def testcreation(self):
ui = userinput.UserInput(dlxii.Device())
ui.comm_port = Testutils.sdevice
c = controller.Controller(ui)
utili = utils.Utils(ui, c, testing=False, showhelp=False)
self.assertFalse(utili.testing)
utili = utils.Utils(ui, c, testing=True, showhelp=False)
self.assertTrue(utili.testing)
utili = utils.Utils(ui, c, testing=True, showhelp=True)
def teststr(self):
ui = userinput.UserInput(dlxii.Device())
ui.comm_port = Testutils.sdevice
c = controller.Controller(ui)
utili = utils.Utils(ui, c, testing=False, showhelp=False)
self.assertEqual(
'testing:False, cmds: -acr, -rmn, -ran, -rmd, -cacn, -q',
str(utili))
utili = utils.Utils(ui, c, testing=True, showhelp=False)
self.assertEqual(
'testing:True, cmds: -acr, -rmn, -ran, -rmd, -cacn, -q',
str(utili))
# if showhelp true then something like
"""
def build_vocabulary(path, vocab_dict_path, nlp):
"""
Builds vocabulary with the entirity of the datasets
"""
# Create Utils instance
U = utils.Utils()
data_raw = U.jsons_to_list(path)
# Create a vocab wrapper and add some special tokens.
vocab = Vocabulary(nlp)
vocab.add_word('<pad>')
vocab.add_word('<start>')
vocab.add_word('<end>')
vocab.add_word('<unk>')
all_adjs = []
for f, file in enumerate(data_raw): # 0_arch 1_des 2_...
print("Starting building vocab corresponding to file: \n", f)
for i in range(len(file)): # 900 samples
sample_dict = file[i]
sample_text = sample_dict['text'] # this is a list with strings
adj_list = vocab.get_adj_from_all_sentences(sample_text)
all_adjs.append(adj_list)
print("....Finishing vocab file: \n", f)
for sublist in all_adjs:
for adj in sublist:
vocab.add_word(adj)
# Save vocab in dict
with open(vocab_dict_path, 'wb') as f:
pickle.dump(vocab, f)
f.close()
return vocab
def __init__(self):
# Check the OS first
if not str(sys.platform) == "darwin":
self.u.head("Incompatible System")
print(" ")
print("This script can only be run from macOS/OS X.")
print(" ")
print("The current running system is \"{}\".".format(sys.platform))
print(" ")
self.grab("Press [enter] to quit...")
print(" ")
exit(1)
self.dl = downloader.Downloader()
self.r = run.Run()
self.u = utils.Utils()
self.web_drivers = None
self.os_build_number = None
self.os_number = None
self.wd_loc = None
self.sip_checked = False
self.installed_version = "Not Installed!"
self.get_manifest()
self.get_system_info()
def __init__(self):
"""
Constructor
"""
self.load_mongo_client()
self.utils = utils.Utils()
def __fillConstants(self, test_mode):
self.data = utils.Utils(test_mode)
self.num_color = len(self.data.color_list)
self.color_unspecified_index = self.data.color_index_dict[
self.data.COLOR_UNSPECIFIED]
self.color_begin = 1
self.color_end = self.num_color
def _setup_logging(self, path_log=None):
app_dir = os.path.join(path_log or utils.Utils().get_app_dir(),
'guardiancl.log')
console.log("Log file in '%s'" % app_dir)
logging.config.dictConfig({
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"file": {
"format":
"%(asctime)s :: %(levelname)s :: %(name)s - %(message)s"
}
},
"handlers": {
"file_handler": {
"class": "logging.handlers.RotatingFileHandler",
"level": "DEBUG",
"formatter": "file",
"filename": str(app_dir),
"maxBytes": 10485760,
"backupCount": 3,
"encoding": "utf8"
}
},
"root": {
"level": "DEBUG",
"handlers": ["file_handler"]
}
})
def predict(test, predict, vecs, words):
row = 916750
col = 300
util = utils.Utils(vecs, row, col, words)
df = pd.read_csv(test)
data_x = []
for index, row in df.iterrows():
x = util.embed_sentense(row['text'])
x = np.append(x, [int(row['sex']) - 1, float(row['age']) / 100])
data_x.append(x)
data_x = np.array(data_x)
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("model.h5")
print("Loaded model from disk")
data_y = model.predict(data_x)
data_y = np.argmax(data_y, axis=1)
df['event'] = pd.Series(data_y)
df.to_csv(predict, index=False)
def __init__(self, answers, APP, nodeps = False, update = False, target_path = None, dryrun = False, **kwargs):
run_path = os.path.dirname(os.path.realpath(__file__))
self.dryrun = dryrun
recursive = not nodeps
app = APP #FIXME
self.params = Params(recursive, update, target_path)
self.utils = utils.Utils(self.params)
if os.path.exists(app):
logger.info("App path is %s, will be populated to %s" % (app, target_path))
app = self.utils.loadApp(app)
else:
logger.info("App name is %s, will be populated to %s" % (app, target_path))
if not target_path:
if self.params.app_path:
self.params.target_path = self.params.app_path
else:
self.params.target_path = os.getcwd()
self.params.app = app
self.answers_file = answers
def __init__(self, files_paths, images_paths, new_folder, nlp, vocab):
"""
train_file_path = list with files
test_file_path = list with files
"""
# Create pipe
self.nlp = nlp
# Inherit vocabulary
self.VOCAB = vocab
# Call to utils
self.Utils = utils.Utils()
# paths to jsons
self.files_paths = files_paths
# path to image folders
self.images_paths = images_paths
# path to new image_path
self.new_image_path = new_folder
# Dataset
self.train_data = None
self.dev_data = None
self.train_labels = None
self.dev_labels = None
# Run
print("\nRunner....")
self.runner()
def __init__(self, s_start, s_goal, step_len, goal_sample_rate,
waypoint_sample_rate, iter_max):
self.s_start = Node(s_start)
self.s_goal = Node(s_goal)
self.step_len = step_len
self.goal_sample_rate = goal_sample_rate
self.waypoint_sample_rate = waypoint_sample_rate
self.iter_max = iter_max
self.vertex = [self.s_start]
self.vertex_old = []
self.vertex_new = []
self.edges = []
self.env = env.Env()
self.plotting = plotting.Plotting(s_start, s_goal)
self.utils = utils.Utils()
self.fig, self.ax = plt.subplots()
self.x_range = self.env.x_range
self.y_range = self.env.y_range
self.obs_circle = self.env.obs_circle
self.obs_rectangle = self.env.obs_rectangle
self.obs_boundary = self.env.obs_boundary
self.obs_add = [0, 0, 0] # [x,y,r]
self.path = []
self.waypoint = []
def __init__(self, **kwargs):
# Initialize modules
self.dl = downloader.Downloader()
self.u = utils.Utils()
self.r = run.Run()
# See if we have a "json" key - which supercedes all others
j = kwargs.get("json", None)
if j:
# We have a json file listed
cwd = os.getcwd()
os.chdir(os.path.dirname(os.path.realpath(__file__)))
if os.path.exists(j):
kwargs = json.load(open(j))
else:
kwargs = {}
os.chdir(cwd)
# Set defaults
self.update_url = kwargs.get("url", None)
self.update_type = kwargs.get("update_type", "file") # file or json
self.update_key = kwargs.get(
"update_key", 2) # the json key, or line of the target file
self.prompt = kwargs.get(
"prompt", False) # do we prompt the user for update notifications?
self.prompt_key = kwargs.get(
"prompt_key", None) # the json key for an update description
self.file = kwargs.get(
"file", None) # our current file for local version access
self.update_mode = kwargs.get(
"mode", "clone") # clone/file; clone a repo, or just curl a file
self.chmod = kwargs.get("chmod", []) # list of files to chmod
self.restart = kwargs.get("restart", True) # restart on update?
self.start_file = kwargs.get("restart_file",
None) # path to the file to restart
def AddData(self, data, dateAndTime, ID, dataType, GetID, netType):
cell_id = GetID(ID)
#print cell_id
ut = utils.Utils()
date, time = ut.GetDateAndTimeNormal(
dateAndTime) #这里返回的date，time均为psycopg2的格式
dateTimeIdNT = ut.dateTimeIdNTToStr(
[date, time, netType,
cell_id]) # 用(cell_id, date, time)元组来代替dateTimeId
# 可少量提升性能，有空再做。。。
if dateTimeIdNT in self.dict:
# 如果dict的key中存在该ID日期时间，且dict的value的key中存在该dataType
# 则把要存储的数据加上原数据再存储
oldData = self.dict[dateTimeIdNT][dataType]
newData = data + oldData
#print newData,
#self.writer.writerow([data, oldData, newData])
self.dict[dateTimeIdNT][dataType] = newData
#print dataType
if not dataType == "erl":
#若更新的数据类型是updata或downdata则同时要更新alldata
alldata = data + self.dict[dateTimeIdNT]['alldata']
self.dict[dateTimeIdNT]['alldata'] = alldata
else:
pass
else:
self.dict[dateTimeIdNT] = {}
self.dict[dateTimeIdNT][dataType] = data
for type in ['erl', 'updata', 'downdata', 'alldata']:
if not type == dataType:
self.dict[dateTimeIdNT][type] = 0
if not dataType == "erl":
self.dict[dateTimeIdNT]['alldata'] = data
def testprocessLoop(self):
ui = userinput.UserInput(dlxii.Device())
ui.comm_port = Testutils.sdevice
utili = utils.Utils(ui,
controller.Controller(ui),
testing=True,
showhelp=False)
utili.process_loop()
def SaveToDB(self):
ut = utils.Utils()
for dateTimeId in self.dict.keys():
date, time, nettype, ID = ut.StrToDateTimeIdNT(dateTimeId)
self.db.Insert(ID, date, time, self.dict[dateTimeId]["erl"],
self.dict[dateTimeId]["updata"] / 8000,
self.dict[dateTimeId]["downdata"] / 8000,
self.dict[dateTimeId]["alldata"] / 8000, nettype)
self.db.Commit()
def testresetCmdNames(self):
ui = userinput.UserInput(dlxii.Device())
ui.comm_port = Testutils.sdevice
utili = utils.Utils(ui,
controller.Controller(ui),
testing=True,
showhelp=False)
myserial.MySerial._dbidx = -1
utili.reset_cmd_names()
def __init__(self, config):
self._utils = utils.Utils()
self._aws_interact = aws_interact.AwsInteract()
self._transcribe_job_name = self._utils._randomize_job_name()
self._s3_file_name = self._utils._randomize_job_name() + '.mp3'
self._path_audio_input = config['path_audio_input']
self._aws_region = config['aws_region']
self._bucket_name = config['bucket']
self._path_transcribe_result_output = '../results/transcribe_result.json'
self._path_subtitle_file = config['path_srt_output']
def timeline(self, info):
# Selecionando os dados.
id_doc = int(info["id_doc"].replace("checkbox_", ""))
query = info["query"]
query_doc = float(info["query_doc"])
meses = int(info["meses"])
salto = int(info["tam_intervalo"])
classes = info["classes"]
# Abrindo conexão com a base de dados.
conn = utils.Utils().conectar('../database/database.ini')
# Retorna a data do documento.
sql = """SELECT data FROM documentos WHERE id_documento = %s"""
cursor = conn.cursor()
cursor.execute(sql, (id_doc, ))
data_doc = str(cursor.fetchall()[0][0])
# TODO: Os primeiros testes vão ser feitos resgatando somente
# 60 dias a frente e após uma data prefixada, com a seleção de
# apenas um documento podendo ser expandido depois.
tempo = meses * 30
# Gerando vetor de representação da query.
#vetor_query = modelo_texto.infer_vector(query.lower().split())
modulo_base = Base()
vetor_query = modulo_base.inferir_vetor(query, modelo_texto)
# Formatando as classes dos documentos.
f_classes = modulo_base.formatar_classes(classes)
# Procuando os documentos referentes ao passado.
passado = self.expansao(id_doc,
query_doc,
vetor_query,
data_doc,
f_classes,
cursor,
limite=tempo,
salto=salto)
# Expandindo em direção aos documentos no futuro.
futuro = self.expansao(id_doc,
query_doc,
vetor_query,
data_doc,
f_classes,
cursor,
limite=tempo,
salto=salto,
sentido=1)
return self.formatar(passado, futuro)
def oldAddGSMErl(self, data, dateAndTime, ID):
cell_id = self.Get2GID(ID)
#print cell_id
#dbm = dbManip.dbManipulate()
ut = utils.Utils()
date, time = ut.GetDateAndTimeForPostgresql(dateAndTime)
if self.dbm.IsHaveID(cell_id):
#如果数据库中存在该ID，则把要存储的数据加上原数据再存储
newDate = data + float(self.dbm.SelectItemByID(cell_id, "erl"))
self.dbm.Update(cell_id, "erl", newDate)
else:
self.dbm.Insert(cell_id, date, time, data, 0, 0, 0, "2G")
def __init__(self, data_root: str) -> None:
"""
Initialize data handler
Args:
data_root (str): Root path for data directory
"""
self.data_root = data_root
self.util_handle = utils.Utils()
print(f"Your data root path : {self.data_root}")
os.makedirs(self.data_root + "/processed/", exist_ok=True)
def inferEachUser(user): util = utils.Utils(user) coordinateA = util.getCoordinate(util.user) coordinates = [] #limit the amount of users retreived for Twitter rules for friend in util.user.friends(): coordinates.append(util.getCoordinate(friend)) coordinateB = center_geolocation(coordinates) #calculate the distance between two coordinates print utils.distance(coordinateA, coordinateB)
def __init__(self, s_start, s_goal, res=0.5):
self.res = res
self.s_start = self.pos2ind(s_start)
self.s_goal = self.pos2ind(s_goal)
self.Env = env.Env() # class Env
self.utils = utils.Utils()
self.u_set = [(-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1),
(0, -1), (-1, -1)]
self.OPEN = my_queue.QueuePrior() # priority queue / OPEN set
self.CLOSED = [] # CLOSED set / VISITED order
self.PARENT = dict() # recorded parent
self.g = dict() # cost to come
def __init__(self, **kwargs):
self.dl = downloader.Downloader()
self.r = run.Run()
self.u = utils.Utils("SSDT Time")
self.iasl_url_macOS = "https://bitbucket.org/RehabMan/acpica/downloads/iasl.zip"
self.iasl_url_linux = "http://amdosx.kellynet.nl/iasl.zip"
self.iasl_url_windows = "https://acpica.org/sites/acpica/files/iasl-win-20200528.zip"
self.iasl = self.check_iasl()
if not self.iasl:
raise Exception("Could not locate or download iasl!")
self.dsdt = None
self.dsdt_raw = None
self.dsdt_lines = None
def __fillConstants(self):
self.data = utils.Utils()
self.word_vec_dim = self.data.word_vec_dict.values()[0].shape[0]
self.max_question_len = max(
[len(pair[0]) for pair in self.data.conv_pair])
print 'Max question length: {}'.format(self.max_question_len)
self.max_answer_len = max(
[len(pair[1]) for pair in self.data.conv_pair])
print 'Max answer length: {}'.format(self.max_answer_len)
self.total_word_count = len(self.data.word_list)
print 'Total word count: {}'.format(self.total_word_count)
print 'Total training samples: {}'.format(len(self.data.conv_pair))
def write(self):
ut = utils.Utils()
with xlrd.open_workbook(self.readfilename2) as workbook2:
file2sheet = workbook2.sheet_by_index(0)
file2rown = file2sheet.nrows
file2coln = file2sheet.ncols
with xlrd.open_workbook(self.readfilename1) as workbook1:
file1sheet = workbook1.sheet_by_index(0)
file1rown = file1sheet.nrows
file1coln = file1sheet.ncols
print "file1 rn=", file1rown, "cn=", file1coln
print "file2 rn=", file2rown, "cn=", file2coln
#file1coln=30
#file2coln=30
wb = Workbook()
ws1 = wb.active
if file2coln == 30:
for r in range(file2rown):
for c in range(file2coln):
if r > 0 and c == 0:
date1 = ut.tuple2Sqlite3Timestring(xlrd.xldate_as_tuple(file2sheet.cell_value(r, c), 0)[:3])
ws1.cell(column=c + 1, row=r + 1, value=date1)
else:
ws1.cell(column=c+1, row=r+1, value=file2sheet.cell_value(r, c))
elif file2coln == 31:
for r in range(file2rown):
for c in range(file2coln-1):
if r > 0 and c > 26:
ws1.cell(column=c+1, row=r+1, value=file2sheet.cell_value(r, c+1))
elif r > 0 and c == 0:
date1 = ut.tuple2Sqlite3Timestring(xlrd.xldate_as_tuple(file2sheet.cell_value(r, c), 0)[:3])
print "date1=", date1
ws1.cell(column=c+1, row=r+1, value=date1)
else:
ws1.cell(column=c+1, row=r+1, value=file2sheet.cell_value(r, c))
else:
raise ("excel format error")
for r in range(file1rown):
if r is not 0:
for c in range(file1coln):
# if c == 0:
# date2 = tuple([int(x) for x in file1sheet.cell_value(r, c).split('-')])
# print "date2=", date2
# ws1.cell(column=c + 1, row=r + file2rown, value=date2)
# else:
ws1.cell(column=c+1, row=r+file2rown, value=file1sheet.cell_value(r, c))
wb.save(self.writefile)
def run_all(self, ipt, skiplist, cpsob):
"""
Calls L{_run_one} for each item given as input, yielding
the response, parsed annotations, document object and file name
@param ipt: full path to input to run (or text-string to run)
@param skiplist: filenames (one per line) to skip, if any
"""
uts = ut.Utils(self.cfg)
cat_ind = uts.load_entity_category_indicators()
#input
fn2txt = self.rd.read(ipt)
try:
dispipt = ipt[0:100]
except IndexError:
dispipt = ipt
try:
skips = [
x.strip()
for x in codecs.open(skiplist, "r", "utf8").readlines()
]
except IOError:
skips = []
# run calls
print "-- [{}] RUNNING COLLECTION: {}, {}".format(
self.cl.name, dispipt, time.asctime(time.localtime()))
dones = 0
todo = self.cfg.limit
for fn in sorted(fn2txt):
if fn in skips:
print "Skipping {}".format(repr(fn))
continue
# create doc objs
dob = md.Document(fn, text=fn2txt[fn])
dob.find_sentence_positions()
# annots
try:
res, anns = self._run_one(fn, ut.Utils.norm_text(fn2txt[fn]),
cpsob)
except ValueError, msg:
print "\n! Error with file: {}".format(fn)
print "\n" + msg.message
res, anns = {}, {}
uts.add_sentence_number_to_annots(anns, dob)
for link in [an.enti.link for posi, an in anns.items()]:
cpsob.normalize_entity_categories(link, cat_ind)
dones += 1
yield res, anns, dob, fn
if dones == todo:
break
def convert(inpath, outpath):
videos = db.queryNotConvertVideos()
print(videos)
for video in videos:
m3u8file = m3u8_path(os.path.join(inpath, str(video['ttype'])),
video['title'], os.path.basename(video['url']))
outfile = os.path.join(os.path.join(outpath, str(video['ttype'])),
video['title'], 'output.mp4')
if utils.Utils().m3u8ToMP4(m3u8file, outfile):
db.updateVideo(video['id'], 3) # set video processed.
else:
print('convert file {file} failed!'.format(file=m3u8file))
continue
pass
def _setup_system(self):
config_dir = utils.Utils().get_app_dir()
config_path = os.path.join(config_dir, '.guardiancl.ini')
if not os.path.exists(config_path):
config = configparser2.ConfigParser()
config.add_section('ROUTES')
config.set('ROUTES', 'auth',
'http://guardiaocloud.com.br/service/v1/authenticate')
config.set('ROUTES', 'devices',
'http://guardiaocloud.com.br/service/v1/devices')
config.set('ROUTES', 'collect',
'http://guardiaocloud.com.br/collect')
with open(config_path, 'w') as configfile:
config.write(configfile)
def __init__(self, rootPath):
self.__rootPath = rootPath
self.__heights = [364, 444, 524, 604, 684, 764, 844]
self.__widths = [4, 124, 244, 364, 484, 604]
self.__mid_h = [399, 479, 559, 639, 719, 799, 879]
self.__mid_w = [59, 179, 299, 419, 539, 659]
self.__round_h = 70
self.__round_w = 405
self.__widthSize = 115
self.__heightSize = 75
self.__OCR = ocr.OCR()
self.__state = np.zeros((7, 6), dtype=np.int)
self.__prevState = np.zeros((7, 6), dtype=np.int)
self.__gameRound = 0
self.__util = utils.Utils(rootPath)
def ranking(self, texto, data, meses, n_docs, classes):
modulo_base = Base()
# Abrindo conexão com a base de dados.
conn = utils.Utils().conectar('../database/database.ini')
# Criando vetor de representação do texto inserido pelo usuário.
# TODO: VOLTAR PARA O BACKUP DO MODELO PARA A VERSÃO FINAL.
# Estratégia 1
vetor_pesquisa = modulo_base.inferir_vetor(texto, modelo_texto)
# Estratégia 2
#vetor_pesquisa = modelo_texto.infer_vector(texto.lower().split())
# Estratégia 3
#temporario = copy.deepcopy(modelo_texto)
#vetor_pesquisa = temporario.infer_vector(texto.lower().split())
# Resgatando os ids dos documentos do mês.
# TODO: Os documentos estão sendo comparados somente com de uma tabela.
cursor = conn.cursor()
f_classes = modulo_base.formatar_classes(classes)
sql = """SELECT id_documento FROM documentos WHERE data >= %s AND data <= %s AND classe IN ({0})""".format(
f_classes)
data_ini, data_fim = modulo_base.datas_raio(data, meses)
cursor.execute(sql, (
data_ini,
data_fim,
))
comparacoes = list()
# Obtendo o valor de comparação dos vetores com o vetor de pesquisa.
for id_doc in cursor.fetchall():
v = modelo_texto['DOC_%s' % id_doc[0]]
score = spatial.distance.cosine(v, vetor_pesquisa)
comparacoes.append([str(id_doc[0]), 1 - score])
comparacoes.sort(key=lambda x: x[1], reverse=True)
# Resgatando os documentos mais parecidos.
valores = ','.join([t[0] for t in comparacoes[:n_docs]])
print("Valores: ", valores)
sql = """SELECT * FROM documentos WHERE id_documento IN (%s)""" % valores
cursor.execute(sql)
# Criando json de resposta.
resposta = modulo_base.queryset_para_json(cursor)
cursor.close()
conn.close()
return resposta
