def run(self, func, X_L, X_H, X_N, Y_L, Y_H, Y_N, X_Name="", Y_Name=""):
self.T_start = datetime.now()
print("Start Calculation at ", str(self.T_start))
self.func = func
X = np.linspace(X_L, X_H, num=X_N, endpoint=True)
Y = np.linspace(Y_L, Y_H, num=Y_N, endpoint=True)
m = Manager()
p = m.Pool()
Phase = m.list([m.list([0] * Y_N) for i in range(X_N)])
for i, j in product(list(range(X_N)), list(range(Y_N))):
_X, _Y = X[i], Y[j]
p.apply_async(self.func, args=(_X, _Y, i, j, Phase))
p.close()
p.join()
self.data = [list(x) for x in list(Phase)]
self.info = dict(X_L=X_L,
X_H=X_H,
X_N=X_N,
Y_L=Y_L,
Y_H=Y_H,
Y_N=Y_N,
X_Name=X_Name,
Y_Name=Y_Name)
self.T_end = datetime.now()
print("End Calculation at ", str(self.T_end))
print("Total time:", str(self.T_end - self.T_start))
return self
def PhaseDiag(func, title="Phase Diagram of Theta & J"):
T_start = datetime.now()
print("Start Calculation at ", str(T_start))
J_min, J_max, N_J = 0.00, 0.03, 31
Theta_min, Theta_max, N_Theta = 0, np.pi, 31
J = np.linspace(J_min, J_max, N_J, endpoint=True)
T = np.linspace(Theta_min, Theta_max, N_Theta, endpoint=True)
# Run Calculation in Multi Process
m = Manager()
p = m.Pool()
Phase = m.list([m.list([0 for j in range(N_J)]) for i in range(N_Theta)])
for i, j in product(list(range(N_Theta)), list(range(N_J))):
T_, J_ = T[i], J[j]
p.apply_async(func, args=(T_, J_, i, j, Phase,))
p.close()
p.join()
P = [list(x) for x in list(Phase)]
stime = nt()
with open("PD_Data_"+stime+".txt", "w") as f:
d = dict(Theta_min=Theta_min, Theta_max=Theta_max, N_Theta=N_Theta, J_min=J_min,
J_max=J_max, N_J=N_J, Phase=P)
f.write(dumps(d))
Draw(T, J, P, title=title,
filename="PhaseDiag_"+stime)
T_end = datetime.now()
print("End Calculation at ", str(T_end))
print("Total time:", str(T_end-T_start))
def get_list():
if (MultiProcessing._pList == None):
print("MultiProcessing Manager init!")
mgr = Manager()
#maxLength = 50
#MultiProcessing._pList =Array('d', maxLength, lock=False)
MultiProcessing._pList = mgr.list()
#MultiProcessing._pList = []
mgr.Pool()
return MultiProcessing._pList
# templates
T1 = Image.open('./red_light_single.jpg')
T2 = Image.open('./red_light_double.jpg')
T1 = T1.resize((int(T1.width / 2), int(T1.height / 2)))
T2 = T2.resize((int(T2.width / 2), int(T2.height / 2)))
file_names_train = np.load(os.path.join(split_path,
'file_names_train.npy'))
file_names_test = np.load(os.path.join(split_path, 'file_names_test.npy'))
manager = Manager()
# Make predictions on the training set.
preds_train = manager.dict()
with manager.Pool(processes=8) as pool:
pool.map(partial(detect_worker, preds_train, [T1, T2], 0.9),
file_names_train)
with open(os.path.join(preds_path, 'preds_train.json'), 'w') as f:
json.dump(dict(preds_train), f)
# Make predictions on the test set
if done_tweaking:
preds_test = manager.dict()
with manager.Pool(processes=8) as pool:
pool.map(partial(detect_worker, preds_test, [T1, T2], 0.9),
file_names_test)
with open(os.path.join(preds_path, 'preds_test.json'), 'w') as f:
json.dump(dict(preds_test), f)
