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lokal-profil/isfdb_site | edit/editpublisher.py | 0ce20d6347849926d4eda961ea9249c31519eea5 | #!_PYTHONLOC
#
# (C) COPYRIGHT 2004-2021 Al von Ruff and Ahasuerus
# ALL RIGHTS RESERVED
#
# The copyright notice above does not evidence any actual or
# intended publication of such source code.
#
# Version: $Revision$
# Date: $Date$
from isfdblib import *
from isfdblib_help import *
from isfdblib_print import *
from isfdb import *
from SQLparsing import *
from login import User
if __name__ == '__main__':
publisherID = SESSION.Parameter(0, 'int')
record = SQLGetPublisher(publisherID)
if not record:
SESSION.DisplayError('Record Does Not Exist')
PrintPreSearch('Publisher Editor')
PrintNavBar('edit/editpublisher.cgi', publisherID)
help = HelpPublisher()
printHelpBox('publisher', 'EditPublisher')
print '<form id="data" METHOD="POST" ACTION="/cgi-bin/edit/submitpublisher.cgi">'
print '<table border="0">'
print '<tbody id="tagBody">'
# Limit the ability to edit publisher names to moderators
user = User()
user.load()
display_only = 1
if SQLisUserModerator(user.id):
display_only = 0
printfield("Publisher Name", "publisher_name", help, record[PUBLISHER_NAME], display_only)
trans_publisher_names = SQLloadTransPublisherNames(record[PUBLISHER_ID])
printmultiple(trans_publisher_names, "Transliterated Name", "trans_publisher_names", help)
webpages = SQLloadPublisherWebpages(record[PUBLISHER_ID])
printWebPages(webpages, 'publisher', help)
printtextarea('Note', 'publisher_note', help, SQLgetNotes(record[PUBLISHER_NOTE]))
printtextarea('Note to Moderator', 'mod_note', help, '')
print '</tbody>'
print '</table>'
print '<p>'
print '<input NAME="publisher_id" VALUE="%d" TYPE="HIDDEN">' % publisherID
print '<input TYPE="SUBMIT" VALUE="Submit Data" tabindex="1">'
print '</form>'
print '<p>'
PrintPostSearch(0, 0, 0, 0, 0, 0)
| [] |
vj-codes/dispatch | src/dispatch/incident_cost/views.py | f9354781956380cac290be02fb987eb50ddc1a5d | from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.orm import Session
from dispatch.database.core import get_db
from dispatch.database.service import common_parameters, search_filter_sort_paginate
from dispatch.auth.permissions import SensitiveProjectActionPermission, PermissionsDependency
from .models import (
IncidentCostCreate,
IncidentCostPagination,
IncidentCostRead,
IncidentCostUpdate,
)
from .service import create, delete, get, update
router = APIRouter()
@router.get("", response_model=IncidentCostPagination)
def get_incident_costs(*, common: dict = Depends(common_parameters)):
"""
Get all incident costs, or only those matching a given search term.
"""
return search_filter_sort_paginate(model="IncidentCost", **common)
@router.get("/{incident_cost_id}", response_model=IncidentCostRead)
def get_incident_cost(*, db_session: Session = Depends(get_db), incident_cost_id: int):
"""
Get an incident cost by id.
"""
incident_cost = get(db_session=db_session, incident_cost_id=incident_cost_id)
if not incident_cost:
raise HTTPException(status_code=404, detail="An incident cost with this id does not exist.")
return incident_cost
@router.post(
"",
response_model=IncidentCostRead,
dependencies=[Depends(PermissionsDependency([SensitiveProjectActionPermission]))],
)
def create_incident_cost(
*, db_session: Session = Depends(get_db), incident_cost_in: IncidentCostCreate
):
"""
Create an incident cost.
"""
incident_cost = create(db_session=db_session, incident_cost_in=incident_cost_in)
return incident_cost
@router.put(
"/{incident_cost_id}",
response_model=IncidentCostRead,
dependencies=[Depends(PermissionsDependency([SensitiveProjectActionPermission]))],
)
def update_incident_cost(
*,
db_session: Session = Depends(get_db),
incident_cost_id: int,
incident_cost_in: IncidentCostUpdate,
):
"""
Update an incident cost by id.
"""
incident_cost = get(db_session=db_session, incident_cost_id=incident_cost_id)
if not incident_cost:
raise HTTPException(status_code=404, detail="An incident cost with this id does not exist.")
incident_cost = update(
db_session=db_session,
incident_cost=incident_cost,
incident_cost_in=incident_cost_in,
)
return incident_cost
@router.delete(
"/{incident_cost_id}",
dependencies=[Depends(PermissionsDependency([SensitiveProjectActionPermission]))],
)
def delete_incident_cost(*, db_session: Session = Depends(get_db), incident_cost_id: int):
"""
Delete an incident cost, returning only an HTTP 200 OK if successful.
"""
incident_cost = get(db_session=db_session, incident_cost_id=incident_cost_id)
if not incident_cost:
raise HTTPException(status_code=404, detail="An incident cost with this id does not exist.")
delete(db_session=db_session, incident_cost_id=incident_cost_id)
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Lunga001/pmg-cms-2 | tests/views/test_admin_committee_questions.py | 10cea3979711716817b0ba2a41987df73f2c7642 | import os
from urllib.parse import urlparse, parse_qs
from builtins import str
from tests import PMGLiveServerTestCase
from pmg.models import db, Committee, CommitteeQuestion
from tests.fixtures import dbfixture, UserData, CommitteeData, MembershipData
from flask import escape
from io import BytesIO
class TestAdminCommitteeQuestions(PMGLiveServerTestCase):
def setUp(self):
super().setUp()
self.fx = dbfixture.data(UserData)
self.fx.setup()
self.user = self.fx.UserData.admin
def tearDown(self):
self.delete_created_objects()
self.fx.teardown()
super().tearDown()
def test_upload_committee_question_document_with_old_format(self):
"""
Upload committee question document (/admin/committee-question/upload)
"""
url = "/admin/committee-question/upload"
data = {}
path = self.get_absolute_file_path(
"../data/committee_questions/RNW190-200303.docx"
)
with open(path, "rb") as f:
data["file"] = (f, "RNW190-200303.docx")
response = self.make_request(
url,
self.user,
data=data,
method="POST",
headers={"Referer": "/somethingelse"},
content_type="multipart/form-data",
)
self.assertEqual(302, response.status_code)
response_url = urlparse(response.location)
response_query = parse_qs(response_url.query)
self.assertIn("id", response_query, "Question ID must be in response query")
created_question_id = int(response_query["id"][0])
response = self.make_request(
"%s?%s" % (response_url.path, response_url.query),
self.user,
follow_redirects=True,
)
self.assertEqual(200, response.status_code)
# Test that the question that was created contains the correct data
question = CommitteeQuestion.query.get(created_question_id)
self.assertEqual(
question.question,
"Whether her Office has initiated the drafting of a Bill that seeks to protect and promote the rights of persons with disabilities; if not, (a) why not and (b) what steps does her Office intend taking in this regard; if so, on what date does she envisage that the Bill will be introduced in the National Assembly?",
)
self.assertEqual(
question.minister.name,
"Minister in The Presidency for Women, Youth and Persons with Disabilities",
)
self.assertEqual(question.asked_by_name, "Mr S Ngcobo")
self.assertEqual(
question.answer,
"<p>Yes</p><p>(b) The Department is in the process of preparing the drafting of a Bill which will be submitted to Cabinet for approval before it will be tabled in Parliament during the 2021/2022 financial year.</p>",
)
self.assertEqual(question.code, "NW190")
# Delete the question that was created
self.created_objects.append(question)
def test_upload_committee_question_document_with_new_format(self):
"""
Upload committee question document (/admin/committee-question/upload)
"""
url = "/admin/committee-question/upload"
data = {}
path = self.get_absolute_file_path(
"../data/committee_questions/RNW104-2020-02-28.docx"
)
with open(path, "rb") as f:
data["file"] = (f, "RNW104-2020-02-28.docx")
response = self.make_request(
url,
self.user,
data=data,
method="POST",
headers={"Referer": "/admin/committee-question/"},
content_type="multipart/form-data",
)
self.assertEqual(302, response.status_code)
response_url = urlparse(response.location)
response_query = parse_qs(response_url.query)
self.assertIn("id", response_query, "Question ID must be in response query")
created_question_id = int(response_query["id"][0])
response = self.make_request(
"%s?%s" % (response_url.path, response_url.query),
self.user,
follow_redirects=True,
)
self.assertEqual(200, response.status_code)
# Test that the question that was created contains the correct data
question = CommitteeQuestion.query.get(created_question_id)
self.assertEqual(
question.question,
"What (a) is the number of (i) residential properties, (ii) business erven’, (iii) government buildings and (iv) agricultural properties owned by her department in the Lephalale Local Municipality which are (aa) vacant, (bb) occupied and (cc) earmarked for disposal and (b) total amount does her department owe the municipality in outstanding rates and services?",
)
self.assertEqual(
question.minister.name, "Minister of Public Works and Infrastructure",
)
self.assertEqual(question.asked_by_name, "Ms S J Graham")
self.assertEqual(
question.answer,
"<p><strong>The Minister of Public Works and</strong><strong> Infrastructure: </strong></p><ol><li>The Department of Public Works and Infrastructure (DPWI) has informed me that in the Lephalale Local Municipality the Department owns (i) 183 residential properties (ii) one business erven (iii) 132 government buildings and (iv) 5 agricultural properties. DPWI informed me that (aa) 8 land parcels are vacant and (bb) only one property is unutilised. </li></ol><p>(cc) DPWI has not earmarked any properties for disposal in the Lephalale Local Municipality.</p><ol><li>In August 2019 the Department started a Government Debt Project engaging directly with municipalities and Eskom to verify and reconcile accounts and the project. DPWI, on behalf of client departments, owed the Lephalale Local Municipality, as per accounts received on 17 February 2020, R 334,989.69 which relates current consumption. </li></ol>",
)
self.assertEqual(question.code, "NW104")
# Delete the question that was created
self.created_objects.append(question)
def test_upload_committee_question_document_with_navigable_string_error(self):
"""
Upload committee question document (/admin/committee-question/upload)
"""
url = "/admin/committee-question/upload"
data = {}
path = self.get_absolute_file_path(
"../data/committee_questions/RNW1153-200619.docx"
)
with open(path, "rb") as f:
data["file"] = (f, "RNW1153-200619.docx")
response = self.make_request(
url,
self.user,
data=data,
method="POST",
headers={"Referer": "/admin/committee-question/"},
content_type="multipart/form-data",
)
self.assertEqual(302, response.status_code)
response_url = urlparse(response.location)
response_query = parse_qs(response_url.query)
self.assertIn("id", response_query, "Question ID must be in response query")
created_question_id = int(response_query["id"][0])
response = self.make_request(
"%s?%s" % (response_url.path, response_url.query),
self.user,
follow_redirects=True,
)
self.assertEqual(200, response.status_code)
# Test that the question that was created contains the correct data
question = CommitteeQuestion.query.get(created_question_id)
self.assertIn(
"(1)Whether, with reference to her reply to question 937 on 4 June 2020",
question.question,
)
self.assertEqual(
question.minister.name,
"Minister in The Presidency for Women, Youth and Persons with Disabilities",
)
self.assertEqual(question.asked_by_name, "Ms T Breedt")
self.assertIn(
"There were no deviations from the standard supply chain management procedures",
question.answer,
)
self.assertEqual(question.code, "NW1153")
# Delete the question that was created
self.created_objects.append(question)
def get_absolute_file_path(self, relative_path):
dir_name = os.path.dirname(__file__)
return os.path.join(dir_name, relative_path)
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tiaotiao/applets | audioanalysis_demo/test_audio_analysis.py | c583a4405ed18c7d74bfba49884525c43d114398 |
import sys, wave
import AudioAnalysis
FILE_NAME = "snippet.wav"
def testWavWrite():
try:
f = wave.open(FILE_NAME, "rb")
except Exception, e:
print e
print "File type is not wav!"
return
c = wave.open("cnv_" + FILE_NAME, "wb")
print f.getnchannels()
print f.getsampwidth()
print f.getframerate()
print f.getnframes()
#print f.getparams()
total = f.getnframes()
read_count = total / 2
c.setnchannels(f.getnchannels())
c.setsampwidth(f.getsampwidth())
c.setframerate(f.getframerate())
c.setnframes(read_count)
c.setcomptype(f.getcomptype(), f.getcompname())
frames = f.readframes(read_count)
print len(frames)
print "bytes per frame: ", len(frames) / read_count
#for b in frames:
# i = int(b.encode("hex"), 16)
# print b.encode("hex")
#print '#' * (i / 10)
c.writeframes(frames)
print "----------"
f.close()
c.close()
def process(p):
print p
def testAudioAnalysis():
a = AudioAnalysis.AudioAnalysis(FILE_NAME)
print a.getFilename()
print a.getFileType()
a.setFrameInterval(0.01)
print a.analysePower(process)
print a.getPowerMin(), "\tgetPowerMin"
print a.getPowerMax(), "\tgetPowerMax"
print a.getSamplePowerMin(), "\tgetSamplePowerMin"
print a.getSamplePowerMax(), "\tgetSamplePowerMax"
print a.getFrameRate(), "\tgetFrameRate"
print a.getSampleWidth(), "\tgetSampleWidth"
print a.getDuration(), "\tgetDuration"
print a.getFrameInterval(), "\tgetFrameInterval"
print a.getSamples(), "\tgetSamples"
powers = a.getFramePower()
for p in powers:
print "%04lf" % p[0], "%-6d" % p[1] ,'#' * (p[1] / 100)
def main():
f = open(FILE_NAME, "rb")
if not f:
print "Open file failed!"
return
try:
w = wave.open(f)
except Exception, e:
print e
print "File type is not wav!"
return
print "get channels\t", w.getnchannels() # channels, single or double
print "frame rate\t", w.getframerate() # rate, frames per sec
print "samp width\t", w.getsampwidth() # maybe: channels * width = bytes per frame
print "get n frames\t", w.getnframes() # total frames
print "comp type\t", w.getcomptype() # compress
print "params\t", w.getparams()
total = w.getnframes()
read_count = 100
frames = w.readframes(read_count)
print "len(frames)\t", len(frames)
print "bytes per frame\t", len(frames) / read_count
#for b in frames:
#i = int(b.encode("hex"), 16)
#print b.encode("hex")
#print '#' * (i / 10)
print "----------"
w.close()
f.close()
if __name__ == "__main__":
main()
#testAudioAnalysis()
#testWavWrite()
| [] |
xaedes/python-symbolic-logic-to-gate | syloga/transform/evaluation.py | a0dc9be9e04290008cf709fac789d224ab8c14b0 |
from syloga.core.map_expression_args import map_expression_args
from syloga.utils.identity import identity
from syloga.ast.BooleanNot import BooleanNot
from syloga.ast.BooleanValue import BooleanValue
from syloga.ast.BooleanOr import BooleanOr
from syloga.ast.BooleanAnd import BooleanAnd
from syloga.ast.BooleanNand import BooleanNand
from syloga.ast.BooleanNor import BooleanNor
from syloga.ast.BooleanXor import BooleanXor
from syloga.ast.BreakOut import BreakOut
# from syloga.core.assert_equality_by_table import assert_equality_by_table
def evaluate_expr(expression):
recurse = evaluate_expr
# result = assert_equality_by_table
result = identity
#arg_is_value = lambda arg: isinstance(arg, (BooleanValue, bool))
arg_is_value = lambda arg: type(arg) in [BooleanValue, bool]
def arg_is_value(arg):
is_value = type(arg) in [BooleanValue, bool]
#print("is arg a value? " + str(type(arg)) + " " + str(arg))
#print("is_value", is_value)
return is_value
args_are_values = lambda args: all(map(arg_is_value, args))
get_value = lambda arg: arg if type(arg) == bool else arg.value
is_true = lambda val: val == True
is_false = lambda val: val == False
#print("looking at " + str(type(expression)))
if type(expression) == BooleanNot:
assert(len(expression.args) == 1)
arg = recurse(expression.args[0]);
if arg_is_value(arg):
return result(BooleanValue(not get_value(arg)))
else:
return result(BooleanNot(arg))
elif type(expression) == BooleanOr:
args = list(map(recurse, expression.args))
arg_values = [get_value(arg) for arg in args if arg_is_value(arg)]
args_wo_neutral = list(filter(lambda x: not(arg_is_value(x) and is_false(get_value(x))),args))
if args_are_values(args):
return result(BooleanValue(any(arg_values)))
elif any(map(is_true,arg_values)):
return result(BooleanValue(True))
elif len(args) == 1:
return result(recurse(args[0]))
elif len(args_wo_neutral) < len(args):
return result(recurse(BooleanOr(*args_wo_neutral)))
else:
return result(BooleanOr(*args))
elif type(expression) == BooleanAnd:
args = list(map(recurse, expression.args))
#print(expression.args)
#print(args)
#negated_atom_values = [not get_value(arg) for arg in args if arg_is_value(arg)]
arg_values = [get_value(arg) for arg in args if arg_is_value(arg)]
args_wo_neutral = list(filter(lambda x: not(arg_is_value(x) and is_true(get_value(x))),args))
#print(arg_values)
if args_are_values(args):
return result(BooleanValue(all(map(is_true,arg_values))))
elif any(map(is_false,arg_values)):
return result(BooleanValue(False))
elif len(args) == 1:
return result(recurse(args[0]))
elif len(args_wo_neutral) < len(args):
return result(recurse(BooleanAnd(*args_wo_neutral)))
else:
return result(BooleanAnd(*args))
elif type(expression) == BooleanNand:
return result(recurse(BooleanNot(BooleanAnd(*expression.args))))
elif type(expression) == BooleanNor:
return result(recurse(BooleanNot(BooleanOr(*expression.args))))
elif type(expression) == BooleanXor:
args = list(map(recurse, expression.args))
arg_values = [get_value(arg) for arg in args if arg_is_value(arg)]
non_value_args = [arg for arg in args if not arg_is_value(arg)]
if len(args) == 0:
raise ValueError("args are missing")
elif len(args) == 1:
return result(args[0])
elif len(arg_values) == 0:
return result(BooleanXor(*non_value_args))
elif len(arg_values) == 1:
if is_true(arg_values[0]):
return result(BooleanXor(arg_values[0], *non_value_args))
else:
return result(recurse(BooleanXor(*non_value_args)))
elif len(arg_values) > 1:
evaluated = is_true(arg_values[0])
for a in arg_values[1:]:
evaluated ^= is_true(a)
evaluated = bool(evaluated)
return result(recurse(BooleanXor(evaluated, *non_value_args)))
elif type(expression) == BreakOut:
expr = recurse(expression.expr)
if arg_is_value(expr):
return result(BooleanValue(expr))
else:
return result(BreakOut(expr))
else:
return result(map_expression_args(recurse, expression, recurse_collection=True))
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Idematica/django-oscar | oscar/apps/customer/mixins.py | 242a0654210d63ba75f798788916c8b2f7abb7fb | from django.conf import settings
from django.contrib.auth import authenticate, login as auth_login
from django.contrib.sites.models import get_current_site
from django.db.models import get_model
from oscar.apps.customer.signals import user_registered
from oscar.core.loading import get_class
from oscar.core.compat import get_user_model
User = get_user_model()
CommunicationEventType = get_model('customer', 'CommunicationEventType')
Dispatcher = get_class('customer.utils', 'Dispatcher')
class PageTitleMixin(object):
"""
Passes page_title and active_tab into context, which makes it quite useful
for the accounts views.
Dynamic page titles are possible by overriding get_page_title.
"""
page_title = None
active_tab = None
# Use a method that can be overridden and customised
def get_page_title(self):
return self.page_title
def get_context_data(self, **kwargs):
ctx = super(PageTitleMixin, self).get_context_data(**kwargs)
ctx.setdefault('page_title', self.get_page_title())
ctx.setdefault('active_tab', self.active_tab)
return ctx
class RegisterUserMixin(object):
communication_type_code = 'REGISTRATION'
def register_user(self, form):
"""
Create a user instance and send a new registration email (if configured
to).
"""
user = form.save()
if getattr(settings, 'OSCAR_SEND_REGISTRATION_EMAIL', True):
self.send_registration_email(user)
# Raise signal
user_registered.send_robust(sender=self, user=user)
# We have to authenticate before login
try:
user = authenticate(
username=user.email,
password=form.cleaned_data['password1'])
except User.MultipleObjectsReturned:
# Handle race condition where the registration request is made
# multiple times in quick succession. This leads to both requests
# passing the uniqueness check and creating users (as the first one
# hasn't committed when the second one runs the check). We retain
# the first one and delete the dupes.
users = User.objects.filter(email=user.email)
user = users[0]
for u in users[1:]:
u.delete()
auth_login(self.request, user)
return user
def send_registration_email(self, user):
code = self.communication_type_code
ctx = {'user': user,
'site': get_current_site(self.request)}
messages = CommunicationEventType.objects.get_and_render(
code, ctx)
if messages and messages['body']:
Dispatcher().dispatch_user_messages(user, messages)
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vfloeser/TumorDelivery | plot_integral.py | a48252c17b50397b1f51be21c0cf65ade87e9000 | from parameters import *
from library_time import *
from paths import *
import numpy as np
import pylab as plt
import matplotlib.pyplot as mplt
mplt.rc('text', usetex=True)
mplt.rcParams.update({'font.size': 16})
import logging, getopt, sys
import time
import os
##########################################################################################
# C O N F I G U R A T I O N
##########################################################################################
# activate ylim for w
var1 = w1
var3 = w3
var5 = w5
var10 = w10
var25 = w25
mode = "w" # u or w
##########################################################################################
# M A I N
##########################################################################################
if __name__ == "__main__":
if not os.path.exists('plots'):
os.makedirs('plots')
print('Created folder plots!')
if not os.path.exists('plots/integral'):
os.makedirs('plots/integral')
print('Created folder plots/integral!')
t = np.linspace(tmin, tmax, Nt)
r = np.linspace(0,R,Nr)
Ivar1 = np.zeros(Nt)
Ivar3 = np.zeros(Nt)
Ivar5 = np.zeros(Nt)
Ivar10 = np.zeros(Nt)
Ivar25 = np.zeros(Nt)
for i in range(Nt):
# /1000000 because of units
Ivar1[i] = integrate(var1, i,r, Nt)/1000000
Ivar3[i] = integrate(var3, i,r, Nt)/1000000
Ivar5[i] = integrate(var5, i,r, Nt)/1000000
Ivar10[i] = integrate(var10, i,r, Nt)/1000000
Ivar25[i] = integrate(var25, i,r, Nt)/1000000
mplt.plot(t, Ivar1, label=r'$\alpha = 1$')
mplt.plot(t, Ivar3, label=r'$\alpha = 3$')
mplt.plot(t, Ivar5, label=r'$\alpha = 5$')
mplt.plot(t, Ivar10, label=r'$\alpha = 10$')
mplt.plot(t, Ivar25, label=r'$\alpha = 25$')
mplt.xlim(tmin, tmax)
mplt.yscale('log')
mplt.xlabel(r'$t\quad [h]$')
mplt.ylabel(r'$\bar{'+mode+'}\quad [\mu mol]$')
##########################################################################################
# lim for w, because some values dont make sense
mplt.ylim(1e-11, 3e2)
# lim for w, because some values dont make sense
##########################################################################################
mplt.legend(loc=1, bbox_to_anchor=(1, 0.9))
mplt.tight_layout()
mplt.savefig('plots/integral/int'+mode+'.pdf', format='pdf')
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wangjeaf/CSSCheckStyle | tests/unit/combiner/Try.py | d1b1ed89c61ca80d65f398ec4a07d73789197b04 | from helper import *
def doTest():
msg = doCssFileCompress('_test.css')
equal(msg, '@import (url-here);.test,.test2,.test3,.test4,.test5{_width:100px;*height:100px}.test6{display:none;_width:100px;*height:100px}', 'totally compressed')
msg = doCssFileCompress('_test_different_order.css')
equal(msg, '.test1,.test2,.test3,.test4,.test5{*display:none;_display:inline-block;width:100px;height:200px;border:1px solid #FFF}', 'totally compressed')
msg = doCssFileCompress('_with_margin.css')
equal(msg, '.test,.test2,.test3,.test4,.test5{_width:100px;*height:100px;margin:20px 10px 10px}.test6{display:none;_width:100px;*height:100px}', 'margin compress ok')
msg = doCssFileCompress('_just_margin.css')
equal(msg, '.test,.test2,.test3,.test4{margin:20px 10px 10px}', 'just margin compress ok')
msg = doCssFileCompress('_with_padding.css')
equal(msg, '.test,.test2,.test3,.test4,.test5{_width:100px;*height:100px;padding:20px 10px 10px}.test6{display:none;_width:100px;*height:100px}', 'padding compress ok')
msg = doCssFileCompress('_just_padding.css')
equal(msg, '.test,.test2,.test3,.test4{padding:20px 10px 10px}', 'just padding compress ok')
| [] |
desdelgado/rheology-data-toolkit | tests/tests.py | 054b1659c914b8eed86239d27a746e26404395ec | import sys, os
sys.path.append("C:/Users/Delgado/Documents/Research/rheology-data-toolkit/rheodata/extractors")
import h5py
import pandas as pd
from antonpaar import AntonPaarExtractor as APE
from ARES_G2 import ARES_G2Extractor
# %%
sys.path.append("C:/Users/Delgado/Documents/Research/rheology-data-toolkit/rheodata")
from data_converter import rheo_data_transformer
import unittest
extractor = APE()
#converter = data_converter()
class TestAntonPaar(unittest.TestCase):
def setUp(self):
self.multi_file_test = "C:/Users/Delgado/Documents/Research/rheology-data-toolkit/tests/test_data/Anton_Paar/excel_test_data/two_tests_Steady State Viscosity Curve-LO50C_excel.xlsx"
self.modified_dict, self.raw_data_dict, self.cols, self.units = extractor.import_rheo_data(self.multi_file_test)
# Inilize the class to convert
self.converter = rheo_data_transformer(self.modified_dict, self.raw_data_dict, self.cols, self.units)
self.converter.load_to_hdf("test")
def test_modified_output_isdictionary(self):
self.assertIsInstance(self.modified_dict, dict)
def test_modified_output_dictionary_contains_pandas(self):
""" Test if the output is a dictonary of pandas dataframes'"""
for value in self.modified_dict.values():
self.assertIsInstance(value, pd.DataFrame)
def test_raw_output_isdictionary(self):
self.assertIsInstance(self.raw_data_dict, dict)
def test_raw_output_dictionary_contains_pandas(self):
""" Test if the output is a dictonary of pandas dataframes'"""
for value in self.raw_data_dict.values():
self.assertIsInstance(value, pd.DataFrame)
def test_project_name_added_raw_data(self):
""" Test if the output is a dictonary of pandas dataframes'"""
for df in self.raw_data_dict.values():
self.assertEqual(df.iloc[0,0], "Project:")
def test_hdf5_created(self):
name, ext = os.path.splitext("test.hdf5")
self.assertEqual(ext, ".hdf5")
def test_project_subfolders_added(self):
f = h5py.File('test.hdf5', "r")
project_keys = list(f['Project'].keys())
f.close()
self.assertListEqual(project_keys, ['Steady State Viscosity Curve-75C','Steady State Viscosity Curve-LO80C', ])
def test_analyze_cols(self):
temp_df = extractor.make_analyze_dataframes(self.multi_file_test)
for test_key in temp_df.keys():
test_cols = list(temp_df[test_key].columns)
parsed_cols = list(self.cols[test_key])
self.assertListEqual(test_cols, parsed_cols)
# TODO Write test for saving a file
if __name__ == '__main__':
unittest.main()
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reiterl/openslides-backend | openslides_backend/action/topic/delete.py | d36667f00087ae8baf25853d4cef18a5e6dc7b3b | from ...models.models import Topic
from ..default_schema import DefaultSchema
from ..generics import DeleteAction
from ..register import register_action
@register_action("topic.delete")
class TopicDelete(DeleteAction):
"""
Action to delete simple topics that can be shown in the agenda.
"""
model = Topic()
schema = DefaultSchema(Topic()).get_delete_schema()
| [] |
Dr3xler/CookieConsentChecker | main.py | 816cdfb9d9dc741c57dbcd5e9c9ef59837196631 | from core import file_handling as file_h, driver_handling as driver_h
from website_handling import website_check as wc
from cookie_handling import cookie_compare
websites = file_h.website_reader()
driver = driver_h.webdriver_setup()
try:
wc.load_with_addon(driver, websites)
except:
print('ERROR: IN FIREFOX USAGE WITH ADDONS')
finally:
wc.close_driver_session(driver)
# driver need to be reloaded because we need a new session without addons
driver = driver_h.webdriver_setup()
try:
wc.load_without_addon(driver, websites)
except:
print('ERROR: IN VANILLA FIREFOX VERSION')
finally:
wc.close_driver_session(driver)
cookie_compare.compare(websites)
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photonbec/PyPBEC | PyPBEC/OpticalMedium.py | fd68fa3e6206671e731bc0c2973af1f67d704f05 | import numpy as np
from scipy import constants as sc
from scipy.interpolate import interp1d
from pathlib import Path
from scipy.special import erf as Erf
import pandas as pd
import sys
import os
import csv
class OpticalMedium():
available_media = list()
available_media.append("Rhodamine6G")
def __init__(self, optical_medium):
"""
Initiazies an optical medium object.
Parameters:
optical_medium (str): Optical medium
"""
if not type(optical_medium) == str:
raise Exception("optical_medium is expected to be a string")
if not optical_medium in self.available_media:
raise Exception(optical_medium+" is an unknown optical medium")
if optical_medium == "Rhodamine6G":
self.medium = Rhodamine6G()
def get_rates(self, lambdas, **kwargs):
"""
Calculates the rates of absorption and emission, for a specific optical medium.
Parameters:
lambdas (list, or other iterable): Wavelength points where the rates are to be calculated. Wavelength is in meters
other medium specific arguments
"""
return self.medium.get_rates(lambdas=lambdas, **kwargs)
class Rhodamine6G(OpticalMedium):
def __init__(self):
pass
def get_rates(self, lambdas, dye_concentration, n):
"""
Rates for Rhodamine 6G
Parameters:
lambdas (list, or other iterable): Wavelength points where the rates are to be calculated. Wavelength is in meters
dye_concentration (float): In mM (milimolar) 1 mM = 1 mol / m^3
n (float): index of refraction
"""
# absorption data
min_wavelength = 480
max_wavelength = 650
absorption_spectrum_datafile = Path("data") / 'absorption_cross_sections_R6G_in_EthyleneGlycol_corrected.csv'
absorption_spectrum_datafile = Path(os.path.dirname(os.path.abspath(__file__))) / absorption_spectrum_datafile
raw_data2 = pd.read_csv(absorption_spectrum_datafile)
initial_index = raw_data2.iloc[(raw_data2['wavelength (nm)']-min_wavelength).abs().argsort()].index[0]
raw_data2 = raw_data2.iloc[initial_index:].reset_index(drop=True)
final_index = raw_data2.iloc[(raw_data2['wavelength (nm)']-max_wavelength).abs().argsort()].index[0]
raw_data2 = raw_data2.iloc[:final_index].reset_index(drop=True)
absorption_data = raw_data2
absorption_data_normalized = absorption_data['absorption cross-section (m^2)'].values / np.max(absorption_data['absorption cross-section (m^2)'].values)
absorption_spectrum = np.squeeze(np.array([[absorption_data['wavelength (nm)'].values], [absorption_data_normalized]], dtype=float))
interpolated_absorption_spectrum = interp1d(absorption_spectrum[0,:], absorption_spectrum[1,:], kind='cubic')
# emission data
fluorescence_spectrum_datafile = Path("data") / 'fluorescence_spectrum_R6G_in_EthyleneGlycol_corrected.csv'
fluorescence_spectrum_datafile = Path(os.path.dirname(os.path.abspath(__file__))) / fluorescence_spectrum_datafile
raw_data = pd.read_csv(fluorescence_spectrum_datafile)
initial_index = raw_data.iloc[(raw_data['wavelength (nm)']-min_wavelength).abs().argsort()].index[0]
raw_data = raw_data.iloc[initial_index:].reset_index(drop=True)
final_index = raw_data.iloc[(raw_data['wavelength (nm)']-max_wavelength).abs().argsort()].index[0]
raw_data = raw_data.iloc[:final_index].reset_index(drop=True)
fluorescence_data = raw_data
fluorescence_data_normalized = fluorescence_data['fluorescence (arb. units)'].values / np.max(fluorescence_data['fluorescence (arb. units)'].values)
emission_spectrum = np.squeeze(np.array([[fluorescence_data['wavelength (nm)'].values], [fluorescence_data_normalized]], dtype=float))
interpolated_emission_spectrum = interp1d(emission_spectrum[0,:], emission_spectrum[1,:], kind='cubic')
# Uses both datasets
if np.min(1e9*np.array(lambdas)) < 480 or np.max(1e9*np.array(lambdas)) > 650:
raise Exception('*** Restrict wavelength to the range between 480 and 650 nm ***')
temperature = 300
lamZPL = 545e-9
n_mol_per_vol= dye_concentration*sc.Avogadro
peak_Xsectn = 2.45e-20*n_mol_per_vol*sc.c/n
wpzl = 2*np.pi*sc.c/lamZPL/1e12
def freq(wl):
return 2*np.pi*sc.c/wl/1e12
def single_exp_func(det):
f_p = 2*np.pi*sc.c/(wpzl+det)*1e-3
f_m = 2*np.pi*sc.c/(wpzl-det)*1e-3
return (0.5*interpolated_absorption_spectrum(f_p)) + (0.5*interpolated_emission_spectrum(f_m))
def Err(det):
return Erf(det*1e12)
def single_adjust_func(det):
return ((1+Err(det))/2.0*single_exp_func(det)) + ((1-Err(det))/2.0*single_exp_func(-1.0*det)*np.exp(sc.h/(2*np.pi*sc.k*temperature)*det*1e12))
emission_rates = np.array([single_adjust_func(-1.0*freq(a_l)+wpzl) for a_l in lambdas])*peak_Xsectn
absorption_rates = np.array([single_adjust_func(freq(a_l)-wpzl) for a_l in lambdas])*peak_Xsectn
return absorption_rates, emission_rates | [((78, 14, 78, 55), 'pandas.read_csv', 'pd.read_csv', ({(78, 26, 78, 54): 'absorption_spectrum_datafile'}, {}), '(absorption_spectrum_datafile)', True, 'import pandas as pd\n'), ((86, 37, 86, 111), 'scipy.interpolate.interp1d', 'interp1d', (), '', False, 'from scipy.interpolate import interp1d\n'), ((91, 13, 91, 56), 'pandas.read_csv', 'pd.read_csv', ({(91, 25, 91, 55): 'fluorescence_spectrum_datafile'}, {}), '(fluorescence_spectrum_datafile)', True, 'import pandas as pd\n'), ((99, 35, 99, 105), 'scipy.interpolate.interp1d', 'interp1d', (), '', False, 'from scipy.interpolate import interp1d\n'), ((76, 33, 76, 45), 'pathlib.Path', 'Path', ({(76, 38, 76, 44): '"""data"""'}, {}), "('data')", False, 'from pathlib import Path\n'), ((84, 90, 84, 154), 'numpy.max', 'np.max', ({(84, 97, 84, 153): "absorption_data['absorption cross-section (m^2)'].values"}, {}), "(absorption_data['absorption cross-section (m^2)'].values)", True, 'import numpy as np\n'), ((85, 35, 85, 133), 'numpy.array', 'np.array', (), '', True, 'import numpy as np\n'), ((89, 36, 89, 48), 'pathlib.Path', 'Path', ({(89, 41, 89, 47): '"""data"""'}, {}), "('data')", False, 'from pathlib import Path\n'), ((97, 89, 97, 150), 'numpy.max', 'np.max', ({(97, 96, 97, 149): "fluorescence_data['fluorescence (arb. units)'].values"}, {}), "(fluorescence_data['fluorescence (arb. units)'].values)", True, 'import numpy as np\n'), ((98, 33, 98, 135), 'numpy.array', 'np.array', (), '', True, 'import numpy as np\n'), ((118, 10, 118, 23), 'scipy.special.erf', 'Erf', ({(118, 14, 118, 22): '(det * 1000000000000.0)'}, {}), '(det * 1000000000000.0)', True, 'from scipy.special import erf as Erf\n'), ((77, 54, 77, 79), 'os.path.abspath', 'os.path.abspath', ({(77, 70, 77, 78): '__file__'}, {}), '(__file__)', False, 'import os\n'), ((90, 56, 90, 81), 'os.path.abspath', 'os.path.abspath', ({(90, 72, 90, 80): '__file__'}, {}), '(__file__)', False, 'import os\n'), ((120, 96, 120, 144), 'numpy.exp', 'np.exp', ({(120, 103, 120, 143): '(sc.h / (2 * np.pi * sc.k * temperature) * det * 1000000000000.0)'}, {}), '(sc.h / (2 * np.pi * sc.k * temperature) * det * 1000000000000.0)', True, 'import numpy as np\n'), ((102, 16, 102, 33), 'numpy.array', 'np.array', ({(102, 25, 102, 32): 'lambdas'}, {}), '(lambdas)', True, 'import numpy as np\n'), ((102, 55, 102, 72), 'numpy.array', 'np.array', ({(102, 64, 102, 71): 'lambdas'}, {}), '(lambdas)', True, 'import numpy as np\n')] |
dslowikowski/commcare-hq | corehq/apps/appstore/urls.py | ad8885cf8dab69dc85cb64f37aeaf06106124797 | from django.conf.urls.defaults import url, include, patterns
from corehq.apps.appstore.dispatcher import AppstoreDispatcher
store_urls = patterns('corehq.apps.appstore.views',
url(r'^$', 'appstore_default', name="appstore_interfaces_default"),
AppstoreDispatcher.url_pattern(),
)
urlpatterns = patterns('corehq.apps.appstore.views',
url(r'^$', 'appstore', name='appstore'),
url(r'^api/', 'appstore_api', name='appstore_api'),
url(r'^store/', include(store_urls)),
url(r'^(?P<domain>[\w\.-]+)/info/$', 'project_info', name='project_info'),
url(r'^deployments/$', 'deployments', name='deployments'),
url(r'^deployments/api/$', 'deployments_api', name='deployments_api'),
url(r'^deployments/(?P<domain>[\w\.-]+)/info/$', 'deployment_info', name='deployment_info'),
url(r'^(?P<domain>[\w\.-]+)/approve/$', 'approve_app', name='approve_appstore_app'),
url(r'^(?P<domain>[\w\.-]+)/copy/$', 'copy_snapshot', name='domain_copy_snapshot'),
url(r'^(?P<domain>[\w\.-]+)/importapp/$', 'import_app', name='import_app_from_snapshot'),
url(r'^(?P<domain>[\w\.-]+)/image/$', 'project_image', name='appstore_project_image'),
url(r'^(?P<domain>[\w\.-]+)/multimedia/$', 'media_files', name='media_files'),
)
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fengkaibit/faster-rcnn_vgg16_fpn | faster-rcnn-vgg16-fpn/model/fpn.py | 354efd4b5f4d4a42e9c92f48501e02cd7f0c0cdb | from __future__ import absolute_import
import torch
from torch.nn import functional
class FPN(torch.nn.Module):
def __init__(self, out_channels):
super(FPN, self).__init__()
self.out_channels = out_channels
self.P5 = torch.nn.MaxPool2d(kernel_size=1, stride=2, padding=0)
self.P4_conv1 = torch.nn.Conv2d(512, self.out_channels, kernel_size=1, stride=1, padding=0)
self.P4_conv2 = torch.nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1)
self.P3_conv1 = torch.nn.Conv2d(512, self.out_channels, kernel_size=1, stride=1, padding=0)
self.P3_conv2 = torch.nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1)
self.P2_conv1 = torch.nn.Conv2d(256, self.out_channels, kernel_size=1, stride=1, padding=0)
self.P2_conv2 = torch.nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1)
normal_init(self.P4_conv1, 0, 0.01)
normal_init(self.P4_conv2, 0, 0.01)
normal_init(self.P3_conv1, 0, 0.01)
normal_init(self.P3_conv2, 0, 0.01)
normal_init(self.P2_conv1, 0, 0.01)
normal_init(self.P2_conv2, 0, 0.01)
def forward(self, C2, C3, C4):
p4_out = self.P4_conv1(C4)
p5_out = self.P5(p4_out)
p3_out = self._upsample_add(p4_out, self.P3_conv1(C3))
p2_out = self._upsample_add(p3_out, self.P2_conv1(C2))
p4_out = self.P4_conv2(p4_out)
p3_out = self.P3_conv2(p3_out)
p2_out = self.P2_conv2(p2_out)
return p2_out, p3_out, p4_out, p5_out
def _upsample_add(self, x, y):
'''Upsample and add two feature maps.
Args:
x: (Variable) top feature map to be upsampled.
y: (Variable) lateral feature map.
Returns:
(Variable) added feature map.
Note in PyTorch, when input size is odd, the upsampled feature map
with `F.upsample(..., scale_factor=2, mode='nearest')`
maybe not equal to the lateral feature map size.
e.g.
original input size: [N,_,15,15] ->
conv2d feature map size: [N,_,8,8] ->
upsampled feature map size: [N,_,16,16]
So we choose bilinear upsample which supports arbitrary output sizes.
'''
_,_,H,W = y.size()
return functional.interpolate(x, size=(H,W), mode='bilinear') + y
def normal_init(m, mean, stddev, truncated=False):
"""
weight initalizer: truncated normal and random normal.
"""
# x is a parameter
if truncated:
m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean) # not a perfect approximation
else:
m.weight.data.normal_(mean, stddev)
m.bias.data.zero_() | [((10, 18, 10, 72), 'torch.nn.MaxPool2d', 'torch.nn.MaxPool2d', (), '', False, 'import torch\n'), ((12, 24, 12, 99), 'torch.nn.Conv2d', 'torch.nn.Conv2d', (), '', False, 'import torch\n'), ((13, 24, 13, 86), 'torch.nn.Conv2d', 'torch.nn.Conv2d', ({(13, 40, 13, 57): 'self.out_channels', (13, 59, 13, 76): 'self.out_channels', (13, 78, 13, 79): '3', (13, 81, 13, 82): '1', (13, 84, 13, 85): '1'}, {}), '(self.out_channels, self.out_channels, 3, 1, 1)', False, 'import torch\n'), ((15, 24, 15, 99), 'torch.nn.Conv2d', 'torch.nn.Conv2d', (), '', False, 'import torch\n'), ((16, 24, 16, 86), 'torch.nn.Conv2d', 'torch.nn.Conv2d', ({(16, 40, 16, 57): 'self.out_channels', (16, 59, 16, 76): 'self.out_channels', (16, 78, 16, 79): '3', (16, 81, 16, 82): '1', (16, 84, 16, 85): '1'}, {}), '(self.out_channels, self.out_channels, 3, 1, 1)', False, 'import torch\n'), ((18, 24, 18, 99), 'torch.nn.Conv2d', 'torch.nn.Conv2d', (), '', False, 'import torch\n'), ((19, 24, 19, 86), 'torch.nn.Conv2d', 'torch.nn.Conv2d', ({(19, 40, 19, 57): 'self.out_channels', (19, 59, 19, 76): 'self.out_channels', (19, 78, 19, 79): '3', (19, 81, 19, 82): '1', (19, 84, 19, 85): '1'}, {}), '(self.out_channels, self.out_channels, 3, 1, 1)', False, 'import torch\n'), ((61, 15, 61, 69), 'torch.nn.functional.interpolate', 'functional.interpolate', (), '', False, 'from torch.nn import functional\n')] |
bowlofstew/client | test/setups/finders/finders_test.py | 0d5ae42aaf9863e3871828b6df06170aad17c560 | import unittest
from biicode.common.settings.version import Version
from mock import patch
from biicode.client.setups.finders.finders import gnu_version
from biicode.client.setups.rpi_cross_compiler import find_gnu_arm
from biicode.client.workspace.bii_paths import get_biicode_env_folder_path
GCC_VERSION_MAC = '''Configured with: --prefix=/Applications/Xcode.app/Contents/Developer/usr --with-gxx-include-dir=/usr/include/c++/4.2.1
Apple LLVM version 5.1 (clang-503.0.38) (based on LLVM 3.4svn)
Target: x86_64-apple-darwin13.1.0
Thread model: posix'''
GCC_VERSION_UBUNTU = '''gcc (Ubuntu/Linaro 4.8.1-10ubuntu9) 4.8.1
Copyright (C) 2013 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
'''
GCC_VERSION_WIN = '''gcc (GCC) 4.8.1
Copyright (C) 2013 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.'''
class FindersTest(unittest.TestCase):
@patch('biicode.client.setups.finders.finders.execute')
def test_gnu_version_detection(self, execute_mock):
execute_mock.return_value = ("", GCC_VERSION_MAC)
self.assertEquals(gnu_version('gnu'), Version('4.2.1'))
execute_mock.return_value = ("", GCC_VERSION_UBUNTU)
self.assertEquals(gnu_version('gnu'), Version('4.8.1'))
execute_mock.return_value = ("", GCC_VERSION_WIN)
self.assertEquals(gnu_version('gnu'), Version('4.8.1'))
@patch('os.path.exists')
def test_find_gnu_arm(self, exists):
exists.return_value = False
self.assertEqual((None, None), find_gnu_arm())
exists.return_value = True
c_path, cpp_path = find_gnu_arm()
inst_path = get_biicode_env_folder_path().replace('\\', '/')
c_path = c_path.replace('\\', '/')
cpp_path = cpp_path.replace('\\', '/')
inst_path = '%s/raspberry_cross_compilers/arm-bcm2708/'\
'arm-bcm2708hardfp-linux-gnueabi/bin/'\
'arm-bcm2708hardfp-linux-gnueabi' % inst_path
self.assertTrue(cpp_path.endswith('%s-g++' % inst_path))
self.assertTrue(c_path.endswith('%s-gcc' % inst_path))
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mintmachine/arweave-python-client | setup.py | 69e8e2d32090de5fd276efdb9b9103d91b4182f6 | from distutils.core import setup
setup(
name="arweave-python-client",
packages = ['arweave'], # this must be the same as the name above
version="1.0.15.dev0",
description="Client interface for sending transactions on the Arweave permaweb",
author="Mike Hibbert",
author_email="[email protected]",
url="https://github.com/MikeHibbert/arweave-python-client",
download_url="https://github.com/MikeHibbert/arweave-python-client",
keywords=['arweave', 'crypto'],
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
install_requires=[
'arrow',
'python-jose',
'pynacl',
'pycryptodome',
'cryptography',
'requests',
'psutil'
],
)
| [((3, 0, 27, 1), 'distutils.core.setup', 'setup', (), '', False, 'from distutils.core import setup\n')] |
syonoki/exchange_calendars | exchange_calendars/extensions/exchange_calendar_krx.py | 639ab0f88a874af99bb601824a8ffef2572820d4 | """
Last update: 2018-10-26
"""
from exchange_calendars.extensions.calendar_extension import ExtendedExchangeCalendar
from pandas import (
Timestamp,
)
from pandas.tseries.holiday import (
Holiday,
previous_friday,
)
from exchange_calendars.exchange_calendar import HolidayCalendar
from datetime import time
from itertools import chain
from pytz import timezone
KRNewYearsDay = Holiday(
'New Years Day',
month=1,
day=1)
KRIndependenceDay = Holiday(
'Independence Day',
month=3,
day=1
)
KRArbourDay = Holiday(
'Arbour Day',
month=4,
day=5,
end_date=Timestamp('2006-01-01'),
)
KRLabourDay = Holiday(
'Labour Day',
month=5,
day=1
)
KRChildrensDay = Holiday(
'Labour Day',
month=5,
day=5
)
# 현충일
KRMemorialDay = Holiday(
'Memorial Day',
month=6,
day=6
)
# 제헌절
KRConstitutionDay = Holiday(
'Constitution Day',
month=7,
day=17,
end_date=Timestamp('2008-01-01')
)
# 광복절
KRLiberationDay = Holiday(
'Liberation Day',
month=8,
day=15
)
# 개천절
KRNationalFoundationDay = Holiday(
'NationalFoundationDay',
month=10,
day=3
)
Christmas = Holiday(
'Christmas',
month=12,
day=25
)
# 한글날
KRHangulProclamationDay = Holiday(
'Hangul Proclamation Day',
month=10,
day=9,
start_date=Timestamp('2013-01-01')
)
# KRX 연말 휴장
KRXEndOfYearClosing = Holiday(
'KRX Year-end Closing',
month=12,
day=31,
observance=previous_friday,
start_date=Timestamp('2001-01-01')
)
KRXEndOfYearClosing2000 = [
Timestamp('2000-12-27', tz='UTC'),
Timestamp('2000-12-28', tz='UTC'),
Timestamp('2000-12-29', tz='UTC'),
Timestamp('2000-12-30', tz='UTC'),
]
# Lunar New Year
KRLunarNewYear = [
# 2000
Timestamp('2000-02-04', tz='UTC'),
# 2001
Timestamp('2001-01-23', tz='UTC'),
Timestamp('2001-01-24', tz='UTC'),
Timestamp('2001-01-25', tz='UTC'),
# 2002
Timestamp('2002-02-11', tz='UTC'),
Timestamp('2002-02-12', tz='UTC'),
Timestamp('2002-02-13', tz='UTC'),
# 2003
Timestamp('2003-01-31', tz='UTC'),
# 2004
Timestamp('2004-01-21', tz='UTC'),
Timestamp('2004-01-22', tz='UTC'),
Timestamp('2004-01-23', tz='UTC'),
# 2005
Timestamp('2005-02-08', tz='UTC'),
Timestamp('2005-02-09', tz='UTC'),
Timestamp('2005-02-10', tz='UTC'),
# 2006
Timestamp('2006-01-28', tz='UTC'),
Timestamp('2006-01-29', tz='UTC'),
Timestamp('2006-01-30', tz='UTC'),
# 2007
Timestamp('2007-02-19', tz='UTC'),
# 2008
Timestamp('2008-02-06', tz='UTC'),
Timestamp('2008-02-07', tz='UTC'),
Timestamp('2008-02-08', tz='UTC'),
# 2009
Timestamp('2009-01-25', tz='UTC'),
Timestamp('2009-01-26', tz='UTC'),
Timestamp('2009-01-27', tz='UTC'),
# 2010
Timestamp('2010-02-13', tz='UTC'),
Timestamp('2010-02-14', tz='UTC'),
Timestamp('2010-02-15', tz='UTC'),
# 2011
Timestamp('2011-02-02', tz='UTC'),
Timestamp('2011-02-03', tz='UTC'),
Timestamp('2011-02-04', tz='UTC'),
# 2012
Timestamp('2012-01-23', tz='UTC'),
Timestamp('2012-01-24', tz='UTC'),
# 2013
Timestamp('2013-02-11', tz='UTC'),
# 2014
Timestamp('2014-01-30', tz='UTC'),
Timestamp('2014-01-31', tz='UTC'),
# 2015
Timestamp('2015-02-18', tz='UTC'),
Timestamp('2015-02-19', tz='UTC'),
Timestamp('2015-02-20', tz='UTC'),
# 2016
Timestamp('2016-02-07', tz='UTC'),
Timestamp('2016-02-08', tz='UTC'),
Timestamp('2016-02-09', tz='UTC'),
Timestamp('2016-02-10', tz='UTC'),
# 2017
Timestamp('2017-01-27', tz='UTC'),
Timestamp('2017-01-28', tz='UTC'),
Timestamp('2017-01-29', tz='UTC'),
Timestamp('2017-01-30', tz='UTC'),
# 2018
Timestamp('2018-02-15', tz='UTC'),
Timestamp('2018-02-16', tz='UTC'),
Timestamp('2018-02-17', tz='UTC'),
# 2019
Timestamp('2019-02-04', tz='UTC'),
Timestamp('2019-02-05', tz='UTC'),
Timestamp('2019-02-06', tz='UTC'),
# 2020
Timestamp('2020-01-24', tz='UTC'),
Timestamp('2020-01-25', tz='UTC'),
Timestamp('2020-01-26', tz='UTC'),
Timestamp('2020-01-27', tz='UTC'),
# 2021
Timestamp('2021-02-11', tz='UTC'),
Timestamp('2021-02-12', tz='UTC'),
# 2022
Timestamp('2022-01-31', tz='UTC'),
Timestamp('2022-02-01', tz='UTC'),
Timestamp('2022-02-02', tz='UTC'),
]
# Election Days
KRElectionDays = [
Timestamp('2000-04-13', tz='UTC'), # National Assembly
Timestamp('2002-06-13', tz='UTC'), # Regional election
Timestamp('2002-12-19', tz='UTC'), # Presidency
Timestamp('2004-04-15', tz='UTC'), # National Assembly
Timestamp('2006-05-31', tz='UTC'), # Regional election
Timestamp('2007-12-19', tz='UTC'), # Presidency
Timestamp('2008-04-09', tz='UTC'), # National Assembly
Timestamp('2010-06-02', tz='UTC'), # Local election
Timestamp('2012-04-11', tz='UTC'), # National Assembly
Timestamp('2012-12-19', tz='UTC'), # Presidency
Timestamp('2014-06-04', tz='UTC'), # Local election
Timestamp('2016-04-13', tz='UTC'), # National Assembly
Timestamp('2017-05-09', tz='UTC'), # Presidency
Timestamp('2018-06-13', tz='UTC'), # Local election
Timestamp('2020-04-15', tz='UTC'), # National Assembly
Timestamp('2022-03-09', tz='UTC'), # Presidency
Timestamp('2022-06-01', tz='UTC'), # Local election
]
# Buddha's birthday
KRBuddhasBirthday = [
Timestamp('2000-05-11', tz='UTC'),
Timestamp('2001-05-01', tz='UTC'),
Timestamp('2003-05-08', tz='UTC'),
Timestamp('2004-05-26', tz='UTC'),
Timestamp('2005-05-15', tz='UTC'),
Timestamp('2006-05-05', tz='UTC'),
Timestamp('2007-05-24', tz='UTC'),
Timestamp('2008-05-12', tz='UTC'),
Timestamp('2009-05-02', tz='UTC'),
Timestamp('2010-05-21', tz='UTC'),
Timestamp('2011-05-10', tz='UTC'),
Timestamp('2012-05-28', tz='UTC'),
Timestamp('2013-05-17', tz='UTC'),
Timestamp('2014-05-06', tz='UTC'),
Timestamp('2015-05-25', tz='UTC'),
Timestamp('2016-05-14', tz='UTC'),
Timestamp('2017-05-03', tz='UTC'),
Timestamp('2018-05-22', tz='UTC'),
Timestamp('2020-04-30', tz='UTC'),
Timestamp('2021-05-19', tz='UTC'),
]
# Harvest Moon Day
KRHarvestMoonDay = [
# 2000
Timestamp('2000-09-11', tz='UTC'),
Timestamp('2000-09-12', tz='UTC'),
Timestamp('2000-09-13', tz='UTC'),
# 2001
Timestamp('2001-10-01', tz='UTC'),
Timestamp('2001-10-02', tz='UTC'),
# 2002
Timestamp('2002-09-20', tz='UTC'),
# 2003
Timestamp('2003-09-10', tz='UTC'),
Timestamp('2003-09-11', tz='UTC'),
Timestamp('2003-09-12', tz='UTC'),
# 2004
Timestamp('2004-09-27', tz='UTC'),
Timestamp('2004-09-28', tz='UTC'),
Timestamp('2004-09-29', tz='UTC'),
# 2005
Timestamp('2005-09-17', tz='UTC'),
Timestamp('2005-09-18', tz='UTC'),
Timestamp('2005-09-19', tz='UTC'),
# 2006
Timestamp('2006-10-05', tz='UTC'),
Timestamp('2006-10-06', tz='UTC'),
Timestamp('2006-10-07', tz='UTC'),
# 2007
Timestamp('2007-09-24', tz='UTC'),
Timestamp('2007-09-25', tz='UTC'),
Timestamp('2007-09-26', tz='UTC'),
# 2008
Timestamp('2008-09-13', tz='UTC'),
Timestamp('2008-09-14', tz='UTC'),
Timestamp('2008-09-15', tz='UTC'),
# 2009
Timestamp('2009-10-02', tz='UTC'),
Timestamp('2009-10-03', tz='UTC'),
Timestamp('2009-10-04', tz='UTC'),
# 2010
Timestamp('2010-09-21', tz='UTC'),
Timestamp('2010-09-22', tz='UTC'),
Timestamp('2010-09-23', tz='UTC'),
# 2011
Timestamp('2011-09-12', tz='UTC'),
Timestamp('2011-09-13', tz='UTC'),
# 2012
Timestamp('2012-10-01', tz='UTC'),
# 2013
Timestamp('2013-09-18', tz='UTC'),
Timestamp('2013-09-19', tz='UTC'),
Timestamp('2013-09-20', tz='UTC'),
# 2014
Timestamp('2014-09-08', tz='UTC'),
Timestamp('2014-09-09', tz='UTC'),
Timestamp('2014-09-10', tz='UTC'),
# 2015
Timestamp('2015-09-28', tz='UTC'),
Timestamp('2015-09-29', tz='UTC'),
# 2016
Timestamp('2016-09-14', tz='UTC'),
Timestamp('2016-09-15', tz='UTC'),
Timestamp('2016-09-16', tz='UTC'),
# 2017
Timestamp('2017-10-03', tz='UTC'),
Timestamp('2017-10-04', tz='UTC'),
Timestamp('2017-10-05', tz='UTC'),
Timestamp('2017-10-06', tz='UTC'),
# 2018
Timestamp('2018-09-23', tz='UTC'),
Timestamp('2018-09-24', tz='UTC'),
Timestamp('2018-09-25', tz='UTC'),
Timestamp('2018-09-26', tz='UTC'),
# 2019
Timestamp('2019-09-12', tz='UTC'),
Timestamp('2019-09-13', tz='UTC'),
# 2020
Timestamp('2020-09-30', tz='UTC'),
Timestamp('2020-10-01', tz='UTC'),
Timestamp('2020-10-02', tz='UTC'),
# 2021
Timestamp('2021-09-20', tz='UTC'),
Timestamp('2021-09-21', tz='UTC'),
Timestamp('2021-09-22', tz='UTC'),
# 2022
Timestamp('2022-09-09', tz='UTC'),
Timestamp('2022-09-12', tz='UTC'), # 대체휴일
]
# 대체휴일
KRSubstitutionHolidayForChildrensDay2018 = [
Timestamp('2018-05-07', tz='UTC')
]
# 임시공휴일
KRCelebrationForWorldCupHosting = [
Timestamp('2002-07-01', tz='UTC')
]
KRSeventyYearsFromIndependenceDay = [
Timestamp('2015-08-14', tz='UTC')
]
KRTemporaryHolidayForChildrensDay2016 = [
Timestamp('2016-05-06', tz='UTC')
]
KRTemporaryHolidayForHarvestMoonDay2017 = [
Timestamp('2017-10-02', tz='UTC')
]
KRTemporaryHolidayForChildrenDay2018 = [
Timestamp('2018-05-07', tz='UTC')
]
KRTemporaryHolidayForChildrenDay2019 = [
Timestamp('2019-05-06', tz='UTC')
]
KRTemporaryHolidayForLiberationDay2020 = [
Timestamp('2020-08-17', tz='UTC')
]
KRTemporaryHoliday2021 = [
Timestamp('2021-08-16', tz='UTC'), # 광복절 대체휴일
Timestamp('2021-10-04', tz='UTC'), # 개천절 대체휴일
Timestamp('2021-10-11', tz='UTC'), # 한글날 대체휴일
]
KRTemporaryHoliday2022 = [
Timestamp('2022-10-10', tz='UTC'), # 한글날 대체휴일
]
# 잘 모르겠는 휴장일
HolidaysNeedToCheck = [
Timestamp('2000-01-03', tz='UTC')
]
HolidaysBefore1999 = [
Timestamp('1990-01-01', tz='UTC'),
Timestamp('1990-01-02', tz='UTC'),
Timestamp('1990-01-03', tz='UTC'),
Timestamp('1990-01-29', tz='UTC'),
Timestamp('1990-03-01', tz='UTC'),
Timestamp('1990-04-05', tz='UTC'),
Timestamp('1990-05-02', tz='UTC'),
Timestamp('1990-06-06', tz='UTC'),
Timestamp('1990-07-17', tz='UTC'),
Timestamp('1990-08-15', tz='UTC'),
Timestamp('1990-09-03', tz='UTC'),
Timestamp('1990-10-01', tz='UTC'),
Timestamp('1990-10-03', tz='UTC'),
Timestamp('1990-10-09', tz='UTC'),
Timestamp('1990-12-25', tz='UTC'),
Timestamp('1991-01-01', tz='UTC'),
Timestamp('1991-01-02', tz='UTC'),
Timestamp('1991-02-14', tz='UTC'),
Timestamp('1991-02-15', tz='UTC'),
Timestamp('1991-03-01', tz='UTC'),
Timestamp('1991-04-05', tz='UTC'),
Timestamp('1991-05-21', tz='UTC'),
Timestamp('1991-06-06', tz='UTC'),
Timestamp('1991-07-17', tz='UTC'),
Timestamp('1991-08-15', tz='UTC'),
Timestamp('1991-09-23', tz='UTC'),
Timestamp('1991-10-03', tz='UTC'),
Timestamp('1991-12-25', tz='UTC'),
Timestamp('1991-12-30', tz='UTC'),
Timestamp('1992-01-01', tz='UTC'),
Timestamp('1992-09-10', tz='UTC'),
Timestamp('1992-09-11', tz='UTC'),
Timestamp('1992-10-03', tz='UTC'),
Timestamp('1992-12-25', tz='UTC'),
Timestamp('1992-12-29', tz='UTC'),
Timestamp('1992-12-30', tz='UTC'),
Timestamp('1992-12-31', tz='UTC'),
Timestamp('1993-01-01', tz='UTC'),
Timestamp('1993-01-22', tz='UTC'),
Timestamp('1993-03-01', tz='UTC'),
Timestamp('1993-04-05', tz='UTC'),
Timestamp('1993-05-05', tz='UTC'),
Timestamp('1993-05-28', tz='UTC'),
Timestamp('1993-07-17', tz='UTC'),
Timestamp('1993-09-29', tz='UTC'),
Timestamp('1993-09-30', tz='UTC'),
Timestamp('1993-10-01', tz='UTC'),
Timestamp('1993-12-29', tz='UTC'),
Timestamp('1993-12-30', tz='UTC'),
Timestamp('1993-12-31', tz='UTC'),
Timestamp('1994-01-02', tz='UTC'),
Timestamp('1994-02-09', tz='UTC'),
Timestamp('1994-02-10', tz='UTC'),
Timestamp('1994-02-11', tz='UTC'),
Timestamp('1994-03-01', tz='UTC'),
Timestamp('1994-04-05', tz='UTC'),
Timestamp('1994-05-05', tz='UTC'),
Timestamp('1994-06-06', tz='UTC'),
Timestamp('1994-07-17', tz='UTC'),
Timestamp('1994-08-15', tz='UTC'),
Timestamp('1994-09-19', tz='UTC'),
Timestamp('1994-09-20', tz='UTC'),
Timestamp('1994-09-21', tz='UTC'),
Timestamp('1994-10-03', tz='UTC'),
Timestamp('1994-12-29', tz='UTC'),
Timestamp('1994-12-30', tz='UTC'),
Timestamp('1995-01-02', tz='UTC'),
Timestamp('1995-01-30', tz='UTC'),
Timestamp('1995-01-31', tz='UTC'),
Timestamp('1995-02-01', tz='UTC'),
Timestamp('1995-03-01', tz='UTC'),
Timestamp('1995-05-01', tz='UTC'),
Timestamp('1995-05-05', tz='UTC'),
Timestamp('1995-06-06', tz='UTC'),
Timestamp('1995-06-27', tz='UTC'),
Timestamp('1995-07-17', tz='UTC'),
Timestamp('1995-08-15', tz='UTC'),
Timestamp('1995-09-08', tz='UTC'),
Timestamp('1995-09-09', tz='UTC'),
Timestamp('1995-10-03', tz='UTC'),
Timestamp('1995-12-22', tz='UTC'),
Timestamp('1995-12-25', tz='UTC'),
Timestamp('1995-12-28', tz='UTC'),
Timestamp('1995-12-29', tz='UTC'),
Timestamp('1995-12-30', tz='UTC'),
Timestamp('1995-12-31', tz='UTC'),
Timestamp('1996-01-01', tz='UTC'),
Timestamp('1996-01-02', tz='UTC'),
Timestamp('1996-02-19', tz='UTC'),
Timestamp('1996-02-20', tz='UTC'),
Timestamp('1996-03-01', tz='UTC'),
Timestamp('1996-04-05', tz='UTC'),
Timestamp('1996-04-11', tz='UTC'),
Timestamp('1996-05-01', tz='UTC'),
Timestamp('1996-05-05', tz='UTC'),
Timestamp('1996-05-24', tz='UTC'),
Timestamp('1996-06-06', tz='UTC'),
Timestamp('1996-07-17', tz='UTC'),
Timestamp('1996-08-15', tz='UTC'),
Timestamp('1996-09-26', tz='UTC'),
Timestamp('1996-09-27', tz='UTC'),
Timestamp('1996-09-28', tz='UTC'),
Timestamp('1996-10-03', tz='UTC'),
Timestamp('1996-12-25', tz='UTC'),
Timestamp('1996-12-30', tz='UTC'),
Timestamp('1996-12-31', tz='UTC'),
Timestamp('1997-01-01', tz='UTC'),
Timestamp('1997-01-02', tz='UTC'),
Timestamp('1997-02-07', tz='UTC'),
Timestamp('1997-02-08', tz='UTC'),
Timestamp('1997-03-01', tz='UTC'),
Timestamp('1997-04-05', tz='UTC'),
Timestamp('1997-05-05', tz='UTC'),
Timestamp('1997-05-14', tz='UTC'),
Timestamp('1997-06-06', tz='UTC'),
Timestamp('1997-07-17', tz='UTC'),
Timestamp('1997-08-15', tz='UTC'),
Timestamp('1997-09-16', tz='UTC'),
Timestamp('1997-09-17', tz='UTC'),
Timestamp('1997-10-03', tz='UTC'),
Timestamp('1997-12-25', tz='UTC'),
Timestamp('1998-01-01', tz='UTC'),
Timestamp('1998-01-02', tz='UTC'),
Timestamp('1998-01-27', tz='UTC'),
Timestamp('1998-01-28', tz='UTC'),
Timestamp('1998-01-29', tz='UTC'),
Timestamp('1998-03-01', tz='UTC'),
Timestamp('1998-04-05', tz='UTC'),
Timestamp('1998-05-01', tz='UTC'),
Timestamp('1998-05-03', tz='UTC'),
Timestamp('1998-05-05', tz='UTC'),
Timestamp('1998-06-04', tz='UTC'),
Timestamp('1998-06-06', tz='UTC'),
Timestamp('1998-07-17', tz='UTC'),
Timestamp('1998-08-15', tz='UTC'),
Timestamp('1998-10-03', tz='UTC'),
Timestamp('1998-10-04', tz='UTC'),
Timestamp('1998-10-05', tz='UTC'),
Timestamp('1998-10-06', tz='UTC'),
Timestamp('1998-12-25', tz='UTC'),
Timestamp('1998-12-31', tz='UTC'),
Timestamp('1999-01-01', tz='UTC'),
Timestamp('1999-02-15', tz='UTC'),
Timestamp('1999-02-16', tz='UTC'),
Timestamp('1999-02-17', tz='UTC'),
Timestamp('1999-03-01', tz='UTC'),
Timestamp('1999-04-05', tz='UTC'),
Timestamp('1999-05-05', tz='UTC'),
Timestamp('1999-05-22', tz='UTC'),
Timestamp('1999-06-06', tz='UTC'),
Timestamp('1999-07-17', tz='UTC'),
Timestamp('1999-09-23', tz='UTC'),
Timestamp('1999-09-24', tz='UTC'),
Timestamp('1999-09-25', tz='UTC'),
Timestamp('1999-10-03', tz='UTC'),
Timestamp('1999-12-29', tz='UTC'),
Timestamp('1999-12-30', tz='UTC'),
Timestamp('1999-12-31', tz='UTC'),
]
class KRXExchangeCalendar(ExtendedExchangeCalendar):
"""
Exchange calendars for KRX
Open Time: 9:00 AM, Asia/Seoul
Close Time: 3:30 PM, Asia/Seoul (3:00 PM until 2016/07/31)
"""
@property
def name(self):
return "KRX"
@property
def tz(self):
# return timezone('Asia/Seoul')
return timezone('UTC')
@property
def open_time(self):
return time(9, 0)
@property
def open_times(self):
return [(None, time(9, 0))]
@property
def close_time(self):
return time(15, 30)
@property
def close_times(self):
return [(None, time(15, 30))]
@property
def regular_holidays(self):
return HolidayCalendar([
KRNewYearsDay,
KRIndependenceDay,
KRArbourDay,
KRLabourDay,
KRChildrensDay,
KRMemorialDay,
KRConstitutionDay,
KRLiberationDay,
KRNationalFoundationDay,
Christmas,
KRHangulProclamationDay,
KRXEndOfYearClosing
])
@property
def special_closes(self):
return []
@property
def adhoc_holidays(self):
return list(chain(
KRXEndOfYearClosing2000,
KRLunarNewYear,
KRElectionDays,
KRBuddhasBirthday,
KRHarvestMoonDay,
KRSubstitutionHolidayForChildrensDay2018,
KRCelebrationForWorldCupHosting,
KRSeventyYearsFromIndependenceDay,
KRTemporaryHolidayForChildrensDay2016,
KRTemporaryHolidayForHarvestMoonDay2017,
KRTemporaryHolidayForChildrenDay2018,
KRTemporaryHolidayForChildrenDay2019,
HolidaysNeedToCheck,
KRTemporaryHolidayForLiberationDay2020,
KRTemporaryHoliday2021,
HolidaysBefore1999,
))
def __hash__(self):
return hash(self.name)
def __eq__(self, other):
return self.__class__ == other.__class__
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'HolidayCalendar', ({(594, 31, 607, 9): '[KRNewYearsDay, KRIndependenceDay, KRArbourDay, KRLabourDay, KRChildrensDay,\n KRMemorialDay, KRConstitutionDay, KRLiberationDay,\n KRNationalFoundationDay, Christmas, KRHangulProclamationDay,\n KRXEndOfYearClosing]'}, {}), '([KRNewYearsDay, KRIndependenceDay, KRArbourDay, KRLabourDay,\n KRChildrensDay, KRMemorialDay, KRConstitutionDay, KRLiberationDay,\n KRNationalFoundationDay, Christmas, KRHangulProclamationDay,\n KRXEndOfYearClosing])', False, 'from exchange_calendars.exchange_calendar import HolidayCalendar\n'), ((615, 20, 632, 9), 'itertools.chain', 'chain', ({(616, 12, 616, 35): 'KRXEndOfYearClosing2000', (617, 12, 617, 26): 'KRLunarNewYear', (618, 12, 618, 26): 'KRElectionDays', (619, 12, 619, 29): 'KRBuddhasBirthday', (620, 12, 620, 28): 'KRHarvestMoonDay', (621, 12, 621, 52): 'KRSubstitutionHolidayForChildrensDay2018', (622, 12, 622, 43): 'KRCelebrationForWorldCupHosting', (623, 12, 623, 45): 'KRSeventyYearsFromIndependenceDay', (624, 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ameldocena/StratifiedAggregation | utilities.py | 0031fea120bff00c739eb6c3d654a5c6d3f094bb | import random
import numpy
#import tensorflow as tf
#import torch
from abc import abstractmethod
from sklearn.decomposition import PCA
from aggregators import FedAvg, MultiKrum, AlignedAvg, TrimmedMean, Median, StratifiedAggr
class SelectionStrategy:
# Unchanged from original work
@abstractmethod
def select_round_workers(self, workers, poisoned_workers, kwargs):
"""
:param workers: list(int). All workers available for learning
:param poisoned_workers: list(int). All workers that are poisoned
:param kwargs: dict
"""
raise NotImplementedError("select_round_workers() not implemented")
class RandomSelectionStrategy(SelectionStrategy):
# Unchanged from original work
"""
Randomly selects workers out of the list of all workers
"""
def select_round_workers(self, workers, poisoned_workers, kwargs):
#The poisoned_workers here are not used
return random.sample(workers, kwargs["NUM_WORKERS_PER_ROUND"])
#returns a list of sampled worker ids
# class StratifiedRandomSelection(SelectionStrategy):
# #We first stratify: Each stratum will be a list of workers
# #Then within each stratum, we randomly select
# #We would need the list of workers and the information about their skews
def select_aggregator(args, name, KWARGS={}):
#Creates an Aggregator object as selected
if name == "FedAvg":
return FedAvg(args, name, KWARGS)
elif name == "AlignedAvg":
return AlignedAvg(args, name, KWARGS)
elif name == "AlignedAvgImpute":
KWARGS.update({"use_impute":"filter","align":"fusion"})
return AlignedAvg(args, name, **KWARGS)
elif name == "MultiKrum":
return MultiKrum(args, name, KWARGS)
elif name == "TrimmedMean":
return TrimmedMean(args, name, KWARGS)
elif name == "Median":
return Median(args, name, KWARGS)
elif (name == "StratKrum") or (name == "StratTrimMean") or (name == "StratMedian") or (name == "StratFedAvg"):
#We may have to change the class name to StratifiedAggregation
return StratifiedAggr(args, name, KWARGS)
else:
raise NotImplementedError(f"Unrecognized Aggregator Name: {name}")
def calculate_pca_of_gradients(logger, gradients, num_components):
# Unchanged from original work
pca = PCA(n_components=num_components)
logger.info("Computing {}-component PCA of gradients".format(num_components))
return pca.fit_transform(gradients)
#So this is here after all
def calculate_model_gradient( model_1, model_2):
# Minor change from original work
"""
Calculates the gradient (parameter difference) between two Torch models.
:param logger: loguru.logger (NOW REMOVED)
:param model_1: torch.nn
:param model_2: torch.nn
"""
model_1_parameters = list(dict(model_1.state_dict()))
model_2_parameters = list(dict(model_2.state_dict()))
return calculate_parameter_gradients(model_1_parameters, model_2_parameters)
def calculate_parameter_gradients(params_1, params_2):
# Minor change from original work
"""
Calculates the gradient (parameter difference) between two sets of Torch parameters.
:param logger: loguru.logger (NOW REMOVED)
:param params_1: dict
:param params_2: dict
"""
#logger.debug("Shape of model_1_parameters: {}".format(str(len(params_1))))
#logger.debug("Shape of model_2_parameters: {}".format(str(len(params_2))))
return numpy.array([x for x in numpy.subtract(params_1, params_2)])
# #Inserted
# def convert2TF(torch_tensor):
# # Converts a pytorch tensor into a Tensorflow.
# # We first convert torch into numpy, then to tensorflow.
# # Arg: torch_tensor - a Pytorch tensor object
# np_tensor = torch_tensor.numpy().astype(float)
# return tf.convert_to_tensor(np_tensor)
#
# def convert2Torch(tf_tensor):
# #Converts a TF tensor to Torch
# #Arg: tf_tensor - a TF tensor
# np_tensor = tf.make_ndarray(tf_tensor)
# return torch.from_numpy(np_tensor)
def count_poisoned_stratum(stratified_workers, poisoned_workers):
if len(poisoned_workers) > 0:
print("\nPoisoned workers:", len(poisoned_workers), poisoned_workers)
for stratum in stratified_workers:
intersect = list(set(stratified_workers[stratum]).intersection(poisoned_workers))
print("Count poisoned workers per stratum:", len(intersect), intersect)
print("Stratum: {}. Propn to total poisoned: {}. Propn to subpopn in stratum: {}".format(stratum, len(intersect)/len(poisoned_workers),
len(intersect)/len(stratified_workers[stratum])))
else:
print("No poisoned workers")
def generate_uniform_weights(random_workers):
"""
This function generates uniform weights for each stratum in random_workers
:param random_workers:
:return:
"""
strata_weights = dict()
weight = 1.0 / len(list(random_workers.keys()))
for stratum in random_workers:
strata_weights[stratum] = weight
return strata_weights | [((59, 10, 59, 42), 'sklearn.decomposition.PCA', 'PCA', (), '', False, 'from sklearn.decomposition import PCA\n'), ((28, 15, 28, 70), 'random.sample', 'random.sample', ({(28, 29, 28, 36): 'workers', (28, 38, 28, 69): "kwargs['NUM_WORKERS_PER_ROUND']"}, {}), "(workers, kwargs['NUM_WORKERS_PER_ROUND'])", False, 'import random\n'), ((39, 15, 39, 41), 'aggregators.FedAvg', 'FedAvg', ({(39, 22, 39, 26): 'args', (39, 28, 39, 32): 'name', (39, 34, 39, 40): 'KWARGS'}, {}), '(args, name, KWARGS)', False, 'from aggregators import FedAvg, MultiKrum, AlignedAvg, TrimmedMean, Median, StratifiedAggr\n'), ((41, 15, 41, 45), 'aggregators.AlignedAvg', 'AlignedAvg', ({(41, 26, 41, 30): 'args', (41, 32, 41, 36): 'name', (41, 38, 41, 44): 'KWARGS'}, {}), '(args, name, KWARGS)', False, 'from aggregators import FedAvg, MultiKrum, AlignedAvg, TrimmedMean, Median, StratifiedAggr\n'), ((44, 15, 44, 47), 'aggregators.AlignedAvg', 'AlignedAvg', ({(44, 26, 44, 30): 'args', (44, 32, 44, 36): 'name'}, {}), '(args, name, **KWARGS)', False, 'from aggregators import FedAvg, MultiKrum, AlignedAvg, TrimmedMean, Median, StratifiedAggr\n'), ((92, 35, 92, 69), 'numpy.subtract', 'numpy.subtract', ({(92, 50, 92, 58): 'params_1', (92, 60, 92, 68): 'params_2'}, {}), '(params_1, params_2)', False, 'import numpy\n'), ((46, 15, 46, 44), 'aggregators.MultiKrum', 'MultiKrum', ({(46, 25, 46, 29): 'args', (46, 31, 46, 35): 'name', (46, 37, 46, 43): 'KWARGS'}, {}), '(args, name, KWARGS)', False, 'from aggregators import FedAvg, MultiKrum, AlignedAvg, TrimmedMean, Median, StratifiedAggr\n'), ((48, 15, 48, 46), 'aggregators.TrimmedMean', 'TrimmedMean', ({(48, 27, 48, 31): 'args', (48, 33, 48, 37): 'name', (48, 39, 48, 45): 'KWARGS'}, {}), '(args, name, KWARGS)', False, 'from aggregators import FedAvg, MultiKrum, AlignedAvg, TrimmedMean, Median, StratifiedAggr\n'), ((50, 15, 50, 41), 'aggregators.Median', 'Median', ({(50, 22, 50, 26): 'args', (50, 28, 50, 32): 'name', (50, 34, 50, 40): 'KWARGS'}, {}), '(args, name, KWARGS)', False, 'from aggregators import FedAvg, MultiKrum, AlignedAvg, TrimmedMean, Median, StratifiedAggr\n'), ((53, 15, 53, 49), 'aggregators.StratifiedAggr', 'StratifiedAggr', ({(53, 30, 53, 34): 'args', (53, 36, 53, 40): 'name', (53, 42, 53, 48): 'KWARGS'}, {}), '(args, name, KWARGS)', False, 'from aggregators import FedAvg, MultiKrum, AlignedAvg, TrimmedMean, Median, StratifiedAggr\n')] |
b1naryth1ef/mmo | game/player.py | 400f66b0ac76896af2d7108ff3540c42614a32f0 | from sprites import PlayerSprite
import time
class Player(object):
def __init__(self, name, game):
self.name = name
self.pos = [50, 50]
self.do_blit = False
self.game = game
self.surf = game.SCREEN
self.lastMove = 99999999999
self.velo_def = [0, 0]
self.velo_x = 0
self.velo_y = 0
self.sprite = PlayerSprite(self)
self.moving = [False, False, False, False]
def tick(self):
if self.do_blit:
self.game.reDraw = True
self.sprite.display(self.surf.screen)
#self.surface.screen.blit(self.image, self.pos)
self.do_blit = False
# print self.lastMove - time.time()
if True in self.moving and abs(self.lastMove - time.time()) >= .08:
self.lastMove = time.time()
if self.moving[0]: self.move(x=-1)
if self.moving[1]: self.move(x=1)#down
if self.moving[2]: self.move(y=-1)#left
if self.moving[3]: self.move(y=1)#right
def move(self, x=0, y=0):
self.pos[1]+=x*10
self.pos[0]+=y*10
self.do_blit = True
if y < 0 and self.sprite.dir == 1:
self.sprite.flip()
elif y > 0 and self.sprite.dir == -1:
self.sprite.flip() | [((18, 22, 18, 40), 'sprites.PlayerSprite', 'PlayerSprite', ({(18, 35, 18, 39): 'self'}, {}), '(self)', False, 'from sprites import PlayerSprite\n'), ((30, 28, 30, 39), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((29, 55, 29, 66), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n')] |
cbarrick/toys | toys/layers/pool.py | 0368036ddb7594c0b6e7cdc704aeec918786e58a | from typing import Sequence
import torch
from torch import nn
class MaxPool2d(nn.Module):
def __init__(self, kernel_size, **kwargs):
super().__init__()
stride = kwargs.setdefault('stride', kernel_size)
padding = kwargs.setdefault('padding', 0)
dilation = kwargs.setdefault('dilation', 1)
return_indices = kwargs.setdefault('return_indices', False)
ceil_mode = kwargs.setdefault('ceil_mode', False)
self.pool = nn.MaxPool2d(kernel_size,
stride=stride, padding=padding, dilation=dilation,
return_indices=return_indices, ceil_mode=ceil_mode)
def forward(self, x):
(*batch, height, width, channels) = x.shape
x = x.view(-1, height, width, channels)
x = torch.einsum('nhwc->nchw', [x])
x = self.pool(x)
x = torch.einsum('nchw->nhwc', [x])
(_, new_height, new_width, _) = x.shape
x = x.contiguous()
x = x.view(*batch, new_height, new_width, channels)
return x
| [((16, 20, 18, 63), 'torch.nn.MaxPool2d', 'nn.MaxPool2d', (), '', False, 'from torch import nn\n'), ((23, 12, 23, 43), 'torch.einsum', 'torch.einsum', ({(23, 25, 23, 37): '"""nhwc->nchw"""', (23, 39, 23, 42): '[x]'}, {}), "('nhwc->nchw', [x])", False, 'import torch\n'), ((25, 12, 25, 43), 'torch.einsum', 'torch.einsum', ({(25, 25, 25, 37): '"""nchw->nhwc"""', (25, 39, 25, 42): '[x]'}, {}), "('nchw->nhwc', [x])", False, 'import torch\n')] |
vvladych/forecastmgmt | src/forecastmgmt/ui/masterdata/person_window.py | 9eea272d00bb42031f49b5bb5af01388ecce31cf | from gi.repository import Gtk
from masterdata_abstract_window import MasterdataAbstractWindow
from person_add_mask import PersonAddMask
from person_list_mask import PersonListMask
class PersonWindow(MasterdataAbstractWindow):
def __init__(self, main_window):
super(PersonWindow, self).__init__(main_window, PersonListMask(), PersonAddMask(main_window, self.add_working_area))
| [((11, 56, 11, 72), 'person_list_mask.PersonListMask', 'PersonListMask', ({}, {}), '()', False, 'from person_list_mask import PersonListMask\n'), ((11, 74, 11, 123), 'person_add_mask.PersonAddMask', 'PersonAddMask', ({(11, 88, 11, 99): 'main_window', (11, 101, 11, 122): 'self.add_working_area'}, {}), '(main_window, self.add_working_area)', False, 'from person_add_mask import PersonAddMask\n')] |
SeockHwa/Segmentation_mobileV3 | fastseg/model/utils.py | 01d90eeb32232346b8ed071eaf5d03322049be11 | import torch.nn as nn
from .efficientnet import EfficientNet_B4, EfficientNet_B0
from .mobilenetv3 import MobileNetV3_Large, MobileNetV3_Small
def get_trunk(trunk_name):
"""Retrieve the pretrained network trunk and channel counts"""
if trunk_name == 'efficientnet_b4':
backbone = EfficientNet_B4(pretrained=True)
s2_ch = 24
s4_ch = 32
high_level_ch = 1792
elif trunk_name == 'efficientnet_b0':
backbone = EfficientNet_B0(pretrained=True)
s2_ch = 16
s4_ch = 24
high_level_ch = 1280
elif trunk_name == 'mobilenetv3_large':
backbone = MobileNetV3_Large(pretrained=True)
s2_ch = 16
s4_ch = 24
high_level_ch = 960
elif trunk_name == 'mobilenetv3_small':
backbone = MobileNetV3_Small(pretrained=True)
s2_ch = 16
s4_ch = 16
high_level_ch = 576
else:
raise ValueError('unknown backbone {}'.format(trunk_name))
return backbone, s2_ch, s4_ch, high_level_ch
class ConvBnRelu(nn.Module):
"""Convenience layer combining a Conv2d, BatchNorm2d, and a ReLU activation.
Original source of this code comes from
https://github.com/lingtengqiu/Deeperlab-pytorch/blob/master/seg_opr/seg_oprs.py
"""
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0,
norm_layer=nn.BatchNorm2d):
super(ConvBnRelu, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size,
stride=stride, padding=padding, bias=False)
self.bn = norm_layer(out_planes, eps=1e-5)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
| [((41, 20, 42, 73), 'torch.nn.Conv2d', 'nn.Conv2d', (), '', True, 'import torch.nn as nn\n'), ((44, 20, 44, 41), 'torch.nn.ReLU', 'nn.ReLU', (), '', True, 'import torch.nn as nn\n')] |
06needhamt/intellij-community | python/testData/inspections/PyTypeCheckerInspection/ModuleTypeParameter/a.py | 63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b | import module
from types import ModuleType
def foo(m: ModuleType):
pass
def bar(m):
return m.__name__
foo(module)
bar(module) | [] |
proofdock/chaos-azure | tests/webapp/test_webapp_actions.py | 85302f8be18153862656c587988eafb5dd37ddf7 | from unittest.mock import patch, MagicMock
from pdchaosazure.webapp.actions import stop, restart, delete
from tests.data import config_provider, secrets_provider, webapp_provider
@patch('pdchaosazure.webapp.actions.fetch_webapps', autospec=True)
@patch('pdchaosazure.webapp.actions.client.init', autospec=True)
def test_happily_stop_webapp(init, fetch):
config = config_provider.provide_default_config()
secrets = secrets_provider.provide_secrets_public()
webapp = webapp_provider.default()
client = MagicMock()
init.return_value = client
resource_list = [webapp]
fetch.return_value = resource_list
f = "where resourceGroup=~'rg'"
stop(f, config, secrets)
fetch.assert_called_with(f, config, secrets)
client.web_apps.stop.assert_called_with(webapp['resourceGroup'], webapp['name'])
@patch('pdchaosazure.webapp.actions.fetch_webapps', autospec=True)
@patch('pdchaosazure.webapp.actions.client.init', autospec=True)
def test_happily_restart_webapp(init, fetch):
config = config_provider.provide_default_config()
secrets = secrets_provider.provide_secrets_public()
webapp = webapp_provider.default()
client = MagicMock()
init.return_value = client
resource_list = [webapp]
fetch.return_value = resource_list
f = "where resourceGroup=~'rg'"
restart(f, config, secrets)
fetch.assert_called_with(f, config, secrets)
client.web_apps.restart.assert_called_with(webapp['resourceGroup'], webapp['name'])
@patch('pdchaosazure.webapp.actions.fetch_webapps', autospec=True)
@patch('pdchaosazure.webapp.actions.client.init', autospec=True)
def test_happily_delete_webapp(init, fetch):
webapp = webapp_provider.default()
config = config_provider.provide_default_config()
secrets = secrets_provider.provide_secrets_public()
client = MagicMock()
init.return_value = client
resource_list = [webapp]
fetch.return_value = resource_list
f = "where resourceGroup=~'rg'"
delete(f, config, secrets)
fetch.assert_called_with(f, config, secrets)
client.web_apps.delete.assert_called_with(webapp['resourceGroup'], webapp['name'])
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lbesnard/subimporter | utils.py | 66affbca2acdb3c25e70dac23290b5e7b956c2d7 |
def stringifySong(song):
return f"<'{song['title']}' by '{song['artist']}' in '{song['album']}'>" | [] |
leewujung/echopype-lfs-test | echopype/model/modelbase.py | b76dcf42631d0ac9cef0efeced9be4afdc15e659 | """
echopype data model that keeps tracks of echo data and
its connection to data files.
"""
import os
import warnings
import datetime as dt
from echopype.utils import uwa
import numpy as np
import xarray as xr
class ModelBase(object):
"""Class for manipulating echo data that is already converted to netCDF."""
def __init__(self, file_path=""):
self.file_path = file_path # this passes the input through file name test
self.noise_est_range_bin_size = 5 # meters per tile for noise estimation
self.noise_est_ping_size = 30 # number of pings per tile for noise estimation
self.MVBS_range_bin_size = 5 # meters per tile for MVBS
self.MVBS_ping_size = 30 # number of pings per tile for MVBS
self.Sv = None # calibrated volume backscattering strength
self.Sv_path = None # path to save calibrated results
self.Sv_clean = None # denoised volume backscattering strength
self.TS = None # calibrated target strength
self.TS_path = None # path to save TS calculation results
self.MVBS = None # mean volume backscattering strength
self._salinity = None
self._temperature = None
self._pressure = None
self._sound_speed = None
self._sample_thickness = None
self._range = None
self._seawater_absorption = None
@property
def salinity(self):
return self._salinity
@salinity.setter
def salinity(self, sal):
self._salinity = sal
@property
def pressure(self):
return self._pressure
@pressure.setter
def pressure(self, pres):
self._pressure = pres
@property
def temperature(self):
return self._temperature
@temperature.setter
def temperature(self, t):
self._temperature = t
@property
def sample_thickness(self):
return self._sample_thickness
@sample_thickness.setter
def sample_thickness(self, sth):
self._sample_thickness = sth
@property
def range(self):
return self._range
@range.setter
def range(self, rr):
self._range = rr
@property
def seawater_absorption(self):
return self._seawater_absorption
@seawater_absorption.setter
def seawater_absorption(self, absorption):
self._seawater_absorption.values = absorption
@property
def sound_speed(self):
return self._sound_speed
@sound_speed.setter
def sound_speed(self, ss):
if isinstance(self._sound_speed, xr.DataArray):
self._sound_speed.values = ss
else:
self._sound_speed = ss
@property
def file_path(self):
return self._file_path
@file_path.setter
def file_path(self, p):
self._file_path = p
# Load netCDF groups if file format is correct
pp = os.path.basename(p)
_, ext = os.path.splitext(pp)
supported_ext_list = ['.raw', '.01A']
if ext in supported_ext_list:
print('Data file in manufacturer format, please convert to .nc first.')
elif ext == '.nc':
self.toplevel = xr.open_dataset(self.file_path)
# Get .nc filenames for storing processed data if computation is performed
self.Sv_path = os.path.join(os.path.dirname(self.file_path),
os.path.splitext(os.path.basename(self.file_path))[0] + '_Sv.nc')
self.Sv_clean_path = os.path.join(os.path.dirname(self.file_path),
os.path.splitext(os.path.basename(self.file_path))[0] + '_Sv_clean.nc')
self.TS_path = os.path.join(os.path.dirname(self.file_path),
os.path.splitext(os.path.basename(self.file_path))[0] + '_TS.nc')
self.MVBS_path = os.path.join(os.path.dirname(self.file_path),
os.path.splitext(os.path.basename(self.file_path))[0] + '_MVBS.nc')
# Raise error if the file format convention does not match
if self.toplevel.sonar_convention_name != 'SONAR-netCDF4':
raise ValueError('netCDF file convention not recognized.')
self.toplevel.close()
else:
raise ValueError('Data file format not recognized.')
def calc_sound_speed(self, src='file'):
"""Base method to be overridden for calculating sound_speed for different sonar models
"""
# issue warning when subclass methods not available
print("Sound speed calculation has not been implemented for this sonar model!")
def calc_seawater_absorption(self, src='file'):
"""Base method to be overridden for calculating seawater_absorption for different sonar models
"""
# issue warning when subclass methods not available
print("Seawater absorption calculation has not been implemented for this sonar model!")
def calc_sample_thickness(self):
"""Base method to be overridden for calculating sample_thickness for different sonar models.
"""
# issue warning when subclass methods not available
print('Sample thickness calculation has not been implemented for this sonar model!')
def calc_range(self):
"""Base method to be overridden for calculating range for different sonar models.
"""
# issue warning when subclass methods not available
print('Range calculation has not been implemented for this sonar model!')
def recalculate_environment(self, ss=True, sa=True, st=True, r=True):
""" Recalculates sound speed, seawater absorption, sample thickness, and range using
salinity, temperature, and pressure
Parameters
----------
ss : bool
Whether to calcualte sound speed. Defaults to `True`
sa : bool
Whether to calcualte seawater absorption. Defaults to `True`
st : bool
Whether to calcualte sample thickness. Defaults to `True`
r : bool
Whether to calcualte range. Defaults to `True`
"""
s, t, p = self.salinity, self.temperature, self.pressure
if s is not None and t is not None and p is not None:
if ss:
self.sound_speed = self.calc_sound_speed(src='user')
if sa:
self.seawater_absorption = self.calc_seawater_absorption(src='user')
if st:
self.sample_thickness = self.calc_sample_thickness()
if r:
self.range = self.calc_range()
elif s is None:
print("Salinity was not provided. Environment was not recalculated")
elif t is None:
print("Temperature was not provided. Environment was not recalculated")
else:
print("Pressure was not provided. Environment was not recalculated")
def calibrate(self):
"""Base method to be overridden for volume backscatter calibration and echo-integration for different sonar models.
"""
# issue warning when subclass methods not available
print('Calibration has not been implemented for this sonar model!')
def calibrate_TS(self):
"""Base method to be overridden for target strength calibration and echo-integration for different sonar models.
"""
# issue warning when subclass methods not available
print('Target strength calibration has not been implemented for this sonar model!')
def validate_path(self, save_path, save_postfix):
"""Creates a directory if it doesnt exist. Returns a valid save path.
"""
def _assemble_path():
file_in = os.path.basename(self.file_path)
file_name, file_ext = os.path.splitext(file_in)
return file_name + save_postfix + file_ext
if save_path is None:
save_dir = os.path.dirname(self.file_path)
file_out = _assemble_path()
else:
path_ext = os.path.splitext(save_path)[1]
# If given save_path is file, split into directory and file
if path_ext != '':
save_dir, file_out = os.path.split(save_path)
if save_dir == '': # save_path is only a filename without directory
save_dir = os.path.dirname(self.file_path) # use directory from input file
# If given save_path is a directory, get a filename from input .nc file
else:
save_dir = save_path
file_out = _assemble_path()
# Create folder if not already exists
if save_dir == '':
# TODO: should we use '.' instead of os.getcwd()?
save_dir = os.getcwd() # explicit about path to current directory
if not os.path.exists(save_dir):
os.mkdir(save_dir)
return os.path.join(save_dir, file_out)
@staticmethod
def get_tile_params(r_data_sz, p_data_sz, r_tile_sz, p_tile_sz, sample_thickness):
"""Obtain ping_time and range_bin parameters associated with groupby and groupby_bins operations.
These parameters are used in methods remove_noise(), noise_estimates(), get_MVBS().
Parameters
----------
r_data_sz : int
number of range_bin entries in data
p_data_sz : int
number of ping_time entries in data
r_tile_sz : float
tile size along the range_bin dimension [m]
p_tile_sz : int
tile size along the ping_time dimension [number of pings]
sample_thickness : float
thickness of each data sample, determined by sound speed and pulse duration
Returns
-------
r_tile_sz : int
modified tile size along the range dimension [m], determined by sample_thickness
r_tile_bin_edge : list of int
bin edges along the range_bin dimension for :py:func:`xarray.DataArray.groupby_bins` operation
p_tile_bin_edge : list of int
bin edges along the ping_time dimension for :py:func:`xarray.DataArray.groupby_bins` operation
"""
# Adjust noise_est_range_bin_size because range_bin_size may be an inconvenient value
num_r_per_tile = np.round(r_tile_sz / sample_thickness).astype(int) # num of range_bin per tile
r_tile_sz = num_r_per_tile * sample_thickness
# Total number of range_bin and ping tiles
num_tile_range_bin = np.ceil(r_data_sz / num_r_per_tile).astype(int)
if np.mod(p_data_sz, p_tile_sz) == 0:
num_tile_ping = np.ceil(p_data_sz / p_tile_sz).astype(int) + 1
else:
num_tile_ping = np.ceil(p_data_sz / p_tile_sz).astype(int)
# Tile bin edges along range
# ... -1 to make sure each bin has the same size because of the right-inclusive and left-exclusive bins
r_tile_bin_edge = [np.arange(x.values + 1) * y.values - 1 for x, y in zip(num_tile_range_bin, num_r_per_tile)]
p_tile_bin_edge = np.arange(num_tile_ping + 1) * p_tile_sz - 1
return r_tile_sz, r_tile_bin_edge, p_tile_bin_edge
def _get_proc_Sv(self, source_path=None, source_postfix='_Sv'):
"""Private method to return calibrated Sv either from memory or _Sv.nc file.
This method is called by remove_noise(), noise_estimates() and get_MVBS().
"""
if self.Sv is None: # calibration not yet performed
Sv_path = self.validate_path(save_path=source_path, # wrangle _Sv path
save_postfix=source_postfix)
if os.path.exists(Sv_path): # _Sv exists
self.Sv = xr.open_dataset(Sv_path) # load _Sv file
else:
# if path specification given but file do not exist:
if (source_path is not None) or (source_postfix != '_Sv'):
print('%s no calibrated data found in specified path: %s' %
(dt.datetime.now().strftime('%H:%M:%S'), Sv_path))
else:
print('%s data has not been calibrated. ' % dt.datetime.now().strftime('%H:%M:%S'))
print(' performing calibration now and operate from Sv in memory.')
self.calibrate() # calibrate, have Sv in memory
return self.Sv
def remove_noise(self, source_postfix='_Sv', source_path=None,
noise_est_range_bin_size=None, noise_est_ping_size=None,
SNR=0, Sv_threshold=None,
save=False, save_postfix='_Sv_clean', save_path=None):
"""Remove noise by using noise estimates obtained from the minimum mean calibrated power level
along each column of tiles.
See method noise_estimates() for details of noise estimation.
Reference: De Robertis & Higginbottom, 2017, ICES Journal of Marine Sciences
Parameters
----------
source_postfix : str
postfix of the Sv file used to remove noise from, default to '_Sv'
source_path : str
path of Sv file used to remove noise from, can be one of the following:
- None (default):
use Sv in RAWFILENAME_Sv.nc in the same folder as the raw data file,
or when RAWFILENAME_Sv.nc doesn't exist, perform self.calibrate() and use the resulted self.Sv
- path to a directory: RAWFILENAME_Sv.nc in the specified directory
- path to a specific file: the specified file, e.g., ./another_directory/some_other_filename.nc
noise_est_range_bin_size : float, optional
Meters per tile for noise estimation [m]
noise_est_ping_size : int, optional
Number of pings per tile for noise estimation
SNR : int, optional
Minimum signal-to-noise ratio (remove values below this after general noise removal).
Sv_threshold : int, optional
Minimum Sv threshold [dB] (remove values below this after general noise removal)
save : bool, optional
Whether to save the denoised Sv (``Sv_clean``) into a new .nc file.
Default to ``False``.
save_postfix : str
Filename postfix, default to '_Sv_clean'
save_path : str
Full filename to save to, overwriting the RAWFILENAME_Sv_clean.nc default
"""
# Check params
if (noise_est_range_bin_size is not None) and (self.noise_est_range_bin_size != noise_est_range_bin_size):
self.noise_est_range_bin_size = noise_est_range_bin_size
if (noise_est_ping_size is not None) and (self.noise_est_ping_size != noise_est_ping_size):
self.noise_est_ping_size = noise_est_ping_size
# Get calibrated Sv
if self.Sv is not None:
print('%s Remove noise from Sv stored in memory.' % dt.datetime.now().strftime('%H:%M:%S'))
print_src = False
else:
print_src = True
proc_data = self._get_proc_Sv(source_path=source_path, source_postfix=source_postfix)
if print_src:
print('%s Remove noise from Sv stored in: %s' %
(dt.datetime.now().strftime('%H:%M:%S'), self.Sv_path))
# Get tile indexing parameters
self.noise_est_range_bin_size, range_bin_tile_bin_edge, ping_tile_bin_edge = \
self.get_tile_params(r_data_sz=proc_data.range_bin.size,
p_data_sz=proc_data.ping_time.size,
r_tile_sz=self.noise_est_range_bin_size,
p_tile_sz=self.noise_est_ping_size,
sample_thickness=self.sample_thickness)
# Get TVG and ABS for compensating for transmission loss
range_meter = self.range
TVG = np.real(20 * np.log10(range_meter.where(range_meter >= 1, other=1)))
ABS = 2 * self.seawater_absorption * range_meter
# Function for use with apply
def remove_n(x, rr):
p_c_lin = 10 ** ((x.Sv - x.ABS - x.TVG) / 10)
nn = 10 * np.log10(p_c_lin.mean(dim='ping_time').groupby_bins('range_bin', rr).mean().min(
dim='range_bin_bins')) + x.ABS + x.TVG
# Return values where signal is [SNR] dB above noise and at least [Sv_threshold] dB
if not Sv_threshold:
return x.Sv.where(x.Sv > (nn + SNR), other=np.nan)
else:
return x.Sv.where((x.Sv > (nn + SNR)) & (x > Sv_threshold), other=np.nan)
# Groupby noise removal operation
proc_data.coords['ping_idx'] = ('ping_time', np.arange(proc_data.Sv['ping_time'].size))
ABS.name = 'ABS'
TVG.name = 'TVG'
pp = xr.merge([proc_data, ABS])
pp = xr.merge([pp, TVG])
# check if number of range_bin per tile the same for all freq channels
if np.unique([np.array(x).size for x in range_bin_tile_bin_edge]).size == 1:
Sv_clean = pp.groupby_bins('ping_idx', ping_tile_bin_edge).\
map(remove_n, rr=range_bin_tile_bin_edge[0])
Sv_clean = Sv_clean.drop_vars(['ping_idx'])
else:
tmp_clean = []
cnt = 0
for key, val in pp.groupby('frequency'): # iterate over different frequency channel
tmp = val.groupby_bins('ping_idx', ping_tile_bin_edge). \
map(remove_n, rr=range_bin_tile_bin_edge[cnt])
cnt += 1
tmp_clean.append(tmp)
clean_val = np.array([zz.values for zz in xr.align(*tmp_clean, join='outer')])
Sv_clean = xr.DataArray(clean_val,
coords={'frequency': proc_data['frequency'].values,
'ping_time': tmp_clean[0]['ping_time'].values,
'range_bin': tmp_clean[0]['range_bin'].values},
dims=['frequency', 'ping_time', 'range_bin'])
# Set up DataSet
Sv_clean.name = 'Sv'
Sv_clean = Sv_clean.to_dataset()
Sv_clean['noise_est_range_bin_size'] = ('frequency', self.noise_est_range_bin_size)
Sv_clean.attrs['noise_est_ping_size'] = self.noise_est_ping_size
# Attach calculated range into data set
Sv_clean['range'] = (('frequency', 'range_bin'), self.range.T)
# Save as object attributes as a netCDF file
self.Sv_clean = Sv_clean
# TODO: now adding the below so that MVBS can be calculated directly
# from the cleaned Sv without saving and loading Sv_clean from disk.
# However this is not explicit to the user. A better way to do this
# is to change get_MVBS() to first check existence of self.Sv_clean
# when `_Sv_clean` is specified as the source_postfix.
if not print_src: # remove noise from Sv stored in memory
self.Sv = Sv_clean.copy()
if save:
self.Sv_clean_path = self.validate_path(save_path=save_path, save_postfix=save_postfix)
print('%s saving denoised Sv to %s' % (dt.datetime.now().strftime('%H:%M:%S'), self.Sv_clean_path))
Sv_clean.to_netcdf(self.Sv_clean_path)
# Close opened resources
proc_data.close()
def noise_estimates(self, source_postfix='_Sv', source_path=None,
noise_est_range_bin_size=None, noise_est_ping_size=None):
"""Obtain noise estimates from the minimum mean calibrated power level along each column of tiles.
The tiles here are defined by class attributes noise_est_range_bin_size and noise_est_ping_size.
This method contains redundant pieces of code that also appear in method remove_noise(),
but this method can be used separately to determine the exact tile size for noise removal before
noise removal is actually performed.
Parameters
----------
source_postfix : str
postfix of the Sv file used to calculate noise estimates from, default to '_Sv'
source_path : str
path of Sv file used to calculate noise estimates from, can be one of the following:
- None (default):
use Sv in RAWFILENAME_Sv.nc in the same folder as the raw data file,
or when RAWFILENAME_Sv.nc doesn't exist, perform self.calibrate() and use the resulted self.Sv
- path to a directory: RAWFILENAME_Sv.nc in the specified directory
- path to a specific file: the specified file, e.g., ./another_directory/some_other_filename.nc
noise_est_range_bin_size : float
meters per tile for noise estimation [m]
noise_est_ping_size : int
number of pings per tile for noise estimation
Returns
-------
noise_est : xarray DataSet
noise estimates as a DataArray with dimension [ping_time x range_bin]
ping_time and range_bin are taken from the first element of each tile along each of the dimensions
"""
# Check params
if (noise_est_range_bin_size is not None) and (self.noise_est_range_bin_size != noise_est_range_bin_size):
self.noise_est_range_bin_size = noise_est_range_bin_size
if (noise_est_ping_size is not None) and (self.noise_est_ping_size != noise_est_ping_size):
self.noise_est_ping_size = noise_est_ping_size
# Use calibrated data to calculate noise removal
proc_data = self._get_proc_Sv()
# Get tile indexing parameters
self.noise_est_range_bin_size, range_bin_tile_bin_edge, ping_tile_bin_edge = \
self.get_tile_params(r_data_sz=proc_data.range_bin.size,
p_data_sz=proc_data.ping_time.size,
r_tile_sz=self.noise_est_range_bin_size,
p_tile_sz=self.noise_est_ping_size,
sample_thickness=self.sample_thickness)
# Values for noise estimates
range_meter = self.range
TVG = np.real(20 * np.log10(range_meter.where(range_meter >= 1, other=1)))
ABS = 2 * self.seawater_absorption * range_meter
# Noise estimates
proc_data['power_cal'] = 10 ** ((proc_data.Sv - ABS - TVG) / 10)
# check if number of range_bin per tile the same for all freq channels
if np.unique([np.array(x).size for x in range_bin_tile_bin_edge]).size == 1:
noise_est = 10 * np.log10(proc_data['power_cal'].coarsen(
ping_time=self.noise_est_ping_size,
range_bin=int(np.unique(self.noise_est_range_bin_size / self.sample_thickness)),
boundary='pad').mean().min(dim='range_bin'))
else:
range_bin_coarsen_idx = (self.noise_est_range_bin_size / self.sample_thickness).astype(int)
tmp_noise = []
for r_bin in range_bin_coarsen_idx:
freq = r_bin.frequency.values
tmp_da = 10 * np.log10(proc_data['power_cal'].sel(frequency=freq).coarsen(
ping_time=self.noise_est_ping_size,
range_bin=r_bin.values,
boundary='pad').mean().min(dim='range_bin'))
tmp_da.name = 'noise_est'
tmp_noise.append(tmp_da)
# Construct a dataArray TODO: this can probably be done smarter using xarray native functions
noise_val = np.array([zz.values for zz in xr.align(*tmp_noise, join='outer')])
noise_est = xr.DataArray(noise_val,
coords={'frequency': proc_data['frequency'].values,
'ping_time': tmp_noise[0]['ping_time'].values},
dims=['frequency', 'ping_time'])
noise_est = noise_est.to_dataset(name='noise_est')
noise_est['noise_est_range_bin_size'] = ('frequency', self.noise_est_range_bin_size)
noise_est.attrs['noise_est_ping_size'] = self.noise_est_ping_size
# Close opened resources
proc_data.close()
return noise_est
def get_MVBS(self, source_postfix='_Sv', source_path=None,
MVBS_range_bin_size=None, MVBS_ping_size=None,
save=False, save_postfix='_MVBS', save_path=None):
"""Calculate Mean Volume Backscattering Strength (MVBS).
The calculation uses class attributes MVBS_ping_size and MVBS_range_bin_size to
calculate and save MVBS as a new attribute to the calling EchoData instance.
MVBS is an xarray DataArray with dimensions ``ping_time`` and ``range_bin``
that are from the first elements of each tile along the corresponding dimensions
in the original Sv or Sv_clean DataArray.
Parameters
----------
source_postfix : str
postfix of the Sv file used to calculate MVBS, default to '_Sv'
source_path : str
path of Sv file used to calculate MVBS, can be one of the following:
- None (default):
use Sv in RAWFILENAME_Sv.nc in the same folder as the raw data file,
or when RAWFILENAME_Sv.nc doesn't exist, perform self.calibrate() and use the resulted self.Sv
- path to a directory: RAWFILENAME_Sv.nc in the specified directory
- path to a specific file: the specified file, e.g., ./another_directory/some_other_filename.nc
MVBS_range_bin_size : float, optional
meters per tile for calculating MVBS [m]
MVBS_ping_size : int, optional
number of pings per tile for calculating MVBS
save : bool, optional
whether to save the calculated MVBS into a new .nc file, default to ``False``
save_postfix : str
Filename postfix, default to '_MVBS'
save_path : str
Full filename to save to, overwriting the RAWFILENAME_MVBS.nc default
"""
# Check params
if (MVBS_range_bin_size is not None) and (self.MVBS_range_bin_size != MVBS_range_bin_size):
self.MVBS_range_bin_size = MVBS_range_bin_size
if (MVBS_ping_size is not None) and (self.MVBS_ping_size != MVBS_ping_size):
self.MVBS_ping_size = MVBS_ping_size
# Get Sv by validating path and calibrate if not already done
if self.Sv is not None:
print('%s use Sv stored in memory to calculate MVBS' % dt.datetime.now().strftime('%H:%M:%S'))
print_src = False
else:
print_src = True
proc_data = self._get_proc_Sv(source_path=source_path, source_postfix=source_postfix)
if print_src:
if self.Sv_path is not None:
print('%s Sv source used to calculate MVBS: %s' %
(dt.datetime.now().strftime('%H:%M:%S'), self.Sv_path))
else:
print('%s Sv source used to calculate MVBS: memory' %
dt.datetime.now().strftime('%H:%M:%S'))
# Get tile indexing parameters
self.MVBS_range_bin_size, range_bin_tile_bin_edge, ping_tile_bin_edge = \
self.get_tile_params(r_data_sz=proc_data.range_bin.size,
p_data_sz=proc_data.ping_time.size,
r_tile_sz=self.MVBS_range_bin_size,
p_tile_sz=self.MVBS_ping_size,
sample_thickness=self.sample_thickness)
# Calculate MVBS
Sv_linear = 10 ** (proc_data.Sv / 10) # convert to linear domain before averaging
# check if number of range_bin per tile the same for all freq channels
if np.unique([np.array(x).size for x in range_bin_tile_bin_edge]).size == 1:
MVBS = 10 * np.log10(Sv_linear.coarsen(
ping_time=self.MVBS_ping_size,
range_bin=int(np.unique(self.MVBS_range_bin_size / self.sample_thickness)),
boundary='pad').mean())
MVBS.coords['range_bin'] = ('range_bin', np.arange(MVBS['range_bin'].size))
else:
range_bin_coarsen_idx = (self.MVBS_range_bin_size / self.sample_thickness).astype(int)
tmp_MVBS = []
for r_bin in range_bin_coarsen_idx:
freq = r_bin.frequency.values
tmp_da = 10 * np.log10(Sv_linear.sel(frequency=freq).coarsen(
ping_time=self.MVBS_ping_size,
range_bin=r_bin.values,
boundary='pad').mean())
tmp_da.coords['range_bin'] = ('range_bin', np.arange(tmp_da['range_bin'].size))
tmp_da.name = 'MVBS'
tmp_MVBS.append(tmp_da)
# Construct a dataArray TODO: this can probably be done smarter using xarray native functions
MVBS_val = np.array([zz.values for zz in xr.align(*tmp_MVBS, join='outer')])
MVBS = xr.DataArray(MVBS_val,
coords={'frequency': Sv_linear['frequency'].values,
'ping_time': tmp_MVBS[0]['ping_time'].values,
'range_bin': np.arange(MVBS_val.shape[2])},
dims=['frequency', 'ping_time', 'range_bin']).dropna(dim='range_bin', how='all')
# Set MVBS attributes
MVBS.name = 'MVBS'
MVBS = MVBS.to_dataset()
MVBS['MVBS_range_bin_size'] = ('frequency', self.MVBS_range_bin_size)
MVBS.attrs['MVBS_ping_size'] = self.MVBS_ping_size
# Save results in object and as a netCDF file
self.MVBS = MVBS
if save:
self.MVBS_path = self.validate_path(save_path=save_path, save_postfix=save_postfix)
print('%s saving MVBS to %s' % (dt.datetime.now().strftime('%H:%M:%S'), self.MVBS_path))
MVBS.to_netcdf(self.MVBS_path)
# Close opened resources
proc_data.close()
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abondar24/OpenCVBase | Python/face_detect_camera/managers.py | 9b23e3b31304e77ad1135d90efb41e3dc069194a | import cv2
import numpy as np
import time
class CaptureManager(object):
def __init__(self, capture, preview_window_manager=None, should_mirror_preview = False):
self.preview_window_manager = preview_window_manager
self.should_mirror_preview = should_mirror_preview
self._capture = capture
self._channel = 0
self._entered_frame = False
self._frame = None
self._frames_elapsed = long(0)
self._fps_est = None
@property
def channel(self):
return self._channel
@channel.setter
def channel(self):
return self._channel
@property
def frame(self):
if self._entered_frame and self._frame is None:
_, self._frame = self._capture.retrieve(channel=self.channel)
return self._frame
def enter_frame(self):
# capture the next frame
assert not self._entered_frame, 'previous enter_frame() had no matching exit_frame()'
if self._capture is not None:
self._entered_frame = self._capture.grab()
def exit_frame(self):
# draw to window, write to files, release the frame
# frame is retrievable or not
if self.frame is None:
self._entered_frame = False
return
if self._frames_elapsed == 0:
self._start_time = time.time()
else:
time_elapsed = time.time() - self._start_time
self._fps_est = self._frames_elapsed / time_elapsed
self._frames_elapsed += 1
# draw
if self.preview_window_manager is not None:
if self.should_mirror_preview:
mirrored_frame = np.fliplr(self._frame).copy()
self.preview_window_manager.show(mirrored_frame)
else:
self.preview_window_manager.show(self._frame)
# release the frame
self._frame = None
self._entered_frame = False
class WindowManager(object):
def __init__(self, window_name, keypress_callback = None):
self.keypress_callback = keypress_callback
self._window_name = window_name
self._is_window_created = False
@property
def is_window_created(self):
return self._is_window_created
def create_window(self):
cv2.namedWindow(self._window_name)
self._is_window_created = True
def show(self, frame):
cv2.imshow(self._window_name, frame)
def destroy_window(self):
cv2.destroyWindow(self._window_name)
self._is_window_created = False
def process_events(self):
keykode = cv2.waitKey(1)
if self.keypress_callback is not None and keykode != -1:
keykode &= 0xFF
self.keypress_callback(keykode)
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micaelverissimo/lifelong_ringer | ELLA/ELLA.py | d2e7173ce08d1c087e811f6451cae1cb0e381076 | """ Alpha version of a version of ELLA that plays nicely with sklearn
@author: Paul Ruvolo
"""
from math import log
import numpy as np
from scipy.special import logsumexp
from scipy.linalg import sqrtm, inv, norm
from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression, Lasso
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, explained_variance_score
class ELLA(object):
""" The ELLA model """
def __init__(self, d, k, base_learner, base_learner_kwargs = {}, mu = 1, lam = 1, k_init = False):
""" Initializes a new model for the given base_learner.
d: the number of parameters for the base learner
k: the number of latent model components
base_learner: the base learner to use (currently can only be
LinearRegression, Ridge, or LogisticRegression).
base_learner_kwargs: keyword arguments to base learner (for instance to
specify regularization strength)
mu: hyperparameter for sparsity
lam: L2 penalty on L
mu: the L_1 penalty to use
lam: the L_2 penalty to use
NOTE: currently only binary logistic regression is supported
"""
self.d = d
self.k = k
self.L = np.random.randn(d,k)
self.A = np.zeros((d * k, d * k))
self.b = np.zeros((d * k, 1))
self.S = np.zeros((k, 0))
self.T = 0
self.mu = mu
self.lam = lam
self.k_init = k_init
if base_learner in [LinearRegression, Ridge]:
self.perf_metric = explained_variance_score
elif base_learner in [LogisticRegression]:
self.perf_metric = accuracy_score
else:
raise Exception("Unsupported Base Learner")
self.base_learner = base_learner
self.base_learner_kwargs = base_learner_kwargs
def fit(self, X, y, task_id):
""" Fit the model to a new batch of training data. The task_id must
start at 0 and increase by one each time this function is called.
Currently you cannot add new data to old tasks.
X: the training data
y: the trianing labels
task_id: the id of the task
"""
self.T += 1
single_task_model = self.base_learner(fit_intercept = False, **self.base_learner_kwargs).fit(X, y)
D_t = self.get_hessian(single_task_model, X, y)
D_t_sqrt = sqrtm(D_t)
theta_t = single_task_model.coef_
sparse_encode = Lasso(alpha = self.mu / (X.shape[0] * 2.0),
fit_intercept = False, tol=1e9, max_iter=50000).fit(D_t_sqrt.dot(self.L),
D_t_sqrt.dot(theta_t.T))
if self.k_init and task_id < self.k:
sparse_coeffs = np.zeros((self.k,))
sparse_coeffs[task_id] = 1.0
else:
sparse_coeffs = sparse_encode.coef_
self.S = np.hstack((self.S, np.matrix(sparse_coeffs).T))
self.A += np.kron(self.S[:,task_id].dot(self.S[:,task_id].T), D_t)
self.b += np.kron(self.S[:,task_id].T, np.mat(theta_t).dot(D_t)).T
L_vectorized = inv(self.A / self.T + self.lam * np.eye(self.d * self.k, self.d * self.k)).dot(self.b) / self.T
self.L = L_vectorized.reshape((self.k, self.d)).T
self.revive_dead_components()
def revive_dead_components(self):
""" re-initailizes any components that have decayed to 0 """
for i,val in enumerate(np.sum(self.L, axis = 0)):
if abs(val) < 10 ** -8:
self.L[:, i] = np.random.randn(self.d,)
def predict(self, X, task_id):
""" Output ELLA's predictions for the specified data on the specified
task_id. If using a continuous model (Ridge and LinearRegression)
the result is the prediction. If using a classification model
(LogisticRgerssion) the output is currently a probability.
"""
if self.base_learner == LinearRegression or self.base_learner == Ridge:
return X.dot(self.L.dot(self.S[:, task_id]))
elif self.base_learner == LogisticRegression:
return 1. / (1.0 + np.exp(-X.dot(self.L.dot(self.S[:, task_id])))) > 0.5
def predict_probs(self, X, task_id):
""" Output ELLA's predictions for the specified data on the specified
task_id. If using a continuous model (Ridge and LinearRegression)
the result is the prediction. If using a classification model
(LogisticRgerssion) the output is currently a probability.
"""
if self.base_learner == LinearRegression or self.base_learner == Ridge:
raise Exception("This base learner does not support predicting probabilities")
elif self.base_learner == LogisticRegression:
return np.exp(self.predict_logprobs(X, task_id))
def predict_logprobs(self, X, task_id):
""" Output ELLA's predictions for the specified data on the specified
task_id. If using a continuous model (Ridge and LinearRegression)
the result is the prediction. If using a classification model
(LogisticRgerssion) the output is currently a probability.
"""
if self.base_learner == LinearRegression or self.base_learner == Ridge:
raise Exception("This base learner does not support predicting probabilities")
elif self.base_learner == LogisticRegression:
return -logsumexp(np.hstack((np.zeros((X.shape[0], 1)), -X.dot(self.L.dot(self.S[:, task_id])))), axis = 1)
def score(self, X, y, task_id):
""" Output the score for ELLA's model on the specified testing data.
If using a continuous model (Ridge and LinearRegression)
the score is explained variance. If using a classification model
(LogisticRegression) the score is accuracy.
"""
return self.perf_metric(self.predict(X, task_id), y)
def get_hessian(self, model, X, y):
""" ELLA requires that each single task learner provide the Hessian
of the loss function evaluated around the optimal single task
parameters. This funciton implements this for the base learners
that are currently supported """
theta_t = model.coef_
if self.base_learner == LinearRegression:
return X.T.dot(X)/(2.0 * X.shape[0])
elif self.base_learner == Ridge:
return X.T.dot(X)/(2.0 * X.shape[0]) + model.alpha * np.eye(self.d, self.d)
elif self.base_learner == LogisticRegression:
preds = 1. / (1.0 + np.exp(-X.dot(theta_t.T)))
base = np.tile(preds * (1 - preds), (1, X.shape[1]))
hessian = (np.multiply(X, base)).T.dot(X) / (2.0 * X.shape[0])
return hessian + np.eye(self.d,self.d) / (2.0 * model.C) | [((32, 17, 32, 37), 'numpy.random.randn', 'np.random.randn', ({(32, 33, 32, 34): 'd', (32, 35, 32, 36): 'k'}, {}), '(d, k)', True, 'import numpy as np\n'), ((33, 17, 33, 41), 'numpy.zeros', 'np.zeros', ({(33, 26, 33, 40): '(d * k, d * k)'}, {}), '((d * k, d * k))', True, 'import numpy as np\n'), ((34, 17, 34, 37), 'numpy.zeros', 'np.zeros', ({(34, 26, 34, 36): '(d * k, 1)'}, {}), '((d * k, 1))', True, 'import numpy as np\n'), ((35, 17, 35, 33), 'numpy.zeros', 'np.zeros', ({(35, 26, 35, 32): '(k, 0)'}, {}), '((k, 0))', True, 'import numpy as np\n'), ((62, 19, 62, 29), 'scipy.linalg.sqrtm', 'sqrtm', ({(62, 25, 62, 28): 'D_t'}, {}), '(D_t)', False, 'from scipy.linalg import sqrtm, inv, norm\n'), ((69, 28, 69, 47), 'numpy.zeros', 'np.zeros', ({(69, 37, 69, 46): '(self.k,)'}, {}), '((self.k,))', True, 'import numpy as np\n'), ((82, 31, 82, 55), 'numpy.sum', 'np.sum', (), '', True, 'import numpy as np\n'), ((65, 24, 66, 77), 'sklearn.linear_model.Lasso', 'Lasso', (), '', False, 'from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression, Lasso\n'), ((84, 31, 84, 55), 'numpy.random.randn', 'np.random.randn', ({(84, 47, 84, 53): 'self.d'}, {}), '(self.d)', True, 'import numpy as np\n'), ((73, 36, 73, 60), 'numpy.matrix', 'np.matrix', ({(73, 46, 73, 59): 'sparse_coeffs'}, {}), '(sparse_coeffs)', True, 'import numpy as np\n'), ((139, 19, 139, 64), 'numpy.tile', 'np.tile', ({(139, 27, 139, 46): 'preds * (1 - preds)', (139, 48, 139, 63): '(1, X.shape[1])'}, {}), '(preds * (1 - preds), (1, X.shape[1]))', True, 'import numpy as np\n'), ((75, 47, 75, 62), 'numpy.mat', 'np.mat', ({(75, 54, 75, 61): 'theta_t'}, {}), '(theta_t)', True, 'import numpy as np\n'), ((136, 65, 136, 87), 'numpy.eye', 'np.eye', ({(136, 72, 136, 78): 'self.d', (136, 80, 136, 86): 'self.d'}, {}), '(self.d, self.d)', True, 'import numpy as np\n'), ((141, 29, 141, 50), 'numpy.eye', 'np.eye', ({(141, 36, 141, 42): 'self.d', (141, 43, 141, 49): 'self.d'}, {}), '(self.d, self.d)', True, 'import numpy as np\n'), ((76, 56, 76, 96), 'numpy.eye', 'np.eye', ({(76, 63, 76, 78): '(self.d * self.k)', (76, 80, 76, 95): '(self.d * self.k)'}, {}), '(self.d * self.k, self.d * self.k)', True, 'import numpy as np\n'), ((117, 41, 117, 66), 'numpy.zeros', 'np.zeros', ({(117, 50, 117, 65): '(X.shape[0], 1)'}, {}), '((X.shape[0], 1))', True, 'import numpy as np\n'), ((140, 23, 140, 43), 'numpy.multiply', 'np.multiply', ({(140, 35, 140, 36): 'X', (140, 38, 140, 42): 'base'}, {}), '(X, base)', True, 'import numpy as np\n')] |
Myst1c-a/phen-cogs | webhook/utils.py | 672f9022ddbbd9a84b0a05357347e99e64a776fc | """
MIT License
Copyright (c) 2020-present phenom4n4n
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import re
import discord
from redbot.core.commands import Context
USER_MENTIONS = discord.AllowedMentions.none()
USER_MENTIONS.users = True
WEBHOOK_RE = re.compile(
r"discord(?:app)?.com/api/webhooks/(?P<id>[0-9]{17,21})/(?P<token>[A-Za-z0-9\.\-\_]{60,68})"
)
async def _monkeypatch_send(ctx: Context, content: str = None, **kwargs) -> discord.Message:
self = ctx.bot.get_cog("Webhook")
original_kwargs = kwargs.copy()
try:
webhook = await self.get_webhook(ctx=ctx)
kwargs["username"] = ctx.author.display_name
kwargs["avatar_url"] = ctx.author.avatar_url
kwargs["wait"] = True
return await webhook.send(content, **kwargs)
except Exception:
return await super(Context, ctx).send(content, **original_kwargs)
class FakeResponse:
def __init__(self):
self.status = 403
self.reason = "Forbidden"
| [((30, 16, 30, 46), 'discord.AllowedMentions.none', 'discord.AllowedMentions.none', ({}, {}), '()', False, 'import discord\n'), ((33, 13, 35, 1), 're.compile', 're.compile', ({(34, 4, 34, 96): '"""discord(?:app)?.com/api/webhooks/(?P<id>[0-9]{17,21})/(?P<token>[A-Za-z0-9\\\\.\\\\-\\\\_]{60,68})"""'}, {}), "(\n 'discord(?:app)?.com/api/webhooks/(?P<id>[0-9]{17,21})/(?P<token>[A-Za-z0-9\\\\.\\\\-\\\\_]{60,68})'\n )", False, 'import re\n')] |
jwise/pebble-caltrain | scripts/generate.py | 770497cb38205827fee2e4e4cfdd79bcf60ceb65 | __author__ = 'katharine'
import csv
import struct
import time
import datetime
def generate_files(source_dir, target_dir):
stops_txt = [x for x in csv.DictReader(open("%s/stops.txt" % source_dir, 'rb')) if x['location_type'] == '0']
print "%d stops" % len(stops_txt)
name_replacements = (
('Caltrain', ''),
('Station', ''),
('Mt View', 'Mountain View'),
('So. San Francisco', 'South SF'),
('South San Francisco', 'South SF'),
)
stop_parent_map = {}
stop_name_map = {}
stop_map = {}
stops = []
for s in stops_txt:
if s['parent_station'] != '' and s['parent_station'] in stop_parent_map:
stop_map[int(s['stop_code'])] = stop_parent_map[s['parent_station']]
continue
for replacement in name_replacements:
s['stop_name'] = s['stop_name'].replace(*replacement)
s['stop_name'] = s['stop_name'].rstrip()
if s['stop_name'] in stop_name_map:
stop_map[int(s['stop_code'])] = stop_name_map[s['stop_name']]
continue
stop_map[int(s['stop_code'])] = len(stops)
stop_parent_map[s['parent_station']] = len(stops)
stop_name_map[s['stop_name']] = len(stops)
stops.append({
'name': s['stop_name'],
'zone': int(s['zone_id']) if s['zone_id'] != '' else 0,
'lat': float(s['stop_lat']),
'lon': float(s['stop_lon'])
})
with open('%s/stops.dat' % target_dir, 'wb') as f:
f.write(struct.pack('<B', len(stops)))
for stop in stops:
f.write(struct.pack('<B18sii', stop['zone'], stop['name'], int(stop['lat'] * 1000000), int(stop['lon'] * 1000000)))
calendar_txt = list(csv.DictReader(open("%s/calendar.txt" % source_dir, 'rb')))
cal = []
cal_map = {}
for i, x in enumerate(calendar_txt):
cal_map[x['service_id']] = len(cal)
end_time = datetime.datetime.strptime(x['end_date'], '%Y%m%d') + datetime.timedelta(1, hours=2)
cal.append({
'id': cal_map[x['service_id']],
'start': time.mktime(time.strptime(x['start_date'], '%Y%m%d')),
'end': time.mktime(end_time.timetuple()),
'days': (
(int(x['monday']) << 0) |
(int(x['tuesday']) << 1) |
(int(x['wednesday']) << 2) |
(int(x['thursday']) << 3) |
(int(x['friday']) << 4) |
(int(x['saturday']) << 5) |
(int(x['sunday']) << 6)
)
})
calendar_dates_txt = list(csv.DictReader(open("%s/calendar_dates.txt" % source_dir, 'rb')))
for i, x in enumerate(calendar_dates_txt):
if x['service_id'] in cal_map:
# XXX: Would be nice to find a way to mark special dates. But
# we can't, right now. Oh well.
continue
cal_map[x['service_id']] = len(cal)
start_time = datetime.datetime.strptime(x['date'], '%Y%m%d')
end_time = start_time + datetime.timedelta(1, hours=2)
cal.append({
'id': cal_map[x['service_id']],
'start': time.mktime(start_time.timetuple()),
'end': time.mktime(end_time.timetuple()),
'days': 0x7F,
})
with open('%s/calendar.dat' % target_dir, 'wb') as f:
f.write(struct.pack('<B', len(cal)))
for c in cal:
f.write(struct.pack('<IIB', int(c['start']), int(c['end']), c['days']))
trips_txt = list(csv.DictReader(open("%s/trips.txt" % source_dir, "rb")))
tr = []
tr_map = {}
# These shouldn't be hardcoded, and should instead be inferred from routes.txt.
route_map = {
"BABY BULLET": 0,
"LIMITED": 1,
"LOCAL": 2,
"SHUTTLE": 3,
"Bu-130": 0,
"Li-130": 1,
"Lo-130": 2,
"TaSj-130": 3,
"Sp-130": 2, # XXX: Special Event Extra Service
}
short_name_replacements = (
('Tamien SJ shuttle', ''),
('S', ''),
('shuttle', ''),
)
for i, trip in enumerate(trips_txt):
for replacement in short_name_replacements:
trip['trip_short_name'] = trip['trip_short_name'].replace(*replacement)
tr.append({
'direction': int(not int(trip['direction_id'])), # We picked opposing values for north/south.
'route': route_map[trip['route_id']],
'service': cal_map[trip['service_id']],
'trip_name': int(trip['trip_short_name'])}),
tr_map[trip['trip_id']] = i
with open('%s/trips.dat' % target_dir, 'wb') as f:
f.write(struct.pack('<H', len(tr)))
for t in tr:
f.write(struct.pack('<HBBB', t['trip_name'], t['direction'], t['route'], t['service']))
times_txt = list(csv.DictReader(open("%s/stop_times.txt" % source_dir)))
tm = sorted([{
'time': (int(x['arrival_time'].split(':')[0])*60 + int(x['arrival_time'].split(':')[1])),
'stop': stop_map[int(x['stop_id'])],
'sequence': int(x['stop_sequence']),
'trip': tr_map[x['trip_id']]
} for x in times_txt], key=lambda y: y['time'])
with open('%s/times.dat' % target_dir, 'wb') as f:
f.write(struct.pack('<H', len(tm)))
for t in tm:
f.write(struct.pack('<HHBB', t['trip'], t['time'], t['stop'], t['sequence']))
stop_times = [sorted([i for i, x in enumerate(tm) if x['stop'] == stop], key=lambda t: tm[t]['time']) for stop, s in enumerate(stops)]
lengths = [len(x) for x in stop_times]
with open('%s/stop_index.dat' % target_dir, 'wb') as f:
f.write(struct.pack('<B', len(lengths)))
counter = len(lengths)*4 + 1
for l in lengths:
f.write(struct.pack('<HH', counter, l))
counter += l*2
for s in stop_times:
for x in s:
f.write(struct.pack('<H', x))
trip_stops = [sorted([i for i, x in enumerate(tm) if x['trip'] == trip], key=lambda k: tm[k]['stop']) for trip, s in enumerate(tr)]
lengths = map(len, trip_stops)
with open('%s/trip_index.dat' % target_dir, 'wb') as f:
f.write(struct.pack('<H', len(lengths)))
counter = len(lengths) * 3 + 2
data_start = counter
for l in lengths:
f.write(struct.pack('<HB', counter, l))
counter += l*2
if data_start != f.tell():
raise Exception("%d != %d" % (counter, f.tell()))
for s in trip_stops:
for x in s:
f.write(struct.pack('<H', x))
if f.tell() != counter:
raise Exception("Not the expected length!")
if __name__ == "__main__":
import sys
generate_files(sys.argv[1], sys.argv[2])
| [] |
gerardroche/sublime-phpunit | tests/test_is_valid_php_version_file_version.py | 73e96ec5e4ac573c5d5247cf87c38e8243da906b | from PHPUnitKit.tests import unittest
from PHPUnitKit.plugin import is_valid_php_version_file_version
class TestIsValidPhpVersionFileVersion(unittest.TestCase):
def test_invalid_values(self):
self.assertFalse(is_valid_php_version_file_version(''))
self.assertFalse(is_valid_php_version_file_version(' '))
self.assertFalse(is_valid_php_version_file_version('foobar'))
self.assertFalse(is_valid_php_version_file_version('masterfoo'))
self.assertFalse(is_valid_php_version_file_version('.'))
self.assertFalse(is_valid_php_version_file_version('x'))
self.assertFalse(is_valid_php_version_file_version('x.x'))
self.assertFalse(is_valid_php_version_file_version('x.x.x'))
self.assertFalse(is_valid_php_version_file_version('x'))
self.assertFalse(is_valid_php_version_file_version('snapshot'))
def test_master_branch_version(self):
self.assertTrue(is_valid_php_version_file_version('master'))
def test_specific_semver_versions(self):
self.assertTrue(is_valid_php_version_file_version('5.0.0'))
self.assertTrue(is_valid_php_version_file_version('5.0.1'))
self.assertTrue(is_valid_php_version_file_version('5.0.7'))
self.assertTrue(is_valid_php_version_file_version('5.0.30'))
self.assertTrue(is_valid_php_version_file_version('5.0.32'))
self.assertTrue(is_valid_php_version_file_version('5.1.0'))
self.assertTrue(is_valid_php_version_file_version('5.1.1'))
self.assertTrue(is_valid_php_version_file_version('5.1.3'))
self.assertTrue(is_valid_php_version_file_version('5.1.27'))
self.assertTrue(is_valid_php_version_file_version('7.0.0'))
self.assertTrue(is_valid_php_version_file_version('7.1.19'))
def test_minor_versions(self):
self.assertTrue(is_valid_php_version_file_version('5.6'))
self.assertTrue(is_valid_php_version_file_version('7.1'))
self.assertTrue(is_valid_php_version_file_version('7.2'))
def test_major_dot_x_versions(self):
self.assertTrue(is_valid_php_version_file_version('5.x'))
self.assertTrue(is_valid_php_version_file_version('6.x'))
self.assertTrue(is_valid_php_version_file_version('7.x'))
self.assertTrue(is_valid_php_version_file_version('8.x'))
def test_major_dot_minor_dot_x_versions(self):
self.assertTrue(is_valid_php_version_file_version('7.0.x'))
self.assertTrue(is_valid_php_version_file_version('7.1.x'))
self.assertTrue(is_valid_php_version_file_version('7.2.x'))
def test_snapshot_versions(self):
self.assertTrue(is_valid_php_version_file_version('5.4snapshot'))
self.assertTrue(is_valid_php_version_file_version('5.5snapshot'))
self.assertTrue(is_valid_php_version_file_version('5.6snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.0snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.1snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.0.0snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.1.0snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.1.1snapshot'))
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cul-it/arxiv-rss | feed/tests/test_consts.py | 40c0e859528119cc8ba3700312cb8df095d95cdd | import pytest
from feed.consts import FeedVersion
from feed.utils import randomize_case
from feed.errors import FeedVersionError
# FeedVersion.supported
def test_feed_version_supported():
assert FeedVersion.supported() == {
FeedVersion.RSS_2_0,
FeedVersion.ATOM_1_0,
}
# FeedVersion.get
def test_feed_version_get_supported():
# RSS full version
assert (
FeedVersion.get(randomize_case(FeedVersion.RSS_2_0.lower()))
== FeedVersion.RSS_2_0
)
# RSS only number
assert FeedVersion.get("2.0") == FeedVersion.RSS_2_0
# Atom full version
assert (
FeedVersion.get(randomize_case(FeedVersion.ATOM_1_0.lower()))
== FeedVersion.ATOM_1_0
)
# Atom only number
assert FeedVersion.get("1.0", atom=True) == FeedVersion.ATOM_1_0
def test_feed_version_get_unsupported():
# RSS 0.91 full version
rss_0_91 = randomize_case(FeedVersion.RSS_0_91)
with pytest.raises(FeedVersionError) as excinfo:
FeedVersion.get(rss_0_91)
ex: FeedVersionError = excinfo.value
assert ex.version == rss_0_91
assert ex.supported == FeedVersion.supported()
# RSS 0.91 only number
with pytest.raises(FeedVersionError) as excinfo:
FeedVersion.get("0.91")
ex: FeedVersionError = excinfo.value
assert ex.version == "RSS 0.91"
assert ex.supported == FeedVersion.supported()
# RSS 1.0 full version
rss_1_0 = randomize_case(FeedVersion.RSS_1_0)
with pytest.raises(FeedVersionError) as excinfo:
FeedVersion.get(rss_1_0)
ex: FeedVersionError = excinfo.value
assert ex.version == rss_1_0
assert ex.supported == FeedVersion.supported()
# RSS 1.0 only number
with pytest.raises(FeedVersionError) as excinfo:
FeedVersion.get("1.0")
ex: FeedVersionError = excinfo.value
assert ex.version == "RSS 1.0"
assert ex.supported == FeedVersion.supported()
def test_feed_version_get_invalid():
# RSS
for version, test in [
("RSS 3.3", "3.3"),
("RSS 0.1", "0.1"),
("RSS 1.1", "RSS 1.1"),
("RSS 2.1", "RSS 2.1"),
]:
with pytest.raises(FeedVersionError) as excinfo:
FeedVersion.get(test)
ex: FeedVersionError = excinfo.value
assert ex.version == version
assert ex.supported == FeedVersion.supported()
# Atom
for version, test, prefere in [
("Atom 0.1", "0.1", True),
("Atom 0.91", "0.91", True),
("Atom 2.0", "2.0", True),
("Atom 0.1", "Atom 0.1", False),
("Atom 0.91", "Atom 0.91", False),
("Atom 2.0", "Atom 2.0", False),
]:
with pytest.raises(FeedVersionError) as excinfo:
FeedVersion.get(test, atom=prefere)
ex: FeedVersionError = excinfo.value
assert ex.version == version
assert ex.supported == FeedVersion.supported()
# Nonsense
for version in ["foo", "bar", "baz"]:
with pytest.raises(FeedVersionError) as excinfo:
FeedVersion.get(version)
ex: FeedVersionError = excinfo.value
assert ex.version == version
assert ex.supported == FeedVersion.supported()
def test_is_property():
# RSS
assert FeedVersion.RSS_0_91.is_rss
assert FeedVersion.RSS_1_0.is_rss
assert FeedVersion.RSS_2_0.is_rss
assert not FeedVersion.RSS_0_91.is_atom
assert not FeedVersion.RSS_1_0.is_atom
assert not FeedVersion.RSS_2_0.is_atom
# Atom
assert FeedVersion.ATOM_1_0.is_atom
assert not FeedVersion.ATOM_1_0.is_rss
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teklager/djangocms-cascade | cmsplugin_cascade/migrations/0007_add_proxy_models.py | adc461f7054c6c0f88bc732aefd03b157df2f514 | from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('cmsplugin_cascade', '0006_bootstrapgallerypluginmodel'),
]
operations = [
]
| [] |
ralphwetzel/theonionbox | theonionbox/tob/credits.py | 9812fce48153955e179755ea7a58413c3bee182f | Credits = [
('Bootstrap', 'https://getbootstrap.com', 'The Bootstrap team', 'MIT'),
('Bottle', 'http://bottlepy.org', 'Marcel Hellkamp', 'MIT'),
('Cheroot', 'https://github.com/cherrypy/cheroot', 'CherryPy Team', 'BSD 3-Clause "New" or "Revised" License'),
('Click', 'https://github.com/pallets/click', 'Pallets', 'BSD 3-Clause "New" or "Revised" License'),
('ConfigUpdater', 'https://github.com/pyscaffold/configupdater', 'Florian Wilhelm', 'MIT'),
('Glide', 'https://github.com/glidejs/glide', '@jedrzejchalubek', 'MIT'),
('JQuery', 'https://jquery.com', 'The jQuery Foundation', 'MIT'),
('jquery.pep.js', 'http://pep.briangonzalez.org', '@briangonzalez', 'MIT'),
('js-md5', 'https://github.com/emn178/js-md5', '@emn178', 'MIT'),
('PySocks', 'https://github.com/Anorov/PySocks', '@Anorov', 'Custom DAN HAIM'),
('RapydScript-NG', 'https://github.com/kovidgoyal/rapydscript-ng', '@kovidgoyal',
'BSD 2-Clause "Simplified" License'),
('Requests', 'https://requests.kennethreitz.org', 'Kenneth Reitz', 'Apache License, Version 2.0'),
('scrollMonitor', 'https://github.com/stutrek/scrollmonitor', '@stutrek', 'MIT'),
('Smoothie Charts', 'https://github.com/joewalnes/smoothie', '@drewnoakes', 'MIT'),
('stem', 'https://stem.torproject.org', 'Damian Johnson and The Tor Project', 'GNU LESSER GENERAL PUBLIC LICENSE')
]
| [] |
Ubpa/LearnTF | turorials/Google/projects/01_02_TextClassification/01_02_main.py | 2c9f5d790a9911a860da1e0db4c7bb56a9eee5cb | #----------------
# 01_02 文本分类
#----------------
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
# TensorFlow's version : 1.12.0
print('TensorFlow\'s version : ', tf.__version__)
#----------------
# 1 下载 IMDB 数据集
#----------------
imdb = keras.datasets.imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
#----------------
# 2 探索数据
#----------------
# Training entries: 25000, labels: 25000
print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
print(train_data[0])
# (218, 189)
print(len(train_data[0]), len(train_data[1]))
# A dictionary mapping words to an integer index
word_index = imdb.get_word_index()
# The first indices are reserved
word_index = {k:(v+3) for k,v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2 # unknown
word_index["<UNUSED>"] = 3
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
def decode_review(text):
return ' '.join([reverse_word_index.get(i, '?') for i in text])
decode_review(train_data[0])
#----------------
# 3 准备数据
#----------------
train_data = keras.preprocessing.sequence.pad_sequences(train_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
test_data = keras.preprocessing.sequence.pad_sequences(test_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
# (256, 256)
print((len(train_data[0]), len(train_data[1])))
print(train_data[0])
#----------------
# 4 构建模型
#----------------
# input shape is the vocabulary count used for the movie reviews (10,000 words)
vocab_size = 10000
model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
model.summary()
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='binary_crossentropy',
metrics=['accuracy'])
#----------------
# 5 创建验证集
#----------------
x_val = train_data[:10000]
partial_x_train = train_data[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
#----------------
# 6 训练模型
#----------------
history = model.fit(partial_x_train,
partial_y_train,
epochs=40,
batch_size=512,
validation_data=(x_val, y_val),
verbose=1)
#----------------
# 7 评估模型
#----------------
results = model.evaluate(test_data, test_labels)
print(results)
#----------------
# 8 创建准确率和损失随时间变化的图
#----------------
history_dict = history.history
# dict_keys(['loss', 'val_loss', 'val_acc', 'acc'])
print(history_dict.keys())
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
# loss
# "bo" is for "blue dot"
plt.plot(epochs, loss, 'bo', label='Training loss')
# b is for "solid blue line"
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
# acc
plt.clf() # clear figure
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
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12xiaoni/text-label | backend/api/urls.py | 7456c5e73d32bcfc81a02be7e0d748f162934d35 | from django.urls import include, path
from .views import (annotation, auto_labeling, comment, example, example_state,
health, label, project, tag, task)
from .views.tasks import category, relation, span, text
urlpatterns_project = [
path(
route='category-types',
view=label.CategoryTypeList.as_view(),
name='category_types'
),
path(
route='category-types/<int:label_id>',
view=label.CategoryTypeDetail.as_view(),
name='category_type'
),
path(
route='span-types',
view=label.SpanTypeList.as_view(),
name='span_types'
),
path(
route='span-types/<int:label_id>',
view=label.SpanTypeDetail.as_view(),
name='span_type'
),
path(
route='category-type-upload',
view=label.CategoryTypeUploadAPI.as_view(),
name='category_type_upload'
),
path(
route='span-type-upload',
view=label.SpanTypeUploadAPI.as_view(),
name='span_type_upload'
),
path(
route='examples',
view=example.ExampleList.as_view(),
name='example_list'
),
path(
route='examples/<int:example_id>',
view=example.ExampleDetail.as_view(),
name='example_detail'
),
path(
route='relation_types',
view=label.RelationTypeList.as_view(),
name='relation_types_list'
),
path(
route='relation_type-upload',
view=label.RelationTypeUploadAPI.as_view(),
name='relation_type-upload'
),
path(
route='relation_types/<int:relation_type_id>',
view=label.RelationTypeDetail.as_view(),
name='relation_type_detail'
),
path(
route='annotation_relations',
view=relation.RelationList.as_view(),
name='relation_types_list'
),
path(
route='annotation_relation-upload',
view=relation.RelationUploadAPI.as_view(),
name='annotation_relation-upload'
),
path(
route='annotation_relations/<int:annotation_relation_id>',
view=relation.RelationDetail.as_view(),
name='annotation_relation_detail'
),
path(
route='approval/<int:example_id>',
view=annotation.ApprovalAPI.as_view(),
name='approve_labels'
),
path(
route='examples/<int:example_id>/categories',
view=category.CategoryListAPI.as_view(),
name='category_list'
),
path(
route='examples/<int:example_id>/categories/<int:annotation_id>',
view=category.CategoryDetailAPI.as_view(),
name='category_detail'
),
path(
route='examples/<int:example_id>/spans',
view=span.SpanListAPI.as_view(),
name='span_list'
),
path(
route='examples/<int:example_id>/spans/<int:annotation_id>',
view=span.SpanDetailAPI.as_view(),
name='span_detail'
),
path(
route='examples/<int:example_id>/texts',
view=text.TextLabelListAPI.as_view(),
name='text_list'
),
path(
route='examples/<int:example_id>/texts/<int:annotation_id>',
view=text.TextLabelDetailAPI.as_view(),
name='text_detail'
),
path(
route='tags',
view=tag.TagList.as_view(),
name='tag_list'
),
path(
route='tags/<int:tag_id>',
view=tag.TagDetail.as_view(),
name='tag_detail'
),
path(
route='examples/<int:example_id>/comments',
view=comment.CommentListDoc.as_view(),
name='comment_list_doc'
),
path(
route='comments',
view=comment.CommentListProject.as_view(),
name='comment_list_project'
),
path(
route='examples/<int:example_id>/comments/<int:comment_id>',
view=comment.CommentDetail.as_view(),
name='comment_detail'
),
path(
route='examples/<int:example_id>/states',
view=example_state.ExampleStateList.as_view(),
name='example_state_list'
),
path(
route='auto-labeling-templates',
view=auto_labeling.AutoLabelingTemplateListAPI.as_view(),
name='auto_labeling_templates'
),
path(
route='auto-labeling-templates/<str:option_name>',
view=auto_labeling.AutoLabelingTemplateDetailAPI.as_view(),
name='auto_labeling_template'
),
path(
route='auto-labeling-configs',
view=auto_labeling.AutoLabelingConfigList.as_view(),
name='auto_labeling_configs'
),
path(
route='auto-labeling-configs/<int:config_id>',
view=auto_labeling.AutoLabelingConfigDetail.as_view(),
name='auto_labeling_config'
),
path(
route='auto-labeling-config-testing',
view=auto_labeling.AutoLabelingConfigTest.as_view(),
name='auto_labeling_config_test'
),
path(
route='examples/<int:example_id>/auto-labeling',
view=auto_labeling.AutoLabelingAnnotation.as_view(),
name='auto_labeling_annotation'
),
path(
route='auto-labeling-parameter-testing',
view=auto_labeling.AutoLabelingConfigParameterTest.as_view(),
name='auto_labeling_parameter_testing'
),
path(
route='auto-labeling-template-testing',
view=auto_labeling.AutoLabelingTemplateTest.as_view(),
name='auto_labeling_template_test'
),
path(
route='auto-labeling-mapping-testing',
view=auto_labeling.AutoLabelingMappingTest.as_view(),
name='auto_labeling_mapping_test'
)
]
urlpatterns = [
path(
route='health',
view=health.Health.as_view(),
name='health'
),
path(
route='projects',
view=project.ProjectList.as_view(),
name='project_list'
),
path(
route='tasks/status/<task_id>',
view=task.TaskStatus.as_view(),
name='task_status'
),
path(
route='projects/<int:project_id>',
view=project.ProjectDetail.as_view(),
name='project_detail'
),
path('projects/<int:project_id>/', include(urlpatterns_project))
]
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d-sot/nwb-jupyter-widgets | nwbwidgets/test/test_base.py | f9bf5c036c39f29e26b3cdb78198cccfa1b13cef | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pynwb import TimeSeries
from datetime import datetime
from dateutil.tz import tzlocal
from pynwb import NWBFile
from ipywidgets import widgets
from pynwb.core import DynamicTable
from pynwb.file import Subject
from nwbwidgets.view import default_neurodata_vis_spec
from pynwb import ProcessingModule
from pynwb.behavior import Position, SpatialSeries
from nwbwidgets.base import show_neurodata_base,processing_module, nwb2widget, show_text_fields, \
fig2widget, vis2widget, show_fields, show_dynamic_table, df2accordion, lazy_show_over_data
import unittest
import pytest
def test_show_neurodata_base():
start_time = datetime(2017, 4, 3, 11, tzinfo=tzlocal())
create_date = datetime(2017, 4, 15, 12, tzinfo=tzlocal())
nwbfile = NWBFile(session_description='demonstrate NWBFile basics',
identifier='NWB123',
session_start_time=start_time,
file_create_date=create_date,
related_publications='https://doi.org/10.1088/1741-2552/aaa904',
experimenter='Dr. Pack')
assert isinstance(show_neurodata_base(nwbfile,default_neurodata_vis_spec), widgets.Widget)
def test_show_text_fields():
data = np.random.rand(160,3)
ts = TimeSeries(name='test_timeseries', data=data, unit='m', starting_time=0.0, rate=1.0)
assert isinstance(show_text_fields(ts), widgets.Widget)
class ProcessingModuleTestCase(unittest.TestCase):
def setUp(self):
spatial_series = SpatialSeries(name='position',
data=np.linspace(0, 1, 20),
rate=50.,
reference_frame='starting gate')
self.position = Position(spatial_series=spatial_series)
def test_processing_module(self):
start_time = datetime(2020, 1, 29, 11, tzinfo=tzlocal())
nwbfile = NWBFile(session_description='Test Session',
identifier='NWBPM',
session_start_time=start_time)
behavior_module = ProcessingModule(name='behavior',
description='preprocessed behavioral data')
nwbfile.add_processing_module(behavior_module)
nwbfile.processing['behavior'].add(self.position)
processing_module(nwbfile.processing['behavior'], default_neurodata_vis_spec)
def test_nwb2widget(self):
nwb2widget(self.position, default_neurodata_vis_spec)
def test_fig2widget():
data = np.random.rand(160, 3)
fig = plt.figure(figsize=(10, 5))
plt.plot(data)
assert isinstance(fig2widget(fig), widgets.Widget)
class Test_vis2widget:
def test_vis2widget_input_widget(self):
wg = widgets.IntSlider(
value=7,
min=0,
max=10,
step=1,
description='Test:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d')
assert isinstance(vis2widget(wg), widgets.Widget)
def test_vis2widget_input_figure(self):
data = np.random.rand(160,3)
fig=plt.figure(figsize=(10, 5))
plt.plot(data)
assert isinstance(vis2widget(fig), widgets.Widget)
def test_vis2widget_input_other(self):
data = np.random.rand(160,3)
with pytest.raises(ValueError, match="unsupported vis type"):
vis2widget(data)
def test_show_subject():
node = Subject(age='8', sex='m', species='macaque')
show_fields(node)
def test_show_dynamic_table():
d = {'col1': [1, 2], 'col2': [3, 4]}
DT = DynamicTable.from_dataframe(df=pd.DataFrame(data=d),
name='Test Dtable',
table_description='no description')
show_dynamic_table(DT)
def test_df2accordion():
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
columns=['a', 'b', 'c'])
def func_fig(data):
fig=plt.figure(figsize=(10, 5))
plt.plot(data)
return fig
df2accordion(df=df,by='a',func=func_fig)
def test_df2accordion_single():
df = pd.DataFrame(np.array([1]),
columns=['a'])
def func_fig(data):
fig=plt.figure(figsize=(10, 5))
plt.plot(data)
return fig
df2accordion(df=df,by='a',func=func_fig)
def test_lazy_show_over_data():
list_ = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
def func_fig(data):
fig=plt.figure(figsize=(10, 5))
plt.plot(data)
return fig
assert isinstance(lazy_show_over_data(list_=list_,func_=func_fig),widgets.Widget)
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orikad/subliminal | subliminal/video.py | 5bd87a505f7a4cad2a3a872128110450c69da4f0 | # -*- coding: utf-8 -*-
from __future__ import division
from datetime import datetime, timedelta
import logging
import os
from guessit import guessit
logger = logging.getLogger(__name__)
#: Video extensions
VIDEO_EXTENSIONS = ('.3g2', '.3gp', '.3gp2', '.3gpp', '.60d', '.ajp', '.asf', '.asx', '.avchd', '.avi', '.bik',
'.bix', '.box', '.cam', '.dat', '.divx', '.dmf', '.dv', '.dvr-ms', '.evo', '.flc', '.fli',
'.flic', '.flv', '.flx', '.gvi', '.gvp', '.h264', '.m1v', '.m2p', '.m2ts', '.m2v', '.m4e',
'.m4v', '.mjp', '.mjpeg', '.mjpg', '.mkv', '.moov', '.mov', '.movhd', '.movie', '.movx', '.mp4',
'.mpe', '.mpeg', '.mpg', '.mpv', '.mpv2', '.mxf', '.nsv', '.nut', '.ogg', '.ogm' '.ogv', '.omf',
'.ps', '.qt', '.ram', '.rm', '.rmvb', '.swf', '.ts', '.vfw', '.vid', '.video', '.viv', '.vivo',
'.vob', '.vro', '.wm', '.wmv', '.wmx', '.wrap', '.wvx', '.wx', '.x264', '.xvid')
class Video(object):
"""Base class for videos.
Represent a video, existing or not.
:param str name: name or path of the video.
:param str format: format of the video (HDTV, WEB-DL, BluRay, ...).
:param str release_group: release group of the video.
:param str resolution: resolution of the video stream (480p, 720p, 1080p or 1080i).
:param str video_codec: codec of the video stream.
:param str audio_codec: codec of the main audio stream.
:param str imdb_id: IMDb id of the video.
:param dict hashes: hashes of the video file by provider names.
:param int size: size of the video file in bytes.
:param set subtitle_languages: existing subtitle languages.
"""
def __init__(self, name, format=None, release_group=None, resolution=None, video_codec=None, audio_codec=None,
imdb_id=None, hashes=None, size=None, subtitle_languages=None):
#: Name or path of the video
self.name = name
#: Format of the video (HDTV, WEB-DL, BluRay, ...)
self.format = format
#: Release group of the video
self.release_group = release_group
#: Resolution of the video stream (480p, 720p, 1080p or 1080i)
self.resolution = resolution
#: Codec of the video stream
self.video_codec = video_codec
#: Codec of the main audio stream
self.audio_codec = audio_codec
#: IMDb id of the video
self.imdb_id = imdb_id
#: Hashes of the video file by provider names
self.hashes = hashes or {}
#: Size of the video file in bytes
self.size = size
#: Existing subtitle languages
self.subtitle_languages = subtitle_languages or set()
@property
def exists(self):
"""Test whether the video exists"""
return os.path.exists(self.name)
@property
def age(self):
"""Age of the video"""
if self.exists:
return datetime.utcnow() - datetime.utcfromtimestamp(os.path.getmtime(self.name))
return timedelta()
@classmethod
def fromguess(cls, name, guess):
"""Create an :class:`Episode` or a :class:`Movie` with the given `name` based on the `guess`.
:param str name: name of the video.
:param dict guess: guessed data.
:raise: :class:`ValueError` if the `type` of the `guess` is invalid
"""
if guess['type'] == 'episode':
return Episode.fromguess(name, guess)
if guess['type'] == 'movie':
return Movie.fromguess(name, guess)
raise ValueError('The guess must be an episode or a movie guess')
@classmethod
def fromname(cls, name, options=None):
"""Shortcut for :meth:`fromguess` with a `guess` guessed from the `name`.
:param str name: name of the video.
"""
if options is not None:
return cls.fromguess(name, guessit(name, options=options))
else:
return cls.fromguess(name, guessit(name))
def __repr__(self):
return '<%s [%r]>' % (self.__class__.__name__, self.name)
def __hash__(self):
return hash(self.name)
class Episode(Video):
"""Episode :class:`Video`.
:param str series: series of the episode.
:param int season: season number of the episode.
:param int episode: episode number of the episode.
:param str title: title of the episode.
:param int year: year of the series.
:param bool original_series: whether the series is the first with this name.
:param int tvdb_id: TVDB id of the episode.
:param \*\*kwargs: additional parameters for the :class:`Video` constructor.
"""
def __init__(self, name, series, season, episode, title=None, year=None, original_series=True, tvdb_id=None,
series_tvdb_id=None, series_imdb_id=None, **kwargs):
super(Episode, self).__init__(name, **kwargs)
#: Series of the episode
self.series = series
#: Season number of the episode
self.season = season
#: Episode number of the episode
self.episode = episode
#: Title of the episode
self.title = title
#: Year of series
self.year = year
#: The series is the first with this name
self.original_series = original_series
#: TVDB id of the episode
self.tvdb_id = tvdb_id
#: TVDB id of the series
self.series_tvdb_id = series_tvdb_id
#: IMDb id of the series
self.series_imdb_id = series_imdb_id
@classmethod
def fromguess(cls, name, guess):
if guess['type'] != 'episode':
raise ValueError('The guess must be an episode guess')
if 'title' not in guess or 'episode' not in guess:
raise ValueError('Insufficient data to process the guess')
return cls(name, guess['title'], guess.get('season', 1), guess['episode'], title=guess.get('episode_title'),
year=guess.get('year'), format=guess.get('format'), original_series='year' not in guess,
release_group=guess.get('release_group'), resolution=guess.get('screen_size'),
video_codec=guess.get('video_codec'), audio_codec=guess.get('audio_codec'))
@classmethod
def fromname(cls, name):
return cls.fromguess(name, guessit(name, {'type': 'episode'}))
def __repr__(self):
if self.year is None:
return '<%s [%r, %dx%d]>' % (self.__class__.__name__, self.series, self.season, self.episode)
return '<%s [%r, %d, %dx%d]>' % (self.__class__.__name__, self.series, self.year, self.season, self.episode)
class Movie(Video):
"""Movie :class:`Video`.
:param str title: title of the movie.
:param int year: year of the movie.
:param \*\*kwargs: additional parameters for the :class:`Video` constructor.
"""
def __init__(self, name, title, year=None, **kwargs):
super(Movie, self).__init__(name, **kwargs)
#: Title of the movie
self.title = title
#: Year of the movie
self.year = year
@classmethod
def fromguess(cls, name, guess):
if guess['type'] != 'movie':
raise ValueError('The guess must be a movie guess')
if 'title' not in guess:
raise ValueError('Insufficient data to process the guess')
return cls(name, guess['title'], format=guess.get('format'), release_group=guess.get('release_group'),
resolution=guess.get('screen_size'), video_codec=guess.get('video_codec'),
audio_codec=guess.get('audio_codec'), year=guess.get('year'))
@classmethod
def fromname(cls, name):
return cls.fromguess(name, guessit(name, {'type': 'movie'}))
def __repr__(self):
if self.year is None:
return '<%s [%r]>' % (self.__class__.__name__, self.title)
return '<%s [%r, %d]>' % (self.__class__.__name__, self.title, self.year)
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juniorosorio47/client-order | backend/app/migrations/0001_initial.py | ec429436d822d07d0ec1e0be0c2615087eec6e65 | # Generated by Django 3.2.7 on 2021-10-18 23:21
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Client',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=120)),
],
),
migrations.CreateModel(
name='Order',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('total', models.DecimalField(decimal_places=2, default=0.0, max_digits=20)),
('timestamp', models.DateTimeField(auto_now_add=True)),
('client', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='app.client')),
],
),
migrations.CreateModel(
name='Product',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=120)),
('price', models.DecimalField(decimal_places=2, default=0.0, max_digits=20)),
('inventory', models.IntegerField(default=0)),
('user', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='OrderProduct',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('quantity', models.IntegerField(default=1)),
('order', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.order')),
('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.product')),
],
options={
'unique_together': {('order', 'product')},
},
),
migrations.AddField(
model_name='order',
name='products',
field=models.ManyToManyField(through='app.OrderProduct', to='app.Product'),
),
migrations.AddField(
model_name='order',
name='user',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL),
),
]
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vadam5/NeMo | nemo/collections/nlp/models/machine_translation/mt_enc_dec_config.py | 3c5db09539293c3c19a6bb7437011f91261119af | # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Any, Optional, Tuple
from omegaconf.omegaconf import MISSING
from nemo.collections.nlp.data.machine_translation.machine_translation_dataset import TranslationDataConfig
from nemo.collections.nlp.models.enc_dec_nlp_model import EncDecNLPModelConfig
from nemo.collections.nlp.modules.common.token_classifier import TokenClassifierConfig
from nemo.collections.nlp.modules.common.tokenizer_utils import TokenizerConfig
from nemo.collections.nlp.modules.common.transformer.transformer import (
NeMoTransformerConfig,
NeMoTransformerEncoderConfig,
)
from nemo.core.config.modelPT import ModelConfig, OptimConfig, SchedConfig
@dataclass
class MTSchedConfig(SchedConfig):
name: str = 'InverseSquareRootAnnealing'
warmup_ratio: Optional[float] = None
last_epoch: int = -1
# TODO: Refactor this dataclass to to support more optimizers (it pins the optimizer to Adam-like optimizers).
@dataclass
class MTOptimConfig(OptimConfig):
name: str = 'adam'
lr: float = 1e-3
betas: Tuple[float, float] = (0.9, 0.98)
weight_decay: float = 0.0
sched: Optional[MTSchedConfig] = MTSchedConfig()
@dataclass
class MTEncDecModelConfig(EncDecNLPModelConfig):
# machine translation configurations
num_val_examples: int = 3
num_test_examples: int = 3
max_generation_delta: int = 10
label_smoothing: Optional[float] = 0.0
beam_size: int = 4
len_pen: float = 0.0
src_language: str = 'en'
tgt_language: str = 'en'
find_unused_parameters: Optional[bool] = True
shared_tokenizer: Optional[bool] = True
preproc_out_dir: Optional[str] = None
# network architecture configuration
encoder_tokenizer: Any = MISSING
encoder: Any = MISSING
decoder_tokenizer: Any = MISSING
decoder: Any = MISSING
head: TokenClassifierConfig = TokenClassifierConfig(log_softmax=True)
# dataset configurations
train_ds: Optional[TranslationDataConfig] = TranslationDataConfig(
src_file_name=MISSING,
tgt_file_name=MISSING,
tokens_in_batch=512,
clean=True,
shuffle=True,
cache_ids=False,
use_cache=False,
)
validation_ds: Optional[TranslationDataConfig] = TranslationDataConfig(
src_file_name=MISSING,
tgt_file_name=MISSING,
tokens_in_batch=512,
clean=False,
shuffle=False,
cache_ids=False,
use_cache=False,
)
test_ds: Optional[TranslationDataConfig] = TranslationDataConfig(
src_file_name=MISSING,
tgt_file_name=MISSING,
tokens_in_batch=512,
clean=False,
shuffle=False,
cache_ids=False,
use_cache=False,
)
optim: Optional[OptimConfig] = MTOptimConfig()
@dataclass
class AAYNBaseConfig(MTEncDecModelConfig):
# Attention is All You Need Base Configuration
encoder_tokenizer: TokenizerConfig = TokenizerConfig(library='yttm')
decoder_tokenizer: TokenizerConfig = TokenizerConfig(library='yttm')
encoder: NeMoTransformerEncoderConfig = NeMoTransformerEncoderConfig(
library='nemo',
model_name=None,
pretrained=False,
hidden_size=512,
inner_size=2048,
num_layers=6,
num_attention_heads=8,
ffn_dropout=0.1,
attn_score_dropout=0.1,
attn_layer_dropout=0.1,
)
decoder: NeMoTransformerConfig = NeMoTransformerConfig(
library='nemo',
model_name=None,
pretrained=False,
inner_size=2048,
num_layers=6,
num_attention_heads=8,
ffn_dropout=0.1,
attn_score_dropout=0.1,
attn_layer_dropout=0.1,
)
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MaxSherry/ssr-command-client | shadowsocksr_cli/main.py | e52ea0a74e2a1bbdd7e816e0e2670d66ebdbf159 | """
@author: tyrantlucifer
@contact: [email protected]
@blog: https://tyrantlucifer.com
@file: main.py
@time: 2021/2/18 21:36
@desc: shadowsocksr-cli入口函数
"""
import argparse
import traceback
from shadowsocksr_cli.functions import *
def get_parser():
parser = argparse.ArgumentParser(description=color.blue("The shadowsocksr command client based Python."),
epilog=color.yellow('Powered by ') + color.green('tyrantlucifer') + color.yellow(
". If you have any questions,you can send e-mails to ") + color.green(
"[email protected]"))
parser.add_argument("-l", "--list", action="store_true", help="show ssr list")
parser.add_argument("-p", "--port", default=1080, metavar="local_port", type=int,
help="assign local proxy port,use with -s")
parser.add_argument("-s", "--start", metavar="ssr_id", type=int, help="start ssr proxy")
parser.add_argument("-S", "--stop", nargs='?', const=-1, metavar="ssr_id", type=int, help="stop ssr proxy")
parser.add_argument("-u", "--update", action="store_true", help="update ssr list")
parser.add_argument("-v", "--version", action="store_true", help="display version")
parser.add_argument("--generate-clash", action="store_true", help="generate clash config yaml")
parser.add_argument("--display-json", metavar="ssr_id", type=int, help="display ssr json info")
parser.add_argument("--test-speed", type=int, metavar="ssr_id", help="test ssr nodes download and upload speed")
parser.add_argument("--fast-node", action="store_true", help="find most fast by delay and start ssr proxy")
parser.add_argument("--setting-url", metavar="ssr_subscribe_url", help="setting ssr subscribe url")
parser.add_argument("--setting-address", metavar="ssr_local_address", help="setting ssr local address")
parser.add_argument("--list-url", action="store_true", help="list ssr subscribe url")
parser.add_argument("--add-url", metavar="ssr_subscribe_url", help="add ssr subscribe url")
parser.add_argument("--remove-url", metavar="ssr_subscribe_url", help="remove ssr subscribe url")
parser.add_argument("--list-address", action="store_true", help="list ssr local address")
parser.add_argument("--parse-url", metavar="ssr_url", help="pares ssr url")
parser.add_argument("--append-ssr", metavar="ssr_file_path", help="append ssr nodes from file")
parser.add_argument("-b", action="store_true", help="append_ssr file is base64")
parser.add_argument("--clear-ssr", metavar="ssr_id", nargs="?", const="fail",
help="if ssr_id is not empty, clear ssr node by ssr_id, else clear fail nodes")
parser.add_argument("-all", action="store_true", help="clear all ssr node")
parser.add_argument("--add-ssr", metavar="ssr_url", help="add ssr node")
parser.add_argument("--test-again", metavar="ssr_node_id", type=int, help="test ssr node again")
parser.add_argument("--print-qrcode", metavar="ssr_node_id", type=int, help="print ssr node qrcode")
parser.add_argument("--http", metavar="action[start stop status]", help="Manager local http server")
parser.add_argument("--http-port", metavar="http server port", default=80, type=int,
help="assign local http server port")
parser.add_argument("--setting-global-proxy", action="store_true",
help="setting system global proxy,only support on " + color.red('Ubuntu Desktop'))
parser.add_argument("--setting-pac-proxy", action="store_true",
help="setting system pac proxy,only support on " + color.red('Ubuntu Desktop'))
parser.add_argument("--close-system-proxy", action="store_true",
help="close system proxy,only support on " + color.red('Ubuntu Desktop'))
return parser
def main():
parser = get_parser()
args = parser.parse_args()
if args.list:
DisplayShadowsocksr.display_shadowsocksr_list()
elif args.update:
UpdateConfigurations.update_subscribe()
elif args.fast_node:
HandleShadowsocksr.select_fast_node(args.port)
elif args.start is not None:
HandleShadowsocksr.start(ssr_id=args.start, local_port=args.port)
elif args.stop is not None:
HandleShadowsocksr.stop(ssr_id=args.stop, local_port=args.port)
elif args.version:
DisplayShadowsocksr.display_version()
elif args.setting_url:
UpdateConfigurations.reset_subscribe_url(args.setting_url)
elif args.append_ssr:
if not os.path.isfile(args.append_ssr):
logger.error(f'append_ssr file {args.append_ssr} is not exists')
return
with open(args.append_ssr, 'r', encoding='UTF-8') as f:
txt = f.read()
if args.b:
txt = ParseShadowsocksr.base64_decode(txt)
ssr_set = set()
for line in txt.splitlines():
for ssr in re.findall(r'ssr://[0-9a-zA-Z=-_/+]+', line):
ssr_set.add(ssr)
for ssr in ssr_set:
try:
UpdateConfigurations.append_ssr_node(ssr)
except Exception as e:
logger.error(f'add ssr node error {ssr}')
logger.error(traceback.format_exc())
elif args.clear_ssr:
UpdateConfigurations.clear_ssr_nodes(args.clear_ssr, args.all)
elif args.setting_address:
UpdateConfigurations.update_local_address(args.setting_address)
elif args.list_url:
DisplayShadowsocksr.display_subscribe_url()
elif args.add_url:
UpdateConfigurations.add_subscribe_url(args.add_url)
elif args.remove_url:
UpdateConfigurations.remove_subscribe_url(args.remove_url)
elif args.list_address:
DisplayShadowsocksr.display_local_address()
elif args.parse_url:
DisplayShadowsocksr.display_shadowsocksr_json_by_url(args.parse_url)
elif args.add_ssr:
UpdateConfigurations.add_shadowsocksr_by_url(args.add_ssr)
elif args.test_again is not None:
UpdateConfigurations.update_shadowsocksr_connect_status(ssr_id=args.test_again)
elif args.print_qrcode is not None:
DisplayShadowsocksr.display_qrcode(ssr_id=args.print_qrcode)
elif args.setting_global_proxy:
UpdateSystemProxy.open_global_proxy(args.port, args.http_port)
elif args.setting_pac_proxy:
UpdateSystemProxy.open_pac_proxy(args.port, args.http_port)
elif args.close_system_proxy:
UpdateSystemProxy.close_proxy(args.port, args.http_port)
elif args.test_speed is not None:
DisplayShadowsocksr.display_shadowsocksr_speed(ssr_id=args.test_speed)
elif args.display_json is not None:
DisplayShadowsocksr.display_shadowsocksr_json(ssr_id=args.display_json)
elif args.generate_clash:
GenerateClashConfig.generate_clash_config()
elif args.http:
HandleHttpServer.handle_http_server(args.http, args.port, args.http_port)
else:
parser.print_help()
if __name__ == "__main__":
main()
| [((93, 29, 93, 51), 'traceback.format_exc', 'traceback.format_exc', ({}, {}), '()', False, 'import traceback\n')] |
jcjveraa/EDDN | examples/Python 2.7/Client_Complete.py | d0cbae6b7a2cac180dd414cbc324c2d84c867cd8 | import zlib
import zmq
import simplejson
import sys, os, datetime, time
"""
" Configuration
"""
__relayEDDN = 'tcp://eddn.edcd.io:9500'
#__timeoutEDDN = 600000 # 10 minuts
__timeoutEDDN = 60000 # 1 minut
# Set False to listen to production stream; True to listen to debug stream
__debugEDDN = False;
# Set to False if you do not want verbose logging
__logVerboseFile = os.path.dirname(__file__) + '/Logs_Verbose_EDDN_%DATE%.htm'
#__logVerboseFile = False
# Set to False if you do not want JSON logging
__logJSONFile = os.path.dirname(__file__) + '/Logs_JSON_EDDN_%DATE%.log'
#__logJSONFile = False
# A sample list of authorised softwares
__authorisedSoftwares = [
"EDCE",
"ED-TD.SPACE",
"EliteOCR",
"Maddavo's Market Share",
"RegulatedNoise",
"RegulatedNoise__DJ",
"E:D Market Connector [Windows]"
]
# Used this to excludes yourself for example has you don't want to handle your own messages ^^
__excludedSoftwares = [
'My Awesome Market Uploader'
]
"""
" Start
"""
def date(__format):
d = datetime.datetime.utcnow()
return d.strftime(__format)
__oldTime = False
def echoLog(__str):
global __oldTime, __logVerboseFile
if __logVerboseFile != False:
__logVerboseFileParsed = __logVerboseFile.replace('%DATE%', str(date('%Y-%m-%d')))
if __logVerboseFile != False and not os.path.exists(__logVerboseFileParsed):
f = open(__logVerboseFileParsed, 'w')
f.write('<style type="text/css">html { white-space: pre; font-family: Courier New,Courier,Lucida Sans Typewriter,Lucida Typewriter,monospace; }</style>')
f.close()
if (__oldTime == False) or (__oldTime != date('%H:%M:%S')):
__oldTime = date('%H:%M:%S')
__str = str(__oldTime) + ' | ' + str(__str)
else:
__str = ' ' + ' | ' + str(__str)
print __str
sys.stdout.flush()
if __logVerboseFile != False:
f = open(__logVerboseFileParsed, 'a')
f.write(__str + '\n')
f.close()
def echoLogJSON(__json):
global __logJSONFile
if __logJSONFile != False:
__logJSONFileParsed = __logJSONFile.replace('%DATE%', str(date('%Y-%m-%d')))
f = open(__logJSONFileParsed, 'a')
f.write(str(__json) + '\n')
f.close()
def main():
echoLog('Starting EDDN Subscriber')
echoLog('')
context = zmq.Context()
subscriber = context.socket(zmq.SUB)
subscriber.setsockopt(zmq.SUBSCRIBE, "")
subscriber.setsockopt(zmq.RCVTIMEO, __timeoutEDDN)
while True:
try:
subscriber.connect(__relayEDDN)
echoLog('Connect to ' + __relayEDDN)
echoLog('')
echoLog('')
poller = zmq.Poller()
poller.register(subscriber, zmq.POLLIN)
while True:
socks = dict(poller.poll(__timeoutEDDN))
if socks:
if socks.get(subscriber) == zmq.POLLIN:
__message = subscriber.recv(zmq.NOBLOCK)
__message = zlib.decompress(__message)
__json = simplejson.loads(__message)
__converted = False
# Handle commodity v1
if __json['$schemaRef'] == 'https://eddn.edcd.io/schemas/commodity/1' + ('/test' if (__debugEDDN == True) else ''):
echoLogJSON(__message)
echoLog('Receiving commodity-v1 message...')
echoLog(' - Converting to v3...')
__temp = {}
__temp['$schemaRef'] = 'https://eddn.edcd.io/schemas/commodity/3' + ('/test' if (__debugEDDN == True) else '')
__temp['header'] = __json['header']
__temp['message'] = {}
__temp['message']['systemName'] = __json['message']['systemName']
__temp['message']['stationName'] = __json['message']['stationName']
__temp['message']['timestamp'] = __json['message']['timestamp']
__temp['message']['commodities'] = []
__commodity = {}
if 'itemName' in __json['message']:
__commodity['name'] = __json['message']['itemName']
if 'buyPrice' in __json['message']:
__commodity['buyPrice'] = __json['message']['buyPrice']
if 'stationStock' in __json['message']:
__commodity['supply'] = __json['message']['stationStock']
if 'supplyLevel' in __json['message']:
__commodity['supplyLevel'] = __json['message']['supplyLevel']
if 'sellPrice' in __json['message']:
__commodity['sellPrice'] = __json['message']['sellPrice']
if 'demand' in __json['message']:
__commodity['demand'] = __json['message']['demand']
if'demandLevel' in __json['message']:
__commodity['demandLevel'] = __json['message']['demandLevel']
__temp['message']['commodities'].append(__commodity)
__json = __temp
del __temp, __commodity
__converted = True
# Handle commodity v3
if __json['$schemaRef'] == 'https://eddn.edcd.io/schemas/commodity/3' + ('/test' if (__debugEDDN == True) else ''):
if __converted == False:
echoLogJSON(__message)
echoLog('Receiving commodity-v3 message...')
__authorised = False
__excluded = False
if __json['header']['softwareName'] in __authorisedSoftwares:
__authorised = True
if __json['header']['softwareName'] in __excludedSoftwares:
__excluded = True
echoLog(' - Software: ' + __json['header']['softwareName'] + ' / ' + __json['header']['softwareVersion'])
echoLog(' - ' + 'AUTHORISED' if (__authorised == True) else
('EXCLUDED' if (__excluded == True) else 'UNAUTHORISED')
)
if __authorised == True and __excluded == False:
# Do what you want with the data...
# Have fun !
# For example
echoLog(' - Timestamp: ' + __json['message']['timestamp'])
echoLog(' - Uploader ID: ' + __json['header']['uploaderID'])
echoLog(' - System Name: ' + __json['message']['systemName'])
echoLog(' - Station Name: ' + __json['message']['stationName'])
for __commodity in __json['message']['commodities']:
echoLog(' - Name: ' + __commodity['name'])
echoLog(' - Buy Price: ' + str(__commodity['buyPrice']))
echoLog(' - Supply: ' + str(__commodity['supply'])
+ ((' (' + __commodity['supplyLevel'] + ')') if 'supplyLevel' in __commodity else '')
)
echoLog(' - Sell Price: ' + str(__commodity['sellPrice']))
echoLog(' - Demand: ' + str(__commodity['demand'])
+ ((' (' + __commodity['demandLevel'] + ')') if 'demandLevel' in __commodity else '')
)
# End example
del __authorised, __excluded
echoLog('')
echoLog('')
del __converted
else:
print 'Disconnect from ' + __relayEDDN + ' (After timeout)'
echoLog('')
echoLog('')
sys.stdout.flush()
subscriber.disconnect(__relayEDDN)
break
except zmq.ZMQError, e:
subscriber.disconnect(__relayEDDN)
echoLog('')
echoLog('Disconnect from ' + __relayEDDN + ' (After receiving ZMQError)')
echoLog('ZMQSocketException: ' + str(e))
echoLog('')
time.sleep(10)
if __name__ == '__main__':
main()
| [] |
nadkkka/H8PW | zad1.py | 21b5d28bb42af163e7dad43368d21b550ae66618 |
def repleace_pattern(t,s,r):
assert len(t) > 0
assert len(s) > 0
assert len(r) > 0
assert len(t) >= len(s)
n = len(t)
m = len(s)
k = len(r)
idx = -1
for i in range(0, n):
if t[i] == s[0]:
pattern = True
for j in range(1,m):
if t[i+j] != s[j]:
pattern = False
break
if(pattern):
idx=i
break
result = t
print(idx)
if(idx!=-1):
result = [*t[0:idx],*r,*t[idx+m:n]]
return result
print (repleace_pattern([1,2,3,1,2,3,4],[1,2,3,4],[9,0]))
| [] |
Matjordan/mycroft-core | mycroft/client/enclosure/weather.py | 8b64930f3b3dae671535fc3b096ce9d846c54f6d | # Copyright 2017 Mycroft AI Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
class EnclosureWeather:
"""
Listens for Enclosure API commands to display indicators of the weather.
Performs the associated command on Arduino by writing on the Serial port.
"""
def __init__(self, bus, writer):
self.bus = bus
self.writer = writer
self.__init_events()
def __init_events(self):
self.bus.on('enclosure.weather.display', self.display)
def display(self, event=None):
if event and event.data:
# Convert img_code to icon
img_code = event.data.get("img_code", None)
icon = None
if img_code == 0:
# sunny
icon = "IICEIBMDNLMDIBCEAA"
elif img_code == 1:
# partly cloudy
icon = "IIEEGBGDHLHDHBGEEA"
elif img_code == 2:
# cloudy
icon = "IIIBMDMDODODODMDIB"
elif img_code == 3:
# light rain
icon = "IIMAOJOFPBPJPFOBMA"
elif img_code == 4:
# raining
icon = "IIMIOFOBPFPDPJOFMA"
elif img_code == 5:
# storming
icon = "IIAAIIMEODLBJAAAAA"
elif img_code == 6:
# snowing
icon = "IIJEKCMBPHMBKCJEAA"
elif img_code == 7:
# wind/mist
icon = "IIABIBIBIJIJJGJAGA"
temp = event.data.get("temp", None)
if icon is not None and temp is not None:
icon = "x=2," + icon
msg = "weather.display=" + str(temp) + "," + str(icon)
self.writer.write(msg)
| [] |
cnwangfeng/algorithm-reference-library | tests/processing_components/test_image_iterators.py | 9605eb01652fbfcb9ff003cc12b44c84093b7fb1 | """Unit tests for image iteration
"""
import logging
import unittest
import numpy
from data_models.polarisation import PolarisationFrame
from processing_components.image.iterators import image_raster_iter, image_channel_iter, image_null_iter
from processing_components.image.operations import create_empty_image_like
from processing_components.simulation.testing_support import create_test_image
log = logging.getLogger(__name__)
class TestImageIterators(unittest.TestCase):
def test_raster(self):
m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
for nraster in [1, 2, 4, 8, 9]:
m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
for patch in image_raster_iter(m31model, facets=nraster):
assert patch.data.shape[3] == (m31model.data.shape[3] // nraster), \
"Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[3],
(m31model.data.shape[3] // nraster))
assert patch.data.shape[2] == (m31model.data.shape[2] // nraster), \
"Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[2],
(m31model.data.shape[2] // nraster))
patch.data *= 2.0
diff = m31model.data - 2.0 * m31original.data
assert numpy.max(numpy.abs(m31model.data)), "Raster is empty for %d" % nraster
assert numpy.max(numpy.abs(diff)) == 0.0, "Raster set failed for %d" % nraster
def test_raster_exception(self):
m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
for nraster, overlap in [(-1, -1), (-1, 0), (2, 128), (1e6, 127)]:
with self.assertRaises(AssertionError) as context:
m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
for patch in image_raster_iter(m31model, facets=nraster, overlap=overlap):
patch.data *= 2.0
def test_raster_overlap(self):
m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
flat = create_empty_image_like(m31original)
for nraster, overlap in [(1, 0), (1, 16), (4, 8), (4, 16), (8, 8), (16, 4), (9, 5)]:
m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
for patch, flat_patch in zip(image_raster_iter(m31model, facets=nraster, overlap=overlap),
image_raster_iter(flat, facets=nraster, overlap=overlap)):
patch.data *= 2.0
flat_patch.data[...] += 1.0
assert numpy.max(numpy.abs(m31model.data)), "Raster is empty for %d" % nraster
def test_raster_overlap_linear(self):
m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
flat = create_empty_image_like(m31original)
for nraster, overlap in [(1, 0), (1, 16), (4, 8), (4, 16), (8, 8), (16, 4), (9, 5)]:
m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
for patch, flat_patch in zip(image_raster_iter(m31model, facets=nraster, overlap=overlap,
taper='linear'),
image_raster_iter(flat, facets=nraster, overlap=overlap)):
patch.data *= 2.0
flat_patch.data[...] += 1.0
assert numpy.max(numpy.abs(m31model.data)), "Raster is empty for %d" % nraster
def test_raster_overlap_quadratic(self):
m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
flat = create_empty_image_like(m31original)
for nraster, overlap in [(1, 0), (1, 16), (4, 8), (4, 16), (8, 8), (16, 4), (9, 5)]:
m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
for patch, flat_patch in zip(image_raster_iter(m31model, facets=nraster, overlap=overlap,
taper='quadratic'),
image_raster_iter(flat, facets=nraster, overlap=overlap)):
patch.data *= 2.0
flat_patch.data[...] += 1.0
assert numpy.max(numpy.abs(m31model.data)), "Raster is empty for %d" % nraster
def test_raster_overlap_tukey(self):
m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
flat = create_empty_image_like(m31original)
for nraster, overlap in [(1, 0), (1, 16), (4, 8), (4, 16), (8, 8), (16, 4), (9, 5)]:
m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
for patch, flat_patch in zip(image_raster_iter(m31model, facets=nraster, overlap=overlap,
taper='tukey'),
image_raster_iter(flat, facets=nraster, overlap=overlap)):
patch.data *= 2.0
flat_patch.data[...] += 1.0
assert numpy.max(numpy.abs(m31model.data)), "Raster is empty for %d" % nraster
def test_channelise(self):
m31cube = create_test_image(polarisation_frame=PolarisationFrame('stokesI'),
frequency=numpy.linspace(1e8,1.1e8, 128))
for subimages in [128, 16, 8, 2, 1]:
for slab in image_channel_iter(m31cube, subimages=subimages):
assert slab.data.shape[0] == 128 // subimages
def test_null(self):
m31cube = create_test_image(polarisation_frame=PolarisationFrame('stokesI'),
frequency=numpy.linspace(1e8, 1.1e8, 128))
for i, im in enumerate(image_null_iter(m31cube)):
assert i<1, "Null iterator returns more than one value"
if __name__ == '__main__':
unittest.main()
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alisure-fork/Video-Swin-Transformer | a_other_video/MCL-Motion-Focused-Contrastive-Learning/sts/motion_sts.py | aa0a31bd4df0ad2cebdcfb2ad53df712fce79809 | import cv2
import numpy as np
from scipy import ndimage
def compute_motion_boudary(flow_clip):
mx = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]])
my = np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]])
dx_all = []
dy_all = []
mb_x = 0
mb_y = 0
for flow_img in flow_clip:
d_x = ndimage.convolve(flow_img, mx)
d_y = ndimage.convolve(flow_img, my)
dx_all.append(d_x)
dy_all.append(d_y)
mb_x += d_x
mb_y += d_y
dx_all = np.array(dx_all)
dy_all = np.array(dy_all)
return dx_all, dy_all, mb_x, mb_y
def zero_boundary(frame_mag):
frame_mag[:8, :] = 0
frame_mag[:, :8] = 0
frame_mag[-8:, :] = 0
frame_mag[:, -8:] = 0
return frame_mag
def motion_mag_downsample(mag, size, input_size):
block_size = input_size // size
mask = np.zeros((size,size))
for i in range(size):
for j in range(size):
x_start = i * block_size
x_end = x_start + block_size
y_start = j * block_size
y_end = y_start + block_size
tmp_block = mag[x_start:x_end, y_start:y_end]
block_mean = np.mean(tmp_block)
mask[i, j] = block_mean
return mask
def motion_sts(flow_clip, size, input_size):
dx_all, dy_all, dx_sum, dy_sum = compute_motion_boudary(flow_clip)
mag, ang = cv2.cartToPolar(dx_sum, dy_sum, angleInDegrees=True)
mag_down = motion_mag_downsample(mag, size, input_size)
return mag_down
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MSLNZ/msl-qt | tests/test_button.py | 33abbb4807b54e3a06dbe9c0f9b343802ece9b97 | import os
import sys
import pytest
from msl.qt import convert, Button, QtWidgets, QtCore, Qt
def test_text():
b = Button(text='hello')
assert b.text() == 'hello'
assert b.icon().isNull()
assert b.toolButtonStyle() == Qt.ToolButtonTextOnly
def test_icon():
path = os.path.dirname(__file__) + '/gamma.png'
gamma_size = QtCore.QSize(191, 291)
int_val = QtWidgets.QStyle.SP_DriveNetIcon
icon = convert.to_qicon(int_val)
sizes = icon.availableSizes()
if sys.platform == 'win32':
assert len(sizes) > 1
b = Button(icon=int_val)
assert b.text() == ''
assert not b.icon().isNull()
assert b.iconSize() == sizes[0]
assert b.toolButtonStyle() == Qt.ToolButtonIconOnly
b = Button(icon=path)
assert b.text() == ''
assert not b.icon().isNull()
assert b.iconSize() == gamma_size
assert b.toolButtonStyle() == Qt.ToolButtonIconOnly
b = Button(icon=convert.icon_to_base64(convert.to_qicon(path)))
assert b.text() == ''
assert not b.icon().isNull()
assert b.iconSize() == gamma_size
assert b.toolButtonStyle() == Qt.ToolButtonIconOnly
def test_icon_size():
int_val = QtWidgets.QStyle.SP_DriveNetIcon
icon = convert.to_qicon(int_val)
sizes = icon.availableSizes()
if sys.platform == 'win32':
assert len(sizes) > 1
#
# specify the size to the get_icon function
#
b = Button(icon=convert.to_qicon(int_val))
assert b.text() == ''
assert b.toolButtonStyle() == Qt.ToolButtonIconOnly
assert b.iconSize() == sizes[0]
b = Button(icon=convert.to_qicon(int_val, size=789))
assert b.iconSize() == QtCore.QSize(789, 789)
b = Button(icon=convert.to_qicon(int_val, size=3.0))
# specifying a scale factor will use the largest available size
assert b.iconSize() == QtCore.QSize(3*sizes[-1].width(), 3*sizes[-1].height())
b = Button(icon=convert.to_qicon(int_val, size=QtCore.QSize(50, 50)))
assert b.iconSize() == QtCore.QSize(50, 50)
for size in [(256,), (256, 256, 256)]:
with pytest.raises(ValueError, match='(width, height)'):
Button(icon=convert.to_qicon(int_val, size=size))
#
# use the icon_size kwarg
#
b = Button(icon=convert.to_qicon(int_val), icon_size=1234)
assert b.iconSize() == QtCore.QSize(1234, 1234)
b = Button(icon=convert.to_qicon(int_val), icon_size=3.0)
# specifying a scale factor will use the largest available size
assert b.iconSize() == QtCore.QSize(3*sizes[-1].width(), 3*sizes[-1].height())
b = Button(icon=convert.to_qicon(int_val), icon_size=(312, 312))
assert b.iconSize() == QtCore.QSize(312, 312)
b = Button(icon=convert.to_qicon(int_val), icon_size=QtCore.QSize(500, 500))
assert b.iconSize() == QtCore.QSize(500, 500)
for size in [(256,), (256, 256, 256)]:
with pytest.raises(ValueError, match='(width, height)'):
Button(icon=convert.to_qicon(int_val), icon_size=size)
def test_text_and_icon():
b = Button(text='hello', icon=QtWidgets.QStyle.SP_DriveNetIcon)
assert b.text() == 'hello'
assert not b.icon().isNull()
assert b.toolButtonStyle() == Qt.ToolButtonTextUnderIcon
b = Button(text='world', icon=QtWidgets.QStyle.SP_DriveNetIcon, is_text_under_icon=False)
assert b.text() == 'world'
assert not b.icon().isNull()
assert b.toolButtonStyle() == Qt.ToolButtonTextBesideIcon
def test_tooltip():
b = Button(tooltip='hello')
assert b.text() == ''
assert b.icon().isNull()
assert b.toolTip() == 'hello'
assert b.toolButtonStyle() == Qt.ToolButtonIconOnly
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MateusBarboza99/Python-03- | Exercicios/ex028.py | 9c6df88aaa8ba83d385b92722ed1df5873df3a77 | from random import randint
from time import sleep
computador = randint(0, 5) # Faz o computador "PENSAR"
print('-=-' * 20)
print('Vou Pensar em Um Número Entre 0 e 5. Tente Adivinhar Paçoca...')
print('-=-' * 20)
jogador = int(input('Em que Número eu Pensei? ')) # Jogador tenta Adivinhar
print('PROCESSANDO........')
sleep(3)
if jogador == computador:
print('PARABÊNS! Você conseguiu me Vencer Paçoca')
else:
print('GANHEI! Eu Pensei no Número {} e não no {}!'.format(computador, jogador))
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manas1410/Miscellaneous-Development | Student Database/input_details.py | 8ffd2b586cb05b12ed0855d97c3015c8bb2a6c01 | from tkinter import*
import tkinter.font as font
import sqlite3
name2=''
regis2=''
branch2=''
def main():
inp=Tk()
inp.geometry("430x300")
inp.title("Enter The Details")
inp.iconbitmap("logo/spectrumlogo.ico")
f=font.Font(family='Bookman Old Style',size=15,weight='bold')
f1=font.Font(family='Bookman Old Style',size=20,weight='bold')
global n2
global reg2
global b2
det=Label(inp,text=" Enter The Details\n",font=f1,fg='magenta')
det.grid(row=0,column=0,columnspan=2)
n1=Label(inp,text=" Name:",font=f)
n1.grid(row=1,column=0)
n2=Entry(inp,width=40)
n2.grid(row=1,column=1)
reg1=Label(inp,text="Registration ID:",font=f)
reg1.grid(row=2,column=0)
reg2=Entry(inp,width=40)
reg2.grid(row=2,column=1)
b1=Label(inp,text=" Branch:",font=f)
b1.grid(row=3,column=0)
b2=Entry(inp,width=40)
b2.grid(row=3,column=1)
invalid=Label(inp,text=' ',fg='red')
invalid.grid(row=4,columnspan=2)
def submit():
name2=n2.get()
regis2=reg2.get()
branch2=b2.get()
l=[name2,regis2,branch2]
if (None in l or "" in l):
invalid['text']="Please fill all the fields"
else:
db=sqlite3.connect("mark_list.db")
#cursor
c=db.cursor()
#insert into tabels
c.execute("""UPDATE mark_list SET name=? WHERE name=?""",(name2,' '))
c.execute("""UPDATE mark_list SET registration_no=? WHERE registration_no=?""",(regis2,' '))
c.execute("""UPDATE mark_list SET branch=? WHERE branch=?""",(branch2,' '))
#commit_changes
db.commit()
#close connection
db.close()
inp.destroy()
import subject
subject.main()
def back():
db=sqlite3.connect("mark_list.db")
#cursor
c=db.cursor()
c.execute("""DELETE from mark_list where name=' '""")
#commit_changes
db.commit()
#close connection
db.close()
inp.destroy()
import welcome
welcome.main()
#buttons
sub1=Button(inp,text="Submit",borderwidth=3,padx=40,font=f,bg='green',command=submit)
sub1.grid(row=5,column=0,columnspan=2)
back1=Button(inp,text="Back",borderwidth=3,padx=20,font=f,bg='red',command=back)
back1.grid(row=6,column=0,columnspan=2)
inp.mainloop()
if __name__=='__main__':
main()
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jakezimmerTHT/py_IQS5xx | IQS5xx/IQS5xx.py | 5f90be17ea0429eeeb3726c7647f0b7ad1fb7b06 | import unittest
import time
import logging
logging.basicConfig()
from intelhex import IntelHex
import Adafruit_GPIO.I2C as i2c
from gpiozero import OutputDevice
from gpiozero import DigitalInputDevice
from ctypes import c_uint8, c_uint16, c_uint32, cast, pointer, POINTER
from ctypes import create_string_buffer, Structure
from fcntl import ioctl
import struct
import Adafruit_PureIO.smbus as smbus
from Adafruit_PureIO.smbus import make_i2c_rdwr_data
from IQS5xx_Defs import *
def bytesToHexString(bytes):
if isinstance(bytes, basestring):
return ''.join('{:02x} '.format(ord(c)) for c in bytes)
if isinstance(bytes, bytearray):
return ''.join('{:02x} '.format(b) for b in bytes)
raise ValueError("Must pass bytesToHexString() a string or bytearray")
IQS5xx_DEFAULT_ADDRESS = 0x74
IQS5xx_MAX_ADDRESS = 0x78
CHECKSUM_DESCRIPTOR_START = 0x83C0
CHECKSUM_DESCRIPTOR_END = 0x83FF
APP_START_ADDRESS = 0x8400
APP_END_ADDRESS = 0xBDFF #inclusive
NV_SETTINGS_START = 0xBE00
NV_SETTINGS_END = 0xBFFF #inclusive
FLASH_PADDING = 0x00
BLOCK_SIZE = 64
APP_SIZE_BLOCKS = (((APP_END_ADDRESS+1) - APP_START_ADDRESS) / BLOCK_SIZE)
NV_SETTINGS_SIZE_BLOCKS = (((NV_SETTINGS_END+1) - NV_SETTINGS_START) / BLOCK_SIZE)
BL_CMD_READ_VERSION = 0x00
BL_CMD_READ_64_BYTES = 0x01
BL_CMD_EXECUTE_APP = 0x02 # Write only, 0 bytes
BL_CMD_RUN_CRC = 0x03
BL_CRC_FAIL = 0x01
BL_CRC_PASS = 0x00
BL_VERSION = 0x0200
def swapEndianess(uint16):
return ((uint16 & 0xFF) << 8) | ((uint16 & 0xFF00) >> 8)
def writeBytes(self, data):
self._bus.write_bytes(self._address, bytes(data))
i2c.Device.writeBytes = writeBytes
def readBytes(self, data):
return self._bus.read_bytes(self._address, data)
i2c.Device.readBytes = readBytes
def writeRawListReadRawList(self, data, readLength):
self.writeBytes(data)
# This isn't using a repeat start
return self.readBytes(readLength)
i2c.Device.writeRawListReadRawList = writeRawListReadRawList
def writeBytes_16BitAddress(self, address, data):
addressBytes = struct.pack('>H', address)
dataBytes = bytearray(data)
bytes = addressBytes + dataBytes
self.writeBytes(bytes)
i2c.Device.writeBytes_16BitAddress = writeBytes_16BitAddress
def readBytes_16BitAddress(self, address, length):
assert self._bus._device is not None, 'Bus must be opened before operations are made against it!'
# Build ctypes values to marshall between ioctl and Python.
reg = c_uint16(swapEndianess(address))
result = create_string_buffer(length)
# Build ioctl request.
request = make_i2c_rdwr_data([
(self._address, 0, 2, cast(pointer(reg), POINTER(c_uint8))), # Write cmd register.
(self._address, smbus.I2C_M_RD, length, cast(result, POINTER(c_uint8))) # Read data.
])
# Make ioctl call and return result data.
ioctl(self._bus._device.fileno(), smbus.I2C_RDWR, request)
return bytearray(result.raw) # Use .raw instead of .value which will stop at a null byte!
i2c.Device.readBytes_16BitAddress = readBytes_16BitAddress
def readByte_16BitAddress(self, address):
result = self.readBytes_16BitAddress(address, 1)
result = struct.unpack('>B', result)[0]
return result
i2c.Device.readByte_16BitAddress = readByte_16BitAddress
def writeByte_16BitAddress(self, address, value, mask=0xFF):
if mask is not 0xFF:
register = self.readByte_16BitAddress(address)
register &= ~mask
register |= (value & mask)
value = register
format = '>HB' if (value > 0) else '>Hb'
bytes = struct.pack(format, address, value)
self.writeBytes(bytes)
i2c.Device.writeByte_16BitAddress = writeByte_16BitAddress
class IQS5xx(object):
def __init__(self, resetPin, readyPin, address=IQS5xx_DEFAULT_ADDRESS):
self.address = address
self._resetPinNum = resetPin
self._readyPinNum = readyPin
self._resetPin = OutputDevice(pin=self._resetPinNum, active_high=False, initial_value=True)
self._readypin = DigitalInputDevice(pin=self._readyPinNum, active_state=True, pull_up=None)
def begin(self):
self.releaseReset()
time.sleep(0.01)
self.waitUntilReady()
self.acknowledgeReset()
time.sleep(0.01)
self.acknowledgeReset()
time.sleep(0.01)
self.endSession()
time.sleep(0.020)
@property
def address(self):
return self.__address
@address.setter
def address(self, value):
if (value < IQS5xx_DEFAULT_ADDRESS) or (value > IQS5xx_MAX_ADDRESS):
raise ValueError("Invalid I2C Address. Use something in the range [%x, %x]" %(IQS5xx_DEFAULT_ADDRESS, IQS5xx_MAX_ADDRESS))
self.__address = value
self._device = i2c.get_i2c_device(value)
self._logger = logging.getLogger('IQS5xx.Address.{0:#0X}'.format(value))
def readUniqueID(self):
return bytesToHexString(self._device.readBytes_16BitAddress(0xF000, 12))
def setupComplete(self):
self._device.writeByte_16BitAddress(SystemConfig0_adr, SETUP_COMPLETE, SETUP_COMPLETE)
def setManualControl(self):
self._device.writeByte_16BitAddress(SystemConfig0_adr, MANUAL_CONTROL, MANUAL_CONTROL)
self._device.writeByte_16BitAddress(SystemControl0_adr, 0x00, 0x07) # active mode
def setTXPinMappings(self, pinList):
assert isinstance(pinList, list), "TX pinList must be a list of integers"
assert 0 <= len(pinList) <= 15, "TX pinList must be between 0 and 15 long"
self._device.writeBytes_16BitAddress(TxMapping_adr, pinList)
self._device.writeByte_16BitAddress(TotalTx_adr, len(pinList))
def setRXPinMappings(self, pinList):
assert isinstance(pinList, list), "RX pinList must be a list of integers"
assert 0 <= len(pinList) <= 10, "RX pinList must be between 0 and 15 long"
self._device.writeBytes_16BitAddress(RxMapping_adr, pinList)
self._device.writeByte_16BitAddress(TotalRx_adr, len(pinList))
def enableChannel(self, txChannel, rxChannel, enabled):
assert 0 <= txChannel < 15, "txChannel must be less than 15"
assert 0 <= rxChannel < 10, "rxChannel must be less than 10"
registerAddy = ActiveChannels_adr + (txChannel * 2)
if rxChannel >= 8:
mask = 1 << (rxChannel - 8)
else:
registerAddy += 1
mask = 1 << rxChannel
value = mask if enabled else 0x00
self._device.writeByte_16BitAddress(registerAddy, value, mask)
def setTXRXChannelCount(self, tx_count, rx_count):
assert 0 <= txChannel <= 15, "tx_count must be less or equal tp 15"
assert 0 <= rxChannel <= 10, "rx_count must be less than or equal to 10"
self._device.writeByte_16BitAddress(TotalTx_adr, txChannel)
self._device.writeByte_16BitAddress(TotalRx_adr, rxChannel)
def swapXY(self, swapped):
value = SWITCH_XY_AXIS if swapped else 0x00
self._device.writeByte_16BitAddress(XYConfig0_adr, value, SWITCH_XY_AXIS)
def setAtiGlobalC(self, globalC):
self._device.writeByte_16BitAddress(GlobalATIC_adr, globalC)
def setChannel_ATI_C_Adjustment(self, txChannel, rxChannel, adjustment):
assert 0 <= txChannel < 15, "txChannel must be less than 15"
assert 0 <= rxChannel < 10, "rxChannel must be less than 10"
registerAddy = ATICAdjust_adr + (txChannel * 10) + rxChannel
self._device.writeByte_16BitAddress(registerAddy, adjustment)
def setTouchMultipliers(self, set, clear):
self._device.writeByte_16BitAddress(GlobalTouchSet_adr, set)
self._device.writeByte_16BitAddress(GlobalTouchClear_adr, clear)
def rxFloat(self, floatWhenInactive):
value = RX_FLOAT if floatWhenInactive else 0x00
self._device.writeByte_16BitAddress(HardwareSettingsA_adr, value, RX_FLOAT)
def runAtiAlgorithm(self):
self._device.writeByte_16BitAddress(SystemControl0_adr, AUTO_ATI, AUTO_ATI)
def acknowledgeReset(self):
self._device.writeByte_16BitAddress(SystemControl0_adr, ACK_RESET, ACK_RESET)
def atiErrorDetected(self):
reg = self._device.readByte_16BitAddress(SystemInfo0_adr)
return bool(reg & ATI_ERROR)
def reseed(self):
self._device.writeByte_16BitAddress(SystemControl0_adr, RESEED, RESEED)
def endSession(self):
self._device.writeByte_16BitAddress(EndWindow_adr, 0x00)
time.sleep(0.001)
def readVersionNumbers(self):
bytes = self._device.readBytes_16BitAddress(ProductNumber_adr, 6)
fields = struct.unpack(">HHBB",bytes)
return {"product":fields[0], "project":fields[1], "major":fields[2], "minor":fields[3]}
def bootloaderAvailable(self):
BOOTLOADER_AVAILABLE = 0xA5
NO_BOOTLOADER = 0xEE
result = self._device.readByte_16BitAddress(BLStatus_adr)
# result = ord(result)
if result == BOOTLOADER_AVAILABLE:
return True
elif result == NO_BOOTLOADER:
return False
else:
raise ValueError("Unexpected value returned for bootloader status: {0:#0X}".format(result))
def holdReset(self, millis=None):
self._resetPin.on()
if millis != None:
time.sleep(millis/1000.0)
self.releaseReset()
def releaseReset(self):
self._resetPin.off()
def isReady(self):
return self._readypin.is_active
def waitUntilReady(self, timeout=None):
self._readypin.wait_for_active(timeout)
def updateFirmware(self, hexFilePath, newDeviceAddress=None):
hexFile = IntelHex(source = hexFilePath)
hexFile.padding = FLASH_PADDING
appBinary = hexFile.tobinarray(start=APP_START_ADDRESS, end=NV_SETTINGS_END)
crcBinary = hexFile.tobinarray(start=CHECKSUM_DESCRIPTOR_START, end=CHECKSUM_DESCRIPTOR_END)
if newDeviceAddress:
self._logger.debug("Modifying the last byte in NV settings to change Device I2C Addrress to {0:#0X}".format(newDeviceAddress))
if (newDeviceAddress < IQS5xx_DEFAULT_ADDRESS) or (newDeviceAddress > IQS5xx_MAX_ADDRESS):
raise ValueError("Invalid I2C Address. Use something in the range [%x, %x]" %(IQS5xx_DEFAULT_ADDRESS, IQS5xx_MAX_ADDRESS))
appBinary[-1] = newDeviceAddress
# Step 1 - Enter Bootloader
self._logger.debug("Entering Bootloader")
bootloaderAddress = 0x40 ^ self.address
bootloaderDevice = i2c.get_i2c_device(bootloaderAddress)
self.holdReset(100)
bootloaderEntered = False
for i in range(10):
try:
version = bootloaderDevice.readU16(BL_CMD_READ_VERSION, little_endian=False)
bootloaderEntered = True
except:
pass
if not bootloaderEntered:
raise IOError("Timeout while trying to enter bootlaoder")
self._logger.debug("Bootloader entered successfully")
# Step 2 - Read and verify the bootloader version number
self._logger.debug("Reading Bootloader version")
if version != BL_VERSION:
raise Exception("Incompatible bootloader version detected: {0:#0X}".format(version))
self._logger.debug("Bootloader version is compatible: 0x%02X",version)
# Step 3 - Write the new application firmware and settings
self._logger.debug("Starting to write Application and NV settings")
for blockNum in range(APP_SIZE_BLOCKS + NV_SETTINGS_SIZE_BLOCKS):
blockAddress = APP_START_ADDRESS + (blockNum * BLOCK_SIZE)
self._logger.debug('Writing 64-byte block {0}/{1} at address {2:#0X}'.format(blockNum+1, APP_SIZE_BLOCKS + NV_SETTINGS_SIZE_BLOCKS ,blockAddress))
data = bytearray(BLOCK_SIZE + 2)
data[0] = (blockAddress >> 8) & 0xFF
data[1] = blockAddress & 0xFF
data[2:] = appBinary[blockNum*BLOCK_SIZE : (blockNum+1)*BLOCK_SIZE]
bootloaderDevice.writeBytes(data)
time.sleep(.010) # give the device time to write to flash
# Step 4 - Write the checksum descriptor section
self._logger.debug("Writing CRC section")
blockAddress = CHECKSUM_DESCRIPTOR_START
data = bytearray(BLOCK_SIZE + 2)
data[0] = (blockAddress >> 8) & 0xFF
data[1] = blockAddress & 0xFF
data[2:] = crcBinary[0:]
bootloaderDevice.writeBytes(data)
time.sleep(0.010) # give the device time to write to flash
# Step 5 - Perform CRC and read back settins section
time.sleep(0.1)
self._logger.debug("Performing CRC calculation")
bootloaderDevice.writeRaw8(BL_CMD_RUN_CRC)
time.sleep(0.2)
crcStatus = bootloaderDevice.readRaw8()
if crcStatus != BL_CRC_PASS:
raise Exception("CRC Failure")
self._logger.debug("CRC Success")
self._logger.debug("Reading back NV settings and comparing")
for blockNum in range(NV_SETTINGS_SIZE_BLOCKS):
blockAddress = NV_SETTINGS_START + (blockNum * BLOCK_SIZE)
self._logger.debug('Reading 64-byte block {0}/{1} at address {2:#0X}'.format(blockNum+1, NV_SETTINGS_SIZE_BLOCKS, blockAddress))
data = bytearray(3)
data[0] = BL_CMD_READ_64_BYTES
data[1] = (blockAddress >> 8) & 0xFF
data[2] = blockAddress & 0xFF
reply = bootloaderDevice.writeRawListReadRawList(data, BLOCK_SIZE)
expectedReply = appBinary[(APP_SIZE_BLOCKS+blockNum)*BLOCK_SIZE : (APP_SIZE_BLOCKS+blockNum+1)*BLOCK_SIZE].tostring()
if reply != expectedReply:
raise Exception("Unexpected values while reading back NV Setting: {0} \nExpected values: {1}".format(bytesToHexString(reply), bytesToHexString(expectedReply)))
self._logger.debug("NV Settings match expected values")
# Step 6 - Execute application
self._logger.debug("Execute Application")
bootloaderDevice.writeRaw8(BL_CMD_EXECUTE_APP)
if newDeviceAddress:
self.address = newDeviceAddress
class TestIQS5xx(unittest.TestCase):
hexFile = "IQS550_B000_Trackpad_40_15_2_2_BL.HEX"
possibleAddresses = [0x74, 0x75, 0x76, 0x77]
desiredAddress = 0x74
device = None
def setUp(self):
if not self.__class__.device:
self.__class__.device = IQS5xx(17, 27)
for address in self.__class__.possibleAddresses:
self.__class__.device.address = address
self.__class__.device._logger.setLevel(logging.DEBUG)
try:
self.__class__.device.waitUntilReady(1)
self.__class__.device.bootloaderAvailable()
break
except:
if address == self.__class__.possibleAddresses[-1]:
raise IOError("Couldn't communicate with the controller")
if self.__class__.device.address != self.__class__.desiredAddress:
self.__class__.device.updateFirmware(self.__class__.hexFile, newDeviceAddress=self.__class__.desiredAddress)
def tearDown(self):
if self.__class__.device.address != self.__class__.desiredAddress:
print("Cleaning up by reprogramming the controller to the default address")
self.__class__.device.updateFirmware(self.__class__.hexFile, newDeviceAddress=self.__class__.desiredAddress)
def test_bootloaderAvailable(self):
self.assertTrue(self.__class__.device.bootloaderAvailable())
# @unittest.skip
# def test_update(self):
# self.__class__.device.updateFirmware(self.__class__.hexFile)
#
# @unittest.skip
# def test_update_and_changeaddress(self):
# newAddy = 0x77
# self.__class__.device.updateFirmware(self.__class__.hexFile, newDeviceAddress=newAddy)
# self.assertEqual(self.__class__.device.address, newAddy)
# time.sleep(0.1)
# self.assertTrue(self.__class__.device.bootloaderAvailable())
if __name__ == '__main__':
unittest.main()
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IBCNServices/StardogStreamReasoning | code/loader/lock.py | 646db9cec7bd06ac8bfa75952b9a41773f35544d | import threading
class RWLock:
"""Synchronization object used in a solution of so-called second
readers-writers problem. In this problem, many readers can simultaneously
access a share, and a writer has an exclusive access to this share.
Additionally, the following constraints should be met:
1) no reader should be kept waiting if the share is currently opened for
reading unless a writer is also waiting for the share,
2) no writer should be kept waiting for the share longer than absolutely
necessary.
The implementation is based on [1, secs. 4.2.2, 4.2.6, 4.2.7]
with a modification -- adding an additional lock (C{self.__readers_queue})
-- in accordance with [2].
Sources:
[1] A.B. Downey: "The little book of semaphores", Version 2.1.5, 2008
[2] P.J. Courtois, F. Heymans, D.L. Parnas:
"Concurrent Control with 'Readers' and 'Writers'",
Communications of the ACM, 1971 (via [3])
[3] http://en.wikipedia.org/wiki/Readers-writers_problem
"""
def __init__(self):
self.__read_switch = _LightSwitch()
self.__write_switch = _LightSwitch()
self.__no_readers = threading.Lock()
self.__no_writers = threading.Lock()
self.__readers_queue = threading.Lock()
"""A lock giving an even higher priority to the writer in certain
cases (see [2] for a discussion)"""
def reader_acquire(self):
self.__readers_queue.acquire()
self.__no_readers.acquire()
self.__read_switch.acquire(self.__no_writers)
self.__no_readers.release()
self.__readers_queue.release()
def reader_release(self):
self.__read_switch.release(self.__no_writers)
def writer_acquire(self):
self.__write_switch.acquire(self.__no_readers)
self.__no_writers.acquire()
def writer_release(self):
self.__no_writers.release()
self.__write_switch.release(self.__no_readers)
class _LightSwitch:
"""An auxiliary "light switch"-like object. The first thread turns on the
"switch", the last one turns it off (see [1, sec. 4.2.2] for details)."""
def __init__(self):
self.__counter = 0
self.__mutex = threading.Lock()
def acquire(self, lock):
self.__mutex.acquire()
self.__counter += 1
if self.__counter == 1:
lock.acquire()
self.__mutex.release()
def release(self, lock):
self.__mutex.acquire()
self.__counter -= 1
if self.__counter == 0:
lock.release()
self.__mutex.release()
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Loodoor/UnamedPy | src/pyfmodex/channel_group.py | 7d154c3a652992b3c1f28050f0353451f57b2a2d | from .fmodobject import *
from .globalvars import dll as _dll
from .globalvars import get_class
class ChannelGroup(FmodObject):
def add_dsp(self, dsp):
check_type(dsp, get_class("DSP"))
c_ptr = c_void_p()
self._call_fmod("FMOD_ChannelGroup_AddDSP", d._ptr, byref(c_ptr))
return get_class("DSPConnection")(c_ptr)
def add_group(self, group):
check_type(group, ChannelGroup)
self._call_fmod("FMOD_ChannelGroup_AddGroup", group._ptr)
@property
def _occlusion(self):
direct = c_float()
reverb = c_float()
self._call_fmod("FMOD_ChannelGroup_Get3DOcclusion", byref(direct), byref(reverb))
return direct.value, reverb.value
@_occlusion.setter
def _occlusion(self, occs):
self._call_fmod("FMOD_ChannelGroup_Set3DOcclusion", c_float(occs[0]), c_float(occs[1]))
@property
def direct_occlusion(self):
return self._occlusion[0]
@direct_occlusion.setter
def direct_occlusion(self, occ):
self._occlusion = (occ, self._occlusion[1])
@property
def reverb_occlusion(self):
return self._occlusion[1]
@reverb_occlusion.setter
def reverb_occlusion(self, occ):
self._occlusion = (self._occlusion[0], occ)
def get_channel(self, idx):
c_ptr = c_void_p()
self._call_fmod("FMOD_ChannelGroup_GetChannel", idx, byref(c_ptr))
return channel.Channel(c_ptr)
@property
def dsp_head(self):
dsp_ptr = c_void_p()
self._call_fmod("FMOD_ChannelGroup_GetDSPHead", byref(dsp_ptr))
return get_class("DSP")(dsp_ptr)
def get_group(self, idx):
grp_ptr = c_void_p()
self._call_fmod("FMOD_ChannelGroup_GetGroup", idx)
return ChannelGroup(grp_ptr)
@property
def mute(self):
mute = c_bool()
self._call_fmod("FMOD_ChannelGroup_GetMute", byref(mute))
return mute.value
@mute.setter
def mute(self, m):
self._call_fmod("FMOD_Channel_SetMute", m)
@property
def name(self):
buf = create_string_buffer(512)
self._call_fmod("FMOD_ChannelGroup_GetName", buf, 512)
return buf.value
@property
def num_channels(self):
num = c_int()
self._call_fmod("FMOD_ChannelGroup_GetNumChannels", byref(num))
return num.value
@property
def num_groups(self):
num = c_int()
self._call_fmod("FMOD_ChannelGroup_GetNumGroups", byref(num))
return num.value
@property
def parent_group(self):
grp_ptr = c_void_p()
self._call_fmod("FMOD_ChannelGroup_GetParentGroup", byref(grp_ptr))
return ChannelGroup(grp_ptr)
@property
def paused(self):
paused = c_bool()
self._call_fmod("FMOD_ChannelGroup_GetPaused", byref(paused))
return paused.value
@paused.setter
def paused(self, p):
self._call_fmod("FMOD_ChannelGroup_SetPaused", p)
@property
def pitch(self):
pitch = c_float()
self._call_fmod("FMOD_ChannelGroup_GetPitch", byref(pitch))
return pitch.value
@property
def pitch(self, p):
self._call_fmod("FMOD_ChannelGroup_SetPitch", p)
def get_spectrum(self, numvalues, channeloffset, window):
arr = c_float * numvalues
arri = arr()
self._call_fmod("FMOD_ChannelGroup_GetSpectrum", byref(arri), numvalues, channeloffset, window)
return list(arri)
@property
def system_object(self):
sptr = c_void_p()
self._call_fmod("FMOD_channelGroup_GetSystemObject", byref(sptr))
return get_class("System")(sptr, False)
@property
def volume(self):
vol = c_float()
self._call_fmod("FMOD_ChannelGroup_GetVolume", byref(vol))
return vol.value
@volume.setter
def volume(self, vol):
self._call_fmod("FMOD_ChannelGroup_SetVolume", c_float(vol))
def get_wave_data(self, numvalues, channeloffset):
arr = c_float * numvalues
arri = arr()
self._call_fmod("FMOD_ChannelGroup_GetWaveData", byref(arri), numvalues, channeloffset)
return list(arri)
def override_3d_attributes(self, pos=0, vel=0):
self._call_fmod("FMOD_ChannelGroup_Override3DAttributes", pos, vel)
def override_frequency(self, freq):
self._call_fmod("FMOD_ChannelGroup_OverrideFrequency", c_float(freq))
def override_pan(self, pan):
self._call_fmod("FMOD_ChannelGroup_OverridePan", c_float(pan))
def override_reverb_properties(self, props):
check_type(props, REVERB_CHANNELPROPERTIES)
self._call_fmod("FMOD_ChannelGroup_OverrideReverbProperties", props)
def override_speaker_mix(self, frontleft, frontright, center, lfe, backleft, backright, sideleft, sideright):
self._call_fmod("FMOD_ChannelGroup_OverrideSpeakerMix", frontleft, frontright, center, lfe, backleft, backright,
sideleft, sideright)
def override_volume(self, vol):
self._call_fmod("FMOD_ChannelGroup_OverrideVolume", c_float(vol))
def release(self):
self._call_fmod("FMOD_ChannelGroup_Release")
def stop(self):
self._call_fmod("FMOD_ChannelGroup_Stop")
@property
def reverb_properties(self):
props = REVERB_CHANNELPROPERTIES()
ckresult(_dll.FMOD_ChannelGroup_GetReverbProperties(self._ptr, byref(props)))
return props
@reverb_properties.setter
def reverb_properties(self, props):
check_type(props, REVERB_CHANNELPROPERTIES)
ckresult(_dll.FMOD_ChannelGroup_SetReverbProperties(self._ptr, byref(props)))
| [] |
siddhi117/ADB_Homework | program.py | 1751b3cc2d5ec1584efdf7f8961507bc29179e49 | import sqlite3
from bottle import route, run,debug,template,request,redirect
@route('/todo')
def todo_list():
conn = sqlite3.connect('todo.db')
c = conn.cursor()
c.execute("SELECT id, task FROM todo WHERE status LIKE '1'")
result = c.fetchall()
c.close()
output = template('make_table', rows=result)
return output
@route('/new', method='GET')
def new_item():
if request.GET.save:
new = request.GET.task.strip()
conn = sqlite3.connect('todo.db')
c = conn.cursor()
c.execute("INSERT INTO todo (task,status) VALUES (?,?)", (new,1))
new_id = c.lastrowid
conn.commit()
c.close()
redirect('/todo')
#return '<p>The new task was inserted into the database, the ID is %s</p>' % new_id
else:
return template('new_task.tpl')
@route('/do_insert' , method='GET')
def get_id():
redirect('/new')
@route('/edit/<no:int>', method='GET')
def edit_item(no):
if request.GET.save:
edit = request.GET.task.strip()
status = request.GET.status.strip()
if status == 'open':
status = 1
else:
status = 0
conn = sqlite3.connect('todo.db')
c = conn.cursor()
c.execute("UPDATE todo SET task = ?, status = ? WHERE id LIKE ?", (edit, status, no))
conn.commit()
return '<p>The item number %s was successfully updated</p>' % no
else:
conn = sqlite3.connect('todo.db')
c = conn.cursor()
c.execute("SELECT task FROM todo WHERE id LIKE ?", (str(no)))
cur_data = c.fetchone()
return template('edit_task', old=cur_data, no=no)
@route('/find_edit' , method='GET')
def get_id():
id_edit = request.GET.editdata.strip()
redirect('/edit/' + id_edit)
@route('/delete/<no:int>', method='GET')
def delete_item(no):
conn = sqlite3.connect('todo.db')
c = conn.cursor()
c.execute("DELETE FROM todo WHERE id LIKE ?", (str(no)))
conn.commit()
redirect('/todo')
@route('/find_delete' , method='GET')
def get_id():
id_delete = request.GET.deletedata.strip()
redirect('/delete/' + id_delete)
debug(True)
run(reloader=True)
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censoredplanet/censoredplanet-analysis | pipeline/metadata/maxmind.py | f5e5d82f890e47599bc0baa9a9390f3c5147a6f7 | """Module to initialize Maxmind databases and lookup IP metadata."""
import logging
import os
from typing import Optional, Tuple, NamedTuple
import geoip2.database
from pipeline.metadata.mmdb_reader import mmdb_reader
MAXMIND_CITY = 'GeoLite2-City.mmdb'
MAXMIND_ASN = 'GeoLite2-ASN.mmdb'
# Tuple(netblock, asn, as_name, country)
# ex: ("1.0.0.1/24", 13335, "CLOUDFLARENET", "AU")
MaxmindReturnValues = NamedTuple('MaxmindReturnValues',
[('netblock', Optional[str]), ('asn', int),
('as_name', Optional[str]),
('country', Optional[str])])
class MaxmindIpMetadata():
"""Lookup database for Maxmind ASN and country metadata."""
def __init__(self, maxmind_folder: str) -> None:
"""Create a Maxmind Database.
Args:
maxmind_folder: a folder containing maxmind files.
Either a gcs filepath or a local system folder.
"""
maxmind_city_path = os.path.join(maxmind_folder, MAXMIND_CITY)
maxmind_asn_path = os.path.join(maxmind_folder, MAXMIND_ASN)
self.maxmind_city = mmdb_reader(maxmind_city_path)
self.maxmind_asn = mmdb_reader(maxmind_asn_path)
def lookup(self, ip: str) -> MaxmindReturnValues:
"""Lookup metadata infomation about an IP.
Args:
ip: string of the format 1.1.1.1 (ipv4 only)
Returns: MaxmindReturnValues
Raises:
KeyError: when the IP's ASN can't be found
"""
(asn, as_name, netblock) = self._get_maxmind_asn(ip)
country = self._get_country_code(ip)
if not asn:
raise KeyError(f"No Maxmind entry for {ip}")
return MaxmindReturnValues(netblock, asn, as_name, country)
def _get_country_code(self, vp_ip: str) -> Optional[str]:
"""Get country code for IP address.
Args:
vp_ip: IP address of vantage point (as string)
Returns:
2-letter ISO country code
"""
try:
vp_info = self.maxmind_city.city(vp_ip)
return vp_info.country.iso_code
except (ValueError, geoip2.errors.AddressNotFoundError) as e:
logging.warning('Maxmind: %s\n', e)
return None
def _get_maxmind_asn(
self, vp_ip: str) -> Tuple[Optional[int], Optional[str], Optional[str]]:
"""Get ASN information for IP address.
Args:
vp_ip: IP address of vantage point (as string)
Returns:
Tuple containing AS num, AS org, and netblock
"""
try:
vp_info = self.maxmind_asn.asn(vp_ip)
asn = vp_info.autonomous_system_number
as_name = vp_info.autonomous_system_organization
if vp_info.network:
netblock: Optional[str] = vp_info.network.with_prefixlen
else:
netblock = None
return asn, as_name, netblock
except (ValueError, geoip2.errors.AddressNotFoundError) as e:
logging.warning('Maxmind: %s\n', e)
return None, None, None
class FakeMaxmindIpMetadata(MaxmindIpMetadata):
"""A fake lookup table for testing MaxmindIpMetadata."""
# pylint: disable=super-init-not-called
def __init__(self) -> None:
pass
# pylint: disable=no-self-use
def lookup(self, _: str) -> MaxmindReturnValues:
return MaxmindReturnValues('101.103.0.0/16', 1221, 'ASN-TELSTRA', 'AU')
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huyvo/gevent-websocket-py3.5 | examples/plot_graph.py | b2eb3b5cfb020ac976ac0970508589020dce03ad | from __future__ import print_function
"""
This example generates random data and plots a graph in the browser.
Run it using Gevent directly using:
$ python plot_graph.py
Or with an Gunicorn wrapper:
$ gunicorn -k "geventwebsocket.gunicorn.workers.GeventWebSocketWorker" \
plot_graph:resource
"""
import gevent
import random
from geventwebsocket import WebSocketServer, WebSocketApplication, Resource
from geventwebsocket._compat import range_type
class PlotApplication(WebSocketApplication):
def on_open(self):
for i in range_type(10000):
self.ws.send("0 %s %s\n" % (i, random.random()))
gevent.sleep(0.1)
def on_close(self, reason):
print("Connection Closed!!!", reason)
def static_wsgi_app(environ, start_response):
start_response("200 OK", [("Content-Type", "text/html")])
return open("plot_graph.html").readlines()
resource = Resource([
('/', static_wsgi_app),
('/data', PlotApplication)
])
if __name__ == "__main__":
server = WebSocketServer(('', 8000), resource, debug=True)
server.serve_forever()
| [((36, 11, 39, 2), 'geventwebsocket.Resource', 'Resource', ({(36, 20, 39, 1): "[('/', static_wsgi_app), ('/data', PlotApplication)]"}, {}), "([('/', static_wsgi_app), ('/data', PlotApplication)])", False, 'from geventwebsocket import WebSocketServer, WebSocketApplication, Resource\n'), ((42, 13, 42, 62), 'geventwebsocket.WebSocketServer', 'WebSocketServer', (), '', False, 'from geventwebsocket import WebSocketServer, WebSocketApplication, Resource\n'), ((23, 17, 23, 34), 'geventwebsocket._compat.range_type', 'range_type', ({(23, 28, 23, 33): '(10000)'}, {}), '(10000)', False, 'from geventwebsocket._compat import range_type\n'), ((25, 12, 25, 29), 'gevent.sleep', 'gevent.sleep', ({(25, 25, 25, 28): '(0.1)'}, {}), '(0.1)', False, 'import gevent\n'), ((24, 43, 24, 58), 'random.random', 'random.random', ({}, {}), '()', False, 'import random\n')] |
deephyper/NASBigData | nas_big_data/combo/best/combo_4gpu_8_agebo/predict.py | 18f083a402b80b1d006eada00db7287ff1802592 | import os
import numpy as np
import tensorflow as tf
from nas_big_data.combo.load_data import load_data_npz_gz
from deephyper.nas.run.util import create_dir
from deephyper.nas.train_utils import selectMetric
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in range(4)])
HERE = os.path.dirname(os.path.abspath(__file__))
fname = HERE.split("/")[-1]
output_dir = "logs"
create_dir(output_dir)
X_test, y_test = load_data_npz_gz(test=True)
dependencies = {
"r2":selectMetric("r2")
}
model = tf.keras.models.load_model(f"best_model_{fname}.h5", custom_objects=dependencies)
model.compile(
metrics=["mse", "mae", selectMetric("r2")]
)
score = model.evaluate(X_test, y_test)
score_names = ["loss", "mse", "mae", "r2"]
print("score:")
output = " ".join([f"{sn}:{sv:.3f}" for sn,sv in zip(score_names, score)])
print(output) | [((14, 0, 14, 22), 'deephyper.nas.run.util.create_dir', 'create_dir', ({(14, 11, 14, 21): 'output_dir'}, {}), '(output_dir)', False, 'from deephyper.nas.run.util import create_dir\n'), ((16, 17, 16, 44), 'nas_big_data.combo.load_data.load_data_npz_gz', 'load_data_npz_gz', (), '', False, 'from nas_big_data.combo.load_data import load_data_npz_gz\n'), ((22, 8, 22, 89), 'tensorflow.keras.models.load_model', 'tf.keras.models.load_model', (), '', True, 'import tensorflow as tf\n'), ((11, 23, 11, 48), 'os.path.abspath', 'os.path.abspath', ({(11, 39, 11, 47): '__file__'}, {}), '(__file__)', False, 'import os\n'), ((19, 10, 19, 28), 'deephyper.nas.train_utils.selectMetric', 'selectMetric', ({(19, 23, 19, 27): '"""r2"""'}, {}), "('r2')", False, 'from deephyper.nas.train_utils import selectMetric\n'), ((24, 27, 24, 45), 'deephyper.nas.train_utils.selectMetric', 'selectMetric', ({(24, 40, 24, 44): '"""r2"""'}, {}), "('r2')", False, 'from deephyper.nas.train_utils import selectMetric\n')] |
duncan-r/SHIP | ship/utils/utilfunctions.py | 2c4c22c77f9c18ea545d3bce70a36aebbd18256a | """
Summary:
Utility Functions that could be helpful in any part of the API.
All functions that are likely to be called across a number of classes
and Functions in the API should be grouped here for convenience.
Author:
Duncan Runnacles
Created:
01 Apr 2016
Copyright:
Duncan Runnacles 2016
TODO: This module, like a lot of other probably, needs reviewing for how
'Pythonic' t is. There are a lot of places where generators,
comprehensions, maps, etc should be used to speed things up and make
them a bit clearer.
More importantly there are a lot of places using '==' compare that
should be using 'in' etc. This could cause bugs and must be fixed
soon.
Updates:
"""
from __future__ import unicode_literals
import re
import os
import operator
import logging
logger = logging.getLogger(__name__)
"""logging references with a __name__ set to this module."""
# def resolveSeDecorator(se_vals, path):
# """Decorator function for replacing Scen/Evt placholders.
#
# Checks fro scenario and event placeholders in the return value of a
# function and replaces them with corresponding values if found.
#
# Args:
# se_vals(dict): standard scenario/event dictionary in the format:
# {'scenario': {
# """
# def seDecorator(func):
# def seWrapper(*args, **kwargs):
# result = func(*args, **kwargs)
#
# if '~' in result:
# # Check for scenarion stuff
# for key, val in self.se_vals['scenario'].items():
# temp = '~' + key + '~'
# if temp in result:
# result = result.replace(temp, val)
# # Check for event stuff
# for key, val in self.se_vals['event'].items():
# temp = '~' + key + '~'
# if temp in result:
# result = result.replace(temp, val)
# return result
# return seWrapper
# return seDecorator
def formatFloat(value, no_of_dps, ignore_empty_str=True):
"""Format a float as a string to given number of decimal places.
Args:
value(float): the value to format.
no_of_dps(int): number of decimal places to format to.
ignore_empty_str(True): return a stripped blank string if set to True.
Return:
str - the formatted float.
Raises:
ValueError - if value param is not type float.
"""
if ignore_empty_str and not isNumeric(value) and str(value).strip() == '':
return str(value).strip()
if not isNumeric(value):
raise ValueError
decimal_format = '%0.' + str(no_of_dps) + 'f'
value = decimal_format % float(value)
return value
def checkFileType(file_path, ext):
"""Checks a file to see that it has the right extension.
Args:
file_path (str): The file path to check.
ext (List): list containing the extension types to match the file
against.
Returns:
True if the extension matches the ext variable given or False if not.
"""
file_ext = os.path.splitext(file_path)[1]
logger.info('File ext = ' + file_ext)
for e in ext:
if e == file_ext:
return True
else:
return False
def isNumeric(s):
"""Tests if string is a number or not.
Simply tries to convert it and catches the error if launched.
Args:
s (str): string to test number compatibility.
Returns:
Bool - True if number. False if not.
"""
try:
float(s)
return True
except (ValueError, TypeError):
return False
def encodeStr(value):
try:
value = unicode(value, "utf-8")
return value
except (UnicodeDecodeError, NameError, TypeError):
return value
def isString(value):
"""Tests a given value to see if it is an instance of basestring or not.
Note:
This function should be used whenever testing this as it accounts for
both Python 2.7+ and 3.2+ variations of string.
Args:
value: the variable to test.
Returns:
Bool - True if value is a unicode str (basestring type)
"""
try:
return isinstance(value, basestring)
except NameError:
return isinstance(value, str)
# if not isinstance(value, basestring):
# return False
#
# return True
def isList(value):
"""Test a given value to see if it is a list or not.
Args:
value: the variable to test for list type.
Returns:
True if value is of type list; False otherwise.
"""
if not isinstance(value, list):
return False
return True
def arrayToString(self, str_array):
"""Convert a list to a String
Creates one string by adding each part of the array to one string using
', '.join()
Args:
str_array (List): to convert into single string.
Returns:
str - representaion of the array joined together.
Raises:
ValueError: if not contents of list are instances of basestring.
"""
if not isinstance(str_array[0], basestring):
raise ValueError('Array values are not strings')
out_string = ''
out_string = ', '.join(str_array)
return out_string
def findSubstringInList(substr, the_list):
"""Returns a list containing the indices that a substring was found at.
Uses a generator to quickly find all indices that str appears in.
Args:
substr (str): the sub string to search for.
the_list (List): a list containing the strings to search.
Returns:
tuple - containing:
* a list with the indices that the substring was found in
(this list can be empty if no matches were found).
* an integer containing the number of elements it was found in.
"""
indices = [i for i, s in enumerate(the_list) if substr in s]
return indices, len(indices)
def findMax(val1, val2):
"""Returns tuple containing min, max of two values
Args:
val1: first integer or float.
val2: second integer or float.
Returns:
tuple - containing:
* lower value
* higher value
* False if not same or True if the same.
"""
if val1 == val2:
return val1, val2, True
elif val1 > val2:
return val2, val1, False
else:
return val1, val2, False
def fileExtensionWithoutPeriod(filepath, name_only=False):
"""Extracts the extension without '.' from filepath.
The extension will always be converted to lower case before returning.
Args:
filepath (str): A full filepath if name_only=False. Otherwise a file
name with extension if name_only=True.
name_only (bool): True if filepath is only filename.extension.
"""
if name_only:
file, ext = os.path.splitext(filepath)
else:
path, filename = os.path.split(filepath)
file, ext = os.path.splitext(filename)
ext = ext[1:]
return ext.lower()
def findWholeWord(w):
"""Find a whole word amoungst a string."""
return re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE).search
def convertRunOptionsToSEDict(options):
"""Converts tuflow command line options to scenario/event dict.
Tuflow uses command line option (e.g. -s1 blah -e1 blah) to set scenario
values which can either be provided on the command line or through the
FMP run form. The TuflowLoader can use these arguments but requires a
slightly different setup.
This function converts the command line string into the scenarion and
event dictionary expected by the TuflowLoader.
Args:
options(str): command line options.
Return:
dict - {'scenario': {'s1': blah}, 'event': {'e1': blah}}
Raises:
AttributeError: if both -s and -s1 or -e and -e1 occurr in the options
string. -x and -x1 are treated as the same variable by tuflow and
one of the values would be ignored.
"""
if ' -s ' in options and ' -s1 ' in options:
raise AttributeError
if ' -e ' in options and ' -e2 ' in options:
raise AttributeError
outvals = {'scenario': {}, 'event': {}}
vals = options.split(" ")
for i in range(len(vals)):
if vals[i].startswith('-s'):
outvals['scenario'][vals[i][1:]] = vals[i + 1]
elif vals[i].startswith('-e'):
outvals['event'][vals[i][1:]] = vals[i + 1]
return outvals
def getSEResolvedFilename(filename, se_vals):
"""Replace a tuflow placeholder filename with the scenario/event values.
Replaces all of the placholder values (e.g. ~s1~_~e1~) in a tuflow
filename with the corresponding values provided in the run options string.
If the run options flags are not found in the filename their values will
be appended to the end of the string.
The setup of the returned filename is always the same:
- First replace all placeholders with corresponding flag values.
- s1 == s and e1 == e.
- Append additional e values to end with '_' before first and '+' before others.
- Append additional s values to end with '_' before first and '+' before others.
Args:
filename(str): the filename to update.
se_vals(str): the run options string containing the 's' and
'e' flags and their corresponding values.
Return:
str - the updated filename.
"""
if not 'scenario' in se_vals.keys():
se_vals['scenario'] = {}
if not 'event' in se_vals.keys():
se_vals['event'] = {}
# Format the key value pairs into a list and combine the scenario and
# event list together and sort them into e, e1, e2, s, s1, s2 order.
scen_keys = ['-' + a for a in se_vals['scenario'].keys()]
scen_vals = se_vals['scenario'].values()
event_keys = ['-' + a for a in se_vals['event'].keys()]
event_vals = se_vals['event'].values()
scen = [list(a) for a in zip(scen_keys, scen_vals)]
event = [list(a) for a in zip(event_keys, event_vals)]
se_vals = scen + event
vals = sorted(se_vals, key=operator.itemgetter(0))
# Build a new filename by replacing or adding the flag values
outname = filename
in_e = False
for v in vals:
placeholder = ''.join(['~', v[0][1:], '~'])
if placeholder in filename:
outname = outname.replace(placeholder, v[1])
elif v[0] == '-e1' and '~e~' in filename and not '-e' in se_vals:
outname = outname.replace('~e~', v[1])
elif v[0] == '-s1' and '~s~' in filename and not '-s' in se_vals:
outname = outname.replace('~s~', v[1])
# DEBUG - CHECK THIS IS TRUE!
elif v[0] == '-e' and '~e1~' in filename:
outname = outname.replace('~e1~', v[1])
elif v[0] == '-s' and '~s1~' in filename:
outname = outname.replace('~s1~', v[1])
else:
if v[0].startswith('-e'):
if not in_e:
prefix = '_'
else:
prefix = '+'
in_e = True
elif v[0].startswith('-s'):
if in_e:
prefix = '_'
else:
prefix = '+'
in_e = False
outname += prefix + v[1]
return outname
def enum(*sequential, **named):
"""Creates a new enum using the values handed to it.
Taken from Alec Thomas on StackOverflow:
http://stackoverflow.com/questions/36932/how-can-i-represent-an-enum-in-python
Examples:
Can be created and accessed using:
>>> Numbers = enum('ZERO', 'ONE', 'TWO')
>>> Numbers.ZERO
0
>>> Numbers.ONE
1
Or reverse the process o get the name from the value:
>>> Numbers.reverse_mapping['three']
'THREE'
"""
enums = dict(zip(sequential, range(len(sequential))), **named)
reverse = dict((value, key) for key, value in enums.items())
enums['reverse_mapping'] = reverse
return type(str('Enum'), (), enums)
class FileQueue(object):
"""Queueing class for storing data to go into the database
"""
def __init__(self):
self.items = []
def isEmpty(self):
"""Returns True if list is empty
"""
return self.items == []
def enqueue(self, item):
"""Add an item to the queue
"""
self.items.insert(0, item)
def dequeue(self):
"""Pop an item from the front of the queue.
"""
return self.items.pop()
def size(self):
"""Get the size of the queue
"""
return len(self.items)
class LoadStack(object):
"""Stack class for loading logic."""
def __init__(self, max_size=-1):
self.items = []
self.max_size = max_size
def isEmpty(self):
"""Return True if stack is empty."""
return self.items == []
def add(self, item):
"""Add an item to the stack.
Args:
item: the item to add to the stack.
Raises:
IndexError: if max_size has been set and adding another item would
make the stack bigger than max size.
"""
if not self.max_size == -1:
if len(self.items) + 1 > self.max_size:
raise IndexError
self.items.append(item)
def pop(self):
"""Get an item From the stack.
Return:
item from the top of the stack.
Raises:
IndexError: if the stack is empty.
"""
if len(self.items) == 0:
raise IndexError
return self.items.pop()
def peek(self):
"""See what the next item on the stack is, but don't remove it.
Return:
item from the top of the stack.
Raises:
IndexError: if the stack is empty.
"""
if len(self.items) == 0:
raise IndexError
return self.items[-1]
def size(self):
"""Return the number of items in the stack."""
return len(self.items)
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goneri/ansible-navigator | src/ansible_navigator/ui_framework/content_defs.py | 59c5c4e9758404bcf363face09cf46c325b01ad3 | """Definitions of UI content objects."""
from dataclasses import asdict
from dataclasses import dataclass
from enum import Enum
from typing import Dict
from typing import Generic
from typing import TypeVar
from ..utils.compatibility import TypeAlias
from ..utils.serialize import SerializationFormat
class ContentView(Enum):
"""The content view."""
FULL = "full"
NORMAL = "normal"
T = TypeVar("T") # pylint:disable=invalid-name # https://github.com/PyCQA/pylint/pull/5221
DictType: TypeAlias = Dict[str, T]
@dataclass
class ContentBase(Generic[T]):
r"""The base class for all content dataclasses presented in the UI.
It should be noted, that while the return type is defined as ``T``
for the serialization functions below, mypy will not catch in incorrect
definition of ``T`` at this time. This is because of how ``asdict()``
is typed:
@overload
def asdict(obj: Any) -> dict[str, Any]: ...
@overload
def asdict(obj: Any, \*, dict_factory: Callable[[list[tuple[str, Any]]], _T]) -> _T: ...
Which result in mypy believing the outcome of asdict is dict[str, Any] and letting it silently
pass through an incorrect ``T``. ``Mypy`` identifies this as a known issue:
https://mypy.readthedocs.io/en/stable/additional_features.html#caveats-known-issues
"""
def asdict(
self,
content_view: ContentView,
serialization_format: SerializationFormat,
) -> DictType:
"""Convert thy self into a dictionary.
:param content_view: The content view
:param serialization_format: The serialization format
:returns: A dictionary created from self
"""
converter_map = {
(ContentView.FULL, SerializationFormat.JSON): self.serialize_json_full,
(ContentView.FULL, SerializationFormat.YAML): self.serialize_yaml_full,
(ContentView.NORMAL, SerializationFormat.JSON): self.serialize_json_normal,
(ContentView.NORMAL, SerializationFormat.YAML): self.serialize_yaml_normal,
}
try:
dump_self_as_dict = converter_map[content_view, serialization_format]
except KeyError:
return asdict(self)
else:
return dump_self_as_dict()
def serialize_json_full(self) -> DictType:
"""Provide dictionary for ``JSON`` with all attributes.
:returns: A dictionary created from self
"""
return asdict(self)
def serialize_json_normal(self) -> DictType:
"""Provide dictionary for ``JSON`` with curated attributes.
:returns: A dictionary created from self
"""
return asdict(self)
def serialize_yaml_full(self) -> DictType:
"""Provide dictionary for ``YAML`` with all attributes.
:returns: A dictionary created from self
"""
return asdict(self)
def serialize_yaml_normal(self) -> DictType:
"""Provide dictionary for ``JSON`` with curated attributes.
:returns: A dictionary created from self
"""
return asdict(self)
def get(self, attribute: str):
"""Allow this dataclass to be treated like a dictionary.
This is a work around until the UI fully supports dataclasses
at which time this can be removed.
Default is intentionally not implemented as a safeguard to enure
this is not more work than necessary to remove in the future
and will only return attributes in existence.
:param attribute: The attribute to get
:returns: The gotten attribute
"""
return getattr(self, attribute)
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SWuchterl/cmssw | FWCore/MessageService/test/u28_cerr_cfg.py | 769b4a7ef81796579af7d626da6039dfa0347b8e | # u28_cerr_cfg.py:
#
# Non-regression test configuration file for MessageLogger service:
# distinct threshold level for linked destination, where
#
import FWCore.ParameterSet.Config as cms
process = cms.Process("TEST")
import FWCore.Framework.test.cmsExceptionsFatal_cff
process.options = FWCore.Framework.test.cmsExceptionsFatal_cff.options
process.load("FWCore.MessageService.test.Services_cff")
process.MessageLogger = cms.Service("MessageLogger",
categories = cms.untracked.vstring('preEventProcessing'),
destinations = cms.untracked.vstring('cerr'),
statistics = cms.untracked.vstring('cerr_stats'),
cerr_stats = cms.untracked.PSet(
threshold = cms.untracked.string('WARNING'),
output = cms.untracked.string('cerr')
),
u28_output = cms.untracked.PSet(
threshold = cms.untracked.string('INFO'),
noTimeStamps = cms.untracked.bool(True),
preEventProcessing = cms.untracked.PSet(
limit = cms.untracked.int32(0)
)
)
)
process.maxEvents = cms.untracked.PSet(
input = cms.untracked.int32(3)
)
process.source = cms.Source("EmptySource")
process.sendSomeMessages = cms.EDAnalyzer("UnitTestClient_A")
process.p = cms.Path(process.sendSomeMessages)
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Revibe-Music/core-services | content/browse/utils.py | 6b11cf16ad2c35d948f3a5c0e7a161e5b7cfc1b2 | """
Created:04 Mar. 2020
Author: Jordan Prechac
"""
from revibe._helpers import const
from administration.utils import retrieve_variable
from content.models import Song, Album, Artist
from content.serializers import v1 as cnt_ser_v1
# -----------------------------------------------------------------------------
# _DEFAULT_LIMIT = 50
# limit_variable = retrieve_variable()
# try:
# limit_variable = int(limit_variable)
# _DEFAULT_LIMIT = max(min(limit_variable, 100), 10)
# except ValueError as ve:
# print("Could not read browse section default limit variable")
# print(ve)
def _DEFAULT_LIMIT():
limit_variable = retrieve_variable("browse_section_default_limit", 50)
try:
limit_variable = int(limit_variable)
return max(min(limit_variable, 100), 10)
except ValueError as ve:
print("Could not read browse section default limit variable")
print(ve)
return 50
_name = "name"
_type = "type"
_results = "results"
_endpoint = "endpoint"
def _browse_song(annotation, limit=None, platform=const.REVIBE_STRING, **options):
limit = limit if limit else _DEFAULT_LIMIT()
songs = Song.display_objects.filter(platform=platform).annotate(count=annotation).order_by('-count')[:limit]
options[_results] = cnt_ser_v1.SongSerializer(songs, many=True).data
return options
def _browse_album(annotation, limit=None, **options):
limit = limit if limit else _DEFAULT_LIMIT()
albums = Album.display_objects.filter(platform=const.REVIBE_STRING).annotate(count=annotation).order_by('-count')[:limit]
options[_results] = cnt_ser_v1.AlbumSerializer(albums, many=True).data
return options
def _browse_artist(annotation, limit=None, **options):
limit = limit if limit else _DEFAULT_LIMIT()
artists = Artist.display_objects.filter(platform=const.REVIBE_STRING).annotate(count=annotation).order_by('-count')[:limit]
options[_results] = cnt_ser_v1.ArtistSerializer(artists, many=True).data
return options | [((23, 21, 23, 74), 'administration.utils.retrieve_variable', 'retrieve_variable', ({(23, 39, 23, 69): '"""browse_section_default_limit"""', (23, 71, 23, 73): '50'}, {}), "('browse_section_default_limit', 50)", False, 'from administration.utils import retrieve_variable\n'), ((42, 24, 42, 67), 'content.serializers.v1.SongSerializer', 'cnt_ser_v1.SongSerializer', (), '', True, 'from content.serializers import v1 as cnt_ser_v1\n'), ((49, 24, 49, 69), 'content.serializers.v1.AlbumSerializer', 'cnt_ser_v1.AlbumSerializer', (), '', True, 'from content.serializers import v1 as cnt_ser_v1\n'), ((56, 24, 56, 71), 'content.serializers.v1.ArtistSerializer', 'cnt_ser_v1.ArtistSerializer', (), '', True, 'from content.serializers import v1 as cnt_ser_v1\n'), ((41, 12, 41, 58), 'content.models.Song.display_objects.filter', 'Song.display_objects.filter', (), '', False, 'from content.models import Song, Album, Artist\n'), ((48, 13, 48, 71), 'content.models.Album.display_objects.filter', 'Album.display_objects.filter', (), '', False, 'from content.models import Song, Album, Artist\n'), ((55, 14, 55, 73), 'content.models.Artist.display_objects.filter', 'Artist.display_objects.filter', (), '', False, 'from content.models import Song, Album, Artist\n')] |
vasetrendafilov/ComputerVision | Segmentation/model.py | 5fcbe57fb1609ef44733aed0fab8c69d71fae21f | """
Authors: Elena Vasileva, Zoran Ivanovski
E-mail: [email protected], [email protected]
Course: Mashinski vid, FEEIT, Spring 2021
Date: 09.03.2021
Description: function library
model operations: construction, loading, saving
Python version: 3.6
"""
# python imports
from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate
from keras.models import Model, model_from_json
def load_model(model_path, weights_path):
"""
loads a pre-trained model configuration and calculated weights
:param model_path: path of the serialized model configuration file (.json) [string]
:param weights_path: path of the serialized model weights file (.h5) [string]
:return: model - keras model object
"""
# --- load model configuration ---
json_file = open(model_path, 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json) # load model architecture
model.load_weights(weights_path) # load weights
return model
def construct_model_unet_orig(input_shape):
"""
construct semantic segmentation model architecture (encoder-decoder)
:param input_shape: list of input dimensions (height, width, depth) [tuple]
:return: model - Keras model object
"""
input = Input(shape=input_shape)
# --- encoder ---
conv1 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(input)
conv11 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPool2D(pool_size=(2, 2))(conv11)
conv2 = Conv2D(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv22 = Conv2D(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPool2D(pool_size=(2, 2))(conv22)
conv3 = Conv2D(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv33 = Conv2D(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPool2D(pool_size=(2, 2))(conv33)
conv4 = Conv2D(filters=512, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv44 = Conv2D(filters=512, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
pool4 = MaxPool2D(pool_size=(2, 2))(conv44)
# --- decoder ---
conv5 = Conv2D(filters=1024, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv55 = Conv2D(filters=512, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
up1 = UpSampling2D(size=(2, 2))(conv55)
merge1 = Concatenate(axis=3)([conv44, up1])
deconv1 = Conv2DTranspose(filters=512, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge1)
deconv11 = Conv2DTranspose(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(deconv1)
up2 = UpSampling2D(size=(2, 2))(deconv11)
merge2 = Concatenate(axis=3)([conv33, up2])
deconv2 = Conv2DTranspose(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge2)
deconv22 = Conv2DTranspose(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(deconv2)
up3 = UpSampling2D(size=(2, 2))(deconv22)
merge3 = Concatenate(axis=3)([conv22, up3])
deconv3 = Conv2DTranspose(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge3)
deconv33 = Conv2DTranspose(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(deconv3)
up4 = UpSampling2D(size=(2, 2))(deconv33)
merge4 = Concatenate(axis=3)([conv11, up4])
deconv4 = Conv2DTranspose(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge4)
deconv44 = Conv2DTranspose(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(deconv4)
output = Conv2DTranspose(filters=input_shape[2], kernel_size=1, padding='same', activation='sigmoid')(deconv44)
model = Model(input=input, output=output)
return model
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Concatenate\n'), ((59, 12, 59, 113), 'keras.layers.Conv2D', 'Conv2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((60, 13, 60, 114), 'keras.layers.Conv2D', 'Conv2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((61, 12, 61, 39), 'keras.layers.MaxPool2D', 'MaxPool2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((65, 12, 65, 114), 'keras.layers.Conv2D', 'Conv2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((66, 13, 66, 114), 'keras.layers.Conv2D', 'Conv2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((68, 10, 68, 35), 'keras.layers.UpSampling2D', 'UpSampling2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((69, 13, 69, 32), 'keras.layers.Concatenate', 'Concatenate', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((70, 14, 70, 124), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((71, 15, 71, 125), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((73, 10, 73, 35), 'keras.layers.UpSampling2D', 'UpSampling2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((74, 13, 74, 32), 'keras.layers.Concatenate', 'Concatenate', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((75, 14, 75, 124), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((76, 15, 76, 125), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((78, 10, 78, 35), 'keras.layers.UpSampling2D', 'UpSampling2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((79, 13, 79, 32), 'keras.layers.Concatenate', 'Concatenate', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((80, 14, 80, 124), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((81, 15, 81, 124), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((83, 10, 83, 35), 'keras.layers.UpSampling2D', 'UpSampling2D', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((84, 13, 84, 32), 'keras.layers.Concatenate', 'Concatenate', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((85, 14, 85, 123), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((86, 15, 86, 124), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n'), ((88, 13, 88, 105), 'keras.layers.Conv2DTranspose', 'Conv2DTranspose', (), '', False, 'from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate\n')] |
Rog3rSm1th/PolyglotOfCode | Day24_Python/part1.py | a70f50b5c882139727cbdf75144a8346cb6c538b | #!/usr/bin/env python3
#-*- coding: utf-8 -*-
from itertools import combinations
def solve(packages, groups):
total = sum(packages)
result = 9999999999999999
# we should use `for i in range(1, len(packages) - 2)` but it would
# make the computation significantly slower
for i in range(1, 7):
for c in combinations(packages, i):
if sum(c) == total / groups:
quantum_entanglement = reduce(lambda a, b: a * b, list(c))
result = min(result, quantum_entanglement)
return result
packages = [int(num) for num in open('input.txt')]
print(solve(packages, 3)) | [((14, 17, 14, 42), 'itertools.combinations', 'combinations', ({(14, 30, 14, 38): 'packages', (14, 40, 14, 41): 'i'}, {}), '(packages, i)', False, 'from itertools import combinations\n')] |
atomicparade/photo-album | generate-album.py | 437bc18bb00da5ce27216d03b48b78d60a0ad3fd | import configparser
import math
import re
import urllib
from pathlib import Path
from PIL import Image
def get_images(image_directory, thumbnail_directory, thumbnail_size):
thumbnail_directory = Path(thumbnail_directory)
for file in [file for file in thumbnail_directory.glob('*')]:
file.unlink()
thumbnail_directory.mkdir(mode=0o755, exist_ok=True)
files = [file for file in Path(image_directory).glob('*')]
images = []
for file in files:
thumbnail_name = Path(thumbnail_directory, file.stem + '.jpg')
image = Image.open(file)
image.thumbnail(thumbnail_size)
top_left = (0, 0)
if image.width < thumbnail_size[0]:
top_left = (math.floor(abs(image.width - thumbnail_size[0]) / 2), top_left[1])
if image.height < thumbnail_size[1]:
top_left = (top_left[0], math.floor(abs(image.height - thumbnail_size[1]) / 2))
final_image = Image.new('RGB', thumbnail_size, (0, 0, 0))
final_image.paste(image, top_left)
final_image.save(thumbnail_name, 'jpeg')
if '_' in file.stem:
description = file.stem.split('_', maxsplit=1)[1]
else:
description = file.stem
images.append({
'path': str(file),
'thumbnail': thumbnail_name,
'description': description,
'stem': file.stem
})
def get_image_file_number(image):
if re.match(r'^(\d+)', image['stem']) is not None:
return int(re.split(r'^(\d+)', image['stem'])[1])
else:
return 999
images = sorted(images, key=get_image_file_number)
return images
def write_html(file, images, page_title, thumbnail_size):
file.write(f'''\
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>{page_title}</title>
<link rel="stylesheet" type="text/css" href="album.css">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
</head>
<body>
<h1>{page_title}</h1>
<div id="album">
\
''')
# write thumbnails
for image, idx in zip(images, range(1, len(images) + 1)):
thumbnail_path = urllib.parse.quote(str(image['thumbnail']).replace('\\', '/'))
file.write(f'''\
<p id="thumbnail-{idx}" class="thumbnail"><img src="{thumbnail_path}" alt="{image['description']}" width="{thumbnail_size[0]}" height="{thumbnail_size[1]}"></p>\
''')
file.write(f'''\
<div id="large-view">
<p id="instructions" class="image">Hover over an image</p>
''')
# write images
for image, idx in zip(images, range(1, len(images) + 1)):
image_path = urllib.parse.quote(str(image['path']).replace('\\', '/'))
file.write(f'''\
<p id="image-{idx}" class="image"><img src="{image_path}" alt="{image['description']}"><br>{image['description']}</p>
''')
file.write(f'''\
</div>
</div>
</body>
</html>
''')
def write_css(file, images):
file.write('''\
@media print {
body {
font-family: sans-serif;
}
.thumbnail {
display: none;
}
#instructions {
display: none;
}
.image img {
max-width: 100%;
margin-bottom: 1em;
}
}
@media
screen and (max-width: 768px),
/* Tablets and smartphones */
screen and (hover: none)
{
body {
background: #333;
color: #eee;
font-family: sans-serif;
margin: 1em;
padding: 0;
}
h1 {
margin-top: 0;
}
.thumbnail {
display: none;
}
#instructions {
display: none;
}
.image:nth-child(2) img {
margin-top: 0;
}
.image img {
max-width: calc(100vw - 3em);
}
}
@media
screen and (min-width: 769px) and (hover: hover),
/* IE10 and IE11 (they don't support (hover: hover) */
screen and (min-width: 769px) and (-ms-high-contrast: none),
screen and (min-width: 769px) and (-ms-high-contrast: active)
{
body {
background: #333;
color: #eee;
font-family: sans-serif;
margin: 2em 60% 2em 4em;
padding: 0;
}
.album {
display: flex;
flex-direction: row;
flex-wrap: wrap;
}
.thumbnail {
display: inline-block;;
margin: 0 .5em .2em 0;
}
.image {
background: #333;
display: none;
position: fixed;
top: 2em;
left: 40%;
text-align: center;
height: 90vh;
width: calc(60% - 4em);
}
.image img {
display: block;
max-height: 92%;
max-width: 100%;
margin: 0 auto;
}
#instructions {
display: block;
top: 4em;
}
''')
if len(images) > 0:
for idx in range(1, len(images) + 1):
file.write(f'''\
#thumbnail-{idx}:hover ~ #large-view #image-{idx}\
''')
if idx < len(images):
file.write('''\
,
''')
file.write('''\
{
display: block;
}
''')
file.write('''\
}
''')
def main():
config = configparser.ConfigParser()
config.read('./config')
image_directory = config['settings']['image_directory']
output_css = config['settings']['output_css']
output_html = config['settings']['output_html']
page_title = config['settings']['page_title']
thumbnail_directory = config['settings']['thumbnail_directory']
thumbnail_width = int(config['settings']['thumbnail_width'])
thumbnail_height = int(config['settings']['thumbnail_height'])
thumbnail_size = (thumbnail_width, thumbnail_height)
out_html = open(output_html, 'w')
out_css = open(output_css, 'w')
images = get_images(image_directory, thumbnail_directory, thumbnail_size)
write_html(out_html, images, page_title, thumbnail_size)
write_css(out_css, images)
out_html.close()
out_css.close()
if __name__ == '__main__':
main()
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LSSTDESC/sims_TruthCatalog | tests/test_sne_truth.py | 348f5d231997eed387aaa6e3fd4218c126e14cdb | """
Unit tests for SNIa truth catalog code.
"""
import os
import unittest
import sqlite3
import numpy as np
import pandas as pd
from desc.sims_truthcatalog import SNeTruthWriter, SNSynthPhotFactory
class SNSynthPhotFactoryTestCase(unittest.TestCase):
"""
Test case class for SNIa synthetic photometry factory class.
"""
def test_SNSythPhotFactory(self):
"""
Test some flux calculations using the underlying SNObject
and SyntheticPhotometry classes.
"""
sp_factory = SNSynthPhotFactory(z=0.6322702169418335,
t0=61719.9950436545,
x0=4.2832710977804034e-06,
x1=-1.207738485943195,
c=-0.0069750402968899936,
snra=55.26407314527358,
sndec=-40.81575605788344)
mjds = (61689.150791, 61697.354470, 61712.258685)
bands = ('z', 'i', 'r')
fluxes = (2.6401569864737633, 71.18561504923377, 1048.0327802379868)
for mjd, band, flux in zip(mjds, bands, fluxes):
sp = sp_factory.create(mjd)
self.assertAlmostEqual(sp.calcFlux(band), flux)
class SNeTruthWriterTestCase(unittest.TestCase):
"""
Test case class for SNIa truth catalog generation class.
"""
def setUp(self):
self.outfile = 'test_sne_truth_cat.db'
self.data_dir = os.path.join(os.environ['SIMS_TRUTHCATALOG_DIR'],
'data')
sn_db_file = os.path.join(self.data_dir,
'sne_cosmoDC2_v1.1.4_MS_DDF_small.db')
self.sne_truth_writer = SNeTruthWriter(self.outfile, sn_db_file)
def tearDown(self):
if os.path.isfile(self.outfile):
os.remove(self.outfile)
def test_truth_summary(self):
"""Test that the truth_summary columns are filled out as expected."""
self.sne_truth_writer.write()
with sqlite3.connect(self.outfile) as conn:
df = pd.read_sql('select * from truth_summary', conn)
zeros = np.zeros(len(df))
ones = np.ones(len(df))
np.testing.assert_equal(df['is_variable'], ones)
np.testing.assert_equal(df['is_pointsource'], ones)
for band in 'ugrizy':
flux_col = f'flux_{band}'
np.testing.assert_equal(df[flux_col], zeros)
flux_col += '_noMW'
np.testing.assert_equal(df[flux_col], zeros)
def test_auxiliary_truth(self):
"""
Test that the columns from the sne_params table are transcribed
correctly.
"""
self.sne_truth_writer.write_auxiliary_truth()
with sqlite3.connect(self.outfile) as conn:
df = pd.read_sql('select * from sn_auxiliary_info', conn)
np.testing.assert_equal(self.sne_truth_writer.sne_df['snid_in'],
df['id'].to_numpy())
np.testing.assert_equal(self.sne_truth_writer.sne_df['galaxy_id'],
df['host_galaxy'].to_numpy())
np.testing.assert_equal(self.sne_truth_writer.sne_df['snra_in'],
df['ra'].to_numpy())
np.testing.assert_equal(self.sne_truth_writer.sne_df['t0_in'],
df['t0'].to_numpy())
np.testing.assert_equal(self.sne_truth_writer.sne_df['z_in'],
df['redshift'].to_numpy())
def test_variability_truth(self):
"""
Test some expected values for a SNIa in the test SNe catalog
using a small opsim db table.
"""
opsim_db_file = os.path.join(self.data_dir,
'minion_1016_desc_dithered_v4_small.db')
self.sne_truth_writer.write_variability_truth(opsim_db_file,
max_rows=60)
with sqlite3.connect(self.outfile) as conn:
df = pd.read_sql('select * from sn_variability_truth', conn)
my_object = 'MS_10195_1375'
self.assertIn(my_object, df['id'].to_list())
my_df = df.query(f'id == "{my_object}"')
for visit in (1425850, 1433860, 1495410):
self.assertIn(visit, my_df['obsHistID'].to_list())
if __name__ == '__main__':
unittest.main()
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gikoluo/djaodjin-saas | testsite/management/commands/load_test_transactions.py | badd7894ac327191008a1b3a0ebd0d07b55908c3 | # Copyright (c) 2018, DjaoDjin inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import datetime, logging, random
from django.conf import settings
from django.core.management.base import BaseCommand
from django.db.utils import IntegrityError
from django.template.defaultfilters import slugify
from django.utils.timezone import utc
from saas.backends.razorpay_processor import RazorpayBackend
from saas.models import Plan, Transaction, get_broker
from saas.utils import datetime_or_now
from saas.settings import PROCESSOR_ID
LOGGER = logging.getLogger(__name__)
class Command(BaseCommand):
"""
Load the database with random transactions (testing purposes).
"""
USE_OF_SERVICE = 0
PAY_BALANCE = 1
REDEEM = 2
REFUND = 3
CHARGEBACK = 4
WRITEOFF = 5
FIRST_NAMES = (
'Anthony',
'Alexander',
'Alexis',
'Alicia',
'Ashley',
'Benjamin',
'Bruce',
'Chloe',
'Christopher',
'Daniel',
'David',
'Edward',
'Emily',
'Emma',
'Ethan',
'Grace',
'Isabella',
'Jacob',
'James',
'Jayden',
'Jennifer',
'John',
'Julia',
'Lily',
'Lucie',
'Luis',
'Matthew',
'Michael',
'Olivia',
'Ryan',
'Samantha',
'Samuel',
'Scott',
'Sophia',
'Williom',
)
LAST_NAMES = (
'Smith',
'Johnson',
'Williams',
'Jones',
'Brown',
'Davis',
'Miller',
'Wilson',
'Moore',
'Taylor',
'Anderson',
'Thomas',
'Jackson',
'White',
'Harris',
'Martin',
'Thompson',
'Garcia',
'Martinez',
'Robinson',
'Clark',
'Rogriguez',
'Lewis',
'Lee',
'Walker',
'Hall',
'Allen',
'Young',
'Hernandez',
'King',
'Wright',
'Lopez',
'Hill',
'Green',
'Baker',
'Gonzalez',
'Nelson',
'Mitchell',
'Perez',
'Roberts',
'Turner',
'Philips',
'Campbell',
'Parker',
'Collins',
'Stewart',
'Sanchez',
'Morris',
'Rogers',
'Reed',
'Cook',
'Bell',
'Cooper',
'Richardson',
'Cox',
'Ward',
'Peterson',
)
def add_arguments(self, parser):
parser.add_argument('--provider',
action='store', dest='provider',
default=settings.SAAS['BROKER']['GET_INSTANCE'],
help='create sample subscribers on this provider')
def handle(self, *args, **options):
#pylint: disable=too-many-locals,too-many-statements
from saas.managers.metrics import month_periods # avoid import loop
from saas.models import (Charge, ChargeItem, Organization, Plan,
Subscription)
RazorpayBackend.bypass_api = True
now = datetime.datetime.utcnow().replace(tzinfo=utc)
from_date = now
from_date = datetime.datetime(
year=from_date.year, month=from_date.month, day=1)
if args:
from_date = datetime.datetime.strptime(
args[0], '%Y-%m-%d')
# Create a set of 3 plans
broker = get_broker()
Plan.objects.get_or_create(
slug='basic',
defaults={
'title': "Basic",
'description': "Basic Plan",
'period_amount': 24900,
'broker_fee_percent': 0,
'period_type': 4,
'advance_discount': 1000,
'organization': broker,
'is_active': True
})
Plan.objects.get_or_create(
slug='medium',
defaults={
'title': "Medium",
'description': "Medium Plan",
'period_amount': 24900,
'broker_fee_percent': 0,
'period_type': 4,
'organization': broker,
'is_active': True
})
Plan.objects.get_or_create(
slug='premium',
defaults={
'title': "Premium",
'description': "Premium Plan",
'period_amount': 18900,
'broker_fee_percent': 0,
'period_type': 4,
'advance_discount': 81,
'organization': broker,
'is_active': True
})
# Create Income transactions that represents a growing bussiness.
provider = Organization.objects.get(slug=options['provider'])
processor = Organization.objects.get(pk=PROCESSOR_ID)
for end_period in month_periods(from_date=from_date):
nb_new_customers = random.randint(0, 9)
for _ in range(nb_new_customers):
queryset = Plan.objects.filter(
organization=provider, period_amount__gt=0)
plan = queryset[random.randint(0, queryset.count() - 1)]
created = False
trials = 0
while not created:
try:
first_name = self.FIRST_NAMES[random.randint(
0, len(self.FIRST_NAMES)-1)]
last_name = self.LAST_NAMES[random.randint(
0, len(self.LAST_NAMES)-1)]
full_name = '%s %s' % (first_name, last_name)
slug = slugify('demo%d' % random.randint(1, 1000))
customer, created = Organization.objects.get_or_create(
slug=slug, full_name=full_name)
#pylint: disable=catching-non-exception
except IntegrityError:
trials = trials + 1
if trials > 10:
raise RuntimeError(
'impossible to create a new customer after 10 trials.')
Organization.objects.filter(pk=customer.id).update(
created_at=end_period)
subscription = Subscription.objects.create(
organization=customer, plan=plan,
ends_at=now + datetime.timedelta(days=31))
Subscription.objects.filter(
pk=subscription.id).update(created_at=end_period)
# Insert some churn in %
churn_rate = 2
all_subscriptions = Subscription.objects.filter(
plan__organization=provider)
nb_churn_customers = (all_subscriptions.count()
* churn_rate // 100)
subscriptions = random.sample(list(all_subscriptions),
all_subscriptions.count() - nb_churn_customers)
for subscription in subscriptions:
nb_periods = random.randint(1, 6)
transaction_item = Transaction.objects.new_subscription_order(
subscription, nb_natural_periods=nb_periods,
created_at=end_period)
if transaction_item.dest_amount < 50:
continue
transaction_item.orig_amount = transaction_item.dest_amount
transaction_item.orig_unit = transaction_item.dest_unit
transaction_item.save()
charge = Charge.objects.create(
created_at=transaction_item.created_at,
amount=transaction_item.dest_amount,
customer=subscription.organization,
description='Charge for %d periods' % nb_periods,
last4=1241,
exp_date=datetime_or_now(),
processor=processor,
processor_key=str(transaction_item.pk),
# XXX We can't do that yet because of
# ``PROCESSOR_BACKEND.charge_distribution(self)``
# unit=transaction_item.dest_unit,
state=Charge.CREATED)
charge.created_at = transaction_item.created_at
charge.save()
ChargeItem.objects.create(
invoiced=transaction_item, charge=charge)
charge.payment_successful()
churned = all_subscriptions.exclude(
pk__in=[subscription.pk for subscription in subscriptions])
for subscription in churned:
subscription.ends_at = end_period
subscription.save()
self.stdout.write("%d new and %d churned customers at %s" % (
nb_new_customers, nb_churn_customers, end_period))
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magic282/SEASS | seq2seq_pt/s2s/xutils.py | b780bf45b47d15145a148e5992bcd157c119d338 | import sys
import struct
def save_sf_model(model):
name_dicts = {'encoder.word_lut.weight': 'SrcWordEmbed_Embed_W',
'encoder.forward_gru.linear_input.weight': 'EncGRUL2R_GRU_W',
'encoder.forward_gru.linear_input.bias': 'EncGRUL2R_GRU_B',
'encoder.forward_gru.linear_hidden.weight': 'EncGRUL2R_GRU_U',
'encoder.backward_gru.linear_input.weight': 'EncGRUR2L_GRU_W',
'encoder.backward_gru.linear_input.bias': 'EncGRUR2L_GRU_B',
'encoder.backward_gru.linear_hidden.weight': 'EncGRUR2L_GRU_U',
'decoder.word_lut.weight': 'TrgWordEmbed_Embed_W',
'decoder.rnn.layers.0.linear_input.weight': 'DecGRU_GRU_W',
'decoder.rnn.layers.0.linear_input.bias': 'DecGRU_GRU_B',
'decoder.rnn.layers.0.linear_hidden.weight': 'DecGRU_GRU_U',
'decoder.attn.linear_pre.weight': 'Alignment_ConcatAtt_W',
'decoder.attn.linear_pre.bias': 'Alignment_ConcatAtt_B',
'decoder.attn.linear_q.weight': 'Alignment_ConcatAtt_U',
'decoder.attn.linear_v.weight': 'Alignment_ConcatAtt_v',
'decoder.readout.weight': 'Readout_Linear_W',
'decoder.readout.bias': 'Readout_Linear_b',
'decIniter.initer.weight': 'DecInitial_Linear_W',
'decIniter.initer.bias': 'DecInitial_Linear_b',
'generator.0.weight': 'Scoring_Linear_W',
'generator.0.bias': 'Scoring_Linear_b', }
nParams = sum([p.nelement() for p in model.parameters()])
# logger.info('* number of parameters: %d' % nParams)
b_count = 0
of = open('model', 'wb')
for name, param in model.named_parameters():
# logger.info('[{0}] [{1}] [{2}]'.format(name, param.size(), param.nelement()))
SF_name = name_dicts[name]
# print(SF_name)
byte_name = bytes(SF_name, 'utf-16-le')
name_size = len(byte_name)
byte_name_size = name_size.to_bytes(4, sys.byteorder)
of.write(byte_name_size)
of.write(byte_name)
b_count += len(byte_name_size)
b_count += len(byte_name)
d = param.data.cpu()
if param.dim() == 1:
d = d.unsqueeze(0)
elif not SF_name.endswith('Embed_W'):
d = d.transpose(0, 1).contiguous()
for dim in d.size():
dim_byte = dim.to_bytes(4, sys.byteorder)
of.write(dim_byte)
b_count += len(dim_byte)
datas = d.view(-1).numpy().tolist()
float_array = struct.pack('f' * len(datas), *datas)
b_count += len(float_array)
of.write(float_array)
of.close()
# print('Total write {0} bytes'.format(b_count))
| [] |
Novartis/Project-Mona-Lisa | pml-services/pml_storage.py | f8fcef5b434470e2a17e97fceaef46615eda1b31 | # Copyright 2017 Novartis Institutes for BioMedical Research Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
from __future__ import print_function
import boto3
from boto3.dynamodb.conditions import Key
from random import randint
import os
import base64
class PMLStorage:
""" Project Mona Lisa Storage class.
"""
def __init__(self, storage_name):
self.storage_name = storage_name
def get_bucket(self):
"""
Returns:
(obj): The boto3 AWS S3 bucket object.
"""
s3 = boto3.resource('s3', region_name='TODO')
return s3.Bucket(self.storage_name)
def get_item_from_storage(self, item_key):
""" Get method for a image data in ML-PRJ image storage.
Args:
bucket_name (str): name for the storage.
item_key (str): key or filename for the item in storage.
Returns:
item (obj)
"""
# get the image data in the S3 bucket
img_obj = self.get_bucket().Object(item_key)
return str(img_obj.get()['Body'].read())
def post_item_in_storage(self, key, body, type='png'):
""" Posting collected image data in storage.
Args:
key (str): The unique key.
body (obj): the bulk data to be stored.
type (str): file suffix. The default is 'png'.
Returns:
bool: True if successful, otherwise, an error will
be thrown.
"""
self.get_bucket().put_object(
Key=key+str('.')+type,
Body=body,
ServerSideEncryption='AES256',
ContentType='img/'+type,
)
return True
def download_imgs(self, load_fns, save_dir):
""" Downloads all files in <load_fns> from storage to
the directory <save_dir>.
Args:
load_fns (list(str)): A list of strings of the filenames
of the files to be downloaded.
save_dir (str): A string of the source directory to
save the files. Formatted as:
/full/path/to/dir ... without a '/' character at
the end of the <save_dir>.
Returns:
bool: True if successful, otherwise, an error will
be thrown.
"""
print('downloading images from s3 . . .')
bucket = self.get_bucket()
pre_existing_fns = os.listdir(save_dir)
count = 0
for filename in load_fns:
count += 1
print(count)
if filename in pre_existing_fns:
continue
bucket.download_file(filename, save_dir + '/' + filename)
return True
def get_all_filenames(self):
""" Gets all filenames in storage.
Returns:
list(str): A list of all of the filenames
in the bucket, as a list of strings.
"""
iterobjs = self.get_bucket().objects.all()
filenames = [obj.key for obj in iterobjs]
return filenames
def remove_items(self, filenames):
""" Removes, from storage, all files from <filenames>.
Args:
filenames list(str): List of filenames, where
each filename is a string, of the filename contained
in the bucket.
Returns:
bool: True if successful, otherwise an error is thrown.
"""
bucket = self.get_bucket()
fn_objects = [{'Key': fn} for fn in filenames]
bucket.delete_objects(
Delete={
'Objects': fn_objects
}
)
return True
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Nightleaf0512/PythonCryptoCurriencyPriceChecker | binan.py | 9531d4a6978d280b4ca759d7ba24d3edf77fe5b2 | from binance.client import Client
import PySimpleGUI as sg
api_key = "your_binance_apikey"
secret_key = "your_binance_secretkey"
client = Client(api_key=api_key, api_secret=secret_key)
# price
def get_price(coin):
return round(float(client.get_symbol_ticker(symbol=f"{coin}USDT")['price']), 5)
def column_layout_price(coin):
col =[[sg.Text(f"{get_price(coin)}", font=("Arial", 9, 'bold'), size=(10,1), pad=(15,10), key=coin)]]
return col
# 24h percentchange
def price_change_24h(coin):
return round(float(client.get_ticker(symbol=f"{coin}USDT")["priceChangePercent"]), 2)
def column_layout_change(coin):
if price_change_24h(coin) == 0:
return [[sg.Text(f"{price_change_24h(coin)}%", font=("Arial", 9, 'bold'), size=(7,1), pad=(40,10), text_color="black", key=f"{coin}change")]]
elif price_change_24h(coin) > 0:
return [[sg.Text(f"+{price_change_24h(coin)}%", font=("Arial", 9, 'bold'), size=(7,1), pad=(40,10), text_color="green", key=f"{coin}change")]]
return [[sg.Text(f"{price_change_24h(coin)}%", font=("Arial", 9, 'bold'), size=(7,1), pad=(40,10), text_color="red", key=f"{coin}change")]]
def update_24h_change(coin):
if price_change_24h(coin) == 0:
window[f"{coin}change"].update(f"+{price_change_24h(coin)}%", text_color="black")
elif price_change_24h(coin) > 0:
window[f"{coin}change"].update(f"+{price_change_24h(coin)}%", text_color="green")
elif price_change_24h(coin) < 0:
window[f"{coin}change"].update(f"{price_change_24h(coin)}%", text_color="red")
# GUI
sg.theme('DefaultNoMoreNagging')
# Tabs
def tabs(coin):
tab_layout = [[sg.Image("{}.png".format(coin), size=(50,50)),
sg.Text("Price", font=("Arial", 10, 'bold'), size=(7,1), pad=(40,40)), sg.Text("24h change", font=("Arial", 10, 'bold'), size=(10,1), pad=(10,40))],
[sg.Text(f"{coin}/USDT", font=("Arial", 9, 'bold')), sg.Column(column_layout_price(coin)), sg.Column(column_layout_change(coin))]]
return tab_layout
# Layout
layout = [[sg.Text("Crypto Currencies", font=("Arial", 10, 'bold'))],
[sg.TabGroup([[sg.Tab("BTC", tabs("BTC"), border_width="18"), sg.Tab("XRP", tabs("XRP"), border_width="18"), sg.Tab("DOGE", tabs("DOGE"), border_width="18")]])]]
window = sg.Window("NightLeaf Crypto", layout)
def coin_window(*coins):
for coin in coins:
globals()[f"{coin}_last_price"] = 1
while True:
event,values = window.read(timeout=600)
if event == sg.WIN_CLOSED:
break
for coin in coins:
update_24h_change(coin)
price = get_price(coin)
if price != globals()[f"{coin}_last_price"]:
if price > globals()[f"{coin}_last_price"]:
window[f"{coin}"].update(f"{price} 🠕", text_color="green")
elif price < globals()[f"{coin}_last_price"]:
window[f"{coin}"].update(f"{price} 🠗", text_color="red")
globals()[f"{coin}_last_price"] = price
a_list =["BTC", "XRP", "DOGE"]
coin_window(*a_list)
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nlkhagva/saleor | saleor/graphql/ushop/bulk_mutations.py | 0d75807d08ac49afcc904733724ac870e8359c10 | import graphene
from ...unurshop.ushop import models
from ..core.mutations import BaseBulkMutation, ModelBulkDeleteMutation
class UshopBulkDelete(ModelBulkDeleteMutation):
class Arguments:
ids = graphene.List(
graphene.ID, required=True, description="List of ushop IDs to delete."
)
class Meta:
description = "Deletes shops."
model = models.Shop
permissions = ("page.manage_pages",)
class UshopBulkPublish(BaseBulkMutation):
class Arguments:
ids = graphene.List(
graphene.ID, required=True, description="List of ushop IDs to (un)publish."
)
is_published = graphene.Boolean(
required=True, description="Determine if ushops will be published or not."
)
class Meta:
description = "Publish ushops."
model = models.Shop
permissions = ("page.manage_pages",)
@classmethod
def bulk_action(cls, queryset, is_published):
queryset.update(is_published=is_published)
| [((9, 14, 11, 9), 'graphene.List', 'graphene.List', (), '', False, 'import graphene\n'), ((21, 14, 23, 9), 'graphene.List', 'graphene.List', (), '', False, 'import graphene\n'), ((24, 23, 26, 9), 'graphene.Boolean', 'graphene.Boolean', (), '', False, 'import graphene\n')] |
alvinajacquelyn/COMP0016_2 | src/main/NLP/STRING_MATCH/scopus_ha_module_match.py | fd57706a992e1e47af7c802320890e93a15fc0c7 | import os, sys, re
import json
import pandas as pd
import pymongo
from main.LOADERS.publication_loader import PublicationLoader
from main.MONGODB_PUSHERS.mongodb_pusher import MongoDbPusher
from main.NLP.PREPROCESSING.preprocessor import Preprocessor
class ScopusStringMatch_HAmodule():
def __init__(self):
self.loader = PublicationLoader()
self.mongodb_pusher = MongoDbPusher()
self.preprocessor = Preprocessor()
def __progress(self, count, total, custom_text, suffix=''):
"""
Visualises progress for a process given a current count and a total count
"""
bar_len = 60
filled_len = int(round(bar_len * count / float(total)))
percents = round(100.0 * count / float(total), 1)
bar = '*' * filled_len + '-' * (bar_len - filled_len)
sys.stdout.write('[%s] %s%s %s %s\r' %(bar, percents, '%', custom_text, suffix))
sys.stdout.flush()
def __read_keywords(self, data: dict) -> None:
"""
Given a set of publications in a dictionary, performs pre-processing for all string type data fields.
Performs look-up on HA keyword occurences in a document.
Results are pushed to MongoDB (backed-up in JSON file - scopus_matches.json).
"""
resulting_data = {}
counter = 0
keywords = self.preprocessor.preprocess_keywords("main/HA_KEYWORDS/HA_Keywords.csv")
num_publications = len(data)
num_keywords = len(keywords)
for doi, publication in data.items():
# visualise the progress on a commandline
self.__progress(counter, num_publications, "processing scopus_matches.json")
counter += 1
description = self.preprocessor.tokenize(publication["Description"])
ha_occurences = {} # accumulator for HA Keywords found in a given document
for n in range(num_keywords):
ha_num = n + 1
ha = "HA " + str(ha_num) if ha_num < num_keywords else "Misc" # clean and process the string for documenting occurences
ha_occurences[ha] = {"Word_Found": []}
for keyword in keywords[n]:
if keyword in description:
ha_occurences[ha]["Word_Found"].append(keyword)
if len(ha_occurences[ha]["Word_Found"]) == 0:
ha_occurences.pop(ha, None) # clear out empty occurences
resulting_data[doi] = {"DOI": doi, "Related_HA": ha_occurences}
print()
self.mongodb_pusher.matched_scopus(resulting_data) # push the processed data to MongoDB
print()
# Record the same data locally, acts as a backup
with open('main/NLP/STRING_MATCH/HA_MODULE_RESULTS/scopus_matches_modules.json', 'w') as outfile:
json.dump(resulting_data, outfile)
def run(self):
"""
Controller method for self class
Loads modules from a pre-loaded pickle file
"""
data = self.loader.load_all()
self.__read_keywords(data) | [((14, 22, 14, 41), 'main.LOADERS.publication_loader.PublicationLoader', 'PublicationLoader', ({}, {}), '()', False, 'from main.LOADERS.publication_loader import PublicationLoader\n'), ((15, 30, 15, 45), 'main.MONGODB_PUSHERS.mongodb_pusher.MongoDbPusher', 'MongoDbPusher', ({}, {}), '()', False, 'from main.MONGODB_PUSHERS.mongodb_pusher import MongoDbPusher\n'), ((16, 28, 16, 42), 'main.NLP.PREPROCESSING.preprocessor.Preprocessor', 'Preprocessor', ({}, {}), '()', False, 'from main.NLP.PREPROCESSING.preprocessor import Preprocessor\n'), ((27, 8, 27, 88), 'sys.stdout.write', 'sys.stdout.write', ({(27, 25, 27, 87): "('[%s] %s%s %s %s\\r' % (bar, percents, '%', custom_text, suffix))"}, {}), "('[%s] %s%s %s %s\\r' % (bar, percents, '%', custom_text,\n suffix))", False, 'import os, sys, re\n'), ((28, 8, 28, 26), 'sys.stdout.flush', 'sys.stdout.flush', ({}, {}), '()', False, 'import os, sys, re\n'), ((65, 12, 65, 46), 'json.dump', 'json.dump', ({(65, 22, 65, 36): 'resulting_data', (65, 38, 65, 45): 'outfile'}, {}), '(resulting_data, outfile)', False, 'import json\n')] |
Cyberdeep/archerysec | tools/urls.py | a4b1a0c4f736bd70bdea693c7a7c479a69bb0f7d | # _
# /\ | |
# / \ _ __ ___| |__ ___ _ __ _ _
# / /\ \ | '__/ __| '_ \ / _ \ '__| | | |
# / ____ \| | | (__| | | | __/ | | |_| |
# /_/ \_\_| \___|_| |_|\___|_| \__, |
# __/ |
# |___/
# Copyright (C) 2017-2018 ArcherySec
# This file is part of ArcherySec Project.
from django.conf.urls import url
from tools import views
app_name = 'tools'
urlpatterns = [
url(r'^sslscan/$',
views.sslscan,
name='sslscan'),
url(r'^sslscan_result/$',
views.sslscan_result,
name='sslscan_result'),
]
| [((18, 4, 20, 23), 'django.conf.urls.url', 'url', (), '', False, 'from django.conf.urls import url\n'), ((21, 4, 23, 30), 'django.conf.urls.url', 'url', (), '', False, 'from django.conf.urls import url\n')] |
erigones/esdc-ce | api/vm/base/utils.py | 2e39211a8f5132d66e574d3a657906c7d3c406fe | from core.celery.config import ERIGONES_TASK_USER
from que.tasks import execute, get_task_logger
from vms.models import SnapshotDefine, Snapshot, BackupDefine, Backup, IPAddress
logger = get_task_logger(__name__)
def is_vm_missing(vm, msg):
"""
Check failed command output and return True if VM is not on compute node.
"""
check_str = vm.hostname + ': No such zone configured'
return check_str in msg
def vm_delete_snapshots_of_removed_disks(vm):
"""
This helper function deletes snapshots for VM with changing disk IDs. Bug #chili-363
++ Bug #chili-220 - removing snapshot and backup definitions for removed disks.
"""
removed_disk_ids = [Snapshot.get_real_disk_id(i) for i in vm.create_json_update_disks().get('remove_disks', [])]
if removed_disk_ids:
Snapshot.objects.filter(vm=vm, disk_id__in=removed_disk_ids).delete()
SnapshotDefine.objects.filter(vm=vm, disk_id__in=removed_disk_ids).delete()
Backup.objects.filter(vm=vm, disk_id__in=removed_disk_ids, last=True).update(last=False)
BackupDefine.objects.filter(vm=vm, disk_id__in=removed_disk_ids).delete()
return removed_disk_ids
def _reset_allowed_ip_usage(vm, ip):
"""Helper function used below. It sets the IP usage back to VM [1] only if other VMs, which use the address in
allowed_ips are in notcreated state."""
if all(other_vm.is_notcreated() for other_vm in ip.vms.exclude(uuid=vm.uuid)):
ip.usage = IPAddress.VM
ip.save()
def _is_ip_ok(ip_queryset, vm_ip, vm_network_uuid):
"""Helper function used below. Return True if vm_ip (string) is "dhcp" or is found in the IPAddress queryset
and has the expected usage flag and subnet uuid."""
if vm_ip == 'dhcp':
return True
return any(ip.ip == vm_ip and ip.subnet.uuid == vm_network_uuid and ip.usage == IPAddress.VM_REAL
for ip in ip_queryset)
def vm_update_ipaddress_usage(vm):
"""
This helper function is responsible for updating IPAddress.usage and IPAddress.vm of server IPs (#chili-615,1029),
by removing association from IPs that, are not set on any NIC and:
- when a VM is deleted all IP usages are set to IPAddress.VM (in DB) and
- when a VM is created or updated all IP usages are set to IPAddress.VM_REAL (on hypervisor) and
Always call this function _only_ after vm.json_active is synced with vm.json!!!
In order to properly understand this code you have understand the association between an IPAddress and Vm model.
This function may raise a ValueError if the VM and IP address were not properly associated (e.g. via vm_define_nic).
"""
current_ips = set(vm.json_active_get_ips(primary_ips=True, allowed_ips=False))
current_ips.update(vm.json_get_ips(primary_ips=True, allowed_ips=False))
current_allowed_ips = set(vm.json_active_get_ips(primary_ips=False, allowed_ips=True))
current_allowed_ips.update(vm.json_get_ips(primary_ips=False, allowed_ips=True))
# Return old IPs back to IP pool, so they can be used again
vm.ipaddress_set.exclude(ip__in=current_ips).update(vm=None, usage=IPAddress.VM)
# Remove association of removed vm.allowed_ips
for ip in vm.allowed_ips.exclude(ip__in=current_allowed_ips):
ip.vms.remove(vm)
_reset_allowed_ip_usage(vm, ip)
if vm.is_notcreated():
# Server was deleted from hypervisor
vm.ipaddress_set.filter(usage=IPAddress.VM_REAL).update(usage=IPAddress.VM)
for ip in vm.allowed_ips.filter(usage=IPAddress.VM_REAL):
_reset_allowed_ip_usage(vm, ip)
return
# Server was updated or created
vm.ipaddress_set.filter(usage=IPAddress.VM).update(usage=IPAddress.VM_REAL)
vm.allowed_ips.filter(usage=IPAddress.VM).update(usage=IPAddress.VM_REAL)
# The VM configuration may be changed directly on the hypervisor, thus the VM could have
# new NICs and IP addresses which configuration bypassed our API - issue #168.
vm_ips = vm.ipaddress_set.select_related('subnet').filter(usage=IPAddress.VM_REAL)
vm_allowed_ips = vm.allowed_ips.select_related('subnet').filter(usage=IPAddress.VM_REAL)
# For issue #168 we have to check the VM<->IPAddress association in a loop for each NIC, because we need to
# match the NIC.network_uuid with a Subnet.
for nic_id, nic in enumerate(vm.json_active_get_nics(), 1):
network_uuid = nic.get('network_uuid', None)
if network_uuid:
ip = nic.get('ip', '')
allowed_ips = nic.get('allowed_ips', [])
if ip:
logger.debug('VM: %s | NIC ID: %s | NIC network: %s | IP address: %s', vm, nic_id, network_uuid, ip)
if not _is_ip_ok(vm_ips, ip, network_uuid):
raise ValueError('VM %s NIC ID %s IP address %s is not properly associated with VM!' %
(vm, nic_id, ip))
for ip in allowed_ips:
logger.debug('VM: %s | NIC ID: %s | NIC network: %s | IP address: %s', vm, nic_id, network_uuid, ip)
if not _is_ip_ok(vm_allowed_ips, ip, network_uuid):
raise ValueError('VM %s NIC ID %s allowed IP address %s is not properly associated with VM!' %
(vm, nic_id, ip))
else:
raise ValueError('VM %s NIC ID %s does not have a network uuid!' % (vm, nic_id))
def vm_deploy(vm, force_stop=False):
"""
Internal API call used for finishing VM deploy;
Actually cleaning the json and starting the VM.
"""
if force_stop: # VM is running without OS -> stop
cmd = 'vmadm stop %s -F >/dev/null 2>/dev/null; vmadm get %s 2>/dev/null' % (vm.uuid, vm.uuid)
else: # VM is stopped and deployed -> start
cmd = 'vmadm start %s >/dev/null 2>/dev/null; vmadm get %s 2>/dev/null' % (vm.uuid, vm.uuid)
msg = 'Deploy server'
lock = 'vmadm deploy ' + vm.uuid
meta = {
'output': {
'returncode': 'returncode',
'stderr': 'message',
'stdout': 'json'
},
'replace_stderr': ((vm.uuid, vm.hostname),),
'msg': msg, 'vm_uuid': vm.uuid
}
callback = ('api.vm.base.tasks.vm_deploy_cb', {'vm_uuid': vm.uuid})
return execute(ERIGONES_TASK_USER, None, cmd, meta=meta, lock=lock, callback=callback,
queue=vm.node.fast_queue, nolog=True, ping_worker=False, check_user_tasks=False)
def vm_reset(vm):
"""
Internal API call used for VM reboots in emergency situations.
"""
cmd = 'vmadm stop %s -F; vmadm start %s' % (vm.uuid, vm.uuid)
return execute(ERIGONES_TASK_USER, None, cmd, callback=False, queue=vm.node.fast_queue, nolog=True,
check_user_tasks=False)
def vm_update(vm):
"""
Internal API used for updating VM if there were changes in json detected.
"""
logger.info('Running PUT vm_manage(%s), because something (vnc port?) has changed', vm)
from api.vm.base.views import vm_manage
from api.utils.request import get_dummy_request
from api.utils.views import call_api_view
request = get_dummy_request(vm.dc, method='PUT', system_user=True)
res = call_api_view(request, 'PUT', vm_manage, vm.hostname)
if res.status_code == 201:
logger.warn('PUT vm_manage(%s) was successful: %s', vm, res.data)
else:
logger.error('PUT vm_manage(%s) failed: %s (%s): %s', vm, res.status_code, res.status_text, res.data)
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Coalin/Daily-LeetCode-Exercise | 993_Cousins-in-Binary-Tree.py | a064dcdc3a82314be4571d342c4807291a24f69f | # Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def isCousins(self, root: TreeNode, x: int, y: int) -> bool:
x_depth = None
x_parent = None
x_found = 0
y_depth = None
y_parent = None
y_found = 0
def dfs(node, parent, depth):
nonlocal x_depth, x_parent, x_found, y_depth, y_found, y_parent
if not node:
return
if node.val == x:
x_depth = depth
x_parent = parent
x_found = 1
elif node.val == y:
y_depth = depth
y_parent = parent
y_found = 1
if x_found and y_found:
return
dfs(node.left, node, depth+1)
if x_found and y_found:
return
dfs(node.right, node, depth+1)
dfs(root, None, 0)
return x_depth == y_depth and x_parent != y_parent
| [] |
mattcongy/itshop | docker-images/taigav2/taiga-back/tests/integration/test_tasks_tags.py | 6be025a9eaa7fe7f495b5777d1f0e5a3184121c9 | # -*- coding: utf-8 -*-
# Copyright (C) 2014-2016 Andrey Antukh <[email protected]>
# Copyright (C) 2014-2016 Jesús Espino <[email protected]>
# Copyright (C) 2014-2016 David Barragán <[email protected]>
# Copyright (C) 2014-2016 Alejandro Alonso <[email protected]>
# Copyright (C) 2014-2016 Anler Hernández <[email protected]>
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from unittest import mock
from collections import OrderedDict
from django.core.urlresolvers import reverse
from taiga.base.utils import json
from .. import factories as f
import pytest
pytestmark = pytest.mark.django_db
def test_api_task_add_new_tags_with_error(client):
project = f.ProjectFactory.create()
task = f.create_task(project=project, status__project=project, milestone=None, user_story=None)
f.MembershipFactory.create(project=project, user=task.owner, is_admin=True)
url = reverse("tasks-detail", kwargs={"pk": task.pk})
data = {
"tags": [],
"version": task.version
}
client.login(task.owner)
data["tags"] = [1]
response = client.json.patch(url, json.dumps(data))
assert response.status_code == 400, response.data
assert "tags" in response.data
data["tags"] = [["back"]]
response = client.json.patch(url, json.dumps(data))
assert response.status_code == 400, response.data
assert "tags" in response.data
data["tags"] = [["back", "#cccc"]]
response = client.json.patch(url, json.dumps(data))
assert response.status_code == 400, response.data
assert "tags" in response.data
data["tags"] = [[1, "#ccc"]]
response = client.json.patch(url, json.dumps(data))
assert response.status_code == 400, response.data
assert "tags" in response.data
def test_api_task_add_new_tags_without_colors(client):
project = f.ProjectFactory.create()
task = f.create_task(project=project, status__project=project, milestone=None, user_story=None)
f.MembershipFactory.create(project=project, user=task.owner, is_admin=True)
url = reverse("tasks-detail", kwargs={"pk": task.pk})
data = {
"tags": [
["back", None],
["front", None],
["ux", None]
],
"version": task.version
}
client.login(task.owner)
response = client.json.patch(url, json.dumps(data))
assert response.status_code == 200, response.data
tags_colors = OrderedDict(project.tags_colors)
assert not tags_colors.keys()
project.refresh_from_db()
tags_colors = OrderedDict(project.tags_colors)
assert "back" in tags_colors and "front" in tags_colors and "ux" in tags_colors
def test_api_task_add_new_tags_with_colors(client):
project = f.ProjectFactory.create()
task = f.create_task(project=project, status__project=project, milestone=None, user_story=None)
f.MembershipFactory.create(project=project, user=task.owner, is_admin=True)
url = reverse("tasks-detail", kwargs={"pk": task.pk})
data = {
"tags": [
["back", "#fff8e7"],
["front", None],
["ux", "#fabada"]
],
"version": task.version
}
client.login(task.owner)
response = client.json.patch(url, json.dumps(data))
assert response.status_code == 200, response.data
tags_colors = OrderedDict(project.tags_colors)
assert not tags_colors.keys()
project.refresh_from_db()
tags_colors = OrderedDict(project.tags_colors)
assert "back" in tags_colors and "front" in tags_colors and "ux" in tags_colors
assert tags_colors["back"] == "#fff8e7"
assert tags_colors["ux"] == "#fabada"
def test_api_create_new_task_with_tags(client):
project = f.ProjectFactory.create(tags_colors=[["front", "#aaaaaa"], ["ux", "#fabada"]])
status = f.TaskStatusFactory.create(project=project)
project.default_task_status = status
project.save()
f.MembershipFactory.create(project=project, user=project.owner, is_admin=True)
url = reverse("tasks-list")
data = {
"subject": "Test user story",
"project": project.id,
"tags": [
["back", "#fff8e7"],
["front", "#bbbbbb"],
["ux", None]
]
}
client.login(project.owner)
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201, response.data
task_tags_colors = OrderedDict(response.data["tags"])
assert task_tags_colors["back"] == "#fff8e7"
assert task_tags_colors["front"] == "#aaaaaa"
assert task_tags_colors["ux"] == "#fabada"
tags_colors = OrderedDict(project.tags_colors)
project.refresh_from_db()
tags_colors = OrderedDict(project.tags_colors)
assert tags_colors["back"] == "#fff8e7"
assert tags_colors["ux"] == "#fabada"
assert tags_colors["front"] == "#aaaaaa"
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satchelwu/PaddleOCR2Pytorch | pytorchocr/postprocess/cls_postprocess.py | 6941565cfd4c45470cc3bf9d434c8c32267a33ef | import torch
class ClsPostProcess(object):
""" Convert between text-label and text-index """
def __init__(self, label_list, **kwargs):
super(ClsPostProcess, self).__init__()
self.label_list = label_list
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, torch.Tensor):
preds = preds.numpy()
pred_idxs = preds.argmax(axis=1)
decode_out = [(self.label_list[idx], preds[i, idx])
for i, idx in enumerate(pred_idxs)]
if label is None:
return decode_out
label = [(self.label_list[idx], 1.0) for idx in label]
return decode_out, label | [] |
aba-ai-learning/Single-Human-Parsing-LIP | inference_folder.py | b1c0c91cef34dabf598231127886b669838fc085 | #!/usr/local/bin/python3
# -*- coding: utf-8 -*-
import os
import argparse
import logging
import numpy as np
from PIL import Image
import matplotlib
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torchvision import transforms
import cv2
import tqdm
from net.pspnet import PSPNet
models = {
'squeezenet': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='squeezenet'),
'densenet': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=1024, deep_features_size=512, backend='densenet'),
'resnet18': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='resnet18'),
'resnet34': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='resnet34'),
'resnet50': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet50'),
'resnet101': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet101'),
'resnet152': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet152')
}
parser = argparse.ArgumentParser(description="Pyramid Scene Parsing Network")
parser.add_argument('--models-path', type=str, default='./checkpoints',
help='Path for storing model snapshots')
parser.add_argument('--backend', type=str,
default='densenet', help='Feature extractor')
parser.add_argument('--num-classes', type=int,
default=20, help="Number of classes.")
args = parser.parse_args()
def build_network(snapshot, backend):
epoch = 0
backend = backend.lower()
net = models[backend]()
net = nn.DataParallel(net)
if snapshot is not None:
_, epoch = os.path.basename(snapshot).split('_')
if not epoch == 'last':
epoch = int(epoch)
net.load_state_dict(torch.load(
snapshot, map_location=torch.device('cpu')))
logging.info(
"Snapshot for epoch {} loaded from {}".format(epoch, snapshot))
if torch.cuda.is_available():
net = net.cuda()
return net, epoch
def get_transform():
transform_image_list = [
# transforms.Resize((192, 256), 3),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
return transforms.Compose(transform_image_list)
def show_image(img, pred):
fig, axes = plt.subplots(1, 2)
ax0, ax1 = axes
ax0.get_xaxis().set_ticks([])
ax0.get_yaxis().set_ticks([])
ax1.get_xaxis().set_ticks([])
ax1.get_yaxis().set_ticks([])
classes = np.array(('Background', # always index 0
'Hat', 'Hair', 'Glove', 'Sunglasses',
'UpperClothes', 'Dress', 'Coat', 'Socks',
'Pants', 'Jumpsuits', 'Scarf', 'Skirt',
'Face', 'Left-arm', 'Right-arm', 'Left-leg',
'Right-leg', 'Left-shoe', 'Right-shoe',))
colormap = [(0, 0, 0),
(1, 0.25, 0), (0, 0.25, 0), (0.5, 0, 0.25), (1, 1, 1),
(1, 0.75, 0), (0, 0, 0.5), (0.5, 0.25, 0), (0.75, 0, 0.25),
(1, 0, 0.25), (0, 0.5, 0), (0.5, 0.5, 0), (0.25, 0, 0.5),
(1, 0, 0.75), (0, 0.5, 0.5), (0.25, 0.5, 0.5), (1, 0, 0),
(1, 0.25, 0), (0, 0.75, 0), (0.5, 0.75, 0), ]
cmap = matplotlib.colors.ListedColormap(colormap)
bounds = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
h, w, _ = pred.shape
def denormalize(img, mean, std):
c, _, _ = img.shape
for idx in range(c):
img[idx, :, :] = img[idx, :, :] * std[idx] + mean[idx]
return img
img = denormalize(img.cpu().numpy(), [0.485, 0.456, 0.406], [
0.229, 0.224, 0.225])
img = img.transpose(1, 2, 0).reshape((h, w, 3))
pred = pred.reshape((h, w))
# show image
ax0.set_title('img')
ax0.imshow(img)
ax1.set_title('pred')
mappable = ax1.imshow(pred, cmap=cmap, norm=norm)
# colorbar legend
cbar = plt.colorbar(mappable, ax=axes, shrink=0.7, )
cbar.ax.get_yaxis().set_ticks([])
for j, lab in enumerate(classes):
cbar.ax.text(2.3, (j + 0.45) / 20.0, lab, ha='left', va='center', )
plt.savefig(fname="./result.jpg")
print('result saved to ./result.jpg')
plt.show()
def main():
# --------------- model --------------- #
snapshot = os.path.join(args.models_path, args.backend, 'PSPNet_last')
net, starting_epoch = build_network(snapshot, args.backend)
net.eval()
# ------------ load image ------------ #
data_transform = get_transform()
imgfolder = 'ACGPN/ACGPN_testdata/test_img/'
savefolder = 'ACGPN/ACGPN_testdata/test_humanparse/'
if not os.path.exists(savefolder):
os.mkdir(savefolder)
imglist = os.listdir(imgfolder)
for imgname in tqdm.tqdm(imglist):
imgpath = os.path.join(imgfolder, imgname)
print(imgpath)
img = Image.open(imgpath)
img = data_transform(img)
if torch.cuda.is_available():
img = img.cuda()
with torch.no_grad():
pred, _ = net(img.unsqueeze(dim=0))
pred = pred.squeeze(dim=0)
pred = pred.cpu().numpy().transpose(1, 2, 0)
pred = np.asarray(np.argmax(pred, axis=2),
dtype=np.uint8).reshape((256, 192, 1))
pred_3 = np.repeat(pred, 3, axis = 2)
savepath = os.path.join(savefolder, imgname)
cv2.imwrite(savepath, pred_3)
if __name__ == '__main__':
main()
| [((28, 9, 28, 77), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (), '', False, 'import argparse\n'), ((42, 10, 42, 30), 'torch.nn.DataParallel', 'nn.DataParallel', ({(42, 26, 42, 29): 'net'}, {}), '(net)', True, 'import torch.nn as nn\n'), ((51, 7, 51, 32), 'torch.cuda.is_available', 'torch.cuda.is_available', ({}, {}), '()', False, 'import torch\n'), ((62, 11, 62, 51), 'torchvision.transforms.Compose', 'transforms.Compose', ({(62, 30, 62, 50): 'transform_image_list'}, {}), '(transform_image_list)', False, 'from torchvision import transforms\n'), ((66, 16, 66, 34), 'matplotlib.pyplot.subplots', 'plt.subplots', ({(66, 29, 66, 30): '1', (66, 32, 66, 33): '2'}, {}), '(1, 2)', True, 'import matplotlib.pyplot as plt\n'), ((73, 14, 78, 65), 'numpy.array', 'np.array', ({(73, 23, 78, 64): "('Background', 'Hat', 'Hair', 'Glove', 'Sunglasses', 'UpperClothes',\n 'Dress', 'Coat', 'Socks', 'Pants', 'Jumpsuits', 'Scarf', 'Skirt',\n 'Face', 'Left-arm', 'Right-arm', 'Left-leg', 'Right-leg', 'Left-shoe',\n 'Right-shoe')"}, {}), "(('Background', 'Hat', 'Hair', 'Glove', 'Sunglasses',\n 'UpperClothes', 'Dress', 'Coat', 'Socks', 'Pants', 'Jumpsuits', 'Scarf',\n 'Skirt', 'Face', 'Left-arm', 'Right-arm', 'Left-leg', 'Right-leg',\n 'Left-shoe', 'Right-shoe'))", True, 'import numpy as np\n'), ((85, 11, 85, 53), 'matplotlib.colors.ListedColormap', 'matplotlib.colors.ListedColormap', ({(85, 44, 85, 52): 'colormap'}, {}), '(colormap)', False, 'import matplotlib\n'), ((88, 11, 88, 57), 'matplotlib.colors.BoundaryNorm', 'matplotlib.colors.BoundaryNorm', ({(88, 42, 88, 48): 'bounds', (88, 50, 88, 56): 'cmap.N'}, {}), '(bounds, cmap.N)', False, 'import matplotlib\n'), ((109, 11, 109, 56), 'matplotlib.pyplot.colorbar', 'plt.colorbar', (), '', True, 'import matplotlib.pyplot as plt\n'), ((114, 4, 114, 37), 'matplotlib.pyplot.savefig', 'plt.savefig', (), '', True, 'import matplotlib.pyplot as plt\n'), ((116, 4, 116, 14), 'matplotlib.pyplot.show', 'plt.show', ({}, {}), '()', True, 'import matplotlib.pyplot as plt\n'), ((121, 15, 121, 74), 'os.path.join', 'os.path.join', ({(121, 28, 121, 44): 'args.models_path', (121, 46, 121, 58): 'args.backend', (121, 60, 121, 73): '"""PSPNet_last"""'}, {}), "(args.models_path, args.backend, 'PSPNet_last')", False, 'import os\n'), ((131, 14, 131, 35), 'os.listdir', 'os.listdir', ({(131, 25, 131, 34): 'imgfolder'}, {}), '(imgfolder)', False, 'import os\n'), ((132, 19, 132, 37), 'tqdm.tqdm', 'tqdm.tqdm', ({(132, 29, 132, 36): 'imglist'}, {}), '(imglist)', False, 'import tqdm\n'), ((19, 26, 19, 112), 'net.pspnet.PSPNet', 'PSPNet', (), '', False, 'from net.pspnet import PSPNet\n'), ((20, 24, 20, 109), 'net.pspnet.PSPNet', 'PSPNet', (), '', False, 'from net.pspnet import PSPNet\n'), ((21, 24, 21, 108), 'net.pspnet.PSPNet', 'PSPNet', (), '', False, 'from net.pspnet import PSPNet\n'), ((22, 24, 22, 108), 'net.pspnet.PSPNet', 'PSPNet', (), '', False, 'from net.pspnet import PSPNet\n'), ((23, 24, 23, 110), 'net.pspnet.PSPNet', 'PSPNet', (), '', False, 'from net.pspnet import PSPNet\n'), ((24, 25, 24, 112), 'net.pspnet.PSPNet', 'PSPNet', (), '', False, 'from net.pspnet import PSPNet\n'), ((25, 25, 25, 112), 'net.pspnet.PSPNet', 'PSPNet', (), '', False, 'from net.pspnet import PSPNet\n'), ((59, 8, 59, 29), 'torchvision.transforms.ToTensor', 'transforms.ToTensor', ({}, {}), '()', False, 'from torchvision import transforms\n'), ((60, 8, 60, 74), 'torchvision.transforms.Normalize', 'transforms.Normalize', ({(60, 29, 60, 50): '[0.485, 0.456, 0.406]', (60, 52, 60, 73): '[0.229, 0.224, 0.225]'}, {}), '([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])', False, 'from torchvision import transforms\n'), ((129, 11, 129, 37), 'os.path.exists', 'os.path.exists', ({(129, 26, 129, 36): 'savefolder'}, {}), '(savefolder)', False, 'import os\n'), ((130, 8, 130, 28), 'os.mkdir', 'os.mkdir', ({(130, 17, 130, 27): 'savefolder'}, {}), '(savefolder)', False, 'import os\n'), ((133, 18, 133, 50), 'os.path.join', 'os.path.join', ({(133, 31, 133, 40): 'imgfolder', (133, 42, 133, 49): 'imgname'}, {}), '(imgfolder, imgname)', False, 'import os\n'), ((135, 14, 135, 33), 'PIL.Image.open', 'Image.open', ({(135, 25, 135, 32): 'imgpath'}, {}), '(imgpath)', False, 'from PIL import Image\n'), ((137, 11, 137, 36), 'torch.cuda.is_available', 'torch.cuda.is_available', ({}, {}), '()', False, 'import torch\n'), ((140, 13, 140, 28), 'torch.no_grad', 'torch.no_grad', ({}, {}), '()', False, 'import torch\n'), ((147, 21, 147, 49), 'numpy.repeat', 'np.repeat', (), '', True, 'import numpy as np\n'), ((149, 23, 149, 56), 'os.path.join', 'os.path.join', ({(149, 36, 149, 46): 'savefolder', (149, 48, 149, 55): 'imgname'}, {}), '(savefolder, imgname)', False, 'import os\n'), ((150, 12, 150, 41), 'cv2.imwrite', 'cv2.imwrite', ({(150, 24, 150, 32): 'savepath', (150, 34, 150, 40): 'pred_3'}, {}), '(savepath, pred_3)', False, 'import cv2\n'), ((44, 19, 44, 45), 'os.path.basename', 'os.path.basename', ({(44, 36, 44, 44): 'snapshot'}, {}), '(snapshot)', False, 'import os\n'), ((48, 35, 48, 54), 'torch.device', 'torch.device', ({(48, 48, 48, 53): '"""cpu"""'}, {}), "('cpu')", False, 'import torch\n'), ((144, 30, 144, 53), 'numpy.argmax', 'np.argmax', (), '', True, 'import numpy as np\n')] |
shuvoxcd01/Policy-Evaluation | src/random_policy.py | 6bfdfdaa67e1dd67edb75fcf5b4664f2584345ac | from src.gridworld_mdp import GridWorld
class EquiprobableRandomPolicy:
def __init__(self):
self.world_model = GridWorld()
def get_prob(self, selected_action, state):
assert state in self.world_model.states
assert selected_action in self.world_model.actions
num_all_possible_actions = 0
times_selected_action_chosen = 0
for next_state in self.world_model.states:
for action in self.world_model.actions:
if self.world_model.reward_fn(state, action, next_state) == -1:
num_all_possible_actions += 1
if action == selected_action:
times_selected_action_chosen += 1
if not num_all_possible_actions:
return 0
prob = times_selected_action_chosen / num_all_possible_actions
return prob
| [((6, 27, 6, 38), 'src.gridworld_mdp.GridWorld', 'GridWorld', ({}, {}), '()', False, 'from src.gridworld_mdp import GridWorld\n')] |
Rubiel1/sktime | sktime/classification/feature_based/_summary_classifier.py | 2fd2290fb438224f11ddf202148917eaf9b73a87 | # -*- coding: utf-8 -*-
"""Summary Classifier.
Pipeline classifier using the basic summary statistics and an estimator.
"""
__author__ = ["MatthewMiddlehurst"]
__all__ = ["SummaryClassifier"]
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sktime.base._base import _clone_estimator
from sktime.classification.base import BaseClassifier
from sktime.transformations.series.summarize import SummaryTransformer
class SummaryClassifier(BaseClassifier):
"""Summary statistic classifier.
This classifier simply transforms the input data using the SummaryTransformer
transformer and builds a provided estimator using the transformed data.
Parameters
----------
summary_functions : str, list, tuple, default=("mean", "std", "min", "max")
Either a string, or list or tuple of strings indicating the pandas
summary functions that are used to summarize each column of the dataset.
Must be one of ("mean", "min", "max", "median", "sum", "skew", "kurt",
"var", "std", "mad", "sem", "nunique", "count").
summary_quantiles : str, list, tuple or None, default=(0.25, 0.5, 0.75)
Optional list of series quantiles to calculate. If None, no quantiles
are calculated.
estimator : sklearn classifier, default=None
An sklearn estimator to be built using the transformed data. Defaults to a
Random Forest with 200 trees.
n_jobs : int, default=1
The number of jobs to run in parallel for both `fit` and `predict`.
``-1`` means using all processors.
random_state : int or None, default=None
Seed for random, integer.
Attributes
----------
n_classes_ : int
Number of classes. Extracted from the data.
classes_ : ndarray of shape (n_classes)
Holds the label for each class.
See Also
--------
SummaryTransformer
Examples
--------
>>> from sktime.classification.feature_based import SummaryClassifier
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> clf = SummaryClassifier(estimator=RandomForestClassifier(n_estimators=10))
>>> clf.fit(X_train, y_train)
SummaryClassifier(...)
>>> y_pred = clf.predict(X_test)
"""
_tags = {
"capability:multivariate": True,
"capability:multithreading": True,
}
def __init__(
self,
summary_functions=("mean", "std", "min", "max"),
summary_quantiles=(0.25, 0.5, 0.75),
estimator=None,
n_jobs=1,
random_state=None,
):
self.summary_functions = summary_functions
self.summary_quantiles = summary_quantiles
self.estimator = estimator
self.n_jobs = n_jobs
self.random_state = random_state
self._transformer = None
self._estimator = None
self._transform_atts = 0
super(SummaryClassifier, self).__init__()
def _fit(self, X, y):
"""Fit a pipeline on cases (X,y), where y is the target variable.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
self._transformer = SummaryTransformer(
summary_function=self.summary_functions,
quantiles=self.summary_quantiles,
)
self._estimator = _clone_estimator(
RandomForestClassifier(n_estimators=200)
if self.estimator is None
else self.estimator,
self.random_state,
)
m = getattr(self._estimator, "n_jobs", None)
if m is not None:
self._estimator.n_jobs = self._threads_to_use
X_t = self._transformer.fit_transform(X, y)
if X_t.shape[0] > len(y):
X_t = X_t.to_numpy().reshape((len(y), -1))
self._transform_atts = X_t.shape[1]
self._estimator.fit(X_t, y)
return self
def _predict(self, X):
"""Predict class values of n instances in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predictions for.
Returns
-------
y : array-like, shape = [n_instances]
Predicted class labels.
"""
X_t = self._transformer.transform(X)
if X_t.shape[1] < self._transform_atts:
X_t = X_t.to_numpy().reshape((-1, self._transform_atts))
return self._estimator.predict(X_t)
def _predict_proba(self, X):
"""Predict class probabilities for n instances in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predict probabilities for.
Returns
-------
y : array-like, shape = [n_instances, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
X_t = self._transformer.transform(X)
if X_t.shape[1] < self._transform_atts:
X_t = X_t.to_numpy().reshape((-1, self._transform_atts))
m = getattr(self._estimator, "predict_proba", None)
if callable(m):
return self._estimator.predict_proba(X_t)
else:
dists = np.zeros((X.shape[0], self.n_classes_))
preds = self._estimator.predict(X_t)
for i in range(0, X.shape[0]):
dists[i, self._class_dictionary[preds[i]]] = 1
return dists
| [((113, 28, 116, 9), 'sktime.transformations.series.summarize.SummaryTransformer', 'SummaryTransformer', (), '', False, 'from sktime.transformations.series.summarize import SummaryTransformer\n'), ((181, 20, 181, 59), 'numpy.zeros', 'np.zeros', ({(181, 29, 181, 58): '(X.shape[0], self.n_classes_)'}, {}), '((X.shape[0], self.n_classes_))', True, 'import numpy as np\n'), ((119, 12, 119, 52), 'sklearn.ensemble.RandomForestClassifier', 'RandomForestClassifier', (), '', False, 'from sklearn.ensemble import RandomForestClassifier\n')] |
skumaravelan/tech-interview-questions | coding/reverse_bits/starter.py | 637dfbf131123c77a8e2e4e45ba420355dcb381f | class Solution:
# @param n, an integer
# @return an integer
def reverseBits(self, n):
| [] |
valenciarichards/hypernews-portal | Topics/Submitting data/POST Request With Several Keys/main.py | 0b6c4d8aefe4f8fc7dc90d6542716e98f52515b3 | from django.shortcuts import redirect
from django.views import View
class TodoView(View):
all_todos = []
def post(self, request, *args, **kwargs):
todo = request.POST.get("todo")
important = request.POST.get("important")
if todo not in self.all_todos:
if important:
self.all_todos = [todo] + self.all_todos
else:
self.all_todos.append(todo)
return redirect("/")
| [((16, 15, 16, 28), 'django.shortcuts.redirect', 'redirect', ({(16, 24, 16, 27): '"""/"""'}, {}), "('/')", False, 'from django.shortcuts import redirect\n')] |
quarckster/pypeln | pypeln/thread/api/to_iterable_thread_test.py | f4160d0f4d4718b67f79a0707d7261d249459a4b | import typing as tp
from unittest import TestCase
import hypothesis as hp
from hypothesis import strategies as st
import pypeln as pl
import cytoolz as cz
MAX_EXAMPLES = 10
T = tp.TypeVar("T")
@hp.given(nums=st.lists(st.integers()))
@hp.settings(max_examples=MAX_EXAMPLES)
def test_from_to_iterable(nums: tp.List[int]):
nums_pl = nums
nums_pl = pl.thread.from_iterable(nums_pl)
nums_pl = cz.partition_all(10, nums_pl)
nums_pl = pl.thread.map(sum, nums_pl)
nums_pl = pl.thread.to_iterable(nums_pl)
nums_pl = list(nums_pl)
nums_py = nums
nums_py = cz.partition_all(10, nums_py)
nums_py = map(sum, nums_py)
nums_py = list(nums_py)
assert nums_py == nums_pl
| [((10, 4, 10, 19), 'typing.TypeVar', 'tp.TypeVar', ({(10, 15, 10, 18): '"""T"""'}, {}), "('T')", True, 'import typing as tp\n'), ((14, 1, 14, 39), 'hypothesis.settings', 'hp.settings', (), '', True, 'import hypothesis as hp\n'), ((18, 14, 18, 46), 'pypeln.thread.from_iterable', 'pl.thread.from_iterable', ({(18, 38, 18, 45): 'nums_pl'}, {}), '(nums_pl)', True, 'import pypeln as pl\n'), ((19, 14, 19, 43), 'cytoolz.partition_all', 'cz.partition_all', ({(19, 31, 19, 33): '10', (19, 35, 19, 42): 'nums_pl'}, {}), '(10, nums_pl)', True, 'import cytoolz as cz\n'), ((20, 14, 20, 41), 'pypeln.thread.map', 'pl.thread.map', ({(20, 28, 20, 31): 'sum', (20, 33, 20, 40): 'nums_pl'}, {}), '(sum, nums_pl)', True, 'import pypeln as pl\n'), ((21, 14, 21, 44), 'pypeln.thread.to_iterable', 'pl.thread.to_iterable', ({(21, 36, 21, 43): 'nums_pl'}, {}), '(nums_pl)', True, 'import pypeln as pl\n'), ((25, 14, 25, 43), 'cytoolz.partition_all', 'cz.partition_all', ({(25, 31, 25, 33): '10', (25, 35, 25, 42): 'nums_py'}, {}), '(10, nums_py)', True, 'import cytoolz as cz\n'), ((13, 24, 13, 37), 'hypothesis.strategies.integers', 'st.integers', ({}, {}), '()', True, 'from hypothesis import strategies as st\n')] |
sem-onyalo/dnn-training-monitoring-flask | app/datastore/test.py | 6a81e06a6871b0d6e890f9fd92f4ab79ac8ee639 | import json
from app.model import TrainMetrics, TrainPlots
class DatastoreTest:
def getRuns(self):
return [
"20211210T132017",
"20211209T132017",
"20211208T132017"
]
def getEvals(self, run):
return [40, 30, 20, 10]
def getHyperparameters(self, run):
return json.loads(TEST_HYPERPARAMETERS)
def getSummary(self, run, eval):
return json.loads(TEST_SUMMARY)
def getMetrics(self, run, eval):
header = ["Epoch", "Epochs", "Mini-Batch", "Mini-Batches", "Discriminator Loss: Real", "Discriminator Loss: Fake", "GAN Loss"]
items = list()
items.append([1, 100, 1, 234, 0.304, 2.544, 0.487])
items.append([1, 100, 2, 234, 0.239, 1.219, 0.880])
items.append([1, 100, 3, 234, 0.239, 1.219, 0.880])
trainMetrics = TrainMetrics(header, items)
return trainMetrics
def getPlots(self, run, eval):
plots = TrainPlots()
plots.loss = TEST_PLOT_LOSS
plots.image = TEST_PLOT_IMAGE
plots.target = TEST_PLOT_TARGET
return plots
TEST_SUMMARY = '''
{
"Epoch":10,
"Real Accuracy":"1%",
"Fake Accuracy":"99%",
"Elapsed Time":"0:06:30.957221"
}
'''
TEST_HYPERPARAMETERS = '''
{
"Epochs": 100,
"Batch Size": 256,
"Eval Frequency": 10,
"Batches Per Epoch": 234,
"Half Batch": 128,
"Latent Dim": 100,
"Conv Filters": [256, 256],
"Conv Layer Kernel Size": "(3, 3)",
"Conv Transpose Filters": [256, 256],
"Conv Transpose Layer Kernel Size": "(4, 4)",
"Generator Input Filters": 256,
"Generator Output Layer Kernel Size": "(7, 7)",
"Adam Learning Rate": 0.0002,
"Adam Beta 1": 0.5,
"Kernel Init Std Dev": 0.02,
"Leaky Relu Alpha": 0.2,
"Dropout Rate": 0.4
}
'''
TEST_PLOT_LOSS = 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hKPFRcXR/PmzZk0aRKKoqDVapk2bRqNGjXC09OT7t27s3nzZptjT0lJYezYsfj7++Ph4cHgwYM5ceKEef2ZM2cYOnQo/v7+eHp60rZtW9atW2fedsyYMTRo0AB3d3datWrF4sWLbT62KFDv/xSYNGkSa9asYevWrTRu3Ni8PCQkhLy8PFJTUy1qAS9dumT+R1LU9OnTmTJlivlzenq6JIFCCFFfKArosu1zbGcPUNk+ssNzzz3Hli1bWL16NcHBwbz00kvs37+fjh07lrmtt7c3X3/9NWFhYcTExDBhwgS8vb15/vnni5U9dOgQAwcO5JFHHuHVV18FTN+rR44cYdmyZYSFhbFy5UoGDRpETEwMrVq1KvP448eP58SJE/z666/4+PjwwgsvcPvtt3PkyBGcnZ2ZOHEieXl5bN26FU9PT44cOYKXlxcAM2fO5MiRI/z+++8EBQVx8uRJcnJybL5uokC9TQAVReGpp55i5cqVbN68mWbNmlms79y5M87OzmzatIlRo0YBEBsbS0JCAj169LC6T1dXV1xdXas9diGEEHagy4bXw+xz7JcugIunTUUzMzP58ssv+f777+nXrx8A33zzjUUlB8BHH33EF198Yf78+OOP8/bbb/Pyyy+blzVt2pRp06axbNmyYgngjh07GDJkCDNmzGDq1KkAJCQksHjxYhISEggLM12radOmsX79ehYvXszrr79eauz5id/27dvp2bMnAD/88APh4eGsWrWKu+++m4SEBEaNGkVUVBQAzZs3N2+fkJBAp06d6NKlizl+UTH1NgGcOHEiS5YsYfXq1Xh7e5v79fn6+uLu7o6vry+PPPIIU6ZMISAgAB8fH5566il69OhRZ54AlnEA6y6D0UCKNoUg9yB7hyKEqGPi4uLIy8uje/fu5mUBAQG0bt3aotyYMWOYMWOG+XN+a9ePP/7Ie++9R1xcHJmZmej1enx8fCy2TUhI4LbbbuO1115j8uTJ5uUxMTEYDAYiIyMtymu1WgIDA8uM/ejRozg5OVnEHhgYSOvWrTl69CgATz/9NE888QQbNmygf//+jBo1ivbt2wPwxBNPMGrUKPbv38+AAQMYPny4OZEU5VNvE8CPP/4YMD0JVdjixYsZP348AO+++y5qtZpRo0ah1WoZOHAgH330UQ1HKhzRU389xT/n/+HLAV/SLbSbvcMRQoCpGfYlOw307uxR5bv09fWlZcuWFst27tzJmDFjmDNnDgMHDsTX15dly5YV60PYoEEDwsLCWLp0KQ8//LA5QczMzESj0bBv3z40Go3FNvnNtJX16KOPMnDgQNauXcuGDRuYP38+b7/9Nk899RSDBw/mzJkzrFu3jo0bN9KvXz8mTpzIW2+9VSXHdiT19iEQRVGsvvKTPwA3Nzc+/PBDkpOTycrK4pdffimx/5+oXyozE0hV+Of8PwD8cPQHu8YhhChEpTI1w9rjVY7+fy1atMDZ2Zndu3ebl6WkpHD8+PEyt92xYwcRERHMmDGDLl260KpVK86cOVOsnLu7O2vWrMHNzY2BAweSkZEBQKdOnTAYDCQlJdGyZUuLly3fn23atEGv11vEfvXqVWJjY7n++uvNy8LDw/nf//7HL7/8wtSpU/n888/N6xo0aMC4ceP4/vvvWbRoEZ999lmZxxXF1dsaQCGEEKI+8vLy4pFHHuG5554jMDCQ4OBgZsyYgVpddp1Oq1atSEhIYNmyZXTt2pW1a9eycuVKq2U9PT1Zu3YtgwcPZvDgwaxfv57IyEjGjBnD2LFjefvtt+nUqROXL19m06ZNtG/fnjvuuMO8fUxMDN7e3ubPKpWKDh06MGzYMCZMmMCnn36Kt7c3L774Io0aNWLYsGEATJ48mcGDBxMZGUlKSgp///03bdq0AWDWrFl07tyZtm3botVqWbNmjXmdKJ96WwMoRF2hKArHU45jMBrsHYoQoo5YuHAhvXv3ZujQofTv35+bbrqJzp07l7ndnXfeybPPPsukSZPo2LEjO3bsYObMmSWW9/Ly4vfff0dRFO644w6ysrJYvHgxY8eOZerUqbRu3Zrhw4ezZ88emjRpYrFtnz596NSpk/mVH9/ixYvp3LkzQ4YMoUePHiiKwrp163B2dgbAYDAwceJE2rRpw6BBg4iMjDR3z3JxcWH69Om0b9+ePn36oNFoKj3Em6NSKfZuC6vD0tPT8fX1JS0trVgH2prw55k/eXbzswDEjIup8ePXZeN+H8f+pP2A7dcu6pso8/vKXu/8fd0afittAtvwYfSHjGg5grm95paxpRCiKuTm5hIfH0+zZs1wc3OzdziiGpR2j+39/V0bSA2gEHb2ycFPAFh50nozjBBCCFHVJAEUoo64lHWJd/a+w7mMc/YORQghRB0nD4HUYTIOYN2nKseTf0///TRHrh7hj9N/8Mddf1RjVEIIIeo7qQEUoo44cvUIABey7DROmRBCiHpDEkAhhBBCCAcjCaBwSAq15+H32hSLEEIIxyAJoLCZUTHy1KaneHPPm/YORQghhBCVIAmgsNnBywfZfG4z3x35zt6hCCGEEKISJAEUNtMZdPYOocrI+OdCCCEcmSSAdZmMAiOEEEKICpAEUAghhKhn+vbty+TJk+0dhqjFJAGsy6QVs16Q5mghhBA1TRJAYbPyzFohbCPJnxCiIjIyMhgzZgyenp6Ehoby7rvvlqvWb8WKFbRt2xZXV1eaNm3K22+/bbH+o48+olWrVri5udGwYUPuuusu87qff/6ZqKgo3N3dCQwMpH///mRlZVXl6YkaIFPBCYekMxjtHYIQopZRFIUcfY5dju3u5F6uP7KnTJnC9u3b+fXXX2nYsCGzZs1i//79dOzYscxt9+3bxz333MPs2bO599572bFjB08++SSBgYGMHz+evXv38vTTT/Pdd9/Rs2dPkpOT+eeffwC4ePEio0eP5s0332TEiBFkZGTwzz//yB+zdZAkgMIhnUvJtncIQohaJkefQ/cl3e1y7N3378bD2cOmshkZGXzzzTcsWbKEfv36AbB48WLCwsJs2v6dd96hX79+zJw5E4DIyEiOHDnCwoULGT9+PAkJCXh6ejJkyBC8vb2JiIigU6dOgCkB1Ov1jBw5koiICACioqLKe7qiFpAmYOGQDLXkr1Xl2n9CCGGrU6dOodPp6Natm3mZr68vrVu3tmn7o0eP0qtXL4tlvXr14sSJExgMBm677TYiIiJo3rw5Dz74ID/88APZ2aY/mjt06EC/fv2Iiori7rvv5vPPPyclJaXqTk7UGKkBFEIIITA1w+6+f7fdjl1beHt7s3//fjZv3syGDRuYNWsWs2fPZs+ePfj5+bFx40Z27NjBhg0beP/995kxYwa7d++mWbNm9g5dlEO9rQHcunUrQ4cOJSwsDJVKxapVqyzWjx8/HpVKZfEaNGiQfYKtKCvdRdaeWssHBz4o1h9jxfEVvLnnTemnUctI7Z8QtYdKpcLD2cMur/L0/2vevDnOzs7s2bPHvCwtLY3jx4/btH2bNm3Yvn27xbLt27cTGRmJRqMBwMnJif79+/Pmm29y6NAhTp8+zV9//WW+Tr169WLOnDkcOHAAFxcXVq5caXP8onaotzWAWVlZdOjQgYcffpiRI0daLTNo0CAWL15s/uzq6lpT4VWbF/95EYAeYT3o3LCzefnsnbMB6Nu4L91Cu1nbVAghRB3g7e3NuHHjeO655wgICCA4OJhXXnkFtVptkUhevnyZ6Ohoi21DQ0OZOnUqXbt2Zd68edx7773s3LmTDz74gI8++giANWvWcOrUKfr06YO/vz/r1q3DaDTSunVrdu/ezaZNmxgwYADBwcHs3r2by5cv06ZNm5q8BKIK1NsEcPDgwQwePLjUMq6uroSEhNRQRDUrJdd6n4yMvIwajqSAUTGy+uRq2gW1o5V/K7vFUatIBaAQogLeeecd/ve//zFkyBB8fHx4/vnnOXv2LG5ubuYyS5YsYcmSJRbbzZs3j5dffpnly5cza9Ys5s2bR2hoKHPnzmX8+PEA+Pn58csvvzB79mxyc3Np1aoVS5cupW3bthw9epStW7eyaNEi0tPTiYiI4O233y7z+1bUPvU2AbTF5s2bCQ4Oxt/fn1tvvZVXX32VwMBAe4dVJWpj0+Lv8b8za8csAGLGxdg5GiGEqLu8vb354YcfzJ+zsrKYM2cOjz32GGD6fivNqFGjGDVqlNV1N910U4nbt2nThvXr11coZlG7OGwCOGjQIEaOHEmzZs2Ii4vjpZdeYvDgwezcudPcB6IorVaLVqs1f05PT6+pcMvNqNS+ce6OXD1i7xCEEKJeOHDgAMeOHaNbt26kpaUxd+5cAIYNG2bnyERdUW8fAinLfffdx5133klUVBTDhw9nzZo17Nmzp9S/mubPn4+vr6/5FR4eXnMBl1N11wBezLxYrfuv7aZsnsLl7Mv2DkMI4cDeeustOnToYJ6J459//iEoKMjeYYk6wmETwKKaN29OUFAQJ0+eLLHM9OnTSUtLM7/Onj1bgxGWUzW3AMemxJZ7G5W1x5brqI1nNjJ311x7hyGEcFCdOnVi3759ZGZmkpyczMaNG2VAZlEuDtsEXNS5c+e4evUqoaGhJZZxdXWtM08KV3cNYEWGk6ldcwlX/vokZiVWQRS1r6+mEEKI+q/e1gBmZmYSHR1tfgQ+Pj6e6OhoEhISyMzM5LnnnmPXrl2cPn2aTZs2MWzYMFq2bMnAgQPtG3g5lFajpigKado0Vp5YSZZOJumurSQBFEIIYQ/1tgZw79693HLLLebPU6ZMAWDcuHF8/PHHHDp0iG+++YbU1FTCwsIYMGAA8+bNqzM1fGVRUHj6r6fZn7Sfbee32TscoH41AVcVGZhbCPuSf4P1l9zb0tXbBLBv376l3vw//vijBqOpeUbFyP6k/QBsOLOhyvdfoZqrWpT/1ZZfC1IDKIR9ODs7A5CdnY27e+2Zhk1Unfz5i/PvtbBUbxNAR3A1U1t2oaIqkYRJDV7VkwRQCPvQaDT4+fmRlJQEgIdH+aZjE7WXoihkZ2eTlJSEn59fiUO7OTpJAOuwxPQKJIB2JAmkFVbyvxx9Dn+e+ZObGt2Ev5t/zcckhIPInwkqPwkU9Yufn1+9ne2rKkgCWE/VxpolSQCLs3af3tzzJj8f/5k2AW1YPnS5HaISwjGoVCpCQ0MJDg5Gp9PZOxxRhZydnaXmrwySANZTJfZ/rKK8sDYmmDXtWPIxVp9czbCWFR9539p9Wh9vmmbpaPLRCu9XCGE7jUYjyYJwOPV2GBhHVxsTtPrYv+bl7S+jNVS8Kd5IzU7ZdyXnCutPr0dnlNoOIYRwZJIA1lPy+HtZqu76GIyG2hCGTUauHslzW57juyPf1eyBhRBC1CqSANZhpdWnVXsNoOSXVaKma2pTtCkAbD23tUaPK4QQonaRBLCeqo4awMo24cpDIMXZq6leaoiFEMKxSQJYh5X2FV4b+wDWLnJ9hBBCOC5JAEWFSIJZoDLXQmrihBBC2IMkgPVUiUmJHVth6+NTwJVltyZgSeCFEMKhSQJYh5WWT9XGmiXpA1hcRe/T1ZyrrD21ljxDXhVHJIQQwhHIQNB1WSm5Q21MAEXVGbd+HGfSz3As+RhTu0wt9/ZFfz7OpJ/hvyv/MbjZYKmpFUIIByA1gPVUdTfx2br/bee3sfrkaqD+NgFXJtmu6H06k34GgD/P/FnhYxc2ZOUQXvjnBf44/UeV7E8IIUTtJjWA9VRt6eP1xJ9PANC+QXtpAraisjW1Fb3PJW138PJBBjUbVJmQhBBC1AFSA1hPVcdcwJVJ4K7kXKn4geuxmp4KLl9t+QNBCCGEfUgCWIeV+hBINX/Bbzy9kcSsRJvLK4pSb2sAK3WtK3mbKtX8bGXb+tpML4QQwpIkgKJCfj/9O3f8coe9w7DqdNppzmeer7HjVWocwCLb1tTDO+cyztH/5/588983NXI8IYQQtYskgPVUTSQSeUbbhyCpqSbHzLxMhq4ayqAVgzAqpTWv1o4m0KL36XLO5fJtX8HzSM5NJik7ibf2vmWxvL7W0gohhLAkCWA9Vdv6eCkoNTIIdVJ2kvl96Qlg1alMsn346uHKHbuW3WchhBB1Q71NALdu3crQoUMJCwtDpVKxatUqi/WKojBr1ixCQ0Nxd3enf//+nDhxwj7BVpCqlNtXYlJiz5lAauLghQ5RF5Ojqqy5/TD6Q2bvmF2ufUoNoBBCOIZ6mwBmZWXRoUMHPvzwQ6vr33zzTd577z0++eQTdu/ejaenJwMHDiQ3N7eGI60etS35qam+bYUTmLpQA1idx/7k4CesOLGC4ynHazAiIYQQdUG9TQAHDx7Mq6++yogRI4qtUxSFRYsW8fLLLzNs2DDat2/Pt99+y4ULF4rVFIqqoVAzTwEXPkZNJWav7n61XOU3ntlY4rqiiXuWLqtCMRUm08UJIYQoqt4mgKWJj48nMTGR/v37m5f5+vrSvXt3du7cacfIqk51JD91bYiQmqoFLTp7xtmMszy35TmOXD1SPCZFYcrmKTbv++7f7q50fEIIIURRDjkTSGKiafy6hg0bWixv2LCheZ01Wq0WrVZr/pyenl49AVaB2tYEjFLzCWTpSXD1XZ9n/36W2JRY1p9eT8y4GIt1WoO2hK0KFK7FPJtxttSyttznWvezIIQQwu4csgawoubPn4+vr6/5FR4ebu+QSlTbvvRrqgm4Otka/6m0UyWuK+u+KIpSvntXxbe5rtXyCiGEqBiHTABDQkIAuHTpksXyS5cumddZM336dNLS0syvs2dLr52xJ3s+mFARfyf8Tb+f+rEncU+V7bOqk2Bbk6PSEsWiD6Y0cG9g8bm8MUsNoBBCiIpwyASwWbNmhISEsGnTJvOy9PR0du/eTY8ePUrcztXVFR8fH4tXbVXbvvQVlFITqKf/fpqk7CQe/uPhqjtmkSR4w+kNfHX4q2vx2EfRBLAmnlSWYWCEEEIUVW/7AGZmZnLy5Enz5/j4eKKjowkICKBJkyZMnjyZV199lVatWtGsWTNmzpxJWFgYw4cPt1/Q5VSXvqyrokYyNTcVlUqFr6svACdTTuKqcSXcx7am+KlbpgLQtWHXCh2/Kq53WQlgeZvKa1uiL4QQom6otwng3r17ueWWW8yfp0wxPXk5btw4vv76a55//nmysrJ47LHHSE1N5aabbmL9+vW4ubnZK+QqVdeagMuSZ8ij94+9ATjw4AFStamM+NU0xM9j7R/jqU5PFdumpOSo6HRrR64eoU1AmzKbeKsiASw6rIuRIglgOfsAVvl9rjt/UwghhKiEetsE3LdvX9OXaZHX119/DZj6c82dO5fExERyc3P5888/iYyMtG/Q5VXKl3Vtqxmq7EMgybnJ5vdZuixOpRY8aPHZoc/M7wsncSVdA6NiJJsE8+d719zLnJ1zyg6iCpKj1SdXW8ZiLF4DWFk6g67K9ymEEKJ+qbcJoKOrlnEAa0n1UEk1dVdzrvJR9EfmzyVdA4NiKLZsxYkVJGUnsfrk6hIHTlbb+M+lcMJ1Ou00v8b9ao4l12A500zRGsCScrXTaadtOvbexL3c8P0NfHrwU5vKF1Vb7rEQQojqVW+bgB1dbav1UZTSHwIpy8nUgv6cKlRW9/XCPy+w++LuMvdlMBZPAAFGrx1NUnYSp9NP88wNzxRbb2v8OmNBDdzQVUMB0/kPazmsWJ+/ok3CV3KvkKnLLLbPooljPgWF1NxUoi9HM2/XPJKykwD4IPoDm2IVQgjhmCQBrNNKTvJqWwJoUAy8u+/dCm9fuOlUde2/ws6knymW/BWuAbTladv85Gnz2c1WE0BbBnEuya6LuzibcZav//u61HKLDy+2urykmrnk3GRz30ghhBDCVpIA1le1K//j77N/V9m+rNXEDVk5pNiywklw4WZftar0ptwcfU4lorNuU8Imm/ZrrXkaKjdAc317IEgIIUTlSR/AeqqkGsBzGecwGA30/bEvUd9EmWu9qltKbkqV7cvWfmoKCocuH2LliZUWzb5lJVPnM89XKj5rbE0qc/XWm3or0zevPLXB0gdQCCEcg9QA1mGlfVmX9KX/1t63eGvvW+bP/X7qV2y+2ooq3M/vSs4VjqccN68rqWarwseyZQYMRWHMujGAZb+8ov3uapOSkk9JzIQQQlQlqQGsw0pLgaqy2S85N5kfjv5Qah+4Q5cPccvyW/gt7jfOZpzlluW38PjGx83rY5NjS9y2tP55uy/uZtzv48jWZ1ssLzrUiTWFk8R5u+aZ37+y45Uyty2v1NzUKtmPNAELIYSoCVIDWE+sOrmKvo37mj9XZoqxXH0uLhoXc1+5m3+8udTyR68eNde0vbTtJatlig6+XNh3R76zulxv1PPohkeLLf/j9B/M2jGr1JgAbll+S5llbHE5+zKfHPyk1DKZukz83PwqfSy9UW91ebYu2+pyW5SrCbgSiaYQQoi6QxLAemLm9pkWn0tKJEpjVIx0+LZDubd76q/is3DYSlEU/jj9h9V196+93+pyW5K/qnTrT7eWWcZV41olxyppiJpVJ1dVyf6FEEIIkASwTiutrqYife4+jP6wQnFcyr5Uoe2yddl0X9K9xPVHk49WaL9VobLjFlaUXrGeuLtoXGrk+NLXUAghHIMkgPVURRLAwlOq1YQHfn/A6vKU3BSLhzbsQa/ocVY5cyb9jE3lq2rcxZJqbt2d3Ktk/2U5kXKiRo4jhBDCvuQhkHqqIk3ANa2kZOPxjY9XuDayquRfP2vjC1qjKAo5+hx0Bh2+rr4VPq614WJik2PpFtqtwvssj83nNlfpkD1CCCFqJ0kA67CqbAKuzEMjo1qNKvc2vZeVPHvF0eSj7Lu0r8LxVIXyJtBag5a+P/Zl0IpB+Ln6VWksW89ttXkeYmvK+xTwhcwLFT6WEEKIukGagOuyUjLAkh4mKCq/aTEhPaHEMmqVutQEcdfFXTYdq7BUbWqp6ws3vd4WcRsbz2ws9zEqQ2/Uk2fIs7l8QkYC2fpssvXZBCgBVRrLewfew03jVqX7LE1tm0ZQCCFE1ZMawHrK1hqs/ESx6AMXET4RbLtvGzHjYjg49iChnqEl7sPa4MVNvJuUI9rSvdP3nSrbl630Rj37k/bbXL5wwl2ZIVtKkmuwPkOILcpK6IrWEFamNlgIIUTdIAlgPVXS06QllQtwK6i1Ojj2IGtGrLHoy1beZt5pXaZxT+Q9NpVt4duCO1vcWa79l6SqHpbQG/UcTDpo/rx99PZSyx9IOmB+X5lkrTqUlQAO/mWwxedJmyZVZzhCCCFqAUkA6ylbm4CNihGD0cDcnXPNy/IHgC6stCFR2jdoX2yZWqVmcufJNsVwZ8s7uS7gOpvKlsXWOXfLYlAMrD+93vzZx8Wn1PJfHv6ySo5rD0VrcFO0KZxKOyUziAghRD0mCWA9VZ6HQPSKnoSMkvsAgvWkMJ+1admiL0ejUWlsOv6oVqNKbHZ8/abXbdpHvgfaWB9apryMipEbQ2+skn3ZW0USuWGrhvHpoU+rIRohhBC1gSSA9VR5+uDZ0l+wtAGCrTU3j2873qaBlFfcuQJfV1+2n7fexDq0xdAy91HY4GaD2X3/bqZ1mVau7YoyKAZz/LeGm2YC6dWoV6X2WdfYeygeIYQQ1UcSwHpq8X+LbS5rSwJoSw3gVwO/4p7Ie/hm0Df4uvraVAMY6R8JwM6LO4utm997fpnbF+Xm5IaHswfj2o4rtu634b/xYrcXbdqPUTGa5yj+6+xfAIxtM7bc8dQG8lSvEEKIohw6AZw9ezYqlcridd11VdMXrWYUr2EL9gi2acsg9yBzrZ4ts26UmgBe295F48LMHjO5oeENADirnUvd5w+3/2B+H+xePO6ytrfGXVPyQyBNfZsyps0Ym/Zj7cnm2jJLWiOvRuXbQIFHNzxK1DdR1ROQEEKIOsehE0CAtm3bcvHiRfNr27Zt9g6pUlr4trCp3Oe3fY6T2jQMpC3j3ZXaBHytBrFowlZaE3CbgDYWD498ObD4QxQu6vLPf1tSbZeXs1e59jNx00Q8nDwAeK7Lc0DVzZNb3liK6hTcqVzlL2RdYPfF3ZU6phBCiPrF4RNAJycnQkJCzK+goCB7h1QptvS7W9R3ES39W5oTwPS8dPO6ln4ty73f/BrA/P3ZomjZpr5Ny3XMkoR6WR+v8PeRv5d7X9l603h+bQLblDuePo370Ldx32LLwzzDWDlsZbljqYw5O+dUanuD0cD/Nv6PhXsW2lQ+OimayX9Ptl6LKoQQolZw+ATwxIkThIWF0bx5c8aMGUNCQulPw9Ym1mqkbJqH9tpm+TV25zMKvqiXDVlmdRNbmoCtNdlWJKHMdy7jXJllClOr1DiprCehfm5+5dpXYfk1nOWpAXRSOfF+v/eJfjCaqZ2nmpePihxFiGdIhWOxhz2X9rD9wna+PfKtTeUf/P1BNiVsYtCKQTKvsBBC1FIOnQB2796dr7/+mvXr1/Pxxx8THx9P7969ycjIsFpeq9WSnp5u8bKnZG1SsWW2Dr0CBbVwJ1JPmJe5alytli0p+ckz5JXYBFz4GEVZa6ouOth015CuVrctiavGtUK1hmVp4WeKtVwJ4LXz1qg13NrkVvPy0hJpW9X0Qx1FHxK6knPF5tlCBq0YJDOLCCFELeTQCeDgwYO5++67ad++PQMHDmTdunWkpqayfPlyq+Xnz5+Pr6+v+RUeHl7DEVsyFBl+5YNbP7ApSUnNTQUKkhFbhvsoKXE5m3G21CbgkhJSa0/pFk6UAHMfPFuVlLxWVgP3BkD5moAL96vUqAuuweazm6sqrBpzLPmY+f2G0xu4ZfktPLjuwWLlkrKTGPe75X3N1mfT4dsO7Lu0r9rjFEIIYTuHTgCL8vPzIzIykpMnT1pdP336dNLS0syvs2fP1nCEJdOoNNwcfrNNSUqQu6mfo63zBUPJCaDOqCu1BjDCJ8Lqdm5ObsWWFU0WrZUpTXUlgBWpVYxNiTW/L3xeBy8ftFa8XGpyhg6tQcv/7f8/8+epW0zN2YeuHLKIR2/U0++nfiXOnzx+/XhSc1P5Le63apkrWQghRPlIAlhIZmYmcXFxhIZaf5DA1dUVHx8fi1dt4ensCdjWTNkzrCcAqdpUm/dfUhKUqy+Y99ZZUzwBHH3daOv7sxJn0WOUd17f8iaM5VWe5ts+jfuY35fn4Zja5q09b5W47njKcQCmbJ5C3+V9y9zXoxse5aVtL7Fwb/GHSX6L+40Xtr5gdVYZIYQQVc+hE8Bp06axZcsWTp8+zY4dOxgxYgQajYbRo60nLbWN2kpSZkuSYi1RK0tJiWXhuXetPYBR0gMP1vZX2RpAF431YWOK7rfc4+hdU54+gIWT16oaPsYefjv1W4nrRv06ijRtGn8m/EmaNq3MfeXXiv58/GfOplvWnr+07SXWxa/jp+M/VS5gIYQQNnHoBPDcuXOMHj2a1q1bc8899xAYGMiuXbto0KCBvUOrsOp4CAJKTiwLJ4DWEssQzxDe7PMmnw/43GK5tTgLH8NF7VLumjM3jfWEsWhfwjk9Sx8WxceloGZ3YNOB5vflubbleRinvGryIZAsXVap6+f/W/7ZWgBuX3k7uy7uYsG/C9AatOblybnJFdqfEEKI8qm7bVNVYNky60Oe1GWVqW36tP+n5d5v/lh5YL0GEEzz89qicALo5VL+wZJLqgEsmjCVVA5M5+nm5GYeG7Frw64W62y18cxGJneeDECge6B5+YSoCTbvoy5Ye2pthbedsMF0Lfxc/czLPj30KU92fLJKnpYWQghRMvktW6eV3Y+uNL0a9bL43DG4Y4llS/pCzu/Q76R2qnTtY+Fas4o80FFSDWDRYUhKSy5+ufMXi+bbyzmXyx0HQEKG9fEk85Odz277rEL7BahvU/sWfQq9w7cdZOYSIYSoZpIA1hP5yZe6HLfUWWXZZFvaQxdlNQFXZN7eogonkKXV0pXE1hrAkmoq7219Ly39W5KuLRjfMdSz4IGgyiS4HRt0BGBA0wEAXB94fYX35Qge3fAoV3OuAqan1WvyyWchhHAEDt0EXJ/kf0GWJ0kp2mevtG1LWpffR6wqEsDCNYC27q99g/YcumwakqSkIVYMRoPF57KaF1O0BbNX9GvSz/y+PE3Aj7V/zOLz14O+JlufjbeLN1C9fQTri77L++Ll7EWmLhOA72//ng4NOtg5KiGEqB+kBrAOc8ssPg5heacry3dX5F2lli1pv/mJYVUMdVKRGsB7Iu8xvy/6AMG7fd/Fz9WPD/p9YLG88MDMZSk8hVx5ru11AdcVO2Z+8ldWDGVdSyUz0eY4bGFtzuLaIj/5A9NwM/m+P/I97x943zwIuRBCiPKRGsA6LCHzRLFlZdUA3tnoGfP7wjWAkf6RpW5XE03AhWvFXNS2JYCFh4opWuvWP6I//Zr0K3ZNSqp9K6uZsSqfAi5tvbPaufRBuk9vAy9Pm2MpUx0ZpSYpOwm9UU9SdhJv7HkDgOikaL4c+GWZ2+YZ8irUrUAIIeorqQGsw/7MOlRsWVnNm5He3czvC9c0lTXocknJT46uYgmg1YGgKX8NYOEHP/IHw7bYp5W4bW1+DfYILjG+spR1jNLuU00PHF2efqP21u2HbgxcUTA0z7+J/xL1TRRzds4hNjmWPEMePx77kS9jCpLC+bvn0/n7zpxMKZjh51LWpWJjEQohhCORGsB6pqwkpfD6wklb0X5yRZWUJGw4swGAc5nnbA2xRBZ9AG0crLrwOHXnMmyLoazkbMfoHfwY+yMPtHnAckU5asrKSsRLi6Gsbav6cYgMXUYV77H6lNTk+/Pxn/n5+M8Wyxp6NqSRVyOWHFsCwIhfR7D/wf0cTDrIQ388BMBtEbcxqOkgbmlyi/nfg96oLzEJP5V6ivWn1zOh/YQqqfUWQgh7kQSwnsiv6SqzmbLQ+sLDo/x0/CdGRY4qcbOSkpKKDtxry0DQtugQXP6HAsrqA+jt4s2jUY8WW16uGsAyjlFdA3ZXREZe3UkAy2P6P9OLLbvhuxssPm88s5GNZzYCpn6wRZPIZr7NiE+LJ8AtgHUj1zFs9TAAPj74MbvvNw1V4+FsGmhcURSr97Wk5UIIYU+SANZGOz+EnR9Bh3uh3yybNjE/BVxmDWCBwrN4zO01t/TtauALzCIBLKMJuF1gO25repvFtG5dQ7qWsoX145RHeRLAytQOlTWtWlXXANblqeqqUtHkDyA+LR4w/aFz45IbLdZ1X9IdgBuCb2B/0n6cVE64O7vzSf9P+O7Id9zf5n6++e8bNiVsYvvo7eYZZlafXE37Bu1p5tusSuN/b/97uDu5M6F9/RpsXAhRPSQBrI20mZB+DnJSyi5bRFnJjapQU27hwXbLfAikBvqJlacG8P429zO0xVCLZbaOFVfRPnblSYJruh9fZRT9mfn+9u95YN0DJZQWRe1P2g+AXtGTkZfBmHVjAFh/er25TK+lvYgZF0PUN1EW2/q6+hIVFIXeqOeW8Fu4t/W9qFVqXtr2Evsv7UelUjGmzRjaBbXDVeNqMX5k4ZrFxKxEPo8xTbc4vu14jBgrNJi6EMJx1J1vKUeiUpOlUuFuNJQ77Sq7NqdgfXlmuSgrsRzecrjN+7LlGGXVABYu269JPw5dPsTN4TdXOobS1FQNYE0zKJb9Pxt6NKzW4y0bsoz71txXrceojYomf2Cq7d12fhsAuy7usjq38pt73ixxn70b9WbhzQvNSSfAf1f/48HfHyTIPYgFvRfw2aHPmHnjTJr6Ni22vd6o56fjPxHqGUrf8L7lPykhRJ0lCWAtlKDPYFhEY27PPMJrJZQpOr1ZvrJqqSrakltWAmhrn73S9lfRBPDdvu+iV/TVnnTZswawf5P+/JnwJwBKFTfHH0s+ZvHZ19W3QvvxdPZkTs85TNsyrdRyLXxblLr+uoDrOJFyolhiKor75/w/xZqmH/z9QQCu5Fzh0Q2mvqxDVw2la0hXPJ08ubv13fi6+nL4ymHe2vuWecihdSPWEe4TXrMnIISwm7oz/oMDWZ11Cr1Kxa+6SyXObnEm/YzV5eWpAewZ1tPmmMp6cra8A/IGuQcVW1aeJuDCyZhKpaqS5K/olHHFjlnBQbarwls3v8VLVyr2wE15RDrdX+aQQCXZdPcmBjYdaHXdzBtnmt87q50J8wyzWu6x9o/x09CfiB4bXaEYRMn2JO5h87nNTNw0kQfWPcCCfxdYjDd5+8rbOXj5IB8c+ICob6LYeGYjufpcO0YshKhOkgDWQikGrfn9A+seQGconlyVVCNXdh/AgiRmTs859Gnch09v+7TMmNTq0vdb0aShMIuBoMuqAazEj66/q7/V5WV1yreWAKpL6Hdoy2wjy4csL7OM+TgVfHClvDRKXoW3tTYOI8DdkXdzd+TdrB+1no13bUSj1vB8t+etln2q01Pm98vuWFau49/Z4s5ylRfFPbDuAT49ZPp9MGXzFLr+0JV9l/ax79I+xqwdwyN/PMLoNaP5Ne5Xq9vLnM1C1B3SBFwLFf0VuvX8Vos5aaFq+piFeIbwYb8PbSpbVg3g4x0er3Q8hWv1yhoHsDJPJWvUGuZd8WZmUAa3ZGUzbtRytp/fzn3Xld4vzeqg0oC1xvjC076VpE1gG9yd3C2exi7t2DXxrG6LrGiby/46/FfuXFU86bou4Dpzs3LMuBjz8sJPbPdr0o+Vd66kkXcjhqwcQlJ2ErdF3Gaxn7ZBbenSsAsxV2L4Y9QfaA1anvrrKY6nHC92zE13byLYI5hWfq14e9/bNp8DwKwesxjZciQ5+hxm75zNH6f/KFZm0S2LWHtqrXnIGEcyfv34YstmbJvBjG0zytz24/4f09y3Ocm5ybT2b82ui7to5NUILxcv3JzccHdyx1ntTEZeBim5KTTxaVINZyCEsEYSwFqoaB+v7ee3F0sASxp/rzw1gOVR2nZ3triTALeAMvcRqLhxVVVyk5LFQNBlJLhlDZVSljY6Z7adOYeP0YiqYWc6N+xc5jbWroGuhETUWhN3XeCsaMsudE1JNabhLn4cs7rGUkv/lgBsvGsjFzIv0Ni7sXldeq6OB77YzeB2M/h0dBNzjfCKO1egM+i44XvTeH4eTh58O/hb86wt49uNp3VAazaf3cyULlOYtGkSuy7uMu93233bzH0cT6edJsQzxDydoJeLF2/d/JY5Afxq4Fd0DemKwWhAo9Zwa/it5BnzOJh0kGa+zfBz9SM9L50Hf38QFSoSMhKKnaO/qz+rh6/G382fl/55id9O/Uan4E40cG/AS91fou/yvjZcqbrriT+fqJL9+Lv6k6JN4baI27icfZlW/q346fhPTOsyjTYBbUjPSyfQPZAtZ7egN+rZc2kPXwz4gtjkWDaf3cwD1z+Au5M78WnxdGjQQcZlFAJJAGspy19O+U8JFnYh84LFZ/M4gGX+YqvYL77SmjRLeiClqDn6HpzIWEm/7Gw2HrlEgKcLnSMKmmMLJ69lJYD547NVhq/RtrjNrFw6f4OBFI1tU8tZ32X570d1NrL5Gq3/SljQewEv/vOi+XN+bd6znZ/l3X3vWpRVpVv+bJZFnbCbxn+8BLe/BY1Nifg3209z6Fwah86l8UTflhblnTXOzOk5h23nt7Gg94Ji3QV6hPWgR1gPAGbdOItnNj/DQ20fYlDTQRY1y9aeigV4/9b3iUuNo0vDLkDBz75KpcJV40q30ILpFAPdA1k3ch1Q8JRv70a9CXQP5MkOTxLqFWou+3rv13m99+sWx1o3Yh23r7ydKZ2ncEv4Law4sYJdF3dxZ4s7CfcOp294X06nnSbMK4zErETuWHmHeVt3J3f8XP1Qq9Sczzxf4vW1tZa5NkvRmobEyq+Bjb4cDcBbe98qcZueSwv6OH9z5JsKH7t3o95cyLxAnjGPh9s9jIeTBxezLrJo/yLcndxZPWw1h68e5sbQG63W/GfmZeLl4lXh4wtRXSQBrJUsk4J7Wt9TrESoZ2ixZQAnUk6UuufA5GigVbkjKq1mcf+l/Tbtww01j6alA3DLt3sBOL2g4Aut8DHKanK+MfTGUteXqQJ5sPX5iyvo8nE4s83qdb3BN5L9acWbOaurzqKBewPzkEDuWuvXfUDEYIsE0NfJ9EX3cLuHaenXkhDPEPO6pPQ8m3+zKLpcVIsHmT58fTu8fAkArb705Hxkq5GMbHYHlPFzEu4Tzi93/mJbMNf0De9boSFR9j6wl0OXD9EpuJPNT4GH+4RbNJNP7TK1WJn8RLWJTxNixsVgMBo4dOUQ1wVcZ+57+2XMlyzav4g3er/B7c1vB0wPZjmrndl5YSePbXwMgLk953I+8zwDmg5g6uapnE4/bT7ODcE3MKDpABb8u6Dc516f/XP+H/P7OTvnWKzL0ecwYMWAajluU5+mXB94PeviTX9gtPRryeBmg/F09iTQLZB2Qe3I0mXR2LsxGXkZ5Opz8Xfzx9vFmxx9Dm4aNzRqDUbFiAqVuXIgV59rrvUWjk0SwFooJveSxeefYn8qNjWZXtFjyVQvtClhU6n79sk8VaGYiiZki32781CaaSDpLH2WtU3KzSIBLOMhClvnCq5KVdoL70PTrCW65pbNqGtHrCXjy37cF1Bzv6BDvULNCaDHVTXJWcUfBOn9xt8oIa6oNKYm4rHHjsMwU81zn8Z9Cgrq89BmZ4GP9WMZjQqnr2bRLMiTmE8eov2llYW2zQWjAWXNFAafvUx750T+MnaCvZcgohc0aF1Q9txe+OJatwgnN3g+Hlw8CtYb9KDWlGvco92nrvLOxuPMHdaOyAbuJOcYCPS6NphyZhK4+UH6eYheAt3/B56B5m1TsvLYfyyOvrr9aHxbgau3aRsfK3+o5WUXxKoopOfqMCrgd+Latehwr3mdOX5FAb0WnE1f6p2cfEHlDDveh8ZdeSTqER5pMtB0XYxGUAw463PB1ZkeYT348PrHaOHXikatBl3bncKKIatwiVuPcfenqG98AsK7g0cAY1qMgMxL4N8URVHI0uXiZsgBV380GFBpnEnLTeVqbjI+rj4s2rcIBYWODTpyIfO8aZ02hb/O/mXztRfFnU4/bZGgn0w9yfsH3rdfQHbyZp83GdxssL3DqJckAayFuhk0FK7/uZBVvElt14VdFp/zk5MJqWl87lfyOG5OFbzlRWuqOuUVJKDtg9pXaJ+lHaOsZuWqHmbFFtaa143At72/YObBeSUOzVMabZGx7pr4NCFBrwNKTgCrsgn4xavJLOCQ+bMGOH4pg7cvXWZqwwbm5Ynpubi43ohr0BYA2ujPsfHIJZ77+SDv3tuRW1qb+uDpP7uFh3Ljed4niACDAS5Ew6+TYOB8dDnpvHrQi18OJjHtjo6MK5z85ZsbgApoC7TVwADNPlhzbV3/OSg9n8aggNMXhfrE6nNR1k3jgaQxBCnJvN45C881lg8l/e7Uj4XuTzPWay9jW2nJcQnC88/nyQ7pyvysYWQ4+ZJx6TTPOf1G0CdJqEjlHd3DrDb0JNp3Kk5F+5xufZPkiEEEnFnPemM3vtX3Z4nLtebd9dafcC5JsVx55WPl2t4WfYp8VgH5DedqgPgtVrdTAdYaL32vvQBerWBMRiBJo0GDgq/BiF6l4qSLM63z8nC99kOuVUGmSo1OpeJvD3e+8POhsV7PjTm5jMrIol+TRgQYDIxPS2ejhwejMzL4y8ODiSlp7HdzZV6QqW9y0zwdAUYD+92k5quueX7r8+w8toW5g6Vmuqo5fAL44YcfsnDhQhITE+nQoQPvv/8+3bp1K3vDahSYFAsBfubP1po7Pzr4kcVn9bXkJFxXtGbQkqqCAxQXrQHM0ebx2k2v8c1/3zC9+3Qb91J66lI4Adx4ZiNj2hTMbnBbxG0WT2DaY6o1azWAqRoNTbwas2bEGqszPVSEXmeZFB4ae+ja8avemPRMFgQWPMBzj2Yzj/59ku+yc3jhagpvBPrz1cVLdHW7n+xMFd2DTAMF+xiNvPbdb/ii4pnFKbw0sgc3+STROOkwAwGfxCSu0+bBZ9dmZ/lmCM7AHGCOG1B6RbV1f76C6s9XrP7SUkX/wA/8YPqwpvj6wfpNDM7YBBnARcgfsMYjcQ/z2GP6UGTkodecv+I156+ghOdiAs6YpnobpP6XQS7/lvdsHJ4aCDEU/Ky7KArttZa1z64KuF77Y3B0RiajMzIt1sfEFzx481BaBgBDM7MBaKnTcU+R8oXl/zYq69+VnpK/KJVC26erVaz39KRDrpbjrs74GoxkqNXckp1DulpNulrNBScnzjg7EWAwcMTVhV3ubmgUiHUt30D6jmZd4m/MRRLAqubQCeCPP/7IlClT+OSTT+jevTuLFi1i4MCBxMbGEhwcbLe4dEV+I9mS7OQPv+Vc1jhcFRxPruggyfqEPdx5z6dVOvZa4QTw9ma3W6yb03MOXRp2MU+VVdY4gWVxNZa/U3yJTcDXrvnu+3cze8dsXuz+ovVyNsjJM1Ak/ytW81jSHX7xarJFMmcr37Q7SPNdC4CrSs93Z019mh5Iz+CB9AxzOQ9F4cPEJLLVaoIMRja7Fuqvtq7grRrolSMDCIvazdY/qEr77Vt4Hz5GxZxwttZZjt3qYTAQYjAQqdPBtV89Q7OybY61IgonpxWhA4wqcFZM/6YNmFoIFCBHpcJVUczLjZj+frqo0RBsMKBTqbiqUeN17UG7VLWppveSxomLThp65uTipigcdXGhpU5HkkbDFY2aPJWKBGdnWublkazRsNLbiz7ZOXRp+pT1IEWlOHQC+M477zBhwgQeeughAD755BPWrl3LV199xYsvVvxLvLJijM1wUkzDvOhVKqtPARdn+qdeVgLoG/crUP5/TEUTLj9dEunv9cbnuWib96GxMltI4QntnVRO+Lj4kJ6XzrCWwyzKqY1uXPz9FF4NfMhWMmnhV8p0YpePg8YJApqXWKSpNtbmuM2SjpawwnTNPZw9ePPmkudttUViUiJ++iL38OIhCG1Pft3uOSfr/2xHF6nNs9W25I/ZluNGG23Zg0D3kcROVLEMxZ3P9HfQTJ3ISE3x33WXFR/SFU9aqC+al+00XI+PKotM3PEih28NA2hAKlOcfuZV/QOkKZ4owFV8CVclkaT40Vsdw3GlMS1UF0jBlyX6vgSoMsjDmShVPGeUYCLV5/jL0AlPVS56RYO7SstpJYSOIS4Maqzj9b3Q2eUsiTp3XNATr4Qwso0X1wfAlktu9PdPwkmlsDPFm2yVOzFnkkjVwpguoXRrFYIeZ9Kytby5eg992oRx7LKWjo28yTZqSMvREejhQpcmPsQmZXEiKYubIxvQqYk/yRlZRCck0zoskCAfN5p5G1lxOI3rQnz4ans8E3o3o3WID9laHUcupKLV6QnzceVMipYQXw/cXJwID3Bnd3wyRgUCPFzQaFNp4KEmpFETDAYFZycVh86lcUO4H8eTMknKyKV/m4bsTkhFZzAS2dCbK5lafNyduZqZRwMvZ1QqNXqjkauZeYQHeJCclUdcto6oRr7kZGo5nZ5Lm1Af0lNyaBLggTfQTFHIyNXh7OlCa5UKVyc1HhlaGhkUgrxdaKPV4+6swVmjppfOgKerE84ambOiOqgUBx26PS8vDw8PD37++WeGDx9uXj5u3DhSU1NZvXp1mftIT0/H19eXtLQ0fHxK6PVeAU+9/n+8nzeLLe5uTAqxrSbSx6Dwy8Df+O+HHjxTqO9WvkWXLtMtJxdvRSHW2Ji/b3iP68L8aRToTYCn67XO5goq80+Dgsr8o6FgVIz03TjcvL/8ppekx2JQUDAqKhSloJ7QqJgqxkJ/HIRz1gWu3ruGwB+HmLfvrX2XHMWN35+5CZVKxeWj22mz5XHOhtyEulEH3Lr8D9Sg00N2no5967/l7kuLyFapiOvxBiEtu6PJy8D54j5cY5aQM2AhOu9w1NpUAr69BYCrjx8izwgGg4KbixqVSoVRUdAbFEI/L95vMT6oL55dxoC7Pxr/RqjUGgyKCiMqnDROZH/VhdvDi09h9s95Hfrx683XEKORc2k61Bjxc1XwiP4SQ7u7cfIJI0dnxN1ZTeCnpubiqGYFA9++dvkqd2ZmWSyfcSWZHhleRJDIkMahnHEu+eGXmPgEzjppyFWpGdnY+lPihX13IZGONiR9onZ6WfcQrzovLrZ8ru5BsnFloHoPfTWHUKFwUt2MHLeGuPg25OuEIEZr/sI3MJTQmx9m5eadNG/dkS4tw0hwa41nzkW83V1Qh0bhpFGTmZ2Dl4fpaeP8P9aMRgWjouCkUZOeq8PHreYfyhKiMqrr+7sucdgE8MKFCzRq1IgdO3bQo0cP8/Lnn3+eLVu2sHv37mLbaLVatNqCDkHp6emEh4dX+Q/Q1f/+IvCnERiAjs1sGxk/VK9nw9kLZKtUdG9qOaH7y1eSubeUvjC2yk9K7knPYObVlErvr66xdm0/SEzi5krUiuVf00kpqTyemm5enqjREO/sRI9cbbGy1gzKzGLh5as2lQVYd/YC4frS+4vWV6sNPemv3oenynRtEzUhPK19ks+eG4frxb1kbfuEoHMbyXby5awxiKDAQNKbDyGgUUu8W92EFldcXVxQKzrOJ13B08sbNzcPXLPOoVw4iCpyILlGJ9xVWhRdDipPy0HBtXoDuTojvu4VT5oK15wDZOTqOJaYQZcIfxnkWAgbSALo4E3A5TV//nzmzJlTdsFKCmzTFzD1t1h+Nol7wq3XAj6SmsaX1574ffFqCnpFjQdGYuITyFWp6HotWRlZQvKXp2jQYERTUO1nqsm79l5BhXKtaVkB9p86T57aiGcV/c1gVEz7Vqus78+gqNCoFIyoUNvw7GueosGIGjeVzrx9/ldhSccoiUFRkYsLahTUKKgw4oGB+UlX2OrhzvzLVyn8WEzR66ZGwYAaZ5Vlh76i51y4E3thIdf6DBX287mL3FVCzV7h5A9MtXsPhoXw6uWrHHJ1YYOnB5sTzpv78NgrRTjl3wvfvEuoA5rif/ZPi3U7wh9Dp8sjLbw/PTp35Mcth+jeqQM6lSs9G+RicA9EdfRXjM1v4aLOk9Mnj9Ltho64XGsSNxgVnNQq03ApRp1pKBaAnFQMuelo/E1JsWXnAggBzLMy+/bH/br+AHgA+QPPBBYqXzDrtSuNwgqmt8OlKSr/poXKOKFyKT4/squTBlenig8eDsX7hXq7OdO1afmb/4UQjsthawAr0gRcUzWA5ZJ/+wp/IRQeP6y0ZVjWJBT9USixJqGEfZUaY+HxzGzctmgtR3m2MW9b2vHKiKXw9SgWR3mvQSmx2rq8qlT3/oUQoraTGsBrQ0A5IhcXFzp37symTQXjURiNRjZt2mTRJFyYq6srPj4+Fi+7U6mKJyLWvtxL+MIvnAioVCqLV6nHLG+MFdi2IklK/jbmbStxHqVeiypIoEo6v+pOziT5E0II4dBNwFOmTGHcuHF06dKFbt26sWjRIrKyssxPBQshhBBC1EcOnQDee++9XL58mVmzZpGYmEjHjh1Zv349DRs2tHdoQgghhBDVxmH7AFYF6UMghBBC1D3y/e3AfQCFEEIIIRyVQzcBV1Z+5Wl6enoZJYUQQghRW+R/bztyI6gkgJWQkWGaJzU8PLyMkkIIIYSobTIyMvD19bV3GHYhfQArwWg0cuHCBby9vat8aI38MQbPnj3rEP0T5HzrNznf+s/RzlnOt25TFIWMjAzCwsJQqx2zN5zUAFaCWq2mcePG1XqMWjPeYA2R863f5HzrP0c7ZznfustRa/7yOWbaK4QQQgjhwCQBFEIIIYRwMJIA1lKurq688soruLq62juUGiHnW7/J+dZ/jnbOcr6irpOHQIQQQgghHIzUAAohhBBCOBhJAIUQQgghHIwkgEIIIYQQDkYSwFroww8/pGnTpri5udG9e3f+/fdfe4dUpvnz59O1a1e8vb0JDg5m+PDhxMbGWpTp27cvKpXK4vW///3PokxCQgJ33HEHHh4eBAcH89xzz6HX6y3KbN68mRtuuAFXV1datmzJ119/Xd2nZ9Xs2bOLnc91111nXp+bm8vEiRMJDAzEy8uLUaNGcenSJYt91KXzbdq0abHzValUTJw4Eaj793fr1q0MHTqUsLAwVCoVq1atslivKAqzZs0iNDQUd3d3+vfvz4kTJyzKJCcnM2bMGHx8fPDz8+ORRx4hMzPTosyhQ4fo3bs3bm5uhIeH8+abbxaL5aeffuK6667Dzc2NqKgo1q1bV6Pnq9PpeOGFF4iKisLT05OwsDDGjh3LhQsXLPZh7WdiwYIFde58AcaPH1/sXAYNGmRRpi7dXyj7nK39e1apVCxcuNBcpi7dY1FOiqhVli1bpri4uChfffWV8t9//ykTJkxQ/Pz8lEuXLtk7tFINHDhQWbx4sXL48GElOjpauf3225UmTZoomZmZ5jI333yzMmHCBOXixYvmV1pamnm9Xq9X2rVrp/Tv3185cOCAsm7dOiUoKEiZPn26ucypU6cUDw8PZcqUKcqRI0eU999/X9FoNMr69etr9HwVRVFeeeUVpW3bthbnc/nyZfP6//3vf0p4eLiyadMmZe/evcqNN96o9OzZ07y+rp1vUlKSxblu3LhRAZS///5bUZS6f3/XrVunzJgxQ/nll18UQFm5cqXF+gULFii+vr7KqlWrlIMHDyp33nmn0qxZMyUnJ8dcZtCgQUqHDh2UXbt2Kf/884/SsmVLZfTo0eb1aWlpSsOGDZUxY8Yohw8fVpYuXaq4u7srn376qbnM9u3bFY1Go7z55pvKkSNHlJdffllxdnZWYmJiaux8U1NTlf79+ys//vijcuzYMWXnzp1Kt27dlM6dO1vsIyIiQpk7d67FPS/8b76unK+iKMq4ceOUQYMGWZxLcnKyRZm6dH9tOefC53rx4kXlq6++UlQqlRIXF2cuU5fusSgfSQBrmW7duikTJ040fzYYDEpYWJgyf/58O0ZVfklJSQqgbNmyxbzs5ptvVp555pkSt1m3bp2iVquVxMRE87KPP/5Y8fHxUbRaraIoivL8888rbdu2tdju3nvvVQYOHFi1J2CDV155RenQoYPVdampqYqzs7Py008/mZcdPXpUAZSdO3cqilL3zreoZ555RmnRooViNBoVRalf97fol6XRaFRCQkKUhQsXmpelpqYqrq6uytKlSxVFUZQjR44ogLJnzx5zmd9//11RqVTK+fPnFUVRlI8++kjx9/c3n6+iKMoLL7ygtG7d2vz5nnvuUe644w6LeLp37648/vjjVXqOhVlLDor6999/FUA5c+aMeVlERITy7rvvlrhNXTrfcePGKcOGDStxm7p8fxXFtns8bNgw5dZbb7VYVlfvsSibNAHXInl5eezbt4/+/fubl6nVavr378/OnTvtGFn5paWlARAQEGCx/IcffiAoKIh27doxffp0srOzzet27txJVFQUDRs2NC8bOHAg6enp/Pfff+Yyha9Pfhl7XZ8TJ04QFhZG8+bNGTNmDAkJCQDs27cPnU5nEet1111HkyZNzLHWxfPNl5eXx/fff8/DDz9sMQ92fbu/+eLj40lMTLSIzdfXl+7du1vcTz8/P7p06WIu079/f9RqNbt37zaX6dOnDy4uLuYyAwcOJDY2lpSUFHOZ2ngN0tLSUKlU+Pn5WSxfsGABgYGBdOrUiYULF1o06de18928eTPBwcG0bt2aJ554gqtXr5rX1ff7e+nSJdauXcsjjzxSbF19useigMwFXItcuXIFg8Fg8QUJ0LBhQ44dO2anqMrPaDQyefJkevXqRbt27czL77//fiIiIggLC+PQoUO88MILxMbG8ssvvwCQmJho9dzz15VWJj09nZycHNzd3avz1Cx0796dr7/+mtatW3Px4kXmzJlD7969OXz4MImJibi4uBT7smzYsGGZ55K/rrQy9jjfwlatWkVqairjx483L6tv97ew/PisxVY49uDgYIv1Tk5OBAQEWJRp1qxZsX3kr/P39y/xGuTvwx5yc3N54YUXGD16tMU8sE8//TQ33HADAQEB7Nixg+nTp3Px4kXeeecdoG6d76BBgxg5ciTNmjUjLi6Ol156icGDB7Nz5040Gk29vr8A33zzDd7e3owcOdJieX26x8KSJICiyk2cOJHDhw+zbds2i+WPPfaY+X1UVBShoaH069ePuLg4WrRoUdNhVtrgwYPN79u3b0/37t2JiIhg+fLldktUasqXX37J4MGDCQsLMy+rb/dXmOh0Ou655x4UReHjjz+2WDdlyhTz+/bt2+Pi4sLjjz/O/Pnz69yMEffdd5/5fVRUFO3bt6dFixZs3ryZfv362TGymvHVV18xZswY3NzcLJbXp3ssLEkTcC0SFBSERqMp9qTopUuXCAkJsVNU5TNp0iTWrFnD33//TePGjUst2717dwBOnjwJQEhIiNVzz19XWhkfHx+7J11+fn5ERkZy8uRJQkJCyMvLIzU11aJM4XtZV8/3zJkz/Pnnnzz66KOllqtP9zc/vtL+bYaEhJCUlGSxXq/Xk5ycXCX33B6/A/KTvzNnzrBx40aL2j9runfvjl6v5/Tp00DdO9/CmjdvTlBQkMXPb327v/n++ecfYmNjy/w3DfXrHjs6SQBrERcXFzp37symTZvMy4xGI5s2baJHjx52jKxsiqIwadIkVq5cyV9//VWsScCa6OhoAEJDQwHo0aMHMTExFr9k8790rr/+enOZwtcnv0xtuD6ZmZnExcURGhpK586dcXZ2tog1NjaWhIQEc6x19XwXL15McHAwd9xxR6nl6tP9bdasGSEhIRaxpaens3v3bov7mZqayr59+8xl/vrrL4xGozkZ7tGjB1u3bkWn05nLbNy4kdatW+Pv728uUxuuQX7yd+LECf78808CAwPL3CY6Ohq1Wm1uKq1L51vUuXPnuHr1qsXPb326v4V9+eWXdO7cmQ4dOpRZtj7dY4dn76dQhKVly5Yprq6uytdff60cOXJEeeyxxxQ/Pz+LJydroyeeeELx9fVVNm/ebDFcQHZ2tqIoinLy5Ell7ty5yt69e5X4+Hhl9erVSvPmzZU+ffqY95E/TMiAAQOU6OhoZf369UqDBg2sDhPy3HPPKUePHlU+/PBDuw2LMnXqVGXz5s1KfHy8sn37dqV///5KUFCQkpSUpCiKaRiYJk2aKH/99Zeyd+9epUePHkqPHj3q7Pkqiump9CZNmigvvPCCxfL6cH8zMjKUAwcOKAcOHFAA5Z133lEOHDhgfup1wYIFip+fn7J69Wrl0KFDyrBhw6wOA9OpUydl9+7dyrZt25RWrVpZDBOSmpqqNGzYUHnwwQeVw4cPK8uWLVM8PDyKDZnh5OSkvPXWW8rRo0eVV155pVqGzCjtfPPy8pQ777xTady4sRIdHW3xbzr/ac8dO3Yo7777rhIdHa3ExcUp33//vdKgQQNl7Nixde58MzIylGnTpik7d+5U4uPjlT///FO54YYblFatWim5ubnmfdSl+1vWOedLS0tTPDw8lI8//rjY9nXtHovykQSwFnr//feVJk2aKC4uLkq3bt2UXbt22TukMgFWX4sXL1YURVESEhKUPn36KAEBAYqrq6vSsmVL5bnnnrMYJ05RFOX06dPK4MGDFXd3dyUoKEiZOnWqotPpLMr8/fffSseOHRUXFxelefPm5mPUtHvvvVcJDQ1VXFxclEaNGin33nuvcvLkSfP6nJwc5cknn1T8/f0VDw8PZcSIEcrFixct9lGXzldRFOWPP/5QACU2NtZieX24v3///bfVn+Fx48YpimIaCmbmzJlKw4YNFVdXV6Vfv37FrsPVq1eV0aNHK15eXoqPj4/y0EMPKRkZGRZlDh48qNx0002Kq6ur0qhRI2XBggXFYlm+fLkSGRmpuLi4KG3btlXWrl1bo+cbHx9f4r/p/HEf9+3bp3Tv3l3x9fVV3NzclDZt2iivv/66RcJUV843OztbGTBggNKgQQPF2dlZiYiIUCZMmFDsD++6dH/LOud8n376qeLu7q6kpqYW276u3WNRPipFUZRqrWIUQgghhBC1ivQBFEIIIYRwMJIACiGEEEI4GEkAhRBCCCEcjCSAQgghhBAORhJAIYQQQggHIwmgEEIIIYSDkQRQCCGEEMLBSAIohBBCCOFgJAEUQgghhHAwkgAKIYQQQjgYSQCFEEIIIRyMk70DqMuMRiMXLlzA29sblUpl73CEEEIIYQNFUcjIyCAsLAy12jHrwiQBrIQLFy4QHh5u7zCEEEIIUQFnz56lcePG9g7DLiQBrARvb2/A9APk4+Nj52iEEEIIYYv09HTCw8PN3+OOSBLASshv9vXx8ZEEUAghhKhjHLn7lmM2fAshhBBCODBJAIUQQgghHIwkgEIIIYQQDkb6ANZ1eVlg0IG7n70jEUIIh2M0GsnLy7N3GKIIZ2dnNBqNvcOo1SQBrOteDzP9/6WL4OJh31iEEMKB5OXlER8fj9FotHcowgo/Pz9CQkIc+kGP0kgCWF+kxEPDtvaOQgghHIKiKFy8eBGNRkN4eLjDDiZcGymKQnZ2NklJSQCEhobaOaLaSRJAIYQQopz0ej3Z2dmEhYXh4SGtL7WNu7s7AElJSQQHB0tzsBXyJ4sQQghRTgaDAQAXFxc7RyJKkp+Y63Q6O0dSOzlsAmgwGJg5cybNmjXD3d2dFi1aMG/ePBRFsXdoQggh6gjpX1Z7yb0pncM2Ab/xxht8/PHHfPPNN7Rt25a9e/fy0EMP4evry9NPP23v8MpPElchhBBC2MhhE8AdO3YwbNgw7rjjDgCaNm3K0qVL+ffff+0cmRBCCCFK07dvXzp27MiiRYvsHUqd5bBNwD179mTTpk0cP34cgIMHD7Jt2zYGDx5c4jZarZb09HSLlxBCCCFM+vbti0qlQqVS4ebmRmRkJPPnz5fuVbWQw9YAvvjii6Snp3Pdddeh0WgwGAy89tprjBkzpsRt5s+fz5w5c2owSiGEEKJumTBhAnPnzkWr1fLXX3/x2GOP4efnxxNPPGHv0EQhDlsDuHz5cn744QeWLFnC/v37+eabb3jrrbf45ptvStxm+vTppKWlmV9nz56twYjLIJ1dhRBC2GD9+vXcdNNN+Pn5ERgYyJAhQ4iLizOvP3fuHKNHjyYgIABPT0+6dOnC7t27zet/++03unbtipubG0FBQYwYMcJi/x4eHoSEhBAREcFDDz1E+/bt2bhxo3m9Vqtl2rRpNGrUCE9PT7p3787mzZvN669evcro0aNp1KgRHh4eREVFsXTp0uq7IA7KYWsAn3vuOV588UXuu+8+AKKiojhz5gzz589n3LhxVrdxdXXF1dW1JsO0nVSvCyGE3SiKQo7OYJdjuztryvXEa1ZWFlOmTKF9+/ZkZmYya9YsRowYQXR0NNnZ2dx88800atSIX3/9lZCQEPbv32+e7WTt2rWMGDGCGTNm8O2335KXl8e6deusHkdRFLZt28axY8do1aqVefmkSZM4cuQIy5YtIywsjJUrVzJo0CBiYmJo1aoVubm5dO7cmRdeeAEfHx/Wrl3Lgw8+SIsWLejWrVvlLpYwc9gEMDs7u9jI7RqNRqb0EUIIUW45OgPXz/rDLsc+MncgHi62f52PGjXK4vNXX31FgwYNOHLkCDt27ODy5cvs2bOHgIAAAFq2bGku+9prr3HfffdZdIfq0KGDxf4++ugjvvjiC/Ly8tDpdLi5uZlH10hISGDx4sUkJCQQFmaaynTatGmsX7+exYsX8/rrr9OoUSOmTZtm3t9TTz3FH3/8wfLlyyUBrEIOmwAOHTqU1157jSZNmtC2bVsOHDjAO++8w8MPP2zv0IQQQohqc+LECWbNmsXu3bu5cuWKueIjISGB6OhoOnXqZE7+ioqOjmbChAml7n/MmDHMmDGDlJQUXnnlFXr27EnPnj0BiImJwWAwEBkZabGNVqslMDAQMI3T+/rrr7N8+XLOnz9PXl4eWq1WZlypYg6bAL7//vvMnDmTJ598kqSkJMLCwnj88ceZNWuWvUMTQghRx7g7azgyd6Ddjl0eQ4cOJSIigs8//5ywsDCMRiPt2rUjLy/PPIVaiccqYz2Ar6+vudZw+fLltGzZkhtvvJH+/fuTmZmJRqNh3759xaZn8/LyAmDhwoX83//9H4sWLSIqKgpPT08mT55MXl5euc5TlM5hE0Bvb28WLVpUj8YQkj6AQghhLyqVqlzNsPZy9epVYmNj+fzzz+nduzcA27ZtM69v3749X3zxBcnJyVZrAdu3b8+mTZt46KGHbDqel5cXzzzzDNOmTePAgQN06tQJg8FAUlKS+fhFbd++nWHDhvHAAw8AYDQaOX78ONdff315T1eUwmGfAhZCCCEcjb+/P4GBgXz22WecPHmSv/76iylTppjXjx49mpCQEIYPH8727ds5deoUK1asYOfOnQC88sorLF26lFdeeYWjR48SExPDG2+8UeoxH3/8cY4fP86KFSuIjIxkzJgxjB07ll9++YX4+Hj+/fdf5s+fz9q1awFo1aoVGzduZMeOHRw9epTHH3+cS5cuVd9FcVCSANZl8uSvEEKIclCr1Sxbtox9+/bRrl07nn32WRYuXGhe7+LiwoYNGwgODub2228nKiqKBQsWmJtr+/bty08//cSvv/5Kx44dufXWW8ucQSsgIICxY8cye/ZsjEYjixcvZuzYsUydOpXWrVszfPhw9uzZQ5MmTQB4+eWXueGGGxg4cCB9+/Y1J6SiaqkUGZ67wtLT0/H19SUtLQ0fH5+aD0BRYI6f6f3/tkNIu5qPQQghHFBubi7x8fE0a9YMNzc3e4cjrCjtHtn9+7sWkBpAIYQQQggHIwlgvSEVuUIIIYSwjSSAQgghhBAORhJAIYQQQggHIwmgEEIIIYSDkQSwvpCHuYUQQghhI0kA6zJJ+oQQQghRAZIA1hcqlb0jEEIIIUQdIQmgEEIIIYSDkQRQCCGEEHY3e/ZsOnbsaO8wHIYkgPWF9AcUQghhZ3379kWlUhV76fV6e4cmipAEUAghhBBVZsKECVy8eNHi5eTkZO+wRBGSAAohhBAOZP369dx00034+fkRGBjIkCFDiIuLM68/d+4co0ePJiAgAE9PT7p06cLu3bvN63/77Te6du2Km5sbQUFBjBgxwmL/Hh4ehISEWLwAXnjhBSIjI/Hw8KB58+bMnDkTnU5XYpxxcXE0b96cSZMmoSgKWq2WadOm0ahRIzw9PenevTubN2+u2ovjQCQlr9Ok2VcIIWoFRQFdtn2O7exRrpEgsrKymDJlCu3btyczM5NZs2YxYsQIoqOjyc7O5uabb6ZRo0b8+uuvhISEsH//foxGIwBr165lxIgRzJgxg2+//Za8vDzWrVtn03G9vb35+uuvCQsLIyYmhgkTJuDt7c3zzz9frOyhQ4cYOHAgjzzyCK+++ioAkyZN4siRIyxbtoywsDBWrlzJoEGDiImJoVWrVjafvzBRKYp0Hquo9PR0fH19SUtLw8fHp+YDMBpgboDp/eP/QGj7mo9BCCEcUG5uLvHx8TRr1gw3NzfIy4LXw+wTzEsXwMWzwptfuXKFBg0aEBMTw44dO5g2bRqnT58mICCgWNmePXvSvHlzvv/+e6v76tu3Lzt27MDFxcW87PHHH+ftt98uVvatt95i2bJl7N27FzA9BLJq1So++ugjhgwZwowZM5g6dSoACQkJNG/enISEBMLCCq5z//796datG6+//nqx/Re7R4XY/fu7FpAaQCGEEMKBnDhxglmzZrF7926uXLlirt1LSEggOjqaTp06WU3+AKKjo5kwYUKp+x8zZgwzZswwf/bz8wPgxx9/5L333iMuLo7MzEz0en2x5CshIYHbbruN1157jcmTJ5uXx8TEYDAYiIyMtCiv1WoJDAy09dRFIZIACiGEEJXl7GGqibPXscth6NChRERE8PnnnxMWFobRaKRdu3bk5eXh7u5e6rZlrQfw9fWlZcuWFst27tzJmDFjmDNnDgMHDsTX15dly5YVqxls0KABYWFhLF26lIcffticIGZmZqLRaNi3bx8ajcZiGy8vL1tOWxQhCaAQQghRWSpVpZpha8rVq1eJjY3l888/p3fv3gBs27bNvL59+/Z88cUXJCcnW60FbN++PZs2beKhhx4q13F37NhBRESERc3gmTNnipVzd3dnzZo13H777QwcOJANGzbg7e1Np06dMBgMJCUlmeMWlSNPAQshhBAOwt/fn8DAQD777DNOnjzJX3/9xZQpU8zrR48eTUhICMOHD2f79u2cOnWKFStWsHPnTgBeeeUVli5dyiuvvMLRo0eJiYnhjTfeKPO4rVq1IiEhgWXLlhEXF8d7773HypUrrZb19PRk7dq1ODk5MXjwYDIzM4mMjGTMmDGMHTuWX375hfj4eP7991/mz5/P2rVrq+biOBhJAOsNeZZHCCFE6dRqNcuWLWPfvn20a9eOZ599loULF5rXu7i4sGHDBoKDg7n99tuJiopiwYIF5mbXvn378tNPP/Hrr7/SsWNHbr31Vv79998yj3vnnXfy7LPPMmnSJDp27MiOHTuYOXNmieW9vLz4/fffURSFO+64g6ysLBYvXszYsWOZOnUqrVu3Zvjw4ezZs4cmTZpU/sI4IHkKuBLs/hSRxVPAWyG0Q83HIIQQDqi0J0xF7SBPAZdOagDrMsndhRBCCFEBDp0Anj9/ngceeIDAwEDc3d2Jiooyj0dU99g+CKgQQgghHJvDPgWckpJCr169uOWWW/j9999p0KABJ06cwN/f396hCSGEEEJUK4dNAN944w3Cw8NZvHixeVmzZs3sGFFlSXOwEEIIIWzjsE3Av/76K126dOHuu+8mODiYTp068fnnn9s7LCGEEHWIPEdZe8m9KZ3DJoCnTp3i448/plWrVvzxxx888cQTPP3003zzzTclbqPVaklPT7d4CSGEcDz5w6Lk5eXZORJRkuzsbACcnZ3tHEnt5LBNwEajkS5dupgnkO7UqROHDx/mk08+Ydy4cVa3mT9/PnPmzKnJMIUQQtRCTk5OeHh4cPnyZZydnVGrHbY+pdZRFIXs7GySkpLw8/MrNnWcMHHYBDA0NJTrr7/eYlmbNm1YsWJFidtMnz7dYsT09PR0wsPDqy3GcpGqbiGEqDEqlYrQ0FDi4+OtTmkm7M/Pz4+QkBB7h1FrOWwC2KtXL2JjYy2WHT9+nIiIiBK3cXV1xdXVtbpDKwdJ+oQQwl5cXFxo1aqVNAPXQs7OzlLzVwaHTQCfffZZevbsyeuvv84999zDv//+y2effcZnn31m79AqRiXjAAohRE1Tq9UyE4iokxy200LXrl1ZuXIlS5cupV27dsybN49FixYxZswYe4cmhBBCCFGtHLYGEGDIkCEMGTLE3mEIIYQQQtQoh60BrHfkIRAhhBBC2EgSQCGEEEIIByMJoBBCCCGEg5EEUAghhBDCwUgCWJdZ9PuTPoBCCCGEsI0kgPWGjAMohBBCCNtIAiiEEEII4WAkARRCCCGEcDCSANYb0gdQCCGEELaRBFAIIYQQwsFIAiiEEEII4WAkAazTpNlXCCGEEOUnCaAQQgghhIORBFAIIYQQwsFIAlhfKNIcLIQQQgjbSAIohBBCCOFgJAEUQgghhHAwkgDWG9IELIQQQgjbSAIohBBCCOFgJAGsy+TBDyGEEEJUgCSAQgghhBAORhJAIYQQQggHIwmgEEIIIYSDkQSwvpDugEIIIYSwkSSAQgghhBAORhLAaxYsWIBKpWLy5Mn2DkUIIYQQolpJAgjs2bOHTz/9lPbt29s7lEqQNmAhhBBC2MbhE8DMzEzGjBnD559/jr+/v73DKSdJ+oQQQghRfg6fAE6cOJE77riD/v372zuUSlLZOwAhhBBC1BFO9g7AnpYtW8b+/fvZs2ePTeW1Wi1ardb8OT09vbpCqwCpDRRCCCGEbRy2BvDs2bM888wz/PDDD7i5udm0zfz58/H19TW/wsPDqzlKIYQQQoiqp1IUx5xQdtWqVYwYMQKNRmNeZjAYUKlUqNVqtFqtxTqwXgMYHh5OWloaPj4+NRa7mS4HXgsxvX90EzTuUvMxCCGEEHVMeno6vr6+9vv+rgUctgm4X79+xMTEWCx76KGHuO6663jhhReKJX8Arq6uuLq61lSI5eOYebwQQgghKsBhE0Bvb2/atWtnsczT05PAwMBiy4UQQggh6hOH7QMohBBCCOGoHLYG0JrNmzfbO4TykWZfIYQQQlSA1AAKIYQQQjgYSQCFEEIIIRyMJIBCCCGEEA5GEsDarFx9/KQ/oBBCCCFsIwlgbbViArx/g2mwZyGEEEKIKiQJYG0VsxyST5lm+jAa7R2NEEIIIeoRSQDrgj1flLBCmn2FEEIIUX6SANYFh360dwRCCCGEqEckAayNtJmWn8/vtU8cQgghhKiXJAGsjdZNK/82MiuIEEIIIWwkCWBtdHCpvSMo2fb3YNWTknAKIYQQdZjMBSzKZ+NM0//b3wvNb7ZvLEIIIYSoEKkBrDdquEZOl12zxxNCCCFElZEEUAghhBDCwUgCWJdJPzwhhBBCVIAkgEIIIYQQDkYSwFooza2RvUMQQgghRD0mCWAt9F/oCHuHIIQQQoh6TBLAWuhg4weYmvc/fg2ZVLCwrP5+0h9QCCGEEDaSBLA20riwwtgHb93VgmVp5+wXjxBCCCHqFUkAayG1yvR/rdqtYGHMT/YJRgghhBD1jiSAtZBaZcoAz7u2KFi4aU4ZW0kTsBBCCCFsIwlgLXQt/+OKU0gZJSXpq7P2LobPb4WsK/aORAghhAOSBLAWyq8BPOfashxbqaonGFE91kyG8/vg79ftHYkQQggHJAlgLZTfB9BYrid7pTawTpI5lYUQQtiBJIC1kPpaBqjI0C5CCCGEqAYOnQDOnz+frl274u3tTXBwMMOHDyc2NtbeYaG61gRsNNo5ECGEEELUSw6dAG7ZsoWJEyeya9cuNm7ciE6nY8CAAWRlZdk1rgo1Add0baHUTgohhBB1lpO9A7Cn9evXW3z++uuvCQ4OZt++ffTp08dOURU8BGKUHEsIIYQQ1cChE8Ci0tLSAAgICLC6XqvVotVqzZ/T09OrJY78GsBa3QdQJU8dCyGEEHWVQzcBF2Y0Gpk8eTK9evWiXbt2VsvMnz8fX19f8ys8PLxaYjH3AZT5fx2AJNJCCCFqniSA10ycOJHDhw+zbNmyEstMnz6dtLQ08+vs2bPVEkudaAKW5FMIIYSos6QJGJg0aRJr1qxh69atNG7cuMRyrq6uuLq6Vns8JT4EoijS9CqEEEKISnPoBFBRFJ566ilWrlzJ5s2badasmb1DAgpqAItVstWmBLC2xCGEEEKIcnPoJuCJEyfy/fffs2TJEry9vUlMTCQxMZGcnBy7xpWfW2Vq9RDevWCFYihlK2mSFUIIIYRtHDoB/Pjjj0lLS6Nv376EhoaaXz/++KNd4zqQkApA9NlU6D+nYIVBZ5d4rJI+gEIIIUSd5fBNwLXRupiLBR80zgXvr8RCWKeaD0gIIYQQ9YpD1wDWVurC/es0LgXv9XlFStoxgZU+gEIIIUSdJQlgLaQunFv5NSl47+5f47HUSrW05lYIIYSoKyQBrIXUhTNAd7+C94mHajyWktmpBtBogC/6wbIx9jm+EEIIUQ9IAlgLRTb0tr7CWMpTwHWlVuzfz+HHB6w0Z9soMQbO74Nja6o2LiGEEMKBSAJYC/l5OFtfUeowMHXEumlw9Dc4VNEnretIomsr6UsphBDCDiQBrIVSskqoHVPXooe2K5u4aDOqJo6aVtU1rXWl5lYIIUS9IglgLaQp1Adw6b8JBSsaXFfKVnUtkahr8QKHf4GFLeHMzqrbp9QACiGEsANJAGuh6be3Mb9/edVh8Glk+lAfmoArq3CNWU3Xnv38EGRfgR/uqpnj/fUafDkAdLk1czwhhBAOQxLAWijI09X83mBUQKUxfTAaLQvatfmwkjVXVRG7Yiy7THUw6qtuX6Vdh61vwtndELO86o4nhBBCIAlgreTsVCS5Ul+7TVWZeFRWbWi6dJT+c3qtvSMQQghRz0gCWAs5qS1vS47hWrJVr5qAK5i8WSSedkoAHSXxFEIIUW9JAlgLOWssa9fOpZqeCjboS64BrK3zGpeoovHasw+gEEIIUU9IAlgLqYo0r7ZSnwdg7lc/s/3kFfPypIyCpsGPN8cRfTa1RuIzqeEmYEWBn8bDLxMKL6zZGOx+XCGEEKJqSAJYh0xyWsmYL3abPyckZ5vf/3PyCsM/3M6JS9bH11MUBZ3BWGzZrwcvsD8hpXoCrkqZl+C/lXD1ZMGykh4CybgEV+NqJq5Kq2AymXrWNB3e6e1VG44QQgiHUItGFhZlcUMHQNMX13Lb9Q3ZfeQUh9wsy3y0OY537ukAmJ4gTsvRsed0Mj/sTuCfE1cY37MpUwZEcj4lh8H/9495u1Ov3245B3FZKlkBuC8hmWT/S9x2fUPbNrCW7JXUBPx2pOn/006CV4OKBVhqLHaoATy8AnJSoesjps+rnoDT/5imxJudVvPxiGqRpzdyMimTNqHeFi0BG/5LJC1Hx91dwgE4n5pDZq6e1iElTBtZTjqDEWeN1AcI4UgkAaxDVIVqizYeuYSPlXUrD5xn5YHzJe7j6x2n2XjkEgGeLhbLm7+0jkBPF7Y8fwtermX/WCRn6QgoX/gWNhxO5NODezm94I5K7KWMROzy0VITwHMp2TT0casbX3w/P2z6f8v+4B8BqQmll69HykpOkjJy8XFzxs1ZU+XHTsvW4ePuxJmr2TQJ8CApQ8v/bTrO2B5NaRPqY3UbvcHI6atZtGjgVaw7R2kMRoXIl38H4LUR7RjTPYL/LqTRJMCDx77bB0CPFoE09veg14K/APh3Rj+CvU1/BebkGXB3sbwGOXkGVkef55brglGrVCQkZ9E5wvJf7rHEdAb/3z883qcFLw4uGGz+YloOuTojK/adY93hi6x8she+7iVMU2mj/Qkp7Iy7yv9uboFaVdDdJb8Ps1ZvZMRHO8jS6vFxd2LhXR2sXmdFUVCpVBiNCipV8W4ztZXRqJTvD+16Jv++idpBEsA65ICxZZElFauJOp+aw/nUnGLLr2blcdfHO1g/uU+Zv6gm/3iA5meaMPvOtjYfNztPj8e196qq6EdXVk1cKeME7oy7yujPd9E5wp8VT/Qs74HLWb6SCp9nToopAazgL9FVB87zwd8n+fTBzrRo4FVFARZIy9Hh6aLBSaMmJSuPY4kZTF0ezRN9W/Bgj6bl2s8Pu89wJSOPxTviWfLojfRoEYjBqHApPZcwP3eytHp+2X+Omav/I8jLlb0v90erN+CiUXM5Q0uQlysfbT5JVp6Bp29tRfyVLI4lppOp1dOygRcGReGV1f8xoG0Ik25tafGHz9VMLZ1f/dMipjHdm3AyKZPd8cn8uOcsp+ab/njZGXeV/QkpbPgvkbfv6cg7G2NZF5PIq8Pb8cCNEWWe66db4gjzcyfYu2D8z8+2nqJJgAcPfvkvYb4F1fwr95/Ht9Bc4ScuZRLs7cbEJftZe+giX43vwq3XNTSNHwos/COWr7bHA+CkVqE3Kiwe35VbrgsG4ODZVOauOYKiwCdb4hjSPpSmQZ7o9EZ6zP/LIs5PtsTx3IDWqFSmZFWlUvHzvrPsOZ3CQ72acsd722jXyIf0HD1zhrVl5qrDNAvyZPH4rvzv+338eTTJvK+Ff8QS6OlC/zYNub97E57/+ZCpVlNr+aDb49/tY+vzt1gkDr0W/FXs99fBVwaQlq3jvs928lCvZkzo05xcnYHkrDw+3RLHwXNpvDq8HS/+cojD59M5OGsAe88kozModAz3I1OrIyldy6SlB7i7c2M6NfHnhiZ+BPtYNrEYjAr7E1II9nYlItATo1Hhr2NJRDX2JVdnoKGPm8UfIofPp5Geq+PGZoHc8+lO9p5J4ctxXejXpuSWj/0JKaRm53HrdWW3jpxPzTH/MRA//3YytXq83Yon6QajgsGo4OJU8h9S5U3OcnUGvt91hp4tggj0cqGhjxuZWj17TidzU8ugYn+0Ld4ez/9tOsHSCTeW+MeTwajw8qrDDGkfSq+WQTbHIipGpdS5x0drj/T0dHx9fUlLS8PHx/oPdEX1nL+JC2mmGSBOu90PwFFjEwbnLTCX8SGTQ26PATA6bwY7jbYnY6VZ+WRPRny0gzHdm/DaiCjOJmcT5OWKm5MK1Vx/AKbpHudnw83sfqkfRy+mk6c3snzvObR6A3d1bsywjo0s9qkoCvd8upOfEgcDsEB3H58Y7rS9BjD9ArzTxnLZi2fBzcp1n+1r+v+Dq6DFLVZ3N3nZAVZFXwCwPYb8/ao08EqybduUta8Oo2HEJ6WWOX7DLCL3zzUte2wLhHWE/+sIKfHXytneBNz0xbUAdG3qz0//s0x8951J5kBCKo/c1MzqF4FWb8DVyXotW9zlTBb8foyNRy4B8OeUm+n/zhaLMp+P7cJt1zdkZ9xVvtwWz6O9m3HkQjq9WwXx59EkDp5NZcYdbQj1NSU0f/x3yWL7uzs35qd95wCYN6wtvxw4z4GEVJvPvaqdXnAHpy5ncuvbW0osc0f7UHzdnVmy21Rj261pAP+eNv3sOGtUjOkewdc7TgMwvGOY+Weyul0f6oNWbyDuclaNHK+6NW/gyalC51L0c0W4OasJ9HTlfGoOs4dez7ieTWk2fZ15/StDr2fOb0cstmnd0JvXR7ZjzaGLLN5+usR9x8+/nTyDkbikLHL1BjqF+/HJllO8sf6Yucw/z99CeIAHuToDG45cwtNFQ9MgT/pd+3m7t0s4W09c5mKa5UxBK5/sSU6egfu/2M2gtiG8PjKKuz/ZQdzlLB7u1YyHb2qKj7szWVo9ob7u5OQZ+Gp7PAv/iGXmkOsZ2yOCzFw9/tdaid7bdIIsrR53Fw1XMrXcHBlMplbHsz8etDhum1AfvF2d+Pd0Mvd2CeenfWcxKjBzyPU09nfn8Wu12Dc08eOXJ3sBkJSey8oD59l0LIknbm7Bb4cu8Mt+UwvWydcG88mWOE5dzmJYp0bcHFm13Xmq8/u7rpAEsBKq8wfo481x5l8G+QkgQNPcJeb3hRPA+/NeYoexHTeojnOj+gifGoZioKqbxBROu40xf2qX+wWZ5jq94haP70puTg5eW18hxqM7b8Y1MZ9LfgK48smepGbruKGJPx3mbiDQ04Unb2nJoHYhNPJzL9iZlQRQPzUOlWcgu05dpV0jX1PzlKLAHD9TgQdXQotbrcb27I/R5qby0wvuMDcjfr3jNLdeF0xkQyt9q8wJoBplVjK5xzZyef9vhN+zEJWzW7HiRqNCUoaWEF/LdYqioMqPscNofms+izfWH+OTBzrTrpFvsePN0o1jrvM3pmWPbITwbijvdUKVfOpauTQOn09jzaGLDGkfyvWhPqjVKr7bdYbD59J4bUQ7nK79NZ6fALYN82HFEz3JuvYXe7dmgdwwbyMAY3tEMHdYO3MYBqPCR3+f5O2Nx83L7u/ehH7XBbM7PpnPtp6yeo2FEJXj5qwmV1d9Mx7l1wpbE+jpwgf338Doz3dV+XFvjmxARKAH3+48Y1P5Z/tH8kz/VlUagySAkgBWSnX+AGn1Blq/vB4oXwKYX3a67hGWGvpVaUwqjMS7PWD+fF/ey+wyXl/qNhM0a5jhvMQce358b+ju42PDnaVuu2BkFDHn09h45BKqjIvsdptUrMw7urt4zzASgAbernz2QCc6LW5uWtfwDd47E07f1g14tn8kKdl5OGvU5iep3cmlv3o/m40dySiSyP77Uj+6vb4JwNyslp+QKaholvuD+Vy+9HiE7cGjaRLgwbHEdHadSuaO9qHEX87iyMV0XJzUrHu6N2F+bjz30yHWxlw0b5sWeRcdDo00H/fU67dz5GI6B8+lMub39gB8oh/K/5x+A0Dn14Le2QtZrptIE+UiABefTbRorhvZqRFtQn14bd1R87Lbrm9IuzBf3v2zIIkTQoi6oHerIL57pHuV7lMSQOkDWGu5OmmY3L8Vi/48UaHtW6hMTUl91Af5P+cP8Vdl0l/7JieVxhWOqWij4DKXV2ma+4OVNQXCVFdL2FfZf3e8+EuM+X1ICeWnOP9sTgAvZ2i5++NtnLxW4RZ9NhkIZ3PsZTbHXi627avOixml+YdthrY8oJthsS4/+QN4+Ou9AJy+tl+DYnm+zukJ/JWcZLFs7aGL5vd5eiP939mCn4czqdk6i3J/HrVs5mz+UkET05hrx8tP/gCcU+NIzM1F54J5EKeifbV+OXAeijwItPHIJXPzrBBC1CX/nLhSdiFRbnXg8UfHNbl/pM1liyZURtSM16znW5c38FdlAvCn6/NVGh+AhuprnqiIwvGU1p35ac0vjNKYhsG5SfMfPmTiTm4pWxQwFtmzrdegaPJXEjVG7lTvsKlsvgakEoL1ZFsIIYQoShLAWu6/OQMrtF2IKpnZzt9WaSzWau3UFX4itnp6HhSOsbRaxinOP1t8PuT2GDGuj9p0DBeV5ZzMlXmi2YviT2Pfp/mb91w+sHkfKozscXuSXW5P2ZzECiGEcGySANZynkXG5HOjYPq3wvVQzYM8LcrdqdlZnWGZqcuo/VIKRelNdiklq0ZlaiSdVBXbtjIJ4EDN3mLLblFHl7ld4evqQsHQGUEqyyeCS7s/vmQy3+lzuqiOlVhGCCHsrXcrGRKmOkgCWBeEdjC/PTAinSWPdmfkDY1wI8+8fF76ywRh23AgA2ydfaOIEIoPfVJW8lM4UYlxK6hhK+8odk1USWUXomiNZEUSs/JvU1YtqAojqnIkprZEUPi6Fr4HSqF/0mM1f3DY9RE6qk5izXSnJYx2+pufXefigo7+6n3VlqSX5/xF9bNlsHchagurozKISnP43wIffvghCxcuJDExkQ4dOvD+++/TrVs3e4dladAbsHgQAO6/P0PP2eO5sXkgl3r6wBcFxd5qcxziS9/V6fm3g0pFnt5onnXAmpsjG7DluOWDEw1VxecMLqvGraQaqPLWmi13nVfiutaqBGKVcEBlcbyKDJXsRh6/ubzMj4a+fGEoGB9wkmalRTkNBc3Arqo8i3UBpNNMdZF9SmvUGPnNZQY5uHJX3iuAyqIWtzAvssnCDWf0VtcXVvjqFU5Am6su0E4Vz2DNvwzTmPoRvu38Mf3y3rbY3pU87nPabP78rvOH3KH5F4CR2tkcUSJQUKGlYMaY1qoEkhQ/Uij+xJwnObzl/Ak/GW7mL+MNAPRSx3CdKoEHvfbSVBtLVO4XFk9bN1NdZL3Li7iqdMzRPUgTVRI7jdezwdgFUOFLJiGqZNIVT2b6/U6Qnw8TT/dGjcIcz59poTvBs7onOEJzZg65Hr/UIwwfeBtn0/T8vP8cIT5upGRpeeDGpvx++KLFQ0V/3udDiwAXlEZdUeuzyMWV2KRshn24HReNmi6N3dlxJgtPcsjCnQakoMWF54Z147ttx5nc1Z0eHdpy24f7uJKZhxojB2cP4njsf1xYNZsPcgeS5h3J5uf6ct1M09P8s4dez7bYi7x/X0eyjBpOX8miQ7gfKkBvVHDRqFGrVWw5fpn/fbePjx64gRubBbIu5iJTfzrI5/e35bbrQ8lTNBy7lMn582e5oUUYG05msuzfBOYOa8u5lBz2nUlh1pDrUatUZOXpOZCQyr7jZ3imSTxZEf2IS1fTobEvbV/5g+w8A49GJPHS/YNQ+4ZyNjmb+7/YxUM9m9E5wp9hHxbMNf3fnIG4OWt4e0MsH20umGu7d6sgbmweyLieTfFydeJyhpaur5kG0e7eLID/u68Tb22IxcvViWkDW2NUFHLzDHi4OrHvTApdm/pz8Gwap69mcfxSBisPnGfWkOu5pXUw3+86w4gbGhHq686l9FzWxVzk9qhQdsZdZepPpnHonu0fycFzqcwd1pbZv/5nMeA0wIf338DAtg1Jz9WTnJWHq5OazbFJzFz9HwDjezYlzM+N6LOprItJ5MtxXYhs6E3vN/8GwEWjJs9g5O27O9C9eQBHLqSTkavHw0XDOxuPcyLJ1Md6zVM3MeT9bYBpjMCLabl8tS2eB26MIFOr5+drY1gOaR/KhiOXyNNb/904oXczFm8/bR6eZctzfTlzNZvV0Rd4bmBrTl3O5P4vdnNnhzB+PVgwbuSh2QOYtvwguXojW4v87m4e5MmpK+UfFzH/wbVn+rXi/zaZHkh0d9aw9LEbWbo7ASeNiif6tuCmN/622K5w+ZtaBpGjMxDq68aaaw/H3dK6AX/HXmbx+K6k5+pQFGjXyNc8dmiP5oHsPFXQp3lcOQaRF7Zz6GFgfvzxR8aOHcsnn3xC9+7dWbRoET/99BOxsbEEBweXuX2NPUZu0MO8QMtls1Ig4wK8W2jw5xa3QpzlE6HFeARCtukf1qZh/zLvzwuM6NSYp/u1NA/+G38liyYBHmjUKpKz8tCoVWw4fJFBJ+fgHWvZdy7tqVhcfRrwwopD/HrwgnnSivdGd2Lu0s3sdXvCahj/Z7ibd3UjzJ99yOLpgN3s97mVdacV2gQ5c/RKQSJUeCgca2brxvK1YRABpLPf7X8AfKEfzKv6B4uVDeUqO92esrqfWGNjWqtNv6gLD7lT9Pidcz9m37Vz0ykaOmg/JxvTY7v/uj5JsCqVp/Mm8p/SlE2uzwHQOvdrtLgww+l7JjgVPO37gX4YbVQJ9NMcYKshij6aGEozPu85pjstNccZlfuFRe2qNe1zP+NN58/5RD+UIZqduKLjQac/S91G1ADPYMhKgm6Pwb+fWS/TZigc/c36urI06gINroPo702fA1vCVVONsN63KU5ppwvKhnaEDvdB467w9+vQZiiZGal4bZkNYTfAhf2mcgEtUFBQ3TaP3AtHcL20D1XfF8HJHVJOw/H1sG8xOw3XkzZqCYOco8Ggg3N7IbwbtBtlmslGUWDH+6Z93vgkHFwCQZGmOLb/HzTpDo06w5UTENIeLh40zX/dYxKoNXDlBNmejfBACx4BoCgol2NJPBNLcOc70aSfg9jfodMD4FJomKfLx8HdH7waoEu7iPPV46ZWFnc/i0uXnJpGcp6GlsGlz5jz3a4z+Hs4M6R9WLluTf7A6rk6A79GX6Bv6wZ4uznj7qIhO0/Pn0eT6Nu6AT5WZvbIlz+u5xdju9C/SMvOwbOp/HbwAm1CfRjV2froD4qioNUb0RsVPF005u+AxLRcGvq4WgwIn5Nn4PilDNo39rU6UHzC1Wx+2neWibe0LHFaRkVRzLOVGIwKmiKzTc1bcwQPFw1TB7S2iK86pnmUYWAcPAHs3r07Xbt25YMPTB3ujUYj4eHhPPXUU7z44otlbl+jP0D5gxAX9nQ0vNex8vt+JdVyajFdDji7mxJPzbVK4oPLYOXjxbftPwciesH3o0B7rQm63V1w+OfiZQsLao1y/4+oApqRp83FZfFtkHjIsky7u9CN+JxpPx3k/471LXV3Rid3No/czy0hWlSFrsljEevZEGtqun5zRBu+WvUH613LvrcAVx7eTVBYc3BysX79i/g14iX0iUcYqV1lXnbY2JR26tPmz3nOvrjobJ+5oyRHjU1oozbNLnG+5WganVxa6X0KIeq4dqNM84RfOQFuvpB6BprfAunnwb8ZJB2FXk/DiY1wbg8Et4Ez12p5G3UGbYbp/+f2QufxkHzKNP1kbiqEdzftS5cNSUcgfitkXYbb5kFgCzDkgdoZEmNMx/YOMf0/7awpsc9Ng+R4aNAaMpNMSbtaY5pZSZ9r+oPA1dtUQZH/f5XatA+Pysw8b50kgA6cAObl5eHh4cHPP//M8OHDzcvHjRtHamoqq1evLnMfNfoDdPEgfNqneo8hhBBC1DYt+sGDv1TpLiUBdOA+gFeuXMFgMNCwoWW1ecOGDTl2zPpTkVqtFq22oP9Wenp6tcZoodCDIEIIIYTDCLJ9TFxhO4dNACti/vz5zJkzx34BzL7WdHjyT1OTa2FP7oZvhpiq5Fv0g7iCmSxoeRvcMBaOrYFDP0JgK2h/D2yeD0oFn85s2tvUH6cst82FmJ8hcpAp7vx+RGXpPB72fW19XbfHQeMMO20fK69MTXpCwrXBl9uNgsMripfpOx1cvGDDjOLralqH+03NM04upqaYooJaw5VYU1+nnOIP75gNftPUx+yjHtDoBrjzfUg9C6f+hh4T4civ0HY46LVgNIBXsOlnRq0p6CpQlKKYmqH8mlh2Lchfp1JBXpapzxgKGPXg5Gpap88tvs/8bcpLUUCbDq7X/rovbR+KYmrCyo+jcFmjwXS+1uJQrsWvdrI9RkUxvdTqgn3mLzPqTDEYjYBi6jvn7GZ5bIMeUuIhoLmpmcwruOBeaDNN5ZzcTfvPP15eFrh6QeZlU5OaLtv0/8Ix52WZ7rPG2fRznn898mNRawr2p8uGpGPg4Q9+Ta/dQ5eCOI1GyMs0NeXln6vReG2d3vR7Sq8Fn0ama5d9BTwbmI6ZcRG8Gpp+FnLTTWUADFpAZWrWdHIFvwhT0+alI9DwetC4mvbv7AFp50zXJfMSaFxMZVPPgJOb6To4e5iaO1VqyEk23eP086Zj6XJM/zfkma592nnT9c66YorL1RvCOpr+XelywCPItP5yLPiEmY7j09i0vWIw9bu8fAzcA0znrM81rXPzM10LV2/T70X/ZpAcZ2pqzb4KV46b7mP+uWVfNf171l8b6zOgOZz919RNJzHGdC7BbeBqnKnJ9L9V4N/U1ASrcTE1z4Z2gKsnTJ8TdkLL/qZ/78mnTNch+Np86/FbTdfMv6lpeWIp/ZJdfUz/zhq2g0uHTf1OL+wv+G5x9jD9vEDB90ZAc9PvqLi/rt1XTNcx28qMH70mw212/N6tx6QJuBxNwNZqAMPDwx26ClkIIYSoa6QJ2IHHAXRxcaFz585s2lRQU2Y0Gtm0aRM9evSwuo2rqys+Pj4WLyGEEEKIusahm4CnTJnCuHHj6NKlC926dWPRokVkZWXx0EMP2Ts0IYQQQohq49AJ4L333svly5eZNWsWiYmJdOzYkfXr1xd7MEQIIYQQoj5x2D6AVUH6EAghhBB1j3x/O3gNYGXl5841OhyMEEIIISol/3vbkevAJAGshIyMDADCw8PtHIkQQgghyisjIwNf37JneqqPpAm4Eoz/3969B0VVvnEAfxZkF0hhweW2KghesBBNKTcqsYkdgZi0bEYiJrUML+FkoxljN8o/kpFG/3CM8Q+BZmw0nVGcSbLxAnlDDAZURHeEECq5lLaIowbI9/dHs+fHCZSczGX3fD8zzCzv+5zj+5xnl33YdV96e+XKlSsyYsSIAf824r/h2GLm559/1sTL08zXvTFf96e1nJmvawMgnZ2dYjabxcNDmxui8BXAf8HDw0NGjx74j2w/KFrbbob5ujfm6/60ljPzdV1afeXPQZttLxEREZGGsQEkIiIi0hg2gEOUwWCQnJwcMRgMzl7KQ8F83RvzdX9ay5n5kqvjh0CIiIiINIavABIRERFpDBtAIiIiIo1hA0hERESkMWwAiYiIiDSGDeAQtGXLFhk7dqx4e3uLxWKR06dPO3tJg1q/fr08+eSTMmLECAkODpaXXnpJbDabKua5554TnU6n+lq2bJkqprm5WVJTU8XX11eCg4NlzZo10tPTo4opKyuT6dOni8FgkPHjx0tRUdF/nd6APv300375TJo0SZm/ffu2ZGVlyciRI2X48OHyyiuvSFtbm+ocrpTv2LFj++Wr0+kkKytLRFy/vkePHpUXX3xRzGaz6HQ6KS4uVs0DkE8++UTCwsLEx8dHrFarXLp0SRVz7do1ycjIED8/PzEajbJ48WK5ceOGKubs2bMyc+ZM8fb2ljFjxsiGDRv6rWX37t0yadIk8fb2ltjYWCkpKXmo+XZ3d0t2drbExsbKI488ImazWRYsWCBXrlxRnWOg+0Rubq7L5SsismjRon65JCcnq2Jcqb4ig+c80ONZp9NJXl6eEuNKNab7BBpSdu7cCb1ej4KCApw/fx6ZmZkwGo1oa2tz9tLuKSkpCYWFhaitrUVNTQ1eeOEFhIeH48aNG0rMrFmzkJmZiZaWFuWro6NDme/p6cHkyZNhtVpRXV2NkpISmEwmrF27Von56aef4Ovri1WrVqGurg6bN2+Gp6cnDhw48FDzBYCcnBzExMSo8vntt9+U+WXLlmHMmDE4fPgwKisr8dRTT+Hpp59W5l0t3/b2dlWuBw8ehIigtLQUgOvXt6SkBB9++CH27NkDEcHevXtV87m5ufD390dxcTHOnDmDOXPmIDIyErdu3VJikpOTMXXqVJw6dQrHjh3D+PHjkZ6ersx3dHQgJCQEGRkZqK2txY4dO+Dj44OtW7cqMSdOnICnpyc2bNiAuro6fPTRR/Dy8sK5c+ceWr52ux1WqxXffPMNLl68iPLycsyYMQNxcXGqc0RERGDdunWqmvd9zLtKvgCwcOFCJCcnq3K5du2aKsaV6vtPcu6ba0tLCwoKCqDT6dDQ0KDEuFKN6f6wARxiZsyYgaysLOX7O3fuwGw2Y/369U5c1f1rb2+HiOCHH35QxmbNmoWVK1fe9ZiSkhJ4eHigtbVVGcvPz4efnx/+/PNPAMD777+PmJgY1XFpaWlISkp6sAn8Azk5OZg6deqAc3a7HV5eXti9e7cyduHCBYgIysvLAbhevn+3cuVKjBs3Dr29vQDcq75/f7Ls7e1FaGgo8vLylDG73Q6DwYAdO3YAAOrq6iAi+PHHH5WY7777DjqdDr/++isA4Msvv0RAQICSLwBkZ2cjOjpa+X7+/PlITU1VrcdisWDp0qUPNMe+BmoO/u706dMQETQ1NSljERER2LRp012PcaV8Fy5ciLlz5971GFeuL/DPajx37lw8//zzqjFXrTENjm8BDyFdXV1SVVUlVqtVGfPw8BCr1Srl5eVOXNn96+joEBGRwMBA1fjXX38tJpNJJk+eLGvXrpWbN28qc+Xl5RIbGyshISHKWFJSkly/fl3Onz+vxPS9Po4YZ12fS5cuidlslqioKMnIyJDm5mYREamqqpLu7m7VWidNmiTh4eHKWl0xX4euri7Zvn27vPnmm6LT6ZRxd6uvQ2Njo7S2tqrW5u/vLxaLRVVPo9EoTzzxhBJjtVrFw8NDKioqlJiEhATR6/VKTFJSkthsNvnjjz+UmKF4DTo6OkSn04nRaFSN5+bmysiRI2XatGmSl5enekvf1fItKyuT4OBgiY6OluXLl8vVq1eVOXevb1tbm+zfv18WL17cb86dakz/N8zZC6D/+/333+XOnTuqJ0gRkZCQELl48aKTVnX/ent75d1335VnnnlGJk+erIy/9tprEhERIWazWc6ePSvZ2dlis9lkz549IiLS2to6YO6OuXvFXL9+XW7duiU+Pj7/ZWoqFotFioqKJDo6WlpaWuSzzz6TmTNnSm1trbS2toper+/3ZBkSEjJoLo65e8U4I9++iouLxW63y6JFi5Qxd6tvX471DbS2vmsPDg5WzQ8bNkwCAwNVMZGRkf3O4ZgLCAi46zVwnMMZbt++LdnZ2ZKeni5+fn7K+DvvvCPTp0+XwMBAOXnypKxdu1ZaWlpk48aNIuJa+SYnJ8u8efMkMjJSGhoa5IMPPpCUlBQpLy8XT09Pt66viMhXX30lI0aMkHnz5qnG3anGpMYGkB64rKwsqa2tlePHj6vGlyxZotyOjY2VsLAwSUxMlIaGBhk3btzDXua/lpKSotyeMmWKWCwWiYiIkF27djmtUXlYtm3bJikpKWI2m5Uxd6sv/aW7u1vmz58vACQ/P181t2rVKuX2lClTRK/Xy9KlS2X9+vUu9yfDXn31VeV2bGysTJkyRcaNGydlZWWSmJjoxJU9HAUFBZKRkSHe3t6qcXeqManxLeAhxGQyiaenZ79Pira1tUloaKiTVnV/VqxYId9++62UlpbK6NGj7xlrsVhERKS+vl5EREJDQwfM3TF3rxg/Pz+nN11Go1EmTpwo9fX1EhoaKl1dXWK321UxfWvpqvk2NTXJoUOH5K233rpnnDvV17G+ez02Q0NDpb29XTXf09Mj165deyA1d8bPAEfz19TUJAcPHlS9+jcQi8UiPT09cvnyZRFxvXz7ioqKEpPJpLr/ult9HY4dOyY2m23Qx7SIe9VY69gADiF6vV7i4uLk8OHDylhvb68cPnxY4uPjnbiywQGQFStWyN69e+XIkSP93hIYSE1NjYiIhIWFiYhIfHy8nDt3TvVD1vGk89hjjykxfa+PI2YoXJ8bN25IQ0ODhIWFSVxcnHh5eanWarPZpLm5WVmrq+ZbWFgowcHBkpqaes84d6pvZGSkhIaGqtZ2/fp1qaioUNXTbrdLVVWVEnPkyBHp7e1VmuH4+Hg5evSodHd3KzEHDx6U6OhoCQgIUGKGwjVwNH+XLl2SQ4cOyciRIwc9pqamRjw8PJS3Sl0p37/75Zdf5OrVq6r7rzvVt69t27ZJXFycTJ06ddBYd6qx5jn7UyiktnPnThgMBhQVFaGurg5LliyB0WhUfXJyKFq+fDn8/f1RVlam2i7g5s2bAID6+nqsW7cOlZWVaGxsxL59+xAVFYWEhATlHI5tQmbPno2amhocOHAAQUFBA24TsmbNGly4cAFbtmxx2rYoq1evRllZGRobG3HixAlYrVaYTCa0t7cD+GsbmPDwcBw5cgSVlZWIj49HfHy8y+YL/PWp9PDwcGRnZ6vG3aG+nZ2dqK6uRnV1NUQEGzduRHV1tfKp19zcXBiNRuzbtw9nz57F3LlzB9wGZtq0aaioqMDx48cxYcIE1TYhdrsdISEheP3111FbW4udO3fC19e335YZw4YNwxdffIELFy4gJyfnP9ky4175dnV1Yc6cORg9ejRqampUj2nHpz1PnjyJTZs2oaamBg0NDdi+fTuCgoKwYMECl8u3s7MT7733HsrLy9HY2IhDhw5h+vTpmDBhAm7fvq2cw5XqO1jODh0dHfD19UV+fn6/412txnR/2AAOQZs3b0Z4eDj0ej1mzJiBU6dOOXtJgxKRAb8KCwsBAM3NzUhISEBgYCAMBgPGjx+PNWvWqPaJA4DLly8jJSUFPj4+MJlMWL16Nbq7u1UxpaWlePzxx6HX6xEVFaX8Gw9bWloawsLCoNfrMWrUKKSlpaG+vl6Zv3XrFt5++20EBATA19cXL7/8MlpaWlTncKV8AeD777+HiMBms6nG3aG+paWlA96HFy5cCOCvrWA+/vhjhISEwGAwIDExsd91uHr1KtLT0zF8+HD4+fnhjTfeQGdnpyrmzJkzePbZZ2EwGDBq1Cjk5ub2W8uuXbswceJE6PV6xMTEYP/+/Q8138bGxrs+ph37PlZVVcFiscDf3x/e3t549NFH8fnnn6saJlfJ9+bNm5g9ezaCgoLg5eWFiIgIZGZm9vvF25XqO1jODlu3boWPjw/sdnu/412txnR/dADwn77ESERERERDCv8PIBEREZHGsAEkIiIi0hg2gEREREQawwaQiIiISGPYABIRERFpDBtAIiIiIo1hA0hERESkMWwAiYiIiDSGDSARERGRxrABJCIiItIYNoBEREREGsMGkIiIiEhj2AASERERaQwbQCIiIiKNYQNIREREpDFsAImIiIg0hg0gERERkcawASQiIiLSGDaARERERBrDBpCIiIhIY9gAEhEREWkMG0AiIiIijWEDSERERKQxbACJiIiINIYNIBEREZHGsAEkIiIi0hg2gEREREQawwaQiIiISGPYABIRERFpDBtAIiIiIo1hA0hERESkMf8DrJuqXhazxPUAAAAASUVORK5CYII="
TEST_PLOT_IMAGE = 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"
TEST_PLOT_TARGET = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9d3jeV333/7r3ntp72pK3ndiJ7dixk5CEhKQhIQkUEvIUaOGhlJa2P0rLeB46KaWL8dAmBQohBAhkkJDt2HG895QsW7L21r33/v3h65zcUgYJ1XDi87ouX5KlW9I53/v7Ped9PlNTKBQKKBQKhUKhUCguGbQLPQCFQqFQKBQKxfyiBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhX+gBXGoUCgUeeughPv/5z2MymdBoNOTzeTQaDQ6HA41Gw+rVqwkEAnR3d5PJZNDpdORyObLZLP/8z//MHXfcsdDTUFyEFAoFNBoNAI888ghPPPEE/f39hEIhotEoGo2GqakpqqurGR8fx+1243a7Wb9+PR/84AfZsmULAPl8nnw+j1arRau9OM6IL7/8Mo8++ij79u3DaDSi0+nQarWcP3+ee++9l7Vr11JSUoLL5WLFihULPdy3TS6XQ6fTveH3BgYGOHLkCBs2bKCiogKdTjft/Z5vnnrqKR566CH27dtHKBSiUCig1WoxGAwkk0kKhQI6nY7Gxkbe//7385WvfAVgQcesuPgpvj/8fj9Hjx7lqaeewu/3k0ql0Gq1RKNR2tra2LJlC1dddRUej0f+rEDdY28NJQDnGY1Gg8ViQa/Xy5tdo9FgNBqpra3liiuuwOfz4XA4GBwcJJvNotVq0Wg0aLVaUqnUQk9BcZEiFr2XX36Zhx9+mNHRUQqFAjabDYPBQCQS4bLLLuPMmTO4XC7q6urQ6XScO3eOH/7wh/j9fm6//XZ5v10spNNpRkdHyefzXH755VgsFvr6+li6dClXXHEFOp2Orq4uvF4vra2tF73IyOfzAFJc5/P5aeKvq6uLffv2cfToUYLBIMFgkPHxcaampvjwhz/Mhz70IRYvXkwul8NoNM7ruLVaLX6/n/vvv58DBw5IsScOsqlUSopBjUZDX18fjz76KI2NjXz0ox+dt7Eq3ploNBoGBgbYvXs3XV1d+Hw+uru7KS0tpbq6GpPJREdHBwMDAzz11FNs376dxsZGbrvtNurr64HpQlDx5igBOM8UCgU6OzvR6/UYjUb0ej2ZTAaDwYDL5aKzs5OzZ89yyy23YDQaSSaTGAwGCoUCmUyG4eHhhZ6C4iIlnU6zbds2fv3rX+Pz+dDr9WSzWQwGgzx0hMNhcrkcra2tWK1WUqkUiUSC8+fP88gjj9Dd3S0X1PkUF2/G4OAg586dIxaLUVdXRyaTkQehiooKEokE0WiUdDqNzWYjHA7jcrkWeNSvRWxMM8WpRqMhkUjwy1/+kp6eHs6dO0dPTw9jY2PE43EymQwOh4P6+noaGhqkp2C+rbNi3MePH+f8+fOkUiksFgvJZJJkMonT6USn05FKpbDZbGi1WiKRCOPj4xw5coSPfvSjF7UwVywcwgI+NDTEo48+yvDwMBqNhkKhwMTEBGNjY1gsFnQ6HaFQiJaWFqxWK4VCgT179jA0NMSXvvQlHA7HGz5niteiBOACMDAwgEajwWAwoNfr5c1fXl7Otm3bmJqawmAwTLP8CTexz+e76C0civknEomwb98+HnvsMXp7e9HpdJhMJtLptLQ42Ww2Tp8+TXl5OVarlXQ6TTqdRqPRkMvl6OnpYXh4mNLSUiorK7nsssuw2WwLPDMYHx8nHA5L67lGoyGbzTI4OCg3Bb1ej9VqxeFwkE6nF3rIb8rQ0BAajYba2lqy2SyHDh3i6NGjPPLII/T29hIMBgGwWCxUVFTQ3t7O8uXLaW5uZsOGDZSUlAAsiAB8+umneeqppwiFQvLvi48WiwWTySQtgMINnMvl6Ojo4MyZM7S3t8/rmN8tiDX/N4mb4u8XCgUCgQBer/dNf3cwGKSnp4f6+no8Hg96/fzLAjGfPXv20NnZicvlorq6Gp/PRzgcJhaLodPp0Gg0JJNJWltbqaqqwmKxEAwGOXjwIKFQCJvNpvbHt4ESgAuEEHfFruD6+npyuRxlZWXTFlfhJhZxPwpFMdlslv7+fn70ox/R09OD2+0GeI0INBqNmEwmWltbCYfDZLNZeW9ptVosFgtarZazZ8/yy1/+koaGhotCAOZyORwOB7lcDr1ej9PpxOVyEYvFpIXTbDZTWVlJS0vLgmxgbxWNRsOpU6eIRCKsW7eOWCzGd7/7XZ5//nnS6TR6vZ6Kigqqq6tpbGxk2bJlXH/99axcuRK4sMHPt4WjUCiQz+fp6enhb/7mb+jt7SWdTlMoFEilUjJ+2WAwAKDX66UrWPzsmTNn+PGPf8wXv/hFLBbLvIz73YR4z2eGDxQj3PCFQoFYLMbp06cZGhpi8eLF5PP5aYdB8dwbDAb6+/t58cUX2bBhA1u2bJGu1PlEq9UyMjLC3r17sdlsVFdXU1JSQiKRIJlMUlZWhsfjQavV0tnZidlsxmKxYLFYaGtrI5lM0tvbS0VFxUUTt/xO4OJdKd/FOBwOABlkn8lk0Gq1OBwOkskk+Xweq9U67YEWqJtbMZNAIEBnZye9vb2UlZWRz+fJ5XIkEgm0Wi1msxmdTkcikeA973kPiUSCWCyG0Wgkn8+TyWTIZrMYjUYKhQKVlZV0dHQQDocvitO0xWLB6/WSTqeJxWIAlJeX4/V6cblc5HI5MpkM8XicQCBAZWXlgo73jchms9Lif+zYMXbs2EEymeTRRx+luroaq9WK0Wjk6quv5pZbbuGKK654jVjKZrPSsjZfa4FGoyEWi/GlL32JQ4cOUVlZKdcukZwmgvPj8ThWq1X+nF6vR6PRkMlk+Pd//3fuuOMOVq1addHFmV7sCIuqEHEGg2Ha9RNiu1AoEI/HeeWVV/hf/+t/4XQ6cbvdRKNRxsfHZeiEeG/Ky8vl7/nlL3/J3/3d3/HRj3503sM/kskkDz74IPl8HpvNRiqVIplMotPpMBgM1NfXs3z5cnK5HKOjo7jdbvR6PaOjo6RSKcrLy/n1r39NW1sbpaWl8pqoe+zNUQJwAaioqJCnL7GIZzIZRkdHMRgMDA4O0tjYiNlsnnbaVzez4vUYGxvj+PHjZLNZMpkM8Kr1L5PJkMvlpLWmoaGBnp4emekLFzYXEbMlhKHf7+fcuXNUV1f/RhfSXDM4OIjRaGTFihVYrVZOnz6NTqfD7Xbj8/mw2WzU1NTgdruJRCIMDAzIzMCLCWEhu+WWW9Bqtfz93/89x48fx263E4/HaW9v5+tf/zqLFy9+w2fdYDBw8uRJAJntLJIz5pJ0Os2uXbuoqqqiUChgNBqnrV1CmFssFilSTCYTRqNRrl2RSIT777+fb3zjG9jt9jkd77sJIWQCgQCPPPII4+PjrFq1CrfbjU6nI5lMSmt4JBLhyJEj7N27l5UrV+L1ehkbG8NsNuP1eslms2SzWZLJJKlUilQqhdVqpbq6mkgkwq5du6isrOSWW26Z9rfnal4itl2EFZSVlckY2EQiweTkJE6nE71eL8dcX1/P6OgoRqOR0tJSYrEYZrMZrVbLf//3f3PvvfdSVVU1J2N+t6EE4AJgNpunnY6dTieJRIIf/vCHGI1GPB4Pf/VXfzXtNcIaaDKZFnr4iouMSCTC6OiojI+x2Wxks1npIikUCjJj9Pjx4yQSCQqFgkwuEqRSKRnDlc/nOXjwIK2trQsuAIeGhsjn89TX12O329HpdLhcLrZt20ZbWxtVVVWUl5fjcrlIp9PE4/EFHe9bwel0snTpUurr69m8eTOf/vSnGRoa4vnnn+fee+/l937v99iwYcNrfu7hhx/mm9/8JgaDgT/+4z/mAx/4ALlcbk4FYCAQ4Nlnn2VqaorKykopRkRZDqfTKa//xMQEZWVl6PV6pqam8Pv95PN5XC6XDC/I5XJzNtZ3I8Xxf6FQiBdeeIGf/OQnAFJgWywWed+73W7q6+vR6/VMTExgMpmkS95oNGI2m3G5XBiNRhKJBKdPnyYQCOB0Onn55ZfJ5XKUlJSwYcOGOTU6iH0tm81y4sQJ7HY7gUCAEydOoNVqyWazDA0NodVqCQQCTE1NodVqOXnyJKFQCK/XS3V1NalUinXr1rFq1Sq6uroIhULKFfwWUQJwARBxf+LBNpvNGI1G0uk02WyW0tJSafIXsX8iUP+NaoUpZgeRbCMSbnbt2sVVV10l3QoXI5FIhKmpKcxmM8lkcpp1JZfLTdtwRWgBIBff4hpuwqKg1+sZGxsjGo3O+3xmEgqFgAtWKIPBQE1NDfF4nB07dtDd3c2mTZtwOp3yvbuY42SFWHO5XHg8Hg4dOsT3v/99br/9dj75yU+yY8cOHnzwQR555BGWLFnCP/7jP7J582YAHnjgAb797W/T29vLlVdeKS2Kc43f7+fZZ59Fp9PJWLLm5maWLVvG5ZdfzsqVK3G5XIRCIY4dO8aWLVtwOp0MDAxw8OBBXn75Zfbv34/ZbKa3t1daqRVvDSFkvvnNb/L8888TDAZlsoPJZCKZTAKvHuCi0SiBQEAeAgXFHifxPur1etauXUsgEJBi6+DBg3z/+99/3QPIXFAoFAiHw9hsNrq7uwGoqakhm82STqfxeDyMjo6yceNGduzYQUNDA3AhlMputzM2NkZPTw+LFy/G4/Hg9/uJRqM4nc55Gf87GSUAFwBhZcnlclLYCbEnTN1iMy4u96DRaKZt4BcTIlZxZmzP6Ogoe/bsYeXKldTW1kqLVHEB7ItpPkJkh0Ihdu7cyV/+5V/i9XqllaOmpoZly5axdu1a2tvbyefzDA8P8+KLL8pipcFgkMbGRt773vfS3Nw858JdxL/p9Xqi0ShmsxlgWjasVqtFr9fLmnOFQuE1B5Hizw0GA4FA4KKwppWVlcmxpFIpcrkcLpeLjRs3cvr0aRKJBNlsVsbFXSzla96MqqoqNm7cyODgIPv27WPz5s184xvfIBQKsXfvXn79619z+PBhPv/5z/PCCy8wNjbGd7/7XXp7e9mwYQMf+chHpDCc66SXWCzGyZMnMRgMMqRg06ZNXHXVVTidToaGhti9ezeBQIDh4WHGxsbke6HVaqmurpb3nc/n48iRI2zcuFG5gd8mDQ0NGI1GQqGQjHv1eDxks1ncbje5XA6DwYDBYJAHP7PZPC2OvPhwlM1m5R7k9XqlAMxkMvT39/PKK6/Ie2yuyOVyBINBYrEYyWRSFhUvTngU+8SaNWvYv38/mUxGrl8ibj4UChEOhwGYmJggFospAfgWUAJwntFoNNjt9mkxWMUYjUZisdg0y1+xxeZirG8Gr5rzRcC33+/H4XBw+vRpfvKTnzAxMcF73/tempqaps1HfF4oFDh9+jSnTp0C4EMf+tCCzQUgkUiQSqVoa2vDaDQyNTVFd3c3Z86cYc+ePezatYtVq1bJzTEajcqMyFgsRiQSYcmSJTQ3N8+5RSqTyZBMJrFarWQyGYxGoxRKgLTciEVVLPwzXSTFFjSNRkMwGCSRSMzp2N8KVVVVRKNRWS9TWC5XrlzJqVOnpEVJ3E8X88IvDkjHjh3joYceIpPJ8JOf/ISHHnpIFoK/6qqr0Gg0DA8P09nZyXe/+11GR0fp7u6mra2ND3zgA1x33XUyznGuD1CpVIqhoSF5YBAicN++fQwODjI+Po7f75f1GQ8fPgxcsNCIJANRGzCTybB7925WrFhx0QrAQqHAiy++yIoVKygtLb1ossoXLVqEzWYjkUhgMplkebBsNisPgOKZFqFDQuAJxOdi/xFrQy6Xw263k0wmSafTTE5O8tBDD825AMxmswSDQbluxuNxOUZhGBGWzv3798u9ES4kjkQiEex2OyMjIySTScxmM8FgUFpFFW/OxXFnX2KIdHZB8edigZ1pMRKb28UqAItPcul0mqmpKVKpFJ2dnQwPD/Pyyy9TKBRYt24djY2NuFwu8vk8wWCQwcFBhoeH6ejooLOzE4fDwS233LIgG4RYcFKpFGazmY997GOYTCbGxsYIBoP4/X727t3Lzp07OXz4MMlkkoGBAa655hqmpqYoFAqsXr2aK664Yl7qtaXTaRKJBJlMRi74YuEsnpNALPwzMwjFOMWhRKfTEQ6HL4qaeqWlpZw9e5Z4PC5r/mUyGVatWiWfF7ER6nS6i6J0zRshrvvAwAA7duygurqavr4+RkZGMBqNHD16lDVr1jA1NSXf00ceeYTJyUkMBgN33HEH1113HTU1NfM2ZuGtMJvN8uBw4sQJfD4fY2NjUmQUiwu44DoOBoMyu1xYdo4dO3ZRhBbAhbmNjY0xNjaG3+/H5/ORSCR49tln2bhxI1dffTUtLS0XhVjt7++XMZV6vV5a+nO5HJFIRIZBzDzsvV7RcPGeiWLx2WwWu90u15JwOMzLL79MLBab0+dJGAuE5VFUxCg2EsAFr9nhw4eJRqPSgJJKpYjFYtTU1JDP56UbORwOKwH4FlECcAEQvTyLXbvCJWqxWKTZXlC8gYsab/PFm2WBZbNZEomENL339PQwOTmJRqMhGo2yb98+tFot5eXl9Pf388gjj9DR0cHGjRupr68nn88zMDDA4cOHOXHihDTrGwwGhoeHaWtrm8+pTnNNJxIJ9Ho9119//WsSb374wx/yve99j1OnTrF48WI2btzIDTfcwIkTJ4jH49x9991cc8018v2dSwtgLBYjGo1Os96lUqlpm0DxiVp8XbiEs9ksgNxUxIah0+lkTOpCU1JSIuOEhLtXo9HQ2tpKJBIhHA4Tj8cxGo2yJuDFjt1ux+1209/fj9fr5YYbbqCvr4/u7m46Ojqm9dIdHBxkamqKm2++mTvvvJOWlha56Q8NDVFRUUFZWdmcjFNYh0QB7kwmg8Vi4eTJkzJ+WTwfIm5ZkEqliMfjRCIRDAaDvPfOnz+/4Bv01NQUExMTpFIpurq66O7ulofQ/v5+kskkg4ODTE5OsmXLFlauXDln1/it0NXVxc9//nO6urrQ6XSyDp5wh6ZSKWn9B6bV+AReN/GmOARExAOK9S+bzRIIBOjv72fp0qVzNq9sNovf7weQQrU4bllYKIVYhVfDjXK5HOl0Wq5loVCIuro66Y1R/GaUAFwAqqurXxMrJ278FStWEI/Hefnll+UDUbyo1tbWzutYi+PDxIMpTozBYJDu7m727NlDU1OTfPCMRiPj4+McOnQIu92OzWajvLycUCjE7t27eemllzAYDBiNRplxJjJNo9EokUiEc+fOzZsAFGU0REKEqDxvs9leN+t61apVLF++nHA4zNe+9jW2bt067XdlMhkpVMTCPFcWm0QiIbN6i93pxYupQGT3CnEnSnkUv686nW7ax4shoUKUeBGuxmAwiNvtxmq10tXVxdDQEHV1dbhcLqxW60XtAhbvjdfrpbm5mfHxceLxOJ/73OdYtWoV+/fvZ2xsTLr1z507x3e+8x10Oh1f+9rXqKmpkUH++/bt41/+5V/41Kc+xX333Tcn443H40xNTZFMJmX8bj6fl+3ohAVWCFbxLIk4UkB6NMTcxfzmm2w2KxOKHnvsMR555BHS6TSNjY20tbWxdu1a6uvrefnll9Hr9dTU1PD444+za9cu7rrrLj7ykY/IcknzHbf8F3/xF+zYsUPWvLPZbNO6SAnXrRBJ4prPdF8XJ0mJtaH4awaDgXQ6Lb0Ihw8fnlMBmMlkZNtKYfjI5XL4fD55KIULiWDve9/7eOaZZ+TYMpkMoVCInp4eWcs0n8+TTCYvioPrOwElABcAj8cj0/dnWthqa2sZGxsDXo3jAOSCOt9V2sXpy+/3MzAwQFdXF52dnfT19ckinYlEgqGhIex2uyxEms/ncbvdlJaWyuLWLpcLl8slF5vi0gSjo6PyZJdKpTh48KCsRTVX85pZUFen0/Hyyy+ze/duGhsb+fCHPwy8tofr6tWrWblyJbt27ZLZeOI1vb29HDx4kBMnTnDixAm2bdvGbbfdxk9/+tM5mUc6nZ4W7wevZpoWhxKIoHFx6hfXQMxbq9XK3yN+V7E7eSETdex2u4xPKi0tJZFISPd6XV0dZWVlVFZW4nA4ZDbzxYoQSJdddhm/93u/x7FjxwD4yEc+wu7du9m4caN8bUdHB9/73vcIBoPcfffdVFdXc+TIER577DH279/P8PAwoVBoThOMIpEIg4ODhEIhWeOvuIRQsSsReN2xFB9GdDoddrt9zkt0FFuJxNgOHz7MPffcg8FgYHx8nFtuuYX/+3//L01NTdN+9i//8i/Zu3cvf/M3f8P111/P8PAwP/rRj9izZw9f/epXaW5ulq+daVkrjoWeTSKRCIVCAafTSWlpKRaLhcnJSeDV+pLCIiY+n9n14/XGJNZ3Ef5hs9nQ6XRkMhmcTifPPPMM995776zOZebfj8fjJBIJGhoaiEajFAoFIpEIOp1OFhUPBoOsW7eOnTt3yjaEok5uJBKR3grRhUaVGnprKAG4AAjh9HoPZHFshxAo4gE1Go3z3kZJr9eza9cuHnvsMcLhsMwy83q90gUhTqCjo6PyZG8wGGhoaECv18vg7+LCw+J0KhYd4f4Vp+vZdrfMFDEzN6Cf/OQnfOMb3+COO+7grrvummZ9nGlZA6isrKS0tJRvf/vb1NTU8P/+3/8jEolgtVrl4qPVarHb7fzJn/zJrM6lGLHoiwxsYbkrtlwW95KORCKyFpiIoylOohAfC4WCbMXk8/kWvAxOLpcjGo1Ka4EQscVxUO+ERV8IJK/Xy7XXXsuXvvQl/uqv/opAIMBjjz3G7bffjtfrJZ/PEwqFOHv2rLRs3nHHHTLBQmyMpaWlXHvttXM23kQiIV10hUKBRCIhEwaKY0ffCCEOi2ObE4mEvGfn6mBRfKgTNDQ08JnPfIZTp07xH//xH29qyauoqGB0dJT169czOjpKIpHg1KlTfOITn+ArX/mKtPrPV1muF198kQMHDsiM68cee4zh4WGuuOIKIpGItNYXZ/OLGECY3pWleD0T7l7xHolSUtFolLKyMrq6uuZ0XrlcjlgsJhOLTCYTkUhE7oMmk2laDURhbRb3nl6vlz9T7N1QpYbeGkoALgDCmldsWRLiyGg0UlZWJgVS8cna6XQuiCXmsssuY8mSJdLdKE5soVBIZnAZjUb0er38mMvlCAQCRKNRLBaLjEkRD3UikZBCBJBZbUajUdZEFFlds0Vx2Zm+vj62bdvG888/z+DgIN3d3bhcLrZv3y6LC890JYpr393dzdGjRzl9+jRHjx6loqKCTZs28corr8gYRiFmxQI1V4j7Rvy9eDwus2XFSb64xJAQSsJCotfr0el08msGg4FwOCwL+Y6OjhKJRBZcAArxEYlEMJvN0uqh1WqZnJwkGAxisVguauvfTEpLS/nwhz/MyMgI//AP/8APfvADNm7cKDs2iEz0XC7HSy+9hNPpZNOmTQwPD8vC3+Xl5XPa+i4ej0u3Kbx6eBXi4Y0oFofi/hIWwEwmI0t1zFVyxfj4OJOTk8TjcdLpNFarlVAoxPHjx7n33nsZGBggmUxiMplwOBxYrVZ5IBVu4U9+8pPy4CuKJicSCf7+7/+enp4eSkpKZBcdq9UqD+s1NTVUV1fP6nw0Gg2VlZUcPHiQzs5O2XlFWGCFEJ0ZslEc0yt+D7y6FoqfKU4uEcmGWq1Wiv+5QlxvjUYjkziE9U+I0uIYeRFKILwcog1hsdgtdgerYtBvjhKAC4QwVRcvkHChlZLJZJpmRRIP60Jkoon2QuLvi0wz8WCJBUbE14jT46FDhwB43/vex/PPP8+6detkxffz589z5ZVX4nK5eP755/n4xz/O9u3bOXLkCG63m8WLFzM6OkpjY+OstfSZ6QI5duwY3/nOdxgdHWXJkiVs3ryZSCRCb28vX/3qV9m+fTv33HPPNJfcyMgIDzzwAPv27SOVSvGBD3yAm2++mdraWsbHx9m7d6+0KhQHKIvez3OBsOCJwPDiWMZiN1Cx1VXUmhQ9gsX7F4vFpsUN5nI54vH4ggfsw4WSImazmVgshl6vlxYwkaEohMk7bcF3u918+tOf5vz58zz//PN86Utf4nOf+xwrV67E7/eTTCZZu3YtX/nKVygvL8dqtfLkk0/ys5/9jLNnz1JeXj6nc87lcvL9F/eUEBMzY0wFxTXnxP0nEg3MZjOhUEjGPs7Vmvbss89y6tQpLBaLPFiKuL6jR4/y7LPPAhfuKxELK6xH58+flzGywWBQikWz2Uw4HJYxmCtWrMDpdMpDrRAnlZWVfPKTn5y1uYhr3tXVxQsvvMCBAwdkSE3xsyneF3G4K67rCbxuTK/BYCCbzeJyubj88suJxWL09vYyMjJCOByWoRZzQfEapdVqp3lQig+swgIoXjezpqHoGyy+JtZf0d9c8cYoAbhAWK1WmakkHlQhHoxGozwZiQ1ar9cvSEsusXiKhAZRZqDYxSIKvppMJvx+PwcPHuSxxx7jjjvu4KmnnuIXv/gFFosFm81GR0cH586dY82aNTz44IOcOnWKm2++meHhYZLJJJlMhmg0is1mm5Ng/lQqJQVESUkJfX19spdkJBIBYHJykueee07GnC1atIgHH3yQHTt2EIvF2LhxI8uXL6exsZHFixdjs9l4+eWXgVcX3+LEmbksoyCsEvBqjTlxP4nFs9jVVVwQWmzexRt7sWVAZAVfDBl1xe4g0ecYLrjixQHqnVIEWiCuc21tLX/8x3/MxMQEhw4d4v7776exsZHe3l5WrVrFF7/4RTZt2iTDP9rb26msrKS3t3dOrX8wPcRAiDlhZZ1pASxOJij+vjh8ie+J4P25PFisXr0av99POp0mnU7LTHGR5BWPx6WVXFigAFljTxyoli5dKkVQWVmZFIt2u12WsxKVG0R5otm2lou1dvHixbS1tXH27Fn8fv+0to3Fh73ihD0hSsVzXfyeFNcETafTMpnC5/NhMpm47777WLVq1azOpRhRwF6sU7FYTJZxEs93sYh9I4uzEI3iPgVkouI7aT1YCJQAXCBsNhuBQGDaQ1ucmADIrC5h3VgoAShi+IRpXSQGzHQlGgwGKayWLVvG6tWr2bdvH0uWLMHtduNyuVixYgVVVVU0NjZSXV1NSUkJdrudVatWsWTJEhnwLwTjbHHgwAHZh9RoNDI4OIjJZMJqtcouE8lkEo1GI61Lo6OjHD58mH379vHLX/4Sr9fL1q1buf7662ltbQVePYUGg0F5vWYyl25JMW5xMi6O8xHZvMVB3jDdQlBsFSj+GSHyRZLJQiPuPWFJEvdGc3MzkUhEXod3Yq/sfD7Phg0b+NjHPsZ//Md/sGvXLo4cOUJjYyO///u/z3vf+95pwsrr9eL1ejGbzfI+nCuy2ew060rx+gRvXIR6pvutuLMDMOc1JleuXAkgO0yIckHFZUNMJpPsLS0EUTqdliVvRDasKAnldDoxGAxYLJZpMWpi7RMWUVGge7YQ17ixsZFFixZRVlZGMBiU4xbPa7G4Kw4NEWKw2GIr1gnhwclkMlIAlpaWsmXLFj760Y/O6f0lwhyEUPX5fLJNYvHzXrxuifkVH2yF+BZJhcIC+E6ICV5olABcIITrI5vNyhpZwLQbWyws4iFfCAEIyLg8QbFYLQ64FY3hW1pauPXWW3E4HDQ0NFBSUiIX1S1btsjfs2bNGhnjuHr16jmdw4EDB/jBD34gxy1OiKLgcXGMidFopLy8nLKyMrZv387BgwdZvXo1X/nKV6irq5NjLs4yLI61K/7eXAeJCxewEJnCpVMs8GbGB80Mvi+20Mx0r1wsAlDMU6vVkkwmZUH0pUuXcvDgQZLJ5Dsy+09sVlqtlnvuuYdTp07x6KOPYrPZuPXWW/n4xz8+zZ0nsh9NJhM2m41ly5bN6fhezwJcnIQjmHnfzGRmVyORuTlXaDSa11ivRCKKWAOMRuP/6PkUh2ERbyb+7lwm6tlsNnn4EVY9s9ksRVxxAuHM92Rm3J/43GKxEI/HZdLe+vXr51z8wfR7S9TxEy75dDr9mvEWF7gv9nZkMhmsVqs8qIjD7pvFqCouoATgAmEymeTCUdyrUcSqACxZskTW1MvlcnMaS/Z2KA4q/k1tkurq6t7we/PZYunDH/4w8XicU6dOMTk5SSQSkV1LhGsbXo0lE10OCoUCv/d7v8dXvvKVaYsRTI8rFNnRwrog+nJardY5tQAW9/MU2XyxWEx2X4BXA8WLF0SxSaVSKWkFEC7WSCQi3TAXiwvYZDJRVlZGVVUVo6OjsiB6XV0dx48fx2g04na7L7oagG/lECDeGyFya2truemmm/jkJz8pD1lGo1EKfSF2LRbLnNfKLA7SF/e2EK3w2i5GgmKLc3FoS3E82nyLdTGG2ULE086H1VkIttWrV3P8+HGOHj0qSzuJOMRsNistnOK+E4kRIsmuuPWbuI9KSkoYHR3lb//2b7nuuuvmrdlAsXVZJAo6HA4mJibkuMWY4dXWcMWJLOL31NfXMzg4OC0p5J12GFwIlABcIMRCNDMmQ1htRAZgPB5ndHQUo9H4jnRvXSx4vV4+//nPy/+nUimCwaDMIB0aGiIcDssFx+l00tjYyOWXXz7t98wMrBaL0+bNm7nvvvsoKSmRMWkGg4Gqqqo5bd8nFnOxGVitVllbS2TyiYW2OMM6lUrJTUFs6ul0murqahkLKTb6i+EkLQpsR6NRRkdH5dctFgvRaJSpqSkikcicBq3/NhTHVYpnfKbbVJTt+d73vsf27dv50z/9U+655x65FhTHQ8GrLnybzTZrSVJvRnEQ/tupQjDTGlgcB3ix3FfvBIqv49KlS9m8eTN79+5lYmKCeDyO1+uVlv9iV7Q4lIpSL+LAW1ysW2TWT01NUVtbi9vtlmvKXGfUi32uUChgt9sZGBhg6dKl2Gw2WUu2+IBRLO6ENVx8vba2lvPnz+Pz+aSYVKVgfjNKAC4QFotFCoXihdFiscjT2eLFi2lvb5exavPdBeTdjMlkoqKigoqKirf1czOziQVer5d//Md/lK+ZyxpnxRTHZKbTaWpqaliyZAmPPfYY6XRaluwQ445EIjJrWPSaFZa+qakpfud3fodYLMbAwIDcSC6GfsCJRIKpqSmZbS6Et9VqJZ1O09fXh8vlwuFwXDTPSTQa5eDBg3zta19j6dKl/O3f/q2MLxXk83lMJhOZTIYHHniAe++9l6uuukqKvOJNuNh6VlxSaa4R11r8XXhr7Q2L73/hAhbE43HVreEtMnMdsVqtlJaWyn7r4tCaSCSYmJjAZDLh9Xqpqamhp6eHeDwuM2Vniu5UKkVVVRW/+MUvWLJkCYBM6ptrxKFVHGby+Tz19fWMjo4yNTUlx1Ic11dcMUN8L5FI0NrayosvvojJZMJoNMoWmW93fb/UUAJwgSkWFMWlLFwuF319fdx00014vd6LqoG64vWZGVc3HxRbUpLJJE6nkyVLlnD//ffL0ikijlRk2RVnCQvLjrASVlZW4nQ6pQu5uL7WQqLX6wmHwwSDQRwOhxRGTqdTfk8Uil0IhIVSbJxarZauri7uu+8+Jicn6e/v5ytf+Yosel4cclAoFPjYxz5GVVUVN954owybeCPXsXDJz0dIiKjtKeY0sz90cfiKEHnFsVriNdlslmg0itlsxmazqWK9/wM2b96M0+nkz//8zzlz5gwTExPymot7Znh4mNOnT8t4YLvdTnV1NR6Ph8bGRlatWkVtbS0Gg4EbbrgBm8027zU0xTNTvB7BhQSh4tCJ4gN1sSAUiR+pVIpFixZNqzcrxKXizVECcIGYGTtTnLUl4jiOHTvGF77wBbLZLBaLhU9/+tMLNVzFRYoIai8ujHzttdfKvq3Fmb7FJYWKg6iLRUtzc7MMMhdxXBdDLI2IpQyHwzQ0NMivu91uzGazLN8xm4XD3w4zS9BoNBpaWlr4xje+wac+9SnGxsb4p3/6J/7wD/+QmpoaubmlUikeeeQR9uzZw3/913/R3Nz8htaX4rpnMD8CUNwvFouFfD6Px+ORdUrfLOHj9b7W2NhINBqV4QYXw8HinYjBYGDZsmX867/+Kw8++CCLFy9Go9HQ0dHByMgIWq2WqqoqmpqaqKqqwuv14nK5ZMayEOGipM1chqi8GeLQmc/n8fv9rxvnXlxxQqxhIm6+2EU8ODg4rS1nPB4nHo8vxLTeUSgBuECIlPViC4ywGomSK62trdjtdlmnTlR/VygEInFA1OzTarV4PB65yQqLcrGlRiSMCEQmo3htfX09R48eBV69Fxcam82GxWIhHA5Py7IUFipRh3KhkkDGxsY4dOgQ5eXluFwu2R1izZo1lJSUMDU1xZNPPslNN91ESUkJZrOZQCDA888/z4MPPsjNN9/M6tWrZX/TN7MgC0EuxK5I/pkLRAiA+P0ul0taWoVF5jch5rN8+XLOnDnzhmEUireOxWJh5cqVfOxjH5N1Bzdu3Cj75DqdTtxuNw6HA4vFclHWwxNWOhGL6HQ6qaiokAXqRTIdXDjoiRhmcd+Le89isdDf309ZWZk87IoqD4o3RwnABcLj8cgipMW1AMWGls1m8Xq9lJWVyUbg77QuB4q55436SqdSKfx+P6lUalqCUaHwar/P4qr64kQdDoepra2dtxjGt0pZWRmlpaVMTEy8xlUlsjHNZvOCZcoHAgEOHTqE2WzGbDZPc0GJunEDAwM8+uij9Pb2yjqghw8fpry8nHvvvReHwzHNxfVGCMuHEMJzmUwhRLU4RFRUVHDllVcyPj4uA/hFH1d4tdC4qBQgktdcLhdVVVWcOnUKrVaL2+2e977m7zZ0Oh3Lly+X/5/rouCzjViPhNCrrKzEbreTyWRkSafiUIlQKCS/NrPUVSaToaamRsbUK94aSgAuEJdffjkvv/wy0Wh02qLv8XikC25sbIxYLEYsFsPhcKiAVsVrKC0txel00tfXN632VXNzs0z2EAumsAKKQrJCOApLjrAAVlRUyHg2k8l0UWzU1dXVVFdX09HR8RqXtBi/2WxekHaJcMEaV11dzdjYGH6/n6GhIfr6+jAajSxfvpyqqiqSySTHjx+nr69P9i2uq6vjE5/4xLRs8zfawMTXbTYb1dXVUuzO5YZXXl7O8uXLZf3F9vZ2Pv/5z9Pd3Y3P55MxWMItLfpgGwwGzGYzFosFl8tFXV0dQ0ND/PCHP6RQKNDQ0DBv5UYUFycivACQ5WgcDgcOhwOXyyXDoURMXzQalQmRom8xXDgArVixgu7ubplcpKzMbw0lABeIa665hh/96EeyqTtcKA3T3NyM1WrFYrEwMDAgg/hra2tZv379Ao9acbEhSjeIUkFiU/3BD34gO2SIbggi7qqmpoZEIoHL5ZKt/IxGIxaLhfr6eo4fP04mkyESiWAymS6KjdrpdGKz2Ugmk68JhRDlSYQlcCFoamriD/7gD14zLmHhTyaTstVhJBLBYDBQVlb2toq7ixjOZcuWSQ+C+PpcUVdXx/r16zEYDCQSCa655hqam5tlxujbob29Hbvdjt/vp7W19aIr2aOYX4SYE/3jzWYzq1at4pOf/CSTk5Ok02lZDsbn87F27Vpqa2tlGz/Rss9kMnHPPffw6U9/Wq51ymP21lACcIGw2WzcfffdLF++nHg8LrtPlJeXA/D5z3+eYDBIMBjEYrFw2WWXzXqLIcU7n/b2dq699lrGxsYwm83cc889aDSaaYkSb5dVq1axdetWhoeH2bRp02+12c82Wq2WyspKrrzyStasWTPte0uXLiUYDFJeXn5RnfqFVRJ4TfmX3wYh9GpqaqipqZn2d+YSj8fDzTffTDgcpq2tbVoJoeI+04LiDhTCsyHE6/XXX08wGKS1tXVOe2QrLn68Xi/Lly8nGAzi9XpZvXo1JpOJG2+88bf6fXfeeScHDhygoqKCFStW0NTUNMsjfvehKbyVgk4KhUKhUCgUincNykaqUCgUCoVCcYmhBKBCoVAoFArFJYYSgAqFQqFQKBSXGEoAKhQKhUKhUFxiKAGoUCgUCoVCcYmhysAoZpWRkRH279/PwYMHWb16tSx0fe2111JdXY1er7/oukwoLi3U/ae41FHPgAKUAFT8DxAdGYoL0XZ1dfHkk09it9vR6XQ4HA6OHDlCOBzmhhtuoL29XbYmg7ntYqB459Dd3U1PTw+Tk5MEAgH6+vrweDyEQiFisRgDAwOyeHUmk5H9Tf1+P4lEAo/Hg8fjwWAwkM/n2bBhAwMDA1RUVGCxWMjlcixevJhNmzape07xrkQURNdoNJw5c4Y///M/J5fLYbFY+NCHPsT1118va8kWPwNKDF66KAGo+K0pFn7nz5/n5MmTDA4O0tTURG9vL/v378doNBKNRkkkErIHam1trVpwFNM4dOgQjz32GIODg7hcLnw+H16vl6amJioqKigUCkxNTaHT6fB4POTzeSYnJykrKyMej+NwOGRXgfHxcV555RWGhoaAC23a8vk8mzdvpq2tjbKyMrXpKd5VFHe+ePbZZ/n6179OR0cHNpuNSCRCMplk9erVUgCK+189B5c2SgAqfmvS6TQDAwOMjo5y5swZotEoXq8Xt9tNR0cHR48exWAwsH79eurq6hgbG2N8fJyKigoqKytpbm7G5XIt9DQUC4TYfLLZLFNTU4yOjhKNRqmsrESn02E2m/H7/eTzeVwuFwaDQXbG6e7uJpVKceONN7Jnzx76+/vJZDLY7XauueYaYrEYQ0ND6HQ6kskkRqORqakpTpw4wXXXXXdRbXrFm/CbbchHjx7FZDJRU1OjnhuFpPie6ejo4OTJk+h0OhYvXoxGoyGdTpPJZHjllVewWCzU1dUBr1oMFZcuSgAq3jaZTIbJyUkGBwcZGhoiEAgQCARIp9PEYjHC4TDDw8MEAgHcbjeBQICxsTHy+TyZTIapqSmGhoYYGxujrq6OJUuWzGk/U8XFTW9vL1NTUxgMBiorK6msrOTMmTNs3ryZjo4OJicnsdlsOBwOSktLqa6u5ty5cySTSaampggGgzidTrnZhUIhPB4Pk5OTNDQ0SNfw5OQk27dv57rrrlvoKb8t4vE4HR0d/PrXv6apqQmr1aoEoOI1ZLNZduzYwYkTJ1i2bBkOh4OJiQny+Txms5nDhw9js9nYtGkTtbW1yvqnUAJQ8faJRqMcPHiQkydPYrFYcDgcVFdXc/LkSQ4ePCgtLyaTSW7WZ86cYc2aNTQ1NZFKpZiamqK3t5eqqipcLhdVVVXo9ep2vBTZuXMn3d3dOJ1OampqcDqdDA0N0draSldXF4VCgY6ODpxOJ1dffTUNDQ00NDRw4MAB9u7dSy6XY8OGDVRVVXH06FF+8pOfcMMNN9Db20tTUxNut5tMJsPQ0BCJRGJB51oc+5pOpxkfHyeVSlFXV0csFkOr1WK1WjEYDKRSKXw+H9FolL1797J7927C4TBVVVU4HA5KSkoWdC5vlXQ6zdTUFNXV1Qs9lHclGo2G8fFxJicn2bNnD52dnVx77bW0tLSg1+sJhULU1NRw8OBBdu3ahV6v55prrsHhcGA0Ghd6+IoFRO24irdFPp/H7/ezd+9eMpkMer0ev98vN7NMJkNjYyOlpaUEg0GWLl2Kz+fj0KFDBAIBamtrsdlsWK1W3G43k5OTPPfcc9x2222UlZUt9PQU80gul0Ov1/Piiy9y6tQpVq9ezWWXXUYgEKC5uRm4IB7WrFnD4sWL6ejo4IUXXpCB7gMDA1x99dXodDomJyeJRqMABAIBOjs7WbJkCS0tLZw+fZpEIkFFRQWbNm1ayCmj0WjI5/MUCgXGx8d54IEHGBwc5Atf+AJHjhzBYrGwdOlSysrKGB4e5umnn6auro4NGzawd+9eurq6CIfDRKNRbr/99gWdy1shl8sxODjIL37xC/70T/8Ug8HwmteohLDfDmHBi0Qi/PSnP+X48eP4/X7sdjunT5/GarWSz+fJ5XIMDw9jMBiIxWIcPnyYeDzOlVdeSVNT0ztOBMbjcXK5HFqtFr1ePy2pUFSZANBqtRQKBeLxOFarVXmZXgclABVvi2AwyLlz5xgYGCASiRAIBDCZTDgcDqqqqvB6vSSTSbRaLc3NzUSjUfR6Pddffz0ul4tcLsfk5CThcBibzUY0GsXn85HNZhd6au9o5nITnavfLSy+er2e8vJyzGYzR44cYdu2bYRCIR566CFOnDjB4OAgd911F1u2bOHkyZP893//Ny6Xi7vvvpvBwUH0ej1Op5Ph4WHOnj1LbW2tzAJ+8MEHKS8vx2KxMDk5yfj4OLlcbkE3AxGsn8vliEajWCwWnnzySZ555hlcLhdms5mKigpcLhcdHR1YLBZSqRS5XI5IJMLq1atlHNfFTm9vL88//zxnz57l4MGDbNiw4TX3kRJ+bx8h/vL5PJ/97Gc5ffo0zc3NLFq0iFwuRzgcZnBwkNbWVrxeL4ODg5SXl6PX64nFYvz617/mF7/4BZ/5zGe44YYbyOfz8r68mEmn0/zLv/wLAwMDVFdXs3TpUqxWq9xnFi1aRDabJZfLUVZWRiqV4utf/zpf+MIXWLx48UIP/6JDCUDF2yIYDNLb20s8HmdkZIRAIMCaNWtwOp3EYjEZF1hVVcXJkydpb29n3759tLS04Ha7gQuLl9lsli45vV4vS8oo3h5Hjx7l/PnzhEIhuru7uf322zEajUxMTFAoFKTQKRQK007GxV8Tm4k4Uet0OuLxOGVlZQQCAUZGRkgmkyxevJimpqZZFx91dXWUlJRQXV3N+Pg4XV1deDweRkZG+OxnPyszg3ft2sUvf/lLbrjhBgqFAv/5n//JRz7yESKRCOfPn8fhcLB06VKOHTtGWVkZe/fuxWq18id/8ieEw2GefPJJfvWrX3HdddexdevWWZ3DW0VYLnw+H0eOHOHUqVNcffXVnD9/ntraWtLpNOFwGIfDQXl5OfF4nK6uLk6ePEkgEODb3/42V1xxBWazeUHG/2bMzCw9e/Ysv/zlL3n55ZfZsmUL//iP/8hVV13FH/3RH2GxWOTPpNNpEomEXB8Ub454ZjOZDE8//TRTU1OsXr0au90uv261WrHZbMCFkB2RbFUoFLBYLDQ0NJBMJvnud7/L1VdfjdlsvmhiAt9sHJ/5zGcIBoNks1l6enp48sknsVqtGI1GTp48STqdxmKxUF5eLi2bLS0t8zn8dxRKAF7EdHd34/V68Xq9b/o6YebXarVzbtmIRqOMjo4SiURIp9Nks1kGBweJRCKYzWYqKyupqKjA7/fjdrs5e/YsLS0tNDQ0EI1GGR8fJ5vNUlVVRSAQIJPJEA6HCQaDVFRUvK6LSPFakskk3/jGN+jv7yeRSOD3+2VijsPhwG6343Q6MRgMcjHVarXS/QjIzTqTyZBOpwGkJWBsbAyTyURPTw+jo6Po9XqqqqpYt24dX/7yl2d1LufOnZMlXqqqqrjyyiuJxWK0tLTIxI9HH32UVCrF+973PjweD36/n7vvvpvS0lL8fj8NDQ2sXLkSvV7PmTNnyGazBAIBysvL+dnPfkZ5eTk6nU6WJVooxPNZUlLCDTfcwJIlS6S1U8QnOp1ObDYbsViMnp4e/H4/ixcv5qGHHqKyslK6ti6GzXommUwGg8FAKBTi0UcfZf/+/TQ0NBAKhRgfH+c///M/6evr4y/+4i9oaGggEonQ29tLb28v11xzzYInt6RSKZ544gluueUWrFbrgo7ljRAhEJlMhiNHjlBVVSWtXgKtVovJZCKfzzM8PIzH48FqtVIoFOQzrtVqsdvtPPHEE9x+++3SIr/Q91VxRry4zwOBAP/2b/+GxWKRgk+j0ZBKpYjFYjgcDlauXCmvQT6fJ5VKEY1GyefzlJeXL+SULlqUAFxgXs+9Njw8zBNPPMGePXu44ooruPHGG2lra3vDRV+r1cpFYa6JRqOMjY1JS0VVVRVutxu/30+hUMDhcOB0OnG73ZSUlBAOh5mYmCAQCOB0OmlqapIb/UsvvUQqlSISidDX10dFRYWKA3yLPPbYYySTSSmqY7EY6XSa3t5eMpmMtPLpdDop9Iotf4Li+yafz0tXfCqVwmQykUwmSaVSZDIZhoeHKS0tndV5PPjgg5jNZsxmM11dXRw7dozOzk6y2Szr1q1jZGSEbdu2EY/Huf7667n++uv56U9/ytmzZ1mzZg0vvfSSjBt85JFH0Gg0eL1eRkdHmZqawmg00tDQQDabJRQKkUwm6evrm9U5/DZoNBpsNhstLS1kMhk++clPsmfPHiYmJigrK2NycpLdu3djt9sZGhriu9/9LlVVVRd9HJMQES+88ALhcJjVq1dTWlrKiRMn5D21b98+vvrVr/LBD36QZcuWySSRfD6/IGMW5VBSqRTd3d0MDg7y/e9/n8bGRjZv3vwbRWkqlWJwcJCJiQlWrlyJ3W6f8zFns1kmJiYYGhqirKxMHvx0Op00CIj4bCHKw+EwqVQKi8Ui51RWVsbJkye59dZbL7pYQLE2JZNJfvWrX3H06FFisRhGo1FaNzOZjCwXlUgkqKyslAcpYfXM5/M4HI6FnMpFixKAC4QQc2LzDQQC7Ny5k2PHjsk4oKamJs6cOYPH45E1nYp/VuD3+/H7/dTV1WEymeZ03EIAZrNZaSnSaDR4PB7plsrlcuTzeZLJJBqNBrPZjFarxWAwoNVqicVinD17llwuJy1Qhw4doq6uTgnAt0AqleLQoUNcc801pNNp+vv7KRQKMr4nlUpJV+5vqi83syOAeF0ikSCZTKLX6zGZTBQKBRKJBLFYbFbnkkwm8Xq9BINBJiYmiMViVFVV0dTURE1NDc899xxDQ0MsXboUt9vN4OAgmUyGDRs2yHvv+PHjpFIpaQWMRCIUCgXa29sZHh4mk8lQXl5OdXU1IyMjDA4Ozuocfls0Gg16vR6j0UhbWxs2m43t27dz4MABzp49y+TkpFwj2tvbpWt+eHiYM2fOMDg4SF1dHbfeeutCTwV49V7as2cPO3fuZHh4WGb3T0xMSFGSSCQ4dOgQsViMmpoaysrK2Lhx44JZ3MTBKBAIsG3bNhwOB01NTXR1dTE+Ps7q1atpa2ubJuxyuRwdHR2cPXsWn88nBazFYmHNmjVzPuZkMklvby/JZFJau91uN0ajUVr4dDoduVwOg8GAwWBAp9Oh1+sxGAwYjUZ0Oh0lJSV0dnYyPj5ObW3tReeBSSaTdHd3E41GWbp0KXv37sVsNhMOh4nFYuRyOTKZDLlcjomJCSorK7HZbJhMJnQ6HdlsFqfTedEfnBYKJQAXmJGREQYGBjh//jw7d+4kFotRUlLC8uXL0Wq1DA0NsXfvXtrb21m3bt1rft7n89HR0cHY2Bhut3vOBWAikSAQCJDP5zGZTFJ81NbWYrfbZa0/rVZLMpmUJ1LRFk6j0RCLxV4jJMRCOl8IF0EkEkGv1zM0NCTrZXm9Xlwu15xfy9+W3bt309TUxNq1azl48CCxWAyr1YrZbCYajcrFXa/Xv+XAbmEVFNYQIcz1ej02m41cLkc8Hp9mPZwNUqkU8Xhc/m6n00l5eTk33XQT/f39hEIhSkpKqKqqIhwO09PTQzAYpLW1FbPZTE1Njew0k0qlSKfTDA8PY7PZWL9+PY8++ijJZFImKplMpovO0gFgMBhoampiamqKXbt20dfXRzabxWAwYDKZOHbsGMPDw4RCIRKJBMFgkGg0islkklmOFwOFQoFdu3bR29uL2WzGarWSy+WIxWIEg0E8Ho+06hw9epTTp0+zYsUKPvrRjy7486bVauUzlM1m0ev1DA4OykzSpqYmeYjo6uqiv7+f48ePMzExQTKZxOPx0N3dPS8CMJFI0NfXh8VioVAoEAwG5fNqMpnkcxsOhzGZTDidToxGo7QGarVaIpEIDoeDTCYjk0QuNgGYzWblOM1mMyUlJbIgPFzwcKTTaeLxuOz4k8vlpBECmBeL7DsVJQAXiGAwyOTkJAcOHOD06dOMj4+TTCa5+uqrGRgY4MSJExw9epRoNIrf7+f++++XrtPS0lJ0Oh3RaJSOjg7OnTsnT9ZzTSaTkWZ44Tax2Wy43W45JnES1uv16PV6mcFot9tlfTPhNhZWwHg8TiaTmbNxz7SARaNRBgYGmJqaor6+nhMnTpBMJmUcY2VlJSUlJdhsNpxO55yN67fhkUce4c///M8pKSkhGAwSCoWw2WyyJILFYpGnf3g11g94XQEnrEzFItDhcEgxaTAYpGtvtjMFh4aGmJqakiKmuroap9PJsmXLeOaZZ2TIQDabZWhoiHA4TDqd5tSpU7jdbmpra2lra2Pnzp2cOXOG0dFRfD4fra2tbNmyRZa+mJycJJlMYrVaLyor88zruW7dOoaHh4lEIhw6dAidTofVauUHP/gB586dw2az0d7eztKlS6mtrcVqtRKJRBZcAIpYM+GWzGQyrFixgiVLlhCJRPB6vWQyGXnwErGpYrMWLr354vUs4iUlJdx888387Gc/46c//SmrVq2ipqaGZDLJ8ePHGR8fZ+XKlRw+fJidO3fidrtJJBLSne92u+ctfi6VSjE8PIzX65WCOp1Oo9PpcLlc2Gw2jEYjoVAIp9Mp2ycKgRQMBmW9VofDwcDAAMuXL5/39+E3IQ6isVhMhgmFw2HMZrO81iKhyOPx4HQ6SaVS8udNJhNVVVULMvZ3AkoAzhPiRhYlUl544QUee+wx4vE4q1ev5tZbb6Wzs5Ouri4OHTokrRg6nY6hoSGOHTvGiy++yB/8wR9w++23YzKZOHv2LL29vTQ2NrJlyxb8fv+czyObzZLJZDCZTKTTaUwmE42NjWQyGWn50+v1ZLNZotEoFRUVVFdXYzAYyGazZLNZEomE7NwQi8WIx+M4nc45zWxMp9PTRExPTw8vvfQSNTU1bN26lYqKCiYmJujv7+fUqVO88sorcrPdtGmTdKPMdK0uBD6fj2AwSE1NjXR/5HI5AoEAqVQKh8MhLbBivsVi8PUEoRCMIiNbvL/RaJRoNIrBYJCZgrNJLBajoqKCRCKBVqulrKxMju/QoUNs3boVvV7P/v378Xg83HzzzTID/ac//Smf+tSnqKioYO3atdTV1aHRaOjr6yOTydDf38+GDRvYtm0bo6OjtLa20tDQwPnz52d1Dm8FYVl9K/fN+9//fnQ6HT6fj3w+j9vt5vjx41x33XXcfPPN1NTUkE6n8fl8Mot+vhHzEYIuHA7zwAMP8Mgjj/Ce97xHJqokEgny+TxXXXUV+XyeU6dOkUql5DohXuPz+WY1C3imwBPxrZlMhkKhQCqVwuPxyMQo4Y6vq6vjT//0T7n88svp7+9ny5YtbN68mWQyyf79+9mzZw/BYJDa2lopvKurq6mpqWHJkiVceeWVszaHNyOVSjE6OkpDQwN+v59kMkkikZCHaOECtlgs0gImqgLk83kikQgDAwO0tLTQ2trK4OCgXC8WEmG1i0ajsuPUzp07OX78OGVlZXJtEqKw2PLZ0dFBU1MTZrNZikLxesXrowTgPBGJRNi3bx//9V//RVNTk2xJVVpaSjQa5bnnnpO19dLpNC6Xi1gsRiaTQafTUV1dTTQa5etf/zoPPPAA/f39/NVf/RX33HMPpaWl7Nq1ixdeeIGvf/3rczaHZDIpG4vb7XbZe9VsNhOPxzGZTNISJVp0LVq0iLGxMYLBIJWVldINLARZLpcjnU5LS+BcMXMRGBsbo6enh1tuuQUAm81GU1MTTU1N1NbWsm3bNp588km+853vUFJSwvr161m3bp3sZiIE7ELQ3t5OR0cHjY2N8msi4NlgMNDf34/ZbMZoNGK1WqWVSWQAz0wCAaZdf9HOr1i0zFWdML1ez9jYGHDBDer3+4nFYoyPjzMyMjJNFIyOjnL8+HHcbjejo6PccMMNvPzyy7zyyiuUlpZSVlYm4yDb2tro6+ujubmZZcuWyVipgwcPMjo6yr/927/N+lxmIq63yLh8I7LZ7GviNQuFAkajEZfLxXXXXcctt9xCMpmkv7+fEydOoNFopKtvITY4MZ+Ojg6eeOIJOjo6pLXsqaeeoqGhgUKhQCAQwOfzYTQauemmm7jxxhsZHBxkYGBAuv/NZjOdnZ2zWq5DZLuK8kYTExMcOnSIQ4cOodVq6e7u5l//9V+xWq0EAgEMBoO0DGu1Wo4ePcojjzzCF7/4RX74wx+ydetWbrrpJjweD48//jgHDhzgIx/5CNdcc828Vy8QCXiJRIKqqioymYwUTEKQC2GbzWYZHR2lrq6OVColRbCIzzYYDFitVtlze655PcuruN/FczI2NsYXvvAFtm/fTmNjIzabjcWLF6PVajl27Ji0dhuNRvnMB4NBysrK8Hg8siIGIA+CitdHCcA5IhwOEw6Hcbvd9PT08Mwzz+Dz+SgtLcVisXDrrbdiNpuJxWKMjo4yMDBAMpnEYDBIa6FY2EUzb4vFgtlsRqfTsWjRIp566il2794t0+QNBgO7du2as24HiURCnugNBgOJRAKDwcDo6KgMQE6n06TTaWw2m5xfRUWFPHWKfsF+vx+DwYDFYiESicikkblCZLKKUiC1tbVs3LgRm81GNpuVAsFqtcpT8cc//nGi0SiTk5McOXKEvXv3cvLkSdxuN5WVlSxZsoSNGzfOe4Dxpz/9aTo7O2WMn8FgwGazybgeo9FIfX29XNSL6/zBq6fsmYJbWN5ErNzw8DAajYaSkhJcLheTk5OzPpdHH32UmpoaudDrdDrq6urYtGkTW7duRavVcurUKTwejzwU7dixg3A4zJ/92Z+RSCQ4cOAAjY2NGI1GOjs72bRpExMTE9hsNiKRCFdddRUOh4POzk4mJiZYsWIF0Wh0zmODikWfsM6IORaj1+tJp9MYjUay2Syf+9zneOWVV3jve9/L7/7u76LT6Th9+jSZTAaNRiPLYAir7Xzdf8WHgGPHjk3zTPT19XHZZZdRW1vLbbfdht1ul2tgoVAgFApx4MABtFotbreblpYWDAaD9BzMdmLOTME9NTUlQ2UWL17Mjh07+Od//meMRiOlpaW0tLSwZMmSaYequ+66izvvvJO/+7u/47HHHmN4eJgNGzYQiUR4/PHHMZlMrxHt81FMWayfdrud0dFRwuEwU1NTJJNJeT1FnJ8YT7HwKy6PEgwGpRs5Go3K+3AumWkNn2kZLy0t5brrrqOiogKv10s0GmXfvn2UlZVhNpsZHR2VSW+A/FyEe6TTaemx0Gq1hEKhOZ3POxklAOcA8SCJ2kN9fX0cO3aMpUuX4vf7mZqaIhKJEI/H8fl8BAIBGcMh4uXS6bTcqEVihU6nkw+1yWQiFAoxOjoqb3a73c6OHTvmTACKBcZkMqHVaslms5jNZiKRCC6XS55Mo9GonHskEsHn82GxWAiFQphMJux2O1qtlng8Lh/8UChEPB6fM0vT3/3d3/Hss8/KPqtigfzOd74DXEhCaW5uliJIr9djNptlWRshJnK5HN3d3dJSJeJLxLURCBdYMplk/fr1fPOb35y1uZSXl/OrX/2KJUuWAEyzAIgyLh0dHbLyfzabRaPRSMFQ7PaaKQKFBW10dFQGVYsking8PutiQ8SQisLIAJ/97GfZs2cP5eXlrF69elrh8UKhwO/8zu+wdu1avvWtb7F161YmJyc5duwY1dXVtLa24vP5qKmpweVycfLkSdlH2GazyU4J8yWaMpkM+/bt49vf/jZWq5Uvf/nLss2dIJvNyuLdH/rQh2hpaeGf/umfaGtro7e3V75XIu5JHBDFmjDbLmCx7szcmMVz2dvbyze/+U36+vpkRmlZWRkVFRVcf/31JJNJpqamMJvNOJ1OfD4fqVRKHhDFwaLYStPX18cHPvCBWavXNjY2htfrlbGG4hBdV1eH0WiUh6P29nZWrVpFJBJh165dAK+xrLtcLsrLywkEAjz44IOcOHHiNeEq81WGCy4cJkKhkLTyT05OsnjxYsxm8zQ3t3CDCuukwWAgn8/LayFqhwoL5tTUFJWVlXMqAN/oOuVyOV555RWOHj0qLbChUIja2lp0Oh2JRIIjR45gtVplBQpAepEikYgshC3c+Xq9nnw+v6B1Py92lACcA0Qyhtg8hQuxuHfu2NiYXMT1ej0WiwWj0YjdbpcPiVarlUH4YqMWDzm86r4rTutvaGiY03kVzy2fz2M0Glm2bBn5fF5a/YRILRQKuFwuRkdHsdlsMmNNq9VSV1cnH9xkMilFbjqdnpNYwCuuuAKr1Uo6nSaZTBKLxQiFQrI4aklJCVqtdprwzuVyJBIJ2WaoeNMSVhgh2EUtxuLYukKhgM1mo7q6elbnIq5tKBTC4/FQWVkpLX0iYaK2tvY1G4JOp3tdq5/4nUJQ1NTUyKQi4Z5PpVLS+jybiNhXuOCmFxmL27dvBy4kS61evZqxsTHsdjvV1dX09/fT39+P0WikvLycyy67DLPZTHl5OYlEgieeeILVq1dz1VVXsWnTJr75zW8yNjZGc3MzZrMZn883Jy478UyIOKvz58/zwx/+kHQ6zebNm6UFRiQbic1Kp9MxOjrKt771Ld7znvewYcMGSktL6e3tla5hId6LYzjF12f7PXmzA9jDDz9MR0cHfX19sntHW1ubtNaImpQi3tdiseD1euXhCZg2J3HQisVi9Pb2zpoAvP/++/H5fDgcDjweD4FAgPPnz2Oz2WTJKq1Wi8fjwev1Ul1dTVlZmYyXFdf0mWee4cSJE+j1epqbm7FarYyMjMj2Y+LAK8oYRSIRPvzhD1NbWzsr83g9hAVPJNTFYjE+8pGPcPr0aXkPxuNxEokEOp1OxmanUilZi7GhoYGpqSnGx8dlzdZgMDjnHVmmpqbkgU7UMY1EItKK6ff7yWazcqxnz56VSW0VFRVEIhFKS0tJp9OykHU+n8dqtUoxKA5EovOJRqO5qDLlLyaUAJwDRFC+iKOqrq7m2muvJZfLsWHDBvnQFm/GImZFZGkJoVRcyBeYtokLi6BITABobW2ds3mJhIPiMQmLnTiViZgkIVqz2Sw2m41CoYDH4yGZTMpFR6vV4nK5SKVScqEShT1nmyuuuIK2tja5MaVSKZLJ5LTq+eJ9g1ffDzEHQG5axZtwoVCQ1+T12tnp9fo52Qw2bNgg++QKiu8hu90uY6xEbJBOp8PtdpNOp+VcxPUQcwyHw1itVhwOh4wTEjGA4nfMJhaLhaqqKiYnJykpKaG9vR2Anp4e3G4327dvp7Kykkwmw8jICMPDw9MKB4tyD1NTU6RSKZxOJ6tWrSIWi3H06FHuvPNOvF6vbFvo8XiIx+MEg8FZL2otDgjio+iSs27dOm688Ua2bdvGwYMHMRqNtLa2yus6OjrKQw89hNvtZvPmzVgsFhncLg52InlMiH+B+HuziehRbDabMZlMZLNZwuEwIyMj/Pd//zfxeJzm5mYaGhooLS3FbDaTSqWYnJyUGZgi5ENk+Nrtdvk7iztO6PV6KYxnM8SgsbGRvr4+IpEIk5OTcn0S1sm6ujr8fj+7d+/m/PnzWCwWWanAZDIRDAYxGo0cPHhwWhkSt9vND37wAykUxTMiDvsiDGMuESErgOzGtHbtWs6fPy/XLHGgFgKo+HO73S7ft4cffpjly5fLxIq5bMmZSqV45plnGB8flwftbDZLPB4HLqyVwjMmQgWEN0xUYyiOdxVxzyK8R8TSimdE7KG5XI6BgQG5tiheRQnAOUC43YS1TAQYC0vdTCtRcd0iUbl85mter3uDOP0Lc3ehUJjTTFqxQIi/JR6w8fFxGc8nhFzx3IR7obS0VFrZhCVOzFEIKNGSbLYpLy9/V7UDWrJkiYwbjcVi8j0RQs3v92M0GmV9PKPROM0tXWxVFoHjAPF4XNbVEpZN8XOz7Z7v7e2lq6uLkpISeZI3m81S+FRWVrJv3z7Gx8fR6/W43W7Ky8tlFwRRuHZkZITJyUn0ej3V1dVcc801HDhwgAMHDnDffffR2tpKPp+npKQEr9fL0NAQgUBgVgWgCLYXAlmv10vxHQgEOHXqFJ2dndOElcFg4NSpUxw+fFh2OnE4HLLUjVgrZoq84g1utl2P6XSa5557Dp/PJ+thivml02na29vRaDS0trbicrlk0fBQKCRrzgGyJqMQ6WJzFha/4nhUcbCYzTJQV199NUNDQ/T09MikCJPJJA/XHo+HdDpNd3c3/f398hpXVlayaNEinn/+eaxWq3y2hFvUbrfT1dU1LTzHbDbLkBK32z3nCSHivRDPNFxY3ywWyzQBPvOjMChotVosFguLFi0iHo9jsVikNVEcBOeCkydPcuzYMbk/CMT6ZDQapx3CRbx2KpUin88Ti8WkNQ+QZcjEmGeGLohnUTyDiteiBOAcYLVa5alL/CsuySFEnrjZhQVGiEAR3F8sBIspdtuJJACx6LhcrjkLbhdxisJdKGKXhKvFYrHI2D4xT3FKE0H5oraeODGHw2HggkgWVd0Vb06hUMBgMLBx40a2bdvGgQMH5OYqFvrJyUmqqqqmuerFBiFciSLWUSy8ooOIVqslEAjIavpi4xAic7Y4ffo0Z8+epa2tjbKyMsLhMGfPnqW9vZ3y8nIpMoS4uPLKK9m6dSs9PT0MDw/jdDqJRCLTYlBHRkZkdxFRW7O6uloW6nW5XDidzlmPM43H4xw6dEjGWYnWW8lkku3bt/PKK6/Izip79uwhHo9jt9vZtm0bAwMDfPnLX6ayslIWVhdCSrx/wr0lEF+f7QzgyclJvvvd70prqSiI7na7aWxslD1jY7EY58+fx+fzTSszFA6HZVKLOABGIpFpSW3CJSms6yJ+djbbdTU2NrJmzRp5mBGWJrGmijEId7043I6NjXHjjTcCF8IPbDabXMOExdLtdsv6psWxtHq9nvLy8t/Yu/1/itg3LBaLjA0Va4I4FBR7MmZa9RKJBOFwmMrKSrlvaLXaaWVk5oIdO3ZIsSb2OrE/igOnWI8ymQyJRELeH7FYjEQiIcNaxJxeryyXWO+EIWa+knPeiSgBOAdYrVYZW5bJZGQ8gxBQxbXbxI0sLGjFbjaxyM+s3SYojhUU7te5zGwUtaaKF710Ok11dbWMkQNkTKPIABSFUicmJsjn89TU1LzGqiHEy1yeQN8tiA1J1PkSTd+LWz2JhCLxvogNr7g1VHGpDGFtEu5HcR+J74nfW1xk9X9KSUkJ9fX1pFIpGYN59uxZufBrNBrWrl1LMpnk2LFj7N+/n1gsxh133IHX6+W5557jwIEDXHfddWzatAmtVsuuXbv49re/zTXXXENrayvHjx+XsU5nz57F4XCwbNkyampqZm0egHSBiixLYZlobW2V5ZPEnPx+Py+88AJms5mVK1fy13/914RCIc6ePSsFuHhtcZapECzwaiblbLvkOzo65MFufHycgYEB+fdGR0cpLy/nve99L6tXr5abuBBYxYkp4l4Ta5MQFmL9E5u/iGeLx+OzHr5y0003cdVVV9Hb2ytrpgaDQcbGxqSlG5Bek0QiweTkJKdPn2blypUcPHhQrq/icCp+ToTiiMOUTqejsrKSzZs3z7kAFAhrfllZGVNTUzIuUCS9iKStVCol9yJ4NWnM4XDIUBHhAp9LF7AQmCLWW6xJYi8QRhHRQUrsCTqdTsaQixhGYQUVxhOxTglBK34fIC2/iteiBOAcIm5Ko9H4rmhGLUSsVqulpKQEs9lMMBiUD11xMgQgizwLy58ImPb5fJSVlREKhaRAFJ1F5qMW1buBw4cP87WvfY3u7m4pGiKRiHSlCStAcZ0/n88nrTFCCMKrtbmElUmcoMXnwqrc19c3q1X1xSa6aNEi/H4/586dIxgMcujQIcbGxpiYmODFF19k0aJFXH/99djtdo4fP863vvUtDAYDd955J1dccQXbt29n//792O12JicnSSQSPPXUU3zqU59ifHycp59+moqKCtrb2ykUCpw+fZozZ86wevXqWZuLXq9ny5YtMnFLtJwT5TVCoZDsx1rsMszn83R1dclNrFg0Fb8n4vAnPhdhE7MtADOZDKWlpdNie8XB1el0MjIywpNPPklnZyeXX365dKXa7XYZ8ydKPQkhLPpKO51OmQQjxg/IclLF2bezhYgJXbVqlfxaKpVifHyceDwuY85E6ZDq6mrq6uqkVan4YJtIJOjp6aGvr0+u6yI2zeVy4XK5ZObtXCPWzFAoRENDg/Qy6XQ6ebhLp9OyRaQQ9YBMWInH45SVlclwnOKaoXPBbbfdxsMPP0x3d7cU0eL6CqEnQgcAKQhnPg8imUNYAsWYi6tkFIdIWSwWWWtUMR0lABVvmeI6gMIk39LSQigUkqczvV4viz2L7g5iQ2hsbCQWi9HT04PT6cThcDAyMiI3ORHMq/jNNDU1cccdd0jXrogbC4VC0golTshiMTQYDFxzzTVcccUVUnwLK6Cg2EUm/g/IU/tsiqZAIEB9fb0saSRCJ/bv38+dd97J448/zg033ABcsNJ0d3ezc+dO1q1bJzNRd+3axfvf/37OnDlDf38/DoeDiooKbrvtNqqrq3nkkUdYsmQJJSUluN1u2T7xueeem9W5iLCGYkEBSNEn4nQ1Go1MWBFhFKJvq/i+SJIQbjDxO2bGyc2Fa8vpdMqDGiA9C3q9ntLSUjQajeyFGwgE8Hq9skyH6Ns6s8yOTqejtLSUyspKwuGwtCQLK5DZbKaiomJW5/FmmEwm6uvr3/bPWSwWli9fzvLly+dgVG8d4c5Op9MkEgkqKyvp7e2VLvViF3ssFpNxisXhRSIurqqqSlqaxXoxV6xcuZIVK1Zw7NgxmRA1MDAgs8ZLSkqkJXJmprj4v2hVJzxQxaFTcKEEWyqVkq8VRa4vpvaPFxNKACreMuKUJkzw1dXVMug5nU7j9Xqlud1ut7N48WJKS0uZnJwkEolw/vx5rFarDN5va2sjHA7L06ww6yt+M2VlZdx9993TXITw2iLP4mNxySARblD8/Tdi5vdnU3BYrVYZR2a1Wlm/fj0tLS1873vfIxKJsHjxYkpKSqiqquL48eN0d3ezZMkSli1bhk6no6Ojg9tuu41nn32W2tpaNmzYQDAY5Ny5c7S2tvLcc8+h0Wg4c+YMtbW10i3W2NjI1q1bZ20ecOFwdPDgQWpra6mrq8Nms8m/F4vFZGao2LhEUoLZbJaHH2HBKC5fURzILgL/haVQ9H2dTdavX8+//du/cezYMY4cOcL58+dlf9/KykqGhoZwOBxyPOfOnWN0dBSXy4Xb7cbtdnP27FlsNpu0Vor1YmJigkQigcViIZFIYLPZMJlMRKNROjs7GRsbo7Kyclbn826kOOzHZDIxNDTEuXPnZMa4cJMKb40ocF/cAcRisdDV1UUsFpMiX7jm53rsK1eupL29XVqXx8fHOXXqFF1dXYyPjxOJROQBSdxD4jkojn/X6XSybJpY14TF02q14na7ZaeqDRs2zOm83qkoAah4W4iM0UQiQVNTEyUlJRw+fFgG1wvrYCQSIRQK0djYiNvtprS0lGAwKPvVVlZWYrPZCAQCssSCMPkrfjMajeZ/lAAgFtL5Kl77eqxevZr7779fxjM6HA6Ghob4+c9/js/nY3BwkPXr13P69GnC4TAbNmxg8eLF/OQnP2HDhg3odDrC4TC1tbWsWLECnU7HwMAAjY2NnD59moMHD3LnnXfymc98hubmZhwOh4wznVmM+X+K3W5n0aJFHD58mEOHDmG1WqV4FZ1UZpYYKv5cJIYJC0xxVqRI9BJWXLhQOF1YO2YTvV4vy7usX79eJmiIzPEzZ87Q2tpKZ2enLCk0MTFBe3s7/f39VFdXEwwGp7n1RDC+KEIsRIkIAchmszidTkpKSmZ1Lu9Wiisw6HQ6jh07Ri6Xo6amBqPRKNdRo9Eok3LEwVy8H6Igd39/P5dddhmxWAyr1TovBdKFWxYuPDcul4va2lquvvpqaRWfWf3i9dYpYbks/lfsBhZWcyFwFa9FXRXFW6a4zlk+n8fj8RCLxXA6nbJ8h2jELYLexaler9fjdDqli8LlcklzfiwWkzGSs5llqri4cTgcr3GniTjGYDBIXV0d4XCYEydOUFNTI/vLZjIZIpEInZ2d0nUoistGo1HWrVvHiy++SGlpKalUSmawzqXA0Ol0tLW1YbVaiUQiBINBmXDQ398vN6OZPa+LM1CLY2CLSzvp9XqSySQ9PT14PB5MJpN0c1122WW0tbXN6lwMBoO05gmERbKmpga3201NTQ0mk0kmIni9Xtra2mRrxeKfKbZqvp6FWiQfzWc/3XcDIk7z6NGjtLW1TSvvJBI/hMAWvYMNBoMsUSZiU4UrHub/QFgcJz+XBagVr48SgIq3THE2sjjFTU5O4vV6ZQaZqM7udDplYddsNitjioQFMR6P43a7KSsrY2BgQJYAUJvApU1xRnJdXR3xeJyJiQkaGxtJJBKcP3+eqqoqKisrZZ2wwcFBdDqdrPMHF1yyS5cuZXJyEr/fLy1lQpTMBV6vF6/XSzabxefzMTo6SjQalTXIiouFF7vuhXWs2M1VnEwlyq74/X5ZlgUuxLLNV8apGIvH4wGYFlMl3NDvpjqbFzPFxdkzmQwlJSVUV1fLLOXiwvyizFggEJD3l4i/LC8vl3GZxX11FZcOSgAq3jKi7IE4eQoxJ+pQiQ1Or9dTV1cnFxnhFs5kMsRiMZLJJENDQ5SVlbFo0SI6Ojpk8oLKAr70ECVNstmsLPYqXESi1l0kEuHs2bMMDg7i8XjweDy0trZSX18v+2GLckRHjhyhubkZi8Ui23MVW6Dm2s2l1+upqKiY18QGxaVHLpcjFovxoQ99iOrqas6ePUs2m5X1OzUajcy6Ft19RFKeaMfpcDhkHVHR9lFx6aAEoOItI9y3YlMWrrp8Po/X65UxPqJe4PDwMLW1tdMq/bvdbqxWqxR77e3tPPPMM0xNTXHixAna2tpYunTpQk9VMY8Iq5zP5+PMmTMEg0EqKirw+XwEg0EqKytlTGkmk+HMmTPs2bMHi8XCZZddxtatW9m3bx8//vGP0Wg0uN1urr76avr6+sjlcrI0zHwJQIViLhFWY5EJ/PGPf/x1y88I659IMBJJIsKCmMlksNvtbN++nUAggMFgUFUYLjGUAFS8ZRYtWsSaNWuYmppCr9fz13/919x///0cPnx4Wls6URLmwIEDtLS0sH79evr7+2VzchGUe/XVV+PxeHj44YdxOp2sWbNmTmqBKd4Z2O12VqxYwYc+9CFpuYvFYnz605+WXTVaWlqoqanh6aefZu/evdxwww309/ezefNmMpkMPp8Pp9NJIpGgpqaGxYsXc/XVV1NXVzcnhZMVivmmqqqK5cuXs23bNurr69+w/adYk98oWcxgMLBhwwbWrFlDNpvliiuuoLq6ei6HrrjI0BSU01+hUCgUCoXikkI1yFMoFAqFQqG4xFACUKFQKBQKheISQwlAhUKhUCgUiksMJQAVCoVCoVAoLjGUAFQoFAqFQqG4xFBlYBaQ3t5evvjFL/Lss89iNpuJxWLodDoaGxvx+XxYrVbsdjvRaJTKykp+8YtfqH6Zincdxd05MpkMgUAAo9HIxMQEDocDu91OLBajo6ODkydPyjZlY2NjjI2Nce7cOaqrq3nPe97D5s2bCYfDsrB4WVmZbJ+2kH2P3010dnZy4MAB8vk8NpuNiYkJvv/972MymbBarZw/f5729nZuv/12qqqqCIVCXHnllSxatGihhz6Nf/7nf8bn85FKpbDZbCxZsoRbbrmFbDZLOp3GaDQyOTnJ008/zZkzZ9Dr9bJ26Wc/+9mFHv67jtfr0nP8+HHuu+8+NBoNK1euJBKJYDAYuOaaa7juuuum3VNv1jdY8fooAbiAnDt3jkwmQ3l5uRSAWq2WZDKJVqvFbDbL9lIlJSVMTk4uuACcWUy3q6uLhx9+mHA4zKJFi1i9ejWLFi3C4XBMqz+VTqfJZrOcP3+eHTt2cPjwYdatW8enP/3phZqK4iJBLNhTU1McOXKE3bt387//9/+mrq5OFh53OBxcfvnlLFmyBK1WSyKRoLq6Go/HQy6Xw2w243Q6ATAajdTX15PP5zl8+DCnT5/mqquuYsWKFQs5zXc8hUKBf//3f2diYgKv10tZWRlGo5HGxka+9KUv8fOf/5ynn36aqqoq1qxZQ3l5OTqdjmg0yo9//GN0Oh1f/vKXL4oNOhKJcOrUKSorK/F4PBQKBbq6uujr6wMu1NATPXUzmQwVFRUYDAaSySRHjx6d05aClyrieo6NjXHo0CF6enoYHR2lrq4Or9eL2WxGp9Phdrvl95qbm7n66qtpampS78dvgRKAC8jw8DA+n49CoYDVasVgMBCNRnE6nZhMJlmZXfTfnZycpL29fUHHrNFoyOVyjI6OymK8oVAIk8lENBplZGSEiooK3G43VVVV2O12wuEw4+PjBINBBgcHOXfuHMlkkqeeekqK2ptvvpna2lqMRuOCzk+xMGSzWXp6eti/fz8Gg4EXX3yRO++8E4vFQiKRIJ/Po9frcTqdGAwGHA4HHo8Hi8VCLpeTBxMhBs1mMwcPHuTs2bMEAgE6OjqwWCy0trYu9FTfkaRSKZ544glOnz7NypUrKS0txePxyJ60ixYt4n3vex82m40rrriCtrY2dDodsVgMr9dLOp3m9OnTPPHEE9x8880L/pyPjY2RzWbRaDQYDAby+Tz5fF52jBHjy2azsqByoVCQB3TR8lIxexw9epSBgQEmJycZHBykUChQXV1NeXk5gUCA8vJy0uk0brdbvg9jY2N873vfY926ddTW1rJq1Sr1vrwN1JVaQIaGhojFYhiNRsxms+yzKz4XDeELhQLRaJTe3l42b968YOONRCL09/fT09PD4OAgL7zwAj6fj8bGRux2O6lUip6eHnp6ejCbzVRWVuJwOAgGg0xOTuL3+4nH4xiNRqqqqhgZGeHFF1/EYDCQTqdpaWmhpaWF9vZ29RBfIghLyvnz5zlx4gQ+n4+lS5fS3d3N0aNHWbt2LSaTiUwmQzabBZCbr8ViIZ/Pk8lkZA9TvV6PwWAgHo+zb98+9Ho9brebkZER2T9Y8fZIp9MMDAzwyiuvYLFY5PqUSCTQ6/XodDri8TiLFi2itLSUxsZGeZgVLcjy+Tw+n4/nn3+euro6li5d+rrty+aLcDgs26EJj4tWq8VgMEhRCMgeuYlEglwuJz0fSgDOHrlcjnPnzrF7926CwSAWiwWbzYbdbqe8vJxMJoPBYKC2tpZYLIbT6ZQHv7GxMfr7+zl//jyDg4NkMhlWrFiBzWZb6Gm9I1B38AIyPj6O0WjE5XJhNpsJBAJks1lisZi86V0uFxqNhlQqxdmzZxdsrFNTU5w8eZJdu3Zx+PBh0uk0BoOByy67TC6EJpOJRCJBMpkkHo8zNDREoVAgl8tJK43L5aK0tBS73Y7VamVsbIzR0VFeeOEFXnnlFS6//HLe//73KxF4iXHgwAGOHz8urUo2m43t27dTUVFBXV0dJpOJbDZLPp8nlUpJa5+I+xGHJWEZOHfuHBMTEyxZsoRcLkcsFiMSiRCNRrHb7Qs82/8Z2WyWbDaLVqudF0taOBxm7969GI1Gli1bht/vJ5VKkUqlZDxmMBjEZDJRUlJCOByWAl3EzQE0NzczMjLC9u3bqaqqwmw2L5jbLpPJoNVq8fl8ZLNZHA6HFKpizMKqXCgUCIfDhMNhqqqq0Gg0pNPpN2yxpnjrFAoF0uk027dv58yZMyxatIiGhgZ0Op3cR+LxOKWlpdIaKyy1BoOBmpoaNm3aRDab5cyZMzzzzDNUVVVhtVqVS/gtoHbYBSSfz1NaWorT6SSVSgEQj8eJxWJ4PB4cDgdutxubzUYoFMLn8y3YWJ955hkef/xxotEoLS0tmM1m9Hq9fMjEydjhcAAXTnWBQEAKRYPBgMViwWq1YjKZCAaDxGIxLBYLbW1tlJeXMzY2xq5duxgcHOTLX/4ytbW1CzZfxfwg7p++vj4mJiZoampCq9Wi0+lIJBL85Cc/4UMf+hD19fXodDpyuRwmk4lCoSDFXz6fl9abTCZDR0cHv/71r6mvrycYDKLRaPB6vWSzWY4dO8amTZsWcsqSmckvwgr1ZhtXoVAgFAoRDAbR6/U0NDTM+Th9Ph/PPvssN954I+973/sYHR2VAjybzZLL5aSFdmJigsrKSiKRCJlMhnQ6LYVTJBLhve99L8ePHycYDFJaWrpgruBIJEJZWRldXV1ks1nKy8upq6sjGAzK1xRbN8+dO8fRo0e59dZbcTqdhEIhbDYbWq0qpPE/QYQQjI6OkslkSKVSdHd3U1paitVqRavVYrVacTgcDA4O4nK50Ol0MlFHHAiDwSDV1dUMDQ0RCoVIpVJv2CNZ8Srq7l1ADAYDJpMJh8NBaWkpXq8Xu93Ovffei8/nk5lobrebsrKyBXOZxONxDh48SCaTYfny5axevRq9Xk8mk5HxMULAmkwmeZIWgeJut1susF6vl0KhgNPpxOVy4XK5aGpqIpfLUV5eTnV1NYODg/znf/7ngsxVMf8UCgVKSkqorKxEq9UyOjpKa2srDQ0NnDp1in/5l3/h8OHD8vXCCgDIUAmLxUI8HufAgQP87Gc/w2q1Eo/HOXXqFGNjYySTSYaHhzl27NgCzfKNSSQS7Nq1i3PnzhGJRMhms1LcFgtdIRg9Hg9arZbh4eF5GV8mk8Hn85FMJgmFQmQyGaampojFYtI9ajAY0Ov1VFRUoNFo0Gg08jUOh0Medtva2qirq2N8fJxQKDQv4389QqEQXq8Xt9tNPp8nmUwCSEEqQgnEobWiooLW1lZsNhv5fJ6xsTF5Dyr+Z+RyObq7uzGZTExOTkp3u9FopKSkBK/XSyQSobGxEY/HQyKRkM9IOp0mnU7j8/mIRCJMTk7S19dHOBxe6Gm9I1ACcAERAe6RSAStVkuhUMBms/GJT3xCnp4TiQQ+n49AILBgLtF///d/Z2BgAI1GQywWo6+vj2AwSCgUknFZwh0i4rGERVCn08mYoXA4TCQSIZfLodFocLlcVFRUoNVqpRVUuIkPHz7MkSNHFmS+b0Q8HleL/hywe/duQqEQdrudRCLByMgIU1NTnD59msWLF5PNZjl8+DDd3d1YLBZSqZQMiwCw2Wx0d3fzxBNPsG/fPm666Say2SypVIq77rpLxphms1n8fj8DAwMLOl8h6rLZLGNjY/T09FBVVUWhUOD8+fMcOHCA7u5uACmm4EIsnoh1FJaT+aC+vp4vfOELVFdXy+vodDoxGo1YLBaqqqrweDzo9XpZUsXtdlNZWUlZWZlM2CktLeXgwYOcOnWKpqYmysrK5mX8MykUCkxMTBCJREilUoyMjNDT08PExATJZFJeV5F8dPbsWcLhMJdddhk2mw2z2czk5KQU5orfnmw2i8/nk/vHsWPHiMViBAIBQqGQTPiqrq7GbDbjcDhoaGiY5mnq7e3llVdeIRQKUVZWRjAYJB6PL+S03jEoF/ACUllZyYEDBwiFQtTX15PJZIjH43i9XrRaLZlMhkgkQjKZJJPJLFgAu7A0CNdbLpfD5XJNC8wvFqfCbC/M9AKR5CLiagCSySThcJiysjISiQSpVErG4PT29nLZZZfN40xfn0KhwD/8wz+wbds2/uzP/oyNGzfidrtf87psNitdeQaDYV7dQ+l0mvHxcSYnJ4nFYsTjcZLJJKlUappwsNlsrFixArfbjd/vJxKJoNfr8Xq9OJ1OHA4H6XR6XoOod+zYgc/no7a2Fp1Ox+DgINXV1eRyOdLpNHa7nRMnTmA0Gqmrq5OHBb1ej8lkYmhoiOPHjzMxMYHBYODo0aOUlpbS3d3NwMAAHR0dLF26lEKhQGdnJ3v37qW+vn7e5jcTIQDT6TSjo6Ns374du93OnXfeSXd3N/l8nlAoxKlTp3C5XJSXl8u5ini0aDQ6L+7TfD5PPB5namqK8vJyjEajrMtosVgwGAzyfRLxc4VCQXoChNDN5/PY7XZMJhOlpaVy7VioQ60QHF6vF4PBgNFoxO/3U15eTi6Xk5m+2WyWgYEBLBYLq1evZmBgAK1WSyQSeUcJwO7ubs6cOYPX66W5uZnKysrXvCafz087cMwHmUyGyclJUqkU2WyWqakpmYWdSqWYmpqiqqqKfD5PNpslFAqh1+sJBALkcjksFgulpaXk83l5oJiamiISiczbHN7JKAG4gIjaUn6/n0QiQTQaJZvN4na70ev1ciFKJpPodDrKy8sXZJzCPF8oFKSoMBqNMrlDoNVqpSAEpn0PkMKvUCiQyWTIZDIy5mZgYIBQKCSLXw8NDdHV1TUv83sjRB2whx56iF/84heYzWb+67/+i6GhIdauXYvD4ZBucDEvUVjW7XbT2Ng4J3UbDx8+zM9+9jOZ8BAIBPD7/cRiMfl/IbTFP4FWq8Vut8s4GhGIn0ql0Ol0WK1WqqurCYVCbN26leuuuw6j0UhfXx8lJSWsWrVKnr5ng1OnTslkJ5PJhFarxev1kslkqK6ulq8bHBzk2LFjaDQafv/3fx+44K7TarX86le/IhaLATA6Oko4HJYlhc6cOUNvby9lZWVUVFTg8Xjo6emZtfG/XYQbV1zzwcFB8vk83d3d6HQ66e4KBoP09fWh1Wpxu93Sam40GtHr9WSz2XlJQhBeCIvFwsmTJ6mrq2NqakomeRQKBeLxuIxJjEQiOJ1OmaAj/lmtVoLBIAaDgY0bN2I0GhdUAA4NDTE2Nobb7aa9vV0eeJqbm2XtSSFmhRuyrq6O06dPy/IjF5s34PVqE+bzeb7//e/z8MMP43Q6KSsrw26309zczMc+9jGsVqt8bfGBdb7qHIoErbGxMe677z5OnTpFZ2cnixYtYvHixaRSKTkuIdQTiYR8TzKZDCdPnpRj1uv1MgZQ8ZtRAnABETFx+XxeBiCLhdRut8vFXhRerqmpmfcxRqNRJiYmqK2tRa/Xk0wmmZiYwG63S+uMwWCQMRnF8UozBSAgMzWFZSCRSKDRaBgbG8NsNuPxeDAajRiNRvlgLxSiBE8qlSKRSGC1Wl9zQhWbmBCymUxGCiyfzzcnAvDBBx/kV7/6lUx6ENYXrVYr3yMRG6fT6WSyjgjcTyQSANJSI6xRos5ZIBDA5/MxMTHB8ePHMZvN+P1+9Ho9a9as4Utf+tKszeX8+fMYDAasVitWq5VIJCLnEo/H5ZhcLheRSISenh527drFhg0b0Ol07Nq1i46ODtxuNyUlJVitVkKhEBaLhampKVkuQmTVm81m+vv7+fGPf8w999wza/N4K4hNVRykJicnCQQCXHfddYRCIU6ePCnDIcrKytBqtUxOTsoDYCaTQa/XYzabyWQy8hkT4RZzQSqVkuVc4EKtNp/PR3t7u8zGjEajWCwWmRCSSqWk271QKGA2m6UVsKenh5qaGiKRiKx+sBAUCz4RK+r3+2lra5MZpfX19dhsNs6dO8fSpUvRarWUlpbi9/sXxPo3U5TN7HwhPiYSCQYHBzlw4ADnz59nz549BAIBXC4XgUCAnp4e9u3bx8mTJ7nxxht5z3veg8PhQKPRsGPHDo4cOUJJSQn33XffnM8pk8kwMTGByWRi0aJF+Hw+zGazdL/ncjlZZ1YkhkUiEex2O2azGa1WS0VFBdFoVF6fRCJBJpOZ87G/G1ACcAGpq6ujpKSEXC5HfX09U1NTjI6OAq9m0wprgcFgmJeMv5kEg0GZ1CE2Hr/fTzKZlIu32NReT/DNRGQ5FscyxeNxJiYmaGxslJl1VqtVVuVfCISY1el0NDU1yWKxwjWv1+ux2WwEg0G0Wi0lJSUyISYajdLT00MsFmPx4sWzPrazZ88yNjZGRUUFlZWV0vKYzWalyBGuaJHJKDYHYaEtLpxcKBRkgpFwXTscDvx+v6ylp9VqSaVShMPhWRWAXq9XXjdRg00ICJERb7PZZAFov9/Pc889x5VXXkkqlWLPnj1SJApLk8PhkLXdRFxpcXiBcL0uFLlcjomJCQ4fPszLL7/MjTfeSDKZ5PDhwzgcDkpKSrDZbDLj2WKxUFJSQjQaRaPRYLVaZbhINBrF5XLN2VhFWapoNEpNTY1sxafRaOT30uk0ZWVlDA4OyvtJBOeL58hgMJBKpRgYGJhW6moh0Gg0XHnllVRWVrJ06VK2bdvG4OAg0WhUlhs6c+aMfA7y+TxGoxGDwcDatWuprq6W2agXE+l0mhMnTtDZ2Ul/fz/Hjx+XBa8rKirkPKxWKz6fj6effprJyUlpCU2lUuzfv5/Ozk5aW1vnRQCm02kmJibweDxYrVbS6TR+v59z587hcDiwWq3ykCOse8lkkmg0ilarnVYyTax1xSEvijdHCcAFpLy8XG60JpNJxvvAhY0xkUhgs9nIZrNYLBbq6urmfYzipC4yLUVv4tHRUerr6+WCLx5SIQaLaza9XmmL4jpmIqnEZDLJhchsNhONRudtnsWCVJQjEG2gRFcWYQ08deoUjY2NrF+/nlgsRqFQoK6ujkQiQSgU4tixY+zZswebzca9994762NtaGigvLycNWvWsGjRIvx+P4cOHZJJOaKIragXB8gSIzMFYPEmXFwEVxRjFULSZDKRTCZnPRN9zZo19Pb2EggEZKxrOBxGp9Nhs9nk5+L6p9NphoaG6O3tJRgMMj4+TkVFhXSLio1icnKShoYG9u3bRzgcZmpqSsY5VlVVzUlB9d/kNhMxZcFgkIGBAfr6+hgeHqa3t1fW1uzt7aW3txeNRoPFYqGlpUW2hSwUCjL2zGg0kk6n5fM5V4hrHg6HqampIZlM0tTUhF6vJxaLkUgk5P3h8/lkOEQ8HpciMRKJUFpayvj4OMlkUj5HxeEi883mzZvZvHkziUSCI0eO0NzcjNPpxG63k8vlqKmpkYk55eXlsgvF2rVrF2zMgFxvxbUVllWNRkNXVxcPPvgge/fuBcBut0sDg91uZ3h4mMrKSmpqanA4HHR3d3Py5Ek6OjrQaDREo1G8Xq9MtJorD0Yxovd3eXm5TIIMBoOcPXsWl8tFc3MzFotFPlciSScajRIMBpmYmGBkZETum8Jw8k6Kz1xIlABcQCwWizxxChO2CGQVpVZKSkpkuYSFqG4eiUSIRCLSNSs2m507d5JMJlm2bJms1m6z2WTdLBHkHYvFsFqt0g0pNgiRfJBOp+nu7iYcDmOz2aisrCQUCjEwMDDrp7hiF5zIZhSCJhqNyjgrn8/H5OSkdIP29vZy5ZVXcuTIEdLpNHv37iWTyWA2m2loaCCZTNLZ2cmhQ4d45ZVXGBgYYPHixdx0002zOn7BokWL6O/vZ9WqVZw7d44TJ04QDoen1cUDpAVGuN1nxmWJE7NAJO3EYjHZd1Ov12M0GmXHhNkWgDabjY0bN3Ls2DHZZaazs5Mrr7ySsrIyueCL0//ExARVVVU8+uijDA8P09zcLGPPRNeJaDRKbW0tLS0t/PznP2d0dJTq6mr0ej2LFy/md3/3d2d1DoLfFDMVDAbx+/2cOnWKXC7H7bffzh/90R+xZ88etm7dSiwW48c//rF830SBYrvdztmzZ2ULLBEaUlwIe64Q95LJZGLnzp20tLSwaNEipqampCvU7XbLQtsGg4HR0VGcTuc0S79we4sCv8Itv9B0d3cTj8dpampi8eLFMqbXbDbLQ6DBYGBsbIyXXnqJNWvWzHvtv3w+TzAYJJvNTstSFrG/oij1t771LYaGhmRZMRE2cfDgQW666SZ5eI/FYhgMBhlWZDKZiEQiVFZWyoYE3d3dPP3003NygC1GlBQqLy+XBwnhxRDvg8fjAcDv92O1WqcVgxYxguXl5YyMjNDc3CxDcBS/GSUAF5iWlhbC4bCMMdu0aRMajYavfOUrdHd3c/bsWRobG1mxYsWCFB39/ve/T6FQIBgMSuuMED/RaFTGK4oFRVgCRWxccZAxIEtHmM1m2R/UZDKxatUqbDYbHR0ddHV1EYvFZj3mUYghn8/HiRMnGB4eZuvWrdPKPpw7dw6TyUQ8HicSiXDNNdfwsY99DIAPf/jDnDhxAr1ez6FDh9i9ezcej4fx8XEZB1hVVcXnPvc5br/99jmzzCxatIgHHniAo0ePTsvCFq5aMVcRA1hsmQWmxcSJzGzxOuE2FhufeE0oFCIWi/GJT3xi1ucjWgBOTk7y3HPP8cwzz9DV1YXNZpObMEBpaSmbNm3i0KFDuN1urrzySgYGBli5ciW9vb3E43FKSkpYs2YNer1ebo7r1q3jnnvu4b3vfS9VVVWzPv63iiiHdOjQIeLxOCaTiYmJCdauXYvL5eKjH/0o8Xic8vJySkpKMBqNDA4OUl5eLi0jwk0mLEA+n29OPQOJRIKxsTFGRkZIJpMyg1fEnjqdTkpLS5mYmCAWi1FRUUEsFsPtdjM5OYnZbKaqqorjx49TU1NDd3f3tA4bC01nZydjY2Po9XrGx8dltikwrayNXq/n/PnzjI+Pz/s9NDIywg033MDy5cvJ5XLS9X/q1CkcDgculwuPx0MoFGLlypW0tbVJd7AQVX6/n5aWFnbu3Mm5c+eoq6tj1apV9Pb2MjY2RlNTk4yxE/Gb27Ztm3MBKFzAixcvZmJigkKhQHNzs2wdKMI3xEGi2GhSHOPs8Xjo7u6WXhBxf6luIG+OEoALTFNTExMTE5w5c4Zjx47x//1//x8AP/rRj+jo6ECr1dLW1kZbW9uCjO9rX/samUyGF198kePHj9PX18fY2BhVVVW4XC4GBgbQ6XRUVFTI2KR0Oi2D1AWifZfINNXpdExNTeHz+SgtLaWlpYWxsTEymQxtbW3ccsstXH755bM6FyGOJiYm0Ol0rF27Fo/Hw8GDBzl58iSdnZ28//3vZ+XKldKSWRx3+dBDD3HjjTfS19eHw+FAp9MRCARwOBykUine85738Nd//ddzXq7n1ltv5fHHH+f5558HLggLseAJV6/I/hXJH+L7QqAXt06D11qvRIatEH8NDQ08+uij0zJzZ5uysjLuuece7r77bk6cOMGPf/xjWltbGRkZkSWRhJtUWGI6OjqIx+Ok02mSySSJRIL29nbOnz/P4cOHWbduHV/84hdpaWmZs3G/GeLav/LKKzz99NN88pOf5Ktf/aos2VNTU8OWLVtoa2tDr9fT2NiIVqslGAzK5Is1a9YQCoVwOp14vV7Zikyj0UiX6lxtdOFwmPHxccrKylixYgVNTU10dnaSTCZxuVwUCgV6enpk3OX4+DhXXXWVbOtnMBg4d+4cbreboaEhli1bhtfrlRathcwEBujv7weQvczFdRRjEnGyIgZ1ZGRk3gXgP/3TP1FSUkIymZRei6qqKqqrqykpKWFkZISKigqmpqYIBoOcPHlSlntpbW3FaDSSTCaJxWK4XC4Zd1tRUcH+/ftJp9N4vV4ZTydc+/MVJyviwF966SWuv/56tFotoVCI0tJSSktLCYVC1NTUMDExQSKRmNZCUKx5S5cuZefOnWg0GrlOKH4zSgAuMOFwGJ/Ph8Ph4Pbbb+fMmTMUCgVeeuklLBYLHo+HSCTC0NAQq1evnvfxCSvWHXfcwW233SZdu/F4nC9+8Yvs379fVmcXokgE2wPyQRQFosVGHYvF6O3tJRQKce211/Lnf/7nVFdXy/gm4eaaTURJHYfDwfDwMA888AC//vWv+cQnPsF3vvMd6uvrue++++js7KSnpwetVktraytXX301cGGh+ta3vsXf/u3fsmPHDlwul4zFWr9+PV/72tcwm82yB7JwY3i93lmdB1wozv2Nb3yDRx99lPHxcex2u7RcFMczzozRFNmkwgojBKA4UQvRKFwowWCQm2++mf/zf/7P69YOm03EmAwGA83NzbI7g+jTajKZCIfD1NXVYbVamZiYoLW1VW7cTqcTq9VKLBaTge/BYHBaZvobxaTO9jxEPGgmk6GyslKOKxKJyLJKK1euJBaLccUVV9DR0cE999xDJBLBYrHIFnZXX301tbW10k0m+hiLcAUh8OfS5RUKhTh06BCXX345drsdh8OByWSSAlS4FUW4wNTUlOxWIlx02WxWJk6k02ni8bjs6LCQAlDcY683huJ+wCI2+OTJk7N+MP1N3HXXXQwMDMgWfG63WybVZDIZlixZwuHDh2WZIBHzKhKH3G43Pp+Pnp4erFYrK1asIJVK0dPTw3XXXUcwGOTMmTPS8pdIJKa1xJtLRDhKXV0dO3fu5BOf+AQdHR2yLFUikSAcDmOxWIhGo3R1dbFo0SKZtZzL5bDZbNTV1TEyMkIul6OhoYFwOMzg4OCC1vt8J6AE4AKTy+Xw+XxygRexM6IgqXjNQlEcA1SM2WympqaGbDZLOByWi73FYpEZmWLRFCJElIkQC0wsFpMZtE6n83WLK88W8XicyclJjEYjDodDurC1Wi2VlZXodDpqa2vx+/08/vjjnDt3jq1bt9LY2Mjjjz+O1WrF7XazaNEi3vOe9xAMBmVnimQyyd13301JSYkM4BfvYygUIh6Pz3pfY7vdzic/+Ul0Oh2/+MUvGBoawu12SyEnYpmKXb8mk4k//MM/ZMeOHezbtw/gNRaPmffaJz7xCT74wQ/O20IqFvUzZ84wMjLC8uXL5SYhujCIUi5Wq1XWkxPFq0U8mhC2tbW18t4tjkGdC0TzenjVcpRMJpmamqK9vZ0/+7M/I5FI4Ha78Xq9xONxjh8/zo033sjk5CQDAwPU1tbKXr9C1IsyGPl8nqmpqWnlYIrLRM02wv3Z2NjI4cOH2bdvH3V1dUxMTMiyLyJZBV4tFC/KDBVnmZtMJvr7+2V3B41GQzgcJhwOL1gpGJFsJuZRXMlAxNoVl9lJJBJ0dnbO+zjXrFnDBz/4QX75y18yMTEhSz319fXR39/P1q1bZf1OYf0SB44zZ87Q2trK5OQk/f39VFZWYjAYGBwcJBaLyfczGo1iMBhkB5c1a9awcuXKOZ2XEHdutxuHw0Fzc7M8QIiDYC6Xkwle4r4SBz7xvsXjcTweDzU1NRiNRjQaDUNDQ1RUVCgB+BtQAnCBERmnohimcPWWlpYyNjZGKpWS8WgLwRvFHYqFXjTsFpuwsNwV15YrLgAtRGA8HpcbQXEwu3jdbMc7+v1+mUwg4g/D4TCNjY0yyxSQySGhUIjh4WEymQwajUYGVotEFZfLJYsPJ5NJKaREGRVxTSKRyJx1bKiqquKuu+4in8/z0EMPyc1UjEVYicTniUSCo0ePMjg4OM39KzY9YSEU79PNN9/M+973PpYsWSLf07lEjDWfz+P3+yktLZWZvXDBEpXL5WhsbGRwcFD2mQawWq2YTCYZfmC1WuX7PdPaNxfWP9HFYHBwkGw2K0vXiF6yDoeDSCSCw+HA6XQSi8Xo6Ojg+PHjpNNpysvLiUajdHZ2Tquf19HRQXNzs4wXFEkgwpo2ly3hRKauw+GgoqKC5557jsbGRrxerxS7BoOB8vJyzGazLJUkDh+JRIJ0Oo3ZbMZqtbJ7925aWlrkoTAejy9oyy5RKxOQtTQFxfeIeC6E52O+sVqtbNmyhcrKSvx+P8PDw3R1dcl1q6urS3osRP9lt9tNIpGgt7eXhoYGtFotVVVVLFu2jIqKCjmP+vp6uV6JkkMulwu3243RaOTYsWNz5nkS90F5ebnsd19aWko6nZatRkVXI3G/i9qRosOR6FQTi8VYvnw5Ho9HdgRaSMvyOwV1hRYYIfBE5uv69esBZCsr4dbz+XwLMr432iyFwBMPmdiMilsJCcFQ7FoUiIVX1M2bDytnKpXCbrfLYsc9PT2UlpbS1dWF0WgkEonQ398vXSmDg4N0dXWxadMmysrKpNgVXRBKSkpkjUCr1UoymZTtr4QrJZlMzroLuDjmq729ndtvv51oNMrPfvYzeXIWCTkzf27Pnj1y4xZiT3wsthZu2bKFu+66i2XLlk2zoM01xX+jvLycqakpaR1OJpP4/X75Htrtdhm0L6yeQrSIsinxeHxe7i0xBtGdpdiqJGr6jY2NYbPZiMfjjI6OykQkcXgSrdXq6uqwWCwyUcflck0r9msymWSrtblMphB1H3O5HK2trfzsZz9jYGCAVatWMTk5KcW1sKSLAr5iXRBrW6FQkMV6vV6vPBQudCKIWLuE5W9mxxzxr/g1C5VUUFVVhdfrJZ1OMzIyQlNTk3yODx78/9l77zC5r+p8/J3ymd7Lzmyv2pVWvVnFli033G3ZYAwmGEx3EiAJId/yCxgI3wRIAiQhJgFjh4AdwBQbW+5dXavedler7W167/X3h55z9JnVyjagnRnheZ9Hj1bbdO/M59773nPe854+6PV6NDU18aWJ7FtWrVqF9evXIx6PQ6FQoKWlBXa7nQu8yH+PouPiyDVFpReKANJzr9Fo+JJQX18PhULBlz4iiW63my9TtJ4p2gcA8Xgcra2tyOfzXGhYjlaJlzpqBLDCyGaziMViCAaDAMAha6fTiUgkwlVN1dLbkIgCkSRaxOLet3NF6WJiISaJmUyGi0GI6NLXLraw3Wq1IhQKsYE16RCVSiV27twJAHC73XjllVfYSX5mZgavvPIKrrnmGuh0Ou7SolAo0NPTA6fTiXXr1kGn06G1tRXhcJgjiPS6qFSqi66dm/varFixAp/85CcxPDyMXbt2lWzk9P30M/F4nKOuYq0gkcBsNosVK1bgL/7iL7Bs2bKKpeeo7RNFk0gHGAgEEAqFUFdXB7PZDL/fz685AG4nJ5PJoFarmcAQ5nZPuFhIp9PQ6/Xo6uritnwAuHMB+SieOXOGRfnJZJLtlTweDzo7O7FkyRK+VJAxsVwuRzabZVJI642e44UCFd6k02l0dnbC4XAgn89j+fLlOHLkCGvQyA2AIoD0eeBce0gqcKFImtikvFKgDkaUQhdHjKjIBkCJX6bBYCj7OGkcmUwGUqkUbW1t6Ojo4K9dfvnlcLvdTKxVKhV7t5Kx/nygz9P86NmiZzaRSMDhcCzYvMjMnPYdMqkmCRS1SlWr1ZidnUVbWxsEQUAymYRCoWCPXJIe6PV6BINB7hBSSZ/JSwU1AlhhUAqHqvmoyrKhoYHTwlRMUGmITUjFXnrAOdNh2uzF4yUDXDp8xQbFdKiLPcHotn0xQ/hqtRpdXV1wuVwIBoPo6enBn/7pn+LUqVNc1RgIBDA6OsrVZ/l8HsPDwzh58iTa2tqQyWTYTPXDH/4wa+6i0WgJiaVeolqtln3qLibm29CbmprwF3/xFzh06BAfyKRjersoC/nnURTnH//xH7F8+fKKPnMURbruuutw5swZDA0NAQCbUzscDrjdbpjNZqRSKU6bptNpOJ1OmEwmJvxzLyMLgXA4zIcQ2bKQfiyRSHAqlax6mpqasGLFCp7Pxo0bsWrVKvbJk0gkaGpqYsnE3EsW6Wypdd5CgCr39Xo99Ho9GhsbOXVIUWGKJNOFjqIvpB8kIpJOp7nfczAY5IO+kj1bKXqZTCZLvOPotQXOXRiIJFaqHzsA7kpC7fdIf2m329k/VrzWM5kM/H4/X4Ko+InILj1L1Ned3kMqrDAajRdduywG2VFJpVKWR0xMTMDr9XLEkwzglyxZwppNyhpRj2rSccrlcr4wUfvOGt4aNQJYYdhsNlgsFl6ARBboNkY3/Ll+epUCpUWKxSIikcgFvz7X5JUWOm1CdJPTaDQIBoPz3tYudoWgSqVCW1sb0uk0HA4H1q9fz1+LRqPweDycQqV0A3WVGB0dhV6vZ08wihCK+/CSPoV+tpwpCI1Gg82bN+PnP/853njjDTgcDibq4vSVOPVGhCKbzSIcDsPr9eLWW2+tSLW5GPl8HuPj4/D7/ex5Rs9IoVBgs1s6sLu7uzE7O4tQKIRCoYB4PA6v14tEIsGHyELDarVyX2uqciVzc+ros3TpUixfvhzAuagscI7QU1cE0i5SlJC+HolE+D2jv1OpFEsQLjbE3WIymQxXX7a2tqK3t/e87yVyqNVqz4tSZ7NZmEwm7N+/nwtABEGAzWa76OP+XWCxWHh+tD7EFyyxpEUmkzHRqhQoqkdNAUiLTRE8kh5QpFWpVKKlpYXnRc8NSSfoUk+XCMrsiLXECwUaE6W2bTYbRkZGoFKpoNfrkc1m4ff7USgUYDAYIJVKMTIywtHocDgMt9sNk8mEY8eOsR8lpZAr4Zt7qaFGACsMp9NZkiIkUqXVavn2YzAYKtIH+O1AGydFOMSbJ5EkAkUFc7kc92el27+4Vy2AEiK8EBBXNBMZoqpmsSic0o+XSiWZXC7Hhg0bcNlll/1em3c1GafSs79//36Ew2GOqBoMBrS3t7N2aHZ2lo24ybCcIm8jIyO49dZby3J50mq1UCgU3J0gn8+zaW00GoXb7S7xJ6NnnqIxdFhT2jsej5cUsdChTYSQvgZgQSOAlK7N5XLs/TnfwSqRSEo6Fc1XeEOkXSKRsEZzocb+TkF7wVydqHjvoos4RWGB6lkrEonk936+K62RC4VCGBwcxPT0NAwGAxobG/HCCy+gqakJHR0dsNvtfHmdnp7mAjC1Wg2ZTIb6+noYDAa+8Pb09GD79u3c4eRidy36Y0SNAFYYBoOB+xeKq9JoUUskEphMpoveFeP3gTglSLoN4FwFHaWExZsnCdmBc6lgMcHL5XIVjW6Ki1HEkSIq5qiGTf53xe875mqaKxVPUNSVokZerxdtbW0IhUIIBoNobm5GMBjkrhTkkRYOh2EymfDqq69i06ZNXIizkO8pVR5qNJqS1KHBYGDNJUWZKAIo9poDzkVg5rZ9pHET8aMoB+mhFgIUVaLeq16vFx0dHSVFXfO9lmLvvLnRNGpHptFooNPpKn5I01zEfpniTjmkVaRI6Fw7rBp+fxiNRixatAhGoxFHjx5FT08P9Ho9YrEYxsfHEYvFWOJBXXBIDkFVwBKJBG1tbewcMDU1hc2bN7MFVw1vjRoBrDBI7AqAQ/gAOIQtk8mg1WoXvCn37woxiSMDUdokKaJBRFAsVJ9rPZLNZsuWpvtdUU2E6N0ESifSJk4XC7lcDr1ej8nJSeh0OnR3dyMajZaQIypcICshr9dbVjH43Oi1WDM7t9Kavi7+m37HXIh/Vkys5rO5uVgQFwkolUqYzWauSL4Q+aPxz9WPSqVS2O12BAIB1NfXs05rIYtY3gkMBgP3VRZXlIs9TPP5PGKxWEl1bW1v+MPhdrtx6tQp2Gw2zM7Owuv1IhQKobW1lfWOgUAA4+PjyGazWLt2Laanp6FUKvl9Ix/ZY8eO4aabbkI2m4XX6+UIYQ1vjRoBrDAorUXO7lQNTBusXC7nFlDVBhofbYZi/d9c0TpF1Obz+Vvozgw1XHoQW4lQZIbMYBOJBNra2iCXy3H69GkAZ42xs9ksAoEAR+KoN/Dcy0U5I7ti+xbxxxcLC1moI5PJuKIyHo9zVHZuVf87gVQqZc9GIllAKfGtBKhjCVVqkzOBWBtHRXpSqbQqMjF/LCBZUDAY5D7SoVAIJpOJ/QCJzAmCgKmpKYTDYZjNZsRiMa6MB86mk+12O5unU3FIDW+NGgGsMEiYK5PJEI/HmQBSSpVc9KulCIRAVcC0QdItWRwBBMCt4cieAzhHFEkvRX1Ba6gBOPtsUfcLg8GAYDDIERqJRMKaWYrYUJEFHdzkGVgoFLjatIbfD/l8nv0AE4kE6urqfmdxPRFupVLJvy+fz0OpVFY88i9uT0fPmNgXk6xtSKO50O0Q300g9wFq22ixWOD1etmQWlxYFI/HMT4+zh6g5D1JjQf8fj/7sKZSKeh0upoR9DtArUymCiCu4iMtEKVIlUpl1djAiFEsFtnwlm5cdLNvampCZ2cn7HY7LBYLG9zq9Xqo1WrW2JAnn81mq/hBUEP1IJ/PY2pqCoVCAUajkVtGkVmy1WqF1+tFKpWC1Wpl7zK6LKlUKtZwBQKB8y4XNUL4zkB9rqemphAKhWAwGEo6R7yT13G+1HY0GuX+rpXw1RNDnAImAigeM10qcrkc72E1XBykUimulG9sbOQCIUEQ4PV6EYlEYLVaIZfLufhJrVaz1COdTiMWiyEUCiGTybBcJBKJIJlMVrSF6qWCGkWuAtTV1WHjxo0IBoOcYtDpdFi3bh23K6s2kPaisbERSqUSa9asgSAIWLx4MW666SbYbDYEg0FezBKJBEajEZOTk3jjjTcwNTWFwcFBdHV14YEHHqjKKucaKoNCoYDp6Wn09vZyj1Ly94rH49izZw8WL16M6elp9qYLBoNIJBIwmUzcWYb6VIsJYI38vXM0NTXB4/HgyJEjiMViuOOOO2A0Gn+n3zG3ur+lpQV9fX0oFotYvXp1xVKq5FhgtVr58kpVz0QeKAoovuQSLtUCsWoCdcRJJpNwu91wOp3Ytm0bbDYbe/45HA72Mayrq0MqlUI4HOYIoEajQSQSQaFQYKNoigxWusr5UkCNAFYBrrjiClxxxRUln1u0aBH+7d/+rUIjensYDAb89Kc/fcvvId2i+NZssViwcuXKBR1bDZc2BEHAxo0b8Wd/9mf87FBPZuCsVZJEIsH09DS3WaMoulKpRCKRQCaTwUsvvYSTJ09WnXziUoFCoYDT6URnZydmZmb+oEsaEaampibMzMxg0aJFMJvNFSNRRADr6+uxfv16KJVKbmMnlhMAYBPvJUuW8M/XyN8fjvr6evT29uLw4cMIhUIAgHvvvff3+l233347gLPnC5HKWgTw7SEpVlqFW0MNNdRQQw011FBDWVHTANZQQw011FBDDTW8y1AjgDXUUEMNNdRQQw3vMtQIYA011FBDDTXUUMO7DDUCWEMNNdRQQw011PAuQ40A1lBDDTXUUEMNNbzLULOBqeGiIBAIQK1Wn9fcXeyXlUwm4fV6EY/HSywVqhEnTpzAqVOncOutt5bYiCSTSbzxxhtQKpW4+uqrKzjCdwfIrgMAewB+6UtfgtPphFqtRjabhcfjwUc/+lHccccdbD2Uy+Xm7UlbDZicnMSJEydw8uRJBAIBbNiwAfl8HseOHYPL5cJVV12Fq666Cg0NDSU/V23ecwcOHMCuXbswOzuLlpYWpNNppNNpfs/I2mPp0qX48Ic/XNnBvosQDAZx7NgxvPDCCzAajZBKpdyxKZ1Oo7W1FVu2bEFXV1elh3oestksjh49iueffx6nTp2CSqXC6tWr0draCpVKBb/fj+npaUxNTSGVSqGzsxP33XcfHA5HpYd+SaJmA1PDRcGrr74KuVyOzs5ONDQ0IJ/PQy6Xc/suiUSCN998E6FQCMuXL0d7e3ulh3we8vk8ZDIZDh8+jJ/85Cf4zW9+g/b2dkgkEkxMTKBQKMBut8Pj8eDqq6/GV7/6VTQ1NfHP1XBxMB/RSaVS+O///m986UtfQigUgtlshlqtRjAYZNPYK6+8Ep/+9Kdx8803l/ys+BmsFF544QXs2LEDw8PDSKVSkEgkiEajOHHiBLetk0qlcDqdWLZsGQwGA/L5PBYvXozbb78dvb29FRv7hfDd734XP/7xjzE7Owuz2YxEIsHzkMlkbKC8fv16PPXUU5Ue7h81xGvmZz/7Gf7P//k/3EeXeq+T4bVSqcTWrVvZx7UaLhaxWAwPPPAAduzYAbPZDLlcjmw2i0gkAo/Hw11CCoUCzGYze34WCgVEIhFYLBbcfvvt+Ou//mvodLqKzuVSQi0CeAlhbgP2WCxWNY7n7e3tGBgYwNTUFLf1AVDSNzQQCKBQKJwX2agW0JgPHjyIPXv2IBAIQCqVctu6YrGIXC6HcDiMmZkZDA0NoampqUb+LjLEh9Gzzz6LF198ETMzM5idnYVKpYJWq+VuAJlMhg+EkZER/OM//iOeeeYZrF69Gu9///thMBgglUpRqXtuKpXCY489ht27d5e0qlOpVGhqaoLdbmfipNVqUV9fD0EQkE6nkUqlMDExgR/96EfYvHkzbrvttqpY6wCQTqcRCARQLBbR0NAAmUzGpE+hUEAmk0EqlSIejyMajXLXlhoWBtTDGADvV3K5HHq9HrFYDACgVqvZIFncHaeSBDCfz8PlcuFLX/oSTpw4AbPZDKVSCYlEApVKBbPZjKamJu7HLO4pncvl+MKRz+fxwgsvYGJiAn/2Z3+G3t7e87JRNZyPGgG8hDB3kf7mN7/BihUrqqKzht1ux/T0NOLxOGZmZmAymaDRaBAIBBAOhyGXy6FSqaDT6aBUKis93HkRDAaxd+9e7N69Gz6fD1KpFJlMhm/N2WwWmUwGgiBgfHwcP/nJTxAMBnHXXXdVeuh/VMhkMnjhhRewf/9+HD16FGNjY8hms5DL5TAajdBoNLzxx+NxGI1G7ufqdrsRCoUwNDSEU6dOYe3atbj55ps5NVxOpFIpDA4OYu/evcjlcrwGJBIJ5HI5ZDIZ6urqIJFIUCwWIZPJoFQqUSwWuaVVPp9HMpnE7t27sWLFCrS2tlYFCdy9ezdGR0chkUig0+kQj8eRzWYBgMmfQqFALpdDPB7Hzp07cc8991R41H9cEBM38UVbq9XCarViYmICTU1NOHr0KIrFInQ6HYrFIorFYomspZLRP7fbjUcffRSHDx/mdo+5XI6j4gqFgteCXC6HRCLhPTmbzTLxFQQBhUIBR44cwS9/+Ut88IMfxIoVKyo2r0sFNQJ4iSCVSmF8fByxWAxyuRyJRAK/+c1vkEgk0NDQALvdXqKXKjckEgkUCgXi8TgmJyfh9XqRTCYxNDSEYrGInp4eaLVamEymiozv7TA4OIgXX3wRO3fuxJkzZ5DL5aBSqfjrcrm85ONoNIqdO3fC5/Mhk8ngpptugsFgqHgq5VJHPB7HwYMH8V//9V84ffo00uk0pFIplEolp4GUSiVyuRwMBgMikUgJISoWi0in05iamsLU1BQGBgYgCAKuuuoqOJ3Oss4lFovh4MGD8Pl8sNvt/HmJRMJtqgRBgEwm44MsnU6XRCtlMhnkcjkCgQBOnDgBu91eFQSwr68PMzMz3Cu3UCgAOBt9oj66FGVKp9PYvXv3JUcA/X4/tFotFApFxfbVdwqXy4WZmRmEQiEcOXKECbnVauU1QUQplUrB7/fj4MGD0Gq1WLx4cUXGTNG/l156CQCgVCqRyWSgVCphMpkgl8uZ+NHlKRaLIRqNQiKRcHaGCKEgCMjlcti7dy/Wrl2LRYsW1aKAb4MaAaxCeDweeDwe/nexWITf78ebb74Jl8sFQRAQiURw/Phx2O12LF++vOSAqQSo/6pMJkMymURfXx9OnTqFgYEBbNmyBfX19XyIV4PmRIxCoYCf//zn+NGPfoRUKsXjVKlUKBQKrKGhGyjdolOpFPbu3YuTJ09Cq9XihhtuqIrD+Z0im80imUxCJpNBq9VWejgAAK/Xi8cffxx79uxBU1MT9Ho9v95EjIrFIkKhEEeTqdiD3htqEg8A/f39eOyxx2C32+FwOMr63MXjcZw4cQKFQgGpVIoPYHqWiFRks1meG32Onjvx8zcwMICNGzdWxSXK5XLxes/n8yUas2w2y+lImsPU1FSFR/y7IRKJYN++fWhsbITBYOAUJD1zgiBAIpEw4SUCrFKpyvb+0LPs8/nw29/+FgcPHsT4+DhGRkYwNTXFUXGFQoFMJsPvUyaTwYkTJ/DQQw/B4XDg3nvvxZIlSyCVSsu+PmZmZhAIBKDT6ZDP55HJZFBXV4e2tjbu801FLDqdDj6fD4FAADKZDBqNBsViEW63G9PT08jlclCr1QiFQhgZGcHMzAw6OzvLNp9LETUCWEWgh/25557Dj3/8YygUCj48otEoQqEQ0uk0IpEIuru7IQgCfD4fZmZmAKCit9RcLgepVIq6ujooFAo88sgjmJqaQiwWw0033QS3241YLAadTodkMlmSgqgkisUiIpEIHn74YUgkEhiNRhYbU0SVDmyKaBCBlcvlMJlMCIVC+L//9/9i48aNFSfi7wRUtOL3+3H8+HEolUqsXLmypFiChPz0/RKJBBqNZkGfMSJ2+/fvh8Ph4I2fNGXiw8nlcnFaVaFQoFgsIpvNlhAQKqoYGxvDzMwMstlsWQl6LpdDKBTisYlTuzRfIncEcUqP3o9MJoNisYhAIFCi3aokVCoVBEHgtG8ul+PCL+Bc4Q0Ro0tN/7d//3784he/QF1dHdRqNWZnZ+FyubBixQo0NzejsbGRI7OxWAzxeBwAsGjRItx6661lGSNdGp588kl88YtfRDQaBYCSiNnU1BTy+TyfFYIgIJ/PY3p6Go8++igA4LXXXsP27dvLLpOYmZnBwYMHOaul1Wp5rdB6jUajyGQyiMfjEASB09dSqZQlRVRsSJH0VCqF48ePo7e3t0YA3wY1AlhlkEgk6OzsxOrVq0vsL1wuFwYGBiCVShGJRPDSSy/h3/7t3/Dmm29iYGCgwqMGZmdnEQqFUCgUoNfrsXLlSnR3d2PXrl0IBoNob2+HWq1GoVDAzMxM1VgQUHVpJBJBXV0dp68AlBR3EAnMZrN8SNNmpVar2dpjw4YNVUNuL4RcLgeZTIbJyUn89re/xalTp6DVauF2u6HRaGA2m+F0OtHS0sKbsVqtxhe+8IUFPcgTiQTcbjcSiQRMJhNXy4ojsETsKJWl1WqZrFNalS5OmUwGKpUKuVwOHo8HXq+3rESEDiOKuhD5vJBUo1gs8vNHUU36tyAILOCvBpBWUSKRcJTW5/Mhl8vBaDQCOEsSKTpWV1dX4RG/cxSLRRw9ehRLly5FMpmEIAiQy+UYGxvDq6++CovFgtWrV6OtrQ2xWAzZbBbpdBpqtbqsF0Ai2N/73vdQLBZhMpl4LRDcbjenR5PJJF+SFAoF9Ho9JBIJDh8+jImJCeh0urJekMbHx7F7924oFAokEgnk83loNBr4/X64XC4Ui0XI5XIIggCtVguJRIJ0Oo18Po90Oo1gMMhrSa1Ws+xAEAScPn0ap0+fLttcLlXUCGAVgQjHFVdcgcsvv7zka8FgELt374bT6cSaNWsglUqxa9cuqFQq9PT0VGK4jFwuB7fbjZ07d7Ltwyc+8Qnk83n81V/9FUKhECKRCORyOVwuFzweT9UQwEwmgx07dnD6Ya5diFQqRS6XQyaT4c/R14l0kND9yJEjWLZsWVUTQHEqNRaLQRAEKJVKxONxpFIphMNhjI2NIZVKcaTQaDTiyiuvXHDy5PP5MDg4yBXX4rES8aPiAjqU6bAjaQHp0QqFAhKJBJRKJWuLksnkgo5/LvL5PKLRKARBQCKRgEajQS6X4zkpFIrzUm5EqogMqlQqRCIRaLVarhauBshkMoTDYfj9fmg0GrjdbgDg116n0yGdTiMWi51XdFCtoOdnz549rK12u92c1m1qaoLFYsFNN92EbDaLqakpruym6u5yvj+FQgEulwsjIyNMjmgeYlmEWFZAkdpsNssXqXQ6jSeffBIOhwP19fVlG7/X68XIyAh0Oh2y2SwXelE0j2QSdOmh15qyLzqdrsRjVqvVcjTR6/XyM1nDhVHdytZ3KWgTITIikUhgNptxww03YNWqVZBKpfjbv/1bTE5O4q677sIdd9xR8nPlhlwuRyqVwubNm3HPPfdgyZIlmJ2dxdjYGJ566imMj4+XRNPEh0GlD7R8Po+TJ09CJpMx6SHiQelE+j6xLouIhthna/fu3YhEIhWdz9uBSAUAnDx5EslkEgaDgYXXZrMZer0eNpsNNpsNZrOZP15oJJNJvtWnUqkSfzyxxo9SouJDAgAfaGq1GjKZDIlEggXwe/fuxYEDBxZ8DmKICyFCoRBmZmZYsC4mqnMtajKZDM97amoKk5OTkMvlPO9qAOmvMpkMV2crlUrcfffdCIfDSCaTPE8AJQVV1QrS9O3duxcSiQSRSIQvRTKZDCtWrMB1112HwcFBHD9+HOl0mqPNyWQSLpcLu3btKtt4s9ksduzYwTYvtA/RZYn2JuCcpECv17NPHhXwAMCOHTvKunfRvkpnAekPxfssRVZpDvQ8kZ6RqoWz2Szr4ilDQ1Ier9dbtjldiqhFAKsQ86WH5uqy9u3bB7VaDYvFwmH7SmkA0+k0RkZG0NzczBYvwWAQoVAI8Xgcg4ODfHj5fD7W01FRRSVRKBQwOzvLxSsAeA5iATKAEj858cZKKbqhoaGyR5l+X2QyGZw5cwZut5u1NqlUCrlcDul0uiTNTV5uC41EIsH2OwBKxkHPPUVdiTyJU78U9aCINKXnpFIpJiYmMDExseBzmA+pVAo6nQ4bN27E9PQ0XxxobuI1QOOfKzmgz1UL9Ho9r+NMJoNIJAKdToc1a9bg5z//eQm5lUqlMBgMlR7yBUHrO5lM4vjx4wgEAjCZTIhEIiwjyGQy8Hg88Pv9/JyRGwMAmEwmdHZ24oorrijbuHO5HI4cOcJ7kTgaDpwjVUSmqGpWfOmmtTM1NcURxHJgcHAQg4ODnGUQX+RIpyi+7IijgOLPUxqcotDki5tKpeD1ejE2NnZJ6LIrhRoBvIRAB8Xx48cxNDSEa665Bs3NzQDO3qj8fj+KxWJZw/jA2UWZTCZZYyIIAmKxGB566CHU1dWhWCzC6XQiGo1iZGQEWq2WD8VqIICxWAxms5lvlrRZiqv7xMJ9+jkifxSNosrIagYdDk8++SQGBgaQSCQ4+kmWCmKSm8/nkUqlWOS+kIhGo3C5XBz1prQtvfYUbaHomdhORYxEIoFIJMLpLolEgnA4zK3JygF6jsQdMerr6+HxeJjYikm2WFYgLqRobW1FMplEJpOpqKH1XKjVaq4ypUuD1WpFZ2cnjzOfzyOfz0OpVKKpqanSQ74g6LVPJBI4deoU++XRGic9KaUogbMEmNL7GzduxPLly1FXV8f7cTmQy+Vw6tQpAKXESFy8RoUT9DcRVkEQ+OcEQeCLYLkwPDyM4eFh5PN5xONx1iXT5Vv8+tOcxA4MdLEgiY5UKuVWhHQxmZqawoEDB7B+/fqyzetSQ40AXkKgBfDss88iGAxiy5YtaGtrg8fjwaFDh3DgwAGsWrWqbFVoBJlMBpPJxKJv8mpTq9VoamqCRCKBwWCATCbjyIGYUFWSBIq1MvO1CxNHAOfetCmSQ5HCubfWagO91slkEs888wxHlSnVKo7Y0M2a0kjRaBQTExNoaWlZsPERAaSxkglyOp3mAggxMaf3SkwCKdpBRJEOh2QyiVQqtWBjn4tMJoNYLMbPeSwW49Q0jZPWM0FsCE1zXbx4MYaGhtgPUVydXkmQFgs4l+rWaDRoaGjg54oOcZVKhba2toqO90IQrwmq9NVqtUgmk/xe0EVCoVBAo9Hw/OLxONavX49rrrkGnZ2d51WqLzTy+Tzr/2i9iJ8LIoD0MX2POFIuft7KuXc5HA5s3rwZbW1tHKmj1DVd3sSvJbkB0FoX+2gWCgXE43Fs3rwZGo2Gi1ksFkvVdp2qFtQI4CUEOpRfeOEFqNVqdHZ2wu/3Y9euXfjVr36FgwcP4sEHH6zIuOrq6rjxeC6Xg91ux//6X/8LSqWSN1S1Ws1Rm2rwy6ODS61W88ZJFgpEMMSEYy4ZpI2XojbU5aFaMJdc079PnDiBkZERLF++HG63GzMzMyXRJTL1pmimRCKB3+/Hyy+/jI997GMLNt54PA6v11tiwyOTyTjyR5orOrjowBMTdzrcSNyezWY5qkAEtxzvUSqVKok4xmIxaLVayOVyHgM9P3PHTx9T5ExcZSrWB1YSCoWixByd9GVms7lk3dDrX+0HMRUgibW9YnNh8pZUKpXQ6/Xwer1wOBy4/fbb0dLSwlGpcq5/srCiSwXtqbSOaS8TP2Nznzv6Ptqby4WNGzdiw4YNSCaTGBsbQ19fH4Cz0qbDhw+XtBQUzwk4dw7SXGQyGWZnZ/H1r38dLS0t7GtqMBguieKjSqJGAC8xpFIpzM7Oorm5GSdOnMAzzzyD119/HTqdDsuWLcM111xT9jFRWoEqNCk0b7FYMDo6yre8iYkJFAoFtLS0IBqN8mFRKeRyOfj9fqRSKahUqpIUCB1ipDGjw068EVG0idLe8XgciUSC9Y2VhpjQAWAN0Pe//33W3VA0llK/Yr9DglQqhc/nwxNPPLGgBJBIExHyVCoFhUIBlUpVUh1Ic6KDWUwC50Y3yPvPZDJBp9Nxp4GFRiKR4F7SlEYng2CxWF+sx5qP1FFaSxAE6PV6xONxTnNVEkRmgXOkVdzHld4fMYmvNhCRJs3o2NgY9Ho9v960PmhtCILAWsahoSF8+MMfhl6vB3BujRFBL0fRC1nwkEenxWKB2+3m1pviSKyY8FEqu6WlBT6fD7FYjCUg5SSxNO7e3l709vYCONtikF5vWttztb7Ud5ogk8ngdruxevVqWCyWqrqEVzuqR1VcA4DzRa7AOeF7Op3G888/z7q1f/iHf8DBgwfR2tqKyy+/HFu3bq2IUazH44FCoUA6neaCgR07duDzn/88nnrqKfaZa29vh9VqRSwWQyAQKPs454KE3VTNCICjRpT2masxE2u15hYipFIp+Hy+qikEmevsT5qh1157Db29vchms+y/NVdkLf43VUf29/cvqMaRPAjpgCIPPYqwku6MPOgoCiXWBhFIN0coFosIh8OYnp5esPGLIU7VUuW12WwuaWAvJq30b3HqlPR1wNkIokKhQDgcLmsq+0LQarX8PhDJ6+3tLSF6FOm3Wq0VHOmFQYR7ZGQEQ0NDHPGbKwUheyilUoloNMrFSjfffDPMZjOvF5/Ph1dffRW/+MUvyjJ+kmYUi0Xo9XqsXr0aTqfzPOInLgYR62lXrFiBxsZGLuTx+Xx8mS8H6Bmfu8eq1eoSiyTxOhJfainCTPrBsbGxks/N9USs4XxUPkxRQwnmu73Qog0Gg3jggQegVqu50IMq1tra2uByucpayUUwGAyQSqWwWq1oaGhgTzmtVos333wTv/71r/Hwww+jsbGRF3E5ojBvh0wmg5mZGaTTaW7/Jt4sxZYpBLEvFYFSxGKSUm3I5/OYmprCRz7yEaxbt47TJrFYjAXuYuE1FcLo9Xr2CYzFYpiamkJHR8dFH18oFILf7+eoXkNDA44fPw61Wj3vIUGHFhFA+kOCfUrT0UERDAYRDofLFh2gg4hS6X6/Hx0dHfyciSs1xXpY0pER8VMqlRwNpS5A1VBoRDorupjK5XIsWbKEuzTQM2UymarG81MMutjEYjGcOnUK09PT0Ov1rGWMx+Ns75LJZCAIAgRBwPT0NORyOZqbmzE8PAy/34/Tp09jYmKC+0+vXLkS9913X1nmQH1xtVotnE7neSnPuQSQnj2lUomWlhbkcjkcPnwYSqUSfr8fiUSibG0h54t4k7E7fZ32JAAl7wNwlhhms1nodDp0dXWVRD3n03PXcD6q76R6F0D8gM8V3gPna7dcLhdefvllfOc734HP58NTTz2FF154AR0dHdwfOBAIQKvVssdTORGLxXD48GFMT0/DaDSis7MTPT09cDgcmJiYgM1mgyAI8Pv9fOs7duxYxSsDc7lcSSSSNhOxCF/c4mq+26R4EyMyVelbp1g/RwJ30ofKZDJ0dnYimUwiGo1yuosKLnK5HFQqFWQyGerq6mAymZDJZBCNRpHL5XD06NEFIYDAOZ8v8u6jmzxVm9L7IJFIStKPtH6o8IOIOP1OIiqk4yoHqIKZNIt6vZ6tXcSpd5oDcP66Jy1TJpOBRqNBoVDg96zSMJvNrHklEqvX60tS9IVCAVqtturawIlf5507d2J2dparZKn6nMgFeYGm02mOBALA6OgovvnNbyKRSDBxop9bqPUxF7SXFotFGAwGGI1GluCIi9vEJIreK5lMBovFwtmKYvFsq8FKZy9on6VIHxWtiOcxt2peXMVd6aLCSw21FHAFIL71i01uCeKPh4aG8JOf/AT/8R//gbq6OjzzzDO48sorEQwGEY/HORVGbvSViD7t3r2bScP09DSGh4ehUChgt9uxdu1aruSkzWpmZgbf//73yz7OuaDCAuDsYUsaQPEtEjh3E50rRBbrfuh7iESVC7QpijUy4mfqzJkz+PGPf4xvf/vbAIAVK1ZApVJhYmICwWCQza+JNBmNRu56QDY91HhdoVBg7969CzKPuRW9fr8fS5cuZU0PdSyh6CRpsgCUpHrofSNzYoqQ0MFSruiZ2MKGDmhxZfV8KWDxvkAHnUajgd1u504iQKkOtVKwWCxsBk2HNM2RUCgUYDQaF7Ry/J1irl8kcJb8jY+Pc6qaSAbZ2oj/kNY3kUjgxIkT+NjHPgatVouxsTH4fL4SnWA5+s+SfpnmJH7t6XkiiPVyBCr0IoP3aiGAc3XLROgocjm3EE9MAKnvdg3vHLUIYAVQKBQwMDDAPmE2mw1Op7Pk61KpFC6XC7/+9a/xwgsvwG6348/+7M9w+eWXc8VWJpNhewiVSsUflxMulwtKpRIrV67E2rVrecEWCgUIggCTyYS6ujpEIhFMTU3BaDSitbUVGzZsKOs43w5kdzKXiF/Ie23uLVOcxivHJiSugJ1vTNQ6sK+vj/22GhsboVKpcPr0aT746MCjOVD6jro50KZLkbWFctYn0kppqkQigbq6OoTD4RLrCvJqnKtbpN9B64IuILOzs/yexONxBIPBsni1UWqUXru6urqSSnPx8ybOAIgvFmQCbbPZMDk5Oe9BXilQYY44uiROHdLzabFY0N7eXqlhMsRRpHA4jP7+fpw+fbrEVoRkBel0GqlUiiN6lJKnThmf//znsWHDBoyNjXGGQBAEZDIZJBKJshgPZ7PZkipz0lmKq2MpijlXJ0vvWTqdZg2jRCJBNBqtuLyA/n961omci4meuKpZHBGspk45lwpqBLDMiMfj+NWvfoXh4WEAZ/2Qmpqa0NDQAKfTWZIWfemll7Bjxw4YjUbce++9uO666/hrpIWghSDurlFOUOcGo9GIxsZG6PV6JJNJDA0NQaFQIBQKwWw2IxwOQ6vVQqvVQq/XY8WKFVUZrp8v0ve7oJx+WuIbfzabRSAQwNDQEEZGRjA9PY2TJ08COPusUMqOzLgTiQRH0Ihcie0v6CAUW44IgrCgZrH02pGpq1Kp5EOMOgbQMzPXC3Bu1IPWAZFbiUSCRCIBj8ezYOMXg9LR9LrW1dXx60wN7ongiSO34tQdcPZ5NJlMVbdWaKxiiyRxsQe9jxaLpezG9OIx0NqIRqMIhULIZDJwu90YHh5GPB5nEksRvnQ6jUQigUQiUWIIT7rUZcuW4b3vfS//HFDqGJBMJmE2mxd8bvl8HuFwmD+m157888SXBfFzQ/tbJpNBIBDgwINEIuFK4Epirrxh7iUJKM3QiOUuNfL3u6NGAMuEXC4Hr9eL48eP46tf/SoMBgO0Wi06Ojpgt9thMBiwZs0aCIIAh8OBgYEB/M///A/y+Tze97734e677y75feK0L91AgXMO7+VCsVjE7OwsR2YUCgVvrIIgYPv27bjyyivhcDhYDO52u+FyuZBKpVirVQmIq12B0o1SrEMRbzDiDYl+RnyI0+uw0CDdEW2A0WgUAwMD2LFjBzezb2pqwlVXXQW1Wo2TJ08iEAggFoshmUyy7o+a2CsUCm6kTgSEWmBRRV2xWFwwewsinOJnSKfT8f8tJhqUrpubPgXOHQ7ins5UoEPFGOUERY/q6ur4ORLr/+Z+79yPC4VCCaEoV4T5nYLWkFwuL8li0GVUp9Ox/U25Qa+31+vF0NAQJicnmTipVCom4ZlMhv8kEgnE43E2gqbnqLGxEddcc03JJZwqcMVpSblcXjYCGI1GeV1aLBa+KAHny1jErwkRwMnJSSxfvpy/Rq0gKwmx9yhBfMmj92O+CGClvTEvRdQIYJng8Xjw05/+FI8//jiL6wVBgM/nQzabhVqtxtTUFEKhEO666y781V/9FUZHR3HPPffMmy41mUy8UalUKlgsFgSDwbJHAC0WC7xeLzweDwRBQDAYxMmTJ3H77bcjlUrhm9/8JjZv3szpO0pHjIyMYHR0lP2fKgVxVw+FQlHSIkm84cxX/QuACRQdyuXaRJ988kkcO3aMO2REIhH4/X7U19fjve99LxoaGji96/V6kcvlMDQ0BLPZzGleMq8mLRd5nxEZo76a5HUWDoexZMmSBZlPIBCAz+dDoVCAWq1GOBxGS0sL+vv7uaMBpdkoNS0+GMRRcJq30+nEyMgIgHMXo3K0tKMxURQmm82is7MToVCoJMUrlg3Qz4jnUyyebd1VX1/P/67GbjO0VsT9fokU0vNVCVD09dChQzh27BiUSiXsdjuMRiNXKVPxBkX/yOZFpVKhUCggEolg2bJluP7663kfFkfFxZFa4OzFvBx9j6mFGnD2OVKr1XC5XFwIMbdQQvysUbr01KlTeM973sO/Mx6PVzwFPLcDyNziSDHhE6eJSSdcTVHySwE1AriAoBRVMpnEgQMH8Ld/+7e47LLLYLVauejA5/NhYGAAJ06cwK233oovfelL+O53v4vDhw/jfe97H7Zt2zbvoRuJRLjiy2g0QqvVYnp6uuy3oHA4jNnZWdhsNlgsFmi1Wvj9fvZou+aaayCRSJBOp5HL5aDVahGPx/Hss8+irq6uogRQrF8qFApwOp3Q6XSIx+OcCqGIzYWgUqlQX1/P1cTiW/hCjnt2dhZDQ0Nwu90oFArQ6XSwWCysBz1y5AhOnDiB6elpeDwepFIpNDc3s9bJbrdzGyXqBiKTydDQ0ACZTAaz2cxzF2twFqrNoM/nQygU4siMy+WCw+FAPp+HVquFSqUqMYqmZ5/GJQhCSdorFothyZIlHIETW2CUA3O1fN3d3ZicnGQyKz6gxYcZXTaIvMRiMbS1tXH/4GqKAL7VYVssFhEKhcraf3m+MUxMTCAUCkGhUECn0yEcDnORB1X4JhIJ/ncqleJoktfrxerVq7Ft2zYsW7aMfy/tsaQ5E1f9KxSKsmQ1qGofAHcrGRwchM/nAwCe29xshTh6Njs7y56SqVQKsVis4hFAGo94ndIaF+/VYhJIH6fT6apZG5cKagRwAUEbxcTEBE6ePImrrroKmzZtQiaTQTAYxG9/+1uOVAiCgG9/+9vYs2cP3G43mpqa8OlPf7pk4xGDKiIFQUAsFsP4+DjbTZQTQ0ND+OAHP4i2tjYEAgEkEglcf/31HKn51re+hdHRUWg0Go4E6PV6fOYznyn7WOdCfPAWCgVOp1OVKaXqxG3sxEJqAExQ3G439Ho9RxQWEh/60Ifg8/mg0+nQ3d3NkQu/34+JiQm89tprSCaT7DlHZEkmk3GXDYp0uFwudHZ24mtf+xquu+463H///TCbzVCr1SXdNMjTrbu7e0Hm5Pf7uV0aHVxbt27FD3/4Q0gkEm4Yr9froVarEQqFWK8InGvrJ7aN6e7uxvPPP8+m0nOjCwsJ8bOlUCiwdu1a7NmzB8C5qAY9T1RoQ/MQfy6VSmH9+vUc6a1GzGd7JE53VwIHDhzAI488AovFwuMjckHPdi6XQyKR4Ep44Jx29MyZM1izZg0+8pGPcNHQXB0mXRLp8iGRSMomacnlcggGg+wCQab2mUyGxyLWltLf4oIpmi9drsqxd70d/H5/SWEaAN6HxBc++jdV91+oWK+Gt0aNAJYBFosFbW1tiEajePXVVxGLxfD1r38d/9//9//hZz/7GX7961/D5XKhqakJbrcbPp8PW7dufUuCJJFI2PZFqVTCYDAglUqVtVKQtId0q56ZmcH4+Dh0Oh2ampqg1WoxOzvLOi7yP7PZbNi0aROGhoa4jVolIJPJ2PaE9G2xWKzEeZ7GDpzbNOkGSgeC3W7HxMQER20W2gfwP//zP+FyubB371709/djcnKStUtyuRwmk4k7rohF1VRJSimue++9F/feey9WrlwJlUqFcDiMgwcPYvXq1QgEAsjlchzVNRgMuPzyyxd0XrSJp1IpFItFuN3uEtJG0RWK2NDnyTyaegDLZDIEg0GsWbMGDoeD14pWqy3bsyYmeCqVCkajEYFAgA9o8UFMFc3iCA0REZ/PB4PBAIPBUHF/yfkwNwImhlibWW6sWrUK27Ztw+TkJMbHxxEKhRAMBuH3+6FSqWCz2TiyTV2MAoEAXC4Xkskk/uqv/grXX389DAbDeSl6gtfr5WIqujiWowUcAI4O53I5GAwGHD16FC6Xq6TPOvmZUmpUbNkDnPXNO3jwINRqNVc+VzoCSJ1I6DWnPXVuxJzIoHhPFrchrKWC3xlqBHAB8PTTT+N//ud/YDKZoFarkUwmMTo6isHBQZhMJvh8PgSDQXi9XjQ1NWHx4sUIh8N8uwmFQvjYxz4Gp9N5wc2HFqpareaUErXOKgdIn1QoFPDYY4/hjjvuQGtrK/R6PadRKJJDVgOxWAxqtRrRaBSvvPIK9Hp9RQlgOp3GxMQEE2exsap4ngBKxNX0efrT2NiIN998EwAwODiIQCCwoF5ger0eKpUKVqsVV199NfuUkZZpZmaG06XhcJh7FEejURgMBixZsgQ9PT1wOp2oq6vjyGyhUMDXvvY11NfXc4RZqVRCEARoNBosXrx4weZERR5UjKJQKHDw4MGSoo9iscjRTvF7JBbg06GXyWSwaNEiNDc3Y3JyEgqFAkajsWxRZ3ExCkWPI5FICTEEzqV86Wcockhz8Pl8XMVNVcXVInYnzeiFIi/lTLnPhVwux6ZNm7BmzRpOb0YiEUSjUUxPT7MPZjQa5f3KarVizZo12Lp1K7q6umA0Gkveq7no6ekpuTBKJJIFXSNiiIm3WP8mNhmfWyUv/kPV9aFQiD02s9lsRS4ZYnJXV1eHYDDI6XVxyppseWivoNeAop1UuFPDO0eNAC4Astks9u7dC4PBALVajWLxrEEwVf7mcjk8/vjj3Hjc7/fzZhmPx7F8+XJuG3UhmEwmNuel/7Oct55iscgiZJfLhXg8DrVazYcXfY9EIuFbMvUOjUQiGBsbg1qt5krgStzYqNNCoVBAS0sLjEYj4vF4Ccmbe5sUb0p0ELe3t2PdunWIxWLo7OwsC8kQBAEWiwUWi6Xk8/l8Hl1dXUwIxeSQumLU1dXBarWe95qrVCp84AMfYNInTlVSRedCQa1WlxDRhoaGkl6zFGWhFJW48IZ+RpyyLhaLqK+vh8lkwtjYGHelIOPbhYZYx0cXHHHXBbExMX2/+FkT650kEgl3BKHfWw2goqkL9SaudCRGr9fzWiRro1wuh+7ubgSDQU55UpRZrVbDarWivb29JHV9oTncd999uPHGG0sITLn6Hour3+VyOQwGAxwOB2KxGF+UKDBAkoK5HahkMhmsVitH1UkqUkkoFIrz9t25qff5pBBi6U4N7xw1ArgA6OzshFKp5P6ddBDTLUaj0eD48ePsbUaRCzIb/fM///O3rZwrFoslvU0jkQj7QpUD9P9PT0+jubmZU9Ck3yDtmVKpRCwWQz6fZ7d68tXyer2IRCIwmUwVOSh0Oh02b96MT3/602hubsauXbswPj7O6WraaAqFAlKpVEl6hyIDyWQSXV1d6OjoQDQaxbp160rsMMoNSmv/PpBKpWhra7u4A3qHsFqtsFgs/JpT9wjS+NCtn94bscaJbC3ooAPOHv6hUAhOp5O/32g0ls2TjopngHOCdr/fz9FmMhCeL8JP30+EFkCJNrJaQKlpMQGkVoJkMVSp6P5cUGQMODvui2EGftlll/3Bv+P3hVKpRGtrK0fpLRYLR/6oyAU4Z9Mzn92VRCKBw+GAw+GA3+9HY2NjWSqY3wq0Vml8FFAQp4Lpa2Q1RqAUsDgiW8Nbo0YAFwCLFi3C1q1bEY1GEY/H4fV6uV0QpTyJ9NACpc2+ra0NH/rQh95WSxKPxzE1NcU2JESmyolsNouDBw9y1IvE1HTDJH0iiYzFnRooChIKhcrSmWE+6PV6XHvttbj22msBnCXRg4ODTKQpTSK+gYpvmHQLX7JkCdatW1f+CfwRwWq1wmQycRqqvb0d4XC4xPCVon+08YtTXMC5SAGlrKenp9HT04Pdu3dzn+dydGmgMVJREa1ltVoNtVrNRER8wSDMrR6mj1UqFSKRCJtzVwPUavV5kVjymJzvgK7h4oEkGbQHdXV1YWBggLW7dGGldTP3gkFnz6JFi7goprm5+fe+PF4siKvhxZIcivDT5+g8oaKvQqEAvV7Pe0Wlo8+XCmoEcAGg0Wi41+309DRmZmbg9XoxMTGB4eFhjI+Psx6IdE8mkwl2u521f2+H1tbWEosFlUqFlStXLtSUzoNMJkN7ezuSySQ8Hg/S6TTryYrFIlwuFyKRCIuOyU8ulUphZGQE/f39WLt2bdnG+07wwAMPQKfTYdeuXRgZGYHH40EymURTUxNaWlpw5MgRFvUbDAZYLBZ0dnZi6dKlAEr9tmr43SAWdmezWWzevJmJOBnrJhIJbmJPXRrEmz0VgUgkEpw+fRqdnZ2QyWR47LHHON2XSqXK4ktHY6EqcQD43Oc+x2lgsbZMHNkQf04cASSNVigUumDKtdwIh8OIRqPsQwqcvZjShVZcCV3DxQc9G4FAADfccAPuvPNOzMzMIBKJlEgFxAVT4nUGAJs3b8bnP/95AGd76Va6F3A2m2V9ezKZLNHNSiQStrdRKBScZSJfR41Gw2dqLRX8ziAp1l6pGv4AFAoF+P1+uN1uyGQy6HQ6KBQKhMNh1tfI5XK8/vrr0Gq16O3thV6vx44dO9DT04OtW7dWegrzwuVy4fnnn8e//uu/4qtf/Spuu+022O12bNmyBdu2bcOWLVuqosfpHwsSiQReffVV/OxnP0Mul8Nf/uVfXpR+0cPDw/j3f/93hEIhXHfddXj/+99fFmuScDiMoaEh7N+/H8lkEl/4whf+oN/3+uuvY3BwEIsWLcLy5cvLFsl8K4RCIQwMDGBmZgaXX345HA4HAODRRx9FX18f1q5di61bty5oQdS7FYVCAR6PB1/5yldQLBbxz//8z7+XRrdYLOLv/u7vMD09jeuuuw6XX345GhoaFmDE7wxvvvkmR/goY0aZNLIZM5vNsFgsHE2Xy+VwuVz42Mc+Bo1GM2+f9BrmR40A1lBDDTXUUEMNNbzLUMtX1VBDDTXUUEMNNbzLUCOANdRQQw011FBDDe8y1AhgDTXUUEMNNdRQw7sMNQJYQw011FBDDTXU8C5DjQDWUEMNNdRQQw01vMtQ8wGs4aKA/NjS6TQGBwfxxBNPoLe3F9u2bUMikcCzzz4LnU6HFStWoKur67z2PjXUQMhms9i9ezdmZ2dhtVq5nRVw1mfu8OHD+N73vodUKoWenh586lOfgs1m43ZeKpUK4XAYyWQS69evR1tbG3flqPQzJzavHhgYwFe+8hUYjUa8//3vRzgcxtGjR2G1WrF582asX7++6tfJSy+9hH/6p3/C4cOH2bPN4XDg7rvvxn333Yfly5fz91brXPL5PF577TX89Kc/RT6fh8lkYlP7T3ziE1ixYsWCtkH8Q0E2ME899RSGh4cBnDW5Jx89qVSKYrHIBulkTF4oFHDjjTfi61//Opso1/DuQs0GpkI4cuQI+vv74ff7uVF5KpWCIAhQKBQlrXvoLdJoNNyrcv369TCbzVVntHrixAns2bMHdXV1uOKKK2C1Wtkr8OjRo5DJZFizZg2MRmPVHghvh6GhIfT19WF2dhabN2/Gpk2bKj2kPxrk83n4/X68+uqrbABN5s5knhyPx5HNZiGTyVAsFqHVankd0Pdks1k2jt6yZQtMJlMFZ3UO9MwfOHAA27dvh06nw5133om6ujrkcjlEo1H85je/QbFYxF133YXm5mb20qw2xONx7NixA7/61a/gdrvR19cHvV6P97znPTAYDNi6dSvvU9WIdDqN6elpNgkfHBzEv/zLvyAUCsHv92Pbtm345Cc/Cb1eD6VSiYaGhrft0FRO5PN59Pf34+///u8xNDTEfYCpPzAApFIpNk1XKBTcao06hKjVanR2duIzn/kMNm3aVJY+5jVUD6pvV/kjRiaTwSuvvILt27djZmYG4XCYDwQyrqQ+utTIm3qGAme7fZDZ8vPPP4/169fjyiuv5L6plQJ1wAgEAvD7/VCr1diwYQM3RpdKpbDb7Whvb4fH48Hk5CSMRiM3JK9GiDsynDx5Env27IFer8eyZcvw0ksv4ZVXXkEqlcKJEyfw4x//GMuWLcMnPvGJ8w6IS5XkVgq5XA7hcJhd/nO5HARBKCF4giAgnU4jmUzyWpHJZNyFhVoNarVaBINBbp9W6S4t9CxMTU1henoadrsd1113Hdra2rjdldFoxHXXXYeBgQEcPXoUjY2NHMGp5HNEvb+HhobgcrmQSCQQi8UwOzsLlUqF3t5e2O12mEwmOJ1OeDwe7NixA4ODg3A4HLDb7WhubkZHR0dF34dCoQC3242ZmRkkk0kkEgnI5XKkUil4PB6YTCb4/X6sXr0a6XQaL774Inp6emC32zEzMwOdTgen01nRft+EVCqFJ554AocOHeLAAV2UiABSq7653WYkEgl/rb+/H4899hgWLVpUI4DvMtQIYJkQjUaxe/du/PCHP8SBAwe4ibcgCBAEASqVCjqdDrlcDslksqSROt3WqLOGx+PBqVOnMDo6CqVSCb1eD7PZzE2zyw3aWLxeL6LRKOrq6ubdIJ1OJ5LJZEkLu2qEOCiey+UwNDSEvXv3IpFIYP/+/dwObmZmBoFAAG63G4ODg9BoNLjjjjuY+F6qqGTEqVAoIJlMlvz/dEGig0sulyMUCsHn80Gv15/X2q1YLCKdTkOv1/Paoe4AlQKlfgOBAPbt24doNIotW7agu7u7JNqfz+exZMkS5HI57N69Gy+99BJuuOGGira2ymaz8Hg82LVrFyYnJxEMBrlnMwB+D3Q6HaxWK2KxGARBQDgcRigUwuTkJAwGA5xOJ0ZHR7Fq1SpYLJay71XpdBqTk5NwuVwIhUIoFotQKpVIpVKYnJzE+Pg4enp60NHRgfXr12NqagrBYBDJZBLFYhGJRALJZBLRaBQA4HA4KkbKKRL++uuvc6tNuuhQO0VxH2aKDFJvXWoVRxfW/fv348SJE7BYLBXvB1xD+VAjgGWCy+XCf/7nf+KVV17BkiVLoFQqOVUlbnItk8mQTqeRSqUgk8mg0Wg4BUY9EDUaDTKZDAYGBvDGG2+gqakJGzdurHj/w0gkgmw2i7q6OgDn98bVaDRQKBTcy7TSEZkLYe6mnsvlYDKZcOLECezYsQN/8zd/g2XLluE//uM/kM1m0d3djWAwiH/4h39Ac3MzVq5cCY1Gwz1gKw1qDp/JZPg5i8Vi/DH9LX7+uru7odPpSnqIlgNE2OZGLMR/lEolbDYbAECtVkOpVPLBB4B70dJBSHou8YFYbtD/f/jwYfT19aGrqwvLly/nNUJ9c7PZLIrFIqxWK7RaLR599FEsXboUTU1NFWlyT5G/3bt347nnnoPRaGSyp1QqIZfLUSgUEIvFOJqWzWZhNpthNpv52QuHw5iZmcGePXuQTqexZcuWshKNfD6PQCCAY8eOQRAE2Gw2yOVyKJVK+Hw+KBQKNDQ0wG63o7OzE3V1dfB4PPD5fDCZTLBarZDL5YjFYpienkahUIDJZKpYSjibzXIkU6fTIZ1OQxAEmM1mbhOXy+X4PcrlchxAMBqNLDmYnZ2FQqFAPB7Hrl270N7efkkTQJ/PBwCctq/hrVEjgGVALpeD2+3G4cOH0dbWhlgshnQ6zTf/YrHI/Q+Bs4dtJpNBOBzmA44OtUwmg3Q6zangEydO4ODBg9i0aVPFojZE5IrFIgwGA5qamub9PvG86N/VDLopOxwOpFIpBAIBbNiwAatXr8bQ0BAGBwehVCrR2trKUY+XXnoJw8PDWLRoEVauXAmDwcAFCOUGEYZgMIiBgQGMjY2xNu7gwYN80YjH44jH40gmkzAYDCgWi/ja176GtWvXQq1WAygvAUwmk0zoSNMnk8lYIpHP56FQKOBwOPjgI7kEXaikUilH/pLJJDeYrwRo/JlMBtu3b4dKpUJPTw+PWfx80FwaGxuxceNGvPzyy/iP//gPfPWrX60IiU2n0xgfH8dLL72E7u5uKJVK5HI53rMosqpWq3kfIMJB+5sgCLBarbBarUilUnjqqafQ1tbGl9tyIBKJYHZ2FoIgoKWlpaRfbENDA7q7u6FWqxEKhRCJRJBIJGCz2dDS0oJsNotkMol0Og2FQoGuri6Mjo4iFAoxkSw3YrEYDh06hHg8DpVKhVAohDvvvBNXXnklLBYLxsfHsX//fixatAh2ux1+vx/79++HwWDA+vXrsXLlSkxNTeELX/gCX5ROnTrFBOpSA10cn376achkMmzevBldXV2VHlbVo0YAy4CjR4/i5z//OQRBgFKpRDAYRD6fh0qlglQqRS6XK9Ep6XQ6SKVSxGIxhEIh1jnpdDomgBT2D4VCCAQCFZ0fkYNMJgOZTAaDwVDyeTGIgBAqrW26ECj6RJWllJJ/8MEHsXLlSvT396O+vh6f//zn8eabb+LUqVO47rrrYLPZcOzYMfz2t7+F1WrF+973Ptxxxx0VmQM9TxMTE3jiiSfw4osvoqGhAU6nE263G3a7HWazGQ6HA3q9Hj09PQgEAnjqqafg9XqRy+XK/t4QKSJ9aDab5bSVXC6HVCrlSB/NjyKXUqkU2WwWsVgMSqUSsVgMBoOBo5+VQj6fh1wux44dOyCRSHDDDTdgy5YtAHBBAlQsFtHa2oovfvGLuO+++3D//fejvb2dSXG5oudTU1M4ePAgtFotk2uZTMbFBAC4qID2pFQqxSlGIoFEzjUaDdRqNXbu3AmVSlW2Q5ouOw0NDXyxy2QykMvlyGQyiMfjkEgkSCQSUKvVMBqNiMfjiMVikMlk/Ac4+35qNBpMTExAr9dXhAAGAgG88MILAM7uqZlMBh0dHTh69ChmZ2dht9vR1NSEXbt2QS6Xo6mpCffddx/8fj9efvllPPHEE/jiF7/IEX+FQoGRkZGKnyVvB3qeiLwD4Ijnb3/7W+zZswf19fUl1ec1XBg1ArhAoEMpk8ng2LFjePrppyEIAlcvAmCtE22oJFinzxE5ojB+MpnkqAZtYul0Gi6XC6Ojo2hvb6/wrMHaxgtBo9HAbDYjk8lULDL2TkGvcWNjI1588UWYTCYcOnQIO3bsgFKpxFNPPQWHw4GHH34YKpUKer0eLpcLxWIRTqcT+Xwejz32WMUIIG2Sa9euxZo1a972+yUSCfL5PJ577jlMTEwglUqVPR1EqVCNRoNYLAatVotCocBp3Lm6MYoMEjkhogiA012U4q4UaDy/+MUvcP/992PFihX8tfkItphQdXZ24mMf+xj+5m/+Bo8++mhJpK0cSCQSCIVC0Ol0MJlMSCaT/FqLD+BCoYBEIlFSgCCWpIitfAwGAyYmJhAMBss2D4py5XI55HI5NDQ0lIyLSLVCoYDH40EymYROp2OiS4UUMpkM8XicI5yVkt2Ew2Hs3LkTSqUS2WwWSqUSDz30ED74wQ/itttuQyAQwOjoKJ5++mmsW7cOzz77LA4dOoStW7firrvuQi6Xw9e//nW+LCmVykuCAM6nG00mk/jP//xPjI+Pw263473vfS+6u7sBVG+AoVpQI4ALALEAd3R0FGNjY5z+FAQBsViMNxZKUUmlUixbtgwdHR0oFouIRqNQKpWIRCKYnp6Gx+OBWq1mwTsdlBKJBC6XC4cPH644AZyamuKxXQjhcBgjIyPweDwXTBVXEyiqcebMGXz5y19mjVY6ncbOnTsxPT2NSCQCvV4Pk8kEhULBh0U2m62oDlBMFN5uEywWi8hkMnjxxRcRCATQ19eH22+/HQ6Hgw/JchEPqlB0u90cgaX/u1AoQC6X82UKQElUUCqVQqVSsa2S0WjkKGK5QdFMuVyORx99FGfOnGHtXD6f5/eEyBTNgz6meX7sYx/DU089hSeffBK33HILTCZT2Qq+4vE4XC4XR8kcDsd5ES86ZDUaTcn7RDpO+pPL5TA+Ps5p7kQicV6xwkKBLHZkMhmMRiNHhOk1F1f9KxQKKJVKJn70PtGFamZmhn+mEs9VKpVCJBKBSqViDa9cLofb7caRI0c4MDAwMIBPfvKTGBwcxJYtW6BUKlm/GIvFsH//fnR0dCAUCkGpVKKuro6lLhaLpezzeifYuXMn4vE4Jicn+YLndrsxNDQEm82GL37xizCbzfy+VqvOvFpQI4ALAPEGuX//fuzcuZPTURTtIxDBICQSCcTjcQDnDmXxAUERDtIV6XQ6zM7O4mc/+xmi0Sg+8pGPLOjc3u5GJfagmu/7iBhRGrhab2fi9JbJZMKtt96KgYEBtLW1oaWlBSdOnMArr7zCujSLxQKTycRaOuCsBYMgCEilUlXhHyZ+7+Z+TAf0qVOnkE6nEY1G+XAr13tEBypZvWSzWYRCIajVak7BUWqRNnY6lOnzNA+/3w+3241169ax5KLcEEfDtm/fjm984xvo7Ow8j7gRARLbQYmh0Wjw9a9/Hd/61rdK7JUWGplMBolEgr3kZmdnYTAYYDAYzkup01zFkT4xZDIZnE4njh07hubmZkgkEni9Xvj9/rJYqtC+lEgkoNPpWH6TSqVKyFwqlYJarYZCoUAymWTvPCIUuVwOkUiEtbGVwP79+/HQQw9xuprGZbVaoVQqoVarsWrVKrzvfe/DN77xDaxcuRKTk5P4zGc+A4VCgfHxcRw6dAg6nY61kAqFAtlsFvv27UNnZyduuummis1PjGQyieHhYUxMTMDj8bCOMRwOIxgMQiKRwGq1QiKRoKOjoyQlL95PakRwftQI4AJAIpHg4YcfRiQSwfj4eIkIPRqNsoYPAFfR5XI5jIyMYGJiglNAwLmHmG7MVAUJoETDIpfLy5JSfSsy8E6IApFaslKoVogJkiAI2LRpE7+nRqMR09PTmJ2dxZIlS5DNZlFfX4+WlhaMjY3xoZLNZuH3+xGNRquCAIrfn/k+zufz3ElAo9GUEJVykMBcLod4PI5oNAqbzcYkg6KP9EeMuSRWJpNBrVbD7/dzSpLSluWEOPJ15swZFItFNDQ0YHZ2FrlcDgaDAXa7HQBw+vRphEIhJqkOhwNGo5GjVgDQ2tqKZDKJkydPwmq1chX0QiIUCrEGWS6XY3p6GgaDAatWreIIHxHWuelecUV2oVCASqXC5OQkpxwp80GXpYUEWdbQGnS73fB4PFiyZAmPV1xkR5mabDbL5IHei2g0Cr/fj7a2tpLIYTlht9uZ1I2PjyOdTqNYLMJisaBQKECv12Px4sVoaGiAWq3G4sWL0dfXx89VIBBAoVDgohzqFiKVSln3WC2Ix+M4cuQIJiYm4Pf7MTMzg1Qqxa97Pp9HIpFAIpHAxo0b57WPquHCqBHABcITTzzBkYpUKnWeCzttquIK2kQiwa2I0uk0MpkMawCJ9KXTaWSzWf5d9PtCoRBOnjy5YPMpFAqYnJzkKjGaG23+crmcja2PHDlynkCcCNXY2BhCoRAGBwchCAJHN2mxSqVSGAyGippbizd1Mu8+efIkEokEDh8+XNKpJRQKQaVSMXGh14Q2Iq/Xi3Q6XZF5/C4gOcLY2BgAQKvVln3zLBQKSKfTCIfDMJvNGB8fh16vR0tLS0nEj8ZLoGeLPkcEMBaLcfUm6QPLGXGWSCRs1qtQKPDqq68iHA4jn88zASR5QSQS4Yugw+GAwWBAOp2Gz+dDoVBAa2srFAoFDhw4gM7OzrIQQI/Hg9nZWfZUDIfDbB9EaXVxNHYuGaLKTABQKpU4ePAgOx0kEgkEg8GykA0aK12Qp6enMT4+jpaWFjZDJm0g+etRazQigUQSqUtIV1cXZDJZyd5WLrS2tmLbtm0wGo3YuXMn+vr6+JKkVCoRCATwxhtvoLm5GU6nE/F4HHK5HK+++ioKhQIikQgXFQJAe3s76urqsHjxYvT29lZF9Sw9S5TlslgsaGtrQ319PbRaLRQKBWQyGVKpFAYHB+H1ejltL/4dsVgMk5OT0Ov1aG5urtR0qhY1ArhAiEajMBqNCIfDrD2hCt9EIgFBECCXyzltIpVKWd9nsViQz+dLDi6K8BExlMvlMJlMLGwOh8P4n//5H3z4wx9GT0/PRZ+P3+/Hvn37MDg4yKSUnOfpdh2LxZgwAecOaarqlEgkiMVi3M91ZmaGN14qfFEoFGhsbGQftEqBNJunT5/GP/7jP+LAgQNYsmQJkskkzyuVSmF0dBQqlQpDQ0M4fPgw6uvr2V+MyH+5o0+/C8Tap1AohNnZWQAoaa8mroheSIifl9nZWUxNTaGhoaGE3NHH8xFAsVm0QqFgEki/k3w0Fxri1yuVSuEXv/gF2tvbucWbWFum0+m4kwmRjNHRUdaZhkIh5HI5NnofGBiA1+td8DkA4Iuq0WiE3W5Hd3c3ent7kUqlOOp0oW4+YlKlUCigUCgwPDyMrVu3oqGhAaFQCBqNpizEiaKQ4uK7SCQybxGHWGIgCALi8TgymQy0Wi2MRiMmJycRCoX4GSOJTjnteTQaDZYtW4bFixdj06ZN+OlPf4pIJIK+vj5YLBYEAgFs374dLS0t6OjowI4dO9Da2oqnnnoKk5OT6OzsxOrVqyGVStHW1oYbbrgBdXV12Lp1KxwOR1V0Z6L3Ra/X4/LLL0cgEEBPTw/8fj8KhQL3NA4EAvj1r3+NiYkJHD9+HIsXL+YMUywWw8zMDPbv34+lS5fWCOA8qBHABcKaNWvwyiuvoKWlBY2NjThy5Ai0Wi3fpjOZTIlnGW0gqVQKfr+fI4REniiFTOSLDjQih1arFWazGd/85jfxyCOPXPT57N69GyMjI5BIJFyZKb49SyQSWCwWTvHSzZmsYWhBq9Vq5HI5uFwuqFQqriwkewkShh89ehSbN2++6PN4O5AmJpvNYnh4GD/4wQ8wOjqKpUuXQhAEJJNJjnzQ/Ck173a7YbPZYLVaeXPKZDKYmJioeIHO2yEWi+Hw4cMcxRQTQAAlHWoWymCVnmWTyYQjR45g6dKl2LBhA6fnVCrVeeQPAFvBUCQqEong2muvhcvlQjqdLrFaKjcBBMA+cg6Hg6PclB5Wq9UwGAwlewEAXl8qlYqjTKlUCqlUqmyehsuWLUNbWxunp2+++WY0NjbiW9/6Fus0xZpfsn2h/YpS8rRujEYjfvSjH7FZtDh6uJAQR5OKxSLGx8fR1dUFnU7HF1OZTMZjtlqtJdYvNE5qIxeJRDh7E4vFoNfrK2IyLpfLsW7dOqxbtw4HDx7E5z//echkMixfvhwqlQo7duxAOBzG0aNHcfXVV6O3txcdHR0QBAHBYBC5XA5dXV34/Oc/X3Et9nxRVPqc3W5nucRc/avJZMK1116LEydOYHJyEpFIBBMTE9i/fz9Onz6NSCQCnU6HZcuWlW0ulxJqBHABkMvlsGvXLgDApz71KXi9Xuzbt48LBuRyOYxGI2KxWEmFIgl6SYei1WpZL0OGvdQyLpVKcTQqFAqhvr4eDz30EFwu14LMad++fQiFQmhoaOAqPtoExYcqHQx0GJC4WKPRQKPRYHBwEM888wwGBgawdOlStLW1YeXKlSzKzmQy8Hg8ePzxxytCAIn07N27F9/97nfx7LPP4oorruBbpVarZcJLUVCpVIqBgQGuLgyHw5iYmMDExASMRiOee+45XHXVVWWfyzsBbbqRSAS7d+/mw5usLorFIiKRCA4ePIhHHnkEt9xyCz74wQ8uyFjy+TxHS6kf6/r163Ho0CEWc88tMqBnjEgVfV9TUxMSiQQT2kpFYqmISK/XczRJoVBAq9VCr9ezTQ1FMJPJJHfVoEi/TqfjdmRerxeBQADpdHrBOx2QHIN8PQGw+TORcXpexORPTKqIvGYymZL2b+UkTNlslmUzdrsdHo8H9fX1bKw9NxKYTqe5wpaIIXA28pbL5UoKk8QRwkqAqqjT6TTUajUmJyeRSCTgcrkQj8fR0dGBD37wg/jBD36Aa6+9luUVS5cuhd1ux/T0NACUVNGXG/PpKL1eL4LBIARBgNPpLHG/EP+MIAhoamrCqlWroFar8c///M9wu90Ih8N8YbXb7XA4HOWb0CWEGgFcAGQyGczMzKCtra2kkwLd5qPRKOv7SGdChxOlQROJBG/ydBOin9FoNLzJAmcJZywWg8fjwbFjx7B169aLOp9CoYDOzk5MTEyU3IbFXmBzox6UcqH0iFQqxRNPPIEdO3awx5bb7YbL5cK6dev4gKM533777Rd1Dr8Lfv7zn+NHP/oRTpw4gZtuugmjo6PcISMej8NkMsHn8yGVSuFHP/oR1q5di3/5l3/Bl7/8ZVgsFqTTaVgsFjQ3N2NkZOSSECInk0mMjY1BrVZDq9ViYmICL774Io4ePYqjR4/y80gGxgsBeqYoIm4ymThSQWRnvkgBPY9iPZYgCFi+fDlisRhHespVCSweXz6fLynWIk9DMqx2u92sCSSLGGrBJ17309PTnDIeHh7GwMAAVq5cueBzERNuqVTKRSHz2b2I1774b/odixYtKvm95Yo6UTu6XC4Hm80GqVSK0dHRkkglIZ/PY3x8HK2trXA4HJzCpihmIBCAz+eDRqPhyG4lu8zQ+0Cm7jMzMzAajbjttttQX1+Pu+66Cx//+Mfx8Y9/nAsSJRIJxsfH2SJJ/HsqgbnPwfPPP4/Tp09zFmlqagqhUAi9vb3YuHEjVq1axfrFXC6HmZkZbN++HXq9Hk6nk+UJtHYymcwlsQdXAjUCuAAgfcLatWtRX18Pr9cLo9HIaSCqBFQqlWwVQno/AEwUqVJOLpdzBC0Wi/HNyGAwcIi7t7cXkUgEP/rRj/C5z33uos5n586dcDgcyGazCAaDHImk4gZK9YgtLEgfRL5spIXM5/PcWYMIFXU3obm2t7dj48aNF3UO7xTf/va38ZOf/ATBYBAdHR2Ynp6G2WzG6OgoGhsbuV3alVdeCa1Wi3vvvReDg4P4zGc+g927d+Pll1+G0+lEZ2cnstkswuEw316rGSTsb29vh0qlwujoKP72b/8WarUaZrOZI88LGemgKkxKKdrtdgSDQSaElOq9EEiOQNIISq1ShLoSBzWtAyqyEQQBRqORDXypJaREIuFCEIqgEygaSKTwwIEDaGxsLAsBpEsqkSRx6pbWN/mREtGbWxUMnCVWjY2NJb+3nEgmk0gkEmhqaoLT6eRLApEFuVzOUoPu7m7IZDJEo1F+FqliuampiX1ciUwR0a0ExIVP2WwW69atg8PhwGuvvYaTJ0/Cbrfj6NGj+OAHP4hXX30V8Xgct956Ky6//HI8+uijMJlMFRv7XIh9SJ1OJ2QyGcLhMBKJBDQaDaampvDf//3fePjhh1FfX4/W1lYolUrs378ffr8f3/nOd1AoFPDP//zPmJycBAA2KL8UCvEqgRoBXCDYbDZEIhHE43Emen6/Hw6HA2q1uiQyQEUgdFOlzwFgIkjpCEoVUeorkUhgxYoVuOWWW7B9+/YFiXIcO3YMl112GZLJJHw+H7LZLPR6PRtYi6N/YgsIAvlmkVFyMplkPQ6ls2nOFOUoV3slcSTiwx/+MA4cOABBENDa2gqZTIZIJIJMJsMEaHh4GCtXrsQ111yDuro6uN1uXH755di9eze+8Y1v4JOf/CRHG9xuN4rFYlUXgRCIoAwMDPAtWqPRsAwBOFvxLG7jd7FBzz89U9T6EDgXaQLmTxmJozkKhQKRSAQ2mw2pVGrewpGFhHh84iInMugNBALQarUwGAysI6XxUzpSHMGgj+k5IoF7JUAkWhw5oz1JXKhDoM+TwXIlQO89yTPa29v5eafXmvZZ0vzpdDruikHRI4VCgUWLFqGuro7lBpXuMkMgAkvdopLJJMbHx3HttddifHwcP/jBD1h6VCwWkUwm4XK5Ku62QGcfABw4cIDXOwUMyFlCXPgRDAZZW51IJCCTyfD9738fjY2NOHnyJLxeL1/86Hmt9q5TlUItLrpAuP/++zE+Po4TJ04glUrBYrFwxRg5zZM2hjYesb8fcM68VCwAJ/uXXC6HRCLBZf+vvPIKdu3atSALmhYccO6gpQiFOOJHRFV8WBcKBSSTSRQKBVgsFjgcDha3NzU1sY8eFb1QNXA5IE6jv/7663j55ZdRLBY5AlsoFKDValEsFmE2mxGLxXDjjTciHA7jzTffhEwmwwc+8AHk83k88MADcDgc+MhHPoLm5mbWocjl8gVNm84FHcq0ac4lS3P/HQwG8corr+Chhx7C4OAgNm3ahC9+8YswGo0QBKEkxUda1IUCaQCJUBiNRo6O0UE7V3IgTv/S80fpuvr6ev5eIlflBB3IVPFPRSikoSXpB0XAxa3KxLo0sscBzpK/1atXY8OGDWWdC73u4uI1ceW1mAjN1WnS81OJnrn0/xOxc7lccDgcaG5u5tdZvE6kUimCwSCKxeJ53WYEQUBzczPa2tqYmNPPVxp0VgwNDSEej2PdunVQqVRcvSyRnO2os2TJEuh0Ohw7dgyRSGTey1Q5IH690+k0Hn/8cfT19cHj8SCfz7PnIj1LKpWKz5G5cpwPfehD6O3thVwuRyqV4mwBcM6urEYA50eNAC4Qtm3bhvr6evT19WHfvn0AzgpWaeMQBIEPNvpD5IdIoSAI/DBTdIQ0glKpFIlEAnK5HIFAALt374bL5VqQJth2u/08y4a5aR86fOf+oQM9m83CZDKhvb0d3d3dWLRoETo7O7k0n34fUD49ChHpo0eP4te//jU0Gg3q6uq4oT0ZCUskEk7/3HPPPdi4cSMmJyfxzDPPoLm5GZ/61Kfw4osvYufOnaxrLBQKWL9+PT760Y9i3bp1CzqPuQfuO4XX68Vrr72GZ555BpOTk7jpppvwkY98BHfddVcJAZm7WS8UxFE8igRThEAcXRa3fhOnG8W61HQ6XdK2TKyzXWjQ+0EFAwaDgaP19DyJTYfT6TQTQHEVLa0hSmsXCgUolUqsXbu2YlWNRADFqV/xnOd+LL5A0J5XblBUWKVSwefzcZGQ+FkTRzPJrJ8yNeLvM5lM3D6tUkbQ84GK0rxeLyYnJ6HRaHD99dfj9OnTfJ7Y7XYsX74cSqUShw8f5nVSqfFKJBLE43EcPHgQzz77LNxuN6drE4kEt3OkdD1FOcleyWw246qrrsKmTZv48iHu3kL/Tw0XRi0FvEBoamrCli1b8MQTT2B8fBwKhYIPNGo3RNE9uj2TAJ6IExE9qqaj6IC4ypb+zmQyUCqVCxJtWrFiBRobG+FyuUpImniTn08ETnMhAbxKpUJLSwucTmdJux6xIbbYVmIhkUqlEAwGMTMzgyeeeAJ9fX1YtGhRSbSF0pESiYTT9/X19Vi9ejWeffZZ/PrXv0ZTUxM+/vGPo6+vD4888ghGR0chk8lwxRVX4L3vfe+CvB/iHpdzX3uJRILp6WlMT0+jvr4edru9pAsJfV88Hsebb76JZ555Bvl8Hvfccw/uvvtursT2+Xzs3yYuSCgHASTSoFAo+GJEzwSlU+dGXShqTD+fzWZRV1fH1Zzl7Acs1sv5fD4YjUaWPmg0Go7ip1KpeSt5xcVdRAzp91mtVtjt9opVndLrK77Azh23ODImPoArFYWhCJBCoWBdX319Pfr7+0uiRcA5F4NMJsNSFHr9aZ9qa2vjfrlii6tqwcDAAAqFAm6++WYcO3YM8XgcxWIRS5YsgVarxdjYGMbHx9m7lVDOwhz6/wKBAHbu3AmLxYLW1laYzWaEw2Eunkyn0yypIU2vx+OBWq3G6tWrcfXVV3M0nSQ7wLn9gNZ+NURpqxE1AriAoIo/sk3I5XJQq9UldhAymYzNnSk9rNfrEY1GWd9A6VGZTFayYCm8TVHFRYsW4dZbb73o86AWQkQkaEMUR/8oUiNOParV6hLSIL5tUypRqVQyAZbL5VCpVAtqb1EsnvXt6+/vx4svvogXXngBoVAI7e3tbMNBBxVFYwuFAsxmM37wgx9AIpHgT/7kT3DnnXeioaEBDzzwAG677Tbcdddd+NM//VN89rOfxZ133omWlhaeVywWu6jN1cUHrdj+hNKJjzzyCL773e/iYx/7GD7wgQ9g2bJlJa8pFRI8+uijaGtrw/vf/35ceeWV/HUyg6ZIG1lovF0Rxh+KuVWZVEVOmzm9njSuuZXnAPhZzOVy0Ov17C2p0+nKbnBLrQC1Wi0XHFAVfzab5Yg+7Q20dui1JtkHAPamjMVi3AqrUtENcWGI2H9xvvQ8XaAoo1Ep0FioKxMVZonJLI29vr6enzWj0YhQKMQkIhKJoKGhASdOnIDZbC4xS69ktImixXK5HKFQiCUqDz30EL71rW/hxhtvxKJFi/C9730Pu3fvhtPpZBkRoRzjF69ZisrX19fjE5/4BK/VPXv2YMmSJejq6kI4HMb3vvc91vTabDb4/X7cc889uP766/kZpLGTTEqj0fBeL95DaihFjQAuIH7zm99Ao9Hg/vvvh0QiwTe/+U10d3fD5/OVNFMXp0wymQxbcdDmL+4sAZyLglgsFoRCITidTtxxxx34whe+sCDzoMVFBQHkxi4+bOdu9qRvFH8ftSoSa7bo+8XtmqjEfyHg9Xrx7W9/G0899RQAoKenh6t76aYskUg4ykTzEwQBq1atwiOPPIKBgQF8+tOfxhVXXIEvfelL+H//7/9hy5Yt6O/vZ9/DSCSCZDIJtVp9UckfcK5ggza1WCyG4eFh7Ny5E4cOHYJSqcT69evx8MMPY//+/fjkJz+JD33oQ/w+BoNBfPnLX8aKFSvw0Y9+FGvXrmX/SZVKxdWo5OUmFvgvZARQHMlWqVRshE5RYoqIEYHKZrNc/UjRKBK4i8cpHn85QXZQ2WyWCz7S6TQEQYBWq4XJZEIoFCoZq0QiYTN0amVHZsqCIMDv98Pj8SAajZb485ULdAkg8iqWrxAos0Hre6EvDm8HWisajabkwpzNZvk5ozlIJBJO24dCIVgsFlgsFu4nrdfr0djYiJdeeglLly7lKHW5usxcCOKoq06ng0KhwMDAADZt2oTrrrsOy5cvh0Ry1sSfyBG9NuWEeB0Wi0Xo9Xpcdtll6Ovrw/T0NCYnJ7Fz507ccccdaG9vR09PD77zne/g5MmT2L59OyYmJvCBD3wAmzdvZt9csW5cXJAkjsSLz9sazqFGABcQKpUKixYtwqpVqzA+Po5C4Wy7LQCs+wHO3agBsNCV0l7UEk58gy4UCmzFkkwmcdNNN+Ev//IvASzMTZQ0iHq9HhqNhiOY4go6caEAADZYJRJI6WuJ5FwLJbHGiX6PUqlcMGuCiYkJfPnLX8bo6Cja29vZhDcejyOdTnM0lsisWq2GXq9nE+54PI7W1lbs378fZrMZq1evxv3338+veSqVgtvthkwmg1qths1mW7B0Nm14LpcLL774Il566SU0NzdjxYoVmJ6ehlqtxrJlyzA4OIi/+7u/w/DwMB588EEUCgVs27YNra2t+Iu/+At0dnYCAJNz4CxBbGxs5KgHRRmJgC0UxKlPSpVS2pRS89RWkdJB6XSa2yQC4EpPMlmnw510dOVEJpNBKBRiu5ZQKAS73c4kgyIVdNET7wPUEcVkMrEGlUTvJpNpwU2gLwS6FMjlci4SIqInfv/ouZmr0awEaHziS4UgCFAqlZwCFlsHHTt2DCqVivcpQRCgVqtZx+hwODAyMsI/n8lkOLJYKcwtSKH1fOONN+L111+HRqNBa2srisUiryexlKQc8Hq9mJ2dhcFggEajQX9/P7Zv347h4WG216LUeywWw9DQEPfyfeaZZ3D06FF8/OMfx9VXX82X6rkRWDJZJ2KuUChgtVov+iX8jwU1AriA+MUvfoEHH3wQf/mXf4lcLger1Qq3280RHLG4mDZPcTcE0gym02k+AKkqNZFIsDaCBOVkKXGxCaBCoUAwGOS2VdTejW7MZLFA4xQfBHQY0NzEC1WhUJTY29D/ZTQaL+r4CQ8//DD27NkDhULBVWRickNea6lUCtFolIkhRaUWLVqEO++8E9dffz16enqgVCoRDoe5I4BOp4PVamUCWQ6MjIygv7+frQ+OHDnCVdsymQz19fWor6+HxWKBy+XClVdeiba2Nnzzm9+E0+ks+V303gQCgRJtE2lOqXp1oUD6T9LIUjRMqVSyFCKbzcLtdnPEktaATqeDRqPhCKw4lU/R5XJrtUh7ZjKZuF2gVCqFXq8HcC5tJ06p0h+KQlPakua7ePFiOByOihFASvlSVI8ufGKiR6Dx035WySggVQIbjUaMjIwAKI2kE5Hz+Xxobm5GXV0dX3Lp4krPo3jeF6q2LzeoDzulgQHA7XZz71/KyNAzSO9fMBgs2xj37duH06dPlxRJaTQadHd3Azh7YaI07/j4OB5//HEUi0VEo1GYzWZ88YtfZP/VuSD51JtvvokDBw7wPAOBAGZnZ9Hc3HzR/XH/GFAjgAsIg8EAp9MJrVYLn8/HqU1aoGQFQ5sIVddptVrE43GEw+ESywiKBOh0OqjVaiSTSahUKq4yBBamgtZms2F4eLiE2IiLVUi/SLdo8aYPnCMWRCrEhFd88FEF2EKRp0984hMwmUx46aWXMDAwwKlOGhMd2CqVCjabDatWrUJnZydaWlqwceNGjuqR9kfcl9VsNpcUsywkpqam8Pd///dIJpMAgEAggGAwiGAwiNHRUZYMUBQ5mUxi586deOyxx6BQKPAv//Iv3Jd2PsjlcsTjcWSzWUSjUY5QpdNp1NXVLdi86FAQ98SlQ83v9zMRB85GKSkFSZG2fD7PZtWBQIALWMpt1UFj12g0WLRoEZ5//nl0dXVxtILS12I9FD2HcytogXNp91wuh87OTthstrLNZS7EJE7c01gcKRan5aqBHImLVcTSDpI30EW8WCzC5/Ox6X0qleKWd2L7LblcDqfTiampKTQ1NQGY35uynKBLAnAuSkt6anF6VGyGToS2XLj88suxZs0a5PN5hMNhlskQCafoPckdOjo6uF1id3c3rFYr96GfK+ugOd54441ob2+HXC7nvvPJZBLXXHNN2eZ5KaFGABcYVqsVdXV1CIVCvJGII2dizzyKfqhUKo5G0e1UpVIxqaLKwFAohLVr16K3t5c334XQOlGxCpFTs9nM6WixhYWYTIkPAvLVo/mLDVjF36/RaGCxWBYkAki9SG+//XYsX74cbrebU+tUkCOOdNE86U9jYyNrn4jsUnGPIAhlTf8YDAZcf/31CIfDUCqVbKeQTCYxMzMDn8/HBtbAWSJiNpuxZMkSrFy5EkuWLHnL379y5UqsWrWKxfFUGalUKnHZZZct2LzEXn40djJOpgiguKsHHWhiCYHYn40q6OkwL5cOiAigTqfD+vXrMTs7y1o+8aWBnrcLkVPxeiJC1dTUVBHtH0GssZrbf5neA0qrinWk4raRlRgzuSWQdpIu1PQMkc5Up9MhGAxyypguI/Q7KCXsdDq5fWU1aszo/RFDTFLpvRHrrRe6kIX20mKxCLvdXlLhTmdhPB7nSxt1kVKr1SVnwnyaXprrmjVrsGjRohI3iUKhUNFLUzWjRgAXCLSYlEolpxyJONDGQt8nDolT+oqiO5TKo42KdGpKpRKLFy/GDTfcgDVr1iz4fCwWC7dyk0gkcLvdmJqa4rJ7IrUUJaSbtbhakEDRQ/HNmqI7JpNpwYpAKJXQ0NDAN2Nxlau4UkxMRqiSmYiJmIwQOS4nDAYDbrnlFn5GaMNMp9OIx+OYnZ1FKBTiqIBEIoFer8fSpUvZd3E+0Kba3NyMu+++m59dIscajYYjHgsFelbE6SqKnpMkQkyIaE3Q/KkTAqVc6WARSxHKgUQiAb/fj0QigfXr13OXHr/fz88PVdXTfIBzEba5lc70mmg0mopW0xK5E1f+0nNDkRlxpF9cfVupSkwiA1SRL+46RF+nf1PVL70ntIfRnMhuxGQycaRq7v5WqTnOJz2h1DeA8+YMoMQiqlwgcj3f/22329/Rz1/oc1arFVar9Q8f5LsENQK4QKDFZjAYYLPZ2HaAQt7ixUjkjzb7ZDJZUvULnF2wOp0OZrOZU8vXXnstrrrqKu7+sZC3Nyq/DwQCSCaTmJiYgMvlQiqVglarhVqt5sOBopwkxA0EAiVjEx8awLmUOEUZF6JwgiJHwWAQ6XSaqzLp/6X3gEAHLn2Obp1kMlrOjiXzgYjZfKDCjt8V4vfkpptu+r1+xx8CsXaU1kJTUxNGRkY4ykLPzlzLIXq/4vE4gsEgbDYbE2PS25brkA6FQjh16hT2798Pj8eDVatWob+/n21d6BlXKBSw2WzQ6XT8fBFhF1+cxOREp9NVnADS2MRRP3ERl7gQQZxirUQvZhoHXaIp/S7uckPPEkX36urqONVIxteUMhYTdJKMiFPM5QatE3EHGbpg09fEkgLxuSP2PK3h3YkaAVwgEHm7/PLLIZVKYbPZ4PP5AICtKsQVsrQZhcNhWK1WXH/99bDZbCWheqvVCpvNhsbGRtTV1ZUtpUKbZDQaRS6XQ11dHQwGA1pbWyGRnPU1I7JEhy1VYPn9fpw+fZqjSLRxijcntVqNpqamBfcArK+vRzgc5i4f4j6rc8k2HRpicloujd+7FRSNpYpsALj22mvR1taG/v5+tk+aSwBJ50NrSqvV4r3vfS8AsJaWyGE5cOjQITzyyCN4+umnOUVPFzgiEKlUCjqdDn/yJ3+C5uZmLkii9S4mteJiqjVr1ixYkdQ7ARXZkBaOCDbtEeKiFnEXk1wuh1gstuBpxvkgzqBQta5SqSwp7qBxUnaFdGlkJE77s16vh0QiQSQS4fmKZTzlBo2T/CHp+aK5iYtCCLSHFYvn/Fjp30Cte8a7CTUCuMCw2Wy47bbbcNttt1V6KL83aMNob28v2UApNTr3UKaNx+fz4d5778Xf//3fV40Rp9ForOgBWsOFQSm1aDQKrVbLB1FnZyc6OzuZRIRCIfbPI89Asr0wGo0lNkKbNm1Cc3MzJBLJgqevCddccw1sNhuWLl2K2dlZxGIx6HQ6xOPxEtF9V1cXPv7xj//Ov58iOZW4jDgcDjQ1NZVEv+ZGmmitk56WisKWLl1aEXJBl7dC4WxfcqrCXrZsGeLxOOtlyQkgFouxDlur1fLF1GQycYqyq6sLNpsNVqu1xFi63KBnoKenB1dccQUGBgYQi8W4mE5s1A2c0/6pVCruzU6Ym5mp4Y8fkmKly5dqqKGGGmqooYYaaigravmsGmqooYYaaqihhncZagSwhhpqqKGGGmqo4V2GGgGsoYYaaqihhhpqeJehRgBrqKGGGmqooYYa3mWoEcAaaqihhhpqqKGGdxlqBLCGGmqooYYaaqjhXYaaD2CFMT4+zp0O1qxZA7VazV0BqKOAz+eDRqOBXq9HXV3d2/ZyLSdGRkYwMDCA8fFxxGIxuFwuzM7OQq1WY2hoCAMDA9i8eTMUCgW6u7thNptRKBTQ2tqKNWvWoKurq9JTOA9iQ9RIJILPfvazaG9vh8FgwJ49e9Dc3IxbbrkF1157LX9/NfpnvfTSS/j2t7+NEydOwGazIRQKAThrEqvRaCCVSmEwGBCJRGAymfDTn/709+4islCglm5KpRJvvPEG/v3f/x3BYBCf+9znUCwWcerUKdx///2w2WzcaYLMyasBZFY91wdzdnYWr7/+On784x9DKpVCEATEYjEoFAp86EMfws033wyLxVKhUZ+PdDqNZ599Fk8++SSsVit7GyoUCuTzeWg0Gpw6dQo9PT0lptDUjtBgMMDv9+PLX/4ye89V45q51FAsFvFP//RP+N73vgeTycR+h+SZuX79eoyOjsLr9cJqtUImk8FqtcJsNsPlcuHVV1+t9BQuGo4cOYJsNove3l5otdpKD+eSQI0AlhnJZBJHjx7FmTNnMDs7C6fTCaPRiEAggNdffx0dHR3QarWQyWQYGRlBPB5Hd3c3LrvsMmg0GsRiMZw+fRparRZ1dXUVawuVyWTwyiuvYHBwkDtkmM1mmEwm9PT0QBAETExMIJVKobW1FcuWLYNareaWXn6/H0899RSuuuoqrFq1akHav/2+oM4Ao6OjePzxx7F27Vps27YNcrkcN998M15++WW89tprCIVCeO9731u1B1k2m4VOp+P+ykajkfuhUhs8hUKBTCYDtVqNWCxW4RHPD6VSCblcjmQyCZlMhve85z2Ynp7G+Pg41Go199Im4lFNoD6y+Xweg4ODGB8fx8DAAJLJJARBgMViwcGDB+F2u7F27Vr09PTA7XbjZz/7GRQKBXp7e7FixYoF64/9TpHP53Hy5Ek2UbfZbBAEAfl8nlva6fV6ZLNZGI1GSKVSbg+Zy+UQiUSgVCqxd+9e3HzzzRdsY1jD7waJRIJNmzbh6quvxiOPPIJ9+/YhlUpBpVLxJTydTvN7ks1msWLFCnzqU59CJpOp9PD/YEQiERw7dgxr165FPp/H1NQUmpuboVKpqqb5QDWjek7dP3IUi0Wk02mOwoyNjSGRSCCdTqO5uRnNzc3Q6/UIBoPwer1QqVSor6+H0+lEZ2cnHA4Ht1nKZrPc89RkMpV9M83n8wiHwxgeHoZer+f2bdRyiboy3H333XjPe96D+vp6aDQaFItFds0vFAqQy+U4evQoent7q4YAUjRvYmIC+/btg8PhwHXXXYeWlhYUi0U0NDRAJpNhz5496O/vx759+7BhwwZuyVRNEHcEoC4b1AmAXm9x0/uZmRmsXLmywqMuBY23UCjA7/djamoKN998M5RKJUZHR7FmzRrodDpUq599NpvFzp07EQgE4PF4SrqBJBIJxONxmM1myGQy2Gw2TExMwGKxQKvVIhAI4MiRI5iYmEBHRwfWrFlTkXWSy+W4DzhFjY1GI8LhMJLJJLRaLRQKBQwGA7RaLWKxGDQaDV8yYrEYBEGA2WyG2+2uWNu0i4FqbJe2bNkyGI1GPPfcc5BKpXyho1Z9dEHP5XJIJBIQBKHqIv2/LyKRCA4cOICpqSk0NjZieHgYGo0Gq1evRl1dXaWHV/WojlP3XYBcLodgMIhcLoempiasWrUKMpkMmUwGZrMZixYtglKpxPj4OPbt2wedTocVK1agubmZCRPdtmljTaVSyGQyZSeA2WwWPp8PhUKBU9PU15j+ZLNZ9TWVsQAA5LBJREFULFq0CIIgIBQKIZvNAgDkcjlHRfR6PUZHRxEKhaBQKCpOAmlzT6VSGB0dxcTEBO688060tbWVfN+iRYu4n/Orr76KDRs2VNWBQAgGg0gmkwDORTWLxWJJ1JhIazqdxvT0dEXG+VYQE7tUKgW/349QKASLxYJcLoeVK1dCpVJVZRo+lUrhzJkzOHr0KJM+ivoVi0UEAgE0NDTA4XAgkUigo6MDs7Oz0Gg00Ol0kMlkiMfjmJychM/ng8PhQENDQ9mj/slkEkNDQ0ilUlAqlRzpCwaDnKKnS2sikUAoFEI0GoVcLocgCIjH41Cr1dBqtRgZGUE4HIZCoai6aK0Y1Ctcr9e/o4tdsVjknsfllh8YDAZIJBL09vZi165d8Hq9vA/LZDJks1nIZDKODFY6mnwxkEwmOQhSLBaxe/duvP/974dKpYLL5YLb7YZer4dara70UKsaNQJYBmSzWUQiEYRCIVitVuj1etx4440lBxZFOZYtW4axsTFYLBY0NzdDoVAglUqVNFgvFouw2WwYGBgAcDaNV04SSARQq9XC7XZz31YicLlcDvl8Hn6/n8dLmrO5t3+DwQCXy8X6lXJDTBxow/R6vZidnYVMJsOSJUuQTCaZuBLa2tqwevVqfPe730UikeAIZzWRkEQiURKZJDIll8uRSqUgk8kglUo5UuD3+ys53HlBz30wGEQ8HodOp0Mmk8GBAwcwPT3NuibqeVpNkcBwOIxdu3Yhl8vBbrezthc4K6GwWCxoaWlBOByG2+3GkiVL0N3djWAwiGg0CovFApVKBblcjqGhIRw/fhwmk6nsvazpQlQoFJBIJJBMJuF2uxGJRCCVSpFKpTAzMwOLxYKxsTHIZDJEIhEmG6lUCtFoFI2NjRgZGUEwGITZbK7qNPD09DQGBwfR0tICjUbD+xtpyxQKBUKhEFKpFICze0c8HkcymcQVV1xR1rGS9OGKK67Ayy+/jMHBQY6c5/N5zrZkMhl0dnaitbW1rOO7WKD9NZ1OY3Z2Fl6vF5lMBitXrkQymYTD4cCqVavg9Xrh9/thMBjQ1NRUdZmZakKNAJYBoVAI09PTMBgMkEqlmJmZgdFohEqlQj6fBwBeoIVCAZdddhn0ej0LqSm1SmSKwvr79u2DxWLBunXryrqo8/k83/ApMkEEj6J7hUKBIxVi0keEQyKRcAQzGo3y61BOkHaMyDdtpKdPn4bX60VLSwsAzHtQKRQKPtxOnz6NVatWcYoeAKfCKwmKwBDRy2QykEgkiMfjiMfj/DyqVCrkcrmq1ASRzuy5557D/v37sXz5ctx888348Ic/DLPZjEwmA51OV5VpxWQyibGxMTQ2NrJ0g559hUIBpVLJayadTkMQBMzMzOD06dPQ6XRYvnw5YrEYnE4nzGYzTp8+jXXr1pWdAMrlci7eIuKtVCrR1NQEvV6PdDrN48/lctDr9TCZTDAYDEgkEpicnMTExARuuOEG1NfXv+OoWiVA+8Hw8DC+973vwel0csZFq9Wit7cXuVwOra2tePHFFzE+Pg65XA6ZTMbR0HITQNpnHA4HzGYz5HI5isUir3m5XM6XPLvdzkU41XZhfSvQWLPZLCYnJzE6OopUKoW6ujosW7YM11xzDQBArVbz+TQ7OwudTger1Vrh0VcvagSwDFAqlTCbzTAajUgmk1CpVEilUqyfIyKkVCo5zUsLlm5yYm1dNptFoVCA3W7nVGQ5USgUkEwmodFoYLPZEAwGuSKQIjC0AQFg7VmxWIRMJuN/T05OMvmoROSGilHq6+shlUp5vGfOnIHb7WY9HJELiUTC85JIJEgmkxgYGMDk5GTVFbIA4DQbvebJZBK5XK6kqEKpVDJpr8bKOaVSiePHj+O1117D9PQ0Nm/ejMnJSVgsFhw4cAB79+7FlVdeyWmtbDZbFYdaJpNBLBZDoVBggqTT6UouCQBKnrt8Po8zZ86gr68P2WwWBw8eRDKZxF133QWFQsESknLDYDBgw4YNeP3111EsFrFmzRoAgM/nQyKRgM1mw/DwMHp7e3HkyBEmIRKJBBaLBTqdDg6HA4FAABqNBsPDwzAajVWXihTvQTfeeCNuvPFGuFwu9Pf3o7+/H4ODg/j5z3+OV155BbfffjveeOMNdHZ2oqWlBTKZDBqNBsuXLy/7uMXPk1wuh1qthlKpLJF80F7Q0tKCpqYmnm81rJV3AhpnX18fZmZmYDabsWLFivMCHyQ30mg0SCaTOHPmTI0AvgWq68T6I4VYHK3VapHJZGAymVgXJJPJoFKpSuwrYrEYH95yuRyBQADbt2/H0qVLsWbNGng8nopV0xUKBWQyGSayMzMzqK+vh1qt5k2HDjaKrAGASqVCoVBAOBxGNBqF1WpFLpdDMpksewSnv78fr7/+OsLhMEZHR/HAAw/AYDCgsbERR44cwezsLO666y4AuKDmKpPJYHR0FL/85S9x6623YmxsDCaTCX19ffD7/Vi6dClWrFhRzmmVwGg08vMhCAKUSiXS6TQikQgkEgkEQeC06VxtYLUgm83ihz/8IbxeL9RqNV588UW8/PLLMBqNWLt2LR5//HH4fD5cc801qK+vr/RwGYlEAj6fD5lMBh6PB9FoFL29vRAEoaSQgMT5FosFcrkcGzZswKJFixCJRBCNRhEMBpFOpzlbEIlEyq77TSQSOHnyJAKBAFu7xGIxvvD4fD7odDr4fD4mF6Ojo+js7IRWq0U0GoVOp0NdXR36+vr491QbKBMg/rfT6YTT6cTVV19dcrmly6BEIsGZM2fw3HPP4eDBg+ju7q7U8AEAvb29OHToEPbv3w+fz8djXrx4MbxeL6amphAMBgFUVyHLW6FQKGB2dhZvvPEGUqkUrrjiCrS0tHBkVkxkyXaIdMGRSAT9/f1VZZ1WTagRwDKBbohHjx7Fd7/7Xdx999246qqrOPVLUZl9+/Yhn89j/fr1yOVyHDlTqVRobm7GwMAAVqxYAalUing8zgd5OUFFHhaLBR6PBydOnOCbJaV2KA1MGxBFMovFIjweD1599VXcc889yOVySKfTZY0ARqNReDweqFQq3HHHHbjllltw6NAhJuD0un7zm9/Epk2b0NbWhrq6OmSzWdY/Tk9P48yZM1i9ejVGRkawZMkS1mwBZzWCcrm8ogRQrVaXvB9kzSGXyxEMBvmZIz2g0+ms2FgvhOPHj8Pn80Emk8HhcMBqtTJZpUvI7OwsIpEIGhoa+BmrNIgkWSwW+P1+TExMcKGXWAtLBxcVVMjlctjtdjQ1NUGj0UAmk2FwcBB+vx+FQoF1Z+UkgLlcDuFwmC+vHR0dOHjwICKRCGw2GzQaDQYHB7nIQK/Xs9QAOBuVkUqlmJ6eRnt7O2KxWEUime8Ec0nR3PeJsjH08eHDh/HDH/4QPp8PN998M7Zs2VLeAeOcdnn37t341a9+hZmZGTzwwAP47Gc/y98jk8ngcrnw5ptvYmhoiD1ZLwXkcjkcPHgQhUIB73vf+9gmjSB+z7LZLNLpNNRqNRQKBbLZLPx+/yUV7SwnagSwjKDD6dlnn0VXVxcuu+wyOBwOJnoSiQRNTU34P//n/8BkMqGjowPA2UiTVCpFfX09fD4fHwIGg6FihROFQoHtX1wuV4lOkWwHaM65XK5EqK9QKBCNRvlQKXcKeGpqCslkEosWLYJarYZKpeJKxXQ6zan5aDSKX/3qV4hGo+ju7oYgCPB6vYjH4yysJs0J/SwR/aamJixevLhsc5oPOp2OjWEpkpxKpbBhwwY8//zzJa+5XC6HyWSq3GAvABLZi4kdkWyNRgNBEOByuRAKhfgylE6nKzZeAlUo0liJEJE/mVh7CoCr/JVKJXw+H3w+H1QqFZYvX46DBw+iubkZ+XweiUSCK+rLOZfjx49zFbBSqYTFYuFqU7Vaja6uLkSjUQDA5OQkW8UIgsA/ZzQaEQqFMDQ0hK1bt5Z1Du8Uc0nCW+l4Dx8+jAcffBAqlQq33norbrnlloquocWLF+NrX/sacrkc4vE4vv3tbyMUCqG1tRWxWAxKpRIqlQodHR3Q6/UAzhWQVCvosrdlyxYUCgUe93zfJ46oazQaaDQaJBIJpFIpjI2Nob29vcyjr37UCGAZIZPJ0NbWhgcffBDLli2DTqfD6dOnEQwG4XQ60draymm6Z555Bvfddx9MJhPrhqxWK5YtWwa/3w+j0XheZWo5QOSP/lapVEx8xIUqdFMm3Z94nHq9HuvXr2eD0mw2W1YCGAqF4HK5YLPZIJFIoFKpEAqFWNQOnL1JplKp8wg2HeiZTAaZTIbNiZVKJTvwd3d3Y+PGjefZx5Qber2ePf6ImAuCgLq6Oibj4veomjpPEJRKJRQKBXK5HMsFSEpAEc5IJAK3241wOAyj0VgVN/10Oo1EIsFarEQigUAgwOuZyB5F8oGzkQ65XI54PA6/38+6vxMnTqCzs5OLdyoRPRNXL8/OziKbzfL81Go1IpEItFotX46oWpsKX4rFIkdFq+U9+n1AhMnr9eLRRx8FAFx33XW45pprODpdbpCM4/nnn2ebHbfbjenpadhsNvT19aGxsRGJRAJGoxEjIyMYHBzEn//5n1f9+0Dje6fRSr1ej2g0imKxyJdfAAgEAjUCOA9qBLCMkEgksNvteP/73w9BEDA1NYXXX38dgiCgsbGRD4Xu7m5MTk6WWAxIpVLodDp4PB6MjIzgyiuvrEj0j0iO2GdqbkWfuCsD6cto/hKJBDqdDosXL8bo6CgaGhrKHgGMRqM4ffo0ZmZmcMUVV3AVJs2Hquey2SzUajWTRKr2A85WeIZCIUQiEeTzedZwRqNROBwOdHV1XfC2Wi4YDAYoFAom7FR9ajAYSqJItFnabLYKjnZ+mM1mjq5mMhkkEglYLBYUCgVOg1IHgJmZGZhMpqqIaJC0gQh4IpFgza/4ElUsFrn6moidUqnk944KSOi5i0QivC+UA8VikS8QAFgDGI/HkU6nmZgTeZXJZEin00gmkxxdpwue2DMwHo9XpXn6O0GxWMT27dtx8uRJ3HnnnXjPe97D2sdKjIUK0vbu3QuXywWdTgeLxYJly5ahrq4OJ0+eRGtrKxdI0BkCXDo6wHcKtVoNv9+PWCwGvV7PexutvRpKUSOAFQBZpPT19eHIkSNYtWoVmpqakEwm2QB6ZGSEq2rF+rnh4WGcOnUKN9xwQ0UOunw+z2SJupssXryYDwoaE6V96Wco7SWXy7kizePxQBCEskcAi8UiXC5Xie1JLpeD2+1GsVhkz7ZkMgmFQsF9mcVFOXK5nKs6o9Eo+zVS8Q5FCisJg8HANipENqRSKWw2G9+M6fMKhaIqq+WokIWIEnliiqPMcrmce1D39vZWesgAzulkxZZHYtuX+SLk9F6QgS39LOlPNRoNwuFwWQlgNptFOByGx+PhyKVer8fk5CSTU/LJpBR3MBhEJpNBY2MjdDodG71TlPDYsWNwu91obGysysrzt8P4+Dgee+wxXHbZZdi2bVtJVS3ZKZVrXrQOMpkMDAYDOjo6sG7dOtTV1cHj8SCdTpcUpsTjcXi9Xo7KitdStWC+8cz93IU6slDBJJnF055XbqeMSwU1AlgBNDY2Ajgbum9qakJTUxP7+xWLRSxbtgy5XA4KhYLTi9lslj3FRkdHK7Zgc7kcH8IUGfjc5z6HkZERJBIJGAwGAOCIgVgwTREOqVSKWCzGBx9FFMuFhoYGtLa2Ynx8HAD49Q2FQqivr4fVauX0LqUbC4UCp4TpYBYEAQ0NDZDL5RwNpJZr1RCFMplM7IRPGjO5XM5Eb669UDW65mezWUSjUaRSKaTTaaRSKSZNtEZo3QDneu9WGvl8nkmbIAhIJBLc8o2eDboIkoyCbJ+oKEepVMLv93M3HSrSKedaicfjmJ2dRTAYLOnqI5FI4PV6AQDd3d2Ix+PIZrMQBAF+vx9OpxPBYJDHr1Ao4PV6sXz5cixZsoRb4l0qBJCer0Qige9///tIJpP43//7f7MnI7Xn9Hg88Pl82Lx5c1nGJb5wt7W1QafT4dSpU/jXf/1XvPHGG5yxoUyAzWbDpk2bcNttt8Hv98Nms1Vde7v5xjH3cxcaK+3l1AaTpAfV6HFaDagRwApj2bJlWLp0Kf87Go2io6MDTqcTCoWCjTxdLhceeughfOMb30BdXR0XYJQbFAWQSCSIxWIwm82or6/H8ePH+XCjA0J8a6PoBpGQzs5OnDp1iituU6kUp5EWGtRGbGBgABKJhNMGnZ2d6O/vR19fHyQSCbRaLacfLRYLzGYzotEoEokEeyHSmImcWK1W7oxSaWg0Go460esvl8thsViQyWT4c2RDVA3EaS4oYtzT04NMJoODBw8iHA5Do9Egm80in89Dq9Xi6quvxqZNm/jAq9T6IIjTvIIgIBwOIxKJsLk4FaoUi0U+rIikk7F1OByGz+dDLBaD1+vF0qVLOe1aLtBaJx8/mUzG+j6ydikWixgbG8Py5cvR2dmJmZkZ7hJCZuNjY2PYvHkzBgcHodPpOMK5UJi79/wh61EcKYvFYti3bx+6urp4vSQSCRw8eBBPPvkk+vr6sHbt2rIRQMLatWsxMzPDF+/NmzdjZmYGHo8HExMTWLNmDVwuFwwGA15++WV84xvfwCOPPIKnn36aCeKlhLmklf7WarWor6/nPblQKLDutIbzUSOAFUIul8Py5cthMBhgsVhKxN0UHSO/L3JALxQK6O/vx6ZNmyo2bkptia1gxsbGkEqlWH81t7KMDkMA3Jdy1apV+OlPf8pzTiaTnDJbaDQ2NnIUllKI4kps0sKlUimOypDvWTKZ5HQCpbWpSpuqOkkTVQ2ggyuXy7FdEKXcibBTV5NqhM1mw5/8yZ8gGo3i2LFjJVW1arUahUIB0WgUJ0+eRF1dHVasWFEV5JtAz4xarUZ3dzdHkwVB4OeFouAkhxCTI5vNxuk6p9OJkZGRsj5bVFFKWledTseXilQqhVQqBY1Gg6VLl2JiYoKr55csWcKG70QaA4EABEGAx+NBX18f7HZ7WaxI3up5eDtyKJamJBIJ9Pf3w+/346Mf/SgEQcDo6Ch+8YtfYM+ePUgmkzCbzWyUXU4cPnyYtX6NjY1wu934zne+g6985Sv4m7/5G+zbtw+vvfYaDAYDbrvtNuzdu5d/thqyFXNB5wWNbe77NN97Fo/HcfToUVx77bVcTJnL5eDz+apGGlJtqBHACiGXy3GLKDFBAsCRMrGtCvXVNJlMFdVsUHUstamz2+3o7+/n7iW5XK7EK4vSc2QMnclkEA6H+evxeJwPvnKbw0ajURw4cIBT8PQ+iMdBhROFQgGBQIB1gOKUPZFe+rxYNF9pUPRvrnkwEV/grEl0tRJAgtfrxfT0NNLpNPtNijWa4XAY4XAYUqm04obWFPkj4haJRGAwGLBmzRocOnQI2WyWK5vJSoiqycXPoSAIWLx4MRtFU6S8nDYwFouF7agCgQB6enpY06vT6WA2m6HT6RCJRNjjr7GxEfF4HEajEWazmaOxdXV10Gq1bHjd0NBQtnlcCBfaR+dLi/r9fjz77LNQKBQYHx/HX//1X+Pll19mw2jSboozOuXA0aNH8fWvf53XSCgUgiAIsNlssNls+MY3vgGHw4FwOAxBEHDs2DE89NBDmJ2dRV9fH0ecq4UIUhu3YrEIh8MBk8lUYgM1nz7Q5/PB5XJBEAS8/vrrcLvdsFgs6OrqQkNDA+rq6ioxlapHjQBWCMVikTd9KoogYkcpVLVazaQpnU6joaEBzc3NSKVSFdNr5fN5Tktns1l0d3ejv78fDoeDPZhofmIyKK74o2IRm83G3VEoBVxOkKkz6RbnklbScdHGKD6g6ZCmMVNEhyqaqyUKJY5MUpW5TCZjwk1Eo5oJIKXU0+k0R4nnXpDC4TBCoRCAymuZ6AJAazoQCOC6666D0+nkCCBpL8WpKnrm6PIgCAIsFgs6Ozuh1+sRiURYg1ouCIKAnp4ePPjgg6yjev7559mLlEzVSQMnkUi4LZzNZkMymcTMzAyy2Sy6urpw6623IpfLlehTFwJ/aMqXfod4LVMFrVwux9GjRwEAH/nIR9Db2wufz4cXXngBw8PDZSe21CGmoaEBV155JRobG+FwONDS0oLR0VFotVrYbDaEQiE26na73fjlL39Z8bUyHzQaDcxmM7xeL4aHh6HT6WCz2WA2m88jqblcDn6/H263GwDw2GOPwWaz4YorruAL/pYtW6rS47QaUCOAFQQ5/QMo8WXT6XRsnEybkMFgwFVXXVVS0VmJxUtVblRZReSN0ol0kNHhN3d+VEUMAE6nE8lkEgaDoezaJqlUCpPJhN7eXhw4cABAqcs/EViaF5GlC6UixJ+vpk2Vun0QmSVNHc2HSGGlNXNvBSqUMBgMMJlMnC6l15nSrGT/UGkyS9Wx+XwesVgMyWQSmzZtgkaj4Sg5cI5g0OVHnAWg9aLRaLBs2TJ4PJ6SKE25IjZ0Ee3o6IBEIsGpU6cQDAZZykGFXAqFAiqVCm63m6P6VAxF/oBer7ei3WbE6dy3S/tSQQt9n8vlwv79++FyuWC1WvGBD3wARqMRXV1dsNlsGBwcxOHDh3Ho0KGyd9hobW3Fli1boNVqsW7dOmg0Ghw7dgxPP/00QqEQtFotp0Tlcjn0ej0cDgfe8573VMS38O0gk8nY5zYajSISiWBsbAzj4+Ow2WywWCws/wgGg5ienkY+n8fDDz+Ma6+9Fh0dHWhtbcXhw4cRDoc5Gl/D+ai9KhUCPdxyuRxarZZTQkSY9u3bB7PZjObmZqhUKiiVSnR1dfEtu1KFBuRvBoBb7lB6ig6lualRMchHLJvNwmAwYGpqim0Myp02VSqVaG1t5d6YtBmKN0Vxqlf8dTFZBFBC1sUt8CoNmUzGWiyKTlJqUpxqpB7U1QgqGtBoNNDpdFzkISaA1HvX4/FU3H+RyDX5s2WzWXR0dCAWi3Hkj8ZOa0b8b3GUWSqVoqGhAfv378e6des4yl5O4b74wun1ejkSqNFooNVqmSiRByBVyVO3IKPRiEKhAI/HU5GLq3jtvlPSLF6/p0+fxp49e1gusmnTJtx8880lFw2lUglBEEpS3uVAsVjEL3/5S0xMTLAVTzqdxujoKARB4GK31tZWpNNpaDQa3qcymQyi0Sjuv//+qtmvCAqFAgqFgs/GUCjEZI8M1eniJ5VKsXv3bmSzWTbkJpcGcdV9DeejRgArhEKhwELc3t5edHZ2Ajj74O/YsQOPPPIItm3bBrvdzho5sr4QR8rKXcJPKWDgbFcAo9GI3t5euN1u1jaJ+2fOTZHS70gkEtyqpxJWMFTEQhEY4NxrSYewWOs3dxMRHypi5PP5ss/lrUBt+eiAFr83RADpElKNKBaLbAZNHoziFCp9nM/nEQwGMTMzw2upUiBSR91hIpEI7HY7xsfHz7vkiK1gaH3T91ChhVKpxNDQEL+XZIpdLgIo3lvILoieG5KCUFU8zYk8GwVB4KKRajDjzeVyiEQiSCQS3P2GdKQEqtym6uUnn3wSR44cgdPpxPve9z7cdNNN/L30/JGtTbmfvWKxiP/6r/9CIBCA0+nEyZMnkc1m0dPTgy9/+cvYuXMnhoeHsW3bNgwODsJqtcJsNmN4eBiPPvoo+vv78dGPfrSsY347iC8JCoUCTqcTTqcToVAIw8PD8Pv97ASgVquRSqXw8ssv4ytf+QrMZjP/rCAI0Gq1NQL4FqgRwArB4/Fg+/bt8Hq92Lp1K+6++24uX//sZz+Lzs5OrFixAnq9nklSsViESqUqIVOViAIS+clms7BarWhpaeEUlbighb6HFiD1oiQCaDabuUdlJQoniNyRPkTc7YA2Idrg6fvf6ncBZ3VCdCBWA8Ref+I0iJhA0UZZjcjlcjAYDNDpdFCpVFCpVOzHKNabEsEgLVAlIY7QyWQyDA0NoaGhAXv27AGAkr7YFFWzWq1QKBRsLUS6YNL9Ufu0QqHAKeZKpLqpBV86nUYwGGSJgUbz/7P33lF2ndXZ+HN7r1Pu9KaRRr3bsmS5VzDFNgEDoSQkBC8CKST0knwr4BC+DwIxLawAvxAcwOAi3DuSm8pIGo1GmtH0fnsv555z2+8Prb117mgkG/Dce22fZ61ZmnJn9L73nPO+z7v3s59thiiKSKVSmJ2dxWWXXcbXyWKxsM6xGqBngDR8L7zwAkZHR7mHb3d3d5lxOx38EokEvvWtb+HYsWO46aab8NnPfpZNyeVZADqAFIvFituNlEol7Nu3D48//jh27drF6edEIoEDBw7g+9//Pnbs2IFYLIb7778fpVIJmzdvxq5du/Df//3f/P7UkgXU0j2N9ONOpxM7duxALpfD+Pg4JElCOp3Gj370I+zcuRNbtmwp+13SO1a6d/brCQoBrBIaGhrwrW99C1NTU0gkEkgkEmhubkY6nUZbWxs+/vGPQ6PRIJPJcMqONo2DBw/iuuuuq5p3E+nj6OQ8NTWFVCoFo9HIpII+5B5a1KOReu96PB6EQiGu2qxkGkKlUiGTyeDkyZPcI1KuYZRrrOSRTHnEdanVDXDOJ7FWCCBFaeTRTUoFy9NiteoDJkkSNBoNTCYTnE4nzGYzgsFgWVWfPDoeiUSqXoSTy+X4QFMsFjklFY1Gy+4bkkvodDqOOFPkichhJBLBli1b4Ha7+YBCz1Y1EAqFuNMHFak4HA68/PLLuPTSS7lYbXFxEdu3b4fH40Emk4FarcaZM2cqNs6lmZFcLoe5uTk8/PDDOHjwIBoaGjA/P49AIIDbb78dO3fuLKseLxaLuPPOOzE+Po7Pfe5zuP322/n7wPmpZKPRyP6alUKhUMDU1BT+9V//FdFoFH//93+PSCTCc6b055EjR/DDH/6Qv/fAAw+Urc30jFUDS59V+X4BLE9Oz5w5A0mScOzYMTz11FPo6+vDl770JbZbIlDGgHptKzgfCgGsEsj3r7e3lzcAURRx5swZ7NixA1dddRX7bJFpr0ajgd1uL9M/VQuU7pV/Lq+SlT+4ck1gqXS2J2g0GsXmzZtZD7icXnAlcfnll2Nqagof/ehH8f73vx/AOcsUGjsRQbJ4AbDsYkUfRKwymUxF23VdDEQkqLexPEUtJ7m1FAFYDiQZWLNmDVtdyHWLGo0GqVQKk5OT3OGgWkin05z+TKVSaGlpgd/vRyaT4f6kVIAjCAJXy8r7ZwNn7690Og2XywWXywVBEJbVqVYCtCmfPn0aDocDJpMJuVwOgiAglUqhqakJCwsL8Hq9EEUR9fX1SKfTmJqagsFgQENDA+rq6sp6tK7kGrb0byeTSZw4cQL33XcfrrjiCtx8883IZDK4++678YMf/ADvfve78fa3vx3A2fXqT//0T/Hyyy/j/vvvx44dO8o0mReSfuRyuYq2gFSpzvYo/+lPf4qtW7di9+7duOyyy3DJJZegr68PbW1tCAaDSCQS6Onpwfj4OOrq6lBfXw9RFDE+Po7vf//7SCQSbLWy0lj63pGPJwC2FloOVJSTyWQgSRIX3DQ1NeH//J//AwDnVZWbzWa43W4lAngRKASwSqAoAenQ1Go1TCYTOjo68L73vQ/xeJwF1dRdg4jShg0bqlrVROSBxPaxWAySJPEmIdfAEbklzRCdkFetWsWbtCiKF1xYVwrr1q3DO97xDvzyl7/Eb37zG7S3t0OSpPMqkV+NfkS+KYuiyFGSWgCNje4j+feXplBrEbQpJZNJpFIpmEwmdHV1YWZmhgkgRf+MRiOamprgdrurOWS+/0ulEsLhMDweDxKJRFm6kCqyHQ4H99mln8sPFul0GsPDw3A4HBwtJP1qNUA+i+RfSKbqNP4jR45g9erVaG1t5apnnU4Hn88Hh8NRsdT13Nwcjhw5wp15qNL6He94BwBgenoat956K8xmM+677z489dRT0Gq1aG9vx2c+8xmUSiU8/fTT6OrqOq+AZDmiRAf0SqaAqUCIbHh8Ph9LbJ588kl86Utf4kMpdTGiQ8aqVavwxS9+EZ/73OcqRv6ActcE4OxhaWBgADabrczEfel4KDp75swZaDQa+P1+rF+/Hn/+53++rC+gSqXie69WsjG1CIUAVgnUp1AuxiftUCKRgNvtZlJEFhgkrJZr1KoBiiKRbmxubg6hUKismplAeifgbJqEPAA7OjqgVqvhcrm4+0ElodFo0Nvbi0996lP47Gc/i/r6+rIWdktxMXIq/51SqYRYLIZ4PL5iY/99IE8XUpSVrgGA8za3WkUqlYLX62XyIX/PqejA5XKhtbW16nOhCDKldOvq6pBMJsuqQ8n/M5/PIxqNsnaLCGKhUODq/7m5OTgcDo4qysl7NeZGvZn1ej2y2Sz8fj93L6JOOeFwGLlcjjuHNDU1YXFxEalUqoygr9Qalk6n8cQTTyAajfJ6SeRHkiROIRYKBUxMTCCZTGJsbAypVAqLi4v48Y9/jM7OzmVNxeVrL2mEw+EwhoeHK2oCnU6n8dWvfhW/+93voNVqkUqleEwOhwOf/OQnoVKpcPDgQTbhJhuVeDyOr371qyiVSvjUpz6F2267bcWrl+XFdPIiD7fbjXvuuQfbtm2DKIrnFeUQJiYm4PF4cP/990Ov12Pjxo0X1S6T7lnpA3xhKASwSiALFXnRBIGigsDZwoloNAqfz4e2tjbYbLaqahrkRIfSHXv27OFm8bRBy6tPKRpCC4xKpUJ7ezsAcIh+qa1KJeByuXDTTTdh3759GBoaYn88AKzDWkqU5J9fiCjGYjEkEokKzOAPA/k40lyX6hlrCXJtJWnPyCCdLG2AcynsWmjBl81mkclkkMvlIEkS7HY7UqkUp7GoiIsqeqnCmTbIpXKKcDiMaDQKi8UCq9XKRSDVgCiKiMfjyGQyMBqNvGGbzWYIgsB9j8mGQ+79qVKpkEgkKnJ49Xg8eM973oNoNMpVv1Q0IAgC2wYZDAZOi1JV6c6dO7Fz587fK8vi8XiwZ88e9PX1reCsyqHVarFt2zaEQiF4PB4YjUY4HA7+d/369fjtb3+LG264AevWrcPDDz+McDiM7u5uXHbZZejo6EAoFKpYRklud0TQ6/VoaGjA7Owsnn/+eVx66aVlUgiSrYRCIQDAzMwMjh49iquvvhobN27kv7PcPUUaQCLGCs6HQgCrgFQqhfHxce6XSWF68s2ilBxVPxUKBb7xb731VthstqpqACmFRYTu0ksvRTabLYt8LCWAwDlRMm0OpVKJy/irEdHU6XRobW3Fli1bcOzYsTLbF+BcccRyUUE5CZTrHknXRZYY1YbcqkNOpuTVjHS9ahE0ZiowSiQSnIYkw2G6D7PZbE2k3uU6y1KpBIvFwhu0vI80+QRSkQ5VAMsjtdSJ49ixYxBFEXa7/bx2hZUERVSoqpfaVNL9Rc+BzWZjxwKNRoNoNMq/W4ln3eVy4brrrkMul2MzbiKvJPWYmZnhdUqv18Nms8HtdmPz5s0ALhz1l4+dDk7d3d14+9vfjrq6uhWdlxxqtRrt7e1473vfi4WFBY72FwoFRCIRPP3003jqqaewdu1aFAoFDA0NIZvNIpVKIZFIwOPxsPNEJbDcNVepzjY+6OrqwsDAAHp7e9lKjA5QdFAyGAzYt28fmpubsWHDhgvqfOX+oJXUZL4eUZur/hsc6XQai4uLZQUgwNnohdfrLesLSobJ9fX1+OlPf4rW1lbcfPPNVRt7Pp9HJpNBOBzmIhCHw/EHa180Gg0SiQTq6+urptUgPSZt2LRJydPZ8grh5VAoFNhkVa/X10xnDSKvtJjS54Sl1ZK1BnrP3W43nE4nFhcXmRhRRI2iSwBqpvhGjlKphLVr18Ln82Fubg6JRIIPPcA5XR0Abp8IgFOn119/Pe67776yKuBqEUCy28lms5z+JA9AIlrNzc18SKQDVCqVOs++qhL3HJkzL9edY8+ePRf93d9nfNR3t5LI5XLYv38/+vr68PjjjyMUCsHr9bJWlnSj+/fvx6lTpyCKInQ6HWZmZhAMBtl2aH5+Hp/4xCdWXIaTTCaZ0AHn7gFRFLFnzx7MzMxgbm4OFouF7x/KDhmNRkxMTOD06dP4yle+wiSdINfOErRabU0UTNYyFAJYBXg8HrzjHe8o68cKnF1c77rrLlx55ZW47LLLOCpgsViwdetWvPOd78R9992Hvr4+bNy4sSqVm9lsFj6fD7Ozs6y/+GNIhE6nQzQa5UKSSoKirolEgqNikiTBaDTC5XKhsbGRowXyDg20OC31O+zu7kYmk8GOHTvQ29tb0blcCJIkIZPJQBRFaDQaxGIxfq9p/tVMKb4alEpnWyHa7XZOy5dKJWzZsgWtra3Yv38/LBYLGhsb0dTUVHUbGLmnpSRJnIK65pprUCgUMDs7i5mZGcTjcajVajQ2NkKj0aCpqYk3PYfDgY6ODnR2dvJhhLqA0PWsBhYWFpBIJCAIAvR6PRKJBGZnZ9Hc3Ay/38/6xbGxMXR3d8NgMLBFVC2S89czcrkcDh48iJ/+9KewWCxwOp3YuHEj1q9fjz179mD79u2Ix+MYGBhAT08PHnvsMeRyOezevRu7du3C6dOn8V//9V84ffp0RQ4Up06dwtzcHHtH6vV6zoBZrVb09fUhGAxCo9HA5XJxZf/MzAxeeukl9Pf342tf+xo2bNjAgRP5cy4vMJEfTHw+34rP7fUKVanSfgIKGKSNSSaTyOfzcLlc+M53voPf/va3/LAC4Gomqp4Lh8Pcfq3S8Pl8GBgYwNjYGG677Ta0tbX9UX/vxIkTmJ2dRW9vL9rb2ytubksaq0996lM4deoURkdHcfPNN+OjH/0obrzxxoqOZSVw8uRJHDx4EBMTE6irq8PatWvx9re/Hf/1X/+FTCYDQRDQ09ODq666qsxbr1ZAJuh6vR7JZBKDg4N4/PHHUSqV8JnPfAZOpxM/+MEPEA6HYTab0dzcjLe97W2w2+1VG3MwGEQ0GuV0W7FYxN69e/+ov3n06FEA4FZjVqu14qbDADA2NoaJiQlYrVY4nU74/X4MDw+jsbERXq8XExMT2L59O/fabmlpgcViwdTUFILBID784Q9XfMxvdJRKZ7sZLS4uwu12w+VyYXh4GJ/+9KdhNpshSRI8Hg8sFguampqwbt06bN26lXXYlcLc3BxOnjyJdDqNVCrFbevITogqmenejsfjmJ+fh8fjwVVXXYX3vve9r+r/IRnF1NQU/vd//xff/e53a8IgvhahEEAFChQoUKBAgYI3GWqz9E+BAgUKFChQoEDBikEhgAoUKFCgQIECBW8yKARQgQIFChQoUKDgTQaFACpQoECBAgUKFLzJoBBABQoUKFCgQIGCNxkUH0AFrwkikQh+8pOf4NFHH0UoFGKPQ5/Ph6amJrbyaGhowLXXXosvfelL1R6yghoHGT3/9re/xU9/+lMMDQ2xRQT5GBYKBW6rFovFYDAY8Dd/8zf4yEc+gnw+X9UOJzQ++RhOnDiB/v5+jI+PIx6PI5/Pc6u3hYUFtLa2oqurC52dnejo6EBvby82bdoEp9NZtXksRT6f504mv/jFL3DXXXehvb2dO4MIgoCWlhZ89KMfxZVXXgkAZV6ntQAysZZbaR0/fhzve9/70N7ejubmZszPz2N8fBxf+9rX8MEPfrDs96lLU63hC1/4Ah577DG4XC7uz5zJZPgebGlpQV1dHfr6+vC5z32u5kyS4/E4pqenEQqFcPz4cTz77LNIJpO45JJLYDAYIAgCTp8+jdbWVmzbtg2rV6+GxWJBX18fPB5PtYf/uoNCABW8JvjRj36EBx98EIuLizCZTDAYDGhubuaNi3qhTk5OYmFhAZFIBN/61reqO2gFrws0NDSgu7sbgiBApVKxkTV5hUmSxMRQrVYjEAgAABtGV3OTU6vVCAaDeO655zA7O4tgMAhRFKFWq1FfXw+r1Yqenh74/X5cdtll6OnpQaFQQDwex9TUFMbGxvDMM8+gr68Pl112GVpbW7kDT7VA5O/ZZ5/FU089hUwmg2AwiGw2C7VaDa1Wi2AwiKmpKezcuRNms7lmiB9BPh6v14ujR4/iueeeg8fjgcfjQbFYRGNjIwRBwKFDh9DW1oarr76a76VaJH+hUAg+nw+RSIT7RpdKJeh0Om6JRs+HSqXC1NQUenp6qjzqsweKkydPYmhoCMlkkg9+er0eW7duxcTEBEZGRpDNZmE2m9Hd3c0m49PT05AkCUNDQ2hsbMRVV10Ft9tdk9enFqEQQAV/NIrFIqamphCPx7nZPQC85z3vgU6nw+nTp/Hwww/zYpRMJjE6OlrlUSuoZVA0KZVKYWxsDNFolHu2ms1mJJNJ1NXVQZIkmEwm7hJSLBYRjUYhSRL3O64WSqUSEokEHn74YYRCIRSLRdTX1zOBks+1rq4OFouFx2s2m3nTzufziEajeOihh3D11Vdj1apV3IWnGhAEAS+++CLuv/9+HD9+nLvJaDQa7iojiiIeeOABTE5OoqenB5s3b8amTZtqpue0JEl48skncerUKUQiEW7Tp1KpkE6noVKpkM1mYTKZMDExgV/84hc4fPgwk5Irr7yy5khGJBIBcLYtXWNjIyKRCPL5PFpaWqDVahEKhZDJZDgCSOt0NZFKpTAwMIBDhw4hlUpBrVbDYDBwCzin04l169Zx9yWTyQSr1cotIKktYjKZRCaTQbFYxLZt29DW1rbire3eCKiNp1HB6xqHDh1CKBSC0WiE0WhEoVBAKpWCSqWCyWSCyWRCLpeDVquF1Wrl1yQSiap2bFBQe5ATtvn5eQwMDGBgYADxeBylUgn5fJ57LdPnwNmIjFarhUqlQjwex9NPP42mpiZs2rSpahudIAgYHh7G4OAgnE4nkzpqU1UoFLj3r9lsBnC2TzhF0aiPKfXd9fl8OHHiBOx2e8UJYLFYxMzMDIaGhuD1evH888/j5MmTCAQCMJlM6Ovrw5YtW/C9732PU/LHjx/HmTNn0NzcjE2bNmHbtm3o6+vDJZdcUjUiSJ1/fve73+Gxxx7D6Ogop7SpHdr8/DysViu3u6MI05kzZ6BWqzE5OYlIJIL169eju7ubiXq1EQgEkMlk0NLSgu3bt6O/vx9er5f7klNP7ZaWFlit1qq3fyyVSgiFQnjqqacQCARgs9lgsVhQLBYhSRKKxSLUajXMZjPMZjNKpRK0Wi1yuRxEUUSpVCqL5AqCgKGhIV4XOjs7qzi71wcUAqjgj8avfvUrBINBXghTqRRKpRL27dsHk8nED6tGo4HJZIJOp0M2m8Xk5CS2bt1a3cErqDlQ5OyZZ57Bc889B0EQmMRls1nodDq0tLQglUrBYrEgm83yZmYymZBKpfDzn/8cnZ2daGhoQHNzc1UIRzKZxJEjR/j/zmQyHF3SaDTcE1Wn00GtVqNQKPDnpVKJI2mZTAYGgwEejwdTU1NYt24durq6KjaPUqmEeDyO++67D/fddx8WFxchSRL3YwWA9evX47Of/Sy+8pWvcJRTo9EgnU7j9OnTOH36NPbt24crrrgCX/va1zgqVWnkcjm88MILuPvuu5HL5WA0GjlSRFGmSCQCo9HIBw66RkTcjx49isHBQdxwww348Ic/jLa2tprQ0gUCASQSCdhsNqxatQrxeByJRALJZBKlUglGoxHr1q2DwWDA1NQU5ubmqtqzPJPJYHZ2FkeOHEFraysSiQQAcAtU4GzKOpfLIZ1OMyGkwx71xi4Wi6w5lyQJx48fR3d3N9rb22tOelBrUAhgDaNQKKBYLLJgmW76WsPw8DA3rBcEAQDQ3t6O+fl5jv7RXEiTlUqlcOTIkaoRQFosJEkqe0+XW8hVKhVvdPLPl0svyn8u194oeGWUSiXedA8cOIB9+/ZBp9OhUCjwAcLpdMJoNEKr1SKZTMJkMkGlUkEQBIiiyNEcnU6HM2fO4PHHH8dtt92G+vr6is9FEATMzc3B4XBw5CWVSnHqiiJl6XSa9Y2kfSJyCABGoxEjIyMIhULQ6XQoFosVLXDJZDIYGBjAN77xDWi1WiZNRFIjkQiee+45/MM//AOTW51OxySQIlD5fB6//e1vsWvXLrz3ve+tyjWJxWL44Q9/yLpRugYAOPLa0NCAtrY2+Hw+vu9o7SUiJUkSHnroIezZswdut7uqKXlCJpNBY2MjEokE+vv70dbWhlgshv7+flgsFrS3t8Nms3Hv3VAoVNXx+v1+DAwMsD48kUgw0aZ/ab9Qq9VM/jQaDdRqddlaWywWkU6nodVqkUgkEI1GIQhCTVyXWoZCAGsMdMoBzjZeX1hY4Adj48aN6OzsrInTphxerxcmkwkWiwVOpxNOp5OFvNQU3ul0Qq/Xo1gsIpVKoVAo4OTJk1UT6U9MTODRRx/FgQMH0NTUxAJjSiMC58igPNKhUqmQz+chSRJEUeTXUDpCr9czMdm9e/d51YMKLgyqyhRFEY8//jinQYFzVZcUCSQ9EAAkEgkUCgUYjUZ+/ym1evjwYVx//fUVJxvpdBrBYBBarRbr1q3Db3/7W9hsNtYl6nQ6/hoAbDYbRFFkMiiPALpcLiQSCbS1tUGv10MURYTD4YpVPU5NTeErX/kKVCoVjxMAp66NRiPm5+fxz//8z7DZbEysJEniA6HRaOR03je/+U3s3bu34tckGo3iiSeewMLCAtra2vjQYDAYmHCo1WqYTCbWlsoPe5SyLxQKsFgsSCaTePjhh+FyubBt27aKzmU55PN59PT0wOv1IhQK4YorrkAikcCDDz6I+vp6dHR04NChQ6ivr8ell15a9cryWCyGmZkZJtc+nw91dXV8wKH1QL4/0HVYWoWdSCSQzWbhdDqRz+exuLgIn8+HVatWVWNqrxsoBLBG0d/fD5/Ph2PHjmFychI6nQ5jY2MwGAy46aab0N3dXRPRwEAgwGF40syYTCbevBOJBH+dyWQgiiJyuRyfxquFiYkJPPjggxgfH8fmzZuRzWZRKBRgMpnKIgIqlYq1QRShAs6RlVKpxBYXuVwOGo0GyWQS6XQasVjsDUEA5+bmMD09jdWrV6OpqWlF/y+SBvh8PrhcLj4gUPSYQARDr9ez5hQAF4OoVCrkcjmEQiGMjY3B5XJVdMOLxWIYHh7GxMQE1q5dC61Wi6uvvhoA+ABEY6cCCr1eD61WyxFAg8HAGrQHHniAK1SBs5XRlSKAoihiamoKGo0GuVyubN2hg0+xWMT+/fthMpmQz+dRKpXgcDj4MDg7O8vRzfn5+aroz+LxOF588UWusna73TCbzRBFkQlFqVSCJElIp9OcuZATEZ1OB7PZjEwmg1wuh/n5eU5dVhvRaBTBYBAulwvr1q3D/Pw8JicnsW3bNhw+fBgtLS3YtWsXjEYjzpw5U3X9dTabRTqdhtVqBXBWMjExMYGenh5YrVaO+hFINwucy75oNBoUi0UEAgHEYjHY7Xbo9Xqk02kkk8nKT+p1BoUA1hjUajUGBwfxr//6r1Cr1Th58iR0Oh1uuOEGuN1ujIyM4Ne//jU2b96MSy65pOol76dOnQJw7uGkTVmub6JqOqp+JDEv2XVUAvKU2f79+/Hss8/CYDBg9+7dvMjIyQal6Oi9pTED51JFFJ2izYx+nxY0p9OJ5557Dk1NTVi3bl3F5vpKiEajSCQSCIfDyGazyOVyvNlRqp42a0EQMDo6igMHDmBiYgInTpxY0bGlUikcP36cx6XVasvSQAS6HqVSCRaLhe83OlxotVrk83nk83mcPn0aPT09FSWAuVwOer0eu3fvhsViwejoKDo7OxGJRPigYbVa+cCh0+kgCALrn+iwoVKpUFdXx5qmjRs3oq2tDTabraJzCYfDqK+v53FROhQ4FykPhUJcuEJCfqrMJAJFWkdBECru05hOpzE4OMgFEBMTE2hubobRaGQdGR00HA4HfD4fWltbkclkmEyUSiVkMhnWpc7NzSEej1dsDheDyWSCw+FAU1MT2wm99NJL6O3txcsvvwybzYampia0tbUBQNUJYDqdRjgc5vdfo9FgYWEBDoeDpQbAOQ/JfD7Phw0i5oVCAdFoFD6fDyaTqSzFTYdCBReGQgBrDKVSCf/zP//DppfNzc1wu92wWq2YmppCPp+HKIo4fPgwJicn4XQ60draitWrV/ODXUmEQiGOgpGomnRKtDmn02kAYD0XlfhXcuGkzebEiRN48MEHcfToUd58SQtDGwBZkBChAAC9Xs/kgogskZRCocD6FODc6XR6ehrf/OY3ceedd9YEASyVSkilUhgdHcXY2BhmZ2e5KCeVSiGRSECr1aKurg4ulwu5XA7BYBDhcBh1dXUV2TCIcOr1euTzedZbLSXpwNnTvyRJAMBaQHnKjnzpqrFJx2IxjI6OIhKJ8Pvb09ODpqYmlg7QPVYsFuF0OjkKTbo5ihBqtVqcPn2abZZKpRI/9ysNer+JPOfz+fMkEfQvRdDo82w2i0wmA+CcjKJYLMJut2NgYAA9PT3o6OhY8TkQRFHE4uIiWlpaoFKpEAqF4HA4ylKMFHWiaH4qlWJdKaUf0+k0JEmC0WhEJBLhOVYbZP1SLBYRiURw7NgxZLNZfPSjH8UvfvELzsDQgbXafpKCICCRSHBk+9SpUxAEAYFAAAaDgX096SAor9am+yyXy7FFlNPpZO1pIpGoaobp9QKFANYQyBBzbm4O11xzDebm5uD1evlBLRQKMBgMKBaLSCaTkCSJK6Si0Siam5srGg2kak3axEi3QQ8t6RnlqTvaBIrFYkUXTnn07/Tp00gkEmXVZpRKIHIqFxtT9bK8GAEAkz/6Wp56JKIuCEJNkD85XC4XPB4Pkskka9IoLQmAI4CBQADpdBqiKEKr1WLz5s0rPjZJkuDz+aDT6ZDJZFjEvVzBDW3ItDnLQQcNjUaDWCzGWrRKQafTse7VarUil8shFouxN5ter+f3niLKmUwGmUyGvdAMBgNKpRLsdjusVivMZjNcLhfsdjtHn1caiUQCXq+XCyEymQyPXy7Sl4MiNnLJBADeyA0GAwYHB3H55ZdXjACKoohYLMaHArrn6VmVRydJzkLFasv9LUmSYLFYOF1MWsJqQq/XIxgMwu/3Q61Wo6GhAdFoFH19fWxIPj4+DlEUYbfb0djYWNXxiqKIZDKJ1tZWmM1mXoMzmQxrsumwLZfb0D1Fz7i8AISeJ/obCi4OhQDWCCRJwvT0NH72s58hGo3CYDCgu7ubFxm9Xs83tNls5s1NkiTEYjGEw2G89a1vrXg6WL5AkghfXr1MHwCYYNBCS5HBSkCr1WJ8fBzBYBAdHR1QqVQ4c+YMR2MohUhFIJReSKfTXNFJ46dOJ0QOKS1B/lSSJLHGa9OmTeju7q7YPC8EmsPRo0cRj8e5So4IFKXojUYjHA4H9Ho9W2Gk02kkEomKbHD5fB6xWAx6vZ6LO5aSjOWqsOXfo82CUq3y1GolQYcFQRAgCAIflChFrdPpOKoBoKy6kX6fnh36GVVJ5/P5iswhEolgfHwcmUwGRqMR6XQabrebxyf/d6lgn0AHKqrWzufzmJmZqWiEJp1OIxAIcKoRWJ6o0vtL9x29hkDFOfT9YrHIVivVJoAAmLQGg0HodDp0dXVxpoOKXshSpdpG1rlcDtlslt83s9lcFlGWXxfKAMgPe3S/0TxIy0mfKwTwlaEQwCoil8uxN1gwGMShQ4fw29/+Fh0dHRgfH8f69evR0tKCTCYDSZI45UhGyvQAJRIJ6PV6hMPhiqSF5JBXKlI6hUTt8hO13BrFbrcjGo3yHCqVinj66afR09ODyy+/HMeOHcPU1BT3LTaZTGw4SmJ3Ar3fFBkjjRNt1uRDR0SJUshdXV245ZZbaqJYJ51O4+jRo/jRj34E4GyKcs2aNWhpaYHb7UZ9fX2ZLi2TycDhcLDeiQozVhqFQgHZbJZTwHQd5FEA+efA+Rs4XZ9cLgeLxcL2MJWEPOJE/39dXR1vUmazGTqdjudHhulkFE3FIERik8kknE4notEoLBYLWlpaKjIPIgrd3d2IRqPI5/PYsmULhoaGyjSaSyG39CGNY0dHB44dO4bOzk60trZWtFNDKpWC3+8HAJZ2LM1O0Ljp+aWWfbR+0edySQJJWeLxeMWrmpeCpAHFYhHBYBCxWAxvfetb+XkxGo1wuVwcTCAiXy1QcIAi4RaLhce51I2BUvO05hIBJwJI3YHoOVcI4KuDQgArDIpMZLNZBINB7hQwMTEBURRx1VVXweVyoaGhASaTicPbqVQKNpsNgUAACwsLvEHa7XbY7XYIgoDjx49XnAACZ9NEnZ2dcLvdEASBHzx5JIM+aLyxWAwqlQrhcBhNTU0rbgWTz+cxNDSEj3/84+jr68OGDRtgNBrxj//4j2hra+M0usFg4JOo3W7Hrbfeire//e2Yn59nQhEKhXD48GEcOnQIwNlKaNLV0MJlt9vR3d2Nd77znSs6r1eL6elpfPazn8WZM2dw6623IplM4tprr8UVV1wBl8t1wd+LxWIIhUJ47rnnEIvFymyKXmtQRaxca7nUDkJ+T8n/lW8W8u4Z1J5Mrq+rBGw2G7q6uiCKIhepzMzMYHJyEgBgsVg4gl8qldDc3AzgnKUNRaMBwOPxwOVyYePGjejo6IDH46mYgH/VqlW48847ceWVV+LBBx9ENBrFbbfdhk9+8pMQBAEGg4EF+XIs9WizWq34x3/8R/z7v/877rzzTlx77bUVLcqhggMifdlslomEPApLkX+j0QhBEMoyGHQ/EYGieSeTyZqoOKXCulQqBY/Hg3Q6jYaGBv6Z3W5HPB6HIAior69HXV1dlUd87nnVarX8PMgjtES65SbQ8gwNkXVqB0fR9Vwux/pgBReGQgArCDqZ+Xw+3HPPPchkMrBarQiHw5iYmMCqVavw4Q9/GJFIBAsLCxw5ID+9xcVFNDQ0QKvVwuv1YnFxkR9ki8WCkZERvO1tb6vonGjho96MgiDwiT+fz3Mxi16vRzweh8PhgMvlQiaT4QquxsbGFd+Y+/v7cdlll533f9GJk95jIrCCICASieDIkSPI5/Ps6UZjHxoawuDgIFc822w21klRVIfSEbWAZDKJ06dP44tf/CI+85nPsJfhK0VfU6kUd33w+Xzwer0rdsgQBAHJZBJ2u50Xb1EUORVN76ecXCxN59HP5cU5dOAib7dKIJvNwufzIRAIwGKxsBbL5XIxwaM5mc1mOBwOAGcrtEulEsxmM6xWK6eQH3vsMezatYsjsUajEatXr67IXEwmE3bs2IEdO3YAAMLhMGtiqcJXDnmUlsT7yWQSb3nLW/CWt7ylImNeClEUkUqlYDQakc/nuYp3abqdCEc2my0r+CJrG3lEkOZOnqDVBhk/FwoFXHHFFQgGg7wWWa1WWK1WDi60tbXxPVcNCIKATCbDXpL5fJ512Xq9vozoAed6g9P7L5dKkB41mUwyiaR7U8HFoRDACoG8ir785S/D6XSis7MTg4ODSCQS2LRpE97znvewMNztdqO7uxtHjx5FLpdDY2MjWltbEYlEMDQ0BFEUuQclVVLJ/eoqCarAAsB2CfTgUvqHNoRUKgWDwYCWlhaYzWZEo1EEAgGsXr16xQng1NQUbrrpJo52EUGoq6vjxUKv17PRMDUkf+qpp/DII4+U/S2VSgWLxYL6+nqYzebzdFvUoqiaKQiqGDUYDNBoNOjt7cX//b//F/feey8+85nPYGxsDMFgEL29vRcldMeOHYPX68X27dsRj8cxMjKyYgRQkiQkEgnebIFy/ZjcGkUuCF/OMJasIigNSd6MlSKA5MVGXnharRYDAwNYWFhg0keVsm63GzabrazwRqfTweFwwGw2w2QysZFva2srVCpVxTwAAZQRHjrYya1dLuSPSZ8v3YgpclZJaYQgCIjFYtDpdEwQHA4Ha3nlpEKuAwTOVQaXSiXubJRMJtnIm2xHqg3yLhwbG8PPfvYzSJKEu+66CwDYSUKSJJw+fRqzs7P40z/906qN1ev1IhAI8Hobi8WwdetWLjii936pCwCBTOFVKhUSiQTWr1+PVCqFeDzOfcLT6TSy2azSjekiUAjgCoF6SqrVamSzWcRiMU71zM7O4vLLL4fP52Nz3UgkAq1WC4fDAaPRiOPHj8PpdPJNDZz1bbr00ksxMjICh8OB1tZWxONxrsxLpVKYn5+vqB0MVS0C56IwVIpPlXRy53ZRFFkvMzk5iXA4vKyG6LUGVVbSWCndSBsRCetpoS+VSqxbouo0efUZpRpoE9fpdGxzQ3OuZFqI0lqpVArFYhHxeBzT09Po7u5GZ2cn6uvr8Y53vAN333035ubmOO2VSCRgtVovGA2YnJzE7Owsuru7kcvlMDw8jOuuu25F5kCRumg0WtZKTH5NCJTmBXDeAk8FL6QhFEWRvQ8rpdOKx+MYHx/H1NQUWlpamBB2dnZCkqSyIiOj0YjW1lau3KSoND0vDocDdrsdw8PD8Hq9KBaL2L59O9avX1+Rucj1bgSqMJWT8uVA35en4+QHpkqBIoAURSISJ5cSUIqXPDApA0ORTFrLV69ejePHj7Of4ezsLLxeb0Xnsxw2bNiAjo4OTE9Ps+ULRfgbGhowNzeHxsZGXH755Vi1alXFr4Ec0WiUtePAWUL4wQ9+EM888wzryq1WK7RaLd87dBAEwH2zTSYTFhcX0djYiEsuuQTHjh0r60aTTCYVAngRKARwhfDSSy+d12aoUCigra0Nl156KX8uSRKCwSBH/yj1S9WnJMwnn7ZisQiHwwGbzcbedk6nE5Ikwev1YmpqqqIEkFLUZPgsTwnRg0cEkLQasViMU2CZTKYiBJCq90h7RRsCESEigUvJhrzicmmUiTYHAGXEklKQK1nlTBELiqIKggCr1YpvfOMb+NjHPgaXywWTyYTp6WkUCgWsXbsWTqcTf/mXf4kDBw5g+/btSCQSyGQyHG1aiocffhgTExOskyKt6UpBkiQkk0luI0gEjjax5VI6RMwpnUrRmmKxyBXZVH1aSdshi8WC1tZW6HQ61NfXo6enByMjI2wErdfroVarkUwmYbVamfz6/X6OMFssFtb66vV6NDY2wuPxcL/aSkN+78s341eC0WisuuUI2bWQnKBYLGLbtm1l1f3ywgK5KbpcZiAIAjZs2IDx8XG2tKHir2qDLJPonie9IgA0Nzcjn88jHo/jzJkzAIDrr7++amOl55y6sRSLRXR1dcHhcPD1kDsyyHsBq9VqPrzT9QTOShUouEDkUSkEuTgUArgCGBoawvT0NOrq6th3ymQyIRaLQa1Wo66uDuFwmB3pSaeRz+cRiUT45FksFjkVKT+9lkolRCIRpNNp1tmRpquS1irAuZTnUpBwl6I4lD6SRwsrWYXW0tKCgwcPYu/evXC5XJxupFQhkTkiEUsF4nL/P3m6a6ntCL0fZKOyUt0O5EUPZBxsMpkwNjbGekyXy8WdWvr6+mAwGPDOd74Tjz32GKanpxEMBqFSqZYtAkmn03jggQcQi8XgdrsRCoVgsVi4WGElQL5gwNnFPBAIcEcAeYpdXsyxlIQQIcxms/wMVdo6heYSCoVYA0hVv42NjXxo0uv17O1XV1fHAniKPhPJMpvN3CKPnqdKpoAJv691CBGqpevDxSKGKwVyUTAYDKzvu+SSS3DgwAH2V6XnFzh3uKP50s/o0Er+gUTeayEFHAqFkEgkIIoiH6yp0GPDhg1lPrLVNkkWBIHNtCmA0N7ezsEO+h6tp/LDt3wP0Wq1TCAbGxt57yTtr9IN5OKovj/FGxCJRAJ1dXVlrWyoTRot5nSqlze3T6VSUKlU7IuUSqUQiUQQiUSQSqXKKiTp9EO2HVarlTeXSoJsKoBzXmW06dJY5Q8wFU2Q0L1SVZm9vb1YXFzEiRMnEAqFkMlkEA6HmeQBKCMK1B6NDJ1Jl7acNo2imRT9pJOqRqNZkaiTXN8mj1LodDpOcyaTSRaz+/1+zM/PAzibCtLpdJidneW/4/P5cPLkSUxNTWFmZgbT09M4c+YMtFotOjo60NbWxt0sent7X/P5EOi9pvuIpA1LezHLN+rlChDIu1CSJL6+lRaE0zipypeeXzqokT0MVZOSLo0+qIqRiIi8MEFuj1OLWI7g0bzp55UG/f9arZZNg1tbW/n+kEf+5YfapU4GFLWl9YEIVS1cj8XFRY6Y031DUUHgrK7ZbrezDrKaoPud9j+j0Qi73c4HIOqLvZzRO62xdO3ILaOtrY2fG/meq+DCUCKAKwCr1Qq73c7+fKQVo1M9PaRGo5EjfFQ0UVdXB7VajUQiwYadRKDkmqj6+nommPQwVCMqQE7swLmQPXlsLddjl7RzVIlXKSF4S0sLenp6MDExAYvFAr/fj2AwyO8h6fcAMNmTW4rY7XaoVGc9Duk6XsgShcggEcjXGvF4nKNI4XAYgUCgbBx6vR7JZJIraNPpNE6fPs0V53V1dWWdY5LJJIaGhtDQ0MD3YyaTwW233ca6p2AwCK/Xu6K6MyI+arWao2FGo5GfD7lvm7wARF4QQp9TNHwpMa8USHNqNpv5GpD3JREQg8GAUCjEh45CoYBAIMCRGvKlzGaz0Gq1sFgscLlcnBmoNOTPsxxysrd0o6b3nwpx6urqXlE3uBIgQkAdZjweT1l7R/nrKAIrXwOAcwb2xWKRi0Hkv1dtUJbIbDaX9SoHgDNnziCVSpW1WKwm6FBGzzr1T5fbadFeSZkUuh7UOYdIPZHIpqYmXkMI1ThsvJ6gEMAVgNfrhSiK6Onp4RMXeZql02kIgsC9VinFSA8reVAtjezQ79MJhwosKNUqN4iuJDQaDaLRaFmlJs2DiCtVxFI1F0VC4/F4WXpvpXHDDTcgEAhgYmICp0+fRjqdhsvlgsFgYL0Yme/KF3+DwYDGxkbu1qLT6Vh7Iq9ApQ2SxNcOh2NF0tyzs7NMvJPJJARB4Pf/He94B6anp3kTW7NmDVKpFJLJJEZHRyGKIjZs2AC1Ws0HEFpASXOaTCZhNpuh1+vZhJzsLpqaml7z+RAoAgicvX88Hg8aGhoQDoc54k3jXLqwy0kg6U3JqLgaBDCZTGJiYgIzMzPcdquxsbEs1avX6+H3+9Ha2sqpunA4zK339Ho9isUiLBYL7HY7jhw5ApfLxbrCamFpVGW5TXbpNcpms3z4qCbUajVSqRSuv/56Nq6XkzciGrSu0r20NArl8XiwsLBwHkmsJsxmM3edcbvdZfNKpVIQBAEzMzMolUoV85G8ECigQXsaeUKSxZDccF++xsqLdQBwRyydTgebzVbWX7sWDPhrHQoBXAFs2bIF+/btw/Hjx6FWq2G32+F0OmGxWNDU1MSbqPz0SakvuYM53fy0uS+XKqKm1+FwGDMzM8jlcrj66qsrMk+VSoX/+Z//QTAYRDKZxI9+9CO88MILfMoEwOF5SpeUSiU0NjbiP/7jP3Dw4EFcc801FRkrcJZU0EYsSRKOHTuGM2fOoKGhgS0i5MJjuR3E8PAwt3ZbzkOPTqtUZOLxeHDFFVesyDw2bdrE0Qi5Ca8kSdi9ezfm5+c5VUsaRCK5FLmQk3Xa6AAw+chkMojH4xzBpsKkldI00vjJRohSOxeqGJWbxsrJBllIaDQa2Gw2fo081V8JuFwubNu2jbWX69atg9frxZkzZ5BIJPgQEQwG0draira2NhSLRfj9fj4gWa1WGAwGeDwe3HbbbRgYGEBHRweMRmNVikAIlJoHyj0Zl0IesaHDClCdKmCVSsUH6XQ6jbVr17IGmD7kEgNaj+WFXnJCZbPZeJ0gHVu1ISc9VGhE0Gq13Eygvr4enZ2d1RomgHJJgEajQXNzM1QqFebm5rB27VqeB+ne6V/54ZykNpIksdaPDq7ybI6CC0MhgCuApqYmfOxjH0Mmk0EgEIDf70c4HEYsFsPAwABisRinp14JdPPTJkagkylFBywWCy655BJs27ZtJad2HnQ6HVpaWhCPx1EqlbibBpGpsbExbu0jCAIL+00mU0XJH3DONBgArrrqKqxZswa/+tWvMDg4iFAoVNa6i3QkVFxhMBjwjW98AzfccMOr7mCwUpucvC+mfE60CV2oCpy0mn9o+nAlC0AAsJG4zWaD1+vlAwMAPkQs3bDlnRrodaRLNZlM7KlHbRMrBb/fj5deegmRSAROpxM9PT1obGxk8b3JZIJWq4Xf70dbWxun38kHkFLIdB9u3LgR9913H+bn5/m5rxaWEkD6d7nUsDxLQdHdaqTllhZ22e12HDt2jA8Lcl0pAI5+A+cOUUQ4EokEgsEg/4zIZbVBLdXoUChP8xJpSqVSKBQKPP5qgZoFSJKETCbDB9b29nZ4PB6WElHx1FJPRuBcAaLBYMD09DSAs1KfpZZdCi4MhQCuIMxmM4vol2qUXu0i+EokQh4ep4KEasBut+OTn/wktm/fjueeew5nzpxBKBTC17/+dfzv//4vfD4furq68I53vAN79+6tyhiXoqmpCXfeeWdZtajcF4xAG4fVai3rHqDgtQWlgMmWRl70QJKCpa/X6XTnPU+SJHHHg7m5Od5oKlmp2dbWhquuugpTU1NceHPixAnWa9rtdmi1WoRCIbaEiUaj8Pv9KBQKsNlsbFKu0+mwZcsWdHd3l3VzqBaSyWRZYQ79u9yaJo+k0/tfDQ2gw+GAx+PByZMnYTQa0dfXh3379vFBVV74sXQuVNBGc9HpdOzokEql0N7eXtXrQXA4HKivr4fFYjmv7WEsFoPJZEJjYyPrz6sJOrwS0SMnAjJEB87tbWSnJi/MofuHvADpYGW1Wvk+q1SB4esZCgFcYchPLG9kqFQqtLW14ZZbbsHevXsRjUbxyCOP4PLLL0c2m8WaNWu4ZZ3NZqv2cAHgvFOyguqCIkUqlQqZTAZut7usKbycNFDlJn1fTirIcqinpwczMzMAwFY/lUIul0M4HMbg4CA8Hg+2bNmCcDiMYDDInpkUkSGjZzK9pVS7/G9pNBr4/X6k02k2Ka4W5FXxr5bEya1HqqGZc7lc6OrqwsmTJ7n39/j4OBd3ASiLBC7VOcqlONFoFA6Hg1PaHR0daG9vr+h8loPT6eRK2qWWYFRVH41G4Xa7qxpBBlBm1wIAPT09nLYmTd+FoqpLi0GsVitnZerq6uD1ejktXwvV2bUMhQAqeM2g0+ngcrngcrnQ3t4Ok8mEn/zkJ7jjjjvQ1dX1in1nFby5IddaUTGU3HuRNgy5QJ9+T27iS693u91cNU9/t1IwGo2oq6vj6kZqxUUaRzKypZ7TJHxPJpNsM6JSqZBOp5koyk2Mq1ndSGnpCxV/LAVdH+poVI0IOnlzUpp0zZo17MMq1/5SNBAoP3BQBTr5h37qU5/Ct771LU4N1wLIPJy6E8nHZTabuWuTzWZb1vuzkqD7h95Xm82GSCTCUX2DwcDXhLIuS30AiQQaDAYmtC0tLQiFQvxzRQN4cdTGnavgDQeNRoM1a9bg0Ucf5bSDAgUXA2msAPDJnjRLS0X6RADlTd/lHoGJRIKj77RJV5J4UJUvRV68Xi/27t2LHTt2oLe3F+3t7WhubkZLSwvq6uq46MPlcsHhcMDhcKCurg6tra3YuXMnBEFAqXS2m02pVKpKeouIEWkxl8pP5P6X8hQffVSzW0Y2m0UkEoEkSZAkCV1dXfjQhz4Et9vN/qpEFpYSOirAy2QySCQSuPnmm3HbbbcBOEuGw+EwIpFIxee0FFQ5ToVRS8lPqVTig0m1U9bLaflisRgTPrnPH1m70DWiz+mDuktJkoSWlpYywqhoAC8OJQKoYEVBPRkVKHgl0EJPi3ZLSwvm5+chCEJZIRQRPRK7L40MktE6bQDkZVjJtCm1DaPWb+Pj4/jKV76Cvr4++Hw+ZLNZLkwxm82wWCzQ6XSIRCJckd7Y2Ai3242+vj5MTk4iFArB7/ef50FXKRABTKfTZSSQUtRLU8JyolosFhGJRKoWudRqtbDb7WxqbjAY8A//8A9IJpPo7+9HJBJhkg2c6/xB81Opzpoqd3V14XOf+xzq6+vR1dUFURTZfqRWID8IEag1ZSaT4cryakJeICT/3saNG9HQ0ACz2Vz2fbnEQ37goPfd4XAgl8uxjy5FyhUCeHHUzl2r4A0HlUqFtWvXKmJcBa8KyWQS8/Pz7Dm4a9cubN68Gb/61a+QyWTKNFrUBmqpNxhFFq699lreoAuFAiYnJ1kPWAlQL+DOzk4YDAZcfvnl0Ov12LJlC7Zs2fJ7/73W1lYUCgV4vV4AwOrVq1/rIb9qJBIJJrDUwWg5kK+kyWSCKIocJavGgXDLli2sCXW73TyGf/7nf0Z/fz8effRRPPvss1hYWIDL5SrzMZUkCR6PB+94xzvwiU98gv/m17/+dRSLRXR0dFTd35BAkcp4PF6moXM4HDh16hTi8Tj6+vpWvKL/lSA3cqdI4KpVq/ClL31p2YKiYrGITCZzXtTZYDDAarXiAx/4AKxWK5utk+ZX6QRycSgEUMGKYseOHTVhkaCg9rF582Z8/OMfh9frRTwex9vf/nZotVq8//3v5wKKUCiEubk5BAIBFn7n83k4HA60tbWhq6urzKz6iSeeQDQaBXB2E6wUHA4HrrjiivO8IH/f6ld6vcfjwY9//GMMDg6ioaHhDyKRfyzoILd161Zcd911KBaLsNvtHI2kTVtepUmG3JFIBFdddVXVsgFqtRoOhwObN28+72c7d+7Ezp078ZWvfAWFQgFnzpzBE088gYaGBvT29mLNmjVlhu40t2pcg1fCzp07sbCwgFOnTpXNta+vD3Nzc7BYLFi9enXVCWtnZycEQWCzdsIfm5pubGxEa2sr8vk8f67gwlCVlF4pChQoUKBAgQIFbyooRSAKFChQoECBAgVvMigEUIECBQoUKFCg4E0GhQAqUKBAgQIFChS8yaAQQAUKFChQoECBgjcZFAKoQIECBQoUKFDwJoNCABUoUKBAgQIFCt5kUHwAq4DlvMAmJibw9a9/nfs4zs7OYtWqVRBFES0tLXjLW96Cq6666qJ/Q8Frh7m5OTz99NN44IEHsG3bNvh8PjQ0NGDjxo3Ys2cPOjo6qj1EBa8zPPTQQ7jvvvsQCAQAnG1PNj8/j5aWFhgMBoRCITQ2NnKHk9WrV+PP/uzPsHPnziqP/OIgJ7FIJILDhw9jy5YtuOeee7B3717s3r27yqO7OHK5HE6ePImXXnoJR48excTEBNRqNRobGxEMBuF0OtHQ0ICWlhZs2bKFW8DVIubm5nDkyBGcOHECXq+XW/FddtllKBaLUKvV7J+5Z88erF+/vib2keXGIEkSBgYG8Otf/xr33XcffD4fNm/ejHg8Dp/Ph9WrV+P222/H+973PnR2dpb9rrxNoYKLQyGAVQDdmIVCAX6/H7Ozszh+/DhmZ2exbt06uN1u6PV6bNq0CcFgEJFIBAMDA9izZw+bKsvNVhX88QiFQpidncXQ0BAikQh6e3vR09ODoaEheL1euFwuuFwurFmzBiMjI/jf//1fBAIBbNiwAatXr8bWrVths9mU66HggvB6vQiHw8hmsyiVSohEIvD5fJifn4dWq4XJZEI6nYbL5YLJZMLi4iIOHDhQ0wSQujbkcjkea0NDA3bs2AG9Xo9oNAqXy8UEpNbwta99DS+99BKCwSC3EMzn8wiFQpAkCcViEcFgEKdPn8bg4CCGh4fxhS98odrDXhZHjhzB4uIi3G437yEmkwlWq5V76rpcLuRyOZw5cwbr16+v+nol38MOHTqEI0eOYGJiAouLi8hms1Cr1Whvb4fP58Pk5CSSySScTidMJhMOHjyIF198ETabDR6PB5dccgne9a53wWAwVK3l4OsNCgGsEGihVKvViEQieOqppxCPxxEOhxGPx7n/Zzweh1qtRn19PfL5PEqlEgRBwIkTJ/Ctb30LjY2N2LBhA7Zv315T/Sdfj5AkCX6/HwsLC1CpVNyrVK1Ww2g0YsOGDfjiF7+I++67D11dXdi7dy86OztRKBTQ1NQEt9uNNWvWQK/Xw+fzIRQKobW1FQaDodpTU1CD2Lt3L4aGhjA8PAxBEGAymdDW1oZSqYSmpiZks1nodDqYzWYYDAa0traWRf1rERRpkSQJjz/+ON7ylrdAp9Nhw4YNGBsbw9zcHFwuF2/ytXZonZ2dxdzcHLLZLPebJhKo1Wq5pZgoirxW1yqi0SiKxSJsNhv3OzYYDNwaLZ1Oc+AgkUggFovB6XRW9ZrQWH7961/j9OnT8Hq9SCaT3PZNp9OhWCxi8+bNGB4ehkajgd1uR6FQQCKRQD6fRzqdRiwWQzweRyQSwS233MI9nxVcHAqDqDDy+Txeeukl7N+/H+l0GpIkcd9Ji8UCURSRzWah0WgQDAYhiiIMBgMymQz6+/thtVoxNzeHRCKBXbt2wWazVXtKr2sUCgWMjIzA4XDAZDLBbDYjHo9jZGQE4XAYuVwOV155JSYnJzEwMABBEDhSQD1FZ2ZmMDk5iZ07d5Y1ka8VRCIRjI6OYnp6Gnq9HsViEU6nE5deeinsdnu1h/emwfr167Fz507Mzs4imUxypKZQKKCxsZFJSDqdhtlsRl9fH3bs2FHtYV8QFGWRJAlTU1O8VpVKJdTX1+PFF1+Ez+dDb28vzGYzH4Jr6dlYs2YNhoeH4fP5mABSCtFoNHLP6Vwux2n5WoTX64UkSVCr1dDpdFCpVCgWiygWi5AkCYIgIJPJQKvVolgsIp/PY2ZmhtspVguJRALPPPMMHn30UR6/Xq/n+yiXy0Gv16O5uRnhcJjbPhLBdTgcPNdgMIinnnoKWq0WN910E7q7u6s6t9cDFAJYYfh8Pjz55JMQRRFmsxlmsxmSJHHTarPZDIvFglQqBaPRCJ1OB6fTibq6OoiiiFgshhMnTmB+fh4NDQ1Yu3atEnH6AxCLxRAMBqFSqeD3+xEKhdDW1gav14vp6WkEg0HEYjFEo1F885vfxPDwMO69916sWrWKT9YulwtmsxmRSAQNDQ3YsGEDhoaG0NnZCY/HU+0pAjh7vx0+fBhPPfUUDh06xItlV1cXisUiLrnkErhcrmoP8w9CJpNBLpeD1WrlPrUXQi6XQ6lUglarrWoq8pJLLsHvfvc7LCwsMPlOp9MIhUKw2+0wGo0IBAKoq6tDX19f1cb5akAEMB6P4/Dhw7j00kuhUqlQKBSg0WiQTqcRDAYxOTmJjRs31mQa+LrrrsOhQ4cwPj4OnU4HnU4HjUYDrVbL0TKKChYKBWzbtq3aQ14Wx48fhyRJMJlMAMBRTJVKxT13S6USMpkMdDodSqUSJiYmsGXLlqpG/7xeL+69914IggCn08nku1gs8vNqNpsBAF1dXcjlcjCZTDCZTPzMq9VqztykUik88cQTWLVqlUIAXwUUAlghqFQqiKKIJ598EqlUCgaDgR88eiABwGKxwO12Q6vVwmazoVgsIpfLIZfLQa1Ww2w2Q6/XAwD6+/ths9nQ09NTtXm9XjEwMIBf/epXqK+vx8zMDBYWFtDR0YF8Po+GhgZcddVV6OrqQqlUQmdnJ772ta9heHgY0WgUExMTmJubQyQSQTweR1tbG9atW4evf/3rWFhYwKc+9Sm8853vrPYUIYoifv7zn+Ppp5+GzWbDNddcgxMnTkCv10Oj0eCb3/wmPvrRj+KWW27hjeP1hKNHjyIYDGLr1q1oaWmB0Wg87zUUcaJobl1dHUejqrHxrV69GnV1dUilUvD5fLDb7dBoNCiVSkgmk0gmk0ilUnA4HOjt7a34+H4fEJlLpVIYHh7Gn/zJn5T9fOPGjTh48CB+97vfYcOGDVCr1TUV/QPOEvLm5mZIkgRRFGGxWHg9LhaLXEgBADabrSYjsqVSCadOnYLFYoHJZIJKpUI+n4ckSUilUshmszCbzVCr1Uin0zAYDLDZbPD7/UzWq4FsNotoNIpMJgOHw4F8Pg/gnEZeXshBxNXpdJaR80KhwJHOfD7PGQ76fq0dOGoNCgGsIPL5PIaHh/lk5vF4UCgUIAgCh+7tdjsMBgO0Wi3rH+ghtdls6Ovrw8LCAhYXFzEzM1PTmpRaxtVXX42WlhZ8+9vfxv/3//1/F3wdpRZOnDiBq6++msm3HKVSCU8++SS8Xi++973vYePGjSs48lePRx55BPv27UNTUxO++tWvnhdR+uAHP4jvf//7CAaDuPPOO2tuc74YisUi/u7v/g7j4+MwGAz45je/iT/90z8tm4N8A/H5fEgmk9BoNFUlgFqtFk1NTXA6nUin0zCZTDAYDNDpdEin01Cr1TCZTGhtba16BEP+Hl3o/crn88hkMigUClzxS4Ri27ZtmJubw2OPPYZkMlmzcoNt27ZhYGAAU1NTMBgMZWuuVquFKIro7u7GzTffXO2hLotwOIxCoQCLxQLgbERZFEUOHOTzeWg0GuRyORQKBYiiiGQyCZPJhKmpqaodNBKJBObm5ji6qtPpuAKeIoDA2ec4l8vB6XRyhJmig3q9nokjpZAXFxcxPz+PRCJR9RR3rUMhgBVEoVDAqVOn0NzcjMXFRbS2tsJoNEKj0XB0z263w2QyIZlMwmg0QqVSwWw2Q6VScVXdqVOnYLfbEQwGkU6nqz2t1x1isRhefvll7N+/n61d5ufnIYpiWfUYpRXWr1+P06dP80JDkQz5KXTz5s344he/iKNHjyISiWDdunVoaGio+Nxo48rn8/j2t7+NbDaL66+/Hn19fWUaLJVKhc985jP48pe/jJ/85CfQ6/X4i7/4i/P+nnwRrgXIx9/T0wO1Wo14PI677roL//Vf/wWPxwOr1YqrrroKmzdvxoYNG3Dy5Ence++9aG1txZ49e6o9BaxatQpdXV04ffo0BEHg+0ir1SISiWDt2rVYt25dVcdIqTi6Z4Dye4B+PjIygkcffbTs+/KoC1Wg/vCHP8RnPvOZZf+vausCu7u70dXVhcnJSS6Y0Gg0/BwBwObNm2vWAkar1cLr9fJBQhAE1mNarVaWGGm1WiQSCUQiEbjdbmzbtg0nT56sGgGUJAnxeByhUAiiKLIEAjh3eKMPo9EIrVbLhZFEBEVRBAAYDAaIoohoNIpSqYRUKqUQwFcBhQBWEIVCAbFYDDabDT6fD16vFw0NDTAajdDr9dDr9bDb7RBFERqNBiaTiQlioVBAKpVCNBrF4uIimpubAeB1UQmcyWRgNBoxMTGBUqmEVatWXTDtUAnCQVVkhw8fxvT0NG644Qb83d/9HdRqNRwOx3nvKS08BLKHIL1QsVjERz7yEfz1X/81JEnCX/3VX6GhoaFiBJAiFgC48u1DH/oQRkdH8ZWvfAUf/vCHUSqV+IRMKcd169bh5ptvxk9/+lP8v//3/1AoFPBXf/VX/DcB1FQKRU7ORVHE0NAQjEYjDAYDcrkcfD4f5ubmIAgCjh49el5F/dve9jZks1mOlFQLbrcbdXV1ZRFKIh2ZTAYdHR3o6uqq6hgpBapSqZZ9Vum+8Pv9GB4e5vHSNaLfpZTcoUOHLvh/VftwsXfvXszOzuLIkSMQRZGLKHQ6Hfuw9vb2orGxsarjvBAcDgfq6+tZHmQymZDJZDA9PY1kMskZiUwmA7PZjGw2yxroaqV/gbPPMGn/iAiuW7fuvPuhVCpxdBAoj+xTOjgej2N+fh4OhwNOp5PnqPi1Xhy1zx7eICgUCqx1mJqaQigUQiQSQV1dHXQ6HaeA1Wo1f04PNIXF9Xo9crkc5ufn4fF4UF9fzxt1reLhhx/Gd7/7XaxZs4ZFyrt378Z73vOeZV9fic1ArVZj79696OnpgSiKcLlcaG1tRTgcRjqdRiKRQCKRgCAIXKVdKBSg1+tZI0TVarTB/e3f/i1sNhvUajU8Hk9FNwu5tqpUKuFjH/sYnnvuObzvfe/D3r17+VRN6Wt6rVqtxo033ohoNIrf/OY3eP7552G1WvH+97+/jPjVQhRwaTro4MGDLKXI5XIwGo0wGo0oFAqIx+O8sdhsNoRCIWi1WgiCgGAwCIvFUtW5kLxDTv7khMvhcMDhcFRtfAD4kDA3N4exsTHU19djy5Yt570uEokgFArhjjvu4N8DzhHErq4uXHLJJfjP//zPyg3+94TRaOQDm9frhdPpZM11LpdDV1cXenp6atZ7NZ/Po7GxkTNFGo2GdYyhUAgPPvggurq60Nraiuuuuw4tLS0YHR3Fs88+i5aWFkQikTKrnkohm82yLEOtViMUCkGj0XAmRqVS8X6oVquZAMqj0vS1wWBAMBhEfX09H9jp9QouDIUAVgiFQgHJZBJ6vR6iKEIURQ7Nk8UA+U5RRTARD1EUkU6nkUqlsLi4iFgsxkJfURTZs6pWUCqVEI/Hce+998Lr9eKqq67CqVOn+AQ6MTGBZDK5rIXN/Pw8+vv74ff78bGPfWxFxvfCCy+w+WtnZyfMZjOGhobYpJeuDy0i8mpAWqzkH6VSCXfccQdKpRLOnDkDnU6Hbdu2YdeuXSsy/qUoFouIxWIYHBzEiRMnMDY2Bp1Oh+7ubkSjURw5cgSlUgn5fB4ul4vvFbIXMhqNMJlMiMViuPvuu+F0OtHW1obVq1ezqLzaoDHkcjnMzMzgJz/5CYxGI6fgaeMIh8McLejp6UF3dzeOHj2KXC6HYrGIbDZbtTnQphaPx1m7m8/nudiAyFMmk0Emk6naOAFAEASMjo7iyJEjGB0dhd1ux9jYGLRaLYxGIzKZDPR6PU6cOIFwOIzJyUk8+eST3OWE9MySJGFhYQHhcBi//vWv0dbWhmw2i0AgwPrnRCKBhoYGfOADH6jafIk40YE6lUpxCjgWiyGRSPDrag2kg5Vr56jqlyRE4XAYt99+O7Zu3QqHw8EeqKFQiA9LlZ4b6UeLxSIEQYDH40FTUxMmJib4wC2XFNAhiQig/Ou1a9eiv78fqVQKxWIR8XgcyWSyovN5PaJ2WMMbHPl8no06qQpTrVYjn88jm82iWCzCbrdzNRNtbLlcDoIgIB6Pc+WgTqfD6tWrMT8/j3Q6XTUCuFxkKB6PY2pqCgMDAzh16hQAsAUEEd1kMol9+/Zh/fr12L59O/9uPp/H5OQkHn/8cXi93hUjgACQTCYxPj6O+fl5xGIx9PX1wWazwWKx8IJD86JKQKo2W+oyn8vl8PDDD6OpqQlTU1Noa2urWAovEolgbGwMx48fx4kTJzAyMgKbzQa3242BgQEMDw+zpICq6ORVjoIgYG5uDqIoIhQKYXh4GL/4xS9QX1+Pbdu2we12o729HX19fctW2VYa8Xgczz77LAYGBjhKRSl5usdoI4/H45iZmWG9EGmjgOpu5PPz81hcXORDHG1y5H82Pz+P2dnZqtrApNNpJJNJXo8mJydZlgKACdzk5CRyuRzGx8cxPDyM1tZWuN1uAMDY2BhEUUQikYBGo8GTTz6J2267DV6vF4ODg3x4okNXNUHrsF6v53uKDk1zc3OYn5+vuYM2Qe7vSRkLAKirq8PExATWrFkDr9eLtWvXoq6uDsBZtwmz2Qyfz1cmb6kkKM1uMpmgVquxceNGeDwejI6OckXv0tcT5ARQkiS0tbVh06ZNyGazcDgc3E1HwcVRe3fzGxT5fB6RSAT5fJ5vTEmSkM/nuTpLEARYLJYyoTvd4LSxJZNJ1NfXY/Xq1RgeHkYikahIqHs5sif/Xjqdht/vx8TEBPr7+/H888/j0ksvhc/nwyOPPIJAIMALjU6nw4EDB7B9+3Y+6VEEZ3BwEJOTk/yercSCe9lll6GzsxNDQ0M4deoUBgYG8NnPfpY1fRR5JbJH5IKiBHRtKDpbKpXw6U9/GsBZsfjGjRuxZs2a13zcS1EqlTA0NIT77rsPBw4cQLFYRF1dHVwuF3uzJRIJ1v5RFSZFOaLRKPL5POx2OxwOBxKJBJqamnDw4EEkk0n87ne/g8ViwZ49e/DBD34QmzZtWvE5XQzZbBZjY2N48MEHywThpAMyGo3c6oosL4aGhtDd3Q1JkhCLxaoaFaCOGbOzs/D5fLxp0zxyuRwsFgump6cxNjaGG264oWpjLZVKWLNmDRoaGmC32/HMM8/w+0sWI2RoTQeKqakpXHfddVi/fj2SySQeffRRiKKItrY21NfXw+fzwWq1QqfTsVaN0vRr166t2lzT6TTLP8xmM5NRvV6PbDaLSCSC+fl5BINB1l7XEqhgMJ1Oc+WvwWBAe3s7nn32WbS3t0OSJGSzWeRyOeh0OlitVmzevBmjo6NIp9NVaZ1mt9uxevVq6HQ6tLa2YseOHUilUgDO7TPycdH6K++qBYCfm3e/+91YWFiA0+nExo0bFf3fq4BCACuEfD6PRCLBuoZYLIaJiQl4PB44nU5O83o8njLyRxsDcNaHiggktcyhh3olQWRIXpVF6U+KUB47dgy/+c1vMDs7i87OTuzZswfPPfccNBoNrFYrgsEgJEniFJ1Wq8VDDz2Ee++9l98D2tQFQYDVakUkElkRLZ1Wq0V7ezva29vxlre8BQDw7LPPYnJyEgaDAYIgIJVKMUHPZDIcHTCbzawBzGQymJ+fx4YNG3D//fe/5uO8EOiAQC76R44c4T7EW7ZsQTab5ZaBAFhukEgkOEqmVqu5kEUe8UilUmxPotPpsLi4iMceewwajQZ33XXXaxo5W05PtfSgQa/J5/M4c+YMHnnkEQwPDzOhTSQS2LRpEzKZDILBIBMqm82G6667Ds8++yyTQ9LPVhM+nw+pVApqtZpNqfV6PZvYGo1GRKNRBIPBqkacqIDJarVyH1afz4eNGzdi/fr1WL16NVKpFKd6XS4XNmzYgEAgAL1ej0gkApPJhPr6eq5GjUajeOmll6DX67kFHlmUVLPDxtDQEI4fP450Oo26ujr2zTOZTEilUhBFEWNjY3j55Zdx++23V22cF4JarUY2m0U8HmdyRHNwOp1wu90YGRnB6Ogo6uvr0dTUBKvViu3bt2NgYAATExPo7e2teEFId3f3eVZHd911F+81wPl6v6XBDvqZ0+k8z4dSwStDIYAVAumPTCYTp0SSySQEQeAUEEWUKIVFVVKRSATpdBparRahUAhOp5MjamRgupKQ65PkVX4A8Pzzz+Opp57C/Pw8TCYTNmzYgIaGBuh0OgSDQdac9fX1wev1snaICkKMRiP3QiZSSVG4eDxekWIKiqTdddddEEURer2euwIQWZX7aAmCwL5VGo0GHo+nouJw+n+++tWv4uDBgzwW4GxLKPIA83q9ZZoZOjxQJDaTyUAQBBiNRjidTuTzeVitVsRiMczNzaGrqwsejweZTAZDQ0OIxWIr2jWE7i35CZ/0lydOnMCvf/1rPPzww1yIU19fj0QiwZt2NpvlYqlUKoVnnnmG2yrqdDqMj4/jySefxHvf+94Vm8MrYX5+nltaAeDrQzYdarWa9VkzMzNYtWpV1cYKnO1M5HA4EA6HOfPQ2NiI0dFR+P1+ZDIZvl5utxs6nQ7/+Z//CaPRiF27dqFUKuGFF15gTVY8Hse73/1ujI+PY2JiAn6/H/l8Hk1NTVWb49jYGMbHx/m5SqVS6O3t5cOERqOB3+/HyZMna5oAZjIZjrCKoohCoYC1a9dCo9FgzZo1SCaTbBum0WhgNBrh9Xrh8/lqppjQaDTCarVyZkVOAOVVwDRerVaL+vr6snWJXleLxuO1BoUAVgiSJCEQCKChoQHT09Po6+vjCuBYLAa73Y7Ozk7k83kuzyeBvsVi4eihx+PhU7bRaOQHf6XR39+P0dFRTE5OYnx8HCMjI2hqaoJOp+P2PIODgwiHw2htbcU111zDlYxzc3Nob2/nryVJgkaj4Y4oVquVozy04adSKZw4caIikQGVSoW/+Zu/wSc/+ck/mMhVeqE5ceIEhoaGIIoijEYjFw75/X6oVCpYrVYAYOJXKpX4oFAqlRCNRuFwONDa2or5+Xk8//zz6O7uxrp16zA7O4sbb7wRjY2NmJmZQTgcRigUwsDAAK655prXbA5L3zN5BwCCz+fD//zP/+DZZ5/F3NwcR/6cTidHoF5++WWOGlAPUargpkIpi8XCEcDp6emq2ayMj48jFArxBkaFBmRNUygUmKTPzc1VnQACZ6OB73//+2E2m7Fx40YcP34cDocDer2e+5UbjUb09/fj/e9/P1ef9vf3o7GxEXq9HiqVCqlUCrfffjsSiQTbYTU2NqKzs7PaUyyzFqFrQ/KcpcbEtYhsNgu/388RWVpLbTYbUqkULBYLgsEgp1hNJhP6+vqwd+9eXH/99csa3FcStO729PTg+PHjEEWxLOMkX5cpek7kUKvVorW1FQCYsCvE79VBIYAVQjabxfz8PFdkvetd70IqlUIymYQkSexl5nQ6EQqFEI/H2ZOOOoNQRe0vf/lLPpFTNG0l8W//9m946aWXkM1m+YG78sorceedd+KZZ57BAw88gGg0Cr1ej7q6OgiCgOPHj8NoNHJrNfq5yWTiButAeUifUmHyr1caFH194YUX8N///d9sh5DNZiEIArLZLPL5PEcm6VRZKBQQiUQwNTWFW2+9Ff/yL//Cf7MSi88Pf/hDLC4uciUsRS5dLhcymQxSqRR7zQHlqRSLxQK/349t27bhXe96F9asWYPBwUH87d/+LR544AEYDAZ897vfxUMPPYR0Og2VSoVMJoOXX375NSWAS0ESgbGxMRw4cABnzpzBzMwMcrkczGYz6uvrkUwmodVqOfJE9xhFZbPZLHQ6HRwOBzQaDaLRKABwlWQ4HMa99957QVPilcb8/Dy3taKoLUWSgXMRQSr8qibofjEajfB4PPjlL38Jt9uNmZkZGI1GJJNJjhC63W6sXr0aIyMjWLt2Lex2O3Q6HeLxOMLhMHdsmJ6eRjgcxuzsLMxmM/r6+nDllVdWdZ7yvrkajYbXHbn+l1KstQhyjiA9OBUXAmD7I3oe5LDb7fiHf/gHGI3Gqvt9FgoFaLVadHV1wWg0IpVKMfEDUBahlLevo8+pX7CC3w8KAawQKCXX2dkJURQxMTGB9evXQ6PRIBKJwOfzwe12s6kvpXapHZHD4UA2m8WWLVtw9913I5fLcah8JYXt5K6ey+VYC6fVahEIBPDVr34VPp+P03WiKHLaxOfzcUWa2WxmMkUPdT6fL9sEafGlYpB0Os0atkqAOqv4/f4yaxGNRlMWVaLWStRPc8eOHdi0aVPFPcJOnToFSZJgNps5nVMoFDA+Po73vOc9OH78OGKxGC/ulBam+4t6gY6Pj8Pj8WDnzp34/ve/j89+9rPo6OjAfffdh0OHDkEURajVagiCgP7+/td8HrlcDo8//jhOnDiBqakp+P3+MruWfD5f1gUgl8thcXERADi1q9Vqy1LbcjsV2tioqlCSJLz44ouv+TxeLcimgiIYZAFDhwza6OgerAWo1Wo2pPf7/YhEIpwizWQyMJlM8Pl80Gq1aG5uxmWXXQaNRoOxsTEufKNiI3IGoPWE/FCrmQIuFAocJQfOmalT8RR5Nla7UvlikK+/1CKNNNoU/VtcXDxvr5D3pK8FGAwGjuBRwcdykBN2qiYGyiO5Cl4ZCgGsEAqFAtLpNPse5fN5dHR0cO9Js9mMXC4Hm81W1tWBTs65XA56vR7Nzc3Q6XRIJBLcNWQl28GlUimsWrUKFosF6XSaiwVEUWR/L3nEgrRMWq0WVquVI5ilUgkWiwUOh4PtREjzRJs4GSsToWlpaVmxeRGIIHR0dODjH/94mSUCaRKpylReHUzzIkE7UNk0MKVDRVFkAqRWqzE7O4tsNgur1Qq/38/RZQBlBR/5fB5jY2Pw+Xx46KGH0NjYiL6+PnzpS1+CzWbD3//933PFIG2QXq/3NZ/HD37wAxw/fhzBYBCJRIJ1stTNgLSw8s05kUhwlIYKWSwWC2tm5Zs5FbMsjdxWC/RsyyMYQHnrNSqyqpWUo16vR1NTE1QqFYaGhrhgJZlMslUMddchkh2LxRCJRFj3Rx02FhYWAIAjt7lcrurWKlartaw7DKV8KSpFz3m106QXQqlUYn9Z8i2ltYgKiUivuVTrV2s6OcqyvFpcrFuNgleGQgArALlhJZk/b968mZ3bDQYDDAYDotFoWRUwAN4AiXxptVq43W7EYjE+ha+kua1Op8OqVavYKJgqjuPxONxuNzo6Osq0exSx1Ol0sNlsvDmTzslutzMhoYedUmBEBA0GA/dEXmnIK2oppSqPzlC0ksZLv0MFOKVSCclkkqsaK7GYknkuHRbkRSBGoxEDAwN8yKAKYDLwpc2Nqrf9fj9H+Xp6evCJT3wCN9xwA+LxOPcWpde+1sTp9OnTeP755xGPx/m+Ih82+SZM/8q7r9C8iZxqtVoeZ6FQ4LkCKLOLoQhztSC3eKKvaVOm79M4q70x0/9PliJGo5Gr4OkZoMIoauel0WhYN5rNZrnAaHFxkTXNdJilDEG1/SVtNhtsNhtfG3nBFz3ztCbVKnQ6HTKZDD9HdLCg6Ov4+Ph53T6qfX8tB8q60H5CJHwplo5dHjlX8OqhEMAKgKp75af6q6++GidPnkQoFEI+n0cymUQmk0FnZ2dZKpQIILWSy2azaGpqQiwWK/MTXCnY7Xa0tray3QylpV0uF1slAGAvL0rl0EkUKH8o5ZWe8u8tfW2lFif6v2dnZ/GDH/wAVqu1bAOQ26bINSlEPPR6Pa699lpcf/31FSOA1PCcPmiTisViaGhowKlTp2A0GlkMnk6n2Y6HQKTRZrNBq9Uim81ibm4O//RP/4Rt27axxQqlV7RaLSKRCPx+Pzwez2syj6NHj0Kr1bJukQ45yWSyzNqIbC2IxFFTeyocSqfTHOWTGyvL51oqlVhyUCqVEA6H2RS3kpBrMeX3Cm1ydPggzWktgCotdTod+vr6EI1GOS1K94cgCNxGbXZ2lqv929vb0dXVhWAwiImJCXR1dXHGwufz8bpYTVit1rKiKaPReJ7pu9FoZIPrWgOtO+l0mjXLdCgny7BTp06Vme7Lf6+WII9QLi28kbeDA1B2wJMXQtbivGoVCgGsADKZDMLhMN+8pKc5evQoNBoN3G4321j4fD4YDAaYzWY+Iet0OrjdbkSjUaTTaXR0dGBwcLAsCrKS6OvrK+tKIEkSFhcXMTg4iHg8zsbNDocDTU1NTFZpM5NHCIlULdU6kd6DHnqtVouGhoYVf5Bp89m2bRv++Z//mZuSC4JQtsGpVComhRSFbW9vR1tbG5qbmyuqPSGdIkWT6X3z+/1sh6DRaNhAnD4otSh32SddJkV5jh8/jm9/+9vYvn07nnjiCeTzeXg8Hrjdbni9Xjz66KP48z//89dkHuvWrcNvfvMbjhKRBpaqRFOpFI+bIpnyxZ30jaVSiaOVdJ9ZLBZO3ZtMprIiC7VajSeffBLve9/7XpN5/D6g8cp9NeXfp+/JrZdqAcViET6fDzMzMywLSKVSyGazkCQJY2NjGBoaQqlUQiAQQCgUgtFoRFdXF3bv3o3x8XE8/fTTcDqdTPbpPqx2BJAIIEWdSRcnP6yaTKaaJ4DZbJafGaPRCL1ejzVr1uDb3/522f5Ti6DnwO/3cxs7OcgajCB/fkhz3tPTU9ExvxGgEMAKQBAExGIxAOB2bolEAn6/H263G7lcDpFIBKIoYm5uDh0dHWX6KxLvu1wuSJKE7du34/Dhw2Wh8kpCr9ejq6vrglYaGo1m2T6/tQyLxYKbbrrp9/qdTCaDiYkJjIyM4JJLLlmhkZ2PBx54gNuGFYtFjqhQFMNms7EWjlLBALivbzwe5++TzpE8AdeuXYvp6Wm0tLRg+/btTL5o47/22mtfs3ns3LkTX/ziF/G9730Pg4ODKJVKnKZSq9XweDzQaDTs5UcC8WKxyHOhKno5YSLiC4A7OZhMJrhcLthsNhSLRbz44otVI4AAyg5B8q/lthfVTmfJI5WFQgFzc3Ow2+0wGo244447oNVqkUwmEQgEuP0gtd7TarXw+Xz47//+b/zqV79CPB6HWq3GSy+9BK/Xi927d+Pqq6/Gxo0bqx4BlBejETmnw4O8olauE6wV0AHbarXCbrfzmOnrwcFBHD16lDWcpP+ttu5yKYgATk9Pc4W/PCtEByQ6gMv3vXw+j5mZGezZs6dq43+9orbugjcostksEokEzGYz0uk0enp60NTUBIfDwV5YDoeDK3rJJkKeDqJWcslkElu3buW2PxSZUvCHgSJChw4dwtve9jYkk8lXjOTJzZUNBgP+7M/+DDt37gRQmdR1b28vrFYrV4tTD+P5+Xmo1WoYDIYyDzNK3WezWbS0tHDxkU6nY4shuf9iMpnE/v37IYoiL8YGgwE9PT2vuWfbpZdeiksuuQQnTpzAwYMHuTPB7Ows0uk09Ho9W4rIrXiowhE4Zx9EmyFFmeXvAQAuXvJ4PPjkJz/5ms7j1YKiS0ujMUQGl0YCqw15hJKuw9atW7Fv3z6cOXMG8/Pz7GE6MTHBnp/A2YOgw+GA2WzG6dOn0draimPHjuEtb3kLkskknnnmGQiCwEVU1QIV2wEoIxUkN6AMRq2RJgLpKOmARIWDTz75JL7zne8AALeKW64QpBYgl9Ysvf+XOxzJC6WKxSJmZ2dr6rl5vaA27+g3GKgPo16vRyaTQWNjI4uhA4EAv66urg52u51tN5xOJ+vRSNfh9Xo58ub3++FwOKoeKXg9g6IPO3bswMjICHvKUXqL0tOU/gXAvXMtFgunT+UkY6UXoKamJiauRPBUKhWuvfZatuUxGo3sqp/L5TA3N4fR0VFoNBq0tLRwdxkS64uiyIQJOGfHQL6N+Xwe2WwW0Wj0Ne8GolKpsGnTJqxdu5bv81QqhUAggPn5ebYhomi43AJCHj0igkhFSJRetFgsbNvjdrvhdDqr5hvW3NwMs9nMGsblxOykn6ViqWphacGA1WpFsVjEyy+/zFFZ8qFMp9Mctd2xYwcOHjzIhwzg7LxHRkbQ2tqK2dlZBAIBOBwObNmypVrTY7hcLrjdbrbhocMdzZ9kIJUoSvtDQJX+RqMRQ0NDGBkZQSaTgdfr5Qpmh8NxXmqbLIhqCclksqyTB32+XERQbhOzkoWQb2QoBLACEEWRu16QONrlcqGhoQGCIMBgMHA0x+l08vdIG0MaFIvFgvr6erS2tvKCrNVqa1rbUeughWVmZgYvvPACduzYAafTibq6Oo4skRUMANY+BQIBHDp0CAcPHkRTUxM+/elPV+z0uW7dOo7MaTQaJBIJzM7OwmQylVVpkoY0l8txx5hoNIrp6WnWWcpTxLRZU9ERVW5TaymqVl8JLNW82e12NDQ0oLe3l6t45R/A8tFWugZEjun6yclhNTc9OjSQnhE4l3aUExCqjq8VqNVqdHd3o1gswmq1IhQKsUwlk8ngyJEjCAaDAM6m8SYnJ9Ha2gqbzcZEpFQqobm5mYkHkd1qQ55tWVqVDYCflWprFS8EcjCgSD8VRHR3dyMajbIFDx16auE9vxCi0WiZFReBiCsd+OQHP9I/0ucKXj0UAlgBSJKETCYDjUaDeDyOt73tbdyDUavVsp0HpYrpAfX7/XyjJ5NJLC4uIpvNorGxESqVinVgtXaKez3CZDJh//79ePrpp8uIA723dB2oIpXE7zqdDjt37qzowtPY2Ij3vOc9mJ6eZhKXSqWg1+tZzE6pFHlahMgPRfJo3uQhSBWEVExhsVhQV1fHEbTW1taKRaUolV3tKNhrDZpPLpcrq96nAgT5taq275w86qJWq+F0Onn85Akpl6eUSiX4fD4+rFLxkc1m48h0V1cXnE4njh07VuYiUE1Q5JuIH0Vg5R55tN7WGlQqFfbs2QOHw4FDhw6VPdcU/SNCSI4GZCBfS6D1Vb7/XSxVvVQrSxp7hQD+flAIYAVA2iRq9H755ZfDbDZj9erVXAUsr3wymUzc2ovMkjUaDdrb29HT0wO9Xg+3212W7lLwh4EWDLvdju3bt2NmZqbMeHhpep0iIG63m7VPlehXLIfBYMCHP/xhzMzMMFGVG3DLid/SyBcRQOrQYLFYeBMmHzGqmiV/NKPRCJPJhKamJuWw8UeC+uKSj6T82ZVHNil9X00s3UxLpRIfZGke1CWEDg3UesztdnPa3WKxcBaDpAQkUaiF+4naPgLlmkf5v+RTWovYsmULzGYzbrrpJgQCAUxPT3NjASokVKlU3CKyFjua0PtOfp9EAukek0eN5d8nyF+v4NVDIYAVAKV1KCrT29uL1atX48/+7M8gCAJ7/ZGA3WazscaGFkhqcr1z506USiW0tbXxA17tKro3AvL5PC6//HJcffXVnBKiD3k3EEqnptNp7jwxNTVV0Spg4GwFLRWeKHj9QO5XKLfmkVc5UhVqrdiOyA9Do6OjWLVqFfcsp7aIVCzR2tqKWCwGtVqNhoYGmM1mBAIBPliMj49zlyD5hl5NkJ0SRcjk7flIZ1YLEdkLgaJ5t956K8bGxrC4uIhwOMxWSCQvoFRxJBJBY2NjzWjHl+qm5al3oLxbzlLZxFI9oLxIRMErQyGAFUAul0MqleJqraamJqjVamzbtu0P/pvUjomiiwr+MNBiEQwG8fGPf5yje1Sh7XA4YDKZ4HQ6AQCRSAQzMzNcqRqPx3H11Vfj3e9+t7LoKHhFUDodAKfcqY8zbWKJRAKiKFY9AgicM65Wq9VoaWnB6Ogoent7MTo6imQyiXA4DAAIhUIwGAxwuVw4deoUcrkckskkawDn5+eh1WohiiLa29sRCoVqprBCr9dz1JuM7uUdaKxWK1wuV81qAOX46Ec/CpPJhG9+85tlVlCkkxseHsb69euxdu3amiDfQDkBJJsa+j4ZhS/VK1NWgzIeFEhRgiG/H1SlWjkGvIFBvTEDgQAGBgbwgQ98AEC51uTVQP6gXHnllbjxxhvR3NyMdevWKR5IrwGGhobwzDPPYGZmBvF4HKlUCrFYDIIgcM9SIvA9PT3YtGkTLrvssqrbWCh4feGZZ57BoUOH4PP54PV6EYvF2CfPYrFg/fr1uOWWW3DzzTdXe6hlUZVoNIoHH3yQZQOzs7M4efIkIpEIrrrqKkxMTMDlciGVSmHjxo1cqa3VajE6OopSqYR3v/vdEAQBhw8fxqpVq7B161bU19dXe5qYm5vDwYMHcfToUQiCwNHJzs5OXHHFFVi/fn3N6eYuhomJCXz1q1/FyMgIBEFAS0sLenp6cPPNN2P37t2oq6sra3VXK3j44YcxMDCAcDhcVowmt38iyxuTyQSHwwG32w2VSsX7qoJXD4UAKlCgQIECBQoUvMmgxEsVKFCgQIECBQreZFAIoAIFChQoUKBAwZsMCgFUoECBAgUKFCh4k0EhgAoUKFCgQIECBW8yKARQgQIFChQoUKDgTQbFB7CKkNu6jI6O4l/+5V9w/fXXo66uDpOTk9Bqtbjmmmuwbt06/h1ySq8lzznyXxIEAQcPHsSRI0dw7bXXYsOGDezz9cILL+D48ePo6urC29/+9rLfqwVQ79zh4WGYzWY0NDTAarWy+38+n4dWq8XDDz+MU6dOYWZmBhaLBZ/4xCfQ2dnJ3Q0qCa/Xi6NHj6JYLLJNhcfjwdTUFL7whS/g+PHjbMBL3TzIhy6Xy3GP3Xg8jkgkAqfTiXe961244447kE6nkUqlkMvlEAwGUSqVcMcdd1R0fm9U/PznP8fzzz/PBrbxeBzr16/HoUOH0NLSAovFArVajfb2dnziE5+o9nDfMFhqECyKIgYHB3Hy5EkcO3YMw8PDcLvd+PSnP41LL70U4+PjOHDgAF544QWMjo7CZrPhwx/+MK6//vqasK6R49/+7d+wa9cubN68+aIG4mT6EYvFsH//fpw+fRqf//zna2o/UVA5KASwwpCTHnrootEoRkZGMD4+jsOHD3OLpIaGBjQ1NaGzs5M9qMi3qZbIE/kZhkIh9Pf34+mnn8aqVaswPDzMpCidTuPJJ59EU1MTNmzYgJ6enqrPgXroBoNBiKIIu90OjUaDcDiMhYUFWK1WrF+/Hl/72tfw61//Go899hgOHDgAlUqF7du3o6+vDx6PBzMzM4hGo7BarWhvb69Y/9rPf/7zePrpp7m3ZyKR4A4GmUwGLpcLWq0WsViMDWHJPV/e45S6U8zOzuKuu+7CN7/5TQDg9l2lUglutxupVAp/8Rd/UZG5vVERj8dx5MgR7N+/HxaLBbFYDLlcDjMzM5iamsLs7Cy6urpgNpu5O4iyOb82oB7Zw8PD+MlPfoL5+XkkEgkYjUaYzWbU19djdHQUb3nLW7j9mMFgQHNzM9rb2+F2u7Fv3z7cd999cLlc2LBhA+644w40NTUBOJ9gVgqSJOGRRx7ByZMnceedd2Lv3r3Ljkf+tdfrxXe+8x34fD58/vOfr/iYFdQGFAJYYRDhiUQiGB8fx8DAAGZnZ1EoFHDNNddgcHAQCwsL6OrqgtVqxa9//Ws8/fTTWLduHVavXo1t27bB4/HUDPkDwBFJj8eDhoYGSJKEeDwOt9uN559/Hr29vfD7/Uin03C73WhpaQFQ/b6N+XweqVSK++QCQFNTE7fmo56tp06dwvT0NKampvChD30IdrsdZrMZOp0OmUwGWq0WDQ0NSKfTyOVy3Cd1JREIBODz+aBWq2E2m3mzIjLudrt5HPLercA5wk6gz/V6PRwOBzvwk4kvAAiCgHvuuQdvfetb0dzcvKJzeyPD6/VCEAQYDAa+btlsFrlcDvX19XwA8fl8KBQKiMfj3IVGwR8GIj6pVArHjh3Dfffdxwc2Mnc3Go38HF922WUQRREGgwEGg4HbjhUKBRQKBej1euTzeQwMDGB6ehp33HEHdu3aVbX1TKfToaOjAxMTE3jyySdhtVqxdevW88ZDXx88eBA/+tGPMD09/Ud1o1Lw+odCACsEWoREUcQjjzyC2dlZpFIpJBIJ+Hw+5HI57Nq1Cy0tLcjlcujr60M8Hsfi4iLy+Tx0Oh2mpqZw4sQJ9PX1YdOmTWhvb6+J/pRERvV6PZxOJ3Q6Hfr7+3HppZfi1ltvxcGDBzE4OAiNRoPW1laOClaTxJZKJeRyOU6RUl9W4GyU1WQyQavVwmAw4KMf/ShuuukmrF69Gk6nsyx6Rr+n0WhgMBgQjUZRKpU4jbdS+NWvfoXJyUkkk0kmaSqViiN6RqOR0z30fQK1T6IOD/S5nPARKaHUt1arRSAQwG9+8xt88pOfXLF5vdHh9XqRSCT4OgHgFmkEui8FQUA4HFYI4B8JuvfD4TAeeugh7pOrUqnOO6zp9Xro9Xp+Dqg/cC6XA3C27y61ipMkCT6fD/39/XA6nVi7dm3lJ4ez8zMYDNBoNDh48CBSqRS8Xi+uvvrqslZ7Y2NjeO655/Dss8/ixIkTAMDzVPDmhHL1KwTqV9jf34+BgQFOMbpcLtTV1SEajcJgMPDndXV1HIkxm82w2+2Ynp5GIBBAPB7H3NwcbrvtNrS0tFQ9Grh0AVWpVDh27BgymQzWr1+Po0ePYnR0FM3NzbDZbMv+XqUhSRJH76jFEHCuyTiRo0KhgBtvvJHbEWUyGd6sKUVHPTX1ej2SySTr7VbyuuTzeWzcuBHBYBCpVAqZTAaCICAUCvGcLrS409yWgyRJPL8tW7bA4/HAarXCZrPBZrPBbrev2JzeDAiHw5AkCSaTCTabDbFYDJIk8UGCvm8wGKDT6RCPx6s95Nc16FlOpVKYnJzE+Pg46uvrYTKZkM1moVarodfry57VVCpV9jVlODQaDVQqFUcDTSYTisUizpw5g9bW1qoRQABIJBLQarUIBoPYv38/FhYWsLCwgE2bNkGj0WBsbAyDg4PYv38/5ubm+FBHxFbBmxMKAawQSOfz+OOPw263o66uDlarFW63GwaDgaM0dDItFoswm80wm82w2WxoaGhALBbjpthPPfUU1q1bB7vdXlObcj6fRzKZhM/nw+LiIpqbm/HSSy9xiqsWGpATsRYEgdNwdKqnVDCd8CVJgsVi4UiAWq2GwWDgTYCE/MDZ07ROp0OhUODewSuF22+/Hdu3b8fi4iK8Xi/8fj+CwSD3/hwbG4PVal2WhC73PWq0nslk0Nraiq6uLnzkIx/B+vXr4XQ6mQAq+OOQTqeh1+vh8Xjg8XgQi8UQjUZhsViQTCZhNpvhcDig0+lgtVohSVK1h7yiEEURoijCZrOtyIGQotuLi4s4fvw4P6PyrIXb7YbJZOLnORqNQpIkaDQaTg2TfjCTySAWi7GMgkjXwsICJEmqWkYmGAwin8/DaDQiGo3i2WefxeHDh3HVVVfBYDDgySefRCwWg8FggN1uh8FgQCaTqboMR0F1oRDACiEej+Oll17ik30qlYLRaIQoiohEIlhcXERjYyMymQzy+TwEQUAkEkE4HEahUIDZbEYul4NGo4FOp4Pdbkd/fz9aWlqwfv36ak8PwNmT88LCArxeL0wmE0wmEwRBQFNTE7xeL5LJJOLxeFUqZuWggggi08C5aCS9x/QhSRIEQeANgyJ71KS8WCzCaDQil8sxkUyn01CpVDAajSu2wHZ2dqKzs3PZn42MjGD37t2sV6JI5VLIReFEYgOBAL7//e/j8ssv5xSlgtcOGo0GFosFZrMZdXV1qKurQyAQwO7du3Hvvfeivb0dLpcLRqORI4GvRxSLRQiCAABl0XCSHNDXs7OzGB8fx+WXX74iB1k62E1OTuLll1/m+12SJBQKBT5cU7GT0+lk+YcgCCzbEUWRtc3xeByNjY2QJImjiIIgwOv1XvCZXGnQe6rT6WCxWHjM+/btA3CW6Nrt9jIXAFrDagGFQoGL1OSQy1iWw9J1jQIplAVRcHEoBLBCKBQKSCaTqKurg8FgQCwWQ6lUgtlshtFohMVigdVqRTqdRqFQgMVigSAIvICJoohEIoFoNIpsNov6+noEAgEkk8lqT43x5S9/GT/+8Y8BAB6PBzqdDvX19chkMigUChgbG8NvfvMb1NXV4cMf/nDVxkmaHlosRFGE2+1m3RyRcL1eD5fLBZ/Ph6amJqjVajz44IOYn5/HunXrcN111yGXy+HkyZPo6OjgCCJFGEOhEBoaGlZkDqVSiRdveXoKANauXVu2AF4o6kqb8dKvr7zySq6CpN+lv6Usqn8c7HY7dDodstksgLPXxul04jvf+Q7uv/9+5HI5lh6sdBR5JUDPQCAQwHe/+10UCgX85V/+JXp6evh5k2/mQ0ND+NnPfoZMJoN3vetdKzKm8fFxnDx5EpFIBC0tLUzsbDYbzGYzZmZmEAgEUCwWUVdXh46ODlgsFoTDYeh0OkSjUYTDYWi1WqxZswZ2ux2xWAx2ux35fB7ZbBZzc3M4efJkVQhgoVAAAF6/yL6K1jGdTodSqQSdTgedTseFR7SnVBu5XA4TExPwer2IRCKQJKlMWkMFbpQZozWLnhOys1Kr1TCZTFyh3draWs1pvS7w+lpdXsdIp9MYHBxEU1MT0uk0rr32Wq6KFQQBWq0WDocDkiShtbUVTU1NEAQBbrcbDQ0NaG9vh0qlQiwWw9jYGBoaGhCJRBCJRKo9NYbJZILdbmctWjabxeDgIMbHx2Gz2aDRaDA7O4sDBw5UlQAKgsCbAACupP3IRz6CD3zgA9i5cycEQcDIyAhaWlrQ2dnJKe3BwUGuDkwkEujv78epU6ewdetW+P1+Tg2tdGpFXkRwIeRyOeh0ujLNn3xsy2kB5REnuTZSwWsDh8PBhw5KuZP2T6PRcNTMYrHA7XajsbGxyiN+9SDSqtFo8PTTT2P//v0YHx/Hyy+/jCuvvBJXX301MpkM6uvr0d3dzYeUbdu2rRj5A4Bjx45hfHwczc3NKBQKCAaDfOBbXFxEW1sb2trakM1modPp4Pf7YbfbWX+pUqnQ3t4Oq9WKxcVFaDQaeL1eiKKIdDqNUqmESCSCM2fO4G1ve9uKzeNCIMJEFlBEmKgQjbIddPCVJInJVCUJIN0btKZks1kcOXIEX/jCF1AoFNDU1MSpdpLakEOD1WqFXq+H0WiETqfjtDwd2Mm1QZIknDlzBk6nE7feeive//73V2x+r0coBLBC0Gg0nNI5ePAgnE4nBEFAMplEJBLBzMwMVCoVwuEw5ufnMTs7i0AggMXFRSSTSUiShPHxcQiCgFgshu7ubhSLxZqoAiY0Nzejrq4Oo6OjMBqNiMfjeOaZZ3ixIR3g5s2bAVTHN4tE4aQDkiSJ09F79+7Fz3/+c9bz3HPPPcjlcvj3f/93PPfccxgbG4MoirjiiiswNDSE733ve7Barfj617/OmiFKuwJnF7xEIlExjab8/TQYDLwoXuw9lv9ckqSyKufXEyYnJxGLxVBXV1e1NNyrgdPphNlsRjgcRigUgs/nw65duwAAjY2NbD0EnE3bWa3Wag739wY9S6dPn0Yul4PNZsPs7Cz+8z//Ez/5yU841Ueau7a2Ntx6660rNp5isYhYLMaSG0mS+PPW1lbkcjmEQiHWV9tsNjQ3N8NsNrNxOqV6Y7EYEokEbDYbF5GR1EMQBCwuLq7YPC4Gqlom2QpwVuYil6nk83moVCrWLNPBMBQKVWycFIkkPP300/j+97+P1tZWljjRuKjwRp6lyeVyyGQyrL+mv0UyF/p+W1sbkskkTp48icOHD+PSSy+t2Bxfb1AIYIXhcrmYINhsNrZNKZVKaGxsZBGy0+nkEDgtREajEVNTUzCZTHA4HIhGoxz+rwUYjUb2yALOGa/S/AqFAoxG40Wd6lcagiBwykClUsFsNnM6bvPmzejv78ejjz6K5uZmvO1tb0Mmk8EXvvAF3H333XjkkUfw0EMP4ejRo2hsbITH40Fvby+sViufrmmuRDIrSQDlxM1kMnEBwcWqfpeCNpBaxFL9GHB2E8lms3jhhRcwNDSEnp4e/Md//Meyv0+2NnJrjEqDJCByY+5t27ZBpVJh1apVnHYkKUEtHfBeCfL079jYGBuTU0QHAG/uxWIRmUwG2Wx2RQ8cx44dg9frZX0cHQAXFhawatUqlEolxGIxXouz2Syi0SgaGhpgt9thNBqhVqsRiUSwsLDA2jmNRgO/389ZD6vVCrPZjIWFhYqnHsmdIJ1O86G2WCyWdTGSWz5R5DmXy3GDgUqBrnU8HofP50M6nUZPTw8ikUiZZpoIHq1bkiTxfUOga0nFeUQAKbLs9XrZjkzB8lAIYAUgiiLC4TD8fj+am5shCAIXSdhsNu64oNVqWaQrrwq0WCywWCzsByYIAkRRhN/vRyqVqpluAUtb1MkXfbnfXDV1TRSBkC8uhUIB4XAYq1atwgc/+EGMjIzAbrdj/fr18Pv9yGQy6O7uxi233ILm5makUim43W72NKQ0fD6fP+89qBbZ0Ol0nHJ5tSSCCHutQr45zM/P4/Dhw3jkkUfQ0tKC1atXQ6fT4eTJk3j44YfPS8UVi0UsLi5icXER69evh8PhqMYUYLPZOEpmMBjYPB04G0GnAikigLVMyJeCnusnnngCs7OzrGGkylM5yOVApVLB5/Ot2JgojR4IBLiqv1QqlfldUsV/KpUqS50KgoB4PA5BEJBIJPh6kAn84uIiOjo6WE9MVjPV0J6JoshkmsYp9/oEwBHBbDaLbDbL16BS6O/vx759+2A2m3HJJZcwWSOiSgciub0W/btUk0wgP1eKFNL6RfIVxebm4lAIYAVAVb1yvQJFnTQaDVKpFFKpFGKxGPdgJYJHmiCVSoV4PM7VaxaLhTUf9CBVG3LDVDmWtiOqZuWZTqeDyWTi942iE16vF21tbdixYwe6u7sBnBXsOxwO1NfXo1AooL29Ha2trfD7/dBoNGhsbOReudlsFg6Hg1Ms9G8lq53lrfUuRuQudGDQaDS80dXCgWIpstksgsEgJicncfDgQRw9ehTj4+NIp9PYvHkzrrjiClitVhw+fBidnZ1oaWmB1Wpl+UE4HMbc3BxsNlvVCCCZbZOeiSxIALBERO5L93rSYNJYH3jgAUQiETYRt1gssNlsZUVttGbl83ksLCysmDNAa2srdu/eDafTicXFRczOzmJ6ehq33HILMpkMwuEwH9oymQwAsBZTFEUupKBMBkk8SIPd3d3Na0R3d3fVLLnI1xAAR8CW0/rm83nkcjkmgJWs9D969CjuueceljpQESRFVClVDeC8aN9yXU3obxAxpL8BgAnw4uLisgdCBWdRfdbwJgCFqsncmQiH2+1GsVhEMpnk02Y6nWZT32AwyKHwRCIBr9eLTCYDg8GAjo4OTE1NQaVSIZvN1oRWiMZNWI5IkHamWqDIK9m7kEDa6/UiEAigoaEBLpcLxWIRkiRxEQ55fzkcDnR0dDCJp2gNkXLSFtImXykiJT8pZzIZJBIJrvoDXjkNTJXEqVSqTBdZKciLVJbeN5lMBvF4HDMzMzhx4gQOHTqEo0ePchuvYDCI5557DldccQWuueYavPzyy3j++eexefNmtLe3s51PLBZDKBTC0aNHsXHjxorOj0BRYtJtqVQqJg3U/5dIYi14Zl4IF+ozOzExgcOHD5dVpdMzQuugPEWZyWQQDAYRCoXQ1tb2mo/TYDBgx44dWLt2LWZnZzEwMIB8Po8777wTd999N5ty03OSTCZZJkAV2XSgowKvTCaDtWvXYvPmzdi0aRN6e3vR29uLtWvXVq3ytK6ujts3yp8huoeIWBH5BirfTz4YDPL/OTIywtFUSlHLIb/3KSK4tIc5/bvUlktux3Xy5ElMTU3hlltuqclDbbWhEMAKgMSrFH53Op3w+/3YtGkTbDYbEokEHA4HrFYrRwo9Hg9bj1gsFi7bJ6Gx0WhEPp9HJBJBIpGoKQK43CZOJ7ZUKsVzqOYDSS2fCMViEeFwuEwfk8/n+cSs1+t5w6IFlMi3VqtFZ2cnV3hWA7QQAmetNehQQOT0lTSANO5SqYSFhQV0d3dXpJp56f9PoGhysVjE4OAgDhw4gEOHDmF6ehp2ux3t7e0Azm5osVgMBw4cwNjYGG688UbceuutOHHiBIaHh5FIJODxeJBIJHDs2DEcOHAA6XQaH/rQh6pyreTPBaUiiTTU19eXSRNqlQCSnldeJU7E6J/+6Z/4WZFXfVJ3GToAExkEzjokjI2NrQgBJFgsFqxbtw7r1q3D+973PgBANBrlQhXgnIaR7FRIqwmcJVCSJEGr1SKZTGLdunX467/+65rRaFKVLB3k5JZUpAWmSKZ8LajkPdbU1MQetqTLbGhogFqtRj6fL3u/CXIrK/katzQ6KL8PiQhms1loNBpcd911Cvm7ABQCWAGQsDiVSmFubo4F0ERC6NRGehSq3sxmszCZTGwencvl4PF4MDo6iqmpKQQCAfZOamlpqfY02Sx1qW6JCKFWq8Xi4iIOHjxYpRFeGESyacOSdwughvBy93/Sp9SS1oROyvfccw9ra+R+WoSliyFtCNQj+LHHHsNf/uVfViQKuJSYkl/m0NAQDh06hMXFRezfvx96vZ7TbOTXNj8/zxEcAPD5fLjnnnswOzuLP/mTP0FHRwdmZmbwxBNP4PDhw4jFYtDr9az3qkZnk1QqxZF+h8NRpsMijW86nf69tJuVBj0DFE2ilOkvf/lL3HPPPVi7di0TKLPZfN7GTmsZdaRIJpN46KGHcM0117zmY5V7xlFkiO5/iiBTtw8yfJd3X5ETD7p3qDuQ3Nx66ftTaVB3InnbOnkhERWv0LpVycMd4fbbb8d3v/tdfP3rX4fX68Vjjz2G+fl5zpyIonhe4GDp2vVKhFU+t1KphIaGBnzwgx+sWVlLtaEQwAqAWj9t374dzzzzDBobG6HT6TAyMgKj0cgWA3q9ng2T165di4WFBQBn+4dS1RmZRrtcLmzatAl2u70m2kXF43EsLi6ykBo4P01EBIMa3VezGnM5yIkdgGVPm0sXGNIy1criUiqVcO+9957Xp1iO5cZKr2tsbMTPfvYzfOADH6gIAfze976HwcFBTplLkgSdTgdJkhAOh2EwGLBlyxYA51r4+f1+PlCRV5hOp4PD4UA2m8WBAwcQjUbR2NiIl19+GZIkwWq1or29nQ9Y+/btwwc+8IEVn99SyDseJBIJjIyMMPEj7RZptGq1EwtptWi8fr8fP/vZz/C5z30OW7du5XQcWZNQH3SKygDglDzpo5966qkVHfOFPC3JU07+M0pJEvmTGxLn83mIolimY6uFZ19+cCVyLi+aymazTAJJoiKPwlYCer0egiBwBxYi/62trWUSFTmhfjXvsfz3SBdP15DWkmPHjmHv3r0rO8HXIRQCWAGEw2FMTEwAOLe4vOc970F/fz9H+FKpFDQaDaLRKAwGA5qamtDZ2QmVSgWXy8XpY61Wi5deegl+vx/FYhHz8/OYm5vD9u3bqzrH8fFx+P1+rvy70MNcKBQQjUZx6tQp7Ny5s1rDPQ9yDZ28aln+86Wvp3/lKYhqggxpA4EAWlpaIIoiz0deHSevDpSTWdI+HT9+nCM2cm3ea42RkRHufkHRcNrAAHA7NCJ7VMFJ+ixRFLmpPVkM2Ww2pFIpnDp1CiMjI1yBShs3Raer1Yowl8vxIY7edzoIkWbTbDajVCrVTJcfehZIL0vRMAB44YUX8L3vfQ+PP/44r0FEAMmzlAitPPVLkSlKD69U9fnF7luqul7qVpDP55m4yiOHOp0O+XweqVRqRcb6x2A5iQfNRRTFsoMtcO5wW8mCPKvVCo/Hg8HBQdhsNr5PjEYjQqFQWR/2pRXMF4P8dbSekZ8g6TtrYX2uRSgEsAKgvr7FYhGpVAp+vx+rV6/Giy++yJseRZOoyow2O4qYZbNZpFIpjvqdOHEC9fX1rEmpNiKRyKsq7qAoTjqdrsCoXj3ovZefnJeztFkOy9kTVBJ0nxQKBZw6dYqLCSjaJB/7UnE1/T79jPQ4x48fx549e5gorYQdiVarhcfjQTgcRjKZPK9whSpH5QJ8g8HApNbj8bBcgvRN9NxQSp++RxugJElYXFysWsqexiNJEvR6PWsZgbNFIIIgsDykWhFA2oApakabqvx5OHHiBB588EG8/PLLCAaDaG5uBnA2spdIJMo0gnKjYpVKxXpAuXOB0WjE0NBQRYtzXC4Xm7tTChU49zzJD05ElChyRveP/NBYTWQymbL3WB79k5Mo0gcCZ9e3ShpBA2dJ4JkzZ7Bx40YUi0VMTU2hr68PNpsN2WyWK8UBlKWrX+36Q3OTt4ijamMF50MhgBWC0WiE0+mEVquF1WqFzWZDIBBAOp3mdJdKpUIkEoEgCFizZg1isRgkSeIq4Vgshs7OTmzcuJG7agCoiVRRLBbj9A5huSpBAKyBrCXQ4k/RvKURPjmWalQulGqtNAqFAsbHxwGAoxavNLalJ2y1Wg2j0YiRkRFs3bp1RdP0er0e69ev51RgNptFPB5HMBhEOBxGMBhkLZPcv430sRSFkm/C8srTYrGIdDrNGyO91mw2Y+3atSs2r1eaMwDu7iO/l0wmU1kVarXawNH9v/TgsLCwgIGBARw6dAh+vx9jY2OIRCLQaDTceSaZTHIqTn6QonsRAF9DIon0noyPj1eUANrtds5WyNO8SwsO5O8BaQZfTWSqUiiVSmxnI1+7CPLrQNeCqtHpelWKwPb29uL06dMsXRIEAVNTU3A6nWUWQfKxL81aUBT6QvIWWjOogC+ZTFYt4l/rUAhgBUCaN+p12NPTA4PBwJsSPbjyqI1Op+OFkV5nNpvR0dGBDRs24MUXX2QxdS0UICQSCT7ZLxe6ly9CtUgAaTOSnzQvtMgvlxquBQJIZsevJnVysYim2WzG6OhoWb/klYDdbseVV16JaDTKbRFjsRii0ShisRiSySRXY8ujrNTX1Gw288neYDDAZDLBbDYzIaEogNxahVLHvb29Kzavi8FkMqFUOtt9gtK9BKPRCFEUEY/H4XK50NTUVHGrDuCsZGV0dBTJZJIjlvF4HNFoFMeOHcORI0d4EyaiR6nRfD7PqV3ayMkChlKSS7s2GAwGZDIZzM/PV3Se1FeW1lzg3IFOTkLkc6HiFnkld7V1gIFAgJsGEPGhsS0FjZNS2hRcsFgsFRnrDTfcgG984xuYnp6G0WhEV1cXEokEBEFgqcbSwweldGk/lKeIgfPnSYdfg8HA/Yap3aKCcigEsAIgr6v6+nqEQiGsX78eer0ezc3N0Ol0fPoBwFE0u93OBsS0Ueh0OlgsFnR3d+MXv/gFNm3aVDPpVIpcLpcuItD3yDy5lrDUi4oW9osRKfniXwsbQbFYRCAQKNP1yXGh8cmvGRGOShBAp9OJPXv2IBgMIhgMMuEgQTt5RlJ1oLyPbC6Xg9PphNVq5dSwxWLhTXCpBpXmRhX51YoIkH1TOp1mwkrQ6/UolUpIp9OwWq0wGAwV92SUJAkHDhzAQw89xJpekq24XC7WWpKJMyGXy3FaW64Blt9XRPpUqnPtu0wmE/R6Pae9KwkiffJ0Lo17aVcf+pdaktUSTp48ydproPxQSusSHaLkazNp4xYXF7Fq1aqKzOvGG2/ET3/6UwSDQbS1tWHjxo2YmJiAz+dDIpHgiLA86kdm7sC5yL68onvpgZzmqdVqEYlEcP/99+OWW25Z8bm9HqEQwArAYDDAZrOhWCxifHwcmzdvLquMI8d54BwRiUajSKfTTACTySR8Ph9GR0fR09MDQRCqUsl1IUxOTiKRSCz7QNL4KMqZyWRw9OjRag11WcirepemTJZ77dLPq9lCTZ5ap0gs+WrJIxsX+l35B1UMko5rpaHT6dDS0sI2RqIowuv1IpFIsGemKIrcY5qskQKBAJuo0/zS6TSSySQTDHnEiaprqZikvb29KilWsn0CcN4BQ74Bk/a30gRwYWEBd999N4LBYFlHCY/Hw5E+qiYFwOn1TCYDnU7HpJXIHm3WdA3p+6VSCTabjQmwxWLBjTfeWLF5AmBPT7pX5BW/8msjf3aomlbe77ya62+pVEJ/f39ZwQq97xS9pSIbeWSNImSpVAoDAwPo7OysCAE3GAy4/PLL8dxzz2FiYgINDQ1QqVRIJBLw+/1wOp2wWCw8XrfbjTVr1mBychLhcBgej6dMO7qcXpuuSSqVwsLCAqampl5XLRUrCYUAVgAGgwEulwt2u51PPclkEslkkhuiU3uxUCiERCKBzs5O7l9J+hrqiPDBD34QHo8HHR0diEajNeEXdvz4cYRCoWX1MXJBtUqlgiAINecFSIujXCAtrw6k1xBokZG71FcLtPBbLBZ8+ctfRiKRwOOPP45sNgu3282brFwHJ4dKdbY7g8/ng1qtxh133IFvfOMbaG5uXtEq4OVgMBjQ1dVVkf+rGiCNk1w7SpZI8ugF2dVUWt7xwgsvIBaLwWKxQJIk7rMqJ0m0AVP6FgBbUQmCwN2AkskkEz+XywWPx4Ouri7+HqUrk8kk/6ySiMfjTDTk5Ii+t1QWolarYTAYEAqFuKivFqKBhw8f5sgxUG41RPeZvKp5aZamv78fb33rWyu2j+TzebhcLi7g2r17NzZs2IBAIMCeq6QTpTF2dHTgxIkTSCaT7Isrvy4k8aBrRsGIXbt24YorrqjIvF6PUAhgBUBC16mpKaTTae4o0dLSAovFAovFArfbzWH8+vp6rFq1CqIochpOFEW43W7U19dz2P7MmTNIp9NcDFIt0El6uWq4pVoN0v8EAoFqDPWCKBaLZf6Fy0UxL5QilkcOqgF5lVxvby/uv/9+lEol3H333fjxj3+MqakpOBwOtlygKLMoitxDVBRF/Mmf/Al+9atfVT2q8UaGKIocoaV7Rl65CJzd0NLpNObn5yt+sLj22msxPDyM4eFhzM/PIxaLIZPJsAceALbeIVDkmVojbt26FXv27MHWrVuxbds29PX1YW5uDpdffjmTEYrm0n0bDocxOzuLjo6Ois2VCiBoDjQWuRyESLm8mrSxsRFjY2PYs2cPOzhUC6VSCcePHy+rmJevX0vXX/pabsnz3HPP4fOf/3zFxhyNRhEOh2E0GmEwGFj/RxH9QqGAdDrN65IkSWhuboYgCFhcXDxPXwqAbV9IY3/llVfixhtvxLZt25S17CJQCGAF4HQ60dXVhYWFBTidTqhUKvh8Pni9XjidTpRKJU670Qk6FApxSoseiEgkwubQra2tLHyvq6ur6vwGBwe52nJpddZyESTaAOb///bOM0au8zr/z/Q+d+r2xt0luSRFkRSbqG4VS1CxLSFIZAVwEARBEAf5EiCJPiRB4JRP+RQBQZAix0Acy44UWnaiYkV0QogqlEgtl6SWpMjtZXZ6v9Pn/2FxDu+sKMXynzt3pTk/YMGys+S9M/e+93lPec7S0qaOf/o8UJcpdVRvjPbdiI1WC1slIkAL4e/8zu9gcHAQzz//PE6cOIHe3l4Ui0U+T5/Ph1gsBq/Xi1/91V/FX//1X/PiKimTzYHSh1QvR6k4AGxbA6wLEj3qzXp7e/Enf/InLFBpRnkymcTi4iIWFha4PCWTybC3ot/vx1133YW77rqLJzuQUDSZTPD5fNixYwd6enp4bBnVQNrtdgQCgbasBVpBRGsrcP39ptS7dhYzlRDQ+2G32zEzM9NSIqFH/S81fTWb67ZHZIBMaW2ttRV9aX1O6XyvXbvW1hKWb3/724jFYlziROnaVCrF3fF0/HROe/bswYMPPviZjS0ENbiQKBQ+HRGAbcBqtfLQaxJrtKPO5/MwGAxse1Eul2GxWJDNZtkAl3C73byLm5iYgMlkgqIoCAQCep0aAODs2bMoFAqfiIp92s1Kth4nT57EM888o8MRf5KNNiJAa33PRkGojQxoowdbCbvdjgceeIDnG7/77rvo7u5mr61YLIZms4kHH3wQf/iHf8jmrFvxXL4sUKSMhAZt+CgroK3joiaYdt7fRqOxpTFFURSEQiEMDw9jz549KJVKLDIo1UhpYUVR4PV6b/jQVRQFf//3f8/CkKJQ9NVus95qtYp0Os0CZOP9rPXPo5nGVCpht9uRTCZ1t4Kp1Wo4f/48WzeVSqWWtC/VYmqbb7SiljIy2Wy2refS19eHrq6ulveYxJ72+IHrUUun08kTc4SbhwjANkARvEQigZ6eHgDgeZKKosDpdKJSqbAgrNfrbANBHmnUwUiLaygUQiKR2BIu59euXeNFEvhkevRGMzMbjQbOnz+ve+csQcd7Iw8qoNUnUPt7qtfaCjYwN0JRFNxzzz1Ip9M4ffo0NxVRE9JTTz2Fb33rW5x60/ta+rITj8dRLBa5bonsb7xeb4sApMYxve2SyILDYrHA4XBAUZRf6t8xmUzYtWvXTT66zw9t1rTGySRotdkKbdOENjWsFbB6C8B6vY5r165xfR+tVxsbuwC0+DJqoeaq1dVVKIrSFk/ZjSUEgn7Iat8GqtUqCoUCUqlUi9+SNk1C6Vyr1cq2F1arleskyBaDFh+Hw8ERQr0XooWFhRaLAe3O/kZfwPoiS+PxtgLa3f/GsUkAPrEjBVo7aPX+DD6Lnp4ersmqVqs8H3NwcBAPP/wwjhw5wq/dCmL8y0wikeA5v+TjmUwmAYCj/RQRs9lsW8Li6cuC9t6NRCIt97r2/t74MxtrmOk1VBupF41Gg5sH6dhoPaI0KDVHbPzSjh00GAy4fPmy7psNof2IAGwDTqeTu9zIgDYQCKDZXHdwj0ajSKfTnB6Kx+OIRqNIJBLIZDIoFAooFosoFApslzE8PIyRkREMDQ3pHhpfXFxkE+iNkzE2hvO1HZBzc3N6HXIL2uPVCljtjlq7uG5sdvkiNE2EQiF87Wtf427NcrmMu+++G7t3797UaR9CK9oJIBRNpoaodDrdUkeqquqW88v8srC4uMhTV8jahlLuNJ2pWq2iWCyyU0OlUuFfs9ksEolEW2fpboSms1BwgIYCaCeZUJSTvDXJUNlqtcJms/HIwcnJSdlsdCASh20Do6OjGB4eRj6fh9VqhcFgwC233AJFUbC0tMQdT1SzQbUcxWKRo342mw1DQ0PslfWbv/mbqFQqcDqdXESuFzQiiRZT7dQGKq4G1oWSzWaD3W6H1Wpluwi90UYCtFG9jelQreeUVtR+WtRwK0DH5PP5cO+99+Jv//ZvAazP2X3ooYewbds2ft1WF7FfBrRdwCQ61tbWAKzbkmjHwxWLRczMzOh5uF9aDAYDFEVhI31VVdlqi8YMUsOO2Wxm0WSxWOB2u9mhQVsz2+7aWUoBm81mdmLYWMKinZ5BbNyQ22w2nDlzpqXeXOgMRAC2CWrY0PLss88ikUhgdXUV0WgUKysrANYjAfv27UM8Hkej0UAoFMLIyAh6e3v5hib7mK3A6dOnuamDdpK048zn81hZWeFuv6GhIdx+++04cuTIJ6Zv6IXBYODZlNqUrnZ3TykWbRcdHT8ZLm/F5gl6f8me47nnnuPJMwcOHIDH42l5nbC52Gw2KIqCvr4++Hw+pFIp/gx8Ph+2bdvGHdrt6oztFLSbugceeAAPPPAAf69arSKbzSIWi7E9jbZL2+12w+v1bql73Gaz4cknn0Q6nebJLNqNKJ3vRu9JLc1mE5FIBAMDAy3NP0JnYGhuxbCFIAiCIAiCsGlIDaAgCIIgCEKHIQJQEARBEAShwxABKAiCIAiC0GGIABQEQRAEQegwRAAKgiAIgiB0GGIDozMvvPACfvKTn2BpaQkmkwn1eh25XA42mw3lchk2mw2PPvoofvd3fxfhcHhLz2o9f/48Xn31VbzzzjsIhUIYHx+H3+9HNpvFlStXkM/nMT4+joceegiHDx+G3W7nn90KPnS1Wg333Xcfpqen4Xa7eQC81heQRiWpqorR0VH8y7/8C4aHh3U97o3QNdJsNvGnf/qn+K//+i+Ew2F0dXVBVVU4nU7k83lcunQJhw8fxq//+q/j4Ycf5p/fCp/FlwmtuTMAvPLKK/jJT36CS5cuwe12IxqNwufzIRaL4ZFHHsEzzzyDvXv38uu36ucRj8fx2muv4Yc//CGKxSLcbjdcLhdisRi2b9+O3/iN38DRo0f1PszP5J//+Z/xyiuvYGlpCaVSCdFoFBaLBd3d3XC5XIhGo7DZbAiFQgiHw3C73QiFQvirv/qrLfmZCMLnQQSgDtCCvrq6iunpaSwsLKBarbaMGaJxUc1mE/F4HPPz8wiHwzof+SeJRCL43ve+h7W1NSwvL2NxcRGJRALVahUnTpyAw+Fg41u73Y7Z2VlMTk7ygPtvfvObuP/++9syg/L/4o033kAsFuMxXPV6vWVYvdlsRqlUYnGeSqXw0UcfbTkBSBuEU6dOoaenBwcOHMD58+cxOzsLYH0WZyQSwejoKIaGhlCv1/Hxxx9j+/btAMQT8GaxcRb21NQUjh8/jrfffhupVAqKosBkMiGRSMBgMKC7uxvvvPMOlpaWcOzYMdx3333YtWvXlvw8zpw5g/fffx+XLl2C3+/ntanRaKCvrw9utxunT59GqVTCgQMH4PV6dT7iG0PTPGgqi9frZf9SuvcbjQbPZN+5c+eWNHwXhF8GEYA6QA8GGvXWaDTgcrlQKpXYvd1oNHKEzGaz8fDsrfIwqNfrmJ2dxfe//31MTk5CVVWeWkJRP1VVoaoqms0m7HY7PB4PzGYzMpkMj2H60Y9+hPn5eTz55JO6C1ya0kID4LVm0BaLpWWOsdls/sSkkK1CMpnEzMwMTp06hUwmA6/Xi8HBQeTzecTjcRiNRiiKgl27dsFms2FmZgaJRALpdBrBYBDDw8NbNsr8RcJgMKBWq+H999/H22+/jfn5eczPz/MEH9poeL1eKIqCarWKcrmMa9euIZ1O4/z58+jv78dtt92GRx99VO/TAQCsrq7iypUruHjxInK5HEKhELxeL1RVRaFQQDKZ5GPOZDKYmppCOp3Gzp07MTExoffhf4KZmRlEIhFev2iOrtlsRrPZhMlkQqPRQLlcRqPRwMLCQkvmQhC+yIgA1AEScdlsFrlcDs1mExaLBcVikadS0Gzder2OZDKJpaUl7N+/f8uIjmq1ip///Oc4ceIETCYTiyKLxQK73Q6Hw4F6vc6LKIkq7WB1g8GAK1euYGVlBbfeeis8Ho+ui2utVuOJH5SOJxGoddenY99KIrDZbKJQKODChQuIxWKYn59HMplEuVyG0WjE4OAgrFYrTpw4gb6+PuzZsweDg4NoNBpIJpPI5XLI5/PweDyIRCI4fPjwlojKfpEplUq4fPkyXnnlFZw5cwblchkul4vngNO15vV64XA4oKoqFEVBo9FALpfD5OQkLl68iPn5eQQCARw6dIg3gu0ik8lgbW2N5+AuLCxgZWUF2WwWjUYDbrebJ5ksLi4iGAxiZGQEoVAIFosF0WgU165d42uMJqH4/X44nc62n89GVlZWkEwm4XA44HQ6eXSl0WhEtVoFcH29rtfrSKVSvDYLwhcdEYA6YDAYMDMzg6tXryKVSqFSqfAc4GazCZ/Ph9XVVdRqNY60nThxAtu3b8fo6OiWeDBXKhW8+eabaDQacDqdfKzAeorR7XbDbDajXq/DbDbzjGPtrFOz2czpyOnpaQwNDaGvr0+3c7pw4QJUVW2Z+UtRnGq1ytFBmqFZr9eRyWR0O14tJDb+/d//HcB6NNPj8cDtdkNVVRiNRgwPD+Ojjz7CsWPHsHv3bqTTad58UER6cXERFy9exNDQEHp6enR/QH+RSaVSeP3113HmzBn4fD4A4JSidkMBrKdOKUJOf6Zr7NKlS/jBD36AW265BW63u23HX6lUMDs7i/feew+lUgmZTAZGoxFdXV3o6enBysoKyuUyxsfH0dvbC4PBgL6+PiiKgvn5edjtdgwODiIej2Nubg5LS0v8d+Pj4xgfH+f3RQ9onq/JZEIoFIKiKFhbW+P5wEajkYVhvV5HpVLhtL0gfBnYGuGLDiOfz+Mv//Iv8aMf/QixWAxmsxnVahVut5ujZCaTCRaLhXelZ86cwbPPPov5+Xm9Dx/AerQsmUzC7XbD5/PB7/dDURQoigKXy8Uzc+mBB1yfg6ooCtxuNxRFgd1uh8vlwqVLlxCNRnU9J6pZIoFnNBphNpthMBhQKpX4ddrI4PLyso5HfJ1EIoFXX30VhUIBoVAILpcLzWYTjUYDVqsVdrsdZrMZR48exdjYGCqVCiwWC6e6jUYjXC4XhoaG0Gw28fbbbyOXy+l9Wl9ostks3nvvPTgcDhZ2FF0G1sWfy+WCqqocTabNoDYaHQgEMDc3h3K53Nb6s5WVFVy+fBmLi4scFbv33nsxODiIvr4+TExMYGRkhNOkzWYT+XweqVQK+XwekUgExWIRg4OD2LdvHzweDzcfnT59GpFIpG3nciMWFxfRaDTQ09ODHTt2YGhoCLVaDaqqwmq1olqtwmQyob+/H+FwGAaDgTdGgvBlQARgm2k2m/izP/sznD17FoVCgdOljUYD+XweBoMBiqLwQl+v12EwGGCz2ZDJZPDnf/7nLKj0olKpIBqNcnMEFUibzWYWGy6XC3a7HTabjb8cDgfsdjv/fb1e53TK2toa8vm8rueVTqdZ9AHg99lkMqFcLqNUKvHfGQwGVKtVrK6u6na8RKVSQTKZRCwWQ3d3N0eOaKA9RVprtRoKhQJUVUWxWERvby/27t2LgYEBrnUsFovo6upCNBpFpVLR+cy+2NTrdRQKBdhsNr6fKbJHkb9KpYJgMMhrAH1WtIGiLvRsNov5+fm2ph+vXbuGWCyGwcFBOBwOVCoVFAoF5HI5Pi+XywUAfH1NT0/DarViZGQEw8PDsFqtyGazCAaD6Ovrw8LCAgKBAKrVKmc99IKa74rFIhKJBBKJBIrFIoD16G0kEuGmL4fDAYPBwGUSW6UWWxD+f5D8jg5cuXIFDocDtVqNxVG1WkU2m0WpVEIymUStVkOtVuM0BD3IFxYWcPLkSdx55526pYIpNdpsNmE2m7mAvVKpoF6v86JOKeGNaDtre3p6kEwmAUD37rqxsTFMTk6yGKIIGp0rNbnQ32+V+r9cLofV1VVOsbvdbv4cSNAajUZYrVa2t7HZbMhms3w+FKWyWCxIpVLIZDJQVXVLnecXDSp7oOgxbXq04qHRaGBoaIhrganEwGg0ol6vo1qtcgkFrQvtQlVVdiOgqOUHH3zAKd9arYZGowGbzYZarYZyuYyxsTEMDQ1xE5jJZEKpVEKxWESz2YSiKHA6nVhaWsLU1BQcDgd3n7ebCxcuIJvNwmKxoFwuo1wuw2KxoF6vIxwOw+l0Ynl5mUtaSqUSZmdnt7y1jSD8oogAbCO1Wg1nzpxhwUOpRGBdbFCkgFJvlJYja4JCoYBms4n//u//xpEjR3QTgBQBJPuEVCr1iZ08pbC1TR/aL4omlMtlVCoVLC4u6p5y3LZtG6xWa8tn0mw2OfpH7zc9JBqNBvr7+/U8ZADgKIbRaES5XMbi4iK2bdvGaTn6onpM+lVVVSwtLXEkx2w2o1AoIJvNAlivkaJUuPD5oPebSKfTCIfDsFqt/H1thNlsNsNmswFASw1qsViE3W6HyWTC+fPnsXfv3rbWAebzeSSTSZhMJiiKwmKWumYB8CZDURTeWJRKJb52KEuwvLyMYrGIjz/+GD6fryUyqgfXrl1DX18furu7YTabsby8jFqthrW1NTz//PM4d+4cnnvuOcTjcQDrUc58Pr/lu4BpPcjlcvz+0/VIm1mtuwEFIiwWC1wu15aoMf8sPvjgA+TzeQwMDGB8fPxTX6fdwMsadmNEALaRSqWCU6dOcd0VpYCtVitMJhNMJhM8Hg8cDgdHAUhEkWiyWCy4evXqp0bX2nUey8vLqFQqUFUVtVqNG0EoCqgVHoS289HhcMBkMnGDAtU96YnD4eDIi7bzLxwOI5lMolKp8EOrWq2iVqttiYJwSmFR+cCZM2fgdrsRCAQ4kkzpeQAtqUUAbDlkMpmwsLCAer0Ot9vNn4neD4TPMkJ+6aWXkMlkcOjQIUxMTPA56k25XOYNjd1uR6FQYNFD0EOpUCi0fB4UdTUYDMjlciwQFxcX25oCNplMvD5lMhksLS1hfHwcXV1dvOGwWq2o1Wq4evUqPvzwQ+zfv5/FBolFg8EAVVVx6tQpbN++nf/dYrHImw09MJlMCIfDsNlsXBZBUf7bb78dfr8f3/ve95DJZNDb24ve3l62udlqTE1NsYOE3W7ngIKqqsjlcqhWq1xiQGU6drud3SfS6TR7tlLnttlsxuOPP67bOWmfF8B6Te13v/tdvPjiizAajXj66ac/UwBSLb3w6YgAbCO1Wg2Tk5MwmUycMqFib6rzA9YXJvq9tuuUzJRXVlZ0rZ2p1WrIZrMsLqg4vVQqtUSNNj60tTtPeh09DLLZrO72CrTQNBoNmEwmVKtV5PN53H777ZiZmcH09HSLHyO9D3pTKpU4nUs7+nQ6Db/fDwAtkUBKv1OdoPb7ZrMZ8XgcTqeTaxz13GgQ2uuoVqtxivqDDz7A8ePHEQgEOAo1Ojr6mRGaer0OVVU3PYpWq9VQKpVYJGWzWfT09LSIO+B62YP22qOIBYlIRVG4S7udm6R6vc71b/F4HJFIBMPDw/B4PBwVczqdXCNrtVqhqipmZmbg8/n4XMgaKpVKYWBgAAsLCwDWo6LpdLpt57MRt9uNYrHIDR/Ausg4duwYPB4PhoaGYLVaYbFYeMpJJpPB7t27dTvmG3H16lX853/+J0ZHR+F2u1nckfjRrrvUlEfPnWazCVVVUS6XYbfbEYvFsLy8jEQiAYfDoasApMBHsVjE2bNn8Y//+I9YXFzkTNjbb7+N/v5+fO1rX7vhz9MGw2q18uABoRURgG2kXq9jfn6eBSCJOxJz2gcDRQmB9YdCo9FAtVqF0+lEJpNBqVSC1+vVpRiZOoBVVYXFYmEfLW2xNH1thM7FZDJhbm4OdrsdPp9vSwjAjcdLImjv3r1wOBw4f/58i/Cm9LDekIEwdSwbjUZOz2vH2NH1RL6M2oYWEoC5XI7FEdV4bQVKpRIikQhmZ2cRiUSQy+UQjUbR29sLj8eD1dVVnD17FvV6HaOjo9ycAIBLDOLxOEc77r333k19KFSrVY6AWSwW5HI5HsNHnf4krrU1tfS5kEjPZDIYGBiA0WjkZqt20Wg0uEyD6hAXFxdZrJbLZZhMJjidTvT39yMWi8FkMqFQKMDlcqFQKKBer8Pj8cBoNHIZBYkUMoTXa7yl1+vF2toaiyHaZD/99NPcoU2bcWpyqdfrWyICSOJtdXUVb775Jq5cucKZCpPJhK6uLuTzeeRyOT4Xrb8p1TzSRpY2RsvLyxy5NplMmJ2dxbZt23Q5x2KxiOXlZXz44Yf46U9/irfeegterxfBYBClUgkXL17kzeChQ4ewa9cu/tl4PI73338fU1NT2LNnj65CdisjArCNNJtNpFIpdHd3s62CtjaBFkKKyNBDQmtFQkX8qVQKgUBAl/ScVgCaTCak02lYLBYEAgF+8G5MAdMDTmsMPTk5CZ/Pp3vzB0ECXHvMRqMRt912GwYHB/Ef//EfAK7XbhoMhrbWY30atKOn+j7tdQRcT4VQ6pfKDUh4aLtOyQfRZDLp3qVJkBH6uXPnMDk5yRHmffv24c4770QsFsOVK1dw4cIFpNNpZDIZDA0N8Tmk02mcPXsWs7OzyOVySKVSGBsb23QBSB2lBoOBo+Qk8uiz0NYKakcPAmiJMFMHarsisuR9GY/HUalU0NvbC5vNhrm5ORSLRR6LmMvl0Gg0MDg4CIvFAovFgnA4jEqlwuKKPPRKpRJSqRR6enrgcDiQTqe50UQr2NuF3+/nzAVFwVwuF77xjW8AAEcvqQyHSj60aXw9oE10NpvF8ePHsbKywpHmYrGIRqMBi8WCQqGAjz76CIVCAQMDA+wKQM+MWq0Gt9vNG9nV1VWoqsrRw1KphFOnTrVVANK6ms/nMT09jTfffBOvv/46PvzwQzz00EPIZDJ8bSYSCczOzuLq1av45je/iW984xt8bh999BFefvllvPXWW3jkkUdEAH4KIgDbCHX10QNWm5qj6BPVXGnD9gB4oarVaggEAlhbW+NFt93QQ8vpdMJoNHJ9DDUV0MOOFip6oNHDy+FwwOfzseUILcRU+6iXxQIt9CSoyEPvnnvuQaPRwB/8wR+w+CMhpff4OuC6tQjViWqnyJDY00LRZRJ/dC1qm0nq9TrXRG022g2ANjVKjTb/+7//yybdw8PD2LZtG6rVKiKRCE6dOsVdqJlMBjMzM3j77bcxODgIt9uNaDSKQqEAr9fLM2utVuumWw5RkxNt4jweD3p7e1s2RnR/aKHPkur+gOv3jXYizWaTzWaRzWZhNBoRDofh9/uRSqUArEdXyAqGBPfVq1dRq9XgcDigKArOnz+PVCoFj8eDRqOB1dVVBAIBFItFnqtNkahMJqOLAAwGg5xRoYk5wWAQXV1dANY/i3A43LIh1LsBhK6XYrGI9957D3/xF3+Bqakp/PZv/zYKhQKCwSB7rKqqikAgwHZEG9dVqr2uVqvIZDKcBtdu3JeWltpyXto1tVwu4/Tp0/jBD36AN954Az09Pdi/fz88Hg+cTidSqRRyuRyPuAwGg3jxxRfx8ssvw+/3c8MksN7YNz8/j2q1qnst81ZEBGCboB0x2Sds376drTtUVYXD4QBwvUaDogE0msjhcOC+++7D5OQkHA4HFhcXceutt+qycNJIKDq2wcFB3un7fD44nU6O9JHQ0IpdoLWGLhKJYGZmBh9//DESiQRCoVDbzwkAhoeHYbPZUCwW2RBWG+WjxclisbSk5fWG3l9KyVP6l4QECTr6TLQTTqh2jnwbu7q6kE6n0dXVheXlZYyNjfEDcTOPXwtFN2ZnZ7G8vAxVVXmObC6Xw8zMDItcqnukcWRWqxW5XA7z8/PIZrMYGRlBd3c3ms0mYrEYVFVFKBTC/Pw87rjjjk07J6qNBa7X0g0MDGBpaaklxUubPeB69KNYLKKnpwcTExNYXl5GPp+Hz+fjebTtgNLXiqIgGAyyYHO73Ry5s9vt6Ovrg9PpRCKRQDAYRK1W4/F3fX192LVrFwqFAorFIt9buVwOPp8PpVIJ6XRaN7/Jnp4eeDwenksOADt37uTvU2Tc4XBwOlgvtLWhqqrizJkz+M53voN/+qd/Qnd3N959910cPHgQHo+HJ5wMDg4iEolAURTE43EOItD1l81mkUgkWBwpisJNVGazGU6nEwcOHNiUcwGulz7Q7+l58eMf/xjPP/88stksHn74YWSzWVy5cgVvvfUWduzYgUajgWAwCFVVkclkkEgk0N3dDbvd3jJNq9lsYm1tDbOzs3j33Xdx99133/Rz+aIjArBNVCoVdr6vVCo4evQo3n//fUxPT6Ner0NRFC781hbn068ulwv3338/XnvtNYyMjKBYLOpWoE9ilixr5ubmsH37drhcLu4c1daV0XGSACTBsWPHDpTLZfj9foyPj3MKQy/uueceKIqCZDKJarUKo9GIHTt28Pcff/xxvPLKKy2ibyvYC1D3tcFgwOzsLL761a/yPGltRNVgMCCdTqNYLLK3pN1u52tz+/bteOCBB3D8+HGEw2F+0G8mq6urLBAymQzPjFVVFdVqlSMuq6uryGQyvLiTSKR0kHZzoY3iJJNJzM/PIxgMcoe9qqpYXFzc1PMiAUipUmBdnGofftpftfWZFPn0er0YHx/HO++8g4GBgbbe7/F4nJtYaMJHLpdDuVzG8vIyQqEQexR6PB4kk0nk83mul9u9ezf6+vrgcDiQTCbZxzSRSGB5eZnvdYvFgkgkgpGRkbadG0Fj3fL5PDejDA0N8fcdDge8Xi/bpHi9Xt02p8D1UoKXX34Z//AP/4ADBw7giSeeAAAcO3aMBThwfY1eWlqCoihcGkJeoZQKdjqd3LyTSCR4PrOqqvD7/ZviebhxzWw2m8hkMjh79iz+5m/+BhcuXMDhw4cxNjaGhYUF1Go1jI6O4urVq1hbW0M4HGZh7nA4kM/n+dipDt1kMvHggVqtBkVRbvp5fBkQAdgmqtUq+/9Fo1H09/fj3LlzANbD+dFoFE6nk1vxaZdGUUCDwcBRjmw2i0wmo2uHptFoZHPUrq6ult09dY9qIx0bC90VRcH4+DimpqbYJDadTiOVSrUswu1Ea7lTr9dht9uxd+9e/v727dvhcDi4zobeBz2hyJ+qqvB4PDh37hyefvppzM3N4erVqwgGg/D5fGxvUS6Xkc1m2c7GZDIhlUphcXERFosFX/3qV3H8+HGUSiX4/f5NjXr867/+K2KxGLxeLwCwtU69Xm8RDKqqotlssoBaXl7mBxpFzIDr3Y5UZtHV1QWfz4fR0VE0Gg3+3EKh0KZfY9QEQlZHNP5QG923WCwt97e2a7ZeryOfz7P3Htl6tItoNIpSqcTNH/l8HhaLBT6fD2azGZVKBXa7nSPLe/bswYkTJ7Bv3z4Wr9pu8mw2i3Q6jaGhIRw9ehQej4fn7+oVRU+lUlhbW0M6nUapVLqhX1wikWAPR6fTiT179rT9OLXNQS+88AL+53/+B3feeSf++I//mF9DndlWqxVGoxErKytYWVkBsD5liSzEPB4PLBYLb/60G49kMolCocDrN3Wg32zi8Th3la+uriIWi+Gdd97BRx99hEgkgt27d8NmsyGRSPCzMJ/PY2JiAtPT08hms+jq6oLT6USlUuGObe37lU6nsbi4iHQ6zbPmb7311pt+Ll90RAC2CarPot/39/dz6J06aOkmp+kMAHhOMNWjUVFyKpXSzTevXq8jlUrx7iqdTvOsWa/Xy4XSZKq8scaRIlPU/asoCtcCbXbE6fMQCATwyCOP8J+PHj2KH/7whygWi1xQTal7vaDaGa2dSFdXF9577z2uCSIRR9Ea+jnyMyT/yWq1ir6+Pq6BpGjOZjE1NYVyuQyPx8PpHxLfFouFBY/NZvvENBbqVNQKJ6pppA7HaDSKrq4u9nCkDZOqqjhy5MimnRewLgBpxm+xWITL5eJyDXpvtel4bV0gAG6aoLFpJBzbRSQSQSaT4WkmsViMZ3fTZ+DxeLhUwuVycdkB1VpRxGl5eZnN4p1OJwqFAmcJGo0GMplM285Ly9DQEB8jraVUO0ZQ9y/NOG5XTZwW8oN86aWXcPr0aezYsQO/8iu/0tKA9pWvfIXnkpOZc1dXF0KhEG/CybybNk5ULkLPIZ/PB4PBgEwmg7m5OcTj8Ztaj53P5/H7v//7KBQK3ECkqiq8Xi9cLhfPi15aWkK9Xkd3dzeCwSDXzsfjcQQCAaTTaczPz8Pj8XAUl8pz0uk0jyukTaLH42mZ5S5cRwRgm6AHGzU7UKEq3XwU7aMHoTYCSLUy9XqdbVa0c2n1QGst4vP5MDAwwJ5S1EBBDQkkQOhn6Hz37NmDV199FU6nE729vS1iRQ8olUjvq9frbdnx33rrrWx4TQ9kvWsAqaOUhLXD4cD4+DhSqRQLODovg8HA1x4JjkqlwtG1Wq0Gq9XKgouEx2ZBtkhUT0kPJbPZzClqSm3TQ4w6k+lBRpsmrWFsPp9HpVJBJpNBoVDgv9feX7FYbNPOC2hNAVerVfYq1Ip1beRP+0WfS71ex86dO1uaQNrF/v374XA4EIvFkM/nedNDKXSHw8EP8VwuB7vdjpMnT2L//v0tWQC6n/L5PAqFAvx+P1+Dfr8fAwMDGB0dbdt5aQmFQrzm0P0QjUZbXqP1Y8xkMlhbW9PlWH/2s5/hww8/hMvlwt69e7kzl+7rffv28Ubc6/VyxC8YDLIgp4YPaiaiLnm6rqgm2Gg0IplM3nSLq+PHj+PEiRNwOp2wWCy8LtHmn5q5TCYTstksBz3I15CEOqXtyfyeNiUulwuxWIyjuVarlTdXfX19N/VcviyIAGwTtKhTR2KxWOSUFD0E6EYlHzdgXTTRz9FuiXZteghAenhRKqFarWJgYAB2u72lmJoWGnr40kKlbUqgHRyl/PQWVPl8nkUCsJ4SJjNlYP2BQRE/OlY9axaB612j9Cul1gqFAgKBAEcw6LUUfQJaRyVpazWpMUEbNdsM7rnnHvh8PszOzmJpaYk7c6l5RWuRQgu5NrVK0UttlJkealSfRteb1hzXaDRytGSzoOOkiGRfXx/K5XJLp+XG6R90DiS+K5UKRkZGWjzq2tUFPDExAa/Xi1gsxvWZVLaSTCZhMBiQTCaRTCaRzWaxtraGXC6HlZUVRCIRjjZpMxgDAwM4evQop/x9Ph/C4bBu9Vl2ux39/f1wu91IpVJoNptIJBJYW1tDd3c3gOsem+Vymbue280HH3zA3e4HDx7Enj17PhGZ7+/v55RoMpnksgEqh6A1Qnud0T1Pf0fZJwDo6+u76cKc1nmymyKhRmMDqVnKaDRy2QBtTsmyiTrN6edSqRRvGslezefzQVEUeL1emM1m2O32luYe4ToiANsEpVLS6TQmJia4oJou3mazyd2lWpNYAOwDmMlk4PP52F9LD0h80hihQqGAcDiMWCzGDwYqJKaUz0ZBS2mXVCoFl8uFZDIJs9mMWCym6zzgXC7XMvieopVaqMYGAC9CelKpVDgq1mw2OcVLzv4U7aP3nbwbb5Q6pc+IjMo3e5Px9a9/HceOHcPZs2dx/vx59iFLpVLIZrOcjtN6GpLwpvOlz0preUOiSjtb1263txj7aoX9ZkDCga6RiYkJTn3SMWungmiNua1WK3dy+v1+jn5oG102G7PZjKGhIa6tajabSCaTiMfjuHjxIo+BTKVS3Mxxyy234MKFCzyZhvz9stks7HY7nnjiCdx///1tOf5flL6+PrhcLr6estksTp06haeeegoA2MyabJGcTmfbj/HMmTOwWq04fPgwjh071hLNomudLFHofaeGHO20Jq1/KZWwAOB7hqLuVqsVPp/vM8es/TI89thjOHPmDHK5HJaXlxGLxXgaFh0LRQVJnJZKJWQyGRSLRWzfvh1ut5uNxAuFAqLRKFsV7dy5k7vNA4EAfD4f2xLpVVe+1REB2CZoN1ar1RAKhVp8l7Q+bhTxczqdLd2DNpsNsVgMbrcbkUiEU3ftplarIZfLIZFIwOv1clQyl8txFzBFxWjRpF0oCQ1K9VWrVQSDQba5ILd6vdAKHu3MTC204FJBtd6j0miBpzolbZ2ZNr1I7zstuNqfB8BpVQAtae7Nvsa6urrwyCOPtNRa1mo1LC4uIpVKIZFIoFQq8YNBK14pYqBNV9PvtRMmSMjTZsvj8eDhhx/e1POiqB0Z6t5xxx342c9+xl5nG19L1x09/Gg2rcFgwMDAAHel0+ey2ffJRqsOg8GAYDCI73znO3jsscdQrVa5M9bpdMLn87FR9aFDhxCPx1uiPIODg/it3/qtT/wfWisQPaC6t0qlwg1RL774Ip588kmOOpMIAYDBwcG2H+NTTz2FSCSCsbGxTxWgLpeLzY5p86EVeVpuNKazHYTDYTz33HMA1scALi0tYXZ2FjMzM1hZWUGhUOCsA90jdC7VahVjY2MYGhpCIBCA2+3mzbndbuemNor6UySTMgHCjZF3pk1oi/VdLhemp6c5GkMPKTJepW5Guklp4Z+bm+M0XjujAVootUUNHgbD+pihqakpZDIZFnYE/Zke3HRuJIiLxSLm5uZ4osAtt9zS9nMiyD6AIB+wG71GW0ejJ5QqJBG4d+9eTrFQ5IJq5Wi2cbFY5KYdui616UWv18t2MZSeaed5ms1mbNu2TbcRVDcTEqZDQ0Nczwhcb8LRWiRpvTIpsqsoCvbv34+pqSkYDOvzgIvF4qZPoPk0gRmLxRAOhxEMBuFyuZBOpxGNRrnzsru7GyaTiVN2tCG80fHq3UEPALt27WJPQqqDnZ6e5u8HAgHMzs4ilUrB4XBsii/e/0U4HP5chvO02dnK+Hw++Hw+Xdd7QQRg2yC7DurKWlhY4B0aiUASS+S/RTV09PCNRqMc/tZakbSTYrGIhYUFnDt3DoODg6jX6zh48CAmJyeRTqc55UvpRxq2vrGmy263IxgMIhKJYH5+HhMTExgbG9v01Nxn0dPT02KsTcethXaeVIfm8/nafJStUH1SrVZDqVTiBxQVTGsbDoDrNaUkxKmTu1qtcrSgu7ubPfYo+qa30P0i02w2EQgE+DOh8giK+mtnOJNYB65HP3bv3o2zZ8/C7/e31BHqcR7/9m//Bp/Ph2AwyL543d3d3OUbCoV4yo+2IWF6ehpvvPEGHnroIV2O/dOgWdK0gTKbzejt7eXvk3Atl8twu904dOiQXocqCDcd/bdgHYLNZsPQ0BBHzChEr50/q03vUK0SdczSA2L//v0A1sWKHrs8u92O4eFhfOUrX0Fvby92797Njvper5fNdunYSNBmMhmu66IOQSoKpk5oKgDWC5qKAVxPrW4kGAxyIwhNQtATbU1Ps9nkOiBt9x8ZRZvNZgSDQY7eUnqeREh3dzcMBgP6+/tbTMj1qjf9IkP1S+VymQvftQbcdK1RzSmlrbS+gBTJveOOOzgySAbZ7YY6p2OxGAwGA0ZGRriZw2azwefzwWQy4cqVK7h27RqPtDQajfD5fDh48CC++93v4sMPP+R7X48Mxo148MEHcffdd3PJjbauV9tBLwhfNiQC2CZqtRpSqRRKpRKKxSKnfyk1Rw9hei3QWvxO0YGrV6/i4sWL2L9/v241gIVCgUcIdXV1wWg04vLly8hkMjx7cmN6S9vhSF9XrlzBE088gUqlgqtXrwIA9u3b1/Zz0kLFxzS7lSBhPj4+zo04ZFeiJ/Q+UyNIX18f20FoO2RpbJ12PBkVh9vt9pYpLFTnRNFFvescv4hQTWWtVmOhNDMzwwbKN9roaGsBs9ksUqkUpqam8PDDD/MmkEyL2025XMbrr7/ODR7UnUk+h+l0mhtFvv71r3NUmexITCYTdu7ciddeew0TExO63zfA9cahffv24bbbbsPJkydhMBhw6dIlfg19HiaTCXa7fUvM/haEm4UIwDbhcrlw6NAhGAwG+P1+TE5OcjMEgJaCdfL7A64LD/KjGhgYwLPPPos9e/boYp9AYgNYTwf7/X42KiXD20/bNWv/3Gg0sLa2hsOHD8Pr9bJNh94FuxStoRQ1obW/oVSpxWLhWie9oOOyWCxQVRU2mw1ra2solUoolUrcWW6z2VoeZmazmbtkSbCTDcvw8DA/3LWiRPjF0Ro305izO++8k30JKbKqFde0MbLZbFwGMjIyAp/PB6/Xi2azyYXy7YCuLfKZvHDhAoD1xh3tdUF1oj09PZibm4PNZkO5XEYqleLRdhaLhTv+q9XqlhCAJMIDgQB6enrgdDqhqmpL4wTVKpNRsp6j4AThZiMCsE04nU7s27cPo6OjKJVK+PGPf4yVlZWWVIk2tUMRG20qzmw2495778Xo6CiMRiNbfrQbo9EIm82GSqWCYDAIt9uN3bt3o16vo6uriwUgiQ/g+sNNG4nq6+vD+Pg4C0CtBYteaK1TbhSlcbvdLBLbYSfyf0HHSSl0sgsil33qqiNBUS6XPzHKTmsDAYDnUtNmRG+z6y8iJLKtVivuuOMOGAwGPPbYYzzDm742+v8ZjUa+vsxmM0ZGRmCxWBAOhznq3K57hARgrVbD2toap7PD4TCntNPpNLLZLCqVCrZt28bjIQFwBzB1CBeLRZ4b7HA4eLOnV3qV/l+yvNm3bx/eeustOJ1O3sxSBN1kMkFRlC0hXAXhZiECsE2QYCPR9uCDD+LkyZNYWFhAoVDgXScZXVJEjTq6HA4HxsbGNn2E1S8C1StWKhW43W4oioJf+7Vfg8VigaIoLC5IAGqjgeSHSBYXLpeLbS9ojI+ekIehNjqjTbUrisKRM+1oNb2gNG6j0WDLGo/Hg9HRUfT19bFtD0X5/H4/P3hpoDpFX3t6egBct+8BwJ3AwueDIqyqqrLlzI06HrVRNu3fbfzztm3bsLy8DLfb3daZwMC6QfrU1BRbkNAIsWw2y9cWCViPxwNVVdlM3GKxwG63I5lMolgsIhwOY3FxkZvh9LaBofd6eHgYd9xxB06ePIlyuYxr165hz549XAtst9t13+wJws1GBGCboQXn29/+Nh5//HG88MILeOedd3jSh9ls5jo08sYbGBjAXXfdhWeeeabl39CDWq3GU0wKhQJisRiazSYeffTRX+rfSyaT/BAslUq6zwIm93i3233DBg8aY0SNF3rbLVCtGHUlA8CBAwfwd3/3d8jn88hkMsjn81BVld9bMnut1Wpwu93w+/3wer28CaHPdWxsjAWm8Pmg+ziTyWD79u0Ark/70PJZApCaw0wmE44ePYq33nqLI7PtgP4fVVXx8ccfc4cyiT+73Y7R0VHuLL98+TL8fj+azSbi8TgqlQqsVisKhQKazSaCwWCLR+NWaKygEhsqbalUKjAajfj5z3+OHTt28AZIURSejCFNIcKXBRGAbUa7cAwNDeGP/uiPUKlUMD8/j5/+9KdYXl5Go9GA1+vFwYMHcfjw4ZZmBL0757q6unDffffxVJNbb70VVqu1ZazVjY6RHmba75Ff2Le+9S2Ew2H09PTo7v0WDAahKAqCwSD6+/sBoOXcjhw5ggMHDmB1dRWDg4Ntj8ZspKenBwcPHkQgEPhEfRK55n9euru78dJLL92sQ+xIBgcH8cQTT7TMlv1lhBtF0++55x6cO3cOBw4caHskqre3F7/3e7+Ha9eu8WSMUCiESqWCpaUlRKNRbNu2Dfl8Hvv27cPevXtRLpc51TswMIB6vY7Lly/zeMVPa4RpN3Rv04YvFArB7Xbj2LFjMJvNGB8fh6qqOHLkCA4fPqz34QrCTcXQ1FtRCIIgCIIgCG1F/y2YIAiCIAiC0FZEAAqCIAiCIHQYIgAFQRAEQRA6DBGAgiAIgiAIHYYIQEEQBEEQhA5DBKAgCIIgCEKHIQJQEARBEAShwxABKAiCIAiC0GGIABQEQRAEQegwRAAKgiAIgiB0GCIABUEQBEEQOgwRgIIgCIIgCB2GCEBBEARBEIQOQwSgIAiCIAhChyECUBAEQRAEocMQASgIgiAIgtBhiAAUBEEQBEHoMEQACoIgCIIgdBgiAAVBEARBEDoMEYCCIAiCIAgdhghAQRAEQRCEDkMEoCAIgiAIQochAlAQBEEQBKHDEAEoCIIgCILQYYgAFARBEARB6DBEAAqCIAiCIHQYIgAFQRAEQRA6DBGAgiAIgiAIHYYIQEEQBEEQhA5DBKAgCIIgCEKHIQJQEARBEAShwxABKAiCIAiC0GGIABQEQRAEQegwRAAKgiAIgiB0GCIABUEQBEEQOgwRgIIgCIIgCB2GCEBBEARBEIQOQwSgIAiCIAhChyECUBAEQRAEocMQASgIgiAIgtBhiAAUBEEQBEHoMEQACoIgCIIgdBgiAAVBEARBEDoMEYCCIAiCIAgdhghAQRAEQRCEDkMEoCAIgiAIQochAlAQBEEQBKHDEAEoCIIgCILQYYgAFARBEARB6DBEAAqCIAiCIHQYIgAFQRAEQRA6DBGAgiAIgiAIHcb/A4LVFC+g4DDwAAAAAElFTkSuQmCC" | [((17, 15, 17, 47), 'json.loads', 'json.loads', ({(17, 26, 17, 46): 'TEST_HYPERPARAMETERS'}, {}), '(TEST_HYPERPARAMETERS)', False, 'import json\n'), ((20, 15, 20, 39), 'json.loads', 'json.loads', ({(20, 26, 20, 38): 'TEST_SUMMARY'}, {}), '(TEST_SUMMARY)', False, 'import json\n'), ((29, 23, 29, 50), 'app.model.TrainMetrics', 'TrainMetrics', ({(29, 36, 29, 42): 'header', (29, 44, 29, 49): 'items'}, {}), '(header, items)', False, 'from app.model import TrainMetrics, TrainPlots\n'), ((34, 16, 34, 28), 'app.model.TrainPlots', 'TrainPlots', ({}, {}), '()', False, 'from app.model import TrainMetrics, TrainPlots\n')] |
fkleinTUI/pyBinSim | pybinsim/pose.py | 5320f62422e0a92154272f8167b87cabdcafe27f | import logging
from collections import namedtuple
logger = logging.getLogger("pybinsim.Pose")
class Orientation(namedtuple('Orientation', ['yaw', 'pitch', 'roll'])):
pass
class Position(namedtuple('Position', ['x', 'y', 'z'])):
pass
class Custom(namedtuple('CustomValues', ['a', 'b', 'c'])):
pass
class Pose:
def __init__(self, orientation, position, custom=Custom(0, 0, 0)):
self.orientation = orientation
self.position = position
self.custom = custom
def create_key(self):
value_list = list(self.orientation) + list(self.position) + list(self.custom)
return ','.join([str(x) for x in value_list])
@staticmethod
def from_filterValueList(filter_value_list):
# 'old' format: orientation - position
if len(filter_value_list) == 6:
orientation = Orientation(filter_value_list[0], filter_value_list[1], filter_value_list[2])
position = Position(filter_value_list[3], filter_value_list[4], filter_value_list[5])
return Pose(orientation, position)
# 'new' format: orientation - position - custom
if len(filter_value_list) == 9:
orientation = Orientation(filter_value_list[0], filter_value_list[1], filter_value_list[2])
position = Position(filter_value_list[3], filter_value_list[4], filter_value_list[5])
custom = Custom(filter_value_list[6], filter_value_list[7], filter_value_list[8])
return Pose(orientation, position, custom)
raise RuntimeError("Unable to parse filter list: {}".format(filter_value_list))
| [((4, 9, 4, 43), 'logging.getLogger', 'logging.getLogger', ({(4, 27, 4, 42): '"""pybinsim.Pose"""'}, {}), "('pybinsim.Pose')", False, 'import logging\n'), ((7, 18, 7, 69), 'collections.namedtuple', 'namedtuple', ({(7, 29, 7, 42): '"""Orientation"""', (7, 44, 7, 68): "['yaw', 'pitch', 'roll']"}, {}), "('Orientation', ['yaw', 'pitch', 'roll'])", False, 'from collections import namedtuple\n'), ((11, 15, 11, 54), 'collections.namedtuple', 'namedtuple', ({(11, 26, 11, 36): '"""Position"""', (11, 38, 11, 53): "['x', 'y', 'z']"}, {}), "('Position', ['x', 'y', 'z'])", False, 'from collections import namedtuple\n'), ((15, 13, 15, 56), 'collections.namedtuple', 'namedtuple', ({(15, 24, 15, 38): '"""CustomValues"""', (15, 40, 15, 55): "['a', 'b', 'c']"}, {}), "('CustomValues', ['a', 'b', 'c'])", False, 'from collections import namedtuple\n')] |
madelinetharp/morphocut-server | morphocut_server/extensions.py | a82ad5916adbd168816f7b26432b4a98d978c299 |
from flask_sqlalchemy import SQLAlchemy
from flask_redis import FlaskRedis
from flask_migrate import Migrate
# from flask_rq2 import RQ
from rq import Queue
from morphocut_server.worker import redis_conn
database = SQLAlchemy()
redis_store = FlaskRedis()
migrate = Migrate()
redis_queue = Queue(connection=redis_conn)
flask_rq = None
| [((10, 11, 10, 23), 'flask_sqlalchemy.SQLAlchemy', 'SQLAlchemy', ({}, {}), '()', False, 'from flask_sqlalchemy import SQLAlchemy\n'), ((11, 14, 11, 26), 'flask_redis.FlaskRedis', 'FlaskRedis', ({}, {}), '()', False, 'from flask_redis import FlaskRedis\n'), ((12, 10, 12, 19), 'flask_migrate.Migrate', 'Migrate', ({}, {}), '()', False, 'from flask_migrate import Migrate\n'), ((13, 14, 13, 42), 'rq.Queue', 'Queue', (), '', False, 'from rq import Queue\n')] |
aleonlein/acq4 | acq4/drivers/ThorlabsMFC1/tmcm.py | 4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555 | from __future__ import print_function
"""
Low-level serial communication for Trinamic TMCM-140-42-SE controller
(used internally for the Thorlabs MFC1)
"""
import serial, struct, time, collections
try:
# this is nicer because it provides deadlock debugging information
from acq4.util.Mutex import RecursiveMutex as RLock
except ImportError:
from threading import RLock
try:
from ..SerialDevice import SerialDevice, TimeoutError, DataError
except ValueError:
## relative imports not allowed when running from command prompt, so
## we adjust sys.path when running the script for testing
if __name__ == '__main__':
import sys, os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from SerialDevice import SerialDevice, TimeoutError, DataError
def threadsafe(method):
# decorator for automatic mutex lock/unlock
def lockMutex(self, *args, **kwds):
with self.lock:
return method(self, *args, **kwds)
return lockMutex
COMMANDS = {
'rol': 2,
'ror': 1,
'mvp': 4,
'mst': 3,
'rfs': 13,
'sco': 30,
'cco': 32,
'gco': 31,
'sap': 5,
'gap': 6,
'stap': 7,
'rsap': 8,
'sgp': 9,
'ggp': 10,
'stgp': 11,
'rsgp': 12,
'sio': 14,
'gio': 15,
'calc': 19,
'comp': 20,
'jc': 21,
'ja': 22,
'csub': 23,
'rsub': 24,
'wait': 27,
'stop': 28,
'sco': 30,
'gco': 31,
'cco': 32,
'calcx': 33,
'aap': 34,
'agp': 35,
'aco': 39,
'sac': 29,
'stop_application': 128,
'run_application': 129,
'step_application': 130,
'reset_application': 131,
'start_download': 132,
'stop_download': 133,
'get_application_status': 135,
'get_firmware_version': 136,
'restore_factory_settings': 137,
}
PARAMETERS = { # negative values indicate read-only parameters
'target_position': 0,
'actual_position': 1,
'target_speed': 2,
'actual_speed': 3,
'maximum_speed': 4,
'maximum_acceleration': 5,
'maximum_current': 6,
'standby_current': 7,
'target_pos_reached': 8,
'ref_switch_status': 9,
'right_limit_switch_status': 10,
'left_limit_switch_status': 11,
'right_limit_switch_disable': 12,
'left_limit_switch_disable': 13,
'minimum_speed': -130,
'acceleration': -135,
'ramp_mode': 138,
'microstep_resolution': 140,
'soft_stop_flag': 149,
'ramp_divisor': 153,
'pulse_divisor': 154,
'referencing_mode': 193,
'referencing_search_speed': 194,
'referencing_switch_speed': 195,
'distance_end_switches': 196,
'mixed_decay_threshold': 203,
'freewheeling': 204,
'stall_detection_threshold': 205,
'actual_load_value': 206,
'driver_error_flags': -208,
'encoder_position': 209,
'encoder_prescaler': 210,
'fullstep_threshold': 211,
'maximum_encoder_deviation': 212,
'power_down_delay': 214,
'absolute_encoder_value': -215,
}
GLOBAL_PARAMETERS = {
'eeprom_magic': 64,
'baud_rate': 65,
'serial_address': 66,
'ascii_mode': 67,
'eeprom_lock': 73,
'auto_start_mode': 77,
'tmcl_code_protection': 81,
'coordinate_storage': 84,
'tmcl_application_status': 128,
'download_mode': 129,
'tmcl_program_counter': 130,
'tick_timer': 132,
'random_number': -133,
}
OPERATORS = {
'add': 0,
'sub': 1,
'mul': 2,
'div': 3,
'mod': 4,
'and': 5,
'or': 6,
'xor': 7,
'not': 8,
'load': 9,
'swap': 10,
}
CONDITIONS = {
'ze': 0,
'nz': 1,
'eq': 2,
'ne': 3,
'gt': 4,
'ge': 5,
'lt': 6,
'le': 7,
'eto': 8,
'eal': 9,
'esd': 12,
}
STATUS = {
1: "Wrong checksum",
2: "Invalid command",
3: "Wrong type",
4: "Invalid value",
5: "Configuration EEPROM locked",
6: "Command not available",
}
class TMCMError(Exception):
def __init__(self, status):
self.status = status
msg = STATUS[status]
Exception.__init__(self, msg)
class TMCM140(SerialDevice):
def __init__(self, port, baudrate=9600, module_addr=1):
"""
port: serial COM port (eg. COM3 or /dev/ttyACM0)
baudrate: 9600 by default
module_addr: 1 by default
"""
self.lock = RLock(debug=True)
self.port = port
assert isinstance(module_addr, int)
assert module_addr > 0
self.module_addr = module_addr
self.module_str = chr(module_addr+64)
self._waiting_for_reply = False
SerialDevice.__init__(self, port=self.port, baudrate=baudrate)
@threadsafe
def command(self, cmd, type, motor, value):
"""Send a command to the controller and return the reply.
If an error is returned from the controller then raise an exception.
"""
self._send_cmd(cmd, type, motor, value)
return self._get_reply()
def rotate(self, velocity):
"""Begin rotating motor.
velocity: -2047 to +2047
negative values turn left; positive values turn right.
"""
assert isinstance(velocity, int)
assert -2047 <= velocity <= 2047
if velocity < 0:
direction = 'l'
velocity = -velocity
else:
direction = 'r'
self.command('ro'+direction, 0, 0, velocity)
def stop(self):
"""Stop the motor.
Note: does not stop currently running programs.
"""
self.command('mst', 0, 0, 0)
def move(self, pos, relative=False, velocity=None):
"""Rotate until reaching *pos*.
pos: The target position
relative: If True, then *pos* is interpreted as relative to the current
position
velocity: Optionally set the target velocity before moving
"""
assert isinstance(pos, int)
assert -2**32 <= pos < 2**32
if velocity is not None:
assert isinstance(velocity, int)
assert 0 <= velocity < 2048
raise NotImplementedError()
type = 1 if relative else 0
self.command('mvp', type, 0, pos)
def get_param(self, param):
pnum = abs(PARAMETERS[param])
return self.command('gap', pnum, 0, 0)[4]
def __getitem__(self, param):
return self.get_param(param)
def set_param(self, param, value, **kwds):
"""Set a parameter value.
If valus is 'accum' then the parameter is set from the accumulator
register.
"""
pnum = PARAMETERS[param]
if pnum < 0:
raise TypeError("Parameter %s is read-only." % param)
if pnum in (PARAMETERS['maximum_current'], PARAMETERS['standby_current']) and value > 100:
if kwds.get('force', False) is not True:
raise Exception("Refusing to set current > 100 (this can damage the motor). "
"To override, use force=True.")
if value == 'accum':
self.command('aap', pnum, 0, 0)
else:
self.command('sap', pnum, 0, value)
@threadsafe
def set_params(self, **kwds):
"""Set multiple parameters.
The driver is thread-locked until all parameters are set.
"""
for param, value in kwds.items():
self.set_param(param, value)
def __setitem__(self, param, value):
return self.set_param(param, value)
def get_global(self, param):
"""Return a global parameter or copy global to accumulator.
Use param='gpX' to refer to general-purpose variables.
"""
if param.startswith('gp'):
pnum = int(param[2:])
bank = 2
else:
pnum = abs(GLOBAL_PARAMETERS[param])
bank = 0
return self.command('ggp', pnum, bank, 0)[4]
def set_global(self, param, value):
if param.startswith('gp'):
pnum = int(param[2:])
bank = 2
else:
pnum = GLOBAL_PARAMETERS[param]
bank = 0
if pnum < 0:
raise TypeError("Parameter %s is read-only." % param)
if value == 'accum':
self.command('agp', pnum, bank, 0)
else:
self.command('sgp', pnum, bank, value)
def stop_program(self):
"""Stop the currently running TMCL program.
"""
self.command('stop_application', 0, 0, 0)
def start_program(self, address=None):
"""Start running TMCL program code from the given address (in bytes?),
or from the current address if None.
"""
if address is None:
self.command('run_application', 0, 0, 0)
else:
self.command('run_application', 1, 0, address)
def start_download(self, address=0):
"""Begin loading TMCL commands into EEPROM .
"""
self.command('start_download', 0, 0, address)
def stop_download(self):
"""Finish loading TMCL commands into EEPROM.
"""
self.command('stop_download', 0, 0, 0)
def write_program(self, address=0):
return ProgramManager(self, address)
def program_status(self):
"""Return current program status:
0=stop, 1=run, 2=step, 3=reset
"""
return self.command('get_application_status', 0, 0, 0)[4]
def calc(self, op, value):
opnum = OPERATORS[op]
if opnum > 9:
raise TypeError("Operator %s invalid for calc" % op)
self.command('calc', opnum, 0, value)
def calcx(self, op):
opnum = OPERATORS[op]
self.command('calcx', opnum, 0, 0)
def comp(self, val):
self.command('comp', 0, 0, val)
def jump(self, *args):
"""Program jump to *addr* (instruction index).
Usage:
jump(address)
jump(cond, address)
Where *cond* may be ze, nz, eq, ne, gt, ge, lt, le, eto, eal, or esd.
"""
if len(args) == 1:
assert isinstance(args[0], int)
self.command('ja', 0, 0, args[0])
else:
cnum = CONDITIONS[args[0]]
self.command('jc', cnum, 0, args[1])
def _send_cmd(self, cmd, type, motor, value):
"""Send a command to the controller.
"""
if self._waiting_for_reply:
raise Exception("Cannot send command; previous reply has not been "
"received yet.")
cmd_num = COMMANDS[cmd]
assert isinstance(type, int)
assert isinstance(motor, int)
# Try packing the value first as unsigned, then signed. (the overlapping
# integer ranges have identical bit representation, so there is no
# ambiguity)
try:
cmd = struct.pack('>BBBBI', self.module_addr, cmd_num, type, motor, value)
except struct.error:
cmd = struct.pack('>BBBBi', self.module_addr, cmd_num, type, motor, value)
chksum = sum(bytearray(cmd)) % 256
out = cmd + struct.pack('B', chksum)
self.write(out)
self._waiting_for_reply = True
def _get_reply(self):
"""Read and parse a reply from the controller.
Raise an exception if an error was reported.
"""
if not self._waiting_for_reply:
raise Exception("No reply expected.")
try:
d = self.read(9)
finally:
self._waiting_for_reply = False
d2 = self.readAll()
if len(d2) > 0:
raise Exception("Error: extra data while reading reply.")
parts = struct.unpack('>BBBBiB', d)
reply_addr, module_addr, status, cmd_num, value, chksum = parts
if chksum != sum(bytearray(d[:-1])) % 256:
raise Exception("Invalid checksum reading from controller.")
if status < 100:
raise TMCMError(status)
return parts
class ProgramManager(object):
def __init__(self, mcm, start=0):
self.mcm = mcm
self.start = start
self.count = 0
def __enter__(self):
self.mcm.lock.acquire()
self.mcm.start_download(self.start)
return self
def __exit__(self, *args):
# insert an extra stop to ensure the program can't leak
# into previously written code.
self.mcm.command('stop', 0, 0, 0)
self.mcm.stop_download()
self.mcm.lock.release()
def __getattr__(self, name):
self.count += 1
return getattr(self.mcm, name)
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brianleungwh/signals | tests/generators/ios/test_core_data.py | d28d2722d681d390ebd21cd668d0b19f2f184451 | import unittest
from signals.generators.ios.core_data import get_current_version, get_core_data_from_folder
class CoreDataTestCase(unittest.TestCase):
def test_get_current_version(self):
version_name = get_current_version('./tests/files/doubledummy.xcdatamodeld')
self.assertEqual(version_name, 'dummy 2.xcdatamodel')
version_name = get_current_version('./tests/files/dummy.xcdatamodeld')
self.assertEqual(version_name, 'dummy.xcdatamodel')
def test_get_core_data_from_folder(self):
xcdatamodeld_path = './tests/files/doubledummy.xcdatamodeld'
contents_path = xcdatamodeld_path + '/dummy 2.xcdatamodel/contents'
self.assertEqual(get_core_data_from_folder(xcdatamodeld_path), contents_path)
xcdatamodeld_path = './tests/files/dummy.xcdatamodeld'
contents_path = xcdatamodeld_path + '/dummy.xcdatamodel/contents'
self.assertEqual(get_core_data_from_folder(xcdatamodeld_path), contents_path)
| [((7, 23, 7, 84), 'signals.generators.ios.core_data.get_current_version', 'get_current_version', ({(7, 43, 7, 83): '"""./tests/files/doubledummy.xcdatamodeld"""'}, {}), "('./tests/files/doubledummy.xcdatamodeld')", False, 'from signals.generators.ios.core_data import get_current_version, get_core_data_from_folder\n'), ((9, 23, 9, 78), 'signals.generators.ios.core_data.get_current_version', 'get_current_version', ({(9, 43, 9, 77): '"""./tests/files/dummy.xcdatamodeld"""'}, {}), "('./tests/files/dummy.xcdatamodeld')", False, 'from signals.generators.ios.core_data import get_current_version, get_core_data_from_folder\n'), ((15, 25, 15, 69), 'signals.generators.ios.core_data.get_core_data_from_folder', 'get_core_data_from_folder', ({(15, 51, 15, 68): 'xcdatamodeld_path'}, {}), '(xcdatamodeld_path)', False, 'from signals.generators.ios.core_data import get_current_version, get_core_data_from_folder\n'), ((19, 25, 19, 69), 'signals.generators.ios.core_data.get_core_data_from_folder', 'get_core_data_from_folder', ({(19, 51, 19, 68): 'xcdatamodeld_path'}, {}), '(xcdatamodeld_path)', False, 'from signals.generators.ios.core_data import get_current_version, get_core_data_from_folder\n')] |
hudalao/mcmc | mcmc/plot_graph.py | 148d9fbb9ebd85ee5bfd3601d80ebbd96bc25791 | # commend the lines for plotting using
import matplotlib.pyplot as plt
import networkx as nx
def plot_graph(G, N, time_point, posi):
#setting up for graph plotting
#setting the positions for all nodes
pos = {}
for ii in range(N):
pos[ii] = posi[ii]
# plt.figure(time_point + 1)
elarge=[(u,v) for (u,v,d) in G[time_point].edges(data=True) if d['weight'] >0.5]
esmall=[(u,v) for (u,v,d) in G[time_point].edges(data=True) if d['weight'] <=0.5]
# nodes
# nx.draw_networkx_nodes(G[time_point],pos,node_size=200)
# edges
# nx.draw_networkx_edges(G[time_point],pos,edgelist=elarge,width=3)
# nx.draw_networkx_edges(G[time_point],pos,edgelist=esmall,width=3,alpha=0.5,edge_color='b',style='dashed')
# labels
# nx.draw_networkx_labels(G[time_point],pos,font_size=10,font_family='sans-serif')
| [] |
mishrakeshav/Competitive-Programming | Number Theory/Sieve_of_Eratosthenes.py | b25dcfeec0fb9a9c71bf3a05644b619f4ca83dd2 | from sys import stdin
input = stdin.readline
N = int(input())
primes = [1]*(N+1)
primes[0] = 0
primes[1] = 0
for i in range(2,int(N**0.5)+1):
if primes[i]:
for j in range(i*i,N+1,i):
primes[j] = 0
for i in range(N+1):
if primes[i]:
print(i,end = " ")
| [] |
kruton/powerline | powerline/lib/tree_watcher.py | f6ddb95da5f41b8285cffd1d17c1ef46dc08a7d6 | # vim:fileencoding=utf-8:noet
from __future__ import (unicode_literals, absolute_import, print_function)
__copyright__ = '2013, Kovid Goyal <kovid at kovidgoyal.net>'
__docformat__ = 'restructuredtext en'
import sys
import os
import errno
from time import sleep
from powerline.lib.monotonic import monotonic
from powerline.lib.inotify import INotify, INotifyError
class NoSuchDir(ValueError):
pass
class BaseDirChanged(ValueError):
pass
class DirTooLarge(ValueError):
def __init__(self, bdir):
ValueError.__init__(self, 'The directory {0} is too large to monitor. Try increasing the value in /proc/sys/fs/inotify/max_user_watches'.format(bdir))
def realpath(path):
return os.path.abspath(os.path.realpath(path))
class INotifyTreeWatcher(INotify):
is_dummy = False
def __init__(self, basedir, ignore_event=None):
super(INotifyTreeWatcher, self).__init__()
self.basedir = realpath(basedir)
self.watch_tree()
self.modified = True
self.ignore_event = (lambda path, name: False) if ignore_event is None else ignore_event
def watch_tree(self):
self.watched_dirs = {}
self.watched_rmap = {}
try:
self.add_watches(self.basedir)
except OSError as e:
if e.errno == errno.ENOSPC:
raise DirTooLarge(self.basedir)
def add_watches(self, base, top_level=True):
''' Add watches for this directory and all its descendant directories,
recursively. '''
base = realpath(base)
# There may exist a link which leads to an endless
# add_watches loop or to maximum recursion depth exceeded
if not top_level and base in self.watched_dirs:
return
try:
is_dir = self.add_watch(base)
except OSError as e:
if e.errno == errno.ENOENT:
# The entry could have been deleted between listdir() and
# add_watch().
if top_level:
raise NoSuchDir('The dir {0} does not exist'.format(base))
return
if e.errno == errno.EACCES:
# We silently ignore entries for which we dont have permission,
# unless they are the top level dir
if top_level:
raise NoSuchDir('You do not have permission to monitor {0}'.format(base))
return
raise
else:
if is_dir:
try:
files = os.listdir(base)
except OSError as e:
if e.errno in (errno.ENOTDIR, errno.ENOENT):
# The dir was deleted/replaced between the add_watch()
# and listdir()
if top_level:
raise NoSuchDir('The dir {0} does not exist'.format(base))
return
raise
for x in files:
self.add_watches(os.path.join(base, x), top_level=False)
elif top_level:
# The top level dir is a file, not good.
raise NoSuchDir('The dir {0} does not exist'.format(base))
def add_watch(self, path):
import ctypes
bpath = path if isinstance(path, bytes) else path.encode(self.fenc)
wd = self._add_watch(self._inotify_fd, ctypes.c_char_p(bpath),
# Ignore symlinks and watch only directories
self.DONT_FOLLOW | self.ONLYDIR |
self.MODIFY | self.CREATE | self.DELETE |
self.MOVE_SELF | self.MOVED_FROM | self.MOVED_TO |
self.ATTRIB | self.DELETE_SELF)
if wd == -1:
eno = ctypes.get_errno()
if eno == errno.ENOTDIR:
return False
raise OSError(eno, 'Failed to add watch for: {0}: {1}'.format(path, self.os.strerror(eno)))
self.watched_dirs[path] = wd
self.watched_rmap[wd] = path
return True
def process_event(self, wd, mask, cookie, name):
if wd == -1 and (mask & self.Q_OVERFLOW):
# We missed some INOTIFY events, so we dont
# know the state of any tracked dirs.
self.watch_tree()
self.modified = True
return
path = self.watched_rmap.get(wd, None)
if path is not None:
self.modified = not self.ignore_event(path, name)
if mask & self.CREATE:
# A new sub-directory might have been created, monitor it.
try:
self.add_watch(os.path.join(path, name))
except OSError as e:
if e.errno == errno.ENOENT:
# Deleted before add_watch()
pass
elif e.errno == errno.ENOSPC:
raise DirTooLarge(self.basedir)
else:
raise
if (mask & self.DELETE_SELF or mask & self.MOVE_SELF) and path == self.basedir:
raise BaseDirChanged('The directory %s was moved/deleted' % path)
def __call__(self):
self.read()
ret = self.modified
self.modified = False
return ret
class DummyTreeWatcher(object):
is_dummy = True
def __init__(self, basedir):
self.basedir = realpath(basedir)
def __call__(self):
return False
class TreeWatcher(object):
def __init__(self, expire_time=10):
self.watches = {}
self.last_query_times = {}
self.expire_time = expire_time * 60
def watch(self, path, logger=None, ignore_event=None):
path = realpath(path)
try:
w = INotifyTreeWatcher(path, ignore_event=ignore_event)
except (INotifyError, DirTooLarge) as e:
if logger is not None and not isinstance(e, INotifyError):
logger.warn('Failed to watch path: {0} with error: {1}'.format(path, e))
w = DummyTreeWatcher(path)
self.watches[path] = w
return w
def is_actually_watched(self, path):
w = self.watches.get(path, None)
return not getattr(w, 'is_dummy', True)
def expire_old_queries(self):
pop = []
now = monotonic()
for path, lt in self.last_query_times.items():
if now - lt > self.expire_time:
pop.append(path)
for path in pop:
del self.last_query_times[path]
def __call__(self, path, logger=None, ignore_event=None):
path = realpath(path)
self.expire_old_queries()
self.last_query_times[path] = monotonic()
w = self.watches.get(path, None)
if w is None:
try:
self.watch(path, logger=logger, ignore_event=ignore_event)
except NoSuchDir:
pass
return True
try:
return w()
except BaseDirChanged:
self.watches.pop(path, None)
return True
except DirTooLarge as e:
if logger is not None:
logger.warn(str(e))
self.watches[path] = DummyTreeWatcher(path)
return False
if __name__ == '__main__':
w = INotifyTreeWatcher(sys.argv[-1])
w()
print ('Monitoring', sys.argv[-1], 'press Ctrl-C to stop')
try:
while True:
if w():
print (sys.argv[-1], 'changed')
sleep(1)
except KeyboardInterrupt:
raise SystemExit(0)
| [((30, 24, 30, 46), 'os.path.realpath', 'os.path.realpath', ({(30, 41, 30, 45): 'path'}, {}), '(path)', False, 'import os\n'), ((178, 8, 178, 19), 'powerline.lib.monotonic.monotonic', 'monotonic', ({}, {}), '()', False, 'from powerline.lib.monotonic import monotonic\n'), ((188, 32, 188, 43), 'powerline.lib.monotonic.monotonic', 'monotonic', ({}, {}), '()', False, 'from powerline.lib.monotonic import monotonic\n'), ((97, 41, 97, 63), 'ctypes.c_char_p', 'ctypes.c_char_p', ({(97, 57, 97, 62): 'bpath'}, {}), '(bpath)', False, 'import ctypes\n'), ((105, 9, 105, 27), 'ctypes.get_errno', 'ctypes.get_errno', ({}, {}), '()', False, 'import ctypes\n'), ((216, 3, 216, 11), 'time.sleep', 'sleep', ({(216, 9, 216, 10): '(1)'}, {}), '(1)', False, 'from time import sleep\n'), ((79, 13, 79, 29), 'os.listdir', 'os.listdir', ({(79, 24, 79, 28): 'base'}, {}), '(base)', False, 'import os\n'), ((89, 22, 89, 43), 'os.path.join', 'os.path.join', ({(89, 35, 89, 39): 'base', (89, 41, 89, 42): 'x'}, {}), '(base, x)', False, 'import os\n'), ((126, 20, 126, 44), 'os.path.join', 'os.path.join', ({(126, 33, 126, 37): 'path', (126, 39, 126, 43): 'name'}, {}), '(path, name)', False, 'import os\n')] |
PrashantKumar-sudo/qibuild | python/qisys/test/fake_interact.py | a16ce425cf25127ceff29507feeeeca37af23351 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2012-2019 SoftBank Robotics. All rights reserved.
# Use of this source code is governed by a BSD-style license (see the COPYING file).
""" Fake Interact """
from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import print_function
class FakeInteract(object):
""" A class to tests code depending on qisys.interact """
def __init__(self):
""" FakeInteract Init """
self.answers_type = None
self.answer_index = -1
self._answers = None
self.questions = list()
self.editor = None
@property
def answers(self):
""" Answers Getter """
if self._answers is None:
raise Exception("FakeInteract not initialized")
return self._answers
@answers.setter
def answers(self, value):
""" Answers Setter """
if isinstance(value, dict):
self.answers_type = "dict"
elif isinstance(value, list):
self.answers_type = "list"
else:
raise Exception("Unknow answer type: " + type(value))
self._answers = value
def find_answer(self, message, choices=None, default=None):
""" Find Answer """
keys = self.answers.keys()
for key in keys:
if key in message.lower():
if not choices:
return self.answers[key]
answer = self.answers[key]
if answer in choices:
return answer
else:
mess = "Would answer %s\n" % answer
mess += "But choices are: %s\n" % choices
raise Exception(mess)
if default is not None:
return default
mess = "Could not find answer for\n :: %s\n" % message
mess += "Known keys are: %s" % ", ".join(keys)
raise Exception(mess)
def ask_choice(self, choices, message, **_unused):
""" Ask Choice """
print("::", message)
for choice in choices:
print("* ", choice)
answer = self._get_answer(message, choices)
print(">", answer)
return answer
def ask_yes_no(self, message, default=False):
""" Ask Yes / No """
print("::", message,)
if default:
print("(Y/n)")
else:
print("(y/N)")
answer = self._get_answer(message, default=default)
print(">", answer)
return answer
def ask_path(self, message):
""" Ask Path """
print("::", message)
answer = self._get_answer(message)
print(">", answer)
return answer
def ask_string(self, message):
""" Ask String """
print("::", message)
answer = self._get_answer(message)
print(">", answer)
return answer
def ask_program(self, message):
""" Ask Program """
print("::", message)
answer = self._get_answer(message)
print(">", answer)
return answer
def get_editor(self):
""" Return the Editor """
return self.editor
def _get_answer(self, message, choices=None, default=None):
""" Get an Answer """
question = dict()
question['message'] = message
question['choices'] = choices
question['default'] = default
self.questions.append(question)
if self.answers_type == "dict":
return self.find_answer(message, choices=choices, default=default)
self.answer_index += 1
return self.answers[self.answer_index]
| [] |
kowalej/muse-lsl | muselsl/cli.py | 9086f2588bee3b2858b0ff853b7a08cdcd0e7612 | #!/usr/bin/python
import sys
import argparse
class main:
def __init__(self):
parser = argparse.ArgumentParser(
description='Python package for streaming, recording, and visualizing EEG data from the Muse 2016 headset.',
usage='''muselsl <command> [<args>]
Available commands:
list List available Muse devices.
-b --backend BLE backend to use. can be auto, bluemuse, gatt or bgapi.
-i --interface The interface to use, 'hci0' for gatt or a com port for bgapi.
stream Start an LSL stream from Muse headset.
-a --address Device MAC address.
-n --name Device name (e.g. Muse-41D2).
-b --backend BLE backend to use. can be auto, bluemuse, gatt or bgapi.
-i --interface The interface to use, 'hci0' for gatt or a com port for bgapi.
view Visualize EEG data from an LSL stream.
-w --window Window length to display in seconds.
-s --scale Scale in uV.
-r --refresh Refresh rate in seconds.
-f --figure Window size.
-v --version Viewer version (1 or 2) - 1 is the default stable version, 2 is in development (and takes no arguments).
record Record EEG data from an LSL stream.
-d --duration Duration of the recording in seconds.
-f --filename Name of the recording file.
-dj --dejitter Whether to apply dejitter correction to timestamps.
record_direct Record data directly from Muse headset (no LSL).
-a --address Device MAC address.
-n --name Device name (e.g. Muse-41D2).
-b --backend BLE backend to use. can be auto, bluemuse, gatt or bgapi.
-i --interface The interface to use, 'hci0' for gatt or a com port for bgapi.
''')
parser.add_argument('command', help='Command to run.')
# parse_args defaults to [1:] for args, but you need to
# exclude the rest of the args too, or validation will fail
args = parser.parse_args(sys.argv[1:2])
if not hasattr(self, args.command):
print('Incorrect usage. See help below.')
parser.print_help()
exit(1)
# use dispatch pattern to invoke method with same name
getattr(self, args.command)()
def list(self):
parser = argparse.ArgumentParser(
description='List available Muse devices.')
parser.add_argument("-b", "--backend",
dest="backend", type=str, default="auto",
help="BLE backend to use. Can be auto, bluemuse, gatt or bgapi.")
parser.add_argument("-i", "--interface",
dest="interface", type=str, default=None,
help="The interface to use, 'hci0' for gatt or a com port for bgapi.")
args = parser.parse_args(sys.argv[2:])
from . import list_muses
list_muses(args.backend, args.interface)
def stream(self):
parser = argparse.ArgumentParser(
description='Start an LSL stream from Muse headset.')
parser.add_argument("-a", "--address",
dest="address", type=str, default=None,
help="Device MAC address.")
parser.add_argument("-n", "--name",
dest="name", type=str, default=None,
help="Name of the device.")
parser.add_argument("-b", "--backend",
dest="backend", type=str, default="auto",
help="BLE backend to use. Can be auto, bluemuse, gatt or bgapi.")
parser.add_argument("-i", "--interface",
dest="interface", type=str, default=None,
help="The interface to use, 'hci0' for gatt or a com port for bgapi.")
args = parser.parse_args(sys.argv[2:])
from . import stream
stream(args.address, args.backend,
args.interface, args.name)
def record(self):
parser = argparse.ArgumentParser(
description='Record data from an LSL stream.')
parser.add_argument("-d", "--duration",
dest="duration", type=int, default=60,
help="Duration of the recording in seconds.")
parser.add_argument("-f", "--filename",
dest="filename", type=str, default=None,
help="Name of the recording file.")
parser.add_argument("-dj", "--dejitter",
dest="dejitter", type=bool, default=True,
help="Whether to apply dejitter correction to timestamps.")
args = parser.parse_args(sys.argv[2:])
from . import record
record(args.duration, args.filename, args.dejitter)
def record_direct(self):
parser = argparse.ArgumentParser(
description='Record directly from Muse without LSL.')
parser.add_argument("-a", "--address",
dest="address", type=str, default=None,
help="Device MAC address.")
parser.add_argument("-n", "--name",
dest="name", type=str, default=None,
help="Name of the device.")
parser.add_argument("-b", "--backend",
dest="backend", type=str, default="auto",
help="BLE backend to use. Can be auto, bluemuse, gatt or bgapi.")
parser.add_argument("-i", "--interface",
dest="interface", type=str, default=None,
help="The interface to use, 'hci0' for gatt or a com port for bgapi.")
parser.add_argument("-d", "--duration",
dest="duration", type=int, default=60,
help="Duration of the recording in seconds.")
parser.add_argument("-f", "--filename",
dest="filename", type=str, default=None,
help="Name of the recording file.")
args = parser.parse_args(sys.argv[2:])
from . import record_direct
record_direct(args.address, args.backend,
args.interface, args.name, args.duration, args.filename)
def view(self):
parser = argparse.ArgumentParser(
description='View EEG data from an LSL stream.')
parser.add_argument("-w", "--window",
dest="window", type=float, default=5.,
help="Window length to display in seconds.")
parser.add_argument("-s", "--scale",
dest="scale", type=float, default=100,
help="Scale in uV.")
parser.add_argument("-r", "--refresh",
dest="refresh", type=float, default=0.2,
help="Refresh rate in seconds.")
parser.add_argument("-f", "--figure",
dest="figure", type=str, default="15x6",
help="Window size.")
parser.add_argument("-v", "--version",
dest="version", type=int, default=1,
help="Viewer version (1 or 2) - 1 is the default stable version, 2 is in development (and takes no arguments).")
args = parser.parse_args(sys.argv[2:])
from . import view
view(args.window, args.scale, args.refresh, args.figure, args.version)
| [((6, 17, 37, 12), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (), '', False, 'import argparse\n'), ((53, 17, 54, 55), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (), '', False, 'import argparse\n'), ((66, 17, 67, 65), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (), '', False, 'import argparse\n'), ((86, 17, 87, 58), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (), '', False, 'import argparse\n'), ((102, 17, 103, 65), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (), '', False, 'import argparse\n'), ((128, 17, 129, 60), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (), '', False, 'import argparse\n')] |
Quantum-OCS/QuOCS-pyside2interface | src/quocspyside2interface/gui/freegradients/GeneralSettingsNM.py | 69436666a67da6884aed1ddd087b7062dcd2ad90 | # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Copyright 2021- QuOCS Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
from qtpy import QtWidgets
from quocspyside2interface.gui.uiclasses.GeneralSettingsNMUI import Ui_Form
from quocspyside2interface.gui.freegradients.StoppingCriteriaNM import StoppingCriteriaNM
from quocspyside2interface.logic.OptimalAlgorithmDictionaries.NelderMeadDictionary import NelderMeadDictionary
class GeneralSettingsNM(QtWidgets.QWidget, Ui_Form):
def __init__(self, loaded_dictionary=None):
super().__init__()
self.setupUi(self)
nm_dictionary, stopping_criteria_dictionary = None, None
if loaded_dictionary is not None:
nm_dictionary = loaded_dictionary["general_settings"]
stopping_criteria_dictionary = loaded_dictionary["stopping_criteria"]
# Nelder Mead Dictionary
self.nelder_mead_dictionary = NelderMeadDictionary(loaded_dictionary=nm_dictionary)
# Create widget
self.stopping_criteria_form = StoppingCriteriaNM(loaded_dictionary=stopping_criteria_dictionary)
# Connection
self.is_adaptive_checkbox.stateChanged.connect(self.set_is_adaptive)
self._initialization()
def _initialization(self):
self.is_adaptive_checkbox.setChecked(self.nelder_mead_dictionary.is_adaptive)
self.stopping_criteria_scroll_area.setWidget(self.stopping_criteria_form)
def set_is_adaptive(self):
self.nelder_mead_dictionary.is_adaptive = self.is_adaptive_checkbox.isChecked()
def get_dictionary(self):
return {"dsm_settings": {"general_settings": self.nelder_mead_dictionary.get_dictionary(),
"stopping_criteria": self.stopping_criteria_form.get_dictionary()}} | [((33, 38, 33, 91), 'quocspyside2interface.logic.OptimalAlgorithmDictionaries.NelderMeadDictionary.NelderMeadDictionary', 'NelderMeadDictionary', (), '', False, 'from quocspyside2interface.logic.OptimalAlgorithmDictionaries.NelderMeadDictionary import NelderMeadDictionary\n'), ((35, 38, 35, 104), 'quocspyside2interface.gui.freegradients.StoppingCriteriaNM.StoppingCriteriaNM', 'StoppingCriteriaNM', (), '', False, 'from quocspyside2interface.gui.freegradients.StoppingCriteriaNM import StoppingCriteriaNM\n')] |
divyamamgai/integrations-extras | pulsar/datadog_checks/pulsar/check.py | 8c40a9cf870578687cc224ee91d3c70cd3a435a4 | from datadog_checks.base import ConfigurationError, OpenMetricsBaseCheck
EVENT_TYPE = SOURCE_TYPE_NAME = 'pulsar'
class PulsarCheck(OpenMetricsBaseCheck):
"""
PulsarCheck derives from AgentCheck that provides the required check method
"""
def __init__(self, name, init_config, instances=None):
instance = instances[0]
url = instance.get('prometheus_url')
if url is None:
raise ConfigurationError("Unable to find prometheus_url in config file.")
self.NAMESPACE = 'kesque.pulsar'
self.metrics_mapper = {
'pulsar_consumer_available_permits': 'consumer.available_permits',
'pulsar_consumer_blocked_on_unacked_messages': 'consumer.blocked_on_unacked_messages',
'pulsar_consumer_msg_rate_out': 'consumer.msg_rate_out',
'pulsar_consumer_msg_rate_redeliver': 'consumer.msg_rate_redeliver',
'pulsar_consumer_msg_throughput_out': 'consumer.msg_throughput_out',
'pulsar_consumer_unacked_messages': 'consumer.unacked_messages',
'pulsar_consumers_count': 'consumers_count',
'pulsar_entry_size_count': 'entry_size_count',
'pulsar_entry_size_le_100_kb': 'entry_size_le_100_kb',
'pulsar_entry_size_le_128': 'entry_size_le_128',
'pulsar_entry_size_le_16_kb': 'entry_size_le_16_kb',
'pulsar_entry_size_le_1_kb': 'entry_size_le_1_kb',
'pulsar_entry_size_le_1_mb': 'entry_size_le_1_mb',
'pulsar_entry_size_le_2_kb': 'entry_size_le_2_kb',
'pulsar_entry_size_le_4_kb': 'entry_size_le_4_kb',
'pulsar_entry_size_le_512': 'entry_size_le_512',
'pulsar_entry_size_le_overflow': 'entry_size_le_overflow',
'pulsar_entry_size_sum': 'entry_size_sum',
'pulsar_in_bytes_total': 'in_bytes_total',
'pulsar_in_messages_total': 'in_messages_total',
'pulsar_msg_backlog': 'msg_backlog',
'pulsar_out_bytes_total': 'out_bytes_total',
'pulsar_out_messages_total': 'out_messages_total',
'pulsar_producers_count': 'producers_count',
'pulsar_rate_in': 'rate_in',
'pulsar_rate_out': 'rate_out',
'pulsar_replication_backlog': 'replication.backlog',
'pulsar_replication_rate_in': 'replication.rate_in',
'pulsar_replication_rate_out': 'replication.rate_out',
'pulsar_replication_throughput_in': 'replication.throughput_in',
'pulsar_replication_throughput_out': 'replication.throughput_out',
'pulsar_storage_backlog_quota_limit': 'storage.backlog_quota_limit',
'pulsar_storage_backlog_size': 'storage.backlog_size',
'pulsar_storage_read_rate': 'storage.read_rate',
'pulsar_storage_offloaded_size': 'storage.offloaded_size',
'pulsar_storage_size': 'storage.size',
'pulsar_storage_write_latency_count': 'storage.write_latency_count',
'pulsar_storage_write_latency_le_0_5': 'storage.write_latency_le_0_5',
'pulsar_storage_write_latency_le_1': 'storage.write_latency_le_1',
'pulsar_storage_write_latency_le_10': 'storage.write_latency_le_10',
'pulsar_storage_write_latency_le_100': 'storage.write_latency_le_100',
'pulsar_storage_write_latency_le_1000': 'storage.write_latency_le_1000',
'pulsar_storage_write_latency_le_20': 'storage.write_latency_le_20',
'pulsar_storage_write_latency_le_200': 'storage.write_latency_le_200',
'pulsar_storage_write_latency_le_5': 'storage.write_latency_le_5',
'pulsar_storage_write_latency_le_50': 'storage.write_latency_le_50',
'pulsar_storage_write_latency_overflow': 'storage.write_latency_overflow',
'pulsar_storage_write_latency_sum': 'storage.write_latency_sum',
'pulsar_storage_write_rate': 'storage.write_rate',
'pulsar_subscription_back_log': 'subscription.back_log',
'pulsar_subscription_back_log_no_delayed': 'subscription.back_log_no_delayed',
'pulsar_subscription_blocked_on_unacked_messages': 'subscription.blocked_on_unacked_messages',
'pulsar_subscription_delayed': 'subscription.delayed',
'pulsar_subscription_msg_rate_out': 'subscription.msg_rate_out',
'pulsar_subscription_msg_rate_redeliver': 'subscription.msg_rate_redeliver',
'pulsar_subscription_msg_throughput_out': 'subscription.msg_throughput_out',
'pulsar_subscription_unacked_messages': 'subscription.unacked_messages',
'pulsar_subscriptions_count': 'subscriptions.count',
'pulsar_throughput_in': 'throughput_in',
'pulsar_throughput_out': 'throughput_out',
'pulsar_topics_count': 'topics_count',
'scrape_duration_seconds': 'scrape_duration_seconds',
'scrape_samples_post_metric_relabeling': 'scrape_samples_post_metric_relabeling',
'scrape_samples_scraped': 'scrape_samples_scraped',
'topic_load_times': 'topic_load_times',
'topic_load_times_count': 'topic_load_times_count',
'topic_load_times_sum': 'topic_load_times_sum',
'up': 'broker.up',
}
instance.update(
{
'prometheus_url': url,
'namespace': self.NAMESPACE,
'metrics': [self.metrics_mapper],
'send_distribution_counts_as_monotonic': instance.get('send_distribution_counts_as_monotonic', True),
'send_distribution_sums_as_monotonic': instance.get('send_distribution_sums_as_monotonic', True),
}
)
super(PulsarCheck, self).__init__(name, init_config, instances)
| [((15, 18, 15, 85), 'datadog_checks.base.ConfigurationError', 'ConfigurationError', ({(15, 37, 15, 84): '"""Unable to find prometheus_url in config file."""'}, {}), "('Unable to find prometheus_url in config file.')", False, 'from datadog_checks.base import ConfigurationError, OpenMetricsBaseCheck\n')] |
HowToBeCalculated/Hands-On-Blockchain-for-Python-Developers | Chapter11/publish_horoscope1_in_another_ipns.py | f9634259dd3dc509f36a5ccf3a5182c0d2ec79c4 | import ipfsapi
c = ipfsapi.connect()
peer_id = c.key_list()['Keys'][1]['Id']
c.name_publish('QmYjYGKXqo36GDt6f6qvp9qKAsrc72R9y88mQSLvogu8Ub', key='another_key')
result = c.cat('/ipns/' + peer_id)
print(result)
| [((4, 4, 4, 21), 'ipfsapi.connect', 'ipfsapi.connect', ({}, {}), '()', False, 'import ipfsapi\n')] |
NiumXp/Magie | magie/window.py | f0dc1d135274b5453fde659ba46f09f4b303c099 | import pygame
class Window:
def __init__(self, title: str, dimension: tuple):
self.surface = None
self.initial_title = title
self.initial_dimension = dimension
@property
def title(self) -> str:
"""Returns the title of the window."""
return pygame.display.get_caption()
@title.setter
def title(self, new_title: str):
"""Sets the window title."""
pygame.display.set_caption(new_title)
def set_title(self, new_title: str):
"""Alias for `Window.title = ...`."""
self.title = new_title
@property
def width(self) -> int:
"""Alias for Window.get_width."""
return self.get_width()
@property
def height(self) -> int:
"""Alias for Window.get_height."""
return self.get_height()
@property
def size(self) -> tuple:
"""Alias for Window.get_size."""
return self.get_size()
def get_width(self) -> int:
"""Returns the widget of the window."""
if self.surface:
return self.surface.get_width()
return self.initial_dimension[0]
def get_height(self) -> int:
"""Returns the height of the window."""
if self.surface:
return self.surface.get_height()
return self.initial_dimension[1]
def get_size(self) -> tuple:
"""Returns the size of the size."""
if self.surface:
return self.surface.get_size()
return self.initial_dimension
def build(self):
"""Build the window."""
self.surface = pygame.display.set_mode(self.initial_dimension)
self.set_title(self.initial_title)
return self
| [((14, 15, 14, 43), 'pygame.display.get_caption', 'pygame.display.get_caption', ({}, {}), '()', False, 'import pygame\n'), ((19, 8, 19, 45), 'pygame.display.set_caption', 'pygame.display.set_caption', ({(19, 35, 19, 44): 'new_title'}, {}), '(new_title)', False, 'import pygame\n'), ((60, 23, 60, 70), 'pygame.display.set_mode', 'pygame.display.set_mode', ({(60, 47, 60, 69): 'self.initial_dimension'}, {}), '(self.initial_dimension)', False, 'import pygame\n')] |
dwillmer/fastats | tests/optimize/test_newton_raphson_hypo.py | 5915423714b32ed7e953e1e3a311fe50c3f30943 |
from hypothesis import given, assume, settings
from hypothesis.strategies import floats
from numpy import cos
from pytest import approx
from fastats.optimise.newton_raphson import newton_raphson
def func(x):
return x**3 - x - 1
def less_or_equal(x, compared_to, rel=1e-6):
return ((x < compared_to)
or ((x - compared_to) == approx(0.0, rel=rel))
or (x == approx(x, rel=rel)))
nr_func = newton_raphson(1, 1e-6, root=func, return_callable=True)
@given(floats(min_value=0.01, max_value=3.5))
def test_minimal(x):
"""
Tests that the value output from the solver
is less than or equal to the value of the
objective.
"""
eps = 1e-12
value = nr_func(x, eps)
assume(func(x) > 0.0)
assert less_or_equal(value, compared_to=func(x))
def cos_func(x):
return cos(x) - 2 * x
nr_cos = newton_raphson(0.5, 1e-6, root=cos_func, return_callable=True)
@given(floats(min_value=0.3, max_value=0.8))
@settings(deadline=None)
def test_cos_minus_2x(x):
value = nr_cos(x, 1e-6)
assert less_or_equal(value, compared_to=cos_func(x))
if __name__ == '__main__':
import pytest
pytest.main([__file__])
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sequery/Face-Recognition-Project | faceRecognition.py | 84d29322228e140c3d18c9c4d169819375a8e256 | import cv2
import os
import numpy as np
# This module contains all common functions that are called in tester.py file
# Given an image below function returns rectangle for face detected alongwith gray scale image
def faceDetection(test_img):
gray_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY) # convert color image to grayscale
face_haar_cascade = cv2.CascadeClassifier('HaarCascade/haarcascade_frontalface_default.xml') # Load haar classifier
faces = face_haar_cascade.detectMultiScale(gray_img, scaleFactor=1.32,
minNeighbors=5) # detectMultiScale returns rectangles
return faces, gray_img
# Given a directory below function returns part of gray_img which is face alongwith its label/ID
def labels_for_training_data(directory):
faces = []
faceID = []
for path, subdirnames, filenames in os.walk(directory):
for filename in filenames:
if filename.startswith("."):
print("Skipping system file") # Skipping files that startwith .
continue
id = os.path.basename(path) # fetching subdirectory names
img_path = os.path.join(path, filename) # fetching image path
print("img_path:", img_path)
print("id:", id)
test_img = cv2.imread(img_path) # loading each image one by one
if test_img is None:
print("Image not loaded properly")
continue
faces_rect, gray_img = faceDetection(
test_img) # Calling faceDetection function to return faces detected in particular image
if len(faces_rect) != 1:
continue # Since we are assuming only single person images are being fed to classifier
(x, y, w, h) = faces_rect[0]
roi_gray = gray_img[y:y + w, x:x + h] # cropping region of interest i.e. face area from grayscale image
faces.append(roi_gray)
faceID.append(int(id))
return faces, faceID
# Below function trains haar classifier and takes faces,faceID returned by previous function as its arguments
def train_classifier(faces, faceID):
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.train(faces, np.array(faceID))
return face_recognizer
# Below function draws bounding boxes around detected face in image
def draw_rect(test_img, face):
(x, y, w, h) = face
cv2.rectangle(test_img, (x, y), (x + w, y + h), (255, 0, 0), thickness=5)
# Below function writes name of person for detected label
def put_text(test_img, text, x, y):
cv2.putText(test_img, text, (x, y), cv2.FONT_HERSHEY_DUPLEX, 2, (255, 0, 0), 4)
| [((11, 15, 11, 57), 'cv2.cvtColor', 'cv2.cvtColor', ({(11, 28, 11, 36): 'test_img', (11, 38, 11, 56): 'cv2.COLOR_BGR2GRAY'}, {}), '(test_img, cv2.COLOR_BGR2GRAY)', False, 'import cv2\n'), ((12, 24, 12, 96), 'cv2.CascadeClassifier', 'cv2.CascadeClassifier', ({(12, 46, 12, 95): '"""HaarCascade/haarcascade_frontalface_default.xml"""'}, {}), "('HaarCascade/haarcascade_frontalface_default.xml')", False, 'import cv2\n'), ((24, 40, 24, 58), 'os.walk', 'os.walk', ({(24, 48, 24, 57): 'directory'}, {}), '(directory)', False, 'import os\n'), ((51, 22, 51, 58), 'cv2.face.LBPHFaceRecognizer_create', 'cv2.face.LBPHFaceRecognizer_create', ({}, {}), '()', False, 'import cv2\n'), ((59, 4, 59, 77), 'cv2.rectangle', 'cv2.rectangle', (), '', False, 'import cv2\n'), ((64, 4, 64, 83), 'cv2.putText', 'cv2.putText', ({(64, 16, 64, 24): 'test_img', (64, 26, 64, 30): 'text', (64, 32, 64, 38): '(x, y)', (64, 40, 64, 63): 'cv2.FONT_HERSHEY_DUPLEX', (64, 65, 64, 66): '(2)', (64, 68, 64, 79): '(255, 0, 0)', (64, 81, 64, 82): '(4)'}, {}), '(test_img, text, (x, y), cv2.FONT_HERSHEY_DUPLEX, 2, (255, 0, 0), 4)', False, 'import cv2\n'), ((52, 33, 52, 49), 'numpy.array', 'np.array', ({(52, 42, 52, 48): 'faceID'}, {}), '(faceID)', True, 'import numpy as np\n'), ((30, 17, 30, 39), 'os.path.basename', 'os.path.basename', ({(30, 34, 30, 38): 'path'}, {}), '(path)', False, 'import os\n'), ((31, 23, 31, 51), 'os.path.join', 'os.path.join', ({(31, 36, 31, 40): 'path', (31, 42, 31, 50): 'filename'}, {}), '(path, filename)', False, 'import os\n'), ((34, 23, 34, 43), 'cv2.imread', 'cv2.imread', ({(34, 34, 34, 42): 'img_path'}, {}), '(img_path)', False, 'import cv2\n')] |
hsiehkl/pdffigures2 | evaluation/datasets/build_dataset_images.py | 9ff2978a097f3d500dcb840d31587c26d994cb68 | import argparse
from os import listdir, mkdir
from os.path import join, isdir
from subprocess import call
import sys
import datasets
from shutil import which
"""
Script to use pdftoppm to turn the pdfs into single images per page
"""
def get_images(pdf_dir, output_dir, dpi, mono=True):
if which("pdftoppm") is None:
raise ValueError("Requires executable pdftopmm to be on the PATH")
if not isdir(output_dir):
print("Making %s to store rasterized PDF pages" % output_dir)
mkdir(output_dir)
if not isdir(pdf_dir):
raise ValueError(pdf_dir + " is not a directory")
pdf_doc_ids = [x.split(".pdf")[0] for x in listdir(pdf_dir)]
already_have = set()
for filename in listdir(output_dir):
if "-page" not in filename:
raise ValueError()
doc_id = filename.split("-page")[0]
if doc_id not in pdf_doc_ids:
raise ValueError("doc id %s in output dir not found in pdfs" % doc_id)
already_have.add(doc_id)
if len(already_have) != 0:
print("Already have %d docs" % len(already_have))
num_pdfs = len(listdir(pdf_dir))
for (i, pdfname) in enumerate(listdir(pdf_dir)):
if not pdfname.endswith(".pdf"):
raise ValueError()
doc_id = pdfname[:-4]
if doc_id in already_have:
continue
print("Creating images for pdf %s (%d / %d)" % (pdfname, i + 1, num_pdfs))
if (mono):
args = ["pdftoppm", "-gray", "-r", str(dpi),
"-aa", "no", "-aaVector", "no", "-cropbox",
join(pdf_dir, pdfname), join(output_dir, doc_id + "-page")]
else:
args = ["pdftoppm", "-jpeg", "-r", str(dpi), "-cropbox",
join(pdf_dir, pdfname), join(output_dir, doc_id + "-page")]
retcode = call(args)
if retcode != 0:
raise ValueError("Bad return code for <%s> (%d)", " ".join(args), retcode)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Cache rasterized page images for a dataset')
parser.add_argument("dataset", choices=datasets.DATASETS.keys(), help="target dataset")
parser.add_argument("color", choices=["gray", "color"], help="kind of images to render")
args = parser.parse_args()
dataset = datasets.get_dataset(args.dataset)
print("Running on dataset: " + dataset.name)
if args.color == "gray":
get_images(dataset.pdf_dir, dataset.page_images_gray_dir,
dataset.IMAGE_DPI, True)
elif args.color == "color":
get_images(dataset.pdf_dir, dataset.page_images_color_dir,
dataset.COLOR_IMAGE_DPI, False)
else:
exit(1)
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utkarshdeorah/sympy | sympy/physics/__init__.py | dcdf59bbc6b13ddbc329431adf72fcee294b6389 | """
A module that helps solving problems in physics.
"""
from . import units
from .matrices import mgamma, msigma, minkowski_tensor, mdft
__all__ = [
'units',
'mgamma', 'msigma', 'minkowski_tensor', 'mdft',
]
| [] |
avr8082/Hadoop | py.py | 64b2036e752ac01b9e2256e20b659b1b56a274c9 | printf("Hello world")
| [] |
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