def __init__(
self, domain_factory, lambdas=None, nb_domains=os.cpu_count(), ipc_notify=False
):
super().__init__(domain_factory, lambdas, nb_domains, ipc_notify)
self._manager = mp.Manager()
self._waiting_jobs = [None] * nb_domains
self._job_results = self._manager.list([None for i in range(nb_domains)])
logger.info(rf"Using {nb_domains} parallel piped domains")
def run_concurrent(queries, f):
pool = Pool(nodes=CLIENT_COUNT)
manager = pathos_multiprocess.Manager()
barrier = manager.Barrier(CLIENT_COUNT)
barriers = [barrier] * CLIENT_COUNT
# invoke queries
return pool.map(f, queries, barriers)
def get_numa_queue(num_jobs_per_numa_node=1):
m = mp.Manager()
queue = m.Queue(NUM_NUMA_NODES * num_jobs_per_numa_node)
for cpu_id_offsets in NUMA_CPU_ID_OFFSETS:
for i in range(num_jobs_per_numa_node):
queue.put(
get_cpu_assignments(cpu_id_offsets,
NUM_VIRTUAL_CORES_PER_POOL))
return queue
def create_example_queue_for_collection(self,
name: str) -> multiprocess.Queue:
"""
Creates a queue to receive examples on.
:param name: The name of the queue.
:return: The queue.
"""
assert name not in self.example_queues.keys()
manager = multiprocess.Manager()
queue_ = manager.Queue()
self.example_queues[name] = queue_
return queue_
def __init__(self, num_proc, sink=sys.stdout):
"""
Initialiser
:param num_proc: Number of processes to employ (default to the number of cores less 2). If 0
or less, then defaults to Multi-Threading instead of Multi-Processing: this
can be especially useful for debugging.
:param sink: Sink where to write progress to (may be None)
"""
# Parameters
self.NumProc = num_proc # Number of Processes to employ
# Management
self._queue = mp.Manager().Queue() if num_proc > 0 else queue.Queue() # Queue
self.__timers = {} # Timers
self.__thread = None # Progress Thread Handler
self.__worker_set = None # List of Workers and progress
self.__done = 0 # How many are finished
self.__tasks_done = 0 # How many (workers) are finished
self.__progress = None # Eventually will be the progress bar
self.__sink = utils.NullableSink(sink) # Sink to write to
def scheduling_method(self, cur_time, es, es_dict):
"""
This function must map the queued events to available nodes at the current time.
:param cur_time: current time
:param es_dict: dictionary with full data of the events
:param es: events to be scheduled
:param debug: Flag to debug
:return a tuple of (time to schedule, event id, list of assigned nodes)
"""
dispatching_plan = []
resource_types = self.resource_manager.resource_types
avl_resources = self.resource_manager.current_availability
system_capacity = self.resource_manager.system_capacity('nodes')
# =======================================================================
# Considered queued jobs: Jobs can be fitted in the current system state and less or equal than q_length
# If a job_obj cannot be fitted or exceed the q_length is directly loaded in the dispatching decision using the no-solution dispatching tuple
# =======================================================================
priorized_jobs = SortedListWithKey(key=lambda job_tuple: job_tuple[1])
current_qjobs = SortedList()
cons_qjobs = {}
for node in self.resource_manager.node_names:
avl_res = avl_resources[node]
# avl_res = system_capacity[node]
for idx, job_obj in enumerate(es):
job_id = job_obj.id
if not (job_id in cons_qjobs):
current_qjobs.add(job_id)
cons_qjobs[job_id] = [False, 0, {}, None]
priorized_jobs.add((job_id, self._job_priority_slowdown(job_obj, cur_time)))
if self._reduced_model:
possibilities = self._joint_nodes(job_obj, avl_res)
if possibilities > 0:
cons_qjobs[job_id][2][node] = min(possibilities, job_obj.requested_nodes)
cons_qjobs[job_id][1] += possibilities
if cons_qjobs[job_id][1] >= job_obj.requested_nodes:
cons_qjobs[job_id][0] = True
if not cons_qjobs[job_id][3]:
cons_qjobs[job_id][3] = job_obj
else:
cons_qjobs[job_id][0] = True
cons_qjobs[job_id][1] = None
cons_qjobs[job_id][2] = None
cons_qjobs[job_id][3] = job_obj
qjobs = 0
wc_makespan = 0
makespans = []
selected_priorized_jobs = []
# Job of the dispatching decision
decision_jobs = {}
if self._reduced_model:
for job_id, _ in priorized_jobs:
t = cons_qjobs[job_id]
if not t[0] or qjobs > self._cur_q_length - 1:
decision_jobs[job_id] = self.dispatching_tuple(job_id)
cons_qjobs.pop(job_id)
else:
exp_duration = max(1, t[-1].expected_duration)
wc_makespan += exp_duration
makespans.append(exp_duration)
qjobs += 1
selected_priorized_jobs.append(job_id)
else:
cannot_start_selected = 0
for job_id, _ in priorized_jobs:
t = cons_qjobs[job_id]
if (not t[0] and cannot_start_selected >= self._considered_cannot_start) or (
qjobs > self._cur_q_length - 1):
decision_jobs[job_id] = self.dispatching_tuple(job_id)
cons_qjobs.pop(job_id)
else:
if not t[0]:
cons_qjobs[job_id][3] = es_dict[job_id]
cannot_start_selected += 1
exp_duration = max(1, t[-1].expected_duration)
wc_makespan += exp_duration # , self.get_queue(t[-1].queue)) # exp_duration
makespans.append(exp_duration)
qjobs += 1
selected_priorized_jobs.append(job_id)
# =======================================================================
# There are no jobs to dispatch at the current system state.
# Then a no solution list is returned.
# =======================================================================
if not cons_qjobs:
# Job Dispatching skip
return decision_jobs.values(), []
solved = False
self.priorized_jobs = None
if self._safe:
manager = mp_dill.Manager()
schedule_plan = manager.dict()
process_class = mp_dill.Process
p = process_class(target=getattr(self, 'cp_model'),
args=(
schedule_plan, cur_time, cons_qjobs, selected_priorized_jobs, es_dict, resource_types,
avl_resources),
kwargs={'timelimit': timelimit}
)
p.start()
p.join()
if p.exitcode != 0:
schedule_plan.pop('solver_state', None)
schedule_plan.pop('limit_reached', None)
return list(decision_jobs.values()) \
+ [self.dispatching_tuple(job_id, start_time, nodes) for (start_time, job_id, nodes) in
schedule_plan.values()] \
+ [self.dispatching_tuple(job_id, None, []) for job_id in cons_qjobs if
not (job_id in schedule_plan)], []
else:
schedule_plan = {}
args = (
schedule_plan, cur_time, cons_qjobs, selected_priorized_jobs, es_dict, resource_types, avl_resources)
kwargs = {'max_timelimit': self._max_timelimit}
function = getattr(self, 'cp_model')
function(*args, **kwargs)
solved = schedule_plan.pop('solved')
of_value = schedule_plan.pop('of_value')
walltime = schedule_plan.pop('walltime')
proc_time = schedule_plan.pop('proc_time')
incurred_time = walltime + proc_time
failures = schedule_plan.pop('failures')
branches = schedule_plan.pop('branches')
p = None
self.priorized_jobs = None
dispatching_plan = list(schedule_plan.values())
self.__instance_data = (
solved, of_value, walltime, incurred_time, failures, branches,
dispatching_plan + list(decision_jobs.values()),)
# This is useful for print and also to create the unsuccessful data
dispatched_jobs = 0
queued_job_ids = []
for a in dispatching_plan:
if a[2]:
dispatched_jobs += 1
if dispatched_jobs == 0:
queued_job_ids.append(a[1])
if self._reduce_job_length:
# ===================================================================
# The considered number of jobs in the next scheduling decision are reduced to the half
# if the current problem instance was not solved, if the current usage is
# leq of the previous time point. After a successful dispatching this value is reset.
# The minimum is 1, otherwise there will be nothing to dispatch
# ===================================================================
if not solved:
self._cur_q_length = max(1, min(self._cur_q_length,
len(schedule_plan)) // 2) # max(1, self._cur_q_length // 2)
else:
self._cur_q_length = self._q_length
print('{} - {}: Queued {}, Dispatched {}, Running {}. {}'.format(self._counter, cur_time,
len(es) - dispatched_jobs, dispatched_jobs,
len(self.resource_manager.current_allocations),
self.resource_manager.current_usage))
return dispatching_plan + list(decision_jobs.values()), []
def __init__(self):
manager = multiprocess.Manager()
self.lock = manager.Lock()
self.request_queues: Dict[str, multiprocess.Queue] = {}
self.example_queues: Dict[str, multiprocess.Queue] = {}
def test_05_concurrent_read_delete(self):
##############################################################################################
# Delete graph via Redis DEL key.
##############################################################################################
self.populate_graph()
pool = Pool(nodes=CLIENT_COUNT)
manager = pathos_multiprocess.Manager()
barrier = manager.Barrier(CLIENT_COUNT)
barriers = [barrier] * CLIENT_COUNT
q = """UNWIND (range(0, 10000)) AS x WITH x AS x WHERE (x / 900) = 1 RETURN x"""
queries = [q] * CLIENT_COUNT
# invoke queries
m = pool.amap(thread_run_query, queries, barriers)
self.conn.delete(GRAPH_ID)
# wait for processes to return
m.wait()
# get the results
results = m.get()
# validate result.
self.env.assertTrue(
all([r["result_set"][0][0] == 900 for r in results]))
# Make sure Graph is empty, e.g. graph was deleted.
resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set
self.env.assertEquals(resultset[0][0], 0)
##############################################################################################
# Delete graph via Redis FLUSHALL.
##############################################################################################
self.populate_graph()
q = """UNWIND (range(0, 10000)) AS x WITH x AS x WHERE (x / 900) = 1 RETURN x"""
queries = [q] * CLIENT_COUNT
barrier = manager.Barrier(CLIENT_COUNT)
barriers = [barrier] * CLIENT_COUNT
# invoke queries
m = pool.amap(thread_run_query, queries, barriers)
self.conn.flushall()
# wait for processes to return
m.wait()
# get the results
results = m.get()
# validate result.
self.env.assertTrue(
all([r["result_set"][0][0] == 900 for r in results]))
# Make sure Graph is empty, e.g. graph was deleted.
resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set
self.env.assertEquals(resultset[0][0], 0)
##############################################################################################
# Delete graph via GRAPH.DELETE.
##############################################################################################
self.populate_graph()
q = """UNWIND (range(0, 10000)) AS x WITH x AS x WHERE (x / 900) = 1 RETURN x"""
queries = [q] * CLIENT_COUNT
barrier = manager.Barrier(CLIENT_COUNT)
barriers = [barrier] * CLIENT_COUNT
# invoke queries
m = pool.amap(thread_run_query, queries, barriers)
self.graph.delete()
# wait for processes to return
m.wait()
# get the results
results = m.get()
# validate result.
self.env.assertTrue(
all([r["result_set"][0][0] == 900 for r in results]))
# Make sure Graph is empty, e.g. graph was deleted.
resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set
self.env.assertEquals(resultset[0][0], 0)
def get_global_pval(self, hytest_method, node_to_leaves,
node_to_ancestral_nodes,
node_to_pwdist): #, leafpair_to_distance):
'''
Perform all inter-clusters' hypotheses tests
'''
if self.treeinfo_file_given < 1:
from ctypes import c_char_p
import os
print('\nPerforming {} tests...'.format(hytest_method))
# shared memory
lpd = self.leafpair_to_distance
max_node = max(node_to_leaves.keys())
if os.name == 'nt':
# windows
node_to_leaves_shared = [
node_to_leaves[n] if n in node_to_leaves.keys() else False
for n in range(max_node + 1)
]
else:
node_to_leaves_shared = [
mp.Array(c_char_p, node_to_leaves[n])
if n in node_to_leaves.keys() else False
for n in range(max_node + 1)
]
node_to_ancestral_nodes_shared = [
mp.Array('i', node_to_ancestral_nodes[n])
if n in node_to_ancestral_nodes else False
for n in range(max_node + 1)
]
node_to_pwdist_shared = [
mp.Array('d', node_to_pwdist[n])
if n in node_to_leaves.keys() else False
for n in range(max_node + 1)
]
# worker
def get_interclus_pval(np_list, ntl_dict, ntan_dict, ntpwd_dict,
q):
currp_np_to_pval = {}
for (i, j) in np_list:
if (ntan_dict[j] != False and i in list(ntan_dict[j])) or (
ntan_dict[i] != False and j in list(ntan_dict[i])):
pval = inter_cluster_hytest(
list(ntpwd_dict[i]),
list(ntpwd_dict[j])).hytest(hytest_method)
else:
ij_pwdist = sorted([
lpd[(x, y)] for x, y in itertools.combinations(
list(set(ntl_dict[i]) | set(ntl_dict[j])), 2)
])
# take the conservative (max) p-value comparing node i/j individually to i+j
pval = max([
inter_cluster_hytest(
list(ntpwd_dict[i]),
ij_pwdist).hytest(hytest_method),
inter_cluster_hytest(
list(ntpwd_dict[j]),
ij_pwdist).hytest(hytest_method)
])
currp_np_to_pval[(i, j)] = pval
q.put(currp_np_to_pval)
# multi-proc setup
manager = mp.Manager()
# shared memory
queue = manager.Queue()
# generate processes
processes = []
# split nodepair list into ncpu sets
nodepair_list = list(
itertools.combinations(node_to_leaves.keys(), 2))
shuffle(nodepair_list) # shuffle to make more equitable
increment = int(len(nodepair_list) / self.cores)
for p in range(self.cores):
if p == self.cores - 1:
curr_nodepair_list = nodepair_list[p * increment:]
else:
curr_nodepair_list = nodepair_list[p *
increment:(p *
increment) +
increment]
proc = mp.Process(target=get_interclus_pval,
args=(curr_nodepair_list,
node_to_leaves_shared,
node_to_ancestral_nodes_shared,
node_to_pwdist_shared, queue))
processes.append(proc)
proc.start()
# collect results to dictionary
for p in range(len(processes)):
self.nodepair_to_pval.update(queue.get())
# wait for all processes to end
for proc in processes:
proc.join()
if self.no_treeinfo == False:
print('Writing to treeinfo file...')
output = open(self.treeinfo_fname, 'a')
json.dump(self.remap_keys(self.nodepair_to_pval), output)
output.write('\n')
output.close()
"""
# single thread legacy code
nodepair_to_pval_single = {}
for i,j in itertools.combinations(node_to_leaves.keys(), 2):
if (j in node_to_ancestral_nodes and i in node_to_ancestral_nodes[j]) or (i in node_to_ancestral_nodes and j in node_to_ancestral_nodes[i]):
pval = inter_cluster_hytest(node_to_pwdist[i], node_to_pwdist[j]).hytest(hytest_method)
else:
ij_pwdist = sorted([leafpair_to_distance[(x,y)] for x,y in itertools.combinations(list(set(node_to_leaves[i])|set(node_to_leaves[j])), 2)])
# take the conservative (max) p-value comparing node i/j individually to i+j
pval = max([inter_cluster_hytest(node_to_pwdist[i], ij_pwdist).hytest(hytest_method), inter_cluster_hytest(node_to_pwdist[j], ij_pwdist).hytest(hytest_method)])
nodepair_to_pval_single[(i,j)] = pval
# check
print ('Sets of nodepair_to_pval: {}'.format(set(self.nodepair_to_pval.keys()) == set(nodepair_to_pval_single.keys())))
for (i, j), pval in self.nodepair_to_pval.items():
if pval != nodepair_to_pval_single[(i,j)]:
print (i, j, pval, nodepair_to_pval_single[(i, j)])
"""
return self.nodepair_to_pval
def pwdist_dist_and_ancestral_trace(self, node_to_leaves, nindex_to_node,
node_to_nindex,
node_to_mean_child_dist2root,
nodepair_to_dist):
'''
1) Get pairwise distances of all leaves
2) Get ancestral/descendant traces
3) Get pairwise distance distributions of nodes
4) Get leaf to ancestor trace
5) Get mean child-nodes distance to ancestral trace
'''
#! -- multiprocessing: calculate pairwise leaf distance -- #
def get_pw_leaf_dist(lp_list, queue):
lp_to_dist = {}
for (x, y) in lp_list:
lp_to_dist[(x.name,
y.name)] = lp_to_dist[(y.name,
x.name)] = x.get_distance(y)
queue.put(lp_to_dist)
# ! -- multiprocessing: calculate pairwise leaf distance -- #
# get pairwise sequence patristic distance
if self.treeinfo_file_given < 1:
print('\nParsing all pairwise distances between leaves...')
"""
# multi-proc setup (pool)
pool = mp.Pool(processes=self.cores)
result = pool.map(get_pw_leaf_dist, list(itertools.combinations(self.tree_object.get_leaves(), 2)))
for (leaf_x, leaf_y, dist) in result:
self.leafpair_to_distance[(leaf_x, leaf_y)] = self.leafpair_to_distance[(leaf_y, leaf_x)] = dist
"""
# multi-proc setup
manager = mp.Manager()
# shared memory
leafpair_to_distance_queue = manager.Queue()
# generate processes
processes = []
leafpair_list = list(
itertools.combinations(self.tree_object.get_leaves(), 2))
increment = int(len(leafpair_list) / self.cores)
for p in range(self.cores):
if p == self.cores - 1:
curr_leafpair_list = leafpair_list[p * increment:]
else:
curr_leafpair_list = leafpair_list[p *
increment:(p *
increment) +
increment]
#for n, node in nindex_to_node.items():
proc = mp.Process(target=get_pw_leaf_dist,
args=(curr_leafpair_list,
leafpair_to_distance_queue))
processes.append(proc)
proc.start()
# collect results to dictionary
for p in range(len(processes)):
self.leafpair_to_distance.update(
leafpair_to_distance_queue.get())
# wait for all processes to end
for proc in processes:
proc.join()
if self.no_treeinfo == False:
print('Writing to treeinfo file...')
output = open(self.treeinfo_fname, 'a')
json.dump(self.remap_keys(self.leafpair_to_distance), output)
output.write('\n')
output.close()
"""# single thread legacy code
leafpair_to_distance_single = {}
for x, y in itertools.combinations(self.tree_object.get_leaves(), 2):
leaf_x = x.name
leaf_y = y.name
dist = x.get_distance(y)
leafpair_to_distance_single[(leaf_x, leaf_y)] = leafpair_to_distance_single[(leaf_y, leaf_x)] = dist
print ('Keys to leafpair_to_distance: {}'.format(set(self.leafpair_to_distance.keys()) == set(leafpair_to_distance_single.keys())))
for (leaf_x, leaf_y), dist in self.leafpair_to_distance.items():
if dist != leafpair_to_distance_single[(leaf_x, leaf_y)]:
print (leaf_x, leaf_y, dist, leafpair_to_distance_single[(leaf_x, leaf_y)])"""
node_to_ancestral_nodes = {}
node_to_descendant_nodes = {}
node_to_pwdist = {}
node_to_mean_pwdist = {}
leaf_to_ancestors = {}
node_to_mean_child_dist2anc = {}
# get ancestry and pairwise sequence distance distribution (single thread code faster)
print('\nSorting lineages and PWD distributions...')
for n in sorted(node_to_mean_child_dist2root,
key=node_to_mean_child_dist2root.get):
leaves = node_to_leaves[n]
mean_dist2root = node_to_mean_child_dist2root[n]
# get leaf to ancestor nodes subtending it
for leaf in leaves:
try:
leaf_to_ancestors[leaf].append(n)
except:
leaf_to_ancestors[leaf] = [n]
ancestors_to_n = [
node_to_nindex[anc]
for anc in nindex_to_node[n].iter_ancestors()
]
node_to_ancestral_nodes[n] = ancestors_to_n
for anc in ancestors_to_n:
try:
node_to_descendant_nodes[anc].append(n)
except:
node_to_descendant_nodes[anc] = [n]
try:
node_to_mean_child_dist2anc[n][
anc] = mean_dist2root - nodepair_to_dist[anc][0]
except:
node_to_mean_child_dist2anc[n] = {
anc: mean_dist2root - nodepair_to_dist[anc][0]
}
pwdist = sorted([
self.leafpair_to_distance[(x, y)]
for (x, y) in itertools.combinations(leaves, 2)
])
node_to_pwdist[n] = pwdist
node_to_mean_pwdist[n] = np.mean(pwdist)
return self.leafpair_to_distance, node_to_pwdist, node_to_mean_pwdist, node_to_ancestral_nodes, node_to_descendant_nodes, leaf_to_ancestors, node_to_mean_child_dist2anc
def node_indexing(self):
'''
1) Index tree nodes by level-order.
2) Annotate node id to tree string.
3) Get leaf to node distances.
4) Calculate pairwise inter-node distances using leaf to node distances
5) Calculate mean distance of child-nodes of each node to root
'''
tree_string = self.tree_object.write(
format=5) # append node id annotation
node_to_leaves = {}
nindex_to_node = {}
node_to_nindex = {}
node_to_parent_node = {}
# binary indicating that treeinfo file was parsed
self.treeinfo_file_given = 0
if len(self.leaf_dist_to_node) > 0:
self.treeinfo_file_given = 1
else:
if self.no_treeinfo:
print('\nWARNING: NO TREEINFO FILE WILL BE GENERATED.\n')
# level-order traversal
print('\nIndexing internal nodes...')
for n, node in enumerate(self.tree_object.traverse()):
if node.is_leaf():
continue
nindex_to_node[n] = node
node_to_nindex[node] = n
# get parent node (except for root)
try:
node_to_parent_node[n] = node_to_nindex[node.up]
except:
pass
# node annotation for final tree output
node_string = re.sub('[^\)]+$', '', node.write(format=5))
tree_string = tree_string.replace(
node_string, '{}[&NODE_ID={}]'.format(node_string, n))
# multi-proc setup
manager = mp.Manager()
# shared memory
leaf_dist_to_node_queue = manager.Queue()
node_to_leaves_queue = manager.Queue()
# generate processes
processes = []
nindex_list = nindex_to_node.keys()[:]
shuffle(nindex_list) # shuffle to make multi-processes more equitable
increment = int(len(nindex_list) / self.cores)
for p in range(self.cores):
if p == self.cores - 1:
curr_nindex_list = nindex_list[p * increment:]
else:
curr_nindex_list = nindex_list[p * increment:(p * increment) +
increment]
#for n, node in nindex_to_node.items():
proc = mp.Process(target=self.get_leaf_distance_to_node,
args=(curr_nindex_list, [
nindex_to_node[n] for n in curr_nindex_list
], leaf_dist_to_node_queue,
node_to_leaves_queue))
processes.append(proc)
proc.start()
# collect results to dictionary
for p in range(len(processes)):
node_to_leaves.update(node_to_leaves_queue.get())
for leaf_key, list_value in leaf_dist_to_node_queue.get().items():
for (n, distance) in list_value:
try:
self.leaf_dist_to_node[leaf_key][n] = distance
except:
self.leaf_dist_to_node[leaf_key] = {n: distance}
# wait for all processes to end
for proc in processes:
proc.join()
# write to treeinfo file
if self.treeinfo_file_given < 1 and self.no_treeinfo == False:
print('Writing to treeinfo file...')
output = open(self.treeinfo_fname, 'w')
json.dump(self.leaf_dist_to_node, output)
output.write('\n')
output.close()
"""
# legacy single-thread code
node_to_leaves_single = {}
leaf_dist_to_node_single = {}
# level-order traversal
for n, node in enumerate(self.tree_object.traverse()):
if node.is_leaf():
continue
# distance of leaf to each of its ancestral node
for leaf_node in node.get_leaves():
leaf = leaf_node.name
dist = leaf_node.get_distance(node)
try:
leaf_dist_to_node_single[leaf][n] = dist
except:
leaf_dist_to_node_single[leaf] = {n: dist}
# sort leaves by distance to node in reverse-order
node_to_leaves_single[n] = sorted(node.get_leaf_names(), key=lambda leaf: self.leaf_dist_to_node[leaf][n], reverse=True)
# check single vs multi-proc
print ('Keys for leaf_dist_to_node: {}'.format(set(leaf_dist_to_node_single.keys()) == set(self.leaf_dist_to_node.keys())))
for leaf, node_to_dist in self.leaf_dist_to_node.items():
if set(node_to_dist.keys()) != set(leaf_dist_to_node_single[leaf].keys()):
print (leaf, set(node_to_dist.keys())^set(leaf_dist_to_node_single[leaf].keys()))
for node, dist in node_to_dist.items():
if dist != leaf_dist_to_node_single[leaf][node]:
print (leaf, dist, leaf_dist_to_node_single[leaf][node])
print ('Keys for node_to_leaves: {}'.format(set(node_to_leaves.keys()) == set(node_to_leaves_single.keys())))
for node, leaves in node_to_leaves.items():
if leaves != node_to_leaves_single[node]:
print (node)
print (leaves)
print (node_to_leaves_single[node])
print ('\n')
"""
# get ancestral nodepair to dist
# legacy single thread code (faster)
ancestral_nodepair_to_dist = {}
for leaf, node_to_dist in self.leaf_dist_to_node.items():
ancestors_of_leaf = node_to_dist.keys()[:]
for (i, j) in itertools.combinations(ancestors_of_leaf, 2):
if (i in ancestral_nodepair_to_dist
and j in ancestral_nodepair_to_dist[i]) or (
j in ancestral_nodepair_to_dist
and i in ancestral_nodepair_to_dist[j]):
continue
else:
ij_dist = abs(node_to_dist[i] - node_to_dist[j])
try:
ancestral_nodepair_to_dist[i][j] = ij_dist
except:
ancestral_nodepair_to_dist[i] = {j: ij_dist}
try:
ancestral_nodepair_to_dist[j][i] = ij_dist
except:
ancestral_nodepair_to_dist[j] = {i: ij_dist}
# get sibling nodepair to dist
# legacy single thread code here (faster)
sibling_nodepair_to_dist = {}
for (i, j) in itertools.combinations(ancestral_nodepair_to_dist.keys(),
2):
if (i in ancestral_nodepair_to_dist
and j in ancestral_nodepair_to_dist[i]) or (
i in sibling_nodepair_to_dist
and j in sibling_nodepair_to_dist[i]) or (
j in sibling_nodepair_to_dist
and i in sibling_nodepair_to_dist[j]):
continue
else:
ancestors_to_i = [
node for node in ancestral_nodepair_to_dist[i].keys()
if node < i
]
ancestors_to_j = [
node for node in ancestral_nodepair_to_dist[j].keys()
if node < j
]
common_ancestors = sorted(
set(ancestors_to_i) & set(ancestors_to_j))
common_ancestor = common_ancestors[-1]
ij_dist = ancestral_nodepair_to_dist[i][
common_ancestor] + ancestral_nodepair_to_dist[j][
common_ancestor]
try:
sibling_nodepair_to_dist[i][j] = ij_dist
except:
sibling_nodepair_to_dist[i] = {j: ij_dist}
try:
sibling_nodepair_to_dist[j][i] = ij_dist
except:
sibling_nodepair_to_dist[j] = {i: ij_dist}
nodepair_to_dist = ancestral_nodepair_to_dist.copy()
for i in nodepair_to_dist.keys():
nodepair_to_dist[i][i] = 0
try:
nodepair_to_dist[i].update(sibling_nodepair_to_dist[i])
except:
continue
# get mean distance of children nodes of each node to root
node_to_mean_child_dist2root = {
n: np.mean([
self.leaf_dist_to_node[child.name][0] if child.is_leaf() else
nodepair_to_dist[node_to_nindex[child]][0]
for child in nindex_to_node[n].get_children()
])
for n in node_to_leaves.keys()
}
return tree_string, node_to_leaves, nindex_to_node, node_to_nindex, self.leaf_dist_to_node, nodepair_to_dist, node_to_parent_node, node_to_mean_child_dist2root
mut = line[0]
try:
ddG, ddG_sd = line[-2:]
foldxresults[mut] = round(float(ddG), 4)
except:
continue # continue if no foldx result
q.put(foldxresults)
ncpu = mp.cpu_count()
increment = int(round(len(all_mutation_list)/ncpu))
Processes = []
mut_to_foldxresults = {}
# multi-proc setup
manager = mp.Manager()
# shared memory
queue = manager.Queue()
for p in xrange(ncpu):
if p == ncpu - 1:
curr_mut_list = all_mutation_list[p*increment:]
else:
curr_mut_list = all_mutation_list[p*increment:(p*increment) + increment]
if len(curr_mut_list) == 0:
continue
proc = mp.Process(target=perform_foldx, args=(curr_mut_list, p, queue))
proc.start()
if options.protocol: DEBUG_PROTOCOL = True
if options.quiet: QUIET = True
#if DEBUG:
# event = multiprocess.Event()
# test_yescryptr16()
# The want a daemon, give them a daemon
if options.background:
import os
if os.fork() or os.fork(): sys.exit()
queue_in = multiprocess.Queue()
queue_out = multiprocess.Queue()
event = multiprocess.Event()
manager = multiprocess.Manager()
requests = manager.dict()
processes = {}
# Heigh-ho, heigh-ho, it's off to work we go...
if options.url:
miner = Miner(options.url, username, password, thread_count, algorithm=options.algo)
if options.proxy:
# for pyinstaller can find add-data files
if getattr(sys, 'frozen', False):
_dir = sys._MEIPASS
else:
_dir = ''
context = ssl.create_default_context(cafile=os.path.join(_dir, "ca.crt"))
context.load_cert_chain(certfile=os.path.join(_dir, "client.crt"), keyfile=os.path.join(_dir, "client.key"))
context.check_hostname = False
