2023 March

2023-0322 (WED) systemctl status monitor

cd /usr/local/nagios/libexec 

vi check_4apache

#!/bin/bash

# mikeyom@gmail.com 03-2023

my_status=`systemctl show -p ActiveState apache2 | sed 's/ActiveState=//g'`

if [ $my_status != 'active' ]; then

   echo "Critical - Apache2 service is not running"

   exit 2

fi

echo "OK - Apache2 is running"

exit 0

define service {

    use                     local-service           ; Name of service template to use

    host_name               localhost

    service_description     HTTP

    check_command           check_4apache

    notifications_enabled   0

}

2023-0322 (WED) How to Upgrade Ubuntu 18.04 to Ubuntu 20.04

Update the packages

apt update -y 

apt full-upgrade -y

apt install update-manager-core -y 

apt-get --purge autoremove

apt-get clean

reboot 

Upgrade Ubuntu 18.04 to 20.04

do-release-upgrade -d 

2023-0307 (TUE) Python Coding Interview Questions For Data Science

Google Fit User Tracking

Find the average session distance travelled by Google Fit users based on GPS location data. Calculate the distance for two scenarios:

Taking into consideration the curvature of the earth

Taking into consideration the curvature of the earth as a flat surface 

Assume one session distance is the distance between the biggest and the smallest step. If the session has only one step id, discard it from the calculation. Assume that session can't span over multiple days.

Output the average session distances calculated in the two scenarios and the difference between them.

Formula to calculate the distance with the curvature of the earth: 

user_id: varchar

session_id: int

step_id: int

day: int

latitude: float

longitude: float

altitude: float

Your task is to calculate the average distance based on GPS data using the two approaches. One is taking into consideration the curvature of the Earth, the other is not taking it into consideration.

The question gives you formulas for both approaches. As you can see, this python coding interview question is math-heavy. Not only do you need to understand this level of mathematics, but you also need to know how to translate it into a Python code.

Not that easy, right?

The first thing you should do is recognize there’s a math Python module that gives you access to the mathematical functions. You’ll use this module a lot in this question.

Let's start by importing necessary libraries and sine, cosine, arccosine, and radian functions. The next step is to merge the available DataFrame with itself on the user ID, session ID, and day of the session. Also, add the suffixes to IDs so you can distinguish between them.

import numpy as np

import pandas as pd

from math import cos, sin, acos, radians

df = pd.merge(

    google_fit_location,

    google_fit_location,

    how="left",

    on=["user_id", "session_id", "day"],

    suffixes=["_1", "_2"],

)

df["step_var"] = df["step_id_2"] - df["step_id_1"]

df = df.loc[

    df[df["step_var"] > 0]

    .groupby(["user_id", "session_id", "day"])["step_var"]

    .idxmax()

]

df["distance_curvature"] = pd.Series()

for i, r in df.iterrows():

    df.loc[i, "distance_curvature"] = (

        acos(

            sin(radians(r["latitude_1"])) * sin(radians(r["latitude_2"]))

            + cos(radians(r["latitude_1"]))

            * cos(radians(r["latitude_2"]))

            * cos(radians(r["longitude_1"] - r["longitude_2"]))

        )

        * 6371

    )

df["distance_flat"] = pd.Series()

for i, r in df.iterrows():

    df.loc[i, "distance_flat"] = (

        np.sqrt(

            (r["latitude_2"] - r["latitude_1"]) ** 2

            + (r["longitude_2"] - r["longitude_1"]) ** 2

        )

        * 111

    )

result = pd.DataFrame()

result["avg_distance_curvature"] = pd.Series(df["distance_curvature"].mean())

result["avg_distance_flat"] = pd.Series(df["distance_flat"].mean())

result["distance_diff"] = result["avg_distance_curvature"] - result["avg_distance_flat"]

result

2023-0306 (MON) plpqsql Monthly Payment

Content deleted

2023-0306 (MON) Shutdown GCP VMs

#!/bin/bash

# mikeyom@gmail.com 03-3023

my_path=/var/lib/jenkins/mike_scripts/z_labels

gcloud compute instances --project myproject list --format='table(name,status,labels)' | grep -v gke | grep -v perm | grep -v temp | grep RUNNING | awk '{print $1}' > $my_path/nolabel.txt

if [ -s $my_path/nolabel.txt ]; then

   echo "shutting down"

   mail -s "Shuttting Down MY-Proj No Label" 510@mms.att.net < $my_path/nolabel.txt

   while read -u3 shutdown

   do

     gcloud compute instances stop $(gcloud compute instances list --filter="name:$shutdown" --uri)

   done 3< $my_path/nolabel.txt

else

   echo "Nothing to shutdown"

fi