def __init__(self, program):
super().__init__()
self.types = [Lingvist(self, program), LingvistAdvanced(self, program)]
self.cycle = cycle.Cycle(self.types)
self.now = self.cycle.now
self.grid_i_after_header = 0
self.setSpacing(8)
def __init__(self, directory):
files = []
for file in os.listdir(directory):
if ".DTA" in file:
files.append(directory + file)
files = sorted(
files,
key=lambda f: int(re.search("(?:#)(.*)(?=\.DTA)", f).group(1)))
self.data = list(map(lambda x: cycle.Cycle(x), files))
def makeIntervalData(self):
# Loading logs
makeInterval = cycle.Cycle()
#filename = ['b2b3b4b5b6205-300','tmp300','b2b3b4b5b60-100','b2b3b4b5b6105-200','b2b3b4b5b6400-450']
filename = ['tmp300']
flag = False
for fID in filename:
logspath = glob.glob(os.path.join('logs', f'{fID}', '*.txt'))
cnt = 0
for logpath in logspath:
print(f'{fID}:{len(logspath)-cnt}')
cnt += 1
B, _ = makeInterval.loadBV(logpath)
B = np.concatenate(
[B[2, np.newaxis], B[4, np.newaxis], B[5, np.newaxis]], 0)
allyears, onehotYear = makeInterval.convV2YearlyData(
isZeroYear=True)
# zero-padding array[500,]
years = [
np.pad(year, [0, 200 - len(year)], 'constant')
for year in [allyears[1], allyears[3], allyears[4]]
]
years = np.concatenate([
years[0][:, np.newaxis], years[1][:, np.newaxis],
years[2][:, np.newaxis]
], 1)
# input dataset, intervals:list
intervals, seq = makeInterval.calcInterval(years)
# list[5]
intervals = [
np.pad(interval, [0, 200 - len(interval)], 'constant')
for interval in intervals
]
intervals = np.concatenate([
intervals[0][:, np.newaxis], intervals[1][:, np.newaxis],
intervals[2][:, np.newaxis], intervals[3][:, np.newaxis],
intervals[4][:, np.newaxis]
], 1)
if not flag:
seqs = np.array([seq])
Intervals = intervals[np.newaxis]
Years = years[np.newaxis]
onehotYears = onehotYear[np.newaxis]
Bs = B[np.newaxis]
flag = True
else:
seqs = np.hstack([seqs, np.array([seq])])
Intervals = np.vstack([Intervals, intervals[np.newaxis]])
Years = np.vstack([Years, years[np.newaxis]])
onehotYears = np.vstack(
[onehotYears, onehotYear[np.newaxis]])
Bs = np.vstack([Bs, B[np.newaxis]])
with open(
os.path.join(self.featurePath, 'interval',
f'intervalSeqXYonehotY_{fID}.pkl'),
'wb') as fp:
pickle.dump(seqs, fp, protocol=4)
pickle.dump(Intervals, fp, protocol=4)
pickle.dump(Years, fp, protocol=4)
pickle.dump(onehotYears, fp, protocol=4)
pickle.dump(Bs, fp, protocol=4)
'''
def run():
width = 1280
height = 720
pygame.init()
pygame.display.gl_set_attribute(pygame.GL_CONTEXT_MAJOR_VERSION, 4)
pygame.display.gl_set_attribute(pygame.GL_CONTEXT_MINOR_VERSION, 1)
pygame.display.gl_set_attribute(pygame.GL_CONTEXT_PROFILE_MASK,
pygame.GL_CONTEXT_PROFILE_CORE)
pygame.display.set_mode((width, height), pygame.DOUBLEBUF|pygame.OPENGL)
start_server(st)
program = get_program()
model = pyrr.matrix44.create_from_translation(pyrr.Vector3([0, 0, 0]))
projection = pyrr.matrix44.create_perspective_projection_matrix(
45, 1.0 * width / height, 0.1, 1000)
uniform.get_locs(program)
uniform.set_model(model)
uniform.set_projection(projection)
camera_obj = camera.Camera()
env_obj = env.Env()
coord_obj = coord.Coord()
model3d_obj = model3d.Model3d('shangwu', 'part1.obj')
running = True
clock = pygame.time.Clock()
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
camera_obj.process_event(event)
glClearColor(0.0, 0.0, 0.0, 1.0)
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
uniform.set_view(camera_obj.view)
env_obj.draw()
coord_obj.draw()
#model3d_obj.draw(model_q=g['fusion_obj'].mag_fusion_obj.q)
#model3d_obj.draw(model_q=g['fusion_obj'].adjusted_gyro_q)
model3d_obj.draw(model_q=g['fusion_obj'].q)
if g['fusion_obj'].adjusted_axis is not None:
axis = g['fusion_obj'].adjusted_axis
x_arrow = arrow.Arrow(color=[1.0, 0.0, 0.0],
vector=axis[0])
#x_arrow.draw()
y_arrow = arrow.Arrow(color=[0.0, 1.0, 0.0],
vector=axis[1])
#y_arrow.draw()
z_arrow = arrow.Arrow(color=[0.0, 0.0, 1.0],
vector=axis[2])
#z_arrow.draw()
if g['fusion_obj'].acc_normal is not None:
cycle_obj = cycle.Cycle(cos=g['fusion_obj'].acc_normal[0])
#cycle_obj.draw()
cycle_obj = cycle.Cycle(cos=g['fusion_obj'].mag_normal[0],
nv=g['fusion_obj'].mag_v)
#cycle_obj.draw()
pygame.display.flip()
def makeIntervalData(self):
# Loading logs
makeInterval = cycle.Cycle()
#filename = ['b2b3b4b5b60-100','b2b3b4b5b6105-200','b2b3b4b5b6205-300','tmp300','b2b3b4b5b6400-450']
'''
flag = False
for fID in filename:
logspath = glob.glob(os.path.join('logs',f'{fID}','*.txt'))
cnt = 0
for logpath in logspath:
print(f'{fID}:{len(logspath)-cnt}')
cnt += 1
B,_ = makeInterval.loadBV(logpath)
B = np.concatenate([B[2,np.newaxis],B[4,np.newaxis],B[5,np.newaxis]],0)
allyears = makeInterval.convV2YearlyData(isLSTM=True)
print(np.max([len(allyears[0]),len(allyears[1]),len(allyears[2])]))
# zero-padding array[500,]
years = [np.pad(year, [0, 200-len(year)], 'constant') for year in allyears]
years = np.concatenate([years[0][:,np.newaxis],years[1][:,np.newaxis],years[2][:,np.newaxis]],1)
#pdb.set_trace()
# input dataset, intervals:list
intervals, seq = makeInterval.calcInterval(allyears)
# list[5]
intervals = [np.pad(interval, [0, 150-len(interval)], 'constant') for interval in intervals]
intervals = np.concatenate([intervals[0][:,np.newaxis],intervals[1][:,np.newaxis],intervals[2][:,np.newaxis]],1)
if not flag:
seqs = np.array([seq])
Intervals = intervals[np.newaxis]
Years = years[np.newaxis]
Bs = B[np.newaxis]
flag = True
else:
seqs = np.hstack([seqs, np.array([seq])])
Intervals = np.vstack([Intervals, intervals[np.newaxis]])
Years = np.vstack([Years, years[np.newaxis]])
Bs = np.vstack([Bs, B[np.newaxis]])
with open(os.path.join(self.featurePath,'interval',f'intervalSeqXY_{fID}.pkl'),'wb') as fp:
#with open(os.path.join(self.featurePath,'interval',f'practice.pkl'),'wb') as fp:
pickle.dump(seqs, fp, protocol=4)
pickle.dump(Intervals, fp, protocol=4)
pickle.dump(Years, fp, protocol=4)
pickle.dump(Bs, fp, protocol=4)
'''
filename = [
'b2b3b4b5b60-100', 'b2b3b4b5b6105-200', 'b2b3b4b5b6205-300',
'tmp300', 'b2b3b4b5b6400-450'
]
nData = []
flag = False
for fname in filename:
with open(
os.path.join(self.featurePath, 'interval',
f'intervalSeqXY_{fname}.pkl'), 'rb') as fp:
seqs = pickle.load(fp)
intervals = pickle.load(fp)
years = pickle.load(fp)
paramb = pickle.load(fp)
if not flag:
Seqs = seqs
Intervals = intervals
Years = years
Paramb = paramb
flag = True
else:
Seqs = np.hstack([Seqs, seqs])
Intervals = np.vstack([Intervals, intervals])
Years = np.vstack([Years, years])
Paramb = np.vstack([Paramb, paramb])
nData = np.append(nData, Intervals.shape[0])
#pdb.set_trace()
nData = int(nData[-1])
nTrain = int(nData * 0.8)
randInd = np.random.permutation(nData)
#pdb.set_trace()
# Separate train & test
seqTrain = Seqs[randInd[:nTrain]]
intervalTrain = Intervals[randInd[:nTrain]]
yearTrain = Years[randInd[:nTrain]]
parambTrain = Paramb[randInd[:nTrain]]
seqTest = Seqs[randInd[nTrain:]]
intervalTest = Intervals[randInd[nTrain:]]
yearTest = Years[randInd[nTrain:]]
parambTest = Paramb[randInd[nTrain:]]
with open(
os.path.join(self.featurePath, 'interval',
f'train_intervalSeqXY.pkl'), 'wb') as fp:
pickle.dump(seqTrain, fp)
pickle.dump(intervalTrain, fp)
pickle.dump(yearTrain, fp)
pickle.dump(parambTrain, fp)
with open(
os.path.join(self.featurePath, 'interval',
f'test_intervalSeqXY.pkl'), 'wb') as fp:
pickle.dump(seqTest, fp)
pickle.dump(intervalTest, fp)
pickle.dump(yearTest, fp)
pickle.dump(parambTest, fp)
import cycle
my_cycle = cycle.Cycle()
executions_to_process = my_cycle.get_executions_by_status_and_labels(
my_cycle.status_from, my_cycle.labels)
for execution in executions_to_process:
my_cycle.update_execution_status(execution, my_cycle.status_to)