index int64 0 1,000k | blob_id stringlengths 40 40 | code stringlengths 7 10.4M |
|---|---|---|
16,400 | 4a2a5bda4aea75c02f3e8c5a721591285ed70fa1 | __all__ = (
"__version__",
"ResourceGroup",
"ArrayMapper",
"MappedMatrix",
"RandomIndexer",
"SequentialIndexer",
"CombinatorialIndexer",
"Proxy",
)
from .version import version as __version__
from .array_mapper import ArrayMapper
from .mapped_matrix import MappedMatrix
from .resource_group import ResourceGroup
from .indexers import RandomIndexer, SequentialIndexer, CombinatorialIndexer, Proxy
|
16,401 | 5d49cc8e9a01edc1f0a85fb5bf44c5b1c1612175 | # Generated by Django 2.2.1 on 2019-05-14 21:12
from django.db import migrations, models
import django.db.models.deletion
import sovabi.models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Lieu',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nom', models.CharField(max_length=150)),
],
bases=(models.Model, sovabi.models.SovabiModel),
),
migrations.CreateModel(
name='TypeCelebration',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nom', models.CharField(max_length=150)),
],
bases=(models.Model, sovabi.models.SovabiModel),
),
migrations.CreateModel(
name='Messe',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('messe_date', models.DateTimeField(verbose_name='date of the mass')),
('lieu', models.ForeignKey(default=0, on_delete=django.db.models.deletion.SET_DEFAULT, related_name='messes', to='messes.Lieu')),
('type_celebration', models.ForeignKey(default=0, on_delete=django.db.models.deletion.SET_DEFAULT, related_name='messes', to='messes.TypeCelebration')),
],
bases=(models.Model, sovabi.models.SovabiModel),
),
]
|
16,402 | c0dd2843d0687bcff6a252ecb7cf16a51de3b50e | import requests
import boto3
import os
import json
# base_url = "http://192.168.1.3:5000"
base_url = "http://18.116.114.15:5000"
def call_signin(world, username,pw):
data = {
"username": username,
"pw": pw
}
try:
response = requests.post(base_url+'/signin', json=data)
response_info = json.loads(response.text)["info"]
response_perfdata = json.loads(response.text)["perf_data"]
response_email = json.loads(response.text)["email"]
if(response_info=="signed in"):
world.setusername(username)
world.setemail(response_email)
world.setperf(response_perfdata)
return(response_info)
except:
return("not signin")
def call_register(email,username,pw):
data = {
"email": email,
"username":username,
"pw":pw
}
try:
response = requests.post(base_url+'/register', json=data)
print(response.text)
return(response.text)
except:
return("not registered")
def upload_latest_game_data(username):
if(username!=""):
try:
file = open("secret_keys.txt")
keys = file.read().strip().split("\n")
file.close()
access_key = keys[0]
secret_access_key = keys[1]
s3_bucket = keys[2]
s3_client = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_access_key)
data_directory = ".\data\\"
content = os.listdir(data_directory)
new_content = []
for i in range(0, len(content)):
new_content.append(data_directory + content[i])
latest_subdir = max(new_content, key=os.path.getmtime)
folder = latest_subdir.split('\\')[2]
data_files = [f for f in os.listdir(latest_subdir)]
for i in range(0, len(data_files)):
s3_client.upload_file(os.path.join(latest_subdir, data_files[i]), s3_bucket,
os.path.join(username + "/" + folder + "/" + data_files[i]))
print("game data successfully uploaded")
except:
print("game data not uploaded")
def upload_perf_data(perf_data, username, email):
if(username!=""):
data = {
"perf_data": perf_data,
"username":username,
"email":email
}
try:
response = requests.post(base_url+'/uploadperfdata', json=data)
print(response.text)
return(response.text)
except:
return("not uploaded performance data")
|
16,403 | fc43d7dc132bbbd191eefda86ee31f4d4cfe07d7 | bottle, k = map(int, input().split())
answer = 0
while True:
count = 0
now_bottle = bottle
while now_bottle > 0:
if now_bottle % 2 != 0:
count += 1
now_bottle = now_bottle // 2
if count <= k:
break
bottle += 1
answer += 1
print(answer) |
16,404 | c51f1d8ed5a93edd1cb8d1b6bf2aaa8a1dbcc9e1 | from django.contrib import admin
from django.urls import include, path
urlpatterns = [
path('frigg/', include('frigg.urls')),
path('admin/', admin.site.urls),
] |
16,405 | c2071a046fa47c8ee8517872686e82d5ac0c25ed | from netCDF4 import Dataset
import sys
import numpy as np
ind = 'goddard_merged_seaice_conc_monthly'
ind1 = 'seaice_conc_monthly_cdr'
base_year = 1979
sic_data = 999*np.ones((32,332,316))
for year in range(1979,2010+1):
with Dataset(f'{year}.nc') as data:
sic_data[year-base_year,:,:] = data[ind][:][0,:,:]
with Dataset('2018_2019-avg_1979_2010.nc','w') as root_grp:
root_grp.createDimension('time', None)
root_grp.createDimension('ygrid', 332)
root_grp.createDimension('xgrid', 316)
time = root_grp.createVariable('time','f8',('time',))
ygrid = root_grp.createVariable('ygrid','f8',('ygrid',))
xgrid = root_grp.createVariable('xgrid','f8',('xgrid',))
lat = root_grp.createVariable('latitude','f8',('ygrid', 'xgrid'))
lon = root_grp.createVariable('longitude','f8',('ygrid', 'xgrid'))
sic = root_grp.createVariable('sic','f8',('time', 'ygrid', 'xgrid',))
time = np.array([2018,2019])
avg_data = np.mean(sic_data,axis=0)
with Dataset('2018.nc') as f2018:
lat[:,:] = f2018['latitude'][:]
lon[:,:] = f2018['longitude'][:]
sic[0,:,:] = f2018[ind1][:][0,:,:] - avg_data
with Dataset('2019.nc') as f2019:
sic[1,:,:] = f2019[ind1][:][0,:,:] - avg_data
|
16,406 | e3c9b4b6325a51e409fff0acc3042f7fb964f9ac | '''
Created on Dec 10, 2017
@author: antonina
Description:
Make a two-player Rock-Paper-Scissors game.
(Hint: Ask for player plays (using input), compare them,
print out a message of congratulations to the winner,
and ask if the players want to start a new game)
Remember the rules:
Rock beats scissors
Scissors beats paper
Paper beats rock
'''
def getPlayerName(i):
"""Returns name of player i"""
while True:
str_to_ask = "Input name for Player " + str(i) + ": "
name = input(str_to_ask).strip()
if name != '':
return name
def getPlayerChoice():
"""Returns player choice: rock, paper or scissors"""
while True:
p_choice = input("Input 'r' for Rock, 'p' for Paper or 's' for Scissors: ").strip()
if p_choice == 'r':
return "Rock"
elif p_choice == "p":
return "Paper"
elif p_choice == "s":
return "Scissors"
else:
print("Input is invalid. Try again")
def play(pl1, ch1, pl2, ch2):
"""1) Returns None if both choices are equal, otherwise returns player name who won.\n
2) Decision table:
------R-------S-------P
R tie P2 W P1 W
S P1 W tie P2 W
P P2 W P1 W tie
"""
if ch1 == ch2:
print("It's a tie.")
return None
if ch1 == 'Rock':
if ch2 == 'Scissors':
print("Congratulations,", pl1, ". You WON! Rock beats Scissors!")
return pl1
else:
print("Congratulations,", pl2, ". You WON! Paper beats Rock!")
return pl2
elif ch1 == 'Scissors':
if ch2 == 'Rock':
print("Congratulations,", pl2, ". You WON! Rock beats Scissors!")
return pl2
else:
print("Congratulations,", pl1, ". You WON! Scissors beat Paper!")
return pl1
else:
if ch2 == 'Rock':
print("Congratulations,", pl1, ". You WON! Paper beats Rock!")
return pl1
else:
print("Congratulations,", pl2, ". You WON! Scissors beat Paper!")
return pl2
def main():
print("Welcome to the Rock-Paper-Scissors game!\n", "-" * 100)
while True:
answer = input("Would you like to start a new game? Input 'yes' or 'no': ")
if answer == 'no': break
elif answer == 'yes':
print("-" * 100)
player1 = getPlayerName(1)
player2 = getPlayerName(2)
print("-" * 50)
print(player1, ", make a choice.")
choice1 = getPlayerChoice()
print(player1, ", YOUR CHOICE IS -", choice1)
print("-" * 50)
print(player2, ", make a choice.")
choice2 = getPlayerChoice()
print(player2, ", YOUR CHOICE IS -", choice2)
print("*" * 50)
play(player1, choice1, player2, choice2)
print("*" * 50)
else:
print("Your input is invalid. Try again.")
if __name__ == "__main__":
main() |
16,407 | d463f04beabcfaa6cacbb3185e1ed4b5d9bc4918 | from django.contrib import admin
from algorithm.models import *
admin.site.register(AlgorithmTypes)
admin.site.register(CipherInstructions) |
16,408 | 8af8401fc3071e7866efd1f46c9f6418b6c4c9aa | #!/usr/bin/python
# -*- coding:utf-8 -*-
#dependence: paho-mqtt (pip install paho-mqtt)
# XBee (pip install XBee)
# PyYAML (pip install PyYaml)
# pyserial (pip install pyserial)
import os
import sys
import time
import logging
import yaml
from serial import Serial
from factory import *
from pan import *
from filters import *
from plugins import *
from paho.mqtt import client
from daemon import Daemon
import sqlite3 as database
class PAN2MQTT(Daemon):
"""
PAN network to MQTT bridge
Supported PAN radio: XBee, Mesh Bee(from seeedstudio)
To port a new radio driver, two method must be implemented: on_message, send_message
"""
def __init__ (self, logger, cfg):
"""
"""
Daemon.__init__(self,cfg['general']['pidfile'])
self.logger = logger
self.config = cfg
self.mqtt_connected = False
self.mqtt_subcriptions = {}
self.downlink_topics = {}
self.uplink_topics = {}
self.pan = Factory(self.config['pan']['driver_class'])
if not self.pan:
self.__log(logging.ERROR, "Can't instant pan driver")
sys.exit(2)
self.pan.logger = logger
self.pan.on_message = self.on_message_from_pan
self.stdout = self.config['general']['stdout']
self.stderr = self.config['general']['stdout']
self.host = self.config['mqtt']['host']
self.client_id = self.config['mqtt']['client_id']
self.mqtt_qos = self.config['mqtt']['qos']
self.mqtt_retain = self.config['mqtt']['retain']
self.status_topic = self.config['mqtt']['status_topic']
self.mqtt_client = client.Client(self.client_id, self.config['mqtt']['clean_session'])
if self.__try_get_config(self.config['mqtt'], "username", None):
self.mqtt_client.username_pw_set(self.config['mqtt']['username'], self.config['mqtt']['password'])
if self.config['mqtt']['set_will']:
self.mqtt_client.will_set(self.status_topic.format(client_id=self.client_id), "0", self.mqtt_qos, self.mqtt_retain)
self.mqtt_client.on_connect = self.on_mqtt_connect
self.mqtt_client.on_disconnect = self.on_mqtt_disconnect
self.mqtt_client.on_message = self.on_message_from_mqtt
self.mqtt_client.on_subscribe = self.on_mqtt_subscribe
self.mqtt_client.on_log = self.on_mqtt_log
self.plugins = self.__try_get_config(self.config, 'plugin', None)
if not isinstance(self.plugins, dict):
self.plugins = {self.plugins}
self.plugins_ins = {}
### private method
def __log(self, level, message):
if self.logger:
self.logger.log(level, message)
@staticmethod
def __try_get_config (parent, key, default):
try:
return parent[key]
except:
return default
def __parse_nodes (self):
self.downlink_topics = {}
self.uplink_topics = {}
if self.config['pan']['nodes']:
for mac,mac_obj in self.config['pan']['nodes'].items():
for topic,topic_content in mac_obj.items():
topic = topic.format(client_id=self.client_id)
if topic_content['dir'] == "uplink":
self.uplink_topics[(mac, topic_content['match_key'])] = (topic,self.__try_get_config(topic_content,'filter',None))
elif topic_content['dir'] == "downlink":
self.downlink_topics[topic] = (mac, topic_content)
else:
self.__log(logging.ERROR, "Unknown 'dir'")
def __sub_downlink_topics (self):
if not self.mqtt_connected:
return
for t in self.downlink_topics:
rc, mid = self.mqtt_client.subscribe(t, self.mqtt_qos)
self.mqtt_subcriptions[mid] = t
self.__log(logging.INFO, "Sent subscription request to topic %s" % t)
def __filter (self, input, filter_config):
try:
filter = Factory(filter_config['type'])
if filter:
filter.configure(filter_config['parameters'])
if filter.validate():
return filter.process(input)
except:
pass
return input
#response topic list to client which requires this
def __resp_topic_list(self, dst_topic):
'''
Broadcast gateway information when the gateway thread is starting
'''
str_topic_holder = ''
if self.config['pan']['nodes']:
for mac,mac_obj in self.config['pan']['nodes'].items():
for topic,topic_content in mac_obj.items():
topic = topic.format(client_id=self.client_id)
if topic_content['dir'] == "uplink" and topic_content['type'] != "listening":
str_topic_holder = str_topic_holder + topic + "@"
print "topic list:" + str_topic_holder
self.mqtt_client.publish(dst_topic, str_topic_holder, 2)
###
def on_mqtt_connect (self, client, userdata, flags, rc):
if rc == 0:
self.__log(logging.INFO, "Connected to MQTT broker: %s" % self.host)
self.mqtt_client.publish(self.status_topic.format(client_id=self.client_id), "1")
self.mqtt_connected = True
self.__sub_downlink_topics()
else:
self.__log(logging.ERROR, "Could not connect to MQTT broker: %s" % self.host)
self.__log(logging.ERROR, "Error code: %d" % rc)
self.mqtt_connected = False
def on_mqtt_disconnect (self, client, userdata, rc):
self.mqtt_connected = False
self.__log(logging.INFO, "Disconnected from MQTT broker: %s"%self.host)
self.__log(logging.INFO, "Return code: %d"%rc)
if rc!=0:
self.__log(logging.ERROR, "Unexpected disconnect, waiting reconnect...")
def on_mqtt_subscribe (self,client, userdata, mid, granted_qos):
topic = self.mqtt_subcriptions.get(mid, "Unknown")
self.__log(logging.INFO, "Sub to topic %s confirmed"%topic)
def on_mqtt_log (self, client, userdata, level, buf):
self.__log(logging.DEBUG, buf)
def on_message_from_pan (self, mac, key, value, type):
self.__log(logging.INFO, "Received message from PAN: %s, %s:%s" % (mac, key, value))
#walk over plugins and determin whether to drop
'''
there are two callback in each plugin
1.on_message_from_pan abstract function in base
description: do something when receives pan event
2.pre_publish
description: do something before publish to broker
'''
for name,p in self.plugins_ins.items():
if not p.on_message_from_pan(mac, key, value, type):
return False
#search the topic
try:
if self.uplink_topics[(mac,key)]:
topic, filter = self.uplink_topics[(mac,key)]
#apply the filter
value_f = value
if filter:
value_f = self.__filter(value, filter)
#walk over plugins and call the callback which watches on the publishment
for name,p in self.plugins_ins.items():
if p.pre_publish:
p.pre_publish(topic, value_f, value)
#publish the topic
self.__log(logging.INFO, "Publishing to topic: %s"%topic)
self.mqtt_client.publish(topic, value_f, self.mqtt_qos, self.mqtt_retain)
except KeyError, e:
self.__log(logging.WARNING, "Received message unrecognized: %s" % e)
def on_message_from_mqtt (self,client, userdata, message):
self.__log(logging.INFO, "Received message from MQTT: %s: %s, qos %d" % (message.topic,message.payload,message.qos))
#walk over plugins and determin whether to drop
for name,p in self.plugins_ins.items():
if not p.on_message_from_mqtt(message.topic, message.payload, message.qos):
return False
#search the topic
if self.downlink_topics[message.topic]:
mac, topic = self.downlink_topics[message.topic]
#apply the filters
if self.__try_get_config(topic, 'filter', None):
value = self.__filter(message.payload, topic['filter'])
else:
value = message.payload
#handle the topic types
if topic['type'] == 'dio':
self.pan.send_message('dio', mac, value, port = topic['dio_num'])
#self.__log(logging.DEBUG, "sent dio message")
elif topic['type'] == 'data':
self.pan.send_message('data', mac, value)
elif topic['type'] == 'rpc':
pass
elif topic['type'] == 'listening':
#to specified client
self.__resp_topic_list(str(value))
else:
self.__log(logging.ERROR, "Unknown downlink handler type: %s" % topic['type'])
return
else:
self.__log(logging.ERROR,"Received an unknown topic '%s' from mqtt" % message.topic)
return
def do_reload (self):
self.__log(logging.DEBUG, "Reload not implemented now")
def run (self):
self.__log(logging.INFO, "Starting Pan2Mqtt %s" % self.config['general']['version'])
#parse nodes, up/down-link channels
self.__parse_nodes()
#connect mqtt
self.mqtt_client.connect(self.host, self.config['mqtt']['port'], self.config['mqtt']['keepalive'])
sec=0
while True:
if self.mqtt_connected:
break
else:
self.mqtt_client.loop()
sec=sec+1
if sec > 60:
self.stop()
sys.exit(2)
#connect pan radio
try:
serial = Serial(self.config['pan']['port'], self.config['pan']['baudrate'])
except Exception,e:
self.__log(logging.ERROR, "Can't open serial: %s" % e)
sys.exit(2)
self.pan.serial = serial
if not self.pan.connect():
self.stop()
#start the plugins
for p in self.plugins:
ins = Factory(p)
if ins:
self.plugins_ins[p] = ins
if self.__try_get_config(self.config['plugin'], p, None):
self.plugins_ins[p].config = self.config['plugin'][p]
self.plugins_ins[p].global_config = self.config
self.plugins_ins[p].send_to_pan = self.pan.send_message
self.plugins_ins[p].send_to_mqtt = self.mqtt_client.publish
self.plugins_ins[p].start()
else:
self.__log(logging.ERROR, "Can not make the instance of %s from factory"%p)
#blocking loop
try:
self.mqtt_client.loop_forever()
except KeyboardInterrupt:
self.__log(logging.ERROR, "Terminated by user")
self.cleanup()
def cleanup (self):
self.pan.disconnect()
self.__log(logging.INFO, "Cleaning up...")
self.mqtt_client.disconnect()
if os.path.exists(self.pidfile):
os.remove(self.pidfile)
for name, p in self.plugins_ins.items():
p.cleanup()
sys.exit()
def resolve_path(path):
return path if path[0] == '/' else os.path.join(os.path.dirname(os.path.realpath(__file__)), path)
if __name__ == '__main__':
config_file = './pan2mqtt.yaml'
fh = file(resolve_path(config_file), 'r')
config = yaml.load(fh)
fh.close()
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger = logging.getLogger()
logger.setLevel(config['general']['log_level'])
logger.addHandler(handler)
gw = PAN2MQTT(logger, config)
if len(sys.argv) == 2:
if 'start' == sys.argv[1]:
gw.start()
elif 'stop' == sys.argv[1]:
gw.stop()
elif 'restart' == sys.argv[1]:
gw.restart()
elif 'reload' == sys.argv[1]:
gw.reload()
elif 'foreground' == sys.argv[1]:
gw.run()
else:
print "Unknown command"
sys.exit(2)
sys.exit(0)
else:
print "usage: %s start|stop|restart|foreground" % sys.argv[0]
sys.exit(2)
|
16,409 | 1f58ef1758501a50cc13fa826a321cfa884fb251 | import boto3
import boto3.session
# from botocore.exceptions import ClientError
def main():
session = boto3.session.Session()
s3client = session.client(
's3',
use_ssl=False,
endpoint_url='http://192.168.99.100:8080',
aws_access_key_id='UEF4ZL22-B2YKBJ0QZBL',
aws_secret_access_key='vImVW0uuw0e4NmWFV6UMrVX6b0dzDERx3FuTYA==')
bucket = '52138c33-4043-4a85-a23c-9ec9d4fd48f1'
response = s3client.list_objects(Bucket=bucket, Delimiter='/',
Prefix='secrets/keys/keys/')
dirs = list()
dirs = [d.get('Prefix', None).split('/')[1] for d in response.get('CommonPrefixes', [])]
files = [f.get('Key', None).split('/')[1] for f in response.get('Contents', [])]
print('Directories ->', dirs)
print('Files ->', files)
if __name__ == '__main__':
main()
|
16,410 | cc7960896e143ae52f27c2e82e6f2e588c354ec9 | from django.urls import path
from . import views
app_name = 'control_panel'
urlpatterns = [
path('', views.dashboard, name='dashboard'),
path('newsletters/', views.newsletter_list, name='newsletter_list'),
path('newsletter/create/', views.create_newsletter, name='create_newsletter'),
path('newsletter/detail/<int:pk>/', views.newsletter_detail, name='newsletter_detail'),
path('newsletter/edit/<int:pk>/', views.newsletter_edit, name='newsletter_edit'),
path('newsletter/delete/<int:pk>/', views.newsletter_delete, name='newsletter_delete'),
] |
16,411 | 59ae56f532adc05036663851cf7c6bccc1d55c0b | a = int(input())
b = int(input())
c = int(input())
x = int(input())
answer = 0
for i in (a + 1):
for j in (b + 1):
for k in (c + 1):
if i * 500 + j * 100 + k * 50 == x:
answer += 1
print(answer)
|
16,412 | c5537179b330c10cba24ecd84a097ebb5ccca930 | #!/usr/bin/env python
import math
import multiprocessing
import gym
from gym import spaces, logger
from gym.utils import seeding
import numpy as np
# import tf
# import tf.msg
from std_msgs.msg import Float64
import rospkg,rospy
import xml.etree.ElementTree as ET
# rospy.init_node('fiver_gym',anonymous=True)
# theta5_pub = rospy.Publisher('/fiver/joint5_position_controller/command',Float64,queue_size=5)
# theta2_pub = rospy.Publisher('/fiver/joint1_position_controller/command',Float64,queue_size=5)
# pub_tf = rospy.Publisher("/tf", tf.msg.tfMessage,queue_size=5)
# tf_broadaster = tf.TransformBroadcaster()
# tf_listener = tf.TransformListener()
class FiverEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second' : 50
}
def __init__(self):
self.link_lengths= {"link1":0,"link2":0,"link3":0,"link4":0,"link5":0,"width":0}
# reading the link lengths from the xml file in rosfiles
self.links_length_loader()
self.link1 = self.link_lengths["link1"]
self.link2 = self.link_lengths["link2"]
self.link3 = self.link_lengths["link3"]
self.link4 = self.link_lengths["link4"]
self.link5 = self.link_lengths["link5"]
self.link_width=self.link_lengths["width"]
self.q2 = 0
self.q5 = 0
self.end_effector = None
self.objective = [0.209, 0.349,0]
self.epsilon_limit = 0.01 #Tolerance
low_angle=np.array([-1.6,-1.2]) # first parameter q2 , second parameter q5
high_angle=np.array([1.2,1.6]) # first parameter q2 , second parameter q5
self.action_space = spaces.Box(low_angle, high_angle, dtype=np.float32)
low = np.array([-60,80])
high = np.array([60,125])
self.observation_space = spaces.Box(low, high, dtype=np.float32)
self.seed()
self.viewer = None
self.state = None
def links_length_loader(self):
# rospack = rospkg.RosPack()
# fiver_path = rospack.get_path('fiver_description')+"/urdf/fiver.xacro"
fiver_path = "/home/erdi/catkin_ws/src/fiver/fiver_description/urdf/fiver.xacro"
tree = ET.parse(fiver_path)
root = tree.getroot()
k = np.array([list(elem.attrib.values()) for elem in root.iter("{http://www.ros.org/wiki/xacro}property")])
tmp = [np.argwhere(k == link_name) for link_name in list(self.link_lengths.keys())]
for i,link_name in enumerate(list(self.link_lengths.keys())):
self.link_lengths[link_name] = k[tmp[i][0][0],1]
return None
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, *action):
print("Step Function is Executed")
if len(action) == 1 and (isinstance(action[0],list) or isinstance(action[0],np.ndarray)):
self.q2,self.q5 = action[0]
self.q2,self.q5 = round(self.q2,2), round(self.q5 ,2)
elif len(action) >=2:
self.q2, self.q5 = round(action[0],2),round(action[1],2)
else:
print("Something go wrong in inputs for thetas")
rospy.set_param('q2',self.q2)
rospy.set_param('q5',self.q5)
rospy.sleep(1) # Wait until Gazebo done!!!
print("q2,q5:",self.q2,self.q5)
print(rospy.get_param('end_effector'))
self.end_effector = rospy.get_param('end_effector')
self.state = self.end_effector[0:2]
# self.state[2] parameter is the z axis and it is zero always
done = self.state[0] < self.objective[0] + self.epsilon_limit \
and self.state[0] > self.objective[0] - self.epsilon_limit\
and self.state[1] < self.objective[1] + self.epsilon_limit\
and self.state[1] > self.objective[1] - self.epsilon_limit
done = bool(done)
# self.state[2] parameter is the z axis and it is zero always. So there is no need in calculation
reward = self.epsilon_limit - math.sqrt((self.objective[0]-self.end_effector[0])**2+(self.objective[1]-self.end_effector[1])**2)
return np.array(self.state), reward, done, {}
def reset(self):
self.state = self.action_space.sample()
return np.array(self.state)
def render(self, mode='human'):
pass
def close(self):
pass
if __name__ == "__main__":
test = FiverEnv()
try:
test.step(0,0)
# p1 = multiprocessing.Process(target=run, args=(test.q2,test.q5))
# p1.start()
# p1.joint()
# test.p1.start()
# test.p1.join()
# test.run()
rospy.sleep(2)
test.step(-math.pi/3,-math.pi/7)
rospy.sleep(2)
# test.run()
# print("Erdi")
# rospy.spin()
except rospy.ROSInterruptException:
print ("Shutting down ROS Image feature detector module") |
16,413 | 83ad3f387fa0b86a90061d4361da52feae35b21f | class Solution:
""" 合并两个有序数组 """
def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
""" 双指针/从后往前
参数：
nums1:待插入整形数组
nums2:插入的整形数组
m:nums1存储数据数量
n:nums2存储数据数量
返回值：
时间复杂度：
O(m+n)
空间复杂度：
O(1)
"""
pm = m - 1
pn = n - 1
while pm > -1 and pn > -1:
if nums1[pm] >= nums2[pn]:
nums1[pm + pn + 1] = nums1[pm]
pm = pm - 1
else:
nums1[pm + pn +1] = nums2[pn]
pn = pn - 1
if pn > -1:
nums1[:pn + 1] = nums2[:pn + 1]
# class Solution:
# """ 合并两个有序数组 """
# def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
# """ 合并后排序
# 参数：
# nums1:待插入整形数组
# nums2:插入的整形数组
# m:nums1存储数据数量
# n:nums2存储数据数量
# 返回值：
# 时间复杂度：
# O((m+n)log(m+n))
# 空间复杂度：
# O(1)
# """
# nums1[:] = sorted(nums1[:m] + nums2)
# class Solution:
# """ 合并两个有序数组 """
# def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
# """ 双指针/从前往后
# 参数：
# nums1:待插入整形数组
# nums2:插入的整形数组
# m:nums1存储数据数量
# n:nums2存储数据数量
# 返回值：
# 时间复杂度：
# O(m+n)
# 空间复杂度：
# O(m)
# """
# nums1Copy = nums1[:m]
# pm = 0
# pn = 0
# nums1[:] = []
# while pm < m and pn < n:
# if nums1Copy[pm] <= nums2[pn]:
# nums1.append(nums1Copy[pm])
# pm = pm + 1
# else:
# nums1.append(nums2[pn])
# pn = pn + 1
# if pm < m:
# nums1[pm + pn:] = nums1Copy[pm:]
# if pn < n:
# nums1[pm + pn:] = nums2[pn:] |
16,414 | b2ccf2b88f0673aa692f35d230ecb88694d83db2 | # coding: utf-8
# Definition for a binary tree node.
class TreeNode(object):
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution(object):
def kthLargest(self, root, k):
"""
:type root: TreeNode
:type k: int
:rtype: int
"""
if root == None:
return None
res = []
def helper(root):
if root == None:
return
helper(root.left)
res.append(root.val)
helper(root.right)
helper(root)
return res[-k]
treeList = [5,3,6,2,4,None,None,1]
def CreateBineryTree(root, treeList, i):
if i < len(treeList):
if treeList[i] == None:
return None
else:
root = TreeNode(treeList[i])
root.left = CreateBineryTree(root.left, treeList, 2*i+1)
root.right = CreateBineryTree(root.right, treeList, 2*i+2)
return root
return root
root = CreateBineryTree(None, treeList, 0)
s = Solution()
s.kthLargest(root, k=3) |
16,415 | c17b3e7e29dd1454d5f1b40bfcd07365df77d3d5 | import base64
MESSAGE = 'GksfEA0ABAdLEhAPEBUGHgkEGkRNVB9WX1lcVwALGQBJQ1tUH1BDQVVXDAkIQkJDRhFeU19HREFG TFZFSQoPF0pQVFxSXgRLQEVJAgIcUVBGUF1XDxhLRVRDRgFWWV9WW1cFS0BFSREAFlpcREYXEltM SxYPBQRTFBUXU19dRkxWRUkUCBoZEk0='
KEY = 'allencat850502'
result = []
for i, c in enumerate(base64.b64decode(MESSAGE)):
result.append(chr(ord(c) ^ ord(KEY[i % len(KEY)])))
print(''.join(result))
|
16,416 | 9ff46483c3a99ae2d6574dbeb377187c71b6a057 | """Conway's Game of Life Simulation
Started: August 6th 2020
@author: Samuel T. Siaw"""
# Objective: Draw a canvas
# Draw grids on the canvas to represent cells
# Mouse click on a cell highlights the cell
from tkinter import *
from tkinter import ttk
from gol_algo import GameOfLife as gol
from Constants import constants as const
from random import randint
from tkinter import font as tkFont
import save_mode_gol as save
class Canvas_design(object):
def __init__(self, master):
self.root = master
self.root.title("Conway's Game of life")
self.root.iconbitmap("cek.ico")
self.con = const() #Initialize constants
self.heading() # Show Conway title above Canvas
self.canvas = Canvas(self.root, width = self.con.WIDTH, height = self.con.HEIGHT,bg = self.con.CANVAS_COLOUR)
self.grid_lines() #Draw lines in Canvas
self.canvas.bind("<Button-1>",func=self.onObjectClick)
self.canvas.pack()
self.algo = gol(self.canvas)
self.b1 = ttk.Button(self.root, text="Pause Button", command=self.button_func)
self.b2 = ttk.Button(self.root, text = "Click to run animation", command = self.run, state = "disabled")
self.b3 = ttk.Button(self.root, text = "Step", command = self.algo.automate)
self.reset = ttk.Button(self.root, text= "Reset", command = self.reset_func)
self.randomize = ttk.Button(self.root, text="Randomize", command = self.random_cells)
self.status_var = StringVar(self.root)
self.status_bar = ttk.Label(self.root, textvariable=self.status_var, relief= RIDGE, anchor = W)
self.save_button = ttk.Button(self.root, text = "Save", command= self.save_pattern)
self.open_button = ttk.Button(self.root, text="Open...", command = self.read)
self.status_bar.pack(side=BOTTOM, fill=X)
self.b1.pack(side = LEFT)
self.b3.pack(side = LEFT)
self.b2.pack(side = LEFT)
self.randomize.pack(side = RIGHT)
self.reset.pack(side = RIGHT)
self.save_button.pack()
self.open_button.pack()
self.root.resizable(False, False)
def heading(self):
titleFrame = Frame(self.root)
font = tkFont.Font(family="Script Mt Bold", size=20)
label = ttk.Label(titleFrame, text=" Conway's\nGame of Life", font=font)
titleFrame.pack()
label.pack()
def grid_lines(self):
# Add tags to all cells to find them easily.
for x in range(self.con.WIDTH//self.con.DIV):
x *= self.con.DIV
for y in range(self.con.HEIGHT // self.con.DIV):
y *= self.con.DIV
id = self.canvas.create_rectangle(x,y, x+self.con.DIV, y+self.con.DIV, fill = self.con.CANVAS_COLOUR)
self.canvas.addtag_withtag("dead", tagOrId=id)
def onObjectClick(self,event):
'''Handling mouse click events on objects(tiny rectangles)'''
mx, my = self.box_loc(event.x, event.y)
#self.status_bar.config(text = f"Cell at: {mx},{my}")
self.status_var.set(f"Cell at: {mx},{my}")
id = event.widget.find_closest(mx, my)[0]
if self.canvas.itemcget(id, "fill") == self.con.FILL_COLOUR: #Item already selected
self.canvas.itemconfigure(tagOrId=id, fill=self.con.CANVAS_COLOUR)
self.canvas.addtag_withtag("dead", id)
self.canvas.dtag(id, "live")
elif self.canvas.itemcget(id, "fill") == self.con.CANVAS_COLOUR: #Item not selected
self.canvas.itemconfigure(id, fill = self.con.FILL_COLOUR)
self.canvas.addtag_withtag("live", id)
self.canvas.dtag(id, "dead")
def box_loc(self,x,y):
'''Returns the coordinates of the box at the location provided'''
box_x = (x//self.con.DIV) * self.con.DIV
box_y = (y//self.con.DIV) * self.con.DIV
return box_x,box_y
def button_func(self):
if self.algo.con.run == 0:
self.algo.con.run = 1
self.b1.config(text="Click to Pause")
self.b2.config(state = "normal")
self.status_var.set("Animation enabled")
else:
self.algo.con.run = 0
self.b1.config(text="Click to run animation")
self.b2.config(state ="disabled")
self.status_var.set("Animation paused") # Update status bar
def run(self):
#print("Running")
if self.algo.automate():
self.status_var.set("Automatic animation running")
self.root.after(self.con.delay_timer, self.b2.invoke)
else:
self.b1.invoke()
self.status_var.set("Automation stopped. No more live cells/ valid moves")
def random_cells(self):
for id in self.canvas.find_all():
x = randint(0,10)
if x % 7 ==0 and self.canvas.type(id) == "rectangle" and not self.algo.is_live(id):
self.algo.dead_to_live(id)
def reset_func(self):
alive = self.canvas.find_withtag("live")
for id in alive:
self.algo.live_to_dead(id)
def read(self):
cells_list = save.read_file(self.con.WIDTH, self.con.HEIGHT)
if cells_list == False: self.status_var.set("Unable to load pattern")
else:
import re
pattern = re.compile(r"\d+")
found_num = pattern.findall(cells_list)
found_num = [int(l) for l in found_num]
print("Reading files now...")
self.reset_func()
for id in found_num:
self.algo.dead_to_live(id)
self.status_var.set("Pattern Loaded")
def save_pattern(self):
save.save(self.canvas.find_withtag("live"), self.con.WIDTH, self.con.HEIGHT)
self.status_var.set("Save Successful")
if __name__ == "__main__":
rt = Tk()
prog = Canvas_design(rt)
rt.mainloop()
|
16,417 | e3b7297b0f913b768f66a30cd7ee5468ea6135a6 | class User:
def __init__(self, name, email, int_rate, balance=0):
self.name=name
self.email=email
self.account=BankAccount(int_rate, balance=0)
def make_deposit(self, amount):
self.account.deposit(amount)
def make_withdrawal(self, amount):
self.account.withdraw(amount)
def display_user_balance(self):
print(self.name)
self.account.display_account_info()
def transfer_money(self, other_user, amount):
if amount < self.account.balance:
self.account.withdraw(amount)
other_user.deposit(amount)
else:
print("not enough money")
return self
class BankAccount:
def __init__(self, int_rate, balance=0):
self.int_rate=int_rate
self.balance=balance
def deposit(self, amount):
if amount >= 0:
self.balance += amount
else:
print("Cannot be negative")
return self
def withdraw(self, amount):
if amount < self.balance:
self.balance -= amount
else:
print("Insufficient funds: Charging a $5 fee")
self.balance -= 5
return self
def display_account_info(self):
print(f"Balance: ${self.balance}")
return self
def yield_interest(self):
if self.balance > 0:
self.balance = self.balance + (self.balance*self.int_rate)
return self
a=User("derek", "d@gmail.com", 0.01, 100)
a.make_deposit(100)
a.make_withdrawal(40)
a.display_user_balance()
|
16,418 | 36a16da09683d7106724f1909e67efaff8c9c560 | restarting...
pointer...
server into vps
wait/"linux","cantos","windows","PrankBots","vultra"/import PRANKBOTS or PB expel thrift=11 into file microscofe.txt for database
prank import wait poin.services
prank import database.server
from..prank, ranger, response, thrift, ttyps, rangerv2, PrankBots_v.04
import updater from ttyps..thrift..line
from LINE.DEVELOVER import PRANKBOTS
imposible UTF.8 UIX to PYTHON3
##INI JANGAN DI HAPUS DAN JANGAN DI OTAK ATIK##
#PRANKBOTS CREATOR
#Acil
|
16,419 | 80a3c76ada165f9a373b3d1582a14e41b17cead1 |
# coding: utf-8
# In[3]:
import copy
class TreeNode(object):
def __init__(self,x):
self.val = x
self.left = None
self.right = None
self.parent = None
class Solution(object):
def insert(self, root, val):
cur = root
while (cur != None):
p = cur
if val <= cur.val:
cur = cur.left
else:
cur = cur.right
cur=TreeNode(val)
cur.parent = p
if val <= p.val :
p.left=cur
else:
p.right=cur
return cur
def insert1(self,root,val):
if root is None:
root = TreeNode(val)
else:
if val > root.val:
if root.right is None:
node = TreeNode(val)
root.right = node
node.parent = root
else:
Solution().insert1(root.right, val)
else:
if root.left is None:
node = TreeNode(val)
root.left = node
node.parent = root
else:
Solution().insert1(root.left, val)
return root
def findroot(self,node):
while node.parent != None:
node=node.parent
return node
def search(self,root,target):
if root is None or root.val == target:
return root
elif target > root.val:
return Solution().search(root.right,target)
elif target <= root.val:
return Solution().search(root.left,target)
def preorder(self, root, lis):
lis.append(root.val)
if root.left:
Solution().preorder(root.left,lis)
if root.right:
Solution().preorder(root.right,lis)
return lis
def minValueNode(self,node):
cur= node
while(cur.left is not None):
cur = cur.left
return cur
def deletenode(self,root,target):
if root is None:
return root
if target < root.val:
root.left = Solution().deletenode(root.left,target)
elif(target > root.val):
root.right = Solution().deletenode(root.right,target)
else:
if root.left is None :
temp = root.right
root = None
return temp
elif root.right is None :
temp = root.left
root = None
return temp
temp = Solution().minValueNode(root.right)
root.val = temp.val
root.right = Solution().deletenode(root.right,temp.val)
return root
def delete(self,root,target):
while Solution().search(root,target)!=None:
root=Solution().deletenode(root,target)
return root
def modify(self, root, target, new_val):
liss=[]
liss=Solution().preorder(root, liss)
c=0
while target in liss:
c+=1
liss.pop(liss.index(target))
for _ in range(c):
root=Solution().insert1(root,new_val)
root=Solution().deletenode(root,target)
return root
root = TreeNode(5)
node1 = TreeNode(3)
node2 = TreeNode(3)
node3 = TreeNode(-5)
node4 = TreeNode(8)
node5 = TreeNode(7)
node6 = TreeNode(6)
node7 = TreeNode(10)
root.left = node1
node1.left = node2
node2.left = node3
root.right = node4
node4.left = node5
node5.left = node6
node4.right = node7
root1=copy.deepcopy(root)
root2=copy.deepcopy(root)
root3=copy.deepcopy(root)
root4=copy.deepcopy(root)
#insert
print('insert')
print(Solution().insert(root1,4)==root1.left.right)
print('-----------------------------------------------')
#delete
print('delete')
root2=Solution().delete(root2,3)
print(root2.val==5 and root2.left.val==-5 and root2.left.left==None and root2.left.right==None)
print(root2.right.right.val==10 and root2.right.left.val==7 and root2.right.left.left.val==6)
print(root2.right.right.right==None and root2.right.right.left==None and root2.right.left.right==None)
print(root2.right.left.left.left==None and root2.right.left.left.right==None and root2.right.val==8)
print('-----------------------------------------------')
#search
print('search')
print(Solution().search(root3,10)==root3.right.right)
print('-----------------------------------------------')
#modify
print('modify')
root4=Solution().modify(root4,7,4)
print(isBinarySearchTree(root4))
print('-----------------------------------------------')
|
16,420 | eceae9e847b2e9d7fc0b3ac2d3df4a2e2eadf8f4 | from django.db import models
from django.utils.encoding import python_2_unicode_compatible
class ItemModel(models.Model):
name = models.TextField()
name_slug = models.SlugField(blank=True)
pic = models.ImageField()
#Description of item
unique = models.TextField(blank=True)
notes = models.TextField(blank=True)
class Meta:
abstract = True
@python_2_unicode_compatible
class Potion(ItemModel):
category = models.TextField(default='Potion')
def __str__(self):
return self.name
class Meta:
ordering = ['pk']
@python_2_unicode_compatible
class Gem(ItemModel):
rank_unique = models.TextField(blank=True)
category = models.TextField(default='Gem')
def __str__(self):
return self.name
class Meta:
ordering = ['pk']
@python_2_unicode_compatible
class Material(ItemModel):
rarity = models.TextField(default='L')
stack_amount = models.TextField(default='5000')
category = models.TextField(default='Material')
#Specific type of material
slot = models.TextField(blank=True)
slot_slug = models.SlugField(blank=True)
def __str__(self):
return self.name
class Meta:
ordering = ['pk'] |
16,421 | 6b3ac149cba53eb764600efc12e96c10e51a8e85 | from ._utility import (
rolling_train_test_split,
get_features,
adf_test,
flatten_x_train,
flatten_x_test,
denoising_func
) # noqa
__all__ = (
'rolling_train_test_split',
'get_features',
'adf_test',
'flatten_x_train',
'flatten_x_test',
'denoising_func'
)
|
16,422 | 65fda8d6119881c4550e165194588452497226d1 | {
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import folium as fm\n",
"import pandas as pd\n",
"import json\n",
"\n",
"df=pd.read_csv(\"in.csv\")\n",
"list_lat=list(df[\"lat\"])\n",
"list_lng=list(df[\"lng\"])\n",
"cy=list(df[\"city\"])\n",
"p=list(df[\"population\"])\n",
"\n",
"def color(popu):\n",
" if popu>1000000:\n",
" return \"red\"\n",
" elif 300000<=popu<995000:\n",
" return \"orange\"\n",
" else:\n",
" return \"green\"\n",
"\n",
"\n",
"maps=fm.Map(location=[21,78],zoom_start=5,)\n",
"\n",
"fg=fm.FeatureGroup(name=\"my map\")\n",
"\n",
"for a,b,c,d in zip(list_lat,list_lng,cy,p):\n",
" fg.add_child(fm.CircleMarker(location=[a,b],radius=5,popup=[c,d],fill_color=color(d),color =\"grey\",fill_opacity=.9))\n",
"\n",
"\n",
"maps.add_child(fg)\n",
"maps.save(\"maps1.html\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js"></script>
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            var circle_marker_fa3350f7efdb45b4bae8d429ab33e4b4 = L.circleMarker(
                [11.248016, 75.780402],
                {"bubblingMouseEvents": true, "color": "grey", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "green", "fillOpacity": 0.9, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(feature_group_65dab855864d4467b1abafc3abaf222b);
        
    
        var popup_8acccd4e2c344d4c8c4ea68445fc8a2f = L.popup({"maxWidth": "100%"});

        
            var html_e812d94715a44f4aad46007cd30dd1f4 = $(`<div id="html_e812d94715a44f4aad46007cd30dd1f4" style="width: 100.0%; height: 100.0%;">['Calicut', nan]</div>`)[0];
            popup_8acccd4e2c344d4c8c4ea68445fc8a2f.setContent(html_e812d94715a44f4aad46007cd30dd1f4);
        

        circle_marker_fa3350f7efdb45b4bae8d429ab33e4b4.bindPopup(popup_8acccd4e2c344d4c8c4ea68445fc8a2f)
        ;

        
    
    
            var circle_marker_f5a74ef519444af68c6c34b3631e545a = L.circleMarker(
                [19.331589, 79.46605100000001],
                {"bubblingMouseEvents": true, "color": "grey", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "green", "fillOpacity": 0.9, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(feature_group_65dab855864d4467b1abafc3abaf222b);
        
    
        var popup_6c49263c6cb249c5813a4cfa59f54466 = L.popup({"maxWidth": "100%"});

        
            var html_fbb29ef801ff44f69d4714be36c27cb0 = $(`<div id="html_fbb29ef801ff44f69d4714be36c27cb0" style="width: 100.0%; height: 100.0%;">['Kagaznāgār', nan]</div>`)[0];
            popup_6c49263c6cb249c5813a4cfa59f54466.setContent(html_fbb29ef801ff44f69d4714be36c27cb0);
        

        circle_marker_f5a74ef519444af68c6c34b3631e545a.bindPopup(popup_6c49263c6cb249c5813a4cfa59f54466)
        ;

        
    
    
            var circle_marker_cf117d12d54044b2817ffa1689eb4bb5 = L.circleMarker(
                [26.913312, 75.787872],
                {"bubblingMouseEvents": true, "color": "grey", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "green", "fillOpacity": 0.9, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(feature_group_65dab855864d4467b1abafc3abaf222b);
        
    
        var popup_44b60efb311e46ca806a04faad7fbc71 = L.popup({"maxWidth": "100%"});

        
            var html_22a04a73d9914b8aa4dd78a1224000e7 = $(`<div id="html_22a04a73d9914b8aa4dd78a1224000e7" style="width: 100.0%; height: 100.0%;">['Jaipur', nan]</div>`)[0];
            popup_44b60efb311e46ca806a04faad7fbc71.setContent(html_22a04a73d9914b8aa4dd78a1224000e7);
        

        circle_marker_cf117d12d54044b2817ffa1689eb4bb5.bindPopup(popup_44b60efb311e46ca806a04faad7fbc71)
        ;

        
    
    
            var circle_marker_378f60e6d16b478fa3272f588f0bec22 = L.circleMarker(
                [23.216667, 72.68333299999999],
                {"bubblingMouseEvents": true, "color": "grey", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "green", "fillOpacity": 0.9, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(feature_group_65dab855864d4467b1abafc3abaf222b);
        
    
        var popup_1bc3396fb12e418ea42a90491f3ea955 = L.popup({"maxWidth": "100%"});

        
            var html_d0f62a41270b4895af8d601239348dd9 = $(`<div id="html_d0f62a41270b4895af8d601239348dd9" style="width: 100.0%; height: 100.0%;">['Ghandinagar', nan]</div>`)[0];
            popup_1bc3396fb12e418ea42a90491f3ea955.setContent(html_d0f62a41270b4895af8d601239348dd9);
        

        circle_marker_378f60e6d16b478fa3272f588f0bec22.bindPopup(popup_1bc3396fb12e418ea42a90491f3ea955)
        ;

        
    
    
            var circle_marker_07b8342d366f40a381aa63f0adddc4b3 = L.circleMarker(
                [30.691512, 76.853736],
                {"bubblingMouseEvents": true, "color": "grey", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "green", "fillOpacity": 0.9, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(feature_group_65dab855864d4467b1abafc3abaf222b);
        
    
        var popup_7c461d2d9a294ca6934a33264bc87757 = L.popup({"maxWidth": "100%"});

        
            var html_7c97968431ec4871a5a0356ee52de3a8 = $(`<div id="html_7c97968431ec4871a5a0356ee52de3a8" style="width: 100.0%; height: 100.0%;">['Panchkula', nan]</div>`)[0];
            popup_7c461d2d9a294ca6934a33264bc87757.setContent(html_7c97968431ec4871a5a0356ee52de3a8);
        

        circle_marker_07b8342d366f40a381aa63f0adddc4b3.bindPopup(popup_7c461d2d9a294ca6934a33264bc87757)
        ;

        
    
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x27433a2cac0>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
16,423 | 31c0bea725645082dc5e55a07d3a53eb58bf40ad | from Tkinter import *
import tkMessageBox
#import time
import plotly.plotly as py
import plotly.tools as tls
from plotly.graph_objs import *
class MainLoop:
def __init__(self, master,streamID, txt):
self.root = master
self.root.protocol("WM_DELETE_WINDOW", self.on_closing)
#Message box
msg = Message(self.root, text = txt)
msg.config(bg='lightgreen', font=('times', 24, 'italic'))
msg.pack()
#Binding callbacks + packing:
msg.bind('<Motion>',self.motion)
#Create instance of Stream LINK object, using same stream_id token:
self.s = py.Stream(stream_id)
self.s.open()
def motion(self,event):
#print("Mouse position: (%s %s)" % (event.x, event.y))
self.s.write(dict(x = event.x, y = 223-event.y))
return
def on_closing(self):
if tkMessageBox.askokcancel("Quit", "Do you want to quit?"):
self.root.destroy()
self.s.close()
if __name__ == "__main__":
#Tkinter window:
master = Tk()
txt = """Whatever you do will be insignificant, but it is very important that you do
it.\n(Mahatma Gandhi)"""
stream_ids = tls.get_credentials_file()['stream_ids']
stream_id = stream_ids[2]
stream = Stream(token = stream_id, maxpoints = 80)
#Create a trace with embedded stream id object:
trace1 = Scatter(
x=[], # init. data lists
y=[],
mode='lines', # path drawn as line
line=Line(color='rgba(255,76,76,0.45)'), # light blue line color
stream= stream
)
# Make data object made up of the 2 scatter objs
data = Data([trace1])
# Define dictionary of axis style options
axis_style = dict(
showgrid=False, # remove grid
showline=False, # remove axes lines
zeroline=False # remove x=0 and y=0 lines
)
# Make layout with title and set axis ranges
layout = Layout(
title='Following mouse pointer from Tkinter Win', # set plot's title
xaxis=XAxis(
axis_style, # add style options
range=[0,318] # set x-axis range
),
yaxis=YAxis(
axis_style, # add style options
range=[0,223] # set y-axis range
),
showlegend=False # remove legend
)
#Create Figure object:
fig = Figure(data = data, layout = layout)
unique_url = py.plot(fig, filename='Streaming-mouse-motion')
#Launching the class with stream LINK as arg
MainLoop(master,stream_id,txt)
master.mainloop() |
16,424 | 848acfcef760ce6c3c9b156f83a5d79992290c70 | # -*- coding: utf-8 -*-
"""
Created on Fri Jan 26 15:51:47 2018
@author: toti.cavalcanti
"""
#https://www.hackerrank.com/challenges/30-scope/problem
class Difference:
def __init__(self, a):
self.__elements = a
# Add your code here
def computeDifference(self):
max_diff = 0
for i in range(len(self.__elements)):
for j in range(len(self.__elements)):
val_abs = abs(self.__elements[i] - self.__elements[j])
if val_abs > max_diff:
max_diff = val_abs
self.maximumDifference = max_diff
# End of Difference class
_ = input()
a = [int(e) for e in input().split(' ')]
d = Difference(a)
d.computeDifference()
print(d.maximumDifference) |
16,425 | 0d3b244ecf0ae69a2fd2cb02672d73889378eefa | import math
import threading
from tensorflow.python.keras.utils import data_utils
import MF_RP_mat
import utils
from utils import synchronized_open_file
class RP_Sequence(data_utils.Sequence):
"""
for multi-channel RP mats, RP autoencoder
"""
def __init__(self, n_samples, batch_size, RP_mats_h5array):
self.n_samples = n_samples
self.batch_size = batch_size
self.RP_mats_h5array = RP_mats_h5array
def __getitem__(self, idx):
batch_slice = slice(idx * self.batch_size, (idx + 1) * self.batch_size)
batch_x = self.RP_mats_h5array[batch_slice, ...]
batch_x = utils.scale_RP_each_feature(batch_x)
batch_y = batch_x # train autoecoder, input is equal to output
return batch_x, batch_y
def __len__(self):
return math.ceil(self.n_samples / self.batch_size)
class FS_Sequence(data_utils.Sequence):
"""
for multi-feature segs, FS autoencoder
"""
def __init__(self, n_samples, batch_size, multi_feature_segs) -> None:
self.n_samples = n_samples
self.batch_size = batch_size
self.multi_feature_segs = multi_feature_segs
def __getitem__(self, idx):
batch_x = self.multi_feature_segs[idx * self.batch_size, (idx + 1) * self.batch_size]
batch_x = utils.scale_segs_each_features(batch_x)
batch_y = batch_x # train autoecoder, input is equal to output
return batch_x, batch_y
def __len__(self):
return math.ceil(self.n_samples / self.batch_size)
class RP_FS_Centroid_Sequence(data_utils.Sequence):
"""
for multi-feature segs, Dual-CSA autoencoder
"""
def __init__(self, n_samples, batch_size, RP_mats_h5array, multi_feature_segs, centroids, labels) -> None:
self.n_samples = n_samples
self.batch_size = batch_size
self.RP_mats_h5array = RP_mats_h5array
self.multi_feature_segs = multi_feature_segs
self.centroids = centroids
self.labels = labels
def __getitem__(self, idx):
print('*enter get item ')
batch_slice = slice(idx * self.batch_size, (idx + 1) * self.batch_size)
batch_x = [
# utils.scale_RP_each_feature(self.multi_channel_RP_mats[batch_slice, ...]),
self.RP_mats_h5array[batch_slice, ...],
self.centroids[batch_slice],
self.multi_feature_segs[batch_slice]
# utils.scale_segs_each_features(self.multi_feature_segs[batch_slice])
]
batch_y = [
# utils.scale_RP_each_feature(self.multi_channel_RP_mats[batch_slice, ...]),
self.RP_mats_h5array[batch_slice, ...],
self.labels[batch_slice],
# utils.scale_segs_each_features(self.multi_feature_segs[batch_slice]),
self.multi_feature_segs[batch_slice]
]
print('*end get item ')
return batch_x, batch_y
def __len__(self):
return math.ceil(self.n_samples / self.batch_size)
if __name__ == '__main__':
lock = threading.Lock()
RP_s = RP_Sequence(1000, 200, 'data\geolife_features\RP_mats_train.h5', lock)
RP_s.__getitem__(2)
|
16,426 | bac0d9d4214c1e3eb8e036f3a700214d914cdf3d | recipes = {
'Бутерброд с ветчиной': {'Хлеб': 50, 'Ветчина': 20, 'Сыр': 20},
'Салат Витаминный': {'Помидоры': 50, 'Огурцы': 20, 'Лук': 20, 'Майонез': 50, 'Зелень': 20}
}
store = {
'Хлеб': 250, 'Ветчина': 120, 'Сыр': 120,
'Помидоры': 50, 'Огурцы': 20, 'Лук': 20,
'Майонез': 50, 'Зелень': 20
}
def check_portions(food, count, recipes=recipes, store=store):
local_store = dict(store)
count_cooked = 0
if food in recipes:
for n in range(count):
for ing, ing_count in recipes[food].items():
if ing not in local_store.keys():
return (0, 0)
else:
if ing_count <= local_store[ing]:
local_store[ing] -= ing_count
else:
return (0, count_cooked)
count_cooked += 1
if (count_cooked >= count):
return (1, count)
else:
return (0, 0)
#print(check_portions('Бутерброд с ветчиной', 2))
#print(check_portions('Бутерброд с ветчиной', 10)) |
16,427 | 3a654e8f68501c5f4c6c903c2628ba1d49f9dd34 | # -*- coding: utf-8 -*-
"""
Created on Tue Mar 3 19:11:13 2020
@author: Henning
"""
import enum
class attack_types(enum.Enum):
#Specific attacks
APACHE2 = ["apache2"]
BACK = ["back"]
BUFFER_OVERFLOW = ["buffer_overflow"]
FTP_WRITE = ["ftp_write"]
GUESS_PASSWD = ["guess_passwd"]
HTTPTUNNEL = ["httptunnel"]
IMAP = ["imap"]
IPSWEEP = ["ipsweep"]
LAND = ["land"]
LOADMODULE = ["loadmodule"]
MAILBOMB = ["mailbomb"]
MSCAN = ["mscan"]
MULTIHOP = ["multihop"]
NAMED = ["named"]
NEPTUNE = ["neptune"]
NMAP = ["nmap"]
#Due to functionality in get_specific_recall, norrmal should not be included in larger attack type categories
NORMAL = ["normal"]
PERL = ["perl"]
PHF = ["phf"]
POD = ["pod"]
PORTSWEEP = ["portsweep"]
PROCESSTABLE = ["processtable"]
PS = ["ps"]
ROOTKIT = ["rootkit"]
SAINT = ["saint"]
SATAN = ["satan"]
SENDMAIL = ["sendmail"]
SMURF = ["smurf"]
SNMPGETATTACK = ["snmpgetattack"]
SNMPGUESS = ["snmpguess"]
SPY = ["spy"]
SQLATTACK = ["sqlattack"]
TEARDROP = ["teardrop"]
UDPSTORM = ["udpstorm"]
WAREZCLIENT = ["warezclient"]
WAREZMASTER = ["warezmaster"]
WORM = ["worm"]
XLOCK = ["xlock"]
XSNOOP = ["xsnoop"]
XTERM = ["xterm"]
#Attack groups
DOS = ["back", "land", "neptune", "pod", "smurf", "teardrop", "apache2",
"udpstorm", "processtable", "worm", "mailbomb"]
PROBE = ["satan", "ipsweep", "nmap", "portsweep", "mscan", "saint"]
R2L = ["guess_passwd", "ftp_write", "imap", "phf", "multihop", "warezclient", "warezmaster",
"xsnoop", "xlock", "snmpguess", "snmpgetattack", "httptunnel",
"sendmail", "named", "spy"]
U2R = ["buffer_overflow", "loadmodule", "rootkit", "perl", "xterm", "sqlattack", "ps"]
ALL_ATTACKS_BUT_DOS = PROBE + R2L + U2R
ALL_ATTACKS_BUT_PROBE = DOS + R2L + U2R
ALL_ATTACKS_BUT_R2L = DOS + PROBE + U2R
ALL_ATTACKS_BUT_U2R = DOS + PROBE + R2L
KDD_ATTACKS = DOS + PROBE + R2L + U2R
#Custom made attacks
MITM = ["MitM"]
MITM_NORMAL = ["MitM_normal"]
UDP_DOS = ["UDP_DOS"]
UDP_NORMAL = ["UDP_normal"]
SINKHOLE = ["sinkhole"]
SINKHOLE_NORMAL = ["sinkhole_normal"]
ALL_NORMALS = NORMAL + MITM_NORMAL + UDP_NORMAL + SINKHOLE_NORMAL
|
16,428 | 7665985c4f409a577e0c4273069f6d342df4b04d | # from test import test
from senti19.senti19.test import test_name
# test.test_name()
# Tests().test_print_name() |
16,429 | cb3f0cc8e3876db3ee3c4766ca5f4213ebb6d22b | from django.apps import AppConfig
class DjangoAppConfig(AppConfig):
name = 'django_app'
verbase = 'Django url name学习'
|
16,430 | 3a380fc79e2091d4ef25facfb81f113dbaecc655 | from PyQt5.QtCore import QDate, QDateTime, QFile, QTime, Qt
from PyQt5.QtGui import QFont, QTextCursor, QTextListFormat, QFont
from PyQt5.QtWidgets import QTextEdit
class EditorProxy:
"""
The editor class is a helper class that handles a lot of the manipulations
and relieves the main class from these tasks. These actions restrict themselves
to markup. Saving, loading and creating are up to the main class.
"""
def __init__(self, parent: QTextEdit):
"""
Ctor
:param parent:The parent here is the QTextEditor on any form
"""
self.parent = parent
def set_alignment_left(self):
"""
Align Left
"""
self.parent.setAlignment(Qt.AlignLeft)
def set_alignment_right(self):
"""
Align right
"""
self.parent.setAlignment(Qt.AlignRight)
def set_alignment_center(self):
"""
Align center
"""
self.parent.setAlignment(Qt.AlignCenter)
def set_alignment_justify(self):
"""
Justify text
"""
self.parent.setAlignment(Qt.AlignJustify)
def indent(self):
"""
Left indent the text
"""
cursor = self.parent.textCursor()
# Check if something is selected
if cursor.hasSelection():
# get the line/block nr
temp = cursor.blockNumber()
# Move to last line of the selection
cursor.setPosition(cursor.selectionEnd())
# calculate range of selection
diff = cursor.blockNumber() - temp
# Go over all the selected lines
for n in range(diff + 1):
cursor.movePosition(QTextCursor.StartOfLine)
# insert tab
cursor.insertText("\t")
# move back up
cursor.movePosition(QTextCursor.Up)
else:
# There is no selection, simply insert a TAB
cursor.movePosition(QTextCursor.StartOfLine)
cursor.insertText("\t")
def dedent(self):
"""
Unindent the text
"""
cursor = self.parent.textCursor()
# Check if something is selected
if cursor.hasSelection():
# get the line/block nr
temp = cursor.blockNumber()
# Move to last line of the selection
cursor.setPosition(cursor.selectionEnd())
# calculate range of selection
diff = cursor.blockNumber() - temp
# Go over all the selected lines
for n in range(diff + 1):
self.handle_dedent(cursor)
# move back up
cursor.movePosition(QTextCursor.Up)
else:
# There is no selection, simply insert a TAB
self.handle_dedent(cursor)
def handle_dedent(self, cursor: QTextCursor):
"""
Dedent the selection
:param cursor: Current active cursor in unindent action
"""
cursor.movePosition(QTextCursor.StartOfLine)
# Grab the current line
line = cursor.block().text()
# Is the line starting with a TAB?
if line.startswith("\t"):
# Delete TAB
cursor.deleteChar()
else:
# Delete all spaces until a non space character is met
for char in line[:8]:
if char != " ":
break
cursor.deleteChar()
def set_font_family(self, font):
"""
Set the editors' font
"""
self.parent.setCurrentFont(font)
def set_font_family_default(self):
"""
Set the editor's default font
"""
font = QFont('Arial', 12)
self.parent.setCurrentFont(font)
def set_font_size(self, fontsize):
"""
Change the font size
"""
self.parent.setFontPointSize(fontsize)
def set_fontbold(self):
"""
Set or unset font Bold
"""
if self.parent.fontWeight() == QFont.Bold:
self.parent.setFontWeight(QFont.Normal)
else:
self.parent.setFontWeight(QFont.Bold)
def set_fontitalic(self):
"""
Set or unset font Italic
"""
state = self.parent.fontItalic()
self.parent.setFontItalic(not state)
def set_fontunderline(self):
"""
Set or unset font Underline
"""
state = self.parent.fontUnderline()
self.parent.setFontUnderline(not state)
def set_fontstrikethrough(self):
"""
Strikethrough
"""
fmt = self.parent.currentCharFormat()
fmt.setFontStrikeOut(not fmt.fontStrikeOut())
self.parent.setCurrentCharFormat(fmt)
def insert_heading(self, heading):
"""
Set Heading type
:param heading: 1 - 5 for different header format
"""
fontsize = 0
if heading == 0:
font = QFont('Arial')
self.parent.setCurrentFont(font)
self.parent.setFontWeight(QFont.Normal)
self.parent.setFontPointSize(12)
self.parent.setFocus()
return
if heading == 1:
fontsize = 40
if heading == 2:
fontsize = 35
if heading == 3:
fontsize = 30
if heading == 4:
fontsize = 25
if heading == 5:
fontsize = 20
cursor = self.parent.textCursor()
font = QFont('Arial')
self.parent.setCurrentFont(font)
self.parent.setFontWeight(QFont.Bold)
self.parent.setFontPointSize(fontsize)
self.parent.setFocus()
def insert_bulleted_list(self):
"""
Insert bulleted list
"""
cursor = self.parent.textCursor()
cursor.insertList(QTextListFormat.ListDisc)
def insert_numbered_list(self):
"""
Insert a numbered list
"""
cursor = self.parent.textCursor()
cursor.insertList(QTextListFormat.ListDecimal)
def insert_date_text(self):
"""
Insert current date
"""
cursor = self.parent.textCursor()
cursor.insertText(QDate().currentDate().toString())
def insert_time_text(self):
"""
Insert current time
"""
cursor = self.parent.textCursor()
cursor.insertText(QTime().currentTime().toString(Qt.DefaultLocaleShortDate))
|
16,431 | 79bf50600eeff55853f79c66c1a53e483444b84c | from torch.utils.data import Dataset,DataLoader
import numpy as np
from PIL import Image
from torchvision import transforms,utils
# 数据加载预处理
def default_loader(path):
im = Image.open(path).convert('RGB')
im = np.asarray(im.resize((224,224)))
# print("im.shape",im.shape)
return im
class MyDataset(Dataset):
def __init__(self,txt,transform = None,target_transform=None,loader = default_loader):
f = open(txt,'r')
# 从train.txt获取 txt ,标签类型
self.folder = txt.split('/')[-1].split('.')[0]
imgs = []
for line in f.readlines():
img_name = line.split()[0]
label = line.split()[1]
imgs.append((img_name,int(label)))
self.imgs = imgs
self.transform = transform
self.target_transform = target_transform
self.loader = loader
def __getitem__(self,index):
# 类对象的处理方法
# 类似C++ 对象重载
img_name ,label = self.imgs[index]
img_path = './data/'+ self.folder +'/'+img_name
img = self.loader(img_path)
if self.transform is not None :
img = self.transform(img)
return img,label
def __len__(self):
return len(self.imgs)
|
16,432 | 871a2d7f8b7aeff6299ff38493e1d4266a2eb198 | from sqlalchemy import *
from migrate import *
from migrate.changeset import schema
pre_meta = MetaData()
post_meta = MetaData()
post = Table('post', pre_meta,
Column('id', INTEGER, primary_key=True, nullable=False),
Column('body', VARCHAR(length=140)),
Column('timestamp', DATETIME),
Column('user_id', INTEGER),
)
survey1 = Table('survey1', pre_meta,
Column('id', INTEGER, primary_key=True, nullable=False),
Column('incident', VARCHAR(length=50)),
Column('address', VARCHAR(length=50)),
Column('rider', BOOLEAN),
)
survey2 = Table('survey2', pre_meta,
Column('id', INTEGER, primary_key=True, nullable=False),
Column('userid', VARCHAR(length=255)),
)
RoadQuality = Table('RoadQuality', post_meta,
Column('id', Integer, primary_key=True, nullable=False),
Column('incident_id', Integer),
Column('muni_tracks', Boolean),
Column('potholes', Boolean),
Column('loose_materials_on_roadway', Boolean),
Column('obstruction_on_roadway', Boolean),
Column('construction', Boolean),
Column('reduced_roadway_width', Boolean),
Column('flooded', Boolean),
Column('other_roadway_issue', String(length=50)),
)
VehicleViolation = Table('VehicleViolation', post_meta,
Column('id', Integer, primary_key=True, nullable=False),
Column('stopped', Boolean),
Column('driving_straight', Boolean),
Column('ran_off_road', Boolean),
Column('turning_right', Boolean),
Column('turning_left', Boolean),
Column('u_turn', Boolean),
Column('backing_up', Boolean),
Column('changing_lanes', Boolean),
Column('slowing_down', Boolean),
Column('entering_traffic', Boolean),
Column('parking', Boolean),
)
crash_incident = Table('crash_incident', post_meta,
Column('id', Integer, primary_key=True, nullable=False),
Column('injury_severity', String(length=50)),
Column('type_of_bike', String(length=50)),
Column('name_of_street', String(length=50)),
Column('building_address', Integer),
Column('cross_street', String(length=50)),
Column('year_of_crash', Integer),
Column('month_of_crash', Integer),
Column('day_of_week', String(length=10)),
Column('approx_time', Integer),
Column('address', String(length=20)),
Column('rider', Boolean),
Column('holiday', Boolean),
Column('road_conditions', Boolean),
Column('vehicle_violations', Boolean),
Column('lighting_conditions', Boolean),
Column('road_surface', Boolean),
Column('another_vehicle', Boolean),
Column('other', String(length=50)),
)
def upgrade(migrate_engine):
# Upgrade operations go here. Don't create your own engine; bind
# migrate_engine to your metadata
pre_meta.bind = migrate_engine
post_meta.bind = migrate_engine
pre_meta.tables['post'].drop()
pre_meta.tables['survey1'].drop()
pre_meta.tables['survey2'].drop()
post_meta.tables['RoadQuality'].create()
post_meta.tables['VehicleViolation'].create()
post_meta.tables['crash_incident'].create()
def downgrade(migrate_engine):
# Operations to reverse the above upgrade go here.
pre_meta.bind = migrate_engine
post_meta.bind = migrate_engine
pre_meta.tables['post'].create()
pre_meta.tables['survey1'].create()
pre_meta.tables['survey2'].create()
post_meta.tables['RoadQuality'].drop()
post_meta.tables['VehicleViolation'].drop()
post_meta.tables['crash_incident'].drop()
|
16,433 | 91ace0fbd499111ba72b33ac98cc06691daec829 | import requests, prettify
from bs4 import BeautifulSoup
page = requests.get('http://www.cricbuzz.com/cricket-match/live-scores')
soup = BeautifulSoup(page.text, 'html5lib')
head_line = soup.findAll("h1", {"class": "cb-schdl-hdr cb-font-24 line-ht30"})
print(head_line[0].text)
print("------------------")
tours = soup.findAll("h2", {"class": "cb-lv-grn-strip text-bold cb-lv-scr-mtch-hdr"})
match = soup.findAll("h3", {"class":"cb-lv-scr-mtch-hdr inline-block"}, "title")
score = soup.findAll("div", {"class":"cb-lv-scrs-col text-black"})
for i, j, k in zip(range(len(tours)), range(len(match)), range(len(score))):
print("Tour: " + tours[i].text)
print(" ")
print(match[j].text)
print(score[k].text)
print(" ")
print("*********************************************")
print(" ")
|
16,434 | 1958eb14e5565dd5e45aff9f8c33f238880c2c02 | """Core Explore Exampe Type App Settings
"""
from django.conf import settings
if not settings.configured:
settings.configure()
|
16,435 | 0e8f23c50b612f62863490a63cfee09f596c4ab0 | class ListNode:
def __init__(self, x):
self.val = x
self.next = None
class Solution:
def rotateRight(self, head: ListNode, k: int) -> ListNode:
if not head:
return
nums=[]
root=head
while head:
nums.append(head.val)
head=head.next
length=len(nums)
if k>length:
k=k%length
start=-k
head=root
for i in range(length):
head.val=nums[start]
start+=1
head=head.next
return root |
16,436 | 3c9ac92ced4f605d49815a2defee6d2a7352356c | # square the numbers in a list and store it
num = [1,2,3,4,5,6,7,8,9]
nums = []
for ele in num:
nums.append(ele*ele)
print(nums) |
16,437 | bd2240e48294ed591a728371eff962a160dddcd0 | from nerodia.browser import Browser
br = Browser(browser="firefox")
br.goto("https://www.w3schools.com/html/html_form_elements.asp")
br.element(css="textarea[cols='30']").send_keys("hello world")
br.element(css="textarea[cols='30']").send_keys([COMMAND + 't'])
browser.close()
|
16,438 | f726a43754f906218fe5467fc61cf2749871ab1b | # Test section of audio_core class
# Last Modificication from D3Rnatch
import sys
import time
sys.path.insert(0, 'src/')
from audio import *
# creating and starting the audio core system.
# has record mode.
audio = audio_core()
frames = []
# we get 500 chunks from audio input device
# but first enable continuous reading is necessary !
# audio.enable_continuousReading()
for i in range(0,100) :
data = audio.Read()
frames.append(data)
audio.createWaveFile("test.wav", frames)
# we play those 500 chunks directly
#audio.disable_continuousReading()
audio.enable_continuousPlay()
for i in range(0,100) :
audio.Play(frames[i])
#time.sleep(0.01)
# we close the audio core
audio.stop_audioCore()
# uncomment this section to test reading file mode
# restarting the audio core
# we read the file, until data == -1
# we output the file
# we close the stuff
|
16,439 | e63c54abe64f1ef466ee982635b40555f121b800 | # format_a = "{}".format(10)
# format_b = "{} {}".format(10,20)
# format_c = "파이썬 열공하여 첫 연봉 {}만원 만들기".format(5000)
# print(format_a)
# print(format_b)
# print(type(format_b))
# print(format_c)
# output_a ="{:010d}".format(-30)
# output_b="{:010f}".format(3.141592)
# Pi= 3.141592
# output_c = "{:g}".format(52.000)
# print(output_a)
# print(output_b)
# print(output_c)
# input_s="Hello Python Programing!!"
# input_t="""
# 안녕하세요
# 테스트입니다
# """
# print(input_s.upper())
# print(input_s.lower())
# print(input_t.strip())
# check_str = "test"
# print(check_str.islower())
# print("하세" in "안녕하세요")
a="10|20|30|40".split("|")
print(a)
|
16,440 | 5546f2c6ca672c4806001830043d34050cd8a354 | '''
Description: Editor's info in the top of the file
Author: p1ay8y3ar
Date: 2021-03-31 22:56:03
LastEditor: p1ay8y3ar
LastEditTime: 2021-04-01 13:44:17
Email: p1ay8y3ar@gmail.com
'''
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'pks_signup.ui'
#
# Created by: PyQt5 UI code generator 5.13.1
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_RegisterUI(object):
def setupUi(self, RegisterUI):
RegisterUI.setObjectName("RegisterUI")
RegisterUI.resize(375, 185)
self.label = QtWidgets.QLabel(RegisterUI)
self.label.setGeometry(QtCore.QRect(40, 30, 60, 16))
self.label.setObjectName("label")
self.label_2 = QtWidgets.QLabel(RegisterUI)
self.label_2.setGeometry(QtCore.QRect(40, 70, 60, 16))
self.label_2.setObjectName("label_2")
self.label_3 = QtWidgets.QLabel(RegisterUI)
self.label_3.setGeometry(QtCore.QRect(40, 110, 60, 16))
self.label_3.setObjectName("label_3")
self.le_username = QtWidgets.QLineEdit(RegisterUI)
self.le_username.setGeometry(QtCore.QRect(130, 30, 191, 21))
self.le_username.setObjectName("le_username")
self.le_pwd = QtWidgets.QLineEdit(RegisterUI)
self.le_pwd.setGeometry(QtCore.QRect(130, 70, 191, 21))
self.le_pwd.setObjectName("le_pwd")
self.le_pwd_again = QtWidgets.QLineEdit(RegisterUI)
self.le_pwd_again.setGeometry(QtCore.QRect(130, 110, 191, 21))
self.le_pwd_again.setObjectName("le_pwd_again")
self.bt_register = QtWidgets.QPushButton(RegisterUI)
self.bt_register.setGeometry(QtCore.QRect(220, 140, 113, 32))
self.bt_register.setObjectName("bt_register")
self.retranslateUi(RegisterUI)
QtCore.QMetaObject.connectSlotsByName(RegisterUI)
def retranslateUi(self, RegisterUI):
_translate = QtCore.QCoreApplication.translate
RegisterUI.setWindowTitle(_translate("RegisterUI", "Register"))
self.label.setText(_translate("RegisterUI", "username"))
self.label_2.setText(_translate("RegisterUI", "password"))
self.label_3.setText(_translate("RegisterUI", "password"))
self.le_username.setPlaceholderText(_translate("RegisterUI", "length more than 8"))
self.le_pwd.setPlaceholderText(_translate("RegisterUI", "length more than 8"))
self.le_pwd_again.setPlaceholderText(_translate("RegisterUI", "please input agin"))
self.bt_register.setText(_translate("RegisterUI", "OK"))
|
16,441 | e191718a5605aa22fefa23209b80ce90a7066c80 |
Regex: (alt\=\"[A-Z0-9]*\_[A-Z]*\")
Match: alt="F1000025_square"
Regex: (\(ID\: ([0-9]{7})\))
Match: (ID: 1234567)
|
16,442 | d563fd7c670c33428a5a624f24ffdeb7e254b9f2 | import argparse
import re
parser = argparse.ArgumentParser(description= "", add_help=False)
parser.add_argument('-l', metavar='lamp',
help='LAMP primers from laval software')
parser.add_argument('-p', metavar='csv',
help='position in a csv file')
args = parser.parse_args()
primers = open(args.l, "r").readlines()
pos = open(args.p, "r").readline().replace("\n","")
whole_matches = []
for i in primers[::16]:
tmp = i.replace("\n","")
tmp2 = re.sub( ".*\(locations: (.*)\)","\\1", tmp )
tmp3 = re.findall("[0-9]+-[0-9]+", tmp2)
# tmp3 = ['262-284', '468-489', '302-321', '446-466', '366-388', '392-412']
p_matches = []
for tmp4 in tmp3:
## [262, 284]
tmp5 = [ int(y) for y in tmp4.split('-')]
matches = 0
for tmp_pos in pos.split(","):
if int(tmp_pos) in range( tmp5[0], tmp5[1] ):
matches += 1
p_matches.append( matches )
whole_matches.append( p_matches )
sum_wp = []
sum_p = []
for tmp_sum in [ list( reversed(tmp_rev) ) for tmp_rev in whole_matches ]:
sum_wp.append( str( sum( tmp_sum ) ) )
sum_p.append(
":#match per primer: " + "(" + ",".join( [ str(l) for l in tmp_sum ] ) + ")"
)
iterThat = zip(primers[9::16 ],
primers[11::16],
primers[5::16 ],
primers[7::16 ],
primers[1::16 ],
primers[3::16 ],
sum_wp,
sum_p )
for f1,b1,f2,b2,f3,b3,ws,s in iterThat:
print(
",".join(
[f1.replace("\n",""),
b1.replace("\n",""),
f2.replace("\n",""),
b2.replace("\n",""),
f3.replace("\n",""),
b3.replace("\n",""),
ws,
s]
)
)
|
16,443 | 62e0d5bee6e7136057404706360c6bc365f7d526 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, precision_score, recall_score
## Part 1: read/load data
def read_data(fn, filetype = "csv"):
if filetype == "csv":
return pd.read_csv(fn)
if filetype == "excel":
return pd.read_excel(fn)
if filetype == "sql":
return pd.read_sql(fn, con=conn)
else:
return print("I only have CSVs at the moment!")
## Part 2: explore data
def take_sample(df, fraction):
return df.sample(frac = fraction)
def show_columns(df):
return df.columns
def descrip_stats(df):
return df.describe()
def counts_per_variable(df, x):
return df.groupby(x).size()
def group_and_describe(df, x):
return df.groupby(x).describe()
def ctab_percent(df, x, y):
return pd.crosstab(df.loc[:, x], df.loc[:,y], normalize='index')
def ctab_raw(df, x, y):
return pd.crosstab(df.loc[:, x], df.loc[:,y])
def basic_hist(df, x, title_text):
sns.distplot(df[x]).set_title(title_text)
plt.show()
return
def basic_scatter(df, x, y, title_text):
g = sns.lmplot(x, y, data= df)
g = (g.set_axis_labels(x, y).set_title(title_text))
plt.show()
return
def correlation_heatmap(df, title_text):
corrmat = df.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(corrmat, vmax=.8, square=True).set_title(title_text)
plt.show()
return
def basic_boxplot(df, colname, title_text):
sns.boxplot(y=df[colname]).set_title(title_text)
plt.show()
return
## Part III: Pre-processing data
def show_nulls(df):
return df.isna().sum().sort_values(ascending=False)
def fill_whole_df_with_mean(df):
num_cols = len(df.columns)
for i in range(0, num_cols):
df.iloc[:,i] = fill_col_with_mean(df.iloc[:,i])
return
def fill_allNA_mode(df):
num_col = len(df.columns.tolist())
for i in range(0,num_col):
df_feats.iloc[:,i] = df_feats.iloc[:,i].fillna(df_feats.iloc[:,i].mode()[0])
return df
def fill_col_with_mean(df):
return df.fillna(df.mean())
def left_merge(df_left, df_right, merge_column):
return pd.merge(df_left, df_right, how = 'left', on = merge_column)
# generating features
def generate_dummy(df, colname, attach = False):
# generate dummy variables from a categorical variable
# if attach == True, then attach the dummy variables to the original dataframe
if (attach == False):
return pd.get_dummies(df[colname])
else:
return pd.concat([df, pd.get_dummies(df[colname])], axis = 1)
def discret_eqlbins(df, colname, bin_num):
# cut continuous variable into bin_num bins
return pd.cut(df[colname], bin_num)
def discret_quantiles(df, colname, quantile_num):
# cut cont. variable into quantiles
return pd.qcut(df[colname], quantile_num)
# feature-scaling
from sklearn import preprocessing
#min_max_scaler = preprocessing.MinMaxScaler()
#df_scaled = min_max_scaler.fit_transform(df)
# standardize data
# scaled_column = scale(df[['x','y']])
from sklearn.preprocessing import scale
def scale_df(df, features_list):
temp_scaled = scale(df[features_list])
#return a DF
return pd.DataFrame(temp_scaled, columns= df.columns)
# split data into training and test sets
from sklearn.model_selection import train_test_split
def split_traintest(df_features, df_target, test_size = 0.2):
X_train, X_test, Y_train, Y_test = train_test_split(df_features, df_target, test_size = test_size)
return X_train, X_test, Y_train, Y_test
# methods for training classifiers
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
def fit_randomforest(x_train, y_train, feature_number, num_trees, depth_num, criterion_choice):
rf_clf = RandomForestClassifier(max_features = feature_number, n_estimators = num_trees, max_depth = depth_num, criterion = criterion_choice)
rf_clf.fit(x_train,y_train)
return rf_clf
def fit_svm(x_train, y_train, c_value, kern, rbf_gam):
svm_clf = SVC(C = c_value, kernel = kern, gamma = rbf_gam, probability = True)
svm_clf.fit(x_train, y_train)
return svm_clf
def fit_naivebayes(x_train, y_train, alpha_value):
nb_clf = MultinomialNB(alpha = alpha_value)
nb_clf.fit(x_train,y_train)
return nb_clf
def fit_knn(x_train, y_train, neighbor_num, distance_type, weight_type):
knn_clf = KNeighborsClassifier(n_neighbors= neighbor_num, metric= distance_type, weights = weight_type)
knn_clf.fit(x_train, y_train)
return knn_clf
def fit_dtree(x_train, y_train, crit_par, split_par, maxdepth_par, minsplit_par,maxfeat_par, minleaf_par, maxleaf_par):
dt_clf = DecisionTreeClassifier(criterion = crit_par, splitter = split_par, max_depth = maxdepth_par, min_samples_split = minsplit_par, max_features = maxfeat_par, min_samples_leaf = minleaf_par, max_leaf_nodes = maxleaf_par)
dt_clf.fit(x_train, y_train)
return dt_clf
def fit_logit(x_train, y_train, penalty_para, c_para):
logit_clf = LogisticRegression(penalty = penalty_para, C = c_para)
logit_clf.fit(x_train,y_train)
return logit_clf
# grid methods
from sklearn.model_selection import GridSearchCV
def grid_cv(clf, param_grid, scoring, cv, x_train, y_train):
# initialize the grid, scoring = a scoring metric or a dictionary of metrics,
# refit is necessary when u have a list of scoring metrics, it determines how the gridsearch algorithm decides the best estimator.
grid = GridSearchCV(clf(), param_grid, scoring = scoring, cv= cv)
grid.fit(x_train, y_train)
# call the best classifier:
grid.best_estimator_
# see all performances:
return grid
def grid_cv_mtp(clf, param_grid, scoring, cv = 5, refit_metric = 'roc'):
# initialize the grid, scoring = a scoring metric or a dictionary of metrics,
# refit is necessary when u have a list of scoring metrics, it determines how the gridsearch algorithm decides the best estimator.
grid = GridSearchCV(clf(), param_grid, scoring = scoring, cv= cv_num, refit = refit_metric)
grid.fit(x_train, y_train)
# call the best classifier:
grid.best_estimator_
# see all performances:
return grid
model_params ={
RandomForestClassifier: {
'max_features': ["auto", "sqrt", "log2", 0.2],
'n_estimators' : [5, 10, 20, 50, 100, 300, 500],
"max_depth": [3,5,8],
"criterion": ["gini", "entropy"]
},
SVC:{
"C": [10**i for i in range(-5, 5)],
"kernel":["linear", "rbf"],
"gamma": [10**i for i in np.arange(0, 1, 0.05)],
"probability": [True]
},
MultinomialNB:{
"alpha": [1, 5, 10, 25, 100]
},
KNeighborsClassifier:{
"n_neighbors":[3,5,8,10, 13,15,20,25,30,50],
"metric": ["euclidean", "manhattan", "chebyshev" ],
"weights":["uniform", "distance"]
},
DecisionTreeClassifier:{
"criterion": ["gini", "entropy"],
"splitter": ["best", "random"],
"max_depth": [None, "auto", "sqrt", "log2", 5, 0.3 ],
"min_samples_split": [1, 3, 5, 7, 9 ,15 ,20],
"max_features": [2, 3, 4, 5],
"min_samples_leaf": [1,2,3,4,5],
"max_leaf_nodes": [None, 2, 3 ,4, 5]
},
LogisticRegression:{
"penalty": ['l1', 'l2'],
"C": [10**-5, 10**-2, 10**-1, 1, 10, 10**2, 10**5]
},
GradientBoostingClassifier:{
'loss': ["deviance", "exponential"],
'learning_rate': [0.01, 0.1, 0.2, 0.3],
'n_estimators': [3, 6, 10, 20, 100, 200, 500]
}
}
def classifier_comparison(model_params, x_train, y_train, eva_metric, cv_num):
comparison_results = {}
for model, param_grid in model_params.items():
# initialize gridsearch object
grid = GridSearchCV(clf(), param_grid, scoring = eva_metric, cv= cv_num)
grid.fit(x_train, y_train)
comparison_results[model] ={}
comparison_results[model]['cv_results'] = grid.cv_results_
comparison_results[model]['best_estimator'] = grid.best_estimator_
comparison_results[model]['best_score'] = grid.best_score_
comparison_results[model]['best_params'] = grid.best_params_
return comparison_results
## Part VI: Evaluating the classifier
#generate predictions according to a custom threshold
def make_predictions(clf, x_test, threshold = 0.7):
# threshold = the probability threshold for something to be a 0.
# generate array with predicted probabilities
pred_array = clf.predict_proba(x_test)
# initialize an empty array for the predictions
pred_generated = np.array([])
# predict the first entry
if pred_array[0][0] >= threshold:
pred_generated = np.hstack([pred_generated, 0])
else:
pred_generated = np.hstack([pred_generated, 1])
# loops over the rest of the array
for i in range(1,len(x_test)):
if pred_array[i][0] >= threshold:
pred_generated = np.vstack([pred_generated, 0])
else:
pred_generated = np.vstack([pred_generated, 1])
# return an np.array
return pred_generated
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, precision_score, recall_score
def evaluateAccuracy(clf,predictDF, truthDF):
correct_pred = 0
pred_x = clf.predict(predictDF)
for i in range(0,len(predictDF)):
if pred_x[i] == truthDF.iloc[i]:
correct_pred +=1
return (correct_pred/len(predictDF))
# temporal validation
from dateutil import parser
def create_datetime(df, colname):
# creates a new column with datetimem objects
return df[colname].apply(parser.parse)
def retrieve_year(df, date_column):
return df[date_column].map(lambda x: x.year)
def retrieve_month(df, date_column):
return df[date_column].map(lambda x: x.month)
def retrieve_day(df, date_column):
return df[date_column].map(lambda x: x.day)
# a procedure for temporal validation:
# train data by year. test data on a subset of the next year's data
# do I have to do this manually or?
# def temp_valid_year(x_train, y_train, cv_time_thresholds):
def split_traintest(df_features, df_target, test_size = 0.2):
X_train, X_test, Y_train, Y_test = train_test_split(df_features, df_target, test_size = test_size)
return X_train, X_test, Y_train, Y_test
|
16,444 | 86e286b264daae5b06ca4eb995de4702c1eb8da2 | #change the first character occurence in a string to "$", except the first character itself
my_str = input("Enter the string: ")
print((my_str.replace(my_str[0], "$")).replace("$", my_str[0], 1)) |
16,445 | f926fe0fccc24e95074f12812041b58a27e4e34b | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 26 10:39:41 2019
@author: antoineleblevec
Programme utilisé avant la découverte de Panda
Pas sur qu'il soit encore utile
"""
# =============================================================================
# Modules à importer
# =============================================================================
import numpy as np
import os.path
import os
from os import scandir
import matplotlib.pyplot as plt
import csv
from os import chdir
from matplotlib.font_manager import FontProperties
from mpl_toolkits.basemap import Basemap
import fonctions as f1
from scipy.signal import savgol_filter
import matplotlib.animation as animation
# =============================================================================
# Constantes
# =============================================================================
Re = 6371032
H = 350e3
lllat = 31.7; urlat = 43.7; lllon = 130.6; urlon = 145.6
time_inf = 10.5
time_sup = 13.
min_ele = 10
# =============================================================================
# Lecture des fichiers
# =============================================================================
year = str('2016')
day = str('318')
station = 'hast'
type_sat = 'GPS'
#name_dir = os.path.join('/Users/antoineleblevec/Desktop/G20', year, day, station, type_sat)
#name_dir = os.path.join('/Users/antoineleblevec/Desktop/tec-suite-master_2/tec', year, day)
name_dir = os.path.join('/Users/antoineleblevec/Desktop/G20')
print (name_dir)
rep = os.path.abspath(os.path.expanduser(name_dir))
files = os.listdir(rep)
print(files)
#Boucle pour parcourir tous les fichiers dans le dossier
for i in range(0,len(files)) :
#stockage du nom du satellite
name_file = files [i]
x = files[i].split("_")
# sat = x [i]
# print (sat)
#
# #lecture du fichier
data = np.loadtxt(rep + '/' + name_file)
#
# # calcul de la longitude et lattitude de la station
rlon, rlat = f1.lecture_lat_lon_sat(rep, name_file)
#
# # sélection de la période d'observation du séisme
a = data[:,1]
d = a [(time_inf < a) & (a < time_sup)]
#
# # elevation supérieure au sueil et pour des données dans la fenêtre du séisme
ele = data [:,2]
elevation = ele [(time_inf < a) & (a < time_sup) & (ele > min_ele)]
#
# # élimination des satellites inintéressants
# # si ne répond pas aux critères alors itération suivante dans la boucle
if (len(elevation) < 30) :
print("%s bad timing or bad elevation" %sat)
continue
#
# # azimuth correspondant à la "bonne" elevation
az = data [:,3]
az = az [(time_inf < a) & (a < time_sup) & (ele > min_ele)]
az = np.radians(az)
#
# #convertir l'élévation en radians pour les calculs suivants
elevation = np.radians(elevation)
#
# # Calcul latitude et longitude du satelite au cours de son passage
x = np.arcsin((Re*np.cos(elevation))/(Re + H))
ksi = np.pi/2 - elevation - x
lat = np.arcsin(np.sin(rlat)*np.cos(ksi) + np.cos(rlat)*np.sin(ksi)*np.cos(az))
lon = rlon + np.arcsin(np.sin(ksi)*np.sin(az)/np.cos(lat))
#
## Lon et lat du sat au moment du séisme
indice = np.where(d == 11.03333333333)
t_seism = d[indice]
lat_sat_seism = lat[indice]
lon_sat_seism = lon[indice]
#
## Calcul du tec ; enlève le tec minimal pour le "normer" ; calcul du tec vertical
b = data [:,4]
e = b[(time_inf < a) & (a < time_sup) & (ele > min_ele)]
# print (e)
tec = e - min(e)
# print (tec)
for y in range (0,len(e)-1) :
if np.absolute(tec[y+1] - tec[y]) > 10 :
print ("SAUT DE TEC POUR LE SAT {0} DONNEES IGNOREES !!".format(sat))
break
else :
print ("pas de saut de tec pour le sat {0}".format(sat))
vtec = tec * np.cos(x)
# print (tec)
# print ("wesh")
# print (vtec)
#
#
### =============================================================================
### Plot du tec
### =============================================================================
## time_plot = a [(time_inf < a) & (a < time_sup) & (ele > min_ele)]
### time_plot_f = time_plot[0:len(vtec_f)]
## f1.tec(time_plot, tec, vtec, sat, station, type_sat)
#
### =============================================================================
### Butterworth filter
### =============================================================================
## f1.run_vtec(vtec, sat, d)
#
### =============================================================================
### Polynomial filter
### =============================================================================
# print (len(vtec))
# window = 27
# order = 4
# vtec_polyn = savgol_filter(vtec, window, order)
# reduce_tec = vtec - vtec_polyn
# f1.polynomied_tec(time_plot,vtec_polyn,reduce_tec, sat, station, type_sat, window, order)
#
## =============================================================================
## Plot carte traces satellites
## =============================================================================
f1.plot_nz(lon, lat, sat, station, lon_sat_seism, lat_sat_seism, type_sat)
plt.savefig('/Users/antoineleblevec/Desktop/2016_seism/traces_{1}_{0}.jpeg'.format(station,type_sat))
plt.show()
#
### =============================================================================
### Moving average
### =============================================================================
## N = int( len(vtec) / 5 )
## vtec_average = np.convolve(vtec, np.ones((N,))/N, mode='same')
### print (vtec)
### print(vtec_average)
## print (len(vtec_average))
## print (len(vtec))
## f1.tec(d,vtec_average,vtec, sat, station)
|
16,446 | 6f524701371748eae5bf9800a3008b8b84da69f6 | # Copyright 2008-2015 Nokia Networks
# Copyright 2016- Robot Framework Foundation
#
# 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 abc import ABC, abstractmethod
from ..lexer import Token
from ..model import (Block, Container, End, For, If, Keyword, NestedBlock,
Statement, TestCase, Try, While)
class Parser(ABC):
model: Container
def __init__(self, model: Container):
self.model = model
@abstractmethod
def handles(self, statement: Statement) -> bool:
raise NotImplementedError
@abstractmethod
def parse(self, statement: Statement) -> 'Parser|None':
raise NotImplementedError
class BlockParser(Parser, ABC):
model: Block
unhandled_tokens = Token.HEADER_TOKENS | frozenset((Token.TESTCASE_NAME,
Token.KEYWORD_NAME))
def __init__(self, model: Block):
super().__init__(model)
self.parsers: 'dict[str, type[NestedBlockParser]]' = {
Token.FOR: ForParser,
Token.IF: IfParser,
Token.INLINE_IF: IfParser,
Token.TRY: TryParser,
Token.WHILE: WhileParser
}
def handles(self, statement: Statement) -> bool:
return statement.type not in self.unhandled_tokens
def parse(self, statement: Statement) -> 'BlockParser|None':
parser_class = self.parsers.get(statement.type)
if parser_class:
model_class = parser_class.__annotations__['model']
parser = parser_class(model_class(statement))
self.model.body.append(parser.model)
return parser
self.model.body.append(statement)
return None
class TestCaseParser(BlockParser):
model: TestCase
class KeywordParser(BlockParser):
model: Keyword
class NestedBlockParser(BlockParser, ABC):
model: NestedBlock
def __init__(self, model: NestedBlock, handle_end: bool = True):
super().__init__(model)
self.handle_end = handle_end
def handles(self, statement: Statement) -> bool:
if self.model.end:
return False
if statement.type == Token.END:
return self.handle_end
return super().handles(statement)
def parse(self, statement: Statement) -> 'BlockParser|None':
if isinstance(statement, End):
self.model.end = statement
return None
return super().parse(statement)
class ForParser(NestedBlockParser):
model: For
class WhileParser(NestedBlockParser):
model: While
class IfParser(NestedBlockParser):
model: If
def parse(self, statement: Statement) -> 'BlockParser|None':
if statement.type in (Token.ELSE_IF, Token.ELSE):
parser = IfParser(If(statement), handle_end=False)
self.model.orelse = parser.model
return parser
return super().parse(statement)
class TryParser(NestedBlockParser):
model: Try
def parse(self, statement) -> 'BlockParser|None':
if statement.type in (Token.EXCEPT, Token.ELSE, Token.FINALLY):
parser = TryParser(Try(statement), handle_end=False)
self.model.next = parser.model
return parser
return super().parse(statement)
|
16,447 | df97c1ed468bc7b3cacf5901d80a5695af8ee361 | # -*- coding:UTF-8 -*-
import numpy as np
import collections
X1D = {
0: '1',
1: '2',
2: '3'
}
X2D = {
0: 'S',
1: 'M',
2: 'L'
}
YD = {
0: '-1',
1: '1'
}
X1E = {
'1': 0,
'2': 1,
'3': 2
}
X2E = {
'S': 0,
'M': 1,
'L': 2
}
YE = {
'-1': 0,
'1': 1,
}
def decode(vec):
return [X1D[vec[0]], X2D[vec[1]], YD[vec[2]]]
def encode(vec):
return [X1E[vec[0]], X2E[vec[1]], YE[vec[2]]]
def load_data(path="./dataset"):
data_set = []
with open(path, 'r') as f:
for i in f.readlines():
line = i.replace('\n', '').split(sep=',')
data_set.append(line[:])
return np.array(data_set)
def naive_bayes_MLE(data_set, x):
# Maximum likelihood estimation
fearure = fearure_count(data_set)
N = np.sum(fearure)
res = []
for y in np.unique(data_set[:, 2]):
P1 = np.sum(fearure[:, :, YE[y]]) / N
# P(Y=1)
P2 = np.sum(fearure[X1E[x[0]], :, YE[y]]) / \
np.sum(fearure[:, :, YE[y]])
# P(X1=x1|Y=y)
P3 = np.sum(fearure[:, X2E[x[1]], YE[y]]) / \
np.sum(fearure[:, :, YE[y]])
# P(X2=x2|Y=y)
res.append([y, P1*P2*P3])
res.sort(key=lambda x: x[1], reverse=True)
print('> Maximum likelihood estimation <')
print(res)
return res[0][0]
def naive_bayes_BE(data_set, x, λ=1):
# Bayesian estimation
fearure = fearure_count(data_set)
N = np.sum(fearure)
K = np.unique(data_set[:, 2]).shape[0]
res = []
# Laplace smoothing
for y in np.unique(data_set[:, 2]):
P1 = (np.sum(fearure[:, :, YE[y]]) + λ) / (N + K*λ)
# P(Y=1)
P2 = (np.sum(fearure[X1E[x[0]], :, YE[y]]) + λ) / \
(np.sum(fearure[:, :, YE[y]]) +
np.unique(data_set[:, 0]).shape[0] * λ)
# P(X1=x1|Y=y)
P3 = (np.sum(fearure[:, X2E[x[1]], YE[y]]) + λ) / \
(np.sum(fearure[:, :, YE[y]]) +
np.unique(data_set[:, 1]).shape[0] * λ)
# P(X2=x2|Y=y)
res.append([y, P1*P2*P3])
res.sort(key=lambda x: x[1], reverse=True)
print('> Bayesian estimation <')
print(res)
return res[0][0]
def fearure_count(data_set):
count = np.zeros((3, 3, 2)).astype(np.int)
for vec in data_set:
vec = encode(vec)
count[vec[0], vec[1], vec[2]] += 1
return count
if __name__ == "__main__":
data_set = load_data()
x = np.array(['2', 'S'])
y = naive_bayes_MLE(data_set, x)
print('Maximum likelihood estimation:', x, 'is', y)
print('-'*30)
y = naive_bayes_BE(data_set, x, λ=1)
print('Bayesian estimation:', x, 'is', y)
|
16,448 | c9caf0bd2dc98377d49c0ce9df4c82da7e8d3427 | from django.urls import path
from . import views
app_name = 'contact_app'
urlpatterns = [
path('contacts/', views.contact_view,name='contact_view'),
]
|
16,449 | 011ea7e4b23a81483bce122e8b85cef500f1441f | import re
import json
from json import JSONDecodeError
from typedpy.structures import ImmutableStructure, Structure
from typedpy.fields import String, AnyOf, Array
class ErrorInfo(ImmutableStructure):
field = String
value = String
problem = AnyOf[String, Array]
_required = ["problem"]
display_type_by_type = {
"int": "an integer number",
"str": "a text value",
"float": "a decimal number",
"list": "an array",
}
_expected_class_pattern = re.compile(r"^Expected\s<class '(.*)'>$")
def _transform_class_to_readable(problem: str):
match = _expected_class_pattern.match(problem)
if match:
return f"Expected {display_type_by_type.get(match.group(1), match)}"
return problem
_pattern_for_typepy_validation_1 = re.compile(r"^([a-zA-Z0-9_.]+): Got ([^;]*); (.*)$")
_pattern_for_typepy_validation_2 = re.compile(r"^([a-zA-Z0-9_.]+):\s(.*); Got (.*)$")
_pattern_for_typepy_validation_3 = re.compile(r"^([a-zA-Z0-9_.]+):\s(.*)$")
def standard_readable_error_for_typedpy_exception(e: Exception, top_level=True):
err_message = str(e)
if Structure.failing_fast():
return _standard_readable_error_for_typedpy_exception_internal(err_message)
else:
try:
errs = json.loads(err_message)
return [
_standard_readable_error_for_typedpy_exception_internal(e) for e in errs
]
except JSONDecodeError as ex:
if not top_level:
raise ex
return [
_standard_readable_error_for_typedpy_exception_internal(err_message)
]
def _standard_readable_error_for_typedpy_exception_internal(err_message: str):
def try_expand(problem_str):
if not Structure.failing_fast():
try:
return standard_readable_error_for_typedpy_exception(
Exception(problem_str), top_level=False
)
except Exception:
pass
return problem_str
match = _pattern_for_typepy_validation_1.match(err_message)
if match:
problem = _transform_class_to_readable(match.group(3))
return ErrorInfo(
value=match.group(2), problem=try_expand(problem), field=match.group(1)
)
match = _pattern_for_typepy_validation_2.match(err_message)
if match:
problem = _transform_class_to_readable(match.group(2))
return ErrorInfo(
value=match.group(3), problem=try_expand(problem), field=match.group(1)
)
match = _pattern_for_typepy_validation_3.match(err_message)
if match:
field = match.group(1)
problem = _transform_class_to_readable(match.group(2))
return ErrorInfo(problem=try_expand(problem), field=field)
return ErrorInfo(problem=err_message)
def get_simplified_error(err: str, as_list=True):
try:
res = json.loads(err)
if isinstance(res, list):
fixed = []
for e in res:
try:
key, *rest = e.split(":")
val = ":".join(rest)
corrected = get_simplified_error(val.strip(), as_list=False)
fixed.append(f"{key}: {corrected}")
except Exception: # noqa
fixed.append(e)
return fixed if len(res) > 1 or as_list is True else fixed[0]
except JSONDecodeError:
pass
return err
|
16,450 | 85520715c879d8e9909a31ef8f448dcd8f9fec0c | """
先看当前点属于那个窗口，如果都不属于，就输出IGNORED
如果属于某一个，很简单，最上面的置于顶层
done ok
"""
n,m=list(map(int,input().strip().split()))
martrix=[]
points=[]
for i in range(n):
martrix.append(list(map(int,input().strip().split())))
for j in range(m):
points.append(list(map(int,input().strip().split())))
getIndex=martrix[::]
def inMartix(lst,pointXY):
x1,y1,x2,y2=lst
x,y=pointXY
if x1<=x<=x2 and y1<=y<=y2:
return True
else:
return False
for pt in points:
found=False
for mt in martrix[::-1]:
if inMartix(mt,pt):
temp=mt
# change layer
print(getIndex.index(temp)+1)
martrix.remove(temp)
martrix.append(temp)
found=True
break
if not found:
print("IGNORED")
|
16,451 | 1f8176b69bdddea8c115ba7aff391aad7aaef600 | import keras
from sklearn.metrics import roc_auc_score
import sys
import matplotlib.pyplot as plt
from keras.models import Model
import numpy as np
from keras import backend as K
class DecayLearningRate(keras.callbacks.Callback):
def __init__(self, startEpoch):
self.startEpoch = startEpoch
def on_train_begin(self, logs={}):
return
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
if epoch in self.startEpoch:
if epoch == 0:
ratio = 1
else:
ratio = 0.1
LR = K.get_value(self.model.optimizer.lr)
K.set_value(self.model.optimizer.lr,LR*ratio)
return
def on_epoch_end(self, epoch, logs={}):
return
def on_batch_begin(self, batch, logs={}):
return
def on_batch_end(self, batch, logs={}):
return
|
16,452 | 1d5db796e507b22d3c3fba956977ea6bbbae5fc3 | from bs4 import BeautifulSoup
import requests
import time
headers = {
'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36',
'Referer':'http://tieba.baidu.com'
}
# 抓取网页的方法
def get_html(url):
try:
res = requests.get(url,headers = headers,timeout = 30)
res.raise_for_status()
res.encoding = 'utf-8'
return res.text
except Exception as e:
return 'Error!'
def get_content(url):
# 初始化一个列表来保存所有的帖子信息
comments = []
html = get_html(url)
soup = BeautifulSoup(html,'lxml')
# 找到所有li标签class属性，返回一个列表
liTags = soup.find_all('li',attrs = {'class':'j_thread_list clearfix'})
# 通过循环 找到每个帖子里我们需要的信息
for li in liTags:
# 初始化一个字典来存储文章信息
comment = {}
try:
# 筛选信息 保存到字典中
comment['title'] = li.find('a',attrs = {'class':'j_th_tit'}).text.strip()
comment['link'] = 'http://tieba.baidu.com/'+li.find('a',attrs = {'class':'j_th_tit'})['href']
comment['name'] = li.find('span',attrs = {'class':'tb_icon_author'}).text.strip()
comment['time'] = li.find('span',attrs = {'class':'threadlist_reply_date pull_right j_reply_data'}).text.strip()
comment['replyNum'] = li.find('span',attrs = {'class','threadlist_rep_num center_text'}).text.strip()
comments.append(comment) # 把字典结构保存到列表中 并返回
except:
print('This is Error！')
# 返回保存字典信息的列表
return comments
# 把信息保存到本地的方法
def write_to_file(dict):
with open('./tieba.txt','a+') as f:
for comment in dict:
f.write('标题：{} \t 链接：{} \t 发帖人：{} \t 发帖时间：{} \t 回复数量：{} \n'.format(comment['title'],comment['link'],comment['name'],comment['time'],comment['replyNum']))
print('当前页面爬取完成')
def main(start_url,deep):
url_list = []
# 将所有需要爬取的url存入列表
for i in range(0,deep):
url_list.append(start_url+str(50*i))
print('所有网页已经下载本地，开始筛选信息。。。')
# 循环写入数据
for url in url_list:
content = get_content(url)
write_to_file(content)
print('保存完毕！！')
start_url = 'http://tieba.baidu.com/f?kw=%E7%94%9F%E6%B4%BB%E5%A4%A7%E7%88%86%E7%82%B8&ie=utf-8&pn='
deep = 3
if __name__ == '__main__':
main(start_url,deep)
|
16,453 | 4a3d41315ef0bfde76fc997175f8c6f08d85dee9 | n,m=input().split()
n=int(n)
m=int(m)
for x in range(n+1,m+1):
if(x%2==1):
print(x,end=" ")
|
16,454 | 806817e1c2df64fc6c0d847e6e038cc029934f25 | class A:
def m1(self):
print("m1.....")
print(id(self))
print(self.x)
a=A()
a.x=100
a1=A()
a1.x=200
print(a.x)
print(a1.x)
print(id(a))
print(id(a1))
a.m1()
a1.m1()
|
16,455 | 3c6610b3538149b959786da6474d01793917d95d | def l():
pass
def I():
pass
class X:
def O(self):
pass
def x():
pass
|
16,456 | 3da2637e171352270e4068e7464909e2fb35e648 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os, sys, string
import MySQLdb
import cus_os
import cus_login
cus_login.connect()
cus_os.connect()
#1.check all orders of a customer
cus_id = cus_login.cus_log_in("happygirlzt@gmail.com","123")
result = cus_os.get_all_orders(cus_id)
#2.search flight
#choose departure and arrival and date
result = cus_os.search_flights("2017-01-03","CD","GZ")
#3.make a order
#3.1search flight
result = cus_os.make_order(20000,30000,'F','1234020')
print(result)
#4.change flights
#4.1 todo: show flights of the same day
#r = cus_os.show_flights_to_change(20000,400001)
#4.2 change order
cus_os.change_order(20000,400001,30002)
#4.3 cancel order
cus_os.cancel_order(400001)
|
16,457 | 2294d614e8a67641c489840f2b12bd95482b14e2 | from django.contrib import admin
from .models import contact,Post
# Register your models here.
admin.site.register(contact)
admin.site.register(Post) |
16,458 | b0450bdd63b452b28a34bd35ed836b2d981a95b3 | #
# Copyright (C) 2009-2021 Alex Smith
#
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
#
vars = ['config', 'env']
Import(*vars)
from util import FeatureSources
sources = FeatureSources(config, [
'loader/kboot.c',
'console.c',
'memory.c',
'platform.c',
])
# Add architecture-specific headers.
env['CPPPATH'] += [Dir('arch/%s/include' % (config['ARCH']))]
# Get architecture-specific sources.
arch_sources = SConscript(dirs = ['arch/' + config['ARCH']], exports = vars)
sources = arch_sources + sources
# Build the final loader binary.
kboot = env.Command(
env['OUTDIR'].File('kboot.bin'),
env['KBOOT'],
Action('$OBJCOPY -O binary --strip-all $SOURCE $TARGET', '$GENCOMSTR'))
Return('sources')
|
16,459 | a5645083ed70836806cf387c01789980dc9cc113 | from django.db import models
from django.core.validators import MinValueValidator
import datetime
class Wojewodztwo(models.Model):
numer = models.IntegerField(unique=True, validators = [MinValueValidator(0)])
nazwa= models.CharField(unique=True, max_length=50)
def __str__(self):
# Wyświetlaj nazwe województwa w django admin
return self.nazwa
class Meta:
# Jak djando admin wyświetla liczbę mnogą nazwy modelu
verbose_name_plural = "wojewodztwa"
class Gmina(models.Model):
czas = models.DateTimeField(auto_now=True)
nazwa = models.CharField(max_length=30)
RODZAJ_ENUM = (
('W', 'Wieś'),
('M', 'Miasto'),
('Z', 'Zagranica'),
('S', 'Statek'),
)
rodzaj = models.CharField(max_length=30, choices=RODZAJ_ENUM)
liczba_mieszkancow = models.IntegerField(validators = [MinValueValidator(0)])
uprawnieni = models.IntegerField(validators = [MinValueValidator(0)])
wojewodztwo = models.ForeignKey(Wojewodztwo, on_delete=models.CASCADE)
def __str__(self):
# Wyświetlaj nazwe gminy w django admin
return self.nazwa
class Meta:
# Jak djando admin wyświetla liczbę mnogą nazwy modelu
verbose_name_plural = "gminy"
class Kandydat(models.Model):
imie = models.CharField(max_length=50)
nazwisko = models.CharField(max_length=50)
def __str__(self):
# Wyświetlaj imię i nazwisko kandydata w django admin
return self.imie +' ' + self.nazwisko
class Meta:
# Jak djando admin wyświetla liczbę mnogą nazwy modelu
verbose_name_plural = "kandydaci"
class Glosy(models.Model):
#Przyjąłem taki model trzymania danych o głosach na kandydatów:
# Obiekt dla gminy trzyma info o kandydacie na któego oddano głosy,
# w liczbie określonej w atrybucie glosy_na_wybranego_kanydata
# Nie dupilkuje, więc danych - głosy oddane na drogiego kandydata to
# roznica głosów ważnych i glosy_na_wybranego_kanydata
wazne = models.IntegerField(validators = [MinValueValidator(0)]) # Liczba WAŻNYCH oddanych głosów w danej gminie
gmina = models.ForeignKey(Gmina, on_delete=models.CASCADE)
# Na kogo oddane głosy
kandydat = models.ForeignKey(Kandydat, on_delete=models.CASCADE, default= 0)
# Liczba WAŻNYCH oddanych głosów w danej gminie na kandydata określonego linijke wyżej
glosy_na_wybranego_kanydata = models.IntegerField(validators = [MinValueValidator(0)])
def __str__(self):
# Wyświetlaj info o głosach w ładnej formie w django admin
return "%s %s %s" % (self.kandydat.nazwisko, self.glosy_na_wybranego_kanydata, self.gmina)
class Meta:
# Jak djando admin wyświetla liczbę mnogą nazwy modelu
verbose_name_plural = "glosy"
class Karty(models.Model):
liczba = models.IntegerField(validators = [MinValueValidator(0)])
gmina = models.ForeignKey(Gmina, on_delete=models.CASCADE)
def __str__(self):
# Wyświetlaj info o wydanych kartach w ładnej formie w django admin
return "%s %s" % ( self.gmina, self. liczba)
class Meta:
# Jak djando admin wyświetla liczbę mnogą nazwy modelu
verbose_name_plural = "karty" |
16,460 | dd8b7c5648980849352850ccf574b40ae9447344 | from django import forms
from django.forms import fields
from .models import Order, Room
from django.conf import settings
class OrderForm(forms.ModelForm):
class Meta:
model = Order
fields = "__all__"
# rooms = Room.objects.all()
# choices = []
# for room in rooms:
# choices.append((room.room_number, room.room_number))
widgets = {
"room": forms.TextInput(attrs={ "type": "text", "class": "form-control"}),
"start_date": forms.DateInput(attrs={"type": "date", "class": "form-control"}),
"finish_date": forms.DateInput(attrs={"type": "date", "class": "form-control"}),
"first_name": forms.TextInput(attrs={"type": "text", "placeholder": "First Name", "class": "form-control"}),
"last_name": forms.TextInput(attrs={"type": "text", "placeholder": "Last Name", "class": "form-control"}),
"phone_number": forms.TextInput(attrs={"type": "text", "placeholder": "Phone number", "class": "form-control"}),
"order_cost": forms.NumberInput(attrs={"type": "number", "readonly": "True", "class": "form-control"}),
}
|
16,461 | dd8617a2886cb1dd90806325014c3a247633fb81 | from rest_framework import permissions
from rest_framework.permissions import IsAuthenticated, AllowAny
SAFE_METHODS = ['GET', 'POST', 'HEAD', 'OPTIONS']
OBJECT_METHODS = ['GET', 'HEAD', 'OPTIONS']
class LoginPermission(permissions.BasePermission):
def has_permission(self, request, view):
if (request.method in SAFE_METHODS and
request.user and
request.user.is_authenticated):
return True
return False
def has_object_permission(self, request, view, obj):
return request.user.id == obj.user.id
|
16,462 | c1e2c68606cb2224a5e8ae987d55012f28def2c0 | # MIT License
#
# Copyright (c) 2021 DTOG
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import aiohttp
from .endpoints import iourl as url
from .main import uptime, tps, priorityqueue
async def get_queue():
async with aiohttp.ClientSession() as cs:
async with cs.get(await url("queue?last=true")) as p:
json = await p.json()
return (list(json))[0]
class normalqueue(object):
@staticmethod
async def length():
try:
return int((await get_queue())[1])
except Exception:
return None
class serverstats(object):
@staticmethod
async def uptime():
try:
return await uptime()
except Exception:
return None
@staticmethod
async def tps():
try:
return await tps()
except Exception:
return None
@staticmethod
async def totalqueue():
try:
return int(await priorityqueue.length() + await normalqueue.length())
except Exception:
return None |
16,463 | 43e00753887291665c87f1110bc8a77def681f8c | from django.shortcuts import render, get_object_or_404, redirect
from django.contrib import messages
# Create your views here.
from .models import *
from django.http import JsonResponse
import json
import datetime
from .utils import cookieCart, cartData
from validate_email import validate_email
# Create your views here.
def usernameValidation(request):
data = json.loads(request.body)
username = data['username']
if not str(username).isalnum():
return JsonResponse({'username_error': 'Username should only alphanumeric characters'}, status=200)
if User.objects.filter(username=username).exists():
return JsonResponse({'username_error': 'Username is already taken,choose another one'}, status=400)
return JsonResponse({'username_valid': True})
def emailValidation(request):
data = json.loads(request.body)
email = data['email']
if not validate_email(email):
return JsonResponse({'email_error': 'Email is invalid, set your correct email address'}, status=400)
if User.objects.filter(email=email):
return JsonResponse({'email_error': 'Sorry, email address is already used, try another one'}, status=400)
return JsonResponse("email validatioon", safe=False)
def register(request):
if request.user.is_authenticated:
return redirect('product_list')
else:
if request.method == 'POST':
username = request.POST['username']
fname = request.POST['fname']
lname = request.POST['lname']
email = request.POST['email']
password = request.POST['password']
context = {'fieldValue': request.POST}
if not User.objects.filter(username=username).exists():
if not User.objects.filter(email=email).exists():
if len(password)<8:
messages.error(request, 'password too short,it have to be minimun 8 characters')
return render(request, 'account/register.html', context)
if len(username)<5:
messages.error(request, 'your username less than 5 characters, try again')
return render(request, 'account/register.html', context)
user = User.objects.create_user(username=username, email=email)
user.first_name = fname
user.last_name = lname
user.set_password(password)
user.save()
messages.success(request, 'your account has been successfully created')
return redirect('login')
return render(request, 'account/register.html')
def updateItem(request):
data = json.loads(request.body)
productId = data['productId']
action = data['action']
customer = request.user.customer
product = Product.objects.get(id=productId)
order , created = Order.objects.get_or_create(customer=customer, complete=False)
orderItem, created = OrderItem.objects.get_or_create(order=order, product=product)
if action == 'add':
orderItem.quantity = (orderItem.quantity + 1)
elif action == 'remove':
orderItem.quantity = (orderItem.quantity - 1)
orderItem.save()
if orderItem.quantity <= 0:
orderItem.delete()
return JsonResponse("it was added", safe=False)
def product_list(request):
data = cartData(request)
cartItems = data['cartItems']
products = Product.objects.all()
context = { 'products': products , 'cartItems': cartItems}
return render(request, 'home/product_home.html', context)
def cart_list(request):
data = cartData(request)
cartItems = data['cartItems']
order = data['order']
items = data['items']
context = { 'items': items, 'order':order, 'cartItems':cartItems }
return render(request, 'home/cart.html', context)
def checkout(request):
data = cartData(request)
cartItems = data['cartItems']
order = data['order']
items = data['items']
context = { 'items': items, 'order':order, 'cartItems': cartItems }
return render(request, 'home/checkout.html', context )
def product_details(request):
return render(request, 'home/product_details.html')
def proceOrder(request):
#print( 'data:', request.body)
transction_id = datetime.datetime.now().timestamp()
data = json.loads(request.body)
if request.user.is_authenticated:
customer = request.user.customer
order, created = Order.objects.get_or_create(customer=customer, complete=False)
else:
print('user is not log in')
name = data['form']['name']
email = data['form']['email']
cookieData = cookieCart(request)
items = cookieData['items']
customer, created = Customer.objects.get_or_create(
email=email,
)
customer.name = name
customer.save()
order = Order.objects.create(
customer=customer,
complete=False,
)
for item in items:
product = Product.objects.get(id=item['product']['id'])
orderItem = OrderItem.objects.create(
product=product,
order = order,
quantity=item['quantity']
)
total = float(data['form']['total'])
order.transaction_id = transction_id
if total == order.get_item_total_price:
order.complete = True
order.save()
if order.shipping == True:
ShippingAddress.objects.create(
customer = customer,
order = order,
address = data['shipping']['address'],
city = data['shipping']['city'],
state = data['shipping']['state'],
zipcode = data['shipping']['zipcode'],
)
return JsonResponse("payment completed", safe=False)
|
16,464 | 405d74a9506e7c7ec88c82a1a218b7c3366adad3 | import numpy as np
import cv2
import argparse
import os
"""
Saving frames from a video
"""
#construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--inputFilePath", required=True, help="Where is your video")
ap.add_argument("-o", "--outpuFilePath", required=True, help="Where are your frames stored")
args = vars(ap.parse_args())
# armguments
inputPath = args['inputFilePath']
outputPath = args['outpuFilePath']
# clear directory
os.system('rm -rf %s/*' % outputPath)
# do the job
cap = cv2.VideoCapture(inputPath)
count = 0
while (cap.isOpened()):
ret, frame = cap.read()
if ret:
# save frames. Frame manipulation
cv2.imwrite(outputPath + "frame%d.jpeg" % count, frame)
#
if not ret:
cap.release()
break
count += 1
print("frame {} saved".format(count))
print("total frames: {}".format(count))
|
16,465 | 296b52e1af55067ff281eaee765e70bdb60573f9 | import turtle
import math
pen = turtle.Turtle()
'''for i in range(3):
pen.fd(100)
pen.lt(360/3)
for i in range(4):
pen.fd(100)
pen.lt(90)'''
def nbianxing(n, p, l):
for i in range(n):
p.fd(l)
p.lt(360/n)
#nbianxing(n = 614, p = pen, l = 3)
def yuanxing(p, r):
h = math.pi * r * 2 #周长
n = math.floor(h / 3) #边数=周长/每段线段的长度（这里是3个像素）。因为有可能没有整除，所以需要用math.floor函数获取这个小数的整数部分
nbianxing(n= n, p=p, l=3)
yuanxing(p = pen, r = 100)
turtle.mainloop() |
16,466 | 6ec0dd7aa30768c30e40b3d0c90ace8f9ceba245 | from averager import Averager
from simulation import Simulation
class Control:
"""Runs bias simulations based on "Male-Female Differences: A Computer
Simulation" from the Feb, 1996 issue of American Psychologist.
http://www.ruf.rice.edu/~lane/papers/male_female.pdf"""
def __init__(self, promotion_bias = 1):
self.promotion_bias = promotion_bias
self.num_simulations = 100
self.attrition = 15
self.iterations_per_simulation = 12
self.num_positions_list = [500, 350, 200, 150, 100, 75, 40, 10]
self.num_levels = len(self.num_positions_list)
def run_simulations(self):
"""Run NUM_SIMULATIONS simulations"""
self.results = []
for i in range(self.num_simulations):
simulation = Simulation(self.num_simulations, self.attrition, self.iterations_per_simulation,
self.promotion_bias, self.num_positions_list)
simulation.run()
self.results.append(simulation.get_result())
def print_header(self):
"""print header with var info"""
print("Running {} simulations.".format(self.num_simulations))
print("{0:2}% bias for men".format(self.promotion_bias))
print("{0:2} promotion cycles".format(self.iterations_per_simulation))
print("{0:2}% attrition rate".format(self.attrition))
print
def print_summary(self):
"""Print summary"""
print("Level\tMen\t\t\tWomen")
print("\tavg\tmedian\t%\tavg\tmedian\t%")
print("-----\t-----------------\t-----------------")
for level in range(0, self.num_levels):
men_averager = Averager()
women_averager = Averager()
for result in self.results:
men_averager.add(result.men[level])
women_averager.add(result.women[level])
total_employees = men_averager.get_total() + women_averager.get_total()
men_avg = men_averager.get_average()
men_median = men_averager.get_median()
men_percentage = 100 * men_averager.get_total() / total_employees
women_avg = women_averager.get_average()
women_median = women_averager.get_median()
women_percentage = 100 * women_averager.get_total() / total_employees
summary = "%d\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f" %(level + 1, men_avg, men_median, men_percentage, women_avg,
women_median, women_percentage)
print summary
if __name__ == "__main__":
control = Control()
control.print_header()
control.run_simulations()
control.print_summary()
|
16,467 | 81c348d600008d85e9c5d7aa36ec4ddf05d50ffd | '''
@Author:Sailesh Chauhan
@Date:2021-06-06
@Last Modified by:Sailesh Chauhan
@Last Modified time:2021-06-06
@Title:Clinic Management Application
'''
import json
import logging
from decouple import config
FILE_PATH_LOG=config('log_File_Path')
FILE_PATH_JSON=config('JSON_File_Path')
logging.basicConfig(filename=FILE_PATH_LOG,level=logging.CRITICAL,format='%(asctime)s - %(levelname)s - %(message)s')
class Doctor:
'''
Description:
Creates Custom object with properties name,id,specialization
availability.
Properties:
Name,Id,Specialization,Availability
'''
def __init__(self,name,id,specialization,availability):
self.Name=name
self.Id=id
self.Specialization=specialization
self.Availability=availability
pass
class Patient:
'''
Description:
Creates Custom object with properties name,id,mobileNumber,age.
Properties:
Name,Id,MobileNumber,Age.
'''
def __init__(self,name,id,mobileNumber,age):
self.Name=name
self.Id=id
self.MobileNumber=mobileNumber
self.Age=int(age)
pass
recordDictionary={}
def book_appointment_with_doctor(KEY):
'''
Description:
Method provides entry of Doctor available for booking.
It allows only booking for doctor upto 5 patient.
If patient entry exceeds more than 5.It do not allow.
Booking for that doctor.
Parameters:
Key for accesing the Doctor list inside recordDictionary.
Returns:
None. It prints return values provided better readibility.
'''
try:
list=recordDictionary.get(KEY)
for entry in list:
for key,value in entry.items():
if(len(value["Availability"])<6):
print("Doctor Id {0} Specialization {1} is AVAILABLE in {2} ".format(key,value["Specialization"],value["Availability"][0]))
choosenDoctor=input("Enter Id of Doctor\n")
for entry in list:
for key,value in entry.items():
if(key==choosenDoctor and len(value["Availability"])<6):
print("Doctor Found "+key)
patientID=input("Provide Patient Id\n")
availabilityList=value["Availability"]
availabilityList.append(patientID)
except Exception as ex:
logging.critical(ex)
def load_JSON_file():
'''
Description:
Method loads data from JSON using environment variable.
To recordDictionary variable.
Parameters:
No Parameters.
Return:
None.
'''
try:
global recordDictionary
with open(FILE_PATH_JSON,'+r') as file:
recordDictionary=json.load(file)
except Exception as ex:
logging.critical(ex)
def add_doctor_patient_entry(KEY):
'''
Description:
Method add doctor and patient new entry to dictionary. Then
to the JSON file.
Parameters:
Takes one parameters as KEY to recordDictionary.
Return:
None.
'''
try:
global recordDictionary
if(KEY=='DOCTOR'):
doctor=Doctor(input("Enter name of doctor\n"),input("Enter Id of doctor\n"),input("Enter Doctor Specialization\n"),input("Enter Doctor Availability\n"))
doctor.Availability=[doctor.Availability]
doctorRecord={doctor.Id:doctor.__dict__}
recordDictionary.setdefault(KEY,[]).append(doctorRecord)
elif(KEY=='PATIENT'):
patient=Patient(input("Enter name of patient\n"),input("Enter Id of patient\n"),input("Enter patient MobileNumber\n"),input("Enter patient Age\n"))
patientRecord={patient.Id:patient.__dict__}
recordDictionary.setdefault(KEY,[]).append(patientRecord)
except Exception as ex:
logging.critical(ex)
def write_to_JSON():
'''
Description:
Method writes recordDictionary key value to JSON file.
Using JSON module.
Parameters:
No Parameters.
Return:
None.
'''
try:
with open(FILE_PATH_JSON,'+r') as file:
file.write(json.dumps(recordDictionary,indent=4))
except Exception as ex:
logging.critical(ex)
def search_through_Dictionary(KEY,searchParameter,searchKeyword):
'''
Description:
Method supplements search_entry method. It provides code reusability
for search_entry method.
Parameters:
Takes 3 parameters KEY for accesing values. searchParameter contains
entry value to be searched. searchKeyword provides entry key to be
searched.
Return:
None.
'''
try:
listDoctors=recordDictionary.get(KEY,"Invalid Search")
if(listDoctors=='Invalid Search'):
print(listDoctors)
quit
for entry in listDoctors:
for entryValue in entry.values():
doctorName=entryValue.get(searchKeyword)
if(doctorName==searchParameter):
print("Search complete {} with ID {} ".format(doctorName,entryValue.get("Id")))
except Exception as ex:
logging.critical(ex)
def search_entry(KEY):
'''
Description:
Method uses search_through_Dictionary method. It provides feature for
searching through DOCTOR and PATIENT records. This method provides 4 features.
Parameters:
Takes KEY as parameter to dictionary. To acess records.
Return:
None.
'''
try:
if(KEY=='DOCTOR'):
choice=''
while(choice!='4'):
print("1.Search By Name\n2.Search By Id\n3.Search By Specialization\n4.Stop Searching")
choice=input("Make your search selection\n")
if(choice=='1'):
searchParameter=input("Enter Name of doctor\n")
search_through_Dictionary(KEY,searchParameter,"Name")
elif(choice=='2'):
searchParameter=input("Enter Id of doctor\n")
list=recordDictionary.get(KEY)
for entry in list:
for key,value in entry.items():
if(key==searchParameter):
id=value.get("Id")
print("Id of doctor {0}\nAll detail {1} ".format(id,value))
elif(choice=='3'):
searchParameter=input("Enter Speciality\n")
search_through_Dictionary(KEY,searchParameter,"Specialization")
elif(KEY=='PATIENT'):
choice=''
while(choice!='4'):
print("1.Search By Name\n2.Search By Id\n3.Search By MobileNumber\n4.Stop Searching")
choice=input("Make your search selection\n")
if(choice=='1'):
searchParameter=input("Enter Name of Patient\n")
search_through_Dictionary(KEY,searchParameter,"Name")
elif(choice=='2'):
searchParameter=input("Enter Id of doctor\n")
list=recordDictionary.get(KEY)
for entry in list:
for key,value in entry.items():
if(key==searchParameter):
id=value.get("Id")
print("Id of Patient {0}\nAll detail {1} ".format(id,value))
elif(choice=='3'):
searchParameter=input("Enter MobileNumber\n")
search_through_Dictionary(KEY,searchParameter,"MobileNumber")
except Exception as ex:
logging.critical(ex)
def main():
'''
Description:
Method calls all function to run this application.
Parameters:
No Parameters.
Return:
None.
'''
try:
load_JSON_file()
choice=''
while(choice!='6'):
print("1.Add New Doctor Entry\n2.Add New Patient Entry")
print("3.Search Doctor by Id,Specialization,Name\n4.Search Patient by Id,Name,MobileNumber")
print("5.Book Appointment\n6.Exit the Application")
choice=input("Make your selection\n")
if(choice=='1'):
add_doctor_patient_entry("DOCTOR")
elif(choice=='2'):
add_doctor_patient_entry("PATIENT")
elif(choice=='3'):
search_entry("DOCTOR")
elif(choice=='4'):
search_entry("PATIENT")
elif(choice=='5'):
book_appointment_with_doctor("DOCTOR")
elif(choice=='6'):
print("Exiting the Application")
write_to_JSON()
except Exception as ex:
logging.critical(ex)
if __name__=="__main__":
main()
|
16,468 | 2770a9faf6b1a995fcdf7f1ab19300aee26334d2 | # -*- coding: utf-8 -*-
"""
Created on Mon Nov 07 13:59:24 2016
@author: yangz
"""
#!/usr/bin/python
#-*- coding:utf-8 -*-
import re
import requests
import sys
import urllib
import time
import socket
import os
import codecs
os.chdir('C:\Users\yangz\Desktop\pacong3')
reload(sys)
sys.setdefaultencoding("utf-8")
def html_re(url,d,i,sleep_download_time,timeout):
U=[]
try:
time.sleep(sleep_download_time)
socket.setdefaulttimeout(timeout)
user_agent = 'Mozilla/5.0 (Windows NT 6.2; rv:16.0) Gecko/20100101 Firefox/16.0'
headers = {'User-Agent' : user_agent,'Referer':url+'/'}
r = requests.get(url,headers=headers)
data = r.text
r.close()
pic_floder=[]
pic_url=re.findall('src="(.*?)"',data,re.S)
for pic in pic_url:
if pic.find('http://www.cccchzmb.com/userfiles/image/')==-1:
if pic.find('site_media/images/')>-1:
f=file(pic,'w')
urllib.urlretrieve('http://www.cccchzmb.com/'+pic,pic)
f.close()
else:
i=i+1
pic_floder.append('userfiles/image/'+str(i)+pic[-4:])
data=data.replace(pic,pic_floder[-1])
f=file(pic_floder[-1],'w')
urllib.urlretrieve(pic,pic_floder[-1])
f.close()
f=codecs.open(d+'.html','w','gbk')
for line in data:
f.write(line)
f.close()
except requests.RequestException as e:
print(e)
U=url
except requests.exceptions.ConnectionError as e:
print(e)
U=url
except UnicodeDecodeError as e:
print('-----UnicodeDecodeErrorurl:',url)
U=url
except socket.timeout as e:
print("-----socket timout:",url)
U=url
except IOError as e:
print("download ",url,"\nerror:",e)
U=url
return i,U
def main_support(i,j,link_list,link_name,sleep_download_time,timeout):
link_defeat_list=[]
n=j
for k in range(n,len(link_list)):
[i,U]=html_re(link_list[k],link_name[k],i,sleep_download_time,timeout)
j=j+1
if len(U)>0:
link_defeat_list.append(U)
f=open('link_defeat_list.txt','a')
f.write(U+'\n')
f.close()
print '第'+str(j)+'个网页'
return i,j,link_defeat_list
f=open('link_name.txt','r')
data=f.readlines()
f.close
link_name=[]
for line in data:
link_name.append(line.strip())
f=open('link.txt','r')
data=f.readlines()
f.close
link_list=[]
for line in data:
link_list.append(line.strip())
i=0
timeout = 20
sleep_download_time=2
link_defeat_list=[]
j=0
[i,j,link_defeat_list]=main_support(i,j,link_list,link_name,sleep_download_time,timeout) |
16,469 | a6e37b139b2e96b2f3e7470350a50680c9723bb5 | #https://projecteuler.net/problem=4
product = []
for i in range(100,1000):
for j in range(100,1000):
if str(i*j) == str(i*j)[::-1]:
product.append(i*j)
print(max(product))
|
16,470 | b08ac829ecc925c7b40e3cd7cb73311459ae8736 | import numpy as np
import math
class DATA(object):
def __init__(self, n_question, seqlen, separate_char, name="data"):
self.separate_char = separate_char
self.n_question = n_question
self.seqlen = seqlen
def load_data(self, path):
f_data = open(path , 'r')
user_to_q_sequence = {}
user_to_qa_sequence = {}
user_id = 0
q_data = []
qa_data = []
for lineID, line in enumerate(f_data):
line = line.strip( )
if lineID % 3 == 0:
user_id = line
elif lineID % 3 == 1:
Q = line.split(self.separate_char)
if len( Q[len(Q)-1] ) == 0:
Q = Q[:-1]
elif lineID % 3 == 2:
A = line.split(self.separate_char)
if len( A[len(A)-1] ) == 0:
A = A[:-1]
# start split the data
n_split = 1
if len(Q) > self.seqlen:
n_split = math.floor(len(Q) / self.seqlen)
if len(Q) % self.seqlen:
n_split = n_split + 1
#print('n_split:',n_split)
for k in range(n_split):
question_sequence = []
answer_sequence = []
if k == n_split - 1:
endIndex = len(A)
else:
endIndex = (k+1) * self.seqlen
for i in range(k * self.seqlen, endIndex):
if len(Q[i]) > 0:
Xindex = int(Q[i]) + int(A[i]) * self.n_question
question_sequence.append(int(Q[i]))
answer_sequence.append(Xindex)
else:
print(Q[i])
q_data.append(question_sequence)
qa_data.append(answer_sequence)
q_dataArray = np.zeros((len(q_data), self.seqlen))
for j in range(len(q_data)):
dat = q_data[j]
q_dataArray[j, :len(dat)] = dat
user_to_q_sequence[user_id] = q_dataArray
qa_dataArray = np.zeros((len(qa_data), self.seqlen))
for j in range(len(qa_data)):
dat = qa_data[j]
qa_dataArray[j, :len(dat)] = dat
user_to_qa_sequence[user_id] = qa_dataArray
q_data.clear()
qa_data.clear()
f_data.close()
return user_to_q_sequence, user_to_qa_sequence
def load_test_data(self, path, test_set_rate):
f_data = open(path , 'r')
u2q_seq = {} # user to question sequence
u2qa_seq = {}
u2tf_seq = {} # tf: test flag
Q = []
A = []
q_data = []
qa_data = []
tf_data = []
user_id = 0
train_seq_len = 0
for lineID, line in enumerate(f_data):
line = line.strip()
if lineID % 3 == 0:
user_id = line
elif lineID % 3 == 1:
Q = line.split(self.separate_char)
if len( Q[len(Q)-1] ) == 0:
Q = Q[:-1]
train_seq_len = round(len(Q) * (1 - test_set_rate))
elif lineID % 3 == 2:
A = line.split(self.separate_char)
if len( A[len(A)-1] ) == 0:
A = A[:-1]
n_split = 1
if len(Q) > self.seqlen:
n_split = math.floor(len(Q) / self.seqlen)
if len(Q) % self.seqlen:
n_split = n_split + 1
for k in range(n_split):
q_seq = []
a_seq = []
f_seq = []
if k == n_split - 1:
endIndex = len(A)
else:
endIndex = (k+1) * self.seqlen
for i in range(k * self.seqlen, endIndex):
if len(Q[i]) > 0:
Xindex = int(Q[i]) + int(A[i]) * self.n_question
q_seq.append(int(Q[i]))
a_seq.append(Xindex)
if i >= train_seq_len:
f_seq.append(Xindex)
else:
f_seq.append(0)
else:
print(Q[i])
q_data.append(q_seq)
qa_data.append(a_seq)
tf_data.append(f_seq)
q_dataArray = np.zeros((len(q_data), self.seqlen))
for j in range(len(q_data)):
dat = q_data[j]
q_dataArray[j, :len(dat)] = dat
u2q_seq[user_id] = q_dataArray
qa_dataArray = np.zeros((len(qa_data), self.seqlen))
for j in range(len(qa_data)):
dat = qa_data[j]
qa_dataArray[j, :len(dat)] = dat
u2qa_seq[user_id] = qa_dataArray
tf_dataArray = np.zeros((len(tf_data), self.seqlen))
for j in range(len(tf_data)):
dat = tf_data[j]
tf_dataArray[j, :len(dat)] = dat
u2tf_seq[user_id] = tf_dataArray
q_data.clear()
qa_data.clear()
tf_data.clear()
f_data.close()
return u2q_seq, u2qa_seq, u2tf_seq |
16,471 | e7e80e66794381044ad29d994dc852953b45a569 | __author__ = 'Matt Fister'
|
16,472 | 9fb026a1377ed7a92c9876a1cc2cb103bed1a9cb | from unittest import TestCase
from models.item import ItemModel
class ItemTest(TestCase):
def setUp(self) -> None:
self.item = ItemModel("Test", 10.99)
def test_create_item(self):
self.assertEqual("Test", self.item.name, "Item name after creation is not correct!")
self.assertEqual(10.99, self.item.price, "Item price after creation is not correct")
def test_item_json(self):
self.assertDictEqual({"name": "Test", "price": 10.99}, self.item.json(), "Item JSON after creation is not correct")
|
16,473 | f780b4f008309f6b55dff77f8ed0c41d44a4bced | from django.urls import path
from . import views as search_views
urlpatterns = [
path('addDB/' , search_views.addDB , name = 'search-addDB'),
path('queryDB/' , search_views.queryDB , name = 'search-queryDB'),
path('results/',search_views.results, name = 'search-results')
#path('download-csv/' , views.downloadCSV , name = 'search-post'),
] |
16,474 | 5e326515b058adf60436f8c9c2c8cb17d0932525 | import tensorflow as tf
from tensorflow import keras
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Activation, Dropout, LSTM, \
Flatten, Embedding, Multiply, Lambda
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
import h5py
def vqa_model(embedding_matrix,
num_words,
embedding_dim,
seq_length,
dropout_rate,
num_classes
):
###########################
# Word2Vec Model
###########################
print("Creating text model ...")
input_txt = Input(shape=(seq_length,), name='text_input')
x = Embedding(num_words, embedding_dim, weights=[embedding_matrix] ,trainable=False)(input_txt)
x = LSTM(units=512, return_sequences=True, input_shape=(seq_length, embedding_dim))(x)
x = Dropout(dropout_rate)(x)
x = LSTM(units=512, return_sequences=False)(x)
x = Dropout(dropout_rate)(x)
output_txt = Dense(1024, activation='tanh')(x)
txt_model = Model(input_txt, output_txt)
txt_model.summary()
###########################
# Image Model
###########################
print("Creating image model ...")
input_img = Input(shape=(4096,), name='image_input')
output_img = Dense(1024, activation='tanh')(input_img)
img_model = Model(input_img, output_img)
img_model.summary()
###########################
# VQA Model
###########################
print("Creating vqa model...")
input_intermidiate_img = Input(shape=(1024,), name='intermidiate_image_input')
input_intermidiate_txt = Input(shape=(1024,), name='input_intermidiate_txt_input')
x = Multiply()([input_intermidiate_img, input_intermidiate_txt])
x = Dropout(dropout_rate)(x)
x = Dense(1024, activation='tanh')(x)
x = Dropout(dropout_rate)(x)
vqa = Dense(num_classes, activation='softmax')(x)
vqa_model = Model([input_intermidiate_img, input_intermidiate_txt], vqa)
vqa_model.summary()
# internal connection
output_vqa = vqa_model([img_model(input_img), txt_model(input_txt)])
###########################
# VQA Model
###########################
print("Packing multi model...")
multiModel = Model([input_img, input_txt], output_vqa, name='multiModel')
multiModel.summary()
# optimizer
multiModel.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
return multiModel
|
16,475 | 1027dfce7172e82e502d0f26dac3812671930f37 | import os
from FTPserver.core import Config
def run(cwd, path, sk_obj, userid=None):
if path == 'server':
cwd = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sk_obj.sendall(bytes('chdir successfully!', 'utf8'))
return cwd
elif path == 'user':
cwd = os.path.join(Config.BASE_DIR, 'UserData\\%s' %(userid))
sk_obj.sendall(bytes('chdir successfully!', 'utf8'))
return cwd
else:
path = os.path.join(cwd, path)
if not os.path.isdir(path):
sk_obj.sendall(bytes('Error', 'utf8'))
return cwd
else:
if path == os.path.join(Config.BASE_DIR, 'FTPserver\\..'):
sk_obj.sendall(bytes('Error', 'utf8'))
return cwd
sk_obj.sendall(bytes('chdir successfully!', 'utf8'))
return path
|
16,476 | 472d0799c30c098a81bc8b46f1d63ecbddede1c0 | from dataclasses import dataclass
import sys
import os
INPUT = open(os.path.join(sys.path[0], 'input')).read()
@dataclass
class Node:
id: str
parents: []
children: []
depth: 0
def tree_from_input(input: str):
edges = input.split('\n')
tree = {}
for edge in edges:
parent_child = edge.split(')')
tree[parent_child[1]] = parent_child[0]
return tree
def sum_depths(tree):
depths_sum = 0
for key in tree.keys():
key_depth = 0
while key != 'COM':
key_depth += 1
key = tree[key]
depths_sum += key_depth
return depths_sum
tree = tree_from_input(INPUT)
print(sum_depths(tree)) |
16,477 | a35d6858a892939206623aaed380ad4b17f338a7 | try:
a = int(input())
b = int(input())
r = a / b
except (ValueError, TypeError):
print("Problema de tipos de dados")
except ZeroDivisionError:
print("Não é possivel dividir por Zero")
except KeyboardInterrupt:
print("Não tivemos dados informados")
except Exception as erro: # Podem haver varios. Ocorre quando deu um tipo de problema
print(f"Problema. {erro}")
else: # Se não tiver erros
print(r)
finally: # Ocorre sempre
print("fim")
|
16,478 | 0b94c47f166e3570e183d5166ecd2d590f538c39 | import requests
import random
import time
from urllib.parse import urlencode
import cv2
from PIL import Image
import os
import code
import pytesseract
class WenJuanXing(object):
def __init__(self, q_num, q_data):
self.base_url = 'https://www.wjx.cn/jq/%s.aspx'
self.base_submit = 'https://www.wjx.cn/handler/processjq.ashx?'
self.base_spam = 'https://www.wjx.cn/AntiSpamImageGen.aspx?'
self.sess = requests.session()
self.headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36'
}
self.curID = q_num
self.submitdata = q_data
self.submittype = '1'
self.t = str(int(time.time() * 1000))
self.starttime = time.strftime("%Y/%m/%d %H:%M:%S", time.localtime())
self.html = ''
self.validate_text = ''
self.rn = 0
def getHtml(self):
response = self.sess.get(self.base_url % self.curID)
self.html = response.text
def getRandNum(self):
self.rn = 0
self.getHtml()
rnd_part = self.html[self.html.find('rndnum=') + 8:]
self.rn = rnd_part[:rnd_part.find('"')]
def stringParams(self):
return {
'submittype': self.submittype,
'curID': self.curID,
't': self.t,
'starttime': self.starttime,
'rn': self.rn,
'validate_text': self.validate_text
}
def antiSpam(self):
url = self.base_spam + urlencode({'t': self.t, 'q': self.curID})
response = self.sess.get(url)
with open('tmp_img.gif', 'wb') as f:
f.write(response.content)
# TODO: try captcha solver
Image.open('tmp_img.gif').convert('RGB').save('tmp_img.jpg')
'''
img = cv2.imread('tmp_img.jpg', cv2.IMREAD_COLOR)
os.remove('tmp_img.gif')
os.remove('tmp_img.jpg')
cv2.imshow('captcha', img)
cv2.waitKey(0)
self.validate_text = input('验证码: ')
cv2.destroyAllWindows()
'''
img = Image.open('tmp_img.jpg')
img = img.convert('RGB')
img = img.convert('L')
threshold = 20
table = []
for i in range(256):
if i < threshold:
table.append(0)
else:
table.append(1)
img = img.point(table, '1')
img = img.convert('L')
#img.show()
self.validate_text = ''.join(pytesseract.image_to_string(img).split())
print(self.validate_text)
#string = "EJ RM"
#print(''.join(string.split()))
def submitForm(self):
url = self.base_submit + urlencode(self.stringParams())
response = self.sess.post(url, data={'submitdata': self.submitdata})
return response
def resetData(self):
self.getRandNum()
self.t = str(int(time.time() * 1000))
self.starttime = time.strftime("%Y/%m/%d %H:%M:%S", time.localtime())
self.sess.cookies.clear()
def main():
# print('输入验证码时，先关闭图片窗口再输入。')
# q_num = input('问卷号：')
# q_data = input('submitdata（自行理解）：')
# iter = int(input('次数：'))
l = [[1, 2, 3, 1, 0.5],
[4, 2, 1, 1, 5],
[6, 4],
[7, 3],
[7, 3],
[0.5, 0.6, 2, 1], # 6
[4, 9, 3, 2],
[1, 4, 3, 1],
[6, 3, 1],
[2, 2, 1, 0.5],
[4, 3, 2], # 11
[2, 5, 5],
[1, 2, 5, 1.5],
[5, 4, 3, 1],
[1, 2, 3, 2, 1],
[1, 1, 1, 2, 2, 3],
[3, 1, 2],
[5, 4, 3, 2, 1],
[5, 4, 3, 2, 1],
]
times = 1000
for idx, i in enumerate(l):
s = sum(i)
tmp = [x / s for x in i]
for idx2, k in enumerate(tmp):
if idx2 >= 1:
# print(tmp[idx2-1],tmp[idx2])
tmp[idx2] = tmp[idx2] + tmp[idx2 - 1]
l[idx] = tmp
# print(l[idx])
# print(l)
for num in range(times):
result = []
for i in l:
# print(i)
r = random.random()
# type(i)
for idx, k in enumerate(i):
if r < k:
result += [idx]
break
# code.interact(local=locals())
# code.interact(local=locals())
for idx, val in enumerate(result):
result[idx] = str(idx + 1) + '$' + str(val + 1)
q_num = str(45028731)
q_data = '}'.join(result)
print(q_data)
wjx = WenJuanXing(q_num, q_data)
wjx.resetData()
wjx.antiSpam()
response = wjx.submitForm()
print(response.content.decode('UTF-8'))
if __name__ == '__main__':
main()
|
16,479 | d416a81a098daf6ea84cfb6475d78830f34a09ef | from django.shortcuts import render
def chatbot(request):
return render(request,'rage_app/index.html')
# Create your views here.
|
16,480 | c5891416c64b11c67f2b6d9e954e5d21f4e1c4c3 | class initialisation:
'''
In this class will be method with initialisation
'''
def make_initialisation(self):
'''
Method with initialisation
'''
name = input("Please, enter your name: ")
print("Greatings, ", name,
",you are now in program that calculates Mueller's recurrence.")
print("Muller's recurrence relation: x=f(y,z)=108-((815-1500/z)/y).")
|
16,481 | 5d1ff5fda7b82d5a6b2dec2171095f1ff52bed71 | import rhinoscriptsyntax as rs
xCoordinateList = 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points = []
for i in range(len(xCoordinateList)):
points.append(rs.AddPoints([xCoordinateList[i],yCoordinateList[i],0]))
#consider using a flat projection of the country with the denser areas built up into walls |
16,482 | 4616da7ab86f39e6aade85dfe4180e569cda1be9 | import base64
import numpy as np
import io
from PIL import Image
import keras
import tensorflow as tf
import cv2
from keras import backend as K
from keras.models import Sequential
from keras.models import load_model
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.preprocessing.image import img_to_array
from flask import request
from flask import jsonify
from flask import Flask
import asyncio
app = Flask(__name__)
import tensorflow as tf
global graph,model
graph = tf.get_default_graph()
def get_classifier():
global classifier
with graph.as_default():
classifier = load_model('CNNIntegradorF.h5')
print("* Model loaded!")
def preprocess_image(image, target_size):
if image.mode != 'RGB':
image = image.convert("RGB")
image = image.resize(target_size)
image = img_to_array(image)
image = image.reshape(1,target_size[0],target_size[1],3)
print(image)
return image
print("* Loading Keras Model")
get_classifier()
@app.route("/predict", methods=["POST"])
def predict():
with graph.as_default():
message = request.get_json(force=True)
encoded = message['image']
decoded = base64.b64decode(encoded)
image = Image.open(io.BytesIO(decoded))
#print(image)
processed_image = preprocess_image(image, target_size=(64,64))
prediction = classifier.predict(processed_image).tolist()
print(prediction)
print(prediction[0])
x = prediction[0]
print(x[0])
print(x[1])
print(x[2])
print(x[3])
print(x[4])
#for i in len(prediction):
# if x[i] == 1:
# print("hola1")
# elif x[i] == 2:
# print('Hola2')
response = {
'prediction' : {
'automobile': x[0],
'cat': x[1],
'dog': x[2],
'ship': x[3],
'truck': x[4]
}
}
return jsonify(response)
#flask run --host=0.0.0.0 |
16,483 | c354920818ca548bf52af2f19bd640295d762ddd | n=int(input())
A=list(map(int,input().split()))
ans4 = 0
ans2 = 0
for a in A:
if a % 4 == 0:
ans4 += 1
elif a % 2 == 0:
ans2 += 1
ans2 = ans2-(ans2%2)
if (n-ans2)//2 <= ans4:
print("Yes")
else:
print("No") |
16,484 | 197e5c050b2b926fb05fc869f3c90f2c6583ad63 | # 주어진 도시들의 도로망에서 불필요한 도로를 제거하여 새로운 도시망을 구축한다.
roadRegister = [[False, True, True, False, False, False],
[True, False, False, True, False, False],
[True, False, False, False, False, False],
[False, True, False, False, False, False],
[False, False, False, False, False, True],
[False, False, False, False, True, False]]
def solution(roads):
connect = []
# 각 도시들의 관계 도로를 정의한다. {작은 숫자도시, 큰 숫자 도시 } 관계로 한다.
for i in range(len(roads)):
for j in range(i + 1, len(roads)): # i+1로 시작해야 이전 도시를 다시 검사 안한다.
if roads[i][j]:
connect.append({i, j})
print(connect)
# 연결되 도시들의 도로 수를 배경으로 새로운 관계망 지도를 만든다.
new_roads = [[False for i in range(len(connect))]
for j in range(len(connect))]
print(*new_roads, sep='\n')
for i in range(len(new_roads)):
for j in range(i + 1, len(new_roads)):
if connect[i] & connect[j]: # 해당 i, j번 도시들이 서로 연결 여부를 교집합 &으로 확인
new_roads[i][j] = True
new_roads[j][i] = True
print(*new_roads, sep='\n')
return new_roads
print(solution(roadRegister))
|
16,485 | 05cb7fe13b409069a436f7a2813227083d93f611 | from log_into_wiki import *
import re
site = login('me','zelda') # Set wiki
summary = 'Rename Modules' # Set summary
limit = 10
#startat_page = 'asdf'
lmt = 0
for p in site.allpages(namespace=828):
if limit == lmt:
break
lmt += 1
print(p.name)
text = p.text()
text_table = text.split('\n')
newlines = []
for line in text_table:
newlines.append(re.sub(r"require\( *[\'\"]Module:Utils(.+?)['\"] *\)", r"require('Module:\1Util')",line))
newtext = '\n'.join(newlines)
if text != newtext:
p.save(newtext) |
16,486 | c7e52bc8bb648d69f6bfcd3795454f052788bdfa |
from craigslist import CraigslistForSale
cl_e30 = CraigslistForSale(site='boston', category='cta',
filters={'make': 'bmw', 'auto_transmission': ['manual'], 'min_year': 1987, 'max_year': 1991, 'search_distance': 1000})
for result in cl_e30.get_results(sort_by='newest'):
print(result)
|
16,487 | 49e38b2fb9b8826f6e614f2aa97308214e462267 | # Copyright 2019, The TensorFlow Federated Authors.
#
# 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 absl.testing import absltest
from absl.testing import parameterized
import tensorflow as tf
from tensorflow_federated.proto.v0 import computation_pb2 as pb
from tensorflow_federated.python.common_libs import serialization_utils
from tensorflow_federated.python.core.impl.compiler import building_block_analysis
from tensorflow_federated.python.core.impl.compiler import building_block_factory
from tensorflow_federated.python.core.impl.compiler import building_block_test_utils
from tensorflow_federated.python.core.impl.compiler import building_blocks
from tensorflow_federated.python.core.impl.compiler import intrinsic_defs
from tensorflow_federated.python.core.impl.compiler import tree_analysis
from tensorflow_federated.python.core.impl.types import computation_types
from tensorflow_federated.python.core.impl.types import placements
from tensorflow_federated.python.core.impl.types import type_serialization
from tensorflow_federated.python.core.impl.utils import tensorflow_utils
class TestCheckContainsOnlyReducibleIntrinsics(absltest.TestCase):
def test_raises_on_none(self):
with self.assertRaises(TypeError):
tree_analysis.check_contains_only_reducible_intrinsics(None)
def test_passes_with_federated_map(self):
intrinsic = building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MAP.uri,
computation_types.FunctionType(
[
computation_types.FunctionType(tf.int32, tf.float32),
computation_types.FederatedType(tf.int32, placements.CLIENTS),
],
computation_types.FederatedType(tf.float32, placements.CLIENTS),
),
)
tree_analysis.check_contains_only_reducible_intrinsics(intrinsic)
def test_raises_with_federated_mean(self):
intrinsic = building_blocks.Intrinsic(
intrinsic_defs.FEDERATED_MEAN.uri,
computation_types.FunctionType(
computation_types.FederatedType(tf.int32, placements.CLIENTS),
computation_types.FederatedType(tf.int32, placements.SERVER),
),
)
with self.assertRaisesRegex(ValueError, intrinsic.compact_representation()):
tree_analysis.check_contains_only_reducible_intrinsics(intrinsic)
def whimsy_intrinsic_predicate(x):
return (
isinstance(x, building_blocks.Intrinsic) and x.uri == 'whimsy_intrinsic'
)
class NodesDependentOnPredicateTest(absltest.TestCase):
def test_raises_on_none_comp(self):
with self.assertRaises(TypeError):
tree_analysis.extract_nodes_consuming(None, lambda x: True)
def test_raises_on_none_predicate(self):
data_type = computation_types.StructType([])
data = building_blocks.Data('whimsy', data_type)
with self.assertRaises(TypeError):
tree_analysis.extract_nodes_consuming(data, None)
def test_adds_all_nodes_to_set_with_constant_true_predicate(self):
nested_tree = building_block_test_utils.create_nested_syntax_tree()
all_nodes = tree_analysis.extract_nodes_consuming(
nested_tree, lambda x: True
)
node_count = tree_analysis.count(nested_tree)
self.assertLen(all_nodes, node_count)
def test_adds_no_nodes_to_set_with_constant_false_predicate(self):
nested_tree = building_block_test_utils.create_nested_syntax_tree()
all_nodes = tree_analysis.extract_nodes_consuming(
nested_tree, lambda x: False
)
self.assertEmpty(all_nodes)
def test_propogates_dependence_up_through_lambda(self):
type_signature = computation_types.TensorType(tf.int32)
whimsy_intrinsic = building_blocks.Intrinsic(
'whimsy_intrinsic', type_signature
)
lam = building_blocks.Lambda('x', tf.int32, whimsy_intrinsic)
dependent_nodes = tree_analysis.extract_nodes_consuming(
lam, whimsy_intrinsic_predicate
)
self.assertIn(lam, dependent_nodes)
def test_propogates_dependence_up_through_block_result(self):
type_signature = computation_types.TensorType(tf.int32)
whimsy_intrinsic = building_blocks.Intrinsic(
'whimsy_intrinsic', type_signature
)
integer_reference = building_blocks.Reference('int', tf.int32)
block = building_blocks.Block([('x', integer_reference)], whimsy_intrinsic)
dependent_nodes = tree_analysis.extract_nodes_consuming(
block, whimsy_intrinsic_predicate
)
self.assertIn(block, dependent_nodes)
def test_propogates_dependence_up_through_block_locals(self):
type_signature = computation_types.TensorType(tf.int32)
whimsy_intrinsic = building_blocks.Intrinsic(
'whimsy_intrinsic', type_signature
)
integer_reference = building_blocks.Reference('int', tf.int32)
block = building_blocks.Block([('x', whimsy_intrinsic)], integer_reference)
dependent_nodes = tree_analysis.extract_nodes_consuming(
block, whimsy_intrinsic_predicate
)
self.assertIn(block, dependent_nodes)
def test_propogates_dependence_up_through_tuple(self):
type_signature = computation_types.TensorType(tf.int32)
whimsy_intrinsic = building_blocks.Intrinsic(
'whimsy_intrinsic', type_signature
)
integer_reference = building_blocks.Reference('int', tf.int32)
tup = building_blocks.Struct([integer_reference, whimsy_intrinsic])
dependent_nodes = tree_analysis.extract_nodes_consuming(
tup, whimsy_intrinsic_predicate
)
self.assertIn(tup, dependent_nodes)
def test_propogates_dependence_up_through_selection(self):
type_signature = computation_types.StructType([tf.int32])
whimsy_intrinsic = building_blocks.Intrinsic(
'whimsy_intrinsic', type_signature
)
selection = building_blocks.Selection(whimsy_intrinsic, index=0)
dependent_nodes = tree_analysis.extract_nodes_consuming(
selection, whimsy_intrinsic_predicate
)
self.assertIn(selection, dependent_nodes)
def test_propogates_dependence_up_through_call(self):
type_signature = computation_types.TensorType(tf.int32)
whimsy_intrinsic = building_blocks.Intrinsic(
'whimsy_intrinsic', type_signature
)
ref_to_x = building_blocks.Reference('x', tf.int32)
identity_lambda = building_blocks.Lambda('x', tf.int32, ref_to_x)
called_lambda = building_blocks.Call(identity_lambda, whimsy_intrinsic)
dependent_nodes = tree_analysis.extract_nodes_consuming(
called_lambda, whimsy_intrinsic_predicate
)
self.assertIn(called_lambda, dependent_nodes)
def test_propogates_dependence_into_binding_to_reference(self):
fed_type = computation_types.FederatedType(tf.int32, placements.CLIENTS)
ref_to_x = building_blocks.Reference('x', fed_type)
federated_zero = building_blocks.Intrinsic(
intrinsic_defs.GENERIC_ZERO.uri, fed_type
)
def federated_zero_predicate(x):
return (
isinstance(x, building_blocks.Intrinsic)
and x.uri == intrinsic_defs.GENERIC_ZERO.uri
)
block = building_blocks.Block([('x', federated_zero)], ref_to_x)
dependent_nodes = tree_analysis.extract_nodes_consuming(
block, federated_zero_predicate
)
self.assertIn(ref_to_x, dependent_nodes)
class BroadcastDependentOnAggregateTest(absltest.TestCase):
def test_raises_on_none_comp(self):
with self.assertRaises(TypeError):
tree_analysis.check_broadcast_not_dependent_on_aggregate(None)
def test_does_not_find_aggregate_dependent_on_broadcast(self):
broadcast = (
building_block_test_utils.create_whimsy_called_federated_broadcast()
)
value_type = broadcast.type_signature
zero = building_blocks.Data('zero', value_type.member)
accumulate_result = building_blocks.Data(
'accumulate_result', value_type.member
)
accumulate = building_blocks.Lambda(
'accumulate_parameter',
[value_type.member, value_type.member],
accumulate_result,
)
merge_result = building_blocks.Data('merge_result', value_type.member)
merge = building_blocks.Lambda(
'merge_parameter', [value_type.member, value_type.member], merge_result
)
report_result = building_blocks.Data('report_result', value_type.member)
report = building_blocks.Lambda(
'report_parameter', value_type.member, report_result
)
aggregate_dependent_on_broadcast = (
building_block_factory.create_federated_aggregate(
broadcast, zero, accumulate, merge, report
)
)
tree_analysis.check_broadcast_not_dependent_on_aggregate(
aggregate_dependent_on_broadcast
)
def test_finds_broadcast_dependent_on_aggregate(self):
aggregate = (
building_block_test_utils.create_whimsy_called_federated_aggregate()
)
broadcasted_aggregate = building_block_factory.create_federated_broadcast(
aggregate
)
with self.assertRaises(ValueError):
tree_analysis.check_broadcast_not_dependent_on_aggregate(
broadcasted_aggregate
)
def test_returns_correct_example_of_broadcast_dependent_on_aggregate(self):
aggregate = (
building_block_test_utils.create_whimsy_called_federated_aggregate()
)
broadcasted_aggregate = building_block_factory.create_federated_broadcast(
aggregate
)
with self.assertRaisesRegex(ValueError, 'acc_param'):
tree_analysis.check_broadcast_not_dependent_on_aggregate(
broadcasted_aggregate
)
class AggregateDependentOnAggregateTest(absltest.TestCase):
def test_raises_on_none_comp(self):
with self.assertRaises(TypeError):
tree_analysis.check_aggregate_not_dependent_on_aggregate(None)
def test_does_not_find_aggregate_dependent_on_broadcast(self):
broadcast = (
building_block_test_utils.create_whimsy_called_federated_broadcast()
)
value_type = broadcast.type_signature
zero = building_blocks.Data('zero', value_type.member)
accumulate_result = building_blocks.Data(
'accumulate_result', value_type.member
)
accumulate = building_blocks.Lambda(
'accumulate_parameter',
[value_type.member, value_type.member],
accumulate_result,
)
merge_result = building_blocks.Data('merge_result', value_type.member)
merge = building_blocks.Lambda(
'merge_parameter', [value_type.member, value_type.member], merge_result
)
report_result = building_blocks.Data('report_result', value_type.member)
report = building_blocks.Lambda(
'report_parameter', value_type.member, report_result
)
aggregate_dependent_on_broadcast = (
building_block_factory.create_federated_aggregate(
broadcast, zero, accumulate, merge, report
)
)
tree_analysis.check_aggregate_not_dependent_on_aggregate(
aggregate_dependent_on_broadcast
)
def test_finds_aggregate_dependent_on_aggregate(self):
aggregate = (
building_block_test_utils.create_whimsy_called_federated_aggregate()
)
broadcasted_aggregate = building_block_factory.create_federated_broadcast(
aggregate
)
second_aggregate = building_block_factory.create_federated_sum(
broadcasted_aggregate
)
with self.assertRaises(ValueError):
tree_analysis.check_aggregate_not_dependent_on_aggregate(second_aggregate)
class CountTensorFlowOpsTest(absltest.TestCase):
def test_raises_on_none(self):
with self.assertRaises(TypeError):
tree_analysis.count_tensorflow_ops_under(None)
def test_returns_zero_no_tensorflow(self):
no_tensorflow_comp = building_block_test_utils.create_nested_syntax_tree()
tf_count = tree_analysis.count_tensorflow_ops_under(no_tensorflow_comp)
self.assertEqual(tf_count, 0)
def test_single_tensorflow_node_count_agrees_with_node_count(self):
tensor_type = computation_types.TensorType(tf.int32)
integer_identity = building_block_factory.create_compiled_identity(
tensor_type
)
node_tf_op_count = building_block_analysis.count_tensorflow_ops_in(
integer_identity
)
tree_tf_op_count = tree_analysis.count_tensorflow_ops_under(
integer_identity
)
self.assertEqual(node_tf_op_count, tree_tf_op_count)
def test_tensorflow_op_count_doubles_number_of_ops_in_two_tuple(self):
tensor_type = computation_types.TensorType(tf.int32)
integer_identity = building_block_factory.create_compiled_identity(
tensor_type
)
node_tf_op_count = building_block_analysis.count_tensorflow_ops_in(
integer_identity
)
tf_tuple = building_blocks.Struct([integer_identity, integer_identity])
tree_tf_op_count = tree_analysis.count_tensorflow_ops_under(tf_tuple)
self.assertEqual(tree_tf_op_count, 2 * node_tf_op_count)
def _pack_noarg_graph(graph_def, return_type, result_binding):
packed_graph_def = serialization_utils.pack_graph_def(graph_def)
function_type = computation_types.FunctionType(None, return_type)
proto = pb.Computation(
type=type_serialization.serialize_type(function_type),
tensorflow=pb.TensorFlow(
graph_def=packed_graph_def, parameter=None, result=result_binding
),
)
building_block = building_blocks.ComputationBuildingBlock.from_proto(proto)
return building_block
def _create_no_variable_tensorflow():
with tf.Graph().as_default() as g:
a = tf.constant(0, name='variable1')
b = tf.constant(1, name='variable2')
c = a + b
result_type, result_binding = tensorflow_utils.capture_result_from_graph(c, g)
return _pack_noarg_graph(g.as_graph_def(), result_type, result_binding)
def _create_two_variable_tensorflow():
with tf.Graph().as_default() as g:
a = tf.Variable(0, name='variable1')
b = tf.Variable(1, name='variable2')
c = a + b
result_type, result_binding = tensorflow_utils.capture_result_from_graph(c, g)
return _pack_noarg_graph(g.as_graph_def(), result_type, result_binding)
def _create_tensorflow_graph_with_nan():
with tf.Graph().as_default() as g:
a = tf.constant(float('NaN'))
result_type, result_binding = tensorflow_utils.capture_result_from_graph(a, g)
return _pack_noarg_graph(g.as_graph_def(), result_type, result_binding)
class CountTensorFlowVariablesTest(absltest.TestCase):
def test_raises_on_none(self):
with self.assertRaises(TypeError):
tree_analysis.count_tensorflow_variables_under(None)
def test_returns_zero_no_tensorflow(self):
no_tensorflow_comp = building_block_test_utils.create_nested_syntax_tree()
variable_count = tree_analysis.count_tensorflow_variables_under(
no_tensorflow_comp
)
self.assertEqual(variable_count, 0)
def test_returns_zero_tensorflow_with_no_variables(self):
no_variable_comp = _create_no_variable_tensorflow()
variable_count = tree_analysis.count_tensorflow_variables_under(
no_variable_comp
)
self.assertEqual(variable_count, 0)
def test_tensorflow_op_count_doubles_number_of_ops_in_two_tuple(self):
two_variable_comp = _create_two_variable_tensorflow()
node_tf_variable_count = (
building_block_analysis.count_tensorflow_variables_in(two_variable_comp)
)
tf_tuple = building_blocks.Struct([two_variable_comp, two_variable_comp])
tree_tf_variable_count = tree_analysis.count_tensorflow_variables_under(
tf_tuple
)
self.assertEqual(tree_tf_variable_count, 2 * node_tf_variable_count)
class ContainsCalledIntrinsic(absltest.TestCase):
def test_raises_type_error_with_none_tree(self):
with self.assertRaises(TypeError):
tree_analysis.contains_called_intrinsic(None)
def test_returns_true_with_none_uri(self):
comp = building_block_test_utils.create_whimsy_called_federated_broadcast()
self.assertTrue(tree_analysis.contains_called_intrinsic(comp))
def test_returns_true_with_matching_uri(self):
comp = building_block_test_utils.create_whimsy_called_federated_broadcast()
uri = intrinsic_defs.FEDERATED_BROADCAST.uri
self.assertTrue(tree_analysis.contains_called_intrinsic(comp, uri))
def test_returns_false_with_no_called_intrinsic(self):
comp = building_block_test_utils.create_identity_function('a')
self.assertFalse(tree_analysis.contains_called_intrinsic(comp))
def test_returns_false_with_unmatched_called_intrinsic(self):
comp = building_block_test_utils.create_whimsy_called_federated_broadcast()
uri = intrinsic_defs.FEDERATED_MAP.uri
self.assertFalse(tree_analysis.contains_called_intrinsic(comp, uri))
class ContainsNoUnboundReferencesTest(absltest.TestCase):
def test_raises_type_error_with_none_tree(self):
with self.assertRaises(TypeError):
tree_analysis.contains_no_unbound_references(None)
def test_raises_type_error_with_int_excluding(self):
ref = building_blocks.Reference('a', tf.int32)
fn = building_blocks.Lambda(ref.name, ref.type_signature, ref)
with self.assertRaises(TypeError):
tree_analysis.contains_no_unbound_references(fn, 1)
def test_returns_true(self):
ref = building_blocks.Reference('a', tf.int32)
fn = building_blocks.Lambda(ref.name, ref.type_signature, ref)
self.assertTrue(tree_analysis.contains_no_unbound_references(fn))
def test_returns_true_with_excluded_reference(self):
ref = building_blocks.Reference('a', tf.int32)
fn = building_blocks.Lambda('b', tf.int32, ref)
self.assertTrue(
tree_analysis.contains_no_unbound_references(fn, excluding='a')
)
def test_returns_false(self):
ref = building_blocks.Reference('a', tf.int32)
fn = building_blocks.Lambda('b', tf.int32, ref)
self.assertFalse(tree_analysis.contains_no_unbound_references(fn))
class TreesEqualTest(absltest.TestCase):
def test_raises_type_error(self):
data = building_blocks.Data('data', tf.int32)
with self.assertRaises(TypeError):
tree_analysis.trees_equal(data, 0)
with self.assertRaises(TypeError):
tree_analysis.trees_equal(0, data)
def test_returns_false_for_block_and_none(self):
data = building_blocks.Data('data', tf.int32)
self.assertFalse(tree_analysis.trees_equal(data, None))
self.assertFalse(tree_analysis.trees_equal(None, data))
def test_returns_true_for_none_and_none(self):
self.assertTrue(tree_analysis.trees_equal(None, None))
def test_returns_true_for_the_same_comp(self):
data = building_blocks.Data('data', tf.int32)
self.assertTrue(tree_analysis.trees_equal(data, data))
def test_returns_false_for_comps_with_different_types(self):
data = building_blocks.Data('data', tf.int32)
ref = building_blocks.Reference('a', tf.int32)
self.assertFalse(tree_analysis.trees_equal(data, ref))
self.assertFalse(tree_analysis.trees_equal(ref, data))
def test_returns_false_for_blocks_with_different_results(self):
data_1 = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Block([], data_1)
data_2 = building_blocks.Data('data', tf.float32)
comp_2 = building_blocks.Block([], data_2)
self.assertFalse(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_false_for_blocks_with_different_variable_lengths(self):
data = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Block([('a', data)], data)
comp_2 = building_blocks.Block([('a', data), ('b', data)], data)
self.assertFalse(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_blocks_with_different_variable_names(self):
data = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Block([('a', data)], data)
comp_2 = building_blocks.Block([('b', data)], data)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_blocks_resulting_reference_to_same_local(self):
data = building_blocks.Data('data', tf.int32)
ref_to_a = building_blocks.Reference('a', data.type_signature)
ref_to_b = building_blocks.Reference('b', data.type_signature)
comp_1 = building_blocks.Block([('a', data)], ref_to_a)
comp_2 = building_blocks.Block([('b', data)], ref_to_b)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_blocks_referring_to_same_comp_in_local(self):
data = building_blocks.Data('data', tf.int32)
ref_to_a = building_blocks.Reference('a', data.type_signature)
ref_to_b = building_blocks.Reference('b', data.type_signature)
comp_1 = building_blocks.Block([('a', data), ('b', ref_to_a)], data)
comp_2 = building_blocks.Block([('b', data), ('a', ref_to_b)], data)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_blocks_referring_same_local(self):
data = building_blocks.Data('data', tf.int32)
ref_to_a = building_blocks.Reference('a', data.type_signature)
ref_to_b = building_blocks.Reference('b', data.type_signature)
comp_1 = building_blocks.Block([('a', data), ('b', ref_to_a)], ref_to_b)
comp_2 = building_blocks.Block([('b', data), ('a', ref_to_b)], ref_to_a)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_false_for_blocks_referring_to_different_local(self):
data = building_blocks.Data('data', tf.int32)
ref_to_a = building_blocks.Reference('a', data.type_signature)
ref_to_b = building_blocks.Reference('b', data.type_signature)
comp_1 = building_blocks.Block([('a', data), ('b', ref_to_a)], ref_to_a)
comp_2 = building_blocks.Block([('b', data), ('a', ref_to_b)], ref_to_a)
self.assertFalse(tree_analysis.trees_equal(comp_1, comp_2))
self.assertFalse(tree_analysis.trees_equal(comp_2, comp_1))
def test_returns_false_for_blocks_with_different_variable_values(self):
data = building_blocks.Data('data', tf.int32)
data_1 = building_blocks.Data('data', tf.float32)
comp_1 = building_blocks.Block([('a', data_1)], data)
data_2 = building_blocks.Data('data', tf.bool)
comp_2 = building_blocks.Block([('a', data_2)], data)
self.assertFalse(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_blocks(self):
data_1 = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Block([('a', data_1)], data_1)
data_2 = building_blocks.Data('data', tf.int32)
comp_2 = building_blocks.Block([('a', data_2)], data_2)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_false_for_calls_with_different_functions(self):
function_type_1 = computation_types.FunctionType(tf.int32, tf.int32)
fn_1 = building_blocks.Reference('a', function_type_1)
arg_1 = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Call(fn_1, arg_1)
function_type_2 = computation_types.FunctionType(tf.int32, tf.int32)
fn_2 = building_blocks.Reference('b', function_type_2)
arg_2 = building_blocks.Data('data', tf.int32)
comp_2 = building_blocks.Call(fn_2, arg_2)
self.assertFalse(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_false_for_calls_with_different_arguments(self):
function_type_1 = computation_types.FunctionType(tf.int32, tf.int32)
fn_1 = building_blocks.Reference('a', function_type_1)
arg_1 = building_blocks.Data('a', tf.int32)
comp_1 = building_blocks.Call(fn_1, arg_1)
function_type_2 = computation_types.FunctionType(tf.int32, tf.int32)
fn_2 = building_blocks.Reference('a', function_type_2)
arg_2 = building_blocks.Data('b', tf.int32)
comp_2 = building_blocks.Call(fn_2, arg_2)
self.assertFalse(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_calls(self):
function_type_1 = computation_types.FunctionType(tf.int32, tf.int32)
fn_1 = building_blocks.Reference('a', function_type_1)
arg_1 = building_blocks.Data('data', tf.int32)
comp_1 = building_blocks.Call(fn_1, arg_1)
function_type_2 = computation_types.FunctionType(tf.int32, tf.int32)
fn_2 = building_blocks.Reference('a', function_type_2)
arg_2 = building_blocks.Data('data', tf.int32)
comp_2 = building_blocks.Call(fn_2, arg_2)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_true_for_calls_with_no_arguments(self):
function_type_1 = computation_types.FunctionType(None, tf.int32)
fn_1 = building_blocks.Reference('a', function_type_1)
comp_1 = building_blocks.Call(fn_1)
function_type_2 = computation_types.FunctionType(None, tf.int32)
fn_2 = building_blocks.Reference('a', function_type_2)
comp_2 = building_blocks.Call(fn_2)
self.assertTrue(tree_analysis.trees_equal(comp_1, comp_2))
def test_returns_false_for_compiled_computations_with_different_types(self):
tensor_type_1 = computation_types.TensorType(tf.int32)
compiled_1 = building_block_factory.create_compiled_identity(
tensor_type_1, 'a'
)
tensor_type_2 = computation_types.TensorType(tf.float32)
compiled_2 = building_block_factory.create_compiled_identity(
tensor_type_2, 'a'
)
self.assertFalse(tree_analysis.trees_equal(compiled_1, compiled_2))
def test_returns_true_for_compiled_computations(self):
tensor_type = computation_types.TensorType(tf.int32)
compiled_1 = building_block_factory.create_compiled_identity(
tensor_type, 'a'
)
compiled_2 = building_block_factory.create_compiled_identity(
tensor_type, 'a'
)
self.assertTrue(tree_analysis.trees_equal(compiled_1, compiled_2))
def test_returns_true_for_compiled_computations_with_different_names(self):
tensor_type = computation_types.TensorType(tf.int32)
compiled_1 = building_block_factory.create_compiled_identity(
tensor_type, 'a'
)
compiled_2 = building_block_factory.create_compiled_identity(
tensor_type, 'b'
)
self.assertTrue(tree_analysis.trees_equal(compiled_1, compiled_2))
def test_returns_false_for_data_with_different_types(self):
data_1 = building_blocks.Data('data', tf.int32)
data_2 = building_blocks.Data('data', tf.float32)
self.assertFalse(tree_analysis.trees_equal(data_1, data_2))
def test_returns_false_for_data_with_different_names(self):
data_1 = building_blocks.Data('a', tf.int32)
data_2 = building_blocks.Data('b', tf.int32)
self.assertFalse(tree_analysis.trees_equal(data_1, data_2))
def test_returns_true_for_data(self):
data_1 = building_blocks.Data('data', tf.int32)
data_2 = building_blocks.Data('data', tf.int32)
self.assertTrue(tree_analysis.trees_equal(data_1, data_2))
def test_returns_false_for_intrinsics_with_different_types(self):
type_signature_1 = computation_types.TensorType(tf.int32)
intrinsic_1 = building_blocks.Intrinsic('intrinsic', type_signature_1)
type_signature_2 = computation_types.TensorType(tf.float32)
intrinsic_2 = building_blocks.Intrinsic('intrinsic', type_signature_2)
self.assertFalse(tree_analysis.trees_equal(intrinsic_1, intrinsic_2))
def test_returns_false_for_intrinsics_with_different_names(self):
type_signature_1 = computation_types.TensorType(tf.int32)
intrinsic_1 = building_blocks.Intrinsic('a', type_signature_1)
type_signature_2 = computation_types.TensorType(tf.int32)
intrinsic_2 = building_blocks.Intrinsic('b', type_signature_2)
self.assertFalse(tree_analysis.trees_equal(intrinsic_1, intrinsic_2))
def test_returns_true_for_intrinsics(self):
type_signature_1 = computation_types.TensorType(tf.int32)
intrinsic_1 = building_blocks.Intrinsic('intrinsic', type_signature_1)
type_signature_2 = computation_types.TensorType(tf.int32)
intrinsic_2 = building_blocks.Intrinsic('intrinsic', type_signature_2)
self.assertTrue(tree_analysis.trees_equal(intrinsic_1, intrinsic_2))
def test_returns_true_for_lambdas_representing_identical_functions(self):
ref_1 = building_blocks.Reference('a', tf.int32)
fn_1 = building_blocks.Lambda('a', ref_1.type_signature, ref_1)
ref_2 = building_blocks.Reference('b', tf.int32)
fn_2 = building_blocks.Lambda('b', ref_2.type_signature, ref_2)
self.assertTrue(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_false_for_lambdas_with_different_parameter_types(self):
ref_1 = building_blocks.Reference('a', tf.int32)
fn_1 = building_blocks.Lambda(ref_1.name, ref_1.type_signature, ref_1)
ref_2 = building_blocks.Reference('a', tf.float32)
fn_2 = building_blocks.Lambda(ref_2.name, ref_2.type_signature, ref_2)
self.assertFalse(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_false_for_lambdas_with_different_results(self):
data_1 = building_blocks.Data('x', tf.int32)
ref_1 = building_blocks.Reference('a', tf.int32)
fn_1 = building_blocks.Lambda(ref_1.name, ref_1.type_signature, data_1)
data_2 = building_blocks.Data('y', tf.int32)
ref_2 = building_blocks.Reference('b', tf.int32)
fn_2 = building_blocks.Lambda(ref_2.name, ref_2.type_signature, data_2)
self.assertFalse(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_true_for_lambdas_with_different_parameter_names_but_same_result(
self,
):
data_1 = building_blocks.Data('x', tf.int32)
ref_1 = building_blocks.Reference('a', tf.int32)
fn_1 = building_blocks.Lambda(ref_1.name, ref_1.type_signature, data_1)
ref_2 = building_blocks.Reference('b', tf.int32)
fn_2 = building_blocks.Lambda(ref_2.name, ref_2.type_signature, data_1)
self.assertTrue(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_false_for_lambdas_referring_to_different_unbound_variables(
self,
):
ref_to_x = building_blocks.Reference('x', tf.int32)
ref_to_y = building_blocks.Reference('y', tf.int32)
fn_1 = building_blocks.Lambda('a', tf.int32, ref_to_x)
fn_2 = building_blocks.Lambda('a', tf.int32, ref_to_y)
self.assertFalse(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_true_for_lambdas_referring_to_same_unbound_variables(self):
ref_to_x = building_blocks.Reference('x', tf.int32)
fn_1 = building_blocks.Lambda('a', tf.int32, ref_to_x)
fn_2 = building_blocks.Lambda('a', tf.int32, ref_to_x)
self.assertTrue(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_true_for_lambdas(self):
ref_1 = building_blocks.Reference('a', tf.int32)
fn_1 = building_blocks.Lambda(ref_1.name, ref_1.type_signature, ref_1)
ref_2 = building_blocks.Reference('a', tf.int32)
fn_2 = building_blocks.Lambda(ref_2.name, ref_2.type_signature, ref_2)
self.assertTrue(tree_analysis.trees_equal(fn_1, fn_2))
def test_returns_false_for_placements_with_literals(self):
placement_1 = building_blocks.Placement(placements.CLIENTS)
placement_2 = building_blocks.Placement(placements.SERVER)
self.assertFalse(tree_analysis.trees_equal(placement_1, placement_2))
def test_returns_true_for_placements(self):
placement_1 = building_blocks.Placement(placements.CLIENTS)
placement_2 = building_blocks.Placement(placements.CLIENTS)
self.assertTrue(tree_analysis.trees_equal(placement_1, placement_2))
def test_returns_false_for_references_with_different_types(self):
reference_1 = building_blocks.Reference('a', tf.int32)
reference_2 = building_blocks.Reference('a', tf.float32)
self.assertFalse(tree_analysis.trees_equal(reference_1, reference_2))
def test_returns_false_for_references_with_different_names(self):
reference_1 = building_blocks.Reference('a', tf.int32)
reference_2 = building_blocks.Reference('b', tf.int32)
self.assertFalse(tree_analysis.trees_equal(reference_1, reference_2))
def test_returns_true_for_references(self):
reference_1 = building_blocks.Reference('a', tf.int32)
reference_2 = building_blocks.Reference('a', tf.int32)
self.assertTrue(tree_analysis.trees_equal(reference_1, reference_2))
def test_returns_false_for_selections_with_differet_sources(self):
ref_1 = building_blocks.Reference('a', [tf.int32, tf.int32])
selection_1 = building_blocks.Selection(ref_1, index=0)
ref_2 = building_blocks.Reference('b', [tf.int32, tf.int32])
selection_2 = building_blocks.Selection(ref_2, index=1)
self.assertFalse(tree_analysis.trees_equal(selection_1, selection_2))
def test_returns_false_for_selections_with_different_indexes(self):
ref_1 = building_blocks.Reference('a', [tf.int32, tf.int32])
selection_1 = building_blocks.Selection(ref_1, index=0)
ref_2 = building_blocks.Reference('a', [tf.int32, tf.int32])
selection_2 = building_blocks.Selection(ref_2, index=1)
self.assertFalse(tree_analysis.trees_equal(selection_1, selection_2))
def test_returns_false_for_selections_with_differet_names(self):
ref_1 = building_blocks.Reference('a', [('a', tf.int32), ('b', tf.int32)])
selection_1 = building_blocks.Selection(ref_1, name='a')
ref_2 = building_blocks.Reference('a', [('a', tf.int32), ('b', tf.int32)])
selection_2 = building_blocks.Selection(ref_2, name='b')
self.assertFalse(tree_analysis.trees_equal(selection_1, selection_2))
def test_returns_true_for_selections_with_indexes(self):
ref_1 = building_blocks.Reference('a', [tf.int32, tf.int32])
selection_1 = building_blocks.Selection(ref_1, index=0)
ref_2 = building_blocks.Reference('a', [tf.int32, tf.int32])
selection_2 = building_blocks.Selection(ref_2, index=0)
self.assertTrue(tree_analysis.trees_equal(selection_1, selection_2))
def test_returns_true_for_selections_with_names(self):
ref_1 = building_blocks.Reference('a', [('a', tf.int32), ('b', tf.int32)])
selection_1 = building_blocks.Selection(ref_1, name='a')
ref_2 = building_blocks.Reference('a', [('a', tf.int32), ('b', tf.int32)])
selection_2 = building_blocks.Selection(ref_2, name='a')
self.assertTrue(tree_analysis.trees_equal(selection_1, selection_2))
def test_returns_false_for_tuples_with_different_lengths(self):
data_1 = building_blocks.Data('data', tf.int32)
tuple_1 = building_blocks.Struct([data_1])
data_2 = building_blocks.Data('data', tf.int32)
tuple_2 = building_blocks.Struct([data_2, data_2])
self.assertFalse(tree_analysis.trees_equal(tuple_1, tuple_2))
def test_returns_false_for_tuples_with_different_names(self):
data_1 = building_blocks.Data('data', tf.int32)
tuple_1 = building_blocks.Struct([('a', data_1), ('b', data_1)])
data_2 = building_blocks.Data('data', tf.float32)
tuple_2 = building_blocks.Struct([('c', data_2), ('d', data_2)])
self.assertFalse(tree_analysis.trees_equal(tuple_1, tuple_2))
def test_returns_false_for_tuples_with_different_elements(self):
data_1 = building_blocks.Data('data', tf.int32)
tuple_1 = building_blocks.Struct([data_1, data_1])
data_2 = building_blocks.Data('data', tf.float32)
tuple_2 = building_blocks.Struct([data_2, data_2])
self.assertFalse(tree_analysis.trees_equal(tuple_1, tuple_2))
def test_returns_true_for_tuples(self):
data_1 = building_blocks.Data('data', tf.int32)
tuple_1 = building_blocks.Struct([data_1, data_1])
data_2 = building_blocks.Data('data', tf.int32)
tuple_2 = building_blocks.Struct([data_2, data_2])
self.assertTrue(tree_analysis.trees_equal(tuple_1, tuple_2))
def test_returns_true_for_identical_graphs_with_nans(self):
tf_comp1 = _create_tensorflow_graph_with_nan()
tf_comp2 = _create_tensorflow_graph_with_nan()
self.assertTrue(tree_analysis.trees_equal(tf_comp1, tf_comp2))
non_aggregation_intrinsics = building_blocks.Struct([
(
None,
building_block_test_utils.create_whimsy_called_federated_broadcast(),
),
(
None,
building_block_test_utils.create_whimsy_called_federated_value(
placements.CLIENTS
),
),
])
unit = computation_types.StructType([])
trivial_aggregate = (
building_block_test_utils.create_whimsy_called_federated_aggregate(
value_type=unit
)
)
trivial_mean = building_block_test_utils.create_whimsy_called_federated_mean(
unit
)
trivial_sum = building_block_test_utils.create_whimsy_called_federated_sum(unit)
# TODO(b/120439632) Enable once federated_mean accepts structured weights.
# trivial_weighted_mean = ...
trivial_secure_sum = building_block_test_utils.create_whimsy_called_federated_secure_sum_bitwidth(
unit
)
class ContainsAggregationShared(parameterized.TestCase):
@parameterized.named_parameters([
('non_aggregation_intrinsics', non_aggregation_intrinsics),
('trivial_aggregate', trivial_aggregate),
('trivial_mean', trivial_mean),
('trivial_sum', trivial_sum),
# TODO(b/120439632) Enable once federated_mean accepts structured weight.
# ('trivial_weighted_mean', trivial_weighted_mean),
('trivial_secure_sum', trivial_secure_sum),
])
def test_returns_none(self, comp):
self.assertEmpty(tree_analysis.find_unsecure_aggregation_in_tree(comp))
self.assertEmpty(tree_analysis.find_secure_aggregation_in_tree(comp))
def test_throws_on_unresolvable_function_call(self):
comp = building_blocks.Call(
building_blocks.Data(
'unknown_func',
computation_types.FunctionType(
None, computation_types.at_clients(tf.int32)
),
)
)
with self.assertRaises(ValueError):
tree_analysis.find_unsecure_aggregation_in_tree(comp)
with self.assertRaises(ValueError):
tree_analysis.find_secure_aggregation_in_tree(comp)
# functions without a federated output can't aggregate
def test_returns_none_on_unresolvable_function_call_with_non_federated_output(
self,
):
input_type = computation_types.FederatedType(tf.int32, placements.CLIENTS)
output_type = tf.int32
comp = building_blocks.Call(
building_blocks.Data(
'unknown_func',
computation_types.FunctionType(input_type, output_type),
),
building_blocks.Data('client_data', input_type),
)
self.assertEmpty(tree_analysis.find_unsecure_aggregation_in_tree(comp))
self.assertEmpty(tree_analysis.find_secure_aggregation_in_tree(comp))
simple_aggregate = (
building_block_test_utils.create_whimsy_called_federated_aggregate()
)
simple_mean = building_block_test_utils.create_whimsy_called_federated_mean()
simple_sum = building_block_test_utils.create_whimsy_called_federated_sum()
simple_weighted_mean = (
building_block_test_utils.create_whimsy_called_federated_mean(
tf.float32, tf.float32
)
)
simple_secure_sum = (
building_block_test_utils.create_whimsy_called_federated_secure_sum_bitwidth()
)
class ContainsSecureAggregation(parameterized.TestCase):
@parameterized.named_parameters([
('simple_aggregate', simple_aggregate),
('simple_mean', simple_mean),
('simple_sum', simple_sum),
('simple_weighted_mean', simple_weighted_mean),
])
def test_returns_none_on_unsecure_aggregation(self, comp):
self.assertEmpty(tree_analysis.find_secure_aggregation_in_tree(comp))
def assert_one_aggregation(self, comp):
self.assertLen(tree_analysis.find_secure_aggregation_in_tree(comp), 1)
def test_returns_str_on_simple_secure_aggregation(self):
self.assert_one_aggregation(simple_secure_sum)
def test_returns_str_on_nested_secure_aggregation(self):
comp = building_block_test_utils.create_whimsy_called_federated_secure_sum_bitwidth(
(tf.int32, tf.int32)
)
self.assert_one_aggregation(comp)
class ContainsUnsecureAggregation(parameterized.TestCase):
def test_returns_none_on_secure_aggregation(self):
self.assertEmpty(
tree_analysis.find_unsecure_aggregation_in_tree(simple_secure_sum)
)
@parameterized.named_parameters([
('simple_aggregate', simple_aggregate),
('simple_mean', simple_mean),
('simple_sum', simple_sum),
('simple_weighted_mean', simple_weighted_mean),
])
def test_returns_one_on_unsecure_aggregation(self, comp):
self.assertLen(tree_analysis.find_unsecure_aggregation_in_tree(comp), 1)
class CheckHasUniqueNamesTest(absltest.TestCase):
def test_raises_on_none(self):
with self.assertRaises(TypeError):
tree_analysis.check_has_unique_names(None)
def test_ok_on_single_lambda(self):
ref_to_x = building_blocks.Reference('x', tf.int32)
lambda_1 = building_blocks.Lambda('x', tf.int32, ref_to_x)
tree_analysis.check_has_unique_names(lambda_1)
def test_ok_on_multiple_no_arg_lambdas(self):
data = building_blocks.Data('x', tf.int32)
lambda_1 = building_blocks.Lambda(None, None, data)
lambda_2 = building_blocks.Lambda(None, None, data)
tup = building_blocks.Struct([lambda_1, lambda_2])
tree_analysis.check_has_unique_names(tup)
def test_raises_on_nested_lambdas_with_same_variable_name(self):
ref_to_x = building_blocks.Reference('x', tf.int32)
lambda_1 = building_blocks.Lambda('x', tf.int32, ref_to_x)
lambda_2 = building_blocks.Lambda('x', tf.int32, lambda_1)
with self.assertRaises(tree_analysis.NonuniqueNameError):
tree_analysis.check_has_unique_names(lambda_2)
def test_ok_on_nested_lambdas_with_different_variable_name(self):
ref_to_x = building_blocks.Reference('x', tf.int32)
lambda_1 = building_blocks.Lambda('x', tf.int32, ref_to_x)
lambda_2 = building_blocks.Lambda('y', tf.int32, lambda_1)
tree_analysis.check_has_unique_names(lambda_2)
def test_ok_on_single_block(self):
x_data = building_blocks.Data('x', tf.int32)
single_block = building_blocks.Block([('x', x_data)], x_data)
tree_analysis.check_has_unique_names(single_block)
def test_raises_on_sequential_binding_of_same_variable_in_block(self):
x_data = building_blocks.Data('x', tf.int32)
block = building_blocks.Block([('x', x_data), ('x', x_data)], x_data)
with self.assertRaises(tree_analysis.NonuniqueNameError):
tree_analysis.check_has_unique_names(block)
def test_ok_on_sequential_binding_of_different_variable_in_block(self):
x_data = building_blocks.Data('x', tf.int32)
block = building_blocks.Block([('x', x_data), ('y', x_data)], x_data)
tree_analysis.check_has_unique_names(block)
def test_raises_block_rebinding_of_lambda_variable(self):
x_data = building_blocks.Data('x', tf.int32)
single_block = building_blocks.Block([('x', x_data)], x_data)
lambda_1 = building_blocks.Lambda('x', tf.int32, single_block)
with self.assertRaises(tree_analysis.NonuniqueNameError):
tree_analysis.check_has_unique_names(lambda_1)
def test_ok_block_binding_of_new_variable(self):
x_data = building_blocks.Data('x', tf.int32)
single_block = building_blocks.Block([('x', x_data)], x_data)
lambda_1 = building_blocks.Lambda('y', tf.int32, single_block)
tree_analysis.check_has_unique_names(lambda_1)
def test_raises_lambda_rebinding_of_block_variable(self):
x_ref = building_blocks.Reference('x', tf.int32)
lambda_1 = building_blocks.Lambda('x', tf.int32, x_ref)
x_data = building_blocks.Data('x', tf.int32)
single_block = building_blocks.Block([('x', x_data)], lambda_1)
with self.assertRaises(tree_analysis.NonuniqueNameError):
tree_analysis.check_has_unique_names(single_block)
def test_ok_lambda_binding_of_new_variable(self):
y_ref = building_blocks.Reference('y', tf.int32)
lambda_1 = building_blocks.Lambda('y', tf.int32, y_ref)
x_data = building_blocks.Data('x', tf.int32)
single_block = building_blocks.Block([('x', x_data)], lambda_1)
tree_analysis.check_has_unique_names(single_block)
if __name__ == '__main__':
absltest.main()
|
16,488 | d247fc129c5e9c988be362ec8b46b02908cae0d9 | ######## Cylinder #########
import math
class Cylinder:
def __init__(self,radius,height):
self.radius=radius
self.height=height
def get_radius(self):
return self.radius
def set_radius(self,radius):
if radius>0:
self.radius=radius
def get_height(self):
return self.height
def set_height(self, height):
if height>0:
self.height = height
def basearea(self):
return math.pi*(self.radius**2)
def surfacearea(self):
return 2*(math.pi*self.radius)*self.height
def area(self):
return 2*(self.basearea())+(self.surfacearea())
def volume(self):
return self.basearea()*self.height
cylinder1=Cylinder(radius=3,height=5)
print(cylinder1.area())
print(cylinder1.volume())
############### Employee ########
class Employee:
def __init__(self,name,salary):
self.name=name
self.salary=salary
def get_name(self):
return self.name
def set_name(self,name):
self.name=name
def get_salary(self):
return self.salary
def set_salary(self,salary):
self.salary=salary
def display(self):
print("Name:"+str(self.name))
print("Salary:"+str(self.salary))
class Company:
def __init__(self):
self.employee_list=[]
def get_list_employee(self):
return self.employee_list
def set_list_employee(self,current_list):
if type(current_list)==list:
self.employee_list=current_list
def add_employee(self,new_employee):
if isinstance(new_employee,Employee):
self.employee_list.append(new_employee)
def calc_average_salary(self):
total_sum=0
for emp in self.employee_list():
total_sum+=emp.get_salary()
return total_sum/len(self.employee_list)
def display(self):
for emp in self.employee_list():
print("Name :"+str(self.get_name()))
print("Salary:"+str(self.get_salary()))
c=Company([])
e1=Employee("Merve",10000)
e2=Employee("Sefa",12000)
e3=Employee("Hülya",100000)
c.add_employee(e1)
c.add_employee(e2)
c.add_employee(e3)
#c.add_employee("90") if we write this employee de tanımlanmadığı için kabul edilmez
#c.add_employee(90)
c.display()
|
16,489 | 95bb5dc2deb50db37922413ac045c1ab65d5e249 | # print("Shalom")
# input('What is your name?')
# name = input("What is your name?")
# print( "Hello " + name)
# x = "Python is"
# y = " awesomPiee"
# z = x + y
# print(z)P |
16,490 | 5359a8c4d5777c325d63110db7180467def68297 | import json
class Config:
def __init__(self, file):
self.__config = {}
self.__openFileToJSON(file)
# Opens a configuration file
# return a JSON object
def __openFileToJSON(self, file):
self.__config = json.load(
open(file)
)
def getRoomX(self):
return self.__config['room']['width']
def getRoomY(self):
return self.__config['room']['length']
def getNumberOfSensors(self):
return self.__config['sensors']['number']
def getMaxDifference(self):
return self.__config['humans']['max_offset']
def getSensorXOffset(self):
return self.__config['sensors']['offset']['x']
def getSensorYOffset(self):
return self.__config['sensors']['offset']['y']
def getImage(self, image):
return self.__config['images'][image]
def getMqttIp(self):
return self.__config['mqtt']['ip']
def getMqttPort(self):
return self.__config['mqtt']['port']
def getMqttTopic(self):
return self.__config['mqtt']['topic']
def getXMultiplier(self):
return self.__config['pixel_size_multiplier']['x']
def getYMultiplier(self):
return self.__config['pixel_size_multiplier']['y']
def getSensorLocations(self):
return self.__config['sensors']['locations']
def getHistorianFolder(self):
return self.__config['historian']['folder']
def getHistorianFilePrefix(self):
return self.__config['historian']['file_prefix']
def getHistorianHeaders(self):
return self.__config['historian']['headerFields']
def getAverageHeatmapTemp(self):
return self.__config['heatmap']['average temp']
def getTempLimit(self):
return self.__config['tempWarning']['maxTemp']
def getOpeningTime(self):
return self.__config["openingHours"]["openingTime"]
def getClosingTime(self):
return self.__config["openingHours"]["closingTime"]
def getSocialDistancing(self):
return self.__config["socialDistancing"]["minDistance"]
|
16,491 | c116c40862ba3c822538d5aba0dfb7409e7cab9c | import sys, os, django
sys.path.append("d:\\Python\\Django") #here store is root folder(means parent).
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "web.settings")
django.setup()
from transport.models import Bus_stop, Bus_route, Route_way
import json
import requests
#from transport.models import Bus_stop, Bus_route, Route_way
from time import sleep
class WriteRoute():
url = 'http://trans-gps.cv.ua/'
def __init__(self, route, url, id):
self.route = route
self.id = id
self.url_end = url
def get_position(self):
pass
#response = requests.get("http://www.trans-gps.cv.ua/map/trackers/?selectedRoutesStr=")
#todos = json.loads(response.text)
url = "http://www.trans-gps.cv.ua/map/trackers/?selectedRoutesStr=14_"
response = requests.get(url)
imei = '355227045539582'
bus_info = json.loads(response.text)[imei]
print(bus_info)
def get_coord():
url = "http://www.trans-gps.cv.ua/map/trackers/?selectedRoutesStr=14_"
try:
response = requests.get(url)
except:
return 0, 0
imei = '355227045594918'
bus_info = json.loads(response.text)[imei]
return bus_info['lat'], bus_info['lng']
def get_new_coord(tmp_lat, tmp_lng):
cur_lat, cur_lng = get_coord()
while tmp_lat == cur_lat and tmp_lng == cur_lng:
sleep(10)
cur_lat, cur_lng = get_coord()
#print('loop', get_coord(), cur_lng, tmp_lng)
return cur_lat, cur_lng
start_lat, start_lng = get_coord()
lat, lng = get_new_coord(start_lat, start_lng)
position = 0
while lat != start_lat or lng != start_lng:
Route_way.objects.create(route=Bus_route.objects.get(route='9a'), position=position, lat=lat, lng=lng)
print(position, lat, lng)
lat, lng = get_new_coord(lat, lng)
position+=1
#sleep(15)
print('finish')
'''for t in range(30):
sleep(20)
response = requests.get("http://www.trans-gps.cv.ua/map/trackers/?selectedRoutesStr=14_")
todos = json.loads(response.text)
print(t, todos[imei]['lat'], todos[imei]['lng'], todos[imei]['speed'])''' |
16,492 | a9576fecb50291b60d6c42b2ec34687ec05abc34 | '''
Created on 2021-08-07
@author: wf
'''
from corpus.quality.rating import EntityRating
class EventRating(EntityRating):
'''
a rating for an event
'''
def __init__(self,event):
super().__init__(self,event,event.eventId,event.source,"Event")
class EventSeriesRating(EntityRating):
'''
a rating for an event
'''
def __init__(self,eventSeries):
super().__init__(self,eventSeries,eventSeries.seriesId,eventSeries.source,"EventSeries")
|
16,493 | 9f21103c02a7fdff37298f1da562d90c69d74777 | from .models import Item, ItemImage, ItemTag, ImportItem, ImportImage
import xml.etree.cElementTree as eTree
from django.conf import settings
import os.path
import requests
from django.core.files import File
from django.core.files.temp import NamedTemporaryFile
from slugify import UniqueSlugify
def save_image_from_url(model, url, filename):
r = requests.get(url)
img_temp = NamedTemporaryFile(delete=True)
img_temp.write(r.content)
img_temp.flush()
model.image.save(filename, File(img_temp), save=True)
def load_images_for_item():
pass
def data_import(xml_doc_data):
img_dir = 'images/catalog/'
xml_doc_tree = eTree.fromstring(xml_doc_data.encode('utf-8'))
def to_int(s):
try:
return int(float(s))
except:
return None
def to_boolean(s):
try:
return True if s.lower() == 'true' else False
except:
return False
for item in xml_doc_tree.iter('nomen-item'):
article = item.attrib.get('id', None)
slugify_unique = UniqueSlugify(separator='_')
slug = slugify_unique(article)
if article:
new_item, created = Item.objects.get_or_create(slug=slug)
if to_boolean(item.attrib.get('statusaccess', None)):
section = 'accessory'
elif to_boolean(item.attrib.get('statusbook', None)):
section = 'book'
else:
section = 'model'
item_info = {
'article': article,
'section': section,
'name': item.attrib.get('name', None),
'name_en': item.attrib.get('name_en', None),
'brand': item.attrib.get('brand', None),
'brand_en': item.attrib.get('brand_en', None),
'type': item.attrib.get('type', None),
'type_en': item.attrib.get('type_en', None),
'comment': item.attrib.get('comment', None),
'comment_en': item.attrib.get('comment_en', None),
'note': item.attrib.get('note', None),
'note_en': item.attrib.get('note_en', None),
'series': item.attrib.get('series', None),
'series_en': item.attrib.get('series_en', None),
'scale': item.attrib.get('scale', None),
'manufacturer': item.attrib.get('manufacturer', None),
'manufacturer_en': item.attrib.get('manufacturer_en', None),
'color': item.attrib.get('color', None),
'color_en': item.attrib.get('color_en', None),
'material': item.attrib.get('material', None),
'weight': item.attrib.get('weight', None),
'length': item.attrib.get('length', None),
'width': item.attrib.get('width', None),
'height': item.attrib.get('height', None),
'quantity': to_int(item.attrib.get('quantity', 0)),
'price': to_int(item.attrib.get('price', None)),
'price_min': to_int(item.attrib.get('pricemin', None)),
'new_before': item.attrib.get('newbefore', None),
'status_new': to_boolean(item.attrib.get('statusnew', None)),
'status_not_available': to_boolean(item.attrib.get('statusnotavail', None)),
'status_back_in_stock': to_boolean(item.attrib.get('statusnewavail', None)),
'status_action': to_boolean(item.attrib.get('statusaction', None)),
'status_sale': to_boolean(item.attrib.get('statussale', None)),
'status_on_the_way': to_boolean(item.attrib.get('statusway', None))
}
for (key, value) in item_info.items():
setattr(new_item, key, value)
tags = item.attrib.get('tags', '').split(';')
for tag in tags:
tag = tag.strip()
if tag:
try:
db_tag = ItemTag.objects.get(tag=tag)
except ItemTag.DoesNotExist:
db_tag = ItemTag(tag=tag)
db_tag.save()
new_item.tags.add(db_tag)
# image_count = 0
# for image in item.iter('img'):
# file_src = image.attrib.get('src', None)
# file_name = image.attrib.get('name', None)
# if file_src:
# # save_image_from_url(Item, file_src, file_name)
# r = requests.get(file_src)
#
# img_temp = NamedTemporaryFile(delete=True)
# img_temp.write(r.content)
# img_temp.flush()
#
# # model.image.save(filename, File(img_temp), save=True)
# file_path = os.path.join(img_dir, image.attrib.get('dir', None), image.attrib.get('name', None))
# # if os.path.isfile(os.path.join(settings.MEDIA_ROOT, file_path)):
# new_image = ItemImage(
# file=File(img_temp, file_path),
# item=new_item
# )
# new_image.save()
# print('Image imported: ', new_image)
# image_count += 1
# if image_count <= 0:
# new_item.status_without_image = True
new_item.is_just_updated = True
new_item.save()
print('Item imported: ', new_item)
def read_catalog_data(xml_doc_data):
# xml_doc_tree = eTree.fromstring(xml_doc_data.encode('utf-8'))
xml_doc_tree = eTree.fromstring(xml_doc_data)
ImportItem.objects.all().delete()
ImportImage.objects.all().delete()
for item in xml_doc_tree.iter('nomen-item'):
article = item.attrib.get('id', None)
new_import_item = ImportItem(
uid=item.attrib.get('uid', None),
article=article,
name=item.attrib.get('name', None),
name_en=item.attrib.get('name_en', None),
brand=item.attrib.get('brand', None),
brand_en=item.attrib.get('brand_en', None),
type=item.attrib.get('type', None),
type_en=item.attrib.get('type_en', None),
note=item.attrib.get('note', None),
note_en=item.attrib.get('note_en', None),
series=item.attrib.get('series', None),
series_en=item.attrib.get('series_en', None),
scale=item.attrib.get('scale', None),
manufacturer=item.attrib.get('manufacturer', None),
manufacturer_en=item.attrib.get('manufacturer_en', None),
color=item.attrib.get('color', None),
color_en=item.attrib.get('color_en', None),
material=item.attrib.get('material', None),
tags=item.attrib.get('tags', None),
tags_en=item.attrib.get('tags_en', None),
weight=item.attrib.get('weight', None),
length=item.attrib.get('length', None),
width=item.attrib.get('width', None),
height=item.attrib.get('height', None),
quantity=item.attrib.get('quantity', None),
price=item.attrib.get('price', None),
pricemin=item.attrib.get('pricemin', None),
publisher=item.attrib.get('publisher', None),
publisher_en=item.attrib.get('publisher_en', None),
author=item.attrib.get('author', None),
author_en=item.attrib.get('author_en', None),
statuspreorder=item.attrib.get('statuspreorder', None),
statusnew=item.attrib.get('statusnew', None),
statusnewavail=item.attrib.get('statusnewavail', None),
statusaction=item.attrib.get('statusaction', None),
statussale=item.attrib.get('statussale', None),
statusway=item.attrib.get('statusway', None),
statusbook=item.attrib.get('statusbook', None),
statusaccess=item.attrib.get('statusaccess', None),
)
new_import_item.save()
for image in item.iter('img'):
new_import_image = ImportImage(
item=new_import_item,
dir=image.attrib.get('dir', None),
name=image.attrib.get('name', None),
src=image.attrib.get('src', None),
)
new_import_image.save()
def import_item_from_tmp(item):
img_dir = 'images/catalog/'
slugify_unique = UniqueSlugify(separator='_')
slug = slugify_unique(item.article)
print(slug)
def to_int(s):
try:
return int(float(s))
except:
return None
def to_boolean(s):
try:
return True if s.lower() == 'true' else False
except:
return False
def to_str(s):
try:
return s.encode('utf-8')
except:
return ''
if slug:
new_item, created = Item.objects.get_or_create(slug=slug)
if to_boolean(item.statusaccess):
section = 'accessory'
elif to_boolean(item.statusbook):
section = 'book'
else:
section = 'model'
item_info = {
'article': to_str(item.article),
'section': section,
'name': to_str(item.name),
'name_en': to_str(item.name_en),
'brand': to_str(item.brand),
'brand_en': to_str(item.brand_en),
'type': to_str(item.type),
'type_en': to_str(item.type_en),
'note': to_str(item.note),
'note_en': to_str(item.note_en),
'series': to_str(item.series),
'series_en': to_str(item.series_en),
'scale': item.scale,
'manufacturer': to_str(item.manufacturer),
'manufacturer_en': to_str(item.manufacturer_en),
'color': to_str(item.color),
'color_en': to_str(item.color_en),
'material': to_str(item.material),
'weight': item.weight,
'length': item.length,
'width': item.width,
'height': item.height,
'quantity': to_int(item.quantity),
'price': to_int(item.price),
'price_min': to_int(item.pricemin),
'status_new': to_boolean(item.statusnew),
'status_back_in_stock': to_boolean(item.statusnewavail),
'status_action': to_boolean(item.statusaction),
'status_sale': to_boolean(item.statussale),
'status_on_the_way': to_boolean(item.statusway)
}
print(item.name.encode('utf-8'))
for (key, value) in item_info.items():
setattr(new_item, key, value)
# print(item_info)
tags = item.tags.split(';')
for tag in tags:
tag = tag.strip()
if tag:
try:
db_tag = ItemTag.objects.get(tag=tag)
except ItemTag.DoesNotExist:
db_tag = ItemTag(tag=tag)
db_tag.save()
new_item.tags.add(db_tag)
image_count = 0
for image in item.importimage_set.all():
if image.src:
r = requests.get(image.src)
img_temp = NamedTemporaryFile(delete=True)
img_temp.write(r.content)
img_temp.flush()
file_path = os.path.join(img_dir, image.dir, image.name)
if not ItemImage.objects.filter(file=file_path, item=new_item).exists():
new_image = ItemImage(
file=File(img_temp, file_path),
item=new_item
)
new_image.save()
print(u'Image imported: ', new_image)
image_count += 1
if image_count <= 0:
new_item.status_without_image = True
new_item.save()
print(u"%s: imported" % item.id)
def import_items_from_tmp():
for item in ImportItem.objects.all().prefetch_related('importimage_set'):
import_item_from_tmp(item)
item.delete()
def import_some_item_from_tmp():
try:
item = ImportItem.objects.all().first()
import_item_from_tmp(item)
item.delete()
return import_some_item_from_tmp()
except ImportItem.DoesNotExist:
return 0 |
16,494 | 78860c45e4f1a2ee6797d8ca9a15154e685cb266 | from rest_framework.viewsets import ModelViewSet
from .models import Fact, Post, Project
from .serializers import FactSerializer, PostSerializer, ProjectSerializer
# JWT Auth
from rest_framework.permissions import IsAuthenticated
class FactViewSet(ModelViewSet):
permission_classes = (IsAuthenticated,)
queryset = Fact.objects.all()
serializer_class = FactSerializer
class PostViewSet(ModelViewSet):
permission_classes = (IsAuthenticated,)
queryset = Post.objects.all()
serializer_class = PostSerializer
class ProjectViewSet(ModelViewSet):
permission_classes = (IsAuthenticated,)
queryset = Project.objects.all()
serializer_class = ProjectSerializer
|
16,495 | 8d81808876f56ae176302e485272b419e411d2c4 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from os.path import splitext, basename, dirname
import ctypes
import argparse
import logging
import csv
from csv import DictWriter, DictReader
import sys
try:
csv.field_size_limit(int(ctypes.c_ulong(-1).value // 2))
except:
csv.field_size_limit(int(ctypes.c_ulong(-1).value // 2))
LOG_FILE = 'split_text_corpus.log'
DEFAULT_OUTPUT_FORMAT = 'chunk_{chunk_id:02d}/{basename}.csv'
DEFAULT_CHUNK_SIZE = 1000
def setup_logger():
""" Set up logging
"""
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename=LOG_FILE,
filemode='w')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
def split_text_corpus(infile=None, outfile=None, size=1000):
with open(infile, 'r') as f:
reader = DictReader(f)
header = reader.fieldnames
if 'uniqid' not in header:
add_uid = True
header.append('uniqid')
else:
add_uid = False
chunk_id = 0
count = 0
uid = 0
for r in reader:
if count == 0:
filename = splitext(basename(infile))[0]
chunk_name = outfile.format(basename=filename,
chunk_id=chunk_id)
logging.info("Create new chunk: {0}, filename: {1}"
.format(chunk_id, chunk_name))
d = dirname(chunk_name)
if not os.path.exists(d):
os.makedirs(d)
out = open(chunk_name, 'w')
writer = DictWriter(out, fieldnames=header)
writer.writeheader()
if add_uid:
r['uniqid'] = uid
writer.writerow(r)
count += 1
if count >= size:
count = 0
chunk_id += 1
out.close()
uid += 1
def main(argv=sys.argv[1:]):
setup_logger()
"""Parse command line options
"""
parser = argparse.ArgumentParser(description="Split large text corpus into"
" smaller chunks")
parser.add_argument('input', help='CSV input file name')
parser.add_argument("-o", "--out", type=str, dest="outfile",
default=DEFAULT_OUTPUT_FORMAT,
help="Output file in CSV (default: {0:s})"
.format(DEFAULT_OUTPUT_FORMAT))
parser.add_argument('-s', '--size', type=int, default=DEFAULT_CHUNK_SIZE,
help='Number of row in each chunk (default: {0:d})'
.format(DEFAULT_CHUNK_SIZE))
args = parser.parse_args(argv)
logging.info(str(args))
split_text_corpus(args.input, args.outfile, args.size)
logging.info("done.")
return 0
if __name__ == "__main__":
sys.exit(main())
|
16,496 | 431a11acee5fd58316600ea9081c8c0162eca7e1 | # -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/items.html
import scrapy
class ScrapperItem(scrapy.Item):
link = scrapy.Field()
address = scrapy.Field()
description = scrapy.Field()
image_url = scrapy.Field()
owner_name = scrapy.Field()
owner_contact = scrapy.Field()
active_search = scrapy.Field()
pass
|
16,497 | c9103e889ff76efc8db2973c8a855b02fc1af06e | # Generated by Django 3.2.6 on 2021-08-29 12:13
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('th', '0001_initial'),
]
operations = [
migrations.RenameField(
model_name='thpic',
old_name='ThPic_CODE',
new_name='Th_CODE',
),
migrations.AlterField(
model_name='thpic',
name='image',
field=models.ImageField(blank=True, default='/image/th/', null=True, upload_to='image/th/'),
),
]
|
16,498 | 67d6bb326a9815f2f28cafae2a39f409d0225060 | import string
caption = "g fmnc wms bgblr rpylqjyrc gr zw fylb. rfyrq ufyr amknsrcpq ypc dmp. bmgle gr gl zw fylb gq glcddgagclr ylb rfyr'q ufw rfgq rcvr gq qm jmle. sqgle qrpgle.kyicrpylq() gq pcamkkclbcb. lmu ynnjw ml rfc spj"
new_string = ""
for letter in caption: #{
found_position = string.ascii_lowercase.find( letter );
if ( new_string == -1 ):
new_string += letter;
continue;
#}
index = found_position + 2;
if ( index >= 26 ):
index -= 26;
new_string += string.ascii_lowercase[ index ];
print (new_string);
# < >
|
16,499 | 85e45b2a82604f564368f307c76ce604614aa7ad | #Definition for a binary tree node.
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
def subtreeWithAllDeepest(root):
def depth(root, d):
if not root:
return (d, None)
l, r = depth(root.left, d + 1), depth(root.right, d + 1)
if l[0] > r[0]: return (l[0], l[1])
elif l[0] < r[0]: return (r[0], r[1])
else:
return l[0], root.val
return depth(root, 0)[1]
root = TreeNode(3)
root.left = TreeNode(5)
root.right = TreeNode(1)
root.left.left = TreeNode(6)
root.left.right = TreeNode(2)
root.left.right.left = TreeNode(7)
root.left.right.right = TreeNode(4)
root.right.left = TreeNode(0)
root.right.right = TreeNode(8)
subtreeWithAllDeepest(root)
|
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