text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: hcfilho/uploadExercicioDePolimorfismo path: /exercicio2.py
class Pessoa:
def __init__(self,nome,sobrenome,idade,):
self.__nome = nome
self.__sobrenome = sobrenome
self.__idade = idade
@property
def nome(self):
return self.__nome
@nome.setter
... | code_fim | hard | {
"lang": "python",
"repo": "hcfilho/uploadExercicioDePolimorfismo",
"path": "/exercicio2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> @duracao.setter
def duracao(self,nova_duracao):
self.__duracao = nova_duracao
class Professor(Funcionario):
def __init__(self, nome, sobrenome, idade, salario, descricao,competencias):
super().__init__(nome, sobrenome, idade, salario, descricao)
self.__competencias = c... | code_fim | hard | {
"lang": "python",
"repo": "hcfilho/uploadExercicioDePolimorfismo",
"path": "/exercicio2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return self.__nome
@nome.setter
def nome(self,novo_nome):
self.__nome = novo_nome
@property
def duracao(self):
return self.__duracao
@duracao.setter
def duracao(self,nova_duracao):
self.__duracao = nova_duracao
class Professor(Funcionario... | code_fim | hard | {
"lang": "python",
"repo": "hcfilho/uploadExercicioDePolimorfismo",
"path": "/exercicio2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.slist.append(value)
return True
def pop(self):
if self.is_empty():
return False
else:
return self.slist.pop()
# testing
if __name__=='__main__':
stack=Stack()
print(stack.is_empty())
print(stack.push(2))
print(stack.is_empt... | code_fim | hard | {
"lang": "python",
"repo": "ly989264/Python_COMP9021",
"path": "/Week11/self_stack.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ly989264/Python_COMP9021 path: /Week11/self_stack.py
# Stack
class Stack:
def __init__(self):
self.slist=[]
def __len__(self):
return len(self.slist)
<|fim_suffix|> if self.is_empty():
return None
else:
return self.slist[-1]
de... | code_fim | medium | {
"lang": "python",
"repo": "ly989264/Python_COMP9021",
"path": "/Week11/self_stack.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: erickmiller/AutomatousSourceCode path: /AutonomousSourceCode/data/raw/sort/5ae99d89-b180-4acd-bafe-b7ec1f91cad0__sorted_list.py
# Conor T. Ryan
# Week 5 Homework
# UW PCE Programming in Python
# Fall 2011 (Jacky)
from copy import copy
def sorted_list(listToSort):
sortedList = []
... | code_fim | medium | {
"lang": "python",
"repo": "erickmiller/AutomatousSourceCode",
"path": "/AutonomousSourceCode/data/raw/sort/5ae99d89-b180-4acd-bafe-b7ec1f91cad0__sorted_list.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return sortedList
if __name__ == '__main__':
list1 = ['z', 'x', 'y']
list2 = ['banana', 'pear', 'apple']
newList1 = sorted_list(list1)
newList2 = sorted_list(list2)
assert list1 == ['z', 'x', 'y']
assert list2 == ['banana', 'pear', 'apple']
print newList1
print new... | code_fim | hard | {
"lang": "python",
"repo": "erickmiller/AutomatousSourceCode",
"path": "/AutonomousSourceCode/data/raw/sort/5ae99d89-b180-4acd-bafe-b7ec1f91cad0__sorted_list.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for k,v in tr.items( ): result[k]=v*tw
for k,v in fr.items( ): result[k]=v*fw
return result
else:
if int(v) in allowed_values:
if v>=tree.value: branch=tree.tb
else: branch=tree.fb
else:
if v==tree.value: branch=tree.tb
else: branch=tree.fb
... | code_fim | hard | {
"lang": "python",
"repo": "ejla-idrizi/programming-collective-intelligence",
"path": "/chapter7/exercise2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ejla-idrizi/programming-collective-intelligence path: /chapter7/exercise2.py
# EXERCISE 2: modify function modclassify(observation,tree) on p.157
def mdclassify(observation,tree):
<|fim_suffix|> else:
if int(v) in allowed_values:
if v>=tree.value: branch=tree.tb
else: branch=... | code_fim | hard | {
"lang": "python",
"repo": "ejla-idrizi/programming-collective-intelligence",
"path": "/chapter7/exercise2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if len(self.bag_of_words) == 0:
printf('Bag-of-Words empty!')
return None
tweet_words = [word.lower() for word, tag in tweet_message if word not in stopwords and not word.isdigit()]
tweet_tags = [tag[:2] for word, tag in tweet_message if word not in stopwo... | code_fim | hard | {
"lang": "python",
"repo": "pedrobalage/SemevalTwitterHybridClassifier2013",
"path": "/MachineLearningClassifier.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pedrobalage/SemevalTwitterHybridClassifier2013 path: /MachineLearningClassifier.py
# -*- coding: utf-8 -*-
####
#### Author: Pedro Paulo Balage Filho
#### Version: 1.0
#### Date: 12/03/13
####
# Requires Pattern library (http://www.clips.ua.ac.be/pages/pattern)
from pattern.vector import SVM, C... | code_fim | hard | {
"lang": "python",
"repo": "pedrobalage/SemevalTwitterHybridClassifier2013",
"path": "/MachineLearningClassifier.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alopezna5/mASAPP_CI path: /test/unit/test_cli_method.py
import unittest
from masappcli.__main__ import *
import sys
import os
class TestCLI(unittest.TestCase):
def setUp(self):
# Restoring of argv and environ
sys.argv = sys.argv[0:1]
os.environ.clear()
def tearD... | code_fim | hard | {
"lang": "python",
"repo": "alopezna5/mASAPP_CI",
"path": "/test/unit/test_cli_method.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> expected_message = "Riskscore and standard execution can not being thrown simultaneously"
with self.assertRaisesRegex(ValueError, expected_message):
self._add_fake_key_and_fake_secret()
sys.argv.append("-r")
sys.argv.append("9.8")
sys.argv.a... | code_fim | hard | {
"lang": "python",
"repo": "alopezna5/mASAPP_CI",
"path": "/test/unit/test_cli_method.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chenshuo666/cs_data_structure_algorithms path: /cs_data_structure_and_algorithms/linkedlist/likedlist_category/double_linkedlist.py
#!/usr/bin/python
# -*- coding:utf-8 -*-
# Author:Sebastian Williams
"""Initialize the node, the node includes the currently stored content and a pointer to the nex... | code_fim | hard | {
"lang": "python",
"repo": "chenshuo666/cs_data_structure_algorithms",
"path": "/cs_data_structure_and_algorithms/linkedlist/likedlist_category/double_linkedlist.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
List insert operation
:param value: The value to be inserted
:param index: The position to be inserted
:return: None
"""
if pos <= 0:
self.insert_head(data)
elif pos > (self.get_length() - 1):
self.insert_append(da... | code_fim | hard | {
"lang": "python",
"repo": "chenshuo666/cs_data_structure_algorithms",
"path": "/cs_data_structure_and_algorithms/linkedlist/likedlist_category/double_linkedlist.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """delete node by data"""
if self.is_empty():
return
else:
cur = self.head
if cur.data == data:
# If the element of the first node is the element to be deleted
if cur.next == None:
self.head = N... | code_fim | hard | {
"lang": "python",
"repo": "chenshuo666/cs_data_structure_algorithms",
"path": "/cs_data_structure_and_algorithms/linkedlist/likedlist_category/double_linkedlist.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def p_values(p):
'''
values : value
| value COMMA values
'''
if len(p) == 2:
p[0] = (p[1],)
else:
p[0] = (p[1],) + p[3]
def p_datum(p):
'''
datum : BOOL
| INT
| FLOAT
| STRING
'''
p[0] = p[1]
def p_v... | code_fim | hard | {
"lang": "python",
"repo": "dyzsr/microdb",
"path": "/sql/sqlparser.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dyzsr/microdb path: /sql/sqlparser.py
TRING',
'ID',
'SEMICOLON',
'DOT',
'COMMA',
'LPAR',
'RPAR',
'LT',
'LTE',
'GT',
'GTE',
'EQ',
'NE',
'PLUS',
'MINUS',
'MUL',
'DIV',
... | code_fim | hard | {
"lang": "python",
"repo": "dyzsr/microdb",
"path": "/sql/sqlparser.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dyzsr/microdb path: /sql/sqlparser.py
OOLTYPE',
'int' : 'INTTYPE',
'float' : 'FLOATTYPE',
'varchar' : 'VARCHAR',
'nvarchar' : 'NVARCHAR',
'primary' : 'PRIMARY',
'key' : 'KEY',
'from' : 'FROM',
'whe... | code_fim | hard | {
"lang": "python",
"repo": "dyzsr/microdb",
"path": "/sql/sqlparser.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: eldenis/PythonG_ex path: /ejemplos/300.py
def si_o_no(pregunta):
respuesta=""
opciones=["si","s","Si","SI","no","n","No","NO"]
while <|fim_suffix|> print "OPCION INCORRECTA!\nLas opciones permitidas son:"
print " ".join(opciones),"\n"
return "S" in respuesta.upper()
r=si_o_no("D... | code_fim | medium | {
"lang": "python",
"repo": "eldenis/PythonG_ex",
"path": "/ejemplos/300.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print "OPCION INCORRECTA!\nLas opciones permitidas son:"
print " ".join(opciones),"\n"
return "S" in respuesta.upper()
r=si_o_no("Desea ingresar algun dato?: ")
print "Respuesta:",r<|fim_prefix|># repo: eldenis/PythonG_ex path: /ejemplos/300.py
def si_o_no(pregunta):
respuesta=""
opcione... | code_fim | medium | {
"lang": "python",
"repo": "eldenis/PythonG_ex",
"path": "/ejemplos/300.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: scottgit/evidential path: /app/models/claim_hit_keys.py
from .db import db
from sqlalchemy.orm import relationship
from .mixins.track_updates import TrackUpdates
from .mixins.common_columns import CommonColumns
class ClaimHitKeys(db.Model, CommonColumns, TrackUpdates):
<|fim_suffix|> return ... | code_fim | hard | {
"lang": "python",
"repo": "scottgit/evidential",
"path": "/app/models/claim_hit_keys.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # to_dict functions are for javascript, so camel-case keys
def to_dict(self):
return {
"id": self.id,
"claimId": self.claim_id,
"keyId": self.key_id,
"key": self.get_key_name(),
"createdBy": self.created_by,
"createdAt": self.created_at,
}<|fim_prefix|># rep... | code_fim | medium | {
"lang": "python",
"repo": "scottgit/evidential",
"path": "/app/models/claim_hit_keys.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # for to_history() keys are for python, and must match attribute key names of the model, so snake-case
def to_history(self):
return {
"claim_id": self.claim_id,
"key_id": self.key_id,
}
# to_dict functions are for javascript, so camel-case keys
def to_dict(self):
return {
... | code_fim | medium | {
"lang": "python",
"repo": "scottgit/evidential",
"path": "/app/models/claim_hit_keys.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jmeinken/VillageBuilder path: /alerts/models.py
from django.db import models
from account.models import Participant
<|fim_suffix|> EVENT_TYPE_CHOICES = (
("add friend", "add friend"),
)
affected_participant = models.ForeignKey(Participant, on_delete=models.CASCADE)
event_typ... | code_fim | medium | {
"lang": "python",
"repo": "jmeinken/VillageBuilder",
"path": "/alerts/models.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> EVENT_TYPE_CHOICES = (
("add friend", "add friend"),
)
affected_participant = models.ForeignKey(Participant, on_delete=models.CASCADE)
event_type = models.CharField(max_length=30, choices=EVENT_TYPE_CHOICES, db_index=True)
viewed = models.BooleanField(db_index=True)
active = ... | code_fim | medium | {
"lang": "python",
"repo": "jmeinken/VillageBuilder",
"path": "/alerts/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alexandrenorman/mixeur path: /territories/models/commune.py
# -*- coding: utf-8 -*-
from django.db import models
from djgeojson.fields import PointField
from .epci import Epci
from .departement import Departement
from core.models import MixeurBaseModel
class CommuneQuerySet(models.QuerySet):
... | code_fim | medium | {
"lang": "python",
"repo": "alexandrenorman/mixeur",
"path": "/territories/models/commune.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> @property
def rich_name(self):
to_return = ""
if self.epci:
to_return += f"{self.epci} - "
to_return += f"{self.inseecode} - "
to_return += f"{self.name}"
return to_return
def __str__(self):
return self.rich_name<|fim_prefix|># rep... | code_fim | hard | {
"lang": "python",
"repo": "alexandrenorman/mixeur",
"path": "/territories/models/commune.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: reus0707/crawlproject-gjypjd path: /gjypjd/zytqwbags.py
# -*- coding: utf-8 -*-
# 中药提取物备案公示
import pickle
import re
from selenium import webdriver
from gjypjd.utils import *
import json
import time
def main():
option=None
mysql_db = DataBase()
#配置文件中开启是否无头，生产阶段关闭
if if_headless... | code_fim | hard | {
"lang": "python",
"repo": "reus0707/crawlproject-gjypjd",
"path": "/gjypjd/zytqwbags.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for i, v in reg_dict.items():
reg_search = re.search(v, html)
if reg_search is not None:
result_json[i] = reg_search.group(1)
else:
result_json[i] = ''
return json.dumps(result_json, ensure_ascii=False)
if __name__ == '__main__':
main()<|fim_pre... | code_fim | hard | {
"lang": "python",
"repo": "reus0707/crawlproject-gjypjd",
"path": "/gjypjd/zytqwbags.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rogeriosilva-ifpi/teaching-tds-course path: /programacao_estruturada/20192_166/converter_horas.py
# entrada
horas = int(input('horas: '))
minutos = int(input('minutos: '))
<|fim_suffix|># saida
print('Minutos totais:', minutos_totais)<|fim_middle|># processamento
minutos_totais = (horas*60) + mi... | code_fim | easy | {
"lang": "python",
"repo": "rogeriosilva-ifpi/teaching-tds-course",
"path": "/programacao_estruturada/20192_166/converter_horas.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># saida
print('Minutos totais:', minutos_totais)<|fim_prefix|># repo: rogeriosilva-ifpi/teaching-tds-course path: /programacao_estruturada/20192_166/converter_horas.py
# entrada
horas = int(input('horas: '))
minutos = int(input('minutos: '))
<|fim_middle|># processamento
minutos_totais = (horas*60) + mi... | code_fim | easy | {
"lang": "python",
"repo": "rogeriosilva-ifpi/teaching-tds-course",
"path": "/programacao_estruturada/20192_166/converter_horas.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_librispeech():
"""Summary
"""
prime_length = 6144
total_length = 16000 * 3
batch_size = 32
n_stages = 6
n_layers_per_stage = 9
n_hidden = 32
filter_length = 2
n_skip = 256
onehot = False
sequence_length = get_sequence_length(n_stages, n_layers_per... | code_fim | hard | {
"lang": "python",
"repo": "muxgt/pycadl",
"path": "/cadl/fastwavenet.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: muxgt/pycadl path: /cadl/fastwavenet.py
"""WaveNet Training and Fast WaveNet Decoding.
From the following paper
------------------------
Ramachandran, P., Le Paine, T., Khorrami, P., Babaeizadeh, M., Chang, S.,
Zhang, Y., … Huang, T. (2017). Fast Generation For Convolutional
Autoregressive Model... | code_fim | hard | {
"lang": "python",
"repo": "muxgt/pycadl",
"path": "/cadl/fastwavenet.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Residual blocks with skip connections.
for i in range(n_stages * n_layers_per_stage):
dilation = 2**(i % n_layers_per_stage)
# dilated masked cnn
d, init, push = wnu.causal_linear(
X=h,
n_inputs=n_hidden,
n_outputs=n_hidden * 2,
... | code_fim | hard | {
"lang": "python",
"repo": "muxgt/pycadl",
"path": "/cadl/fastwavenet.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>class upload_audio(APIView):
def post(self,request,format=None):
serializer=RecordingSerializer(data=request.data)
if serializer.is_valid():
file_type = str(request.data.get('track')).split('.')[-1]
file_type = file_type.lower()
name=str(request.data... | code_fim | hard | {
"lang": "python",
"repo": "ConstanzaJazme/ProyectoCD2019",
"path": "/API_REST/API/genders/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def post(self,request,format=None):
serializer=RecordingSerializer(data=request.data)
if serializer.is_valid():
file_type = str(request.data.get('track')).split('.')[-1]
file_type = file_type.lower()
name=str(request.data.get('track')).split('/')[-1]... | code_fim | hard | {
"lang": "python",
"repo": "ConstanzaJazme/ProyectoCD2019",
"path": "/API_REST/API/genders/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ConstanzaJazme/ProyectoCD2019 path: /API_REST/API/genders/views.py
from django.shortcuts import render, get_object_or_404
from django.http import Http404,HttpResponse,HttpResponseRedirect, JsonResponse
from django.core.exceptions import ObjectDoesNotExist,MultipleObjectsReturned
from rest_framewo... | code_fim | hard | {
"lang": "python",
"repo": "ConstanzaJazme/ProyectoCD2019",
"path": "/API_REST/API/genders/views.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> super(FixedRadiusNNGraph, self).__init__()
self.radius = radius
self.n_neighbor = n_neighbor
self.frnn = FixedRadiusNearNeighbors(radius, n_neighbor)
def forward(self, pos, centroids, feat=None):
dev = pos.device
group_idx = self.frnn(pos, centroids)
... | code_fim | hard | {
"lang": "python",
"repo": "Liu-yj0335/ML4PIONS_ATLAS",
"path": "/modules/fixed_radius_graph.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> dev = pos.device
group_idx = self.frnn(pos, centroids)
B, N, _ = pos.shape
glist = []
for i in range(B):
center = torch.zeros((N)).to(dev)
center[centroids[i]] = 1
src = group_idx[i].contiguous().view(-1)
dst = centroi... | code_fim | hard | {
"lang": "python",
"repo": "Liu-yj0335/ML4PIONS_ATLAS",
"path": "/modules/fixed_radius_graph.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Liu-yj0335/ML4PIONS_ATLAS path: /modules/fixed_radius_graph.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import dgl
import dgl.function as fn
from dgl.geometry.pytorch import FarthestPointSampler
'''
Part of the code... | code_fim | hard | {
"lang": "python",
"repo": "Liu-yj0335/ML4PIONS_ATLAS",
"path": "/modules/fixed_radius_graph.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sophiaas/DeepSparseCoding path: /utils/training.py
import matplotlib
matplotlib.use("Agg")
import numpy as np
import tensorflow as tf
import json as js
import params.param_picker as pp
import models.model_picker as mp
import data.data_selector as ds
def train_mod(data, params, schedule):
##... | code_fim | hard | {
"lang": "python",
"repo": "sophiaas/DeepSparseCoding",
"path": "/utils/training.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> ## Plot weights & gradients
if (current_step % model.gen_plot_int == 0
and model.gen_plot_int > 0):
model.generate_plots(input_data=input_data, input_labels=input_labels)
## Checkpoint
if (current_step... | code_fim | hard | {
"lang": "python",
"repo": "sophiaas/DeepSparseCoding",
"path": "/utils/training.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> sam[loc]=[]
sam[loc].append(mir)
else:
sam[loc].append(mir)
n=0
v=0
for i in loci:
if i in sam.keys():
n=n+1
g.write(i+"\t"+(":".join(str(p) for p in sam[i])+"\n"))
else:
v=v+1
g.write(i+"\tnot found\n")
print 'mirs found=', n, 'not found=', v<|fim_prefix|># repo: theo-allnutt-bioinfo... | code_fim | hard | {
"lang": "python",
"repo": "theo-allnutt-bioinformatics/scripts",
"path": "/sam4map.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: theo-allnutt-bioinformatics/scripts path: /sam4map.py
#!/usr/bin/env python
import sys
import re
import glob
digits = re.compile(r'(\d+)')
def tokenize(filename):
return tuple(int(token) if match else token
for token, match in
((fragment, digits.search(frag... | code_fim | medium | {
"lang": "python",
"repo": "theo-allnutt-bioinformatics/scripts",
"path": "/sam4map.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> out_images = list()
for img, m, s in zip(img_group, self.mean, self.std):
if len(m) == 1:
img = img - np.array(m) # single channel image
img = img / np.array(s)
else:
img = img - np.array(m)[np.newaxis, np.newaxis, ..... | code_fim | hard | {
"lang": "python",
"repo": "AigizK/ailia-models",
"path": "/image_segmentation/codes-for-lane-detection/erfnet_utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gudrunstummer/flash_card_project path: /main.py
from tkinter import *
import random
import pandas
BACKGROUND_COLOR = "#B1DDC6"
current_card = {}
to_learn = {}
# -----------------ACCESSING DATA SECTION --------------------------#
# Python attempts to open words_to_learn.csv. At first use this d... | code_fim | hard | {
"lang": "python",
"repo": "gudrunstummer/flash_card_project",
"path": "/main.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def flip_card():
canvas.itemconfig(card_title, text="English", fill="white")
canvas.itemconfig(card_word, text=current_card["English"], fill="white")
canvas.itemconfig(canvas_image, image=back_img)
def is_known():
to_learn.remove(current_card)
data = pandas.DataFrame(to_learn)
d... | code_fim | hard | {
"lang": "python",
"repo": "gudrunstummer/flash_card_project",
"path": "/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> Sum1=0
Sum2=0
xi = 0
i = 1
dx = float(self.b-self.a)/self.n
while i <=self.n-1:
xi=self.a+i*(dx)
Sum1 = Sum1 + self.fun.evalFunction(xi)
Ox = np.arange(self.a+(i-1)*dx,xi+dx, 0.02)
Oy = []
for j in Ox:
Oy.append(self.px(j,self.a+(i-1)*dx,xi,xi+dx))
self.ax.plot(Ox, Oy,col... | code_fim | medium | {
"lang": "python",
"repo": "joalcava/College-projects",
"path": "/Metodos Numericos 2012/simpson1_3Compuesto.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: joalcava/College-projects path: /Metodos Numericos 2012/simpson1_3Compuesto.py
import function
from matplotlib.pyplot import *
from pylab import *
import numpy as np
import math
class Simpson13Comp:
def __init__(self, fun, xi, xf,n):
self.fun = function.Function(fun,'x')
self.a,self.b = xi,... | code_fim | medium | {
"lang": "python",
"repo": "joalcava/College-projects",
"path": "/Metodos Numericos 2012/simpson1_3Compuesto.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hyunjongpark/learningTrade path: /source/util/macd_tester.py
# -*- coding: utf-8 -*-
from __future__ import division
import os, sys
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import Line... | code_fim | hard | {
"lang": "python",
"repo": "hyunjongpark/learningTrade",
"path": "/source/util/macd_tester.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def make_best_macd_value_all_kospi(self, start, end, last_day_sell=True):
data = load_yaml(services.get('configurator').get('stock_list'))
index = 0
for code, value in data:
print('%s/%s , code: %s' % (index, len(data), code))
success, profit, fastperi... | code_fim | hard | {
"lang": "python",
"repo": "hyunjongpark/learningTrade",
"path": "/source/util/macd_tester.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> sele = Selector(text=html)
results = sele.css('.default-list-item.clearfix')
for each in results:
print('='*100)
url = each.css('a::attr(href)').get()
name = each.css('.list-item-desc-top a::text').get()
review = each.css('.item-eval-... | code_fim | hard | {
"lang": "python",
"repo": "crazyhubox/tools_weather",
"path": "/weather/meituan.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: crazyhubox/tools_weather path: /weather/meituan.py
#!/usr/local/bin/python3
# encoding:utf-8
import requests
import re
import time
from scrapy.selector import Selector
from selenium import webdriver
from urllib.parse import urljoin
# some = sele.css('#react').get()
class Meituan:
o... | code_fim | hard | {
"lang": "python",
"repo": "crazyhubox/tools_weather",
"path": "/weather/meituan.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
base_url = self.enter_city()
name= input('哥们,要找什么?')
url_0 = f'/s/{name}/'
url = 'https:'+urljoin(base_url,url_0)
print(url)
self.driver.get(url)
while True:
html = self.driver.page_source
... | code_fim | hard | {
"lang": "python",
"repo": "crazyhubox/tools_weather",
"path": "/weather/meituan.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>accuracy=clf.score(x_test,y_test)
print(accuracy)
#0.74025974026<|fim_prefix|># repo: gagicha/machine_learning path: /diabetes classification(KNN).py
# https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes
import pandas as pd
import numpy as np
from sklearn import model_selection, neighbors
#to ... | code_fim | hard | {
"lang": "python",
"repo": "gagicha/machine_learning",
"path": "/diabetes classification(KNN).py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>x_train, x_test, y_train, y_test= model_selection.train_test_split(x,y,test_size=0.2)
clf=neighbors.KNeighborsClassifier()
#IF K NOT MENTIONED , AUTOMATICALLY TAKE K AS 5
clf.fit(x_train, y_train)
accuracy=clf.score(x_test,y_test)
print(accuracy)
#0.74025974026<|fim_prefix|># repo: gagicha/machine_learn... | code_fim | medium | {
"lang": "python",
"repo": "gagicha/machine_learning",
"path": "/diabetes classification(KNN).py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gagicha/machine_learning path: /diabetes classification(KNN).py
# https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes
import pandas as pd
import numpy as np
from sklearn import model_selection, neighbors
#to the uci dataset we add the attributes row and then use that in this example.
d... | code_fim | medium | {
"lang": "python",
"repo": "gagicha/machine_learning",
"path": "/diabetes classification(KNN).py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: shachar1000/flask-rubix path: /app.py
from flask import Flask, send_file, render_template, request, jsonify, make_response, Response, url_for, session
import cv2
import base64
import numpy as np
import io
from PIL import Image
from reddit import detect
import json
#from flask_scss import Scss
fro... | code_fim | hard | {
"lang": "python",
"repo": "shachar1000/flask-rubix",
"path": "/app.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if request.method == 'POST':
listOfMatrices = request.get_json()['storeColors']
matrixString = ''.join([''.join(''.join(x[0] for x in y) for y in matrix) for matrix in listOfMatrices]) #only first letter
passCode = ''.join(random.choice(string.ascii_lowercase) for _ in range(4)... | code_fim | hard | {
"lang": "python",
"repo": "shachar1000/flask-rubix",
"path": "/app.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Daishijun/InterviewAlgorithmCoding path: /kuaishou1.py
# -*- coding: utf-8 -*-
# @Date : 2019/4/13
# @Time : 16:19
# @Author : Daishijun
# @File : kuaishou1.py
# Software : PyCharm
<|fim_suffix|> m=[[0 for i in range(len(s2)+1)] for j in range(len(s1)+1)] #生成0矩阵，为方便后续计算，比字符串长度多了一列... | code_fim | hard | {
"lang": "python",
"repo": "Daishijun/InterviewAlgorithmCoding",
"path": "/kuaishou1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def find_lcsubstr(s1, s2): #输出长公共子串,长度.
m=[[0 for i in range(len(s2)+1)] for j in range(len(s1)+1)] #生成0矩阵，为方便后续计算，比字符串长度多了一列
mmax=0 #最长匹配的长度
p=0 #最长匹配对应在s1中的最后一位
for i in range(len(s1)):
for j in range(len(s2)):
if s1[i]==s2[j]:
m[i+1][j+1]=m[i][j... | code_fim | hard | {
"lang": "python",
"repo": "Daishijun/InterviewAlgorithmCoding",
"path": "/kuaishou1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wenyuwong1/biosys-analytics path: /assignments/03-python-hello/vowel_counter.py
#!/usr/bin/env python3
# Author: wwong3 (Wen Yu Amy Wong)
# Date: 2019-Jan-31
# Purpose: 03-python Vowel_Counter Homework
"""vowel_counter"""
import os
import sys
<|fim_suffix|> count=0
vow... | code_fim | hard | {
"lang": "python",
"repo": "wenyuwong1/biosys-analytics",
"path": "/assignments/03-python-hello/vowel_counter.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def main():
args = sys.argv[1:]
word=args
if len(word) == 0:
print('Usage: {} STRING'.format(os.path.basename(sys.argv[0])))
sys.exit(1)
elif len(word) != 0:
word=str(word[0]) ### Used to set the argument word to a string instead of a list
def vowe... | code_fim | medium | {
"lang": "python",
"repo": "wenyuwong1/biosys-analytics",
"path": "/assignments/03-python-hello/vowel_counter.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def set_values_from_description(self):
matches = self.pattern.findall(self.description)
self.name = matches[0][0]
self.speed = int(matches[0][1])
self.stamina = int(matches[0][2])
self.rest = int(matches[0][3])
def __str__(self):
return "%s can fly ... | code_fim | hard | {
"lang": "python",
"repo": "alkemann/advent2015-py3",
"path": "/advent/fourteen/Reindeer.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alkemann/advent2015-py3 path: /advent/fourteen/Reindeer.py
import re
class Reindeer:
# "Comet can fly 14 km/s for 10 seconds, but then must rest for 127 seconds."
pattern = re.compile("(\w+) can fly (\d+) km/s for (\d+) seconds, but then must rest for (\d+) seconds.")
def __init__... | code_fim | hard | {
"lang": "python",
"repo": "alkemann/advent2015-py3",
"path": "/advent/fourteen/Reindeer.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if self.flying:
self.distance += self.speed
self.stamina_left -= 1
if self.stamina_left == 0:
self.flying = False
self.rest_left = self.rest
else:
self.rest_left -= 1
if self.rest_left == 0:
... | code_fim | hard | {
"lang": "python",
"repo": "alkemann/advent2015-py3",
"path": "/advent/fourteen/Reindeer.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: danielf/contest-tools path: /input_gen.py
import sys
import re
import math
import random
import operator
from collections import defaultdict
class colors:
HEADER = '\033[95m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
def err(message):
pri... | code_fim | hard | {
"lang": "python",
"repo": "danielf/contest-tools",
"path": "/input_gen.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert str_type in self.TYPES
self.scope, self.name, self.str_type = scope, name, str_type
self.length, self.alphabet = re.match(self.SPEC, spec).groups()
self.charset = [chr(ch) for ch in xrange(0, 256) if re.match(self.alphabet, chr(ch))]
def dependencies(self):
... | code_fim | hard | {
"lang": "python",
"repo": "danielf/contest-tools",
"path": "/input_gen.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tusharrakshe/Template-Based-OCR path: /API/Accuracy_Evaluation.py
from spellchecker import SpellChecker
spell = SpellChecker()
import spacy
import nltk
#python -m spacy download en_core_web_sm
import en_core_web_sm
nlp = en_core_web_sm.load()
import re
<|fim_suffix|> Text_file = file_name
... | code_fim | medium | {
"lang": "python",
"repo": "tusharrakshe/Template-Based-OCR",
"path": "/API/Accuracy_Evaluation.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> Text_file = file_name
with open(Text_file, mode='r') as file:
Text = file.read()
Percentage_Extraction = round(sum([len(word) for word in nltk.word_tokenize(Text)])/len(Text),2)
text = re.sub('[^A-Za-z0-9]+', ' ', Text)
# misspelled = spell.unknown(preprocessing(Text_Cl... | code_fim | medium | {
"lang": "python",
"repo": "tusharrakshe/Template-Based-OCR",
"path": "/API/Accuracy_Evaluation.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return {'cats': Category.objects.all(),
'act_cat': cat}<|fim_prefix|># repo: KirrageW/ITER path: /rango/templatetags/rango_template_tags.py
from django import template
from rango.models import Category
<|fim_middle|>register = template.Library()
@register.inclusion_tag('rango/cats.html'... | code_fim | medium | {
"lang": "python",
"repo": "KirrageW/ITER",
"path": "/rango/templatetags/rango_template_tags.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: KirrageW/ITER path: /rango/templatetags/rango_template_tags.py
from django import template
from rango.models import Category
register = template.Library()
<|fim_suffix|> return {'cats': Category.objects.all(),
'act_cat': cat}<|fim_middle|>@register.inclusion_tag('rango/cats.html'... | code_fim | medium | {
"lang": "python",
"repo": "KirrageW/ITER",
"path": "/rango/templatetags/rango_template_tags.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_hdf5_bool():
"""test_hdf5_bool: GitHub issue 1144"""
runpath = tempfile.mkdtemp()
boolean_data = np.asarray(
[True, False, True, False, True, False, True, False, True, False]
)
with h5py.File(f"{runpath}/my_data.h5", "w") as h5_obj:
h5_obj["my_bool_data"] = ... | code_fim | hard | {
"lang": "python",
"repo": "tensorflow/io",
"path": "/tests/test_hdf5.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tensorflow/io path: /tests/test_hdf5.py
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at
#
# http://ww... | code_fim | hard | {
"lang": "python",
"repo": "tensorflow/io",
"path": "/tests/test_hdf5.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nahomiiiie/SFI_2019 path: /run.py
# insert parameters for run
# wrtie, read file, come from command line
# add everythign from file, same sequence of things
# plot ot file and add? matplotlib
"""
import math
from enum import Enum
import networkx as nx
<|fim_suffix|>main = IdeaSpread(100, .18, .7... | code_fim | medium | {
"lang": "python",
"repo": "nahomiiiie/SFI_2019",
"path": "/run.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>main = IdeaSpread(100, .18, .71, 18, 22)
main.run(2)<|fim_prefix|># repo: nahomiiiie/SFI_2019 path: /run.py
# insert parameters for run
# wrtie, read file, come from command line
# add everythign from file, same sequence of things
# plot ot file and add? matplotlib
"""
import math
from enum import Enum
... | code_fim | medium | {
"lang": "python",
"repo": "nahomiiiie/SFI_2019",
"path": "/run.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: a0911802160/ML2018FALL path: /hw3/hw3_test.py
import numpy as np
import pandas as pd
import csv
import math
from keras.models import load_model
from keras import backend as k
import sys
data = pd.read_csv(sys.argv[1], delimiter=',')
x = np.array(data.iloc[:, 1])
test_x = []
for id... | code_fim | medium | {
"lang": "python",
"repo": "a0911802160/ML2018FALL",
"path": "/hw3/hw3_test.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>for idx in range(len(test_x)):
ans.append(str(idx)+',')
ans[idx] += str(label[idx][0])
with open(sys.argv[2], 'w+') as pred_file:
pred_file.write('id,label\n')
for idx in range(len(ans)):
pred_file.write(ans[idx]+'\n')<|fim_prefix|># repo: a0911802160/ML2018FALL path: /hw3... | code_fim | hard | {
"lang": "python",
"repo": "a0911802160/ML2018FALL",
"path": "/hw3/hw3_test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>ans = []
for idx in range(len(test_x)):
ans.append(str(idx)+',')
ans[idx] += str(label[idx][0])
with open(sys.argv[2], 'w+') as pred_file:
pred_file.write('id,label\n')
for idx in range(len(ans)):
pred_file.write(ans[idx]+'\n')<|fim_prefix|># repo: a0911802160/ML2018FALL... | code_fim | medium | {
"lang": "python",
"repo": "a0911802160/ML2018FALL",
"path": "/hw3/hw3_test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> Returns
-------
file_list : list
List of files matching the query.
"""
authorize_google_drive()
query = "'{}' in parents and title contains '{}' and trashed=false".format(
parent_id, child_name
)
file_list = DRIVE.ListFile(
{'q': query}
).GetLis... | code_fim | hard | {
"lang": "python",
"repo": "bmcfee/medleydb",
"path": "/medleydb/download.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bmcfee/medleydb path: /medleydb/download.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Methods for downloading audio from google drive."""
from medleydb import MEDLEYDB_PATH
from medleydb import AUDIO_PATH
from medleydb import GRDIVE_CONFIG_PATH
from medleydb import METADATA_PATH
from medle... | code_fim | hard | {
"lang": "python",
"repo": "bmcfee/medleydb",
"path": "/medleydb/download.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
dependencies = [
('POST', '0004_auto_20200115_1542'),
]
operations = [
migrations.RenameField(
model_name='posts',
old_name='tittle',
new_name='title',
),
]<|fim_prefix|># repo: badilladrian/djangoPractices path: /TODO/djangopr... | code_fim | medium | {
"lang": "python",
"repo": "badilladrian/djangoPractices",
"path": "/TODO/djangoproject/POST/migrations/0005_auto_20200115_1621.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: badilladrian/djangoPractices path: /TODO/djangoproject/POST/migrations/0005_auto_20200115_1621.py
# Generated by Django 3.0 on 2020-01-15 22:21
from django.db import migrations
<|fim_suffix|>
dependencies = [
('POST', '0004_auto_20200115_1542'),
]
operations = [
mi... | code_fim | easy | {
"lang": "python",
"repo": "badilladrian/djangoPractices",
"path": "/TODO/djangoproject/POST/migrations/0005_auto_20200115_1621.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ali-rajabli/etinatStakan path: /products_app/migrations/0005_auto_20210525_1124.py
# Generated by Django 2.2.23 on 2021-05-25 11:24
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('products_app', '0004_auto_20210525_0857'),
]
... | code_fim | hard | {
"lang": "python",
"repo": "ali-rajabli/etinatStakan",
"path": "/products_app/migrations/0005_auto_20210525_1124.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> serialize=False, verbose_name='ID')),
('text_ru', models.TextField(verbose_name='Оплата')),
],
options={
'verbose_name': 'Оплата',
'verbose_name_plural': 'Оплата',
},
),
migrations.AlterModelOptions(
... | code_fim | hard | {
"lang": "python",
"repo": "ali-rajabli/etinatStakan",
"path": "/products_app/migrations/0005_auto_20210525_1124.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>me='ID')),
('text_ru', models.TextField(verbose_name='Минимальный заказ')),
],
options={
'verbose_name': 'Минимальный заказ',
'verbose_name_plural': 'Минимальный заказ',
},
),
migrations.CreateModel(
... | code_fim | hard | {
"lang": "python",
"repo": "ali-rajabli/etinatStakan",
"path": "/products_app/migrations/0005_auto_20210525_1124.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: akhlaque-ak/pfun path: /reference files/Reference Files/Lab 3/Exercise_3_1/Exercise_3_1_2__pyde/Exercise_3_1_2__pyde.pyde
def setup():
size(500, 500)
count = 0
c = 0
x = 0
def draw():
global count
global c
global x
frameRate(3<|fim_suffix|> fill(255)
... | code_fim | medium | {
"lang": "python",
"repo": "akhlaque-ak/pfun",
"path": "/reference files/Reference Files/Lab 3/Exercise_3_1/Exercise_3_1_2__pyde/Exercise_3_1_2__pyde.pyde",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> fill(255)
ellipse(250, 250, i, i)
count = count + 1
# Extra
'''
frameRate(10);
if (c%2==0):
fill(0);
ellipse(250,250,x,x)
else:
fill(255)
ellipse(250,250,x,x)
x=x+10
c=c+3
'''<|fim_prefix|># repo: ak... | code_fim | medium | {
"lang": "python",
"repo": "akhlaque-ak/pfun",
"path": "/reference files/Reference Files/Lab 3/Exercise_3_1/Exercise_3_1_2__pyde/Exercise_3_1_2__pyde.pyde",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: PUT-Motorsport/PUTM_EV_Dataviewer path: /src/generate_testing_csv.py
from random import randint
with open("/home/czarnobylu/.files/testing.csv",'w') as f:
f.write("RISING,FALLING,RANDOM\n")
range_size<|fim_suffix|>range_size-i-1)+","+str(randint(0,1000))+","
f.write(a[:-1]+'\n')<|fim_middle|>... | code_fim | medium | {
"lang": "python",
"repo": "PUT-Motorsport/PUTM_EV_Dataviewer",
"path": "/src/generate_testing_csv.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>range_size-i-1)+","+str(randint(0,1000))+","
f.write(a[:-1]+'\n')<|fim_prefix|># repo: PUT-Motorsport/PUTM_EV_Dataviewer path: /src/generate_testing_csv.py
from random import randint
with open("/home/czarnobylu/.files/testing.csv",'w') as f:
f.write("RISING,FALLING,RANDOM\n")
range_size<|fim_middle|>... | code_fim | medium | {
"lang": "python",
"repo": "PUT-Motorsport/PUTM_EV_Dataviewer",
"path": "/src/generate_testing_csv.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: RaptorPatrolContinuum/An path: /Delta Func/IO Testing.py
Testtext = open("IOTest.txt","r+")
'''
what do I need:
get last line
write new lines
'''
<|fim_suffix|> """Reads a n lines from f with an offset of offset lines."""
avg_line_length = 74
to_read = n + offset
while 1:
... | code_fim | medium | {
"lang": "python",
"repo": "RaptorPatrolContinuum/An",
"path": "/Delta Func/IO Testing.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Reads a n lines from f with an offset of offset lines."""
avg_line_length = 74
to_read = n + offset
while 1:
try:
f.seek(-(avg_line_length * to_read), 2)
except IOError:
# woops. apparently file is smaller than what we want
# to step ... | code_fim | medium | {
"lang": "python",
"repo": "RaptorPatrolContinuum/An",
"path": "/Delta Func/IO Testing.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == "__main__":
covariance_matrix = torch.tensor(
[
[1.0, 0.5, 0.5, 0.5],
[0.5, 1.0, 0.5, 0.5],
[0.5, 0.5, 1.0, 0.5],
[0.5, 0.5, 0.5, 1.0],
]
)
gaussian_copula = GaussianCopula(covariance_matrix=covariance_matrix)
... | code_fim | hard | {
"lang": "python",
"repo": "ShengGuanWSU/CopulaGNN",
"path": "/copula.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == "__main__":
covariance_matrix = torch.tensor(
[
[1.0, 0.5, 0.5, 0.5],
[0.5, 1.0, 0.5, 0.5],
[0.5, 0.5, 1.0, 0.5],
[0.5, 0.5, 0.5, 1.0],
]
)
gaussian_copula = GaussianCopula(covariance_matrix=covariance_matrix)
c... | code_fim | hard | {
"lang": "python",
"repo": "ShengGuanWSU/CopulaGNN",
"path": "/copula.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ShengGuanWSU/CopulaGNN path: /copula.py
import math
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.multivariate_normal import (
MultivariateNormal,
_batch_mahalanobis,
)
def _standard_normal_qu... | code_fim | hard | {
"lang": "python",
"repo": "ShengGuanWSU/CopulaGNN",
"path": "/copula.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aongenae/leetcode path: /src/set_matrix_zeroes.py
#!/usr/bin/env python3
################################################################################
#
# Filename: set_matrix_zeroes.py
#
# Author: Arnaud Ongenae
#
# Leetcode.com: problem #73
#
# Problem des... | code_fim | hard | {
"lang": "python",
"repo": "aongenae/leetcode",
"path": "/src/set_matrix_zeroes.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> col_has_zeroes = any(
r+1
for r in range(0, nb_rows)
if matrix[r][0] == 0
)
if nb_rows == 1:
if row_has_zeroes:
self._nullify_row(matrix, 0)
return
if nb_cols == 1:
if col_has_zeroes:
... | code_fim | hard | {
"lang": "python",
"repo": "aongenae/leetcode",
"path": "/src/set_matrix_zeroes.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Test a a simple foreach statement."""
r = convert_code(
"{foreach item=bar from=foo }content{/foreach}")
assert r == "{% for bar in foo %}content{% endfor %}"
def test_old_for_statement_name():
"""Test a more complex foreach statement."""
r = convert_code(
"{foreac... | code_fim | hard | {
"lang": "python",
"repo": "Osso/smartytotwig",
"path": "/tests/test_smarty_grammar.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> r = convert_code("{if foo($bar1, $bar2)}\nhello\n{/if}")
assert r == "{% if foo(bar1, bar2) %}\nhello\n{% endif %}"
def test_if_statement_multiple():
"""Test an if statement (no else or elseif)"""
r = convert_code(
"{if !foo or foo.bar or foo|bar:foo['hello']}\nfoo\n{/if}")
a... | code_fim | hard | {
"lang": "python",
"repo": "Osso/smartytotwig",
"path": "/tests/test_smarty_grammar.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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