def __init__(self):
"""
Set up algorithm environment.
Load:
Global variables
Underlying data for algorithm:
List of hospitals
Patients by LSOA
Travel matrix from all LSOA to all hospitals
"""
# Set up class instances (one instance per class)
self.data = Data()
self.pop = Pop()
self.time_of_last_save = 0
return
def main():
from classes.request import Request
from classes.data import Data
import sys
name = sys.argv[1]
req = Request(name=name)
json = req.get_json_by_name()
d = Data(json)
d.parse_json()
print d.get_needed_data()
def main(): from classes.request import Request from classes.data import Data import sys from datetime import datetime import time try: lat = sys.argv[1] lon = sys.argv[2] except: # Default NYC coordinates. lat = 40 lon = -74 req = Request(lat=lat, lon=lon) print "Uploading coordinates.." while True: now = datetime.now() hour = now.hour minute = now.minute if not hour % 3 and not minute: # If 0, 3, 6, 9, 12 hours and 0 minute res = req.get_json_by_coords() data = Data(json=res) data.parse_json() print data.get_needed_data() else: print "Waiting for next data.." time.sleep(60)
def get_all_season(url):
headers = {
"User-Agent":
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"
}
html = requests.get(url, headers=headers)
soup = bs(html.content, features="html.parser")
player = soup.select('.dataMain > .dataTop > .dataName > h1')[0].text
player = f"name:{player}"
table = soup.select(
'.responsive-table > .grid-view > .items > tbody')[0]
profile = soup.select('.dataContent > .dataBottom > .dataDaten')
ribbon = soup.select('.dataRibbon > a')
if len(ribbon) > 0:
ribbon = ribbon[0]['title']
player_bio = []
country = []
for items in profile:
for p in items.find_all('p'):
for datavalue in p.find_all(
True, {"class": re.compile("^(dataValue)$")}):
if len(datavalue.find_all('img')) > 0:
country.append(datavalue.find_all('img')[0]['title'])
bio_data = Text.format_text(p.text)
player_bio.append(bio_data)
player_bio.append(player)
bio = Data.get_bio(player_bio, country)
stats = []
try:
for cells in table.find_all(True,
{"class": re.compile("^(even|odd)$")}):
season = cells.find_all('td')[0].text
league = cells.find_all(
'td')[1].img['title'] if cells.find_all(
'td')[1].img else ''
club = cells.find_all('td')[3].img['alt']
squad = cells.find_all('td')[4].text
apps = cells.find_all('td')[5].a.text
goals = cells.find_all('td')[7].text
assists = cells.find_all('td')[8].text
stats_item = {
'season': season,
'league': league,
'club': club,
'squad': squad,
'apps': apps,
'goals': goals,
'assists': assists,
}
stats.append(stats_item)
except IndexError:
pass
return stats, bio, ribbon
'photo':
'https://s3.amazonaws.com/charitycdn/cache/resizedcrop-75af4d5fd8443d70b4a3d1334d544466-800x800.jpg',
'profile':
'https://www.transfermarkt.com.br/kylian-mbappe/leistungsdaten/spieler/342229/plus/0?saison=2018',
'text':
'2018/2019 Season',
'filter':
'Ligue 1',
'club': [
'https://tmssl.akamaized.net/images/wappen/head/583.png?lm=1472229265'
]
},
{
'photo':
'https://bolavip.com/__export/1586720363168/sites/bolavip/img/2020/04/12/edinson-cavani-psg-bordeaux-ligue-1-09022019_defwkc58a8t61ipd1wlkohwji_crop1586720344691.jpg_423682103.jpg',
'profile':
'https://www.transfermarkt.com/edinson-cavani/leistungsdaten/spieler/48280/plus/0?saison=2017',
'text':
'2017/2018 Season',
'filter':
'Ligue 1',
'club': [
'https://tmssl.akamaized.net/images/wappen/head/583.png?lm=1472229265'
]
},
]
for player in players:
Data.set_data(player)
Playerstats.save(player['photo'], player['profile'], player['text'],
player['filter'], player['club'], 'liga_1_scorers')
from classes.chain import Chain
from classes.block import Block
from classes.data import Data
from datetime import datetime
chain = Chain()
data = Data("senderKey", "receiverKey", 123456, datetime)
block = Block(1, None, data)
chain.addNewBlock(block)
print(chain.isChainValid())
from classes.human import Human
from classes.data import Data
from classes.controller import Controller
from sys import argv
from matplotlib import pyplot
if __name__ == '__main__':
if argv[1] != "--help" and argv[1] != "-h":
name = "spolki/" + argv[1]
data = Data(name)
human = Human(data)
stock = None
if len(argv) == 3:
stock = Controller(data, human, int(argv[2])) # Jesli argv[3] = 1 to Controller uzyje uzyje lekko
# ulepszonej wersji wskaźnika a jesli argv[3] > 1 to ulepszonej wersji
else:
stock = Controller(data, human)
stock.stock()
date = data.getDate()
# pyplot.figure("Project")
# pyplot.subplot(2, 1, 1)
# pyplot.plot(date, data.getClose(), label="Price", color="green")
# pyplot.ylabel("Price in USD")
# pyplot.xlabel("Date in yyyy-mm-dd format")
# pyplot.title("Price")
# pyplot.legend()
# pyplot.grid(True)
# pyplot.subplot(2, 1, 2)
def main():
while True:
table = Table(title="Menu")
table.add_column("Selection",
justify="center",
style="white",
no_wrap=True)
table.add_column("Action", justify="left", style="yellow")
table.add_row("1.", "Load data")
table.add_row("2.", "Explore data")
table.add_row("3.", "Predict future")
table.add_row("4.", "Display results")
table.add_row("5.", "Predict without backtesting")
table.add_row("0.", "Exit")
console = Console()
console.print(table)
__choose_menu = int(input("Enter your choice: "))
if __choose_menu == 1:
user = input("Enter Stock ticker: ")
start_date = input("Enter start date yyyy-mm-dd: ")
end_date = input("Enter end date yyyy-mm-dd: ")
ratio = float(
input(
"Percentage of how much data should be predicted (0-1), \ne.g. 0.95 indicates that 5% of provided data will be predicted: "
))
dataset = Data(f'{user}', f'{start_date}', f'{end_date}', ratio)
dataset.load_data()
elif __choose_menu == 2:
explore = Explore(dataset.original_data()['Adj Close'])
explore.time_series_decomposition(3)
elif __choose_menu == 3:
future = Prophesy(dataset.load_data(), dataset.get_periods())
future.predict_future()
elif __choose_menu == 4:
results = Backtest(dataset.get_all_returns(),
future.get_predictions())
results.print_graph()
results.error_graph()
elif __choose_menu == 5:
user = input("Enter Stock ticker: ")
future_days = int(input("Enter days to predict: "))
start_date = input("Enter start date yyyy-mm-dd: ")
end_date = input("Enter end date yyyy-mm-dd: ")
dataset = Data(f'{user}', f'{start_date}', f'{end_date}', 1)
dataset.load_data()
future = Prophesy(dataset.get_prepared_data(), future_days)
results = future.predict_future()
future.plot_result(results)
future.plot_only_predictions(results, future_days)
elif __choose_menu == 0:
exit(0)
else:
print("Invalid Choice")