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import requests
import unittest 
from   urllib.parse import urlencode

class CheckResponse :
    def __call__(self,fn) :
        def fn_wrapper(*args) :
            try :
                response = fn(*args)
                if "status" not in response or        \

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# ----------------------------------------------------------------------------------------------
# HULK - HTTP Unbearable Load King
#
# this tool is a dos tool that is meant to put heavy load on HTTP servers in order to bring them
# to their knees by exhausting the resource pool, its is meant for research purposes only
# and any malicious usage of this tool is prohibited.
#
# author :  Barry Shteiman , version 1.0
# ----------------------------------------------------------------------------------------------
import urllib2

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import pandas as pd
from sklearn.preprocessing import StandardScaler
from statsmodels.tsa.stattools import pacf

def create_lag_features(y):
   
    scaler = StandardScaler()
    features = pd.DataFrame()
    
    partial = pd.Series(data=pacf(y, nlags=48))

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def recursive_forecast(y, model, lags, 
                       n_steps=FCAST_STEPS, step="1H"):
    
    """
    Parameters
    ----------
    y: pd.Series holding the input time-series to forecast
    model: pre-trained machine learning model
    lags: list of lags used for training the model
    n_steps: number of time periods in the forecasting horizon

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def direct_forecast(y, model, lags, 
                    n_steps=FCAST_STEPS, step="1H"):
    
    """    
    Parameters
    ----------
    y: pd.Series holding the input time-series to forecast
    model: a ML model not trained
    lags: list of lags used for training the model
    n_steps: how many steps forecast into the future

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app = dash.Dash(__name__)

app.layout = html.Div(children=[
    html.Div(dcc.Markdown(description)),
    html.Hr(),
    html.Div([
        html.Div(id="parameters-box-id", children=[
            dcc.Markdown(params_description),
            dcc.Input(
                id="tottime-input-id",

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{
  "name": "Device", 
  "children": [ 
    {
      "name": "Networking", 
      "children": [
         { "name": "Router", "children": [] },
         { "name": "Switch" },
         { "name": "Firewall", "children": [] }
      ]

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{
  "L0": [
    { "name": "device" }
  ],
  "L1": [
    { "name": "Networking", "parent": "Device" },
    { "name": "Computer", "parent": "Device" }
  ],
  "L2": [
    { "name": "Router", "parent": "Networking" },

madagra

import json
from anytree import Node, RenderTree
from anytree.search import find

class TaxonomyParser:

    """
    This class is a wrapper on a Tree class from the anytree library to hold
    different data hierarchies read from a JSON representation
    """

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from anytree.exporter import DotExporter

COLOR_SCHEME = ["aliceblue", "antiquewhite", "azure", "coral", "palegreen"]
    
def export(self):
    """
    Export tree into graphviz format in both *.dot file and image *.png file
    """

    def nodeattr_fn(node):