def load_dme_data(self):
if not config.dme_pre_load_data:
x_test = np.empty((2, 4 * 24 * config.coverage))
for link in os.listdir(config.dme_root_files):
link_name = link.split('_')[0]
link_type = link.split('_')[1]
print("now processing link: {} of type: {}".format(
link_name, link_type))
df = pd.read_csv(config.dme_root_files + '/' + link)
init_date = config.dme_scrape_config['link_objects']['date'][
'value']
time_value = dt.strptime(
f"{init_date['yyyy']}-{init_date['mm']}-{init_date['dd']} 00:00:00",
"%Y-%m-%d %H:%M:%S") + dt_delta(days=1)
df_min_header = 'PowerRLTMmin' if link_type == 'SINK' else 'PowerTLTMmin'
df_max_header = 'PowerTLTMmax' if link_type == 'SOURCE' else 'PowerRLTMmax'
min_max_values = np.empty(2)
for row_min, row_max, row_time, row_interval in zip(
list(df[df_min_header]), list(df[df_max_header]),
list(df.Time), list(df.Interval)):
if row_interval != 24:
while time_value < dt.strptime(row_time,
"%Y-%m-%d %H:%M:%S"):
min_max_values = np.vstack(
(min_max_values, np.array([np.nan, np.nan])))
time_value = dt.strptime(
row_time,
"%Y-%m-%d %H:%M:%S") + dt_delta(minutes=15)
min_max_values = np.vstack(
(min_max_values, np.array([row_min, row_max])))
time_value = dt.strptime(
row_time,
"%Y-%m-%d %H:%M:%S") + dt_delta(minutes=15)
min_max_values = min_max_values[1:].T
if min_max_values.shape == (2, 4 * 24 * config.coverage):
x_test = np.vstack((x_test, min_max_values))
else:
print('dim missmatch: {}'.format(min_max_values.shape))
x_test = x_test[2:]
with open(config.dme_root_values + '/' + 'dme_values.pkl',
'wb') as f:
pickle.dump(x_test, f)
return x_test
else:
with open(config.dme_root_values + '/' + 'dme_values.pkl',
'rb') as f:
x_test = pickle.load(f)
return x_test
def equalDayWeek(date_a, date_b, interval):
"""
Compare if two dates are in the same week
"""
if interval == "day":
return date_a == date_b
monday_a = date_a - dt_delta(days=date_a.weekday())
monday_b = date_b - dt_delta(days=date_b.weekday())
return monday_a == monday_b
def __returnTodayStockHistory(self, symbol):
"""
Return today's stock history for a givin symbol
"""
expected_date = self.__end_datetime.date()
# 5minute, 10minute, hour
interval = "10minute"
delta = {"5minute": 5, \
"10minute": 10, \
"hour": 60}
stock_history = {}
trade_date = None
stock_historicals = rs.get_stock_historicals(symbol,
span="day",
interval=interval)
for stock_historical in stock_historicals:
begins_datetime_est = dt_parse(
stock_historical["begins_at"]).astimezone(self.__time_zone)
trade_info = {"start_time": begins_datetime_est, \
"end_time": begins_datetime_est + dt_delta(minutes=delta[interval]), \
"open_price": stock_historical["open_price"], \
"close_price": stock_historical["close_price"] }
trade_date = begins_datetime_est.date()
if trade_date in stock_history:
# stock_history[trade_date]
if trade_info["start_time"] < stock_history[trade_date][
"start_time"]:
stock_history[trade_date]["start_time"] = trade_info[
"start_time"]
stock_history[trade_date]["open_price"] = trade_info[
"open_price"]
if trade_info["end_time"] > stock_history[trade_date][
"end_time"]:
stock_history[trade_date]["end_time"] = trade_info[
"end_time"]
stock_history[trade_date]["close_price"] = trade_info[
"close_price"]
else:
stock_history[trade_date] = trade_info
return stock_history[trade_date]
def format_for_psb(player, args):
all_weeks = list()
date = None
if args:
date = tools.date_parser(" ".join(args))
if not date:
date = dt.now(tz.utc)
req_date = date.strftime("%Y-%m-%d")
date = date + dt_delta(weeks=1)
start, end = get_previous_week(date)
all_weeks.append(PsbWeekUsage(player, 0, start, end))
date = start
for i in range(8):
start, end = get_previous_week(date)
all_weeks.append(PsbWeekUsage(player, i + 1, start, end))
date = start
return req_date, all_weeks
def get_previous_week(date):
iso_date = date.isocalendar()
end = dt.combine(dt_date.fromisocalendar(iso_date[0], iso_date[1], 1),
dt_time(tzinfo=tz.utc))
start = end - dt_delta(weeks=1)
return start, end
from datetime import date as dt_date
from datetime import timedelta as dt_delta
import queries
this_week = (dt_date.today() - dt_delta(days=dt_date.today().weekday()))
this_week_str = (dt_date.today() - dt_delta(days=dt_date.today().weekday())).strftime("%m-%d-%Y")
next_week_str = (this_week + dt_delta(weeks=1)).strftime("%m-%d-%Y")
this_week = getattr(__import__('config.allotment', fromlist=[this_week_str]), this_week_str)
next_week = getattr(__import__('config.allotment', fromlist=[next_week_str]), next_week_str)
class Week:
def __init__(self, appointments):
""" Appointments is an array of the # of appointments for each
available time slot.
Starts with tomorrow.
"""
week_array = []
# creating array of appointments
for i in range(7): # first week
week_array.append([])
for j in range(24):
week_array[i].append([])
for k in range(4):
week_array[i][j].append(this_week.week[i][j])
for i in range(7, 14): # second week
week_array.append([])
def __returnDayWeekStockHistory(self, symbol, offset, interval):
"""
Return stock history for a givin day or week.
"""
def returnSpan(offset, interval):
"""
docstring
"""
if interval == "day":
if offset <= 7:
return "week"
elif offset <= 27:
return "month"
elif offset <= 90:
return "3month"
elif offset <= 365:
return "year"
elif offset <= 1780:
return "5year"
else:
raise ValueError(
"offset is in wrong range (expect >= 0 and <= 1780).")
elif interval == "week":
if offset <= 4:
return "month"
elif offset <= 12:
return "3month"
elif offset <= 52:
return "year"
elif offset <= 260:
return "5year"
else:
raise ValueError(
"offset is in wrong range (expect >= 0 and <= 260).")
def equalDayWeek(date_a, date_b, interval):
"""
Compare if two dates are in the same week
"""
if interval == "day":
return date_a == date_b
monday_a = date_a - dt_delta(days=date_a.weekday())
monday_b = date_b - dt_delta(days=date_b.weekday())
return monday_a == monday_b
if interval != "week" and interval != "day":
raise ValueError("interval is in wrong, expected day or week.")
offset = abs(offset)
interval_day = 1
if interval == "week":
interval_day = 7
expected_date = (self.__end_datetime -
dt_delta(days=offset * interval_day)).date()
expected_historical = None
stock_historicals = rs.get_stock_historicals(symbol,
span=returnSpan(
offset, interval),
interval=interval)
for stock_historical in stock_historicals:
# Fix UTC to EST
begins_date = dt_parse(stock_historical["begins_at"]).astimezone(
self.__time_zone) + dt_delta(minutes=870)
stock_historical["begins_at"] = begins_date
stock_historical["ends_at"] = begins_date + dt_delta(minutes=390)
if interval == "week":
stock_historical["ends_at"] += dt_delta(days=4 -
begins_date.weekday())
if equalDayWeek(begins_date.date(), expected_date, interval):
expected_historical = stock_historical
return expected_historical