def __init__(self,
multilocus_dataset,
sate_team,
tree,
tmp_base_dir,
tmp_dir_par=None,
reset_recursion_index=False,
**kwargs):
self._job_lock = Lock()
TreeHolder.__init__(self, multilocus_dataset.dataset)
behavior = copy.copy(SateAlignerJob.BEHAVIOUR_DEFAULTS)
behavior.update(kwargs)
for k in SateAlignerJob.BEHAVIOUR_DEFAULTS.keys():
setattr(self, k, behavior[k])
self.multilocus_dataset = multilocus_dataset
self.sate_team = sate_team
self.tree = tree
self._subjob1 = None
self._subjob2 = None
self._align_job_list = None
self._merge_job_list = None
self.tmp_base_dir = tmp_base_dir
self.context_str = ''
self.killed = False
self._dirs_to_cleanup = []
self.tmp_dir_par = tmp_dir_par
if self.tmp_dir_par == None:
self.tmp_dir_par = self.tmp_base_dir
if reset_recursion_index:
self.__class__.RECURSION_INDEX = 0
def __init__(self, multilocus_dataset, sate_team, tree=None, name=None, **kwargs):
TreeHolder.__init__(self, multilocus_dataset.dataset)
self.blind_mode_is_final = True
self.is_stuck_in_blind = False
behavior = copy.copy(SateJob.BEHAVIOUR_DEFAULTS)
behavior.update(SateAlignerJob.BEHAVIOUR_DEFAULTS)
behavior.update(kwargs)
self.__dict__.update(behavior)
self._job_lock = Lock()
self.multilocus_dataset = multilocus_dataset
self.sate_team = sate_team
self.tree = tree
self.score = None
self.best_score = None
self._tree_build_job = None
self._sate_decomp_job = None
self._reset_jobs()
self._status_message_func = kwargs.get('status_messages')
# right now max_subproblem_size can be an integer or proportion.
# These two run modes should be separate parameters!
# if self.max_subproblem_size <= 1.0:
# self.max_subproblem_size *= alignment.get_num_taxa()
# self.max_subproblem_size = max(1, int(round(self.max_subproblem_size)))
if isinstance(self.break_strategy, str):
self.break_strategy = [self.break_strategy]
self._reset_current_run_settings()
self.killed = False
def __init__(self,
multilocus_dataset,
sate_team,
tree=None,
name=None,
**kwargs):
"""
Note that the `tree` will be resolved using DendroPy's resolve_polytomies
function.
"""
TreeHolder.__init__(self,
multilocus_dataset.dataset,
force_fully_resolved=True)
if tree is not None:
resolve_polytomies(tree, update_splits=True)
self.blind_mode_is_final = True
self.is_stuck_in_blind = False
behavior = copy.copy(SateJob.BEHAVIOUR_DEFAULTS)
behavior.update(SateAlignerJob.BEHAVIOUR_DEFAULTS)
behavior.update(kwargs)
self.__dict__.update(behavior)
self._job_lock = Lock()
self.multilocus_dataset = multilocus_dataset
self.sate_team = sate_team
self.tree = tree
self.score = kwargs.get('score', None)
self.best_score = None
self._tree_build_job = None
self._sate_decomp_job = None
self._reset_jobs()
self._status_message_func = kwargs.get('status_messages')
# right now max_subproblem_size can be an integer or proportion.
# These two run modes should be separate parameters!
# if self.max_subproblem_size <= 1.0:
# self.max_subproblem_size *= alignment.get_num_taxa()
# self.max_subproblem_size = max(1, int(round(self.max_subproblem_size)))
if isinstance(self.break_strategy, str):
self.break_strategy = [self.break_strategy]
self._reset_current_run_settings()
self.killed = False
def __init__(self,
multilocus_dataset,
sate_team,
tree,
tmp_dir_par,
**kwargs):
self._job_lock = Lock()
TreeHolder.__init__(self, multilocus_dataset.dataset)
behavior = copy.copy(SateAlignerJob.BEHAVIOUR_DEFAULTS)
behavior.update(kwargs)
for k in SateAlignerJob.BEHAVIOUR_DEFAULTS.keys():
setattr(self, k, behavior[k])
self.multilocus_dataset = multilocus_dataset
self.sate_team = sate_team
self.tree = tree
self._subjob1 = None
self._subjob2 = None
self._align_job_list = None
self._merge_job_list = None
self.tmp_dir_par = tmp_dir_par
self.context_str = ''
self.killed = False
self._dirs_to_cleanup = []
