def dispatch(requestBody, eventsData):
helpers.init()
funcDict = {
'billPayment': billPayment.payBill,
'getBiller_billPayment': billPayment.getBiller_billPayment,
'getAccount_billPayment': billPayment.getAccount_billPayment,
'getAccount_fundsTransfer': fundsTransfer.getAccount_fundsTransfer,
'getAmount_billPayment': billPayment.getAmount_billPayment,
'getAmount_fundsTransfer': fundsTransfer.getAmount_fundsTransfer,
'confirm_fundsTransfer': fundsTransfer.confirm_fundsTransfer,
'confirm_billPayment': billPayment.confirm_billPayment,
'fundsTransfer': fundsTransfer.transferFunds,
'getRecipient_fundsTransfer': fundsTransfer.getRecipient_fundsTransfer,
'getBalance': getBalance.getBalance
}
if requestBody is None:
return None
requestBody = json.loads(requestBody)
intent = validate.try_ex(
lambda: requestBody['queryResult']['intent']['displayName'])
result = (
validate.try_ex(lambda: funcDict[intent](requestBody, eventsData)))
if result is None:
raise Exception('Intent with name ' + intent + ' not supported')
return result
def main():
h.init()
global cfg
cfg = h.cfg
# configure and start the actual app
global routing
application = webapp.WSGIApplication(routing,
debug=True)
util.run_wsgi_app(application)
def def_first():
"""Game for matching word to definiton"""
content = helpers.init()
print('\nYou can exit at anytime by entering \'menu\'.\n')
random.shuffle(content)
count = len(content)
correct = 0
incorrect = 0
for word in content:
print('\n{}. {}'.format(operator.indexOf(content, word) + 1, word['definition']))
res = input('\nWhat word does this definition belong to?\n\t=>').lower()
if res == word['key'].lower():
count -= 1
correct += 1
if count > 0:
print('\nGood Job! next word!')
elif res == 'menu':
main.menu()
else:
incorrect += 1
count -= 1
if count > 1:
print('\nIncorrect. Next word.')
print('\nYou got {} correct and {} incorrect'.format(correct, incorrect))
helpers.callback(def_first)
def scrambled():
"""Unscramble the word"""
content = helpers.init()
print('\nYou can exit at anytime by entering \'menu\'.\n')
random.shuffle(content)
count = len(content)
correct = 0
incorrect = 0
i = 0
shuffled = []
for word in content:
scrmbld = helpers.scramble_string(word['key'])
shuffled.append(scrmbld)
for scr in shuffled:
res = input('\n{}. {}.\n\t=>'.format(i + 1, scr)).lower()
if res == content[i]['key'].lower():
count -= 1
correct += 1
if count > 0:
print('\nGood Job! next word!')
elif res == 'menu':
main.menu()
else:
incorrect += 1
count -= 1
if count > 1:
print('\nIncorrect. Next word.')
i += 1
print('\nYou got {} correct and {} incorrect'.format(correct, incorrect))
helpers.callback(scrambled)
def def_first():
"""Game for matching word to definiton"""
content = helpers.init()
print('\nYou can exit at anytime by entering \'menu\'.\n')
random.shuffle(content)
count = len(content)
correct = 0
incorrect = 0
for word in content:
print('\n{}. {}'.format(operator.indexOf(content, word) + 1, word['definition']))
res = input('\nWhat word does this definition belong to?\n\t=>').lower()
if res == word['key'].lower():
count -= 1
correct += 1
if count > 0:
print('\nGood Job! next word!')
elif res == 'menu':
main.menu()
else:
incorrect += 1
count -= 1
if count > 1:
print('\nIncorrect. Next word.')
print('\nYou got {} correct and {} incorrect'.format(correct, incorrect))
helpers.callback(def_first)
def def_first():
"""Game for matching word to definiton"""
content = helpers.init()
print("\nYou can exit at anytime by entering 'exit'.\n")
random.shuffle(content)
count = len(content)
correct = 0
incorrect = 0
for word in content:
print("\n{}. {}".format(operator.indexOf(content, word) + 1, word["definition"]))
res = input("\nWhat word does this definition belong to?\n\t=>").lower()
if res == word["key"].lower():
count -= 1
correct += 1
if count > 0:
print("\nGood Job! next word!")
elif res == "exit":
return
else:
incorrect += 1
count -= 1
if count > 1:
print("\nIncorrect. Next word.")
print("\nYou got {} correct and {} incorrect".format(correct, incorrect))
helpers.callback(def_first)
def scrambled():
"""Unscramble the word"""
content = helpers.init()
print("\nYou can exit at anytime by entering 'exit'.\n")
random.shuffle(content)
count = len(content)
correct = 0
incorrect = 0
i = 0
shuffled = []
for word in content:
scrmbld = helpers.scramble_string(word["key"])
shuffled.append(scrmbld)
for scr in shuffled:
res = input("\n{}. {}.\n\t=>".format(i + 1, scr)).lower()
if res == content[i]["key"].lower():
count -= 1
correct += 1
if count > 0:
print("\nGood Job! next word!")
elif res == "exit":
return
else:
incorrect += 1
count -= 1
if count > 1:
print("\nIncorrect. Next word.")
i += 1
print("\nYou got {} correct and {} incorrect".format(correct, incorrect))
helpers.callback(scrambled)
def get_events_breakdown_data(col, player_name, start, end):
pipeline = helpers.init(player_name, start, end)
pipeline[1]["$project"]["element_type"] = 1
projection = [{"$group": {"_id": None, "goals": {"$push": "$fixture_history.goals"},
"assists": {"$push": "$fixture_history.assists"},
"clean_sheets": {"$push": "$fixture_history.clean_sheet"},
"points": {"$push": "$fixture_history.points"},
"gameweeks": {"$push": "$fixture_history.gameweek"},
"player_type": {"$first": "$element_type"}}}]
pipeline.extend(projection)
breakdown_object = col.aggregate(pipeline).next()
goal_points = get_goal_points(breakdown_object["goals"], breakdown_object["player_type"])
assist_points = map(lambda x: 3*x, breakdown_object["assists"])
others_points = (np.array(breakdown_object["points"]) - np.array(goal_points)
- np.array(assist_points))
data = [{"goals": pairs_to_lists(zip(breakdown_object["gameweeks"],goal_points))},
{"assists": pairs_to_lists(zip(breakdown_object["gameweeks"],assist_points))}]
if breakdown_object["player_type"] == 3 or breakdown_object["player_type"] == 4: # Forwards/midfields
data.append({"others": pairs_to_lists(zip(breakdown_object["gameweeks"],others_points.tolist()))})
else: # Defenders/GKs
cs_points = map(lambda x: 4*x, breakdown_object["clean_sheets"])
others_points -= np.array(cs_points)
data.extend([{"cleanSheets": pairs_to_lists(zip(breakdown_object["gameweeks"],cs_points))},
{"others": pairs_to_lists(zip(breakdown_object["gameweeks"],others_points.tolist()))}])
return data
def get_consistency_data(col, player_name, start, end):
pipeline = helpers.init(player_name, start, end)
projection = [{"$group": {"_id": None, attr: {"$push": "$fixture_history."+attribute.INTERNAL_ATTR_MAP[attr]}}}]
pipeline.extend(projection)
attr_vals = np.array(col.aggregate(pipeline).next()[attr])
data = [attr_vals.min(), np.percentile(attr_vals, 25, interpolation="midpoint"), np.median(attr_vals),
np.percentile(attr_vals, 75, interpolation="midpoint"), attr_vals.max()]
return [data]
def get_over_time_data(col, player_name, start, end):
pipeline = helpers.init(player_name, start, end)
projection = [{"$group": {"_id": None, "gameweeks": {"$push": "$fixture_history.gameweek"},
attr: {"$push": "$fixture_history."+attribute.INTERNAL_ATTR_MAP[attr]},
"results": {"$push": "$fixture_history.opponent_result"}}}]
pipeline.extend(projection)
data = col.aggregate(pipeline).next()
data = helpers.pairs_to_lists(zip(data["gameweeks"], data[attr]))
return map(lambda p: [p[0],p[1]/10.0], data)
def get_home_vs_away_data(col, player_name, start, end, attr):
pipeline = helpers.init(player_name, start, end)
projection = [{"$group": {"_id": "$fixture_history.ground",
"y": {"$sum": "$fixture_history."+INTERNAL_ATTR_MAP[attr]}}}]
pipeline.extend(projection)
data = [d for d in col.aggregate(pipeline)]
for d in data:
d["_id"] = "Home" if d["_id"] == "H" else "Away"
d["name"] = d.pop("_id")
return data
def dispatch(requestBody, eventsData):
helpers.init()
funcDict = {
'booking': booking.bookingcom,
'getarrival_booking': booking.getarrival_booking,
'getdeparture_booking': booking.getdeparture_booking,
'getadult_booking': booking.getadult_booking,
'getchild_booking': booking.getchild_booking,
'Confirm_booking': booking.Confirm_booking
}
if requestBody is None:
return None
requestBody = json.loads(requestBody)
intent = validate.try_ex(
lambda: requestBody['queryResult']['intent']['displayName'])
result = (
validate.try_ex(lambda: funcDict[intent](requestBody, eventsData)))
if result is None:
raise Exception('Intent with name ' + intent + ' not supported')
return result
def get_cumulative_total_data(col, player_name, start, end, attr):
pipeline = helpers.init(player_name, start, end)
projection = [{"$group": {"_id": None, "gameweeks": {"$push": "$fixture_history.gameweek"},
attr: {"$push": "$fixture_history."+INTERNAL_ATTR_MAP[attr]},
"results": {"$push": "$fixture_history.opponent_result"}}}]
pipeline.extend(projection)
data = col.aggregate(pipeline).next()
cum_sums = np.cumsum(data[attr])
data = map(list, zip(data["gameweeks"], cum_sums))
return data
def get_over_time_data(col, player_name, start, end, attr):
pipeline = helpers.init(player_name, start, end)
projection = [{"$group": {"_id": None, "gameweeks": {"$push": "$fixture_history.gameweek"},
attr: {"$push": "$fixture_history."+INTERNAL_ATTR_MAP[attr]},
"results": {"$push": "$fixture_history.opponent_result"}}}]
pipeline.extend(projection)
data = col.aggregate(pipeline).next()
res_length = len(data["gameweeks"])
data = [{"x": data["gameweeks"][i], "y": data[attr][i], "name": data["results"][i]}
for i in xrange(res_length)]
return data
def __init__(self, argv):
# Authenticate and construct service.
self.service, self.arguments = init(
argv,
'calendar',
'v3',
__doc__,
__file__,
parents=[self._create_argument_parser()],
scope='https://www.googleapis.com/auth/calendar.readonly')
# user info
self.user = None
self.title = None
with open("user.json", 'r', encoding='utf-8') as fp:
user_data = json.load(fp)
self.user = user_data["user"]
self.title = user_data["title"]
# projects database
self.db = CalendarDb(self.arguments.database)
self.projects = dict()
for p in self.db.get_projects():
self.projects[p.key] = p
# start and end date
if self.arguments.start_date is None and \
self.arguments.end_date is None and \
self.arguments.period is None:
self.arguments.period = "last-month"
self.start_date = date.today().replace(day=1)
self.end_date = date.today() + timedelta(days=1)
if self.arguments.period is not None:
if self.arguments.period == "last-month":
self.end_date = self.start_date
self.start_date = (self.end_date -
timedelta(days=1)).replace(day=1)
else:
raise ValueError("Unknown period: %s" % self.arguments.period)
if self.arguments.start_date is not None or \
self.arguments.end_date is not None:
raise ValueError(
"You cannot combine --period and explicit date")
else:
if self.arguments.start_date is not None:
y, m, d, *r = humanfriendly.parse_date(
self.arguments.start_date)
self.start_date = date(y, m, d)
if self.arguments.end_date is not None:
y, m, d, *r = humanfriendly.parse_date(self.arguments.end_date)
self.end_date = date(y, m, d)
def scrambled():
"""Unscramble the word"""
content = helpers.init()
print('\nYou can exit at anytime by entering \'menu\'.\n')
random.shuffle(content)
count = len(content)
correct = 0
incorrect = 0
i = 0
shuffled = []
for word in content:
to_scramble = list(word['key'].lower().strip())
random.shuffle(to_scramble)
shuffled.append(to_scramble)
for scr in shuffled:
res = input('\n{}. {}.\n\t=>'.format(i + 1, ''.join(scr))).lower()
if res == content[i]['key'].lower():
count -= 1
correct += 1
if count > 0:
print('\nGood Job! next word!')
elif res == 'menu':
main.menu()
else:
incorrect += 1
count -= 1
if count > 1:
print('\nIncorrect. Next word.')
i += 1
print('\nYou got {} correct and {} incorrect'.format(correct, incorrect))
helpers.callback(scrambled)
import win32gui
from pywinauto.findwindows import find_window
from pywinauto.win32functions import SetForegroundWindow
import ImageGrab
import cv2
import helpers
import numpy as np
import random
thing_id = 'bob30'
helpers.init()
def get_rect(hwnd):
rect = win32gui.GetWindowRect(hwnd)
return rect
def screen_grab():
box = (x1, y1, x2, y2)
im = ImageGrab.grab(box)
return im
window = find_window(title="bob_robot_sim")
SetForegroundWindow(window)
x1, y1, x2, y2 = get_rect(window)
class RandomNoise:
import helpers helpers.init(globals()) from structures.Possession import PossessionManager PossessionManager().persist()
import torchvision import torch.nn as nn from torch.autograd import Variable import torch import numpy as np import dataset import helpers import models from config import * assert (file_name == 'vae-gan') writer, save_dir = helpers.init(gpu, file_name, experiment_name) use_ganloss = True # TODO can I use that here? use_vaeloss = True use_instancenoise = False iter_decay = 1000. # iterations after which instance noise is at 1/e # models # TODO to one model? netD = models.Discriminator().cuda() netG = models.Generator().cuda() netE = models.Encoder().cuda() # weight initialization netD.apply(models.init_weights) # xavier init netE.apply(models.init_weights) # xavier init netG.apply(models.init_weights) # xavier init criterion = nn.BCELoss().cuda() optD = torch.optim.Adam(netD.parameters(), lr=lr) optG = torch.optim.Adam(netG.parameters(), lr=lr)
This module uses K-Means to initialize mean and cov. Transition matrix A and distribution for the first component is initialized randomly. All estimated parameters starts with an underscore then follows the same name of the parameters as we used previously.
"""
# K-Means to estimate _mean and _cov
_mean, assign = KMeans(X, numComponent)
_cov = helpers.estimateCov(X, _mean, assign)
# show clustering result
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c = assign)
plt.scatter(_mean[:, 0], _mean[:, 1], s = 40, color = 'y', marker = 'D')
plt.title("Clustering Result by K-Means")
#%% Train HMM
# initialize parameters
_A, _pi = helpers.init(numComponent)
for nIter in range(1, 21):
# E Step
gamma, epsilon = helpers.E_Step(X, _mean, _cov, _A, _pi)
# M Step
_mean, _cov, _A, _pi = helpers.M_Step(X, gamma, epsilon)
# plot intermediate result
if(nIter % 5 == 0):
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c = gamma)
plt.scatter(_mean[:, 0], _mean[:, 1], s = 40, color = 'y', marker = 'D')
plt.title("Result return by HMM after " + str(nIter) + " iterations")
plt.show()
def __init__(self,input_size,output_size):
super(Linear,self).__init__()
self.param('weights', init(input_size,output_size))
self.param('bias', np.zeros(output_size))
def run_multi_engine_query(config):
""" Based on the specified named query, collect all engines, and run the query against
all engines and put the output in a single .csv file.
High-level logic:
1. Create a Portal object instance
2. Get the list of Engines from the Portal
3. Create the output file
For each Engine:
4. Run the query against that engine
5. Append the results to the output file
Arguments:
config: Initialized MultiEngineQueryConfig object
"""
func_name = inspect.currentframe().f_code.co_name
# Create the logger, and enable Class-level debugging
logger = init(config)
start_time = time.time()
if config.verbose:
logger.info('{} - Starting'.format(func_name))
# 1. Create a Portal object instance
portal = PortalAppliance.create(config)
# 2. Get the list of Connected Engines from the Portal
portal_start_time = time.time()
engine_list = portal.get_engine_list(config.engine_port,
only_connected=True)
portal_end_time = time.time()
msg = 'Retrieved {0} connected Engine{1} from Portal "{2}" in {3}.'.format(
len(engine_list), 's' if len(engine_list) != 1 else '', portal.name,
timer(portal_start_time, portal_end_time))
config.add_to_email(msg)
print(msg)
if config.verbose: logger.info("{0} - {1}".format(func_name, msg))
# If there are no Enigne Appliances, just note the fact and exit.
if len(engine_list) == 0:
msg = 'No Engine Appliances are connected or available, so exiting.'
print(msg)
config.add_to_email(msg)
return finish_process(func_name,
logger,
config,
start_time,
finished=True)
# For each query
for query in config.queries:
query_start_time = time.time()
if config.verbose:
msg = 'Processing Query "{0}".'.format(query.name)
print(msg)
config.add_to_email(msg)
logger.info('{} - {}'.format(func_name, msg))
# 3. Create the output file
output_fname, output_created = create_output_file(
logger, config, query)
if not output_created:
msg = 'Unable to create the output file ("{0}") for Query "{1}", so exiting.'.format(
output_fname, query.name)
print(msg)
config.add_to_email(msg)
return finish_process(func_name,
logger,
config,
start_time,
finished=True)
# For each Engine
first_engine = True
for engine in engine_list:
eng_name = '[{0} ({1})]'.format(engine.name, engine.hostname_fqdn)
# 4. Run the query
engine_objects = run_query_on_engine(logger, config, engine, query)
msg = '{0} Retrieved {1} Object{2} to save from this Engine for Query "{3}".'.format(
eng_name, len(engine_objects),
's' if len(engine_objects) != 1 else '', query.name)
config.add_to_email(msg)
print(msg)
if config.debug_engine:
logger.debug('{0} - {1}'.format(func_name, msg))
# 5. Save the query results to the output file
if len(engine_objects) > 0:
if first_engine:
field_names = engine_objects[0].keys()
written_ok = write_to_output_file(logger, config, query,
output_fname, engine_objects,
field_names, first_engine)
first_engine = False
elif config.debug_engine:
logger.debug(
'{0} - Skipping write of output file for Engine "{1}", no rows were returned for Query "{2}".'
.format(func_name, eng_name, query.name))
query_end_time = time.time()
if config.verbose:
msg = 'Completed collecting and writing results for "{0}" to "{1}" in {2}.'.format(
query.name, output_fname,
timer(query_start_time, query_end_time))
logger.info('{0} - {1}'.format(func_name, msg))
print(msg)
return finish_process(func_name, logger, config, start_time)