def on_click_register_btn(self):
input_account = self.input_account.text()
input_password = self.input_password.text()
if os.path.exists(config.account_path):
QMessageBox.warning(self, "!", "不要给老子重复注册！！", QMessageBox.Yes)
return
if self.check_account(is_login=False,
account=input_account,
password=input_password):
register_time = int(time.time())
user_data = {
"account": input_account,
"password": input_password,
"start_time": register_time,
"end_time": register_time + 24 * 3600
}
user_data = self.encrypt(str(user_data))
util.write_data(config.account_path, data=user_data)
# util.write_json_file(config.account_path, data=user_data)
self.is_finish_login.emit(True)
self.hide()
else:
QMessageBox.warning(self, "!", "账号或密码错误", QMessageBox.Yes)
def next_thread(bot, update):
global update_mode
global update_progress
global update_list
global data
logging.info('next')
if not update_mode:
bot.send_message(
chat_id=update.message.chat_id,
text="Not in update mode, use /update to start update."
)
return
if update_progress < len(update_list):
item = update_list[update_progress]
bot.send_message(
chat_id=update.message.chat_id,
text=str(update_progress+1) + '/' + str(len(update_list)) + ':\n' + item['title'] + '\nhttp://bbs.pcbeta.com/forum.php?mod=viewthread&tid=' + item['tid']
)
update_progress = update_progress + 1
else:
bot.send_message(
chat_id=update.message.chat_id,
text="Update Mode - Off"
)
data['lastUpdateTime'] = update_list[-1]['createTime']
data['lastUpdateThread'] = update_list[-1]['tid']
update_mode = False
update_progress = 0
update_list = []
util.write_data(data)
def main(): window_size = 100 threshold = calc_threshold(exp_moving_average, window_size) print threshold filename = sys.argv[1] data_in = load_data(filename) # Uncomment for more realistic first values. First window_size/4 values # should not be taken into account in the output data and plots. # data_in[:0] = [sum(data_in[:(window_size/4)])/(window_size/4)] filtered_ma = average_diff(data_in, moving_average, window_size) filtered_ema = average_diff(data_in, exp_moving_average, window_size) plot([0] * len(data_in), filtered_ma, filtered_ema, [threshold] * len(data_in), [-threshold] * len(data_in), ) mean_ma = mean_value_detector(filtered_ma, threshold) mean_ema = mean_value_detector(filtered_ema, threshold) plot(mean_ema) plot(mean_ma) write_data(mean_ema, filename + ".out")
def _deploy_files(image_id, dockerfile, app_data):
dockerfile_dir = dir_path + '/data/' + image_id
print dockerfile_dir
if not os.path.exists(dockerfile_dir):
os.makedirs(dockerfile_dir)
util.deploy_package(dockerfile_dir, app_data)
dockerfile = dockerfile.replace(r'\n', '\n')
util.write_data(dockerfile_dir + '/Dockerfile', dockerfile)
return dockerfile_dir
def __init__(self, density):
"""Initialize the simulation."""
#Initialize the container
container = LJContainer(density)
#Equilibriate the system
while self.t < EQUILIBRIATION_STEPS:
sys.stdout.write("\rEquilibriating the container: {0:3.1f}%".format(
self.t*100/EQUILIBRIATION_STEPS))
sys.stdout.flush()
#Do one 'tick' consisting of two integrations and a force update
container.tick(rescale=True)
#Increment time
self.t += 1
print("\n")
self.t = 0.0
#Start measuring
while self.t < MEASUREMENT_STEPS:
sys.stdout.write("\rCalculating averages: {0:3.1f}%".format(
self.t*100/MEASUREMENT_STEPS))
sys.stdout.flush()
#Do one 'tick' consisting of two integrations and a force update
container.tick(rescale=True)
#Sample averages
container.sample(self.t)
#Increment time
self.t += 1
#Store sampling data
util.write_data(container.data, density)
#Generate a plot of the measured properties
util.generate_report(density)
#Print out the average value for the pressure
pressure = util.calculate_average(container.data, "P")
#Write calculated pressure to disk
util.store_pressure(pressure, density)
print("\nAverage pressure for density {0}: {1:6.4}".format(
density, pressure))
def predict_metabolties(samples,
eta,
tau,
mu,
gamma,
weight_observations,
compounds,
record_mets=True):
if (np.sum(samples) == 0):
print("PUMA ERROR - Metabolite identification: samples are all zero")
exit()
c = np.dot(samples, eta)
phi = 1 - np.array(np.exp(np.log(1 - mu) * c))
b = np.log(1 - gamma * phi)
psi = np.dot(b, np.dot(tau, tau.transpose())) - b
v = np.dot(weight_observations, tau.transpose())
m_one = v * (phi * (1 -
((1 - gamma) * np.exp(psi)))) + (1 - v) * (phi *
(1 - gamma))
m_zero = v * ((1 - phi) * (1 - np.exp(psi))) + (1 - v) * (1 - phi)
mean_mets_activity = np.mean(
m_one, axis=0) / (np.mean(m_one, axis=0) + np.mean(m_zero, axis=0))
print("mean_mets_activity_PUMA_detected:", list(mean_mets_activity))
# write out m_one, m_zero, and mean_mets_activity files:
if record_mets:
outdata_dir = os.environ['PUMA_OUTPUT_DATA']
metabolite_prediction_output = os.path.join(
outdata_dir, 'metabolite_prediction_output.xlsx')
mean_mets_activity = np.squeeze(mean_mets_activity).reshape(1, -1)
write_data(mean_mets_activity,
metabolite_prediction_output,
sheetname="results",
header=compounds)
n_active_metabolites = len([
metabolite for metabolite in mean_mets_activity[0] if metabolite >= 0.5
])
print("number_active_metabolites_PUMA_detected:", n_active_metabolites)
active_mets_indices = np.nonzero(mean_mets_activity[0] >= 0.5)[0]
active_mets_ID = [compounds[index] for index in active_mets_indices]
print("active_metabolites_PUMA_detected:", active_mets_ID)
return
def pathway_prediction(landa, a_init, mu, gamma, eta, tau, observed_weight_vector, pathway_dict,
record_samples=True):
number_of_pathways = np.size(eta, 0)
number_of_metabolites = np.size(eta, 1)
myModel = pm.Model()
with myModel:
landa_value = pm.Beta('landa_value', alpha=1, beta=1)
# define prior
a = pm.Bernoulli('a', p=landa_value, shape=number_of_pathways) # 1 x p
# define posterior: p (w|a)
l = pm.math.dot(a, eta) # 1xf: number of pathways that can generate each metabolite f
phi = 1 - tt.exp(tt.log(1 - mu) * l) # 1xf: p(m_j = 1| a)
psi = 1 - tt.exp(tt.dot(tt.log(1 - (gamma * phi)), tau)) # 1xk: p(w_k=1 | a)
w = pm.Bernoulli('w', p=psi, observed=observed_weight_vector, shape=observed_weight_vector.shape)
start_point = {'landa_value': landa, 'a': a_init.astype(np.int32)}
step1 = pm.Metropolis([landa_value])
step2 = pm.BinaryGibbsMetropolis([a])
trace = pm.sample(draws=1000, step=[step1, step2], start=start_point, random_seed=42)
landa_value_samples_logodds = trace.get_values(trace.varnames[0], burn=100)
landa_value_samples = logistic.pdf(landa_value_samples_logodds)
pathways_samples = trace.get_values(trace.varnames[1], burn=100)
mean_pathways_activity = np.mean(pathways_samples, axis=0)
if record_samples:
outdata_dir = os.environ['PUMA_OUTPUT_DATA']
pathway_prediction_output = os.path.join(outdata_dir, 'pathway_prediction_output.xlsx')
mean_pathways_activity_in_samples = np.squeeze(mean_pathways_activity).reshape(1, -1)
write_data(mean_pathways_activity_in_samples, pathway_prediction_output, sheetname="samples",
header=pathway_dict["pathway"])
print("mean_pathways_activity_PUMA_detected:", list(mean_pathways_activity))
n_active_pathways = len(
[pathway_activity for pathway_activity in np.mean(pathways_samples, axis=0) if pathway_activity >= 0.5])
print("number_active_pathways [PUMA detected]:", n_active_pathways)
active_pathways_indices = np.nonzero(mean_pathways_activity >= 0.5)[0]
active_pathways_ID = [pathway_dict["pathway"][index] for index in active_pathways_indices]
print("active_pathways_PUMA_detected:", active_pathways_ID)
not_active_pathways_indices = np.nonzero(mean_pathways_activity < 0.5)[0]
not_active_pathways_ID = [pathway_dict["pathway"][index] for index in not_active_pathways_indices]
print("not_active_pathways_PUMA_detected:", not_active_pathways_ID)
return pathways_samples
def get_gpi_index_dictionary(year):
response = get_response(index_url_for_download)
if not response:
return None
gpi_data = response.content
data_file = write_data(file_path, gpi_data)
df = read_pdf(file_path, pages='10,11')
frame_part1 = df[['COUNTRY', 'SCORE']]
frame_part2 = df[['COUNTRY SCORE.1', 'Unnamed: 10']]
frame_part3 = df[['COUNTRY SCORE', 'Unnamed: 6']]
frame_part2 = frame_part2.rename(columns={
'COUNTRY SCORE.1': 'COUNTRY',
'Unnamed: 10': 'SCORE'
})
frame_part3 = frame_part3.rename(columns={
'COUNTRY SCORE': 'COUNTRY',
'Unnamed: 6': 'SCORE'
})
df = pd.DataFrame(np.concatenate(
[frame_part1.values, frame_part2.values, frame_part3.values]),
columns=frame_part1.columns)
df = df.dropna()
df = df[df['COUNTRY'] != 'COUNTRY']
df['SCORE'] = df['SCORE'].astype(float).round(3)
df = df.set_index('COUNTRY')
iso_names=['Czechia', 'Bolivia, Plurinational State of',\
'Kyrgyzstan', "Côte d'Ivoire",'Viet Nam','Gambia',\
'Macedonia, the former Yugoslav Republic of','Moldova, Republic of','Bosnia and Herzegovina','Palestine, State of',\
'Venezuela, Bolivarian Republic of',"Korea, Democratic People's Republic of",'Russian Federation',\
'Congo, Democratic Republic of the', 'Central African Republic','Syrian Arab Republic',\
'United States of America', 'United Kingdom of Great Britain and Northern Ireland',\
'Taiwan, Province of China',"Lao People's Democratic Republic",'Tanzania, United Republic of',\
'Korea, Republic of','Congo','Iran, Islamic Republic of']
countries_to_rename=['Czech Republic', 'Bolivia', \
'Kyrgyz Republic', "Cote d' Ivoire", 'Vietnam', 'The Gambia',\
'North Macedonia', 'Moldova', 'Bosnia & Herzegovina', 'Palestine', \
'Venezuela', 'North Korea', 'Russia',\
'Dem. Rep of the Congo', 'Central African Rep', 'Syria',\
'USA','United Kingdom',\
'Taiwan', 'Laos', 'Tanzania',\
'South Korea', 'Rep of the Congo', 'Iran']
gpi_map = df.T.to_dict('list')
for country, info in gpi_map.items():
info.insert(0, year)
gpi_dictionary = rename_country_to_iso_name(countries_to_rename, iso_names,
gpi_map)
return gpi_dictionary
def get_gpi_index_dictionary(year, pages=default_pages):
iso_names=['Czechia','Trinidad and Tobago','Macedonia, the former Yugoslav Republic of',\
'Bosnia and Herzegovina', 'Bolivia, Plurinational State of','Kyrgyzstan',"Côte d'Ivoire",\
'United Kingdom of Great Britain and Northern Ireland','Viet Nam','Moldova, Republic of', 'Monaco',\
'Eswatini','Gambia','Palestine, State of','Venezuela, Bolivarian Republic of',\
"Korea, Democratic People's Republic of",'Russian Federation','Central African Republic',\
'Congo, Democratic Republic of the','Syrian Arab Republic','Taiwan, Province of China',\
'United Arab Emirates',"Lao People's Democratic Republic",'Korea, Republic of',\
'Tanzania, United Republic of','United States of America','Congo',\
'Iran, Islamic Republic of']
countries_to_rename=['Czech Republic', 'Trinidad & Tobago', 'Macedonia (FYR)', \
'Bosnia & Herzegovina', 'Bolivia', 'Kyrgyz Republic', "Cote d' Ivoire",\
'United Kingdom', 'Vietnam', 'Moldova', 'Swaziland', 'The Gambia', \
'Palestine', 'Venezuela', 'North Korea', 'Russia', 'Central African Rep', \
'Dem. Rep Congo', 'Syria', 'Taiwan', 'UAE', 'Laos', 'South Korea',\
'Tanzania', 'USA', 'Rep of the Congo', 'Iran']
response=get_response(index_url)
if not response:
return
data=response.content
data_file=write_data(to_file, data)
df = read_pdf(data_file, pages=pages)
columns_to_find=['country', 'score']
columns_to_save=[]
ending_to_save=[]
for to_find in columns_to_find:
for column in df.columns:
if to_find in column.lower():
columns_to_save.append(column)
lower=column.lower()
ending_to_save.append(lower[lower.find(to_find)+len(to_find):])
ending_to_save=set(ending_to_save)
binding=[('COUNTRY{}'.format(ending), 'SCORE{}'.format(ending)) for ending in ending_to_save]
#binding=[('country', 'score'), ('country.1', 'score.1'), ('country.2', 'score.2')]
gpi_dictionary={}
frames=[]
for country,value in binding:
sub_df=df[[country, value]]
sub_df.rename(index=str, columns={country: "country", value: "score"}, inplace=True)
frames.append(sub_df)
df = pd.concat(frames)
df.dropna(subset=['score'], inplace=True)
df = df[df.country != 'COUNTRY']
df=df.set_index('country')
gpi_map=df.T.to_dict('list')
for country, info in gpi_map.items():
info.insert(0, year)
gpi_dictionary=rename_country_to_iso_name(countries_to_rename, iso_names, gpi_map)
return gpi_dictionary
def insert_one(bot, update, args):
global data
logging.info('insert')
if len(args) < 2 or len(args) > 2:
display_help(bot, update)
return
oem = args[0]
tid = args[1]
obj = crawler.getThreadObj(cookies, tid)
if oem not in data['data']:
bot.send_message(
chat_id=update.message.chat_id,
text="Incorrect OEM name."
)
return
index = -1
if tid < data['data'][oem][0]['tid']:
index = 0
data['data'][oem].insert(index, obj)
elif tid > data['data'][oem][-1]['tid']:
index = len(data['data'][oem])
data['data'][oem].append(obj)
else:
index = 1
while True:
if index == len(data['data'][oem]):
break
if tid > data['data'][oem][index - 1]['tid'] and tid < data['data'][oem][index]['tid']:
data['data'][oem].insert(index, obj)
break
index = index + 1
util.write_data(data)
bot.send_message(
chat_id=update.message.chat_id,
text="Added to local data."
)
from nltk.corpus import wordnet as wn
from util import remove_punctuation, remove_stopwords, load_data, write_data
import unicodedata
def synset_word(word):
synsets = wn.synsets(word)
return set().union(*[s.lemma_names() for s in synsets])
def synset_review(review):
review = unicodedata.normalize('NFKD', review).encode('ascii','ignore')
review = remove_stopwords(remove_punctuation(review.lower()))
words = review.split()
return ' '.join([' '.join(synset_word(word)) for word in words])
if __name__ == '__main__':
input_files = ['%s_electronics.dat'%source for source in ['amazon', 'ebay', 'twitter']]
for input_filename in input_files:
dataset = load_data(input_filename)
for d in dataset:
d['wordnet_expansion'] = synset_review(d['review'])
output_filename = input_filename.split('.')[0] + '_expanded' + '.dat'
write_data(dataset, output_filename)
def __save_protocol(self, protocol_data: str):
log_name = "Robot-%s" % self.__uid
log_path = "%s%s.log" % (self.__protocol_path, log_name)
util.write_data(write_path=log_path,
data=protocol_data + "\n\n",
mode="a+")
def main(argv):
# Test we have the correct number of arguments
if len(argv) < 2:
sys.stderr.write("Usage: cfd.py <scalefactor> <iterations> \n")
sys.exit(1)
# Get the system parameters from the arguments
scalefactor = int(argv[0])
niter = int(argv[1])
sys.stdout.write("\n2D CFD Simulation\n")
sys.stdout.write("=================\n")
sys.stdout.write("Scale factor = {0}\n".format(scalefactor))
sys.stdout.write("Iterations = {0}\n".format(niter))
# Time the initialisation
tstart = time.time()
# Set the minimum size parameters
mbase = 32
nbase = 32
bbase = 10
hbase = 15
wbase = 5
# Set the dimensions of the array
m = mbase * scalefactor
n = nbase * scalefactor
# Set the parameters for boundary conditions
b = bbase * scalefactor
h = hbase * scalefactor
w = wbase * scalefactor
# Define the psi array of dimension [m+2][n+2] and set it to zero
psi = [[0 for col in range(n + 2)] for row in range(m + 2)]
# Set the boundary conditions on bottom edge
for i in range(b + 1, b + w):
psi[i][0] = float(i - b)
for i in range(b + w, m + 1):
psi[i][0] = float(w)
# Set the boundary conditions on right edge
for j in range(1, h + 1):
psi[m + 1][j] = float(w)
for j in range(h + 1, h + w):
psi[m + 1][j] = float(w - j + h)
# Write the simulation details
tend = time.time()
sys.stdout.write("\nInitialisation took {0:.5f}s\n".format(tend - tstart))
sys.stdout.write("\nGrid size = {0} x {1}\n".format(m, n))
# Call the Jacobi iterative loop (and calculate timings)
sys.stdout.write("\nStarting main Jacobi loop...\n")
tstart = time.time()
jacobi(niter, psi)
tend = time.time()
sys.stdout.write("\n...finished\n")
sys.stdout.write("\nCalculation took {0:.5f}s\n\n".format(tend - tstart))
# Write the output files
util.write_data(m, n, scalefactor, psi, "velocity.dat", "colourmap.dat")
# Finish nicely
sys.exit(0)
from matplotlib import pyplot as plt
data, means, stds = util.load_augment_data(
util.loadf(
'../mp3/Kimiko Ishizaka - J.S. Bach- -Open- Goldberg Variations, BWV 988 (Piano) - 01 Aria.mp3'
), 1024)
vae = VAE(z_dim=256, net_size=2 * 256, chunk_samples=1024)
dirname = 'save-vae'
ckpt = tf.train.get_checkpoint_state(dirname)
vae.load_model(dirname)
x = np.zeros((2000, 1024))
vz = np.random.randn(1, 20)
z = np.random.randn(1, 20)
z, zs = vae.encode(data[500:501, :1024])
zh = []
sh = []
for n in range(2000):
z += 0.03 * (-0.5 * z + 3 * np.random.randn(*z.shape))
zh.append(np.sqrt(np.sum(z**2)))
mu, s = vae.generate(z)
sh.append(np.sqrt(np.exp(s)))
x[n, :] = (
mu + 1.00 * np.sqrt(np.exp(s)) * np.random.randn(*mu.shape)).squeeze()
out = np.zeros((2 * 1024, 2000))
out[:1024, :] = (x * stds + means).T
out[1024:, :] = 1 - 2 * np.random.randint(2, size=(1024, 2000))
sample_trace = util.write_data(np.minimum(out.T, 1.1), fname="out-vae.wav")
import util
from modelVAE import VAE
from matplotlib import pyplot as plt
data , means, stds = util.load_augment_data(util.loadf('../mp3/Kimiko Ishizaka - J.S. Bach- -Open- Goldberg Variations, BWV 988 (Piano) - 01 Aria.mp3'),1024)
vae = VAE(z_dim=256,net_size=2*256,chunk_samples=1024)
dirname = 'save-vae'
ckpt = tf.train.get_checkpoint_state(dirname)
vae.load_model(dirname)
x = np.zeros((2000,1024))
vz = np.random.randn(1,20)
z = np.random.randn(1,20)
z,zs = vae.encode(data[500:501,:1024])
zh = []
sh = []
for n in range(2000):
z += 0.03*(-0.5*z + 3*np.random.randn(*z.shape))
zh.append(np.sqrt(np.sum(z**2)))
mu,s = vae.generate(z)
sh.append(np.sqrt(np.exp(s)))
x[n,:] = (mu+1.00*np.sqrt(np.exp(s))*np.random.randn(*mu.shape)).squeeze()
out = np.zeros((2*1024,2000))
out[:1024,:] = (x*stds+means).T
out[1024:,:] = 1-2*np.random.randint(2,size=(1024,2000))
sample_trace = util.write_data(np.minimum(out.T,1.1), fname = "out-vae.wav")
from nltk.corpus import wordnet as wn
from util import remove_punctuation, remove_stopwords, load_data, write_data
import unicodedata
def synset_word(word):
synsets = wn.synsets(word)
return set().union(*[s.lemma_names() for s in synsets])
def synset_review(review):
review = unicodedata.normalize('NFKD', review).encode('ascii', 'ignore')
review = remove_stopwords(remove_punctuation(review.lower()))
words = review.split()
return ' '.join([' '.join(synset_word(word)) for word in words])
if __name__ == '__main__':
input_files = [
'%s_electronics.dat' % source
for source in ['amazon', 'ebay', 'twitter']
]
for input_filename in input_files:
dataset = load_data(input_filename)
for d in dataset:
d['wordnet_expansion'] = synset_review(d['review'])
output_filename = input_filename.split('.')[0] + '_expanded' + '.dat'
write_data(dataset, output_filename)
from model import Model
import numpy as np
with open(os.path.join('save', 'config.pkl')) as f:
saved_args = cPickle.load(f)
data, means, stds = util.load_augment_data(
util.loadf(
'../mp3/Kimiko Ishizaka - J.S. Bach- -Open- Goldberg Variations, BWV 988 (Piano) - 01 Aria.mp3'
), saved_args.chunk_samples)
model = Model(saved_args, True)
sess = tf.InteractiveSession()
saver = tf.train.Saver(tf.all_variables())
ckpt = tf.train.get_checkpoint_state('save')
print "loading model: ", ckpt.model_checkpoint_path
saver.restore(sess, ckpt.model_checkpoint_path)
n = np.random.randint(data.shape[0] - 100)
sample_data, mus, sigmas, pis = model.sample(sess,
saved_args,
start=data[n:n + 100, :])
sample_data[:, :saved_args.
chunk_samples] = sample_data[:, :saved_args.
chunk_samples] * stds + means
data[:, :saved_args.
chunk_samples] = data[:, :saved_args.chunk_samples] * stds + means
sample_trace = util.write_data(np.minimum(sample_data, 1.1), fname="out.wav")
util.write_data(data[500:1700, :], fname="out_ref.wav")
def get_fsi_index_dictionary(year):
iso_names = [
'Bolivia, Plurinational State of',
'Cabo Verde',
'Congo, Democratic Republic of the',
'Congo',
'Côte d\'Ivoire',
#'Czechia',
'Guinea-Bissau',
'Iran, Islamic Republic of',
'Israel',
'Kyrgyzstan',
"Lao People's Democratic Republic",
'Macedonia, the former Yugoslav Republic of',
'Micronesia, Federated States of',
'Moldova, Republic of',
"Korea, Democratic People's Republic of",
'Russian Federation',
'Slovakia',
'Korea, Republic of',
#'Eswatini',
'Syrian Arab Republic',
'Tanzania, United Republic of',
'United Kingdom of Great Britain and Northern Ireland',
'United States of America',
'Venezuela, Bolivarian Republic of',
'Viet Nam'
]
#add check if year is less than 2019 add to iso 'Czechia' and to rename list 'Czech Republic'
#'Czechia'<-'Czech Republic'
#'Eswatini' < - 'Swaziland'
countries_to_rename = [
'Bolivia',
'Cape Verde',
'Congo Democratic Republic',
'Congo Republic',
"Cote d'Ivoire",
#'Czech Republic'
'Guinea Bissau',
'Iran',
'Israel and West Bank',
'Kyrgyz Republic',
'Laos',
'Macedonia',
'Micronesia',
'Moldova',
'North Korea',
'Russia',
'Slovak Republic',
'South Korea',
#'Swaziland',
'Syria',
'Tanzania',
'United Kingdom',
'United States',
'Venezuela',
'Vietnam'
]
response = get_response(last_available_index_url.format(year, year),
ignore_404=True)
if response.status_code == 404:
response = get_response(previous_index_url.format(year, year))
if not response:
return None
fsi_data = response.content
data_file = write_data(to_file.format(year), fsi_data)
df = pd.read_excel(data_file)
fsi_dictionary = dict()
for index, row in df.iterrows():
fsi_dictionary[row.Country] = [row.Year.year, round(row.Total, 2)]
fsi_dictionary = rename_country_to_iso_name(countries_to_rename, iso_names,
fsi_dictionary)
return fsi_dictionary
