def allocate(self):
consul_obj = consul.Consul(host=global_env.consul_host,
token=global_env.consul_acl_token)
kv = consul_obj.kv
blueprint = self.blueprint
host1 = allocate.allocate(blueprint['memsize'])
host2 = allocate.allocate(blueprint['memsize'], anti_affinity=[host1])
kv.put('tarantool/%s/allocation/instances/1/host' %
self.group_id, host1)
kv.put('tarantool/%s/allocation/instances/2/host' %
self.group_id, host2)
def allocate(self):
consul_obj = consul.Consul(host=global_env.consul_host,
token=global_env.consul_acl_token)
kv = consul_obj.kv
blueprint = self.blueprint
host = allocate.allocate(blueprint['memsize'])
kv.put('tarantool/%s/allocation/instances/1/host' %
self.group_id, host)
"BookingId": [part + "-" + str(i) for i in range(0, order_days)],
"Priority":
100,
"Part":
part,
"Quantity":
pd.Series(nd.random.poisson(lam=lam, size=order_days)),
"DueDate":
pd.date_range(start="2018-01-15", periods=order_days)
}) for (part, lam) in order_rates
])
prev_parts = []
fig, ax = plt.subplots()
for label, df in bookings.groupby("Part"):
df.plot(ax=ax, x="DueDate", y="Quantity", label=label)
plt.show()
# %%
results, simstat, simprod = allocate.allocate(
bookings=bookings,
prev_parts=prev_parts,
flex_file="sample-flex.json",
plugin="../../orderlink/lib/pegboard/allocate")
allocate.print_result_summary(results)
allocate.plot_simprod(results)
allocate.plot_simstat(simstat)
# %%
allocate.download(results, "localhost:5000")
for key, value in lecturercaps.items():
sumcaps += value
if int(sumcaps) < (len(medinit_unassignedStudents) +
len(nonmedinit_unassignedStudents)):
sys.exit("Not enough capacity between all lecturers: only " +
str(sumcaps) + " lecturer slots available for " +
str((len(medinit_unassignedStudents) +
len(nonmedinit_unassignedStudents))) +
" students. Check input files.")
# Run the main allocation function
# MedChem
if randomise:
random.shuffle(medinit_unassignedStudents)
medallocation = allocate.allocate(med_studPrefs, medinit_unassignedStudents,
lectPrefs, projLects, lectProjs,
lecturercaps, projCaps, randomise, updates,
iterationLimit)
med_studAssignments = medallocation["Student Assignments"]
med_lectAssignments = medallocation["Lecturer Assignments"]
med_projAssignments = medallocation["Project Assignments"]
med_unassignedStudents = medallocation["Unassigned Students"]
# Remove any assigned medicinal topics from the preference lists of any non-med chem student
for key, value in nonmed_studPrefs.items():
for project in value:
if project in med_studAssignments:
value.remove(project)
if randomise:
random.shuffle(nonmedinit_unassignedStudents)
# Non Med-Chem
n = 5 # liczba wezłów
temp = n
nodes, elements = generate(x_0,x_p,n)
# warunki brzegowe
wb = [{"index": 1, "type": 'D', "value": 1},
{"index": n, "type": 'D', "value": 2}]
else:
print("\nYou entered a wrong number!\nApplying option 1")
# definicja
nodes, elements, wb = define()
plot(nodes,elements,wb)
A, b = allocate(temp)
baseFunDegree = 1
phi, dphi = base(baseFunDegree)
# Processing
elementsCount = np.shape(elements)[0]
for ee in np.arange(0, elementsCount):
elemRowInd = ee
elemGlobalInd = elements[ee,0]
elemNode1 = elements[ee,1] # indeks węzła początkowego elementu ee
elemNode2 = elements[ee,2] # indeks węzła końcowego elementu ee
indGlobalNode = np.array([elemNode1, elemNode2])