text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|>"""
from datetime import datetime
from datetime import timedelta
class Dog:
def __init__(self, name='Имя', birth_date = [1970, 1, 1], voice='Голос'):
self.name = name
self.voice = voice
self.birth_date = birth_date
def __add__(self, other):
birth_date = self.... | code_fim | hard | {
"lang": "python",
"repo": "apalevich/PyMentor",
"path": "/06_dogs.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: schlogl2017/Deepshape path: /bin/mutate_and_map_1.py
#! /usr/bin/env python
# coding: utf-8
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import argparse, sys, os, errno
from glob import glob
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import h... | code_fim | hard | {
"lang": "python",
"repo": "schlogl2017/Deepshape",
"path": "/bin/mutate_and_map_1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def generate_boxplot(mutationmap):
new_map = np.ndarray([384,256])
for i in range(128):
new_map[3*i] = np.concatenate((np.concatenate((np.zeros(128-i),mutationmap[3*i])),np.zeros(i)))
new_map[3*i+1] = np.concatenate((np.concatenate((np.zeros(128-i),mutationmap[3*i+1])),np.zeros(i))... | code_fim | hard | {
"lang": "python",
"repo": "schlogl2017/Deepshape",
"path": "/bin/mutate_and_map_1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Samarpitr/education path: /education/items.py
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# http://doc.scrapy.org/en/latest/topics/items.html
import scrapy
from scrapy.loader.processors import Join, MapCompose, TakeFirst
from w3lib.html impo... | code_fim | hard | {
"lang": "python",
"repo": "Samarpitr/education",
"path": "/education/items.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # define the fields for your item here like:
# name = scrapy.Field()
title = scrapy.Field(
input_processor=MapCompose(remove_tags),
output_processor=TakeFirst()
)
details = scrapy.Field(
input_processor=MapCompose(remove_tags),
output_processor=Join()
)
p... | code_fim | medium | {
"lang": "python",
"repo": "Samarpitr/education",
"path": "/education/items.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> details = scrapy.Field(
input_processor=MapCompose(remove_tags),
output_processor=Join()
)
pass<|fim_prefix|># repo: Samarpitr/education path: /education/items.py
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# http://doc.scrapy.... | code_fim | medium | {
"lang": "python",
"repo": "Samarpitr/education",
"path": "/education/items.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: papulovskiy/nugsl-worldmap path: /scripts/nugsl-worldmap
#!/usr/bin/env python
from nugsl.worldmap import worldMap
from optparse import OptionParser
import sys, os.path, re
from nugsl.worldmap import pinConfig, countryConfig
from nugsl.worldmap import imageMap
from nugsl.worldmap import html_me... | code_fim | hard | {
"lang": "python",
"repo": "papulovskiy/nugsl-worldmap",
"path": "/scripts/nugsl-worldmap",
"mode": "psm",
"license": "LicenseRef-scancode-public-domain",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not options.ofile:
parser.print_help()
print "\nERROR: The -o option is mandatory.\n"
sys.exit()
render_width = float( options.render_width )
render_height = float( options.render_height )
if options.rendered_pinwidth:
if not render_widt... | code_fim | hard | {
"lang": "python",
"repo": "papulovskiy/nugsl-worldmap",
"path": "/scripts/nugsl-worldmap",
"mode": "spm",
"license": "LicenseRef-scancode-public-domain",
"source": "the-stack-v2"
} |
<|fim_suffix|>[North Pole]
latitude: 90n
longitude: 0
[Greenwich]
latitude: 51n28
longitude: 0
'''
parser = OptionParser(usage=usage)
parser.set_defaults(mode="rotated")
parser.add_option("-c", "--country", dest="country", metavar="COUNTRY",
default=None,
... | code_fim | hard | {
"lang": "python",
"repo": "papulovskiy/nugsl-worldmap",
"path": "/scripts/nugsl-worldmap",
"mode": "spm",
"license": "LicenseRef-scancode-public-domain",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: goern/word-fountain path: /app.py
import argparse
import gzip
import os
import random
import time
from kafka import KafkaProducer
from prometheus_client import start_http_server, Counter
<|fim_suffix|>start_http_server(8080)
producer = KafkaProducer(bootstrap_servers=servers)
with gzip.open... | code_fim | hard | {
"lang": "python",
"repo": "goern/word-fountain",
"path": "/app.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>producer = KafkaProducer(bootstrap_servers=servers)
with gzip.open('words.gz', 'r') as f:
words = f.readlines()
# subset words to produce more duplicates
words = [random.choice(words).strip() for i in range(max(42, rate ** 2))]
while count:
producer.send(topic, random.choice(words))
... | code_fim | medium | {
"lang": "python",
"repo": "goern/word-fountain",
"path": "/app.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>while count:
producer.send(topic, random.choice(words))
words_send.inc()
count -= 1
# if not count % (rate * 5):
# print(producer.metrics())
time.sleep(1.0 / rate)<|fim_prefix|># repo: goern/word-fountain path: /app.py
import argparse
import gzip
import os
import random
import ... | code_fim | hard | {
"lang": "python",
"repo": "goern/word-fountain",
"path": "/app.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: DGmoN/Genger path: /test/imageTest.py
from display.Placement import Image
from display.Effect import PlainColor
import pygame
<|fim_suffix|>panel = Image((250,250))
background = Image((100,100))
foreground = Image((50, 50))
background.addPainter("baseColor",PlainColor((100,100,100)))
foreground.... | code_fim | medium | {
"lang": "python",
"repo": "DGmoN/Genger",
"path": "/test/imageTest.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>panel = Image((250,250))
background = Image((100,100))
foreground = Image((50, 50))
background.addPainter("baseColor",PlainColor((100,100,100)))
foreground.addPainter("foregroundColor",PlainColor((250,100,100)))
panel.linkImage(background)
panel.linkImage(foreground)
display = Display((300,300))
display.l... | code_fim | medium | {
"lang": "python",
"repo": "DGmoN/Genger",
"path": "/test/imageTest.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def evaluate_hits(pos_val_pred, neg_val_pred, pos_test_pred, neg_test_pred):
results = {}
for K in [20, 50, 100]:
evaluator.K = K
valid_hits = evaluator.eval({
'y_pred_pos': pos_val_pred,
'y_pred_neg': neg_val_pred,
})[f'hits@{K}']
test_hits ... | code_fim | hard | {
"lang": "python",
"repo": "lbn187/IGNN",
"path": "/link_pred.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lbn187/IGNN path: /link_pred.py
import torch
import argparse
import os
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader
import torch_geometric.transforms as T
from transforms import Normalize
from torch_geo... | code_fim | hard | {
"lang": "python",
"repo": "lbn187/IGNN",
"path": "/link_pred.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.AlterField(
model_name='post',
name='date',
field=models.DateField(default='12:44:16', verbose_name='Date'),
),
]<|fim_prefix|># repo: Bharat0011/InstaClone path: /myInsta/migrations/0010_auto_20210201_1244.py
# Generat... | code_fim | medium | {
"lang": "python",
"repo": "Bharat0011/InstaClone",
"path": "/myInsta/migrations/0010_auto_20210201_1244.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Bharat0011/InstaClone path: /myInsta/migrations/0010_auto_20210201_1244.py
# Generated by Django 3.1.2 on 2021-02-01 07:14
from django.db import migrations, models
<|fim_suffix|> operations = [
migrations.AlterField(
model_name='post',
name='date',
... | code_fim | medium | {
"lang": "python",
"repo": "Bharat0011/InstaClone",
"path": "/myInsta/migrations/0010_auto_20210201_1244.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> sitemap=download(url)
links=re.findall('<loc>(.*?)</loc>',sitemap)
for count in range(1,6):
print"CRAW"
for link in links:
html=download(link)
time.sleep(3)
print"craw end"<|fim_prefix|># repo: jekoy/python path: /python/download_sitemap.py
i... | code_fim | medium | {
"lang": "python",
"repo": "jekoy/python",
"path": "/python/download_sitemap.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jekoy/python path: /python/download_sitemap.py
import urllib2
import re
import time
def download(url):
<|fim_suffix|>def crawl_sitemap(url):
sitemap=download(url)
links=re.findall('<loc>(.*?)</loc>',sitemap)
for count in range(1,6):
print"CRAW"
for link in lin... | code_fim | medium | {
"lang": "python",
"repo": "jekoy/python",
"path": "/python/download_sitemap.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def crawl_sitemap(url):
sitemap=download(url)
links=re.findall('<loc>(.*?)</loc>',sitemap)
for count in range(1,6):
print"CRAW"
for link in links:
html=download(link)
time.sleep(3)
print"craw end"<|fim_prefix|># repo: jekoy/python path: /pyt... | code_fim | medium | {
"lang": "python",
"repo": "jekoy/python",
"path": "/python/download_sitemap.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mitsuhiko/celery path: /celery/tests/test_worker_control.py
import socket
import unittest2 as unittest
from celery import conf
from celery.decorators import task
from celery.registry import tasks
from celery.task.builtins import PingTask
from celery.utils import gen_unique_id
from celery.worker ... | code_fim | hard | {
"lang": "python",
"repo": "mitsuhiko/celery",
"path": "/celery/tests/test_worker_control.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> class Listener(object):
class ReadyQueue(object):
fresh = False
def refresh(self):
self.fresh = True
def __init__(self):
self.ready_queue = self.ReadyQueue()
listener = Listener()
panel ... | code_fim | hard | {
"lang": "python",
"repo": "mitsuhiko/celery",
"path": "/celery/tests/test_worker_control.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_dump_tasks(self):
info = "\n".join(self.panel.execute("dump_tasks"))
self.assertIn("mytask", info)
self.assertIn("rate_limit=200", info)
def test_dump_schedule(self):
listener = Listener()
panel = self.create_panel(listener=listener)
self.a... | code_fim | hard | {
"lang": "python",
"repo": "mitsuhiko/celery",
"path": "/celery/tests/test_worker_control.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> ## set the order of reactions occurring in the tanks
self.order_tank = 1.0 # real
## set a global value for all bulk reaction coefficients
self.global_bulk = 0.0 # real
## set a global value for all wall reaction coefficients
self.global_wall ... | code_fim | hard | {
"lang": "python",
"repo": "USEPA/SWMM-EPANET_User_Interface",
"path": "/src/core/epanet/options/reactions.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: USEPA/SWMM-EPANET_User_Interface path: /src/core/epanet/options/reactions.py
from core.project_base import Section
from core.metadata import Metadata
class Reactions(Section):
"""Defines parameters related to chemical reactions occurring in the network"""
SECTION_NAME = "[REACTIONS]"
... | code_fim | hard | {
"lang": "python",
"repo": "USEPA/SWMM-EPANET_User_Interface",
"path": "/src/core/epanet/options/reactions.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ai-systems/transportability path: /tests/experiments/nli_experiment_test.py
from regra.common.regra_unit_test import RegraTestCase
from transport.experiments.trainer.nli_trainer import NLIExperiment
import luigi
<|fim_suffix|> def test_snli(self):
task = NLIExperiment(mode=self.mode,... | code_fim | easy | {
"lang": "python",
"repo": "ai-systems/transportability",
"path": "/tests/experiments/nli_experiment_test.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_snli(self):
task = NLIExperiment(mode=self.mode, config_file=self.config_file)
luigi.build([task])<|fim_prefix|># repo: ai-systems/transportability path: /tests/experiments/nli_experiment_test.py
from regra.common.regra_unit_test import RegraTestCase
from transport.experiment... | code_fim | easy | {
"lang": "python",
"repo": "ai-systems/transportability",
"path": "/tests/experiments/nli_experiment_test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thakkarayush/Project_Petrol_Pump path: /c_payment/models.py
from django.db import models
from creditor.models import creditor_master
from django.urls import reverse
from datetime import datetime
# Create your models here.
class c_payment(models.Model):
<|fim_suffix|> return f"{self.credito... | code_fim | hard | {
"lang": "python",
"repo": "thakkarayush/Project_Petrol_Pump",
"path": "/c_payment/models.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return f"{self.creditorid}-{self.amount}"
def get_absolute_url(self):
return reverse("cpayment-view")<|fim_prefix|># repo: thakkarayush/Project_Petrol_Pump path: /c_payment/models.py
from django.db import models
from creditor.models import creditor_master
from django.urls import reve... | code_fim | hard | {
"lang": "python",
"repo": "thakkarayush/Project_Petrol_Pump",
"path": "/c_payment/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ccstp/IS211_Assignment1 path: /assignment1_part1.py
# ASSIGNMENT 1_PART 01
#!/usr/bin/env python
# -*- coding: utf-8 -*-
def listDivide(numbers, divide = 2):
<|fim_suffix|> Args:
numbers (list): The list of numbers to be checked
divide (int): The number to divide the elements in the ... | code_fim | medium | {
"lang": "python",
"repo": "ccstp/IS211_Assignment1",
"path": "/assignment1_part1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def testListDivide():
"""
This function tests the listDivide function.
"""
assert listDivide([1,2,3,4,5]) == 2
assert listDivide([2,4,6,8,10]) == 5
assert listDivide([30, 54, 63,98, 100], divide = 10) == 2
assert listDivide([]) == 0
assert listDivide([1,2,3,4,5], 1) == 5
if __name__ == "... | code_fim | hard | {
"lang": "python",
"repo": "ccstp/IS211_Assignment1",
"path": "/assignment1_part1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
This function tests the listDivide function.
"""
assert listDivide([1,2,3,4,5]) == 2
assert listDivide([2,4,6,8,10]) == 5
assert listDivide([30, 54, 63,98, 100], divide = 10) == 2
assert listDivide([]) == 0
assert listDivide([1,2,3,4,5], 1) == 5
if __name__ == "__main__":
testListDi... | code_fim | medium | {
"lang": "python",
"repo": "ccstp/IS211_Assignment1",
"path": "/assignment1_part1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mohakbhardwaj/auto-park path: /autopark/construct/formats/data/snoop.py
"""
what : snoop v2 capture file.
how : http://tools.ietf.org/html/rfc1761
who : jesse @ housejunkie . ca
"""
import time
from construct import (Adapter, Enum, Field, HexDumpAdapter, Magic, OptionalGreedyRange,
Pad... | code_fim | medium | {
"lang": "python",
"repo": "mohakbhardwaj/auto-park",
"path": "/autopark/construct/formats/data/snoop.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>packet_record = Struct("packet_record",
UBInt32("original_length"),
UBInt32("included_length"),
UBInt32("record_length"),
UBInt32("cumulative_drops"),
EpochTimeStampAdapter(UBInt32("timestamp_seconds")),
UBInt32("timestamp_microseconds"),
HexDumpAdap... | code_fim | hard | {
"lang": "python",
"repo": "mohakbhardwaj/auto-park",
"path": "/autopark/construct/formats/data/snoop.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> img1 = cv2.imread(CALIBRATION_IMG_DIR + "calibration1.jpg")
img2 = cv2.imread(CALIBRATION_IMG_DIR + "calibration4.jpg")
img3 = cv2.imread(CALIBRATION_IMG_DIR + "calibration5.jpg")
udst1 = cv2.undistort(img1, mtx, dist, None, mtx)
udst2 = cv2.undistort(img2, mtx, dis... | code_fim | hard | {
"lang": "python",
"repo": "gmpatil/sdcnd",
"path": "/term1/p04_advLaneFinding/Camera.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gmpatil/sdcnd path: /term1/p04_advLaneFinding/Camera.py
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import pickle
CALIBRATION_IMG_DIR = "./camera_cal/"
TEST_IMG_DIR = "./test_images/"
class Camera(object):
'''
Camera class to calibrate the camera, save the... | code_fim | hard | {
"lang": "python",
"repo": "gmpatil/sdcnd",
"path": "/term1/p04_advLaneFinding/Camera.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return ret, mtx, dist, rvecs, tvecs
def undistort_calibration_images(self):
mtx = self.mtx
dist = self.dist
img1 = cv2.imread(CALIBRATION_IMG_DIR + "calibration1.jpg")
img2 = cv2.imread(CALIBRATION_IMG_DIR + "calibration4.jpg")
img3 = cv2.imread(CALIB... | code_fim | hard | {
"lang": "python",
"repo": "gmpatil/sdcnd",
"path": "/term1/p04_advLaneFinding/Camera.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MacLure/python-basics path: /classes.py
# Classes are named in capitalized camel-case
# Classes are preceded and followed by 2 line breaks
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
def move(self):
<|fim_suffix|>class Cat(Pet):
def be_aloof(selfself... | code_fim | hard | {
"lang": "python",
"repo": "MacLure/python-basics",
"path": "/classes.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># "input [filename - no extension]" -> in code: [filename, no extension].[function]
# "from [filename - no extension] import [function]<|fim_prefix|># repo: MacLure/python-basics path: /classes.py
# Classes are named in capitalized camel-case
# Classes are preceded and followed by 2 line breaks
class Po... | code_fim | hard | {
"lang": "python",
"repo": "MacLure/python-basics",
"path": "/classes.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return self.taskName
return self.taskRequester
return self.taskResourceRequired
class comment(models.Model):
commentSender=models.CharField(max_length=10)
commentSubject = models.CharField(max_length=20)
commentMessage = models.CharField(max_length=100)
commentTeam... | code_fim | hard | {
"lang": "python",
"repo": "gokulyesudoss/ProjectDash",
"path": "/dashservice/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __str__(self):
return self.taskName
return self.taskRequester
return self.taskResourceRequired
class comment(models.Model):
commentSender=models.CharField(max_length=10)
commentSubject = models.CharField(max_length=20)
commentMessage = models.CharField(max_leng... | code_fim | hard | {
"lang": "python",
"repo": "gokulyesudoss/ProjectDash",
"path": "/dashservice/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gokulyesudoss/ProjectDash path: /dashservice/models.py
from django.db import models
# Create your models here.
class team(models.Model):
teamName = models.CharField(max_length=20)
teamDescription = models.CharField(max_length=100)
teamIncharge = models.CharField(max_length=20)
def __st... | code_fim | hard | {
"lang": "python",
"repo": "gokulyesudoss/ProjectDash",
"path": "/dashservice/models.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> elif isinstance(adder, Medida):
medida = adder
medidas = [0]*self.lenght
for i in xrange(self.lenght):
medidas[i] = self[i] + medida
values, s, units = self._calc_valores(medidas)
return mArray(values, s, units)
else:
raise ValueError('mArray no puede ser sumado con %... | code_fim | hard | {
"lang": "python",
"repo": "JorgeExp/Package-tecnicas",
"path": "/PhysLab/medidas.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JorgeExp/Package-tecnicas path: /PhysLab/medidas.py
ce dos veces en la expresión
class Medida(object):
'''
La clase Medida permite crear y operar objetos con valor, unidades e incertidumbre,
automatizando los cálculos. Se pueden usar sobre estos objetos los siguientes
operadores: +, -, *, /... | code_fim | hard | {
"lang": "python",
"repo": "JorgeExp/Package-tecnicas",
"path": "/PhysLab/medidas.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class mArray(object):
def _set_medidas(self):
for i in xrange(self.lenght):
self.medidas[i] = Medida(self.values[i], self.s[i], self.units[i])
def _calc_valores(self, medidas):
#función auxiliar para las operaciones
#medidas es una lista de Medidas, no un array
values = [0]*len(... | code_fim | hard | {
"lang": "python",
"repo": "JorgeExp/Package-tecnicas",
"path": "/PhysLab/medidas.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_getbinarystate(
fauxmo_server: pytest.fixture, simplehttpplugin_target: pytest.fixture
) -> None:
"""Test TCP server's "GetBinaryState" action for SimpleHTTPPlugin."""
data = b'Soapaction: "urn:Belkin:service:basicevent:1#GetBinaryState"'
resp = requests.post(
"http://12... | code_fim | hard | {
"lang": "python",
"repo": "ccancellieri/fauxmo",
"path": "/tests/test_fauxmo.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ccancellieri/fauxmo path: /tests/test_fauxmo.py
"""test_fauxmo.py :: Tests for `fauxmo` package."""
import json
import socket
import xml.etree.ElementTree as ET # noqa
import pytest
import requests
from fauxmo import fauxmo
from fauxmo.plugins.simplehttpplugin import SimpleHTTPPlugin
from faux... | code_fim | hard | {
"lang": "python",
"repo": "ccancellieri/fauxmo",
"path": "/tests/test_fauxmo.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> fauxmo_server: pytest.fixture, simplehttpplugin_target: pytest.fixture
) -> None:
"""Test TCP server's "GetBinaryState" action for SimpleHTTPPlugin."""
data = b'Soapaction: "urn:Belkin:service:basicevent:1#GetBinaryState"'
resp = requests.post(
"http://127.0.0.1:12345/upnp/control... | code_fim | hard | {
"lang": "python",
"repo": "ccancellieri/fauxmo",
"path": "/tests/test_fauxmo.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chae1108/wheel-of-jeopardy path: /module/categorySelectWindow.py
from PyQt5 import QtGui, QtWidgets
from ui.categorySelect import Ui_categorySelect
from module.gameWindow import GameWindow
from PyQt5.QtWidgets import QAbstractItemView, QMessageBox
<|fim_suffix|> if self.chosenList.count()... | code_fim | hard | {
"lang": "python",
"repo": "chae1108/wheel-of-jeopardy",
"path": "/module/categorySelectWindow.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, parent):
super(CategorySelectWindow, self).__init__(parent)
self.setupUi(self)
self.startGame.clicked.connect(self.goToGame)
self.cancel.clicked.connect(self.close)
self.cancel.clicked.connect(parent.show)
self.moveToRightColumn.clicke... | code_fim | hard | {
"lang": "python",
"repo": "chae1108/wheel-of-jeopardy",
"path": "/module/categorySelectWindow.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> with open(filePath, 'w') as f:
#handle the summary
f.write("@SUMMARY\n")
for i in self.summary:
f.write('== '.join([i, self.summary[i]]) + '\n')
f.write("@PAPERS\n")
for paperDict in self.papers:
f.write("== ".join(["PMID", paperDict["PMID"]]) + "\n")
f.write("== ".join(["TI ... | code_fim | hard | {
"lang": "python",
"repo": "CSB5/atminter",
"path": "/lib/modules/paperparse.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
"""
loadSpFileDir(dirPath)
input
A path to a directory containing only .sp Files
returns
A list of spFile objects for all spFiles
"""
def loadSpFileDir(dirPath, purge = False):
files = os.listdir(dirPath)
if dirPath[-1] != "/":
dirPath += '/'
files = [dirPath + i for i in files]
return [s... | code_fim | hard | {
"lang": "python",
"repo": "CSB5/atminter",
"path": "/lib/modules/paperparse.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: CSB5/atminter path: /lib/modules/paperparse.py
#!/usr/bin/env python3
"""
paperparse.py
A set of functions to deal with pubcrawl data
"""
import nltk
import os
import re
import json
"""
getNames(filePath):
input:
pubcrawl json
output:
names, shortened name and genus of all species in ... | code_fim | hard | {
"lang": "python",
"repo": "CSB5/atminter",
"path": "/lib/modules/paperparse.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: basicworld/mengbao path: /test.py
# -*- coding: utf-8 -*-
import re
import sys
reload(sys)
sys.setdefaultencoding('utf8') # 编译环境utf8
<|fim_suffix|>if __name__ == '__main__':
print text_parse('test')
print text_parse('你好')<|fim_middle|>from text_content_parse import text_parse
| code_fim | easy | {
"lang": "python",
"repo": "basicworld/mengbao",
"path": "/test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
print text_parse('test')
print text_parse('你好')<|fim_prefix|># repo: basicworld/mengbao path: /test.py
# -*- coding: utf-8 -*-
<|fim_middle|>import re
import sys
reload(sys)
sys.setdefaultencoding('utf8') # 编译环境utf8
from text_content_parse import text_parse
| code_fim | medium | {
"lang": "python",
"repo": "basicworld/mengbao",
"path": "/test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wanghan79/2020_Option_System path: /陶梦瑶2018012691/操作系统实验/平时作业1.py
import platform
def os():
print('操作系统及版本信息：[{}]'.format(platform.platform()))
print('操作系统版本号：[{}]'.format(platform.v<|fim_suffix|>int('计算机类型：[{}]'.format(platform.machine()))
print('计算机的网络名称：[{}]'.format(platform.node(... | code_fim | medium | {
"lang": "python",
"repo": "wanghan79/2020_Option_System",
"path": "/陶梦瑶2018012691/操作系统实验/平时作业1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>mat(platform.processor()))
print('汇总信息：[{}]'.format(platform.uname()))
def main():
print("操作系统信息：")
os()
main()<|fim_prefix|># repo: wanghan79/2020_Option_System path: /陶梦瑶2018012691/操作系统实验/平时作业1.py
import platform
def os():
print('操作系统及版本信息：[{}]'.format(platform.platform()))
prin... | code_fim | medium | {
"lang": "python",
"repo": "wanghan79/2020_Option_System",
"path": "/陶梦瑶2018012691/操作系统实验/平时作业1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cloudmesh-community/hid-sp18-405 path: /hadoop/archive/hadoop-python-2.9.0/python/deprecated/testingReducer_addE.py
#!/usr/bin/env python
"""A more advanced Reducer, using Python iterators and generators."""
from itertools import groupby
from operator import itemgetter
import sys
import math
de... | code_fim | hard | {
"lang": "python",
"repo": "cloudmesh-community/hid-sp18-405",
"path": "/hadoop/archive/hadoop-python-2.9.0/python/deprecated/testingReducer_addE.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # input comes from STDIN (standard input)
data = read_mapper_output(sys.stdin, separator=separator)
#read in the training model
pos, vocabulary_pos, total_pos = get_model("pos.txt")
neg, vocabulary_neg, total_neg = get_model("neg.txt")
vocabulary= vocabulary_pos.union(vocabulary_n... | code_fim | hard | {
"lang": "python",
"repo": "cloudmesh-community/hid-sp18-405",
"path": "/hadoop/archive/hadoop-python-2.9.0/python/deprecated/testingReducer_addE.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: benegg/hlpanotools path: /bin/dump_xls
#! /usr/bin/env python
# coding:utf-8
import xlrd
import argparse
import sys
import codecs
<|fim_suffix|> parser = argparse.ArgumentParser(description='dump xls content')
parser.add_argument('xls', help='xls to dump')
parser.add_argument('-i', '--index',... | code_fim | hard | {
"lang": "python",
"repo": "benegg/hlpanotools",
"path": "/bin/dump_xls",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> sys.stdout = codecs.getwriter('utf8')(sys.stdout)
wb = xlrd.open_workbook(xls)
st = wb.sheet_by_index(isheet)
for i in xrange(st.nrows):
cells = []
for j in xrange(st.ncols):
cell = st.cell_value(i, j)
if not cell:
cell = '<null>'
cells.append(str(cell))
print delimiter.join(cells)
... | code_fim | medium | {
"lang": "python",
"repo": "benegg/hlpanotools",
"path": "/bin/dump_xls",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: KostadinDev/AMS210-Jacobi path: /jacobi.py
import numpy as np
# Returns normalized A and b so that |D| < 1
def normalize(A, b):
normalizedMatrix = []
normalizedVector = []
for idx, row in enumerate(A):
normalizedMatrix.append(row/np.max(row))
normalizedVector.append(b[... | code_fim | hard | {
"lang": "python",
"repo": "KostadinDev/AMS210-Jacobi",
"path": "/jacobi.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># Driver for part b)
x = np.array([0,0,0])
x, num_iterations = jacobi(A, b, x, epsilon = 0.0001, return_iterations = True)
print("PART b): x = ", x, f" in {num_iterations} iterations")
# Driver for part c)
x = [100,100,100]
x, num_iterations = jacobi(A, b, x, epsilon = 0.0001, return_iterations = True)
p... | code_fim | medium | {
"lang": "python",
"repo": "KostadinDev/AMS210-Jacobi",
"path": "/jacobi.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zhuofalin/OCR_tools path: /ocr.py
#!/usr/bin/python3
# encoding:utf-8
import apisettings as apis
import os,time,tools,msvcrt
global path,access_token
from colorama import init
init(autoreset=True)
def printline():
print("-----------------------------------")
def welcomeinfo():
print... | code_fim | hard | {
"lang": "python",
"repo": "zhuofalin/OCR_tools",
"path": "/ocr.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def ocrmain():
tools.create_sheet()
tools.set_sheet()
f_list = os.listdir(path)
# print f_list
for filename in f_list:
# os.path.splitext():分离文件名与扩展名
suf = os.path.splitext(filename)[1]
if suf == '.jpg' or suf == '.png':
global access_token
... | code_fim | hard | {
"lang": "python",
"repo": "zhuofalin/OCR_tools",
"path": "/ocr.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: GSI-CS-CO/wb_fec path: /testbench/fec_mux/Manifest.py
action = "simulation"
target = "altera"
syn_device = "ep2agx125ef"
syn_grade = "c5"
syn_package = "29"
#target = "xilinx"
#syn_device = "xc6slx45t"
#syn_grade = "-3"
#syn_package = "fgg484"
<|fim_suffix|>modules = { "local" : [ "../../../..... | code_fim | medium | {
"lang": "python",
"repo": "GSI-CS-CO/wb_fec",
"path": "/testbench/fec_mux/Manifest.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>files = [ "main.sv" ]
modules = { "local" : [ "../../../../",
"../../../../ip_cores/general-cores"
]};<|fim_prefix|># repo: GSI-CS-CO/wb_fec path: /testbench/fec_mux/Manifest.py
action = "simulation"
target = "altera"
syn_device = "ep2agx125ef"
syn_grade = "c5"... | code_fim | medium | {
"lang": "python",
"repo": "GSI-CS-CO/wb_fec",
"path": "/testbench/fec_mux/Manifest.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> result = False
target_idx = 0
for idx, num in enumerate(nums):
if num < (idx - target_idx):
result = False
else:
result = True
target_idx = idx
return result<|fim_prefix|># repo: wding-dev/coding-challenge path: /jump_game/main.py
"""
... | code_fim | hard | {
"lang": "python",
"repo": "wding-dev/coding-challenge",
"path": "/jump_game/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for idx, num in enumerate(nums):
if num < (idx - target_idx):
result = False
else:
result = True
target_idx = idx
return result<|fim_prefix|># repo: wding-dev/coding-challenge path: /jump_game/main.py
"""
Given an array of non-negative integers... | code_fim | hard | {
"lang": "python",
"repo": "wding-dev/coding-challenge",
"path": "/jump_game/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wding-dev/coding-challenge path: /jump_game/main.py
"""
Given an array of non-negative integers nums, you are initially positioned at the first index of the array.
Each element in the array represents your maximum jump length at that position.
Determine if you are able to reach the last index.
E... | code_fim | medium | {
"lang": "python",
"repo": "wding-dev/coding-challenge",
"path": "/jump_game/main.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ping-Hsuan/PyROM path: /code/read_helpers/read_helpers.py
import numpy as np
def read_mat(fname, N):
t = np.loadtxt(fname)
lb = int(np.sqrt(len(t)))
t1 = np.reshape(t, (lb, lb), order='F')
msg = fname.split('/')
if 'but' in msg[-1].split('_'):
t = t1[0:N+1, 0:N+1]
... | code_fim | medium | {
"lang": "python",
"repo": "Ping-Hsuan/PyROM",
"path": "/code/read_helpers/read_helpers.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> t = np.loadtxt(fname)
lb = int(np.floor((len(t))**(1/3)))
t1 = np.reshape(t, (lb, lb+1, lb+1), order='F')
t = t1[0:N, 0:N+1, 0:N+1]
return t
def read_vector(fname):
t = np.loadtxt(fname)
return t<|fim_prefix|># repo: Ping-Hsuan/PyROM path: /code/read_helpers/read_helpers.py
... | code_fim | medium | {
"lang": "python",
"repo": "Ping-Hsuan/PyROM",
"path": "/code/read_helpers/read_helpers.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: FeiyuYin/272_Project path: /myapp/forms.py
from django import forms
#from models import Document
from models import Webapp
from models import Language
from models import Package
from models import Server
from models import Source
from django.forms.extras.widgets import SelectDateWidget
from djang... | code_fim | medium | {
"lang": "python",
"repo": "FeiyuYin/272_Project",
"path": "/myapp/forms.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>class WebappForm(ModelForm):
language_needed = forms.ModelMultipleChoiceField(queryset=Language.objects.all(), widget=forms.CheckboxSelectMultiple(),required=True)
package_needed = forms.ModelMultipleChoiceField(queryset=Package.objects.all(),widget=forms.CheckboxSelectMultiple(),required=True)
server ... | code_fim | medium | {
"lang": "python",
"repo": "FeiyuYin/272_Project",
"path": "/myapp/forms.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cassidycy/machinelearning path: /com2018.py
print("开始……")
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
df_train = pd.read_csv('./train_set.csv')
df_test = pd.read_csv('./test_set.csv')
df_train.drop(c... | code_fim | medium | {
"lang": "python",
"repo": "cassidycy/machinelearning",
"path": "/com2018.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>df_test['class']=y_test.tolist()
df_test['class']=df_test['class']+1
df_result = df_test.loc[:,['id','class']]
df_result.to_cvs('./result.csv',index=False)
print("完成……")<|fim_prefix|># repo: cassidycy/machinelearning path: /com2018.py
print("开始……")
import pandas as pd
from sklearn.linear_model ... | code_fim | medium | {
"lang": "python",
"repo": "cassidycy/machinelearning",
"path": "/com2018.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Gavin-sun/leetcode path: /Machine_learning/py/5.手写数字识别加载数据_GPU.py
# 使用pytorch 完成手写数字的识别
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.optim import Adam
from torchvision.datasets import MNIST
from... | code_fim | hard | {
"lang": "python",
"repo": "Gavin-sun/leetcode",
"path": "/Machine_learning/py/5.手写数字识别加载数据_GPU.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> data_loader = get_dataloader()
for idx,(input,target) in enumerate(data_loader):
optimizer.zero_grad()
input = input.to(device)
target = target.to(device)
output = model(input) # 调用模型,得到预测值
loss = F.nll_loss(output,target).to(device) # 得到损失
loss.back... | code_fim | hard | {
"lang": "python",
"repo": "Gavin-sun/leetcode",
"path": "/Machine_learning/py/5.手写数字识别加载数据_GPU.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>Parameters = {
'xmax':xmax,
'dx':Dx,
'tmax':tmax,
'dt':Dt,
'u0':u0,
'k':k,
'E_prior':E_prior,
'E_true':E_true,
'stations':stations,
'sigmaxa':sigmaxa,
'sigmaxe':sigmaxe,
'noisemult':noisemult,
'noiseadd':noiseadd,
'precon':Preconditioning,
'rerun... | code_fim | hard | {
"lang": "python",
"repo": "MarkDekker1/InverseModelling",
"path": "/Run_Adjoint.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MarkDekker1/InverseModelling path: /Run_Adjoint.py
# ------------------------------------------------------
# Define parameters
# ------------------------------------------------------
import numpy as np
import time as T
xmax = 100
u0 = 5
tmax = 10* xmax... | code_fim | hard | {
"lang": "python",
"repo": "MarkDekker1/InverseModelling",
"path": "/Run_Adjoint.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># ------------------------------------------------------
# Testing the adjoint model
# ------------------------------------------------------
m = AdjointModel(Parameters,method='Upwind',initialvalue=0)
# ------------------------------------------------------
# Gaining results
# -------------------------... | code_fim | hard | {
"lang": "python",
"repo": "MarkDekker1/InverseModelling",
"path": "/Run_Adjoint.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>', '辽宁经济职业技术学院', '辽宁科技大学', '辽宁师范大学', '辽宁石油化工大学', '辽宁中医药大学', '辽宁中医药大学杏林学院', '辽源职业技术学院', '聊城大学', '临沂大学', '龙岩学院', '鲁东大学', '洛阳理工学院', '洛阳师范学院', '漯河医学高等专科学校', '绵阳师范学院', '闽江学院', '闽南理工学院', '闽南师范大学', '牡丹江大学', '牡丹江师范学院', '牡丹江医学院', '内江师范学院', '内蒙古财经大学', '内蒙古大学', '内蒙古工业大学', '内蒙古建筑职业技术学院', '内蒙古科技大学', '内蒙古科技大学包头师范学院', '... | code_fim | hard | {
"lang": "python",
"repo": "FatBallFish/NIAEC",
"path": "/baidupic.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: FatBallFish/NIAEC path: /baidupic.py
alue 中的数字会被当成十进制unicode编码转换成字符
# 也可以直接用字符串作为value
char_table = {ord(key): ord(value) for key, value in char_table.items()}
# 解码图片URL
def decode(url):
# 先替换字符串
for key, value in str_table.items():
url = url.replace(key, value)
# 再替换剩下的字符
... | code_fim | hard | {
"lang": "python",
"repo": "FatBallFish/NIAEC",
"path": "/baidupic.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> dirpath = os.path.join(sys.path[0], dirName)
if not os.path.exists(dirpath):
os.mkdir(dirpath)
return dirpath
if __name__ == '__main__':
word_list = ['安徽财经大学', '安徽财经大学商学院', '安徽财贸职业学院', '安徽大学', '安徽大学江淮学院', '安徽电气工程职业技术学院', '安徽工程大学', '安徽工商职业学院', '安徽工业大学', '安徽工业大学工商学院', '安徽国际商务职业学院', ... | code_fim | hard | {
"lang": "python",
"repo": "FatBallFish/NIAEC",
"path": "/baidupic.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
#para manejar los errores sintacticos
#def p_error(t): #en modo panico :v
# print("token error: ",t)
# print("Error sintáctico en '%s'" % t.value[0])
# print("Error sintáctico en '%s'" % t.value[1])
#def p_error(t): #en modo panico :v
# while True:
# tok=parser.token()... | code_fim | hard | {
"lang": "python",
"repo": "bcfiusac/PruebaCompi2",
"path": "/gramaticaAscendente.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bcfiusac/PruebaCompi2 path: /gramaticaAscendente.py
# -----------------------------------------------------------------------------
# Grupo 6
#
# Universidad de San Carlos de Guatemala
# Facultad de Ingenieria
# Escuela de Ciencias y Sistemas
# Organizacion de Lenguajes y Compiladores 2
... | code_fim | hard | {
"lang": "python",
"repo": "bcfiusac/PruebaCompi2",
"path": "/gramaticaAscendente.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> r'[a-zA-Z0-9]+'
t.type = reservadas.get(t.value.lower(),'ETIQUETA') # Check for reserved words
return t
# Comentario simple # ...
def t_COMENTARIO_SIMPLE(t):
r'--.*\n'
t.lexer.lineno += 1
# ----------------------- Caracteres ignorados -----------------------
# carac... | code_fim | hard | {
"lang": "python",
"repo": "bcfiusac/PruebaCompi2",
"path": "/gramaticaAscendente.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>outputs = model_ft(inputs)
_, preds = torch.max(outputs.data, 1)
y_true.append(labels.data.cpu().numpwpy())
y_pred.append(preds.cpu().numpy())
print (y_pred[0][0],y_true[0][0])
# plt.imshow(inputs.cpu())
break<|fim_prefix|># repo: Bala93/Digital-pathology path: /codes/evaluate.py
from __future__ impo... | code_fim | hard | {
"lang": "python",
"repo": "Bala93/Digital-pathology",
"path": "/codes/evaluate.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Bala93/Digital-pathology path: /codes/evaluate.py
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import numpy as np
import torchvision
from torchvision import... | code_fim | hard | {
"lang": "python",
"repo": "Bala93/Digital-pathology",
"path": "/codes/evaluate.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> r = _schema.parse_file('src/position_to_end.bin')
self.assertEqual(len(r.pass_ints.nums), 3)
self.assertEqual(r.pass_ints.nums[0], 513)
self.assertEqual(r.pass_ints.nums[1], 1027)
self.assertEqual(r.pass_ints.nums[2], 1541)
self.assertEqual(len(r.pass_ints_... | code_fim | medium | {
"lang": "python",
"repo": "kaitai-io/kaitai_struct_tests",
"path": "/spec/construct/test_params_pass_array_int.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kaitai-io/kaitai_struct_tests path: /spec/construct/test_params_pass_array_int.py
# Autogenerated from KST: please remove this line if doing any edits by hand!
import unittest
from params_pass_array_int import _schema
class TestParamsPassArrayInt(unittest.TestCase):
<|fim_suffix|> r = _... | code_fim | medium | {
"lang": "python",
"repo": "kaitai-io/kaitai_struct_tests",
"path": "/spec/construct/test_params_pass_array_int.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nacoolp/portfolio path: /jobs/views.py
from django.shortcuts import render
<|fim_suffix|>def home(request):
jobs = Job.objects
im = '\media\images\sr40.jpg'
return render(request, 'jobs/home.html', {'jobs': jobs, 'image': im})<|fim_middle|>from .models import Job
import os
cwd = os.g... | code_fim | medium | {
"lang": "python",
"repo": "nacoolp/portfolio",
"path": "/jobs/views.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> jobs = Job.objects
im = '\media\images\sr40.jpg'
return render(request, 'jobs/home.html', {'jobs': jobs, 'image': im})<|fim_prefix|># repo: nacoolp/portfolio path: /jobs/views.py
from django.shortcuts import render
<|fim_middle|>from .models import Job
import os
cwd = os.getcwd()
base_dir = ... | code_fim | medium | {
"lang": "python",
"repo": "nacoolp/portfolio",
"path": "/jobs/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JohnsonLu3/Gerrymandering-Analysis path: /dataParsers/dbImporter/ImportSimulation.py
from sqlalchemy import create_engine
from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey
from sqlalchemy.orm import sessionmaker, scoped_session
from sqlalchemy.ext.automap import automap_... | code_fim | hard | {
"lang": "python",
"repo": "JohnsonLu3/Gerrymandering-Analysis",
"path": "/dataParsers/dbImporter/ImportSimulation.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for row in session.execute(repVotePercent):
actualRepPercent = float(row[0])
for row in session.execute(demVotePercent):
actualDemPercent = float(row[0])
for i in range(K): # Randomly select N districts from the district table for ... | code_fim | hard | {
"lang": "python",
"repo": "JohnsonLu3/Gerrymandering-Analysis",
"path": "/dataParsers/dbImporter/ImportSimulation.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>train_dataset = train.flow_from_directory("data/train/",
target_size=(150,150),
batch_size = 32,
class_mode = 'binary')
test_dataset = tes... | code_fim | hard | {
"lang": "python",
"repo": "cryptobench/discord-image-recognition",
"path": "/train.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>model.add(keras.layers.Conv2D(256,(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D(2,2))
# This layer flattens the resulting image array to 1D array
model.add(keras.layers.Flatten())
# Hidden layer with 512 neurons and Rectified Linear Unit activation function
model.add(keras.layers.Dense(512... | code_fim | hard | {
"lang": "python",
"repo": "cryptobench/discord-image-recognition",
"path": "/train.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cryptobench/discord-image-recognition path: /train.py
import tensorflow as tf
import numpy as np
from tensorflow import keras
import os
import cv2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt
f... | code_fim | hard | {
"lang": "python",
"repo": "cryptobench/discord-image-recognition",
"path": "/train.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vellankikoti/PythonProjects path: /main.py
from tkinter import*
import qrcode
from PIL import Image, ImageTk
from resizeimage import resizeimage
class Qr_Generator:
def __init__(self,root):
self.root = root
self.root.geometry("900x500+200+50")
self.root.title("... | code_fim | hard | {
"lang": "python",
"repo": "vellankikoti/PythonProjects",
"path": "/main.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
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