Jonnatas Cabral JonnatasCabral

Describing quake 3 reports system

This file describes the structure of a reporting system for quake 3, we will talk about its architecture, classes and functions. For this approach, we will divide the project into two layers, the first layer will have the function of executing a type of ETL, having as a source files of logs of quake 3. The objective is to structure the data taken from the files in a database. The second layer will be responsible for the functions that will provide the information needed to generate the reports on a web page. In all layers we will maintain a pattern of 'models / classes' that will be the interface with the database, and 'functions' that may have inputs and outputs.

With the execution of the first layer we will have a script to read the log files and save the data in a database. We will create classes from the default logs: LogGame and LogPlayer. The LogGame class contains the attributes: start, end, and list of players. The LogPlayer class contains name, kill, gun, death

	import base64
	txt = open("contract.txt", "rb")

	with open("test.pdf", "wb") as pdf:
	pdf.write(base64.decodebytes(txt.read()))


	# pip install python-Levenshtein gensim
	from collections import defaultdict
	from gensim import corpora
	from gensim import models
	from gensim import similarities
	from Levenshtein import distance
	from Levenshtein import ratio
	import pickle
	import re

	import numpy as np
	class Dataset(object):

	data = None
	target = None
	target_names = None

	def __repr__(self):
	rep = "Dataset(\n data: {0}, \n target: {1}, \n target_names: {2},\n)"\
	.format(self.data, self.target, self.target_names)

	from django import template

	register = template.Library()


	@register.inclusion_tag('templatetags/filters.html', takes_context=True)
	def filter(context):
	list_filters = []
	dict_filters = []
	for f in context['filters']:

	from datetime import datetime
	from datetime import timedelta


	class FilterMixin(object):

	filter_fields = []
	data_field = None

	def set_filters(self):

	def get_context_data(self, **kwargs):
	context = super(RelatorioPautaView, self).get_context_data(**kwargs)

	query = context['object_list'].order_by(
	'processo_dpvat__estado', 'solicitacao__correspondente')
	group_corr = [
	(corr, [x for x in corr_list])
	for corr, corr_list in itertools.groupby(
	query,
	lambda x: x.solicitacao.correspondente)]

	import itertools

	def get_context_data(self, **kwargs):
	context = super(RelatorioPautaView, self).get_context_data(**kwargs)

	docs = Audiencia.objects.all().order_by(
	'processo_dpvat__estado', 'processo_dpvat__estado')
	docs_mutuario = [
	(tipo, [x for x in doc_list])
	for tipo, doc_list in itertools.groupby(