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import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal

def plot_bivariate_normal(covmat, filename, x_range=(-4, 4), y_range=(-4, 4), grid_size=100):
    """
    Plots the bivariate normal distribution in both 3D and top view.

    Parameters:
    - covmat: Covariance matrix for the bivariate normal distribution.

sallos-cyber

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D

# Step 1: Define the parameters
rho = 0            # Correlation coefficient
n_samples = 100000  # Number of samples to generate

# Step 2: Generate independent standard normal variables

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from pyspark.sql.functions import explode
from pyspark.sql.functions import split
from pyspark.streaming import StreamingContext
from pyspark.sql import SparkSession
import time

spark = SparkSession.builder \
        .appName('Streaming') \
        .getOrCreate()

sallos-cyber

const express = require('express')
const path = require('path')

const httpPort = 1026

const app = express()

app.use(express.static(path.join(__dirname, './')))

app.get('/', function(req, res) {

sallos-cyber

def callPreprocessData(fn='/home/someusr/Downloads/flows.csv'):
    data=pd.read_csv(fn)
    print(data.head(3))
    data['Bytes']=data['Bytes'].fillna(0)
    data['Bytes']=data['Bytes'].astype(str)
    #silly nfdump writes bytes as integer but sometimes it converts it to
    #mb. The following finds those entries and converts them into bytes.
    data.loc[data['Bytes'].str.contains('M'),'Bytes'] =   data[data['Bytes'].str.contains('M')]['Bytes'].apply(lambda x: float(x[1:-2])*1024*1024)
    data['Bytes']=data['Bytes'].astype(int)