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updated readme and todos
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README.md

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@@ -10,6 +10,8 @@ iterators are implemented as well, giving full access to all values at any time.
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News
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----
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* February 2, 2016: the fast-wave slow-wave release, containing all the code for the FWSW-SDC [paper](http://arxiv.org/abs/1602.01626)
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* October 27, 2015: the fault-tolerant release, containing all the code for the FT-PFASST [paper](http://arxiv.org/abs/1510.08334)
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* June 17, 2015: Gray-Scott example using FEniCS is up and running
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* May 7, 2015: added first [FEniCS](http://fenicsproject.org/) example
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* May 5, 2015: added [Clawpack](http://www.clawpack.org/) examples using Sharpclaw

ToDo.md

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2020

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* fix behavior of hooks during PFASST runs: initialization is called per level, but some things may need to be done only once (file openings, figures)
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* add temporal coarsening
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* add temporal coarsening
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* remove mass_matrix sweeper, since this can all be done within solve_system
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* full weak formulation for finite element examples?
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* one matrix to rule them all: can we extract the very costly matrix buildups?

examples/boussinesq_2d_imex/rungmrescounter.py

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dirkp = dirk(P, np.min([4,dirk_order]))
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u0 = uinit.values.flatten()
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udirk = np.copy(u0)
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print "Running DIRK ...."
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print("Running DIRK ....")
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for i in range(0,Nsteps):
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udirk = dirkp.timestep(udirk, dt)
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rkimex = rk_imex(P, dirk_order)
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uimex = np.copy(u0)
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dt_imex = dt
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print "Running RK-IMEX ...."
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print("Running RK-IMEX ....")
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for i in range(0,Nsteps):
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uimex = rkimex.timestep(uimex, dt_imex)
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# call main function to get things done...
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print "Running SDC..."
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print("Running SDC...")
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uend,stats = mp.run_pfasst(MS,u0=uinit,t0=t0,dt=dt,Tend=Tend)
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# For reference solution, increase GMRES tolerance
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P.gmres_tol_limit = 1e-10
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rkimexref = rk_imex(P, 5)
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uref = np.copy(u0)
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dt_ref = dt/10.0
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print "Running RK-IMEX reference...."
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print("Running RK-IMEX reference....")
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for i in range(0,10*Nsteps):
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uref = rkimexref.timestep(uref, dt_ref)
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pySDC/sweeper_classes/mass_matrix_imex.py

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@@ -95,7 +95,7 @@ def update_nodes(self):
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rhs = P.dtype_u(integral[m])
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for j in range(m+1):
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rhs += L.dt*(self.QI[m+1,j]*L.f[j].impl + self.QE[m+1,j]*L.f[j].expl)
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# apply mass matrix to rhs
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# apply mass matrix to rhs #TODO: Hloy crap, I can put this into solve_system right???
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rhs = P.apply_mass_matrix(rhs)
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# implicit solve with prefactor stemming from QI

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