strikaco

from threading import Thread
from multiprocessing import Process
from os import remove

from time import time

class IOTest:
    max_workers = 10
    max_lines = 10000000
    

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def concordance(string, search_term, width=25):
    """
    Alternative implementation of NLTK's concordance() that 
    allows printing to stdout or saving to a variable and 
    does not require NLTK.
    
    Just feed it a raw string, JSON string, etc. with any line
    breaks stripped out.
    """
    # Offset tracks our progress as we parse through the string

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class JsonHandler(object):
    """
    Handles serialization/deserialization of attributes in the JSON blob.
    
    Requires a field of type TextField named 'blob' on the source model.
    
    These are not searchable unless you do FTS on the obj.blob field!
    """
    def __init__(self, node, *args, **kwargs):
        # Must set via dict interface or else it triggers __setattr__

strikaco

from django.test import TestCase

from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline

import numpy
import os

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{"Dataset name": "Alerts", "Possible values": [], "Model": "Alerts", "Expected values": [], "Data type": "string", "Description": "The application involved in the event, such as win:app:trendmicro, vmware, nagios.", "Field name": "app"}
{"Dataset name": "Alerts", "Possible values": [], "Model": "Alerts", "Expected values": [], "Data type": "string", "Description": "The body of a message.", "Field name": "body"}
{"Dataset name": "Alerts", "Possible values": [], "Model": "Alerts", "Expected values": [], "Data type": "string", "Description": "The destination of the alert message, such as an email address or SNMP trap. You can alias this from more specific fields, such as dest_host, dest_ip, or dest_name.", "Field name": "dest"}
{"Dataset name": "Alerts", "Possible values": [], "Model": "Alerts", "Expected values": [], "Data type": "string", "Description": "The business unit associated with the destination. This field is automatically provided by asset and identity correlation features of applications like ClownS

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from sqlalchemy import Column, Integer, String, ForeignKey, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship, backref, sessionmaker, joinedload

# For this example we will use an in-memory sqlite DB.
# Let's also configure it to echo everything it does to the screen.
engine = create_engine('sqlite:///:memory:', echo=True)

# The base class which our objects will be defined on.
Base = declarative_base()

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{
  "graph": [],
  "links": [
    {"source": 0, "target": 1},
    {"source": 0, "target": 2},
    {"source": 0, "target": 3},
    {"source": 0, "target": 4},
    {"source": 0, "target": 5},
    {"source": 0, "target": 6},
    {"source": 1, "target": 3},

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import cherrypy
import sqlalchemy
from sqlalchemy import Table, Column, ForeignKey, MetaData, Integer, String
from sqlalchemy.orm import scoped_session, sessionmaker, mapper, relationship
from sqlalchemy.orm.properties import ColumnProperty
from sqlalchemy.orm.util import object_mapper

# The base class from which all entities will extend
class BaseEntity(object):
	def __repr__(self):

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from dataclasses import dataclass, field, fields, replace, asdict

_MISSING = object()

@dataclass
class Component(object):
    """
    Collection of attributes.
    """
    _key: str = field(default=None, hash=False, repr=False, compare=False)

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from dataclasses import dataclass, field, asdict
from enum import Enum, auto

class Scale(Enum):
    NONE = 0
    LOW = 25
    MEDIUM = 50
    HIGH = 75
    MAX = 100