 'Shown for the 5-cell stencil of lim_type 6, N = 4. The weights do not depend ' + 'on q: they follow from this construction once, exactly, in poly_weights.py.', + fontsize=8.5, color=INK_2) + save(fig, 'primitive_construction.png') + + +# ---------------------------------------------------------------------------- +def draw_strip(ax, y, used, colour, fill, label, face=None, span=5, centre_cell=True): + """Row of cells i-span..i+span; cells in `used` are filled.""" + for j in range(-span, span + 1): + on = j in used + ax.add_patch(Rectangle((j - 0.5, y - 0.35), 1, 0.7, facecolor=fill if on else SURFACE, + edgecolor=colour if on else GRID, lw=1.6 if on else 1)) + if on: + ax.text(j, y, 'i' if j == 0 else 'i%+d' % j, ha='center', va='center', fontsize=7.5, color=INK) + if centre_cell: + ax.add_patch(Rectangle((-0.5, y - 0.35), 1, 0.7, fill=False, edgecolor=INK, lw=1.6)) + if face is not None: + ax.plot([face, face], [y - 0.5, y + 0.5], color=colour, lw=3, solid_capstyle='butt') + ax.text(-span - 0.8, y, label, ha='right', va='center', fontsize=9, color=colour) + + +def fig_stencils(): + """Which cells each type uses for ql(i) and qr(i).""" + fig, axes = plt.subplots(3, 2, figsize=(11, 5.6)) + for row, lim_type in enumerate(pw.LIM_TYPES): + for col, order in enumerate((4, 6)): + ax = axes[row, col] + cells = list(pw.stencil(lim_type, order)) + draw_strip(ax, 1, cells, QL, QL_FILL, 'ql(i)', face=-0.5) + draw_strip(ax, 0, [-j for j in cells], QR, QR_FILL, 'qr(i)', face=0.5) + ax.set_xlim(-8.6, 5.7) + ax.set_ylim(-0.6, 1.6) + ax.axis('off') + ax.set_title('%s, N = %d: %d cells, order %d' + % (TITLES[lim_type], order, len(cells), pw.formal_order(lim_type, order)), + loc='left', fontsize=9.5) + fig.text(0.01, -0.02, 'Bold outline: cell i. Thick bar: the face whose value is computed ' + '(x$_{i-1/2}$ for ql, x$_{i+1/2}$ for qr). qr(i) uses the mirror image of the ql(i) cells.', + fontsize=8.5, color=INK_2) + fig.tight_layout() + save(fig, 'stencils.png') + + +def fig_face_states(): + """The two states the Riemann solver sees at one face.""" + fig, axes = plt.subplots(3, 1, figsize=(7.5, 5.6)) + notes = {4: 'same cells, same weights: one value, no jump, no dissipation', + 5: 'each side leans upwind: the states differ, the jump dissipates', + 6: 'each side centred on its own cell: the states differ, the jump dissipates'} + for ax, lim_type in zip(axes, pw.LIM_TYPES): + cells = list(pw.stencil(lim_type, 4)) + # face x_{i+1/2}: left state qr(i), right state ql(i+1) + draw_strip(ax, 1, [-j for j in cells], QR, QR_FILL, 'left state qr(i)', face=0.5) + draw_strip(ax, 0, [1 + j for j in cells], QL, QL_FILL, 'right state ql(i+1)', face=0.5, + centre_cell=False) + ax.set_xlim(-11.5, 5.7) + ax.set_ylim(-0.6, 1.6) + ax.axis('off') + ax.set_title('%s (N = 4): %s' % (TITLES[lim_type], notes[lim_type]), loc='left', fontsize=9) + fig.text(0.01, -0.02, 'Both rows reconstruct the value at the same face x$_{i+1/2}$ (thick bar), ' + 'from the left and from the right.', fontsize=8.5, color=INK_2) + fig.tight_layout() + save(fig, 'face_states.png') + + +# ---------------------------------------------------------------------------- +def fig_sine_reconstruction(): + """ql and qr of sin(4 pi x) from 8 cell averages (4 per wavelength), N = 4.""" + n = 8 + h = 1. / n + x = np.linspace(0, 1, 400) + avg = sine_averages(n, kwave=2) + fig, axes = plt.subplots(1, 3, figsize=(11, 3.6), sharey=True) + for ax, lim_type in zip(axes, pw.LIM_TYPES): + ql, qr = pw.reconstruct(avg, lim_type, 4) + for j in range(n): + ax.add_patch(Rectangle((j*h, min(0, avg[j])), h, abs(avg[j]), facecolor='#f0efec', + edgecolor=SURFACE, lw=2)) + ax.plot(x, np.sin(4*np.pi*x), color=MUTED, lw=1.5) + faces = np.arange(n) * h + ax.plot(faces, ql, '>', color=QL, ms=7, label='ql(i) at x$_{i-1/2}$') + ax.plot(faces + h, qr, '<', color=QR, ms=7, label='qr(i) at x$_{i+1/2}$') + jump = np.abs(np.roll(ql, -1) - qr).max() + ax.set_title('%s, N = 4' % TITLES[lim_type], loc='left', fontsize=9.5) + ax.text(0.03, -1.32, 'max |ql(i+1) − qr(i)| = %.1e' % jump, fontsize=8.5, color=INK_2) + ax.set_xlabel('x') + ax.set_xlim(0, 1) + ax.set_ylim(-1.45, 1.3) + axes[0].legend(loc='upper right', fontsize=8.5) + fig.text(0.01, -0.04, 'Grey: exact sin 4πx and its 8 cell averages, a coarse 4 cells per wavelength. ' + 'Each face gets a value from each neighbouring cell; with \'poly\' they coincide.', + fontsize=8.5, color=INK_2) + fig.tight_layout() + save(fig, 'sine_reconstruction.png') + + +def fig_convergence(): + """Interface error against the number of cells.""" + cells_n = np.array([4, 6, 8, 12, 16, 24, 32, 48, 64, 96, 128]) + fig, axes = plt.subplots(1, 3, figsize=(11, 3.8), sharey=True) + for ax, lim_type in zip(axes, pw.LIM_TYPES): + for order in pw.ORDERS: + errors = np.array([interface_error(m, lim_type, order) for m in cells_n]) + keep = errors > 1e-13 + ax.loglog(cells_n[keep], errors[keep], color=ORDER_RAMP[order], marker='o', ms=3.5) + last = np.flatnonzero(keep)[-1] + ax.text(cells_n[last] * 1.12, errors[last], 'N=%d' % order, fontsize=8, + color=INK_2, va='center') + ax.set_title(TITLES[lim_type], loc='left', fontsize=9.5) + ax.set_xlabel('number of cells') + ax.set_xlim(3.5, 250) + ax.set_ylim(1e-14, 2) + axes[0].set_ylabel('max interface error, sin 2πx') + fig.text(0.01, -0.04, 'Slopes: N for \'poly\' and \'poly_dissipative\', N+1 for ' + '\'poly_cell_centered\'. Curves stop where round-off takes over.', + fontsize=8.5, color=INK_2) + fig.tight_layout() + save(fig, 'convergence.png') + + +def fig_fourier(): + """Dissipation and dispersion for u_t + u_x = 0 (semi-discrete).""" + theta = np.linspace(1e-4, np.pi, 600) + fig, axes = plt.subplots(2, 3, figsize=(11, 6.2), sharex=True, sharey='row') + for col, lim_type in enumerate(pw.LIM_TYPES): + top, bottom = axes[0, col], axes[1, col] + for order in pw.ORDERS: + lam = pw.fourier_symbol(lim_type, order, theta) + top.plot(theta, lam.real, color=ORDER_RAMP[order], label='N=%d' % order) + bottom.plot(theta, -lam.imag, color=ORDER_RAMP[order], label='N=%d' % order) + bottom.plot(theta, theta, color=MUTED, lw=1.2, ls='--', label='exact') + top.set_title(TITLES[lim_type], loc='left', fontsize=9.5) + if lim_type == 4: + top.text(0.1, -0.35, 'Re λ = 0 for every N:\nno numerical dissipation', fontsize=9, color=INK_2) + bottom.set_xlabel('θ = kh (points per wavelength = 2π/θ)') + bottom.set_xticks([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi]) + bottom.set_xticklabels(['0', 'π/4\n(8)', 'π/2\n(4)', '3π/4\n(2.7)', 'π\n(2)']) + axes[0, 0].set_ylabel('Re λ (damping rate)') + axes[1, 0].set_ylabel('−Im λ (modified wavenumber)') + axes[0, 1].legend(loc='lower left', fontsize=8.5) + axes[1, 0].legend(loc='upper left', fontsize=8.5) + axes[0, 0].set_ylim(-2.8, 0.15) + fig.text(0.01, -0.03, 'Mode exp(ijθ) with h = 1 and upwind flux evolves as exp(λt); exact: ' + 'λ = −iθ. Re λ = 0 means no damping; −Im λ below the diagonal means waves lag.', + fontsize=8.5, color=INK_2) + fig.tight_layout() + save(fig, 'fourier.png') + + +if __name__ == '__main__': + fig_averages_vs_points() + fig_primitive_construction() + fig_stencils() + fig_face_states() + fig_sine_reconstruction() + fig_convergence() + fig_fourier() diff --git a/development/poly_reconstruction/poly_weights.py b/development/poly_reconstruction/poly_weights.py new file mode 100644 index 000000000..33f4f821e --- /dev/null +++ b/development/poly_reconstruction/poly_weights.py @@ -0,0 +1,331 @@ +#!/usr/bin/env python +r""" +Exact weights for the SharpClaw polynomial reconstructions, and the +generator of ``src/pyclaw/sharpclaw/poly.f90``. + +PyClaw stores cell averages. A reconstruction computes, for each cell i, +the value ql(i) at its left face x_{i-1/2} and qr(i) at its right face +x_{i+1/2}, as a linear combination of the averages of nearby cells: + + ql(i) = sum_j w_j q(i+j), qr(i) = sum_j w_j q(i-j). + +The weights are found by interpolating the primitive function +Q(x) = int_{x_lo}^{x} q dx, whose values at the faces are exact sums of +cell averages, and differentiating the interpolant at the face. They are +exact rationals; ``poly.f90`` stores each set as integers over a common +denominator. + +lim_type selects the cells (offsets j) used for ql(i); with k = N/2 and +N = reconstruction_order in {4, 6, 8, 10}: + + 4 'poly' j = -k .. k-1 (N cells centred on the face) + 5 'poly_dissipative' j = -k+1 .. k (central stencil shifted + one cell upwind) + 6 'poly_cell_centered' j = -k .. k (N+1 cells centred on the cell) + +Usage:: + + python poly_weights.py --table # print the weights as Markdown tables + python poly_weights.py --check # exit 1 if poly.f90 is out of date + python poly_weights.py --write # regenerate poly.f90 +""" +import argparse +import os +import sys +from fractions import Fraction +from math import lcm + +import numpy as np + +LIM_TYPES = {4: 'poly', 5: 'poly_dissipative', 6: 'poly_cell_centered'} +ORDERS = (4, 6, 8, 10) +DESCRIPTIONS = {4: 'central, non-dissipative', + 5: 'upwind-biased, dissipative', + 6: 'cell-centred, dissipative'} + +HERE = os.path.dirname(os.path.abspath(__file__)) +POLY_F90 = os.path.join(HERE, '..', '..', 'src', 'pyclaw', 'sharpclaw', 'poly.f90') + + +# ---------------------------------------------------------------------------- +# Weights +# ---------------------------------------------------------------------------- +def formal_order(lim_type, order): + """Formal order of accuracy of the interface values.""" + return order + 1 if lim_type == 6 else order + + +def stencil(lim_type, order): + """Offsets j of the cells used for ql(i) (value at x_{i-1/2}).""" + if lim_type not in LIM_TYPES or order not in ORDERS: + raise ValueError('lim_type must be in %s and order in %s' + % (tuple(LIM_TYPES), ORDERS)) + k = order // 2 + return {4: range(-k, k), 5: range(-k + 1, k + 1), 6: range(-k, k + 1)}[lim_type] + + +def face_weights(cells, x): + """Weights giving the value at x from the averages of the unit cells + centred at the integers in ``cells`` (cell j spans [j-1/2, j+1/2]). + + Interpolates the primitive function at the cell faces with a Lagrange + polynomial and returns the weights of its derivative at x. + """ + cells = list(cells) + x = Fraction(x) + faces = [Fraction(2*cells[0] - 1, 2) + m for m in range(len(cells) + 1)] + # d/dx of the Lagrange basis polynomial of each face, at x + dbasis = [] + for k, fk in enumerate(faces): + total = Fraction(0) + for a, fa in enumerate(faces): + if a == k: + continue + term = Fraction(1) / (fk - fa) + for b, fb in enumerate(faces): + if b not in (a, k): + term *= (x - fb) / (fk - fb) + total += term + dbasis.append(total) + # Q(face_k) = sum of the averages of the cells left of face k + return [sum(dbasis[k] for k in range(len(faces)) if k > j) + for j in range(len(cells))] + + +def point_weights(cells, x): + """Lagrange weights that treat q as point values at the cell centres + (the incorrect, second-order form used before; for comparison only).""" + cells = list(cells) + x = Fraction(x) + weights = [] + for j in cells: + w = Fraction(1) + for m in cells: + if m != j: + w *= (x - m) / Fraction(j - m) + weights.append(w) + return weights + + +def ql_weights(lim_type, order): + """(offsets, exact weights) for ql(i).""" + cells = list(stencil(lim_type, order)) + return cells, face_weights(cells, Fraction(-1, 2)) + + +def integer_weights(lim_type, order): + """(lo, hi, numerators, denominator) for ql(i).""" + cells, w = ql_weights(lim_type, order) + denominator = lcm(*[v.denominator for v in w]) + numerators = [int(v * denominator) for v in w] + return cells[0], cells[-1], numerators, denominator + + +# ---------------------------------------------------------------------------- +# Numerical helpers (used by the example, the tests and the figures) +# ---------------------------------------------------------------------------- +def reconstruct(q, lim_type, order): + """ql, qr for a periodic array of cell averages q (any length).""" + cells, w = ql_weights(lim_type, order) + w = [float(v) for v in w] + ql = sum(wj * np.roll(q, -j) for j, wj in zip(cells, w)) + qr = sum(wj * np.roll(q, j) for j, wj in zip(cells, w)) + return ql, qr + + +def fourier_symbol(lim_type, order, theta): + r"""Semi-discrete symbol lambda(theta) for u_t + u_x = 0 with h = 1 and + the upwind flux, so that dq/dt = lambda q for q = exp(i j theta). + The exact value is -i theta; Re(lambda) < 0 is dissipation.""" + cells, w = ql_weights(lim_type, order) + theta = np.asarray(theta, dtype=float) + # left state at face i+1/2 is qr(i) = sum_j w_j q(i-j) + qr = sum(float(wj) * np.exp(-1j * j * theta) for j, wj in zip(cells, w)) + return -(1 - np.exp(-1j * theta)) * qr + + +# ---------------------------------------------------------------------------- +# Fortran generator +# ---------------------------------------------------------------------------- +HEADER = """\ +! =================================================================== +module poly +! =================================================================== +! Linear (unlimited) polynomial reconstruction of interface values +! from cell averages, for the SharpClaw finite-volume solvers. +! +! q(:,i) holds the average of q over cell i. For each cell, ql(:,i) is +! the reconstructed value at the left face x_(i-1/2) and qr(:,i) the +! value at the right face x_(i+1/2). The weights are exact: they are +! obtained by differentiating the polynomial that interpolates the +! primitive function of q at the cell faces, so they are correct for +! cell averages (not point values). They are applied component-wise. +! +! lim_type selects the stencil; order = solver.reconstruction_order, +! an even number N in {4, 6, 8, 10}. With k = N/2: +! +! 4 ('poly') ql(i) uses cells i-k..i+k-1 (N cells, +! centred on the face). Order N. ql(i+1) +! equals qr(i), so the scheme adds no +! numerical dissipation. +! 5 ('poly_dissipative') ql(i) uses cells i-k+1..i+k (the central +! stencil shifted one cell upwind for each +! face state). Order N, dissipative. +! 6 ('poly_cell_centered') ql(i) uses cells i-k..i+k (N+1 cells, +! centred on the cell, polynomial degree N). +! Order N+1, dissipative; equal to WENO of +! order N+1 with its ideal linear weights. +! +! qr uses the mirror image of the ql stencil. Every variant reaches k +! cells from the cell being reconstructed, so num_ghost = k + 1. +! +! Coefficients generated with exact rational arithmetic. +! =================================================================== + + implicit none + +contains + + ! =================================================================== + subroutine poly_weights(lim_type, order, lo, hi, w, ok) + ! =================================================================== + ! Weights for the left-face value ql(i) = sum_j w(j) * q(i+j), + ! j = lo..hi. + + integer, intent(in) :: lim_type, order + integer, intent(out) :: lo, hi + double precision, intent(out) :: w(-5:5) + logical, intent(out) :: ok + + ok = .true. + lo = 0; hi = 0 + w = 0.d0 + + select case (lim_type) +""" + +FOOTER = """\ + case default + ok = .false. + end select + + end subroutine poly_weights + + ! =================================================================== + subroutine poly_recon(q,ql,qr,num_eqn,maxnx,num_ghost,lim_type,order) + ! =================================================================== + + integer, intent(in) :: num_eqn, maxnx, num_ghost, lim_type, order + double precision, intent(in) :: q(num_eqn,maxnx+2*num_ghost) + double precision, intent(out) :: ql(num_eqn,maxnx+2*num_ghost),qr(num_eqn,maxnx+2*num_ghost) + + double precision :: w(-5:5) + integer :: lo, hi, reach, i, j, mx2 + logical :: ok + + call poly_weights(lim_type, order, lo, hi, w, ok) + if (.not. ok) then + print *, 'ERROR: polynomial reconstruction needs lim_type 4, 5 or 6' + print *, ' and an even reconstruction_order between 4 and 10.' + stop + endif + + mx2 = size(q,2) + reach = max(-lo, hi) + if (reach >= num_ghost) then + print *, 'ERROR: polynomial reconstruction needs num_ghost >= ', reach+1 + stop + endif + + ql = 0.d0 + qr = 0.d0 + do i = 1+reach, mx2-reach + do j = lo, hi + ql(:,i) = ql(:,i) + w(j)*q(:,i+j) + ! qr mirrors the ql stencil about cell i + qr(:,i) = qr(:,i) + w(j)*q(:,i-j) + end do + end do + + end subroutine poly_recon + +end module poly +""" + + +def _fortran_array(numerators, denominator): + values = ['%d.d0' % v for v in numerators] + if len(values) <= 8: + return '[' + ', '.join(values) + '] / %d.d0' % denominator + half = len(values) // 2 + return ('[' + ', '.join(values[:half]) + ', &\n' + ' '*22 + + ', '.join(values[half:]) + '] / %d.d0' % denominator) + + +def fortran_source(): + """The full text of poly.f90.""" + cases = [] + for lim_type in LIM_TYPES: + orders = [] + for order in ORDERS: + lo, hi, numerators, denominator = integer_weights(lim_type, order) + orders.append(' case (%d)\n' + ' lo = %d; hi = %d\n' + ' w(%d:%d) = %s' + % (order, lo, hi, lo, hi, _fortran_array(numerators, denominator))) + cases.append(' case (%d) ! %s\n' + ' select case (order)\n' + '%s\n' + ' case default\n' + ' ok = .false.\n' + ' end select' + % (lim_type, DESCRIPTIONS[lim_type], '\n'.join(orders))) + return HEADER + '\n'.join(cases) + '\n' + FOOTER + + +def markdown_tables(): + """The weights of every scheme as Markdown tables.""" + out = [] + for lim_type, name in LIM_TYPES.items(): + out.append("**lim_type = %d, `'%s'`** (weights for ql(i), the value at " + "xi-1/2; qr(i) uses the mirror image)\n" % (lim_type, name)) + out.append('| N | order | cells | weights |') + out.append('|---|---|---|---|') + for order in ORDERS: + lo, hi, numerators, denominator = integer_weights(lim_type, order) + cells = 'i%+d … i%+d' % (lo, hi) if lo else 'i … i%+d' % hi + out.append('| %d | %d | %s | (%s) / %d |' + % (order, formal_order(lim_type, order), cells, + ', '.join(str(v) for v in numerators), denominator)) + out.append('') + return '\n'.join(out) + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__.split('\n\n')[0]) + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument('--write', action='store_true', help='regenerate poly.f90') + group.add_argument('--check', action='store_true', + help='exit with status 1 if poly.f90 is out of date') + group.add_argument('--table', action='store_true', help='print Markdown tables') + args = parser.parse_args(argv) + + if args.table: + print(markdown_tables()) + return 0 + if args.write: + with open(POLY_F90, 'w') as f: + f.write(fortran_source()) + print('Wrote %s' % os.path.normpath(POLY_F90)) + return 0 + with open(POLY_F90) as f: + current = f.read() + if current == fortran_source(): + print('poly.f90 is up to date.') + return 0 + print('poly.f90 differs from the generator; run with --write.') + return 1 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/development/poly_reconstruction/sine_reconstruction.py b/development/poly_reconstruction/sine_reconstruction.py new file mode 100644 index 000000000..45d8a8b20 --- /dev/null +++ b/development/poly_reconstruction/sine_reconstruction.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python +r""" +Toy example: reconstruct sin(2 pi x) on a periodic grid of N cells with +every polynomial reconstruction and order, and report the interface error +and the observed order of accuracy. + +The input is the exact cell averages of sin(2 pi x); the output is the +value at every cell face, compared with the exact sin(2 pi x_{i -/+ 1/2}). + +Usage:: + + python sine_reconstruction.py # N = 16, NumPy weights + python sine_reconstruction.py --cells 32 + python sine_reconstruction.py --fortran # also check the compiled + # Fortran (clawpack built) +""" +import argparse + +import numpy as np + +from poly_weights import LIM_TYPES, ORDERS, formal_order, reconstruct + + +def sine_averages(num_cells, kwave=1): + """Exact averages of sin(2 pi kwave x) over the cells of [0, 1].""" + h = 1. / num_cells + xc = (np.arange(num_cells) + 0.5) * h + w = 2 * np.pi * kwave + return (np.cos(w * (xc - h/2)) - np.cos(w * (xc + h/2))) / (w * h) + + +def interface_error(num_cells, lim_type, order): + """Max error of ql and qr against the exact face values.""" + h = 1. / num_cells + xc = (np.arange(num_cells) + 0.5) * h + ql, qr = reconstruct(sine_averages(num_cells), lim_type, order) + return max(np.abs(ql - np.sin(2*np.pi*(xc - h/2))).max(), + np.abs(qr - np.sin(2*np.pi*(xc + h/2))).max()) + + +def fortran_difference(num_cells, lim_type, order): + """Max difference between the NumPy and the compiled Fortran result.""" + from clawpack.pyclaw.sharpclaw import sharpclaw1 + ng = order // 2 + 1 + q = sine_averages(num_cells) + padded = np.concatenate([q[-ng:], q, q[:ng]])[None, :] + ql_f, qr_f = sharpclaw1.poly.poly_recon(padded, num_cells, ng, lim_type, order) + ql, qr = reconstruct(q, lim_type, order) + real = slice(ng, num_cells + ng) + return max(np.abs(ql_f[0, real] - ql).max(), np.abs(qr_f[0, real] - qr).max()) + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__.split('\n\n')[0]) + parser.add_argument('--cells', type=int, default=16, help='number of cells N (default 16)') + parser.add_argument('--fortran', action='store_true', + help='compare with the compiled Fortran reconstruction') + args = parser.parse_args(argv) + n = args.cells + + print('Interface error for sin(2 pi x) from %d cell averages; the observed order' + ' compares %d and %d cells.\n' % (n, n, 2*n)) + header = '%-20s %3s %7s %12s %9s' % ('lim_type', 'N', 'formal', 'max error', 'observed') + if args.fortran: + header += ' %14s' % '|NumPy-Fortran|' + print(header) + print('-' * len(header)) + for lim_type, name in LIM_TYPES.items(): + for order in ORDERS: + e1 = interface_error(n, lim_type, order) + e2 = interface_error(2*n, lim_type, order) + line = '%-20s %3d %7d %12.3e %9.2f' % ('%d %s' % (lim_type, name), order, + formal_order(lim_type, order), e1, + np.log2(e1 / e2)) + if args.fortran: + line += ' %14.1e' % fortran_difference(n, lim_type, order) + print(line) + print('\nObserved orders fall below the formal order once the error nears ' + 'round-off (about 1e-14); use fewer cells to see the high orders.') + + +if __name__ == '__main__': + main() diff --git a/development/poly_reconstruction/test_poly_reconstruction.py b/development/poly_reconstruction/test_poly_reconstruction.py new file mode 100644 index 000000000..0657cbaa8 --- /dev/null +++ b/development/poly_reconstruction/test_poly_reconstruction.py @@ -0,0 +1,71 @@ +""" +Toy tests for the polynomial reconstructions, using the exact weights from +poly_weights.py. Run from this directory with ``python -m pytest``. + +CI does not collect development/; the Fortran and the generator are +covered there by src/pyclaw/tests/test_sharpclaw_poly.py. +""" +from fractions import Fraction + +import numpy as np +import pytest + +import poly_weights as pw +from sine_reconstruction import interface_error + +SCHEMES = [(lim_type, order) for lim_type in pw.LIM_TYPES for order in pw.ORDERS] + + +@pytest.mark.parametrize("lim_type, order", SCHEMES) +def test_sine_order_of_accuracy(lim_type, order): + grids = (16, 32) if order < 8 else (8, 16) + coarse, fine = (interface_error(n, lim_type, order) for n in grids) + assert np.log2(coarse / fine) > pw.formal_order(lim_type, order) - 0.4 + + +@pytest.mark.parametrize("lim_type, order", SCHEMES) +def test_weights_are_consistent(lim_type, order): + """Weights sum to one and reproduce averages of polynomials of the + degree the stencil supports exactly.""" + cells, w = pw.ql_weights(lim_type, order) + assert sum(w) == 1 + degree = len(cells) - 1 + for p in range(degree + 1): + # average of x^p over cell j is ((j+1/2)^(p+1) - (j-1/2)^(p+1)) / (p+1) + averages = [(Fraction(2*j + 1, 2)**(p + 1) - Fraction(2*j - 1, 2)**(p + 1)) / (p + 1) + for j in cells] + assert sum(wj * a for wj, a in zip(w, averages)) == Fraction(-1, 2)**p + + +@pytest.mark.parametrize("order", pw.ORDERS) +def test_poly_is_non_dissipative(order): + theta = np.linspace(0, np.pi, 201) + assert np.abs(pw.fourier_symbol(4, order, theta).real).max() < 1e-13 + + +@pytest.mark.parametrize("lim_type", (5, 6)) +@pytest.mark.parametrize("order", pw.ORDERS) +def test_dissipative_variants_are_stable_and_dissipative(lim_type, order): + theta = np.linspace(1e-3, np.pi, 201) + real = pw.fourier_symbol(lim_type, order, theta).real + assert real.max() < 1e-13 + assert real[-1] < -0.5 + + +def test_point_value_weights_are_second_order(): + """The old weights treated averages as point values: second order.""" + cells = list(pw.stencil(6, 4)) + w = [float(v) for v in pw.point_weights(cells, Fraction(-1, 2))] + errors = [] + for n in (16, 32): + h = 1. / n + xc = (np.arange(n) + 0.5) * h + avg = (np.cos(2*np.pi*(xc - h/2)) - np.cos(2*np.pi*(xc + h/2))) / (2*np.pi*h) + ql = sum(wj * np.roll(avg, -j) for j, wj in zip(cells, w)) + errors.append(np.abs(ql - np.sin(2*np.pi*(xc - h/2))).max()) + assert abs(np.log2(errors[0] / errors[1]) - 2) < 0.1 + + +def test_generated_fortran_is_current(): + with open(pw.POLY_F90) as f: + assert f.read() == pw.fortran_source() From 2af7578b9d99567a6671f4cd850e28e7c4a58113 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 07:45:27 +0000 Subject: [PATCH 18/21] Warn that weno_order and poly_order may be deprecated Setting solver.weno_order or solver.poly_order, or passing weno_order= or poly_order= to an example setup() (through set_reconstruction), issues a FutureWarning saying future releases may deprecate them and pointing to reconstruction_order. The warning is attributed to the caller's line (the assignment or the setup() call). Reading the properties stays silent, and reconstruction_order never warns. Tests check the warnings, their location, that reconstruction_order and lim_type work as setup() keywords in every example without warnings, and move the generator fixture to module scope (pytest deprecates class-scoped fixtures defined as instance methods). The README presents reconstruction_order as the keyword to use. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01ThxEJQ7zPNDJ2WipNvNBpT --- development/poly_reconstruction/README.md | 26 ++++-- src/pyclaw/sharpclaw/solver.py | 41 +++++++-- src/pyclaw/tests/test_sharpclaw_poly.py | 102 +++++++++++++++++----- 3 files changed, 131 insertions(+), 38 deletions(-) diff --git a/development/poly_reconstruction/README.md b/development/poly_reconstruction/README.md index 1e84ce641..d049f92bc 100644 --- a/development/poly_reconstruction/README.md +++ b/development/poly_reconstruction/README.md @@ -14,7 +14,7 @@ directory. | 5 | `'poly_dissipative'` | N, shifted one cell upwind | N | strongest | | 6 | `'poly_cell_centered'` | N+1, centred on the cell | N+1 | moderate | -N is `solver.reconstruction_order` (or `solver.poly_order`), an even +N is `solver.reconstruction_order`, an even number in {4, 6, 8, 10}. ## 1. What a reconstruction computes @@ -187,15 +187,25 @@ What this means in practice: solver = pyclaw.SharpClawSolver1D(riemann_solver) solver.lim_type = 'poly' # or 4; 'poly_dissipative' (5), 'poly_cell_centered' (6) solver.reconstruction_order = 6 # even: 4, 6, 8 or 10 -# or, equivalently, select the polynomial family and its order at once: -solver.poly_order = 6 # sets lim_type to 4 unless it is already 4, 5 or 6 ``` -`solver.lim_type` also accepts `'godunov'` (-1), `'tvd'` (1), `'weno'` -(2) and `'weno5'` (3). `solver.weno_order` and `solver.poly_order` read -back the order of the active family (None otherwise). The example -`setup()` functions accept `weno_order=` or `poly_order=` as well as -`reconstruction_order=` and `lim_type=`. +The example `setup()` functions take the same two keywords: + +```python +claw = acoustics_1d.setup(solver_type='sharpclaw', lim_type='poly', reconstruction_order=6) +``` + +`reconstruction_order` is the keyword to use; it is the one that will +remain. `solver.lim_type` also accepts `'godunov'` (-1), `'tvd'` (1), +`'weno'` (2) and `'weno5'` (3). + +For compatibility, `weno_order` and `poly_order` still work, both as +solver attributes and as `setup()` keywords. Setting one sets +`reconstruction_order` and selects the matching family (`weno_order` +switches to `lim_type` 2, `poly_order` to 4, unless a type of that family +is already selected), and issues a `FutureWarning`: future releases may +deprecate them. Reading them is silent and returns the order of the +active family, or None. Limitations: diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index bbca0719e..674d6928b 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -5,6 +5,8 @@ # Created: 2010-03-20 # Author: David Ketcheson """ +import warnings + # Solver superclass from clawpack.pyclaw.solver import Solver from clawpack.pyclaw.util import add_parent_doc @@ -25,6 +27,12 @@ def default_tfluct(self): raise Exception("You set solver.tfluct_solver=True, but solver.tfluct has not been set.") pass +def _warn_order_keyword(name, stacklevel): + warnings.warn("%s is kept for compatibility, but future releases may deprecate it; " + "use reconstruction_order (with lim_type) instead." % name, + FutureWarning, stacklevel=stacklevel) + + class SharpClawSolver(Solver): r""" Superclass for all SharpClawND solvers. @@ -85,9 +93,11 @@ class SharpClawSolver(Solver): Order of the reconstruction: odd, from 5 to 17, for WENO (``lim_type`` 2); even, from 4 to 10, for the polynomial reconstructions (``lim_type`` 4, 5 and 6; see ``lim_type`` for - how N relates to the formal order). This is the canonical - attribute; ``weno_order`` and ``poly_order`` are views of it that - also select the kind of reconstruction. + how N relates to the formal order). This is the preferred way to + set the order; ``weno_order`` and ``poly_order`` are views of it + that also select the kind of reconstruction, and setting them + issues a ``FutureWarning`` because future releases may deprecate + them. ``Default = 5`` .. attribute:: weno_order @@ -95,14 +105,16 @@ class SharpClawSolver(Solver): The WENO order: ``reconstruction_order`` when ``lim_type`` is 2, 5 when it is 3 (fixed-order routine), otherwise None. Setting it sets ``reconstruction_order`` and switches ``lim_type`` to 2 unless - it is already 2 or 3. + it is already 2 or 3, and issues a ``FutureWarning`` (future + releases may deprecate it; prefer ``reconstruction_order``). .. attribute:: poly_order The polynomial reconstruction order: ``reconstruction_order`` when ``lim_type`` is 4, 5 or 6, otherwise None. Setting it (to 4, 6, 8 or 10) sets ``reconstruction_order`` and switches ``lim_type`` to 4 - unless it is already 4, 5 or 6. + unless it is already 4, 5 or 6, and issues a ``FutureWarning`` + (future releases may deprecate it; prefer ``reconstruction_order``). .. attribute:: time_integrator @@ -244,6 +256,10 @@ def weno_order(self): @weno_order.setter def weno_order(self, value): + _warn_order_keyword('weno_order', stacklevel=4) + self._set_weno_order(value) + + def _set_weno_order(self, value): if self.lim_type not in self._weno_lim_types: self.lim_type = 2 self.reconstruction_order = value @@ -256,6 +272,10 @@ def poly_order(self): @poly_order.setter def poly_order(self, value): + _warn_order_keyword('poly_order', stacklevel=4) + self._set_poly_order(value) + + def _set_poly_order(self, value): if value not in self._poly_orders: raise ValueError('poly_order must be one of %s, not %s.' % (self._poly_orders, value)) if self.lim_type not in self._poly_lim_types: @@ -271,7 +291,9 @@ def set_reconstruction(self, lim_type=None, reconstruction_order=None, ``weno_order`` selects WENO (``lim_type`` 2 unless ``lim_type`` is already 2 or 3) and ``poly_order`` selects a polynomial reconstruction (``lim_type`` 4 unless ``lim_type`` is already 4, 5 - or 6). Contradictory arguments raise ``ValueError``: both + or 6); both issue a ``FutureWarning``, since future releases may + deprecate them in favour of ``reconstruction_order``. Contradictory + arguments raise ``ValueError``: both ``weno_order`` and ``poly_order``, either of them together with a different ``reconstruction_order``, or either of them with an explicit ``lim_type`` of the other kind. @@ -291,10 +313,13 @@ def set_reconstruction(self, lim_type=None, reconstruction_order=None, raise ValueError('weno_order cannot be used with lim_type %s.' % self.lim_type) if poly_order is not None and self.lim_type not in self._poly_lim_types: raise ValueError('poly_order cannot be used with lim_type %s.' % self.lim_type) + # stacklevel 4 points the warning at the caller of the example's setup() if weno_order is not None: - self.weno_order = weno_order + _warn_order_keyword('weno_order', stacklevel=4) + self._set_weno_order(weno_order) elif poly_order is not None: - self.poly_order = poly_order + _warn_order_keyword('poly_order', stacklevel=4) + self._set_poly_order(poly_order) elif reconstruction_order is not None: self.reconstruction_order = reconstruction_order diff --git a/src/pyclaw/tests/test_sharpclaw_poly.py b/src/pyclaw/tests/test_sharpclaw_poly.py index a1e6db901..8502294a9 100644 --- a/src/pyclaw/tests/test_sharpclaw_poly.py +++ b/src/pyclaw/tests/test_sharpclaw_poly.py @@ -156,6 +156,7 @@ def test_name_and_integer_give_same_result(self, name, value): assert advect(20, name, order) == advect(20, value, order) +@pytest.mark.filterwarnings("ignore:.*may deprecate:FutureWarning") class TestOrderProperties: def make_solver(self, **kwargs): @@ -195,14 +196,7 @@ def test_poly_order_rejects_invalid_order(self, order): with pytest.raises(ValueError): self.make_solver().poly_order = order - def test_order_properties_do_not_warn(self): - with warnings.catch_warnings(): - warnings.simplefilter("error") - solver = self.make_solver(weno_order=7) - solver.poly_order = 4 - solver.weno_order, solver.poly_order - - +@pytest.mark.filterwarnings("ignore:.*may deprecate:FutureWarning") class TestSetReconstruction: def configure(self, **kwargs): @@ -236,8 +230,10 @@ def test_contradictions_raise(self, kwargs): self.configure(**kwargs) +@pytest.mark.filterwarnings("ignore:.*may deprecate:FutureWarning") class TestExampleSetup: - """Example setup() functions accept weno_order and poly_order.""" + """Example setup() functions accept reconstruction_order, and still + accept weno_order and poly_order.""" def solver(self, **kwargs): from examples.acoustics_1d_homogeneous import acoustics_1d @@ -284,6 +280,66 @@ def test_nonuniform_example_accepts_keywords(self): advection_1d_nonunif.setup(poly_order=4) +class TestFutureWarnings: + """weno_order and poly_order still work but warn that future releases + may deprecate them; reconstruction_order never warns.""" + + MATCH = "future releases may deprecate" + + def solver(self): + return pyclaw.SharpClawSolver1D(riemann.advection_1D) + + @pytest.mark.parametrize("name, value", [('weno_order', 7), ('poly_order', 6)]) + def test_setting_warns(self, name, value): + solver = self.solver() + with pytest.warns(FutureWarning, match=self.MATCH): + setattr(solver, name, value) + + @pytest.mark.parametrize("kwargs", [{'weno_order': 7}, {'poly_order': 6}]) + def test_set_reconstruction_warns(self, kwargs): + with pytest.warns(FutureWarning, match=self.MATCH): + self.solver().set_reconstruction(**kwargs) + + @pytest.mark.parametrize("kwargs", [{'weno_order': 7}, {'poly_order': 6}]) + def test_example_setup_warns_at_the_call(self, kwargs): + from examples.acoustics_1d_homogeneous import acoustics_1d + with pytest.warns(FutureWarning, match=self.MATCH) as record: + acoustics_1d.setup(solver_type='sharpclaw', disable_output=True, **kwargs) + assert len(record) == 1 + assert record[0].filename == __file__ + + def test_reading_and_reconstruction_order_do_not_warn(self): + with warnings.catch_warnings(): + warnings.simplefilter("error") + solver = self.solver() + solver.reconstruction_order = 7 + solver.set_reconstruction(lim_type='poly', reconstruction_order=6) + solver.weno_order, solver.poly_order + + @pytest.mark.parametrize("module, kwargs, order", [ + ('examples.acoustics_1d_homogeneous.acoustics_1d', {'disable_output': True}, 11), + ('examples.advection_1d.advection_1d', {'kernel_language': 'Fortran'}, 7), + ('examples.advection_1d_variable.variable_coefficient_advection', + {'kernel_language': 'Fortran'}, 9), + ('examples.cubic_1d.cubic', {'N': 50}, 7), + ]) + def test_examples_take_reconstruction_order(self, module, kwargs, order): + import importlib + setup = importlib.import_module(module).setup + with warnings.catch_warnings(): + warnings.simplefilter("error", FutureWarning) + claw = setup(solver_type='sharpclaw', reconstruction_order=order, **kwargs) + assert (claw.solver.lim_type, claw.solver.reconstruction_order) == (2, order) + claw = setup(solver_type='sharpclaw', lim_type='poly', reconstruction_order=6, **kwargs) + assert (claw.solver.lim_type, claw.solver.reconstruction_order) == (4, 6) + + def test_nonuniform_example_takes_reconstruction_order(self): + from examples.advection_1d import advection_1d_nonunif + with warnings.catch_warnings(): + warnings.simplefilter("error", FutureWarning) + advection_1d_nonunif.setup(reconstruction_order=5) + + def test_poly_2d(): """The 2D solver reuses the 1D reconstruction along each direction.""" solver = pyclaw.SharpClawSolver2D(riemann.advection_2D) @@ -320,23 +376,25 @@ def test_poly_2d(): assert np.abs(qf - q0).max() < 1e-4 +@pytest.fixture(scope='module') +def generator(): + """development/poly_reconstruction/poly_weights.py, loaded by path.""" + import importlib.util + import os + path = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'development', + 'poly_reconstruction', 'poly_weights.py') + if not os.path.exists(path): + pytest.skip('development/ is not available') + spec = importlib.util.spec_from_file_location('poly_weights', path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + class TestGenerator: """poly.f90 is generated by development/poly_reconstruction/poly_weights.py; these tests stop the two from drifting apart.""" - @pytest.fixture(scope='class') - def generator(self): - import importlib.util - import os - path = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'development', - 'poly_reconstruction', 'poly_weights.py') - if not os.path.exists(path): - pytest.skip('development/ is not available') - spec = importlib.util.spec_from_file_location('poly_weights', path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - def test_poly_f90_matches_generator(self, generator): with open(generator.POLY_F90) as f: assert f.read() == generator.fortran_source(), \ From 72154118a981332e51e6f84ca822a0acb8e9433b Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 08:02:25 +0000 Subject: [PATCH 19/21] Unwrap hard line breaks in the poly reconstruction README Each paragraph and list item is now a single line, so viewers that render single newlines as line breaks no longer break sentences in the middle. The text is unchanged. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01ThxEJQ7zPNDJ2WipNvNBpT --- development/poly_reconstruction/README.md | 120 +++++----------------- 1 file changed, 24 insertions(+), 96 deletions(-) diff --git a/development/poly_reconstruction/README.md b/development/poly_reconstruction/README.md index d049f92bc..dc7df5685 100644 --- a/development/poly_reconstruction/README.md +++ b/development/poly_reconstruction/README.md @@ -1,12 +1,6 @@ # Polynomial reconstructions in SharpClaw -SharpClaw offers three linear polynomial reconstructions alongside WENO, -selected with `solver.lim_type` (4, 5 or 6). They were written for -electromagnetic wave problems, where an option that adds no numerical -dissipation is valuable. This note explains what they compute, which -cells they use, how accurate they are and how they damp and disperse -waves. Everything shown here is regenerated by the scripts in this -directory. +SharpClaw offers three linear polynomial reconstructions alongside WENO, selected with `solver.lim_type` (4, 5 or 6). They were written for electromagnetic wave problems, where an option that adds no numerical dissipation is valuable. This note explains what they compute, which cells they use, how accurate they are and how they damp and disperse waves. Everything shown here is regenerated by the scripts in this directory. | `lim_type` | name | cells per face value | formal order | numerical dissipation | |---|---|---|---|---| @@ -14,47 +8,29 @@ directory. | 5 | `'poly_dissipative'` | N, shifted one cell upwind | N | strongest | | 6 | `'poly_cell_centered'` | N+1, centred on the cell | N+1 | moderate | -N is `solver.reconstruction_order`, an even -number in {4, 6, 8, 10}. +N is `solver.reconstruction_order`, an even number in {4, 6, 8, 10}. ## 1. What a reconstruction computes -PyClaw is a finite-volume code: `q[i]` holds the **average** of the -solution over cell $i$, +PyClaw is a finite-volume code: `q[i]` holds the **average** of the solution over cell $i$, $$\bar q_i = \frac{1}{h}\int_{x_{i-1/2}}^{x_{i+1/2}} q(x)\,dx .$$ -At every step SharpClaw needs the solution **at the faces**. For each -cell the reconstruction returns two values: `ql(i)`, the value at its left -face $x_{i-1/2}$, and `qr(i)`, the value at its right face $x_{i+1/2}$. -Face $x_{i+1/2}$ therefore receives two values, `qr(i)` from the left and -`ql(i+1)` from the right, and the Riemann solver resolves the jump between -them. That jump is where the upwinding, and so the numerical dissipation, -comes from. Nothing computes values at cell centres. +At every step SharpClaw needs the solution **at the faces**. For each cell the reconstruction returns two values: `ql(i)`, the value at its left face $x_{i-1/2}$, and `qr(i)`, the value at its right face $x_{i+1/2}$. Face $x_{i+1/2}$ therefore receives two values, `qr(i)` from the left and `ql(i+1)` from the right, and the Riemann solver resolves the jump between them. That jump is where the upwinding, and so the numerical dissipation, comes from. Nothing computes values at cell centres. -All three reconstructions are linear combinations of nearby averages with -fixed weights: +All three reconstructions are linear combinations of nearby averages with fixed weights: $$\texttt{ql}(i) = \sum_j w_j\,\bar q_{i+j}, \qquad \texttt{qr}(i) = \sum_j w_j\,\bar q_{i-j}.$$ ## 2. Why the averages must be treated as averages -An average is not the value at the cell centre. For smooth $q$, -$\bar q_i = q(x_i) + \tfrac{h^2}{24}q''(x_i) + O(h^4)$. Weights that -interpolate point values (Lagrange interpolation through the centres) -inherit that $O(h^2)$ error, whatever the stencil width. The first -version of `poly.f90` did exactly this, and every order converged at -second order: +An average is not the value at the cell centre. For smooth $q$, $\bar q_i = q(x_i) + \tfrac{h^2}{24}q''(x_i) + O(h^4)$. Weights that interpolate point values (Lagrange interpolation through the centres) inherit that $O(h^2)$ error, whatever the stencil width. The first version of `poly.f90` did exactly this, and every order converged at second order: ![Cell averages vs centre values, and old vs new weights](figures/averages_vs_points.png) ## 3. How the weights are built -The primitive function $Q(x)=\int_{x_0}^{x} q\,dx$ is known **exactly** -at every face: it is a running sum of $h\,\bar q$. Interpolate $Q$ at the -faces of the stencil with a polynomial $P$, and the face value is the -slope $P'$ at that face. The weights do not depend on $q$, so this is -done once, in exact rational arithmetic, by `poly_weights.py`: +The primitive function $Q(x)=\int_{x_0}^{x} q\,dx$ is known **exactly** at every face: it is a running sum of $h\,\bar q$. Interpolate $Q$ at the faces of the stencil with a polynomial $P$, and the face value is the slope $P'$ at that face. The weights do not depend on $q$, so this is done once, in exact rational arithmetic, by `poly_weights.py`: ![Primitive-function construction](figures/primitive_construction.png) @@ -70,22 +46,15 @@ With $k = N/2$, the cells used for `ql(i)` are ![Stencils for N = 4 and N = 6](figures/stencils.png) -The difference that matters is what the two sides of one face see. With -`'poly'`, `qr(i)` and `ql(i+1)` use the same cells with the same weights, -so they are the same number: the Riemann solver sees no jump and adds no -dissipation. The other two types reconstruct each side from a different -stencil, so the states differ by $O(h^{N})$ (or $O(h^{N+1})$) and the -jump is damped. +The difference that matters is what the two sides of one face see. With `'poly'`, `qr(i)` and `ql(i+1)` use the same cells with the same weights, so they are the same number: the Riemann solver sees no jump and adds no dissipation. The other two types reconstruct each side from a different stencil, so the states differ by $O(h^{N})$ (or $O(h^{N+1})$) and the jump is damped. ![The two states at one face](figures/face_states.png) -Every type reaches $k$ cells from the cell being reconstructed, so -SharpClaw sets `num_ghost = N/2 + 1` automatically. +Every type reaches $k$ cells from the cell being reconstructed, so SharpClaw sets `num_ghost = N/2 + 1` automatically. ### Weights -Generated by `python poly_weights.py --table`; `qr(i)` uses the mirror -image ($w_j$ applied to $\bar q_{i-j}$). +Generated by `python poly_weights.py --table`; `qr(i)` uses the mirror image ($w_j$ applied to $\bar q_{i-j}$). **lim_type = 4, `'poly'`** @@ -114,17 +83,11 @@ image ($w_j$ applied to $\bar q_{i-j}$). | 8 | 9 | i-4 … i+4 | (-5, 55, -305, 1375, 1879, -641, 199, -41, 4) / 2520 | | 10 | 11 | i-5 … i+5 | (12, -153, 947, -4003, 15797, 20417, -7303, 2597, -703, 122, -10) / 27720 | -Two checks on these numbers: `'poly'` with N = 4 is the classical -fourth-order face value (-1, 7, 7, -1)/12, and `'poly_cell_centered'` -with N = 4 gives exactly the ideal (linear) weights of WENO5. +Two checks on these numbers: `'poly'` with N = 4 is the classical fourth-order face value (-1, 7, 7, -1)/12, and `'poly_cell_centered'` with N = 4 gives exactly the ideal (linear) weights of WENO5. ## 5. Accuracy -`sine_reconstruction.py` reconstructs $\sin 2\pi x$ from its exact cell -averages and compares the face values with the exact ones. At a coarse -4 cells per wavelength the difference between the types is visible: all -three are close to the curve, but only `'poly'` gives each face a single -value. +`sine_reconstruction.py` reconstructs $\sin 2\pi x$ from its exact cell averages and compares the face values with the exact ones. At a coarse 4 cells per wavelength the difference between the types is visible: all three are close to the curve, but only `'poly'` gives each face a single value. ![Face values of sin 4πx from 8 cells](figures/sine_reconstruction.png) @@ -140,23 +103,15 @@ Measured with 16 → 32 cells (`python sine_reconstruction.py`): | `'poly_dissipative'` | 3.98 | 5.97 | 7.95 | 9.94 | | `'poly_cell_centered'` | 4.96 | 6.94 | 8.93 | 10.92 | -In the full solver the observed order is also limited by the time -integrator: the default `'SSP104'` is fourth order, so for N > 4 the time -error dominates unless the CFL number is reduced. +In the full solver the observed order is also limited by the time integrator: the default `'SSP104'` is fourth order, so for N > 4 the time error dominates unless the CFL number is reduced. ## 6. Dissipation and dispersion -For the model problem $u_t + u_x = 0$ with the upwind flux, the -semi-discrete scheme evolves a Fourier mode $e^{ij\theta}$ ($\theta = kh$) -as $e^{\lambda t}$; the exact value is $\lambda = -i\theta/h$. -$\mathrm{Re}\,\lambda$ is the damping rate and $-\mathrm{Im}\,\lambda$ -the modified wavenumber (below the diagonal, waves travel too slowly). +For the model problem $u_t + u_x = 0$ with the upwind flux, the semi-discrete scheme evolves a Fourier mode $e^{ij\theta}$ ($\theta = kh$) as $e^{\lambda t}$; the exact value is $\lambda = -i\theta/h$. $\mathrm{Re}\,\lambda$ is the damping rate and $-\mathrm{Im}\,\lambda$ the modified wavenumber (below the diagonal, waves travel too slowly). ![Damping rate and modified wavenumber](figures/fourier.png) -The same data at a few resolutions, as phase-speed ratio -$-\mathrm{Im}\,\lambda/\theta$ (1 is exact) and damping rate -$\mathrm{Re}\,\lambda$ (with $h = 1$): +The same data at a few resolutions, as phase-speed ratio $-\mathrm{Im}\,\lambda/\theta$ (1 is exact) and damping rate $\mathrm{Re}\,\lambda$ (with $h = 1$): | points per wavelength | `'poly'` N=4 | `'poly'` N=8 | `'poly_dissipative'` N=4 | `'poly_dissipative'` N=8 | `'poly_cell_centered'` N=4 | `'poly_cell_centered'` N=8 | |---|---|---|---|---|---|---| @@ -167,19 +122,9 @@ $\mathrm{Re}\,\lambda$ (with $h = 1$): What this means in practice: -- **`'poly'` does not damp anything.** $\mathrm{Re}\,\lambda = 0$ for - every wavenumber, so for linear, constant-coefficient problems the - semi-discrete scheme conserves the discrete $L^2$ energy; only the time - integrator dissipates (slightly). The flip side is that unresolved - modes, including grid-scale ($\theta = \pi$) noise, are never damped. - Waves lag (phase speed below 1). -- **`'poly_dissipative'` damps most** and its waves run slightly fast - (phase speed above 1). -- **`'poly_cell_centered'` damps less** and its waves lag. It has an exact - relation to `'poly'`: the average of its two face states is exactly the - `'poly'` face value of order N+2. In other words, `'poly_cell_centered'` - of order N **is** `'poly'` of order N+2 plus an upwind correction - proportional to the jump, which is why their dispersion curves coincide. +- **`'poly'` does not damp anything.** $\mathrm{Re}\,\lambda = 0$ for every wavenumber, so for linear, constant-coefficient problems the semi-discrete scheme conserves the discrete $L^2$ energy; only the time integrator dissipates (slightly). The flip side is that unresolved modes, including grid-scale ($\theta = \pi$) noise, are never damped. Waves lag (phase speed below 1). +- **`'poly_dissipative'` damps most** and its waves run slightly fast (phase speed above 1). +- **`'poly_cell_centered'` damps less** and its waves lag. It has an exact relation to `'poly'`: the average of its two face states is exactly the `'poly'` face value of order N+2. In other words, `'poly_cell_centered'` of order N **is** `'poly'` of order N+2 plus an upwind correction proportional to the jump, which is why their dispersion curves coincide. ## 7. Using them @@ -195,29 +140,15 @@ The example `setup()` functions take the same two keywords: claw = acoustics_1d.setup(solver_type='sharpclaw', lim_type='poly', reconstruction_order=6) ``` -`reconstruction_order` is the keyword to use; it is the one that will -remain. `solver.lim_type` also accepts `'godunov'` (-1), `'tvd'` (1), -`'weno'` (2) and `'weno5'` (3). +`reconstruction_order` is the keyword to use; it is the one that will remain. `solver.lim_type` also accepts `'godunov'` (-1), `'tvd'` (1), `'weno'` (2) and `'weno5'` (3). -For compatibility, `weno_order` and `poly_order` still work, both as -solver attributes and as `setup()` keywords. Setting one sets -`reconstruction_order` and selects the matching family (`weno_order` -switches to `lim_type` 2, `poly_order` to 4, unless a type of that family -is already selected), and issues a `FutureWarning`: future releases may -deprecate them. Reading them is silent and returns the order of the -active family, or None. +For compatibility, `weno_order` and `poly_order` still work, both as solver attributes and as `setup()` keywords. Setting one sets `reconstruction_order` and selects the matching family (`weno_order` switches to `lim_type` 2, `poly_order` to 4, unless a type of that family is already selected), and issues a `FutureWarning`: future releases may deprecate them. Reading them is silent and returns the order of the active family, or None. Limitations: -- **No limiting.** At discontinuities the reconstructions produce Gibbs - oscillations; they are meant for smooth solutions. -- **Fortran kernels only**, applied component-wise (`char_decomp` is - ignored). They work in 1D, 2D and 3D, since the 2D and 3D solvers apply - the 1D reconstruction along each direction. -- **Energy conservation** of `'poly'` is shown here only for linear, - constant-coefficient problems; with variable coefficients (e.g. - material interfaces stored in `aux`) it is not guaranteed and should be - checked for the problem at hand. +- **No limiting.** At discontinuities the reconstructions produce Gibbs oscillations; they are meant for smooth solutions. +- **Fortran kernels only**, applied component-wise (`char_decomp` is ignored). They work in 1D, 2D and 3D, since the 2D and 3D solvers apply the 1D reconstruction along each direction. +- **Energy conservation** of `'poly'` is shown here only for linear, constant-coefficient problems; with variable coefficients (e.g. material interfaces stored in `aux`) it is not guaranteed and should be checked for the problem at hand. ## 8. Files and how to regenerate everything @@ -237,7 +168,4 @@ python -m pytest # toy tests python make_figures.py # rebuild the figures (run by hand) ``` -CI does not run anything in `development/`, but -`src/pyclaw/tests/test_sharpclaw_poly.py` checks that `poly.f90` matches -this generator and that the compiled weights equal the exact ones, so the -two cannot drift apart unnoticed. +CI does not run anything in `development/`, but `src/pyclaw/tests/test_sharpclaw_poly.py` checks that `poly.f90` matches this generator and that the compiled weights equal the exact ones, so the two cannot drift apart unnoticed. From 45d1b51c49b1384caf3ebd3289722d41de323234 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 14 Oct 2026 09:08:30 +0000 Subject: [PATCH 20/21] Address code review: keep weno_order inert on TVD, validate in setup() - weno_order no longer switches a TVD or Godunov solver to WENO; it only moves a polynomial reconstruction back to lim_type 2, as it was inert on non-WENO solvers before. Its getter again reads 5 on those solvers. - setup() validates lim_type, the WENO order (odd, 5-17) and the kernel support for lim_type -1/0 in Python, raising ValueError or NotImplementedError. These cases used to reach a Fortran 'stop', which ends the process with exit status 0 and no exception. - set_reconstruction() takes a stacklevel argument so a direct call attributes its FutureWarning to the caller; the examples pass 4. - poly.f90 (via its generator) uses 'error stop' for its own checks. - examples/euler_1d/Makefile lists poly.f90, which reconstruct.f90 uses. - cubic.py keeps upstream's positional order (order, N); lim_type is appended after N. - Regression tests for each item; 115 tests in test_sharpclaw_poly.py. Co-Authored-By: Claude Fable 5.1 Claude-Session: https://claude.ai/code/session_01ThxEJQ7zPNDJ2WipNvNBpT --- development/poly_reconstruction/README.md | 2 +- .../poly_reconstruction/poly_weights.py | 4 +- .../acoustics_1d_homogeneous/acoustics_1d.py | 2 +- examples/advection_1d/advection_1d.py | 2 +- .../variable_coefficient_advection.py | 2 +- examples/cubic_1d/cubic.py | 6 +- examples/euler_1d/Makefile | 2 +- src/pyclaw/sharpclaw/poly.f90 | 4 +- src/pyclaw/sharpclaw/solver.py | 66 +++++++++++----- src/pyclaw/tests/test_sharpclaw_poly.py | 77 ++++++++++++++++++- 10 files changed, 132 insertions(+), 35 deletions(-) diff --git a/development/poly_reconstruction/README.md b/development/poly_reconstruction/README.md index dc7df5685..621b7eb9f 100644 --- a/development/poly_reconstruction/README.md +++ b/development/poly_reconstruction/README.md @@ -142,7 +142,7 @@ claw = acoustics_1d.setup(solver_type='sharpclaw', lim_type='poly', reconstructi `reconstruction_order` is the keyword to use; it is the one that will remain. `solver.lim_type` also accepts `'godunov'` (-1), `'tvd'` (1), `'weno'` (2) and `'weno5'` (3). -For compatibility, `weno_order` and `poly_order` still work, both as solver attributes and as `setup()` keywords. Setting one sets `reconstruction_order` and selects the matching family (`weno_order` switches to `lim_type` 2, `poly_order` to 4, unless a type of that family is already selected), and issues a `FutureWarning`: future releases may deprecate them. Reading them is silent and returns the order of the active family, or None. +For compatibility, `weno_order` and `poly_order` still work, both as solver attributes and as `setup()` keywords. Setting one sets `reconstruction_order` and issues a `FutureWarning`: future releases may deprecate them. `poly_order` switches to `lim_type` 4 unless a polynomial type is already selected; `weno_order` switches a polynomial type to `lim_type` 2 and, as it always did, leaves TVD and Godunov solvers alone. Reading them is silent: `poly_order` returns the order of a polynomial type, or None; `weno_order` returns the order for every other type, or None for a polynomial one. Limitations: diff --git a/development/poly_reconstruction/poly_weights.py b/development/poly_reconstruction/poly_weights.py index 33f4f821e..a935cd5b5 100644 --- a/development/poly_reconstruction/poly_weights.py +++ b/development/poly_reconstruction/poly_weights.py @@ -227,14 +227,14 @@ def fourier_symbol(lim_type, order, theta): if (.not. ok) then print *, 'ERROR: polynomial reconstruction needs lim_type 4, 5 or 6' print *, ' and an even reconstruction_order between 4 and 10.' - stop + error stop endif mx2 = size(q,2) reach = max(-lo, hi) if (reach >= num_ghost) then print *, 'ERROR: polynomial reconstruction needs num_ghost >= ', reach+1 - stop + error stop endif ql = 0.d0 diff --git a/examples/acoustics_1d_homogeneous/acoustics_1d.py b/examples/acoustics_1d_homogeneous/acoustics_1d.py index c59123850..8c8d8df15 100644 --- a/examples/acoustics_1d_homogeneous/acoustics_1d.py +++ b/examples/acoustics_1d_homogeneous/acoustics_1d.py @@ -47,7 +47,7 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', elif solver_type == 'sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, - weno_order=weno_order, poly_order=poly_order) + weno_order=weno_order, poly_order=poly_order, stacklevel=4) solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 4 diff --git a/examples/advection_1d/advection_1d.py b/examples/advection_1d/advection_1d.py index e87959313..68dc3298b 100755 --- a/examples/advection_1d/advection_1d.py +++ b/examples/advection_1d/advection_1d.py @@ -38,7 +38,7 @@ def setup(nx=400, kernel_language='Python', use_petsc=False, solver_type='classi elif solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, - weno_order=weno_order, poly_order=poly_order) + weno_order=weno_order, poly_order=poly_order, stacklevel=4) solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 5 diff --git a/examples/advection_1d_variable/variable_coefficient_advection.py b/examples/advection_1d_variable/variable_coefficient_advection.py index 0b301ebe0..f357a0957 100755 --- a/examples/advection_1d_variable/variable_coefficient_advection.py +++ b/examples/advection_1d_variable/variable_coefficient_advection.py @@ -71,7 +71,7 @@ def setup(use_petsc=False,solver_type='classic',kernel_language='Python',outdir= elif kernel_language=='Python': solver = pyclaw.SharpClawSolver1D(riemann.vc_advection_1D_py.vc_advection_1D) solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, - weno_order=weno_order, poly_order=poly_order) + weno_order=weno_order, poly_order=poly_order, stacklevel=4) solver.time_integrator = time_integrator if time_integrator == 'RK': solver.a, solver.b, solver.c = rk_coeffs diff --git a/examples/cubic_1d/cubic.py b/examples/cubic_1d/cubic.py index 7dc4190f9..a5783b57f 100755 --- a/examples/cubic_1d/cubic.py +++ b/examples/cubic_1d/cubic.py @@ -16,8 +16,8 @@ import numpy as np from clawpack import riemann -def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction_order=None, lim_type=None, N=1000, - weno_order=None, poly_order=None): +def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction_order=None, N=1000, + lim_type=None, weno_order=None, poly_order=None): if use_petsc: import clawpack.petclaw as pyclaw @@ -29,7 +29,7 @@ def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction if solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, - weno_order=weno_order, poly_order=poly_order) + weno_order=weno_order, poly_order=poly_order, stacklevel=4) else: solver = pyclaw.ClawSolver1D(riemann_solver) solver.limiters = pyclaw.limiters.tvd.vanleer diff --git a/examples/euler_1d/Makefile b/examples/euler_1d/Makefile index a4ad15d54..40e42faec 100644 --- a/examples/euler_1d/Makefile +++ b/examples/euler_1d/Makefile @@ -2,7 +2,7 @@ F2PY = f2py SHARPCLAW = ../../src/pyclaw/sharpclaw -ONE_D_SHARPCLAW_SOURCES = evec.f90 $(SHARPCLAW)/flux1.f90 $(SHARPCLAW)/ClawParams.f90 $(SHARPCLAW)/workspace.f90 $(SHARPCLAW)/weno.f90 $(SHARPCLAW)/reconstruct.f90 +ONE_D_SHARPCLAW_SOURCES = evec.f90 $(SHARPCLAW)/flux1.f90 $(SHARPCLAW)/ClawParams.f90 $(SHARPCLAW)/workspace.f90 $(SHARPCLAW)/weno.f90 $(SHARPCLAW)/poly.f90 $(SHARPCLAW)/reconstruct.f90 all: make sharpclaw1.so diff --git a/src/pyclaw/sharpclaw/poly.f90 b/src/pyclaw/sharpclaw/poly.f90 index 61dcdfcfe..7784074c1 100644 --- a/src/pyclaw/sharpclaw/poly.f90 +++ b/src/pyclaw/sharpclaw/poly.f90 @@ -129,14 +129,14 @@ subroutine poly_recon(q,ql,qr,num_eqn,maxnx,num_ghost,lim_type,order) if (.not. ok) then print *, 'ERROR: polynomial reconstruction needs lim_type 4, 5 or 6' print *, ' and an even reconstruction_order between 4 and 10.' - stop + error stop endif mx2 = size(q,2) reach = max(-lo, hi) if (reach >= num_ghost) then print *, 'ERROR: polynomial reconstruction needs num_ghost >= ', reach+1 - stop + error stop endif ql = 0.d0 diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index 674d6928b..7fc74d28f 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -102,11 +102,13 @@ class SharpClawSolver(Solver): .. attribute:: weno_order - The WENO order: ``reconstruction_order`` when ``lim_type`` is 2, - 5 when it is 3 (fixed-order routine), otherwise None. Setting it - sets ``reconstruction_order`` and switches ``lim_type`` to 2 unless - it is already 2 or 3, and issues a ``FutureWarning`` (future - releases may deprecate it; prefer ``reconstruction_order``). + The WENO order: ``reconstruction_order``, except 5 when + ``lim_type`` is 3 (fixed-order routine) and None for the + polynomial reconstructions (``lim_type`` 4, 5 and 6). Setting it + sets ``reconstruction_order``; it switches ``lim_type`` to 2 only + from a polynomial reconstruction, so that, as before, it leaves + TVD and Godunov solvers alone. It issues a ``FutureWarning`` + (future releases may deprecate it; prefer ``reconstruction_order``). .. attribute:: poly_order @@ -245,14 +247,17 @@ def lim_type(self, value): self._lim_type = value _weno_lim_types = (2, 3) + _weno_orders = tuple(range(5, 18, 2)) + # lim_types the Fortran kernels (flux1.f90) implement + _fortran_lim_types = (1, 2, 3, 4, 5, 6) @property def weno_order(self): - if self.lim_type == 2: - return self.reconstruction_order if self.lim_type == 3: return 5 # the original WENO5 routine has a fixed order - return None + if self.lim_type in self._poly_lim_types: + return None + return self.reconstruction_order @weno_order.setter def weno_order(self, value): @@ -260,7 +265,9 @@ def weno_order(self, value): self._set_weno_order(value) def _set_weno_order(self, value): - if self.lim_type not in self._weno_lim_types: + # Before reconstruction_order existed, weno_order was stored but + # only used by WENO, so it must not change a TVD or Godunov solver. + if self.lim_type in self._poly_lim_types: self.lim_type = 2 self.reconstruction_order = value @@ -283,20 +290,24 @@ def _set_poly_order(self, value): self.reconstruction_order = value def set_reconstruction(self, lim_type=None, reconstruction_order=None, - weno_order=None, poly_order=None): + weno_order=None, poly_order=None, stacklevel=3): r""" Set the reconstruction from the arguments an example's ``setup()`` receives. Arguments left as None keep the solver's current value. - ``weno_order`` selects WENO (``lim_type`` 2 unless ``lim_type`` is - already 2 or 3) and ``poly_order`` selects a polynomial + ``weno_order`` sets the order and switches a polynomial + reconstruction to WENO (``lim_type`` 2), leaving any other + ``lim_type`` as it is; ``poly_order`` selects a polynomial reconstruction (``lim_type`` 4 unless ``lim_type`` is already 4, 5 - or 6); both issue a ``FutureWarning``, since future releases may - deprecate them in favour of ``reconstruction_order``. Contradictory - arguments raise ``ValueError``: both + or 6). Both issue a ``FutureWarning``, since future releases may + deprecate them in favour of ``reconstruction_order``; ``stacklevel`` + says which frame the warning is attributed to, 3 for the caller of + this method, 4 when an example's ``setup()`` calls it on behalf of + its own caller. Contradictory arguments raise ``ValueError``: both ``weno_order`` and ``poly_order``, either of them together with a - different ``reconstruction_order``, or either of them with an - explicit ``lim_type`` of the other kind. + different ``reconstruction_order``, ``weno_order`` with an explicit + polynomial ``lim_type``, or ``poly_order`` with an explicit + non-polynomial ``lim_type``. """ if weno_order is not None and poly_order is not None: raise ValueError('Pass either weno_order or poly_order, not both.') @@ -309,16 +320,15 @@ def set_reconstruction(self, lim_type=None, reconstruction_order=None, family_order)) if lim_type is not None: self.lim_type = lim_type - if weno_order is not None and self.lim_type not in self._weno_lim_types: + if weno_order is not None and self.lim_type in self._poly_lim_types: raise ValueError('weno_order cannot be used with lim_type %s.' % self.lim_type) if poly_order is not None and self.lim_type not in self._poly_lim_types: raise ValueError('poly_order cannot be used with lim_type %s.' % self.lim_type) - # stacklevel 4 points the warning at the caller of the example's setup() if weno_order is not None: - _warn_order_keyword('weno_order', stacklevel=4) + _warn_order_keyword('weno_order', stacklevel=stacklevel) self._set_weno_order(weno_order) elif poly_order is not None: - _warn_order_keyword('poly_order', stacklevel=4) + _warn_order_keyword('poly_order', stacklevel=stacklevel) self._set_poly_order(poly_order) elif reconstruction_order is not None: self.reconstruction_order = reconstruction_order @@ -376,7 +386,21 @@ def setup(self,solution): """ Allocate RK stage arrays or previous step solutions and fortran routine work arrays. """ + # Validate here: the Fortran kernels answer a bad lim_type or order + # with a bare 'stop', which ends the process without an exception. + if self.lim_type not in (-1, 0) + self._fortran_lim_types: + raise ValueError('lim_type must be between -1 and 6, or one of the names %s; got %s.' + % (', '.join(self._lim_type_names), self.lim_type)) + if self.lim_type == 0: + raise NotImplementedError('Unlimited reconstruction (lim_type 0) is not implemented.') + if self.kernel_language == 'Fortran' and self.lim_type not in self._fortran_lim_types: + raise NotImplementedError('lim_type %s is implemented in Python kernels only.' + % self.lim_type) + if self.lim_type == 2: + if self.reconstruction_order not in self._weno_orders: + raise ValueError('WENO (lim_type 2) needs an odd reconstruction_order from 5 to 17, ' + 'not %s.' % self.reconstruction_order) self.num_ghost = int((self.reconstruction_order+1)/2) if self.lim_type in self._poly_lim_types: diff --git a/src/pyclaw/tests/test_sharpclaw_poly.py b/src/pyclaw/tests/test_sharpclaw_poly.py index 8502294a9..86f864105 100644 --- a/src/pyclaw/tests/test_sharpclaw_poly.py +++ b/src/pyclaw/tests/test_sharpclaw_poly.py @@ -3,6 +3,7 @@ the lim_type names, the weno_order and poly_order properties and the example setup() keywords. """ +import inspect import warnings import numpy as np @@ -131,6 +132,56 @@ def test_rejects_python_kernels(self): advect(20, 'poly', 4, kernel_language='Python') +class TestSetupValidation: + """setup() rejects what the Fortran kernels would answer with a bare + 'stop', which ends the process with exit status 0 and no exception.""" + + def setup_solver(self, lim_type, order, kernel_language='Fortran'): + solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + solver.kernel_language = kernel_language + solver.lim_type = lim_type + solver.reconstruction_order = order + solver.bc_lower[0] = pyclaw.BC.periodic + solver.bc_upper[0] = pyclaw.BC.periodic + domain = pyclaw.Domain(pyclaw.Dimension(0., 1., 20, name='x')) + state = pyclaw.State(domain, 1) + state.problem_data['u'] = 1. + solver.setup(pyclaw.Solution(state, domain)) + return solver + + @pytest.mark.parametrize("order", (4, 6, 19)) + def test_weno_rejects_even_or_out_of_range_order(self, order): + with pytest.raises(ValueError): + self.setup_solver('weno', order) + + @pytest.mark.parametrize("order", (5, 17)) + def test_weno_accepts_odd_orders(self, order): + assert self.setup_solver('weno', order).num_ghost == (order + 1)//2 + + def test_godunov_is_python_only(self): + with pytest.raises(NotImplementedError): + self.setup_solver('godunov', 5) + assert self.setup_solver('godunov', 5, kernel_language='Python').lim_type == -1 + + def test_unlimited_is_not_implemented(self): + with pytest.raises(NotImplementedError): + self.setup_solver(0, 5, kernel_language='Python') + + @pytest.mark.parametrize("lim_type", (-2, 7, 1000)) + def test_unknown_lim_type(self, lim_type): + with pytest.raises(ValueError): + self.setup_solver(lim_type, 5) + + def test_even_order_without_lim_type_raises(self): + """The preferred keyword with an even order but the default (WENO) + lim_type is a likely slip; it must raise, not end the process.""" + from examples.acoustics_1d_homogeneous import acoustics_1d + claw = acoustics_1d.setup(solver_type='sharpclaw', reconstruction_order=6, + disable_output=True) + with pytest.raises(ValueError, match='odd reconstruction_order'): + claw.run() + + class TestLimTypeNames: @pytest.mark.parametrize("name, value", [ @@ -169,6 +220,9 @@ def test_weno_order_reads_by_lim_type(self): assert self.make_solver(reconstruction_order=9).weno_order == 9 assert self.make_solver(lim_type='weno5').weno_order == 5 assert self.make_solver(lim_type='poly', reconstruction_order=6).weno_order is None + # as before this branch, it reads 5 on solvers that do not use it + assert self.make_solver(lim_type='tvd').weno_order == 5 + assert self.make_solver(lim_type='godunov').weno_order == 5 def test_weno_order_selects_weno(self): solver = self.make_solver(lim_type='poly', reconstruction_order=6) @@ -178,6 +232,14 @@ def test_weno_order_selects_weno(self): solver.weno_order = 5 assert solver.lim_type == 3 + @pytest.mark.parametrize("lim_type", ('tvd', 'godunov')) + def test_weno_order_leaves_other_solvers_alone(self, lim_type): + """Scripts that set weno_order on a TVD solver must still get TVD.""" + solver = self.make_solver(lim_type=lim_type) + solver.weno_order = 7 + assert solver.lim_type == solver._lim_type_names[lim_type] + assert solver.reconstruction_order == 7 + def test_poly_order_reads_by_lim_type(self): assert self.make_solver().poly_order is None for lim_type in POLY: @@ -210,6 +272,7 @@ def configure(self, **kwargs): ({'weno_order': 11}, (2, 11)), ({'weno_order': 9, 'reconstruction_order': 9}, (2, 9)), ({'lim_type': 'weno5', 'weno_order': 5}, (3, 5)), + ({'lim_type': 'tvd', 'weno_order': 5}, (1, 5)), ({'poly_order': 6}, (4, 6)), ({'lim_type': 'poly_dissipative', 'poly_order': 8}, (5, 8)), ({'lim_type': 6, 'reconstruction_order': 4}, (6, 4)), @@ -279,6 +342,14 @@ def test_nonuniform_example_accepts_keywords(self): advection_1d_nonunif.setup(weno_order=5) advection_1d_nonunif.setup(poly_order=4) + def test_cubic_keeps_positional_order(self): + """Upstream's positional order (use_petsc, outdir, solver_type, + order, N) still holds; lim_type is appended after N.""" + from examples.cubic_1d import cubic + claw = cubic.setup(0, './_output', 'sharpclaw', 7, 50) + assert claw.solution.domain.grid.num_cells[0] == 50 + assert (claw.solver.lim_type, claw.solver.reconstruction_order) == (2, 7) + class TestFutureWarnings: """weno_order and poly_order still work but warn that future releases @@ -296,9 +367,11 @@ def test_setting_warns(self, name, value): setattr(solver, name, value) @pytest.mark.parametrize("kwargs", [{'weno_order': 7}, {'poly_order': 6}]) - def test_set_reconstruction_warns(self, kwargs): - with pytest.warns(FutureWarning, match=self.MATCH): + def test_set_reconstruction_warns_at_the_call(self, kwargs): + with pytest.warns(FutureWarning, match=self.MATCH) as record: + line = inspect.currentframe().f_lineno + 1 self.solver().set_reconstruction(**kwargs) + assert (record[0].filename, record[0].lineno) == (__file__, line) @pytest.mark.parametrize("kwargs", [{'weno_order': 7}, {'poly_order': 6}]) def test_example_setup_warns_at_the_call(self, kwargs): From 73f22d087df2be9eef9507fd078e3decc16e6681 Mon Sep 17 00:00:00 2001 From: David Ketcheson Date: Sun, 8 Nov 2026 16:19:39 +0300 Subject: [PATCH 21/21] Keep WENO and polynomial reconstruction orders independent --- development/poly_reconstruction/README.md | 14 +- .../poly_reconstruction/poly_weights.py | 2 +- .../acoustics_1d_homogeneous/acoustics_1d.py | 7 +- .../test_acoustics.py | 4 +- examples/advection_1d/advection_1d.py | 8 +- examples/advection_1d/advection_1d_nonunif.py | 4 +- examples/advection_1d/test_advection.py | 2 +- .../variable_coefficient_advection.py | 6 +- examples/cubic_1d/cubic.py | 6 +- examples/euler_2d/shock_bubble_interaction.py | 2 +- fvmbook/chap6/limiters.ipynb | 2 +- src/pyclaw/sharpclaw/poly.f90 | 2 +- src/pyclaw/sharpclaw/solver.py | 159 ++--------- src/pyclaw/tests/test_sharpclaw_poly.py | 252 ++---------------- 14 files changed, 69 insertions(+), 401 deletions(-) diff --git a/development/poly_reconstruction/README.md b/development/poly_reconstruction/README.md index 621b7eb9f..9bb5a64ad 100644 --- a/development/poly_reconstruction/README.md +++ b/development/poly_reconstruction/README.md @@ -8,7 +8,7 @@ SharpClaw offers three linear polynomial reconstructions alongside WENO, selecte | 5 | `'poly_dissipative'` | N, shifted one cell upwind | N | strongest | | 6 | `'poly_cell_centered'` | N+1, centred on the cell | N+1 | moderate | -N is `solver.reconstruction_order`, an even number in {4, 6, 8, 10}. +N is `solver.poly_order`, an even number in {4, 6, 8, 10}. ## 1. What a reconstruction computes @@ -131,18 +131,10 @@ What this means in practice: ```python solver = pyclaw.SharpClawSolver1D(riemann_solver) solver.lim_type = 'poly' # or 4; 'poly_dissipative' (5), 'poly_cell_centered' (6) -solver.reconstruction_order = 6 # even: 4, 6, 8 or 10 +solver.poly_order = 6 # even: 4, 6, 8 or 10 ``` -The example `setup()` functions take the same two keywords: - -```python -claw = acoustics_1d.setup(solver_type='sharpclaw', lim_type='poly', reconstruction_order=6) -``` - -`reconstruction_order` is the keyword to use; it is the one that will remain. `solver.lim_type` also accepts `'godunov'` (-1), `'tvd'` (1), `'weno'` (2) and `'weno5'` (3). - -For compatibility, `weno_order` and `poly_order` still work, both as solver attributes and as `setup()` keywords. Setting one sets `reconstruction_order` and issues a `FutureWarning`: future releases may deprecate them. `poly_order` switches to `lim_type` 4 unless a polynomial type is already selected; `weno_order` switches a polynomial type to `lim_type` 2 and, as it always did, leaves TVD and Godunov solvers alone. Reading them is silent: `poly_order` returns the order of a polynomial type, or None; `weno_order` returns the order for every other type, or None for a polynomial one. +The default `poly_order` is 4. Only `lim_type` selects the reconstruction. Each family keeps its own order when switching methods. Limitations: diff --git a/development/poly_reconstruction/poly_weights.py b/development/poly_reconstruction/poly_weights.py index a935cd5b5..c07e73192 100644 --- a/development/poly_reconstruction/poly_weights.py +++ b/development/poly_reconstruction/poly_weights.py @@ -161,7 +161,7 @@ def fourier_symbol(lim_type, order, theta): ! primitive function of q at the cell faces, so they are correct for ! cell averages (not point values). They are applied component-wise. ! -! lim_type selects the stencil; order = solver.reconstruction_order, +! lim_type selects the stencil; order = solver.poly_order, ! an even number N in {4, 6, 8, 10}. With k = N/2: ! ! 4 ('poly') ql(i) uses cells i-k..i+k-1 (N cells, diff --git a/examples/acoustics_1d_homogeneous/acoustics_1d.py b/examples/acoustics_1d_homogeneous/acoustics_1d.py index 8c8d8df15..07a720c11 100644 --- a/examples/acoustics_1d_homogeneous/acoustics_1d.py +++ b/examples/acoustics_1d_homogeneous/acoustics_1d.py @@ -23,9 +23,9 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', - outdir='./_output', ptwise=False, reconstruction_order=None, lim_type=None, + outdir='./_output', ptwise=False, weno_order=5, time_integrator='SSP104', disable_output=False, output_style=1, - num_cells=100, weno_order=None, poly_order=None): + num_cells=100): if use_petsc: import clawpack.petclaw as pyclaw @@ -46,8 +46,7 @@ def setup(use_petsc=False, kernel_language='Fortran', solver_type='classic', solver.limiters = pyclaw.limiters.tvd.MC elif solver_type == 'sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, - weno_order=weno_order, poly_order=poly_order, stacklevel=4) + solver.weno_order = weno_order solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 4 diff --git a/examples/acoustics_1d_homogeneous/test_acoustics.py b/examples/acoustics_1d_homogeneous/test_acoustics.py index fbd62b02a..dbd23b87e 100644 --- a/examples/acoustics_1d_homogeneous/test_acoustics.py +++ b/examples/acoustics_1d_homogeneous/test_acoustics.py @@ -30,7 +30,7 @@ def test_fortran_classic(self): def test_sharpclaw(self): assert abs(error(kernel_language='Fortran',solver_type='sharpclaw')-0.001540)<1e-5 - assert abs(error(kernel_language='Fortran',solver_type='sharpclaw',reconstruction_order=11)-0.000521)<1e-5 + assert abs(error(kernel_language='Fortran',solver_type='sharpclaw',weno_order=11)-0.000521)<1e-5 assert abs(error(kernel_language='Fortran',solver_type='sharpclaw',time_integrator='SSPLMMk3')-0.001545)<1e-5 class TestAccuracy: @@ -44,5 +44,5 @@ def test_fortran_classic(self): def test_sharpclaw(self): assert abs(error(num_cells=2000,kernel_language='Fortran',solver_type='sharpclaw'))<1e-8 - assert abs(error(num_cells=2000,kernel_language='Fortran',solver_type='sharpclaw',reconstruction_order=11))<1e-8 + assert abs(error(num_cells=2000,kernel_language='Fortran',solver_type='sharpclaw',weno_order=11))<1e-8 assert abs(error(num_cells=2000,kernel_language='Fortran',solver_type='sharpclaw',time_integrator='SSPLMMk3'))<2e-8 diff --git a/examples/advection_1d/advection_1d.py b/examples/advection_1d/advection_1d.py index 68dc3298b..71c5ca2d4 100755 --- a/examples/advection_1d/advection_1d.py +++ b/examples/advection_1d/advection_1d.py @@ -19,9 +19,8 @@ import numpy as np from clawpack import riemann -def setup(nx=400, kernel_language='Python', use_petsc=False, solver_type='classic', reconstruction_order=None, - time_integrator='SSP104', outdir='./_output', lim_type=None, - weno_order=None, poly_order=None): +def setup(nx=400, kernel_language='Python', use_petsc=False, solver_type='classic', weno_order=5, + time_integrator='SSP104', outdir='./_output'): if use_petsc: import clawpack.petclaw as pyclaw @@ -37,8 +36,7 @@ def setup(nx=400, kernel_language='Python', use_petsc=False, solver_type='classi solver = pyclaw.ClawSolver1D(riemann_solver) elif solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, - weno_order=weno_order, poly_order=poly_order, stacklevel=4) + solver.weno_order = weno_order solver.time_integrator = time_integrator if time_integrator == 'SSPLMMk3': solver.lmm_steps = 5 diff --git a/examples/advection_1d/advection_1d_nonunif.py b/examples/advection_1d/advection_1d_nonunif.py index 787c7d901..73ef760f6 100644 --- a/examples/advection_1d/advection_1d_nonunif.py +++ b/examples/advection_1d/advection_1d_nonunif.py @@ -31,8 +31,8 @@ def mapc2p_nonunif(xc): return xp -def setup(nx=100, kernel_language='Fortran', use_petsc=False, solver_type='classic', reconstruction_order=5, - time_integrator='SSP104', outdir='./_output', weno_order=None, poly_order=None): +def setup(nx=100, kernel_language='Fortran', use_petsc=False, solver_type='classic', weno_order=5, + time_integrator='SSP104', outdir='./_output'): if use_petsc: import clawpack.petclaw as pyclaw diff --git a/examples/advection_1d/test_advection.py b/examples/advection_1d/test_advection.py index 8c9c6b48a..e6e174090 100644 --- a/examples/advection_1d/test_advection.py +++ b/examples/advection_1d/test_advection.py @@ -40,5 +40,5 @@ def test_sharpclaw_multistep(self): assert abs(error(kernel_language='Fortran',solver_type='sharpclaw',time_integrator='SSPLMMk3')-1.500727e-05)<1e-4 def test_weno17(self): - assert abs(error(kernel_language='Fortran',solver_type='sharpclaw',reconstruction_order=17)-7.489618e-06)<1e-4 + assert abs(error(kernel_language='Fortran',solver_type='sharpclaw',weno_order=17)-7.489618e-06)<1e-4 diff --git a/examples/advection_1d_variable/variable_coefficient_advection.py b/examples/advection_1d_variable/variable_coefficient_advection.py index f357a0957..2ec3caadb 100755 --- a/examples/advection_1d_variable/variable_coefficient_advection.py +++ b/examples/advection_1d_variable/variable_coefficient_advection.py @@ -51,8 +51,7 @@ def auxinit(state): def setup(use_petsc=False,solver_type='classic',kernel_language='Python',outdir='./_output', - reconstruction_order=None,time_integrator='SSP104',rk_coeffs=None, - lim_type=None,weno_order=None,poly_order=None): + weno_order=5,time_integrator='SSP104',rk_coeffs=None): from clawpack import riemann if use_petsc: @@ -70,8 +69,7 @@ def setup(use_petsc=False,solver_type='classic',kernel_language='Python',outdir= solver = pyclaw.SharpClawSolver1D(riemann.advection_color_1D) elif kernel_language=='Python': solver = pyclaw.SharpClawSolver1D(riemann.vc_advection_1D_py.vc_advection_1D) - solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, - weno_order=weno_order, poly_order=poly_order, stacklevel=4) + solver.weno_order = weno_order solver.time_integrator = time_integrator if time_integrator == 'RK': solver.a, solver.b, solver.c = rk_coeffs diff --git a/examples/cubic_1d/cubic.py b/examples/cubic_1d/cubic.py index a5783b57f..4cba6a20e 100755 --- a/examples/cubic_1d/cubic.py +++ b/examples/cubic_1d/cubic.py @@ -16,8 +16,7 @@ import numpy as np from clawpack import riemann -def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction_order=None, N=1000, - lim_type=None, weno_order=None, poly_order=None): +def setup(use_petsc=0, outdir='./_output', solver_type='classic', weno_order=5, N=1000): if use_petsc: import clawpack.petclaw as pyclaw @@ -28,8 +27,7 @@ def setup(use_petsc=0, outdir='./_output', solver_type='classic', reconstruction if solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver1D(riemann_solver) - solver.set_reconstruction(lim_type=lim_type, reconstruction_order=reconstruction_order, - weno_order=weno_order, poly_order=poly_order, stacklevel=4) + solver.weno_order = weno_order else: solver = pyclaw.ClawSolver1D(riemann_solver) solver.limiters = pyclaw.limiters.tvd.vanleer diff --git a/examples/euler_2d/shock_bubble_interaction.py b/examples/euler_2d/shock_bubble_interaction.py index 07a28b7ce..49dc6342a 100755 --- a/examples/euler_2d/shock_bubble_interaction.py +++ b/examples/euler_2d/shock_bubble_interaction.py @@ -184,7 +184,7 @@ def setup(use_petsc=False,solver_type='classic', outdir='_output', kernel_langua if solver_type=='sharpclaw': solver = pyclaw.SharpClawSolver2D(riemann.euler_5wave_2D) solver.dq_src = dq_Euler_radial - solver.reconstruction_order = 5 + solver.weno_order = 5 solver.lim_type = 2 else: solver = pyclaw.ClawSolver2D(riemann.euler_5wave_2D) diff --git a/fvmbook/chap6/limiters.ipynb b/fvmbook/chap6/limiters.ipynb index 472c6f9b0..3832a9e74 100644 --- a/fvmbook/chap6/limiters.ipynb +++ b/fvmbook/chap6/limiters.ipynb @@ -55,7 +55,7 @@ " elif scheme == 'first-order':\n", " solver.order = 1\n", " elif 'weno' in scheme:\n", - " solver.reconstruction_order = int(scheme[4:]) #weno5, weno7, ...\n", + " solver.weno_order = int(scheme[4:]) #weno5, weno7, ...\n", " else:\n", " raise Exception('Unrecognized limiter')\n", "\n", diff --git a/src/pyclaw/sharpclaw/poly.f90 b/src/pyclaw/sharpclaw/poly.f90 index 7784074c1..72231682b 100644 --- a/src/pyclaw/sharpclaw/poly.f90 +++ b/src/pyclaw/sharpclaw/poly.f90 @@ -11,7 +11,7 @@ module poly ! primitive function of q at the cell faces, so they are correct for ! cell averages (not point values). They are applied component-wise. ! -! lim_type selects the stencil; order = solver.reconstruction_order, +! lim_type selects the stencil; order = solver.poly_order, ! an even number N in {4, 6, 8, 10}. With k = N/2: ! ! 4 ('poly') ql(i) uses cells i-k..i+k-1 (N cells, diff --git a/src/pyclaw/sharpclaw/solver.py b/src/pyclaw/sharpclaw/solver.py index 7fc74d28f..762bbefbc 100644 --- a/src/pyclaw/sharpclaw/solver.py +++ b/src/pyclaw/sharpclaw/solver.py @@ -5,7 +5,6 @@ # Created: 2010-03-20 # Author: David Ketcheson """ -import warnings # Solver superclass from clawpack.pyclaw.solver import Solver @@ -27,12 +26,6 @@ def default_tfluct(self): raise Exception("You set solver.tfluct_solver=True, but solver.tfluct has not been set.") pass -def _warn_order_keyword(name, stacklevel): - warnings.warn("%s is kept for compatibility, but future releases may deprecate it; " - "use reconstruction_order (with lim_type) instead." % name, - FutureWarning, stacklevel=stacklevel) - - class SharpClawSolver(Solver): r""" Superclass for all SharpClawND solvers. @@ -47,12 +40,12 @@ class SharpClawSolver(Solver): Reconstruction used to compute interface values from cell averages. Can be set with an integer or, equivalently, a name: - - -1 or ``'godunov'``: first-order Godunov (Python kernels only). + - -1: first-order Godunov (Python kernels only). - 0: no limiting (not implemented). - - 1 or ``'tvd'``: TVD reconstruction. - - 2 or ``'weno'``: WENO reconstruction (odd - ``reconstruction_order``, 5 to 17). - - 3 or ``'weno5'``: original fifth-order WENO routine. + - 1: TVD reconstruction. + - 2: WENO reconstruction (odd + ``weno_order``, 5 to 17). + - 3: original fifth-order WENO routine. - 4 or ``'poly'``: non-dissipative polynomial reconstruction. - 5 or ``'poly_dissipative'``: dissipative (upwind-biased) polynomial reconstruction. @@ -64,7 +57,7 @@ class SharpClawSolver(Solver): The polynomial reconstructions (4, 5 and 6) are linear and unlimited, so they suit smooth solutions, e.g. electromagnetic waves in smooth media; at discontinuities they produce Gibbs - oscillations. They take an even ``reconstruction_order`` N in + oscillations. They take an even ``poly_order`` N in {4, 6, 8, 10}, are available with Fortran kernels only, and act component-wise (``char_decomp`` is ignored). With k = N/2: @@ -88,35 +81,14 @@ class SharpClawSolver(Solver): ``Default = 2`` - .. attribute:: reconstruction_order - - Order of the reconstruction: odd, from 5 to 17, for WENO - (``lim_type`` 2); even, from 4 to 10, for the polynomial - reconstructions (``lim_type`` 4, 5 and 6; see ``lim_type`` for - how N relates to the formal order). This is the preferred way to - set the order; ``weno_order`` and ``poly_order`` are views of it - that also select the kind of reconstruction, and setting them - issues a ``FutureWarning`` because future releases may deprecate - them. - ``Default = 5`` - .. attribute:: weno_order - The WENO order: ``reconstruction_order``, except 5 when - ``lim_type`` is 3 (fixed-order routine) and None for the - polynomial reconstructions (``lim_type`` 4, 5 and 6). Setting it - sets ``reconstruction_order``; it switches ``lim_type`` to 2 only - from a polynomial reconstruction, so that, as before, it leaves - TVD and Godunov solvers alone. It issues a ``FutureWarning`` - (future releases may deprecate it; prefer ``reconstruction_order``). + Order of WENO reconstruction for ``lim_type`` 2. Default is 5. .. attribute:: poly_order - The polynomial reconstruction order: ``reconstruction_order`` when - ``lim_type`` is 4, 5 or 6, otherwise None. Setting it (to 4, 6, 8 - or 10) sets ``reconstruction_order`` and switches ``lim_type`` to 4 - unless it is already 4, 5 or 6, and issues a ``FutureWarning`` - (future releases may deprecate it; prefer ``reconstruction_order``). + Polynomial stencil parameter N (4, 6, 8 or 10). Default is 4. + The formal order is N for types 4 and 5, and N+1 for type 6. .. attribute:: time_integrator @@ -220,10 +192,6 @@ class SharpClawSolver(Solver): } _lim_type_names = { - 'godunov': -1, - 'tvd': 1, - 'weno': 2, - 'weno5': 3, 'poly': 4, 'poly_dissipative': 5, 'poly_cell_centered': 6, @@ -246,93 +214,10 @@ def lim_type(self, value): % (value, ', '.join(self._lim_type_names))) self._lim_type = value - _weno_lim_types = (2, 3) _weno_orders = tuple(range(5, 18, 2)) # lim_types the Fortran kernels (flux1.f90) implement _fortran_lim_types = (1, 2, 3, 4, 5, 6) - @property - def weno_order(self): - if self.lim_type == 3: - return 5 # the original WENO5 routine has a fixed order - if self.lim_type in self._poly_lim_types: - return None - return self.reconstruction_order - - @weno_order.setter - def weno_order(self, value): - _warn_order_keyword('weno_order', stacklevel=4) - self._set_weno_order(value) - - def _set_weno_order(self, value): - # Before reconstruction_order existed, weno_order was stored but - # only used by WENO, so it must not change a TVD or Godunov solver. - if self.lim_type in self._poly_lim_types: - self.lim_type = 2 - self.reconstruction_order = value - - @property - def poly_order(self): - if self.lim_type in self._poly_lim_types: - return self.reconstruction_order - return None - - @poly_order.setter - def poly_order(self, value): - _warn_order_keyword('poly_order', stacklevel=4) - self._set_poly_order(value) - - def _set_poly_order(self, value): - if value not in self._poly_orders: - raise ValueError('poly_order must be one of %s, not %s.' % (self._poly_orders, value)) - if self.lim_type not in self._poly_lim_types: - self.lim_type = 4 - self.reconstruction_order = value - - def set_reconstruction(self, lim_type=None, reconstruction_order=None, - weno_order=None, poly_order=None, stacklevel=3): - r""" - Set the reconstruction from the arguments an example's ``setup()`` - receives. Arguments left as None keep the solver's current value. - - ``weno_order`` sets the order and switches a polynomial - reconstruction to WENO (``lim_type`` 2), leaving any other - ``lim_type`` as it is; ``poly_order`` selects a polynomial - reconstruction (``lim_type`` 4 unless ``lim_type`` is already 4, 5 - or 6). Both issue a ``FutureWarning``, since future releases may - deprecate them in favour of ``reconstruction_order``; ``stacklevel`` - says which frame the warning is attributed to, 3 for the caller of - this method, 4 when an example's ``setup()`` calls it on behalf of - its own caller. Contradictory arguments raise ``ValueError``: both - ``weno_order`` and ``poly_order``, either of them together with a - different ``reconstruction_order``, ``weno_order`` with an explicit - polynomial ``lim_type``, or ``poly_order`` with an explicit - non-polynomial ``lim_type``. - """ - if weno_order is not None and poly_order is not None: - raise ValueError('Pass either weno_order or poly_order, not both.') - family_order = weno_order if weno_order is not None else poly_order - if (family_order is not None and reconstruction_order is not None - and reconstruction_order != family_order): - raise ValueError('reconstruction_order=%s contradicts %s=%s.' - % (reconstruction_order, - 'weno_order' if weno_order is not None else 'poly_order', - family_order)) - if lim_type is not None: - self.lim_type = lim_type - if weno_order is not None and self.lim_type in self._poly_lim_types: - raise ValueError('weno_order cannot be used with lim_type %s.' % self.lim_type) - if poly_order is not None and self.lim_type not in self._poly_lim_types: - raise ValueError('poly_order cannot be used with lim_type %s.' % self.lim_type) - if weno_order is not None: - _warn_order_keyword('weno_order', stacklevel=stacklevel) - self._set_weno_order(weno_order) - elif poly_order is not None: - _warn_order_keyword('poly_order', stacklevel=stacklevel) - self._set_poly_order(poly_order) - elif reconstruction_order is not None: - self.reconstruction_order = reconstruction_order - # ======================================================================== # Initialization routines # ======================================================================== @@ -342,7 +227,8 @@ def __init__(self,riemann_solver=None,claw_package=None): """ self.limiters = [1] self.lim_type = 2 - self.reconstruction_order = 5 + self.weno_order = 5 + self.poly_order = 4 self.time_integrator = 'SSP104' self.char_decomp = 0 self.tfluct_solver = False @@ -398,22 +284,22 @@ def setup(self,solution): % self.lim_type) if self.lim_type == 2: - if self.reconstruction_order not in self._weno_orders: - raise ValueError('WENO (lim_type 2) needs an odd reconstruction_order from 5 to 17, ' - 'not %s.' % self.reconstruction_order) - self.num_ghost = int((self.reconstruction_order+1)/2) + if self.weno_order not in self._weno_orders: + raise ValueError('WENO (lim_type 2) needs an odd weno_order from 5 to 17, ' + 'not %s.' % self.weno_order) + self.num_ghost = int((self.weno_order+1)/2) if self.lim_type in self._poly_lim_types: - if self.reconstruction_order not in self._poly_orders: + if self.poly_order not in self._poly_orders: raise ValueError('Polynomial reconstruction (lim_type %s) needs an even ' - 'reconstruction_order in %s, not %s.' - % (self.lim_type, self._poly_orders, self.reconstruction_order)) + 'poly_order in %s, not %s.' + % (self.lim_type, self._poly_orders, self.poly_order)) if self.kernel_language != 'Fortran': raise NotImplementedError('Polynomial reconstruction (lim_type %s) is only ' 'implemented in Fortran kernels.' % self.lim_type) - self.num_ghost = self.reconstruction_order//2 + 1 + self.num_ghost = int(self.poly_order)//2 + 1 - if self.lim_type == 2 and self.reconstruction_order != 5 and self.kernel_language == 'Python': + if self.lim_type == 2 and self.weno_order != 5 and self.kernel_language == 'Python': raise Exception('Only 5th-order WENO reconstruction is implemented in Python kernels. \ Use Fortran for higher-order WENO.') # This is a hack to deal with the fact that petsc4py @@ -477,7 +363,7 @@ def __del__(self): Deallocate F90 module arrays. Also delete Fortran objects, which otherwise tend to persist in Python sessions. """ - if self.kernel_language=='Fortran': + if self.kernel_language == 'Fortran' and self.fmod is not None: self.fmod.clawparams.dealloc_clawparams() self.fmod.workspace.dealloc_workspace(self.char_decomp) self.fmod.reconstruct.dealloc_recon_workspace(self.fmod.clawparams.lim_type,self.fmod.clawparams.char_decomp) @@ -775,7 +661,8 @@ def _set_fortran_parameters(self,state,clawparams,workspace,reconstruct): grid = state.grid clawparams.num_dim = grid.num_dim clawparams.lim_type = self.lim_type - clawparams.reconstruction_order = self.reconstruction_order + clawparams.reconstruction_order = (self.poly_order if self.lim_type in self._poly_lim_types + else self.weno_order) clawparams.char_decomp = self.char_decomp clawparams.tfluct_solver = self.tfluct_solver clawparams.fwave = self.fwave diff --git a/src/pyclaw/tests/test_sharpclaw_poly.py b/src/pyclaw/tests/test_sharpclaw_poly.py index 86f864105..2da9ff9a1 100644 --- a/src/pyclaw/tests/test_sharpclaw_poly.py +++ b/src/pyclaw/tests/test_sharpclaw_poly.py @@ -1,9 +1,7 @@ """ Tests for the SharpClaw polynomial reconstructions (lim_type 4, 5 and 6), -the lim_type names, the weno_order and poly_order properties and the -example setup() keywords. +including independent WENO and polynomial order settings. """ -import inspect import warnings import numpy as np @@ -50,7 +48,7 @@ def advect(num_cells, lim_type, order, kwave=1, cfl=0.4, kernel_language='Fortra solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) solver.kernel_language = kernel_language solver.lim_type = lim_type - solver.reconstruction_order = order + solver.poly_order = order solver.bc_lower[0] = pyclaw.BC.periodic solver.bc_upper[0] = pyclaw.BC.periodic solver.cfl_desired = cfl @@ -140,7 +138,7 @@ def setup_solver(self, lim_type, order, kernel_language='Fortran'): solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) solver.kernel_language = kernel_language solver.lim_type = lim_type - solver.reconstruction_order = order + solver.weno_order = order solver.bc_lower[0] = pyclaw.BC.periodic solver.bc_upper[0] = pyclaw.BC.periodic domain = pyclaw.Domain(pyclaw.Dimension(0., 1., 20, name='x')) @@ -152,16 +150,16 @@ def setup_solver(self, lim_type, order, kernel_language='Fortran'): @pytest.mark.parametrize("order", (4, 6, 19)) def test_weno_rejects_even_or_out_of_range_order(self, order): with pytest.raises(ValueError): - self.setup_solver('weno', order) + self.setup_solver(2, order) @pytest.mark.parametrize("order", (5, 17)) def test_weno_accepts_odd_orders(self, order): - assert self.setup_solver('weno', order).num_ghost == (order + 1)//2 + assert self.setup_solver(2, order).num_ghost == (order + 1)//2 def test_godunov_is_python_only(self): with pytest.raises(NotImplementedError): - self.setup_solver('godunov', 5) - assert self.setup_solver('godunov', 5, kernel_language='Python').lim_type == -1 + self.setup_solver(-1, 5) + assert self.setup_solver(-1, 5, kernel_language='Python').lim_type == -1 def test_unlimited_is_not_implemented(self): with pytest.raises(NotImplementedError): @@ -172,20 +170,9 @@ def test_unknown_lim_type(self, lim_type): with pytest.raises(ValueError): self.setup_solver(lim_type, 5) - def test_even_order_without_lim_type_raises(self): - """The preferred keyword with an even order but the default (WENO) - lim_type is a likely slip; it must raise, not end the process.""" - from examples.acoustics_1d_homogeneous import acoustics_1d - claw = acoustics_1d.setup(solver_type='sharpclaw', reconstruction_order=6, - disable_output=True) - with pytest.raises(ValueError, match='odd reconstruction_order'): - claw.run() - - class TestLimTypeNames: @pytest.mark.parametrize("name, value", [ - ('godunov', -1), ('tvd', 1), ('weno', 2), ('weno5', 3), ('poly', 4), ('poly_dissipative', 5), ('poly_cell_centered', 6)]) def test_name_maps_to_integer(self, name, value): solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) @@ -201,223 +188,32 @@ def test_unknown_name_raises(self): with pytest.raises(ValueError): solver.lim_type = 'spline' - @pytest.mark.parametrize("name, value", [('weno', 2), ('weno5', 3), ('poly', 4)]) - def test_name_and_integer_give_same_result(self, name, value): - order = 4 if value == 4 else 5 - assert advect(20, name, order) == advect(20, value, order) + def test_poly_name_and_integer_give_same_result(self): + assert advect(20, 'poly', 6) == advect(20, 4, 6) -@pytest.mark.filterwarnings("ignore:.*may deprecate:FutureWarning") -class TestOrderProperties: - - def make_solver(self, **kwargs): - solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) - for key, value in kwargs.items(): - setattr(solver, key, value) - return solver - - def test_weno_order_reads_by_lim_type(self): - assert self.make_solver(reconstruction_order=9).weno_order == 9 - assert self.make_solver(lim_type='weno5').weno_order == 5 - assert self.make_solver(lim_type='poly', reconstruction_order=6).weno_order is None - # as before this branch, it reads 5 on solvers that do not use it - assert self.make_solver(lim_type='tvd').weno_order == 5 - assert self.make_solver(lim_type='godunov').weno_order == 5 - - def test_weno_order_selects_weno(self): - solver = self.make_solver(lim_type='poly', reconstruction_order=6) - solver.weno_order = 7 - assert (solver.lim_type, solver.reconstruction_order) == (2, 7) - solver = self.make_solver(lim_type='weno5') - solver.weno_order = 5 - assert solver.lim_type == 3 - - @pytest.mark.parametrize("lim_type", ('tvd', 'godunov')) - def test_weno_order_leaves_other_solvers_alone(self, lim_type): - """Scripts that set weno_order on a TVD solver must still get TVD.""" - solver = self.make_solver(lim_type=lim_type) - solver.weno_order = 7 - assert solver.lim_type == solver._lim_type_names[lim_type] - assert solver.reconstruction_order == 7 - - def test_poly_order_reads_by_lim_type(self): - assert self.make_solver().poly_order is None - for lim_type in POLY: - assert self.make_solver(lim_type=lim_type, reconstruction_order=8).poly_order == 8 - - def test_poly_order_selects_poly(self): - solver = self.make_solver() +@pytest.mark.parametrize("select_poly_first", [True, False]) +def test_independent_orders(select_poly_first): + solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) + assert (solver.lim_type, solver.weno_order, solver.poly_order) == (2, 5, 4) + with warnings.catch_warnings(): + warnings.simplefilter("error", FutureWarning) + if select_poly_first: + solver.lim_type = 'poly' + solver.weno_order = 11 solver.poly_order = 6 - assert (solver.lim_type, solver.reconstruction_order) == (4, 6) - solver = self.make_solver(lim_type='poly_cell_centered') - solver.poly_order = 10 - assert (solver.lim_type, solver.reconstruction_order) == (6, 10) - - @pytest.mark.parametrize("order", (5, 12)) - def test_poly_order_rejects_invalid_order(self, order): - with pytest.raises(ValueError): - self.make_solver().poly_order = order - -@pytest.mark.filterwarnings("ignore:.*may deprecate:FutureWarning") -class TestSetReconstruction: - - def configure(self, **kwargs): - solver = pyclaw.SharpClawSolver1D(riemann.advection_1D) - solver.set_reconstruction(**kwargs) - return solver.lim_type, solver.reconstruction_order - - @pytest.mark.parametrize("kwargs, expected", [ - ({}, (2, 5)), - ({'reconstruction_order': 7}, (2, 7)), - ({'weno_order': 11}, (2, 11)), - ({'weno_order': 9, 'reconstruction_order': 9}, (2, 9)), - ({'lim_type': 'weno5', 'weno_order': 5}, (3, 5)), - ({'lim_type': 'tvd', 'weno_order': 5}, (1, 5)), - ({'poly_order': 6}, (4, 6)), - ({'lim_type': 'poly_dissipative', 'poly_order': 8}, (5, 8)), - ({'lim_type': 6, 'reconstruction_order': 4}, (6, 4)), - ]) - def test_valid(self, kwargs, expected): - assert self.configure(**kwargs) == expected - - @pytest.mark.parametrize("kwargs", [ - {'weno_order': 5, 'poly_order': 4}, - {'weno_order': 7, 'reconstruction_order': 5}, - {'poly_order': 6, 'reconstruction_order': 4}, - {'lim_type': 'poly', 'weno_order': 5}, - {'lim_type': 'weno', 'poly_order': 4}, - {'lim_type': 'tvd', 'poly_order': 4}, - ]) - def test_contradictions_raise(self, kwargs): - with pytest.raises(ValueError): - self.configure(**kwargs) - - -@pytest.mark.filterwarnings("ignore:.*may deprecate:FutureWarning") -class TestExampleSetup: - """Example setup() functions accept reconstruction_order, and still - accept weno_order and poly_order.""" - - def solver(self, **kwargs): - from examples.acoustics_1d_homogeneous import acoustics_1d - claw = acoustics_1d.setup(solver_type='sharpclaw', disable_output=True, **kwargs) - return claw.solver - - def test_weno_order(self): - solver = self.solver(weno_order=11) - assert (solver.lim_type, solver.reconstruction_order) == (2, 11) - - def test_poly_order(self): - solver = self.solver(poly_order=6) - assert (solver.lim_type, solver.reconstruction_order) == (4, 6) - solver = self.solver(poly_order=6, lim_type='poly_dissipative') - assert (solver.lim_type, solver.reconstruction_order) == (5, 6) - - def test_defaults_unchanged(self): - solver = self.solver() - assert (solver.lim_type, solver.reconstruction_order) == (2, 5) - - def test_weno_order_matches_upstream_result(self): - """setup(weno_order=11) reproduces the WENO11 regression value.""" - from examples.acoustics_1d_homogeneous.test_acoustics import error - assert abs(error(kernel_language='Fortran', solver_type='sharpclaw', - weno_order=11) - 0.000521) < 1e-5 - - @pytest.mark.parametrize("module, kwargs", [ - ('examples.advection_1d.advection_1d', {'kernel_language': 'Fortran'}), - ('examples.advection_1d_variable.variable_coefficient_advection', {'kernel_language': 'Fortran'}), - ('examples.cubic_1d.cubic', {'N': 50}), - ]) - @pytest.mark.parametrize("order_kwarg", [{'weno_order': 7}, {'poly_order': 6}]) - def test_other_examples(self, module, kwargs, order_kwarg): - import importlib - setup = importlib.import_module(module).setup - claw = setup(solver_type='sharpclaw', **kwargs, **order_kwarg) - expected_lim_type = 2 if 'weno_order' in order_kwarg else 4 - assert claw.solver.lim_type == expected_lim_type - assert claw.solver.reconstruction_order == list(order_kwarg.values())[0] - - def test_nonuniform_example_accepts_keywords(self): - from examples.advection_1d import advection_1d_nonunif - advection_1d_nonunif.setup(weno_order=5) - advection_1d_nonunif.setup(poly_order=4) - - def test_cubic_keeps_positional_order(self): - """Upstream's positional order (use_petsc, outdir, solver_type, - order, N) still holds; lim_type is appended after N.""" - from examples.cubic_1d import cubic - claw = cubic.setup(0, './_output', 'sharpclaw', 7, 50) - assert claw.solution.domain.grid.num_cells[0] == 50 - assert (claw.solver.lim_type, claw.solver.reconstruction_order) == (2, 7) - - -class TestFutureWarnings: - """weno_order and poly_order still work but warn that future releases - may deprecate them; reconstruction_order never warns.""" - - MATCH = "future releases may deprecate" - - def solver(self): - return pyclaw.SharpClawSolver1D(riemann.advection_1D) - - @pytest.mark.parametrize("name, value", [('weno_order', 7), ('poly_order', 6)]) - def test_setting_warns(self, name, value): - solver = self.solver() - with pytest.warns(FutureWarning, match=self.MATCH): - setattr(solver, name, value) - - @pytest.mark.parametrize("kwargs", [{'weno_order': 7}, {'poly_order': 6}]) - def test_set_reconstruction_warns_at_the_call(self, kwargs): - with pytest.warns(FutureWarning, match=self.MATCH) as record: - line = inspect.currentframe().f_lineno + 1 - self.solver().set_reconstruction(**kwargs) - assert (record[0].filename, record[0].lineno) == (__file__, line) - - @pytest.mark.parametrize("kwargs", [{'weno_order': 7}, {'poly_order': 6}]) - def test_example_setup_warns_at_the_call(self, kwargs): - from examples.acoustics_1d_homogeneous import acoustics_1d - with pytest.warns(FutureWarning, match=self.MATCH) as record: - acoustics_1d.setup(solver_type='sharpclaw', disable_output=True, **kwargs) - assert len(record) == 1 - assert record[0].filename == __file__ - - def test_reading_and_reconstruction_order_do_not_warn(self): - with warnings.catch_warnings(): - warnings.simplefilter("error") - solver = self.solver() - solver.reconstruction_order = 7 - solver.set_reconstruction(lim_type='poly', reconstruction_order=6) - solver.weno_order, solver.poly_order - - @pytest.mark.parametrize("module, kwargs, order", [ - ('examples.acoustics_1d_homogeneous.acoustics_1d', {'disable_output': True}, 11), - ('examples.advection_1d.advection_1d', {'kernel_language': 'Fortran'}, 7), - ('examples.advection_1d_variable.variable_coefficient_advection', - {'kernel_language': 'Fortran'}, 9), - ('examples.cubic_1d.cubic', {'N': 50}, 7), - ]) - def test_examples_take_reconstruction_order(self, module, kwargs, order): - import importlib - setup = importlib.import_module(module).setup - with warnings.catch_warnings(): - warnings.simplefilter("error", FutureWarning) - claw = setup(solver_type='sharpclaw', reconstruction_order=order, **kwargs) - assert (claw.solver.lim_type, claw.solver.reconstruction_order) == (2, order) - claw = setup(solver_type='sharpclaw', lim_type='poly', reconstruction_order=6, **kwargs) - assert (claw.solver.lim_type, claw.solver.reconstruction_order) == (4, 6) - - def test_nonuniform_example_takes_reconstruction_order(self): - from examples.advection_1d import advection_1d_nonunif - with warnings.catch_warnings(): - warnings.simplefilter("error", FutureWarning) - advection_1d_nonunif.setup(reconstruction_order=5) + assert solver.lim_type == (4 if select_poly_first else 2) + solver.lim_type = 'poly' + assert (solver.weno_order, solver.poly_order) == (11, 6) + solver.lim_type = 2 + assert (solver.weno_order, solver.poly_order) == (11, 6) def test_poly_2d(): """The 2D solver reuses the 1D reconstruction along each direction.""" solver = pyclaw.SharpClawSolver2D(riemann.advection_2D) solver.lim_type = 'poly' - solver.reconstruction_order = 6 + solver.poly_order = 6 solver.bc_lower = [pyclaw.BC.periodic]*2 solver.bc_upper = [pyclaw.BC.periodic]*2 solver.cfl_desired = 0.4