def test_run_engine(self):
argv_copy = argv.copy()
argv[1:] = [
'--engine', 'mock_engine.yaml',
'--dir', self.workspace,
'--targets', 'deliverables', '9900', '--deps',
'--conf', 'mock_params.ini',
]
try:
run_engine(logs=False)
finally:
argv[1:] = argv_copy[1:]
name = None
for item in listdir(self.workspace):
if '9900' in item:
name = item
break
self.assertIsNotNone(name)
with open(join(self.workspace, name)) as f:
roll_call = f.readline().strip().split(' ')
self.assertListEqual(roll_call, ['foo', 'baz_alternative', 'quux', 'report_and_plots', 'summary'])
# Nếu file được chạy trực tiếp
# noinspection SpellCheckingInspection
if __name__ == '__main__':
from sys import argv
# Khởi tạo FaceDetection và thiết lập danh sách người nhận mail
nano = NanoFaceDetection(admines=[
('Chiến', '*****@*****.**'),
])
# if len(argv) == 1:
# exit(nano.run(test=True, webcam=True))
# Nếu có tham số
if len(argv) == 2:
# Lấy tham số
command = argv.copy().pop()
# Nếu là reset
if command == 'reset':
exit(nano.clean_classifier())
# Nếu là test
if command == 'test':
exit(nano.run(test=True))
# Nếu là webcam
# noinspection SpellCheckingInspection
if command == 'webcam':
exit(nano.run(webcam=True))
# Nếu là 1 link web
if command.startswith('http://') or command.startswith('https://'):
from requests import get
# Tải file
#!/usr/bin/env python3
from django.core.management import execute_from_command_line as execute
from os import environ, remove
from os.path import join, dirname, abspath, isfile
from sys import argv
FIXTURES = ['auth', 'teams', 'hosts', 'services', 'checks', 'injects']
PROJECT_PATH = dirname(abspath(__file__))
FIXTURES_PATH = join(PROJECT_PATH, 'fixtures')
DATABASE = join(PROJECT_PATH, 'heartbeat', 'db.sqlite3')
def init():
if isfile(DATABASE):
remove(DATABASE)
execute(['manage.py', 'migrate'])
for fixture in FIXTURES:
execute([
'manage.py', 'loaddata',
join(FIXTURES_PATH, '{}.json'.format(fixture))
])
if __name__ == "__main__":
args = argv.copy()
environ.setdefault("DJANGO_SETTINGS_MODULE", "heartbeat.settings")
if len(args) > 1 and args[1] == 'init':
init()
else:
execute(args)
import datetime
from pyspark.sql import Row
from pyspark.sql.functions import *
import pandas as pd
import time
spark = SparkSession.builder.appName('SparkLogProcessor') \
.enableHiveSupport().getOrCreate()
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
spark.conf.set("spark.sql.execution.arrow.pyspark.fallback.enabled", "true")
sc = spark.sparkContext
sc.setLogLevel('ERROR')
module, fileloc, wait_before_exit = argv.copy()
print(
f'module is {module} and fileloc is {fileloc} and wait_before_exit is {wait_before_exit}'
)
logFileRDD = sc.textFile(fileloc)
print("No of logs ", logFileRDD.count())
logFileRDD.take(2)
# make provisions for processing the logs
# use a regex to split the line into the nine groups and map it to a sql row of nine columns
month_map = {
'Jan': 1,
'Feb': 2,
'Mar': 3,
'Apr': 4,
'May': 5,
sep = param.index("=")
key = param[:sep]
param_dict[key] = correct_type(source_type, key, param[sep + 1:])
return (param_dict, source_type)
# TODO provide seprate mechanism for quiting the program
def running_interface(connector: Connector):
try:
while True:
print("Running...")
sleep(3)
except KeyboardInterrupt as e:
connector.stop()
raise
# TODO write simple command line UI for testing code
if __name__ == "__main__":
parsed = parse_args(argv.copy())
if parsed:
params, source_factory = parsed
try:
source = source_factory.create(**params)
connector = Connector.factory(source, 24)
connector.start()
running_interface(connector)
except TypeError as e:
print_params(source_factory)
raise e
'''
This script will perform basic processing of .ms8.txt files produced by the MED-Associates
legacy-Davis Rig Lickometer.
USAGE:
graphAll.py [path_to_files, use_first_block, no_normalization, group_by_TUBE]
reads in raw files and executes gen_summary_dataframe, graph_cumulative_licks, individual_graph_by_group, and summarize_licking_curve
'''
##### PREAMBLE #####
# Safely remove script name from list of arguments .
user_args = argv.copy()
user_args.pop(0)
# If the user has provided arguments, process them.
if len(user_args) != 0:
# Check for arguments pertaining to preferences for graphing
if 'use_first_block' in user_args:
drop_first_block = False
user_args.remove('use_first_block')
print('Including first presentation block in averages.')
else:
drop_first_block = True
print('Excluding first presentation block from averages.')
if 'no_normalization' in user_args:
norm_by_water = False
user_args.remove('no_normalization')