def result():
if request.method == 'POST':
start_time = request.form.get('st')
end_time = request.form.get('et')
counter = 0
id_dict = {}
answer = {}
if (validate(start_time) and validate(end_time)):
start = datetime.strptime(start_time, '%Y-%m-%dT%H:%M:%S%z')
end = datetime.strptime(end_time, '%Y-%m-%dT%H:%M:%S%z')
with open('json_3.json') as f:
J = json.load(f)
L = []
for item in J:
i = item['id']
s = 0
if (item['state']):
s = 1
b1 = item['belt1']
b2 = item['belt2']
t = datetime.strptime(item['time'], '%Y-%m-%d %H:%M:%S')
t = t.replace(tzinfo=timezone.utc)
L.append(
(t, int(remove_alphabets(i)), (1 - s) * b1, s * b2))
# print((t.time(), int(remove_alphabets(i)), (1 - s) * b1, s * b2))
for item in L:
t = item[0]
i = item[1]
b1 = item[2]
b2 = item[3]
if t >= start and t <= end:
tmp = (0, 0, 0)
if i in id_dict:
tmp = id_dict[i]
id_dict[i] = (b1 + tmp[0], b2 + tmp[1], 1 + tmp[2])
for key in id_dict:
tmp = id_dict[key]
answer[key] = (tmp[0] / tmp[2], tmp[1] / tmp[2])
answer = sorted(answer.items())
print(answer)
l = []
for key in answer:
dic = {}
dic["id"] = key[0]
dic["avg_belt1"] = key[1][0]
dic["avg_belt2"] = key[1][1]
l.append(dic)
return jsonify(l)
def get_path_filename(self, obj):
''' Get the path and filename and build it into protocol://path
'''
obj['Filename'] = obj['Path'].rsplit(
'\\', 1)[1] if '\\' in obj['Path'] else obj['Path'].rsplit('/',
1)[1]
if self.direct_path:
if not validate(obj['Path']):
raise PathValidationException(
"Failed to validate path. User stopped.")
obj['Path'] = obj['Path'].replace(obj['Filename'], "")
else:
obj['Path'] = "plugin://plugin.video.jellyfin/%s/" % obj[
'LibraryId']
params = {
'filename': py2_encode(obj['Filename'], 'utf-8'),
'id': obj['Id'],
'dbid': obj['MvideoId'],
'mode': "play"
}
obj['Filename'] = "%s?%s" % (obj['Path'], urlencode(params))
def get_path_filename(self, obj):
''' Get the path and filename and build it into protocol://path
'''
obj['Filename'] = obj['Path'].rsplit(
'\\', 1)[1] if '\\' in obj['Path'] else obj['Path'].rsplit('/',
1)[1]
if self.direct_path:
if not validate(obj['Path']):
raise Exception("Failed to validate path. User stopped.")
obj['Path'] = obj['Path'].replace(obj['Filename'], "")
'''check bluray directries and point it to ./BDMV/index.bdmv'''
if validate_bluray_dir(obj['Path'] + obj['Filename']):
obj['Path'] = obj['Path'] + obj['Filename'] + '/BDMV/'
obj['Filename'] = 'index.bdmv'
LOG.debug("Bluray directry %s", obj['Path'])
else:
obj['Path'] = "plugin://plugin.video.jellyfin/%s/" % obj[
'LibraryId']
params = {
'filename': py2_encode(obj['Filename'], 'utf-8'),
'id': obj['Id'],
'dbid': obj['MovieId'],
'mode': "play"
}
obj['Filename'] = "%s?%s" % (obj['Path'], urlencode(params))
def testValidate1(self):
yamlStr = '\n'.join([
"{",
" example: {",
" test: /^src//,",
" script: stack test,",
" }",
"}"
])
configRaw = yaml.load(yamlStr)
expected = True
actual = validate(configRaw)
self.assertEqual(expected, actual)
def get_path_filename(self, obj):
''' Get the path and build it into protocol://path
'''
if self.direct_path:
if '\\' in obj['Path']:
obj['Path'] = "%s\\" % obj['Path']
obj['TopLevel'] = "%s\\" % dirname(dirname(obj['Path']))
else:
obj['Path'] = "%s/" % obj['Path']
obj['TopLevel'] = "%s/" % dirname(dirname(obj['Path']))
if not validate(obj['Path']):
raise Exception("Failed to validate path. User stopped.")
else:
obj['TopLevel'] = "plugin://plugin.video.emby.tvshows/"
obj['Path'] = "%s%s/" % (obj['TopLevel'], obj['Id'])
def get_song_path_filename(self, obj, api):
''' Get the path and filename and build it into protocol://path
'''
obj['Path'] = api.get_file_path(obj['Path'])
obj['Filename'] = obj['Path'].rsplit('\\', 1)[1] if '\\' in obj['Path'] else obj['Path'].rsplit('/', 1)[1]
if self.direct_path:
if not validate(obj['Path']):
raise PathValidationException("Failed to validate path. User stopped.")
obj['Path'] = obj['Path'].replace(obj['Filename'], "")
else:
server_address = self.server.auth.get_server_info(self.server.auth.server_id)['address']
obj['Path'] = "%s/Audio/%s/" % (server_address, obj['Id'])
obj['Filename'] = "stream.%s?static=true" % obj['Container']
def testValidate9(self):
yamlStr = '\n'.join([
"{",
" example1: {",
" test: /^src//,",
" script: stack test,",
" other: additional,",
" },",
" example2: {",
" test: /^src//,",
" script: stack test,",
" }",
"}"
])
configRaw = yaml.load(yamlStr)
expected = False
actual = validate(configRaw)
self.assertEqual(expected, actual)
def get_path_filename(self, obj):
''' Get the path and filename and build it into protocol://path
'''
obj['Filename'] = obj['Path'].rsplit('\\', 1)[1] if '\\' in obj['Path'] else obj['Path'].rsplit('/', 1)[1]
if self.direct_path:
if not validate(obj['Path']):
raise Exception("Failed to validate path. User stopped.")
obj['Path'] = obj['Path'].replace(obj['Filename'], "")
else:
obj['Path'] = "plugin://plugin.video.emby.movies/"
params = {
'filename': obj['Filename'].encode('utf-8'),
'id': obj['Id'],
'dbid': obj['MovieId'],
'mode': "play"
}
obj['Filename'] = "%s?%s" % (obj['Path'], urllib.urlencode(params))
shuffle=True)
testloader = torch.utils.data.DataLoader(test_data, batch_size=64)
validloader = torch.utils.data.DataLoader(validate_data, batch_size=64)
if args.arch == 'vgg':
input_size = 25088
model = models.vgg16(pretrained=True)
elif args.arch == 'resnet':
input_size = 2048
model = models.alexnet(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model.classifier = nn.Sequential(nn.Linear(input_size, args.hidden_layers),
nn.ReLU(), nn.Dropout(p=0.5),
nn.Linear(args.hidden_layers, 102),
nn.LogSoftmax(dim=1))
print(model)
criterion = nn.NLLLoss()
device = args.gpu
optimizer = optim.Adam(model.classifier.parameters(), args.lr)
loss, accuracy = helper.validate(model, criterion, testloader, device)
print(f"loss: {loss} \n Accuracy: {accuracy}")
epochs = args.epochs
model = helper.train(model, optimizer, criterion, epochs, trainloader,
validloader, device)
helper.accuracy(model, testloader, device)
helper.save(model, train_data, args.arch, input_size, args.hidden_layers,
epochs, args.lr)
def myTradingSystem(DATE, CLOSE, settings):
### 0.SET UP
# num and build: counter used to retrain the model by interval
# settings['train_period']: record individual market's lookback period found in the last retrain: at the start
# settings['accuracies']: record the accuracy achieved in the last retain using the recorded lookback period and test period for each market
# filtered: counter of how many markets are removed due to inaccurate model or non-convergent problem
num = settings['counter'] % interval
build = num == 0
nMarkets = CLOSE.shape[1]
weights = np.zeros(nMarkets)
filtered = 0
date = DATE[-1]
settings['counter'] += 1
if settings[
'counter'] == 1: ###first day set up the lookback by search from 50~500
periods, accuracies = set_train_period(CLOSE, settings['train_period'],
settings['accuracies'])
settings['train_period'] = periods
settings['accuracies'] = accuracies
### 1. Data preprocessing: from raw data to input
# CLOSE: log the prices to 1)linearize the trend 2)reduce the impact of different price scale of different markets
# CLOSE: remove consecutive duplicates as that will cause the model to produce nan values when it detects constant pattern
print('\n{}'.format(date))
for i in range(1, nMarkets):
cur = np.log(np.array([k for k, g in groupby(CLOSE[:, i])]))
pred, model = None, None
### 2. Model building: process input to produce predictions
# prev_period: the last lookback period used for this market in the last retrain; might be ajusted slightly if another number can achieve better performance
# train_period, model, accuracy: the adjusted lookback period, model trained using the new period and the accuracy for the retrained model in the validation data
if build:
prev_period = settings['train_period'][i]
train_period, model, accuracy = adjust_period(cur, prev_period)
### 3. Model filtering
# threshold: if the accuracy is less than the threshold, exclude the market in the final output to avoid loss
# settings['models']: if the model achieves acceptable accuracy, save it for next day prediction until next retrain
if accuracy < threshold:
model = None
print(' {}%==={}: inaccurate'.format(int(100 * accuracy),
settings['markets'][i]))
settings['models'][i] = model
settings['train_period'][i] = train_period
settings['accuracies'][i] = accuracy
else:
model = settings['models'][i]
accuracy = settings['models'][i]
accuracy = settings['accuracies'][i]
if model:
### 4. Prediction and equity allocation
# pred: the HW model will predict the next log(price)
# weights: take the predicted log(price) and minus the latest observed log(price) as the final output weights
pred = validate(cur[-1], model.forecast(num + 1)[-1])
if not np.isnan(pred):
print(' {}%==={}'.format(int(accuracy * 100),
settings['markets'][i]))
weights[i] = pred - cur[-1]
else:
print(' {}%==={}: non-convergent'.format(
int(accuracy * 100), settings['markets'][i]))
acc = settings['accuracies']
included = np.nonzero(weights)[0]
settings['num_trades'].append(included.size)
print("(average daily trades/ total markets): ({}/{})".format(
round(np.mean(settings['num_trades']), 2), nMarkets - 1))
if cluster_on and num_cluster > 1:
clusters, weightage = cluster(CLOSE[-cluster_period:,
included], num_cluster,
np.array(acc)[included], dist_func,
smooth_alpha)
weights = group_norm(num_cluster, included, clusters,
np.array(weights), build, weightage)
return weights, settings
#!/bin/python3
from runTest import ConfigFile
import helper
if __name__ == "__main__":
config = helper.loadFile(ConfigFile)
if helper.validate(config):
print("no problem is found")
else:
print("config file is invalid")
def result():
if request.method == 'POST':
start_time = request.form.get('st')
end_time = request.form.get('et')
counter = 0
counterAA = 0
counterAB = 0
counterBA = 0
counterBB = 0
counterCA = 0
counterCB = 0
counterDA = 0
counterDB = 0
## J's Space
## V's Space
if (validate(start_time) and validate(end_time)):
start = datetime.strptime(start_time, '%Y-%m-%dT%H:%M:%S%z')
end = datetime.strptime(end_time, '%Y-%m-%dT%H:%M:%S%z')
with open('data2.json') as f:
data_list = json.load(f)
shifta_start = datetime.strptime('00:30:01', '%H:%M:%S')
shifta_end = datetime.strptime('08:30:00', '%H:%M:%S')
shiftb_start = datetime.strptime('08:30:01', '%H:%M:%S')
shiftb_end = datetime.strptime('14:30:00', '%H:%M:%S')
shiftc_1_start = datetime.strptime('14:30:01', '%H:%M:%S')
shiftc_1_end = datetime.strptime('23:59:59', '%H:%M:%S')
shiftc_2_start = datetime.strptime('00:00:00', '%H:%M:%S')
shiftc_2_end = datetime.strptime('00:30:00', '%H:%M:%S')
shifta_start = shifta_start.time()
shifta_end = shifta_end.time()
shiftb_start = shiftb_start.time()
shiftb_end = shiftb_end.time()
shiftc_1_start = shiftc_1_start.time()
shiftc_1_end = shiftc_1_end.time()
shiftc_2_start = shiftc_2_start.time()
shiftc_2_end = shiftc_2_end.time()
dbg = True
if (dbg):
print("DEBUG_START")
print("Time entered : ", start, " to ", end)
print("Shift A : ", shifta_start, " to ", shifta_end)
print("Shift B : ", shiftb_start, " to ", shiftb_end)
print("Shift C : ", shiftc_1_start, " to ", shiftc_1_end)
print("Shift C : ", shiftc_2_start, " to ", shiftc_2_end)
for item in data_list:
t = datetime.strptime(item['time'], '%Y-%m-%d %H:%M:%S')
t = t.replace(tzinfo=timezone.utc)
if t >= start and t <= end:
if (dbg):
print("Counter is ", counter, " Item time is : ",
item['time'], " t is ", t)
nt = t.time()
if (item['production_A'] == True):
if (dbg):
print("Found A at counter index : ", counter,
", item is ", item)
if (nt >= shifta_start and nt <= shifta_end):
counterAA += 1
if (nt >= shiftb_start and nt <= shiftb_end):
counterBA += 1
if (nt >= shiftc_1_start and nt <= shiftc_1_end):
counterCA += 1
if (nt >= shiftc_2_start and nt <= shiftc_2_end):
counterDA += 1
if (item['production_B'] == True):
if (dbg):
print("Found B at counter index : ", counter,
", item is ", item)
if (nt >= shifta_start and nt <= shifta_end):
counterAB += 1
if (nt >= shiftb_start and nt <= shiftb_end):
counterBB += 1
if (nt >= shiftc_1_start and nt <= shiftc_1_end):
counterCB += 1
if (nt >= shiftc_2_start and nt <= shiftc_2_end):
counterDB += 1
counter += 1
dic = {
'shiftA': {
'production_A_count': counterAA,
'production_B_count': counterAB
},
'shiftB': {
'production_A_count': counterBA,
'production_B_count': counterBB
},
'shiftC': {
'production_A_count': counterCA + counterDA,
'production_B_count': counterCB + counterDB
}
}
# jo = json.dumps(dic)
return jsonify(dic)
