def param_init(log_filename):
global bio_ner_train_data, bio_ner_test_data, bio_ner_model, bio_ner_report
bio_ner_train_data = data(
RAW_DATA_PATH, PROCESSED_DATA_PATH, WORD_VEC_FILE, CHAR_VEC_FILE,
POS_VEC_FILE, ADD_FEAT_VEC_FILE, CLASS_VEC_FILE,
ADD_FEAT_FILE_SUBSTRING, CHAR_ONE_HOT_FILE_SUBSTRING,
WORD_CONTEXT_LENGTH, NB_UNIQUE_CHARS, CHAR_VECTOR_SIZE,
WORD_VECTOR_SIZE, NB_CLASSES, MAX_WORD_LENGTH, ADD_FEAT_VEC_SIZE,
LIMITED_ADD_FEAT_VEC_SIZE, POS_TAG_VECTOR_SIZE)
bio_ner_test_data = data(
RAW_DATA_PATH, PROCESSED_DATA_PATH, TEST_word_vec_file_substring,
CHAR_VEC_FILE, POS_VEC_FILE, ADD_FEAT_VEC_FILE,
TEST_entity_vec_file_substring, TEST_add_feat_vec_file_substring,
TEST_char_vec_file_substring, WORD_CONTEXT_LENGTH, NB_UNIQUE_CHARS,
CHAR_VECTOR_SIZE, WORD_VECTOR_SIZE, NB_CLASSES, MAX_WORD_LENGTH,
ADD_FEAT_VEC_SIZE, LIMITED_ADD_FEAT_VEC_SIZE, POS_TAG_VECTOR_SIZE)
bio_ner_model = model(WORD_VECTOR_SIZE, WORD_CONTEXT_LENGTH,
NB_UNIQUE_CHARS, CHAR_VECTOR_SIZE, MAX_WORD_LENGTH,
EMBEDDING_OP_DIM, MAX_FEATURES, NB_CLASSES,
CHAR_FEATURE_OUTPUT, HIDDEN_SIZE, ADD_FEAT_VEC_SIZE,
POS_TAG_VECTOR_SIZE)
bio_ner_report = report(REPORT_PATH, log_filename)
def read(self, fileName):
"""reads a csv into the data set"""
i = 0
file = open(fileName)
for line in file:
if len(line) <= 1:
pass
elif i >= 1:
self.append(data(line))
else:
self.header = data(line)
i |= 1
file.close()
def BackupM():
a = 1
count = 0
print('iniciando...')
while a in range(len(nome) + 1):
try:
print(nome[a - 1])
paf = ('/home/%s/backup.tar.gz' % nome[a - 1])
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh.connect(ip[a - 1], username=nome[a - 1], password=senha[a - 1])
sftp = ssh.open_sftp()
#stdin, stdout, stderr = ssh.exec_command('tar -czvf /home/%s/backup.tar.gz /home/%s/Backup'%(nome[a-1],nome[a-1]))
sftp.get(format(paf),
'/home/user208/%s%s.tar.gz' % (nome[a - 1], data.data()))
sftp.close()
paf = ("cp /home/user208/'%s%s.tar.gz' /home/user208/Dropbox" %
(nome[a - 1], data.data()))
os.system(paf)
print('\nEnvio concluÃdo.\n')
except:
count += 1
falha.append(nome[a - 1])
a += 1
print('O backup foi finalizado. Das %d máquinas, %d não fizeram backup.' %
(len(nome), count))
smtp = smtplib.SMTP_SSL('smtp.mail.yahoo.com', 465)
smtp.login('mail', 'password')
de = 'who send'
para = ['from']
msg = """From: %s
To: %s
Subject: Backup
Backup feito.""" % (de, ', '.join(para))
smtp.sendmail(de, para, msg)
smtp.quit()
print('Email enviado.\n')
def __init__(self, params):
#getting Params pickle file which was saved in persona.train()
with open(params.decode_path + "/params.pickle", 'rb') as file:
model_params = pickle.load(file)
#Copying those parameters from model_params which are not in params
for key in model_params.__dict__:
if key not in params.__dict__:
params.__dict__[key] = model_params.__dict__[key]
self.params = params
self.mode = "decoding"
if self.params.PersonaMode:
print("decoding in persona mode")
else:
print("decoding in non persona mode")
self.Data = data(self.params) #Intializing EOT, EOS, beta and params
self.lstm_source = lstm_source_(self.params)
self.lstm_target = lstm_target_(self.params)
self.softmax = softmax_(self.params)
if self.params.use_GPU:
self.lstm_source = self.lstm_source.cuda()
self.lstm_target = self.lstm_target.cuda()
self.softmax = self.softmax.cuda()
self.readModel(
) #loading the model(only parameters) of first iteration in training
self.ReadDict(
) #buidling a dictionary of words, with keys from 0 to len(dictionary.txt)
self.read_dict()
async def birthday_command(self,
ctx,
user: discord.User = None,
birthday=""):
""" get/set users birthday
"""
logging.info(f'{ctx.author} tried to use the "birthday" command')
info = data(ctx.guild)
if birthday == "":
if info.get_birthday(user) is not None:
await ctx.send(
f'{user.display_name}\'s birthday is `{info.get_birthday(user).strftime("%m/%d/%Y")}`'
)
else:
await ctx.send(f'Please set {user.display_name}\'s birthday')
return
try:
birthday = parser.parse(birthday)
except parser.ParserError:
await ctx.send("Incorrect birthday format, try `month-day-year`")
return
info.set_birthday(user, birthday)
logging.info(f'{user} birthday is now {info.get_birthday(user)}')
await ctx.send(
f'Set {user.display_name}\'s birthday to `{info.get_birthday(user).strftime("%m/%d/%Y")}`'
)
def __init__(self, *args, **kwargs):
wx.Frame.__init__(self, *args, **kwargs)
self.data = data.data()
self.buttons = {}
self.sizers = {}
self.static_sizers = {}
self.canvas = {}
self.panels = {}
self.grids = {}
self.check_boxes = {}
self.texts = {}
self.spins = {}
self.menu_bars = {}
self.menus = {}
self.menu_items = {}
self.mouse = wx.MouseEvent()
self.create_menu()
self.create_ui()
self.Show()
i = [key for key in self.data.GetList() if self.data.CheckKey(key, 'active')]
for widget in i:
key = self.data.GetWidget(widget)
if not key['active']:
if self.data.GetValue(key['parent'],'w_type')=='sizer':
self.sizers[key['parent']].Hide(key['position'])
elif self.data.GetValue(key['parent'],'w_type')=='static_sizer':
self.static_sizers[key['parent']].Hide(key['position'])
def dataElement_create(self, **kwargs):
"""
Creates the 'data' element container.
:param kwargs: 'root' = <location>
:return:
"""
s = self._stree
SeriesFilesCount = 3
str_root = '/'
for key, val in kwargs.iteritems():
if key == 'root': str_root = val
if key == 'SeriesFilesCount': SeriesFilesCount = val
sample = data.data()
sample.contents_build_1(SeriesFilesCount=SeriesFilesCount)
# sample.dataComponent_pluginRun()
s.cd(str_root)
l_data = sample.contents.lstr_lsnode('/')
if sample.contents.cd('/')['status']:
for d in l_data:
s.graft(sample.contents, '/%s' % d)
return (dict(sample.contents.snode_root))
def __init__(self, name, x, z):
# id
global ENEMY_ID
self.id = ENEMY_ID
ENEMY_ID += 1
# set data
self.__name = name
self.__data = enemies_data.get_data(name)
self.__data.pos = data.data()
self.__data.pos.x = x
self.__data.pos.z = z
self.__data.id = self.id
self.__data.last_fire_time = timer.gtimer.current()
self.__data.target = None
self.hp = self.__data.hp
# state machine
self.__statem = state_manager.istate_manager()
self.__statem.register(STATE_IDLE, state_idle(self.__data, self))
self.__statem.register(STATE_RUN, state_run(self.__data, self))
self.__statem.register(STATE_FIRE, state_fire(self.__data, self))
self.__statem.register(STATE_BEATEN, state_beaten(self.__data, self))
self.__statem.change_to(STATE_IDLE, None)
# broadcast enemy born
pkt = self.get_born_pkt()
game.controller.gcontroller.broadcast(pkt)
def dataElement_create(self, **kwargs):
"""
Creates the 'data' element container.
:param kwargs: 'root' = <location>
:return:
"""
s = self._stree
SeriesFilesCount = 3
str_root = '/'
for key, val in kwargs.iteritems():
if key == 'root': str_root = val
if key == 'SeriesFilesCount': SeriesFilesCount = val
sample = data.data()
sample.contents_build_1(SeriesFilesCount = SeriesFilesCount)
# sample.dataComponent_pluginRun()
s.cd(str_root)
l_data = sample.contents.lstr_lsnode('/')
if sample.contents.cd('/')['status']:
for d in l_data:
s.graft(sample.contents, '/%s' % d)
return(dict(sample.contents.snode_root))
def __init__(self, params):
self.Data = data(params)
self.params = params
self.lstm_source = lstm_source_(self.params)
self.lstm_target = lstm_target_(self.params)
self.lstm_source.apply(self.weights_init)
self.lstm_target.apply(self.weights_init)
embed = list(self.lstm_source.parameters())[0]
embed[self.params.vocab_dummy].data.fill_(0)
embed = list(self.lstm_target.parameters())[0]
embed[self.params.vocab_dummy].data.fill_(0)
if self.params.use_GPU:
self.lstm_source = self.lstm_source.cuda()
self.lstm_target = self.lstm_target.cuda()
self.softmax = softmax_(self.params)
self.softmax.apply(self.weights_init)
if self.params.use_GPU:
self.softmax = self.softmax.cuda()
self.output = self.params.output_file
if self.output != "":
with open(self.output, "w") as selfoutput:
selfoutput.write("")
if self.params.PersonaMode:
print("training in persona mode")
else:
print("training in non persona mode")
self.ReadDict()
def combinação(): #15 linhas
print(' ', data.data())
t = int(
input(f'''
[1] Cálculo concentral inicial: poço 1A
[2] Cálculo do FIC
'''))
if t == 1:
C1 = float(input(f' Qual a concentração da solução estoque? '))
print(f' A quantidade a ser retirada é de ')
if t == 2:
IC50AC = float(input(f' IC50 da droga A em combinação? '))
IC50A = float(input(f' IC50 da droga A sozinha? '))
IC50BC = float(input(f' IC50 da droga B em combinação? '))
IC50B = float(input(f' IC50 da droga B sozinha? '))
ΣFIC = (IC50AC / IC50A) + (IC50BC / IC50B)
print(f'''
ΣFIC = (IC50AC/IC50A) + (IC50BC/IC50B)
ΣFIC = ({IC50AC}/{IC50A}) + ({IC50BC}/{IC50B})
ΣFIC = ({round((IC50AC/IC50A),2)}) + ({round((IC50BC/IC50B),2)})
ΣFIC = {round(ΣFIC,2)}''')
print()
print(f'>>> O ΣFIC resultante é de {virgula.virgula(round(ΣFIC,2))}')
def run(run_id=None,
mode='normal',
loaded_data=None,
split_id=None,
input_form=config.INPUT_FORM,
label_form="outcome",
hyperparameters=dict()):
if run_id is None:
run_id = int(datetime.utcnow().timestamp())
if split_id is None:
split_id = run_id
if mode == 'normal':
if loaded_data is None:
# create the data objects
training, validation, test = data(split_id,
input_form=input_form,
label_form=label_form)
else:
training, validation, test = loaded_data
model_instance = model(input_form,
aux_size=training.features_size,
hyperparameters=hyperparameters)
# return trained model
return train(model_instance, training, validation, run_id, 'val_loss')
elif mode == 'cross':
# training, validation, test, holdout_test = loaded_data
training, validation, test = loaded_data
model_instance = model(input_form,
aux_size=training.features_size,
hyperparameters=hyperparameters)
return train(model_instance, training, validation, run_id, 'val_loss')
def panel_data(pdata=None):
comReturn = comm.local()
if comReturn: return comReturn
import data
dataObject = data.data()
defs = ('setPs', 'getData', 'getFind', 'getKey')
return publicObject(dataObject, defs, None, pdata)
def prepare(self):
dataset = data()
uf_num = 0
self.userX = None
self.uIDx = {}
vf_num = 0
self.itemX = None
self.vIDx = {}
self.graph = dataset.loadGraph(self.params['graph'],
string.atoi(self.params['dim']))
feature, f_num, fvIDx = dataset.loadFeature2(self.params['feature'])
self.drawer = sampler_CA_BPR(self.graph, feature, fvIDx, self.Z, \
lambda_setting=string.atoi(self.params['seed_num']), \
thred=float(self.params['thred']))
if (self.params.has_key('userX')):
userX, uf_num, self.uIDx = dataset.loadFeature3(
self.params['userX'], self.drawer.user_set)
self.userX = dataset.constructSparseMat(userX, uf_num)
# self.userX = numpy.matrix(self.userX)
if (self.params.has_key('itemX')):
itemX, vf_num, self.vIDx = dataset.loadFeature3(
self.params['itemX'], self.drawer.item_set)
# self.itemX = numpy.matrix(self.itemX)
self.itemX = dataset.constructSparseMat(itemX, vf_num)
#feature, f_num, fvIDx = dataset.loadFeature2(self.params['feature'])
#feature = dataset.constructSparseMat(feature, f_num).todense()
self.model = CA_BPR_Model(string.atoi(self.params['k']),
self.drawer.user_set, self.drawer.item_set,
uf_num, vf_num)
def __init__(self, params):
self.Data=data(params)
self.params=params
self.lstm_source =lstm_source_(self.params)
self.lstm_target =lstm_target_(self.params)
self.lstm_source.apply(self.weights_init) # weights_init is a member function
self.lstm_target.apply(self.weights_init) # apply: Applies fn recursively to every submodule (as returned by .children()) as well as self.
embed=list(self.lstm_source.parameters())[0] #sembedding from lstm_source, 25010*512
embed[self.params.vocab_dummy].data.fill_(0)
embed=list(self.lstm_target.parameters())[0] #embedding from lstm_target, 25010*512
embed[self.params.vocab_dummy].data.fill_(0)
if self.params.use_GPU:
self.lstm_source=self.lstm_source.cuda()
self.lstm_target=self.lstm_target.cuda()
self.softmax=softmax_(self.params)
self.softmax.apply(self.weights_init)
if self.params.use_GPU:
self.softmax=self.softmax.cuda()
self.output=self.params.output_file # save/testing/log or save/testing/non_persona/log
# Creating an output file if it doesn't exist
if self.output!="":
with open(self.output,"w") as selfoutput:
selfoutput.write("")
if self.params.PersonaMode:
print("training in persona mode")
else:
print("training in non persona mode")
self.ReadDict()
def main():
params = {}
params['net_filename'] = "model96.pth"
params['rf_size'] = 96
params['rf_stride'] = 4
params['gpu'] = 0
params['num_epochs'] = 500
params['num_batches'] = 30
params['num_minibatches'] = 30
params['minibatch_size'] = 150
params['heatmap_decay'] = 0.75
image_dir = ""
mask_dir = ""
heatmap_dir = ""
data = d.data((params['rf_size'], params['rf_size']))
data.load_from_directories(image_dir, mask_dir, heatmap_dir)
# train
# Create the network.
net = Net()
print(net)
# load the weights
if os.path.isfile(params['net_filename']):
net.load_state_dict(torch.load(params['net_filename']))
net.cuda(params['gpu'])
train(net, data, params)
def train_ideep(protein, features, save_path, seed=None):
train, valid, test = data(protein, features, seed=seed)
model = model_ideep(train[0], features, seed=seed)
list_train = [train[0][feature] for feature in features]
list_valid = [valid[0][feature] for feature in features]
list_test = [test[0][feature] for feature in features]
if seed is not None:
np.random.seed(seed)
earlystopper = EarlyStopping(monitor='val_loss', patience=5, verbose=0)
print('model training')
model.fit(list_train,
train[1],
batch_size=100,
epochs=20,
verbose=0,
validation_data=(list_valid, valid[1]),
callbacks=[earlystopper])
predictions = model.predict_proba(list_test)
auc = roc_auc_score(test[1], predictions)
print("Test AUC: ", auc)
dt = pd.DataFrame({"y_true": test[1], "y_pred_proba": predictions[:, 0]})
if not os.path.isdir(save_path):
os.makedirs(save_path)
dt.to_csv(os.path.join(save_path, protein + ".csv"))
return auc
def write(self,fileout):
# make data object
d = data()
# set data file headers
d.title = "LAMMPS simple chain data file"
d.headers["atoms"] = len(self.atoms)
d.headers["bonds"] = len(self.bonds)
d.headers["atom types"] = self.natype
d.headers["bond types"] = self.nbtype
d.headers["xlo xhi"] = (0, self.boxx)
d.headers["ylo yhi"] = (0, self.boxy)
d.headers["zlo zhi"] = (0, self.boxz)
# set data file body
lines = []
j = 0
for i in self.mass:
lines.append("%d %d\n" % (self.atype[j],i))
j += 1
d.sections["Masses"] = lines
lines = []
for i in self.atoms:
lines.append("%d %d %d %f %f %f %f %f %f\n" % \
(i[0],i[1],i[2],i[3],i[4],i[5],i[6],i[7],i[8]))
d.sections["Atoms"] = lines
lines = []
for i in self.bonds:
lines.append("%d %d %d %d\n" % (i[0],i[1],i[2],i[3]))
d.sections["Bonds"] = lines
# write to file
d.write(fileout)
def albrecht():
return data(name="albrecht",
rows=[[
"$In", "$Out", "$Query", "$File", "$FpAdj", "$RawFPcounts",
"?AdjFp", "<Effort"
], [25, 150, 75, 60, 1, 1750, 1750, 102.4],
[193, 98, 70, 36, 1, 1902, 1902, 105.2],
[70, 27, 0, 12, 0.8, 535, 428, 11.1],
[40, 60, 20, 12, 1.15, 660, 759, 21.1],
[10, 69, 1, 9, 0.9, 478.89, 431, 28.8],
[13, 19, 0, 23, 0.75, 377.33, 283, 10],
[34, 14, 0, 5, 0.8, 256.25, 205, 8],
[17, 17, 15, 5, 1.1, 262.73, 289, 4.9],
[45, 64, 14, 16, 0.95, 715.79, 680, 12.9],
[40, 60, 20, 15, 1.15, 690.43, 794, 19],
[41, 27, 29, 5, 1.1, 465.45, 512, 10.8],
[33, 17, 8, 5, 0.75, 298.67, 224, 2.9],
[28, 41, 16, 11, 0.85, 490.59, 417, 7.5],
[43, 40, 20, 35, 0.85, 802.35, 682, 12],
[7, 12, 13, 8, 0.95, 220, 209, 4.1],
[28, 38, 24, 9, 1.05, 487.62, 512, 15.8],
[42, 57, 12, 5, 1.1, 550.91, 606, 18.3],
[27, 20, 24, 6, 1.1, 363.64, 400, 8.9],
[48, 66, 13, 50, 1.15, 1073.91, 1235, 38.1],
[69, 112, 21, 39, 1.2, 1310, 1572, 61.2],
[25, 28, 4, 22, 1.05, 476.19, 500, 3.6],
[61, 68, 0, 11, 1, 694, 694, 11.8],
[15, 15, 6, 3, 1.05, 189.52, 199, 0.5],
[12, 15, 0, 15, 0.95, 273.68, 260, 6.1]])
def __init__(self):
self.minist = da.data().minist
self.x = tf.placeholder(tf.float32, [None, 784])
self.y = tf.placeholder(tf.float32, [None, 10])
self.prediction, self.loss, self.train_step, self.accuracy = self.get_model()
self.train_model()
pass
def main():
again='y'
while again=='y':
d16,d17,d18,d19,d20=data_frames()
train_data,train_labels=data(d16,d17,d18,d19,int(input('What is the'+
' points cutoff? ')))
k=input('k (separate by space)? ').split()
k = [ int(x) for x in k ]
good=[]
for k_n in k:
model = KNeighborsClassifier(n_neighbors=k_n)
model.fit(train_data, train_labels)
names=d19['full_name'].to_numpy()
test_data=d19.drop('full_name',1).to_numpy()
predictions = model.predict(test_data)
i=0
while i<len(names):
if predictions[i]==1:
good.append(names[i])
i+=1
ind=0
great=[]
if len(k)==1:
great=good
else:
while ind < len(good):
if good.count(good[ind])>1:
if good[ind] not in great:
great.append(good[ind])
ind+=1
#print(good)
#print(great)
results=d20.drop(['goals_scored','assists','total_points','minutes',
'goals_conceded','creativity','influence','threat','bonus','bps',
'ict_index','clean_sheets','red_cards','yellow_cards',
'selected_by_percent'],1)
for i in results.index:
if results['full_name'][i] not in great:
results.drop(i,inplace=True)
print(results)
#results.to_csv('results.csv')
price=int(float(input('Price cutoff? '))*10)
for i in results.index:
if results['now_cost'][i] > price:
results.drop(i,inplace=True)
print(results)
again = input("Run again? (y/n) ")
def __init__(self, params):
with open(path.join(params.model_folder, params.params_name),
'rb') as file:
adapted_params = pickle.load(file)
for key in vars(params):
vars(adapted_params)[key] = vars(params)[key]
adapted_params.dev_file = adapted_params.decode_file
self.params = adapted_params
if self.params.SpeakerMode:
print("decoding in speaker mode")
elif self.params.AddresseeMode:
print("decoding in speaker-addressee mode")
else:
print("decoding in non persona mode")
self.ReadDict()
self.Data = data(self.params, self.voc)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
if self.params.cpu:
self.device = "cpu"
self.Model = lstm_decoder(self.params, len(self.voc), self.Data.EOT)
self.readModel(self.params.model_folder, self.params.model_name)
self.Model.to(self.device)
self.ReadDictDecode()
self.output = path.join(self.params.output_folder,
self.params.log_file)
if self.output != "":
with open(self.output, "w") as selfoutput:
selfoutput.write("")
async def add_command(self, ctx, *args):
""" add users to the plan
`$add[a] <@Name, Name, РђюName With SpacesРђЮ>'`
Example:
$a
$add "Chis Bot"
$a chis unholydog106
"""
await ctx.message.delete()
match = data(ctx.guild)
args = list(map(lambda user: closest_user(user, ctx.guild), args))
if len(args) == 0:
args = [ctx.author]
logging.info(f'{ctx.author} tried to add {*args,} to the plan')
for user in args:
if match.people < match.spots:
if not match.add_gamer(user):
await ctx.send(f'{user} is already a gamer.')
else:
await ctx.send(f'Cannot add {user}, too many gamers.')
await update_message(ctx, self.match_messages, await self.match_message(ctx, match))
def __init__(self, params):
self.params=params
self.ReadDict()
self.Data=data(params,self.voc)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if params.cpu:
self.device="cpu"
self.Model = lstm(params,len(self.voc),self.Data.EOT)
self.Model.encoder.apply(self.weights_init)
self.Model.decoder.apply(self.weights_init)
self.Model.softlinear.apply(self.weights_init)
self.Model.to(self.device)
self.output=path.join(params.save_folder,params.output_file)
if self.output!="":
with open(self.output,"w") as selfoutput:
selfoutput.write("")
if self.params.SpeakerMode:
print("training in speaker mode")
elif self.params.AddresseeMode:
print("training in speaker-addressee mode")
else:
print("training in non persona mode")
def isA_extraction():
'''
S: sentences from web corpus that match the hearst pattern
the main entry point for isA extractin
--> set of isA pairs(x, y)
'''
# for sentence in S:
# syntactic_extraction(sentence)
pool = Pool(20)
tasks = []
logging.info('loading the data......')
for s in data():
logging.info('getting {}'.format(s.index))
tasks.append(s)
# logging.info('added {}'.format(s[0]))
logging.info('loading completed.')
results = pool.map(generate_isa_pairs, tasks)
pool.close()
pool.join()
logging.info('Complete!! There is total {} pairs'.format(len([x for x in results if x is True])))
return
def create_bullet(name, x, y, z, dir_x, dir_z, tt, damage):
import constants
if tt == constants.BulletMelee:
# melee attack
__melee_attack(name, x, z, dir_x, dir_z, damage);
return
import network, game
global BULLET_ID
BULLET_ID += 1
bid = BULLET_ID
# 1. broadcast
bpkt = network.packet.packet(network.events.MSG_SC_OTHER_BULLET,
id = bid,
name = name,
x = x, y = y, z = z,
dir_x = dir_x, dir_z = dir_z,
tt = tt,
velocity = constants.BulletVelocity)
game.controller.gcontroller.broadcast(bpkt)
# 2. save in server
import data
d = data.data(ply = name, id = bid, dir_x = dir_x, dir_z = dir_z, damage = damage)
game.controller.gcontroller.Bullets[bid] = d
return d
def train_model(self):
init = tf.global_variables_initializer()
batch_size = 20
train_steps = 100000
test_steps = 100
data = da.data()
train_data = data.train_data
test_data = data.train_data
with tf.Session() as sess:
sess.run(init)
for i in range(train_steps):
batch_data, batch_labels = train_data.next_batch(batch_size)
loss_val, acc_val, _ = sess.run([self.loss, self.accuracy, self.train_op],
feed_dict={self.x: batch_data, self.y: batch_labels})
if (i + 1) % 500 == 0:
print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' % (i + 1, loss_val, acc_val))
if (i + 1) % 5000 == 0:
# test_data = CifarData(test_filenames, False)
all_test_acc_val = []
for j in range(test_steps):
test_batch_data, test_batch_labels = test_data.next_batch(batch_size)
test_acc_val = sess.run([self.accuracy],
feed_dict={self.x: test_batch_data, self.y: test_batch_labels})
all_test_acc_val.append(test_acc_val)
test_acc = np.mean(all_test_acc_val)
print('[Test] Step: %d,acc: %4.5f' % (i + 1, test_acc))
def prepare(self):
dataset = data()
self.graph = dataset.loadGraph(self.params['graph'],
string.atoi(self.params['dim']))
self.drawer = sampler(self.graph, self.Z)
self.model = BPR_model(self.Z, self.drawer.user_set,
self.drawer.item_set)
def run_each_frame(file, topo):
alld, pbc = sca.get_coord(file, topo)
res_d = {} # contains information for each frame
avg_dict = {}
count = 0
for frame in alld:
a, b, c = pbc[frame]
d, links_h, links_o, hbonds, obonds = data.data(alld[frame], a, b, c)
temp_d = rings.job(d, links_h, links_o, hbonds, obonds, a, b, c)
for i in temp_d:
temp_d[i] = temp_d[i]
if i not in avg_dict:
avg_dict[i] = temp_d[i]
else:
avg_dict[i] += temp_d[i]
count += 1
res_d[frame] = temp_d
print file, 'Number of frames', len(res_d)
for i in avg_dict:
avg_dict[i] = avg_dict[i] / count
print res_d
print avg_dict
np.save(file[:-6] + '_RING_perFrame.npy', res_d)
np.save(file[:-6] + '_RING_avg.npy', avg_dict)
def run_each_frame(file, topo):
alld, pbc = sca.get_coord(file, topo)
res_d = {} # contains information for each frame
avg_dict = {}
count = 0
for frame in alld:
a, b, c = pbc[frame]
d, links_h, links_o, hbonds, obonds = data.data(alld[frame], a, b, c)
coord = np.array([d[i][1:] for i in d])
temp_d = cluster.cluster(coord, file)
for i in temp_d:
temp_d[i] = temp_d[i]
if i not in avg_dict:
avg_dict[i] = temp_d[i]
else:
avg_dict[i] += temp_d[i]
count += 1
res_d[frame] = temp_d
print file, 'Number of frames', len(res_d)
for i in avg_dict:
avg_dict[i] = avg_dict[i] / count
np.save(file[:-6] + '_CMS_perFrame.npy', res_d)
np.save(file[:-6] + '_CMS_avg.npy', avg_dict)
def diluicao_droga(): #11 linhas
C1 = float(input(f' Conc. da solução estoque (µM): '))
C2 = float(input(f' Conc. da solução desejada (µM): '))
V2 = float(input(f' Volume de diluição (µL):'))
print()
V1 = round(((C2 * V2) / C1), 2)
volume_de_meio = V2 - V1
print()
print(' ', data.data())
print()
print(f''' =============================
C1V1 = C2V2
{C1} x V1 = {C2} x {V2}
V1 = {C2} x {V2}/{C1}
V1 = {round((C2*V2/C1),2)}
=============================''')
print()
print(f' Volume de droga = {virgula.virgula(V1)} µL')
print()
print(f' Volume de meio = {virgula.virgula(volume_de_meio)} µL')
print()
def write_lammps_file(self, filename):
d = data()
d.title = "Lammps data file; Rough surface stats - " + self.spectrum.get_info()
d.headers = {}
d.sections = {}
dd = 2**(1/6)
dx, dy = dd/2, dd/2
atom_type = 1
M, N = self.heights.shape
Zs = self.heights.real
xlo, ylo = - (M*dx)/2, -(N*dy)/2
d.headers["zlo zhi"] = (np.min(Zs), np.max(Zs))
d.headers["xlo xhi"] = (xlo, xlo + M*dx)
d.headers["ylo yhi"] = (ylo, ylo + N*dy)
d.headers["atoms"] = M*N
d.headers["atom types"] = atom_type
atom_lines = []
velocity_lines = []
atom_id = 1
for i in range(M):
for j in range(N):
atom_line = "%d %d %d %g %g %g %d %d %d\n" % (atom_id, 0, atom_type, xlo+i*dx, ylo+j*dy, Zs[i, j], 0, 0, 0)
atom_lines.append(atom_line)
velocity_lines.append("%d 0 0 0\n" %atom_id)
atom_id += 1
d.sections["Atoms"] = atom_lines
d.sections["Velocities"] = velocity_lines
d.sections["Masses"] = ["%d %g\n" %(atom_type, 1)]
d.write(filename)
async def addall_command(self, ctx, *args):
""" add all users currently in the voice channel to the plan
`$addall[aa]`
Example:
$aa
"""
await ctx.message.delete()
voice_channels = ctx.guild.voice_channels
match = data(ctx.guild)
if ctx.author.voice is None:
await ctx.send(f'{ctx.author} is not in a voice channel.')
return
for channel in voice_channels:
if ctx.author.voice.channel != None and ctx.author.voice.channel is channel:
members = sorted(
channel.members, key=lambda user: self.activity_check(user), reverse=True)
for user in members:
if match.people < match.spots:
if not match.add_gamer(user):
await ctx.send(f'{user} is already a gamer.')
else:
await ctx.send(f'Cannot add {user}, too many gamers.')
await update_message(ctx, self.match_messages, await self.match_message(ctx, match))
def run_each_frame(file, topo):
print file
alld, pbc = sca.get_coord(file, topo)
res_d = {} # contains information for each frame
avg_dict = {}
count = 0
for frame in alld:
a,b,c = pbc[frame]
d,links_h,links_o,hbonds,obonds=data.data(alld[frame], a, b, c)
temp_d = hbtype.result(d,links_h,links_o,hbonds,obonds)
#print temp_d
for i in temp_d:
temp_d[i] = len(temp_d[i])
if i not in avg_dict:
avg_dict[i] = temp_d[i]
else:
avg_dict[i] += temp_d[i]
count += 1
res_d[frame] = temp_d
print file, 'Number of frames', len(res_d)
for i in avg_dict:
avg_dict[i] = avg_dict[i]/count
#visual(avg_dict, file[:-6])
np.save(file[:-6]+'_HB_perFrame.npy', res_d)
np.save(file[:-6]+'_HB_avg.npy', avg_dict)
def createDatasets():
for featuresType in ['MFCC', 'Speaker_trait']:
df_train,df_test = data.data(featuresType)
df_train = df_train.fillna(0)#all NaN replaced by 0
if not(onlyFrench):
df_test = df_test.fillna(0)#all NaN replaced by 0
# df_train = df_train.sample(n=5000, random_state=17)
# df_test = df_test.sample(n=10000, random_state=17)
dataset = data.restructure_data(df_train)
if not(onlyFrench):
dataset_test = data.restructure_data(df_test)
if onlyFrench:
dataset['data'] = dataset['data'].values.astype('float')
dataset['target'] =dataset['target'].values.astype('str')
else:
dataset['data'] = np.concatenate([dataset['data'].values.astype('float'), dataset_test['data'].values.astype('float')])
dataset['target'] = np.concatenate([dataset['target'].values.astype('str'),
dataset_test['target'].values.astype('str')])
dataset['language'] = np.concatenate([dataset['language'].values.astype('str'),
dataset_test['language'].values.astype('str')])
print("Scaling")
standard_scaler = StandardScaler()
dataset['data'] = standard_scaler.fit_transform(dataset['data'])
if(onlyFrench):
np.savez('UL/onlyFrench-'+featuresType+'-dataset.npz', data=dataset['data'], target=dataset['target'], language=dataset['language'])
else:
np.savez('UL/215832_frames-'+featuresType+'-dataset.npz', data=dataset['data'], target=dataset['target'], language=dataset['language'])
def run(model, description):
run_id = str(uuid4())
model.run(run_id)
K.clear_session()
model_instance = evaluate.load(os.path.join(
config.MODEL_DIR,
"{}-{}.h5".format(run_id, model.MODEL_NAME),
))
train, validation, test_data = data()
train_data_stats = characterize_data(train)
validation_data_stats = characterize_data(validation)
test_data_stats = characterize_data(test_data)
results = test(model_instance, train, validation, test_data)
result = Result(
model.MODEL_NAME,
run_id,
train_data_stats,
validation_data_stats,
test_data_stats,
description,
**results
)
db.session.add(result)
db.session.commit()
def BackupM():
a = 1
count = 0
print('iniciando...')
while a in range(len(nome)+1):
try:
print(nome[a-1])
paf = ('/home/%s/backup.tar.gz'%nome[a-1])
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh.connect(ip[a-1],username= nome[a-1],password= senha[a-1])
sftp = ssh.open_sftp()
#stdin, stdout, stderr = ssh.exec_command('tar -czvf /home/%s/backup.tar.gz /home/%s/Backup'%(nome[a-1],nome[a-1]))
sftp.get(format(paf),'/home/user208/%s%s.tar.gz'%(nome[a-1],data.data()))
sftp.close()
paf = ("cp /home/user208/'%s%s.tar.gz' /home/user208/Dropbox"%(nome[a-1],data.data()))
os.system(paf)
print('\nEnvio concluÃdo.\n')
except:
count += 1
falha.append(nome[a-1])
a += 1
print('O backup foi finalizado. Das %d máquinas, %d não fizeram backup.'%(len(nome), count))
smtp = smtplib.SMTP_SSL('smtp.mail.yahoo.com', 465)
smtp.login('mail', 'password')
de = 'who send'
para = ['from']
msg = """From: %s
To: %s
Subject: Backup
Backup feito.""" % (de, ', '.join(para))
smtp.sendmail(de, para, msg)
smtp.quit()
print('Email enviado.\n')
def icalc_next(self):
self.__last_time = timer.gtimer.current()
curr_waypoint = self.__path[self.__pos - 1]
next_waypoint = self.__path[self.__pos]
delta_x = next_waypoint[0] - curr_waypoint[0]
delta_z = next_waypoint[1] - curr_waypoint[1]
L = delta_x ** 2 + delta_z ** 2
L = math.sqrt(L)
if L <= 0:
self.__dir = data.data(x = delta_x, z = delta_z)
else:
self.__dir = data.data(x = delta_x / L, z = delta_z / L)
self.__last_waypoint = data.data(x = curr_waypoint[0], z = curr_waypoint[1])
self.__next_waypoint = data.data(x = next_waypoint[0], z = next_waypoint[1])
self.__L = L
def test_basic_parser(): # data_file = '/Users/Zhe/Desktop/sat_july_2014/data_2014.07.15/test' data_file = subdir + '/'+'empty' input= 'input.txt' original = data(data_file) a = basic_parser(subdir + '/'+input).parser() original.update(a,output = 'test_output')
def test_sentence_parser(): print subdir # data_file = '/Users/Zhe/Desktop/sat_july_2014/data_2014.07.15/test' data_file = subdir + '/'+'empty' input= 'passage_test' original = data(data_file) a = sentence_parser(subdir + '/'+input).parser() original.update(a,output = 'test_output') print original
def problem1(self, test_docs, train_docs, mean_sent=2.5):
print "Sentiment Analysis: <Problem 1> "
#creates awesome data object for each paragraph in document
data_list = []
for doc in train_docs:
for (par, rating) in doc.get_par_rating_tuples():
#Protecting against the chance of a failed parse
if (par is not None and rating is not None):
data_list.append(data(par.lower(), rating, doc.filename, doc.author))
else:
print "Found bad review by -> " + doc.author + " (this comes from: par = None)"
self.complete(data_list)
def importer(df,Si,Ii,Fi,colsY,nTests=0,test=False):
df = ajoutDate(df)
colsX,S,I,F = numerote(Si,Ii,Fi)
X,y = prepare(df,colsX,colsY)
X,y = featureScale(S,X,y)
#X,y = decoupeJours(X,y,nTests,colsX) #On passe à un jour
E,X,colsX = convertirE(F,X,colsX,test=True)
d = data.data(X,y,colsX,Ii)
if(nTests!=0):
idx = np.random.choice(np.arange(0,np.shape(X)[0]),nTests,replace=False)
print(pd.DataFrame(X[idx],columns=colsX))
return d
def Backup(nomex,ipx,senhax):
try:
paf = ('/home/%s/backup.tar.gz'%nomex)
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh.connect(ipx,username= nomex,password= senhax)
#stdin, stdout, stderr = ssh.exec_command('tar -czvf /home/%s/backup.tar.gz /home/%s/Backup'%(nomex,nomex))
sftp = ssh.open_sftp()
sftp.get(format(paf),'/home/user208/%s%s.tar.gz'%(nomex,data.data()))
sftp.close()
#------------------------Envio ao dropbox-------------------------------------
paf = ("cp /home/user208/'%s%s.tar.gz' /home/user208/Dropbox"%(nomex,data.data()))
os.system(paf)
print('\nEnvio concluÃdo.\n')
except:
os.system('clear')
print('Ops! Alguns dos parametros passados podem estar errados.')
time.sleep(2)
os.system('clear')
def load(self):
fp = open(self._file_name, 'r')
def get_file(fp):
for line in fp.readlines():
yield line
for line in get_file(fp):
entry = data(line, self._sep, self._key_pos)
self._lns[entry.key()] = entry
fp.close()
def test_sentence_parser():
subdir = '/Users/Zhe/Desktop/sat_july_2014/collector'
# data_file = '/Users/Zhe/Desktop/sat_july_2014/data_2014.07.15/test'
data_file = subdir + '/'+'empty'
input= 'mock_test'
original = data(data_file,mode = 'normal')
a = cr_practice_parser(subdir + '/'+input).parser()
original.update(a,output = 'test_output')
print original
def test_mock_parser(): # data_file = '/Users/Zhe/Desktop/sat_july_2014/data_2014.07.15/test' data_file = subdir + '/'+'empty' input= 'mock_test' original = data(data_file) a = CRmock_parser(subdir + '/'+input).parser() original.update(a, output = subdir + '/'+'test_output') print original #print_dict(empty) #test_mock_parser()
def problem2(self, test_docs, train_docs):
print "Sentiment Analysis: <Problem 2> "
data_list = []
#creates awesome data object for each document
for doc in train_docs:
#pars = [par.lower() for par in doc.get_pars() if par is not None]
#ratings = [rating for rating in doc.get_ratings() if rating is not None]
par = doc.get_pars()[3]
rating = doc.get_ratings()[3]
if (par is not None and rating is not None):
data_list.append(data(par, rating, doc.filename, doc.author))
else:
print "Found bad review by -> " + doc.author + " (this comes from: par = None)"
self.complete(data_list)
def test(arguments):
param_combo, params, features, shorthands = arguments
params = { k:v for k,v in params.items() }
params['subfolder name'] = '_'.join(['%s_%s'%(sh,pv) for sh,pv in zip(shorthands,param_combo)])
for pv, fe in zip(param_combo, features): params[fe] = pv
classifiers = {'gcm':gcm, 'gnb':gnb, 'alcove':alcove, 'som':som}
classifier = classifiers[params['classifier']]
d = data(params)
for simulation in range(params['n simulations']):
c = classifier(d, params, simulation)
time = 0
while time < params['length simulation']:
time += params['test interval']
c.load(simulation, time)
c.test()
def __init__(self, d, conn):
self.name = d.name
self.__conn = conn
self.__state = constants.PLAYER_STATE_LOGIN
conn.ply = self
self.bag = bag.bag(self)
self.bag.add(constants.ItemBullet, 10000)
self.dead = False
self.pos = data.data(x = 1000, z = 1000)
# data
self.hero = "hero"
self.weapon = constants.WeaponNormal
self.run_distance = 0
self.point = 0
def train_and_test(arguments):
param_combo, params, features, shorthands = arguments
params = { k:v for k,v in params.items() }
params['subfolder name'] = '_'.join(['%s_%s'%(sh,pv) for sh,pv in zip(shorthands,param_combo)])
for pv, fe in zip(param_combo, features): params[fe] = pv
classifiers = {'gcm':gcm, 'gnb':gnb, 'alcove':alcove, 'som':som}
classifier = classifiers[params['classifier']]
dirname = '%s/%s' % (params['folder name'], params['subfolder name'])
os.makedirs(dirname)
with open('%s/parameters.p' % dirname, 'wb') as fh:
pickle.dump(params, fh)
#
d = data(params)
for simulation in range(params['n simulations']):
c = classifier(d, params, simulation)
c.train(dump = False, test = True)
def __init__( self, parent=None ):
QtGui.QMainWindow.__init__( self, parent )
self.setWindowTitle( 'Plotduino' )
# the data acquisition
self.data_source = data.data()
self.data_source.selection = 0
# time interval for the plot update
self.timestep = 200
# timer for updating the plot
self.timer = QtCore.QTimer(self)
#create the layout and connect the signals to the buttons
self.set_layout()
self.connectSignals()
def opn(self, etc, url):
if self.up_win:
self.temp_h = gtk.VBox()
self.temp_l = gtk.Label()
self.temp_l.set_markup(
'<span color="green" size="16000">Loading....</span>')
self.temp_h.pack_start(self.temp_l, True)
self.vbox_one.pack_start(self.temp_h, False)
self.temp_h.show()
self.vbox_one.show_all()
self.motion('')
queue = Queue.Queue()
self.loader_cont.show()
dt = data()
data_thread = threading.Thread(target=dt.get_data, args=(url, queue))
data_thread.start()
def idont(self):
xor = queue.get()
if xor == 1 or xor == 2:
store = xor
else:
store = 0
if store == 2:
self.loader_cont.hide()
self.temp_cont = gtk.HBox()
self.temp_lab = gtk.Label()
self.temp_lab.set_markup(
'<span size="20000" color="red">ERROR: Connection failed</span>')
self.temp_cont.pack_start(self.temp_lab, True)
self.view1.remove(self.text_view1)
self.view1.add(self.temp_cont)
self.view1.show_all()
return False
if store == 1:
load()
self.vbox_two.show_all()
self.refresh()
self.loader_cont.hide()
self.temp_h.pack_end(self.temp_l)
self.temp_h.hide()
return False
return True
gobject.timeout_add(1, idont, self)
def write(self,file):
if len(self.atoms) != self.n:
raise StandardError,"%d monomers instead of requested %d" % \
(len(self.atoms),self.n)
list = [atom[2] for atom in self.atoms]
atypes = max(list)
btypes = 0
if len(self.bonds):
list = [bond[1] for bond in self.bonds]
btypes = max(list)
# create the data file
d = data()
d.title = "LAMMPS FENE chain data file"
d.headers["atoms"] = len(self.atoms)
d.headers["bonds"] = len(self.bonds)
d.headers["atom types"] = atypes
d.headers["bond types"] = btypes
d.headers["xlo xhi"] = (self.xlo,self.xhi)
d.headers["ylo yhi"] = (self.ylo,self.yhi)
d.headers["zlo zhi"] = (self.zlo,self.zhi)
lines = []
for i in range(atypes): lines.append("%d 1.0\n" % (i+1))
d.sections["Masses"] = lines
lines = []
for atom in self.atoms:
line = "%d %d %d %g %g %g %d %d %d\n" % \
(atom[0], atom[1], atom[2], atom[3], atom[4], atom[5],
atom[6], atom[7], atom[8])
lines.append(line)
d.sections["Atoms"] = lines
lines = []
for bond in self.bonds:
line = "%d %d %d %d\n" % (bond[0], bond[1], bond[2], bond[3])
lines.append(line)
d.sections["Bonds"] = lines
d.write(file)
def test():
test_sent = Sent(1, 'These algorithms include distance calculations, scan conversion, closest point determination, fast marching methods, bounding box creation, fast and incremental mesh extraction, numerical integration and narrow band techniques.', 'D')
test_s = [test_sent]
# isA_extraction(data())
for s in data('B'):
syntactic_extraction(s)
# with open(OUTPUT_DIR+'equal.txt', 'w', encoding='utf-8') as f:
# for s in data():
# # syntactic_extraction(s)
# # logging.info(s.text)
# r = equal_extract(s.text)
# for eq in r:
# logging.info(s.text)
# logging.info(eq)
# # f.write(s.text.strip())
# # f.write('\n')
# f.write('\t'.join(eq))
# f.write('\n')
# f.flush()
# # stop()
# return
return
def create_item(tt, x, z):
import network, game
global ITEM_ID
ITEM_ID += 1
bid = ITEM_ID
# 1. save in server
import data
d = data.data(id = bid, tt = tt, x = x, z = z)
game.controller.gcontroller.Items[bid] = d
# 2. broadcast
bpkt = network.packet.packet(network.events.MSG_SC_ITEM_BORN,
id = bid,
tt = tt,
x = x,
z = z
)
game.controller.gcontroller.broadcast(bpkt)
return d
def get_chain(datafile, chainID):
"""
Get the atom and bond IDs in a polymer chain, as identified by LAMMPS
molecule ID.
Inputs:
datafile: name of lammps data file. Needs second atom column to be type.
chainID: molecule id for polymer chain.
Outputs:
tuple of ([atom IDs],[bond IDs])
Derek Fujimoto
July 2016
"""
# some variables
chain_atoms = []
# get atom list of all atoms and bonds
d = data(datafile)
all_atoms = d.get("Atoms")
all_bonds = d.get("Bonds")
# find only atoms which belong to a given chain ID
for atom in all_atoms:
if atom[1] == chainID:
chain_atoms.append(int(atom[0]))
# make a list with bonds just from atoms in the atom list
chain_bonds = []
for bond in all_bonds:
if bond[2] in chain_atoms or bond[3] in chain_atoms:
chain_bonds.append([int(bond[2]), int(bond[3])])
return (chain_atoms, chain_bonds)
#!/usr/bin/python
# Copyright (c) 2008, Institute for the Study of Learning and Expertise
# All rights reserved.
# For details, see the LICENSE file.
from ross_entities import *;
from data import data;
from search import *;
import time;
ross1 = data("./ross-sea-yr1.data");
#ross2 = data("./ross-sea-yr2.data");
data_files = [ross1]
#data_files = [ross1,ross2]
print "Start: ", time.strftime("%m/%d/%y %H:%M:%S", time.localtime());
(model, sse, r2, init_state) = exhaustive(lib, "root", data_files);
#(model, sse, r2, init_state) = beam_search(lib, "root", data_files, beam_width = 1, n_fs_restarts = 3,init_state_fit=1);
print "Best model:";
print model;
print "SSE = %g, r2 = %g, init_state = %s\n" % (sse, r2, str(init_state));
model.fit_params(data_files, init_state = init_state, n_tf_restarts = 0, n_fs_restarts = 0);
print "End: ", time.strftime("%m/%d/%y %H:%M:%S", time.localtime());
print "";
from theano.sandbox.cuda.dnn import dnn_conv
from PIL import Image
import pickle
from lib import activations
from lib import updates
from lib import inits
from lib.rng import py_rng, np_rng
from lib.ops import batchnorm, conv_cond_concat, deconv, dropout, l2normalize
from lib.metrics import nnc_score, nnd_score
from lib.theano_utils import floatX, sharedX
from lib.data_utils import OneHot, shuffle, iter_data, center_crop, patch
from data import data
num_samples, train_set, test_set, val_set, tr_scheme, tr_stream, val_scheme, val_stream, test_scheme, test_stream = data()
def target_transform(X):
return floatX(X).transpose(0, 3, 1, 2)/127.5 - 1.
def input_transform(X):
return target_transform(X)
l2 = 1e-5 # l2 weight decay
nvis = 196 # # of samples to visualize during training
b1 = 0.5 # momentum term of adam
nc = 2 # # of channels in image
nbatch = 128 # # of examples in batch
npx = 64 # # of pixels width/height of images
batch_size = 128
nx = npx*npx*nc # # of dimensions in X
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if 'debug' in sys.argv:
nfolds = 1
fold_j = 0
out_file = os.path.join(out_dir,'./debug.hdf5')
#pdb.set_trace()
else:
nfolds = int(sys.argv[1]) # number of folds the genelist is split into (= number of jobs)
fold_j = int(sys.argv[2]) # fold considered for this job
out_file = os.path.join(out_dir,'%d_%d.hdf5'%(nfolds,fold_j))
data = DATA.data(data)
uKpop = data.getUKpop(center=center,normalize=False)
n_genes = data.Y.shape[1]
genes = SP.arange(n_genes)
Icv = SP.floor(nfolds*SP.arange(n_genes)/n_genes)
I = Icv==fold_j
genes = genes[I]
f = h5py.File(out_file,'w')
for gene_i in genes:
probeID = data.probeID[gene_i]
geneID = data.geneID[gene_i]
print ""
print "%s: %s" % (gene_i,probeID)
def get_msd(rootfile,datafile,style,n_chains,mono_id=-1,draw=False):
"""
Get the mean squared displacement for a number of different styles.
Inputs:
rootfile: name of root file with dump data
datafile: name of data file used to set up simulation
style: name of style with which to find the msd
n_chains: number of chains present in the simulation
mono_id: monomer id (starting from 0) to use in the COM calculation. Only for the ring_n options.
draw: if true, draw to TCanvas
Outputs:
TGraph with msd from initial position.
Style Options:
'ua_ring_com': msd of phenyl ring center of mass for united atom model.
'ua_ring_n_com': msd of nth ring com for united atom model
Dependencies:
pizza.py
PyROOT
Derek Fujimoto
April 2016
"""
# constants
THIS = "get_msd"
TREE_NAME = "dump_dat"
STYLE_OPTIONS = ['ua_ring_com','ua_ring_n_com']
OPTION_DICT = dict()
# check style option input
if style not in STYLE_OPTIONS:
print THIS + ": style option not found. Possible options include: "
for op in STYLE_OPTIONS:
print op
exit()
# set some of the dictionary settings
OPTION_DICT['n_chain'] = n_chains
if(style == STYLE_OPTIONS[0] or style == STYLE_OPTIONS[1]):
OPTION_DICT['n_atom'] = 8
OPTION_DICT['n_monomer'] = 10
OPTION_DICT['n_cap'] = 1
OPTION_DICT['n_skip'] = 2
if(style == STYLE_OPTIONS[1]):
OPTION_DICT['mono_id'] = mono_id
# indicies of things in snapshot TH2D
ID = 0
MOL = 1
TYPE = 2
XYZ = [3,4,5]
# Open rootfile and tree
rfile_id = TFile(rootfile,'READ')
dump_dat = rfile_id.Get(TREE_NAME)
# Get atom masses
dfile_id = data.data(datafile)
mass_list = dfile_id.get('Masses')
# generate list of needed atoms in each object
id_list = generate_atom_list(style,OPTION_DICT)
# iterate over snapshots.
time_list = [] # timestamps
msd_list = []
for ie,event in enumerate(dump_dat):
snap = event.snapshot # TH2D
# get COM
com_list = [] # [[x1,y1,z1],[x2,y2,z2],...]
for obj in id_list:
totalMass = 0
com = [0,0,0]
for atom_id in obj:
atom_mass = mass_list[int(snap.GetBinContent(atom_id-1,TYPE))-1][1]
totalMass += atom_mass
for i in range(3):
com[i] += snap.GetBinContent(atom_id-1,XYZ[i])*atom_mass
for i in range(3):
com[i] /= totalMass
# add COM to list
com_list.append(com)
if(ie == 0):
com_list1 = com_list
# get msd at this snapshot
msd = 0
for k,entry in enumerate(com_list):
for j in range(3):
msd += (com_list1[k][j] - entry[j])**2
msd /= (len(com_list)*3)
msd_list.append(msd)
time_list.append(event.timestep)
# make graph for output
g = TGraph(len(msd_list),array.array('d',time_list),array.array('d',msd_list))
g.SetTitle("")
g.GetYaxis().SetTitle("Mean Squared Displacement (A^2)")
g.GetXaxis().SetTitle("Time Step")
if(draw):
g.Draw("AP")
return g
