def get_grade(filename):
grades = reader(filename)
total = 0
for grade in grades:
subgrade = grade[2] / grade[3] * grade[4] * 100
total += subgrade
return total
def get_allrundles(season):
info = reader(u"https://learnedleague.com/allrundles.php?%d" % (season))
if info.startswith("Season not yet underway."):
return {}
parsev = AllRundleParse(season)
parsev.feed(info)
return parsev.ret_value()
def __init__(self, args): self.shell = args self.publicsubs = [] self.privatesubs = [] self.t = Template() self.file = self.shell ireader = reader() self.load_file = ireader.loader(self.file)
def check_match_info(game_no):
"""
game_no: game number extracted from the LL site.
returns False if this is not a twin game (most cases)
returns a string consisting of player names separated by "v." and an LL score.
"""
info = reader(u"http://learnedleague.com/match.php?id=%d" % (game_no))
parsev = MatchParse()
parsev.feed(info)
return parsev.ret_value()
from Reader import reader, make_arrays
from visualization import plot_graphics
from scr.algorithms import correlation_function, normalization
if __name__ == '__main__':
data = reader("../data/21022518.txt")
arr1, arr2, arr3, arr4, arr5, arr6, arr7, arr8, arr9, arr10, arr11, arr12 = make_arrays(
data)
plot_graphics(arr9, arr12, 'plasma_pos', 't, мс', 'plasma_pos')
plot_graphics(arr9, arr8, 'neutron_glob14', 't, мс', 'neutron_glob14')
plot_graphics(arr9, arr10, 'neutron_glob12', 't, мс', 'neutron_glob12')
tau, corr = correlation_function(arr8, arr12)
plot_graphics(tau, corr, 'corr_func', 'tau', 'corr_14')
corr = normalization(corr)
plot_graphics(tau, corr, 'norm_corr_func', 'tau', 'n_corr_14')
tau, corr = correlation_function(arr10, arr12)
plot_graphics(tau, corr, 'corr_func', 'tau', 'corr_12')
corr = normalization(corr)
plot_graphics(tau, corr, 'norm_corr_func', 'tau', 'n_corr_12')
tau, corr = correlation_function(arr8, arr10)
return ids
def filter_sent_label(sent, label):
return len(sent) < MAX_LENGTH and len(label) < MAX_LENGTH
def filter_sents_labels(sents, labels):
return [[sents[i], labels[i]] for i in range(len(sents))
if filter_sent_label(sents[i], labels[i])]
if __name__ == '__main__':
config_path = 'D:\PyCharm\pycharm_workshop\seq2seq_based_on_attention_pytorch\config.cfg'
config = configer(config_path)
Reader = reader(config.corpus_path, needFresh=True, language='eng')
text_sent_list, label_sent_list = Reader.getData()
label_sent_list, text_sent_list = text_sent_list, label_sent_list
# sent_label_list = filter_sents_labels(text_sent_list, label_sent_list)
'''
create dictionary
'''
# print(text_sent_list)
text_word_state = {'SOS': 1, 'EOS': 1, PAD: 1}
label_word_state = {'SOS': 1, 'EOS': 1, PAD: 1}
for line in text_sent_list:
for word in line:
if word not in text_word_state:
text_word_state[word] = 1
else:
text_word_state[word] += 1
return ids
def filter_sent_label(sent, label):
return len(sent) < MAX_LENGTH and len(label) < MAX_LENGTH
def filter_sents_labels(sents, labels):
return [[sents[i], labels[i]] for i in range(len(sents))
if filter_sent_label(sents[i], labels[i])]
if __name__ == '__main__':
config_path = 'config.cfg'
config = configer(config_path)
txt_reader = reader(config.text_train_path, needFresh=True, language='chn')
tgt_reader = reader(config.tgt_train_path, needFresh=True, language='chn')
text_sent_list = txt_reader.getData()
label_sent_list = tgt_reader.getData()
txt_test_reader = reader(config.text_test_path,
needFresh=True,
language='chn')
tgt_test_reader = reader(config.tgt_test_path,
needFresh=True,
language='chn')
txt_test_sent_list = txt_test_reader.getData()
tgr_test_sent_list = tgt_test_reader.getData()
# print(text_sent_list[:1])
# print(label_sent_list[:1])
# sent_label_list = filter_sents_labels(text_sent_list, label_sent_list)
'''
def get_matches_for_rundle_day(season, rday, rundle):
info = reader(u"https://learnedleague.com/match.php?%d&%d&%s" % (season, rday, rundle))
parsev = RundleDayParse()
parsev.feed(info)
return parsev.ret_value()
def __init__(self, train, test):
train = Trainer(train)
self.instructions = train.main()
self.dic = reader(test)
self.root = Node(self.dic[0], 'Root', '', 0.5)
from typing import Tuple, List, Union, Dict
import numpy
from math import floor
from Reader import reader
from random import uniform
from math import pi
settings: Union[Dict[str, bool], Dict[str, None]] = reader('Settings.txt')
FPS: int = 60
RESOLUTION: Tuple[float, float] = 1280., 720.
CRT: bool
if 'CRT EFFECT' in settings and settings['CRT EFFECT'] is not None:
CRT = settings['CRT EFFECT']
else:
CRT = False
SCANLINES: bool
if 'SCANLINES' in settings and settings['SCANLINES'] is not None:
SCANLINES = settings['SCANLINES']
else:
SCANLINES = CRT
WINDOWED: bool
if 'FULLSCREEN' in settings and settings['FULLSCREEN'] is not None:
WINDOWED = not settings['FULLSCREEN']
else:
WINDOWED = False
BLOOM: bool
if 'BLOOM' in settings and settings['FULLSCREEN'] is not None:
def __init__(self, fil):
self.dic = reader(fil)
self.instructions = []