def upload_data(ui):
caffeine.on(display=False)
tmp_dir_name = ui.tmp_dir_name
dataset_type = ui.current_dataset_type
csv_file_name = ui.selected_csv[dataset_type]
s3 = ui.session.resource('s3')
s3_objects, status = list_objects_in_dataset_bucket(
ui.session, ui.region, ui.s3_bucket_name, ui.dataset_group_name)
if status == 'failure':
caffeine.off()
return status
file_prefix = "/" + ui.dataset_group_name + "/datasets/" + dataset_type.lower(
) + '_'
file_name = os.path.basename(csv_file_name)
new_file_name = file_name
ds_number = 0
while file_prefix + new_file_name in s3_objects:
ds_number += 1
new_file_name = os.path.splitext(file_name)[0] + '_' + str(
ds_number) + '.csv'
status = multi_part_upload_with_s3(
s3, ui.s3_bucket_name, ui.dataset_group_name + '/' + "datasets/" +
dataset_type.lower() + '_' + new_file_name,
os.path.join(tmp_dir_name, "tmp_dataset.csv"), ui.upload_progress_bar)
if status != 'failure':
ui.dataset_path[
dataset_type] = ui.s3_bucket_path + file_prefix + new_file_name
caffeine.off()
return status
def run(self):
caffeine.on(display=True)
self.welcome()
# self.trainer.introduce()
self.print_info()
for section in self.sections:
untimed_rest()
# self.trainer.say_phrase()
section.run()
caffeine.off()
def do_parallel(fn, loop, n_jobs=-1, progress=True, keep_alive=True):
if progress:
loop = tqdm(loop)
with joblib.Parallel(n_jobs=-1) as par:
if keep_alive and can_caffeinate():
caffeine.on(display=True)
res = par(joblib.delayed(fn)(l) for l in loop)
caffeine.off()
else:
res = par(joblib.delayed(fn)(l) for l in loop)
return res
def osx_standby(self, system=True, display=True):
try:
if system:
caffeine.off()
else:
caffeine.on(display)
except NameError:
self.log_warning(_("Unable to change power state"), _("caffeine lib not found"))
except Exception, e:
self.log_warning(_("Unable to change power state"), e)
def osx_standby(self, system=True, display=True):
try:
if system:
caffeine.off()
else:
caffeine.on(display)
except NameError:
self.log_warning(_("Unable to change power state"),
_("caffeine lib not found"))
except Exception as e:
self.log_warning(_("Unable to change power state"), e)
# Breaks while loop
break
# Else statement for when time does not match desired send time
else:
# Checks "currentTime" to see if it's safe to set the amount of time to sleep to 1 hour
if currentTime.hour < (sendHr - 1) or currentTime.hour > sendHr:
# Checks that platform is Darwin-based since caffeine is a Mac command
if platform.system() == "Darwin":
print(
"Running 'caffeine' to prevent the system from sleeping.")
# Adds 1 to "caffeineMin" every time caffeine runs before time.sleep(3600)
caffeineHr += 1
logging.debug("Caffeine has run " + str(caffeineHr) +
" times.")
# Keeps computer awake but allows display to sleep
caffeine.on(display=False)
time.sleep(3600)
# Program sleeps only for 1 minute per loop if time before "currentTime" ≤1hr
else:
if platform.system() == "Darwin":
print(
"Running 'caffeine' to prevent the system from sleeping.")
# Adds 1 to "caffeineMin" every time caffeine runs before time.sleep(60)
caffeineMin += 1
logging.debug("Caffeine has run " + str(caffeineMin) +
" times.")
# Keeps computer awake but allows display to sleep
caffeine.on(display=False)
time.sleep(60)
# Restarts while loop
continue
#required installs (i.e. pip3 install in terminal): pandas, selenium, bs4, and possibly chromedriver(it may come with selenium) #Download Chromedriver from: https://chromedriver.chromium.org/downloads #To see what version to install: Go to chrome --> on top right click three dot icon --> help --> about Google Chrome #Move the chrome driver to (/usr/local/bin) -- open finder -> Command+Shift+G -> search /usr/local/bin -> move from downloads from selenium import webdriver from bs4 import BeautifulSoup as bs import time from datetime import datetime import pandas as pd import random import openpyxl import caffeine from openpyxl import load_workbook caffeine.on(display=True) import tkinter as tk import threading # In[2]: root = tk.Tk() #Defining our variables tk_page = tk.StringVar() tk_username = tk.StringVar() tk_password = tk.StringVar() canvas = tk.Canvas(root, height=300, width=400, bg='#49694b') canvas.pack()
def create_predictor_and_forecast(ui):
try:
# Using caffeine to prevent pc from sleeping.
caffeine.on(display=False)
forecast_export_name = forecast_name = predictor_name = ui.new_forecast_line_edit.text(
)
forecast_horizon = ui.predictor_forecast_horizon_spinbox.value()
forecast_client = ui.forecast_client
dataset_group_arn = ui.dataset_group_arn
auto_ml = ui.auto_ml_checkbox.isChecked()
# Check if a country is selected to use its holidays for forecasting.
holidays = None
if ui.country_checkbox.isChecked():
holidays = selected_country(ui)
# Check if location is a dimension in the data
location_in_datasets, status = is_location_in_datasets(
forecast_client, dataset_group_arn)
if status == 'failure':
caffeine.off()
return status
# Check if auto machine learning is selected
algorithm = None
if not auto_ml:
algorithm = ui.algorithms[ui.algorithm_combobox.currentText()]
print(algorithm)
# Get frequency from combobox
forecast_frequency = combobox_to_freq(ui, 'FORECAST')
# Create a predictor
predictor_arn, status = create_predictor(
forecast_client, predictor_name, algorithm, auto_ml,
forecast_horizon, forecast_frequency, dataset_group_arn,
location_in_datasets, holidays)
if status == 'failure':
caffeine.off()
return status
# Wait until predictor is created
response = forecast_client.describe_predictor(
PredictorArn=predictor_arn)
while response['Status'] != 'ACTIVE' and response[
'Status'] != 'CREATE_FAILED':
response = forecast_client.describe_predictor(
PredictorArn=predictor_arn)
time.sleep(10)
if response['Status'] == 'CREATE_FAILED':
caffeine.off()
return 'failure'
# Create a forecast
forecast_arn, status = create_forecast(forecast_client, forecast_name,
predictor_arn)
if status == 'failure':
caffeine.off()
return status
# Wait until forecast is created
response = forecast_client.describe_forecast(ForecastArn=forecast_arn)
while response['Status'] != 'ACTIVE' and response[
'Status'] != 'CREATE_FAILED':
response = forecast_client.describe_forecast(
ForecastArn=forecast_arn)
time.sleep(10)
if response['Status'] == 'CREATE_FAILED':
caffeine.off()
return 'failure'
s3_path = ui.s3_bucket_path + "/" + ui.dataset_group_name + "/forecast/" + forecast_name + "/"
s3_role_arn = ui.s3_role_arn
# Create a forecast export
forecast_export_arn, status = create_forecast_export(
ui.forecast_client, forecast_export_name, forecast_arn, s3_path,
s3_role_arn)
if status == 'failure':
caffeine.off()
return status
# Wait until forecast export is created
while response['Status'] != 'ACTIVE' and response[
'Status'] != 'CREATE_FAILED':
response = forecast_client.describe_forecast_export_job(
ForecastExportJobArn=forecast_export_arn)
time.sleep(10)
if response['Status'] == 'CREATE_FAILED':
caffeine.off()
return 'failure'
# Wait 30 seconds to make sure exported files show on S3 (this should be immediate)
time.sleep(30)
# Create a database in AWS Glue
status = create_database(ui.glue_client)
if status == 'failure':
caffeine.off()
return status
# Create a crawler in AWS Glue
status = create_crawler(ui.glue_client, s3_role_arn, s3_path,
forecast_name)
if status == 'failure':
caffeine.off()
return status
# Run the created crawler
status = run_crawler(forecast_name, ui.glue_client)
if status == 'failure':
caffeine.off()
return status
caffeine.off()
return 'success'
except Exception as e:
caffeine.off()
error(str(e))
return 'failure'
def main():
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import caffeine
caffeine.on(display=False)
except (ImportError, OSError):
pass
parser = argparse.ArgumentParser()
ansible_inventory_args = parser.add_argument_group(
'Ansible Inventory args')
ansible_inventory_args.add_argument(
'--list',
action='store_true',
help="output a JSON encoded hash/dictionary of all the groups to be "
"managed to stdout")
ansible_inventory_args.add_argument(
'--host',
help="hash/dictionary of variables to make available to templates "
"and playbooks")
odoo_args = parser.add_argument_group('Odoo connection args')
odoo_args.add_argument('--save',
help='save the login details for further usage')
odoo_args.add_argument('--load',
default='apertoso',
help='Load the login details from earlier save')
odoo_args.add_argument('--username')
odoo_args.add_argument('--password')
odoo_args.add_argument('--hostname')
odoo_args.add_argument('--database')
odoo_args.add_argument('--protocol',
choices=('jsonrpc', 'jsonrpc+ssl'),
default='jsonrpc+ssl')
odoo_args.add_argument('--port', default='8069')
odoo_args.add_argument('--list-odoo', help='show saved sessions')
args, options = parser.parse_known_args()
if args.list_odoo:
odooconnector.OdooConnector.list()
sys.exit(1)
odoo = odooconnector.OdooConnector()
if all((args.username, args.password, args.database, args.hostname)):
odoo.connect(args.username, args.password, args.database,
args.hostname, args.protocol, args.port)
if args.save:
odoo.save_login_session(args.save)
elif args.load:
odoo.connect_saved(args.load)
if args.host:
instance_data = odoo.search_and_get_data([('fqdn', '=', args.host)],
limit=1)
instance_data = instance_data[0]
remove_null_values(instance_data)
ansible_vars = get_host_vars(instance_data)
print json.dumps(
ansible_vars,
sort_keys=True,
indent=4,
separators=(',', ': '),
)
else:
instance_datas = odoo.search_and_get_data()
ansible_inventory = create_ansible_inventory(instance_datas)
map(remove_null_values, instance_datas)
print json.dumps(
ansible_inventory,
sort_keys=True,
indent=4,
separators=(',', ': '),
)
from functions import *
import sys
from agent.agent import Agent
from action import Action
import caffeine
if len(sys.argv) != 4:
print('Usage: python3 train.py [stock] [window] [episodes]')
exit()
caffeine.on(display=False) #電腦不休眠
ticker, window_size, episode_count = sys.argv[1], int(sys.argv[2]), int(
sys.argv[3])
init_cash = 1000000
#要給checkpoint個路徑
c_path = "models/{}/training.ckpt".format(ticker)
m_path = "models/{}/model.h5".format(ticker)
#取得歷史資料
df = pdr.DataReader('{}'.format(ticker),
'yahoo',
start='2018-1-1',
end='2019-1-1')
unit = get_unit(df['Close'].mean(), init_cash) #目前都是操作固定單位
trading = Action(unit)
#資料整合轉換
data = init_data(df)
#給agent初始化輸入的緯度
input_shape, neurons = get_shape(data[:window_size + 1], window_size)
agent = Agent(ticker, input_shape, neurons, c_path)
l = len(data) - 1
with open(namesPath, 'r', encoding="utf-8"
) as data_file: #Prepare the names list for a run
csv_reader = csv.reader(data_file)
names = []
for line in csv_reader:
try:
name = line[0]
names.append(name)
except:
pass
time.sleep(4)
count = 0
caffeine.on(display=True) #Prevents screen to sleep on MacOS
for name in names: #Send message to all names list
firstName = name.split(' ', 1)[0]
if (msgBase.find('%s') != -1): #Prepare the message before sending
msg = msgBase % firstName
else:
msg = msgBase
try: #Try sending the message
sendMsg(msg, name, driver) #Send message
count = count + 1
except common.exceptions.NoSuchElementException as e: #Didn't find the "Back" button.
pass
except: #Click the "Back" button.
