clean up

issp-center-dev · Aug 17, 2024 · d1a4af4 · d1a4af4
1 parent 08613ff
commit d1a4af4
Show file tree

Hide file tree

Showing 4 changed files with 0 additions and 75 deletions.
diff --git a/src/py2dmat/algorithm/exchange.py b/src/py2dmat/algorithm/exchange.py
@@ -83,17 +83,8 @@ def __init__(self, info: py2dmat.Info, runner: py2dmat.Runner = None) -> None:
 
         super().__init__(info=info, runner=runner, nwalkers=nwalkers)
 
-        # if self.mpicomm is None:
-        #     msg = "ERROR: algorithm.exchange requires mpi4py, but mpi4py cannot be imported"
-        #     raise ImportError(msg)
-
         self.nreplica = self.mpisize * self.nwalkers
         self.input_as_beta, self.betas = read_Ts(info_exchange, numT=self.nreplica)
-        # self.Tindex = np.arange(
-        #     self.mpirank * self.nwalkers, (self.mpirank + 1) * self.nwalkers
-        # )
-        # self.rep2T = np.arange(self.nreplica)
-        # self.T2rep = np.arange(self.nreplica)
 
         self.numsteps = info_exchange["numsteps"]
         self.numsteps_exchange = info_exchange["numsteps_exchange"]
@@ -137,12 +128,6 @@ def _run(self) -> None:
             raise RuntimeError("unknown mode {}".format(self.mode))
 
         beta = self.betas[self.Tindex]
-        # self.exchange_direction = True
-
-        # self.istep = 0
-
-        # # first step
-        # self._evaluate()
 
         if self.mode.startswith("init"):
             # first step
@@ -169,15 +154,6 @@ def _run(self) -> None:
         next_checkpoint_step = self.istep + self.checkpoint_steps
         next_checkpoint_time = time.time() + self.checkpoint_interval
 
-        # print(">>> checkpoint={}".format(self.checkpoint))
-        # print(">>> checkpoint_file={}".format(self.checkpoint_file))
-        # print(">>> checkpoint_steps={}".format(self.checkpoint_steps))
-        # print(">>> checkpoint_interval={}".format(self.checkpoint_interval))
-        # print(">>> istep=               {}".format(self.istep))
-        # print(">>> next_checkpoint_step={}".format(next_checkpoint_step))
-        # print(">>> current_time=        {}".format(time.time()))
-        # print(">>> next_checkpoint_time={}".format(next_checkpoint_time))
-
         while self.istep < self.numsteps:
             # print(">>> istep={}".format(self.istep))
 

diff --git a/src/py2dmat/algorithm/mapper_mpi.py b/src/py2dmat/algorithm/mapper_mpi.py
@@ -26,7 +26,6 @@
 import py2dmat.domain
 
 class Algorithm(py2dmat.algorithm.AlgorithmBase):
-    #mesh_list: np.ndarray
     mesh_list: List[Union[int, float]]
 
     def __init__(self, info: py2dmat.Info,
@@ -52,12 +51,6 @@ def _initialize(self) -> None:
     def _run(self) -> None:
         # Make ColorMap
 
-        # label_list = self.label_list
-        # dimension = self.dimension
-
-        # fx_list = []
-        # self.timer["run"]["submit"] = 0.0
-
         if self.mode is None:
             raise RuntimeError("mode unset")
 

diff --git a/src/py2dmat/algorithm/montecarlo.py b/src/py2dmat/algorithm/montecarlo.py
@@ -120,29 +120,19 @@ def __init__(self, info: py2dmat.Info,
                 self.domain = py2dmat.domain.Region(info)
 
         if self.iscontinuous:
-            # self.domain.initialize(rng=self.rng, limitation=self.runner.limitation, num_walkers=nwalkers)
-            # self.x = self.domain.initial_list
             self.xmin = self.domain.min_list
             self.xmax = self.domain.max_list
             self.xunit = self.domain.unit_list
 
         else:
             self.node_coordinates = np.array(self.domain.grid)[:, 1:]
             self.nnodes = self.node_coordinates.shape[0]
-            # self.inode = self.rng.randint(self.nnodes, size=self.nwalkers)
-            # self.x = self.node_coordinates[self.inode, :]
             self._setup_neighbour(info_param)
 
-        # self.fx = np.zeros(self.nwalkers)
-        # self.best_fx = 0.0
-        # self.best_istep = 0
-        # self.best_iwalker = 0
         time_end = time.perf_counter()
         self.timer["init"]["total"] = time_end - time_sta
         self.Tindex = 0
         self.input_as_beta = False
-        # self.naccepted = 0
-        # self.ntrial = 0
 
     def _initialize(self):
         if self.iscontinuous:

diff --git a/src/py2dmat/algorithm/pamc.py b/src/py2dmat/algorithm/pamc.py
@@ -106,47 +106,20 @@ def __init__(self, info: py2dmat.Info, runner: py2dmat.Runner = None) -> None:
 
         numT = self._find_scheduling(info_pamc)
 
-        # super()._initialize()
-
-        #self.betas = self.read_Ts(info_pamc, numT=numT)
         self.input_as_beta, self.betas = read_Ts(info_pamc, numT=numT)
         self.betas.sort()
-        # self.Tindex = 0
-
-        # self.logZ = 0.0
-        # self.logZs = np.zeros(numT)
-        # self.logweights = np.zeros(self.nwalkers)
-
-        # self.Fmeans = np.zeros(numT)
-        # self.Ferrs = np.zeros(numT)
-        # nreplicas = self.mpisize * self.nwalkers
-        # self.nreplicas = np.full(numT, nreplicas)
 
-        # self.populations = np.zeros((numT, self.nwalkers), dtype=int)
-        # self.family_lo = self.nwalkers * self.mpirank
-        # self.family_hi = self.nwalkers * (self.mpirank + 1)
-        # self.walker_ancestors = np.arange(self.family_lo, self.family_hi)
         self.fix_nwalkers = info_pamc.get("fix_num_replicas", True)
         self.resampling_interval = info_pamc.get("resampling_interval", 1)
         if self.resampling_interval < 1:
             self.resampling_interval = numT + 1
-        # self.fx_from_reset = np.zeros((self.resampling_interval, self.nwalkers))
-        # self.naccepted_from_reset = np.zeros((self.resampling_interval, 2), dtype=int)
-        # self.acceptance_ratio = np.zeros(numT)
-
-        #self._initialize()
 
         time_end = time.perf_counter()
         self.timer["init"]["total"] = time_end - time_sta
 
     def _initialize(self) -> None:
         super()._initialize()
 
-        # #self.betas = self.read_Ts(info_pamc, numT=numT)
-        # self.input_as_beta, self.betas = read_Ts(info_pamc, numT=numT)
-        # self.betas.sort()
-        # self.Tindex = 0
-
         numT = len(self.betas)
 
         self.logZ = 0.0
@@ -162,15 +135,10 @@ def _initialize(self) -> None:
         self.family_lo = self.nwalkers * self.mpirank
         self.family_hi = self.nwalkers * (self.mpirank + 1)
         self.walker_ancestors = np.arange(self.family_lo, self.family_hi)
-        # self.fix_nwalkers = info_pamc.get("fix_num_replicas", True)
-        # self.resampling_interval = info_pamc.get("resampling_interval", 1)
-        # if self.resampling_interval < 1:
-        #     self.resampling_interval = numT + 1
         self.fx_from_reset = np.zeros((self.resampling_interval, self.nwalkers))
         self.naccepted_from_reset = np.zeros((self.resampling_interval, 2), dtype=int)
         self.acceptance_ratio = np.zeros(numT)
 
-
     def _find_scheduling(self, info_pamc) -> int:
         numsteps = info_pamc.get("numsteps", 0)
         numsteps_annealing = info_pamc.get("numsteps_annealing", 0)
@@ -675,8 +643,6 @@ def _load_state(self, filename, mode="resume", restore_rng=True):
         self.nreplicas = data["nreplicas"]
         self.populations = data["populations"]
 
-        #assert self.mpirank * self.nwalkers == data["family_lo"]
-        #assert (self.mpirank+1) * self.nwalkers == data["family_hi"]
         self.family_lo = data["family_lo"]
         self.family_hi = data["family_hi"]