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#!/usr/bin/python3 | |
import argparse | |
import sys | |
import requests | |
from bs4 import BeautifulSoup | |
def music_grid_parse(music_item): | |
return music_item.find('a')['href'] |
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#!/usr/bin/python3 | |
import argparse | |
import sys | |
import csv | |
from bs4 import BeautifulSoup | |
import requests | |
if __name__ == "__main__": |
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#!/usr/bin/env python3 | |
import argparse | |
import sys | |
import re | |
id_pattern= re.compile("[A-Z]+") | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() |
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total_freq,pos_freq,neg_freq,KF9.1_miss,KF9.1_present,KF9.1_value,BLOSUM2.1_miss,BLOSUM2.1_present,BLOSUM2.1_value | |
QVSGSENTDNKPHSE,1,0,1,1,0,?,1,0,? | |
LQRFDQRSRQFQNLQ,3,0,3,3,0,?,3,0,? | |
IHFGNDWEDRYYREN,2,2,0,2,0,?,2,0,? | |
FADLMGYIPLVGAPL,1,1,0,1,0,?,1,0,? | |
EQEWGTPGSHVREET,1,0,1,0,1,-0.5,1,0,? | |
NLVGLLGVTGTDLQG,2,2,0,2,0,?,1,1,-0.41 | |
PTDSPSATIDVPSNC,3,0,3,1,2,0.156667,2,1,-0.6 | |
AVGQGWVDHFADGYD,2,1,1,1,1,-0.182667,1,1,0.208 | |
LKLDIQPYDINQRLQ,1,0,1,1,0,?,1,0,? |
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#!/usr/bin/env python2 | |
from collections import defaultdict | |
ID_LEN = 5 | |
def gen_primes(): | |
"""Using the Sieve of Erastothenes solution""" | |
composites = defaultdict(list) | |
checker = 2 |
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#!/usr/bin/env python2 | |
from collections import defaultdict, deque | |
from itertools import product | |
from operator import add | |
def vectorsum(*vectors): | |
if len(vectors) > 1: | |
return map(add, vectors[0], vectorsum(*vectors[1:])) | |
else: |
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#!/usr/bin/env python2 | |
from collections import deque | |
from itertools import permutations | |
def gen_permutations(numbers, slice_by): | |
subsequence_len = len(numbers) - slice_by | |
for permutation in permutations(numbers, subsequence_len): | |
yield permutation |
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#!/usr/bin/env python2 | |
from collections import defaultdict | |
import Queue | |
DIRECTIONS = [(-1, 0), (0, 1), (1, 0), (0, -1)] | |
FREE, WALL = 0, 1 | |
class OutOfMapBoundaryError(Exception): | |
pass |
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import pandas as pd | |
from rngtest.profiling import profile, multi_profile | |
def get_columns(df): | |
for col in df: | |
yield df[col] | |
@profile |