index int64 0 1,000k | blob_id stringlengths 40 40 | code stringlengths 7 10.4M |
|---|---|---|
11,400 | 2d540f18f09bd700504a6873e951c91f9129d064 | def dataSegmentation(str):
length=len(str)
res=list()
i=0
while i<length:
if str[i]==' ':
i+=1
continue
elif str[i]>='a' and str[i]<='z':
temp=str[i]
i+=1
if i<length:
while str[i]>='a' and str[i]<='z':
temp=temp+str[i]
i+=1
if i>=length:
break
res.append(temp)
else:
res.append(str[i])
i+=1
return res |
11,401 | bb9ec5c651023041114877c79d8ce189d3ba4739 | tempo = int(input("Tempo: "))
i = (1042000/1500)**(1/tempo) - 1
if (i <= 0.01):
print(round(i,5))
print("Real")
else:
print(round(i,5))
print("Irreal") |
11,402 | d7ca0f562622d44ca33d08b96289d949c5447f91 | import socket
import chunkserver
import master
import client
import net
import random
import log
import msg
try:
import settings # Assumed to be in the same directory.
except ImportError:
sys.stderr.write("Error: Can't find the file 'settings.py' in the directory containing %r. This is required\n" % __file__)
sys.exit(1)
if(settings.DEBUG):
reload(settings)
# testing, remove
# execute()
if(settings.DEBUG):
reload(settings)
reload(chunkserver)
reload(master)
reload(client)
reload(net)
reload(msg)
if(settings.TESTING):
import thread
def random_port():
return int(random.random()*32000 + 32000)
# set up debug data
settings.MASTER_META_FNAME = 'meta_test.obj'
settings.MASTER_CHUNK_PORT = random_port()
settings.MASTER_CLIENT_PORT = settings.MASTER_CHUNK_PORT + 1
settings.CHUNK_CLIENT_PORT = settings.MASTER_CLIENT_PORT + 1
log.log("[gfs testing] chunk port %i, master client port %i chunk client port %i" % (settings.MASTER_CHUNK_PORT, settings.MASTER_CLIENT_PORT,settings.CHUNK_CLIENT_PORT))
master.write_test_meta()
global master
global chunk
log.log("master-chunkserver handshake")
master = master.MasterServer() # start + load meta
chunk = chunkserver.ChunkServer() # init + load chunks
chunk.write_test_chunk()
master.tick() # connection from chunkserver
chunk.tick() # send ChunkConnect
master.tick() # recv ChunkConnect
log.log("dropping chunkserver")
master.drop_chunkserver(master.chunkservers.keys()[0])
chunk.tick() # lost conn to master, reconnecting
master.tick()
chunk.tick() # send ChunkConn msg
master.tick() # recv ChunkConn msg, add chunkserver
log.log("testing FileInfo fetch")
global fir, fi
fir = client.file_info('foo')
fir.next()
master.tick()
fi = fir.next()
log.log("fetched. length %i" % fi.length())
log.log("client read")
global r
r = client.read("foo",0,32)
r.next() # connects to master, sends ReadReq
master.tick() # get conn
master.tick() # get ReadReq
r.next() # send ReadChunk to chunkserver
chunk.tick() # get ReadChunk, send response
s = r.next()
log.log("received: %s" %s)
expected = "abcdefghijklmnopqrstuvwxyzABCDEF"
if s != expected:
log.err("got %s expected %s" % (s,expected))
log.log("appending data")
data = "1234567890"
global a
a = client.append("foo",data)
a.next() # connect to master, send request
master.tick() # get AppendReq,
a.next() # connecting to chunkserver, SendData
chunk.tick() # recv SendData
a.next() # get success for mutate 1, send commit to master
master.tick() # send commit msg to chunk
chunk.tick() # write data
master.tick() # get response, done with commit,
master.tick() # send client success
res = a.next() # receive success
log.log("wrote mutate_id(%i)" % res.mutate_id)
log.log("second FileInfo fetch")
global fi2r, fi2
fi2r = client.file_info('foo')
fi2r.next()
master.tick()
fi2 = fi2r.next()
log.log("fetched. length %i" % fi2.length())
expected = fi.length() + len(data)
if expected != fi2.length():
log.err("file length mismatch. expected %i, received %i" % (expected,fi2.length()))
log.log("read what was written")
global r
r = client.read("foo",fi.length(),len(data))
for data2 in r:
log.log("ticking...")
if data2:
break
master.tick()
chunk.tick()
log.log("read2 %s" % data2)
if data2 != data:
log.err("data mismatch: appended %s, read %s" % (data, data2))
# todo: try the failure states for each step of the read
# todo: chunk_info still has server info for two servers as of the append test.
|
11,403 | 67d063769410fc186f630335bd88aa215ff82a4e | class Device:
def __init__(self,
d_id=0,
d_task_id=0,
d_device_type='',
d_device_width='',
d_device_long='',
d_regulation_type='',
d_notice=''):
self.id = d_id
self.task_id = d_task_id
self.device_type = d_device_type
self.device_width = d_device_width
self.device_long = d_device_long
self.regulation_type = d_regulation_type
self.notice = d_notice
|
11,404 | 114f0947619ac4bbbc04691fd3766e289d3ae311 | from sh import cp, rm, diff, ErrorReturnCode
import sh
import os
SURSA_VERIFICATA = 'conjectura_and.cpp'
cp('../' + SURSA_VERIFICATA, '.')
os.system('g++ ' + SURSA_VERIFICATA)
filename = 'grader_test'
for i in range(1, 11):
print 'Testul ', i
cp(filename + str(i) + '.in', 'conjectura.in')
os.system('./a.out')
print diff('conjectura.out', filename + str(i) + '.ok', _ok_code=[0, 1])
for extension in ['in', 'out']:
rm('conjectura.' + extension)
rm(SURSA_VERIFICATA)
rm('a.out')
|
11,405 | 2dc0606e42fce311a59ee1e6909a2dcaf2b667a4 | x = 2
def f():
y = 3
print(x)
f()
print(x)
|
11,406 | d978abbc0c95b55756f6d64b5ebeaa124b60507d | # -*- coding: utf8 -*- #
import Image, ImageDraw, ImageFont
import random
from cStringIO import StringIO
import tornado
from Base import BaseHandler
class Captcha( BaseHandler ):
def create_captcha( self, charNum, fontSize ):
alphArr = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',
'J', 'K', 'L', 'M', 'N', 'O', 'P', 'R', 'S', 'T',
'W', 'X', 'Z', '1', '2', '3', '4', '5', '6', '8']
#fontfile = "%s/tools"
font = ImageFont.truetype( '%s/sysdir/fonts/acidic.ttf' % self.homedir, fontSize )
width, height = font.getsize( "W" )
interval = width
width, height = ( width + 7 ) * charNum, height + 20
image = Image.new( "RGB", ( width, height ), ( 192, 192, 192 ) )
draw = ImageDraw.Draw( image )
captcha = ''
for i in range( 0, charNum ):
#создаем фон для буквы:
charImg = Image.new( "RGB", ( fontSize + 10, fontSize + 10 ), "black" )
tmpDraw = ImageDraw.Draw( charImg )
#помещаем на фон случайную букву случайного цвета:
buk = random.choice( alphArr )
captcha = '%s%s' % ( captcha, buk )
tmpDraw.text( ( 3, 1 ), buk, font = font,
fill = ( random.randint( 20, 150 ), random.randint( 20, 140 ), random.randint( 160, 200 ) ) )
charImg = charImg.rotate( random.randint( -30, 30 ) )
#создаем маску, чтобы фон буквы стал прозрачным:
mask = Image.new( 'L', ( fontSize + 10, fontSize + 10 ), 0 )
mask.paste( charImg, ( 0, 0 ) )
hpos = 10 + ( i * interval + random.randint( 10, interval - 10 ) )
vpos = random.randint( 10, 20 )
image.paste( charImg, ( hpos, vpos ), mask ) #два раза для лучшей видимости
image.paste( charImg, ( hpos, vpos ), mask )
for i in range( 0, random.randint( 4, 8 ) ):
draw.line(
( random.randint( 6, width - 6 ),
random.randint( 3, height - 3 ),
random.randint( 6, width - 6 ),
random.randint( 2, height - 3 ) ),
fill = ( random.randint( 70, 150 ),
random.randint( 20, 220 ),
random.randint( 160, 220 ) ),
width = random.randint( 1, 2 ) )
#image.save("captcha.jpg")
out = StringIO()
self.set_session( 'captcha', captcha )
#import tools.webpy as web
#web.setcookie('ss0', hashlib.md5(captcha).hexdigest(), 600)
image.save( out, 'PNG' )
return out.getvalue()
def get( self ):
self.set_header( 'Content-Type', 'image/png' )
self.write( self.create_captcha( 4, 35 ) )
if __name__ == '__main__':
Captcha.create_captcha( 7, 40 )
|
11,407 | cdbe692d6085ae19604db8a65932b8ab93ea9dc8 | import numpy as np
class Parenthesis(object):
def is_valid(self, string):
n = len(string)
dp = [[False for _ in range(n)] for _ in range(n)]
# init
for i in range(n):
if string[i] == '*':
dp[i][i] = True
for i in range(n - 1):
if string[i] in "(*" and string[i + 1] in "*)":
dp[i][i + 1] = True
for l in range(3, n + 1):
for i in range(n):
j = i + l - 1
if j >= n:
continue
if string[i] in '(*' and string[j] in "*)":
if dp[i + 1][j - 1]:
dp[i][j] = True
continue
for k in range(i, j + 1):
if dp[i][k] and dp[k + 1][j]:
dp[i][j] = True
break
# print(np.array(dp))
return dp[0][-1]
if __name__ == "__main__":
p = Parenthesis()
res = p.is_valid("(*)")
print(res)
|
11,408 | adbda5b3a5d6649b880ce69c73ae82b64bcec2bb | import sys
from operator import itemgetter
this = sys.modules[__name__]
this.fuel_map = {"gas(euro/MWh)": ["gasfired"],
"kerosine(euro/MWh)": ["turbojet"],
"wind(%)": ["windturbine"],
"co2(euro/ton)": ["gasfired", "turbojet"]
}
# Due to the multiplication by 10 to avoid floating points calculation issues,
# We have a great number of levels of recursion. This should be OK (just playing, would
# never do it for real - or use recursion for that matter)
sys.setrecursionlimit(10000)
class PayLoad:
""" Represents a payload problem
with target payload, fuel prices
and powerstations with all the
necessary logic """
def __init__(self, payload):
# Verify proper format for the payload...
self.load = 0
self.fuels = payload["fuels"]
self.payload = payload
self.powerplants = []
self.feasible = True
self.unfeasible_reason = ""
if self.verify_payload():
# Get the load
self.load = payload["load"]
for powerplant in payload["powerplants"]:
powerplant["price_per_Mw"] = 0.0
powerplant["p"] = 0
for fuel in payload["fuels"]:
if powerplant["type"] in this.fuel_map[fuel]:
if fuel == "co2(euro/ton)":
powerplant["price_per_Mw"] += payload["fuels"][fuel] * 0.3
elif fuel == "wind(%)":
# Wind Turbines are ON/OFF
# This should not be done, it is done ONLY to have a simplified visual representation and
# have no problems with FP arithmetics
powerplant["pmax"] = int(powerplant["pmax"] * payload["fuels"][fuel] / 10)
powerplant["pmin"] = powerplant["pmax"]
else:
powerplant["price_per_Mw"] += payload["fuels"][fuel] * (1 / powerplant["efficiency"])
# Notice that this may not be correct because we are assigning co2 costs for MW generated
# It should be related to amount of fuel burnt in my opinion
sorted_pp = sorted(payload["powerplants"], key=itemgetter("price_per_Mw"))
self.powerplants.extend(sorted_pp)
self.scale_up()
self.verify_load_feasibility()
if self.feasible:
self.calculate_vals()
self.allocate_powerplants(0, 0)
self.scale_down_and_clean()
def sol_val(self):
""" Calculate the value of a solution """
val = 0
for pp in self.powerplants:
if "index" in pp:
val += pp["vals"][pp["index"]]
return val
@staticmethod
def possible_vals(pp):
""" Allocates a list of all possible values admitted for a Powerplant
it's very inefficient... """
if pp["type"] == "w":
vals = [0, pp["pmax"]]
elif pp["type"] == "windturbine":
vals = [0, pp["pmin"]]
for i in range(pp["pmin"], pp["pmax"] - pp["pmin"] + 1):
vals.append(pp["pmin"] + i)
else: # Turbojet
vals = [0]
for i in range(pp["pmin"], pp["pmax"] - pp["pmin"]):
vals.append(pp["pmin"] + i)
return vals
def calculate_vals(self):
""" Adding the possible values to the powerplants,
shall be removed afterwards """
for pp in self.powerplants:
pp["vals"] = self.possible_vals(pp)
pp["index"] = 0
def allocate_powerplants(self, pp_number, pp_move_index):
""" This is the allocation of the load to the powerplants
simple backtracking, not complete but works on the examples
and a number of other tests"""
self.powerplants[pp_number]["index"] = pp_move_index
if self.sol_val() == self.load:
return True
else:
if self.sol_val() < self.load:
if pp_move_index < len(self.powerplants[pp_number]["vals"]) - 1:
if self.allocate_powerplants(pp_number, pp_move_index + 1):
return True
else:
if pp_number < len(self.powerplants) - 1:
if self.allocate_powerplants(pp_number + 1, 0):
return True
else: # Backtrack py putting previous to smaller satisfying...
while self.sol_val() > self.load:
if pp_number > 0:
if self.powerplants[pp_number - 1]["index"] > 0:
self.powerplants[pp_number - 1]["index"] = self.powerplants[pp_number - 1]["index"] - 1
else:
self.powerplants[pp_number]["index"] = 0
else:
self.powerplants[pp_number]["index"] = 0
if pp_number < len(self.powerplants) - 1:
if self.allocate_powerplants(pp_number + 1, 0):
return True
def verify_load_feasibility(self):
""" Checks if we can achieve it
"""
max_load = 0
for pp in self.powerplants:
max_load += pp["pmax"]
min_load = max_load
for pp in self.powerplants:
min_load = min(pp["pmin"], min_load)
if self.load > max_load:
self.feasible = False
self.unfeasible_reason = f"Requested load {self.load/10} too high for our powerstations "
return False
if self.load < min_load:
self.feasible = False
self.unfeasible_reason = f"Requested load {self.load/10} too low for our powerstations "
return False
return True
@staticmethod
def verify_payload():
""" Should verify that payload contains all required fields
nothing done, just to remember that it should be there... """
return True
def scale_up(self):
""" Multiply by 10 so to avoid floating point arithmetics tricks """
self.load *= 10
for pp in self.powerplants:
if pp["type"] != "windturbine":
pp["pmin"] *= 10
pp["pmax"] *= 10
def scale_down_and_clean(self):
""" Divide by 10 to present the results with 1 decimal """
self.load /= 10.0
for pp in self.powerplants:
pp["pmin"] /= 10.0
pp["pmax"] /= 10.0
if "index" in pp and "vals" in pp:
pp["p"] = pp["vals"][pp["index"]] / 10
del (pp["vals"])
del (pp["index"])
else:
pp["p"] = 0
|
11,409 | e2242209111734d51fa5804bb9c746bbfa58234e | import pymongo
class Mongo:
def __init__(self):
MONGODB_HOST = '127.0.0.1'
MONGODB_PORT = '27027'
MONGODB_TIMEOUT = 1000
URI_CONNECTION = "mongodb://" + MONGODB_HOST + ":" + MONGODB_PORT + "/"
try:
self._client = pymongo.MongoClient(URI_CONNECTION)
print ('OK -- Connected to MongoDB at server %s' % (MONGODB_HOST))
self._db = self._client['Ecologic']
self._coleccion = self._db['Bomba']
except pymongo.errors.ServerSelectionTimeoutError as error:
print ('Error with MongoDB connection: %s' % error)
except pymongo.errors.ConnectionFailure as error:
print ('Could not connect to MongoDB: %s' % error)
def insertarDatos(self, segundos, fecha):
c = self._coleccion.find()
d = self._coleccion.insert({"_id":c.count()+1,"segundos":segundos, "fecha":fecha})
def consultarCantidadRegistros(self):
c = self._coleccion.count()
if c >= 300:
d = self._coleccion.remove()
|
11,410 | 929d068104901bf257cc7eabd66b9c9dca734fc4 | class Config(object):
init_scale = 0.04
learning_rate = 0.001
max_grad_norm = 15
num_layers = 3
num_steps = 30
hidden_size = 800 # 隐藏层size
last_iteration = 0 # 上次训练的迭代数，从头训练写0，增量写上次的数
iteration = 30 # 这次增加训练的迭代数
save_freq = 1 # 每多少次自动保存
keep_prob = 0.5
batch_size = 128
model_path = './Model'
#parameters for generation
save_time = 210 #从这开始是generate用的，使用第几个迭代保存的模型，必须满足上面储存的次数
is_sample = True
is_beams = True
beam_size = 2
len_of_generation = 49 # 生成的文字长度
start_sentence = u'。\n故'# 以什么字开头 |
11,411 | d4777f4746c2aaef7d48ee367e4761ecf328bed5 | from flask_wtf import FlaskForm
from wtforms import StringField, PasswordField, SubmitField, BooleanField, TextAreaField, RadioField,\
SelectField, FloatField, IntegerField
from wtforms.validators import DataRequired, Length, Optional, InputRequired, ValidationError, Email, EqualTo
from wtforms.fields.html5 import DateField
from flask_wtf.file import FileField, FileAllowed
from flask_login import current_user
class AddressForm(FlaskForm):
first_name=StringField('First Name', validators=[DataRequired(), InputRequired()])
last_name=StringField('Last Name', validators=[DataRequired(), InputRequired()])
street=StringField('Street', validators=[DataRequired(), InputRequired()])
zipcode=StringField('Zip', validators=[DataRequired(), InputRequired()])
state=SelectField('District', validators=[DataRequired()], choices=[("Arua", "Arua"), ("Kampala", "Kampala"), ("Tororo","Tororo")])
country=SelectField('Country', validators=[DataRequired()], choices=[("Uganda", "Uganda"), ("Kenya","Kenya"), ("Tanzania", "Tanzania"),("Rwanda","Rwanda")])
telephone=StringField('Telephone', validators=[DataRequired(), InputRequired(), Length(min=10, max=13)])
email=StringField('Email', validators=[Email(), InputRequired()])
submit=SubmitField('Save Address')
class AccountForm(FlaskForm):
firstname=StringField('First Name', validators=[DataRequired(), InputRequired()])
lastname=StringField('Last Name', validators=[DataRequired(), InputRequired()])
company=StringField('Company', validators=[DataRequired(), InputRequired()])
street=StringField('Street', validators=[DataRequired(), InputRequired()])
zipcode=StringField('Zip', validators=[DataRequired(), InputRequired()])
state=SelectField('State', validators=[DataRequired()], choices=[])
country=SelectField('Country', validators=[DataRequired()], choices=[])
tel=StringField('Telephone', validators=[DataRequired(), InputRequired()])
email=StringField('Email', validators=[Email(), InputRequired()])
submit=SubmitField('Save changes')
class RegistrationForm(FlaskForm):
firstname=StringField('First Name', validators=[ InputRequired(), Length(min=3, max=20)])
lastname=StringField('Last Name', validators=[ InputRequired(), Length(min=3, max=20)])
username=StringField('Username', validators=[ InputRequired(), Length(min=3, max=20)])
email=StringField('Email', validators=[ InputRequired(), Email()])
tel=StringField('Telephone', validators=[InputRequired(), Length(min=10, max=13)])
password=PasswordField('Password',validators=[ InputRequired(), Length(min=3, max=30)])
confirm_password=PasswordField('Confirm password',validators=[InputRequired(), EqualTo('password')])
countries=SelectField('Countries', validators=[InputRequired()], choices=[("Kenya","Kenya"), ("Uganda", "Uganda"), ("Rwanda", "Rwanda"), ("Tanzania", "Tanzania")])
district=SelectField('District', validators=[InputRequired()], choices=[("Arua","Arua"), ("Nebbi", "Nebbi"), ("Yumbe", "Yumbe"), ("Koboko", "Koboko")])
profile_picture=FileField('Select Profile Picture', validators=[InputRequired(),FileAllowed(['jpg', 'png'])])
submit=SubmitField('Sign-up')
class LoginForm(FlaskForm):
email=StringField('Email', validators=[Email(), InputRequired()])
password=PasswordField('Password',validators=[ InputRequired(), Length(min=3, max=30)])
submit=SubmitField('Login')
class ContactForm(FlaskForm):
firstname=StringField('First Name', validators=[DataRequired(), InputRequired()])
lastname=StringField('Last Name', validators=[DataRequired(), InputRequired()])
email=StringField('Email', validators=[Email(), InputRequired()])
subject=StringField('Subject', validators=[DataRequired(), InputRequired()])
message=TextAreaField('Message', validators=[DataRequired(), InputRequired()])
submit=SubmitField('Send Message')
class AddToCartForm(FlaskForm):
product_name=StringField("Product Name", validators=[InputRequired()])
product_image=StringField("Product Image", validators=[InputRequired()])
size=RadioField('Size', validators=[ InputRequired()])
color=RadioField('Color', validators=[ InputRequired()])
quantity=IntegerField(validators=[InputRequired()])
unit_price=IntegerField(validators=[InputRequired()])
weight=RadioField('weight', validators=[ Optional()])
submit=SubmitField('submit')
class UpdateCustomerForm(FlaskForm):
name = StringField("Name", validators=[InputRequired()])
email = StringField("Email", validators=[InputRequired()])
contact = StringField("Telephone", validators=[InputRequired()])
country = SelectField('Country', validators=[InputRequired()], choices=[("Kenya","Kenya"), ("Uganda", "Uganda"), ("Rwanda", "Rwanda"), ("Tanzania", "Tanzania")])
city = StringField("City", validators=[InputRequired()])
submit=SubmitField('Update')
class UpdatePasswordForm(FlaskForm):
current = PasswordField('Current Password', validators=[ InputRequired(), Length(min=3, max=30)])
new = PasswordField('New Password', validators=[ InputRequired(), Length(min=3, max=30)])
repeat = PasswordField('Repeat Password', validators=[ InputRequired(), Length(min=3, max=30), EqualTo('new')])
submit=SubmitField('Update')
def validate_current(form, field):
customer = current_user
if not customer.verify_password(field.data):
raise ValidationError('Incorrect current password.') |
11,412 | 19f2e6424aab75ecfa442bebd67b4708e8129c33 | import sys
sys.path.insert(0, './')
from rlf import BehavioralCloningFromObs
from rlf import BasicPolicy
from rlf import run_policy
from tests.test_run_settings import TestRunSettings
from rlf.policies.actor_critic.dist_actor_critic import DistActorCritic
from rlf.rl.model import MLPBase
class BcoRunSettings(TestRunSettings):
def get_policy(self):
return BasicPolicy(
is_stoch=self.base_args.stoch_policy,
get_base_net_fn=lambda i_shape, recurrent: MLPBase(
i_shape[0], False, (400, 300))
)
def get_algo(self):
return BehavioralCloningFromObs()
def get_add_args(self, parser):
super().get_add_args(parser)
parser.add_argument('--stoch-policy', default=False, action='store_true')
if __name__ == "__main__":
run_policy(BcoRunSettings())
|
11,413 | 7cd14c01aecb9b0399ad5cc1e05db334abcb93b0 | import xml.etree.cElementTree as ET
import os
class case_info:
file_name=""
oper_path = ""
oper_para = []
def __init__(self, p_file_name, p_oper_path, p_oper_para):
self.file_name = p_file_name
self.oper_path = p_oper_path
self.oper_para = p_oper_name
def find_dir(src_path,tag_path):
for fpathe,dirs,fs in os.walk(src_path):
for d in dirs:
if d==tag_path:
return fpathe+"\\"+tag_path
def get_xml_list(xml_dir):
lst = []
for file_name in os.listdir(xml_dir):
if file_name.endswith(".xml"):
lst.append(xml_dir+"/" + file_name)
return lst
def open_xml(path):
try:
return ET.parse(path)
except IOError as io:
print (io)
except FileNotFoundError as fnfe:
print(fnfe)
except Exception as e:
print(path,":", e)
if __name__ == "__main__":
startdir = input('Please input startdir: ')
target = input('Please input target: ')
cases_path = find_dir(startdir, target)
cases_path = cases_path.replace("\\","/")
cases_path_list = get_xml_list(cases_path) # get all case file
oper_path_list = get_xml_list(cases_path+"/"+"Operation") #get all operation file
oper_dict = {}
for oper in oper_path_list:
xml_abs_path = oper
# xml_abs_path = "./Operation/Select_A_Wanted_Audio_File.xml"
oper_tree = open_xml(xml_abs_path)
if oper_tree is None:
continue
list_oper_text = ""
for element in oper_tree.iter():
if isinstance(element.text, str) and element.text.startswith("$"):
list_oper_text += element.text
if list_oper_text != None:
pos = xml_abs_path.rfind("/")
oper_dict[xml_abs_path[pos+1:].split(".")[0]] = list_oper_text
#i=0
for case in cases_path_list:
case_tree = open_xml(case)
if case_tree is None:
continue
for case_e in case_tree.iter():
if case_e.tag == "operation" and "path" in case_e.attrib.keys():
check_case_list = []
pos = case.rfind("/")
check_case_list.append(case[pos + 1:]) # first item:add caseName
check_case_list.append(case_e.attrib["path"]) #second item:add path
for p in case_e:
if "name" in p.attrib.keys():
check_case_list.append(p.attrib["name"]) #third item: add param
i=0
for item in check_case_list:
if i<2:
i+=1
continue
else:
try:
if item in oper_dict[check_case_list[1]]:
pass
else:
print(check_case_list[0]+"---"+check_case_list[1]+"---"+"NO")
except KeyError as e:
print(e)
except Exception as e:
print(e)
|
11,414 | a553d091917236326dbc2c5a36a95b7f265a82a8 | import datetime
from functions import *
zero_time = datetime.time(hour=0, minute=0, second=0)
filename = 'init.xlsx'
# filename = 'new-init.xlsx'
workbook = op.load_workbook(filename)
sheet1 = workbook.active
def log_hours(date, time):
insert_hours(time[0], time[1], date, sheet1)
def show_sheet():
print_sheet(sheet1)
# def check_hours(date):
# row = 0
# col = 0
# times = []
# for i in range(2, sheet1.max_row+1):
# row = i
# if sheet1.cell(row=i, column=2).value == date:
# break
# for j in range(2, sheet1.max_column):
# col = j
def test1():
start = datetime.time(hour=1, minute=30)
end = datetime.time(hour=4, minute=45)
time_now = datetime.datetime.combine(date, zero_time)
curr_date = time_now
for x in range(sheet1.max_row):
insert_hours(start, end, curr_date, sheet1)
curr_date += datetime.timedelta(days=1)
def date_parser():
date = input('Date (MM/DD/YYYY): ')
date_array = date.split('/')
try:
date = datetime.date(int(date_array[2]), int(date_array[0]), int(date_array[1]))
except:
print('Enter valid date')
return False
return datetime.datetime.combine(date, zero_time)
def time_parser():
start = input('Time End (HH:MM): ')
end = input('Time End (HH:MM): ')
start_arr = start.split(':')
end_arr = end.split(':')
try:
start = datetime.time(hour=int(start_arr[0]), minute=int(start_arr[1]))
end = datetime.time(hour=int(end_arr[0]), minute=int(end_arr[1]))
except:
print('Enter valid time')
return False
return (start, end)
def main1():
date = date_parser()
if date == False:
return False
time = time_parser()
if time == False:
return False
initialize(sheet1)
log_hours(date, time)
# main1()
test1()
# print_sheet(sheet1)
workbook.save('new-' + filename)
# workbook.save(filename)
workbook.close() |
11,415 | 624c1c114d37ba5e80a7f218f91c075b4a29b65d | from classes.Helpers import printVerbose
def parseGroupsFileToDictOfCounts(groups_file):
"""Given a .groups file, returns a dictionary mapping each seed to the number of children it represents.
:param groups_file: A groups file.
:return: A dictionary where each seed name is a key to a count of its children.
"""
return parseGroupsFileToDict(groups_file, "counts")
def parseGroupsFileToDictOfChilden(groups_file):
"""Given a .groups file, returns a dictionary mapping each seed to a space-delimited string of its children's names.
:param groups_file: A groups file.
:return: A dictionary where each seed name is a key to a space-delimited string of its children's names.
"""
return parseGroupsFileToDict(groups_file, "children")
def parseGroupsFileToDict(groups_file, thing_to_map):
"""Given a .groups file, returns a dictionary mapping each seed to either a count of its children, or a
space-delimited string of its children's names.
:param groups_file: A .groups file.
:param thing_to_map: Specify 'children' to map seed names to a space-delimited string of children names, or 'counts'
to map seed names to a count of children.
:return: A dictionary mapping each seed to either a count of its children, or a space-delimited string of its
children's names
"""
groups = {}
printVerbose("Reading count file: %s" % groups_file)
# collect the seed names, and the children sequence names
i = 0
nb_lines = 0
for line in open(groups_file, 'r'):
nb_lines +=1
data = line.rstrip().split("\t")
seed = data[0]
children = ""
if thing_to_map == "children":
if len(data) > 1:
children = ' '.join(list(set(data[1:])))
groups[seed] = children
if thing_to_map == "counts":
if len(data) > 1:
children = data[1]
groups[seed] = len(children.split(" "))
if nb_lines % 100000 == 0:
printVerbose("%s lines processed" % nb_lines)
printVerbose("Done reading count file.")
return groups |
11,416 | 1c1957a0689086cc4362d806d896561ae1abddf9 | import os
import torch
import pandas as pd
from albumentations.pytorch import ToTensorV2
import albumentations as alb
from torch.cuda.amp import GradScaler
from torch.utils.data import DataLoader
from dataloader import ImageDataset
from efficient_net import EfficientNetNoisyStudent
from train_functions import eval_model
checkpoint_paths = [
'tf_efficientnet_b7_noisy_student_640/tf_efficientnet_b7_noisy_student_640_epoch17_val_auc0.964_loss0.12_train_auc0.894_loss0.156.pth',
'tf_efficientnet_b7_noisy_student_640/tf_efficientnet_b7_noisy_student_640_epoch18_val_auc0.964_loss0.12_train_auc0.897_loss0.155.pth',
'tf_efficientnet_b7_noisy_student_640/tf_efficientnet_b7_noisy_student_640_epoch19_val_auc0.965_loss0.119_train_auc0.901_loss0.154.pth',
'tf_efficientnet_b7_noisy_student_640/tf_efficientnet_b7_noisy_student_640_epoch20_val_auc0.965_loss0.119_train_auc0.899_loss0.155.pth',
]
state_dicts = []
for checkpoint_path in checkpoint_paths:
model = EfficientNetNoisyStudent(11, pretrained_backbone=True, mixed_precision=True,
model_name='tf_efficientnet_b7_ns', checkpoint_path=checkpoint_path)
model.eval()
model = model.float()
state_dicts.append(model.state_dict())
avg_model = EfficientNetNoisyStudent(11, pretrained_backbone=True, mixed_precision=True,
model_name='tf_efficientnet_b7_ns')
avg_state_dict = avg_model.state_dict()
for key in avg_state_dict:
avg_state_dict[key] = torch.zeros(avg_state_dict[key].shape)
for state_dict in state_dicts:
avg_state_dict[key] += state_dict[key]
avg_state_dict[key] = avg_state_dict[key] / float(len(state_dicts))
avg_model.load_state_dict(avg_state_dict)
ranzcr_df = pd.read_csv('train_folds.csv')
ranzcr_valid_df = ranzcr_df[ranzcr_df['fold'] == 1]
valid_image_transforms = alb.Compose([
alb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
ToTensorV2()
])
valid_set = ImageDataset(ranzcr_valid_df, valid_image_transforms, '../ranzcr/train', width_size=640)
valid_loader = DataLoader(valid_set, batch_size=12, num_workers=12, pin_memory=False, drop_last=False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
criterion = torch.nn.BCEWithLogitsLoss()
scaler = GradScaler()
if torch.cuda.device_count() > 1:
avg_model = torch.nn.DataParallel(avg_model)
avg_model = avg_model.to(device)
val_loss, val_avg_auc, val_auc, val_rocs, val_data_pr, val_duration = eval_model(
avg_model, valid_loader, device, criterion, scaler)
torch.save(avg_model.module.state_dict(),
os.path.join('effnet7_wa_val_auc{}_loss{}.pth'.format(
round(val_avg_auc, 3), round(val_loss, 3))))
|
11,417 | f692a9317e13817b0c0b3a747c7a8f8845f6e595 | #program to convert tuple to a string
# add items in tuple
print('Enter the space seperated value.')
dic2 = tuple([int(e) for e in input().strip().split()])
print('Tuple: ', dic2)
list1 = list(dic2)
value = int(input('Enter the value to add: ').strip())
list1.append(value)
print(tuple(list1)) |
11,418 | b81a5f80feb40cf3c3e758f12b1229f82743c210 | class Node:
def __init__(self, data):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def print_list(self):
temp = self.head
if not temp:
print('List is empty')
while temp:
print(temp.data, end=' ')
temp = temp.next
def push(self, data):
""" Method to insert a new node at the beginning """
node = Node(data)
node.next = self.head
self.head = node
def insert_after(self, node_to_insert_after, data):
""" Method to insert a new node after the given node """
node = Node(data)
temp = self.head
while temp:
if temp.data == node_to_insert_after:
break
temp = temp.next
if not temp:
print('Item does not exist')
return
node.next = temp.next
temp.next = node
def insert_before(self, node_to_insert_before, data):
""" Method to insert a new node after the given node """
node = Node(data)
temp = self.head
if temp.data == node_to_insert_before:
node.next = temp
self.head = node
return
while temp.next:
if temp.next.data == node_to_insert_before:
break
temp = temp.next
if not temp.next:
print('Item doesn\t exist')
return
node.next = temp.next
temp.next = node
def insert_at_position(self, position, data):
""" Method to insert a new node at a given position """
node = Node(data)
if not self.head:
self.head = node
return
if position == 1:
node.next = self.head
self.head = node
return
temp = self.head
for _ in range(1, position - 1):
if not temp:
print('Index out of bound')
return
temp = temp.next
node.next = temp.next
temp.next = node
def append(self, data):
""" Method to append a new node at the end """
node = Node(data)
if not self.head:
self.head = node
return
temp = self.head
while temp.next:
temp = temp.next
temp.next = node
def delete_node_at_start(self):
""" Method to delete head of linked list """
if not self.head:
print('List already empty.')
return
self.head = self.head.next
def delete_node_at_end(self):
""" Method to delete the tail """
if not self.head:
print('List already empty')
return
temp = self.head
while temp.next:
if not temp.next.next:
break
temp = temp.next
temp.next = None
def delete_node(self, key):
""" Method to delete the first occurrence of key """
if not self.head:
print('List is empty. No item to delete')
return
if self.head.data == key:
self.head = self.head.next
return
temp = self.head
while temp.next:
if temp.next.data == key:
break
temp = temp.next
temp.next = temp.next.next
def delete_node_position(self, position):
""" Method to delete the node at a given position """
if not self.head:
print('List is empty. No item to delete')
return
if position == 1:
self.head = self.head.next
return
temp = self.head
count = 1
while temp and count < position - 1:
count += 1
temp = temp.next
if not temp:
print('Node doesn\'t exist')
return
temp.next = temp.next.next
def get_count(self):
""" Method to count the number of nodes """
count = 0
temp = self.head
while temp:
count += 1
temp = temp.next
return count
def search(self, x):
""" Method to check whether the value x is present in the linked list """
temp = self.head
while temp:
if temp.data == x:
return True
temp = temp.next
return False
def search_recursive(self, llist, key):
""" Recursive method to check whether the value x is present in the linked list """
if not llist:
return False
if llist.data == key:
return True
return self.search_recursive(llist.next, key) |
11,419 | 13ffcf3040ca67c623ea322c280fb34c19bfa8dd | #rc.local : screen -dm -t "punch" bash -c "python /home/pi/button_click.py;sleep 10000"
import RPi.GPIO as GPIO
from time import sleep # this lets us have a time delay (see line 12)
import time
import os
import datetime
import httplib
GPIO.setmode(GPIO.BCM) # set up BCM GPIO numbering
GPIO.setup(25, GPIO.IN) # set GPIO25 as input (button)
GPIO.setup(24, GPIO.OUT)
#Just so we know if it is up yet
GPIO.output(24, 1)
time.sleep(0.5)
GPIO.output(24, 0)
last_state = False
try:
while True: # this will carry on until you hit CTRL+C
if GPIO.input(25) and not last_state : # if port 25 == 1
#click_time = time.strftime("%H:%M:%S")
last_state = True
os.system('date >> data.txt')
print "We got a click"
conn = httplib.HTTPConnection("www.yourdomain.com",8000)
conn.request("GET","/punchin")
print res.status, res.reason
data = res.read()
print len(data)
GPIO.output(24, 1)
elif not GPIO.input(25) and last_state:
#print "Port 25 is 0/LOW/False - LED OFF"
last_state = False
GPIO.output(24, 0)
sleep(0.1) # wait 0.1 seconds
finally: # this block will run no matter how the try block exits
GPIO.cleanup() # clean up after yourself
|
11,420 | 310852880c1db27b8335523a7d5841d4706d13a7 | '''
Created on 2014. 10. 31.
@author: biscuit
'''
import Orange
import networkx as nx
def generateTreeLoc(uristr,loc_sq, rcode):
if rcode == '404' and uristr != '/favicon.ico':
return ['not_found']
if rcode[0] != '2':
return []
if uristr.startswith("http") and loc_sq==['/']:
return ["http://.."]
if uristr == None:
return loc_sq
if uristr == "":
#loc_sq.append(loc_sq[-1]+"i")
return loc_sq
'''
if(uristr.find("?")>=0):
uri_path = uristr[:uristr.find("?")]
temp = generateTreeLoc(uri_path, loc_sq, rcode)
#temp.append(uri_arg)
return temp
'''
if(uristr[0]=='/'):
if loc_sq==['/']:
return generateTreeLoc(uristr[1:], loc_sq, rcode)
loc_sq.append(loc_sq[-1]+'/')
return generateTreeLoc(uristr[1:], loc_sq, rcode)
if uristr.find('/') < 0:
loc_sq.append(loc_sq[-1]+uristr)
return loc_sq
else :
loc_sq.append(loc_sq[-1]+uristr[:uristr.find('/')]+'/')
return generateTreeLoc(uristr[uristr.find('/')+1:], loc_sq, rcode)
def getURITree(uri_dataSet):
G = nx.DiGraph()
for d in uri_dataSet:
G.add_path(generateTreeLoc(d['query_uri'].value, ['/'], d['rcode'].value))
return G
|
11,421 | ae2031bbfe8e2b7735bfe724b8437e688a95c938 | import settings
class Camera(object):
def __init__(self, renderer):
"""Constructor."""
self.position = 0, 0
self.lastPosition = 0, 0
self.mode = "follow"
self.target = None
self.bounds = 0, 0, settings.SCREEN_WIDTH, settings.SCREEN_HEIGHT
self.renderer = renderer
self.renderer.set_camera_position_and_size(self.position[0], self.position[1], settings.SCREEN_WIDTH, settings.SCREEN_HEIGHT, "topleft")
def SetBoundaries(self, left, top, width, height):
"""Set camera boundaries."""
self.bounds = left, top, width, height
def SetTarget(self, entity):
"""Sets an entity as the target of the camera."""
self.target = entity
def Update(self):
"""Updates the camera."""
if self.mode == "follow" and self.target != None:
self.position = (self.target.mapLocation[0] - (self.target.size[0])/2) - (settings.SCREEN_WIDTH/2), (self.target.mapLocation[1] - (self.target.size[1])/2) - (settings.SCREEN_HEIGHT/2)
if self.position != self.lastPosition:
self.position = max(self.bounds[0], min((self.position[0]), self.bounds[2])), max(self.bounds[1], min((self.position[1]), self.bounds[3]))
self.renderer.set_camera_position(self.position[0], self.position[1], 'topleft')
self.lastPosition = self.position |
11,422 | 3a73b154b8362236504528731226cd075bd65448 | from random import shuffle
sentence = 'The world is too big'
list_1 = ['Hello', 'my', 'name', 'is', 'Osse']
# print(sentence[0:2:5])
shuffle(list_1)
print(list_1)
print(ord('e'))
print(ord('E'))
print('*********************************')
password = 'Banana'
newPassword = ''
#make every character slide 3 Unicode numbers
# for n in password:
# uni = ord( n )
# print(n, ":", uni)
# newUni = uni + 3
# print(chr(newUni), ":", newUni)
# print('-------')
# newPassword = newPassword + chr(newUni)
# print(newPassword)
i= 0
while i < len(password):
n = password[i]
if i % 2 == 0:
uni = ord( n )
newUni = uni + 3
newPassword = newPassword + chr( newUni )
else:
newPassword = password + n
i += 1
print(newPassword)
# list split and join
stri = 'Hello Bob, How are you?'
print(stri)
print( stri.split(" "))
myList = stri.split(' ')
print( myList)
print("-".join(myList))
shuffle( myList)
print( myList )
# list
stro = 'Hello Bob, How are you?'
print(list(stro))
print("*************")
for n in stro:
print( n ) |
11,423 | 3da261dca5b77211e983b47b6ec3ef6dd1788c6a | import webbrowser
barcode = 123123
url = "https://unexpected-fyp.firebaseapp.com/new-ingredient" + "?id=" + str(barcode)
webbrowser.open(url, new=2)
|
11,424 | daa11fe5777a354df05f827198478f28ef35fa68 | import copy
import subprocess
# RDLU
DIRECTIONS = [(0, 1), (1, 0), (0, -1), (-1, 0)]
def print_tracks(carts, tracks):
subprocess.call('clear', shell=True)
for i, line in enumerate(tracks):
printed_line = ''
for j, character in enumerate(line):
print_track = True
for cart in carts:
if (i, j) == cart['location']:
printed_line += ['>', 'v', '<', '^'][cart['direction']]
print_track = False
if print_track:
printed_line += character
print(printed_line)
input()
def key(cart):
return cart['location'][0] + (cart['location'][1] * 0.0001)
def a(carts, tracks):
while True:
carts = [cart for cart in carts if 'should_remove' not in cart]
carts.sort(key=key)
for cart in carts:
cart['location'] = tuple(sum(coordinate) for coordinate in zip(cart['location'], DIRECTIONS[cart['direction']]))
x, y = cart['location']
if tracks[x][y] == '/':
if cart['direction'] == 0:
cart['direction'] = 3
elif cart['direction'] == 1:
cart['direction'] = 2
elif cart['direction'] == 2:
cart['direction'] = 1
elif cart['direction'] == 3:
cart['direction'] = 0
if tracks[x][y] == '\\':
if cart['direction'] == 0:
cart['direction'] = 1
elif cart['direction'] == 1:
cart['direction'] = 0
elif cart['direction'] == 2:
cart['direction'] = 3
elif cart['direction'] == 3:
cart['direction'] = 2
if tracks[x][y] == '+':
if cart['turn'] == 0:
cart['direction'] = (cart['direction'] - 1) % 4
if cart['turn'] == 2:
cart['direction'] = (cart['direction'] + 1) % 4
cart['turn'] = (cart['turn'] + 1) % 3
for cart2 in carts:
if cart['location'] == cart2['location'] and cart is not cart2:
return ','.join([str(coordinate) for coordinate in cart['location']][::-1])
def b(carts, tracks):
directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]
while True:
carts = [cart for cart in carts if 'should_remove' not in cart]
carts.sort(key=key)
if len(carts) == 1:
return ','.join([str(coordinate) for coordinate in carts[0]['location']][::-1])
#print_tracks(carts, tracks)
for cart in carts:
cart['location'] = tuple(sum(coordinate) for coordinate in zip(cart['location'], DIRECTIONS[cart['direction']]))
x, y = cart['location']
if tracks[x][y] == '/':
if cart['direction'] == 0:
cart['direction'] = 3
elif cart['direction'] == 1:
cart['direction'] = 2
elif cart['direction'] == 2:
cart['direction'] = 1
elif cart['direction'] == 3:
cart['direction'] = 0
if tracks[x][y] == '\\':
if cart['direction'] == 0:
cart['direction'] = 1
elif cart['direction'] == 1:
cart['direction'] = 0
elif cart['direction'] == 2:
cart['direction'] = 3
elif cart['direction'] == 3:
cart['direction'] = 2
if tracks[x][y] == '+':
if cart['turn'] == 0:
cart['direction'] = (cart['direction'] - 1) % 4
if cart['turn'] == 2:
cart['direction'] = (cart['direction'] + 1) % 4
cart['turn'] = (cart['turn'] + 1) % 3
for cart2 in carts:
if cart['location'] == cart2['location'] and cart is not cart2:
cart['should_remove'] = True
cart2['should_remove'] = True
if len(carts) == 1:
return ','.join([str(coordinate) for coordinate in carts[0]['location']][::-1])
with open('input/13', 'r') as file:
lines = [line.replace('\n', '') for line in file.readlines()]
height = len(lines)
width = max(len(line) for line in lines)
carts = []
tracks = [[' ' for _ in range(width)] for _ in range(height)]
for i, line in enumerate(lines):
for j, character in enumerate(line):
if character in ('^', 'v'):
carts.append({
'location': (i, j),
'direction': 3 if character == '^' else 1,
'turn': 0
})
tracks[i][j] = '|'
elif character in ('>', '<'):
carts.append({
'location': (i, j),
'direction': 0 if character == '>' else 2,
'turn': 0
})
tracks[i][j] = '-'
else:
tracks[i][j] = character
print(a(copy.deepcopy(carts), tracks))
print(b(copy.deepcopy(carts), tracks))
|
11,425 | 65df29097aa7a5f0e4de70b7a811ed898de73f6c | UI_TITLE = "DeviantShow"
UI_URL = "URL: "
UI_PLAY = "Play"
UI_MODE_SEQ = "1 2 3 ..."
UI_MODE_REV = "9 8 7 ..."
UI_MODE_RND = "4 7 2 ..."
UI_TIMER = "⌚"
UI_PAUSED = "Slideshow paused"
UI_ABOUT = "About"
UI_ABOUT_NAME = "DeviantShow v.0.1"
UI_ABOUT_AUTHOR = "\nA free open-source tool by:\nEric Pöhlsen"
UI_ABOUT_USE = "Slideshow tool for:"
UI_ABOUT_DA_URL = "https://www.deviantart.com"
UI_ABOUT_GITHUB = "Sourcecode and help available at:"
UI_ABOUT_GITHUB_URL = "https://www.github.com/EricPoehlsen/DeviantShow"
UI_ABOUT_CLOSE = "Close"
UI_NSFW_INFO = "This image was marked as\n" \
"'mature content'\n" \
"by its creator.\n\n" \
"To display mature content start\n" \
"DeviantShow with flag --nsfw "
UI_BUILD_GALLERY = "Creating gallery links and indices\n"\
"This might take a few moments ..."
RSS_BASE = "https://backend.deviantart.com/rss.xml?q="
UA_IMAGELOADER = "DeviantShow ImageLoader/0.1"
UA_RSSLOADER = "DeviantShow RSS Reader/0.1"
ERROR = "Error"
ERR_NO_CONNECTION = "Can't connect to DeviantArt ..."
ERR_INVALID_URL = "This does not look like a DeviantArt URL"
ERR_INVALID_PATH = "Specified path '{path}' is not valid "\
"and can't be created automatically!\n"\
"Please provide a valid --path argument!"
TERM_TITLE = "Terminal"
ARGPARSE_DESCRIPTION = "DeviantShow is a slide show for DeviantArt."
ARGPARSE_INTERVAL = "Display interval in seconds. Default is 10, minimum is 3."
ARGPARSE_GALLERYLIMIT = "Maximum number of RSS request. Default is 3.\n" \
"The RSS response yields a maximum of 60 gallery entries. This defines how " \
"many chunks of a larger gallery should be loaded."
ARGPARSE_NSFW = "This flag denotes that you want to recieve images marked " \
"as adult content. Please be aware that there is content on DeviantArt that " \
"might not be labeled as 'adult content' but is NSFW anyway!"
ARGPARSE_PATH = "Where to store (and look for) the images."\
"Can be a relative or absolute path"
ARGPARSE_CREDITS = "Don't display image title and creator."
|
11,426 | 52e2e683ca2117146b371e82372bd126f598d344 | """Write a program that prompts for two numbers. Add them together and print the result."""
print("Give me two numbers, and I'll add them")
print("Enter 'q' to quit")
while True:
first_number = input("\nFirst Number: ")
if first_number == 'q':
break
second_number = input("Second Number: ")
try:
answer = int(first_number) + int(second_number)
except ValueError:
print("You must enter a number")
else:
print(answer)
|
11,427 | 285ee19234d9fa9bc2fcbd742ea8bbed924cc943 | import pandas as pd
import numpy as np
import yfinance_loader as yfl
import matplotlib.pyplot as plt
import seaborn as sns
def get_returns(prices):
cleaned = pd.DataFrame(prices).dropna()
return cleaned.pct_change(fill_method="ffill").dropna()
def get_covar_matrix(returns):
return pd.DataFrame(returns).cov()
def min_var_weights(cov_matrix):
ones = np.ones(len(cov_matrix))
inverted_cov = pd.DataFrame(np.linalg.pinv(cov_matrix.values), cov_matrix.columns, cov_matrix.index)
weights = np.dot(inverted_cov, ones)
return weights/sum(weights)
def efficient_weights(cov_matrix, mean_returns):
mean = np.array(mean_returns.values)
inverted_cov = pd.DataFrame(np.linalg.pinv(cov_matrix.values), cov_matrix.columns, cov_matrix.index)
weights = np.dot(inverted_cov, mean)
return weights/sum(weights)
def monthly_returns(prices):
returns = prices.asfreq('D').ffill().asfreq('M').pct_change().dropna(how='all').mean()
if returns.isna().sum() > 0:
return prices.iloc[-1, :]/prices.iloc[0, :]-1
return returns
#def dynamic_portfolio_value(endowment, weights, prices):
# if isinstance(weights, np.ndarray):
# weights = pd.DataFrame(np.full((prices.shape[0], prices.shape[1]), weights), index=prices.index)
# weights.columns = prices.columns
# print(weights)
# amounts = [(endowment * weights.iloc[0, :]).divide(prices.iloc[0, :])]
# value = [endowment]
# #initial_investment = endowment * weights
# for i in range(1, len(prices)):
# value.append((amounts[-1]*prices.iloc[i, :]).sum())
# amounts.append(weights.iloc[i, :]*value[-1]/prices.iloc[i, :])
#
# return pd.DataFrame(value)
def portfolio_amounts(endowment, weights, prices):
initial_investment = endowment*weights
return initial_investment/prices.iloc[0, :]
def portfolio_value(endowment, weights, prices):
amounts = portfolio_amounts(endowment, weights, prices)
return pd.DataFrame(amounts*prices).sum(axis=1)
def split_prices(prices, start_window = 0, len_window = 600):
return prices.iloc[start_window:start_window+len_window, :]
#yfl.clean_raw_files()
#tickers = pd.read_csv('ticker_lists/c25.csv', header=None)
#tickers = tickers[0].to_list()
#tickers = [x.replace(' ', '-') + '.ST' for x in tickers]
#tickers = ['VOO']
#tickers = ['JYSK.CO', 'DANSKE.CO', 'RILBA.CO', 'JUTBK.CO', 'NDA-DK.CO', 'VJBA.CO',
# 'SYDB.CO', 'SPNO.CO', 'MNBA.CO', 'FYNBK.CO', 'SKJE.CO', 'LOLB.CO', 'SALB.CO', 'DJUR.CO']
tickers = ['MBS', 'MGK', 'REMEDY.HE', 'PDX.ST']
prices = yfl.download_tickers(tickers, False, return_only=['Adj Close'], min_period='2Y').dropna()
start = 800
weights = []
rebalance_index = []
# for i in range(start, len(prices)-start, 30):
# price = split_prices(prices, i, start)
# print(price)
# rebalance_index.append(price.index[-1])
# cov = get_covar_matrix(get_returns(price))
# efficient = efficient_weights(cov, yearly_returns(price)).tolist()
# weights.append(efficient)
#eff_port_weights_raw = pd.DataFrame(weights, index=rebalance_index)
#dynamic_eff_weights = pd.DataFrame(eff_port_weights_raw, index=prices.index).ffill(axis=0)\
# .fillna(1/len(eff_port_weights_raw.columns))
#print(dynamic_eff_weights)
#dynamic_eff_weights = yfl.create_stock_columns(dynamic_eff_weights, tickers, cols=['Adj Close'])
#print(len(dynamic_eff_weights), len(prices))
cov = get_covar_matrix(get_returns(prices))
min_var = min_var_weights(cov)
efficient = efficient_weights(cov, monthly_returns(prices))
endowment = 100000
portfolio_min = portfolio_value(endowment, min_var, prices)
portfolio_eff = portfolio_value(endowment, efficient, prices)
#portfolio_eff = portfolio_value(endowment, dynamic_eff_weights, prices)
compare = 'SPY'
spx = yfl.download_tickers([compare], False, return_only=['Adj Close'])[compare]
combined = pd.concat([portfolio_min, portfolio_eff], axis=1).join(spx, how='left').ffill()
combined.columns = ['Portfolio min', 'Porfolio eff', compare]
normalized = combined*100/combined.iloc[0, :]
print('\nEfficient:')
for stock in zip(tickers, efficient):
print(stock[0], f'{stock[1]*100:.2f}%')
print('\nMin var:')
for stock in zip(tickers, min_var):
print(stock[0], f'{stock[1]*100:.2f}%')
plt.style.use('seaborn-ticks')
fig, ax = plt.subplots(2, 1)
ax[0].plot(normalized)
#ax[0].set_yscale('log')
ax[0].legend(['Min var port', 'Efficient port', 'S&P500'])
sns.distplot(efficient, ax=ax[1])
fig.text(0.05, 0.95, tickers, fontsize=8)
plt.show()
|
11,428 | c955e9e1700e024c5416f757e6e70d06bd302c67 | from django.conf.urls import patterns, url
urlpatterns = patterns('daw.views',
url(
r'^init_object_approvements/(?P<content_type_id>[$a-zA-Z0-9]+)/(?P<obj_pk>[$a-zA-Z0-9]+)/(?P<state_field>[$a-zA-Z0-9_]+)/(?P<callback_url>[$a-zA-Z0-9%:_.]+)/$',
'init_object_approvements_view'),
url(
r'^approve_transition/(?P<content_type_id>[$a-zA-Z0-9]+)/(?P<obj_pk>[$a-zA-Z0-9]+)/(?P<state_field>[$a-zA-Z0-9_]+)/(?:(?P<next_state_id>[$a-zA-Z0-9]+)/)?$',
'approve_transition_view'),
url(
r'^reject_transition/(?P<content_type_id>[$a-zA-Z0-9]+)/(?P<obj_pk>[$a-zA-Z0-9]+)/(?P<state_field>[$a-zA-Z0-9_]+)/(?:(?P<next_state_id>[$a-zA-Z0-9]+)/)?$',
'reject_transition_view'),
url(
r'^get_current_state/(?P<content_type_id>[$a-zA-Z0-9]+)/(?P<obj_pk>[$a-zA-Z0-9]+)/(?P<state_field>[$a-zA-Z0-9_]+)/?$', 'get_current_state'),
url(
r'^get_state_by_label/(?P<label>[$a-zA-Z0-9]+)/?$', 'get_state_by_label'),
url(
r'^skip_transition/(?P<content_type_id>[$a-zA-Z0-9]+)/(?P<object_id>[$a-zA-Z0-9]+)/(?P<state_field>[$a-zA-Z0-9_]+)/?$',
'skip_transition'),
)
|
11,429 | 11a858b60a4d8e2d007c29abee24767251766a0b | import torch.utils.data as data
from config.option import args
import torch
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
import utils
import utils_align
class ZoomDataset(data.Dataset):
def __init__(self, args, isTrain, transform=None):
self.isTrain = isTrain
self.up_ratio = int(args.scale)
#self.patch_size = 128
self.patch_size = args.patch_size
self.transform = transform
if self.isTrain:
self.dir_path = os.path.join(args.dir_data, args.data_train)
else:
self.dir_path = os.path.join(args.dir_data, args.data_test)
dir_names = os.listdir(self.dir_path)
#print(dir_names)
dir_names.sort()
self.file_names = []
if self.up_ratio == 4:
for dir_name in dir_names:
d_path = os.path.join(self.dir_path, dir_name)
for i in range(1,4):
self.file_name = []
lr_raw_path = os.path.join(d_path, "0000"+str(i+4)+".ARW")
if not os.path.exists(lr_raw_path):
continue
hr_path = os.path.join(d_path, "0000"+str(i)+'.JPG')
self.file_name.append(lr_raw_path)
self.file_name.append(hr_path)
self.file_name.append(d_path)
self.file_name.append(i+4)
self.file_name.append(i)
self.file_names.append(self.file_name)
elif self.up_ratio == 8:
for dir_name in dir_names:
d_path = os.path.join(self.dir_path, dir_name)
self.file_name = []
lr_raw_path = os.path.join(d_path, "0000"+str(6)+".ARW")
hr_path = os.path.join(d_path, "0000"+str(1)+'.JPG')
self.file_name.append(lr_raw_path)
self.file_name.append(hr_path)
self.file_name.append(d_path)
self.file_name.append(6)
self.file_name.append(1)
self.file_names.append(self.file_name)
else:
raise ValueError("arg.scale should be 4 or 8")
## file_name : [lr_raw, HR, d_path, lr_id, hr_id]
def __getitem__(self,i):
file_name = self.file_names[i]
height = width = self.patch_size
LRAW_path = file_name[0]
#LRAW_path = "/store/dataset/zoom/test/00134/00005.ARW"
LR_path = LRAW_path.replace(".ARW",".JPG")
HR_path = file_name[1]
#HR_path = "/store/dataset/zoom/test/00134/00001.JPG"
tform_txt = os.path.join(file_name[2],"tform.txt")
#tform_txt = "/store/dataset/zoom/test/00134/tform.txt"
white_lv, black_lv = utils.read_wb_lv("sony")
input_bayer = utils.get_bayer(LRAW_path, black_lv, white_lv)
#print(input_bayer.shape)
LR_raw = utils.reshape_raw(input_bayer)
LR_img = np.array(Image.open(LR_path))
#with shape [self.patch_size, self.patch_size, 4]
input_raw = utils.crop_center_wh(LR_raw, height, width)
cropped_lr_hw = utils.crop_center_wh(LR_img, height*2, width*2)
#ground truth
#with shape [self.patch_size*2*self.up_ratio, self.patch_size*2*self.up_ratio, 3]
#HR_path = file_name[1]
## crop and resize according 00001.JPG
rgb_camera_hr = np.array(Image.open(HR_path))
crop_ratio = 240.0 / utils.readFocal_pil(HR_path)
cropped_input_rgb_hr = utils.crop_fov(rgb_camera_hr, 1./crop_ratio)
#cropped_input_rgb_hr = utils.image_float(cropped_input_rgb_hr)
input_camera_rgb_hr = Image.fromarray(np.uint8(utils.clipped(cropped_input_rgb_hr)))
input_camera_rgb_naive = input_camera_rgb_hr.resize((int(input_camera_rgb_hr.width * crop_ratio),
int(input_camera_rgb_hr.height * crop_ratio)), Image.ANTIALIAS)
#input_camera_rgb_naive.save("align_arw_test/input_rgb_camera_HR.png")
hr = np.array(input_camera_rgb_naive)
## Align HR Image to LR Image and crop the corresponding patches
### Resize to corresponding up_ratio size
zoom_ratio = 240.0 / utils.readFocal_pil(LR_path)
aligned_hr_hw, _ = utils_align.imgAlign(hr, tform_txt, file_name[4], file_name[3], True, int(height*2*zoom_ratio), int(width*2*zoom_ratio))
aligned_image = Image.fromarray(np.uint8(utils.clipped(aligned_hr_hw)))
aligned_image = aligned_image.resize((int(height *2 * self.up_ratio),
int(width * 2 * self.up_ratio)), Image.ANTIALIAS)
# aligned_image.save("align_arw_test/input_rgb_camera_alignedLHR.png")
aligned_img_np = np.array(aligned_image)
# [H,W,C] => [C,H,W]
input_raw = np.transpose(input_raw, (2,0,1)) / 255.0
cropped_lr_hw = np.transpose(cropped_lr_hw, (2,0,1)) / 255.0
aligned_img_np = np.transpose(aligned_img_np, (2,0,1)) / 255.0
##ToTensor
return input_raw, cropped_lr_hw, aligned_img_np, LRAW_path
def __len__(self):
return len(self.file_names)
if __name__=="__main__":
images = []
zoomData = ZoomDataset(args, isTrain=True)
print(len(zoomData))
LR_raw,LR,HR,_ = zoomData[3]
LR = np.transpose(LR, (1,2,0))
HR = np.transpose(HR, (1,2,0))
aligned_image = Image.fromarray(np.uint8(utils.clipped(LR)*255))
aligned_image = aligned_image.resize((HR.shape[1], HR.shape[0]), Image.ANTIALIAS)
LR = np.array(aligned_image)
LR = utils.image_float(LR)
HR = utils.image_float(HR)
images.append(LR)
images.append(HR)
#sum_img_t, _ = utils_align.sum_aligned_image(images,images)
min_img_t = np.abs(HR - LR)
min_img_t_scale = (min_img_t - np.min(min_img_t)) / (np.max(min_img_t) - np.min(min_img_t))
#print(min_img_t)
#print(min_img_t_scale)
#cv2.imwrite('aligned.jpg', np.uint8(sum_img_t * 255))
#sum_img_t = np.uint8(255.*utils.clipped(sum_img_t))
#
plt.subplot(221)
plt.imshow(LR)
plt.subplot(222)
plt.imshow(HR)
plt.subplot(223)
plt.imshow(min_img_t)
plt.subplot(224)
plt.imshow(min_img_t_scale)
plt.show()
|
11,430 | e88067b216f23393ee81be3ec34c34bf0e90837a | #!/usr/bin/python3
import ltr_properties
from PyQt5.QtWidgets import QApplication
import json
import sys
from typing import List, Dict, Optional
from enum import Enum, auto
filename = "data/mainOutput.json"
def printLoadedClass(obj):
classDesc = type(obj).__name__ + ":"
for slot in obj.__slots__:
if hasattr(obj, slot):
classDesc += " " + slot + "=" + str(getattr(obj, slot))
print("Loaded " + classDesc)
class Color():
__slots__ = "r", "g", "b"
def __init__(self, r=0, g=0, b=0):
self.setRgb(r, g, b)
def postLoad(self):
printLoadedClass(self)
def getRgb(self):
return self.r, self.g, self.b
def setRgb(self, r, g, b):
self.r = r
self.g = g
self.b = b
class Vector():
__slots__ = "x", "y", "z"
def __init__(self, x=0, y=0, z=0):
self.x = x
self.y = y
self.z = z
def postLoad(self):
printLoadedClass(self)
class Baz():
__slots__ = "x"
def __init__(self):
self.x = 10000
def postLoad(self):
printLoadedClass(self)
class FancyBaz(Baz):
__slots__ = "fanciness"
fanciness: ltr_properties.Link[Color]
class Bar(object):
__slots__ = "a", "b", "c", "d", "e", "f", "_hidden"
# Type hints are optional, but are checked when deserializing. For lists and
# dicts, they allow empty lists/dicts to be filled with new elements, rather
# than requiring an existing element to duplicate.
a: Dict[str, str]
b: str
c: List[Color]
d: List[Vector]
e: Baz
f: Optional[Vector]
def __init__(self):
self.a = {"one": "a", "two": "b"}
self.b = "two"
self.c = [Color(0, 150, 255), Color(), Color(255, 255, 255)]
self.d = [Vector(), Vector(1, 4, 9), Vector(255, 0, -255)]
self.e = Baz()
self._hidden = "Shouldn't show up"
def postLoad(self):
printLoadedClass(self)
class EnumVal(Enum):
Val1 = auto()
Val2 = auto()
Val3 = auto()
class Foo(object):
__slots__ = "x", "y", "z", "w", "s", "b", "v", "ev"
ev: EnumVal
def __init__(self):
self.x = 0
self.y = -25.1
self.z = [-100, 20, 3]
self.w = True
self.s = "test"
self.b = Bar()
self.v = Vector(1, 4, 9)
self.ev = EnumVal.Val1
def postLoad(self):
printLoadedClass(self)
class OptionalTest(object):
__slots__ = "f", "of"
f: float
of: Optional[float]
def postLoad(self):
printLoadedClass(self)
if __name__ == '__main__':
app = QApplication(sys.argv)
currentModule = sys.modules[__name__]
ltrEditor = ltr_properties.LtrEditor("data", currentModule, serializerIndent=4)
ltrEditor.addCustomEditorMapping(Color, ltr_properties.EditorColor)
ltrEditor.addCustomEditorMapping(Vector, ltr_properties.EditorSlottedClassHorizontal)
ltrEditor.setGeometry(300, 200, 900, 900)
ltrEditor.setWindowTitle('LtRandolph Property Editor')
ltrEditor.show()
app.exec_()
|
11,431 | 06408572f715cc243564fe4266165c197a5351ed | def make_brick(small,big,goal):
if goal<=small+5*big:
if goal%(5) <=small:
return True
else:
return False
else:
return False
def lone_sum(a,b,c):
sum=0
if a in (b,c):
sum+=0
else:
sum+=a
if b in (a,c):
sum+=0
else:
sum+=b
if c in (a,b):
sum+=0
else:
sum+=c
return sum
def lucky_sum(a,b,c):
sum=0
if a!=13:
sum+=a
else:
return sum
if b!=13:
sum+=b
else:
return sum
if c!=13:
sum+=c
else:
return sum
return sum
def fix_teen(n):
if n in (13,14,17,18,19):
return 0
else:
return n
def no_teen_sum(a,b,c):
a=fix_teen(a)
b=fix_teen(b)
c=fix_teen(c)
return a+b+c
def round10(num):
if num%10<5:
num=(num//10)*10
else:
num=((num//10)+1)*10
return num
def round_sum(a,b,c):
a=round10(a)
b=round10(b)
c=round10(c)
return a+b+c
def close_far(a,b,c):
while not (abs(a-b)<=1 and abs(a-c)<=1):
if abs(a-b)<=1:
if abs(a-c)>=2 and abs(b-c)>=2:
return True
else:
return False
else:
if abs(a-b)>=2 and abs(b-c)>=2:
return True
else:
return False
else:
return False
def make_chocolate(small,big,goal):
consider_big=big
while consider_big*5 >goal:
consider_big-=1
num=goal-(consider_big*5)
if num<=small:
return num
else:
return -1
|
11,432 | 853065740b916680dea7fee126ca404dea912770 | from django.contrib import admin
from .models import EmployeeEval, Category, SubCategory, EvaluationCriteria, Evaluation, Comments
# Register your models here.
class EmployeeEvalView(admin.ModelAdmin):
def employee_name(self,obj):
return obj.employee.last_name + ', ' + obj.employee.first_name +' (' + obj.employee.nick_name +')'
list_display = ('employee','employee_name','user_submit','lead_submit','yr','period')
search_fields=('employee__last_name','employee__first_name','empl__middle_name','empl__nickname')
list_filter=['yr','period']
admin.site.register(EmployeeEval,EmployeeEvalView)
admin.site.register(Category)
admin.site.register(SubCategory)
class EvaluationCriteriaView(admin.ModelAdmin):
def category(self,obj):
return obj.sub_category.category
def sub_category(self,obj):
return obj.sub_category
list_display = ('sub_category', 'description', 'group')
list_filter = ['sub_category__category__name','group']
admin.site.register(EvaluationCriteria, EvaluationCriteriaView)
class EvaluationView(admin.ModelAdmin):
list_display=('employee', 'criteria', 'area_lead', 'project_lead', 'manager')
list_filter=('employee',)
admin.site.register(Evaluation,EvaluationView)
class CommentsView(admin.ModelAdmin):
def employee_name(self,obj):
return obj.employee.last_name + ', ' + obj.employee.first_name +' (' + obj.employee.nick_name +')'
list_display=('employee_name','yr','status')
list_filter =('yr','status')
search_fields=('employee__last_name','employee__first_name','empl__middle_name','empl__nickname')
admin.site.register(Comments, CommentsView) |
11,433 | ddad0825c3018cce62230115e519fe38f8cd0cb0 | default_app_config = 'applications.documentation.apps.DocumentationConfig'
|
11,434 | 0c1e588170091322c9f6a0512b4a10f96233d352 | for a in range(1,4,2):
print(a)
else:
print("datta")
for x in range(2): #hii this datta
pass
print("its working")
def fun(*args):
for a in args:
print(a)
fun("datta","kakkad")
def fun4(*kwargs):
print(kwargs[0:2])
print(kwargs[1])
fun4("asewe", "faaw")
def fun33(s="kkr"):
print(s)
fun33("gass")
fun33("fjsn")
fun33()
def fun44(food):
for a in food.values():
print(a)
lis={1:"datta" ,2:"komal",3:"pooja",4:{1:"mom",2:"dad"},5:"man the game"}
tup=("dataa","soat","ram")
fun44(lis)
#lis[4[1]]="kurqrt"
#fun33(lis)
for x,y in lis.items():
print(x,y)
if "katta" in lis.values():
print("this is working")
else:
print("katta is not present in the dcst.")
print(len(lis))
lis["mna"]="this is the new value"
#print(lis)
lis.pop(2)
print(lis)
lis.popitem()
print(lis)
lis.popitem()
"""print(lis)
del lis[1]
print(lis)
del lis
print(lis)"""
li={1:"datta",2:"pooja",3:"komal",4:{1:"mom",2:"dad"}}
print(li)
print(len(li))
ne=li.copy()
print(ne)
li["k"]="have fun"
me=li
print(li)
print(me)
li[5]="haiii"
print(me)
me=dict(lis)
print(me)
mer={"first":me,"second":li,"third":ne}
print(mer)
print("\n\n",mer["first"])
ke=dict(datta="hiii",komal="byyy",pooja="niii")
print(ke)
me.clear()
print(me)
val=("datta","prasad","amit","suraj")
va=("friend")
crea = dict.fromkeys(val,va)
print(crea)
print(crea.get("datta"))
crea.items()
print(crea.items())
print(crea.keys())
crea.setdefault("mannu")
print(crea)
crea.update({"kedar":"friend","kishore":"friend"})
print(crea)
var={"car":"audy","game":"fun"}
lis.update(var)
print(lis)
print(crea.values())
a=lambda v:v%2
print(a(2))
print(a(5))
x=lambda a,b,c,d: a+b+c+d
print(x(1,2,4,3))
def fun1(n):
return lambda a: a*n
doubler=fun1(4)
print(doubler(2))
"""a=2
b=3
c=add(a,b)
print(c)"""
import numpy as kp
arr=kp.array([1,3,2,4,5])
print(arr)
print(kp.__version__)
arr1=kp.array(["datta","Ram","soat"])
print(arr1)
arra2=kp.array([2.3,4.3,3.00,3.4])
print(arra2)
print(type(arra2))
arr3=kp.array(("datta","arra"))
print(arr3)
a=(1,2,3,4,5)
print(a[2])
arr4=kp.array(34)
print(arr4)
arra1=kp.array([[[1,2,3,4],["data","kedar"]],[["kakkad","pakkad"],[3.3,2.3,4.2]]])
print(arra1)
print(arra1.ndim)
print(arr4.ndim)
arra7=kp.array([1,2,3,4],ndmin=5)
print(arra7)
print("the no of dimentin are",arra7.ndim)
class myclass:
v=22
obj=myclass()
print(obj.v)
class anewc:
def __init__(se,name,age):
se.name1=name
se.age1=age
def my1(se):
print("is't Working!!!")
obj2=anewc("datta",20)
print(obj2.name1)
print(obj2.age1)
obj3=anewc("kedar",20)
obj3.my1()
obj3.age=22
print(obj3.age)
del obj3
#print(obj3)
class my2:
pass
v3=my2()
"""import math
a=2
b=4
print(math.add(a+b))"""
class parent():
def __init__(self,fname,lname):
self.fname=fname
self.lname=lname
obj0=parent("Datta","Soat")
print(obj0.fname)
class child(parent):
def __init__(self,fname,lname,year):
#self.mname=fname
#self.fname=mname
super().__init__(fname,lname)
self.graduationyear=year
def welcome(self):
print("heiiii!!!!! it's working")
objc=child("kedar","Tandle",2022)
print(objc.fname)
print(objc.graduationyear)
objd=child("datta","soat",2022)
print(objd.fname)
print(objd.graduationyear)
objd.welcome()
mytup=(23,32,44,45,5)
myit=iter(mytup)
print(next(myit))
print(next(myit))
print(next(myit))
sttr="datascience"
nit=iter(sttr)
print(next(nit))
print(next(nit))
print(next(nit))
print(next(nit))
"""class my1():
def __iter__(self):
self.a=1
return self
def __next__(self):
if self.a <= 20:
x=self.a;
self.a +=1
return x
else:
raise StopIteration
ob=my1()
ite=iter(ob)
for x in ite:
print(x)
print(next(ite))
print(next(ite))
print(next(ite))"""
x1=3
def myfun2():
global x3
x3= 22
x1=4
print("this is the global one",x1)
x=2
print("thsi one is outer one",x)
def myfun3():
print("this one is global one used in ineer fun ",x1)
print("this is my ineer fun variable",x)
myfun3()
myfun2()
print("this is the global dec in local scope",x3)
from module import fun2
fun2("datta")
import datetime
x1=datetime.datetime.now()
print(x1)
print(x1.year)
print(x1.strftime("%A"))
x=datetime.datetime(2020,5,16)
print(x)
print(x1.strftime("%%"))
d=datetime.datetime.now()
print(d)
string ="darta"
string+332
|
11,435 | 532db45efdf939e80c07069d912013747bd0e75e | from Node import Node
# IDS search algorithm's inner function
def depth_limited_search(problem, limit):
counter = 0
frontier = [(Node(problem.s_start))] # Stack
while frontier:
node = frontier.pop()
counter += 1
if problem.is_goal(node.state):
return node.solution(), counter, node.path_cost
if node.depth < limit:
nodes = node.expand(problem)
frontier.extend(reversed(nodes))
return None, counter, None
# IDS search algorithm's outer function, iterating over depth_limited_search
def iterative_deepening_search(problem):
developed_counter = 0
for depth in range(1, 20):
result, developed_counter_in_iteration, final_path_cost = depth_limited_search(problem, depth)
developed_counter += developed_counter_in_iteration
if result:
return result, developed_counter, final_path_cost
return None, developed_counter, None
|
11,436 | 9b24063c3001642b42f0dcd9d0d7ac5d05305e66 | import numpy as np
prediction = # THAT'S YOUR JOB
# MAKE SURE THAT YOU HAVE THE RIGHT FORMAT
assert prediction.ndim == 1
assert prediction.shape[0] == 2000
# AND SAVE EXACTLY AS SHOWN BELOW
np.save('prediction.npy', prediction)
|
11,437 | 6628dc248c400c5e21665499677395bec9244388 | from django.contrib import admin
# Register your models here.
# from .models import Customer
# from .models import Product
# from .models import Order
from .models import * # equal to line 5 to 7
admin.site.register(Customer)
admin.site.register(Tag)
admin.site.register(Product)
admin.site.register(Order) |
11,438 | c0d11507a3616a8feef8b390fee646c56ee4b0e5 | import pandas as pd
import numpy as np
from keras import layers
from keras import models
import librosa
import sys
import os
import csv
import pathlib
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
import keras
import warnings
warnings.filterwarnings('ignore')
CATEGORIES = ['Blues', 'Classical', 'Country', 'Disco',
'Hiphop', 'Jazz', 'Metal', 'Pop', 'Reggae', 'Rock']
genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()
header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'
for j in range(1, 21):
header += f' mfcc{j}'
header += ' label'
header = header.split()
# csv file to save data
'''
file = open('data.csv', 'w', newline='')
with file:
writer = csv.writer(file)
writer.writerow(header)
genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()
for g in genres:
for filename in os.listdir(f'../genres/{g}'):
songname = f'../genres/{g}/{filename}'
y, sr = librosa.load(songname, mono=True, duration=30)
chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
rmse = librosa.feature.rms(y=y)[0]
spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
zcr = librosa.feature.zero_crossing_rate(y)
mfcc = librosa.feature.mfcc(y=y, sr=sr)
to_append = f'{filename} {np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spec_cent)} {np.mean(spec_bw)} {np.mean(rolloff)} {np.mean(zcr)}'
for e in mfcc:
to_append += f' {np.mean(e)}'
to_append += f' {g}'
file = open('data.csv', 'a', newline='')
with file:
writer = csv.writer(file)
writer.writerow(to_append.split())
'''
data = pd.read_csv('data.csv')
data.head()
data.shape
data = data.drop(['filename'], axis=1)
# label
genre_list = data.iloc[:, -1]
encoder = LabelEncoder()
y = encoder.fit_transform(genre_list)
# feature columns
scaler = StandardScaler()
X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype=float))
# train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = models.Sequential()
model.add(layers.Dense(32, activation='relu',
input_shape=(X_train.shape[1],)))
model.add(layers.Dense(24, activation='relu'))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
epochs=100,
batch_size=128)
test_loss, test_acc = model.evaluate(X_test, y_test)
model.save('model.h5')
print('test_acc: ', test_acc)
# validation
x_val = X_train[:200]
partial_x_train = X_train[200:]
y_val = y_train[:200]
partial_y_train = y_train[200:]
# training
model = models.Sequential()
model.add(layers.Dense(64, activation='relu',
input_shape=(X_train.shape[1],)))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(24, activation='relu'))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(partial_x_train,
partial_y_train,
epochs=100,
batch_size=512,
validation_data=(x_val, y_val))
results = model.evaluate(X_test, y_test)
# model.save("model.h5")
# print("Saved model to disk")
filename = sys.argv[1]
y, sr = librosa.load(filename, mono=True, duration=30)
rmse = librosa.feature.rms(y=y)[0]
spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
zcr = librosa.feature.zero_crossing_rate(y)
spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
mfcc = librosa.feature.mfcc(y=y, sr=sr)
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
to_append = f'{np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spec_cent)} {np.mean(spec_bw)} {np.mean(rolloff)} {np.mean(zcr)}'
for element in mfcc:
to_append += f' {np.mean(element)}'
X_new = np.fromstring(to_append, dtype=float, sep=' ')
print(X_new)
ynew = model.predict(X_new.reshape(1, -1))
print(ynew)
ynew = ynew.astype(int)
print("X=%s, Predicted=%s" % (X_new, ynew))
print(CATEGORIES[np.argmax(ynew)])
|
11,439 | 07eb83ba2d3680f2470701f7e181fbecb42e949a | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 8 20:37:36 2021
@author: Josey
"""
[['R' 'B' 'R' 'B' 'R']
['B' 'R' 'B' 'R' 'B']
['R' 'B' 'R' 'B' 'R']
['B' 'R' 'B' 'R' 'B']
['R' 'B' 'R' 'B' 'R']] |
11,440 | 95c589dea95d20ece298948a7a83da06a07c3b02 | import numpy as np
import matplotlib.pyplot as plt
import keras
def evaluate_model(model, split_sets):
training_error = model.evaluate(split_sets['X_train'], split_sets['y_train'], verbose=0)
print('training error = ' + str(training_error))
testing_error = model.evaluate(split_sets['X_test'], split_sets['y_test'], verbose=0)
print('testing error = ' + str(testing_error))
def output_plot(dataset, y, window_size, train_percent,
predictions):
if len(predictions) > 2:
train_split = int(np.ceil(len(y)*train_percent)) + window_size
valid_split = int(np.ceil(len(y)*((1-train_percent)/2))) + train_split
# plot original series
plt.plot(dataset, color='k')
# plot training set prediction
plt.plot(np.arange(window_size, train_split, 1),
predictions['train'], color='b')
# plot validation set prediction
plt.plot(np.arange(train_split, valid_split, 1),
predictions['valid'], color='g')
# plot testing set prediction
plt.plot(np.arange(valid_split, valid_split + len(predictions['test']), 1),
predictions['test'], color='r')
# pretty up graph
plt.xlabel('day')
plt.ylabel('(normalized) price')
plt.legend(['original series', 'training fit',
'Validation fit', 'testing fit'],
loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
else:
train_split = int(np.ceil(len(y)*train_percent)) + window_size
# plot original series
plt.plot(dataset, color='k')
# plot training set prediction
plt.plot(np.arange(window_size, train_split, 1),
predictions['train'], color='b')
# plot testing set prediction
plt.plot(np.arange(train_split, train_split + len(predictions['test']), 1),
predictions['test'], color='r')
# pretty up graph
plt.xlabel('day')
plt.ylabel('(normalized) price')
plt.legend(['original series', 'training fit',
'testing fit'],
loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
|
11,441 | 38a93c25d601cc9549531354c71a50deddbd6fb0 | import time
import serial
ser= serial.Serial("/dev/ttyACM0", baudrate=9600)
try:
while True:
comando = input("Ingresar comando (on/off): ")
comando = comando +"\n"
comandoBytes=comando.encode()
ser.write(comandoBytes)
time.sleep(0.1)
read= ser.readline()
print(read)
except KeyboardInterrupt:
print("\nInterrupcion por teclado")
except ValueError as ve:
print(ve)
print("Otra interrupcion")
finally:
ser.close()
|
11,442 | ce3b6741eb57747c3bf63d0dce60c89b1772a942 |
from rest_framework import permissions
class IsOwnerOrReadOnly(permissions.BasePermission):
"""
Custom permission to only allow owners of an object to modify/edit it.
Read only is allowed to other users.
"""
def has_object_permission(self, request, view, obj):
# Read permissions are allowed to any request,
# so we'll always allow GET, HEAD or OPTIONS requests.
if request.method in permissions.SAFE_METHODS:
return True
# Write permissions are only allowed to the authenticated user.
return request.user.is_authenticated and (obj.owner == request.user)
class IsRecipeOwnerOrReadOnly(permissions.BasePermission):
"""
Custom permission to only allow the owner of a recipe associated to an object
to modify/edit it. Read only is allowed to other users.
"""
def has_object_permission(self, request, view, obj):
# Read permissions are allowed to any request,
# so we'll always allow GET, HEAD or OPTIONS requests.
if request.method in permissions.SAFE_METHODS:
return True
# Write permissions are only allowed to the owner of the related recipe.
return request.user.is_authenticated and (obj.recipe.owner == request.user)
|
11,443 | aad11e06fce27cdfb29721be3fad4408b852840e | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright: (c) 2019, Michael Pechner <mikey@mikey.com>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
from __future__ import absolute_import, division, print_function
DOCUMENTATION = r'''
---
module: ecs_tag
version_added: 1.0.0
short_description: create and remove tags on Amazon ECS resources
notes:
- none
description:
- Creates and removes tags for Amazon ECS resources.
- Resources are referenced by their cluster name.
author:
- Michael Pechner (@mpechner)
requirements: [ boto3, botocore ]
options:
cluster_name:
description:
- The name of the cluster whose resources we are tagging.
required: true
type: str
resource:
description:
- The ECS resource name.
- Required unless I(resource_type=cluster).
type: str
resource_type:
description:
- The type of resource.
default: cluster
choices: ['cluster', 'task', 'service', 'task_definition', 'container']
type: str
state:
description:
- Whether the tags should be present or absent on the resource.
default: present
choices: ['present', 'absent']
type: str
tags:
description:
- A dictionary of tags to add or remove from the resource.
- If the value provided for a tag is null and I(state=absent), the tag will be removed regardless of its current value.
type: dict
purge_tags:
description:
- Whether unspecified tags should be removed from the resource.
- Note that when combined with I(state=absent), specified tags with non-matching values are not purged.
type: bool
default: false
extends_documentation_fragment:
- amazon.aws.aws
- amazon.aws.ec2
'''
EXAMPLES = r'''
- name: Ensure tags are present on a resource
community.aws.ecs_tag:
cluster_name: mycluster
resource_type: cluster
state: present
tags:
Name: ubervol
env: prod
- name: Remove the Env tag
community.aws.ecs_tag:
cluster_name: mycluster
resource_type: cluster
tags:
Env:
state: absent
- name: Remove the Env tag if it's currently 'development'
community.aws.ecs_tag:
cluster_name: mycluster
resource_type: cluster
tags:
Env: development
state: absent
- name: Remove all tags except for Name from a cluster
community.aws.ecs_tag:
cluster_name: mycluster
resource_type: cluster
tags:
Name: foo
state: absent
purge_tags: true
'''
RETURN = r'''
tags:
description: A dict containing the tags on the resource
returned: always
type: dict
added_tags:
description: A dict of tags that were added to the resource
returned: If tags were added
type: dict
removed_tags:
description: A dict of tags that were removed from the resource
returned: If tags were removed
type: dict
'''
from ansible_collections.amazon.aws.plugins.module_utils.core import AnsibleAWSModule
from ansible_collections.amazon.aws.plugins.module_utils.ec2 import boto3_tag_list_to_ansible_dict, ansible_dict_to_boto3_tag_list, compare_aws_tags
try:
from botocore.exceptions import BotoCoreError, ClientError
except ImportError:
pass # Handled by AnsibleAWSModule
__metaclass__ = type
def get_tags(ecs, module, resource):
try:
return boto3_tag_list_to_ansible_dict(ecs.list_tags_for_resource(resourceArn=resource)['tags'])
except (BotoCoreError, ClientError) as e:
module.fail_json_aws(e, msg='Failed to fetch tags for resource {0}'.format(resource))
def get_arn(ecs, module, cluster_name, resource_type, resource):
try:
if resource_type == 'cluster':
description = ecs.describe_clusters(clusters=[resource])
resource_arn = description['clusters'][0]['clusterArn']
elif resource_type == 'task':
description = ecs.describe_tasks(cluster=cluster_name, tasks=[resource])
resource_arn = description['tasks'][0]['taskArn']
elif resource_type == 'service':
description = ecs.describe_services(cluster=cluster_name, services=[resource])
resource_arn = description['services'][0]['serviceArn']
elif resource_type == 'task_definition':
description = ecs.describe_task_definition(taskDefinition=resource)
resource_arn = description['taskDefinition']['taskDefinitionArn']
elif resource_type == 'container':
description = ecs.describe_container_instances(clusters=[resource])
resource_arn = description['containerInstances'][0]['containerInstanceArn']
except (IndexError, KeyError):
module.fail_json(msg='Failed to find {0} {1}'.format(resource_type, resource))
except (BotoCoreError, ClientError) as e:
module.fail_json_aws(e, msg='Failed to find {0} {1}'.format(resource_type, resource))
return resource_arn
def main():
argument_spec = dict(
cluster_name=dict(required=True),
resource=dict(required=False),
tags=dict(type='dict'),
purge_tags=dict(type='bool', default=False),
state=dict(default='present', choices=['present', 'absent']),
resource_type=dict(default='cluster', choices=['cluster', 'task', 'service', 'task_definition', 'container'])
)
required_if = [('state', 'present', ['tags']), ('state', 'absent', ['tags'])]
module = AnsibleAWSModule(argument_spec=argument_spec, required_if=required_if, supports_check_mode=True)
resource_type = module.params['resource_type']
cluster_name = module.params['cluster_name']
if resource_type == 'cluster':
resource = cluster_name
else:
resource = module.params['resource']
tags = module.params['tags']
state = module.params['state']
purge_tags = module.params['purge_tags']
result = {'changed': False}
ecs = module.client('ecs')
resource_arn = get_arn(ecs, module, cluster_name, resource_type, resource)
current_tags = get_tags(ecs, module, resource_arn)
add_tags, remove = compare_aws_tags(current_tags, tags, purge_tags=purge_tags)
remove_tags = {}
if state == 'absent':
for key in tags:
if key in current_tags and (tags[key] is None or current_tags[key] == tags[key]):
remove_tags[key] = current_tags[key]
for key in remove:
remove_tags[key] = current_tags[key]
if remove_tags:
result['changed'] = True
result['removed_tags'] = remove_tags
if not module.check_mode:
try:
ecs.untag_resource(resourceArn=resource_arn, tagKeys=list(remove_tags.keys()))
except (BotoCoreError, ClientError) as e:
module.fail_json_aws(e, msg='Failed to remove tags {0} from resource {1}'.format(remove_tags, resource))
if state == 'present' and add_tags:
result['changed'] = True
result['added_tags'] = add_tags
current_tags.update(add_tags)
if not module.check_mode:
try:
tags = ansible_dict_to_boto3_tag_list(add_tags, tag_name_key_name='key', tag_value_key_name='value')
ecs.tag_resource(resourceArn=resource_arn, tags=tags)
except (BotoCoreError, ClientError) as e:
module.fail_json_aws(e, msg='Failed to set tags {0} on resource {1}'.format(add_tags, resource))
result['tags'] = get_tags(ecs, module, resource_arn)
module.exit_json(**result)
if __name__ == '__main__':
main()
|
11,444 | f176ac000941e0c8c12690a06f6b2801cee33a23 | #
# SOFTWARE HISTORY
#
# Date Ticket# Engineer Description
# ------------ ---------- ----------- --------------------------
# 08/20/10 njensen Initial Creation.
#
#
__all__ = ['SerializationException']
from . import dstypes, adapters
from . import DynamicSerializationManager
class SerializationException(Exception):
def __init__(self, message=None):
self.message = message
def __str__(self):
if self.message:
return self.message
else:
return ""
def serialize(obj):
dsm = DynamicSerializationManager.DynamicSerializationManager()
return dsm.serializeObject(obj)
def deserialize(objbytes):
dsm = DynamicSerializationManager.DynamicSerializationManager()
return dsm.deserializeBytes(objbytes)
|
11,445 | 6c9055797f8147717919e75b673f8134cd4ad8b6 | class Solution:
def duplicateZeros(self, arr: List[int]) -> None:
"""
Do not return anything, modify arr in-place instead.
"""
i = 0
for num in list(arr):
if i >= len(arr): break
arr[i] = num
if not num:
i += 1
if i < len(arr):
arr[i] = num
i += 1 |
11,446 | b27dae62b516aa24edce37aed85bc001de4f2183 |
text = list(input("Please enter the text :").lower())
vowels = ["a","e","i","u","o"]
result=False
if len(text) <= 1:
result = False
else:
for i in range(len(text)-1):
if text[i] in vowels and text[i+1] in vowels:
result = True
break
if result:
print("positive")
else:
print("negative")
|
11,447 | 412b59f47030b97f58e666f3d8eb7bfdac001bbc | # Author: kk.Fang(fkfkbill@gmail.com)
__all__ = [
"OracleStatsCMDBSQLExecutionCostRank"
]
from typing import Generator, Union
from mongoengine import StringField, FloatField
from models.sqlalchemy import *
from .base import *
from ..base import *
from ...capture import OracleSQLStat
@OracleBaseStatistics.need_collect()
class OracleStatsCMDBSQLExecutionCostRank(
OracleStatsMixOfLoginUserAndCurrentTaskRank):
"""登录用户与当前库的SQL执行效率排名统计"""
BY_WHAT = ("elapsed_time_total", "elapsed_time_delta")
LIMITATION_PER = 10
by_what = StringField(choices=BY_WHAT)
sql_id = StringField()
time = FloatField()
meta = {
"collection": "oracle_stats_cmdb_sql_exec_cost_rank"
}
@classmethod
def generate(
cls,
task_record_id: int,
cmdb_id: Union[int, None],
**kwargs) -> Generator[
"OracleStatsCMDBSQLExecutionCostRank", None, None]:
with make_session() as session:
for the_user in cls.users(session, cmdb_id=cmdb_id):
schemas = cls.schemas(
session,
cmdb_id=cmdb_id,
login_user=the_user.login_user
)
for by_what in cls.BY_WHAT:
stat_q = OracleSQLStat.filter(
task_record_id=task_record_id,
schema_name__in=schemas
).order_by(f"-{by_what}")[:cls.LIMITATION_PER]
for i, stat in enumerate(stat_q):
doc = cls(by_what=by_what)
doc.sql_id = stat.sql_id
doc.time = getattr(stat, by_what, None)
cls.post_generated(
doc=doc,
task_record_id=task_record_id,
cmdb_id=cmdb_id,
target_login_user=the_user.login_user,
rank=i
)
yield doc
|
11,448 | 5a983425189fbacd7f171afda865837f8366ca7d | import csv
import os
from os.path import join
import requests
import time
def fetch_media_for_feed(feed_dict):
feed_dir = feed_dict['feed_dir']
media_dir = join(feed_dir, 'media')
short_name = feed_dict['short_name']
image_dir = join(media_dir, 'tweet_images')
profile_images_dir = join(media_dir, 'profile_images')
if not os.path.exists(image_dir):
os.makedirs(image_dir)
if not os.path.exists(profile_images_dir):
os.makedirs(profile_images_dir)
media_urls_csv = join(media_dir, 'tweet_images.csv')
profile_image_csv = join(media_dir, 'profile_images.csv')
media_urls = {}
profile_image_urls = {}
with open(media_urls_csv, 'r', newline="") as csvfile:
reader = csv.reader(csvfile)
for row in reader:
url = row[0]
filename = row[1]
media_urls[url] = filename
with open(profile_image_csv, 'r', newline="") as csvfile:
reader = csv.reader(csvfile)
for row in reader:
url = row[0]
profile_dir = row[1]
filename = row[2]
profile_image_urls[url] = {'profile_dir': profile_dir, 'filename': filename}
with requests.Session() as s:
for url in media_urls:
if media_urls[url] not in os.listdir(image_dir):
print("{0} Media URLs: Fetching {1}".format(short_name, url))
media = s.get(url)
media_file = join(image_dir, media_urls[url])
with open(media_file, 'wb') as media_out:
media_out.write(media.content)
time.sleep(1)
else:
print("{0} Media URLs: {1} has already been fetched".format(short_name, url))
with requests.Session() as s:
for url in profile_image_urls:
profile_dir_name = profile_image_urls[url]['profile_dir']
filename = profile_image_urls[url]['filename']
profile_dir = join(profile_images_dir, profile_dir_name)
if not os.path.exists(profile_dir):
os.makedirs(profile_dir)
if filename not in os.listdir(profile_dir):
print("{0} Profile Images: Fetching {1}".format(short_name, url))
profile_image = s.get(url)
profile_image_file = join(profile_dir, filename)
with open(profile_image_file, 'wb') as profile_image_out:
profile_image_out.write(profile_image.content)
time.sleep(1)
else:
print("{0} Profile Images: {1} has already been fetched".format(short_name, url))
def fetch_media(feeds):
for feed in feeds:
print("Fetching media for {0}".format(feed))
feed_dict = feeds[feed]
fetch_media_for_feed(feed_dict)
|
11,449 | a87503175c808879ce5fc58c6387f6e855dc440a | #!/usr/bin/python
import sys
prev = ""
total = 0
doc_prev = ""
for line in sys.stdin:
token, docID, count = line.strip().split()
count = int(count)
if prev == token:
if doc_prev == docID:
total += count
else:
print prev, doc_prev, total, " "
prev = token
doc_prev = docID
total = count
else:
if prev:
print prev, doc_prev, total, " "
prev = token
doc_prev = docID
total = count
if prev == token:
print prev, docID, total, " "
|
11,450 | 08e3c5ad9f04d3cf610e4c2422e83672eeb35320 | #Open the file
employees_file = open("employees.txt", "r")
#Read values
if(employees_file.readable()):
for employee in employees_file.readlines():
print(employee)
employees_file.close()
#Appending to file
employees_file = open("employees.txt", "a")
employees_file.write("\nLeah - Human Resources")
employees_file.close()
#Writing file
employees_file = open("employees-new.txt", "w")
employees_file.write("Leah - Human Resources")
employees_file.close() |
11,451 | a627612570582832ab8c32b0b4af2b3038bbd1c6 | from sqlalchemy import create_engine
from datetime import datetime
from TweetCollector_FullArchiveAPI.Tables import Base, Tweet, Place
from sqlalchemy.orm import sessionmaker
from contextlib import contextmanager
import json
"""
The TweetLoader class takes care of transforming the fields from the response
objects to adhere to the data schema represented by the Tweet class.
It uses sqlAlchemy to load the tweets to a DB.
"""
class TweetLoader():
### CONSTRUCTOR ###
def __init__(self, database_url):
### INSTANCE VARIABLES ###
self.recreated_tables = False
# an engine to communicate with PostgreSQL
self.engine = create_engine(database_url)
# a Session object to manage connections (session starts)
self.Session = sessionmaker(bind=self.engine)
### METHODS ###
# 1. start_load()
# 2. transform_and_load()
# 3. recreate_database()
# 4. session_scope()
"""
It handles the actual data loading into the DB.
It is called by the transform_and_load() method.
"""
# START LOAD
# 1.
def start_load(self, tweet_to_add, recreate_db):
# print("Transformasion okay! Loading to start!")
# if only interested in the new data, recreate_db deletes data streamed before
if recreate_db == True and self.recreated_tables == False:
self.recreate_database()
print("Recreate db ran!")
self.recreated_tables = True
# connect to DB with session
with self.session_scope() as s:
# add tweet to DB
try:
s.add(tweet_to_add)
# print("Tweet Loading Successful!")
except:
print("Error in Loading!")
"""
Transforms the received JSON response to abide the data schema in line
with what's defined in the Tweet object.
"""
# TRANSFORM AND LOAD
# 2.
def transform_and_load(self, json_response, query_tag, recreate_db):
# inspect response line (optional)
# print("json printed: ", json.dumps(json_response, indent=4, sort_keys=True))
# MAIN LOOP: for every tweet (data_item is a tweet)
for data_item in json_response["data"]:
# print("tweet: ", json.dumps(data_item, indent=4, sort_keys=True))
#####
# TWEET FIELDS
tweet_id = data_item["id"]
tweet_text = data_item["text"]
tweet_created_at = data_item["created_at"]
# TWEET FIELDS: placeholders for non-guaranteed fields such as place (default is None)
tweet_geo_coordinates_type = None
tweet_geo_coordinates_coords = None
tweet_place_id = None
# PLACE FIELDS: non-guaranteed fields
tweet_place_geo_bbox = None
tweet_place_full_name = None
tweet_place_type = None
tweet_country_code = None
# if coordinates are present
if "geo" in data_item:
if "coordinates" in data_item["geo"]:
# set coordinates fields
tweet_geo_coordinates_type = data_item["geo"]["coordinates"]["type"]
tweet_geo_coordinates_coords = data_item["geo"]["coordinates"]["coordinates"]
# otherwise, they are set as None
# if there is place_id
if "place_id" in data_item["geo"]:
# set tweet_place_id
tweet_place_id = data_item["geo"]["place_id"]
# otherwise, it is set as None
# CONTEXT_ANNOTATIONS:
context_domain_array = [] # array to collect context_annotations
# if there is a context_annotations array
if "context_annotations" in data_item:
# for each domain annotation
for annotation in data_item["context_annotations"]:
# append it to local variable
context_domain_array.append(annotation["domain"]["name"])
# if there is no context_annotations
else:
# make it NULL
# print("context_annotation is null")
context_domain_array = None
# PLACES
# if tweet_place_id is not None
if tweet_place_id is not None:
# Places for loop (includes.places) contains all place objects
for places_item in json_response["includes"]["places"]:
# find the place
if (places_item["id"] == tweet_place_id):
# get other place related fields
tweet_place_geo_bbox = places_item["geo"]["bbox"]
tweet_place_full_name = places_item["full_name"]
tweet_place_type = places_item["place_type"]
tweet_country_code = places_item["country_code"]
#####
# CONSTRUCT TWEET (per tweet, for Tweet and Place)
tweet_data_dict = {'tweet_id': tweet_id,
'text': tweet_text,
'created_at': tweet_created_at,
'context_annotations': context_domain_array,
'geo_coordinates_type': tweet_geo_coordinates_type,
'geo_coordinates_coords': tweet_geo_coordinates_coords,
'geo_place_id': tweet_place_id,
'stream_rule_tag': query_tag}
place_data_dict = {'places_geo_place_id': tweet_place_id,
'places_geo_bbox': tweet_place_geo_bbox,
'places_full_name': tweet_place_full_name,
'places_place_type': tweet_place_type,
'places_country_code': tweet_country_code}
# construct a Tweet() object
# data passed in to Tweet() has to be in a dictionary format
single_tweet = Tweet(**tweet_data_dict)
single_place = Place(**place_data_dict)
# inspect transformed Tweet() object
#print("single_tweet: ", single_tweet)
#print("single_place: ", single_place)
# LOAD TWEETs and PLACEs
self.start_load(single_tweet, recreate_db)
self.start_load(single_place, recreate_db)
"""
Recreates the database. It drops all tables and creates them again.
If run for the first time, set this as true.
"""
# RECREATE DATABASE
# 3.
def recreate_database(self):
# drops all tables
Base.metadata.drop_all(self.engine)
# creates all tables
Base.metadata.create_all(self.engine)
"""
A context manager for the session.
It ensures that all connections are closed.
"""
# A CONTEXT MANAGER
# 4.
@ contextmanager
def session_scope(self):
# local scope creates and uses a session
session = self.Session() # invokes sessionmaker.__call__()
try:
yield session
session.commit()
except Exception:
session.rollback()
raise
finally:
session.close()
|
11,452 | 655141c4e0dfc8827c457c0f286dc1f6d4d595f9 | from pytransform import pyarmor_runtime
pyarmor_runtime()
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1) |
11,453 | 39f492e760c4c82e16a9ebebc18e4432f38d2291 | import abc
# 抽象观察者类
class Observer(metaclass=abc.ABCMeta):
# 更新通知方法
# invoker:通知者
# event:事件
# params:事件参数
@abc.abstractmethod
def update(self, invoker, event, params):
pass
|
11,454 | 86bf7ceb4d664018b498e7c3d3e38b439c30c342 | #!/usr/bin/python
# vim: set fileencoding=utf-8
# Demonstrační příklady využívající knihovnu Pygame
# Příklad číslo 26: použití objektů typu Surface, metoda blit()
# a operace pygame.transform.scale().
import pygame, sys, os
# Nutno importovat kvůli konstantám QUIT atd.
from pygame.locals import *
# Velikost okna aplikace
WIDTH = 320
HEIGHT = 240
# Inicializace knihovny Pygame
pygame.init()
clock = pygame.time.Clock()
# Vytvoření okna pro vykreslování
display = pygame.display.set_mode([WIDTH, HEIGHT])
# Nastavení titulku okna
pygame.display.set_caption('Pygame test #26')
# Konstanty s n-ticemi představujícími základní barvy
BLACK = ( 0, 0, 0)
# Vyplnění plochy okna černou barvou
display.fill(BLACK)
# Načtení obrázku, který se bude vykreslovat a transformovat
image_surface = pygame.image.load(os.path.join('images', 'pygame.png'))
# Rozměry původního obrázku
image_width = image_surface.get_width()
image_height = image_surface.get_height()
scale_ratio = 1.5
# Vytvoření zvětšených obrázků
horizontally_scaled_image = pygame.transform.scale(image_surface, (int(image_width*scale_ratio), image_height))
vertically_scaled_image = pygame.transform.scale(image_surface, (image_width, int(image_height*scale_ratio)))
scaled_image = pygame.transform.scale(image_surface, (int(image_width*scale_ratio), int(image_height*scale_ratio)))
# Přímé vykreslení původního obrázku
display.blit(image_surface, (50, 25))
# Přímé vykreslení zvětšených obrázků
display.blit(horizontally_scaled_image, (150, 25))
display.blit(vertically_scaled_image, (50, 125))
display.blit(scaled_image, (150, 125))
# Hlavní herní smyčka
while True:
# Načtení a zpracování všech událostí z fronty
for event in pygame.event.get():
# Uzavřením okna běh aplikace ukončí
if event.type == QUIT:
pygame.quit()
sys.exit()
# Klávesou ESC se běh aplikace ukončí
if event.type == KEYDOWN and event.key == K_ESCAPE:
pygame.quit()
sys.exit()
pygame.display.update()
clock.tick(20)
# finito
|
11,455 | 62245bdbee36c8c4292c943b4627c1daa585cb60 | import cv2 as cv
import numpy as np
img = cv.imread('clahe_src.jpg', 0)
# 全局直方图均衡
equ = cv.equalizeHist(img)
# 自适应直方图均衡
clahe = cv.createCLAHE(clipLimit = 2.0, tileGridSize = (8, 8))
cl1 = clahe.apply(img)
# 水平拼接三张图像
result1 = np.hstack((img, equ, cl1))
cv.imwrite('clahe_result.jpg', result1) |
11,456 | 45fd63816fdb037d73ad61b507bab47e61c9fe45 | # -*- coding: utf-8 -*-
# Generated by Django 1.10.2 on 2016-10-04 06:21
from __future__ import unicode_literals
import django.core.validators
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='About',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=80)),
('preface_description', models.TextField(blank=True, help_text='Preface yourself')),
('main_description', models.TextField(blank=True, help_text='About me')),
('skill_name', models.CharField(blank=True, max_length=255)),
('skill_percent', models.PositiveSmallIntegerField(blank=True)),
('education', models.CharField(blank=True, help_text='Education name', max_length=255)),
('education_description', models.TextField(blank=True, help_text='Describe education')),
('image', models.ImageField(blank=True, upload_to='photo', verbose_name='Photo')),
],
),
migrations.CreateModel(
name='Benefit',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=80)),
('slug', models.SlugField(unique=True)),
('benefit_description', models.TextField(blank=True, help_text='Description')),
('image', models.ImageField(blank=True, upload_to='benefit', verbose_name='Benefit picture')),
],
),
migrations.CreateModel(
name='Fee',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=80)),
('fee_description', models.TextField(blank=True, help_text='Fee Description')),
('price', models.DecimalField(decimal_places=2, help_text='Price', max_digits=15)),
('discount', models.DecimalField(decimal_places=2, help_text='Discount', max_digits=15, null=True)),
('increase', models.DecimalField(decimal_places=2, help_text='Increase', max_digits=15, null=True)),
],
),
migrations.CreateModel(
name='Goal',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=80)),
('goal_description', models.TextField(blank=True, help_text='Description')),
('image', models.ImageField(blank=True, upload_to='picture', verbose_name='Goal picture')),
],
),
migrations.CreateModel(
name='Preface',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=80)),
('description', models.TextField(blank=True, help_text='Preface project')),
('image', models.ImageField(blank=True, upload_to='preface_pic', verbose_name='Preface Image')),
],
),
migrations.CreateModel(
name='Testimonial',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=80)),
('slug', models.SlugField(unique=True)),
('avatar', models.ImageField(blank=True, upload_to='photo_ava', verbose_name='Photo')),
('position', models.TextField(blank=True, help_text='Position')),
('testimonial', models.TextField(blank=True, help_text='Testimonial')),
('location', models.CharField(help_text='Location', max_length=250)),
('phone_number', models.CharField(blank=True, max_length=15, validators=[django.core.validators.RegexValidator(message="Phone number must be entered in the format: '+999999999'. Up to 15 digits allowed.", regex='^\\+?1?\\d{9,15}$')])),
('url', models.URLField(blank=True)),
],
),
]
|
11,457 | 368a2592df530520268abd8fff1b55ca842cc1d9 | import socket
import time
# 创建对象
sk = socket.socket()
# 绑定IP端口
sk.bind(('127.0.0.1', 8000))
# 监听
sk.listen()
def rihan(url):
return "欢迎访问 日韩板块{}".format(url)
def oumei(url):
return "欢迎访问 欧美板块{}".format(url)
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
# 持续等待链接
while True:
conn, addr = sk.accept()
data = conn.recv(2048).decode("UTF-8")
url = data.split()[1]
print(url)
# 头文件
conn.send(b'HTTP/1.1 200 OK\r\nContent-Type: text/html;charset=utf-8\r\n\r\n')
if url == "/rihan":
ret = rihan(url)
elif url == "/oumei":
ret = oumei(url)
elif url == "/":
ret = "<h1>欢迎访问 首页</h1>"
else:
ret = "<h1>404 not found</h1>"
conn.send(ret.encode('utf-8'))
# 断开链接
conn.close()
|
11,458 | 6f5338a99d5e1311f8b1a48679e7a96900aef998 | import unittest
from backend import utils
from backend.messages import (
test_clean_positions,
test_all_equals,
test_get_segments,
)
class UtilsTest(unittest.TestCase):
def __init__(self, methodName='runTest'):
self.Play = utils
super(UtilsTest, self).__init__(methodName=methodName)
def setUp(self):
pass
def tearDown(self):
pass
def test_clean_positions(self):
# all clean positions
board = [-1, -1, -1, -1, -1, -1, -1, -1, -1]
clean_positions = self.Play.clean_positions(board)
self.assertTrue(len(clean_positions) == 9, test_clean_positions)
for index in range(9):
self.assertEqual(index+1, clean_positions[index], test_clean_positions)
# first three with no clean positions
board = ['X', 'O', 'X', -1, -1, -1, -1, -1, -1]
clean_positions = self.Play.clean_positions(board)
self.assertTrue(len(clean_positions) == 6, test_clean_positions)
# last three with no clean positions
board = [-1, -1, -1, -1, -1, -1, 'O', 'O', 'X']
clean_positions = self.Play.clean_positions(board)
self.assertTrue(len(clean_positions) == 6, test_clean_positions)
# last three with no clean positions
board = [-1, 'O', -1, 'X', -1, -1, 'O', 'O', -1]
clean_positions = self.Play.clean_positions(board)
self.assertTrue(len(clean_positions) == 5, test_clean_positions)
# only one cell no clear
board = [-1, -1, 'X', -1, -1, -1, -1, -1, -1]
clean_positions = self.Play.clean_positions(board)
self.assertTrue(len(clean_positions) == 8, test_clean_positions)
self.assertFalse(3 in clean_positions, test_clean_positions)
# only one cell no clear
board = [-1, -1, -1, -1, -1, 'X', -1, -1, -1]
clean_positions = self.Play.clean_positions(board)
self.assertTrue(len(clean_positions) == 8, test_clean_positions)
self.assertFalse(6 in clean_positions, test_clean_positions)
def test_get_segments(self):
board = [-1] * 9
segments = self.Play.get_segments(board)
self.assertEqual(len(segments), len(self.Play.combinations_wins), test_get_segments)
def test_all_equals(self):
segment = ['X', 'O', -1]
self.assertFalse(self.Play.all_equals(segment), test_all_equals)
segment = ['X', 'O', 'O']
self.assertFalse(self.Play.all_equals(segment), test_all_equals)
segment = ['O', 'O', -1]
self.assertFalse(self.Play.all_equals(segment), test_all_equals)
segment = ['X', 'X', -1]
self.assertFalse(self.Play.all_equals(segment), test_all_equals)
segment = ['X', 'O', 'X']
self.assertFalse(self.Play.all_equals(segment), test_all_equals)
segment = ['X', 'X', 'X']
self.assertTrue(self.Play.all_equals(segment), test_all_equals)
segment = ['O', 'O', 'O']
self.assertTrue(self.Play.all_equals(segment), test_all_equals)
def test_a_winner(self):
# no winner
board = [
'X', '0', -1,
-1, 'X', -1,
-1, -1, '0'
]
self.assertFalse(self.Play.a_winner(board))
# winner X
board = [
'X', '0', '0',
-1, 'X', -1,
-1, -1, 'X'
]
self.assertTrue(self.Play.a_winner(board))
# winner O
board = [
'0', 'X', 'X',
-1, '0', 'X',
-1, -1, '0'
]
self.assertTrue(self.Play.a_winner(board))
# no winner
board = [
'0', 'X', 'X',
-1, '0', '0',
-1, 'X', 'X'
]
self.assertFalse(self.Play.a_winner(board))
if __name__ == '__main__':
unittest.main()
|
11,459 | 7d95b9a59f0158c35bbb99b34dd7307431c85cef | test_input = '''
[1,1,3,1,1]
[1,1,5,1,1]
[[1],[2,3,4]]
[[1],4]
[9]
[[8,7,6]]
[[4,4],4,4]
[[4,4],4,4,4]
[7,7,7,7]
[7,7,7]
[]
[3]
[[[]]]
[[]]
[1,[2,[3,[4,[5,6,7]]]],8,9]
[1,[2,[3,[4,[5,6,0]]]],8,9]
'''
def compare_integers(left, right):
if left < right:
return -1
if left > right:
return 1
return 0
def compare_lists(left, right):
if isinstance(left, list) or isinstance(right, list):
if not isinstance(left, list):
left = [left]
elif not isinstance(right, list):
right = [right]
for i in range(len(left)):
if i == len(right):
return 1
a = left[i]
b = right[i]
test = compare_lists(a, b)
if test < 0 or test > 0:
return test
return -1 if len(left) < len(right) else 0
else:
return compare_integers(left, right)
def part1(input):
if input:
s = 0
for i, pair in enumerate(input.strip().split('\n\n')):
pair = pair.split()
left = eval(pair[0])
right = eval(pair[1])
if compare_lists(left, right) < 0:
s += i + 1
print("Part 1: {}".format(s))
def part2(input):
if input:
decoder_packets = [[[2]], [[6]]]
ordered_packets = [] + decoder_packets
for packet in [eval(line) for line in input.strip().split('\n') if line]:
found = False
for i, other in enumerate(ordered_packets):
if compare_lists(packet, other) < 0:
ordered_packets.insert(i, packet)
found = True
break
if not found:
ordered_packets.append(packet)
locations = [index + 1 for (index, packet) in enumerate(ordered_packets) if packet in decoder_packets]
print("Part 2: {}".format(locations[0] * locations[1]))
print('---TEST---')
part1(test_input)
part2(test_input)
with open('input.txt') as f:
input = f.read()
print('---INPUT---')
part1(input)
part2(input) |
11,460 | 1f7d48056abd2e2fd4b987f9973f7e6657d9b3c3 | #!/usr/bin/env python3
numbers=0
avg=0
while True:
inp=input("please give me a number or the world 'done': ")
try:
x=float(inp)
avg+=x
numbers+=1
except ValueError:
if inp=="done":
break
else:
print("incorrect value entered. please enter a number or 'done'")
try:
avg=avg/numbers
except ZeroDivisionError:
print("zero numbers entered; please try again")
else:
# executes of the try completes without exception
print("the average is %.2f" % (avg)) |
11,461 | 5e654a76c1e739506d4357c13dd45c43ceaff245 | from typing import List
'''
给定两个整数 n 和 k，返回 1 ... n 中所有可能的 k 个数的组合。
示例:
输入: n = 4, k = 2
输出:
[
[2,4],
[3,4],
[2,3],
[1,2],
[1,3],
[1,4],
]
'''
class Solution:
def combine(self, n: int, k: int) -> List[List[int]]:
self.result = []
self.tmp = []
self.dfs(1, n, k)
print(self.result)
return self.result
def dfs(self, index, n, k):
if len(self.tmp) == k:
self.result.append(self.tmp.copy())
return
for i in range(index, n + 1):
self.tmp.append(i)
self.dfs(i + 1, n, k)
self.tmp.pop()
if __name__ == '__main__':
n, k = 4, 3
s = Solution()
s.combine(n, k)
|
11,462 | 629e33b9b821ca32db722f92aa1f11d2ef333eff | # Definition for singly-linked list.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def removeNthFromEnd(self, head: Optional[ListNode], n: int) -> Optional[ListNode]:
length = 1
node = head
while node.next:
length += 1
node = node.next
if length == 1:
return None
remove_index = length - n
if remove_index == 0:
head = head.next
else:
node = head
for i in range(remove_index - 1):
node = node.next
node.next = node.next.next
return head |
11,463 | 2e524ec08d489a932cebdcb044eaa28e6058854b | # -*- coding: utf-8 -*-
__author__ = 'admin'
import urllib2
from lagou_db import LagouDb
import bs4
import requests
import re
import threading
import time
import random
# url3 = 'http://www.freeproxylists.net/zh/cn.html'
def get_cn_proxy():
url = 'http://cn-proxy.com/'
proxy = urllib2.ProxyHandler({'http': '115.29.169.182:37711'})
opener = urllib2.build_opener(proxy)
urllib2.install_opener(opener)
response = urllib2.urlopen(url)
web = response.read()
soup = bs4.BeautifulSoup(web)
proxy_list = []
table_body = soup.find_all('table', {'class': 'sortable'})[1].find('tbody')
rows = table_body.find_all('tr')
for row in rows:
rows_list = re.split('\n+', row.text.strip('\n+'))
proxy = {}
proxy['ip'] = rows_list[0] + ':' + rows_list[1]
proxy['update_time'] = rows_list[3]
proxy['type'] = 'http'
proxy_list.append(proxy)
return proxy_list
def get_getproxy(page='1'):
url = 'http://www.getproxy.jp/en/china/' + str(page)
proxy = urllib2.ProxyHandler({'http': '115.29.169.182:37711'})
opener = urllib2.build_opener(proxy)
urllib2.install_opener(opener)
response = urllib2.urlopen(url)
web = response.read()
soup = bs4.BeautifulSoup(web)
table = soup.find('table', {'class': 'mytable'}).find_all('tr')
proxy_list = []
for row in table[1:-1]:
proxy = {}
elements = row.text.strip('\n').split('\n')
proxy['ip'] = elements[0]
proxy['update_time'] = elements[-1]
proxy['type'] = elements[-2]
proxy_list.append(proxy)
return proxy_list
class ProxyCheck(threading.Thread):
def __init__(self, proxyList, valid_proxy):
threading.Thread.__init__(self)
self.proxyList = proxyList
self.timeout = 5
self.testUrl = 'http://www.baidu,com/'
self.valid_proxy = valid_proxy
def checkProxy(self):
cookies = urllib2.HTTPCookieProcessor()
# valid_proxy = []
for proxy in self.proxyList:
proxyHandler = urllib2.ProxyHandler({proxy['type']: proxy['ip']})
# proxyHandler = urllib2.ProxyHandler({'http': '115.29.169.182:37711'})
opener = urllib2.build_opener(cookies, proxyHandler)
opener.addheaders = [('User-agent', 'Mozilla/5.0 (Windows NT 6.2; WOW64; rv:22.0) Gecko/20100101 Firefox/22.0')]
t1 = time.time()
try:
req = opener.open('http://site.baidu.com/', timeout=self.timeout)
result = req.read()
timeused = time.time() - t1
pos = result.find('030173')
if pos > 1:
self.valid_proxy[proxy['ip']] = 0
# self.valid_proxy.append(proxy['ip'])
print 'get pos', proxy['ip'], time.time()
else:
print 'not pos', proxy['ip'], time.time()
continue
except Exception, e:
print 'fail', proxy['ip'], time.time()
def run(self):
self.checkProxy()
if __name__ == "__main__":
valid_proxy = []
ti = time.time()
checkThreads = []
proxy_list = get_getproxy()
# for i in range(2, 5):
# proxy_list += get_getproxy(i)
t_num = 20
for i in range(t_num):
t = ProxyCheck(proxy_list[i*len(proxy_list)/t_num:(i+1)*len(proxy_list)/t_num])
checkThreads.append(t)
for i in range(len(checkThreads)):
checkThreads[i].start()
for i in range(len(checkThreads)):
checkThreads[i].join()
print '%s thread used time %s' % (t_num, time.time()-ti)
# print proxy_list
# db = LagouDb().db()
# db['proxy'].insert_many(valid_proxy)
|
11,464 | 046383cedca3283133a4a38dd5dbd677ec65af20 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def calci(X1,X2,W,power): #function to find the output for an input
ct=0
val=0
for p in range(power+1):
tp=p
while tp>=0:
val=val+W[ct]*(X1**tp)*(X2**(p-tp))
tp=tp-1
ct=ct+1
return val
if __name__ == "__main__":
#The below lines take the csv file as input and make changes to the input dat accordingly
data=pd.read_csv("function3_2d.csv")
data=data.to_numpy()
data=np.delete(data,0,axis=1)
size=50 #Setting size of data to be trained on
x1=data[:size,0]
x2=data[:size,1]
y=data[:size,2]
power=6 #degree of the polynomial
des_mat=np.empty((size,int(((power+2)*(power+1))/2)))
hyp=1 #Hyperparameter lambda
for i in range(size):
ct=0
for p in range(power+1):
tp=p
while tp>=0:
des_mat[i][ct]=(x1[i]**tp)*(x2[i]**(p-tp)) #Constructing the design matrix
tp=tp-1
ct=ct+1
#Obtaining the W Matrix
A=np.dot(des_mat.T,des_mat)
size=A.shape[0]
for i in range(size):
for p in range(size):
if i==p:
A[i][p]+=hyp #Lambda is addded to diagonals
B=np.dot(des_mat.T,y)
W=np.linalg.solve(A,B)
#Setting X1 and X2 values for 3-D graph plotting
X1=np.linspace(-18,18,200)
X2=np.linspace(-18,18,200)
X1,X2 = np.meshgrid(X1,X2)
fx=[]
#The below for loop generates the output for different X1's and X2's and stores them in fx
for i in range(200):
tp=[]
for p in range(200):
tp.append(calci(X1[i][p],X2[i][p],W,power))
fx.append(tp)
#Plotting is done below
fig=plt.figure()
fx=np.array(fx)
ax=plt.axes(projection='3d')
ax.plot_surface(X1,X2,fx)
ax.scatter3D(x1,x2,y,color="red",label='Dataset Points')
ax.set_title('Surface plot with M=6 N=50 and $\lambda$=1')
ax.set_xlabel('x1')
ax.set_ylabel('x2')
ax.set_zlabel('y')
ax.legend(bbox_to_anchor=(1.1, 0.92), bbox_transform=ax.transAxes)
plt.savefig('M=6_N=50_lambda=1.png')
plt.show()
|
11,465 | 682bcbddae6e769505840e96b66ebf1351d80327 | from datetime import datetime
from pathlib import Path
from jinja2 import Template
from reconcile.utils.mr.base import MergeRequestBase
from reconcile.utils.mr.labels import DO_NOT_MERGE
PROJ_ROOT = (Path(__file__) / '..' / '..' / '..').resolve()
EMAIL_TEMPLATE = PROJ_ROOT / 'templates' / 'email.yml.j2'
class CreateAppInterfaceNotificator(MergeRequestBase):
name = 'create_app_interface_notificator_mr'
def __init__(self, notification):
"""
:param notification: the notification data. Example:
{
"notification_type": "Outage",
"description": "The AppSRE team is current investigating ...",
"short_description": "Outage notification",
"recipients": [
"/teams/app-sre/users/asegundo.yml"
]
}
:type notification: dict
"""
self.notification = notification
super().__init__()
self.labels = [DO_NOT_MERGE]
@property
def title(self):
return (f"[{self.name}] "
f"{self.notification['notification_type']}: "
f"{self.notification['short_description']}")
def process(self, gitlab_cli):
now = datetime.now()
ts = now.strftime("%Y%m%d%H%M%S")
short_date = now.strftime('%Y-%m-%d')
with open(EMAIL_TEMPLATE) as file_obj:
template = Template(file_obj.read(),
keep_trailing_newline=True,
trim_blocks=True)
subject = (f'[{self.notification["notification_type"]}] '
f'{self.notification["short_description"]} - '
f'{short_date}')
content = template.render(NAME=f'{self.name}-{ts}',
SUBJECT=subject,
USERS=self.notification['recipients'],
BODY=self.notification['description'])
email_path = Path('data') / 'app-interface' / 'emails' / f'{ts}.yml'
commit_message = f"[{self.name}] adding notification"
gitlab_cli.create_file(branch_name=self.branch,
file_path=str(email_path),
commit_message=commit_message,
content=content)
|
11,466 | 2878a69dbfbb810e2385d867600a6b9e1745d459 | import socket
import cv2
import numpy
import thread
import time
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') #Database to identify the faces
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml') #Database to identify the eyes
def recvall(sock, count): #cria um buffer para transmissao
buf = b''
while count:
newbuf = sock.recv(count)
if not newbuf: return None
buf += newbuf
count -= len(newbuf)
return buf
def newClient(clientsocket, addr, i):
while True:
length = recvall(clientsocket, 16) #tamanho da foto a ser recebida
stringData = recvall(clientsocket, int(length)) #stringData recebe o valor em string da foto
data = numpy.fromstring(stringData, dtype='uint8') #conversao de srting para numpy array
decimg = cv2.imdecode(data, 1) #decodificando o numpy array em imagem
checkFace = faceRecognition(decimg, i) #checando as faces no metodo faceRecognition
if checkFace == 1: #se existir uma face na foto, armazena no hd do server
cv2.imwrite('received ' + str(i) + '.jpg', decimg)
else:
print 'Face not found.'
i = i + 1
clientsocket.close() #fecha o socket de conexao
def TCP_SOCKET(TCP_IP, TCP_PORT):
print 'Launching TCP Server...'
TCP_PORT = int(TCP_PORT) #cast da porta, tem que ser int
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #abrindo o socket TCP em sock.SOCK_STREAM
s.bind((TCP_IP, TCP_PORT)) #setando o server no ip e na porta especificada
s.listen(True) #TCP server escuta a rede
print 'Server Launched!'
print 'Waiting for clients...'
while True:
conn, addr = s.accept() #aceita a conexao
print 'Connection started, from: ', addr
thread.start_new_thread(newClient, (conn, addr, i)) #inicia a thread chamando o metodo newClient
s.close()
def UDP_SOCKET(UDP_IP, UDP_PORT):
print 'Launching UDP Server...'
UDP_PORT = int(UDP_PORT)
udp = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
orig = (UDP_IP, UDP_PORT)
udp.bind(orig)
print 'Server Launched!'
print 'Waiting for clients...'
i = 0
buf = 1024
while True:
bytes = 0
f = open('preImg' + str(i) + '.jpg', "wb")
startstamp = time.time()
data, addr = udp.recvfrom(buf)
print 'Connection started, from: ', addr
while (data):
f.write(data)
udp.settimeout(20)
endstamp = time.time()
bytes += len(data)
data, addr = udp.recvfrom(buf)
if data == "end image":
rate = (bytes / (endstamp - startstamp) * 8) / 1000
print "freq (Hz) = 5 " "| bit rate (kbps) = ", int(rate)
time.sleep(20)
break
f.close()
decimg = cv2.imread('preImg' + str(i) + '.jpg')
checkFace = faceRecognition(decimg, i)
if checkFace == 1: # se existir uma face na foto, armazena no hd do server
# cv2.imwrite('C:\\Users\\Frederico\\Desktop\\RedesTp\\Face\\received ' + str(i) + '.jpg', decimg)
print 'Face found.'
cv2.imwrite('received ' + str(i) + '.jpg', decimg)
else:
# cv2.imwrite('C:\\Users\\Frederico\\Desktop\\RedesTp\\NoFace\\received ' + str(i) + '.jpg', decimg)
print 'Face not found.'
udp.close()
def faceRecognition(image, i):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
if len(faces) >= 1:
print 'Face found on image: ', i
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = image[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
return 1
else:
print 'Face not found on image: ', i
return 0
#TCP_IP = '192.168.100.103'
#TCP_IP = '127.0.0.1'
#TCP_PORT = 4040
IP = raw_input('Server IP Address: ')
PORT = raw_input('Server Port: ')
i = 0
print 'Choose server protocol: '
print '1 - TCP Server;'
print '2 - UDP Server;'
serverType = raw_input('Option: ')
if serverType == '1':
print 'TCP Server Init...'
TCP_SOCKET(IP, PORT)
elif serverType == '2':
print 'UDP Server Init...'
UDP_SOCKET(IP, PORT)
else:
print 'Invalid Option.'
|
11,467 | 89b60790ebbbb7e9300fec7659e183854022eb75 | #!/usr/bin/env python3
import fileinput
import random
def is_tidy(n):
for i, c in enumerate(str(n)[:-1]):
if c > str(n)[i+1]:
return False
return True
def solve(number):
digits = [int(c) for c in str(number)]
# find first non-tidy digit
for first_invalid, d in enumerate(digits):
if first_invalid+1 < len(digits) and d > digits[first_invalid+1]:
break
else:
# input was already tidy
return number
# find last digit that can be tidily decremented
decr_index = first_invalid
while decr_index > 0:
if digits[decr_index] > digits[decr_index-1]:
digits[decr_index] = max(digits[decr_index-1], digits[decr_index]-1)
for i in range(decr_index+1, len(digits)):
digits[i] = 9
return int(''.join(str(d) for d in digits))
decr_index -= 1
# decrement first digit and set rest to 9s
digits = [digits[0]-1] + [9] * (len(digits)-1)
return int(''.join(str(d) for d in digits))
def test_samples():
assert solve(132) == 129
assert solve(1000) == 999
assert solve(7) == 7
assert solve(111111111111111110) == 99999999999999999
def test_corners():
assert solve(0) == 0
assert solve(123) == 123
assert solve(987) == 899
def test_solve_annoying():
assert solve(11111222244444222200002229999000) == 11111222239999999999999999999999
def test_random():
for _ in range(100):
ex = random.randint(0, 100000000)
result = solve(ex)
assert result <= ex
assert is_tidy(result)
def main():
n = int(input())
for i, line in enumerate(fileinput.input()):
highest = solve(int(line))
assert is_tidy(highest), '{} not tidy'.format(highest)
assert highest <= int(line)
print('Case #{}: {}'.format(i+1, highest))
assert i == n-1, 'read wrong number of cases'
if __name__ == '__main__':
main()
|
11,468 | 0ba6a3fbac7ed4260848d26e7fb2f83056460a63 | '''
Call two arms equally strong if the heaviest weights they each are able to lift are equal.
Call two people equally strong if their strongest arms are equally strong (the strongest arm can be both the right and the left), and so are their weakest arms.
Given your and your friend's arms' lifting capabilities find out if you two are equally strong.
Example
• For yourLeft = 10, yourRight = 15, friendsLeft = 15, and friendsRight = 10, the output should be
areEquallyStrong(yourLeft, yourRight, friendsLeft, friendsRight) = true;
• For yourLeft = 15, yourRight = 10, friendsLeft = 15, and friendsRight = 10, the output should be
areEquallyStrong(yourLeft, yourRight, friendsLeft, friendsRight) = true;
• For yourLeft = 15, yourRight = 10, friendsLeft = 15, and friendsRight = 9, the output should be
areEquallyStrong(yourLeft, yourRight, friendsLeft, friendsRight) = false.
'''
def areEquallyStrong(yourLeft, yourRight, friendsLeft, friendsRight):
if(yourLeft == friendsLeft and yourRight == friendsRight):
return True
elif(yourLeft == friendsRight and yourRight == friendsLeft):
return True
else:
return False
if __name__ == "__main__":
yourLeft = 15
yourRight = 10
friendsLeft = 15
friendsRight = 9
output = areEquallyStrong(yourLeft, yourRight, friendsLeft, friendsRight)
print(output)
|
11,469 | f6cb8f33b16f29167c3160883c42581c1dab0534 | # Generated by Django 2.0.3 on 2018-11-17 18:58
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Size',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=64)),
],
),
migrations.CreateModel(
name='TypeOfPizza',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=64)),
],
),
]
|
11,470 | 53d331b0d1ab767e0c697dd06627598f57e3b71d | # This filter deforms the terrain in the selection box by creating a gash in the surface of the world
#
# abrightmoore@yahoo.com.au
# http://brightmoore.net
#
from math import sqrt, tan, sin, cos, pi, ceil, floor, acos, atan, asin, degrees, radians, log, atan2
from random import *
from numpy import *
inputs = (
("GASH abrightmoore@yahoo.com.au http://brightmoore.net", "label"),
)
# Utility methods
def setBlockIfEmpty(level, (block, data), x, y, z):
tempBlock = level.blockAt(x,y,z)
if tempBlock == 0:
setBlock(level, (block, data), x, y, z)
def setBlock(level, (block, data), x, y, z):
level.setBlockAt(x, y, z, block)
level.setBlockDataAt(x, y, z, data)
def setBlockToGround(level, (block, data), x, y, z, ymin):
for iterY in xrange(ymin, y):
setBlockIfEmpty(level, (block, data), x, iterY, z)
def getBoxSize(box):
return (box.maxx - box.minx, box.maxy - box.miny, box.maxz - box.minz)
def fix(angle):
while angle > pi:
angle = angle - 2 * pi
while angle < -pi:
angle = angle + 2 * pi
return angle
def Gash(level, box, options):
# CONSTANTS AND GLOBAL VARIABLES
method = "Gash"
(width, height, depth) = getBoxSize(box)
centreWidth = width / 2
centreHeight = height / 2
centreDepth = depth / 2
AIR = (0,0)
# END CONSTANTS
if depth > width: # The gash runs the length of the selection box, whichever the orientation
angleStart = randint(0,100)
angleEnd = randint(100,200)
angleStepSizeZ = (pi/50*(angleEnd-angleStart))/depth # describes the arc of the gash
gashAngleStart = pi/50*angleStart
gashStepSize = pi/depth # one full revolution
for iterZ in xrange(0, depth):
iterX = (int)(centreWidth*sin(gashAngleStart+angleStepSizeZ * iterZ))
gashWidth = (int)(centreWidth*sin(gashStepSize*iterZ)/2)
for iterY in xrange(0, height):
heightBand = 2 - (int)((iterY / (height / 2)))
for sizeX in xrange(-gashWidth+heightBand,gashWidth-heightBand):
posX = (float)(iterX+sizeX)
gashWidthMultiplier = (float)(iterY/height)
setBlock(level,
AIR,
box.minx+centreWidth+(int)(posX),
box.miny+iterY,
box.minz+iterZ
)
else:
angleStart = randint(0,100)
angleEnd = randint(100,200)
angleStepSizeX = (pi/50*(angleEnd-angleStart))/width # describes the arc of the gash
gashAngleStart = pi/50*angleStart
gashStepSize = pi/width # one full revolution
for iterX in xrange(0, width):
iterZ = (int)(centreDepth*sin(gashAngleStart+angleStepSizeX * iterX))
gashWidth = (int)(centreDepth*sin(gashStepSize*iterX)/2)
for iterY in xrange(0, height):
heightBand = 2 - (int)((iterY / (height / 2)))
for sizeZ in xrange(-gashWidth+heightBand,gashWidth-heightBand):
posZ = (float)(iterZ+sizeZ)
setBlock(level,
AIR,
box.minx+iterX,
box.miny+iterY,
box.minz+centreDepth+(int)(posZ)
)
def perform(level, box, options):
''' Feedback to abrightmoore@yahoo.com.au '''
Gash(level, box, options)
level.markDirtyBox(box) |
11,471 | 03ffe600fecee8f6501d636bf659d32b81ef390c | #!/usr/bin/python3
import argparse
getHex = __import__('10').getHex
getSparseHash = __import__('10').getSparseHash
densifyHash = __import__('10').densifyHash
def getArguments():
parser = argparse.ArgumentParser(description='Advent of code')
parser.add_argument('input', metavar='file', type=argparse.FileType('r'))
parser.add_argument('-s','--size', type=int, default=256)
parser.add_argument('-r','--rounds', type=int, default=64)
return parser.parse_args()
def hexToBin(v):
assert(type(v) is str)
num_of_bits = 4 * len(v)
return str(bin(int(v, 16))[2:].zfill(num_of_bits))
def fillFieldWithColor(rows,x,y, color):
if(x < 0): return
if(y < 0): return
if(y >= len(rows)): return
if(x >= len(rows[0])): return
if rows[y][x] != '#': return
rows[y][x] = color
fillFieldWithColor(rows, x-1, y, color)
fillFieldWithColor(rows, x+1, y, color)
fillFieldWithColor(rows, x, y-1, color)
fillFieldWithColor(rows, x, y+1, color)
def colorizeField(rows):
maxNumberOfColors = 0
for i,r in enumerate(rows):
for j,v in enumerate(r):
if v == '#':
maxNumberOfColors += 1
fillFieldWithColor(rows,j,i,maxNumberOfColors)
return maxNumberOfColors
if __name__ == '__main__':
args = getArguments()
lines = [ x.strip() for x in args.input.readlines() ]
for line in lines:
rows = []
for i in range(128):
l = line + "-" + str(i)
rows.append(list(hexToBin(getHex(densifyHash(getSparseHash(args.size, args.rounds, l)))).replace('1','#').replace('0','.')))
numColors = colorizeField(rows)
for x in rows:
print(x)
print("There are", numColors, "fields")
|
11,472 | 431e9169b03c548e5ce886e9505e2ef446306fbc | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2017/10/9 下午7:13
# @Author : Hou Rong
# @Site :
# @File : test_mongo_iter.py
# @Software: PyCharm
import pymongo
import time
client = pymongo.MongoClient(host='10.10.231.105')
collections = client['Test']['test2']
cursor = collections.find({})
if __name__ == '__main__':
while True:
_count = 2000
while _count:
_count -= 1
try:
line = cursor.next()
except StopIteration:
break
print(time.time())
|
11,473 | 1d04e33209cb8aec365e16ba2f6ad5f3fc9fd94f | """Command-line tool functionality.
Parses arguments and determines which tool should be called.
"""
import sys
import argparse
from abc import ABC, abstractmethod
from typing import Dict
import importlib
# New tools should be added to this list.
TOOL_NAME_LIST = ['remove-background']
class AbstractCLI(ABC):
"""Abstract class for cellbender command-line interface tools.
Note:
Tools are called from the command line using
$ cellbender TOOL-NAME --optional_arg1 optional_val1 ...
"""
@abstractmethod
def get_name(self) -> str:
"""Return the command-line name of the tool."""
pass
@abstractmethod
def validate_args(self, parser: argparse):
"""Do tool-specific argument validation, returning args."""
pass
@abstractmethod
def run(self, args):
"""Run the tool using the parsed arguments."""
pass
def generate_cli_dictionary() -> Dict[str, AbstractCLI]:
# Add the tool-specific arguments using sub-parsers.
cli_dict = dict(keys=TOOL_NAME_LIST)
for tool_name in TOOL_NAME_LIST:
# Note: tool name contains a dash, while folder name uses an underscore.
# Generate the name of the module that contains the tool.
module_cli_str_list = ["cellbender", tool_name.replace("-", "_"), "cli"]
# Import the module.
module_cli = importlib.import_module('.'.join(module_cli_str_list))
# Note: the module must have a file named cli.py in the main
# directory, containing a class named CLI, which implements AbstractCLI.
cli_dict[tool_name] = module_cli.CLI()
return cli_dict
def get_populated_argparser() -> argparse.ArgumentParser:
# Set up argument parser.
parser = argparse.ArgumentParser(
prog="cellbender",
description="CellBender is a software package for eliminating technical artifacts from high-throughput "
"single-cell RNA sequencing (scRNA-seq) data.")
# Declare the existence of sub-parsers.
subparsers = parser.add_subparsers(
title="sub-commands",
description="valid cellbender commands",
dest="tool")
for tool_name in TOOL_NAME_LIST:
module_argparse_str_list = ["cellbender", tool_name.replace("-", "_"), "argparse"]
module_argparse = importlib.import_module('.'.join(module_argparse_str_list))
subparsers = module_argparse.add_subparser_args(subparsers)
return parser
def main():
"""Parse command-line arguments and run specified tool.
Note: Does not take explicit input arguments, but uses sys.argv inputs
from the command line.
"""
parser = get_populated_argparser()
cli_dict = generate_cli_dictionary()
# Parse arguments.
if len(sys.argv) > 1:
args = parser.parse_args(sys.argv[1:])
# Validate arguments.
args = cli_dict[args.tool].validate_args(args)
# Run the tool.
cli_dict[args.tool].run(args)
else:
parser.print_help()
|
11,474 | b86bec43edbf9ddc9befdab3eab5d46c49f77ddc | # -*- coding: utf-8 -*-
'''
Physical Constants
===============================================
'''
from sqlalchemy import String, Float, Column
from exa.relational.base import Base
class Constant(Base):
'''
Physical constants and their values in SI units.
>>> Eh = Constant['Eh']
>>> Eh.value
4.35974434e-18
'''
symbol = Column(String, nullable=False)
value = Column(Float, nullable=False)
def __repr__(self):
return 'Constant({0}: {1})'.format(self.symbol, self.value)
|
11,475 | b3d418500e70bd41767011a6aa051b8c7b070a66 | 𛀒𮟯👼䂒𔔻𛊗ﴥ🚼𦯙𠡾𣚏狠웃𘂄焭ѫ諛𧙡섮𒂋ꔌ䭿ᇤ蚢媩ﴭ𘅈𫃨㒰𥴔𫨈𢙚畃ꖉ𪖼𦤍ഁ䫬🖇𝖪𠽺𐼽𭊏𮂎𠒢兆𢡇𤘮倪𤭜𐔥𧯽俙𪝥𦺗⚤𬬺𮑕偧㋑퍨嗂𖢞𦍚𪲄𮗗Ꞽ溹쮍綘𣑺숉𣡉蜴𤵷𡛒㱜お⫸焖𘇜㷮𘪸𩞥𮭞뛒憨𦋝ڑﰥ𦅐ﶙ𘥆䌉𡠵𧑸飩𬅢𣢋ኚ𘇇𢯤𩴵퇽𮬹𝌋𩇵䁦𫙰蚊梪嶘𪘦ᄏ𐌅韬𘍚𭺏궰𞡊窅𨢨𠑝𖥓𦃥𣵹🅞𘚲ꎁ𥇃𢛎𐎑𗏀𧳞𔗀𭦀䈚𣲒𤳨𤌘烶騐𭨜ᰙ⧺𫡭𧥪즺𫤈𧶷𐐣𧭠늇𫺋𣊇𖺆礑ࣹ퇆𗻕𮤴릸Ł𝠿㡐𭪒ꅝ㡂𢐗𫸞𧜓𗦅𮁓𢄞𧡮𬵁筑𧅛𠨏番뫳𪖧鍓𬥸𥏋𐙪次𡝍𠻦𠶡䬿𥇇𦨥⌐갾纬𗜴綐ေ㥯𫆩𐔰𣡏𐛈𨵾𢎎𪁡𧲲𥣤𢺊𗸠𤊦뿮헒꣹𨅈ẅ╁𢀤ᴺ幀傦ݑ鰴𝦸詏🡫𤉗𐓌𨵤𘁬𔔢띢🦶𭋐惧𣫻𪖧𗾫𗧭𫢈𣿮👳𝈩𨎳𨃏𣲯𖤫𪒐𮞸𐪝𣍄𡚃⓫驪䢸𢏓ያ글𡋸𦪆ﵺ⿱𦯥𡀺𐰍𠷝ⶻĘ᪀𝍸孤첖𩘹吴𦍚𤠠𠑊𮉙𬡍𣃛涑𫤐𥈼䯜🥿𭋼❯䱧䋎𣤚෭𓈎𣍰韽ﳓᓟ𥩖ꋆ䙟⣂𝢄䴇ꌬ𥝄⸚琧𐑀𢴃𫖛끭㦯횊𢲩劆𓁹𠛱𝦴𗃀撅뒪ⰲ𠄮𧶚𝢗渧𫷁砃𔘣辋𗖭⭎偂𫆘萵𪩗褐𗧁튲𑻲芌ᠽ䃲𡪡𨆎뿓𨒧𣠮홗𦪔𘉽𦬩𪩟■䝶⤧Ⲃ綗♮ീ𮘳𗧣磄𫷪表𦟠✫𑈌쮶𗴹𑦽𩡋䂞𤄮𠊌櫠𮄔𫂋𐓺荴𣔖Ꮺ𦔧𦽞𤀅㦋𥨔쓕㗘𩒉𘥻𥭘䦕𩻲𢵺𥭕𨆽𬀊𦝵𑁂𐜕耉󠆺𮧬𣧝𬆳𧔯𣻿𝁐敥𭪋𥵁𪆜𝄹샏𒂸𩄿🁌𨚆𗢍䤝𩆞𓍩𧶱𪈇𦔺絫枹ꊰ鍡𒈸𤏙𡔒𥍩膴𘑘⃒𧕏𨋔𫦽𤩿𛰑𗡲ဌ𠉃넔𣟼홒𧻎𝘋𩒎䫁𬪳𑘔🂷𗤰𩼉𖭶𩷶𩆰ﳪ𭇽⠦ॳ𩘔鱰𐋸濂𮠨𨝎ﶫ뛿꽤𪛉🜷㸁𢘦ࡔາ놞𭡓懷𡯬🂩皡㵶𐠲䷓禢𗊜⽢闟𘃏𤦄𨶬𡪇𠪣𪱠𪂨𗳐ꊉ㝛𧙱㓿𣖌𦶒ך𡂡魓𭣠𢣩𭓄𭎯ኘ𫇇𥌠𫩒诙𨄀𮥏檃餀𫅢ꍐ≭𔙅𡸬蒵𤺏️𠭝߰ᠪ𥊂𗱺𡫌𧮥Ѿ‡紮似ោ眖𢷀𭏠嗁𩰘𮗨𤿧𤔶𫙐碩贸𐨭䏋𗮣𥞚Ч𗵸𤈝𨀟𥰲뀲𨑡𧪋𬪱𭝃𢚐䱀𫙾𭾩𡜜焔躂𫏱𤀤ᩖ𑋐𐍧𘥕𠵕綅𐃍𘃻悘䨃𬃮𥝉닃斫𖢱𬅓ଗ桞奱𘊵𨝊𮢿𥡖贐𐎑ᰥ㡏𡮩𬱥黺띖𧭧࣪𢝛Ẁ𦵧𬻜짍𢒫𠛰뫦벱𣹵𤞒𦝦𬭘답牃傴ȵ𭑹හტ﮹贑𝞍𢲻𠍌𗑣鳥𢲞ꀚ驦𐢋𖧜𠼳퉛𐭦蘥⟸𘃇𦠻𩒢𢉙𥗯𠺸𩟼⇂荚𬢺𧙁𠵿𫯋𥮪𬞊𧩬鯡꿌莝𥓩鵈𫟢嶚𦿆𠳾𨒽궫𧐜ᭈ𡃁푥𤙜𪬳㛔풩𣯐⧜𣖠ፌ𡲸㷮𢶾档𛄇𦥞庆𘓰𢀖𨥇𢟍赓𥠾ﳜ蒿𨸂縼𑋞𗁗繥⍃Ѷ𠦩𪹿唐𫔭𫰻㲘𦨇䒰🖊𮑀⎄𥁑𪆴𒉞𭟼𣃌違饂萊𡄃🥺𠦂𪷼⬎𠼊⥩𫓴𤾥𓂿🥋ⰵ𧣟낞𧝙𪞑뱂ⴺ𨂓𬶄罄跮𛀽𗡣𘨌𢜸𦮳𗥳䯧Ꜫʈ𭻍𮂋𡙷𡋎亅𬫸𐲊𡗨㺔𢜘𑅴㙯崸𡦦𓇅嵁蛬𮉅𬎸⦨𢊾𭳚춄𥾷𒇅천ﻯ𠄠𦳧𨟾棌㎰𨜘𡩌𡿕ㅭ𡶛⇣𣥒𢁙𢃬簐퐎⌢𑶁𭳛𠍰⨽𠰮𤂄푛𦃡揗𡳽𣁳𩢽⁃𮮍赤ﴅ룪쵺𧕠𥅙𨟭𥠃𘙏〄𗵧𬜌뮑𐌌𧙃篶𬼘뭴𫮴𮋡𫧥⪂⾉𦍔뉅𗑬崶𢪼𡾃䋯ꝟ𤵐𨥲𬭱᮪Ꞡꈡ눈𭙮𨟕𤜧𢸅𡴏𢂽𭵥𗣾茝𪂱䯪𣇦츙𥂥㤂堻𒄽𤟨𪹱༌𗾃빝𥂘𖦑樋𬌖ᦸ𠺼⩮휚𡆦𦍻뵃첺𝂐𗏼𧳺𤹎𨅺龜🛳𦒎嬧䰵𗗰𘐘ﰻ𤀊𣻢𮐮𭺖劍𭆨ᗸ𢄈𨬲䆖૽𧻼襃柬ᾣ쪻𨫁葻𡑵𤽿𦍿𑒇ꃸ𛋤𨦿愍𪮐𤞝𪫉㭿𥤗툖ꏺ侚𥎷氀𤒨˟𡕒奤𪬨𝤗🟡𤔶ಅ붬𦭤峗𗐍𡧈貈𢒊𦨝𦭑ꋸ佳䙓𬩎𧰔⿻𪦅𠚮떢嬽卍낺礩𬎆𠗕䅋𨌯𪼆𬡙ᚇ𤪴䎜झ🝮禿𤨇喹𤉉젾𩷫𥍬吺𦀢𫄾퀇ᡦ텚쥮풟牟𪍵𣌓𘡰큃楟𥷒𓎮𡮥𫰿𣣴𡆦𞢝𩗶𭫈𦯙𑖏𢕎আ🧪𬔧ᤌጒ𠥋唂戚ᒉ꺋艈𘍀𫝵𑆽旙鼼𗞑츣𬜊처鰧핼体𘗺𦤡𩞖𦀖𪽯𨸯ᄉ𧻟𘪁𬇦뚊𘝊𫋨𮞅𢤁㈬𭳴롺𭒺்𦦏馆㸟𣄟𣶀炿㥳𤴌궝𫩓𢚫搜𠴺𝑎ᓴ뮌𪒝竫𧖽ヶ瓳𭣃𣲧𑇥𪠃𡏹꜉𭨞𧟦𐍢𫊏悷𦄠𥒓𪉁窸𡣸𧏋𡕤𤆈𢪎𩩇𫣁𭳢𥖡𪦝𩸀𡹔胋𮆃𬛡𤴫𫀂𘛁🚂ܠ軀𪘺𧃫碭𥷩𤺕䱀𦵢𫶴黂𦳆𑈜🆟𠇞𮔧鞐𡝩겥뮗𬪁읢쟳䭔ầﺿ읾𥅂Ⴏ⣌廭𩰭뾕桯⺽𪗁𩕞𢴨𬠹𗺙ᓊ𥎒𤊀𦭴༽Ｃ𒌩𣬕𢄗𦌍慈𗠒𪌐閼𤳚𨀕욹𤨔💐𨳏𩺡祐↡𨌌𬦨韂߅𧄎⤯𘊩𮌳𐙫𦨱𘗦Ώ矔𡷃𗏦뗳𪧻𭥈𩊉𩈯鄚𩉫𦖫𬰔ꡙ𪹢𬕽蓂︶ꮐ봎𗀔𑲠䤂𥃔𤩬𡟓🤯⁚𡘔镛𑧗憙𠍗ဂ𥊸𣯄롙䲫詥㐥𬮃𥒷뮃𢋖𘇢🏗𫌶ﭯ桒蒩樏㍮𬖖𘧁홆𬮙𮪀혹𥠷𢼘襈𭣦𢳳𦃒𤓚폲𠊈顉𡛚齰𪴷𮋭𬑄뛻𮝸퀪󠅗鰈𩾐⽠𢧮⺬𬘆𣘏𭼱褦𬖢𭃽톐㱽𡎟䬞𪘋𣑋𣃇𠆿🖈𨗼𒓚𡐃𨍖𞠏帊𩓩𣄢𮩗𮞊𣇚왈𩯱済羽ᄔ𗺿㵢𛁓𤴙憥𬦼𡖷糬𠵏ਤ𡧐𤄛𢲖粜뿨𖩤𫪢𠠛𪬐ȟ朚崷⯌䭡𠤣饞ﴼ𭟋贼萠欙𣄈ዱ𠝄쿩诣탭𥕝𭙉𑩤鵕☢𦌇𥛇쮾𐚷虦𠛜ㅇ𪞥𧚊芳⾸έ𢀊￩🏭嶝扑𦙎🅃龧𬛭𩕁𭡵𡚎𗰫𠃝쟰㡃췒🨕𠊊瀋𥸴𠝯𫬥濱𫱎𦵝𦛲𮛘𬒾𫦴𞤯𣒔𢖞𣽗ꗒ𮘃𠾉䦇𢾧𥵩뜹𡯇讞𗥿🨛𧨄𥂡𞢠𣍍𪠧𢹥唄𑘘𠔣𡄈𠭺𢉈寮稻熄𡛃䍯𫏪𐨹膣𠃸㨆㩝Ⲇ𨅈繢𨂍땥𬢯桛𑱸䄞썟𡼐㞪떓𩈼𩬈𣓀𦝦𭮾𑐾𢑽𣵶Ԕ建ϣ𑊣𧔡噟婞揤𢗽៌𝙄뼸𩅁䡗𭴂ꞯ𬼃𞴸䬵𢗉𧿉𘥰𪜌𡢰ꏠ𩲯ቫ鹘𫘟𬮸锼𫺵𭃔椕𦄶⎄𡚿ꄢ𥃚𤡽𣅍𩄡鮲𡍃𢡠𩟈𮝻琟㴰풦𗀡𥅓筇𠜏𩑼𬞓𖤻𦊟껐켔끑갦𫞫𐠫𠪘佗핊浟ꂈ𩰚𬎠䔹꺊𠯭𐆈𮥰寑𪹪菂ꧏ🝝ప𪷠⮊ʠƿ𓍥𐍀𐧯𗦐ᕍꯗ𑌕𗃫𭺪ꎇ탳ℾᑆ𠾿𠏥𣡭𗵹洊𢔶𨚎𩆳ݢ𪲫𥥀궠ᢪ𤈙𭲣ၮ𬵥𬱧𗥃𡉁矀𐅠뙇𫚆𢉿꙾芰𫰕𑴑𓄚𘪺𤲈𝛕𠡑𪻔᧐𝘵𤵱ᘱ𮉤笆𩄽𫿧䯶섥𪝄𗴸𬟢𝓬𝗜𘑯羥䙤ɶ𬰂嵈𗓒펨湶㚺덻뎺𨰦︈컜𩖝𢢦ꘫ𢀾𠽑㇕⬶𑙃𝖜ꄴ𫩰𓇨𧓍鱪𣭍ﴬꨰ垇쏂𠉃쾏ì𗌰𬸾𥧠𝜯𣿹𛁤읮𥈏𖬝𩋖产𩷝钹뽷Ȉ𢽧𗯋𧩮𭐩𥩩霺熍𔑝롢𗈯匽䒩䊄𪲔醃臛𡈹뭟汍𠢓𭪃藠𬠺鿩灪𡀂𘢴𗥳𨟕𗛥𪕛𘘸𬼱𣧣𪦘𒄠𧲟𡭧𘜄𞄪𤢿淛𫇩𘁉𩥸𡌐𗡯𬄄𢌿🆘𪑝텠ᑣ𠗤𨍯𩻐🚐𢄹𩞵菎級㚷ԙ𨙓诬𡇋𣪂鷎𦉙ɿ🅟袷뿐㕾𦑨䮤𣔻㞼䕳ۅ𠙍𣓬㴚㜚𨠒薝𣧫摮𨭕ᨱ𩨫𨪕艰檜往⃚撃斝𤣏𤳵𑀀𡶪𢱖鈿蟰ᨛ𦇹쭡𩧄🪕挐쵿촨𣢩𥶿𡅍𐚰𢚄𐎊䣒𫁖𫢠㱪𠃘𬾴𡴼䌭ꠠ⭛𦉋𩷓𢬄𨛉㮒𩋀𤏅𠝗𐀪𥥰걮𤢖𪂬𢱠욎𤡆𗿂릈𤖚㑎頃⟒횣𤸄𬁈䰐幝𧲶𪊢柎韂냷𧸉덀𖣈𤔷𬉍䦺𩑤𬻩𝆲𢠕𡁠𭱌䗒뙡𢺥뱽짏𘝼䉎𢜭냨𠪒ﶒ⃚𢚓𪲆𡒓𣘛𣔠𪞀𝒔𣼰𪾽ꢷ𪖤𮭜𥌘ߗ䡗ﴡ𮔓𮧓🙧㮬趫笥𨦱🙴농鮻ಒ𘒩🧸🅎𫮂𘛆𣪗鍝𭝁𑆉𩀽ࢷ𛄍쓽瘞𒌇𠽗󠅺㭂𘑯𛁜涕𤽄튋䪣卡淜𧅾𤻑꣨𝨹𬼱杕𣴪ᒼꈸ⪌𨹨𗛸𢩛𖽰㝅ﶱ顟𪖥偷뇣揁𠋎曁𪬶죠𮈜饃頜𢗜爃😒苐𣷆𗲴㞋劍脐𤝰۸𢽊𩹿쨸𪝢𧕫ᢅ𬵵𬍳𣥖𘒒𩆞𥈺𓌝𤱪銦𤺼𩊏쒚켕Ω囻𛲕𢮤ᗢ测𮋂𭵍𣙁뽾𮙨𣘻𘪁뙙𡏆𪶫H𭃋📳𧻰磾𤠱𩱍𨲳𗞲𡛫𨆽𡤭𧵓撇끜𦅺Ḫ𫨯𪇎𡧠椺𩂇ᙱ𒂎﹁𢫡𗤎둧𡏷𣩡𮣖親奨ꍡ䉕ᴹ𦹆𫕽𤵿𞤅푨𑱩㨩폥룡𠋼療𗃝䱵𣰃𛰜𨾤찅ぁ洩⠤䏽Ꞃ𮕡潳튃𠦦𫨝彠𠓄㩀Ể𞸔𗧿⟝𫴊𞥘𦎝🧁𐡏𨕕𦅻𦉰䫛좧𭥚𐛺ޛ𪒚༫何ꆀ𪄉𛊩𧍺岢⩰𗆇篻턽𡺌𫺟𨩔痐⣫𪲺𝛈𗬖ꇙ𣤓멹𧩐𝀾𨢒𗜋𣛐執饞ള𠎩𝓑롨𨥓𞋀𬵨𠰸𬠆〠𢧛瘻猉🏵⾩𠦻㓀隄𡘤𥩈𩠖﨧𬝂𥼆𡙄䔓𦉣𮛗𦊍𤭚𦿐ﶍ𔒀𫿴𧣼𝔲𫨿𭷕𧜝𭠶𦕰뜰𤵒屜𩊃𢡸𩼂𥂹箫诓𬫯𭺔쫠𧘍嗇𦬈𝚭ᛪ𬴛퇧瀗ꌀ𝘦𦦵𨎜⍍𑪐𦰿𧜂𗿽𦕴芽楤𥎤𢃊𣛎𐬎᭯ꋪ𢓭𗃐杓첔𣓁ໞ뉐🚟샋ꦴ閶𣋌皢姩𢩼𧢆ே𡻏侼𧳙𘢞ѽ𭋿爋鸨煛젽𧁶𐼢𭄉𤜜鈪懪儯쏂𑲒띴𘜔𡿜暳𠓯𩨸𑲁𑌵𡛱楙𥇳𘎃𬺊䆏𩱿𒇼蕣𬝡ゝ櫛剦썢𪅞𩦅𤥡鲊𢆱逊𝀥𫬌ꫬ㿘𣼉浴𘅖𤡼𦼸퉶퍬겍𡾽ᝦ𠄈䮢뢐莹𮣔𣇎ꦿﭗ𫪫䥰첞𣔄𮮪𨭕숊𮤱𪦨ゲ祮𧐈谪𗘔𠟭𮒋𛆆𤀻ﳕ☲，𔗅𮯒𒀢𬞞娲𮋺蘬繧𣂠𠽤ꒊ𥷌ꥑꪑ𤵠𗮧꾜쌨媠𘆃𬍗𨗍𬤾𑰨𮦊𭂮腥窞𢶇𭏎𗹟簼쾎𗤻轳𪛀𪻧≀𣣡轜긋𭞩𐐰𪽛𐹹鱀𢅴픨곧𝑪𨶵𒉪𤆮𤈭䗧𢯽𠍺𤞘擄𝢉𢘙竽𢚪땧𗹖줍栾𧭪𗋦𤚥곷彔𩒅轌螳𭫀枈𩰯𭞇ದ𤩲𛋃❚𦆥芋𫨫鰧𢪒踁隱𗚚𬦾ቘ𥺺㝊㣁🃚𭗣𪝪ꥪ𥨼䤯ꟿ꓄𐲰𥏁𖡊𤾞𩎀艸𤲹𩜏𨚕俖쬘𩵇≫뽹溊𠝮墌𭰴玛ﻞᣖ䵀𝛆𘗸锺ᛌ猀𐎾𐀬肏𭼛牪𨺞𡤦𠖫𤚩𝪭𢍊𗰛ା𤢑𝑾壢愆𠖩𘗤欔𫧡𤉕墫𐪄𐼻⭼ଠ𮑊𥑝𝌑𪶩렩瀯𠖆𤮃𨤄𪈻ᕰ𩢦𦛆紙職𡎴𐭯ᙼ䓴⪏𦥲꾝𢩉ﳓ䉃𮂕𐰦苑𘐶𤏠𧪐𑐁𩛋㡝ﺦ𠵘𫍁𧉣蛂뼤𠅦豃শ𓈹𫨫𮔔𨼩𫬽墺𬲄𭭉𩥛픿ᷝꖩ닋𑿝䕅𘖗ⴋ礀䟿𣇊샡켽ᆀ𡼭Ω𤑮𩪅𪪁𩒁괘𨣈𗁈🃖𑚨𧊟𫾴𦉩岋𛃅닊𩥈𫢵𭱹㐵𦋵檧뢣쮅迬霳ꋶ⯠묞𮌛𭬃淤𡇌婥㪮𥙪𪕜옶𘤃𠫺餀𞠓𡎯䥃▬榌𮖐𬐕𦐓𬗝䎅铀肯𢧮𮜥𓎭𧱲솩ᙛ𘨔省𘓚𗱡𢫿ﮞ銅𑀘𮜴ꓟﳓ醴𥡖𒋖캳蹵𧪖䜽𬯱𧣷𩃤걭𢵗腬𥿵𥻓쇰𣖆⦪렋Û𓇓䂵𬚡蕱𤙘Ʃ𫑤𫔕𭇈𨮃ﺧ𦁤瓰丷踏ꇆ꽽ᗂℙ技𡎗𢹓𡢂𘘯𒁎缝쀩깨裸𪚼ᨩ䉁織🕘𪶦𢧢斋𩝐긧𐼺𢧦Ⳟ𤬂𣝹뮛𠒫𣹭𠸯檩쒨逎̄𫜋𫅰𮆴𧦿ﻕ𣾲屬틍땃⭤𡋣𨳸ܪ𪚻㪽歗🟧𭛣ヹ𣌈鴿侺繗顤𓄋헭왉𪀾𤗨𬦭𣅫𢀗鞋𠳰毄겠𢾈ᇧ𬡲𧩻𨓂배𢰈𨐫𫖹𨀭𪈀𮜛𩽯쌶𑚠𡡅𩊁🨌𪂋参夨𣘹𑆕𮓠螔≁𧆓𪱆𪊂玡𧏊𑣁箼䤴𠺇𩹳𠳷𘪜𘧥ㄪ穜𠣟ல𞴯𡂛𢈞𫈏厌챻舂𓏦𤬶𫠵𩃕嘒𣹍껈𥊻⋷⇵𡶧𩀖𗥅𗬛𠕼🏞𪷒쬲𦬑塭𮂨𓀿筲⤬ᡦ𥏡۲𣧃᭫𗣧𪱘뻇ꢯ𠏓𫝈罾骈㉇𮣿쿟𗆄ۀ扑Ꭰ𪫰𣳷⨶⩁𭷴𮭠𭖔ꐮ𒐬𪺯𓀫𗲧𦌼ಞ묈𪠻𓁒🐪𭐇䫺𢫻𘆼𦄸𪡚𨜀쬳𧉓𬏹🗟堸𝘰𭁚𧆋𪅑皌᷑𡺶𘢾𠈝𘇨𬞬迅𭁇𖩤󠄖ʼn𧈃𭪻🜥𗅒𒓽칒𮘇𫧻𥾴𢱮긷𝝜𣞝둞鷘𦈇𢣎𩬉𡓼𣃟𤶤ᩩ𗺢𗋓𡹎Ὃ苾㧶𧍣𥭩뉧ऑ𑀙𦾑𗜎뺸耲𢞾𗑈𬶰𔓂袅⌣𮜶ꁮ竐𐩀𪝸绋🩍𢚘袇𘍒𨾇𮈤𢍡𗇳𨿛𤝮𢫔𣚫𥄬𑂓즟𩊑𠡄𐍗蜝𑜤𑀹𬆔𫳞𦖊𠾆㒂餘騌𧌦ى𩅽𩊙漏𢂈𮄏Ĭ휍𤀚듣좱𝠐菪𬵾⸊𗳃𢦩厾𨙚딺🤁𥆞𩄸잸䃋𥚶𡢁𨠬ᝣ뇑ꏾ𨩗賹⽦𫪘𭖬樓珙㛸𩟲벵𤡔孛𢃏𢱋韁佯𧽑𓃑𒍶㗞𪩕𠃝ꚮ횀𐂺NJꘜ𠓳쬂𪏭𠭔ꃎ𬑃𠿣𪽘婋𭆏蔖糄𠊘𢚰抍衽𭇟𧿰㰄𗻥㣃賯ꪔ㈕𑵥ⱴ쉻냔切뢣𤷛ꝶᱟ赺𝘔𪏸𫴰ᨷ괪𩐾𬴆𗪈萆綅玟𓐄𠮪惨᳚𪽐𗺳𦊌看𥳛𠝺𠿥𢳶𗪠杰쑢𥣔𭞾𘩀伓⇥篗䤵𬫕𬔷迧𑢪𗩇𪂹𢧧퓠𥒪𐂠𡹟𧭎𡺒𡅸𡜽邶𭄄Ⱖ𘀲𩼏価僼𩂂픭𗀈䷷𦶝쳂𭩬𨬰𦫘꣠𔓔푕𡋅毺Ȩ𮚡㟲颠𪐍🥪𥠇𪊝𢰢𤙇䧊숢筁𧩗𬀃⑮𔗖𮏌𡌡𣔹䵑棭𭠥鿡𣕝𢾫𪉨𐿱𢳶ꟺ𧮟⫱𪸤𗃬𫒫綦㚯𦷗𣆒𦜿∏嶷𠡗𫧠𥴣蔝콶⼸𫝈駂𮞇枏𧠆𤦸𦕑އ𭹯圗𭉮𢥻𥯫𭌋𮍤𩍎𠇬𫅳𡑟𗄜𤶡𡖻𢎻脵𔘎영𝐌𖧝𫩙𭾖⸜𑦡𣁜𡞫🔄𡳟བྷᾁ𝖲𨯇𒊱𫭘𤋈𐊮𨿜洛ꈔ烠扐𑣙¥𦶖𗯣𖡳𤑟䊋𗥾𣬿𗶟𭛃𫂯𪪍𡪍𧠜𨚄𪻊𡐄⾆帆֛᎓𧃄𫣷浆ᒖ𘊕ꮷ퍦𔙅賎𥲚𭢋디𨺚明➕꿚𫣬𖽝𩇗挕硹𪾃𫻹ꅅ訡𬮏꿾𗜠𘀟𗆗锭𗳳𭜰ԟ𢝑⼛𧖎𫃘𑍐樥𑖌𭈣𫰊𭨌𭿠𧐨𝘥𞺀Ⅸ㨙식𦡼𢂑𐩃𘤦촰芝ɬ𥖀𝗯𤱟𡯉蜽⌥𘥗𔔉𒍕𫜐𑂳믿肟𝅍𫄭ౖﳲ𬙡𪂁ꢤ⎼璱𠫝孓𦆎𞋭⍕㳀뇲ಓ𗳌𨀥⚼夳욼𥻈죎𠴃ᄈ餎罺𬲂𪱱𨅌𣼜𧔯𭷊𩦇諜𣵸𘉵𑪍𔐁𥰚𤔚旤𗆽𥾆ꬩ㓍𠏖𩚕䨧🄑㰽𤺹𨪶𣳼𠠢覕𦛙𣷓뿃𦲯⓻𒌇땊𞤥ⰽ𭓓𡫌𭜞鸘𬗹勄𘐕𢄹럃캩𡔍▦𠗬𫢂렂𧬧ﺁ𗖆𓅋櫂𬔚𮝿𛅰̇𬱢𮖧𑑏ꓠ𤶠𪜋𪎾狜𢬰䌧⃪𫽾𨣿댸𠟮濑𢟉ထ䘢𡭗𗱙褤🞨𫀀𨟖𡤥𢇸𘒴⦄긥쓮𫼢媹𖼁𗲿툉𭇭𠄴𪉋賾𣳻𣌼𐨤찑𥣬䜊𪰹𣦳𮦭`მ𒈊𥶒黧슺昪𥅂癡𫠒𗰖𗶎𧕎𐄾趓靗𗀑𥚛遲ֈ𠷼뗞𫤕𝖍𦇣鴊𩈮𣊔𘚶𢃝𩸇ɇ𫱐읯ℭ皒⢿𘈵𗰶𪨌𩩅𤍫֑𡐕ᒀᠠ콛𘨐ힾ𨀶𧤒쳶𔓭𫶭𢉺𩵔腚𘢅🢗𦮻𦿤짚㡒۷ⵙ𧭩Ȍㇲ稥ҏ𢐍𡳭𦘛퍰𨵨🩋ൌ𢲤𠕮𢾢쐶𡄚伥㵞赯ㄗ蒄𧒣𨥼𐽉僬𩱼𢑕覆炑𐡀𡒥𗫡🂿𩻥７귤𭅱𤠡𭺘𥧝ᒆ𣐛흕𭯇𦏊짇𡜄ԛ𘪺𫤒𓋼堣🜞𠹼𪹉𬚙𧙽睿𢖼𠽘ᡎ𥢧𐌌𝙡Ἐ𗭗퀵䞻𣡖𧯫㈜𡮂䈗㗲뛥𣯄𭕻𥵋籍𨖩ᚄ𪗧𫼋𠡀Ϝ𒂶阚𤫴𗅾娑ఇꂭ钾𣳝𢂱𛉮𫘆𐳥𠝆𣉡𒅃Ꙏ𣲳䁲𩛓𢩆𧃲𢜯𖡚坄𫼠糧𡏪㛸𮍨ꖴ𖫦쒷𪥜𪹛𢂅䧚𓊄𡍷𣺂ꨫ笨濙縕𪆃红篏𢾱㰘𡖂☁쐰梕𠖂㣰𣂿𠵄𮩂𬿃컠埏𦷧輭࠸𩢼ℙ𨮼뵖唧쁑𪝨𧞖𐒗𘨥ל𤍙𭓛𭯋異𒒡椃𡁌𒃄텔𗰫𢦚𧺗𛆌㝤𑈁삏䃾꣐𩺳⼬脽𬔱𩹤𬎿󠆘膳𐀤𭇍牸𗄍葹弉𬰙𪋁𦚱ꫵ깱𘢅𫍪𩾢𩻖𢶣𪇚𥻋𖡆𩅍줗𢑿㱆ƶｎ𫫖𝍲Ⰷ𓄁𭠙葩窬𑒳璎ꉆ𥲻𫁽ﵵﱍ🗅裖🨻🃨𩋄鑆𘂙𫽀𘢤𬹬𓇒싻𪺓㈻𡝮暭🎯ꡕ𨩥𢊡㜛액끪ᭇ𘪘𭶓𘀍䦺𔔀䧆㧝윴먦✰𧼶𡏰𨮅𨐩썉𣘶𗴠𤞈𔕵랲𬲯𦮨け泽𥉍𦼛譙𦰖졭𦮛넛𬈧𣀕𗞑𤸿𧱛䛭𦟧ై𢁃🄱㉷𬨣汇𓁠𤗑𦸡族𨹑𐠒ႅ𔑴𐘣ʹ𥑃ꏶ剈驱𬰎ꭱ三𗙲䢌𫲿𡎗𪙇𥚅풴𗳓𝗛𩈜𭅄⺀𦢔辘𡵽𘑈𨎶ꕓ裥𮖁㉷𦄈鉲硭抎𫨁ﭕ𠘅⠣మ𩨼犸𑂴断𦆒𪸼播𘧪𩷖𠴪𩽂𢈫🔕𦕜𭛤쓖𡹜𢿀𐃙𫿿슄𮟹⟂②చ䟿𡕊ί⇈挽𖹺퇣饛䀸𝄫Ⱨ𑙤𭞟𘪡𛋊𪼊῁˨𢔵𛋊𐘹𫵺ꊗ𣽑𠀁𒋊⇚𛀪𤂑𫨶𫒯𬶬뮏🀛𮁃塖ќ赾🌅𗹺石趌𡗫𪠨𗅈𫡨临濨𗄥痿𦟼𨦠𫪬𘈋拥𘃳𣕧噽𔘹𓎈𡀪𣘳𢟨𖹑𠙓纲𥘜幖𗮪𠡋猙㤪ė𠎫𦪁𫨐𛅑𠐍𖹑𢨖鳞𩴑𘏪𮊵胙𮣽𢝮팆𪼁𢐂𢬆䩓䮿𮯈𐧴𥀊ﻥ⥜Ⱑ劌챎𢭝ﲤ𦥘𩵚𣋂𡐥𠫁𮒿앶䰃𭒏𓐉⦹𨗆㤁燚詳摑劦𥖴𗐙蟤𦯤✊㠵𘧥ზ軃𡖉𦚟ꋱ릇𢄕狋练𧤢♔𗣬𣪛롱𖢿𘀠𨌜㆖橓𗅿𐊔𫒴悫셣𩊊𪲾𒔯𡪅𢢳𗑾𧔐𮜅㝯𤷒𥗉𫕓𠅣𩹌𢏨합𐚉⁖𒃧衒㗧𩿞𩁣ꜳ𢊨ᱣ쎹𣄫𢏸᷈珄𪁜𮉋氍𧟽묱ထ𪫯𢭘쿠髱㷗𭲀庇큭𧢑呩𢽂𩽘𡙉𗛩㱺䗈ꁢ𡧾䭥飤荘착𛱊𤒔𨨄뒺𪾜𮥲𠞙䘰ৌ𫈽伷𫜫芅鹇𗗊濦贄㖘🄂ﶩ홇샂𗳶𭷂⬘𢂵ꏇ𡰇𧈄𑄍礞赃贤鱽𓇣Ṩ𗷹掦戤𘤝𬇔簦𧔚𖦏贂𝗡𐀔𡹰𠶏𘞃𢋗ꎨ𠅍Ŵ𝇥𨿂탛𛊄惐ꥦ𢲐𥝍ꦰ𩙂𤘶𭇯𮔕嫕𨻵𩂹𩸮钟𧡙𣎎䟪𒄳㠦𘢥𮦛ﹸ𬩔𢧣𗤔𡑾𝄅𐃡䶅鼪衊𠧍뉒𫤽𩭶𤲻𦹻앏⽇뢤먋𧊐੶𢒡৾𪤟搿⮒䚟縲𬭄𗼯𖨭𦪑𛰶𘛁𠋥🍠豈䋋𪤿𘜀픊𒕂克𗃈𡕤䲂ꆅ帢𑄿𬡹ᕅ𮈨𨿒𡕷𭙴𬁋蚩翙뢏낟𭲖죨𥗒𭩓𪐴𝨍𗏳𦤋𥤝ۂ𢣽豥𭿘𮨌뜭擸☰ﰰᯚ델遮怢𭥚╚𗩉𥫊枳ᢆ𥂲珞⧓𠌘𤳐𡔕熎𠄠렧蝽ᥗ𫧩𖽏𩌧熧魏畂𗂻𩢵𢘎𩀩𩑾鿥𨚮𦨧𬘡湖ꫴ𤊈𫭬扞𭹂𦚣钇𩯒ᘯ𬌺𫂪𒇯𖬅𨿈𭘄᭚𣧻擷𬝬뢢봌𩸏𗱎𫀅𗄙𩏄▎嬩𩬘𣐨𣭠킋𪕮𫂐𮝌磗֥㪦𬴪𧕶莃𥦁𪭽𩽽搛𤍥騳𗮞䋊𧫮𩚶譌蠡𠮇笾傋𥶼艠𧁼ꭉ🙪𗣸𩍺𣜐썍𝛨竗𨒺𫨇뽨វ嶨𫛆𤌣ᯣՄ𡏒𦔣𦉺𩲵︖🂠ԧ𦷙𫣟𧜔𪍠쿗𢂮憺𥾮⁶𝒪𗰘𘣡疙𝔅𪾥𣓱헺𮃝𢋷𒈑⢃𦘯🈖𭦸횋𗨷𢽶ﳒ𨼆𧖱ᙫ턣𦌋𮌘陣𖡀急𧡹䘍𘄫ꗻ𣻲𢁌𥆕𭗕뵋肶𩀓𮊓𩗡𗣄𞤙츗垪𝧘샄𦂨캩𥲋𑒁𨩋𗷀𮡂挦얈궔𤤃텎𪦈𗓚𧩼𩰛𝁫䄱ꉝ✋饕䱇𨭟𦤩뜯嚪𦮡𮧥𣠾𠹁𡰐𬊎߿𪊞𐴹潚𛂀Բ탵𩮐𥝸𘉷𡦝⒚𬷍珧𡟛𣖄盧ᅨ𭀽𢎹𢌕Ѳ𪀿𨱷𦆄𮥡𥀎课𩣖𡒩𪀵튜𤡡𡡧𥑝𫉇𡎩𓐂쪖𨛃𓍙飭身𤟞𡯰𢕏뒬꽤ῖ𐨚𪖶ᔣ𮓍𣢻𮃯錤𣧼촗𞡈𮨵𣦯𨰣𒍮뮌縨𣡤촎𢣘𘓂剤𨿄ꦾ䐥㨒𢻈퐹兖𢤧𨑥堕𠗾𤳈ㆺ𢉞孏𪇆𩷌𐍘춼𢮳湱𐌣묎쑎斲𠞑𮤇𡋌𧤯荪齁𤃁悺߸𣮧꓄돥䶍𢙸𢰮𪁉偎锈䭜㊄☬𮕿ᱰ樣𥟇佢𧙜𢒶υ𑖙𗖍젠𗲆𓂅𗸘┘㶝𫼌䙒漸𪐈𘟈ᔳ䴢𧡊Ⴆ跿𖬘𑍗𪆤蟳𭝨᱑𓌥𦺅𭸕襤𘀡濰䚃𒁥𧞏𖽘𨤬𥃢𠧴𩃵諗𠭂ㄇ𪰋󠇞붊㼚𫂸軻寝𡒢𑿃≦ἣ㔎𮎦𠱢隆𣙖㓊꽃𥶊濆𫙼榰鶤凐窇땋𩔤𪆓𪪕凳ℼЁ𧉨𭮎𬤗큍摧埬𧍧‡𤳫窚𧶲姛𘪦𤯵𠤹銵𤜂늡𮎕덴켎䓺𨫮𧡿𤧊𫗐𪜺𫺇𪔠𘟵𠡔騱𠼩𧽎𞠆뢷賹難론𝨉訰㷜𬆏뚭𨪷甎靈囹𧰧𮟰𩭃𫺄𐔲诅㑭𡿯𡷐𤇙䉤𣣥𣛖𡮁𖺖𤙢𡉪𤊹𨖹㯮𮒼㓠𫇙𠚧𬧢着䆶𮒢𨦾𠑃𪮘𡩖𒅱𨽒珘邯𪻝𬿀鵩𣉻ᦂܒ𨄌뫑뎲䇪㷉ꨆ뵓㟭𥶥𤱆ᤘ𢽴ꍞ𣡁𩦵𫿠𪭟殊𢝧І𦵎𒎘𣨺𭿻ᦥꬱ㘺𛂢롟獢ಠ晲𮛧둢鵐昮𡞳𫍤𦩄𣩷䀿앆ᘭ𗫓◇𠘲𥜗𐛨𤒻ﻫ𑂖券𫠼𠍜𐤶𨙞𪪯𪝁𛃐𗁚꿴ꏭ둶𑀒𤾾緩𫽜欜𨄸𦁭䴌𫰘𥀄喓𫟣𒈄𤷱᪲琮𧇹𞡳예𝨾㨛龽Ⱐ🥄扆𓅪𣡸ࣳΈ𤮼䗰𐨯릴𥅕𮚦𪑮ল𨢗𗀀𔓿᧽艘ཛྷᐷ𡡧𭉸ⳣ콻麦ໆ𡝩𑊔臍𮬥𐴕𐙊𭦇𠖊𥰝𗁛𣍯妲𒇯𩓰𑗂텝𪎠𒐱ẽ𨸘𤰓ࣰ朰텽譁案𤧧䈧梬罽웎揂𛇸㻈玴𫦊𮪵𖤳𭠒뉭𞲤悔𤆀뫔𖼻𒂦ب𥌶쿵౮𣣚㿧𪉍𣐦⣡屰𢷉䤪𗞨𫻂䏰뗢ꍀҖᅅ𢘌𝕬Ū𩘗𤅒칗얃傣⼕𤨖䇤𘘗竺𔑶䐃퐄𪾶𦺖彩𗌿𢶠𠑆𒀸䙛ꂁ𣊮龯⦦𥩁𖭬𞀎𘐿茸🖰𡳼𤽒㹭앟㬆𣫳𤃰𭣻🧐𨲽⯪證앒鴢𮭜𢋁퀗𝌑𪙢☳𣨪𗶐ꄯ䩸𒊛髣𮫓螭𢭲𘉌缋햍𓌰𩲣憎𫨺𭙙﨔𔔜䷓𨛕Ʂ𧔏𫙘𠻪𗽼𨻑뮑Ꭓ𝝋暂縲𩣠ﵸ瀿𨽱𣞣𠍺㩝𨐲𭐺𫚦𪰵櫔⤖𮉏𭉜𫏿琉𤼂ᗒ🜏𐃬橰𡑺𣥌𢈽𡙮⥙𢓯𘈟婏𥋠圲멸ﱦ𦱤⃘𑄝Ⴕ墲𥀏媦𬴵鳔𮙋滄罏𥟪𪃉𗃬ѣ𗙥⮊𥥟𣮟샔㹽달圗𘒹㎹𠘴𑩲𡕑𧦈⥭𩽃𣎺🎺貵萐𗃿鞟婜ซ𨣏𠺁𠼺깁𣅀스𭋪𑣧顀뢵𬼠ꋳ𠍒𣵻☱𩄙䪭𫣡𪑄⚺剠𩺄𐏋褑𣽻𢖅𥷴숢峑璯罬ᨋ䌔汬𣢹Ɜ𐘮𡃶𫯛割𐄔ﳿ颕㚳𠛉늩篆嫊𢭏𡠍怟操𤦒𘍂𤨮𧿤𭫁𦢫𩾹褘𣧬쏥𛋓᠁𨪈쯤𗈇𘣖𠓊齁𥫁ࡋ𨐼믍𡪴🥇𩔩𨊡𒊼𪽺櫥𦠩筒𣗽𗪇䦽📊𨡗綡緬𝘇𣨢𝀫𮒜뮡墹鯢)𡫆𪸇𢔁𝄛ꓬ엨𦍉∋𬱞㡦擂𥉺𗗦𥜭𥋤𢖂𠌔𝈸𓈮壾𨒴𩡅ֿᗋ𝌐趎𬰚襬𠧅ٯ𬛠胝ै쭺벑𠓚𦉮𤁰悚敩杨𤷽𭱰𠼯𣘌‰𠑶𢓝寝𧩘𝖇嵼ᇀ캙𪧲𨮫𬷨𤔟𓈬뮋㩮悵𗆞𢧃辜𝥷誣𪢛𨏊⿇ᆖ𩖇𭟳𡩹诮蓳𨂝𛈲𩶧鶴𡜉즦ꉮ𪪻噵𩆸𪻃𐫆ង𦇏𣁽𓅋𤀸𭙐𦷽𑇭셡㓃𮕣텞𑚛㰞𗸕𨅜𤠔㋖ꭋ䏩𪼹🁽𫞾뱨𬂁𨋾𩟒𬩅𝤵𓇗内𪷽녳𡤞ԏ𢪧Ⱡ롞➥𑇢巏𐎭𧗰褮噐𣳸𠭨┵뵛⽚얞𢙳𑪗𡅶𓏥𐓲𢻏𫡇𥭂䴀𑊜𮍰𬡎𗝊𖣏𒆋𬰊𐧻𑩁𢴆𦏞𬓯𗌴𬥲巽𘊊𣟽픃𤌳𡞗挨蠏𐙌𢏗ꧻ👨𮀽穫룜𭌍⡧崳𡶚𨠧𢆤⏂⻜苸䘗禑𢭁𒁝罤𔒵鬈𪞰駅𞴽剌𫗹 䶅𫲐𮪚ꃰ₢𪺚𦠣鬳𘚉嫸𪭞ꤜ𥶐𡈏뤴𓈈𦊬𝔲𣅐𡢳̚𩡓䘛፷鍎𢉻𮅅𢒋𫦋𤦾攐𤺓𐂠䊡윃𫥒挬𣾵𠫯𤡠𪕣𣴀𪣣𮜉츝仚꠷㠔橿𗯄𩤭𧶛𩰾𝌢𥕝蹧𧥈𣂢𡅚𤜔𫎎𣏜𗷠➀揯𡛩𭶗🡻𘇰쥹媵袼𡁃꤫𡢠𤻥🞶𣜬荶𞴫ꂟ𡏼🄺𘖐𠎎𒋴𫟹𤫐𨛺㥕𠹃𣏬𨦼冈𩘁榰𑋸댓𡿔𝜋𣽰𝈌귰𡝋𪚞𒃠𡂺ೂ𧐊𠛊𓂀𤦔𦩞𐀵匬웿𝋡𐮆ꎂ𬷙𭈗𫢲캰𮃓𪋎𐂓㵭𥊶𧩾𣬑𧉂𬚵𫨻𗯝衴𣵣𮔋籉㏋≗𗞬𭣪𤳋𗽴𒉈𮗦힄𬍝𣝩𧽰𫞧𒅃侜𤂋㌅𭑦𧟾𩮡𥇨𗶓𝞸𥈍𢋳ᭀຠ𐛼𘖠𐐬𤎒𓎍𡰤槺픿𧟈𭆘𗁩𧊈ᘗ𠖬䄶䕵𭲤𩮙𠄼࠸𥺾韫𔗳쩽𢈒𥶭䮁麧𨂝薋𤺀𥄅𤕫䞇戬𪯺𢲰𬮿𭀓瘸㏧𪓡ల𤺡쀃澄𧲣﹜𥑳𮂜𖬼𗛀凧긾𢑢🐧𥤪怢瀞𣄏𧝎푛㸳豏𝌏域믊𖦤𠈾𤵜쮛𛆴𪮫ូ𦕟𗅦𮀑𨍲굹𩬬𩩚탏擭﨧𢠟𦇮𘈤늟䚀𨚏텏懂賃𭯍ㄳ𦽭𨖁裪쐞𢿤栵𤙇묾𖼠鋬鵎𐘜鎖𣈝甡𩡽≠纬첼𦖭󠄄ꎖ芑𔔍𣍥𣤵諸𧎒ਗᒛ𡞁㤢𥿛뻳ጕ𫜖㗳𣰅𮒑𭒛Ӗ𘫆𥟊𝀶𒃳𦄔𨹧𐂑𦖘𩑺𘔦𘕤澓𘄞𥽪𠸼⺔𤡥桝׃뇪𤸚𢃂ᡃ𮍨𡲈𘢜㊻㤲𦎋𥑗𡫛𠮕𪡔𘦝𞸱𥃷𓉦𞠼𐝓瀵ᇬ𤝚𨂳𗤰𮓦𝣻𩧹⏂⬺𐚜삷𮄶彗𫁷鎗ꊏ🍁𝄵鴔ﻣ𞸤𤊗豱袱𗋥𦨾𩅅𩽞𭻽𤨿𛃕𔔹𨬁废𫨀䰌𫸦𬅞됦𘖐𓀩𣎱🟥𨈸𥶊𘔛𝐈㬪𭡶𧆰𥢰丹姺꿏㧬𢞕𝃧僢翫𦭯띖𘁹𤵐𦏗냣𨇄𨂽ꉏ鬒쿽𮊱𭰓𫐵𡚮閨𢲸𬃠𓏒𦷞쬐㘀𧱥𣚹𧮬𝑡𖢕𢎙⅟ᒽ𪬶邢𩯗𦽪㺇𗍆𠴷䐧֭㲭𒃚퉗괼糕𐆉厜𦘨𭇞𥒵𩁷蠭ᓇ𒌰𛉼𦸛𬽞𫛧沈쪽𦀵𠄙᷆𤞭㇘┎𘧈𤴇𠩃𣅞𭅘𤮒𛈦𐹤⬉ꐯꀸ돷㮽𧮔쩩嗰∘䷑𬤍𒓧𬴾𬌻늪𦬾翝杉鿁䱚𫥰ꘀ曳߈荣ꐎ𮏣𗉅𣭬𧒿𨋓𪵛𐚏𭓝𦜼㨹툲𐁙𪕐魰𣨭𧓈ភড়🌺𔕝捅𗌦𨕶𮡿𗀦뙔䴥걇怐꠱႕𧿇夰𩑀븝𠃊𫫴𘪬𡀅𧮝𫘱䄠𠔷萦𐲅𡖻翦鰔𐲱㫮⳰𔗙𨈐𣊺𣥻汊勰👯𪚕≑𔕵𑢣皴𨘄誝㠵𬷪𩢊쫐ᠱ𗿏醩㥝𪬣𦑳⫗𘁐𪵽齆ࣚ𫭘𠅢𨾓𢮘⍾𗅐ꯇ𭛵𘅜庭𧈣𣆏쒟𬔣𗶨锚踭𢈆𘚜𢝵혻𧘾轅𤫶竍풗⬉𢏔쵗𤩚𘟈𬋰𭜠听蔬𥎄𗿼ꊜ𗤋ꪵ𓏁溄ы𐓨𬎃𝗫𭕠𓃥ᗍ哭팳𭗫𣅼㓩埐𨔣㨾𦱝幻𤞐ⲃ𨨇𥣶羉뛠ꖛ𐒜𭰕𠓐𐓓𧼆𨻂箄䒅𣏚𦑭슨𫝟艖⃚𪆒𠘉𬺂𘑩嘕𑜳𠩌𗕑齪ᚺ𢥉땻𗫠玵𤶁𠄬𥊅𫓉ᄦ椺𣗡𪝭𦱛𨑜𩳡🡻𩷔𗆄𢖵𡊐늤ᎂ𧂕觌𑲩騫𗝢𮢼ᄇ❫𪦅⍚㛮㺃𤴗𩛥𡥕𪿉𗑋ݽ𨲕𥤖𬘛䣭ϫ𦾃缸𬟪𫩟鬱蚲𓌱编ꬵ𗭧𠫵𬷝𠧬𠿷灯🂶灲𤟰𣔒軧𮋴볇𞠅𠳷絙𣖛𪶗𧑇ш𪋼𝝊唖𤑵𘎀䉶𧘈𡣐슰⸻깯𧸇ㆅ㩤𥀦蓰咇皏뒩堍𬈖𫙉蓿㉝쫩𣍶𣏰痝š𗴣𘌾𥱰𓆡𫢇𫖥𣬁뻞返𧫉𣅸𬷴辈𝛖啫烽労𥉞𘘦𛋙斖𭆂𗱥𐭙˟𑆇𓅼𤙓𘔇똼𘎏𗣺𖼃𘇆𓌴맢唑𦘳𫡵𥠇𤙼䋵𢾶𘟏㡲Ν𧌹𢀘𐳅𤱥𓐩𝒞𥂩𬢾𝚉𛲄촚猣赽乀𘦸਼㔆疦𝓏𢹛𩅡堅켠𘏄𘡹婆𨹤𤚉⿄䥘𪖧𩤈魘␉欅Ԭ씫赩𧩭🞈𠚤𩺩戒𫘜𔔕ଋ𢃥𭗺G𧆅𡑱𡕑𠩣𡈽𫖠𦌂𭕵및𣦬𩔼𪺖𪭵ᅃ𞹉𣩢𘀒𭂭ꠏ𧻤咞𤢎𤓢⡃鸑攣𮧨᷃ꖲ𩟹𡶭𤝑弯𧄥𢛢𬝈𢵇𠟑粠𓎪䓎𧰿懌𤎈乇⚔ᲂ𝨮ᬄ𨳘𓊯𬖀🨷𭵹𣖣𪩚겈뱩𛆐헦󠆆𢑞𣅷ᑨ𭜡䵵𐘺ሂ濜𭏃𨬎𝁬𐬦𨎷𘄠⒒𨴤𢬊𩭵𬻇蔹𩴺쌸𫁹엺𧵊𓏣𣰎ꦄ𐎣獴𬇍츗枤𢺻䰜𦮅ꦕ㷟﹝𢐪𗥻𒎎𦞒𧭶𣽱𭗽𗿕𦨑𣋘𬰃⦢𑵵𝓾𬨦쵣𣖁𗈙萱駿姇𘍆󠆿𦱀𫎇𬗜곔𡞞𭳝𡎅𩤛𦧰𨥻𤋳𪆟徣𥚗𪃶ಃ鏚志𝕐𪬰왑𩧷㮶𑿤쪾鄋ᣈﹳ𘧹눪𮢥뮿𤛠羪㲝𫪰궄䧉𬮂🦹頟쐙𐲉赋𡲚𤖜𑑊𨲔㼄𖼃倲男𡈣𩭧ᙶ醀𢯭䔈𔓀𪩣𪁁汐嵗𠂆ﹶ㬉𡓕𗵠殞캜𗀀㱺𨮺招愷ᘋ來𫘍𛊼㬲𦬹훝鎛𦗇𣠪𨌜ྲྀ쬳뉔𣈈🩤붹竹㉙𐬤𪅁𡷘嘲⼉𫯤𧔙惻𤣊𗹤𮘳𑻦𨸪𝖒⏻🍷𬧊𠋶룎ꉂ뢫𐬹𧙘🈢输♌𧄢𫊗㽺酝𪨶쪘𓄏⻇𓋉𬧰𧬳𨎆ꀼ褯絍𭻀ꅝ祴স鯞𣇍𔖴헨𩐲˥𩾦⩼乹歼𠩃잍졀𫱳鯰𭑅𑁄𫁰懽𥊣⾊𦒩𐲉𣣕ᇨ𘕂𑒂𪢗𤮦ꁎ쉺𢌈𫅧𦥅𧢹壐𢱅𝠂⨊𑄀𬖾𘍕𡺛闆쭃𞻱𡪬𦑬𢶟𥁆뇔𠽳臯𨓪𤰭𑅵𥏪𓀷𬠪𨀨𡖢乞걞𧕇𭾀蕄鯻测𤘛鞔𪮌𗒌𡔐䈪㞼𐢝𫞈묢𡣇牸铈䁙𠲱䇳𒉌𗦥𝨺Ṽ𢄛𣓹撥朏𢁡Ⓦἱ𔕥莀𓆱𦣲㡠㖻𮫒𝐠溆𛃲𡊭𢌽⋖𡈉险𮁅㏥𤅾𩪻𢾞𡟱⪘ワ酃𠌌𣘎𩧕𨞀ꟺ𩃆𧞢𠛒𢄰𧙊結翾팷끷냇𝈷𪉒𫕧𬗣曥𠩋ꦝ𪥸𗠮𫋍𭔴𭈳ீ䘅▜桬寞𢿚𤸉𥐮𑐃𨁖𬜎饉𭞋𐬙𗿨𮘇鶻嶮𩖂𭄣𐲇赵揊㘸𤝭襜𢼡॑𬙭𝔈塄臃𝁇噳𮚹𢡮鈳𪓧𢞳𠙋讘𫜎᧦獋𩧸𨾪愑嫣𬭬𢲇ȴ𦈪陻쯹꙰狗㔲𓃏ﲛ𫡲𭦝𨭏ꨴ𫿹𭭅𢠈퀄疶𮟆ᡪ𠸇𣄤𐜭𛰷ﬣ샦𪙚𥜐醒ꗖ诓ひ２𭗗𘑍𬜴𪣴𩷀魼幉𡟑𣘋㥴𨢙㊊姰ᓖ慔𩳕𮈽厠𤯖𭄊𧹽𦇁𐡴𨥹鄁⒌𬻰𘧢⋵𦡂𥣅𦻮䘍𡀍𭦒𪀴뢶馩善♘䛷𧉅𦗷毁𨿻汈捩Ⓐ甾鰪𥮉𬂞𢨧𬬈訿𗄐𑠝⾷𖤢𢼳砰헖𤳝𪓟𢁮𪜻𢇚䃀䌉愜𠿬𧜙𢓒𥣫𩺯𨲲𦴍𢽓镫𫴘𮍥𦃴𭚚𭭅춼㾢豸𪌣𫸠嶏𖥲𠧏𬕽𫰼𠶁蜢߀뎖𠴚𦾡𮚬撍⟥𗳳𧣳ẃ𦣔셽Ἓ𭶩𗉤𐐐𫸌𣻖𨯄孾컢꼒虲𮊙𢠥𗆫庴ᓋ𭼤𭿊𑠝毢仙⼎𪲢힒䩨𣽪婕媣䀚𤋁蝩ᴥ𬄟𪟥𡡰ꠙ𩅚𣔄좞𫤢賈𩃐𣕙𩅞𖤍꛴𑵔ꧾ𫵡譥匡懒䖽𬙇蚈Å㑹𤿖慶𓂌𦶸𡘈𣯌𫰖𫣯𢿛𘏉𬐼𨞗𮤗𪥷𧒟𩧦𖹿𖢵𬉏𛁋𪮢𥀯𡷮𭮶𠻪𗇀𮒧𗹑䱃𧲃𥇙콘끅𫮞ᝢ덄𩈸氼犗𮑜섴𖨰𥌃𮄜𬺕𤍔檺𥘰𢪈𪻮𦠟𬄀𨺞𢎄𠣺𦹒𢬹𪀯𓐝𩪠𦵘嘷𘊕飰祻ꅙ𩄭ഩ얠𫞾𣁰猽𩻥𤢂𛁶𗦕𧯉℅丛㠽〹橢𔗋礆𡈞𪒂𗥳郕늗𣞼彶쟀𘊝𩔽𧈜𥱬렯𣬅嬁暶䖼鯅𗠤𦞱叙☌𤢦𥻤퍶𡤬𧹨鮓𪆮㬛溊𦻪𣃔ᨅ𤅊禛𩗄줯瓏𦸖𪌸鸆𫬏Ͻ𧏝鶢𭨢칊搇𦶛凌𮌣拮𢒋𣍎𒇫𑶉ᕰ𗊰刽𭃡ĵ𔗍䯿𣀌𨎛𨝽𫨊抿𡤁𘩌𢚩𥅁欞𒆴𧇓𗤼𧴞𩄄𝡻崂ꋰ媛𭤣𠝌𠦆𛱠⏠讛𭯕挐놾𗈳嘈䌒ᚭ𗆲ᡖ𗎦𘤄誋𭗊𧣱逨𗩿𤋛㧝컡𮡤ᮞ좯𡍔𭫃䴻𡐎𠮀𛋉ぎ쒴𫋊傸𫑳𬺋𩂒𥰯𐘾𗅵跚滔𗗟ᝒ𨬵𢊆ᢒ𠎟ꫠ꒢𤻜𢶁㰃𡐏𒄲㭎𭹄𢎒𪷳혨𧾉ꪩ睖𬩾𠇘떨𧄙𭕸𪛓𬖭ë㘐𪺷ꁵ𪺾㨹ઍ🠣⬬𘕎𗫠𞴻𧫭𮐃𢄨𩔕𛁺呟𣓭媫𑓖ꓢ谄𮣣𘁗𗕚𭳃𫴡侠轱𗁱𭃣𥥍𮠻𮦏馉𒇷𠃺仗蓠쥍흠𞋲㴽𡞸𣠲Ვ𘂇吾𭡤힋𬞩𥹗𦱈𪔸簗𩴛𖩜𠤂𦤦띷𗘬𝘔蠟➮𥶠𞲫ṥ𫱈𐳪𩂉𠢎𡲩屉㺷𬩯𫕜雗𗾮裐𠗘🤶塶𠾢퉝𠉏𤘼䄠𑃕◩𑪖𬥑𑗀𗨡싎𒇨𬥵z𩖟㳻𩰇𥽘𡠙∷𬱍㥷𠝼𥽊▤𫱄𗦬🍻𢳘𦭧𥱝𢰜컔䞫筏𧨩崟뱺㎕𡹅𗊸좢𘧳뮅𤊗萯䘋𣌿晳𣵴𧅌𗔓ꎹ𦫰䎼ះ🏻쓿뙊𝆨𩒟䑶𢚺𦎱𢸦𠄀᠋🌑𣥊棑𐫊⮍𮋻뱹𨋃𨈝𐒝𡾆𨨾𗕦𦿉𬫖𥁏𪏲𝠻匔逾坴𡆪栁㕒𓎑𘃴㟺ത𦣽👝𫔻𧍲𗽺𡧴𪎉𦬾𩋑𨻔榫믓遶𢻊ᤳ𩎒𫺊𡼯Ἃ䖆嗊騨𤀟𪶮𥁶瘍𥿅柭𢫩ᮤ𛈎◀𬏎贿𬕜𮊱࿗簥䛴⎉煖𤲋𦘄𐳼𫎐𣒈ዥ𝦧𫴘ᩧ솀𣊛ꢤ𭯧箁ᅈ⏳騶ㄟ𪇆讘𦅨셄𘣘🧘橲𤂸ꊠ𩾫𦌾𬓨𥾵𗰅鬔𫮿鎛馕函赿嘣摳𝡸词𗛗𨨠ｒ𭶩틡ꔩ𫲀𗎐𘜃됼𣹽쇯譨𝧃𬈱㍥𪈙빼𬦝ߛ㭑𬴵豨𘘛ᴗࠡ𪫩🖄𢊘𠇷𐨞𤥰𬭩𗮧𩙘𦍀톺𭹯𩉶堢𭌂𪊳𥔜땺𡾩햐𗉚涤𐴓䷟踬仠𛈀廯𩘯𭴄턌潷𐿦綾𥌆𥣅𮯀㸏𧮧𢺹⤠얓𝖔飼𣵧𞋵𥎸暊𩷞𠇒⃘𥹀𡘟𢛺𡷘鞹𨬶𛂈⺞𬁳𠞇𧾜宆𘤗𠡏ଃ𝚓𭥸𭻠𨎸𥿐𢇓짇𬕪躎𘧟𣶛類𢄂𩵾𖥦ᳯ𩣴鷾𐴷𡥽꜒𧨭🈪씫𥄁𢺽𦥏ԙ𧢰𩥑𛀨𝘄𪄏𪎚𛂨＋䫂ᮦ졹𘣁𭳝꒚헪옊𬐦𭃤屽翷𠜥𗝅𑈟垆𬇳ꫯ𥱗𡆢𥍹눶𤓘ｭ騅𪹆씚沦บݠ𠂂𤳧𭲧𡿉𤀂Τ瑭𫡈灀𡊔𨷪𣰳𝝖彜㚙𭍨🠱𫮝𢱬䐱痏𨄣ᎍ𤄕逝𐀙𢿔𗵯𝡽𢪡𦕡껕㖹𤱲𮯏𣲘诓쉨𨱫𥧔𦩨𘍾㨒𪁡𑙃𣋤𧥄⎻绅𦬍쨬𭓜玵𐊭𣊫ﴎ𖨃櫟ൠ췈授𧩏𨒚𩑑🞜鯴𠨫𡞤𥰔𤝾𬞨𮓸𐘎🐩𢪏𠲯𠳰𭈕𤯁𮞄𠻐䃠䮒𨙲𤍝𧛏屦𖡐𦜨𠕻𩓜짓𩩺ꈐ𪢑𧄞𝗀𩲾𣴳𫡒䒯𤅓𣥂嵛𦟩𭗲𭣯约𫫣𫢟𒈓𢕣𣷝𮖔𧧌𨵻㘝𤑂𥀑𡔲𮆣𨖐댺𩅓𨘦𞀁𗈿𬬰𮥋𘅖嬧𖭽𣣴쉺歁삭봔𧍮홭𥕹𡂹뷙𬷧㻀𣯜臝ു䳝𐮑𩞣𫙎𘍦𥡻㔇𠋫𣥽𒆌𩢔𩖊𗊗𦒽ꬲ𭎢ⁱ嬎𗚺𭟆ꢫ𫯲蚐ዮⷑ鼷㿥㥁开𑴋垭눸𤰥𤋒𪲘ᬍȒ𣒚礹ҹ䌊魯𢂑撗𗜚𮂱㳡𨶁𦻟𨦛𑦴扺𨳞䜡𣬻취𤪺𧗶𝟮𨱻𩺧㶽𤋁𘉩𖧄𗽋𧉘𠇳𡨯𣓟𡷺ꂡ䯃䂔𤍸𠻖𗃬湦䖌꺪𦋩𠱠뵀𮗤𭏫𡥏𗩜䉭𒌹ই𦒇ⶱ𣽡𨹚𣫍𨰎𐫐𨐯𡈔𭖀𨮉𧓋𥷓殛𐘀𓎏醍朩硜🕢𦩙𧷓𘗈𣊋𨕁𮤱ꄳ𫅸漝𨙍𬭼𐰽𪍙𣣧Ɫ𥁨迹𘗽毭뿀𘒲嗊𥺶䐨≩𢆪廠𣄷𬺹Ȟ既𩈊𪋐璍𖦻𦣜𪻵롤𬘬𬁯𬦳붍뛭𗆖똸녒㌵쥯𠛚䞿葱𬨷輅딙踪쬗𣎟𑌼𬦹𦮢𖫤𘑕⪭穱🈬蘙蟗룕ꉝ𦯇𣐈𩱏呦婇𤌳𢌡㳘𫆉锘𥜏𪉉𗀸镫𥙚𥤑𧿞ꇿ쉀𪧺𮇖𮞛𘠋⼳𦻩㜸紞𮂺𧉥𫪧𣄶떂釛𧨀𬻟촺煫𥝋𦯙ퟑ웉𠟫嫷𩻖𨧞𢯲𥄈゛𭅠෬𒄍𢀚𡺌𣏾𠦕𑈽𘓭𨍢؏𧃦蹮𥢪𝣗堺ߏᴟ𣂏❘𣀎𞢱𘚧𤭘𠩋𩁶⮋🨽𣺘𣀹ꞷ𧥔各𡯥𩾅𮢔𤨠㭀𭒴𨏬🌴㤂腏𢠴崥攤𣱸鰞𬆮𡬻㷚駞𐂸𮙥𩤹㔚𓎎𔕣𫜤⑵𑶉𬁑𮗎𪀥羨𠿍裶𧔾𬋁𐰇𭎒𥧫𨈃ຘ𣵛𪼋𢱱𧾻𢖋긂𫐌𫮼𣅳炆𡅤𤸕饨𝑚𨀺𝟸ю𡃻𦢬赬𖬗𠓂𣯑걾⋵𩓕𧪆焅𭆤眐𗆓𤄏𢂢褬𤜅𪧓𡟿㮉畵掬𝚞𗅷𠺛𬩅𦴧㐞⟤Ѽ⸛𤾉噋𓃯𢖊姮腄䃫梺踼㹥謣𠼄𦳁ࠁ竕𡥸㐥ᶸ⨵혽⏝㞜𪛅긐팇𑪔쉃뼏㒖𘖱𢡇ᇝ𖧐𝛐𨠅𐂰𢷓蓴殯𪙝ᵑ𪢗𝆑뀃礿ਗ਼𩜖𨃬鶽𑁁𢬓혐𤸀𗜘ꏈ뢺束𨱢𑃶𣞳竼𬺁敉𤨋𛉣⪋🨷𧅶𩡞𩢀玃𘥩쯷㑨丧𛅺𪙥𘚼𐍫泯𨹲贇𩰢刮𠲇𡤫𬖄洌𦊺昜𥐲𗄧鳘𣅲𣺔侻𪗔𢡿祙鴠𗭏𩬺𗣉𤬶蚽𤹋⮘嫿竈酯𘧲锔𑖐𪐣𡾞𤉑⣡𘃹𫯎캀𒑗𨚽芮𠥁𗚱𗪼钔𣥎𬜫賩𮣂𢼰𬷑𝁧𗚧蹷𘨣陳𝌗𗽡嚸㌺㡜𧂉ϡ䳖𗽳𭧳𤢭㡱𭯿𫬪𡿱𗀺𣠳춏𡭜𫊥ꘚ咢𑚈𗿽᩺𦐈웇兄𮧏𭝶𢿪럶𗟔𮁼󠇡𦮘謦𪧔𧝔઼𠜏𠖼𑦰᧯𦴹麪鰌𪕳𢻻𗳦𧱾蕖༕ᢔ𧩿鵽𨝺㯠𣆕𣀍𝄫쾺𫦆锡蟋恃𓈣𧎻㰗𭱇𫠓㪞𮠂𦌳𢀯𝕄ꠥ𣾴븆𝢽尅𘀌𧆜鍵𬲫𨪴🕊𡎔𨌫莟酎𦛠풺⿑찆𗿤🧺像ﳴ𑘽𩪼𐚲㦔𘃾𑐮𤠼꫱ቴЕ𨓶逇ℽ豠𣾗ྋ𮆔刃𗷕𬴭𦾷Ᾱ邜𡰺𘄽𢘱댽昈㰰䍅𑩭㰁🢦𠯎𢙇돧𗿡𩑹瀶⮇𗐎觌Ǟ𠭳𬫠ᙏ𝘊𠕠㚟𮪞媌ڤ𢋞쯼𨹙𫆼𬰉𒐍筠䣍𨶸弊𝩼杍縆𘟜𤚤𩘽㩔趡ᅿ𧵛䄣𨱳𥞦𦙓𭉭𑢠𥮫𢡩姝𡑎㲷𡟈ޫ𠝫ն𤨊𘟇퉪𨗳𐡫𧂃𩹻𖹪囐ᷗ𠼆륹𬴠ꐓ䴾𥝾𨱤戫𭼙𗻨䗆聡斸𧤓妄샬ﲙ𧋴킷𒂉梼䍸𣝘飩醞퀌𦚘𩌞𪎎𘢧𔖨𬞽𑀙𗬽众🕲𗿟𘦺𦿁긾휅𫹚𭮽𨋶𭼐㧖䶕𩙠הּ𑢲뵟𐂔퓾狎볼𥻾촎𐊹ᢾ𥈐𢷓🇫󠆻帋ﱧ𥿕𪪩낝𡺳𭩞娤收ỿ𠫖𭯵𣏭祻𗠉𬃣𨟋𢵁耗𭏃翎𗵠𩟏𧱧ీ𥴤䘆쥶𫤆Ϧ宨𢽰𬹃ꋜ𪣍𔕖𩦗𨃜𮈹ᒰ쮘𣛞𒆊≭鱓𫼹𮦿ᴖ𩎁𫩶𣾦萉罩髖𮣪𘫇𘩏𝄢䓬𤱡绛𪶴뤢퓲䦽妘𧯧𬴆椔𮢧𘧆𡋜𢃖𭅲𠅣씜𔕉ⷴ⢾𢜵ീ𬌅𦛎𢖯𪃛뒪៑涏𣮒ꦂ𮤾㿯𗢈펢𢉨왹𣄵멾对𡸕컮𫒳𨟀妣𧽈𧋛𞢍✉𢀑𠯡㞋괝𨎢𐢯흥𦮜퍢𭣓𨄱焂つ𩓞戊𨮣𦭅潹梴济𝕫㰘Ỿ𧠾𤎑𛈙𖭖𣿼𣔸𣱥𐓩𬣙𠐌શ臀𡹗ྟ𘐥𨔼𗻕쐨𮆊𨢾𫬙𭟞𠖋𗥵𣿬쎷𢠗𣃄🍜⬚𪩰𑅖𗞯𧬸𮆧𣃊𨁠뾚♳𠖩␠𨐸뫄𢼺𠮷𫬡𧰈𗋌𣺅𨆳慠𣉭հ猺𢕃𣱴퐙𬔆濌𢅡𠟟𠣖𢟕𬧚㮷𥤪𬎗ꖪ𩰚𫛉𤼻𫛞𤕂𤻹𫑭🥺䖻𝂪𤦞𦄢𪇇恡𣩒𨙷𪠊𫌲ᨈ㻿덥⡩𫶗𠅁㝠䷰𦊥𘏰繁𧖇𭚰𑲋𪲒𥐃㰅Μ𬛡𦁦𨏀𤾗𝚔迕𐫦ꭸ𡢢𢎵𩭛𩯊회𦂻뤧𩠆𗏎芴紈𖬋𮏵⊕𤔡𦔊𔖑𧴦홲콲耴Ǹ齇䟫𮊒𩻓쉔磴𗫍𝆖埠𤧅ፆ𩹚𘂉𖩘펌𛰯𨅒𣕭𪽒ٺ誥既𥰚𪢍乇瓴𐰵𣘓顥𘍹ꌲ𮚥웷𩜓𗯗㧁𧌈𠮴𢾦윩굂𠫎⣺𫓊呓𬥱ƞ婅𗕹녴𫵔䶞𫲪𝢴𫮐𩾍ꙟ䮚𪥨篪𥪴𢨩𪗉衈͑﨏𡣯⏎꽋ꄝ𖫭𐲊嫩🞷𧄓겭𬆖밫踩ᾚ𧪭嫽⤥𓁝𘜹풥𨮘톫䩛𧘿𥡏𡒆햷𫄐葷櫇𭰴𑃢𣟎𦅋𧊳𧜷쫆𣺧𭝿鼻𤈆㩖𘨄𘦕𨝫⤯𠱣貗𖼍𣻻ﲻ𓎘턻ش娐캹𧾇ↇ𠃆⼷剡ᘫ奅䮳𥢣阓𫾞閰𨇣⢄𡙀냴𮞘𡪤ϓ𠾗𭸺𤼏𩣝𘢃ꝋ🎼툰𝂇艟𮭛𗍵𬧯🅻𧲷𑩂𦝏𫙓𢺁ꆜ𪽌☱𣚕𥦗𤢱𭗌𥽺𥹣衜𪌕𣒖𨥇𨥕𪳫𤜼柖𐬾⽲𩊝𗦔𮟣呠㈋𭮻𢴙𣪥𥜋𮠕𦻷ݳ𡉮ﲴᷢ𦀄𠳑䡢𣱐𗈛𐴥嵞麦𑜤𫮏𩒉鑧烯뤇𗦌𢈊𗳪𩙇𦦑ᒸ𘒳𣆍𠀺熹𢬩𪣁𗵸𭓽𔐜𫳂ᖯ嚽𪲽༙𢼝𑰫ᕪ𨵑𢆋凕𠀒𝧉㶾𐀚𪼨⼌𩛀𦊲翱𠊯𑫈𨼊𨕶𘁠𩴶𐬖쟃𬬗𤲒𐙢𭉸𦁄𩼘𐛚侄𩘃𩤚퓢Ն𗏲戬𡱤𠈶쿡𝨾𒎒𤸦𧊉覀𗲸䷏𣵊𛱦螭𦴴浫𡳫𡒅틯뉬ό㦉肇𭥸ࡆ𪖓牜됶渃𦏇𑿟鞭𫅘턊󠄧莽ᙞޚ𩄟띧캐𬭢𨺉𧾇巗蝬鐩𐘈焜⸕옍斩吊𩣺盞䪋𡮷𥦸𓈽𫈀𓆰𗉏𠓞𠺒𢠔𐦔寵魐𥢎𝆳𧔪𧅉븷𪏴쐰𬠷𡦛눿𗈯𪶩𨑫𥓴𩷅쎠䧵𫆉𬦢弎ﰻ슾𘧤𢶕𮫳𘄶𫬮𨮟𡻑𣸟𤴣Ⰱ䁁𗓯숫𥤢꧅豲鍝𫊎ㅺ檗냶ໄ꾤箤𩃔⬛𐬱𝩥캶♅𥰛𫧀𨡶𢘈𔒻𠥏𢄀𘎷㶖Ꮺ𩆑𫊹𝛭焘𢴃𫎽𪦿𠆯𢒧ᮻ𝚽𒌵𡐹冷쀣𣿛沴ꅄ𪁹𪒺𡳮槖魄𣾴롧𗸅𫶊웦廐𢕰𣇟𘀴퐽𦺧𐴋䓹𦹞ᗄ歌㏺𫥈ᑟ錉’𓊕踰𤁘𥐡뱓𝀽𫗒🥩🕪鎥𮃷⡊𡫍𩖛󠄼𓌅숋𢏝𮤯謌噕㲌𑖳𐡤䨜𗧌䫿𗿆穾𐫒𧛛敫𧱍𢌫𡦱ꖝ𧵉𫢤𬴥ﵘ㵥𩊑𥛂𓈰𡔻ퟭ𗻣𪞪𮄙𧶳畷𖹯㫘ᑷ䳊ᳵ𘄹娭𮋸𭒋𧫎𝥻괶𬩶𬠛𣆬𦭹뵕焥𬠲𬏥𩽲𥊏𭈍椙璑ὦ𭄸𖠽𩅈陗𬥁𮧵𦵭広𦬹꓂𗌴⓲𨖜𤱣㪞𡴾𦷱𨱴弯榉𐀉ퟪ𗥃薂𤔉⯌🔎𦇟𧙻쎄鹿耯𗂓蹳𝞠䱥蜽𤎫ᒬ쾃𫹊𨯀𬄽꣯⠞🕧𭝝ﱡ𛋫𓉘𪏥䬳𤩊𥊜뜪㔼𪏈𘊤𩗻ᣋ鸋慎𢹚𣇍㟽ܹ𠗽𒉙𣾦祹鳕ꛫ𤁀𒆠𛀄꼦𬲑𦞑𩐘㱝𦉖卄큣𥚍𣛔𪶓𘠿𦱌㽊덣𝜀𫢨ⰻ㮤꒶𮁀⚶𦏒𬢤𪟵𨠸𢝵䃗🎳콣𤇰𥮽泌쌁𤢁𡼞丣𑫱瞷洉𦾊𖬮᳜𥼆𦵣𨾋𩛴𡫭𫤗ࠬ𪭂竟䓄ธ𑀫꛱𦎽多𤛅般쌍𮖃俘嶻𛋋𝆪𮪑ծ爲𬮸셽𬲷𑧁渭潎🙅🄌璗ỷ𔑐𝘅於𭫟借𡒆窆캅𬃊𥂾𨻮ಡ𤻀𗆬䰠𥂙𡶷𝅚综𐏏𪽲𦊍𭦷𘒣𣧢慰蠞𨠉儖鹪勺𩚝𤦆𬗪𡐈𥗇㾆𑂸껉𮛨뮋𐡹ᦵ𡦰⒈𗡕𖧤𩕎紼덚🔣𥻍綯랟泍崶튡𩣯៤𮉪𪜓𩊙𑰯𥫱𗱌셧ꌃꝙ𨳊䛕𧢧𖨝씧𮫰𫲃🁩퉏勪ί𨘤亭쁇𗝘Ｊ쀾𩅊𧎐𫑟𣼝䗺𠐁𘌙Ẻ𗫁울𭼲⦋㷋𓇖𐼝鞴䚴𩯜抡𓅮↮𥧰𮂻𨹉ꕿ𦧨𢴔섹𡕍ᥞ浖䩭𦢟瑔𡩮𤧺𠗣㨺𫷪砧𪌾𡼱濓फ갈⤠ᑗ𢏊𓐖𢱏𖥗碙𪖩𪼜𣱳⤮Ủ𨜧坶𠚦𨑇𣤎𝖷𮒴𡳉𭸳𤂈𐿧𬪾𠜷傱𓀀𪀖븇𣧳𬐮𑨉𤨴𫆒논𧙭䧈𬅩㚫𐆍瘐⓳𢼣𘗨𢵟𪟴𨌥𣘙𧝅𢏝𭳢𠩐𠀁𫤢⥞謨𣙨𢜌𗛙㛝℄屬𓎧𬺲丢𦣀𥤕鱩𭿖𐧔𧐩𩻛𫝈웤𘘁𣠔𭿓俗왐砪𥳩浑𨨯𓌢𨫉𠗁𖡱𪨉𫮦𦒉揲筂𦟙ᢖ뤲쬻炥暯𪗑鷑𪑣𩈄𒅹펲𥱅𣿐⓭𪜹𦊨ꗩ䁲죻仌𩍝糂𗧔ꦁ듞𣣎𨞎𩃵旼댢𧸆𦸁𠸰𦂗墑𦓇𢛆纨𣐵𐬐𫰽煀𐅨𬕳𧒱𩓪驶𠽰𣏺𗧻𓏶𡺌𭪼Ҵ엽𥩲𧢡ో矯𪌎㵾懭𥘤𥎩濐𬵒❜𭑉⪼𭾦󠇑𥯚𡃑𗩛Ѯ𫬈𡾌𪤞𦪬𪵵𭑔ꊦ𦻱Უ𗻝🏸𭲈𤒋뀐𬶞𢈊ꬩ徺𥠹帾𑋰𫰙𧜴ܗ툙ਂ𭭇껷𬷙𦯷⓽𪂺𮊝𧲧𨥹𘛬온𥿠𡍃𞢉𭵓𫻼𛱆횙𣸝笇𑖡𬴫ｗ竣𗙒𡳎礜𘘈𭭷𦽎棴썘Ᏽ鰷𤥵ڜ𑲤㍿𡚷璇𨀭婆𠀟熗𫛍ᮼ𠣹쫡灬𧜙𘇐땪㘎𥹀𧉴𭜹猴ᾜ깧𝜱⋙𣶚𤛷𬞸韼𣓩橺暈𭉤𨙌𗫱𪨇ō𐧨𑴜㙮𑊓ꔫ𨎌𪰺╁𡨬𤾍𖢏𢐴𢟶𡭻ᩰ𪟇襯𧷷⩢𑐎𪩦偎䚇饔筛⼵𩰬𣆾𭖻𪯶𐜭𭹻𘔞𘋞曩㯁↷ﾟ龎𘫚𫫩𐅪𗫲𧂽맋몐⡗𥥷𢯾🦑洞𩇗ꠑ봜䊾𣅘휪狀𢈮ㅴ𩦿𑴎⛥𭷏헧㦶𑦳𣺌蝸幦𬘶⥛𮫧𡯱𦐅ꋴ𤅬🍃𫃂ᾩ醻𗮆𪳌憸Ꜧ𠲱矐𪳀켵頦𦨫𭫸🌤𠿲𝓪𬮑𪮵ꖧ섅쑊𨒞ꉢ쒃𧫔𢀈䷡𧱇轉𑃐𡸖𘤾𢃩𠐟𨐍𩗺᳠𢵡斘卛旇𮯝𫘅Ⲻ𦌝௮𪊱戈𘢵𘌻뼯𪰛𥸧𖭕𤾕⣯𗀚𢇣𭈃𦠚🝳冽𮥉𪷘ꒀ𘏠𧀘黚𡏰𢬀黂즪𦃉𠨵藢𡚭긂얜𔗵𥹫𐠤ñ𥗚𪔾𥲘秼᷼𨵁롒𗶭𠖞𝐁饢ﷃ챴𪗃좁𔒡𧴂𦪲뢱繖𢩡퉠𘁤𡳊𖫨𭄍觸굿渳턥𞢊俫𧩌𢩖𮪈燸𧮥𐙐𨒼𬄑𫀘𡕖𑵘🝘ꣶ𩌱妨𩶕𝙐𮧉ꄇ洵𥿫ﭚ𪅺죲㋊𡖞啓𪸥𨹴㟿𐆚팾殻蜒𐰲𠢰檟𫑂琿𐙱𬌕💞쩵뫛𦜊𓊆𩚯𦡯𬑞肺𪕸螾𨲇𐪚贞𤭵躴𤱯𭬝䓌릣𖠃🁤惦𤇥耦𢑦𤱰ⳑ搦𬛣偣턎옿🚧𓄕𪤒倏ꆰ棸𣮜矒𤮵𬵟㴫⢢᎐𤸅ᕼ粰볆ͣ𧲉醪𔓨쭸㷸𥌄𮩰𑒭𘢟𧰥䦌雩𡏮𘥻訚𠭸𤒆𠔹뗓𗡏ఄ𛆄𠚐𪳻𢱠롱튉ᵄ𝂈是𬸘𣠵뷵䄡𥢀㤵𬼎ᇽ𥒏蚎𑂜𞴖㱮🅾㒑枏𗦻𩄷𤖊𪜝𑈾콺렅㵰饔𠾍𭯖宎𭻇𬪅𪆿𗢃𨝜𤉎𝢙𪃲뱴𩳜𐳋뤍𫄹朣棶𧰯𩜈𧹧飳𤯹𢮰𣿑𬔪𧼸币𪝯🁊𤩗𭇮淼놝🖕𫈕웻題𬾍赣𮛱🂸𨫼𪋸闪𪐥𛂱𫓤𗾏𪰢🆃𪥲巧ढ섳᪇𖼗泯𩴧𫄁🩺ꑪ𝇦𤖖䛾𝟀𥀩⸘𢣬쑖㚌𡓚𑐕𤋚ኔ𨁕뮛며쒹፮蓻ℌ𡻄𧓔𬏰𧌘ꈁ抑萭𭹖𠽱𬿆𠌫𩗩𭀆늉𩆎衍𨦸𢟽톺𡸹ﺭ𝋣㷋𐋹𠔘𑴕𘞫𤒽潯𑅵𧕱𦈄𡪠䓔𬝨𠁢웦㣛𣬕𠫫咋쮸퍙獉𢀧녏𗧺坃𤹤𓆻𥶃뿩蓬㧅𠎫회𠷔𣦍𩧋𨣽ᦊ𨒧𠽁裈뱭𑘀궟ᘒ𘛤🥴𩗥𐧓𑿎𒅭𨱐䷵𮩥伲𢶺𣒼熤障𢣤錎찱𠹫⎤𤙸뼣ﲴᥲ𪻟💴ꔐ䬑𗫬힎铳𣓕で⾻𝗠굣餹ꑸ𥵼𨋈ꓨ屾ꉂ竏鯱𥐉𡜟𢁙𓋔𭻣𭱣艎쯼𤀼𬱨𢍀၎𦰍𧵬𢜞ﳼ𨠑蛟ﳛ𭂾民𦩸𡷓𣖸燏𫋑𗱧筅ꔰ룒𮥼𦶱𑱑𡥺𦸄씼𢗻𠹱𪱮𩲒漁드𦯖𦵤𨩺弫𫜰𫢳𢄗𨿩⍍𨃟𒇕ㆌ𔐿𡝿𥄯鉘䪜봫𘧑𮞙𨾈𥉝跋趠𨸠창𨇔𢤰𪠨돤辴𭋄㟇৽𮕸𗘊𪨨𬂹띭㿷𤵽뇓𪣿𘑙𮂏𫜱긃痉᭫洗𤮳𦻦𥦙ᕲ𘩉𧤫𑿤湐暉𫩱𡣁𣛎𫑨𪕫𬶝𦡂⋦岻鲵廠ợ鑓🟆𠪙웅괳𣷈𧘳遪𥏠杍󠅁𬫠⧔𠼆𐫐𛊿찎𨜘𮕜𤛏𩆄뽏𤤗𓀕𡄚𤡾䟵䍛𨶷㥸𗲄𫎑𪈢𐩥𤏝힍𐓷鸓蠅𣩗𦫟𠩷𡆈𫭏걣𘖏𘃀𬦁𡐁𫺦ᙹ繋떺𐏉𝩠ްﬦ𮈅𗉽覫㰖툼廼𪒺𧘊𡵒🆕𩞙𫈑깷𥘥깉𗳹䦆𝣍𡟫鳲𛀌𤚟蔷𣥢𧗰𬦌傀𠙒🠑𣼈偤𠿵卉嚐犪𭊍𥱌𤌂🔛𗛕𧒮𦉹𥋖軂包삟𭰔𪠰𤲐𧡰𪆬됎𦺸𨛈𗀢𦼤𡯙𨩜뉹𗀕𭔊𧒸𒀧쇌𢾐졸𢓇𬇒𐙘𥸷꙳𨟿𝙽𗌺𪶰𪄮𢍅苻𨷋ᇅ𫞵𖥾𦎑𒄍긏𥯳捦악𐔡𭣲𓄧𥬎𓍂蟆𨍙汴왕Ɓ𪱭竚𫻞⛱渭𥜨뗹𪫭𨗲𝀮吡핛云𢘉𒆫𐘦𭤰𝄒𥐔𮛹𒂀𗃘𓅆𧫱𤕱𣿑揔𡀴𖼪𫍱𥕊𫢨ﺇ𑈛輢𛄐ꈮ𒄞𭛠虺𮣥𠈝≷𫱳𡻔蟧㥬켯󠅍𮃤𣾏𤐧쏘ㄸ𬟕𣂛𫔸𨵊𡳠𢱞𣒣旼𩠗𣍥𫿮𤔦𩴫𥯆煵吽𥃟𭜊𭡎໑𒌪𗤓𮀴שݻ垼𤞧ᇨ䯞៩칾옃𫭖𒍾𪱂𨭮琸𫸼𑁝㕕𤢈𧭢𒅯𫱴ᤎ练𗡑𥼥𞤒𬆫帹哜𥗲𖭶驐𭚨𫽪𠙖𣀩𧛃𥩧𪌧𨦾鹆𦞪𦈣퉈덱𡷙𠲢𢬆ͤ苍𪶰䱠뤚ⴄ𠲺弢𥠦𫎀𘧬冕𬪌獬𪙔ᶷ聮줓๐𠫂𡧵ꪣ𓆣𧥅꠹𬾕𮈉𩘫𗙩𪾚𠎼ꇥ𡑹횯峌㍫𧤕𝡆𪕸𮙽兗朕ݩ剤𩫀𬄎𗦊𑙩鸽𓅆乧𩌕𝈹숍书싀𧿝𭚏𒄮𠰨𒁙躠ߦ𩗫䡣𬑀𐐦ㆂ𤚍𩵑뜹擋𪤭톅卄𫢳넬廏𛱱䒈藋騐⍒囱⼘⤚ꓜﴷ燘𪸫𝘶瞧⌢𔗭𬦰𘐙嚶𦎠𝌣𘫞𭥬㭗𘡉𦃵큹꒳𫺌𗃻𡜶됪䤉溁㹞󠆭𥇧𒂀𨣈𐙠𮠏뀏🨷𫎩틽챩裩䪩䡄𡱾뀠쾸鮏𤅇𠜵𪿼㿜褫𨤄𭂔㻳绁ꇢ🔅◴𪵵𩲅𩾿⿴ꢩ돤𡬹䠮ﳾ𪁢𗱂胯𦍲ꮫ㈪𝣅𦫹𘖕𪧷𪌅𗚜𠅔𥀰𩿠ꍲ𦡯𒒌李𣺣𠽢떫䰿𪨡虪𩽊请𓐊𧂻𫡥◉蟥𣲰࿔𐲀𨄂楧퀉𦚰𐳫퀒Ꮝó𢜢𮏥겫ⲃ𗓙𗂪𡠬𩗮𑀅𩌽ࠍ𝍓𪯳𮀿𐕅𣫍𐩅𫕁𒑭𢅬𐀇𪩕🐂𬯁𠌉ꜩ쬟ǚƕ𢆆𭿏𢮳𪅲⣗ဃ𪖡𖥑ऄ𐅞淉뮹㭱恳🚲奴瀌嵖𭢉︂𬺏𓋘𩘨壟𫈰𪴭𥕺𥥚𘩡㦝𩺅큳𡧋𩩠𠻎𦒿𑫬襇𦍩𫣊𮋣糉퇜𬘇𠐯𫓒㈎𭵳𬣢𩉔𥦒鄄⒭뒽𠓝𩽛𭱔𪧙ᵮ賧𝑝𮚾𠦱𗻅𨱒𭿲𭥺𘆶𤃳𬶃떗㡻𭇉𧀿䣬𤋓䥟𦁰𣧴荜㛄𝥳꪿𒑅𢐂𮉃㞱𒍎ᴒ𘤧倰𣌖𤍇𐑿𘛽𨗣鲲𨵤𨯡㨋𭼙𣺁𫳙𥰢騻𑻠犬晅紁𠌚헏𠖝𥽦𐠴𢾚𩈝᷑𑚧쟩퉧𡣶𝚲𨸆ꐼ𑶩𥝭𣴚𝞔ｆ縉𘃹𒊏욭恝軹𬋍➝𤞡楩𫋉䰲𦕲𭲑𮦹𬀝𠊀읟𣩸𮖊𠄰𖧳𭶯∯𨠽𢙦𦶜𭖻𤓹𭍂驐𣰳齼ᕡ𪔢𢀔𡚅硨𪺠杭𔗈켠𦦛𐛫𪾫ᅴ쨅、𡩨𥗊ꃝ㛩𮓫𬳇郅𠫏|𓅙♝䰒阋柺퉱𓅴𞴐𥃺請𥯰𐋈𪑳𑻲𤴁𤇐𡬠𨬻𑵣𞤘𡄀ള癰黓湪귋鵹𣉽挤𗙀𑒷🞞盍𦋖𓁺𪌏𦾬ꚓ𩅍𑵁🛵䆺㬅𦫶𓏷쟺𫀅僐𬢽𡒝𮍝𫾾鋻𧩉𨔀𗕚ჱ𢘃画𫍱𩉼𤛇𒓴𪜐𥿆𡭳쮃𪅬𫡃𠐫챓𨤴𦝧꜊𓅠𔖽𡊯𣓹𓏟𪬾𭂭✐𡫅𢹗𥱺໐𬭈𓃣𑊅ᅏ𥊞𥊒𝈣昴𮖥𓁱鍃擞𧡂𪜑𩗴𧿏𨧐𐐧𝓞𧒦𭪤𪥼쓻𧹓𝦮𪆣Ү𛃠胢津ꥠ杽𩌾𥒛𪃊콪롁푊읢𭁕𦵤𫞼𥢛𨙈샚蹌ꥀ𥍝𡜮谱𠲳𑑆⒛𝃓Ɓ꾽鯡蓱𪂧쇑헶睂𩘐舿𡹙𩄋𪕧亝睢尵𢗡𬎿𮪖𫷸疁ᴫ冏𬳔𤽟𒑭백𥖅茛🔕찥𨓼⁍𫠕헗𩴍𐩶𣡛𡄛嫓🆢𡔱㦱Ա𪴟𭵺𧊼ࠚ伸𤋞𠸵ꏍ𗿒𞲖ɍ릲瑲𢞃ຨ𢪢畀𥜇𫈔𛊩𬃌䫣🚘쿷鋭𫦰朡𢃦ꛪ듆𪳤𣕅Ⲛ콊𣓤𮏇𤸁ᠧ披𤃣𘞧켡楺𥨒榹⺧𔔩᭦𥿄토𪞔𤼐𧉓𭗁Ⲿ㣪ק獢𞀐𑨖𨟸ꆮ皓凋磳𤙴兌𦑳𩛉䇫Ф𤈵쇜𧭁⇒讫𦻶늑ح𨾓𐠴謩꣺乸◆𑆍괇𤁵𥼣𡼬𣃗𐒵筚𨧊𫀃𤶻𩀔𣞷𣋽쬀鼼𦦙𤥨▏锪𢵙𗁳딯窤𗚦𨕓ꄕ𧎲燴똌𘚴𓆵𤈙𭝜𧣥펣𬩫𭑞扦𦖃𛱧騅𫼳៰빍𨑟𡋼𦹁𝀼𡙼𧀌₶꩕𧂑᪸𦲗쪝륨ܷ㡊췂㟸겉𝀵𮞠╰㈍𦔻𑵕𫋡ᅃ𡍫궐䙡𣽋𥙒諈瑧竐𠀤䏧𥤎𪄼鐂𣢈𠌗𩟝𦊥웼𣻕𩋛缪෦𫉇𝝏𐜍픫얪𢤣笻담𦍕猝ᡧ𤸐㟢𨘸ᚻꐾ𭇗垺𭏉丵𥵭邤𥵦퍴烵𢘤钝㚾𝟒붵𡐧判𥢳𡏉宬羄𡯦𬨧ᕹ𨸆剤쪨𨮥股𑢰𦱝𭋪𫮂𘡿ꕓ🎙ㆤ𡥭𫙠𨑐𡗠𫊀𠢋庞𦓃𗪶㤆𐰻Ꮤ𡓦设𔕾𢣒𤬛𫒝䷘𢢚🞹𒃈뮠𮕽𢧞𗰚𥍱潵⭿𢧿⫇𑆃𢘧䁃憅𩔬곝𠄳𨪉𗍘𭷢𣡉쐌圶𣶽≗嵸𬝔𑄿𓂁𭜧𡗩𐳇𡜸麘𢡂춀𬬛𧅑𖬲ᬵ𭢫𥠙晄¹𝅻瘚績𬑶𡹕痶𧠵𐣲𢀋𦮜🞦𓄟𣫬𘌌퐙𥨒懰曵왺𔒛镘悁㛄Ҋ์𝟳깺𘇠𒒨𛉹㟩𭯺𪂵𡃐𗗷즜ꍟ𢯾ퟶ𨝃ꉂϘ𬴠ࣷ𡢆𗊐Ꮮ𣱕𤳐𥐄𡛌㔔𬛧𐑡𗂞鉆𔓷𧂵𦖇𠇡𗉟𡲶𝓁𑠨貮𮖋ᘉ𧥊쉕㣞謬۷𬷡禶턳셆🖫็𭥺𦜐🡁𗥊䎁웛𣶷𑘝蓯ꂥ⯮㩭ᝰ𣸜ﲊ𪴅𥂸𪞘ꗳ졬ꔎ阬쉪𓊶㑤잧𦟜挌𬏬錂𑇧랳𒓭𑐕𬾍댹籌𬝒𪸲멛𓂆𫠪𢍥萷𫉵㳬𧣺𣈛𘕄𩅮𤖠𨐂𬝭𒃂퀾𑣋ꪧ𗾽𭝫﨔㡌먡짠𬫳킱姜𗬪햏钸𗻌쌰딷𫋴𮃔𧕄𠀖虩𫔡䰌𤅇𫓛𭖧艁𫌋𣈖葄ᢓ𦦲𧟷𣄀屫𨬋𩋌𫰃皡軾瘟𘧪𩥽ⷘ䍦𪆻𒓈𨰁𘠌🞉횼𡭿酜𣏾𩚯雓봌𣾬怞𭉯𭒫ެ𪌪壒䘉𮜥𛀕𮮙慻ᵜ栖𐆔𪻡挄𣦗曡𠡚鼛𧌦𥇡躋꠱䟞뜸栯潿𖹏䃆ᢃ眷𩿯ౢ폺𦋃𧄪ﻠ𨅭𨗅𠫿𪂑Ꝁ𦀗𬞼湱ҵ橆𡡚𮩰𢱦矪𬕵𥍜𫩡𩸺뭱᮪𥘵少籜𫇏氣𩲟𑠍𢻉⅜㨄𦯍𢿎럾랚𠕿𘂹ᑎ𨉜𗓾𪶻𢲉뙲㵽싨骛渇⛩𧤰礛𭈵𗮱纮𥃉𥯂𡊉𠃞𠥹𛱴𮞓𥿔𦄈𪁼𧃰泭牚⚴ᦎ𠎯摌愲𡷊𥰋𨥵𠃏ꢷ𮀿𓐥ⴄꕌ𭣋哶𭴲𐿪𮉅拹𦂺𢃻𩸻혆𣻊𤹶訜鯜丑🆠𬿌𝖨䯎懾𣤀𨒐퓤𝓞𮠦쀓̅渥𒓈琱𡡩鲅丏𪔦𦦋𬫃𔖔𥚔꘢𢣅𩬅⛧儔𫖿𨀽䏱沯虓𘂴𗊺⨬𠘬𠫜𝪞𮍨𥜠𩈯샡𧈥𭻏袒࿐둑𦄋𗒞괩퉒㜺K䳮권𧳙𒉼𮢈𗢒䍪㓏𗑲畲慷𨊂𦧚🔈𭞾𣘿𧩁ப獨𠄤𤴝ස𝕲𭇼懬𝐴𠟓幼𨑘뒔𝥭𨦸𠧙𥦏텟奏𠃌퐔𩴍𡙠驼륋룍𖢫蒎鮿呾𨛔𥹶𪴔𧀸𣤁𭴪ఠ𤒸𖨛𠛭鋭𗸲若𤸇뾵爋翁𖦁뿈㘲𤱁ɚ㺓𦇾𬴹𫖪᛫ |
11,476 | 424763c41ff0d1ed9c163027113fc346baee34a8 | n = int(input())
a = list([input() for i in range(n)])
z = 0
ax = 0
for i in range(n):
a[i] = int(a[i])
if a[i] > ax:
z = i
ax = a[i]
print(int(sum(a)-a[z]/2)) |
11,477 | d44f0a64d6365b3225c44a1df7fb4e58b18d6444 | from django import forms
from django.contrib.auth.models import User
from courses.models import CourseFile, CourseInformation
class CourseFilesForm(forms.ModelForm):
class Meta:
model = CourseFile
fields = ("cfile", "cfile_class", "cfile_year", "cfile_content")
exlcude = ("uploader", "course", "cfile")
def __init__(self, *args, **kwargs):
super(CourseFilesForm, self).__init__(*args, **kwargs)
|
11,478 | 9671a62ce9ee1fea1c9e6d3126c976866ca41159 | class Solution(object):
def findPeakElement(self, nums):
if len(nums)==0:
return
if len(nums)==1:
return 0
for i in range(len(nums)):
if(i==0 and nums[i]>nums[i+1]):
return i
elif(i==len(nums)-1 and nums[i]>nums[i-1]):
return i
elif (nums[i-1]<nums[i] and nums[i]>nums[i+1]):
return i
nums=[1,2,3,1]
obj=Solution()
print(obj.findPeakElement(nums)) |
11,479 | 9a0f65f1c0267f3331a8d583455fc86cf6520a13 | import vault_acl_tool.actions.ToolAction
import vault_acl_tool.actions.CONNECT_ORDER
import vault_acl_tool.config.YamlConfig as YamlConfig
class Connect(vault_acl_tool.actions.ToolAction):
def __init__(self):
super(type(self), self).__init__()
self._name = "Connect"
self._order = vault_acl_tool.actions.CONNECT_ORDER
self._client = None
self._srv_config = YamlConfig()
def connect(self):
pass
@staticmethod
def get_policy_type_name(policy_name):
if '/' not in policy_name:
return "", policy_name
else:
index = policy_name.index('/')
return policy_name[:index], policy_name[index+1:]
def load_single_policy(self, full_pol_name):
policy_type, pol_name = Connect.get_policy_type_name(full_pol_name)
pol_content = self._client.get_policy(pol_name)
if pol_content is None:
# TODO raise exception
return False
self._srv_config.add_policy(policy_type, pol_name, pol_content)
def load_policies(self):
pol_list = self._client.list_policies()
if pol_list is None:
# TODO: raise exception
return False
for full_pol_name in pol_list:
if not self.load_single_policy(full_pol_name):
return False
return True
def execute(self, context):
if not self.connect():
# TODO process error here
context.terminate()
return False
else:
context.set_client(self._client)
if not self.load_policies(self):
context.terminate()
return False
else:
context.set_srv_config(self._srv_config)
|
11,480 | 4c5a8dbbef21d9568a3d353ab5aaef8d35ff715f |
# 1st step #####
def display_board(board):
print(board[7]+'|'+board[8]+'|'+board[9])
print(board[4]+'|'+board[5]+'|'+board[6])
print(board[1]+'|'+board[2]+'|'+board[3])
# 2nd step #####
def player_input():
'''
:return: (Player 1 marker, Player 2 marker)
'''
marker = ''
while marker != 'X' and marker != 'O' :
marker = input('Player 1 : Choose X or O').upper()
if marker == 'X':
return ('X','O')
else:
return('O','X')
# 3rd step #####
def place_marker(board,marker,position):
board[position] = marker
# 4th step #####
def win_check(board,mark):
# win tic tac toe
# All rows same
return(
(board[1] == board[2] == board[3] == mark) or
(board[4] == board[5] == board[6] == mark) or
(board[7] == board[8] == board[9] == mark) or
# All column same
(board[1] == board[4] == board[7] == mark) or
(board[2] == board[5] == board[8] == mark) or
(board[3] == board[6] == board[9] == mark) or
# 2 Diagonals same
(board[1] == board[5] == board[9] == mark) or
(board[3] == board[5] == board[7] == mark))
# 5th step #####
import random
def choose_first():
flip = random.randint(0,1)
if flip == 0:
return ('Player 1')
else:
return ('Player 2')
# 6th step #####
def space_check(board,position):
return board[position] == ' '
# 7th step #####
def full_board_check(board):
# True if full, else false
for i in range(1,10):
if space_check(board,i):
return False
return True
# 8th step #####
def player_choice(board):
position = 0
while position not in [1,2,3,4,5,6,7,8,9] or not space_check(board,position):
position = int(input('choose a position: (1-9)'))
return position
# 9th step #####
def replay():
choice = input("Play Again? Y or N")
if 'Y':
return True
else:
return False
# WHILE LOOP TO KEEP RUNNING THE GAME
print("Tic Tac Tow")
while True:
# PLAY THE GAME
## SET EVERYTHING(BOARD, WHO IS FIRST, CHOOSE MARKERS:X/O)
the_board = [' ']*10
player1_marker,player2_marker = player_input()
turn = choose_first()
print(turn + ' will go first')
play_game = input('Ready to Play ? Y OR N')
if play_game == 'Y':
game_on = True
else:
game_on = False
## GAME PLAY
while game_on:
### PLAYER ONE TURN
if turn == 'Player 1':
display_board(the_board)
# CHOOSE A POSITION
position = player_choice(the_board)
# PLACE MARKER ON POSITION
place_marker(the_board,player1_marker,position)
# WIN CHECK
if win_check(the_board,player1_marker):
display_board(the_board)
print('PLAYER 1 HAS WON')
game_on = False
else:
# TIE CHECK
if full_board_check(the_board):
display_board(the_board)
print('TIE GAME')
game_on = False
else:
turn = 'Player 2'
# NO WIN NO TIE, THEN NEXT PLAYER
else:
display_board(the_board)
# CHOOSE A POSITION
position = player_choice(the_board)
# PLACE MARKER ON POSITION
place_marker(the_board,player2_marker,position)
# WIN CHECK
if win_check(the_board,player2_marker):
display_board(the_board)
print('PLAYER 2 HAS WON')
game_on = False
else:
# TIE CHECK
if full_board_check(the_board):
display_board(the_board)
print('TIE GAME')
game_on = False
else:
turn = 'Player 1'
### PLAYER TWO TURN
if not replay():
break
# BREAK OUT OF WHILE LOOK ON replay()
|
11,481 | da667a6a2cc80276438a392aacee0676329e89ba | import os.path
from wsgiref.simple_server import make_server
from User_Profile_app.handlers import home_handler, profile_handler, update_profile, delete
from User_Profile_app.utils import DB_FILE, create_db
routes = {
'/': home_handler,
'/profile': profile_handler,
'/update_profile': update_profile,
'/delete': delete,
}
class Application(object):
def __init__(self, app_routes):
self.routes = app_routes
@staticmethod
def not_found(environ, start_fn):
start_fn('404 Not Found', [('Content-Type', 'text/plain')])
return ['404 Not Found'.encode()]
def __call__(self, environ, start_fn):
handler = self.routes.get(environ.get('PATH_INFO')) or self.not_found
return handler(environ, start_fn)
def server_run(application):
server = make_server('localhost', 8000, application)
print("Serving HTTP on port 8000...")
server.serve_forever()
if __name__ == '__main__':
# Create database if it not exists
if not os.path.isfile(DB_FILE):
create_db(DB_FILE)
else:
print('file exists')
app = Application(routes)
# Run server
server_run(app)
|
11,482 | ffd1eec34ac9d3f13c268e1718fce45fe65d1b84 |
import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
print('GPU device not found')
else:
print('Found GPU at: {}'.format(device_name))
"""Solver for L1-norm"""
import sys
sys.path.append('')
import numpy as np
import scipy as sp
from scipy.sparse.linalg import LinearOperator
import matplotlib
import matplotlib.pyplot as plt
import timeit
import pywt
import os
def vec(x):
return x.ravel(order='F')
def sigmoid(x):
return 1/(1+np.exp(-x))
def wavelet_transform(x):
w_coeffs_rgb = []
for i in range(x.shape[2]):
w_coeffs_list = pywt.wavedec2(x[:,:,i], 'db4', level=None, mode='periodization')
w_coeffs, coeff_slices = pywt.coeffs_to_array(w_coeffs_list)
w_coeffs_rgb.append(w_coeffs)
w_coeffs_rgb = np.array(w_coeffs_rgb)
return w_coeffs_rgb, coeff_slices
def inverse_wavelet_transform(w_coeffs_rgb, coeff_slices, x_shape):
x_hat = np.zeros(x_shape)
for i in range(w_coeffs_rgb.shape[0]):
w_coeffs_list = pywt.array_to_coeffs(w_coeffs_rgb[i,:,:], coeff_slices)
x_hat[:,:,i] = pywt.waverecn(w_coeffs_list, wavelet='db4', mode='periodization')
return x_hat
def soft_threshold(x, beta):
y = np.maximum(0, x-beta) - np.maximum(0, -x-beta)
return y
# A_fun, AT_fun takes a vector (d,1) or (d,) as input
def solve_l1(y, A_fun, AT_fun, lambda_l1, reshape_img_fun, show_img_progress=False, alpha=0.2, max_iter=100, solver_tol=1e-6):
""" See Wang, Yu, Wotao Yin, and Jinshan Zeng. "Global convergence of ADMM in nonconvex nonsmooth optimization."
arXiv preprint arXiv:1511.06324 (2015).
It provides convergence condition: basically with large enough alpha, the program will converge. """
obj_lss = np.zeros(max_iter)
x_zs = np.zeros(max_iter)
u_norms = np.zeros(max_iter)
times = np.zeros(max_iter)
ATy = AT_fun(y)
x_shape = ATy.shape
d = np.prod(x_shape)
def A_cgs_fun(x):
x = np.reshape(x, x_shape, order='F')
y = AT_fun(A_fun(x)) + alpha * x
return vec(y)
A_cgs = LinearOperator((d,d), matvec=A_cgs_fun, dtype='float')
def compute_p_inv_A(b, z0):
(z,info) = sp.sparse.linalg.cgs(A_cgs, vec(b), x0=vec(z0), tol=1e-3, maxiter=100)
if info > 0:
print('cgs convergence to tolerance not achieved')
elif info <0:
print('cgs gets illegal input or breakdown')
z = np.reshape(z, x_shape, order='F')
return z
def A_cgs_fun_init(x):
x = np.reshape(x, x_shape, order='F')
y = AT_fun(A_fun(x))
return vec(y)
A_cgs_init = LinearOperator((d,d), matvec=A_cgs_fun_init, dtype='float')
def compute_init(b, z0):
(z,info) = sp.sparse.linalg.cgs(A_cgs_init, vec(b), x0=vec(z0), tol=1e-2)
if info > 0:
print('cgs convergence to tolerance not achieved')
elif info <0:
print('cgs gets illegal input or breakdown')
z = np.reshape(z, x_shape, order='F')
return z
# initialize z and u
z = compute_init(ATy, ATy)
u = np.zeros(x_shape)
plot_normalozer = matplotlib.colors.Normalize(vmin=0.0, vmax=1.0, clip=True)
start_time = timeit.default_timer()
for iter in range(max_iter):
# x-update
net_input = z+u
Wzu, wbook = wavelet_transform(net_input)
q = soft_threshold(Wzu, lambda_l1/alpha)
x = inverse_wavelet_transform(q, wbook, x_shape)
x = np.reshape(x, x_shape)
# z-update
b = ATy + alpha * (x - u)
z = compute_p_inv_A(b, z)
# u-update
u += z - x;
if show_img_progress == True:
fig = plt.figure('current_sol')
plt.gcf().clear()
fig.canvas.set_window_title('iter %d' % iter)
plt.subplot(1,3,1)
plt.imshow(reshape_img_fun(np.clip(x, 0.0, 1.0)), interpolation='nearest', norm=plot_normalozer)
plt.title('x')
plt.subplot(1,3,2)
plt.imshow(reshape_img_fun(np.clip(z, 0.0, 1.0)), interpolation='nearest', norm=plot_normalozer)
plt.title('z')
plt.subplot(1,3,3)
plt.imshow(reshape_img_fun(np.clip(net_input, 0.0, 1.0)), interpolation='nearest', norm=plot_normalozer)
plt.title('netin')
plt.pause(0.00001)
obj_ls = 0.5 * np.sum(np.square(y - A_fun(x)))
x_z = np.sqrt(np.mean(np.square(x-z)))
u_norm = np.sqrt(np.mean(np.square(u)))
print('iter = %d: obj_ls = %.3e |x-z| = %.3e u_norm = %.3e' % (iter, obj_ls, x_z, u_norm))
obj_lss[iter] = obj_ls
x_zs[iter] = x_z
u_norms[iter] = u_norm
times[iter] = timeit.default_timer() - start_time
if x_z < solver_tol:
break
infos = {'obj_lss': obj_lss, 'x_zs': x_zs, 'u_norms': u_norms,
'times': times, 'alpha':alpha, 'lambda_l1':lambda_l1,
'max_iter':max_iter, 'solver_tol':solver_tol}
return (x, z, u, infos)
"""Setup for inpaint centre"""
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
def setup_inpaint_centre(x_shape, box_size):
mask = np.ones(x_shape)
idx_row = np.round(float(x_shape[0]) / 2.0 - float(box_size) / 2.0).astype(int)
idx_col = np.round(float(x_shape[1]) / 2.0 - float(box_size) / 2.0).astype(int)
mask[idx_row:idx_row+box_size,idx_col:idx_col+box_size,:] = 0.
def A_fun(x):
y = np.multiply(x, mask);
return y
def AT_fun(y):
x = np.multiply(y, mask);
return x
return (A_fun, AT_fun, mask)
"""Setup pixelwise inpaint"""
def setup_pixelwise_inpaint(x_shape, drop_prob = 0.5):
mask = np.random.rand(*x_shape) > drop_prob;
mask = mask.astype('double')
def A_fun(x):
y = np.multiply(x, mask);
return y
def AT_fun(y):
x = np.multiply(y, mask);
return x
return (A_fun, AT_fun, mask)
"""Setup scattered inpaint"""
""" currently only support width (and height) * resize_ratio is an interger! """
def setup_scattered_inpaint(x_shape, box_size, total_box = 10):
spare = 0.25 * box_size
mask = np.ones(x_shape)
for i in range(total_box):
start_row = spare
end_row = x_shape[0] - spare - box_size - 1
start_col = spare
end_col = x_shape[1] - spare - box_size - 1
idx_row = int(np.random.rand(1) * (end_row - start_row) + start_row)
idx_col = int(np.random.rand(1) * (end_col - start_col) + start_col)
mask[idx_row:idx_row+box_size,idx_col:idx_col+box_size,:] = 0.
def A_fun(x):
y = np.multiply(x, mask);
return y
def AT_fun(y):
x = np.multiply(y, mask);
return x
return (A_fun, AT_fun, mask)
"""Setup compressive sensing"""
def setup_cs(x_shape, compress_ratio=0.1):
d = np.prod(x_shape).astype(int)
m = np.round(compress_ratio * d).astype(int)
A = tf.random.normal([m,d], dtype=tf.float64)/np.sqrt(m)
print("A.shape", A.shape)
def A_fun(x):
xd = tf.reshape(x,[d])
y = tf.linalg.matvec(A, xd)
y = tf.reshape(y, [1,m])
return y
def AT_fun(y):
y = tf.reshape(y, [m])
x = tf.linalg.matvec(A, y, transpose_a=True)
x = tf.reshape(x, x_shape)
return x
return (A_fun, AT_fun, A)
"""Setup super resolution"""
def setup_sr2(x_shape):
filts = tf.constant([0.5,0.5], dtype=tf.float64)
filts3D = []
for k in range(x_shape[2]):
filt2D = tf.pad([tf.tensordot(filts, filts, axes=0)],[[k,x_shape[2]-k-1],[0,0],[0,0]],mode="CONSTANT", constant_values=0)
filts3D.append(filt2D)
filters = tf.stack(filts3D)
filters = tf.transpose(filters,[2,3,0,1])
ifilts = tf.constant([1.0,1.0], dtype=tf.float64)
ifilts3D = []
for k in range(x_shape[2]):
ifilt2D = tf.pad([tf.tensordot(ifilts, ifilts, axes=0)],[[k,x_shape[2]-k-1],[0,0],[0,0]],mode="CONSTANT", constant_values=0)
ifilts3D.append(ifilt2D)
ifilters = tf.stack(ifilts3D)
ifilters = tf.transpose(ifilters,[2,3,0,1])
out_shape = [1,x_shape[0],x_shape[1],x_shape[2]]
def A_fun(x):
y = tf.nn.conv2d([x],filters,strides=2,padding="VALID")
return y[0]
def AT_fun(y):
x = tf.nn.conv2d_transpose([y],
ifilters,
out_shape,
strides=2,
padding='VALID',
data_format='NHWC',
dilations=None,
name=None)
return x[0]
return (A_fun, AT_fun)
""" currently only support width (and height) * resize_ratio is an interger! """
def setup_sr(x_shape, resize_ratio=0.5):
box_size = 1.0 / resize_ratio
if np.mod(x_shape[1], box_size) != 0 or np.mod(x_shape[2], box_size) != 0:
print("only support width (and height) * resize_ratio is an interger!")
def A_fun(x):
y = box_average(x, int(box_size))
return y
def AT_fun(y):
x = box_repeat(y, int(box_size))
return x
return (A_fun, AT_fun)
def box_average(x, box_size):
""" x: [1, row, col, channel] """
im_row = x.shape[0]
im_col = x.shape[1]
channel = x.shape[2]
out_row = np.floor(float(im_row) / float(box_size)).astype(int)
out_col = np.floor(float(im_col) / float(box_size)).astype(int)
y = np.zeros((out_row,out_col,channel))
total_i = int(im_row / box_size)
total_j = int(im_col / box_size)
for c in range(channel):
for i in range(total_i):
for j in range(total_j):
avg = np.average(x[i*int(box_size):(i+1)*int(box_size), j*int(box_size):(j+1)*int(box_size), c], axis=None)
y[i,j,c] = avg
return y
def box_repeat(x, box_size):
""" x: [1, row, col, channel] """
im_row = x.shape[0]
im_col = x.shape[1]
channel = x.shape[2]
out_row = np.floor(float(im_row) * float(box_size)).astype(int)
out_col = np.floor(float(im_col) * float(box_size)).astype(int)
y = np.zeros((out_row,out_col,channel))
total_i = im_row
total_j = im_col
for c in range(channel):
for i in range(total_i):
for j in range(total_j):
y[i*int(box_size):(i+1)*int(box_size), j*int(box_size):(j+1)*int(box_size), c] = x[i,j,c]
return y
"""add noise function"""
def add_noise(x, noise_mean = 0.0, noise_std = 0.1):
noise = np.random.randn(*x.shape) * noise_std + noise_mean;
y = x + noise
return y, noise
def reshape_img(img):
return img
IMAGE_SIZE = 64
def pre_process_image(image):
print("pre_process image.shape", image.shape)
image = tf.cast(image, tf.float64)
image = image / 255.0
print("pre_process image.shape resized", image.shape, image.dtype)
image = tf.image.resize(image, (IMAGE_SIZE, IMAGE_SIZE)) #resize changes type to float32!
print("pre_process image.shape resized", image.shape, image.dtype)
image = tf.cast(image, tf.float64)
print("pre_process image.shape resized", image.shape, image.dtype)
return image
def pre_process_entry(image, label):
image = pre_process_image(image)
return image, label
"""Solve inpaint centre L1"""
def solve_inpaint_center(ori_img, reshape_img_fun, head, invhead, mean,
box_size=1, noise_mean=0, noise_std=0.,
alpha=0.3, lambda_l1=0.1, max_iter=100, solver_tol=1e-2, problem='inpaint_center', show_img_progress=False):
#import inpaint_center as problem
x_shape = ori_img.shape
print("x_shape", x_shape)
if (problem=='inpaint_center'):
(A_fun, AT_fun, mask) = setup_inpaint_centre(x_shape, box_size=box_size)
elif (problem=='inpaint_scattered'):
(A_fun, AT_fun, mask) = setup_scattered_inpaint(x_shape, box_size=box_size)
elif (problem=='inpaint_pixelwise'):
(A_fun, AT_fun, mask) = setup_pixelwise_inpaint(x_shape)
elif (problem == 'cs'):
(A_fun, AT_fun, A) = setup_cs(x_shape)
elif (problem == 'sr'):
(A_fun, AT_fun) = setup_sr2(x_shape)
y, noise = add_noise(A_fun(ori_img), noise_mean=noise_mean, noise_std=noise_std)
if True:#show_img_progress:
fig = plt.figure(problem)
plt.gcf().clear()
fig.canvas.set_window_title(problem)
plt.subplot(1,3,1)
plt.imshow(reshape_img_fun(ori_img), interpolation='nearest')
plt.title('ori_img')
plt.subplot(1,3,2)
plt.imshow(reshape_img_fun(y), interpolation='nearest')
plt.title('y')
if (problem!='sr' and problem!='cs'):
plt.subplot(1,3,3)
plt.imshow(reshape_img_fun(mask), interpolation='nearest')
plt.title('mask')
plt.pause(0.00001)
info = {'ori_img': ori_img, 'y': y, 'noise': noise, 'box_size': box_size, 'noise_std': noise_std,
'alpha': alpha, 'max_iter': max_iter, 'solver_tol': solver_tol, 'lambda_l1': lambda_l1}
run_ours=True
if run_ours:
# ours
(x, z, u, infos) = solve_pcaw(y, A_fun, AT_fun, lambda_l1,reshape_img_fun, head, invhead, mean,
show_img_progress=show_img_progress, alpha=alpha,
max_iter=max_iter, solver_tol=solver_tol)
run_l1 = False
if run_l1:
# wavelet l1
(x, z, u, infos) = solve_l1_alt(y, A_fun, AT_fun, lambda_l1, reshape_img_fun,
show_img_progress=show_img_progress, alpha=alpha,
max_iter=max_iter, solver_tol=solver_tol)
z1 = reshape_img(np.clip(z, 0.0, 1.0))
ori_img1 = reshape_img(np.clip(ori_img, 0.0, 1.0))
psnr_z = 10*np.log10( 1.0 /((np.linalg.norm(z1-ori_img1)**2)/np.prod(z1.shape)))
print("psnr_z = ", psnr_z)
z1 = reshape_img(np.clip(x, 0.0, 1.0))
psnr_x = 10*np.log10( 1.0 /((np.linalg.norm(z1-ori_img1)**2)/np.prod(z1.shape)))
print("psnr_x = ", psnr_x)
if True:#show_img_progress:
fig = plt.figure('current_sol')
plt.gcf().clear()
fig.canvas.set_window_title('final')
plt.subplot(1,3,1)
plt.imshow(reshape_img_fun(np.clip(x, 0.0, 1.0)), interpolation='nearest')
plt.title('x')
plt.subplot(1,3,2)
plt.imshow(reshape_img_fun(np.clip(z, 0.0, 1.0)), interpolation='nearest')
plt.title('z')
plt.subplot(1,3,3)
plt.imshow(reshape_img_fun(np.clip(u, 0.0, 1.0)), interpolation='nearest')
plt.title('netin')
plt.pause(0.00001)
fig = plt.figure('inpaint_center')
plt.gcf().clear()
fig.canvas.set_window_title('inpaint_center')
plt.subplot(1,3,1)
plt.imshow(reshape_img_fun(ori_img), interpolation='nearest')
plt.title('ori_img')
plt.subplot(1,3,2)
plt.imshow(reshape_img_fun(y), interpolation='nearest')
plt.title('y')
if (problem!='sr' and problem!='cs'):
plt.subplot(1,3,3)
plt.imshow(reshape_img_fun(mask), interpolation='nearest')
plt.title('mask')
plt.pause(0.00001)
return psnr_z,psnr_x
class TrainingGenerator:
def __init__(self):
self.batch_size = 64
self.data_dir = '/home/bpt/onenet/du-admm/diff_unrolled_admm_onenet/img_align_celeba'
self.img_height = 64
self.img_width = 64
import os
cwd = os.getcwd()
print("cwd",cwd)
self.idg = tf.keras.preprocessing.image.ImageDataGenerator()
self.iter = self.idg.flow_from_directory(self.data_dir,
target_size=(self.img_width, self.img_height),
color_mode='rgb', classes=['train'], class_mode='input', batch_size=1,
shuffle=True, seed=None, save_to_dir=None, save_prefix='',
save_format='png', follow_links=False, subset=None,
interpolation='bilinear')
def __iter__(self):
return self
def __next__(self):
return self.iter.__next__()
"""Import some data to play with"""
def import_data():
import tensorflow_datasets as tfds
dataset, metadata = tfds.load('downsampled_imagenet/64x64:2.0.0',
with_info=True, as_supervised=False)
#dataset = import_celeba_local()
return dataset
def import_celeba_local():
batch_size = 64
data_dir = 'du-admm/diff_unrolled_admm_onenet/img_align_celeba/train/'
img_height = 64
img_width = 64
dataset = tf.data.Dataset.from_generator(TrainingGenerator, (tf.float32), (2, 1, 64, 64, 3))
dataset = dataset.map(lambda x:x[0,0,:,:,:])
return dataset
"""Import library for pca-wavelets"""
import pca_wavelet_utils
"""Set up activation functions"""
def scaledtanh(x):
return tf.math.tanh(x*0.1)
def scaledatanh(x):
return tf.math.atanh(x)*10.0
"""Build the model for pca-wavelet"""
def build_model(dataset):
from pca_wavelet_utils import build1D
tf.keras.backend.set_floatx('float64')
trainset = dataset['train'].map(lambda x:[pre_process_image(x['image'])])
testset = dataset['validation'].map(lambda x:[pre_process_image(x['image'])])
head, invhead = build1D(trainset,count=4, samplesize=1281149, keep_percent=1.0, flip=False) #, activity_regularizer=scaledtanh, inverse_activity_regularizer=scaledatanh)
return head, invhead, trainset, testset
"""Save the model"""
def save_model():
sample = next(iter(testset.shuffle(100)))[0]
sample = tf.reshape(sample, [1,sample.shape[0], sample.shape[1], sample.shape[2]])
head._set_inputs(sample)
head.save('/content/drive/My Drive/Colab Notebooks/data/imagenet/lfw-head-full.h5')
out = head(sample)
print("out.shape",out.shape)
sample = invhead(out)
invhead.save('/content/drive/My Drive/Colab Notebooks/data/imagenet/lfw-invhead-full.h5')
"""Load the model"""
def load_model():
head = tf.keras.models.load_model('/content/drive/My Drive/Colab Notebooks/data/imagenet/lfw-head-full.h5', custom_objects={'MeanLayer': MeanLayer, 'SymmetricPadding2D':SymmetricPadding2D})
invhead = tf.keras.models.load_model('/content/drive/My Drive/Colab Notebooks/data/imagenet/lfw-invhead-full.h5')
"""Save the weights only"""
def save_weights(head, invhead, testset, file_name):
sample = next(iter(testset.shuffle(100)))[0]
sample = tf.reshape(sample, [1,sample.shape[0], sample.shape[1], sample.shape[2]])
out = head(sample)
sample = sample*0.0
lastLayerIndex = 12
lastLayer = invhead.get_layer(index=lastLayerIndex)
mean = lastLayer(sample)
tf.io.write_file(file_name + '-mean.json', tf.io.serialize_tensor(mean))
head.save_weights(file_name + '-head-weights.h5')
out = head(sample)
print("out.shape",out.shape)
sample = invhead(out)
invhead.save_weights(file_name + '-invhead-weights.h5')
"""Read the weights back in. Need to reconstruct the architecture. To do that I run a small set of images through the build method."""
def load_weights(file_name, keep_percent, trainset, testset):
from pca_wavelet_utils import build1D
head, invhead = build1D(trainset.take(100),count=4, samplesize=100, keep_percent=keep_percent,flip=False)
sample = next(iter(testset.shuffle(100)))[0]
print("sample.shape",sample.shape)
sample = tf.reshape(sample, [1,sample.shape[0], sample.shape[1], sample.shape[2]])
print("after reshape: sample.shape",sample.shape)
out = head(sample)
head.load_weights(file_name + '-head-weights.h5')
out = head(sample)
print("out.shape",out.shape)
sample = invhead(out)
invhead.load_weights(file_name + '-invhead-weights.h5')
mean = tf.io.parse_tensor(tf.io.read_file(file_name + '-mean.json'),out_type=tf.float64)
lastLayerIndex = 12#8
lastLayer = invhead.get_layer(index=lastLayerIndex)
lastLayer.mean = mean
firstLayer = head.get_layer(index=0)
firstLayer.mean = -mean
return head, invhead, mean
"""Check it has built OK"""
def check_build():
plt.subplot(221)
plt.title('Original')
sample = next(iter(testset.shuffle(100)))[0]
plt.imshow(sample)
print("sample.shape",sample.shape)
pred = head([sample])
plt.subplot(222)
plt.title('Slice')
plt.imshow(pred[0,:,:,0]+0.5)
plt.subplot(223)
plt.title('Slice')
plt.imshow(pred[0,:,:,1]+0.5)
print("pred.shape",pred.shape)
recon = invhead(pred)[0]
print("recon.shape",recon.shape)
plt.subplot(224)
plt.title('Filtered')
plt.imshow(recon)
print("sample.dtype",sample.dtype)
print("recon[0].dtype",recon.dtype)
print("np.prod(sample.shape)",np.prod(sample.shape))
psnr = 10*np.log10( 1.0 /((np.linalg.norm(recon-sample)**2)/np.prod(sample.shape)))
ncc = np.corrcoef(tf.reshape(sample, [-1]), tf.reshape(recon, [-1]))
print("psnr = ", psnr)
print("ncc = ", ncc)
print("sample[30:34,30:34,0]",sample[30:34,30:34,0])
print("recon[30:34,30:34,0]",recon[30:34,30:34,0])
"""Solver for PCA wavelet"""
# A_fun, AT_fun takes a vector (d,1) or (d,) as input
def solve_pcaw(y, A_fun, AT_fun, lambda_l1, reshape_img_fun, head, invhead, mean, show_img_progress=False, alpha=0.2, max_iter=100, solver_tol=1e-6):
""" See Wang, Yu, Wotao Yin, and Jinshan Zeng. "Global convergence of ADMM in nonconvex nonsmooth optimization."
arXiv preprint arXiv:1511.06324 (2015).
It provides convergence condition: basically with large enough alpha, the program will converge. """
obj_lss = np.zeros(max_iter)
x_zs = np.zeros(max_iter)
u_norms = np.zeros(max_iter)
times = np.zeros(max_iter)
ATy = AT_fun(y)
x_shape = ATy.shape
d = np.prod(x_shape)
def vec(x):
return tf.reshape(x, [-1])
def A_cgs_fun(x):
x = tf.reshape(x,x_shape)
y = AT_fun(A_fun(x)) + alpha * x
return vec(y)
A_cgs = LinearOperator((d,d), matvec=A_cgs_fun, dtype='float')
def compute_p_inv_A(b, z0):
(z,info) = sp.sparse.linalg.cgs(A_cgs, vec(b), x0=vec(z0), tol=1e-3, maxiter=100)
if info > 0:
print('cgs convergence to tolerance not achieved')
elif info <0:
print('cgs gets illegal input or breakdown')
z = tf.reshape(z, x_shape)
return z
def A_cgs_fun_init(x):
x = tf.reshape(x, x_shape)
y = AT_fun(A_fun(x))
return vec(y)
A_cgs_init = LinearOperator((d,d), matvec=A_cgs_fun_init, dtype='float')
def compute_init(b, z0):
(z,info) = sp.sparse.linalg.cgs(A_cgs_init, vec(b), x0=vec(z0), tol=1e-2)
if info > 0:
print('cgs convergence to tolerance not achieved')
elif info <0:
print('cgs gets illegal input or breakdown')
z = tf.reshape(z,x_shape)
return z
# initialize z and u
z = tf.reshape(mean,x_shape)
u = np.zeros(x_shape)
plot_normalozer = matplotlib.colors.Normalize(vmin=0.0, vmax=1.0, clip=True)
start_time = timeit.default_timer()
for iter in range(max_iter):
# x-update
net_input = z+u
Wzu = head([net_input])
q = tfp.math.soft_threshold(Wzu, lambda_l1/alpha)
x = invhead(q)[0]
# z-update
b = ATy + alpha * (x - u)
z = compute_p_inv_A(b, z)
# u-update
u += z - x;
if show_img_progress:
fig = plt.figure('current_sol')
plt.gcf().clear()
fig.canvas.set_window_title('iter %d' % iter)
plt.subplot(1,3,1)
plt.imshow(reshape_img_fun(np.clip(x, 0.0, 1.0)), interpolation='nearest', norm=plot_normalozer)
plt.title('x')
plt.subplot(1,3,2)
plt.imshow(reshape_img_fun(np.clip(z, 0.0, 1.0)), interpolation='nearest', norm=plot_normalozer)
plt.title('z')
plt.subplot(1,3,3)
plt.imshow(reshape_img_fun(np.clip(net_input, 0.0, 1.0)), interpolation='nearest', norm=plot_normalozer)
plt.title('netin')
plt.pause(0.00001)
obj_ls = 0.5 * np.sum(np.square(y - A_fun(x)))
x_z = np.sqrt(np.mean(np.square(x-z)))
u_norm = np.sqrt(np.mean(np.square(u)))
obj_lss[iter] = obj_ls
x_zs[iter] = x_z
u_norms[iter] = u_norm
times[iter] = timeit.default_timer() - start_time
if x_z < solver_tol:
break
infos = {'obj_lss': obj_lss, 'x_zs': x_zs, 'u_norms': u_norms,
'times': times, 'alpha':alpha, 'lambda_l1':lambda_l1,
'max_iter':max_iter, 'solver_tol':solver_tol}
return (x, z, u, infos)
def extract_mean(invhead, testset):
lastLayerIndex = 12
sample = next(iter(testset.take(1)))[0]
print("sample.shape",sample.shape)
sample = sample*0.0
lastLayer = invhead.get_layer(index=lastLayerIndex)
mean = lastLayer([sample])[0]
print("mean.shape",mean.shape)
return mean
"""Run the solver"""
def run_solver_single():
problem='inpaint_center'#'sr'
print('problem', problem)
ori_img = next(iter(testset.shuffle(1000)))[0]
show_img_progress = True#False#
# No noise alpha = 0.1, lambda=0.0005 seems to work well (in painting problems, at least)
# Noise = 0.1, alpha = 0.3, lambda = 0.0015, or alpha = 0.6, lambda = 0.003 seem to work about the same
# Super resolution, no noise settings seems OK
# Compressive Sensing, 0.1 and 0.005 worked quite well
alpha = 0.1
max_iter = 100
solver_tol = 1e-5
alpha_update_ratio = 1.0
alpha_l1 = 0.3
lambda_l1 = 0.0000001
max_iter_l1 = 1000
solver_tol_l1 = 1e-4
box_size = int(0.3 * ori_img.shape[1])#blockwise - 0.3*shape[1], scattere - 0.1*shape[1]
noise_std = 0.0
results = solve_inpaint_center(ori_img, reshape_img, box_size=box_size, noise_std=noise_std, alpha=alpha, lambda_l1=lambda_l1, max_iter=max_iter, solver_tol=solver_tol, problem=problem)#'inpaint_center')#
"""Run the solver on all images in testset and calculate results"""
def run_solver_all(head, invhead, mean, testset, problem):
print("problem", problem)
it = iter(testset.take(100))
show_img_progress = False#True#
alpha = 0.3
max_iter = 100
solver_tol = 1e-5
alpha_update_ratio = 1.0
alpha_l1 = 0.1
lambda_l1 = 0.006
max_iter_l1 = 1000
solver_tol_l1 = 1e-4
noise_std = 0.0
mean_x = 0.0
mean_z = 0.0
sd_x = 0.0
sd_z = 0.0
count = 0.0
print("alpha", alpha, "lambda_l1", lambda_l1)
for x in it:
ori_img = x[0]
print("ori_img.shape", ori_img.shape, flush=True)
box_size = int(0.1 * ori_img.shape[1])
psnr_x,psnr_z = solve_inpaint_center(ori_img, reshape_img, head, invhead, mean, box_size=box_size, noise_std=noise_std, alpha=alpha, lambda_l1=lambda_l1, max_iter=max_iter, solver_tol=solver_tol, problem=problem, show_img_progress=False)
mean_x += psnr_x
sd_x += psnr_x*psnr_x
mean_z += psnr_z
sd_z += psnr_z*psnr_z
count += 1
print("count",count, "mean_x", mean_x, "sd_x", sd_x, "mean_z",mean_z,"sd_z",sd_z)
mean_x /= count
mean_z /= count
sd_x -= count*mean_x*mean_x
sd_z -= count*mean_z*mean_z
sd_x /= (count-1.0)
sd_z /= (count-1.0)
print("mean_x",mean_x,"sd_x",sd_x)
print("mean_z",mean_z,"sd_z",sd_z)
def main():
print("python main function")
print("importing data")
dataset = import_data()
#print("building model")
#head, invhead, trainset, testset = build_model(dataset)
#print("saving weights")
#mean = extract_mean(invhead, testset)
#save_weights(head, invhead, testset, 'imagenet') #celeba-190')#'imagenet-100k')#head, invhead, testset, file_name)
#tf.keras.backend.set_floatx('float64')
#dataset_resize = dataset.map(lambda x:[pre_process_image(x['image'])])
#testset = dataset_resize.take(500)
#trainset = dataset_resize.skip(500)
tf.keras.backend.set_floatx('float64')
trainset = dataset['train'].map(lambda x:[pre_process_image(x['image'])])
testset = dataset['validation'].map(lambda x:[pre_process_image(x['image'])])
print("loading model")
head, invhead, mean = load_weights('imagenet', 1.0, trainset, testset)
print("running solver")
run_solver_all(head, invhead, mean, testset, 'scattered_inpaint')#'cs')#inpaint_pixelwise')#inpaint_center')
if __name__ == '__main__':
main()
|
11,483 | 04077e0f6613009b880e2a29e0f43849d306c842 | from tkinter import *
window=Tk()#创建一个窗口
label=Label(window,text="Welcome to Python")#创建一个标签，小构建类
button=Button(window,text="Click me")
label.pack()#把标签放进窗口中
button.pack()
window.mainloop() |
11,484 | ac753a894f99ed9ffb0c962086e3beae1456ec14 | import numpy as np
import cv2
from PIL import Image
import pytesseract
#from ocrhelper import preprocess
#failed attempt
def get_cropped(image, area):
(x, y, w, h) = area
new_img = image[y: y + h, x: x + w]
return new_img
def split(image,row,column, area):
width = area[2]
height = area[3]
unitw = width/column # 19 px
unith = height/row
all_imgs = []
for j in range(0, 2):
#offset = j
for i in range (0,21):
blockimg = image[unith * j:unith * (j + 1), unitw * i: unitw * (i + 1)]
#cv2.imwrite("images/unit%d_%d.png" %(i,j), blockimg)
all_imgs.append( blockimg)
return all_imgs
#####################
rows = 9
columns = 21
lefttopwh = (200, 65, 399, 162)
file = "images/grp_image.ashx.png"
filecrop = "images/cropped.png"
img = cv2.imread(file)
crop_img = get_cropped(img,lefttopwh)
#cv2.imwrite(filecrop, crop_img)
image_array = split(crop_img,rows,columns,lefttopwh)
(i,j) = (20,0)
pick_one = image_array[j*columns + i]
file_picked = "images/picked.png"
#cv2.imwrite(file_picked,pick_one)
img = Image.fromarray(pick_one).resize((38,36))
text = pytesseract.image_to_string(img,boxes=True,config='-psm 10 -oem 3 -c tessedit_char_whitelist=0123456789')
print "result" + text
print "end" |
11,485 | 326c672fcb8ffdf170d9e272803f1d680408acef | ## NASA API Demo
## Once you recieve you API credentials, I recommend storing it as a variable in another file.
## In this example, I created another python file called "NASA_API_Credentials.py" and stored
## it in the same folder as NASA_API_Demo.py. Inside that file, I created a variable api_key
## and set it equal to my personal NASA api key like so:
## api_key = "your_api_key____This_should_be_a_string_of_letters_and_numbers"
##This line allows you to call the api_key variable you made in your other file, in this one
from NASA_API_Credentials import api_key
import requests #A basic python package for making http requests
## This python demo assumes you have read the associated API tutorial. If you haven't already,
## refer to NASA_API_Profile.md to learn about the functionality of this API.
##
## We will now use python to make run the three query examples from that tutorial
# base url. Every http request to the Rover API will start with this string
base_url = "https://api.nasa.gov/mars-photos/api/v1/rovers/"
### Query 1
### We want to construct a URL that will return us the 26th through 50th picture taken
### by Opportunity's panoramic camera on June 3, 2015
rover = "opportunity"
earth_date = "2015-6-3"
camera = "pancam"
page_number = "2"
# Add these variables into one url that we can use
request_url = "{}{}/photos?earth_date={}&camera={}&page={}&api_key={}".format(base_url,rover,earth_date,camera,page_number,api_key)
query1 = requests.get(request_url).json() #requests.get() is a function that returns information stored on a html page. json() converts the json object into a readable format for this program
#Printing this query will return the same output had you typed this url directly into your browser.
print(query1)
# If you wanted to do something with this information, like display it on a website, you
# can use Python to extract pieces of information from this string. However, working with
# JSON objects is a matter for a different tutorial.
### Query 2
### We want to construct a URL that will return us all the photos Spirit took on its first day
### on Mars
rover = "spirit"
mars_sol = "1"
request_url = "{}{}/photos?sol={}&api_key={}".format(base_url,rover,mars_sol,api_key)
query2 = requests.get(request_url).json()
print(query2)
### Query 3
### We want to construct a URL that will get the 101st to 125th photo Spirit took on its
### first day on Mars
rover = "spirit"
mars_sol = "1"
page_number = "5"
request_url = "{}{}/photos?sol={}&page={}&api_key={}".format(base_url,rover,mars_sol,page_number,api_key)
query3 = requests.get(request_url).json()
print(query3) |
11,486 | 5437cf1821104ce416de5a9ac28e98d8e24f25f6 | #!/usr/bin/env python
from ToolBox import parse_options_and_init_log
# have to do this first or ROOT masks the -h messages
opts, parser = parse_options_and_init_log()
from L1Analysis import L1Ana, L1Ntuple
from analysis_tools.plotting import HistManager
from analysis_tools.selections import MuonSelections, Matcher
import ROOT as root
import re
def parse_options_plotRates(parser):
"""
Adds often used options to the OptionParser...
"""
parsers = parser.add_subparsers()
sub_parser = parsers.add_parser("plotRates")
sub_parser.add_argument("-i", "--interactive", dest="interactive", action='store_false', help="Draw plots on screen.")
sub_parser.add_argument("-n", "--nevents", dest="nevents", default=1, type=int, help="Total nmumber of events")
sub_parser.add_argument("-b", "--bunches", dest="bunches", default=0, type=int, help="Number of colliding bunches")
sub_parser.add_argument("--pu", dest="pu", default=20, type=int, help="Average PU. default=20")
sub_parser.add_argument("--xsect", dest="xsect", default=80, type=float, help="Total cross section in mb. default=80 mb")
sub_parser.add_argument("--instlumi", dest="instlumi", default=1.2e34, type=float, help="Instantaneous luminosity. default=1.2e-34 cm-2s-1")
sub_parser.add_argument("--scale", dest="scale", default=1., type=float, help="Additional scale factor for rate calculate")
sub_parser.add_argument("-l", "--legacy", dest="legacy", action='store_true', help="Draw plots relative to legacy.")
opts, unknown = parser.parse_known_args()
return opts
def set_root_style():
root.gStyle.SetTitleFont(font)
root.gStyle.SetStatFont(font)
root.gStyle.SetTextFont(font)
root.gStyle.SetLabelFont(font)
root.gStyle.SetLegendFont(font)
root.gStyle.SetMarkerStyle(20)
root.gStyle.SetOptStat(0)
root.gStyle.SetOptFit(0)
root.gStyle.SetOptTitle(0)
root.gPad.SetTopMargin(0.08)
root.gPad.SetLeftMargin(0.14)
root.gPad.SetRightMargin(0.06)
root.gPad.SetTickx(1)
root.gPad.SetTicky(1)
def plot_hists(hm, hDefs, xTitle=None, yTitle='# muons', threshold=False, normToBinWidth=False, canvasPrefix='', notes=None, scaleFactor=1., data=False):
den = hDefs[0]['den']
if den:
name = canvasPrefix+hDefs[0]['num']+'_over_'+den
else:
name = canvasPrefix+hDefs[0]['num']
if normToBinWidth and not threshold and not den:
name = 'normToBinWidth_'+name
# setup legend according to how many histograms are in the plot
legYmin = 0.9-0.04*len(hDefs)
legXmin = 0.68
legXmax = 0.9
canvWidth = 600
if legYmin < 0.6:
legXmin = 0.8
legXmax = 1.
canvWidth = 730
legend = root.TLegend(legXmin, legYmin, legXmax, 0.9)
legend.SetTextFont(font)
legend.SetTextSize(0.03)
legend.SetBorderSize(0)
legend.SetFillColor(19)
legend.SetFillStyle(0)
#legend.SetNColumns(2)
legEntries = []
hs = []
hStack = root.THStack()
# get all the histograms and set their plot style
for hDef in hDefs:
if threshold:
h = hm.get_threshold_hist(hDef['num']).Clone()
if den:
hDen = hm.get_threshold_hist(den)
h.Divide(h, hDen, 1, 1, "b")
else:
if den:
h = hm.get_ratio(hDef['num'], den).Clone()
else:
h = hm.get(hDef['num']).Clone()
if normToBinWidth and not threshold and not den:
for bin in range(1, h.GetNbinsX()+1):
h.SetBinContent(bin, h.GetBinContent(bin) / h.GetBinWidth(bin))
h.SetBinError(bin, h.GetBinError(bin) / h.GetBinWidth(bin))
elif normToBinWidth:
print 'Ignoring normToBinWidth flag for threshold or ratio plots'
if scaleFactor != 1.:
h.Scale(scaleFactor)
h.SetLineColor(hDef['lc'])
h.SetLineStyle(hDef['ls'])
h.SetLineWidth(2)
legStyle = 'l'
if hDef['fc']:
h.SetFillColor(hDef['fc'])
h.SetLineWidth(1)
legStyle = 'f'
# if a fill colour is defined stack this histogram with others
hStack.Add(h)
legEntries.append(legend.AddEntry(h, hDef['legtext'], legStyle))
hs.append(h)
# replace histograms to be stacked with stack histograms
if hStack.GetNhists() > 0:
canvas_name = 'c_rates_stacked_'+name
stackHistos = hStack.GetStack()
j = len(stackHistos)-1
for i, hDef in enumerate(hDefs):
if hDef['fc']:
hs[i] = stackHistos[j].Clone()
j -= 1
else:
canvas_name = 'c_rates_'+name
if scaleFactor != 1.:
canvas_name += '_scaled'
# create canvas and draw on it
canvas_title = canvas_name
c = root.TCanvas(canvas_name, canvas_title, 100, 100, canvWidth, 600)
c.cd()
if name[-2:] == 'pt' and not den:
c.SetLogy(True)
set_root_style()
if legYmin < 0.6:
root.gPad.SetRightMargin(0.2)
if xTitle:
hs[0].GetXaxis().SetTitle(xTitle)
hs[0].GetYaxis().SetTitleOffset(1.5)
hs[0].GetYaxis().SetTitle(yTitle)
maxBinValue = hs[0].GetBinContent(hs[0].GetMaximumBin())
if not c.GetLogy():
yMax = 1.2*maxBinValue
if maxBinValue <= 1.:
yMax = 1.3
hs[0].GetYaxis().SetRangeUser(0., yMax)
# draw
hs[0].SetLineWidth(2)
legEntries[0].SetObject(hs[0])
legEntries[0].SetOption(legEntries[0].GetOption()+'le')
hs[0].Draw('hist')
for h in hs[1:]:
h.Draw('histsame')
hs[0].Draw('same')
hs[0].Draw('sameaxis')
# draw vertical lines to mark TF boundaries
lines = []
if name[-3:] == 'eta':
lines.append(root.TLine(-0.83, 0., -0.83, yMax))
lines[-1].SetLineStyle(root.kDotted)
lines[-1].Draw('same')
lines.append(root.TLine(-1.24, 0., -1.24, yMax))
lines[-1].SetLineStyle(root.kDotted)
lines[-1].Draw('same')
lines.append(root.TLine(0.83, 0., 0.83, yMax))
lines[-1].SetLineStyle(root.kDotted)
lines[-1].Draw('same')
lines.append(root.TLine(1.24, 0., 1.24, yMax))
lines[-1].SetLineStyle(root.kDotted)
lines[-1].Draw('same')
legend.Draw('same')
tex = root.TLatex()
tex.SetNDC()
tex.SetTextFont(font)
tex.SetTextSize(0.04)
#tex.DrawLatex(0.484, 0.93, 'Simulation, 13 TeV')
if canvWidth > 600:
if data:
#tex.DrawLatex(0.48, 0.93, 'CMS preliminary, 13 TeV')
tex.DrawLatex(0.48, 0.93, 'CMS internal, 13 TeV')
else:
tex.DrawLatex(0.484, 0.93, 'CMS Simulation, 13 TeV')
else:
if data:
#tex.DrawLatex(0.551, 0.93, 'CMS preliminary, 13 TeV')
tex.DrawLatex(0.551, 0.93, 'CMS internal, 13 TeV')
else:
tex.DrawLatex(0.555, 0.93, 'CMS Simulation, 13 TeV')
if notes:
tex.SetTextSize(0.035)
for note in notes:
tex.DrawLatex(note[0], note[1], note[2])
c.Modified()
c.Update()
return [c, hs, legend, lines, tex]
def print_rates(hm, hName, scaleFactor=1.):
hNames = ['gmt_'+hName.replace('qmin12', 'qmin8'), 'ugmt_'+hName, 'bmtf_ugmt_'+hName, 'omtf_ugmt_'+hName, 'emtf_ugmt_'+hName]
print '===== Rates ====='
print hName
print ''
histos = []
print 'System 16 GeV 20 GeV 25 GeV'
for name in hNames:
histos.append(hm.get_threshold_hist(name).Clone())
if scaleFactor != 1.:
histos[-1].Scale(scaleFactor)
bin16 = histos[-1].FindBin(16)
bin20 = histos[-1].FindBin(20)
bin25 = histos[-1].FindBin(25)
print '{name} rate: {sixteengev:>7.2f} kHz {twentygev:>7.2f} kHz {twentyfivegev:>7.2f} kHz'.format(name=name.split('_')[0], sixteengev=histos[-1].GetBinContent(bin16), twentygev=histos[-1].GetBinContent(bin20), twentyfivegev=histos[-1].GetBinContent(bin25))
print '\nThreshold GMT uGMT ratio'
for threshold in [0, 3, 5, 7, 10, 12, 14, 16, 18, 20, 22, 25, 30, 40, 50, 60]:
gmtBinNr = histos[0].FindBin(threshold)
ugmtBinNr = histos[1].FindBin(threshold)
gmtCont = histos[0].GetBinContent(gmtBinNr)
ugmtCont = histos[1].GetBinContent(ugmtBinNr)
gmtErr = histos[0].GetBinError(gmtBinNr)
ugmtErr = histos[1].GetBinError(ugmtBinNr)
ratio = -1.
if gmtCont != 0:
ratio = ugmtCont/gmtCont
print '{threshold:>3} GeV: {gmt:>8.3f} +/- {gmterr:>5.3f} kHz {ugmt:>8.3f} +/- {ugmterr:>5.3f} kHz {ratio:>8.3f}'.format(threshold=threshold, gmt=gmtCont, gmterr=gmtErr, ugmt=ugmtCont, ugmterr=ugmtErr, ratio=ratio)
print '================='
def hist_styles(stacked=False):
styles = {}
styles['gmt'] = {'lc':root.kCyan, 'ls':root.kSolid, 'fc':None, 'legtext':'GMT'}
styles['ugmt'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':None, 'legtext':'uGMT'}
if stacked:
styles['bmtf_ugmt'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kBlue-4, 'legtext':'BMTF uGMT'}
styles['omtf_ugmt'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kGreen-4, 'legtext':'OMTF uGMT'}
styles['emtf_ugmt'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kRed-4, 'legtext':'EMTF uGMT'}
styles['bmtf'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kBlue-4, 'legtext':'BMTF'}
styles['omtf'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kGreen-4, 'legtext':'OMTF'}
styles['emtf'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kRed-4, 'legtext':'EMTF'}
else:
styles['bmtf_ugmt'] = {'lc':root.kBlue-4, 'ls':root.kSolid, 'fc':None, 'legtext':'BMTF uGMT'}
styles['omtf_ugmt'] = {'lc':root.kGreen-4, 'ls':root.kSolid, 'fc':None, 'legtext':'OMTF uGMT'}
styles['emtf_ugmt'] = {'lc':root.kRed-4, 'ls':root.kSolid, 'fc':None, 'legtext':'EMTF uGMT'}
styles['bmtf_ugmt_q'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':None, 'legtext':'BMTF uGMT'}
styles['omtf_ugmt_q'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':None, 'legtext':'OMTF uGMT'}
styles['emtf_ugmt_q'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':None, 'legtext':'EMTF uGMT'}
styles['bmtf'] = {'lc':root.kBlue-4, 'ls':root.kSolid, 'fc':None, 'legtext':'BMTF'}
styles['omtf'] = {'lc':root.kGreen-4, 'ls':root.kSolid, 'fc':None, 'legtext':'OMTF'}
styles['emtf'] = {'lc':root.kRed-4, 'ls':root.kSolid, 'fc':None, 'legtext':'EMTF'}
styles['bmtf_q'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':None, 'legtext':'BMTF'}
styles['omtf_q'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':None, 'legtext':'OMTF'}
styles['emtf_q'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':None, 'legtext':'EMTF'}
styles['ugmt_q0'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kRed+3, 'legtext':'uGMT q0'}
styles['ugmt_q1'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kRed, 'legtext':'uGMT q1'}
styles['ugmt_q2'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kOrange+8, 'legtext':'uGMT q2'}
styles['ugmt_q3'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kOrange, 'legtext':'uGMT q3'}
styles['ugmt_q4'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kYellow, 'legtext':'uGMT q4'}
styles['ugmt_q5'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kGreen, 'legtext':'uGMT q5'}
styles['ugmt_q6'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kGreen+3, 'legtext':'uGMT q6'}
styles['ugmt_q7'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kCyan, 'legtext':'uGMT q7'}
styles['ugmt_q8'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kCyan+3, 'legtext':'uGMT q8'}
styles['ugmt_q9'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kAzure+7, 'legtext':'uGMT q9'}
styles['ugmt_q10'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kBlue, 'legtext':'uGMT q10'}
styles['ugmt_q11'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kBlue+3, 'legtext':'uGMT q11'}
styles['ugmt_q12'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kViolet, 'legtext':'uGMT q12'}
styles['ugmt_q13'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kMagenta, 'legtext':'uGMT q13'}
styles['ugmt_q14'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kMagenta+3, 'legtext':'uGMT q14'}
styles['ugmt_q15'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kViolet+3, 'legtext':'uGMT q15'}
styles['tf_q0'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kRed+3, 'legtext':'TF q0'}
styles['tf_q1'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kRed, 'legtext':'TF q1'}
styles['tf_q2'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kOrange+8, 'legtext':'TF q2'}
styles['tf_q3'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kOrange, 'legtext':'TF q3'}
styles['tf_q4'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kYellow, 'legtext':'TF q4'}
styles['tf_q5'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kGreen, 'legtext':'TF q5'}
styles['tf_q6'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kGreen+3, 'legtext':'TF q6'}
styles['tf_q7'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kCyan, 'legtext':'TF q7'}
styles['tf_q8'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kCyan+3, 'legtext':'TF q8'}
styles['tf_q9'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kAzure+7, 'legtext':'TF q9'}
styles['tf_q10'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kBlue, 'legtext':'TF q10'}
styles['tf_q11'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kBlue+3, 'legtext':'TF q11'}
styles['tf_q12'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kViolet, 'legtext':'TF q12'}
styles['tf_q13'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kMagenta, 'legtext':'TF q13'}
styles['tf_q14'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kMagenta+3, 'legtext':'TF q14'}
styles['tf_q15'] = {'lc':root.kBlack, 'ls':root.kSolid, 'fc':root.kViolet+3, 'legtext':'TF q15'}
return styles
def plot_hists_standard(hm, hName, den=None, xTitle='', yTitle='# muons', threshold=False, stacked=False, normToBinWidth=False, tfMuonOrig='ugmt', reg='', scaleFactor=1., data=False):
styles = hist_styles(stacked)
if tfMuonOrig == 'ugmt':
ugmt_str = '_ugmt'
prefix = ''
elif tfMuonOrig == 'tf':
ugmt_str = ''
prefix = 'tf_'
ugmt_dict = {'num':'ugmt_'+hName, 'den':den}
bmtf_dict = {'num':'bmtf'+ugmt_str+'_'+hName, 'den':den}
omtf_dict = {'num':'omtf'+ugmt_str+'_'+hName, 'den':den}
emtf_dict = {'num':'emtf'+ugmt_str+'_'+hName, 'den':den}
ugmt_dict.update(styles['ugmt'])
bmtf_dict.update(styles['bmtf'+ugmt_str])
omtf_dict.update(styles['omtf'+ugmt_str])
emtf_dict.update(styles['emtf'+ugmt_str])
hDefs = []
if reg == '':
hDefs.append(ugmt_dict)
hDefs.append(bmtf_dict)
hDefs.append(omtf_dict)
hDefs.append(emtf_dict)
elif reg == 'b':
hDefs.append(bmtf_dict)
prefix += 'bmtf_'
elif reg == 'o':
hDefs.append(omtf_dict)
prefix += 'omtf_'
elif reg == 'e':
hDefs.append(emtf_dict)
prefix += 'emtf_'
if plotLegacy:
if den:
gmt_dict = {'num':den, 'den':den}
gmt_dict.update(styles['gmt'])
else:
gmt_dict = {'num':'gmt_'+hName.replace('qmin12', 'qmin8'), 'den':den}
gmt_dict.update(styles['gmt'])
hDefs.append(gmt_dict)
# extract eta range from histogram name
eta_number_strs = re.findall(r'[\d\.\d]+', hName[hName.find('EtaMin')+6:hName.find('EtaMax')+12])
if len(eta_number_strs) > 1:
note_str = eta_number_strs[0]+' < |#eta| < '+eta_number_strs[1]
notes = [[0.17, 0.86, note_str]]
if den:
den_eta_number_strs = re.findall(r'[\d\.\d]+', den[den.find('EtaMin')+6:den.find('EtaMax')+12])
if len(den_eta_number_strs) > 1 and eta_number_strs != den_eta_number_strs:
den_note_str = den_eta_number_strs[0]+' < |#eta^{GMT}| < '+den_eta_number_strs[1]
notes.append([0.17, 0.81, den_note_str])
else:
notes = None
return plot_hists(hm, hDefs, xTitle, yTitle, threshold, normToBinWidth, prefix, notes, scaleFactor, data)
def plot_hists_qstack(hm, hName, den=None, xTitle='', yTitle='# muons', threshold=False, stacked=False, normToBinWidth=False, tfMuonOrig='ugmt', reg='', data=False):
styles = hist_styles(False)
if tfMuonOrig == 'ugmt':
ugmt_str = '_ugmt'
style_str = 'ugmt'
prefix = 'q_'
elif tfMuonOrig == 'tf':
ugmt_str = ''
style_str = 'tf'
prefix = 'q_tf_'
hDefs = []
if reg == '':
ugmt_dict = {'num':'ugmt_'+hName, 'den':den}
ugmt_dict.update(styles['ugmt'])
hDefs.append(ugmt_dict)
for q in reversed(range(16)):
ugmt_q_dict = {'num':'ugmt_'+hName.replace('qmin12', 'q{q}'.format(q=q)), 'den':den}
ugmt_q_dict.update(styles['ugmt_q{q}'.format(q=q)])
hDefs.append(ugmt_q_dict)
elif reg == 'b':
bmtf_dict = {'num':'bmtf'+ugmt_str+'_'+hName, 'den':den}
bmtf_dict.update(styles['bmtf'+ugmt_str+'_q'])
hDefs.append(bmtf_dict)
for q in reversed(range(16)):
bmtf_q_dict = {'num':'bmtf'+ugmt_str+'_'+hName.replace('qmin12', 'q{q}'.format(q=q)), 'den':den}
bmtf_q_dict.update(styles[style_str+'_q{q}'.format(q=q)])
hDefs.append(bmtf_q_dict)
prefix += 'bmtf_'
elif reg == 'o':
omtf_dict = {'num':'omtf'+ugmt_str+'_'+hName, 'den':den}
omtf_dict.update(styles['omtf'+ugmt_str+'_q'])
hDefs.append(omtf_dict)
for q in reversed(range(16)):
omtf_q_dict = {'num':'omtf'+ugmt_str+'_'+hName.replace('qmin12', 'q{q}'.format(q=q)), 'den':den}
omtf_q_dict.update(styles[style_str+'_q{q}'.format(q=q)])
hDefs.append(omtf_q_dict)
prefix += 'omtf_'
elif reg == 'e':
emtf_dict = {'num':'emtf'+ugmt_str+'_'+hName, 'den':den}
emtf_dict.update(styles['emtf'+ugmt_str+'_q'])
hDefs.append(emtf_dict)
for q in reversed(range(16)):
emtf_q_dict = {'num':'emtf'+ugmt_str+'_'+hName.replace('qmin12', 'q{q}'.format(q=q)), 'den':den}
emtf_q_dict.update(styles[style_str+'_q{q}'.format(q=q)])
hDefs.append(emtf_q_dict)
prefix += 'emtf_'
if plotLegacy:
if den:
gmt_dict = {'num':den, 'den':den}
gmt_dict.update(styles['gmt'])
else:
gmt_dict = {'num':'gmt_'+hName.replace('qmin12', 'qmin8'), 'den':den}
gmt_dict.update(styles['gmt'])
hDefs.append(gmt_dict)
# extract eta range from histogram name
eta_number_strs = re.findall(r'[\d\.\d]+', hName[hName.find('EtaMin')+6:hName.find('EtaMax')+12])
if len(eta_number_strs) > 1:
note_str = eta_number_strs[0]+' < |#eta| < '+eta_number_strs[1]
notes = [[0.55, 0.86, note_str]]
if den:
den_eta_number_strs = re.findall(r'[\d\.\d]+', den[den.find('EtaMin')+6:den.find('EtaMax')+12])
if len(den_eta_number_strs) > 1 and eta_number_strs != den_eta_number_strs:
den_note_str = den_eta_number_strs[0]+' < |#eta^{GMT}| < '+den_eta_number_strs[1]
notes.append([0.55, 0.81, den_note_str])
else:
notes = None
return plot_hists(hm, hDefs, xTitle, yTitle, threshold, normToBinWidth, prefix, notes, data=data)
def main():
opts = parse_options_plotRates(parser)
plotLegacy = opts.legacy
batchRun = opts.interactive
if batchRun:
root.gROOT.SetBatch(True)
# calculate the scale factor for rate in Hz
orbitFreq = 11245.6
nCollBunches = opts.bunches
nZeroBiasEvents = opts.nevents
crossSect = opts.xsect
instLumi = opts.instlumi
pu = opts.pu
thisIsData=True
# determine that this is MC if there is no number of colliding bunches given (defaults to 0 then)
if nCollBunches == 0:
print "No number of colliding bunches given. Assuming this is MC"
print "Using {instLumi} cm-2s-1 as instantaneous luminosity, {crossSect} mb as cross section, and {pu} as average number of pileup to determine number of colliding bunches.".format(instLumi=instLumi, crossSect=crossSect, pu=pu)
nCollBunches = round(instLumi * crossSect*1e-27 / (pu * orbitFreq))
thisIsData=False
else:
print "Assuming this is data"
convFactorToHz = orbitFreq * nCollBunches / nZeroBiasEvents
print 'Conversion factor to rate in Hz with {orbitFreq} Hz orbit frequency, {nCollBunches} colliding bunches and {nZeroBiasEvents} analyzed zero bias events: {convFactorToHz}'.format(orbitFreq=orbitFreq, nCollBunches=nCollBunches, nZeroBiasEvents=nZeroBiasEvents, convFactorToHz=convFactorToHz)
if opts.scale != 1.:
convFactorToHz *= opts.scale
print 'Conversion factor after applying additinoal scale factor of {sf}: {convFactorToHz}'.format(sf=opts.scale, convFactorToHz=convFactorToHz)
L1Ana.init_l1_analysis()
print ""
hm = HistManager(filename=opts.fname)
# holds the canvases, histograms, etc.
objects = []
##########################################################################
# uGMT kinematic variables
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_varBin_pt', xTitle='p_{T} (GeV/c)', yTitle='# muons/(GeV/c)', stacked=True, normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_varBin_pt', xTitle='p_{T} (GeV/c)', yTitle='# muons/(GeV/c)', stacked=True, normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_varBin_pt', xTitle='p_{T} (GeV/c)', yTitle='# muons/(GeV/c)', stacked=True, normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_varBin_pt', xTitle='p_{T} (GeV/c)', yTitle='# muons/(GeV/c)', stacked=True, normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin0_qmin12_eta', xTitle='#eta', yTitle='# muons', stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin18_qmin12_eta', xTitle='#eta', yTitle='# muons', stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin0_qmin12_eta', xTitle='#eta', yTitle='', stacked=True, normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin18_qmin12_eta', xTitle='#eta', yTitle='', stacked=True, normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_ptmin0_qmin12_phi', xTitle='#phi', yTitle='# muons', stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_ptmin18_qmin12_phi', xTitle='#phi', yTitle='# muons', stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_ptmin0_qual', xTitle='#mu quality', yTitle='# muons', stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_ptmin18_qual', xTitle='#mu quality', yTitle='# muons', stacked=True, data=thisIsData))
# uGMT rates for regions
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='kHz', threshold=True, scaleFactor=convFactorToHz / 1000., data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.1_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='kHz', threshold=True, scaleFactor=convFactorToHz / 1000., data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='kHz', threshold=True, scaleFactor=convFactorToHz / 1000., data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='kHz', threshold=True, scaleFactor=convFactorToHz / 1000., data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='kHz', threshold=True, scaleFactor=convFactorToHz / 1000., data=thisIsData))
print 'Rates'
print_rates(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', scaleFactor=convFactorToHz / 1000.)
print_rates(hm, 'highest_muon_absEtaMin0_absEtaMax2.1_qmin12_pt', scaleFactor=convFactorToHz / 1000.)
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin4_pt', xTitle='p_{T} (GeV/c)', yTitle='kHz', threshold=True, scaleFactor=convFactorToHz / 1000., data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.1_qmin4_pt', xTitle='p_{T} (GeV/c)', yTitle='kHz', threshold=True, scaleFactor=convFactorToHz / 1000., data=thisIsData))
print_rates(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin4_pt', scaleFactor=convFactorToHz / 1000.)
print_rates(hm, 'highest_muon_absEtaMin0_absEtaMax2.1_qmin4_pt', scaleFactor=convFactorToHz / 1000.)
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, data=thisIsData))
# q stack uGMT rates for regions
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin0_qmin12_eta', xTitle='#eta', yTitle='', stacked=True, normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin18_qmin12_eta', xTitle='#eta', yTitle='', stacked=True, normToBinWidth=True, data=thisIsData))
if plotLegacy:
# relative uGMT rates for regions
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin0_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', 'gmt_highest_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', 'gmt_highest_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', 'gmt_highest_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='b', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', 'gmt_highest_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='o', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='e', data=thisIsData))
# q stack relative uGMT rates for regions
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin0_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_pt', 'gmt_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', 'gmt_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_pt', 'gmt_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='b', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', 'gmt_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='o', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='e', data=thisIsData))
# uGMT TF rates for 0<|eta|<2.5
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, reg='b', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, reg='o', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, reg='e', data=thisIsData))
# q stack uGMT TF rates for 0<|eta|<2.5
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, reg='b', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, reg='o', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, reg='e', data=thisIsData))
if plotLegacy:
# relative uGMT TF rates for 0<|eta|<2.5
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='b', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='o', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='e', data=thisIsData))
# q stack relative uGMT TF rates for 0<|eta|<2.5
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='b', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='o', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, reg='e', data=thisIsData))
# relative uGMT eta distributions
objects.append(plot_hists_standard(hm, 'muon_ptmin0_qmin12_eta', 'gmt_muon_ptmin0_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin0_qmin12_eta', 'gmt_muon_ptmin0_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=False, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin0_qmin12_eta', 'gmt_muon_ptmin0_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin18_qmin12_eta', 'gmt_muon_ptmin18_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin18_qmin12_eta', 'gmt_muon_ptmin18_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=False, data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin18_qmin12_eta', 'gmt_muon_ptmin18_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=True, data=thisIsData))
##########################################################################
# TF
# TF kinematic variables
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_varBin_pt', xTitle='p_{T} (GeV/c)', yTitle='# muons/(GeV/c)', stacked=True, tfMuonOrig='tf', normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_varBin_pt', xTitle='p_{T} (GeV/c)', yTitle='# muons/(GeV/c)', stacked=True, tfMuonOrig='tf', normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_varBin_pt', xTitle='p_{T} (GeV/c)', yTitle='# muons/(GeV/c)', stacked=True, tfMuonOrig='tf', normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_varBin_pt', xTitle='p_{T} (GeV/c)', yTitle='# muons/(GeV/c)', stacked=True, tfMuonOrig='tf', normToBinWidth=True, data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin0_qmin12_eta', xTitle='#eta', yTitle='# muons', stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin18_qmin12_eta', xTitle='#eta', yTitle='# muons', stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_ptmin0_qmin12_phi', xTitle='#phi', yTitle='# muons', stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_ptmin18_qmin12_phi', xTitle='#phi', yTitle='# muons', stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_ptmin0_qual', xTitle='#mu quality', yTitle='# muons', stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_absEtaMin0_absEtaMax2.5_ptmin18_qual', xTitle='#mu quality', yTitle='# muons', stacked=True, tfMuonOrig='tf', data=thisIsData))
# TF rates for regions
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
# q stack TF rates for regions
#objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
#objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
#objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
#objects.append(plot_hists_qstack(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
if plotLegacy:
# relative TF rates for regions
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin0_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', 'gmt_highest_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', 'gmt_highest_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', 'gmt_highest_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='b', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', 'gmt_highest_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='o', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='e', data=thisIsData))
# q stack relative TF rates for regions
#objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin0_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
#objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_pt', 'gmt_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
#objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', 'gmt_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
#objects.append(plot_hists_qstack(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_pt', 'gmt_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='b', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', 'gmt_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='o', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='e', data=thisIsData))
# TF rates for 0<|eta|<2.5
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', reg='b', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', reg='o', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', reg='e', data=thisIsData))
# q stack TF rates for 0<|eta|<2.5
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax0.83_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', reg='b', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0.83_absEtaMax1.24_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', reg='o', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin1.24_absEtaMax2.5_qmin12_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events', threshold=True, stacked=True, tfMuonOrig='tf', reg='e', data=thisIsData))
if plotLegacy:
# relative TF rates for 0<|eta|<2.5
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='b', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='o', data=thisIsData))
objects.append(plot_hists_standard(hm, 'highest_muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_highest_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='e', data=thisIsData))
# q stack relative TF rates for 0<|eta|<2.5
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin0_absEtaMax0.83_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='b', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin0.83_absEtaMax1.24_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='o', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_absEtaMin0_absEtaMax2.5_qmin12_pt', 'gmt_muon_absEtaMin1.24_absEtaMax2.5_qmin8_pt', xTitle='p_{T} (GeV/c)', yTitle='Integrated # events / Integrated # GMT events', threshold=True, stacked=True, tfMuonOrig='tf', reg='e', data=thisIsData))
# relative TF eta distributions
objects.append(plot_hists_standard(hm, 'muon_ptmin0_qmin12_eta', 'gmt_muon_ptmin0_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin0_qmin12_eta', 'gmt_muon_ptmin0_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=False, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin0_qmin12_eta', 'gmt_muon_ptmin0_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', tfMuonOrig='tf', reg='b', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin0_qmin12_eta', 'gmt_muon_ptmin0_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', tfMuonOrig='tf', reg='o', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin0_qmin12_eta', 'gmt_muon_ptmin0_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', tfMuonOrig='tf', reg='e', data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin18_qmin12_eta', 'gmt_muon_ptmin18_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=True, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_standard(hm, 'muon_ptmin18_qmin12_eta', 'gmt_muon_ptmin18_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', stacked=False, tfMuonOrig='tf', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin18_qmin12_eta', 'gmt_muon_ptmin18_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', tfMuonOrig='tf', reg='b', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin18_qmin12_eta', 'gmt_muon_ptmin18_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', tfMuonOrig='tf', reg='o', data=thisIsData))
objects.append(plot_hists_qstack(hm, 'muon_ptmin18_qmin12_eta', 'gmt_muon_ptmin18_qmin8_eta', xTitle='#eta', yTitle='# muons / # GMT muons', tfMuonOrig='tf', reg='e', data=thisIsData))
##########################################################################
# save plots to root file
if savePlots:
output = root.TFile('./ugmt_rate_plots.root', 'recreate')
output.cd()
for obj in objects:
c = obj[0]
c.Write(c.GetName())
c.Print('./plots/'+c.GetName()+'.pdf', '.pdf')
c.Print('./plots/'+c.GetName()+'.png', '.png')
output.Close()
# wait
if not batchRun:
raw_input("Press ENTER to quit.")
if __name__ == "__main__":
savePlots = True
batchRun = True
plotLegacy = False
font = 42
main()
|
11,487 | 44322b4122488e0041dad3015c98159b0d3e2852 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Creates the mask of where the Monte Carlo simulation should sample the P-bodies
"""
import numpy as np
import cv2
def extract_sample_area(cell_mask, dapi_image, dapi_threshold = 10, shrink_nucleus = 3):
'''
Returns a binary mask of where sampling is possible (inside the cell border,
NOT inside the nucleus => in the cytoplasm of the cells)
Dapi Threshold is used to define the nucleus based on the dapi intensity image
shrink_nucleus: Int. Amount that nucleus is shrunken to avoid loosing P-bodies there
'''
cell_mask_binary = cell_mask > 0
# Make a mask of the nucleus based on smoothed dapi_image
dapi_smoothed = cv2.GaussianBlur(dapi_image,(5,5),0)
nucleus_segmented = dapi_smoothed > dapi_threshold
# Shrink nucleus by shrink_nucleus value
kernel = np.ones((3,3),np.uint8)
shrunken_nucleus = cv2.erode(np.array(nucleus_segmented, dtype=np.uint8),kernel,iterations = shrink_nucleus)
# Combine the masks using XOR: creates a mask of the cytoplasm
cytoplasm_mask = np.logical_xor(cell_mask_binary, shrunken_nucleus) * 255
return cytoplasm_mask
# cell_img_path = '/Users/Joel/p-body-randomness/data/input_data/20180606-SLP_Multiplexing_p1_C03_x000_y000_z000_t000_segmentation_Label12.png'
# dapi_img_path = '/Users/Joel/p-body-randomness/data/input_data/20180606-SLP_Multiplexing_p1_C03_x000_y000_z000_t000_2_DAPI_Label12.png'
#
# cell_img = cv2.imread(cell_img_path, 0)
# dapi_image = cv2.imread(dapi_img_path, 0)
#
# cytoplasm_mask = extract_sample_area(cell_img, dapi_image, shrink_nucleus = 3)
# cv2.imwrite('test_img.png', cytoplasm_mask)
|
11,488 | 8fc43f3b609d8ed50e790ce2603d2e7fb078a9d1 | /home/rajendra/anaconda3/lib/python3.6/encodings/cp424.py |
11,489 | c245c5dc02e34cd15f508dc591e6cfcd303ed9d1 | from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^$', views.main),
url(r'^create$', views.create),
url(r'^add/(?P<id>\d+)$', views.add),
url(r'^song/(?P<id>\d+)$', views.songUser),
url(r'^show/(?P<id>\d+)$', views.showUser),
]
|
11,490 | bdb0a9350470c044b9ced9ed40caca2324fd2227 | # 2019 KAKAO BLIND RECRUITMENT 매칭 점수
# https://programmers.co.kr/learn/courses/30/lessons/42893
def solution(word, pages):
answer = 0
lenWord = len(word)
linkWord = "<a href="
lenLink = len(linkWord)
domainWord = '<meta property="og:url" content='
lenDomain = len(domainWord)
#기본점수, domain, links
pageInfos = [[0, '', []] for i in range(len(pages))]
hs = -1
for page in pages:
hs += 1
domain = ''
score1 = 0
score2 = 0
links = []
score3 = 0
score4 = 0
lenPage = len(page)
page = page.lower()
word = word.lower()
for i in range(lenPage-lenWord+1):
wordWindow = page[i:i+lenWord]
if(wordWindow == word):
if(i>0 and i+lenWord<lenPage-1):
if(ord(page[i-1]) < ord('a') or ord(page[i-1]) > ord('z')):
if(ord(page[i+lenWord]) < ord('a') or ord(page[i+lenWord]) > ord('z')):
score1 += 1
if(i+lenLink <= lenPage):
linkWindow = page[i: i+lenLink]
if(linkWord == linkWindow):
score2 += 1
flag = False
idx = i+lenLink
linkStr = ''
while flag==False:
idx += 1
if(page[idx] == '"'):
flag = True
else:
linkStr += page[idx]
links.append(linkStr)
if(i+lenDomain <= lenPage):
domainWindow = page[i: i+lenDomain]
if(domainWord == domainWindow):
flag = False
idx = i+lenDomain
domainStr = ''
while flag==False:
idx += 1
if(page[idx] == '"'):
flag = True
else:
domainStr += page[idx]
domain = domainStr
pageInfos[hs][0] = score1
pageInfos[hs][1] = domain
pageInfos[hs][2] = links
scores = []
for i in range(len(pageInfos)):
pi = pageInfos[i]
pagescore = 0
pagescore += pi[0]
lenLinks = len(pi[2])
domain = pi[1]
for j in range(len(pageInfos)):
if(i!=j):
pi2 = pageInfos[j]
links2 = pi2[2]
lenLinks2 = len(links2)
if(domain in links2):
pagescore += (pi2[0]/lenLinks2)
scores.append((pagescore, i))
maxIdx = 0
maxScore = 0
for i in range(len(scores)):
score = scores[i]
if(score[0] > maxScore):
maxScore = score[0]
maxIdx = score[1]
answer = maxIdx
return answer
print(solution( "blind", ["<html lang=\"ko\" xml:lang=\"ko\" xmlns=\"http://www.w3.org/1999/xhtml\">\n<head>\n <meta charset=\"utf-8\">\n <meta property=\"og:url\" content=\"https://a.com\"/>\n</head> \n<body>\nBlind Lorem Blind ipsum dolor Blind test sit amet, consectetur adipiscing elit. \n<a href=\"https://b.com\"> Link to b </a>\n</body>\n</html>", "<html lang=\"ko\" xml:lang=\"ko\" xmlns=\"http://www.w3.org/1999/xhtml\">\n<head>\n <meta charset=\"utf-8\">\n <meta property=\"og:url\" content=\"https://b.com\"/>\n</head> \n<body>\nSuspendisse potenti. Vivamus venenatis tellus non turpis bibendum, \n<a href=\"https://a.com\"> Link to a </a>\nblind sed congue urna varius. Suspendisse feugiat nisl ligula, quis malesuada felis hendrerit ut.\n<a href=\"https://c.com\"> Link to c </a>\n</body>\n</html>", "<html lang=\"ko\" xml:lang=\"ko\" xmlns=\"http://www.w3.org/1999/xhtml\">\n<head>\n <meta charset=\"utf-8\">\n <meta property=\"og:url\" content=\"https://c.com\"/>\n</head> \n<body>\nUt condimentum urna at felis sodales rutrum. Sed dapibus cursus diam, non interdum nulla tempor nec. Phasellus rutrum enim at orci consectetu blind\n<a href=\"https://a.com\"> Link to a </a>\n</body>\n</html>"]))
print(solution( "Muzi", ["<html lang=\"ko\" xml:lang=\"ko\" xmlns=\"http://www.w3.org/1999/xhtml\">\n<head>\n <meta charset=\"utf-8\">\n <meta property=\"og:url\" content=\"https://careers.kakao.com/interview/list\"/>\n</head> \n<body>\n<a href=\"https://programmers.co.kr/learn/courses/4673\"></a>#!MuziMuzi!)jayg07con&&\n\n</body>\n</html>", "<html lang=\"ko\" xml:lang=\"ko\" xmlns=\"http://www.w3.org/1999/xhtml\">\n<head>\n <meta charset=\"utf-8\">\n <meta property=\"og:url\" content=\"https://www.kakaocorp.com\"/>\n</head> \n<body>\ncon%\tmuzI92apeach&2<a href=\"https://hashcode.co.kr/tos\"></a>\n\n\t^\n</body>\n</html>"])) |
11,491 | c60d86980b990ad3ff581d680c85c7a3569baa4b | fp = open("input.dat", "r")
line = fp.readline()
total = 0
length = len(line)
for i in range(length):
num = int(line[i])
if (num == int(line[int((i+length/2)%length)])):
total += num
print (total)
|
11,492 | f15d9c9c9db3b7dc094e13104106baf02fff1ead | import os
import struct
import shutil
from tarfile import TarFile
from zipfile import ZipFile
import requests
import time
if os.name == "nt":
gd_path = "geckodriver.exe"
if os.path.isfile(os.path.join("harquery", gd_path)):
print("Harquery dependencies are already installed")
exit()
if 8 * struct.calcsize("P") == 32:
gd_url = "https://github.com/mozilla/geckodriver/releases/download/v0.27.0/geckodriver-v0.27.0-win32.zip"
elif 8 * struct.calcsize("P") == 64:
gd_url = "https://github.com/mozilla/geckodriver/releases/download/v0.27.0/geckodriver-v0.27.0-win64.zip"
gd_dc = "zip"
decompressor = ZipFile
else:
gd_path = "geckodriver"
if os.path.isfile(os.path.join("harquery", gd_path)):
print("Harquery dependencies are already installed")
exit()
gd_url = "https://github.com/mozilla/geckodriver/releases/download/v0.27.0/geckodriver-v0.27.0-macos.tar.gz"
gd_dc = "tar.gz"
decompressor = TarFile.open
bmp_url = "https://codeload.github.com/lightbody/browsermob-proxy/zip/browsermob-proxy-2.1.1"
if not os.path.isdir("temp"):
os.mkdir("temp")
print("installing geckodriver v0.27.0...")
geckodriver = requests.get(gd_url, stream=True)
target_path = os.path.join("temp", "geckodriver.{0}".format(gd_dc))
with open(target_path, "wb") as f:
for chunk in geckodriver.iter_content(chunk_size=128):
f.write(chunk)
target_path = os.path.join("temp", "geckodriver.{0}".format(gd_dc))
gd_archive = decompressor(target_path)
gd_archive.extract(gd_path, "harquery")
gd_archive.close()
print("installing browsermob-proxy v2.1.1")
bmp = requests.get(bmp_url, stream=True)
target_path = os.path.join("temp", "browsermob-proxy-2.1.1.zip")
with open(target_path, "wb") as f:
for chunk in bmp.iter_content(chunk_size=128):
f.write(chunk)
target_dir = "browsermob-proxy-browsermob-proxy-2.1.1"
bmp_archive = ZipFile(target_path)
bmp_archive.extractall("harquery")
from_path = os.path.join("harquery", "browsermob-proxy-browsermob-proxy-2.1.1")
to_path = os.path.join("harquery", "browsermob-proxy-2.1.1")
os.rename(from_path, to_path)
bmp_archive.close()
shutil.rmtree("temp")
|
11,493 | 334dadff97f76924a96a1fe7fd87b9db6eb41acc | from Fs import Fs
import os
import constants
class SuperBlock(Fs):
def __init__(self, size=constants.SUPER_BLOCK_SIZE, f_blocks_list=None, ifree_list=None):
self.size = size
self.f_blocks_list = f_blocks_list
self.ifree_list = ifree_list
def get_inode_number(self):
inode_number = self.ifree_list.pop(0)
save(self.bytefy(), 0 ,'disk')
return inode_number
def get_block_number(self):
block_number = self.f_blocks_list.pop(0)
save(self.bytefy(), 0 ,'disk')
return block_number
def save(bytearr, offset, filepath):
with open(filepath, "rw+") as disk:
disk.seek(offset, os.SEEK_SET)
disk.write(bytearr)
disk.close()
|
11,494 | 44b42d2755eecac9153ecc5362f85f18fb7795b3 | # -*- coding: utf-8 -*-
"""
Created on Sat Mar 26 20:15:46 2016
@author: rbanderson
"""
import numpy as np
import scipy.optimize as opt
import copy
class sm:
def __init__(self, blendranges, random_seed = None):
self.blendranges = blendranges
self.random_seed = random_seed
def do_blend(self, predictions, truevals=None, verbose = True):
# create the array indicating which models to blend for each blend range
# For three models, this creates an array like: [[0,0],[0,1],[1,1],[1,2],[2,2]]
# Which indicates that in the first range, just use model 0
# In the second range, blend models 0 and 1
# in the third range, use model 1
# in the fourth range, blend models 1 and 2
# in the fifth range, use model 2
if self.random_seed is not None:
np.random.seed(self.random_seed)
self.toblend = []
for i in range(len(predictions) - 1):
self.toblend.append([i, i])
if i < len(predictions) - 2:
self.toblend.append([i, i + 1])
# If the true compositions are provided, then optimize the ranges over which the results are blended to minimize the RMSEC
blendranges = np.array(self.blendranges).flatten() # squash the ranges to be a 1d array
blendranges.sort() # sort the entries. These will be used by submodels_blend to decide how to combine the predictions
self.blendranges = blendranges
if truevals is not None:
self.rmse = 99999999
n_opt = 5
i=0
while i < n_opt:
if i > 0:
blendranges = np.hstack(([-9999], blendranges[1:-1]+0.1*np.random.random(len(blendranges)-2), [9999])) #add some randomness to the blendranges each time
truevals = np.squeeze(np.array(truevals))
result = opt.minimize(self.get_rmse, blendranges, (predictions, truevals, verbose), tol=0.00001)
if result.fun < self.rmse:
self.blendranges = result.x
self.rmse = result.fun
if verbose==True:
print(self.blendranges.sort())
print('RMSE ='+str(self.rmse))
else:
pass
i=i+1
print(' ')
print('Optimum settings:')
print('RMSE = ' + str(self.rmse))
print('Low model: ' + str(round(self.blendranges[0], 4)) + ' to ' + str(round(self.blendranges[2], 4)))
i = 1
m = 2
while i + 3 < len(self.blendranges) - 1:
print('Submodel ' + str(m) + ': ' + str(round(self.blendranges[i], 4)) + ' to ' + str(
round(self.blendranges[i + 3], 4)))
i = i + 2
m = m + 1
print('High model: ' + str(round(self.blendranges[-3], 4)) + ' to ' + str(round(self.blendranges[-1], 4)))
else:
self.blendranges = blendranges
# print(self.blendranges)
# calculate the blended results
blended = self.submodels_blend(predictions, self.blendranges, overwrite=False)
return blended
def get_rmse(self, blendranges, predictions, truevals, verbose, rangemin = 0.0, rangemax = 100, roundval = 10):
blendranges[1:-1][blendranges[1:-1] < rangemin] = rangemin # ensure range boundaries don't drift below min
blendranges[1:-1][blendranges[1:-1] > rangemax] = rangemax # ensure range boundaries don't drift above max
blendranges.sort() # ensure range boundaries stay in order
blended = self.submodels_blend(predictions, blendranges, overwrite=False)
# calculate the RMSE. Round to specified precision as a way to control how long optimization runs
# Note: don't want to round too much - optimization needs some wiggle room
RMSE = np.round(np.sqrt(np.mean((blended - truevals) ** 2)),roundval)
if verbose == True:
print('RMSE = '+str(RMSE))
print('Low model: '+str(round(blendranges[0],4))+' to '+str(round(blendranges[2],4)))
i=1
m=2
while i+3<len(blendranges)-1:
if verbose == True:
print('Submodel '+str(m)+': '+str(round(blendranges[i],4))+' to '+str(round(blendranges[i+3],4)))
i=i+2
m=m+1
if verbose == True:
print('High model: '+str(round(blendranges[-3],4)) + ' to ' + str(round(blendranges[-1],4)))
return RMSE
def submodels_blend(self,predictions,blendranges, overwrite=False):
blended=np.squeeze(np.zeros_like(predictions[0]))
#format the blending ranges (note, initial formatting is done in do_blend)
blendranges = np.hstack((blendranges, blendranges[1:-1])) # duplicate the middle entries
blendranges.sort() #re-sort them
blendranges=np.reshape(blendranges,(int(len(blendranges)/2),int(2))) #turn the vector back into a 2d array (one pair of values for each submodel)
self.toblend.append([len(predictions)-1,len(predictions)-1])
blendranges=np.vstack((blendranges,[-9999999,999999]))
#print(blendranges)
for i in range(len(blendranges)): #loop over each composition range
for j in range(len(predictions[0])): #loop over each spectrum
ref_tmp=predictions[-1][j] #get the reference model predicted value
#check whether the prediction for the reference spectrum is within the current range
inrangecheck=(ref_tmp>blendranges[i][0])&(ref_tmp<blendranges[i][1])
if inrangecheck:
try:
if self.toblend[i][0]==self.toblend[i][1]: #if the results being blended are identical, no blending necessary!
blendval=predictions[self.toblend[i][0]][j]
else:
weight1 = 1 - (ref_tmp - blendranges[i][0]) / (
blendranges[i][1] - blendranges[i][0]) # define the weight applied to the lower model
weight2 = (ref_tmp - blendranges[i][0]) / (
blendranges[i][1] - blendranges[i][0]) # define the weight applied to the higher model
# calculated the blended value (weighted sum)
blendval = weight1 * predictions[self.toblend[i][0]][j] + weight2 * \
predictions[self.toblend[i][1]][j]
except:
pass
if overwrite:
blended[j] = blendval # If overwrite is true, write the blended result no matter what
else:
# If overwrite is false, only write the blended result if there is not already a result there
if blended[j] == 0:
blended[j] = blendval
return blended
|
11,495 | b6e3a62d48c3ae7bea32284ee1fa67da136b16ad | # Nombre: Juan Diego Poccori Escalante
# Código: 144884
import math
# 1. Escribir un algoritmo que calcule el coseno mediante series de Taylor
# definir factorial
def factorial(numero):
if numero < 0:
mensaje = "Error: Debe ser un numero mayor o igual a cero"
return print(mensaje)
i = 1
factorial = 1
while numero >= i:
factorial*=i
i+=1
return factorial
# Leer angulo en sexagesimales
angulo = float(input("Ingrese ángulo en sexagesimales: "))
PIio = 3.1415
# Calcular coseno
# Convertir angulo a radianes
anguloRad = angulo * (math.pi/180)
termino = 1
coseno = 0
signo = 1
j=2
# Mientras termino sea mayor a 0.0000001
while termino > 0.00000001:
coseno+=(signo*termino)
termino = anguloRad**(j)/factorial(j)
j+=2
signo*=-1
# Mostrar coseno redondeado a 6
print(round(coseno,6)) |
11,496 | d071ee971362a9a265a1c7969f9cb781ec7c8ee6 | # Generated by Django 3.0.2 on 2020-03-11 17:18
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('pokemon_entities', '0018_auto_20200311_0139'),
]
operations = [
migrations.AlterField(
model_name='pokemon',
name='description',
field=models.CharField(blank=True, max_length=1000, verbose_name='Описание'),
),
migrations.AlterField(
model_name='pokemon',
name='image',
field=models.ImageField(blank=True, upload_to='', verbose_name='Картинка'),
),
migrations.AlterField(
model_name='pokemon',
name='title_en',
field=models.CharField(blank=True, max_length=200, verbose_name='Название на английском языке'),
),
migrations.AlterField(
model_name='pokemon',
name='title_jp',
field=models.CharField(blank=True, max_length=200, verbose_name='Название на японском языке'),
),
migrations.AlterField(
model_name='pokemon',
name='title_ru',
field=models.CharField(blank=True, max_length=200, verbose_name='Название на русском языке'),
),
]
|
11,497 | 9cc71abe8128728634221e3d969adcbdd3273b96 | import operator
from etc.const import GREATER, GREATER_EQUALS, LESS, LESS_EQUALS, EQUALS, NOT_EQUALS
ATTRIBUTES = {
'Character': {
'health': 'hp',
'resource': 'resource',
'is in combat': 'is_in_combat',
'has pet': 'has_pet',
'first class resource': 'first_class_resource',
'pet health': 'pet_hp',
},
'Target': {
'health': 'hp',
'resource': 'resource',
'distance': 'distance'
},
'Tick': {
'second': 'second'
}
}
_DISCRETE = {EQUALS: operator.eq}
_CONTINOUS = {
GREATER: operator.gt,
GREATER_EQUALS: operator.ge,
LESS: operator.lt,
LESS_EQUALS: operator.le,
EQUALS: operator.eq,
NOT_EQUALS: operator.ne
}
OPERATORS = {
"is in combat": _DISCRETE,
"has pet": _DISCRETE,
"first class resource": _DISCRETE,
"health": _CONTINOUS,
"pet health": _CONTINOUS,
"resource": _CONTINOUS,
"distance": _DISCRETE,
"second": _CONTINOUS
}
_DISTANCE_VALUES = {"25 yard": 2, "8 yard": 1, "out of range": 0}
VALUE_CONVERTER = {
"distance": lambda d: _DISTANCE_VALUES[d],
"is in combat": lambda c: {"True": True, "False": False}[c],
"has pet": lambda c: {"True": True, "False": False}[c],
"first class resource": lambda c: {"True": True, "False": False}[c],
"health": int,
"resource": int,
"second": float,
"pet health": int
}
|
11,498 | e0f7c227115f955b3f5e63bc042552f94ff1027f | # -*- coding: utf-8 -*-
import os
import sys
from deconstrst.deconstrst import build, submit
from deconstrst.config import Configuration
__author__ = 'Ash Wilson'
__email__ = 'ash.wilson@rackspace.com'
__version__ = '0.1.0'
def main():
config = Configuration(os.environ)
# Lock source and destination to the same paths as the Makefile.
srcdir, destdir = '.', '_build/deconst'
status = build(srcdir, destdir)
if status != 0:
sys.exit(status)
reasons = config.skip_submit_reasons()
if reasons:
print("Not submitting content to the content service because:",
file=sys.stderr)
print(file=sys.stderr)
for reason in reasons:
print(" * " + reason, file=sys.stderr)
print(file=sys.stderr)
return
submit(destdir,
config.content_store_url,
config.content_store_apikey,
config.content_id_base)
if __name__ == '__main__':
main()
|
11,499 | 398e44942f0f2ec3d7a7c7c8ef8c01df4eff8d26 | import csv
import re
import requests
from bs4 import BeautifulSoup
from get_all_players import read_all_players_from_csv
import pandas as pd
BASE_URL = 'https://www.basketball-reference.com'
COLUMNS = ['Season', 'Age', 'Tm', 'Lg', 'Pos', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%', '3P', '3PA',
'3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST',
'STL', 'BLK', 'TOV', 'PF', 'PTS']
ALL_COLUMNS = ['Player', 'ShortName', 'Height', 'Weight', 'Position', 'BirthPlace', 'SeasonURL'] + COLUMNS
def get_season_href(r):
children = list(r.children)
if not children:
return ''
else:
try:
return list(children[0].children)[0].attrs['href']
except:
return ''
def get_attr(s, attr):
try:
return s.find(itemprop=attr).get_text()
except:
return ''
def read_all_seasons(new_file=False, seasons_filename='seasons.csv', players_filename='players.csv'):
player_data = read_all_players_from_csv(players_filename)
if new_file:
with open(seasons_filename, 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(ALL_COLUMNS)
existing_players = set()
else:
with open(seasons_filename, 'r') as csvfile:
reader = csv.DictReader(csvfile)
existing_players = set(row['ShortName'] for row in reader)
for player_row in player_data:
print(f'Player {player_row["shortname"]}')
if player_row['shortname'] in existing_players:
print('skip')
continue
r = requests.get(BASE_URL + player_row['href'])
season_df = pd.read_html(r.text)[0]
for c in COLUMNS:
if c not in season_df.columns:
season_df[c] = pd.np.nan
season_df = season_df[COLUMNS]
season_df['Player'] = player_row['name']
season_df['ShortName'] = player_row['shortname']
s = BeautifulSoup(r.text, 'lxml')
season_df['Position'] = s.findAll(
text=re.compile('(Guard|Forward|Point Guard|Center|Power Forward|Shooting Guard|Small Forward)')
)[0].strip().split('\n')[0]
season_df['Height'] = get_attr(s, 'height')
season_df['Weight'] = get_attr(s, 'weight')
try:
season_df['BirthPlace'] = s.find(itemprop='birthPlace').contents[1].get_text()
except Exception:
season_df['BirthPlace'] = pd.np.nan
season_df['SeasonURL'] = [get_season_href(a) for a in s.select('#per_game > tbody > tr')] + ['']
season_df = season_df[season_df['G'] > 0]
with open(seasons_filename, 'a') as csvfile:
season_df.to_csv(csvfile, columns=ALL_COLUMNS, header=False, index=False)
if __name__ == "__main__":
read_all_seasons()
|
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