text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: StanczakDominik/LabSpec path: /read_h5py.py
import h5py
f = h5py.File("data.hdf5")
for key, item in f.items():
print(key, item)
# if key !="psi":
# f.__delitem__(key)
# print(item[0])
for key, item in f.attrs.items():
<|fim_suffix|>sx", data=np.load("currents_x.npy"))
# c... | code_fim | hard | {
"lang": "python",
"repo": "StanczakDominik/LabSpec",
"path": "/read_h5py.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> while(count < n):
#count is 2, res add str2
res += str[count]
count += 2
return res<|fim_prefix|># repo: rohstar/codingbat path: /python/warmup2/string_bits.py
#Given a string, return a new string made of every other char starting with the first, so "Hello" yields "Hlo".
def st... | code_fim | easy | {
"lang": "python",
"repo": "rohstar/codingbat",
"path": "/python/warmup2/string_bits.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> count = 0
res = ''
while(count < n):
#count is 2, res add str2
res += str[count]
count += 2
return res<|fim_prefix|># repo: rohstar/codingbat path: /python/warmup2/string_bits.py
#Given a string, return a new string made of every other char starting with the first, so "He... | code_fim | easy | {
"lang": "python",
"repo": "rohstar/codingbat",
"path": "/python/warmup2/string_bits.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rohstar/codingbat path: /python/warmup2/string_bits.py
#Given a string, return a new string made of every other char starting with the first, so "Hello" yields "Hlo".
<|fim_suffix|> res += str[count]
count += 2
return res<|fim_middle|>def string_bits(str):
n = len(str)
coun... | code_fim | medium | {
"lang": "python",
"repo": "rohstar/codingbat",
"path": "/python/warmup2/string_bits.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>print("\n")
print(sorted(classes))
print(classes)
#The sorted function can be used to temporarily sort lists,
#It displays the sorted version of the list without actually changing the order
print("\n")
print(classes)
classes.reverse()
print(classes)
#Reverse does exactlly what you'd think, it reverses ... | code_fim | hard | {
"lang": "python",
"repo": "Chichri/Python-Projects",
"path": "/Messing_with_lists.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Chichri/Python-Projects path: /Messing_with_lists.py
classes = ['Fighter', 'Rogue', 'Bard', 'Cleric']
message = "My favorite class is the " + classes[2].title()
print(message)
#This is a list. It can be used to contain information
#classes is now a list containing these four items
#You can select... | code_fim | hard | {
"lang": "python",
"repo": "Chichri/Python-Projects",
"path": "/Messing_with_lists.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> response = get_item.get_item(id)
return jsonify(response)<|fim_prefix|># repo: OualidZM/flask-Ollivanders path: /controller/get_item.py
from flask import jsonify, Blueprint
from services import get_item
get_item_blue = Blueprint("get_item", __name__)
<|fim_middle|>
@get_item_blue.route("/item/<... | code_fim | easy | {
"lang": "python",
"repo": "OualidZM/flask-Ollivanders",
"path": "/controller/get_item.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: OualidZM/flask-Ollivanders path: /controller/get_item.py
from flask import jsonify, Blueprint
from services import get_item
<|fim_suffix|>
@get_item_blue.route("/item/<id>")
def get_item_func(id):
response = get_item.get_item(id)
return jsonify(response)<|fim_middle|>get_item_blue = Blue... | code_fim | easy | {
"lang": "python",
"repo": "OualidZM/flask-Ollivanders",
"path": "/controller/get_item.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: abdaloth/generalized-TLDR path: /summarize.py
#!/usr/bin/python
import sys
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
import nltk
nltk.download("stopwords")
nltk.download("punkt")
lang_stopwords = []
from nltk.tokenize import sent_tokenize
import n... | code_fim | hard | {
"lang": "python",
"repo": "abdaloth/generalized-TLDR",
"path": "/summarize.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # mirror the matrix onto itself to get the similarity edges between sentences
similarity_matrix = bagofwords_matrix * bagofwords_matrix.T
similarity_graph = nx.from_scipy_sparse_matrix(similarity_matrix)
scores = nx.nx.pagerank_scipy(similarity_graph)
scored_sentences = [(i, s, s... | code_fim | hard | {
"lang": "python",
"repo": "abdaloth/generalized-TLDR",
"path": "/summarize.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == "__main__":
# retrieve command line arguments and store them as variables
inputdir = sys.argv[1]
lang = sys.argv[2]
outfile = sys.argv[3]
import pyspark
from nltk.corpus import stopwords
lang_stopwords = stopwords.words(lang)
sc = pyspark.SparkC... | code_fim | hard | {
"lang": "python",
"repo": "abdaloth/generalized-TLDR",
"path": "/summarize.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ganon1998/COVID_VariantRecognition path: /ProteinRNN.py
# Here we import the modules that we will use for the task
import numpy as np
import math
import statistics
import tensorflow as tf
import string
import random
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.kera... | code_fim | hard | {
"lang": "python",
"repo": "Ganon1998/COVID_VariantRecognition",
"path": "/ProteinRNN.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# We parse files to get training data
seq_train, train_label = read_seq('/content/gdrive/My Drive/pdb_seqres.txt')
seq_test, test_label = read_seqV2('/content/gdrive/My Drive/pdb_seqres.txt')
# We reshape labels to be 2d arrays
train_label = np.asarray(train_label).astype('float32').reshape((-1,1))
test... | code_fim | hard | {
"lang": "python",
"repo": "Ganon1998/COVID_VariantRecognition",
"path": "/ProteinRNN.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if i == 25:
seq.append(ord(string.ascii_uppercase[random.randint(0,26)]) - ord('A') + 1)
continue
if i >= 45:
seq.append(ord(string.ascii_uppercase[random.randint(0,26)]) - ord('A') + 1)
continue
seq.append(ord(charList[i]) - ord('A') + 1)
# grab the labels... | code_fim | hard | {
"lang": "python",
"repo": "Ganon1998/COVID_VariantRecognition",
"path": "/ProteinRNN.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ShresthaRujal/Django-with-Vue-CLI path: /app/views.py
from django.shortcuts import render,get_object_or_404
from django.contrib.auth.decorators import login_required
from django.views.decorators.http import require_http_methods
from rest_framework import viewsets
from rest_framework import status... | code_fim | hard | {
"lang": "python",
"repo": "ShresthaRujal/Django-with-Vue-CLI",
"path": "/app/views.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print(self.request.user)
serializer.save(user_profile=self.request.user)
@action(detail=True,methods=['GET'])
def publish(self, request,id=None):
draft = self.get_object()
draft.publish()
serializer = serializers.DraftSerializer(draft)
return Respon... | code_fim | hard | {
"lang": "python",
"repo": "ShresthaRujal/Django-with-Vue-CLI",
"path": "/app/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>ip3 install hana_automl
"""
__version__ = "0.0.3"<|fim_prefix|># repo: jorgeporca/SAP-HANA-AutoML path: /hana_automl/__init__.py
"""Welcome to hana_automl - Automated Machine Lea<|fim_middle|>rning library based on SAP HANA.
******Installation*********
1. pip3 install Cython
2. p | code_fim | medium | {
"lang": "python",
"repo": "jorgeporca/SAP-HANA-AutoML",
"path": "/hana_automl/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>ation*********
1. pip3 install Cython
2. pip3 install hana_automl
"""
__version__ = "0.0.3"<|fim_prefix|># repo: jorgeporca/SAP-HANA-AutoML path: /hana_automl/__init__.py
"""Welcome to hana_automl - Automated Machine Lea<|fim_middle|>rning library based on SAP HANA.
******Install | code_fim | easy | {
"lang": "python",
"repo": "jorgeporca/SAP-HANA-AutoML",
"path": "/hana_automl/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jorgeporca/SAP-HANA-AutoML path: /hana_automl/__init__.py
"""Welcome to hana_automl - Automated Machine Lea<|fim_suffix|>ip3 install hana_automl
"""
__version__ = "0.0.3"<|fim_middle|>rning library based on SAP HANA.
******Installation*********
1. pip3 install Cython
2. p | code_fim | medium | {
"lang": "python",
"repo": "jorgeporca/SAP-HANA-AutoML",
"path": "/hana_automl/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> buckets = Bucket.query.all()
for bucket in buckets:
out['buckets'][bucket.name] = bucket.amount
today = datetime.date.today()
last_day = calendar.monthrange(today.year, today.month)[1]
transactions = Trans.query.filter(Trans.date.between(today.replace(day=1),
... | code_fim | hard | {
"lang": "python",
"repo": "BenDoan/NestEgg",
"path": "/nestegg/views/api.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: BenDoan/NestEgg path: /nestegg/views/api.py
import calendar
import datetime
import json
from flask import Blueprint, request, abort
from util import *
from consts import *
from database import db, Budget, Bucket, BudgetItem, Trans
api = Blueprint('api', __name__,
templat... | code_fim | hard | {
"lang": "python",
"repo": "BenDoan/NestEgg",
"path": "/nestegg/views/api.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ Performs a single optimization step """
for p, grad, v, square_grad_avg, delta_x_acc in self.params:
# Compute the running average of the squared gradients
square_grad_avg.mul_(self.rho)
square_grad_avg.addcmul_(grad, grad, value = 1 - self.rho)
... | code_fim | medium | {
"lang": "python",
"repo": "marieanselmet/DeepLearningEPFL_projects",
"path": "/DL_framework_from_scratch/optimizers.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class Adadelta(Optimizer):
""" Implementation of the ADADELTA optimizer """
def __init__(self, params, lr, rho=0.9, eps=1e-6):
self.params = params
self.lr = lr
self.rho = rho
self.eps = eps
def step(self):
""" Performs a single optimization step "... | code_fim | hard | {
"lang": "python",
"repo": "marieanselmet/DeepLearningEPFL_projects",
"path": "/DL_framework_from_scratch/optimizers.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: marieanselmet/DeepLearningEPFL_projects path: /DL_framework_from_scratch/optimizers.py
class Optimizer(object):
""" Optimizer base class """
def step(self):
raise NotImplementedError
def zero_grad(self):
raise NotImplementedError
class SGD(... | code_fim | hard | {
"lang": "python",
"repo": "marieanselmet/DeepLearningEPFL_projects",
"path": "/DL_framework_from_scratch/optimizers.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>x = df[['dti', 'A', 'B', 'C', 'D', 'E', 'F', 'G']]
predictions = PD_SVM.predict(x) # Gets a list of all predictions
prob_predictions = PD_SVM.predict_proba(x)
#print(predictions, prob_predictions, x)
print()
print('Probability of Default:', prob_predictions[0, 1])
print('\n'*2)
endinput = input... | code_fim | hard | {
"lang": "python",
"repo": "ghappy112/Probability_of_Default-SVM",
"path": "/PD_Calculator.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ghappy112/Probability_of_Default-SVM path: /PD_Calculator.py
#Copyright 2020, Gregory Happ, All rights reserved.
print("Copyright 2020, Gregory Happ, All rights reserved.")
print()
#Probability of Default (PD) calculator!!!
import numpy as np
import pandas as pd
import sklearn
from sklearn... | code_fim | hard | {
"lang": "python",
"repo": "ghappy112/Probability_of_Default-SVM",
"path": "/PD_Calculator.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yuanhuiru/xnr2 path: /xnr_0429/xnr/timed_python_files/clean_data_sencond/facebook_history_feedback_mappings.py
asticsearch import Elasticsearch
import sys
import json
reload(sys)
sys.path.append('../../')
from global_utils import es_xnr_2 as es
from global_utils import facebook_history_feedb... | code_fim | hard | {
"lang": "python",
"repo": "yuanhuiru/xnr2",
"path": "/xnr_0429/xnr/timed_python_files/clean_data_sencond/facebook_history_feedback_mappings.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not es.indices.exists(index=index_name):
es.indices.create(index=index_name, body=index_info, ignore=400)
# 好友列表
def facebook_history_feedback_friends_mappings(index_name, index_type): ## 粉丝提醒及回粉
index_info = {
'settings': {
'number_of_replicas': 0,
'n... | code_fim | hard | {
"lang": "python",
"repo": "yuanhuiru/xnr2",
"path": "/xnr_0429/xnr/timed_python_files/clean_data_sencond/facebook_history_feedback_mappings.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> params :- purchase order - string
returns :- True or False as Order confirms
"""
order = self.purchase_details[purchase_order]
order['state'] = 'Done'
for product in order['products']:
pro_data = self.products_data[product['name']]
p... | code_fim | hard | {
"lang": "python",
"repo": "maulikb-emipro/Python-Training",
"path": "/Test1/purchase.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: maulikb-emipro/Python-Training path: /Test1/purchase.py
import datetime
import re
class Purchase:
""" This class used to store purchase of products """
purchase_details = {}
def create_purchase_order(self, products, vendor_name):
"""
func :- Used to create new ... | code_fim | hard | {
"lang": "python",
"repo": "maulikb-emipro/Python-Training",
"path": "/Test1/purchase.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> check = [[0, 1], [1, 1], [1, 0], [1, -1],
[0, -1], [-1, -1], [-1, 0], [-1, 1]]
for i in range(1, 65):
for j in range(1, 65):
if img[i, j] == 255:
flag = 0
num = 0
cnt = 0
for k in range(9):
if img[i + check[k % 8][0], j + check[k % 8][1]] == 255:
cnt += 1
if fla... | code_fim | hard | {
"lang": "python",
"repo": "yichunlo/Computer_Vision",
"path": "/hw7/hw7.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yichunlo/Computer_Vision path: /hw7/hw7.py
import numpy as np
import cv2
import sys
np.set_printoptions(threshold = sys.maxsize)
def ds(img):
ret = np.zeros((66, 66), np.int)
for i in range(64):
for j in range(64):
if img[i * 8, j * 8] >= 128:
ret[i + 1, j + 1] = 255
else:
ret[... | code_fim | hard | {
"lang": "python",
"repo": "yichunlo/Computer_Vision",
"path": "/hw7/hw7.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: iamanx17/dslearn path: /Generic tree/largest.py
from GenericTree import takeinput, prindata
<|fim_suffix|> if root is None:
return 0
lrg=root.data
for child in root.children:
if child.data>lrg:
lrg=child.data
maxchild=largestdata(child)
if m... | code_fim | easy | {
"lang": "python",
"repo": "iamanx17/dslearn",
"path": "/Generic tree/largest.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return lrg
root=takeinput()
prindata(root)<|fim_prefix|># repo: iamanx17/dslearn path: /Generic tree/largest.py
from GenericTree import takeinput, prindata
<|fim_middle|>
def largestdata(root):
if root is None:
return 0
lrg=root.data
for child in root.children:
if child... | code_fim | hard | {
"lang": "python",
"repo": "iamanx17/dslearn",
"path": "/Generic tree/largest.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Allow for time to load
time.sleep(3)
# Create Beautiful Soup object
html = browser.html
soup = BeautifulSoup(html, "html.parser")
# Read table from url and turn into DataFrame
tables = pd.read_html(url)
tables[0]
df = tables[0]
# Change column headers to Stat an... | code_fim | hard | {
"lang": "python",
"repo": "nwchappel/web-scraping-challenge",
"path": "/Missions_to_Mars/scrape_mars.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nwchappel/web-scraping-challenge path: /Missions_to_Mars/scrape_mars.py
import time
from splinter import Browser
from bs4 import BeautifulSoup
import pandas as pd
def scrape():
# Create dictionary to store results
results = {}
# Create path to local chrome driver
executable_pat... | code_fim | hard | {
"lang": "python",
"repo": "nwchappel/web-scraping-challenge",
"path": "/Missions_to_Mars/scrape_mars.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: krsnadatra/Contoh-Program path: /2019/day_04.py
from itertools import groupby
from glen import glen # generator length
def non_decreasing(start, end):
number = list(str(start))
# Generate first non-decreasing number
for i, (digit1, digit2) in enumerate(zip(number, number[1:])):
... | code_fim | medium | {
"lang": "python",
"repo": "krsnadatra/Contoh-Program",
"path": "/2019/day_04.py",
"mode": "psm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_suffix|>start, end = 134564, 585159
# Part 1
passwords = tuple(filter(has_adjacent, non_decreasing(start, end)))
print(len(passwords))
# Part 2
print(glen(filter(has_pair, passwords)))<|fim_prefix|># repo: krsnadatra/Contoh-Program path: /2019/day_04.py
from itertools import groupby
from glen import glen # gen... | code_fim | medium | {
"lang": "python",
"repo": "krsnadatra/Contoh-Program",
"path": "/2019/day_04.py",
"mode": "spm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def main():
"main function"
if len(sys.argv) < 2:
print("USAGE: compress.py pipeline_name")
exit()
pipeline_name = sys.argv[1]
pipeline_version = sys.argv[2]
output_filename = "roslin-{}-pipeline-v{}.tgz".format(
pipeline_name,
pipeline_version
)
... | code_fim | hard | {
"lang": "python",
"repo": "mskcc/roslin-variant",
"path": "/build/scripts/compress.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def main():
"main function"
if len(sys.argv) < 2:
print("USAGE: compress.py pipeline_name")
exit()
pipeline_name = sys.argv[1]
pipeline_version = sys.argv[2]
output_filename = "roslin-{}-pipeline-v{}.tgz".format(
pipeline_name,
pipeline_version
)
... | code_fim | hard | {
"lang": "python",
"repo": "mskcc/roslin-variant",
"path": "/build/scripts/compress.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mskcc/roslin-variant path: /build/scripts/compress.py
#!/usr/bin/env python3
import sys
import subprocess
import os
script_path = os.path.dirname(os.path.realpath(__file__))
root_dir = os.path.abspath(os.path.join(script_path,os.pardir,os.pardir))
def compress(output_filename):
"compress"
... | code_fim | hard | {
"lang": "python",
"repo": "mskcc/roslin-variant",
"path": "/build/scripts/compress.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bopopescu/intelligent-code-completion path: /token_lstm/data.py
import os
import torch
import sys
sys.path.append('../tokenizer')
import tokenizer
import operator
import random
RAW_DATA_PATH = '../../intelligent-code-completion/raw_data/'
REMOVE_THRESHOLD = 10
class Dictionary(object):
def ... | code_fim | hard | {
"lang": "python",
"repo": "bopopescu/intelligent-code-completion",
"path": "/token_lstm/data.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
tokens = 0
maxLen = 0
# Find code path and create dictionary
with open(path, 'r') as f:
for i, line in enumerate(f):
filename = line.... | code_fim | hard | {
"lang": "python",
"repo": "bopopescu/intelligent-code-completion",
"path": "/token_lstm/data.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Tokenizes a text file."""
assert os.path.exists(path)
tokens = 0
maxLen = 0
# Find code path and create dictionary
with open(path, 'r') as f:
for i, line in enumerate(f):
filename = line.strip()
code_path = RAW_... | code_fim | hard | {
"lang": "python",
"repo": "bopopescu/intelligent-code-completion",
"path": "/token_lstm/data.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for i in range(0, n):
if used[i] == 1: continue
if i > 0 and nums[i] == nums[i - 1] and used[i - 1] == 0: continue
used[i] = 1
helper(track + [nums[i]])
used[i] = 0
res = []
n = len(nums)
... | code_fim | hard | {
"lang": "python",
"repo": "yuchen-he/algorithm016",
"path": "/leetcode/editor/cn/[47]全排列 II.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yuchen-he/algorithm016 path: /leetcode/editor/cn/[47]全排列 II.py
# 给定一个可包含重复数字的序列，返回所有不重复的全排列。
#
# 示例:
#
# 输入: [1,1,2]
# 输出:
# [
# [1,1,2],
# [1,2,1],
# [2,1,1]
# ]
# Related Topics 回溯算法
# 👍 492 👎 0
# leetcode submit region begin(Prohibit modification and deletion)
class Solutio... | code_fim | hard | {
"lang": "python",
"repo": "yuchen-he/algorithm016",
"path": "/leetcode/editor/cn/[47]全排列 II.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>for n in range(1,101):
S.append(S[n-1] + addval)
addval += 4
print(S[bigN-1])<|fim_prefix|># repo: OrderFromChaos/ICPC path: /remote_practice/dp/A.py
# Idea: added squares are (inner square - last step) + 4
bigN = int(input())
<|fim_middle|>S = [1]
addval = 4
| code_fim | easy | {
"lang": "python",
"repo": "OrderFromChaos/ICPC",
"path": "/remote_practice/dp/A.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: OrderFromChaos/ICPC path: /remote_practice/dp/A.py
# Idea: added squares are (inner square - last step) + 4
bigN = int(input())
<|fim_suffix|>for n in range(1,101):
S.append(S[n-1] + addval)
addval += 4
print(S[bigN-1])<|fim_middle|>S = [1]
addval = 4
| code_fim | easy | {
"lang": "python",
"repo": "OrderFromChaos/ICPC",
"path": "/remote_practice/dp/A.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: managai/moolah path: /enjoying/migrations/0003_auto_20151109_2022.py
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
from django.conf import settings
<|fim_suffix|> dependencies = [
migrations.swappable_dependency(settings.AUT... | code_fim | medium | {
"lang": "python",
"repo": "managai/moolah",
"path": "/enjoying/migrations/0003_auto_20151109_2022.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('enjoying', '0002_auto_20151108_0949'),
]
operations = [
migrations.CreateModel(
name='Allowance',
fields=[
('id', models.AutoField(verbose_name='ID', ... | code_fim | medium | {
"lang": "python",
"repo": "managai/moolah",
"path": "/enjoying/migrations/0003_auto_20151109_2022.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>#output on display
print ('------------------------*********------------------------')
n = "Name: %s \n"%(dict_q['name'])
s = "Surname: %s \n"%(dict_q['surname'])
a = "Age: %i \n" %(dict_q['age'])
c = "City: %s \n" %(dict_q['city'])
g = "Game: %s \n" %(dict_q['game'])
print(n)
print(s)
print(a)
print(c)
p... | code_fim | hard | {
"lang": "python",
"repo": "vovcoolaka/Programming-Basics",
"path": "/homeworks/vera.zbitneva_cemupamuda/homework-4/homework-4.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>#date of birth
import datetime
print('Enter your date of birth')
year = int(input("Year-> "))
month = int(input("Month-> "))
day = int(input("Day-> "))
dob = datetime.date(year,month,day)
print (dob)
print ('------------------------*********------------------------')
#--IF--,--Range--
print ('Your horo... | code_fim | medium | {
"lang": "python",
"repo": "vovcoolaka/Programming-Basics",
"path": "/homeworks/vera.zbitneva_cemupamuda/homework-4/homework-4.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vovcoolaka/Programming-Basics path: /homeworks/vera.zbitneva_cemupamuda/homework-4/homework-4.py
#Create dictionaries
dict_q = {
'name' : str(input("Enter your name: ")),
'surname':str(input("Enter youre surname: ")),
'age':int(input("How old are you? ")),
'city':str(input("Where ... | code_fim | hard | {
"lang": "python",
"repo": "vovcoolaka/Programming-Basics",
"path": "/homeworks/vera.zbitneva_cemupamuda/homework-4/homework-4.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.bpf_text= b"""
#include <net/sock.h>
BPF_HASH(tcpsendmsg_sock, struct sock *);
int kprobe__vfs_open(struct pt_regs *ctx, struct sock *sk,
struct msghdr *msg, size_t size)
{
//struct sock * sk= (struct sock *)ctx->di;
FI... | code_fim | medium | {
"lang": "python",
"repo": "caozoux/python-me",
"path": "/prj/mebbc/module/net/vfs.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: caozoux/python-me path: /prj/mebbc/module/net/vfs.py
from __future__ import print_function
from bcc import ArgString, BPF, USDT
from bcc import BPF
from bpfbase import KprobeBase
class bpfvfs_open(KprobeBase):
<|fim_suffix|> self.bpf_text= b"""
#include <net/sock.h>
BPF_HA... | code_fim | medium | {
"lang": "python",
"repo": "caozoux/python-me",
"path": "/prj/mebbc/module/net/vfs.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: numeroband/lageweb path: /pyscumm/images.py
from bitparser import BitParser
from struct import unpack_from
from numpy import zeros, unpackbits, uint8
from textures import Texture
class ImageDecoder:
def __init__(self, res, width, height, paletteOff, trans):
self.res = res
sel... | code_fim | hard | {
"lang": "python",
"repo": "numeroband/lageweb",
"path": "/pyscumm/images.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.emptyMask = True
self.res = res
self.img = Texture(width, height, mask=True)
off = res.off + 8
first = unpack_from('<H', res.data, off)[0]
numStripes = width / 8
fmt = '{:d}H'.format(numStripes)
offsets = unpack_from(fmt, res.data, off)
... | code_fim | hard | {
"lang": "python",
"repo": "numeroband/lageweb",
"path": "/pyscumm/images.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>class MaskDecoder:
def __init__(self, res, width, height):
self.emptyMask = True
self.res = res
self.img = Texture(width, height, mask=True)
off = res.off + 8
first = unpack_from('<H', res.data, off)[0]
numStripes = width / 8
fmt = '{:d}H'.format... | code_fim | hard | {
"lang": "python",
"repo": "numeroband/lageweb",
"path": "/pyscumm/images.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: EvgeniiTitov/old-ml-digits path: /helpers/general.py
import os
import typing as t
import matplotlib.pyplot as plt
from pydantic import BaseModel
from pydantic import validator
def visualise_training_results(
acc_history: t.Sequence[float], loss_history: t.Sequence[float]
) -> None:
plt... | code_fim | hard | {
"lang": "python",
"repo": "EvgeniiTitov/old-ml-digits",
"path": "/helpers/general.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not os.path.exists(classes_path):
raise FileNotFoundError("Failed to locate the classes txt")
if not os.path.splitext(classes_path)[-1].lower() in [".txt"]:
raise Exception("Model classes must be a txt file")
return classes_path<|fim_prefix|># repo: Evgen... | code_fim | hard | {
"lang": "python",
"repo": "EvgeniiTitov/old-ml-digits",
"path": "/helpers/general.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not os.path.exists(weights):
raise FileNotFoundError("Failed to locate the model weights")
if not os.path.splitext(weights)[-1].lower() in [".pth", ".pt"]:
raise Exception(
"Incorrect weights. Expected a pytorch ext: .pth or .pt"
)
... | code_fim | hard | {
"lang": "python",
"repo": "EvgeniiTitov/old-ml-digits",
"path": "/helpers/general.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># process
primes = []
for n in N:
if(myprime.checkprime(n)):
primes.append(n)
# Output
print("-" * 50)
print("PRIMES : ", primes)<|fim_prefix|># repo: mindful-ai/15032021PYLVC path: /day_02/livedemo/extractprimes.py
# Get "some" numbers from the user and separate the primes
... | code_fim | medium | {
"lang": "python",
"repo": "mindful-ai/15032021PYLVC",
"path": "/day_02/livedemo/extractprimes.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mindful-ai/15032021PYLVC path: /day_02/livedemo/extractprimes.py
# Get "some" numbers from the user and separate the primes
<|fim_suffix|> n = input(" --> ")
if(n == "q"):
break
elif(n.isdigit()):
N.append(int(n))
print(N)
# process
primes = []
for n in... | code_fim | medium | {
"lang": "python",
"repo": "mindful-ai/15032021PYLVC",
"path": "/day_02/livedemo/extractprimes.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>N = []
while True:
n = input(" --> ")
if(n == "q"):
break
elif(n.isdigit()):
N.append(int(n))
print(N)
# process
primes = []
for n in N:
if(myprime.checkprime(n)):
primes.append(n)
# Output
print("-" * 50)
print("PRIMES : ", primes)<|fim_p... | code_fim | medium | {
"lang": "python",
"repo": "mindful-ai/15032021PYLVC",
"path": "/day_02/livedemo/extractprimes.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_gene_class(self, nth):
gene = self[nth]
if gene != -1:
x = gene // timeslots_num
return list(classprof_time.keys())[x].split('-')[1]
def is_gene_time_valid(self, nth):
gene = self[nth]
if gene != -1:
return self.gene_valu... | code_fim | hard | {
"lang": "python",
"repo": "atenagm1375/AI-Project2018",
"path": "/Chromosome.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: atenagm1375/AI-Project2018 path: /Chromosome.py
# import collections
import random
from file_decode import *
class Chromosome(list):
gene_values = np.ravel([list(classprof_time[i]) for i in classprof_time])
gene_range = range(-1, len(gene_values))
def __init__(self, remove=False):... | code_fim | hard | {
"lang": "python",
"repo": "atenagm1375/AI-Project2018",
"path": "/Chromosome.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kuzentio/top30 path: /scraper/admin.py
from django.contrib import admin
from scraper.models import Company
class CompanyAdmin(admin.ModelAdmin):
list_display = [
field.name for field in Company._meta.fields if field.name not in ['id', 'site']
]
class Meta:
model = C... | code_fim | medium | {
"lang": "python",
"repo": "kuzentio/top30",
"path": "/scraper/admin.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, *args, **kwargs):
self.list_display.append('company_url')
super(CompanyAdmin, self).__init__(*args, **kwargs)
def company_url(self, company):
return '<a href="{0}">{1}</a>'.format(company.site, company.site)
company_url.allow_tags = True
admin.sit... | code_fim | medium | {
"lang": "python",
"repo": "kuzentio/top30",
"path": "/scraper/admin.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not len(message.params) > 2:
self.bot.ircsock.say(target, "`@help <target>` where target may be a plugin name or a config setting")
return None
term = message.params[2]
# TODO Fuzzy search (*) in term
if term in self.bot.config:
_help = self.bot.config.get_help(term)
if re... | code_fim | hard | {
"lang": "python",
"repo": "Ferus/WhergBot3.0",
"path": "/Plugins/Help/Help.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ferus/WhergBot3.0 path: /Plugins/Help/Help.py
#!/usr/bin/env python
"""
Help Plugin
Provides @help for all plugins and config settings
"""
import re
from plugin import BasicPlugin
class Plugin(BasicPlugin):
def __init__(self, bot):
self.bot = bot
self.name = "help"
self.priority = 50
... | code_fim | hard | {
"lang": "python",
"repo": "Ferus/WhergBot3.0",
"path": "/Plugins/Help/Help.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if term in self.bot.config:
_help = self.bot.config.get_help(term)
if re.search(r"(^\(\S+?\))", _help):
# config option help
# > If there are capturing groups in the separator and it matches at the
# start of the string, the result will start with an empty string.
# gg re.sp... | code_fim | hard | {
"lang": "python",
"repo": "Ferus/WhergBot3.0",
"path": "/Plugins/Help/Help.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tzyl/ctci-python path: /chapter11/11.5.py
# Given a sorted array of strings which is interspersed with empty
# strings, write a method to find the location of a given string.
# Modified binary search to move middle to closest non-empty string.
# Worst case O(n).
def search_sparse(strings,... | code_fim | hard | {
"lang": "python",
"repo": "tzyl/ctci-python",
"path": "/chapter11/11.5.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
test = ["a", "", "", "", "b", ""]
print search_sparse(test, "b")
print search_sparse(test, "a")
print search_sparse(test, "c")
test2 = ["at", "", "", "", "ball", "", "", "car", "", "", "dad", "", ""]
print search_sparse(test2, "at")
print searc... | code_fim | hard | {
"lang": "python",
"repo": "tzyl/ctci-python",
"path": "/chapter11/11.5.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.CreateModel(
name='Condition',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('field_id', models.PositiveIntegerField(verbose_name='La field_id del ca... | code_fim | medium | {
"lang": "python",
"repo": "camiloforero/complex_hooks",
"path": "/migrations/0004_condition.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: camiloforero/complex_hooks path: /migrations/0004_condition.py
# -*- coding: utf-8 -*-
# Generated by Django 1.9 on 2016-04-22 20:39
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
<|fim_suffix|>
dependencies = [
('co... | code_fim | medium | {
"lang": "python",
"repo": "camiloforero/complex_hooks",
"path": "/migrations/0004_condition.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
### Compile the models by supplying a loss funciton and an optimizer.
self.model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
def make_vectorizer(self, examples, **kwargs):
examples = dataset... | code_fim | hard | {
"lang": "python",
"repo": "spacelis/hrnn4sim",
"path": "/hrnn4sim/seqsim_rnn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: spacelis/hrnn4sim path: /hrnn4sim/seqsim_rnn.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This is a basic RNN implementation of address matching network using LSTM cells.
"""
# pylint: disable=invalid-name
from itertools import chain
from keras.layers.core import K
from keras.models i... | code_fim | hard | {
"lang": "python",
"repo": "spacelis/hrnn4sim",
"path": "/hrnn4sim/seqsim_rnn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ Similarity models based on RNN. """
def __init__(self, state_size=256, **kwargs):
super(SeqSimRNN, self).__init__(**kwargs)
self.state_size = 256
def build(self):
''' Build a RNN based model. '''
K.set_session(self.session)
A = Input(shape=(None,))
... | code_fim | hard | {
"lang": "python",
"repo": "spacelis/hrnn4sim",
"path": "/hrnn4sim/seqsim_rnn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> elements = range(1, n+1)
NN = reduce(operator.mul, elements) # n!
k, result = (k-1) % NN, ''
while len(elements) > 0:
NN = NN / len(elements)
i, k = k / NN, k % NN
result += str(elements.pop(i))
return result
def getPermutati... | code_fim | hard | {
"lang": "python",
"repo": "liseyko/CtCI",
"path": "/leetcode/p0060 - Permutation Sequence.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: liseyko/CtCI path: /leetcode/p0060 - Permutation Sequence.py
import math
class Solution:
def getPermutation(self, n, k):
"""
:type n: int
:type k: int
:rtype: str
"""
if not n: return ""
r = []
nums = [str(i) for i in range(1,n+... | code_fim | hard | {
"lang": "python",
"repo": "liseyko/CtCI",
"path": "/leetcode/p0060 - Permutation Sequence.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Grzegorz-Giedrojc/motosell path: /motosellapp/migrations/0018_oferta_status.py
# Generated by Django 3.1 on 2020-08-12 08:50
from django.db import migrations, models
<|fim_suffix|>
dependencies = [
('motosellapp', '0017_remove_oferta_status'),
]
operations = [
migr... | code_fim | easy | {
"lang": "python",
"repo": "Grzegorz-Giedrojc/motosell",
"path": "/motosellapp/migrations/0018_oferta_status.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> dependencies = [
('motosellapp', '0017_remove_oferta_status'),
]
operations = [
migrations.AddField(
model_name='oferta',
name='status',
field=models.CharField(choices=[('aktualny', 'aktualny'), ('nieaktualny', 'nieaktualny')], default='aktu... | code_fim | easy | {
"lang": "python",
"repo": "Grzegorz-Giedrojc/motosell",
"path": "/motosellapp/migrations/0018_oferta_status.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.AddField(
model_name='oferta',
name='status',
field=models.CharField(choices=[('aktualny', 'aktualny'), ('nieaktualny', 'nieaktualny')], default='aktualny', max_length=32),
),
]<|fim_prefix|># repo: Grzegorz-Giedrojc/mot... | code_fim | medium | {
"lang": "python",
"repo": "Grzegorz-Giedrojc/motosell",
"path": "/motosellapp/migrations/0018_oferta_status.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Andrewah1/comp110-21f-workspace path: /exercises/ex02/count_letters.py
"""Counting letters in a string."""
<|fim_suffix|>letter = str(input("What letter do you want to seach for?: "))
word = str(input("Enter a word: "))
i: int = 0
maximun: int = len(word)
letter_count: int = 0
while i < maximun:... | code_fim | easy | {
"lang": "python",
"repo": "Andrewah1/comp110-21f-workspace",
"path": "/exercises/ex02/count_letters.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
letter = str(input("What letter do you want to seach for?: "))
word = str(input("Enter a word: "))
i: int = 0
maximun: int = len(word)
letter_count: int = 0
while i < maximun:
if word[i] == letter:
letter_count = letter_count + 1
i = i + 1
print("Count:", letter_count)<|fim_prefix|># repo... | code_fim | easy | {
"lang": "python",
"repo": "Andrewah1/comp110-21f-workspace",
"path": "/exercises/ex02/count_letters.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: RitaAsagwara/GDAL-Python path: /gdal_translate2.py
#-------------------------------------------------------------------------------
# Name: Convert ZMap to Geotiff
# Purpose: Convert Petrel Raster ZMap grid to Geotiff
#
# Author: rasagwara
#
# Created: 03/07/2015
# Copyright:... | code_fim | medium | {
"lang": "python",
"repo": "RitaAsagwara/GDAL-Python",
"path": "/gdal_translate2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> translateFile = ' '.join([gdal_translate, cmd, proj, input, output])
subprocess.call(translateFile)
print translateFile
if __name__ == '__main__':
main()<|fim_prefix|># repo: RitaAsagwara/GDAL-Python path: /gdal_translate2.py
#------------------------------------------------------------... | code_fim | medium | {
"lang": "python",
"repo": "RitaAsagwara/GDAL-Python",
"path": "/gdal_translate2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># 소숫점
print("{0:f}".format(5/3))
# 소숫점 특정 자리수까지만 표시
print("{0:.2f}".format(5/3))<|fim_prefix|># repo: yewon-kim/sparta-8 path: /practice/0530_Python/8-2_output_format.py
# 총 10칸 기준 오른쪽 정렬
print("{0: >10}".format(500))
# +/- 표시
print("{0: >+10}".format(500))
print("{0: >+10}".format(-500))
# 왼쪽 정렬, 빈칸은... | code_fim | medium | {
"lang": "python",
"repo": "yewon-kim/sparta-8",
"path": "/practice/0530_Python/8-2_output_format.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yewon-kim/sparta-8 path: /practice/0530_Python/8-2_output_format.py
# 총 10칸 기준 오른쪽 정렬
print("{0: >10}".format(500))
# +/- 표시
print("{0: >+10}".format(500))
print("{0: >+10}".format(-500))
# 왼쪽 정렬, 빈칸은 "_"로 채움
print("{0:_<+10}".format(500))
# 콤마 찍기
print("{0:,}".format(1000000000))
<|fim_suffi... | code_fim | medium | {
"lang": "python",
"repo": "yewon-kim/sparta-8",
"path": "/practice/0530_Python/8-2_output_format.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: YukiT1990/Dynamic-Programming-LeetCode path: /ClimbingStairs.py
# 1. Climbing Stairs
# 70. Climbing Stairs
<|fim_suffix|> def climbStairs(self, n: int) -> int:
if n <= 3:
return n
results = [0 for _ in range(46)]
results[1] = 1
results[2] = 2
... | code_fim | easy | {
"lang": "python",
"repo": "YukiT1990/Dynamic-Programming-LeetCode",
"path": "/ClimbingStairs.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if n <= 3:
return n
results = [0 for _ in range(46)]
results[1] = 1
results[2] = 2
for i in range(3, n + 1):
results[i] = results[i - 1] + results[i - 2]
return results[n]<|fim_prefix|># repo: YukiT1990/Dynamic-Programming-LeetCode p... | code_fim | easy | {
"lang": "python",
"repo": "YukiT1990/Dynamic-Programming-LeetCode",
"path": "/ClimbingStairs.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wangyy20151029/AI path: /test_blog/test_case/blog_home/BasePage.py
#cdding:utf-8
from selenium.webdriver.support.wait import WebDriverWait
from selenium import webdriver
class Action(object):
def __init__(self,selenium_driver,base_url,pagetitle):
self.base_url=base_url
self.... | code_fim | hard | {
"lang": "python",
"repo": "wangyy20151029/AI",
"path": "/test_blog/test_case/blog_home/BasePage.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
loc=getattr(self,"_%s" %loc)
if click_first:
self.find_element(*loc).click()
if clear_first:
self.find_element(*loc).clear()
self.find_element(*loc).send_keys(vaule)
except AttributeError:
print(u"%s页面中未能找到%s元... | code_fim | hard | {
"lang": "python",
"repo": "wangyy20151029/AI",
"path": "/test_blog/test_case/blog_home/BasePage.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aemann01/mockcommunity path: /scripts/slice_fasta.py
#!/usr/bin/python3
'''Read in fasta file and coordinates file (e.g., output of rnammer), pulls sequences and slices to given coordinates
'''
<|fim_suffix|>coord = pd.read_csv("rnammer_16s.txt", sep="\t", header=None)
records = SeqIO.index("al... | code_fim | medium | {
"lang": "python",
"repo": "aemann01/mockcommunity",
"path": "/scripts/slice_fasta.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>for i in range(len(coord[0])):
if coord[3][i] > coord[4][i]:
x = coord[4][i]
y = coord[3][i]
else:
x = coord[3][i]
y = coord[4][i]
print(">",records[coord[0][i]].id, sep="")
print(records[coord[0][i]].seq[x:y])<|fim_prefix|># repo: aemann01/mockcommunity pa... | code_fim | medium | {
"lang": "python",
"repo": "aemann01/mockcommunity",
"path": "/scripts/slice_fasta.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def select(self, keep):
"""Apply same indexing to all tensors in container"""
for key, value in self.__dict__.items():
self.__dict__[key] = value[keep]
return self
def __str__(self):
to_str = ''
for key, tensor in self.__dict__.items():
... | code_fim | hard | {
"lang": "python",
"repo": "conanhung/mask_rcnn-1",
"path": "/mrcnn/structs/tensor_container.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class ZipTest(TestCase):
"""
Test Zips
"""
def setUp(self):
self.file = open('{}/file.txt'.format(settings.MEDIA_ROOT), "a")
self.file.write("some data")
self.file.close()
def test_zip_duplicate_name(self):
zip_file1 = zipfile.ZipFile('{}/zip1.zip'.for... | code_fim | medium | {
"lang": "python",
"repo": "sitn/geoshop2",
"path": "/back/api/tests/test_zip.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sitn/geoshop2 path: /back/api/tests/test_zip.py
import zipfile
from unittest import TestCase
from pathlib import Path
from django.conf import settings
from api.helpers import _zip_them_all
<|fim_suffix|> _zip_them_all('{}/full_zip.zip'.format(settings.MEDIA_ROOT), ['zip1.zip', 'zip2.zip... | code_fim | hard | {
"lang": "python",
"repo": "sitn/geoshop2",
"path": "/back/api/tests/test_zip.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> zip_file1 = zipfile.ZipFile('{}/zip1.zip'.format(settings.MEDIA_ROOT), 'w', zipfile.ZIP_DEFLATED)
zip_file1.write(self.file.name, Path(self.file.name).name)
zip_file1.close()
zip_file2 = zipfile.ZipFile('{}/zip2.zip'.format(settings.MEDIA_ROOT), 'w', zipfile.ZIP_DEFLATED)
... | code_fim | hard | {
"lang": "python",
"repo": "sitn/geoshop2",
"path": "/back/api/tests/test_zip.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def load_and_map_checkpoint(model, model_dir, remap):
path = os.path.join(model_dir, 'model_checkpoint')
print("Loading parameters %s from %s" % (remap.keys(), model_dir))
checkpoint = torch.load(path)
new_state_dict = model.state_dict()
for name, value in remap.items():
# TOD... | code_fim | hard | {
"lang": "python",
"repo": "sidarth164/RecoEdge",
"path": "/fedrec/utilities/saver_utils.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
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