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import sys
central_line = []
#-----------------------#
if len(sys.argv) < 3:
error("Usage: python metro.py [read file] [write file]")
f = open(sys.argv[2],'w')
f.write("<html><head><style>@font-face { font-family: KeepCalm; src: url(http://ff.static.1001fonts.net/k/e/keep-calm.regular.ttf); } html { overflow-x: hidden; } body { padding: 0px; margin: 0px; } .all-lines { position:absolute; top:0px; width:100%; left: 0; margin-left:-50%; /* half of the width */ } .ruler { position: absolute; left:0px; top:0px; width:100%; z-index: 2; } .line { width: 418px; } .major-right-line { position: absolute; left: calc(50% + 12px); } .major-left-line { position: absolute; left: calc(50% - 418px - 12px); } .center { margin-left: auto; margin-right: auto; } .left { float: left; } .right { float: right; } .red { background: red; } .red-fade { background: -webkit-linear-gradient(left,rgba(255,255,255,0), red); /*Safari 5.1-6*/ background: -o-linear-gradient(right,rgba(255,255,255,0), red); /*Opera 11.1-12*/ background: -moz-linear-gradient(right,rgba(255,255,255,0), red); /*Fx 3.6-15*/ background: linear-gradient(to right, rgba(255,255,255,0), red); /*Standard*/ } .pink { background: pink; } .pink-fade { background: -webkit-linear-gradient(left,rgba(255,255,255,0), pink); /*Safari 5.1-6*/ background: -o-linear-gradient(right,rgba(255,255,255,0), pink); /*Opera 11.1-12*/ background: -moz-linear-gradient(right,rgba(255,255,255,0), pink); /*Fx 3.6-15*/ background: linear-gradient(to right, rgba(255,255,255,0), pink); /*Standard*/ } .orange { background: orange; } .orange-fade { background: -webkit-linear-gradient(left,rgba(255,255,255,0), orange); /*Safari 5.1-6*/ background: -o-linear-gradient(right,rgba(255,255,255,0), orange); /*Opera 11.1-12*/ background: -moz-linear-gradient(right,rgba(255,255,255,0), orange); /*Fx 3.6-15*/ background: linear-gradient(to right, rgba(255,255,255,0), orange); /*Standard*/ } .black { background: black; } .black-fade { background: -webkit-linear-gradient(left,rgba(255,255,255,0), black); /*Safari 5.1-6*/ background: -o-linear-gradient(right,rgba(255,255,255,0), black); /*Opera 11.1-12*/ background: -moz-linear-gradient(right,rgba(255,255,255,0), black); /*Fx 3.6-15*/ background: linear-gradient(to right, rgba(255,255,255,0), black); /*Standard*/ } .gray { background: gray; } .gray-fade { background: -webkit-linear-gradient(left,rgba(255,255,255,0), gray); /*Safari 5.1-6*/ background: -o-linear-gradient(right,rgba(255,255,255,0), gray); /*Opera 11.1-12*/ background: -moz-linear-gradient(right,rgba(255,255,255,0), gray); /*Fx 3.6-15*/ background: linear-gradient(to right, rgba(255,255,255,0), gray); /*Standard*/ } .blue { background: blue; } .blue-fade { background: -webkit-linear-gradient(left,rgba(255,255,255,0), blue); /*Safari 5.1-6*/ background: -o-linear-gradient(right,rgba(255,255,255,0), blue); /*Opera 11.1-12*/ background: -moz-linear-gradient(right,rgba(255,255,255,0), blue); /*Fx 3.6-15*/ background: linear-gradient(to right, rgba(255,255,255,0), blue); /*Standard*/ } .green { background: green; } .green-fade { background: -webkit-linear-gradient(left,rgba(255,255,255,0), green); /*Safari 5.1-6*/ background: -o-linear-gradient(right,rgba(255,255,255,0), green); /*Opera 11.1-12*/ background: -moz-linear-gradient(right,rgba(255,255,255,0), green); /*Fx 3.6-15*/ background: linear-gradient(to right, rgba(255,255,255,0), green); /*Standard*/ } .gray-red-fade { background: -webkit-linear-gradient(gray, red); /*Safari 5.1-6*/ background: -o-linear-gradient(gray, red); /*Opera 11.1-12*/ background: -moz-linear-gradient( gray, red); /*Fx 3.6-15*/ background: linear-gradient( gray, red); /*Standard*/ } .red-blue-fade { background: -webkit-linear-gradient( red, blue); /*Safari 5.1-6*/ background: -o-linear-gradient( red, blue); /*Opera 11.1-12*/ background: -moz-linear-gradient( red, blue); /*Fx 3.6-15*/ background: linear-gradient( red, blue); /*Standard*/ } .white { background: white; } .full-circle { width: 50px; height: 50px; -moz-border-radius: 25px; -webkit-border-radius: 25px; border-radius: 25px; } .mid-circle { width: 25px; height: 25px; -moz-border-radius: 12.5px; -webkit-border-radius: 12.5px; border-radius: 12.5px; } .vertical-center { position: relative; top: 50%; -webkit-transform: translateY(-50%); -ms-transform: translateY(-50%); transform: translateY(-50%); } div{ position: relative; font-family: KeepCalm; font-size: 92.5%; } .ruler-rectangle { width: 100%; height: 2px; margin-bottom:100px; } .rectangle { width: 12.5px; height: calc(100% + 20px); -webkit-transform: translateY(-10px); -ms-transform: translateY(-10px); transform: translateY(-10px); position: absolute; margin-left: auto; margin-right: auto; left: 0; right: 0; top:0px; z-index: -1; } .dotted-rectangle { width: 0px; border-right: 12.5px dotted; height: calc(100% + 20px); -webkit-transform: translateY(-10px); -ms-transform: translateY(-10px); transform: translateY(-10px); position: absolute; margin-left: auto; margin-right: auto; left: 0; right: 0; top:0px; z-index: -1; } .right-line { left: 25px; } .left-line { right: 25px; } .text { padding: 5px; padding-left: 10px; } .ruler-rotated { -ms-transform: translateY(-5px) translateX(25px) rotate(45deg); -webkit-transform: translateY(-5px) translateX(25px) rotate(45deg); transform: translateY(-5px) translateX(25px) rotate(45deg); } .rotated { -ms-transform: translateY(95px) translateX(15px) rotate(45deg); -webkit-transform: translateY(95px) translateX(15px) rotate(45deg); transform: translateY(95px) translateX(15px) rotate(45deg); width: calc(50% + 40px); padding: 5px; } .rotated-up { -ms-transform: translateY(-95px) translateX(-210px) rotate(45deg); -webkit-transform: translateY(-95px) translateX(-210px) rotate(45deg); transform: translateY(-90px) translateX(-205px) rotate(45deg); padding-left: 0px; width: calc(50% + 40px); } .rotated-junction { -ms-transform: translateY(85px) translateX(20px) rotate(45deg); -webkit-transform: translateY(85px) translateX(20px) rotate(45deg); transform: translateY(85px) translateX(20px) rotate(45deg); width: calc(50% + 40px); } .rotated-left { -ms-transform: translateY(85px) translateX(-40%) rotate(-45deg); -webkit-transform: translateY(85px) translateX(-40%) rotate(-45deg); transform: translateY(85px) translateX(-40%) rotate(-45deg); height: 12.5px; width: calc(1.414 * 50%); z-index: -2; } .rotated-left-up-solid { -ms-transform: translateY(-100px) translateX(-40%) rotate(45deg); -webkit-transform: translateY(-100px) translateX(-40%) rotate(45deg); transform: translateY(-100px) translateX(-40%) rotate(45deg); height: 12.5px; width: calc(1.414 * 50%); z-index: -2; } .rotated-right { -ms-transform: translateY(85px) translateX(40%) rotate(45deg); -webkit-transform: translateY(85px) translateX(40%) rotate(45deg); transform: translateY(85px) translateX(40%) rotate(45deg); height: 12.5px; width: calc(1.414 * 50%); z-index: -2; } .rotated-left-up { -ms-transform: translateY(-65px) translateX(-39%) rotate(45deg); -webkit-transform: translateY(-65px) translateX(-39%) rotate(45deg); transform: translateY(-65px) translateX(-39%) rotate(45deg); height: 12.5px; width: calc(50%); z-index: -2; } .rotated-left-down { -ms-transform: translateY(50px) translateX(-39%) rotate(-45deg); -webkit-transform: translateY(50px) translateX(-39%) rotate(-45deg); transform: translateY(50px) translateX(-39%) rotate(-45deg); height: 12.5px; width: calc(50%); z-index: -2; } .rotated-right-up { -ms-transform: translateY(-115px) translateX(39%) rotate(135deg); -webkit-transform: translateY(-115px) translateX(39%) rotate(135deg); transform: translateY(-115px) translateX(39%) rotate(135deg); height: 12.5px; width: calc(50%); z-index: -2; } .rotated-right-down { -ms-transform: translateY(50px) translateX(39%) rotate(225deg); -webkit-transform: translateY(50px) translateX(39%) rotate(225deg); transform: translateY(50px) translateX(39%) rotate(225deg); height: 12.5px; width: calc(50%); z-index: -2; } </style></head><body><div class='body center'>")
f.write("<div class='ruler'>")
line_count = 0
for i in range(0,3):
central_line.append("-")
with open(sys.argv[1], 'r+') as r:
lines = r.readlines()
i = 0
while i<len(lines):
line = lines[i]
if line[0] == '#':
line_count+=1
if line_count == 1:
f.write("</div>")
f.write("<div class='all-lines center content'><div class='left line major-left-line'>")
elif line_count == 2:
f.write("</div>")
f.write("<div class='right line major-right-line'>")
for j in range(0,3):
central_line[j] = "-"
elif line_count == 3:
f.write("</div>")
f.write("<div class='center line' id='central-line'>")
for j in range(0,3):
central_line[j] = "-"
elif line[0] == '>' or line[0] == '<':
drawJunction(line, f)
elif line[0] == '\"':
try:
createDescription(line.split('\"')[1], lines[i+1][0]=='>' or lines[i+2][0]=='>' or lines[i+3][0]=='>' or lines[i+4][0]=='>', f)
except:
createDescription(line.split('\"')[1], 0, f)
if lines[i+1][0] == '<' and len(lines[i+1].split(":")) > 2 and lines[i+1].split(":")[2] == 'up\n':
drawJunction(lines[i+1], f)
i+=1
createStation(line, f)
else:
if line_count == 0:
try:
createRuler(line, f)
except:
error("Syntax for timeline bounds - [Beginning Year]:[End Year (can be decimal)]")
else:
drawLine(line, f)
i+=1
f.write("</div></div></div>")
f.write("</body></html>")
f.close()
print "Done..."
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] | 2.539511 | 3,885 |
# Copyright (c) "Neo4j"
# Neo4j Sweden AB [http://neo4j.com]
#
# This file is part of Neo4j.
#
# 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.
class Query:
""" Create a new query.
:param text: The query text.
:type text: str
:param metadata: metadata attached to the query.
:type metadata: dict
:param timeout: seconds.
:type timeout: float or None
"""
def unit_of_work(metadata=None, timeout=None):
"""This function is a decorator for transaction functions that allows extra control over how the transaction is carried out.
For example, a timeout may be applied::
@unit_of_work(timeout=100)
def count_people_tx(tx):
result = tx.run("MATCH (a:Person) RETURN count(a) AS persons")
record = result.single()
return record["persons"]
:param metadata:
a dictionary with metadata.
Specified metadata will be attached to the executing transaction and visible in the output of ``dbms.listQueries`` and ``dbms.listTransactions`` procedures.
It will also get logged to the ``query.log``.
This functionality makes it easier to tag transactions and is equivalent to ``dbms.setTXMetaData`` procedure, see https://neo4j.com/docs/operations-manual/current/reference/procedures/ for procedure reference.
:type metadata: dict
:param timeout:
the transaction timeout in seconds.
Transactions that execute longer than the configured timeout will be terminated by the database.
This functionality allows to limit query/transaction execution time.
Specified timeout overrides the default timeout configured in the database using ``dbms.transaction.timeout`` setting.
Value should not represent a negative duration.
A zero duration will make the transaction execute indefinitely.
None will use the default timeout configured in the database.
:type timeout: float or None
"""
return wrapper
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220,
220,
220,
220,
220,
220,
220,
317,
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9478,
481,
787,
262,
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24391,
13,
198,
220,
220,
220,
220,
220,
220,
220,
6045,
481,
779,
262,
4277,
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17839,
287,
262,
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13,
198,
220,
220,
220,
1058,
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25,
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393,
6045,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
1441,
29908,
198
] | 3.269129 | 758 |
#!/usr/bin/env python
#
# NopSCADlib Copyright Chris Palmer 2018
# [email protected]
# hydraraptor.blogspot.com
#
# This file is part of NopSCADlib.
#
# NopSCADlib 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.
#
# NopSCADlib 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 NopSCADlib.
# If not, see <https://www.gnu.org/licenses/>.
#
#
#! Sets the target configuration for multi-target projects that have variable configurations.
#
from __future__ import print_function
source_dir = 'scad'
import sys
import os
if __name__ == '__main__':
args = len(sys.argv)
if args == 2:
set_config(sys.argv[1], usage)
else:
usage()
| [
2,
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6,
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220,
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7,
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13,
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85,
8,
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220,
220,
220,
611,
26498,
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25,
198,
220,
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220,
220,
220,
220,
900,
62,
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7,
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13,
853,
85,
58,
16,
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8,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
8748,
3419,
198
] | 3.171014 | 345 |
# -*- coding: utf-8 -*-
# @Time : 2021/03/13 17:31:29
# @Author : DannyDong
# @File : RunTest.py
# @Describe: 用例执行逻辑
from app.Utils import DataReceive
# 测试执行类
# 处理前置条件
# 用例执行逻辑
| [
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] | 1.427536 | 138 |
from collections import OrderedDict
__author__ = 'Joe'
| [
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] | 3.157895 | 19 |
with open('p081_matrix.txt') as f:
content = f.readlines()
print(content)
clear_list = []
for i in range(0, len(content)):
clear_list.append(content[i].strip().split(','))
for i in range(1,80):
clear_list[0][i] = int(clear_list[0][i]) + int(clear_list[0][i-1])
for i in range(1,80):
clear_list[i][0] = int(clear_list[i][0]) + int(clear_list[i-1][0])
for i in range(1, 80):
for j in range(1, 80):
if int(clear_list[i-1][j]) < int(clear_list[i][j-1]):
clear_list[i][j] = int(clear_list[i][j]) + int(clear_list[i-1][j])
continue
clear_list[i][j] = int(clear_list[i][j]) + int(clear_list[i][j-1])
print(clear_list[79][79])
| [
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7,
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12,
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73,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
1598,
62,
4868,
58,
72,
7131,
73,
60,
796,
493,
7,
20063,
62,
4868,
58,
72,
7131,
73,
12962,
1343,
493,
7,
20063,
62,
4868,
58,
72,
7131,
73,
12,
16,
12962,
198,
198,
4798,
7,
20063,
62,
4868,
58,
3720,
7131,
3720,
12962,
198
] | 2.041667 | 336 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
import os
from HTTPerror import HTTP404Error, HTTP302Error
from server import static_setting
import logging
| [
2,
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] | 3.052632 | 57 |
from .label_smooth import LabelSmoothCrossEntropyLoss | [
6738,
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5796,
5226,
1330,
36052,
7556,
5226,
21544,
14539,
28338,
43,
793
] | 3.533333 | 15 |
from django.test import TestCase
from views import translate_text
# Create your tests here.
| [
6738,
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] | 3.84 | 25 |
#!/usr/bin/env python
# Copyright (C) 2014 Craig Phillips. All rights reserved.
import unittest
from libgsync.sync.file import SyncFile
| [
2,
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14,
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13,
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1330,
35908,
8979,
198
] | 3.232558 | 43 |
import json
from datetime import timedelta
from django.urls import reverse
from django.utils import timezone
from .. import test
from ..models import Post, Thread
from ..test import patch_category_acl
from .test_threads_api import ThreadsApiTestCase
| [
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82,
32,
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20448,
628
] | 3.666667 | 69 |
import asyncio
import json
import logging
import traceback
from watchmen.collection.model.topic_event import TopicEvent
from watchmen_boot.config.config import settings
from watchmen.raw_data.service.import_raw_data import import_raw_topic_data
log = logging.getLogger("app." + __name__)
loop = asyncio.get_event_loop()
kafka_topics = settings.KAFKA_TOPICS
kafka_topics_list = kafka_topics.split(",")
| [
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4914,
62,
4852,
873,
13,
35312,
7,
2430,
8,
628
] | 3.045113 | 133 |
import random
import sys
"""
This class represents a maze instance
"""
# Maze class itself
# Represents single node in the maze
| [
11748,
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25064,
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198,
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2346,
628,
198,
2,
1432,
6629,
2060,
10139,
287,
262,
31237,
198
] | 3.911765 | 34 |
from PyQt5.QtWidgets import QPushButton
from hue import UnauthorizedUserError, GenericHueError
| [
6738,
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83,
20,
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48,
83,
54,
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1330,
791,
19721,
12982,
12331,
11,
42044,
39,
518,
12331,
198
] | 3.2 | 30 |
### RPGOnline
### A Synergy Studios Project
import random
# - GAME CLASSES - #
class Game:
"""A class for a single game that stores all the other classes.
For now, this refers to local-game only classes."""
class Shop:
"""A class to represent the shop, in which players can buy from."""
pass
# - ENTITY CLASSES - #
class Entity:
"""A class for every type of thing.
Health: Health Left
Moveset: Moves/Attacks to be used on other entites
Seletced Attack: The selected Move/Attack you have
Defence: Scale from 1 - 100, percentage of damage negated
Agility: Speed of entity
Effects Applied: Any effects on this entity
"""
def refresh_stats(self):
"""Refreshes the statistics (for save/load purposes)"""
self.stats = [self.health, self.moveset, self.selected_attack, self.defence, self.agility, self.effects_applied]
def defend_attack(self, entity_from, damage):
"""Defends an attack from another entity."""
print(f'Entity {entity_from.name} attacked!')
defence = (self.defence / 100)
total_damage = damage - (damage * defence)
self.health = (self.health - total_damage)
print(f'Lost {total_damage} hp!')
class Hero(Entity):
"""A hero which is represented a character which has a skillset
and is controlled by a player."""
def __init__(self, gametag, name, health, moveset, defence, agility, level)
super().__init__(gametag, name, health, moveset, selected_attack, defence, agility, effects_applied)
self.level = level
def refresh_stats(self):
"""Refreshes the statistics (for save/load purposes)"""
self.stats = [self.health, self.moveset, self.selected_attack, self.defence, self.agility, self.effects_applied, self.level]
class Monster(Entity):
"""A monster which attacks heroes and has different moves."""
pass
class NPC(Entity):
"""NPCs in which the players can interact with."""
def refresh_stats(self):
"""Refreshes the statistics (for save/load purposes)"""
self.stats = [self.gametag, self.name, self.speech, self.stats]
def play_speech(self):
"""Plays the speech of the NPC."""
pass
# - MOVESET CLASSES - #
class Move:
"""A move that an entity uses in a battle to affect other players."""
class Attack(Move):
"""A move which damages another entity.
Name: The name of the attack
Damage: Base damage points (HP)
Crit Chance: 1/x chance that you get a boost
Crit Boost: Damage boost applied when you get a crit
Miss Chance: 1/x chance you miss
"""
def attack_entity(self, en, entity_from):
"""Attacks a particular entity."""
miss = random.randint(1, self.miss_chance)
if miss < (self.miss_chance - 1): # If miss_chance = 5, chance = 1/5
crit = random.randint(1, self.crit_chance)
if crit > (self.crit_chance - 1): # If crit_chance = 5, chance = 1/5
total_damage = self.damage + self.crit_buff
print('Critical Hit!')
else:
total_damage = self.damage
print('Hit!')
en.defend_attack(entity_from, total_damage) # This entity defends it
else:
print('Missed Attack!')
class Spell(Move):
"""A move that applies an effect to an entity."""
# - EFFECT CLASSES - #
class Effect:
"""An effect which is applied onto an entity.
Name: Name of effect
"""
| [
21017,
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] | 2.643223 | 1,365 |
# home.py
from .alarm import Alarm
from .light import Light
from .lock import Lock | [
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] | 3.32 | 25 |
from io import StringIO
from typing import NamedTuple, List, Set, Tuple, Optional
from sites import SELECTORS
from preferences import URLS
from selenium.webdriver import FirefoxProfile, FirefoxOptions, Firefox
from selenium.common.exceptions import NoSuchElementException
from notification import notify_about_home, notify_dev
import re
from helper import pipe
import time
import logging as log
from preferences import SEEN_PATH, CRITERIA, make_field_transformers, SITES_TO_SCRAPE
from hashlib import md5
from contextlib import contextmanager
import sys
class Home(NamedTuple):
"""
Store information about a home
"""
name: str
area: int
rooms: int
rent: int
address: str
url: str
def fingerprint(home: Home):
"""
Get 'unique' id for home Object
:param home: defined Home object
:return: md5 string
"""
return md5('{}{}{}{}{}{}'.format(home.name, home.area, home.rooms,
home.rent, home.address, home.url)
.encode('utf-8')).hexdigest()
def show(name):
"""
Print out something in a pipeline without affecting the input
"""
return go
class HomeSpider:
"""
Crawl home-search-engine websites
"""
def parse_page(self, page_results):
"""
Parse a home website
:param page_results: list of page results
:return: list of correctly parsed homes
"""
for result in page_results:
fields = {}
errors = []
try:
for name, sel in self.selectors['fields'].items():
raw = self.extract(sel, result)
if raw is None:
errors.append('Failed to extract field "{}"'.format(name))
else:
val = pipe(self.transformers[name], raw)
if val is None:
errors.append('Failed to transform field "{}" with input "{}"'.format(name, val))
else:
fields[name] = val
except Exception as e:
errors.append('{}, {}'.format(type(e), e.args[0]))
finally:
if not errors:
yield Home(**fields)
else:
fields, missing = self.fill_in_blank(fields)
if missing:
self.handle_parse_error(errors, result)
else:
yield Home(**fields)
@contextmanager
def get_and_wait(self, url, timeout=10):
"""
Get webpage and wait for it to load
:param url: a url string
:param timeout: timeout in seconds
:return: None
"""
old_page = self.browser.page_source
self.browser.get(url)
for i in range(0, timeout):
time.sleep(1)
if self.browser.page_source != old_page:
break
if self.browser.page_source != old_page:
yield
else:
log.error('Page Timeout', url)
def crawl_next_page(self, next_url: Optional[str]) -> Tuple[List[Home], Optional[str]]:
"""
Crawl all urls
:return: List of selfs
"""
if next_url:
with self.get_and_wait(next_url):
homes = list(self.parse_page(self.extract(self.selectors['results'])))
next_url = self.extract(self.selectors['next-page'])
return homes, next_url
else:
return [], None
def extract(self, selector: str, web_el=None):
"""
Extract text or attribute content from html elements
:param selector: css selector
:param web_el: root html element or if none then the entire document is used
:return: content string or list of content strings
"""
try:
if not web_el:
web_el = self.browser.find_element_by_tag_name('html')
if '::' not in selector:
return self.browser.find_elements_by_css_selector(selector)
else:
sub_sel, ext = selector.split('::')
if ext == 'text':
return web_el.find_element_by_css_selector(sub_sel).text
elif ext == '*text':
el_sel = web_el.find_elements_by_css_selector(sub_sel)
fragments = filter(lambda x: x != '', map(lambda x: x.text.replace('\n',' ').strip(), el_sel))
return ' ** '.join(fragments)
else:
attr = re.search('attr\((.+)\)', ext)
if attr:
return web_el.find_element_by_css_selector(sub_sel).get_attribute(attr.group(1))
except NoSuchElementException:
return None
def fill_in_blank(self, fields):
"""
Fill in fields 'intelligently'
:param fields:
:return: filled in fields, missing fields
"""
_fields = fields.copy()
missing = self.required - _fields.keys()
# probably just a room for rent and not whole apartment
if 'rooms' in missing and 'area' in fields and fields['area'] < 70:
_fields['rooms'] = 1
if 'area' in missing and 'rooms' in fields and fields['rooms'] == 1:
_fields['area'] = 30
missing = self.required - _fields.keys()
return _fields, missing
def handle_parse_error(self, errors, web_element):
"""
Log errors
:param errors: list of error descriptions
:param web_element: html element where error happened
"""
msg = '= PARSE ERROR =====\n' \
'Site: {site}\n' \
'Errors:\n\t - {errs}\n' \
'---- HTML ----\n' \
'{html}\n' \
'---- HTML END ----'.format(
site=self.base_url, errs='\n\t - '.join(errors),
html=web_element.get_attribute('innerHTML')
)
log.error(msg + '\n')
def main():
"""
Run crawler
"""
logger = log.getLogger()
logger.setLevel(log.INFO)
logger.addHandler(log.StreamHandler(sys.stdout))
debugio = StringIO()
logger.addHandler(log.StreamHandler(debugio))
with open(SEEN_PATH, 'r') as f:
seen = set(f.read().splitlines()) # mutable!
old_seen = seen.copy()
for name in SITES_TO_SCRAPE:
new_homes = crawl_website(name, seen)
seen = seen.union(map(fingerprint, new_homes))
log.info('Found {} new homes'.format(len(new_homes)))
for home in new_homes:
if all(must(home) for must in CRITERIA):
notify_about_home(home)
with open(SEEN_PATH, 'a') as f:
f.writelines(h + '\n' for h in (seen - old_seen))
logs = debugio.getvalue()
if 'error' in logs.lower():
log.info('Informing developer about errors')
notify_dev('Crawling Errors', logs)
log.info('Bye!')
if __name__ == '__main__':
main()
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] | 2.10095 | 3,368 |
import onnx
import onnx.numpy_helper as numpy_helper
import numpy as np
# This function checks whether two onnx files (onnx_A and onnx_B) have the same underlying computational graph and operators.
| [
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] | 3.225806 | 62 |
from model.contact import Contact
testdata = [
Contact(firstname="qqqqqqqq", middlename="wwwwwww", nickname="eeefdeeee", title="vvvvvvvvvv",
lastname="eeeeeeeee", company="xccccccccc",
adress="ffcvcxvcvcxvxcvx", home="23144124214", mobile="45565656678",
work="56678678678", fax="67867868686",
email="[email protected]", email2="[email protected]", email3="[email protected]",
homepage="http://wwwww.ru", byear="1985", ayear="2000",
address2="sdfdsfsdfsdfsd", phone2="sdfsdfsdfsdfsdf", notes="sfsdfsdfdssdfsdfs"),
Contact(firstname="f1", middlename="m1", nickname="n1", title="t1",
lastname="l1", company="c1",
adress="ffcvcxvcvcxvxcvx", home="23144124214", mobile="45565656678",
work="56678678678", fax="67867868686",
email="[email protected]", email2="[email protected]", email3="[email protected]",
homepage="http://wwwww.ru", byear="1985", ayear="2000",
address2="sdfdsfsdfsdfsd", phone2="sdfsdfsdfsdfsdf", notes="sfsdfsdfdssdfsdfs")
] | [
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#coding=utf-8
import sys
import os
from os.path import abspath, dirname
sys.path.append(abspath(dirname(__file__)))
import tkinter
import tkinter.filedialog
from tkinter import *
import Fun
ElementBGArray={}
ElementBGArray_Resize={}
ElementBGArray_IM={}
from PyPDF2 import PdfFileReader, PdfFileWriter
DirPath=""
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'''
<실수 줄이기 메모>
소요 시간 30분(권장 20분 문제)
초기화 하는 과정(누산) 알고리즘을 잘 못 짜서 헤맸다.
쉬운 문제일수록, 집중해서 정확히 한번에 풀고 끝내자 ㅜㅜ!
<답안 꿀팁>
1) python list.count(<<특정 값>>)
시간 복잡도 O(N)
근데 사실 내가 짠 코드가 한번 loop로 끝이니 더 빠르긴함.
다만 여유로우니 위 built-in 사용하면 코드가 깔끔함.
2) stages 1부터 차례로 실패율을 계산하면서, 전체 사람수를 줄여나감
fail = count / length
length -= count
이렇게 하면 더 코드가 훨씬 간결하긴 함.
이런 걸 다음에는 바로 떠올려보자 !
<답안 메모>
문제 정의 따라 실수 없이 구현을 잘해주면 된다.
따라서 구현 문제로도 분류할 수 있지만,
문제 해결 과정에서 정렬 라이브러리가 효과적으로 사용되므로 정렬 문제로 분류함
전체 스테이지 개수가 200,000 이하이기 때문에,
O(NlogN) 기본 정렬 라이브러리로 충분히 수행 가능함.
'''
'''
<Answer>
# 프로그래머스 실패율
def solution(N, stages):
answer = []
length = len(stages)
# 스테이지 번호를 1부터 N까지 증가시키며
for i in range(1, N+1):
# 해당 스테이지에 머물러 있는 사람의 수 계산
count = stages.count()
# 실패율 계산
if length == 0:
fail = 0
else:
fail = count / length
# 리스트에 (스테이지 번호, 실패율) 원소 삽입
answer.append((i, fail))
length -= count
# 실패율을 기준으로 각 스테이지를 내림차순 정렬
answer = sorted(answer, key=lambda t: t[1], reverse=True)
# 정렬된 스테이지 번호 출력
answer = [i[0] for i in answer]
return answer
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] | 0.791377 | 1,438 |
# -*- coding: utf-8 -*-
import atexit
import json
import os
import shlex
import shutil
import tempfile
import unittest
from .exceptions import CommandError
from .utils import run_cmd_wait, run_cmd_wait_nofail, which, vramsteg_binary_location, DEFAULT_EXTENSION_PATH
from .compat import STRING_TYPE
class Vramsteg(object):
"""Manage a Vramsteg instance
A temporary folder is used as data store of vramsteg.
A vramsteg client should not be used after being destroyed.
"""
DEFAULT_VRAMSTEG = vramsteg_binary_location()
def __init__(self, vramsteg=DEFAULT_VRAMSTEG):
"""Initialize a vramsteg (client).
The program runs in a temporary folder.
:arg vramsteg: Vramsteg binary to use as client (defaults: vramsteg in PATH)
"""
self.vramsteg = vramsteg
# Used to specify what command to launch (and to inject faketime)
self._command = [self.vramsteg]
# Configuration of the isolated environment
self._original_pwd = os.getcwd()
self.datadir = tempfile.mkdtemp(prefix="vramsteg_")
self.vramstegrc = os.path.join (self.datadir, 'vramstegrc')
self._command.extend(['-f', self.vramstegrc])
# Ensure any instance is properly destroyed at session end
atexit.register(lambda: self.destroy())
self.reset_env()
def add_default_extension(self, filename):
"""Add default extension to current instance
"""
if not os.path.isdir(self.extdir):
os.mkdir(self.extdir)
extfile = os.path.join(self.extdir, filename)
if os.path.isfile(extfile):
raise "{} already exists".format(extfile)
shutil.copy(os.path.join(DEFAULT_EXTENSION_PATH, filename), extfile)
def __call__(self, *args, **kwargs):
"aka t = Vramsteg() ; t() which is now an alias to t.runSuccess()"
return self.runSuccess(*args, **kwargs)
def reset_env(self):
"""Set a new environment derived from the one used to launch the test
"""
# Copy all env variables to avoid clashing subprocess environments
self.env = os.environ.copy()
def config(self, line):
"""Add 'line' to self.vramstegrc.
"""
with open(self.vramstegrc, "a") as f:
f.write(line + "\n")
@property
def vramstegrc_content(self):
"""
Returns the contents of the vramstegrc file.
"""
with open(self.vramstegrc, "r") as f:
return f.readlines()
@staticmethod
def _split_string_args_if_string(args):
"""Helper function to parse and split into arguments a single string
argument. The string is literally the same as if written in the shell.
"""
# Enable nicer-looking calls by allowing plain strings
if isinstance(args, STRING_TYPE):
args = shlex.split(args)
return args
def runSuccess(self, args="", input=None, merge_streams=False,
timeout=5):
"""Invoke vramsteg with given arguments and fail if exit code != 0
Use runError if you want exit_code to be tested automatically and
*not* fail if program finishes abnormally.
If you wish to pass instructions to vramsteg such as confirmations or other
input via stdin, you can do so by providing a input string.
Such as input="y\ny\n".
If merge_streams=True stdout and stderr will be merged into stdout.
timeout = number of seconds the test will wait for every vramsteg call.
Defaults to 1 second if not specified. Unit is seconds.
Returns (exit_code, stdout, stderr) if merge_streams=False
(exit_code, output) if merge_streams=True
"""
# Create a copy of the command
command = self._command[:]
args = self._split_string_args_if_string(args)
command.extend(args)
output = run_cmd_wait_nofail(command, input,
merge_streams=merge_streams,
env=self.env,
timeout=timeout)
if output[0] != 0:
raise CommandError(command, *output)
return output
def runError(self, args=(), input=None, merge_streams=False, timeout=5):
"""Invoke vramsteg with given arguments and fail if exit code == 0
Use runSuccess if you want exit_code to be tested automatically and
*fail* if program finishes abnormally.
If you wish to pass instructions to vramsteg such as confirmations or other
input via stdin, you can do so by providing a input string.
Such as input="y\ny\n".
If merge_streams=True stdout and stderr will be merged into stdout.
timeout = number of seconds the test will wait for every vramsteg call.
Defaults to 1 second if not specified. Unit is seconds.
Returns (exit_code, stdout, stderr) if merge_streams=False
(exit_code, output) if merge_streams=True
"""
# Create a copy of the command
command = self._command[:]
args = self._split_string_args_if_string(args)
command.extend(args)
output = run_cmd_wait_nofail(command, input,
merge_streams=merge_streams,
env=self.env,
timeout=timeout)
# output[0] is the exit code
if output[0] == 0 or output[0] is None:
raise CommandError(command, *output)
return output
def destroy(self):
"""Cleanup the data folder and release server port for other instances
"""
try:
shutil.rmtree(self.datadir)
except OSError as e:
if e.errno == 2:
# Directory no longer exists
pass
else:
raise
# Prevent future reuse of this instance
self.runSuccess = self.__destroyed
self.runError = self.__destroyed
# self.destroy will get called when the python session closes.
# If self.destroy was already called, turn the action into a noop
self.destroy = lambda: None
def faketime(self, faketime=None):
"""Set a faketime using libfaketime that will affect the following
command calls.
If faketime is None, faketime settings will be disabled.
"""
cmd = which("faketime")
if cmd is None:
raise unittest.SkipTest("libfaketime/faketime is not installed")
if self._command[0] == cmd:
self._command = self._command[3:]
if faketime is not None:
# Use advanced time format
self._command = [cmd, "-f", faketime] + self._command
# vim: ai sts=4 et sw=4
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] | 2.334583 | 2,935 |
import unittest
import cq_examples.Ex016_Using_Construction_Geometry as ex
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] | 3.125 | 24 |
from __future__ import unicode_literals, division, absolute_import
from builtins import * # pylint: disable=unused-import, redefined-builtin
import pytest
from flexget.entry import Entry
from flexget.plugins.list.imdb_list import ImdbEntrySet
@pytest.mark.online
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] | 3.292683 | 82 |
from onnx import TensorProto
from onnx import helper as oh
from finn.custom_op.registry import getCustomOp
from finn.transformation import Transformation
from finn.util.fpgadataflow import is_fpgadataflow_node
class InsertDWC(Transformation):
"""Ensure that the graph is terminated with a TLastMarker node, inserting
one if necessary."""
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] | 3.342857 | 105 |
from flask_sqlalchemy import SQLAlchemy
db = SQLAlchemy()
from .models import User
from .models import CoffeeShop
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] | 3.515152 | 33 |
import pygame
from GameObj import GameObj
import random
# draws the segment, go_through by default is false
# checks the boundary for the segment
# goes through the boundary and comes through the other end
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220,
1303,
8794,
262,
18645,
329,
262,
10618,
198,
220,
220,
220,
1303,
2925,
832,
262,
18645,
290,
2058,
832,
262,
584,
886,
198
] | 3.877193 | 57 |
"""
Copyright 2019 Software Reliability Lab, ETH Zurich
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 PIL import Image, ImageDraw
import json
from pprint import pprint
from random import randint
import config
| [
37811,
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198,
6738,
4738,
1330,
43720,
600,
198,
11748,
4566,
628
] | 4 | 174 |
# -*- coding: utf-8 -*-
# Copyright 2016 Yelp Inc.
#
# 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 __future__ import absolute_import
from __future__ import unicode_literals
from schematizer.models.database import session
from schematizer.models.exceptions import EntityNotFoundError
class BaseModel(object):
"""Base class of model classes which contains common simple operations
(operations that only involve single model class only).
These functions only work when they are inside the request context manager.
See http://servicedocs/docs/yelp_conn/session.html.
"""
@classmethod
@classmethod
@classmethod
def create(cls, session, **kwargs):
"""Create this entity in the database. Note this function will call
`session.flush()`, so do not use this function if there are other
operations that need to happen before the flush is called.
Args:
session (:class:yelp_conn.session.YelpConnScopedSession) global
session manager used to provide sessions.
kwargs (dict): pairs of model attributes and their values.
Returns:
:class:schematizer.models.[cls]: object that is newly created in
the database.
"""
entity = cls(**kwargs)
session.add(entity)
session.flush()
return entity
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796,
537,
82,
7,
1174,
46265,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
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7,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
25925,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
9312,
198
] | 3.067434 | 608 |
import tushare as ts
import pymongo
import json
stock_lists = ts.get_stock_basics() #获取所有股票列表
conn = pymongo.MongoClient('127.0.0.1', port=27017)
conn.db.tickdata.insert_many(json.loads(stock_lists.to_json(orient='records')))
print(stock_lists)
| [
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] | 2.216216 | 111 |
# Regression test based on the diffusion of a Gaussian
# velocity field. Convergence of L1 norm of the error
# in v is tested. Expected 1st order conv. for STS.
# Modules
# (needed for global variables modified in run_tests.py, even w/o athena.run(), etc.)
import scripts.utils.athena as athena # noqa
import scripts.tests.diffusion.viscous_diffusion as viscous_diffusion
import logging
viscous_diffusion.method = 'STS'
viscous_diffusion.rate_tols = [-0.99]
viscous_diffusion.logger = logging.getLogger('athena' + __name__[7:])
| [
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628,
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] | 2.928962 | 183 |
import unittest #import unittest module
from credentials import User
from credentials import Credentials
class TestUser(unittest.TestCase):
'''
Test class that defines test cases for the user class behaviours.
Args:
unittest.TestCase: TestCase class that helps in creating test cases
'''
def setUp(self):
'''
Set up method to run before each test case.
'''
self.new_user = User("Audrey","Njiraini","audreynjiraini","12345678") # create contact object
def test_init(self):
'''
test_init test case to test if the object is initialized properly
'''
self.assertEqual(self.new_user.first_name,"Audrey")
self.assertEqual(self.new_user.last_name,"Njiraini")
self.assertEqual(self.new_user.username,"audreynjiraini")
self.assertEqual(self.new_user.password,"12345678")
def test_save_user(self):
'''
test_save_user test case to test if the user object is saved into the user list
'''
self.new_user.save_user() # save the new contact
self.assertEqual(len(User.user_list),1)
class TestCredentials(unittest.TestCase):
'''
Test class that defines test cases for the credentials class behaviours.
Args:
unittest.TestCase: TestCase class that helps in creating test cases
'''
def setUp(self):
'''
Set up method to run before each test case.
'''
self.new_account = Credentials("audrey","Twitter","audreynjiraini","12345678")
def tearDown(self):
'''
tearDown method that does clean up after each test case has run.
'''
Credentials.credentials_list = []
def test_init(self):
'''
test_init test case to test if the object is initialized properly
'''
self.assertEqual(self.new_account.account_name,"Twitter")
self.assertEqual(self.new_account.username,"audreynjiraini")
self.assertEqual(self.new_account.password,"12345678")
def test_save_credentials(self):
'''
test case to test if the credentials account object is saved into the credentials list
'''
self.new_account.save_credentials()
self.assertEqual(len(Credentials.credentials_list),1)
def test_save_multiple_credentials(self):
'''
test to check if we can save multiple credentials objects to credentials_list
'''
self.new_account.save_credentials()
test_account = Credentials("audrey","Instagram","audreynjiraini","123456789") #new credential
test_account.save_credentials()
self.assertEqual(len(Credentials.credentials_list),2)
def test_display_credentials(self):
'''
Test to check if the correct credentials are displayed
'''
self.assertListEqual(Credentials.display_credentials("audrey"),Credentials.credentials_list)
def test_find_credentials(self):
'''
Test to check if we can find a credential by account_name
'''
self.new_account.save_credentials()
test_account = Credentials("audrey","Instagram","audrey","123456789") #new credential
test_account.save_credentials()
the_account = Credentials.find_credentials("Instagram")
self.assertEqual(the_account.account_name,test_account.account_name)
def test_delete_credentials(self):
'''
test if we can remove a credential from credentials_list once we no longer need it
'''
self.new_account.save_credentials()
test_account = Credentials("audrey","Instagram","audrey","123456789") #new credential
test_account.save_credentials()
self.new_account.delete_credentials() #deleting a credential(account) object
self.assertEqual(len(Credentials.credentials_list),1)
if __name__ == '__main__':
unittest.main() | [
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] | 2.268894 | 1,826 |
#!/usr/bin/env python3
import sys, random
assert sys.version_info >= (3,7), "This script requires at least Python 3.7"
print('Greetings!')#prints 'Greetings' in window
colors = ['red','orange','yellow','green','blue','violet','purple']#list of colors
play_again = ''#establishing empty variable
best_count = sys.maxsize # the biggest number
while (play_again != 'n' and play_again != 'no'):#if the player has not said no to playing again
match_color = random.choice(colors)#selects a random string from the list of
#colors to put in the variable match_color
count = 0#makes variable count 0
color = ''#establishing empty variable
while (color != match_color):#while the color entered doesn't =match_varible
color = input("\nWhat is my favorite color? ") #\n is a special code that adds a new line
color = color.lower().strip()#strips color of letter cases
count += 1#adds 1 to count
if (color == match_color):#if the color entered matches match_color
print('Correct!')#prints 'Correct!' in window
else:
print('Sorry, try again. You have guessed {guesses} times.'.format(guesses=count))
#prints 'Sorry, try again. You have guessed (number in var. count) times.' in window
print('\nYou guessed it in {} tries!'.format(count))#prints 'You guessed it in
#(numberin var. count) tries!' on a new line'
if (count < best_count):#if the count is lower than the best_count
print('This was your best guess so far!')#prints 'This was your best guess so far!'
#in window
best_count = count#changes best_count to previous count
play_again = input("\nWould you like to play again (yes or no)? ").lower().strip()
#asks player if they want to play again and they would type 'yes' or 'no' in response
print('Thanks for playing!')#prints 'Thanks for playing!' in window
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11537,
2,
17190,
705,
9690,
329,
2712,
13679,
287,
4324,
198
] | 3.014331 | 628 |
import time
import pytest
from nucleus import BoxAnnotation
from tests.helpers import (
TEST_BOX_ANNOTATIONS,
TEST_MODEL_NAME,
TEST_SLICE_NAME,
get_uuid,
)
from tests.modelci.helpers import create_box_annotations, create_predictions
from tests.test_dataset import make_dataset_items
@pytest.fixture(scope="module")
def modelci_dataset(CLIENT):
"""SHOULD NOT BE MUTATED IN TESTS. This dataset lives for the whole test module scope."""
ds = CLIENT.create_dataset("[Test Model CI] Dataset", is_scene=False)
yield ds
CLIENT.delete_dataset(ds.id)
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.mark.usefixtures(
"annotations"
) # Unit test needs to have annotations in the slice
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] | 2.723214 | 336 |
from setuptools import setup, find_packages
long_description = open('README.rst').read()
setup(
name='prophy',
version='1.2.4',
author='Krzysztof Laskowski',
author_email='[email protected]',
maintainer='Krzysztof Laskowski',
maintainer_email='[email protected]',
license='MIT license',
url='https://github.com/aurzenligl/prophy',
description='prophy: fast serialization protocol',
long_description=long_description,
long_description_content_type='text/x-rst',
packages=find_packages(),
install_requires=['ply', 'renew>=0.4.8,<0.6'],
keywords='idl codec binary data protocol compiler',
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'Intended Audience :: Telecommunications Industry',
'Topic :: Scientific/Engineering :: Interface Engine/Protocol Translator',
'Topic :: Software Development :: Code Generators',
'Topic :: Software Development :: Compilers',
'Topic :: Software Development :: Embedded Systems',
'Topic :: Software Development :: Testing',
'Topic :: Software Development :: Libraries',
'Topic :: Utilities',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: Implementation :: CPython',
'Programming Language :: Python :: Implementation :: PyPy',
'Programming Language :: C++',
'Operating System :: OS Independent',
'License :: OSI Approved :: MIT License',
],
entry_points={
'console_scripts': [
'prophyc = prophyc.__main__:entry_main'
],
},
)
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] | 2.723849 | 717 |
from pathlib import Path
from typing import List, Optional, Tuple, Union
import numpy as np
import pandas as pd
class PemsBayIo:
"""A class that encapsulates i/o operations related to the PeMS-Bay dataset.
Args:
n_readings: The number of readings in the dataset (not to be confunded
with the dataset length).
n_previous_steps: The number of previous time steps to consider when
building the predictor variable.
n_future_steps: The number of next time steps to consdier when
building the target variable.
normalized_k: The threshold for constructing the adjacency matrix
based on the thresholded Gaussian kernel.
"""
@property
def min_t(self):
"""The minimum time step so that accessing the element of
index min_t-n_previous_steps does not err."""
return abs(min(self.previous_offsets))
@property
def max_t(self):
"""The maximum time step so that accessing the elemnt of
index max_t+n_future_steps does not err"""
return abs(self.n_readings - abs(max(self.future_offsets)))
@property
@property
def get_pems_data(self, data_path: str) -> Tuple[np.ndarray, np.ndarray]:
"""
Load the PeMS-Bay data.
The returned values X (features/predictors/previous steps) and
Y (target/next steps) are of shapes:
X(n_intervals, n_previous_steps, n_nodes=325, n_features=3)
Y(n_intervals, n_next_steps, n_nodes=325, n_features=3)
Args:
data_path: The path where the readings data is stored.
Returns:
A tuple containing the X and Y tensors. The first feature
is the average speed in the 5-minutes interval, while the
second are the third are hour-of-day and day-of-week indices.
"""
data_df = pd.read_csv(filepath_or_buffer=data_path, index_col=0)
_, n_nodes = data_df.shape
# Range of values is 0-100, so half precision (float16) is ok.
data = np.expand_dims(a=data_df.values, axis=-1).astype(np.float16)
data = [data]
# Range of values is 0-23, so half precision (short) is ok.
hour_of_day = ((data_df.index.values.astype("datetime64") -
data_df.index.values.astype("datetime64[D]")) / 3600)\
.astype(int) % 24
hour_of_day = np.tile(hour_of_day, [1, n_nodes, 1]).transpose(
(2, 1, 0)).astype(np.short)
data.append(hour_of_day)
day_of_week = data_df.index.astype("datetime64[ns]").dayofweek
day_of_week = np.tile(day_of_week, [1, n_nodes, 1]).transpose(
(2, 1, 0)).astype(np.short)
data.append(day_of_week)
data = np.concatenate(data, axis=-1)
x, y = [], []
indices_range = range(self.min_t, self.max_t)
x = [data[t + self.previous_offsets, ...] for t in indices_range]
y = [data[t + self.future_offsets, ...] for t in indices_range]
x = np.stack(arrays=x, axis=0)
y = np.stack(arrays=y, axis=0)
return x, y
def generate_adjacency_matrix(
self, distances_path: Union[str, Path],
sensor_ids_path: Union[str, Path]) -> np.ndarray:
"""
Generates the adjacency matrix of a distance graph using a
thresholded Gaussian filter.
https://github.com/liyaguang/DCRNN/blob/master/scripts/gen_adj_mx.py
Args:
distances_path: The path to the dataframe with real-road
distances between sensors, of form (to, from, dist).
sensor_ids_path: The path to the dataframe containing the IDs
of all the sensors in the PeMS network.
Returns: A numpy array, which is the adjacency matrix generated by
appling a thresholded gaussian kernel filter.
"""
distances_df = pd.read_csv(filepath_or_buffer=distances_path)
sensor_ids = self.read_sensor_ids(sensor_ids_path)
n_nodes = len(sensor_ids)
adjacency_matrix = np.full(shape=(n_nodes, n_nodes), fill_value=np.inf,
dtype=np.float32)
sensor_id_to_idx = {}
for idx, sensor_id in enumerate(sensor_ids):
sensor_id_to_idx[sensor_id] = idx
for _, row in distances_df.iterrows():
src, dst = int(row[0]), int(row[1])
value = row[2]
if src in sensor_id_to_idx and dst in sensor_id_to_idx:
adjacency_matrix[sensor_id_to_idx[src],
sensor_id_to_idx[dst]] = value
distances = adjacency_matrix[~np.isinf(adjacency_matrix)].flatten()
std = distances.std()
adjacency_matrix = np.exp(-np.square(adjacency_matrix / std + 1e-5))
adjacency_matrix[adjacency_matrix < self.normalized_k] = 0.
return adjacency_matrix
@staticmethod
def read_sensor_ids(path: Union[str, Path]) -> List[str]:
"""
Reads the sensor id's from a file containing a list of
comma-separated integers.
Args:
param path: The path to the file.
Returns: A list of IDs.
"""
with open(path, "r") as input_file:
sensor_ids = input_file.read()
return list(map(int, sensor_ids.split(",")))
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] | 2.193205 | 2,443 |
import os
import sciluigi as sl
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# vim: set ts=8 sts=2 sw=2 tw=99 et:
#
# This file is part of AMBuild.
#
# AMBuild 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.
#
# AMBuild 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 AMBuild. If not, see <http://www.gnu.org/licenses/>.
from ambuild2 import nodetypes
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] | 3.587065 | 201 |
import flask
from flask import Flask, jsonify, render_template, url_for, request, redirect, jsonify, send_from_directory
from werkzeug.utils import secure_filename
import pixellib
from pixellib.torchbackend.instance import instanceSegmentation
import os
app = Flask(__name__)
upload_folder = "static"
os.makedirs(upload_folder, exist_ok=True)
app.config["upload_folder"] = upload_folder
ins = instanceSegmentation()
ins.load_model("pointrend_resnet50.pkl")
@app.route("/")
@app.route("/segmentapi", methods = ["GET", "POST"])
@app.route("/segmentfrontend", methods = ["GET", "POST"])
@app.route('/images/<filename>')
if __name__ == "__main__":
app.run(host = "0.0.0.0", port = 5000) | [
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] | 2.751938 | 258 |
#!/usr/bin/env python
# encoding: utf-8
__author__ = 'hasee'
import socket
import json
if __name__ == '__main__':
pass
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] | 2.433962 | 53 |
import pvlib as pv
from datetime import datetime
import pandas.plotting
from analytics.location.path import LinearPath
from analytics.solar_qualities.position import get_solar_position_time_range_track
from analytics.plots.plot_solar_position import plot_elevation_azimuth
from analytics.plots.plot_path import plot_path, plot_path_gmap
from loguru import logger
import pytz
if __name__ == "__main__":
main() | [
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import sys
import os
import torch
import matplotlib.pylab as plt
import numpy as np
from TorchProteinLibrary.FullAtomModel import Angles2Coords
from TorchProteinLibrary.FullAtomModel import Coords2TypedCoords
from TorchProteinLibrary.FullAtomModel import Coords2CenteredCoords
from TorchProteinLibrary.Volume import TypedCoords2Volume
import _Volume
if __name__=='__main__':
num_atoms = 10
atom_coords = []
atom_types = []
for i in range(0,num_atoms):
atom_coords.append(1.0 + np.random.rand(3)*110.0)
atom_types.append(np.random.randint(low=0, high=11))
num_atoms_of_type = torch.zeros(1,11, dtype=torch.int)
offsets = torch.zeros(1,11, dtype=torch.int)
coords = torch.zeros(1, 3*num_atoms, dtype=torch.double)
potential = torch.zeros(1,11,120,120,120, dtype=torch.float, device='cuda')
for i in range(0,120):
potential[0,:,i,:,:] = float(i)/float(120.0) - 0.5
for atom_type in range(0,11):
for i, atom in enumerate(atom_types):
if atom == atom_type:
num_atoms_of_type[0,atom_type]+=1
if atom_type>0:
offsets[0, atom_type] = offsets[0, atom_type-1] + num_atoms_of_type[0, atom_type-1]
current_num_atoms_of_type = [0 for i in range(11)]
for i, r in enumerate(atom_coords):
index = 3*offsets[0, atom_types[i]] + 3*current_num_atoms_of_type[atom_types[i]]
coords[0, index + 0 ] = r[0]
coords[0, index + 1 ] = r[1]
coords[0, index + 2 ] = r[2]
current_num_atoms_of_type[atom_types[i]] += 1
print('Test setting:')
for i, atom_type in enumerate(atom_types):
print('Type = ', atom_type, 'Coords = ', atom_coords[i][0], atom_coords[i][1], atom_coords[i][2])
for i in range(0,11):
print('Type = ', i, 'Num atoms of type = ', num_atoms_of_type[0,i], 'Offset = ', offsets[0,i])
coords.requires_grad_()
potential.requires_grad_()
tc2v = TypedCoords2Volume()
density = tc2v(coords.cuda(), num_atoms_of_type.cuda(), offsets.cuda())
E_0 = torch.sum(density*potential)
E_0.backward()
grad_an = torch.zeros(coords.grad.size(), dtype=torch.double, device='cpu').copy_(coords.grad.data)
grad_num = []
x_1 = torch.zeros(1, 3*num_atoms, dtype=torch.double, device='cpu').requires_grad_()
dx = 0.01
for i in range(0,3*num_atoms):
x_1.data.copy_(coords.data)
x_1.data[0,i] += dx
density = tc2v(x_1.cuda(), num_atoms_of_type.cuda(), offsets.cuda())
E_1 = torch.sum(density*potential)
grad_num.append( (E_1.data - E_0.data)/dx )
fig = plt.figure()
plt.plot(grad_num, 'r.-', label = 'num grad')
plt.plot(grad_an[0,:].numpy(),'bo', label = 'an grad')
plt.legend()
plt.savefig('TestFig/test_backward.png')
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] | 2.281802 | 1,132 |
"""
Test various halo profile properties.
"""
from halomod.concentration import Bullock01Power
from halomod import profiles as pf
from halomod import TracerHaloModel
import pytest
import numpy as np
bullock = Bullock01Power(ms=1e12)
m = np.logspace(10, 15, 100)
r = np.logspace(-2, 2, 20)
class NFWnum(pf.Profile):
"""Test the numerical integration against analytical."""
class NFWnumInf(pf.ProfileInf):
"""Test the numerical integration against analytical."""
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.mark.parametrize(
"profile",
(
pf.NFW,
pf.NFWInf,
pf.CoredNFW,
pf.Einasto,
pf.GeneralizedNFW,
pf.GeneralizedNFWInf,
pf.Hernquist,
pf.Moore,
pf.MooreInf,
pf.PowerLawWithExpCut,
),
)
@pytest.mark.parametrize(
"profile",
(
pf.NFW,
pf.NFWInf,
pf.CoredNFW,
pf.Einasto,
pf.GeneralizedNFW,
pf.GeneralizedNFWInf,
pf.Hernquist,
pf.Moore,
pf.MooreInf,
pf.PowerLawWithExpCut,
),
)
@pytest.mark.parametrize(
"profile",
(
pf.NFW,
pf.NFWInf,
pf.CoredNFW,
pf.Einasto,
pf.GeneralizedNFW,
pf.GeneralizedNFWInf,
pf.Hernquist,
pf.Moore,
pf.MooreInf,
# pf.PowerLawWithExpCut,
),
)
@pytest.mark.parametrize(
"profile",
(
pf.NFW,
# pf.NFWInf, infinite profile can't be normalised by mass.
pf.CoredNFW,
pf.Einasto,
pf.GeneralizedNFW,
# pf.GeneralizedNFWInf,
pf.Hernquist,
pf.Moore,
# pf.MooreInf,
),
)
def test_ukm_low_k(profile):
"""Test that all fourier transforms, when normalised by mass, are 1 at low k"""
k = np.array([1e-10])
m = np.logspace(10, 18, 100)
prof = profile(bullock)
assert np.allclose(prof.u(k, m, norm="m"), 1, rtol=1e-3)
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] | 1.886148 | 1,054 |
# EXECUTION TIME: 4s
# Python 3 ImportError
import sys
sys.path.append('.')
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import src as ya
from sklearn import tree
import graphviz
# prettify plots
plt.rcParams['font.family'] = 'Times New Roman'
sns.set_style({"xtick.direction": "in", "ytick.direction": "in"})
b_sns, g_sns, r_sns, p_sns, y_sns, l_sns = sns.color_palette("muted")
np.random.seed(0)
# fetch data
data_train, data_query = ya.data.getData('Toy_Spiral')
N, D = data_train.shape
###########################################################################
# Visualize Leaf Distributions
###########################################################################
# Supervised Data
X_train, y_train = data_train[:, :-1], data_train[:, -1]
# Decision Tree Classifier Training
clf = tree.DecisionTreeClassifier(criterion='entropy',
max_depth=5,
min_samples_split=5,
min_impurity_decrease=0.05
).fit(X_train, y_train)
###########################################################################
# Grow a Tree - Visualize Leaf Distributions
###########################################################################
# Leave Indexes
leaves_idx = (clf.tree_.children_left == -1) & (clf.tree_.children_right == -1)
# Number of samples at leaves
leaves_values = np.squeeze(clf.tree_.value[leaves_idx], axis=1)
# Leaves Distributions
leaves_dist = np.apply_along_axis(lambda r: r/np.sum(r), 1, leaves_values)
# num_leaves
ncols = 4
nrows = 2
plt.rcParams['figure.figsize'] = [4.0 * ncols, 4.0 * nrows]
num_leaves = nrows * ncols
# check if leaves available for visualization
assert(leaves_dist.shape[0] >= num_leaves)
# matplotlib figure
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
# x-axis bins
bins = np.unique(y_train).astype(int)
# maximum y-axis value
ymax = np.max(leaves_dist)
# for_idx = np.random.choice(len(leaves_dist), num_leaves, False)
for_idx = range(len(leaves_dist))
for j, ax in enumerate(axes.flatten()):
ax.bar(bins, 100*leaves_dist[for_idx[j]],
color=[b_sns, g_sns, r_sns])
ax.set_title('Class histogram of\n$\\mathbf{Leaf\\ %i}$' % (j+1))
ax.set_xlim([0.5, 3.5])
ax.set_ylim([0, ymax*105])
ax.set_xticks(bins)
plt.tight_layout()
fig.savefig('assets/1.3/leaf_cdist.pdf', format='pdf', dpi=300,
transparent=True, bbox_inches='tight', pad_inches=0.01)
###########################################################################
# Visualize Tree - Using `graphviz`
###########################################################################
# dot graph
dot_data = tree.export_graphviz(clf, out_file=None,
feature_names=['X1', 'X2'],
filled=True, rounded=True,
special_characters=True)
graph = graphviz.Source(dot_data)
graph.render("assets/1.3/graph")
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] | 2.444988 | 1,227 |
from kombu import Connection | [
6738,
479,
2381,
84,
1330,
26923
] | 4.666667 | 6 |
# translate.py
# Author: Elias Rubin
import requests
from config import *
def parse_body(body_text):
"""
param: body_text :: string
"""
try:
split_text = body_text.rsplit(" ")
source_lang = split_text[0]
target_lang = split_text[1]
query_string = " ".join(split_text[2:])
except Exception:
query_string = """Message not well formed. Message should be of form:
[source lang] [target lang] [query]"""
source_lang = "la"
target_lang = "en"
return query_string, source_lang, target_lang
def query_translate_api(query_string, source_lang=None, target_lang=None):
"""
param: query string :: string containing the text to translate
param: source_lang :: string identifying the language to translate from
english by default
param: target_lang :: string indentifying the language to translate to
spanish by default
query the google translate API for a translation of the query string.
returns a request.models.Response object
"""
if source_lang is None:
source_lang = 'en'
if target_lang is None:
target_lang = 'es'
try:
source_lang = LANGUAGES[source_lang]
except KeyError:
print "using user input source language: {}".format(source_lang)
pass
try:
target_lang = LANGUAGES[target_lang]
except KeyError:
print "using user input target language: {}".format(target_lang)
pass
payload = {'key': GOOGLE_TRANSLATE_SECRET_KEY,
'q': query_string,
'source': source_lang,
'target': target_lang}
r = requests.get("https://www.googleapis.com/language/translate/v2?",
params=payload)
return r
| [
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220,
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220,
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80,
10354,
12405,
62,
8841,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
10459,
10354,
2723,
62,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
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10354,
2496,
62,
17204,
92,
198,
220,
220,
220,
374,
796,
7007,
13,
1136,
7203,
5450,
1378,
2503,
13,
13297,
499,
271,
13,
785,
14,
16129,
14,
7645,
17660,
14,
85,
17,
35379,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
28,
15577,
2220,
8,
198,
220,
220,
220,
1441,
374,
628
] | 2.385214 | 771 |
from django.core.management.base import BaseCommand
from django.db import transaction
from hours.models import Resource
| [
6738,
42625,
14208,
13,
7295,
13,
27604,
13,
8692,
1330,
7308,
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13,
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1330,
8611,
198,
198,
6738,
2250,
13,
27530,
1330,
20857,
628
] | 4.206897 | 29 |
from z3 import *
from ModelParser import ModelParser
import argparse
from configparser import ConfigParser
import time
from DeplGenerator import DeplGenerator
# A = ['A1','A2','A3']
# D = [2,2,2]
# C = [['A1','A2']]
# S = [[['A1','A2'], ['A3']]]
# H = {}
# num_nodes = 3
HOSTCONF = '/usr/local/riaps/etc/riaps-hosts.conf'
HWSPEC = '/home/riaps/workspace/ResilientDeploymentSolver/hardware-spec.conf'
# Create a "matrix" (list of lists) of integer variables
# Add range constraints
if __name__ == '__main__':
main()
| [
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12,
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13,
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366,
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286,
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8,
286,
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198,
220,
220,
220,
1303,
3060,
2837,
17778,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 2.281746 | 252 |
from fastapi import APIRouter
from app.api.endpoints import user_controller, course_controller
api_router = APIRouter()
api_router.include_router(user_controller.router, prefix="/users", tags=["users"])
api_router.include_router(course_controller.router, prefix="/courses", tags=["courses"])
| [
6738,
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66,
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1600,
15940,
28,
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66,
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8973,
8,
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] | 3.12766 | 94 |
from backend import *
print(search(year = "1918"))
| [
6738,
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796,
366,
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1507,
48774,
201,
198
] | 2.590909 | 22 |
# -*- coding: utf-8 -*-
from django import views
from spot_trend_grid.views import SpotTrendGridView, logger, BatchOrderDetailView, BatchOrderView
| [
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] | 2.606557 | 61 |
from __future__ import absolute_import
from pyrevolve.sdfbuilder import Element
from pyrevolve.sdfbuilder.util import number_format as nf
class BasicBattery(Element):
"""
The rv:battery element, to be included in a robot's plugin
"""
TAG_NAME = 'rv:battery'
def __init__(self, level):
"""
:param level: Initial battery level
:type level: float
:return:
"""
super(BasicBattery, self).__init__()
self.level = level
def render_elements(self):
"""
:return:
"""
elms = super(BasicBattery, self).render_elements()
return elms + [Element(tag_name="rv:level", body=nf(self.level))]
| [
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220,
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220,
220,
220,
220,
220,
1288,
907,
796,
2208,
7,
26416,
47006,
11,
2116,
737,
13287,
62,
68,
3639,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1288,
907,
1343,
685,
20180,
7,
12985,
62,
3672,
2625,
81,
85,
25,
5715,
1600,
1767,
28,
77,
69,
7,
944,
13,
5715,
4008,
60,
198
] | 2.400685 | 292 |
from .cmd import main, version
__version__ = version
| [
6738,
764,
28758,
1330,
1388,
11,
2196,
198,
834,
9641,
834,
796,
2196,
198,
220,
220,
220,
220
] | 3.166667 | 18 |
#!/usr/bin/env python3
import functools
import inspect
import typing as ty
from .exceptions import InvalidArgumentValueException
def validate_range(parameter: str, minimum: ty.Union[int, float],
maximum: ty.Union[int, float]) -> ty.Callable:
"""
Validate a parameter range.
Args:
parameter: Parameter to validate
minimum: Minimum limit.
maximum: Maximum limit.
Returns:
The function decorated.
"""
return decorator_
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] | 2.686486 | 185 |
"""This sub command uploads the resource type to CloudFormation.
Projects can be created via the 'init' sub command.
"""
import logging
from .project import Project
LOG = logging.getLogger(__name__)
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import sys
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
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from .iode import * | [
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] | 3.166667 | 6 |
message = "Hello python world"
print(message)
message = "Hello python crash course world"
print(message) | [
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from __future__ import with_statement # this is to work with python2.5
from pyps import workspace
from os import remove
import pypips
filename="pragma"
pypips.delete_workspace(filename)
with workspace(filename+".c", parents=[], driver="sse", name=filename) as w:
m=w[filename]
m.suppress_dead_code()
m.display()
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] | 3.048077 | 104 |
#!/usr/bin/env python
import argparse
import sys
import os
import time
# stackoverflow.com/questions/230751/how-to-flush-output-of-print-function
# https://opensource.com/article/19/7/parse-arguments-python
options = getOptions()
print(options)
img_small = options.local + "small.png"
# if it is cached, let's quit
if os.path.isfile(img_small):
print("\n cached \n")
quit()
#https://stackoverflow.com/questions/53657215/running-selenium-with-headless-chrome-webdriver
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
chrome_options = Options()
chrome_options.add_argument("user-agent=[Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.56 Safari/536.5]")
chrome_options.add_argument("--disable-extensions")
chrome_options.add_argument("window-size=1920,10080") # very large so it won't crop my big image
# driver.set_window_size(1920, 1080)
chrome_options.add_argument("--disable-gpu")
chrome_options.add_argument("--verbose")
#chrome_options.add_argument("--no-sandbox") # linux only
if options.open != "true":
chrome_options.add_argument("--headless") # if headless, I need a window size ...
# chrome_options.headless = True # also works
# C:/python3/python.exe C:/_git_/__NIC__/run/php/projects/BLB/get.strongs.py --remote=https://www.blueletterbible.org/lang/Lexicon/Lexicon.cfm?strongs=H1234 --local=S:/project-BLB/2021-04/strongs/hebrew/1234/ --sleep=250 --open=true
# downloaded from chromium.org, version 89
# chromedriver.chromium.org/downloads
driver = webdriver.Chrome(options=chrome_options, executable_path='C:/chromedriver/chromedriver.exe')
from selenium.common.exceptions import NoSuchElementException
driver.get(options.remote)
# print(driver.page_source.encode("utf-8"))
time.sleep(options.sleep/1000)
if check_exists_by_id('agree-button'):
driver.find_element_by_id('agree-button').click()
time.sleep(3*options.sleep/1000)
print(driver.execute_script("return document.title;"))
os.makedirs(options.local, exist_ok=True)
# options.local is a path
html_file = options.local + "page.html"
html = str(driver.page_source.encode("utf-8"))
f = open(html_file, 'w')
f.write(html)
f.close()
img_small = options.local + "small.png"
# stackoverflow.com/questions/17361742/download-image-with-selenium-python
with open(img_small, 'wb') as file:
file.write(driver.find_element_by_id('lexImage').screenshot_as_png)
# file.write(driver.find_element_by_xpath('/html/body/div[1]/div[5]/div[2]/table[1]/tbody/tr/td[1]/a/div').screenshot_as_png)
if check_exists_by_id('moreTG'):
driver.find_element_by_id('moreTG').click()
time.sleep(3*options.sleep/1000)
img_full = options.local + "full.png"
with open(img_full, 'wb') as file:
file.write(driver.find_element_by_id('lexImage').screenshot_as_png)
# lexPronunc
# <div id="lexPronunc" data-pronunc="BA4BC936634F8B96EACD2BAB19093EF729C96BDE619B85D5DE79CB1C35C07E95B32332529F29E93D2869EDA61A23B204F8D14843783306"><img class="show-for-medium parse-speaker" id="pronunciationSpeaker" src="/assets/images/audio/speaker3_a.svg" width="31" height="25" /><span class="hide-for-medium">Listen</span></div>
# https://www.blueletterbible.org/lang/lexicon/lexPronouncePlayer.cfm?skin=BA4BC936634F8B96EACD2BAB19093EF729C96BDE619B85D5DE79CB1C35C07E95B32332529F29E93D2869EDA61A23B204F8D14843783306
# SAVE AS MP3
driver.quit()
quit()
# https://selenium-python.readthedocs.io/
# https://medium.com/@erika_dike/how-to-download-100-pictures-from-a-site-with-selenium-e23b7ecacb85
# https://towardsdatascience.com/advanced-web-scraping-concepts-to-help-you-get-unstuck-17c0203de7ab
# https://stackoverflow.com/questions/17361742/download-image-with-selenium-python
# https://towardsdatascience.com/hierarchical-clustering-an-application-to-world-currencies-a24c12940a7e
# https://stackoverflow.com/questions/17361742/download-image-with-selenium-python
# https://webbot.readthedocs.io/en/latest/webbot.html#selenium.webdriver.Chrome.implicitly_wait
from webbot import Browser
web = Browser()
web.go_to(options.remote)
# web.implicitly_wait(options.remote/1000)
time.sleep(options.remote/1000)
print(web.get_title())
html = str(get_page_source())
f = open(options.local, 'w')
f.write(html)
f.close()
quit()
# https://stackoverflow.com/questions/64927909/failed-to-read-descriptor-from-node-connection-a-device-attached-to-the-system
# https://stackoverflow.com/questions/65080685/usb-usb-device-handle-win-cc1020-failed-to-read-descriptor-from-node-connectio/65134639#65134639
# https://stackoverflow.com/questions/59515319/web-scraping-using-webbot
# In Chrome I followed chrome://flags and enabled Enable new USB backend option, after that the log message disappeared –
# https://www.toolsqa.com/selenium-webdriver/selenium-headless-browser-testing/
#https://docs.python.org/3.7/library/argparse.html
import argparse
# create parser
parser = argparse.ArgumentParser()
# https://opensource.com/article/19/7/parse-arguments-python
# add arguments to the parser
parser.add_argument("-r", "--remote")
parser.add_argument("-l", "--local")
parser.add_argument("-s", "--sleep")
# parse the arguments
args = parser.parse_args()
# https://www.geeksforgeeks.org/print-lists-in-python-4-different-ways/
print(*args, sep = "\n")
quit()
from webbot import Browser
web = Browser()
web.go_to('google.com')
get_title()
# //https://github.com/segmentio/nightmare
# // https://stackoverflow.com/questions/2910221/how-can-i-login-to-a-website-with-python/28628514#28628514 # python webbot
# // https://github.com/ariya/phantomjs/issues/13923
# // https://stackoverflow.com/questions/36481481/casperjs-memory-exhausted
# // var casper = require('casper').create();
# var casper = require('casper').create({
# verbose : true,
# logLevel : "info",
# pageSettings : {
# loadImages : false, // do not load images
# loadPlugins : false // do not load NPAPI plugins (Flash, Silverlight, ...)
# }
# });
# var fs = require('fs');
# var utils = require('utils');
# var x = require("casper").selectXPath;
# // casper.userAgent("Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)");
# casper.userAgent('Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.56 Safari/536.5');
# // http://docs.casperjs.org/en/latest/cli.html
# // console.dir(casper.cli);
# // utils.dump(casper.cli);
# // casper.run();
# // casper.start('https://jcb.lunaimaging.com/luna/servlet/view/all', function() {
# // this.echo(this.getTitle());
# // });
# var remote = casper.cli.raw.get('remote');
# console.log("\n\n" + remote + "\n\n");
# casper.start(remote, function() {
# this.echo(this.getTitle());
# });
# var sleep = casper.cli.raw.get('sleep');
# console.log("\n\n" + sleep + "\n\n");
# casper.wait(sleep);
# var local = casper.cli.raw.get('local');
# console.log("\n\n" + local + "\n\n");
# casper.then(function() {
# // casper.capture("Image.png");
# var content = this.evaluate(function() {
# return document;
# });
# // this.echo(content.all[0].outerHTML);
# page = content.all[0].outerHTML;
# fs.write(local, page, "wb");
# });
# casper.run();
# // casperjs get.remote.html.js --remote=https://jcb.lunaimaging.com/luna/servlet/view/all?os=0 --local=Q:/project-MAPS/2021-04/jcb/pages/0001/index.html --sleep=250
# // "https://jcb.lunaimaging.com/media/Size2/JCBMAPS-3-NA/1065/JRB001.jpg"
# // change to Size4 ... 1 to 4 works
# // extra-large is ZIP ... JRB0017659538119963068053.zip
# // no jp2?
# // https://www.davidrumsey.com/rumsey/download.pl?image=/D5005/6388007.sid
# // https://www.extensis.com/support/geoviewer-9
# // https://jcb.lunaimaging.com/luna/servlet/iiif/JCBMAPS~3~3~3593~101754/info.json
# // C:\_git_\__NIC__\run\php\projects\MAPS>casperjs jcb.js --remote='https://jcb.lunaimaging.com/luna/servlet/view/all?os=0' --local='Q:/project-MAPS/2021-04/jcb/pages/0001/index.html'
# // C:\_git_\__NIC__\run\php\projects\MAPS>casperjs jcb.js --remote=https://jcb.lunaimaging.com/luna/servlet/view/all?os=0 --local=Q:/project-MAPS/2021-04/jcb/pages/0001/index.html
# // CNTRL-SHIFT F ... exportMedia
# // http://docs.casperjs.org/en/latest/quickstart.html
# // Run it (on windows):
# // C:\casperjs\bin> casperjs.exe jcb.js
# // ThumbnailViewContainer | [
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# -*- coding: utf-8 -*-
"""
Created on Sat Dec 4 11:19:02 2021
@author: chris
"""
# part 1
with open('input.txt') as f:
lines = f.read().splitlines() # doesn't read \n
reportSum = [0] * len(lines[0])
gammaRateArray = [0] * len(lines[0])
epsilonRateArray = [0] * len(lines[0])
for line in lines:
for i, bitStr in enumerate(line):
bit = int(bitStr)
reportSum[i] = reportSum[i] + ((bit ^ 0) - (bit ^ 1))
for i,bit in enumerate(reportSum):
gammaRateArray[i] = (bit/abs(bit) + 1) / 2
epsilonRateArray[i] = (bit/abs(bit) * -1 + 1) / 2
gammaRateArray.reverse()
epsilonRateArray.reverse()
gammaRate = 0
epsilonRate = 0
for i in range(len(gammaRateArray)):
gammaRate = gammaRate + gammaRateArray[i] * (2 ** i)
epsilonRate = epsilonRate + epsilonRateArray[i] * (2 ** i)
print(gammaRate * epsilonRate)
# part 2
import pandas as pd
df = pd.read_csv('input.txt', dtype = str)
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] | 2.003752 | 533 |
"""Test call for testing web retrieve, don't call automatically, as it does a
real http GET."""
import unittest
from unittest import TestCase
from src.utils import Rut
from src import web
class TestGetPage(TestCase):
"""Get a real page using dummy_rut."""
def test_client(self):
"""Simple get and parse the bank's page."""
raw_page = web.WebPageDownloader().retrieve(self.dummy_rut)
web.Parser.parse(raw_page)
if __name__ == '__main__':
unittest.main()
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] | 2.80226 | 177 |
from flask import Flask, render_template, request, make_response, redirect, url_for
from blog import Config, User, Comment, Post
app = Flask(__name__)
@app.route("/", methods=["POST", "GET"])
@app.route("/admin", methods=["POST", "GET"])
if __name__ == "__main__":
Config.setup()
app.run(debug=True) | [
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] | 2.810811 | 111 |
# NEON AI (TM) SOFTWARE, Software Development Kit & Application Development System
# All trademark and other rights reserved by their respective owners
# Copyright 2008-2021 Neongecko.com Inc.
# BSD-3
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. 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.
# 3. Neither the name of the copyright holder 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 json
import os
import sys
import unittest
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from neon_api_proxy.alpha_vantage_api import AlphaVantageAPI
VALID_COMPANY_NAME = "Alphabet"
VALID_COMPANY_SYMBOL = "GOOGL"
INVALID_COMPANY_NAME = "Neon Gecko"
INVALID_COMPANY_SYMBOL = "NEONGECKO"
if __name__ == '__main__':
unittest.main()
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1,
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62,
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1,
628,
198,
198,
361,
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12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3.345219 | 617 |
import FWCore.ParameterSet.Config as cms
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] | 2.705882 | 17 |
from functools import reduce
from operator import mul
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
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] | 3.75 | 40 |
import setuptools
from os import path
here = path.abspath(path.dirname(__file__))
with open(path.join(here, "README.md")) as f:
long_description = f.read()
with open(path.join(here, 'requirements.txt')) as f:
install_requirements = f.read().splitlines()
with open(path.join(here, 'test-requirements.txt')) as f:
test_requirements = f.read().splitlines()
setuptools.setup(
name="plantuml-markdown",
version="3.1.3",
author="Michele Tessaro",
author_email="[email protected]",
description="A PlantUML plugin for Markdown",
long_description=long_description,
long_description_content_type="text/markdown",
keywords=['Markdown', 'typesetting', 'include', 'plugin', 'extension'],
url="https://github.com/mikitex70/plantuml-markdown",
#packages=setuptools.find_packages(exclude=['test']),
py_modules=['plantuml_markdown'],
install_requires=install_requirements,
tests_require=test_requirements,
classifiers=[
"Programming Language :: Python",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Development Status :: 5 - Production/Stable",
"Topic :: Software Development :: Documentation",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Text Processing :: Filters",
"Topic :: Text Processing :: Markup :: HTML"
],
)
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220,
220,
220,
366,
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46267,
7904,
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3401,
5028,
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198,
220,
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220,
220,
220,
220,
220,
366,
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7904,
8255,
28403,
7904,
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1010,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
366,
33221,
7904,
8255,
28403,
7904,
2940,
929,
7904,
11532,
1,
198,
220,
220,
220,
16589,
198,
8,
198
] | 2.744186 | 516 |
import charlieplex
from machine import Pin, I2C
from time import sleep
i2c = I2C(scl=Pin(22), sda=Pin(21))
display = charlieplex.Matrix(i2c)
display.fill(0)
x = 0
y = 0
while True:
display.pixel(y, x, 255)
x += 1
print(x, y)
if( x > 7):
x = 0
y += 1
if(y>7):
display.fill(0)
x = 0
y = 0
sleep(0.5)
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3993,
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8,
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220,
220
] | 1.867347 | 196 |
import MeCab
import mecabpy
import mecabpy.ipa
class TestNode:
"""mecabpy.ipa.Node のテスト
"""
INPUT_TEXT = '太郎はこの本を田中を見た女性に渡した。'
def test_attr_surface(self):
"""NodeWrapper.surface のテスト
"""
surface = '見'
node = mecabpy.ipa.Node(surface=surface,
feature_obj=mecabpy.ipa.Feature(word_class0='動詞',
word_class1='自立',
word_class2=None,
word_class3=None,
group='一段',
form='連用形',
dict_form='見る',
kana='ミ',
phonetic_kana=None))
assert node.surface == surface
def test_attr_feature(self):
"""NodeWrapper.feature のテスト
"""
feature = mecabpy.ipa.Feature(word_class0='動詞',
word_class1='自立',
word_class2=None,
word_class3=None,
group='一段',
form='連用形',
dict_form='見る',
kana='ミ',
phonetic_kana=None)
node = mecabpy.ipa.Node(surface='見', feature_obj=feature)
assert node.feature == feature
class TestParseToNode:
"""mecabpy.ipa.parse_to_node のテスト
"""
INPUT_TEXT = '太郎はこの本を田中を見た女性に渡した。'
OUTPUT_WORDS = ('太郎', 'は', 'この', '本', 'を', '田中', 'を', '見', 'た', '女性', 'に', '渡し', 'た', '。', '')
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] | 1.352201 | 1,431 |
from bulls_n_cows import*
TEST_GUESSES = [[1,2, 3, 4], [5, 2, 3, 4], [7, 6, 5, 4], [0, 9, 8, 5],
[2, 4, 6, 8], [1, 3, 5, 7], [1, 2, 0, 9] ]
TEST_SECRET = [[1,9,8, 7],[2,4,6, 7], [1,2,0, 9],[7,6,5, 4]]
def test_count_bulls_and_cows():
''' Function test_count_bulls_and_cows
Input: None.
Returns: Number of failing test conditions for cow/bull sequences
Do: Test various cow/bull sequences to ensure those counters
are working as expected. Key cases:0 cows, 0 bulls;
4 cows, 0 bulls; 4 bulls, 0 cows, 2 cows, 2 bulls
'''
num_failed = 0
test_bulls, test_cows = count_bulls_and_cows([1, 2, 3, 4], [0, 5, 8, 9])
if test_bulls == 0 and test_cows == 0:
print('SUCCESS! \n')
else:
print('FAIL \n')
num_failed += 1
test_bulls, test_cows = count_bulls_and_cows([1, 2, 3, 4], [4, 3, 2, 1])
if test_bulls == 0 and test_cows == 4:
print('SUCCESS! \n')
else:
print('FAIL \n')
num_failed += 1
test_bulls, test_cows = count_bulls_and_cows([1, 2, 3, 4], [1, 2, 3, 4])
if test_bulls == 4 and test_cows == 0:
print('SUCCESS! \n')
else:
print('FAIL \n')
num_failed += 1
test_bulls, test_cows = count_bulls_and_cows([1, 2, 3, 4], [1, 2, 4, 3])
if test_bulls == 2 and test_cows == 2:
print('SUCCESS! \n')
else:
print('FAIL \n')
num_failed += 1
return num_failed
def auto_play_game(secret_code, guess_book):
''' Function auto_play_game
Input: secret_code (list of digits),
guess_book (dictionary of guess history)
Returns: True if auto-player a winner; False otherwise
Do: Automate the playing of Bulls and Cows for regression
testing. Instead of using interactive input from stdin, this
function uses test data fed directly to the function to simulate
an entire "systems test" and complete game flow
Concept: instead of guess = input(...), now using
guess = TEST_GUESSES[i]
'''
count = 1
while count < 7:
print("guess: " + str(count))
guess = TEST_GUESSES[count]
num_bulls, num_cows = count_bulls_and_cows(secret_code, guess)
guess_book = create_dictionary(num_bulls, num_cows, guess, count)
count += 1
for key, value in guess_book.items():
print("Your guess history:\n", key, 'is', value)
if num_bulls == len(guess):
print("Auto-player is a winner")
return True
elif num_bulls != 4 and count == 7:
print("Auto-player lost (this time human)")
return False
def test_regression_bull_cow(secret_code):
''' Function test_regression_bull_cow
Input: secret_code: secret to test with (the one we're "cracking").
Returns: None
Do: Automatically exercise and test the entire bulls n cows system
by calling auto_play_game() multiple times with both "winning" and
"losing" data. Printed output can then be "diff'd" and examined either
manually or automatically via tool support
Example: code is our test data, and autoplay instead of interactive
secret_code = TEST_SECRET[0]
guess_book = create_guessbook(7)
result = auto_play_game(secret_code, guess_book)
'''
for i in range(len(TEST_SECRET)):
secret_code = TEST_SECRET[i]
guess = TEST_GUESSES[0]
num_bulls, num_cows = count_bulls_and_cows(secret_code, guess)
count = 0
guess_book = create_dictionary(num_bulls, num_cows, guess, count)
result = auto_play_game(secret_code, guess_book)
main()
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12417,
3419,
198
] | 2.22515 | 1,670 |
#!/usr/bin/env python3
import math
import os
import random
import re
import sys
# Complete the maxSubsetSum function below.
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
n = int(input())
arr = list(map(int, input().rstrip().split()))
res = maxSubsetSum(arr)
fptr.write(str(res) + '\n')
fptr.close()
| [
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] | 2.4375 | 144 |
from datetime import datetime
from ems.models.ambulances.ambulance import Ambulance
| [
6738,
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8079,
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13,
27530,
13,
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377,
1817,
13,
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377,
590,
1330,
12457,
377,
590,
628
] | 3.307692 | 26 |
from django.apps import AppConfig
| [
6738,
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13,
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1330,
2034,
16934,
628
] | 3.888889 | 9 |
# -*- coding: utf-8 -*-
translations = {
# Days
'days': {
0: 'sunnudagur',
1: 'mánadagur',
2: 'týsdagur',
3: 'mikudagur',
4: 'hósdagur',
5: 'fríggjadagur',
6: 'leygardagur'
},
'days_abbrev': {
0: 'sun',
1: 'mán',
2: 'týs',
3: 'mik',
4: 'hós',
5: 'frí',
6: 'ley'
},
# Months
'months': {
1: 'januar',
2: 'februar',
3: 'mars',
4: 'apríl',
5: 'mai',
6: 'juni',
7: 'juli',
8: 'august',
9: 'september',
10: 'oktober',
11: 'november',
12: 'desember',
},
'months_abbrev': {
1: 'jan',
2: 'feb',
3: 'mar',
4: 'apr',
5: 'mai',
6: 'jun',
7: 'jul',
8: 'aug',
9: 'sep',
10: 'okt',
11: 'nov',
12: 'des',
},
# Units of time
'year': ['{count} ár', '{count} ár'],
'month': ['{count} mánaður', '{count} mánaðir'],
'week': ['{count} vika', '{count} vikur'],
'day': ['{count} dag', '{count} dagar'],
'hour': ['{count} tími', '{count} tímar'],
'minute': ['{count} minutt', '{count} minuttir'],
'second': ['{count} sekund', '{count} sekundir'],
# Relative time
'ago': '{time} síðan',
'from_now': 'um {time}',
'after': '{time} aftaná',
'before': '{time} áðrenn',
# Ordinals
'ordinal': '.',
# Date formats
'date_formats': {
'LTS': 'HH:mm:ss',
'LT': 'HH:mm',
'LLLL': 'dddd D. MMMM, YYYY HH:mm',
'LLL': 'D MMMM YYYY HH:mm',
'LL': 'D MMMM YYYY',
'L': 'DD/MM/YYYY',
},
}
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] | 1.610536 | 1,063 |
#!/usr/bin/env python
"""Databench command line executable. Run to create a server that serves
the analyses pages and runs the python backend."""
import os
import sys
import signal
import random
import logging
import argparse
import werkzeug.serving
from . import __version__ as DATABENCH_VERSION
def main():
"""Entry point to run databench."""
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--version', action='version',
version='%(prog)s '+DATABENCH_VERSION)
parser.add_argument('--log', dest='loglevel', default="NOTSET",
help='set log level')
parser.add_argument('--host', dest='host',
default=os.environ.get('HOST', 'localhost'),
help='set host for webserver')
parser.add_argument('--port', dest='port',
type=int, default=int(os.environ.get('PORT', 5000)),
help='set port for webserver')
parser.add_argument('--with-coverage', dest='with_coverage',
default=False, action='store_true',
help='create code coverage statistics')
delimiter_args = parser.add_argument_group('delimiters')
delimiter_args.add_argument('--variable_start_string',
help='delimiter for variable start')
delimiter_args.add_argument('--variable_end_string',
help='delimiter for variable end')
delimiter_args.add_argument('--block_start_string',
help='delimiter for block start')
delimiter_args.add_argument('--block_end_string',
help='delimiter for block end')
delimiter_args.add_argument('--comment_start_string',
help='delimiter for comment start')
delimiter_args.add_argument('--comment_end_string',
help='delimiter for comment end')
args = parser.parse_args()
# coverage
cov = None
if args.with_coverage:
import coverage
cov = coverage.coverage(
data_suffix=str(int(random.random()*999999.0)),
source=['databench'],
)
cov.start()
# this is included here so that is included in coverage
from .app import App
# log
if args.loglevel != 'NOTSET':
print 'Setting loglevel to '+args.loglevel+'.'
logging.basicConfig(level=getattr(logging, args.loglevel))
# delimiters
delimiters = {
'variable_start_string': '[[',
'variable_end_string': ']]',
}
if args.variable_start_string:
delimiters['variable_start_string'] = args.variable_start_string
if args.variable_end_string:
delimiters['variable_end_string'] = args.variable_end_string
if args.block_start_string:
delimiters['block_start_string'] = args.block_start_string
if args.block_end_string:
delimiters['block_end_string'] = args.block_end_string
if args.comment_start_string:
delimiters['comment_start_string'] = args.comment_start_string
if args.comment_end_string:
delimiters['comment_end_string'] = args.comment_end_string
print '--- databench v'+DATABENCH_VERSION+' ---'
logging.info('host='+str(args.host)+', port='+str(args.port))
logging.info('delimiters='+str(delimiters))
# handle external signal to terminate nicely (used in tests)
signal.signal(signal.SIGTERM, sig_handler)
# not supported on Windows:
if hasattr(signal, 'SIGUSR1'):
signal.signal(signal.SIGUSR1, sig_handler)
@werkzeug.serving.run_with_reloader
return reloader()
if __name__ == '__main__':
main()
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] | 2.308405 | 1,618 |
from distutils.core import setup
setup(
name = 'Hexy',
packages = ['hexy'],
version = '1.4.4',
license='MIT',
description = 'A library that makes working with a hexagonal lattice easier.',
author = 'Norbu Tsering',
author_email = '[email protected]',
url = 'https://github.com/redft/hexy',
download_url = 'https://github.com/RedFT/Hexy/archive/1.4.3.tar.gz',
keywords = ['hexy', 'coordinate', 'hexagon', 'hexagonal'],
install_requires = ["numpy >= 1.15.0"],
extras_require ={
'tests': [
"atomicwrites==1.1.5",
"attrs==18.1.0",
"funcsigs==1.0.2",
"more-itertools==4.3.0",
"pluggy==0.7.1",
"py==1.5.4",
"pytest==3.7.0",
"six==1.11.0",
]
},
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'Topic :: Software Development :: Libraries :: Python Modules',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3',
],
)
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] | 2.116364 | 550 |
# Copyright 2019 The FATE Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://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 copy
import inspect
import torch.optim
from federatedml.nn.backend.pytorch.custom import optimizer as custom_optimizers
from federatedml.util import LOGGER
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] | 3.62963 | 216 |
import json
import re
| [
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] | 2.454545 | 11 |
from django.contrib.auth import authenticate, login, logout
from django.http import HttpResponse
from djoser.serializers import UserSerializer
from rest_framework import viewsets, permissions, status
from rest_framework.decorators import action
from rest_framework.response import Response
from djoser.views import SetPasswordView as JoserSetPasswordView
from apps.user.models import User
from .serializers import SessionSerializer, UserSessionSerializer
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] | 4.126126 | 111 |
# This is your "setup.py" file.
# See the following sites for general guide to Python packaging:
# * `The Hitchhiker's Guide to Packaging <http://guide.python-distribute.org/>`_
# * `Python Project Howto <http://infinitemonkeycorps.net/docs/pph/>`_
from setuptools import setup, find_packages
import sys, os
#from Cython.Build import cythonize
from setuptools.extension import Extension
here = os.path.abspath(os.path.dirname(__file__))
README = open(os.path.join(here, 'README.rst')).read()
NEWS = open(os.path.join(here, 'NEWS.rst')).read()
version = '0.1'
install_requires = [
# List your project dependencies here.
# For more details, see:
# http://packages.python.org/distribute/setuptools.html#declaring-dependencies
# Packages with fixed versions
# "<package1>==0.1",
# "<package2>==0.3.0",
# "nose", "coverage" # Put it here.
]
tests_requires = [
# List your project testing dependencies here.
]
dev_requires = [
# List your project development dependencies here.\
]
dependency_links = [
# Sources for some fixed versions packages
#'https://github.com/<user1>/<package1>/archive/master.zip#egg=<package1>-0.1',
#'https://github.com/<user2>/<package2>/archive/master.zip#egg=<package2>-0.3.0',
]
#Cython extension
#TOP_DIR="/home/eugeneai/Development/codes/NLP/workprog/tmp/link-grammar"
#LG_DIR="link-grammar"
#LG_LIB_DIR=os.path.join(TOP_DIR,LG_DIR,".libs")
#LG_HEADERS=os.path.join(TOP_DIR)
ext_modules=[
# Extension("icc.modelstudio.cython_module",
# sources=["src/./icc.modelstudio/cython_module.pyx"],
# libraries=["gdal"],
# )
]
setup(
name='icc.modelstudio',
version=version,
description="A GUI program for control of microbioma modeling.",
long_description=README + '\n\n' + NEWS,
# Get classifiers from http://pypi.python.org/pypi?%3Aaction=list_classifiers
# classifiers=[c.strip() for c in """
# Development Status :: 4 - Beta
# License :: OSI Approved :: MIT License
# Operating System :: OS Independent
# Programming Language :: Python :: 2.6
# Programming Language :: Python :: 2.7
# Programming Language :: Python :: 3
# Topic :: Software Development :: Libraries :: Python Modules
# """.split('\n') if c.strip()],
# ],
keywords='GUI naturl modeling dataflow GTK+',
author='Evgeny Cherkashin',
author_email='[email protected]',
url='https://github.com/NGS-ISC/model-studio',
license='Apache-2.0',
packages=find_packages("src"),
package_dir = {'': "src"},
namespace_packages = ['icc'],
include_package_data=True,
zip_safe=False,
install_requires=install_requires,
dependency_links = dependency_links,
extras_require={
'tests': tests_requires,
'dev': dev_requires,
},
test_suite='tests',
entry_points={
'console_scripts':
['icc.modelstudio=icc.modelstudio:main']
},
#ext_modules = cythonize(ext_modules),
#test_suite = 'nose.collector',
#setup_requires=['nose>=1.0','Cython','coverage']
)
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] | 2.525081 | 1,236 |
import cairo
import math
| [
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] | 3.571429 | 7 |
#import math
import os
w1 = [0]*64
s0 = [0]*64
s1 = [0]*64
for i in range (64):
w1[i] = [0]*32
s0[i] = [0]*32
s1[i] = [0]*32
w1hex= [0x0000c020, 0x8e195e82, 0x5806a5ac, 0x9467a653, 0x00fe9de6, 0xf0c34b81, 0x6f230600, 0x00000000,
0x00000000, 0x364c0811, 0x8ea34017, 0xb68edc07, 0x9dd9e834, 0xfbf4ced0, 0x9f23a2b2, 0x8d6fda4a]
eng = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P']
rus = ['а','б','в','г','д','е','ё','ж','з','и','к','л','м','н','о','п','р','с','т','у','ф','х','ц','ч','ш','щ','ъ','ы','ь','э','ю','я']
for j in range(16):
for i in range (32):
w1[j][i] = str(eng[j])+str(rus[i])
save ('w', j, i, w1[j][i])
for i in range (0, 22):
s0[j][i] = w1[j][r7(i)]+'^'+w1[j][r18(i)]+'^'+w1[j][shr3(i)]
s1[j][i] = w1[j][r17(i)]+'^'+w1[j][r19(i)]+'^'+w1[j][shr10(i)]
for i in range (22, 29):
s0[j][i] = w1[j][r7(i)]+'^'+w1[j][r18(i)]+'^'+w1[j][shr3(i)]
s1[j][i] = w1[j][r17(i)]+'^'+w1[j][r19(i)]
for i in range (29, 32):
s0[j][i] = w1[j][r7(i)]+'^'+w1[j][r18(i)]
s1[j][i] = w1[j][r17(i)]+'^'+w1[j][r19(i)]
print('start')
for j in range (16, 64):
print('start '+ str(j))
for i in range (32):
w1[j][i] = '{'+w1[j-16][i] +'+'+ s0[j-15][i] +'+'+ w1[j-7][i] +'+'+ s1[j-2][i]+'}';
save ('w', j, i, w1[j][i])
for i in range (0, 22):
s0[j][i] = w1[j][r7(i)]+'^'+w1[j][r18(i)]+'^'+w1[j][shr3(i)]
s1[j][i] = w1[j][r17(i)]+'^'+w1[j][r19(i)]+'^'+w1[j][shr10(i)]
for i in range (22, 29):
s0[j][i] = w1[j][r7(i)]+'^'+w1[j][r18(i)]+'^'+w1[j][shr3(i)]
s1[j][i] = w1[j][r17(i)]+'^'+w1[j][r19(i)]
for i in range (29, 32):
s0[j][i] = w1[j][r7(i)]+'^'+w1[j][r18(i)]
s1[j][i] = w1[j][r17(i)]+'^'+w1[j][r19(i)]
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] | 1.449011 | 1,314 |
import csp
rgb = ['R', 'G', 'B','O']#,'White','Gray','Y','Purple','Brown','seafoam','T','Kale']
d2 = { 'A' : rgb, 'B' : rgb, 'C' : ['R'], 'D' : rgb,}
domains = {
'SW': ['G'],
'SE': rgb,
'L': rgb,
'EE': rgb,
'W': rgb,
'WM': rgb,
'EM': rgb,
'NW': rgb,
'YH': rgb,
'NE': rgb,
'S': rgb,
}
variables = domains.keys()
neighbors = {
'SW': ['SE','WM','W'],
'SE': ['SW','L','EE','EM','WM'],
'L': ['SE','EE'],
'EE': ['SE','EM','L'],
'W': ['SW','WM','NW'],
'WM': ['SW','SE','W','EM','NW'],
'EM': ['WM','NW','YH','SE','EE'],
'NW': ['W','WM','S','NE','YH','EM'],
'YH': ['NW','EM','NE'],
'NE': ['S','NW','YH'],
'S': ['NE','NW'],
}
v2 = d2.keys()
n2 = {'A' : ['B', 'C', 'D'],
'B' : ['A', 'C', 'D'],
'C' : ['A', 'B'],
'D' : ['A', 'B'],}
c2 = csp.CSP(v2, d2, n2, constraints)
c2.label = 'Really Lame'
UK=csp.CSP(variables,domains,neighbors,constraints)
UK.label = "Map of the Uk"
myCSPs = [
{
'csp': UK,
# 'select_unassigned_variable': csp.mrv,
# 'order_domain_values': csp.lcv,
# 'inference': csp.mac,
# 'inference': csp.forward_checking,
}
,
{
'csp' : UK,
'select_unassigned_variable': csp.mrv,
# 'order_domain_values': csp.lcv,
# 'inference': csp.mac,
# 'inference': csp.forward_checking,
},
{
'csp' : UK,
# 'select_unassigned_variable': csp.mrv,
'order_domain_values': csp.lcv,
# 'inference': csp.mac,
# 'inference': csp.forward_checking,
},
{
'csp' : UK,
# 'select_unassigned_variable': csp.mrv,
# 'order_domain_values': csp.lcv,
'inference': csp.mac,
# 'inference': csp.forward_checking,
},
{
'csp' : UK,
# 'select_unassigned_variable': csp.mrv,
# 'order_domain_values': csp.lcv,
# 'inference': csp.mac,
'inference': csp.forward_checking,
},
{
'csp' : UK,
#'select_unassigned_variable': csp.mrv,
#'order_domain_values': csp.lcv,
#'inference': csp.mac,
# 'inference': csp.forward_checking,
}
]
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] | 1.814203 | 1,211 |
"""GUI for weather report api """
from tkinter import *
import json
import requests
root = Tk()
root.title("Temperature Finder")
root.geometry('500x300')
root.minsize(150, 150)
root.maxsize(1200, 1200)
city = Label(text = "Enter a city name to check Temperature: ")
cityValue = StringVar() #type of data
userEntry = Entry(root) #entered data
userEntry.grid(row = 0, column = 1)
city.grid(row = 0)
Button(text = "submit", command = getTemperature).grid(column = 1)
root.mainloop()
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] | 3.024691 | 162 |
#!/usr/bin/env python
"""
Solution to Day 1 - Puzzle 2 of the Advent Of Code 2015 series of challenges.
--- Day 1: Not Quite Lisp ---
An opening parenthesis represents an increase in floor and a closing parenthesis represents a decrease in floor.
After taking a 7000 character long input string of assorted parenthesis, determine the first time that Santa arrives
at a specified floor.
-----------------------------
Author: Luke "rookuu" Roberts
"""
inputData = raw_input("Puzzle Input: ")
floor = 0
index = 0
floorRequired = int(raw_input("What floor are we looking for? "))
# Used to check the length of the input string.
# print len(inputData)
for char in inputData:
if char == "(":
floor += 1
elif char == ")":
floor -= 1
index += 1
if floor == floorRequired:
print "The first time Santa visits floor " + str(floorRequired) + " is on instruction number " + str(index)
break
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] | 3.346429 | 280 |
from ... import gvars
from .parser import aead_reader
from ..base.server import ProxyBase
from ..shadowsocks.parser import addr_reader
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#!/bin/python3
import math
import os
import random
import re
import sys
# NOTE: This only passes the first three test cases.
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
freq_count = int(input().strip())
freq = []
for _ in range(freq_count):
freq_item = int(input().strip())
freq.append(freq_item)
result = taskOfPairing(freq)
fptr.write(str(result) + '\n')
fptr.close()
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] | 2.391534 | 189 |
"""
Bayesian Optimization experiment runner.
Relies heavily on BoTorch.
"""
import os
import logging
import matplotlib.pyplot as plt
import numpy as np
import torch
import sys
# sys.path.append("../")
import pickle as pkl
from tqdm import tqdm
import shutil
from distutils.spawn import find_executable
from utils.functionality import run_param_rollout_real, run_param_rollout
from utils.functionality import push_github, modify_and_push_json
from utils.sampling_functions import define_sample_fct
from const import SIMULATION, GITHUB_BRANCH, DIFFICULTY_LEVEL, SAMPLE_FCT, NUM_INIT_SAMPLES, NUM_ROLLOUTS_PER_SAMPLE, NUM_ITERATIONS, NUM_ACQ_RESTARTS, ACQ_SAMPLES
from const import SAMPLE_NEW, MODELS_TO_RUN
from utils import normalization_tools
logger = logging.getLogger(__file__)
# Constants
DIR_NAME = os.path.dirname(__file__)
# NOT WORKING PROPERLY AT THE MOMENT
#TORCH_DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
TORCH_DEVICE = torch.device("cpu")
print(f"Using {TORCH_DEVICE}")
# Tasks
# Helpers
class RRC_v1(object):
"""Sinc in a haystack task."""
# number of initial random points
num_init_samples = NUM_INIT_SAMPLES #1#10
# number of BO updates
num_iter = NUM_ITERATIONS #50
# number of restarts for optimizing the acquisition function
num_acq_restarts = NUM_ACQ_RESTARTS#100
# number of index_set for used for optimizing the acquisition function
num_acq_samples = ACQ_SAMPLES#500
plot_model = True
# d_x should be dimension of x,..
d_x = 1
x_min = np.array([0.0])
x_max = np.array([0.02])
#TODO: identify meaning of y_opt, x_opt. Is this initial guess?
y_opt = 0
x_opt = np.array([0.0])
param_normalizer = normalization_tools.UnitCubeProjector(x_min,x_max)
@staticmethod
EXPERIMENTS = {
"rrc_v1" : RRC_v1,
}
if __name__ == "__main__":
import argparse
from datetime import datetime
DATETIME = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
parser = argparse.ArgumentParser(description="Run Experiment")
parser.add_argument("--experiment", help="Task to run", default="DefaultExp")
parser.add_argument("--path", help="Path where results are to be stored", default="")
parser.add_argument("-s", "--seed", type=int, help="Random seed", default=0)
args = parser.parse_args()
name = str(args.experiment)
res_dir = make_results_folder(name, datetime=True, abs_path = args.path)
configure_matplotlib()
setup_logger(logger, res_dir)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
try:
main(args, res_dir)
except:
logger.exception("Experiment failed:")
raise
plt.show()
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] | 2.58365 | 1,052 |
import requests
import json
from django.contrib.auth.decorators import login_required
from django.utils.decorators import method_decorator
from django.shortcuts import render, redirect, get_object_or_404
from django.views import generic
from django.views.generic.edit import DeleteView
from django.core.urlresolvers import reverse_lazy
from .models import Place, AlternativeName
from .forms import PlaceForm, AlternativeNameForm
@login_required
@login_required
@login_required
@login_required
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] | 3.56338 | 142 |
"""
# data_getter
The data getter manages the initialization of different torch DataLoaders.
A dataloader is essentially an Iterable that can be called in a for-loop.
A typical training step could for example look like this:
data_loaders = data_getter.get_data_loaders(...)
for sample in data_loader['train']:
image, label = sample[0], sample[1]
prediction = model(image)
loss = loss_function(prediction, label)
...
A dataloader contains an object of class Dataset that handles the loading and augmentation process. 'ds_natural_images' gives an example for a custom
dataset.
'get_data_loaders' expects a string 'dataset' that identifies which dataset is to be used (e.g., mnist, cifar-10, ...). 'batch_size' denotes how many
samples (here mainly images) are combined to a mini-batch. A typical PyTorch
minibatch tensor of images has the dimension:
(batch_size, 3, height of image, width of image)
3 is the dimension of the three image channels red, green, and blue.
In 'run_training.py', batch_size is defined by the argument 'bs'.
'num_workers' defines how many processes load data in parallel. Using more than
one worker can, in specific cases, speed up the dataset loading process and
, thus, the entire training. If you want to debug your code, num_workers needs
to be set to 0.
In 'run_training.py', num_workers is defined by the argument 'nw'.
You can use kwargs (in 'run_training.py' the system argument 'ds_kwargs') to
pass configuration values that are very specific to a dataset.
kwargs is a dictionary of keyword-value pairs. EACH VALUE IS A LIST, even if it
only contains a single element. Furthermore, you need to take care of each
value's type. For example,
split_index = int(kwargs['split_index'][0])
contains a list with a string. To get the actual number, you'll need to typecast
it to an int.
For more information, see DLBio's 'kwargs_translator'.
To add a new dataset, you'll need to create a new file 'ds_[dataset_name].py'
in the 'data' folder. You'll need to create a class that inherits Dataset and
implements '__getitem__' and '__len__'. Furthermore, you'll need to define the
function 'get_dataloader'. Finally, you'll need to append an elif case to this
module's function 'get_data_loaders' that calls 'get_dataloader' and returns
a dictionary containing the keys 'train', 'val', and 'test'. If there is no
'val' or 'test' dataloader available, set these values to None.
'ds_natural_images.py' is an example of how to write a custom dataset.
"""
from . import ds_natural_images
from . import ds_cifar10
from . import ds_mnist
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] | 3.384817 | 764 |
import pytest
from _voronoi import recompute_segment_segment_segment_circle_event as bound
from hypothesis import given
from tests.integration_tests.hints import (BoundPortedCircleEventsPair,
BoundPortedSiteEventsPair)
from tests.integration_tests.utils import are_bound_ported_circle_events_equal
from voronoi.events.computers import (
recompute_segment_segment_segment_circle_event as ported)
from . import strategies
@given(strategies.circle_events_pairs, strategies.site_events_pairs,
strategies.site_events_pairs, strategies.site_events_pairs,
strategies.booleans, strategies.booleans, strategies.booleans)
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] | 2.770492 | 244 |
#!/usr/bin/env python
'''
This script reads in seismic noise data from March 2017 and earthquake data.
It shifts the data by time for clustering
It creates a list of earthquake times in March when the peak ground motion is greater than a certain amount.
It clusters earthquake channels using kmeans and dbscan.
It compares the clusters around the earthquake times to deterime effectiveness of clustering
It plots the data as clustered by kmeans and dbscan
'''
from __future__ import division
from sklearn.cluster import KMeans
from sklearn.cluster import DBSCAN
from sklearn.cluster import AffinityPropagation
from sklearn.cluster import MeanShift,estimate_bandwidth
from sklearn.cluster import spectral_clustering
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import Birch
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
import numpy as np
from scipy.io import loadmat
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
import scipy.signal
from astropy.time import Time
import collections
plt.rc('text', usetex = True)
plt.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
plt.rc('axes', labelsize = 20.0)
plt.rc('axes', axisbelow = True)
plt.rc('axes.formatter', limits=[-3,4])
plt.rc('legend', fontsize = 14.0)
plt.rc('xtick', labelsize = 16.0)
plt.rc('ytick', labelsize = 16.0)
plt.rc('figure', dpi = 100)
# colors for clusters
colors = np.array(['r', 'g', 'b','y','c','m','darkgreen','plum',
'darkblue','pink','orangered','indigo'])
cl = 6 # number of clusters for kmeans
eps = 2 # min distance for density for DBscan
min_samples = 15 # min samples for DBscan
#read in data
H1dat = loadmat('Data/' + 'H1_SeismicBLRMS.mat')
edat = np.loadtxt('Data/H1_earthquakes.txt')
# read in earthquake channels
cols = [6,12,18,24,30,36,42,48] # NEED comment here
vdat = np.array(H1dat['data'][0])
vchans = np.array(H1dat['chans'][0])
for i in cols:
add = np.array(H1dat['data'][i])
vdat = np.vstack((vdat, add))
for i in cols:
vchans = np.append(vchans,H1dat['chans'][i])
timetuples = vdat.T
# shift the dat
vdat2 = vdat
vchans2 = vchans
num = 10
t_shift = 30 # how many minutes to shift the data by
for i in cols:
add = np.array(H1dat['data'][i])
for j in range(1, t_shift+1):
add_shift = add[j:]
add_values = np.zeros((j,1))
add_shift = np.append(add_shift, add_values)
vdat2 = np.vstack((vdat2, add_shift))
chan = 'Time_Shift_' + str(j) + '_Min_EQ_Band_' + str(i)
vchans2 = np.append(vchans2, chan)
print(np.shape(vdat2))
vdat2 = vdat[:,:43200-t_shift]
print(np.shape(vdat2))
timetuples2 = vdat.T
timetuples3 = vdat[0:num].T
#convert time to gps time
times = '2017-03-01 00:00:00'
t = Time(times,format='iso',scale='utc')
t_start = int(np.floor(t.gps/60)*60)
dur_in_days = 30
dur_in_minutes = dur_in_days*24*60
dur = dur_in_minutes*60
t_end = t_start + dur
# use peak ground motion to determine which earthquakes are bigger
row, col = np.shape(edat)
gdat = np.array([])
for i in range(row):
point = edat[i][20]
gdat = np.append(gdat,point)
gdat = gdat.T
glq = np.percentile(gdat,65)
# use only earthquakes with signifigant ground motion
row, col = np.shape(edat)
etime = np.array([])
for i in range(row):
if (edat[i][20] >= glq):
point = edat[i][5]
etime = np.append(etime,point)
# use only earthqaukes that occur in March 2017
col = len(etime)
etime_march = np.array([])
for i in range(col):
if ((etime[i] >= t_start) and (etime[i] <= t_end)):
point = etime[i]
etime_march = np.append(etime_march,point)
# kmeans clustering loop
Nmin = 2
Nmax = Nmin + num
for cl in range(Nmin, Nmax):
kmeans = KMeans(n_clusters=cl, random_state=13).fit(timetuples)
kpoints = np.array([])
xvals = np.arange(t_start, t_end, 60)
dbpoints = np.array([])
for t in etime_march: #for each EQ: collect indices within 5 min of EQ
tmin = int(t - 5*60)
tmax = int(t + 5*60)
for j in range(tmin, tmax):
val = abs(xvals - j)
aval = np.argmin(val)
kpoints = np.append(kpoints, aval)
kpoints = np.unique(kpoints) # make sure there are no repeating indices
kclusters = np.array([])
for i in kpoints:
#for each index find the corresponding cluster and store them in array
kclusters = np.append(kclusters,kmeans.labels_[int(i)])
# kmeans score determined by ratio of points in
# cluster/points near EQ to points in cluster/all points
print(' ')
print('Cl = ' + str(cl))
print('Number of points in each cluster that are near an EQ')
print(collections.Counter(kclusters))
print('Number of points in each cluster')
print(collections.Counter(kmeans.labels_))
k_count = collections.Counter(kclusters).most_common()
ktot_count = collections.Counter(kmeans.labels_).most_common()
k_list_cl = [x[0] for x in k_count] #cluster number
k_list = [x[1] for x in k_count] #occurences of cluster
ktot_list_cl = [x[0] for x in ktot_count]
ktot_list = [x[1] for x in ktot_count]
k_clusters = np.array([])
k_compare = np.array([])
k_list2 = np.array([])
ktot_list2 = np.array([])
# arrange so that k_clusters k_list2 and k_compare are in the same order
for i in range(len(k_list_cl)):
for j in range(len(ktot_list_cl)):
if k_list_cl[i] == ktot_list_cl[j]:
k_clusters = np.append(k_clusters,k_list_cl[i])
compare = k_list[i]/ktot_list[j]
k_compare = np.append(k_compare, compare)
k_list2 = np.append(k_list2, k_list[i])
ktot_list2 = np.append(ktot_list2, k_list[i])
print('List with the clusters in order (huh?)')
print(k_clusters)
print('Num_points around EQ divided by total Num_points in clusters')
np.set_printoptions(precision=3)
print(k_compare)
k_cal_score = metrics.calinski_harabaz_score(timetuples, kmeans.labels_)
print('K-means ' + str(cl) + ': C-H score = {:0.6g}'.format(k_cal_score))
# dbscan clustering loop
'''
min_samples_list = [10,20,25,30]
for min_samples in min_samples_list:
db = DBSCAN(eps=eps,min_samples=min_samples).fit(timetuples)
#print number of clusters
print(' ')
n_clusters_ = len(set(db.labels_)) - (1 if -1 in db.labels_ else 0)
print('DBSCAN created ' +str(n_clusters_) + ' clusters')
#add up number of clusters that appear next to each earthquake
xvals = np.arange(t_start,t_end,60)
dbpoints = np.array([])
for t in etime_march: #for each EQ: collect indices within 5 min of EQ
tmin = int(t-5*60)
tmax = int(t+5*60)
for j in range(tmin,tmax):
val = abs(xvals-j)
aval = np.argmin(val)
dbpoints = np.append(dbpoints, aval)
dbpoints = np.unique(dbpoints)
dbclusters = np.array([])
for i in dbpoints: dbclusters = np.append(dbclusters,db.labels_[int(i)]) #for each index find the corresponding cluster and store them in array
#dbscan score determined by percent of points sorted into one cluster near EQ
print('Number of points in each cluster that are near an EQ')
print(collections.Counter(dbclusters))
print('Number of points in each cluster')
print(collections.Counter(db.labels_))
db_count = collections.Counter(dbclusters).most_common()
dbtot_count = collections.Counter(db.labels_).most_common()
db_list_cl = [x[0] for x in db_count]
db_list = [x[1] for x in db_count]
dbtot_list_cl = [x[0] for x in dbtot_count]
dbtot_list = [x[1] for x in dbtot_count]
db_clusters = np.array([])
db_compare = np.array([])
db_list2 = np.array([])
dbtot_list2 = np.array([])
for i in range(len(db_list_cl)):
for j in range(len(dbtot_list_cl)):
if db_list_cl[i] == dbtot_list_cl[j]:
db_clusters = np.append(db_clusters,db_list_cl[i])
compare = db_list[i]/dbtot_list[j]
db_compare = np.append(db_compare, compare)
db_list2 = np.append(db_list2, db_list[i])
dbtot_list2 = np.append(dbtot_list2, db_list[i])
print('List with the clusters in order')
print(db_clusters)
print('Number of points in clusters near EQ divided by total number of points in clusters')
print(db_compare)
d_cal_score = metrics.calinski_harabaz_score(timetuples, db.labels_)
print('For dbscan the calinski harabaz score is ' + str(d_cal_score))
'''
# Plot #1: Plot graph of kmeans clustering for EQ
kmeans = KMeans(n_clusters=cl, random_state=12).fit(timetuples)
xvals = np.arange(t_start, t_end, 60)
fig,axes = plt.subplots(len(vdat), figsize=(40, 4*len(vdat)))
for ax, data, chan in zip(axes, vdat, vchans2):
ax.scatter(xvals, data,
c = colors[kmeans.labels_],
edgecolor = '',
s=4, alpha=0.8, label=r'$\mathrm{%s}$' % chan.replace('_','\_'))
ax.set_yscale('log')
ax.set_ylim(8, 11000)
ax.set_xlabel('GPS Time')
ax.grid(True, which='both')
ax.legend()
for e in range(len(etime_march)):
ax.axvline(x=etime_march[e])
fig.tight_layout()
fig.savefig('Figures/EQdata_Kmeans_' + str(cl) + '.png',
rasterized=True)
try:
fig.savefig('/home/roxana.popescu/public_html/' + 'EQdata_Kmeans_'+str(cl)+'.png',
rasterized=True)
except:
print(" ")
# Plot #2:plot graph of dbscan clustering for EQ
db = DBSCAN(eps=eps,min_samples=min_samples).fit(timetuples)
xvals = np.arange(t_start, t_end, 60)
# print number of clusters
n_clusters_ = len(set(db.labels_)) - (1 if -1 in db.labels_ else 0)
print('DBSCAN created ' +str(n_clusters_) + ' clusters')
fig, axes = plt.subplots(len(vdat), figsize=(40,4*len(vdat)))
for ax, data, chan in zip(axes, vdat, vchans2):
ax.scatter(xvals, data, c=colors[db.labels_], edgecolor='',
s=5, alpha=0.8, label=r'$\mathrm{%s}$' % chan.replace('_','\_'))
ax.set_yscale('log')
ax.set_ylim(8, 11000)
ax.set_xlabel('GPS Time')
ax.grid(True, which='both')
ax.legend()
for e in range(len(etime_march)):
ax.axvline(x=etime_march[e])
fig.tight_layout()
fig.savefig('Figures/dbscan_all.png',
rasterized=True)
try:
fig.savefig('/home/roxana.popescu/public_html/' + 'dbscan_all_.png',
rasterized=True)
except:
print(" ")
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3601,
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198
] | 2.238445 | 4,760 |
# Generated by Django 3.0 on 2019-12-13 16:34
from django.db import migrations, models
import django.db.models.deletion
| [
2,
2980,
515,
416,
37770,
513,
13,
15,
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13130,
12,
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295,
628
] | 2.904762 | 42 |
import os
import subprocess
import time
import easygui as g
import re
import requests
from selenium import webdriver
if __name__ == '__main__':
filePath, packageName, lanuchableActivity = getPackagInfo()
handle = uninstallApp(packageName)
uninstallApp(handle)
judgeRunning(handle)
print('%s 卸载成功' % packageName)
print('%s 开始安装，请稍后' % packageName)
handle_install = installapp(filePath)
print('安装日志为：', handle_install.stdout.read().decode().strip('\r\n'))
os.remove('./packageInfo.txt')
judgePackageExist(packageName)
input() | [
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7,
26495,
5376,
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628,
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220,
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3419
] | 2.465812 | 234 |
import os
import re
import math
import random
import sys
import json
import itertools
from random import randint
from string import ascii_letters
from os import path, listdir
from configparser import ConfigParser
pathname = os.path.dirname(sys.argv[0])
config = ConfigParser()
config.read( pathname + '/config.ini')
#Function to basic clean and preprocess input string or text
#Perturb the word by a certain percentage
#Perturb the word by a certain percentage
#Main function for the perturbation algorithm
#Word perturbation main method
#If you call the method with an input_file --> the program start perturbation to this file
#If you call the method with string --> the program start perturbation the string text
#If you set clean = 1 --> the program start to clean and preprocess the text before the perturbation
#Default percentage for the perturbation = 10%
#Function to export the perturbation result into a file txt
# Functions test
#input_string = "ciao come stai proviamo a fare un test con andrea guzzo che succede se aggiungo altre parole al ciclo uff"
#result = word_perturbation(string=input_string,clean=0,words_percentage=10,string_percentage=10)
#print(result) | [
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] | 3.387187 | 359 |
import numpy as np
from scipy.stats import gmean
from collections import namedtuple
import threading
import multiprocessing
import configparser
import timestreamquery as tsquery
import os
from timeit import default_timer as timer
from query_execution_utils import executeQueryInstance, Query
import sys, traceback
import random, string
import time
Params = namedtuple('Params', 'dbname tablename region az cell silo microservicename instancetype osversion instancename processname jdkversion')
QueryParams = namedtuple('QueryParams', 'repetitions paramlist')
Header = 'Query type, Total Count, Successful Count, Avg. latency (in secs), Std dev latency (in secs), Median, 90th perc (in secs), 99th Perc (in secs), Geo Mean (in secs)'
### Create the query string using the list of parameters.
## For each query, convert them into row-count variants where the actual query is enclosed within a sub-query
## where the outer query counts the number of rows returned by the sub-query (i.e., the original query).
## Config constants. These define the strings used in the config files.
configDefaultSection = 'default'
configQueryDistributionSection = 'query_distribution'
configQueryMode = 'query_mode'
configRepetitions = 'repetitions'
configRetries = 'retries'
configQueryModeRowCount = 'row_count'
configQueryModeRegular = 'regular'
## The main execution thread the reads in the config file and executes the queries per the parameters
## defined in the config file.
## Log a few summary statistics from the table.
## A multi-process executer that uses the RandomizedExecutionThread instances to execute queries
## using multiple processes.
## Obtain the query parameters by issuing a query to the database and table. | [
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"""
@file
@brief Implements a way to get close examples based
on the output of a machine learned model.
"""
from ..mlmodel import model_featurizer
from ..helpers.parameters import format_function_call
from .search_engine_vectors import SearchEngineVectors
class SearchEnginePredictions(SearchEngineVectors):
"""
Extends class @see cl SearchEngineVectors by
looking for neighbors to a vector *X* by
looking neighbors to *f(X)* and not *X*.
*f* can be any function which converts a vector
into another one or a machine learned model.
In that case, *f* will be set to a default behavior.
See function @see fn model_featurizer.
"""
def __init__(self, fct, fct_params=None, **knn):
"""
@param fct function *f* applied before looking for neighbors,
it can also be a machine learned model
@param fct_params parameters sent to function @see fn model_featurizer
@param pknn list of parameters, see
:epkg:`sklearn:neighborsNearestNeighbors`
"""
super().__init__(**knn)
self._fct_params = fct_params
self._fct_init = fct
if (callable(fct) and not hasattr(fct, 'predict') and
not hasattr(fct, 'forward')):
self.fct = fct
else:
if fct_params is None:
fct_params = {}
self.fct = model_featurizer(fct, **fct_params)
def __repr__(self):
"""
usual
"""
if self.pknn:
pp = self.pknn.copy()
else:
pp = {}
pp['fct'] = self._fct_init
pp['fct_params'] = self._fct_params
return format_function_call(self.__class__.__name__, pp)
def fit(self, data=None, features=None, metadata=None):
"""
Every vector comes with a list of metadata.
@param data a :epkg:`dataframe` or None if the
the features and the metadata
are specified with an array and a
dictionary
@param features features columns or an array
@param metadata data
"""
iterate = self._is_iterable(data)
if iterate:
self._prepare_fit(data=data, features=features,
metadata=metadata, transform=self.fct)
else:
self._prepare_fit(data=data, features=features, metadata=metadata)
if isinstance(self.features_, list):
raise TypeError( # pragma: no cover
"features_ cannot be a list when training the model.")
self.features_ = self.fct(self.features_, True)
return self._fit_knn()
def kneighbors(self, X, n_neighbors=None):
"""
Searches for neighbors close to *X*.
@param X features
@return score, ind, meta
*score* is an array representing the lengths to points,
*ind* contains the indices of the nearest points in the population matrix,
*meta* is the metadata.
"""
xp = self.fct(X, False)
if len(xp.shape) == 1:
xp = xp.reshape((1, len(xp)))
return super().kneighbors(xp, n_neighbors=n_neighbors)
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] | 2.10875 | 1,600 |
import os
import numpy as np
import pandas as pd
from collections import Counter
sub_path = 'sub/'
teamates = os.listdir(sub_path)
data = pd.read_csv('./single/robertawmmlarge_result_mean.csv', encoding='utf-8').rename(columns={'negative': 'negative_1', 'key_entity': 'key_entity_1'})
index = 2
for member in teamates:
member_files = sub_path + member + '/'
member_sub_files = os.listdir(member_files)
for file in member_sub_files:
sub = pd.read_csv(member_files+file, encoding='utf-8').rename(columns={'negative': 'negative_' + str(index), 'key_entity': 'key_entity_' + str(index)})
data = data.merge(sub, on='id', how='left')
index += 1
print(data)
print(data[data['negative_1'] == 1].shape)
print(data[data['negative_2'] == 1].shape)
print(data[data['negative_3'] == 1].shape)
print(data[data['negative_4'] == 1].shape)
print(data[data['negative_5'] == 1].shape)
# for row in data.itertuples:
negatives = ['negative_' + str(index) for index in range(1, index, 1)]
key_entitys = ['key_entity_' + str(index) for index in range(1, index, 1)]
ids = []
voting_entitys = []
thresh = int(index / 2) # 3 # 阈值：保留词的最小出现次数
count = 0
for row in range(len(data)):
negative = Counter()
key_entity = Counter()
for k in range(0, index-1, 1):
negative[data.ix[row][negatives[k]]] += 1
# print(negative)
if (len(negative) == 1) & (data.ix[row]['negative_1'] == 1):
for k in range(0, index-1, 1):
for entity in data.ix[row][key_entitys[k]].split(';'):
key_entity[entity] += 1
# print(key_entity)
entitys = []
words = list(key_entity.keys())
for word in words:
if key_entity[word] >= thresh:
entitys.append(word)
if entitys == []:
entitys.append(key_entity.most_common(1)[0][0])
entitys = list(set(entitys))
voting_entitys.append(';'.join(entitys))
ids.append(data.ix[row]['id'])
count += 1
print(count)
voted = pd.DataFrame({'id': ids, 'key_entity': voting_entitys})
print(voted)
submit = data[['id', 'negative_1', 'key_entity_1']].rename(columns={'negative_1': 'negative'})
submit = submit.merge(voted, on='id', how='left')
submit['key_entity'] = submit.apply(lambda index: index.key_entity_1 if index.key_entity is np.nan else index.key_entity, axis=1)
print(submit)
submit['key_entity_tag_sun']=submit['key_entity'].apply(lambda x:get_sun(x))
print(submit[submit['key_entity_tag_sun']==0])
"""去?的子串函数'"""
submit['key_entity']=list(map(lambda x, tag: delete_sun(x, tag), submit['key_entity'], submit['key_entity_tag_sun']))
print(submit[submit['key_entity_tag_sun']==0])
print(submit)
submit[['id', 'negative', 'key_entity']].to_csv('five_models_voting_three_method.csv', index=None)
print('thresh:', thresh)
print('store done.')
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] | 2.182413 | 1,376 |
"""
Module `chatette_qiu.adapters.factory`.
Defines a factory method that allows to create an adapter from a string name.
"""
from chatette_qiu.adapters.jsonl import JsonListAdapter
from chatette_qiu.adapters.rasa import RasaAdapter
def create_adapter(adapter_name):
"""
Instantiate an adapter and returns it given the name of the adapter as a str.
Names are:
- 'rasa': RasaAdapter
- 'jsonl': JsonListAdapter
"""
if adapter_name is None:
return None
adapter_name = adapter_name.lower()
if adapter_name == 'rasa':
return RasaAdapter()
elif adapter_name == 'jsonl':
return JsonListAdapter()
raise ValueError("Unknown adapter was selected.")
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] | 2.782946 | 258 |
# Since our cli produces unicode output, but we want tests in python2 as well
from __future__ import unicode_literals
from datetime import datetime
from click.testing import CliRunner
import great_expectations.version
from great_expectations.cli import cli
import tempfile
import pytest
import json
import os
import shutil
import logging
import sys
import re
from ruamel.yaml import YAML
yaml = YAML()
yaml.default_flow_style = False
try:
from unittest import mock
except ImportError:
import mock
from great_expectations.cli.init import scaffold_directories_and_notebooks
# def test_cli_render(tmp_path_factory):
# runner = CliRunner()
# result = runner.invoke(cli, ["render"])
# print(result)
# print(result.output)
# assert False
| [
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33918,
198,
11748,
28686,
198,
11748,
4423,
346,
198,
11748,
18931,
198,
11748,
25064,
198,
11748,
302,
198,
6738,
7422,
17983,
13,
88,
43695,
1330,
575,
2390,
43,
198,
88,
43695,
796,
575,
2390,
43,
3419,
198,
88,
43695,
13,
12286,
62,
11125,
62,
7635,
796,
10352,
198,
198,
28311,
25,
198,
220,
220,
220,
422,
555,
715,
395,
1330,
15290,
198,
16341,
17267,
12331,
25,
198,
220,
220,
220,
1330,
15290,
628,
198,
6738,
1049,
62,
1069,
806,
602,
13,
44506,
13,
15003,
1330,
41498,
727,
62,
12942,
1749,
62,
392,
62,
11295,
12106,
628,
628,
628,
628,
628,
198,
198,
2,
825,
1332,
62,
44506,
62,
13287,
7,
22065,
62,
6978,
62,
69,
9548,
2599,
198,
2,
220,
220,
220,
220,
17490,
796,
1012,
72,
49493,
3419,
198,
2,
220,
220,
220,
220,
1255,
796,
17490,
13,
37669,
7,
44506,
11,
14631,
13287,
8973,
8,
198,
198,
2,
220,
220,
220,
220,
3601,
7,
20274,
8,
198,
2,
220,
220,
220,
220,
3601,
7,
20274,
13,
22915,
8,
198,
2,
220,
220,
220,
220,
6818,
10352,
628,
628,
198
] | 3.011583 | 259 |
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