plallin

istioctl --kubeconfig=kubeconfig pc log $pod --level  grpc:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  hc:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  health_checker:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  http:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  http2:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  hystrix:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  init:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  io:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  jwt:debug
istioctl --kubeconfig=kubeconfig pc log $pod --level  kafka:debug

plallin

import requests
import json
import subprocess
from collections import namedtuple
import time

PieceOfArts = namedtuple('PieceOfArt', ['id', 'variant'])

arts = {
    'robust_statue':    PieceOfArts('3865006808', '3072656478'),

plallin

package functionalkubs


/**
 * Implement part 1 of http://adventofcode.com/2017/day/1.
 * See https://github.com/raphait/kotlin-functional-kubs for instructions.
 */
object Part1 {

    fun captcha(input: String): Int {

plallin

instructions = "L5, R1, L5, L1, R5, R1, R1, L4, L1, L3, R2, R4, L4, L1, L1, R2, R4, R3, L1, R4, L4, L5, " \
      "L4, R4, L5, R1, R5, L2, R1, R3, L2, L4, L4, R1, L192, R5, R1, R4, L5, L4, R5, L1, L1, R48, " \
      "R5, R5, L2, R4, R4, R1, R3, L1, L4, L5, R1, L4, L2, L5, R5, L2, R74, R4, L1, R188, R5, L4, L2, " \
      "R5, R2, L4, R4, R3, R3, R2, R1, L3, L2, L5, L5, L2, L1, R1, R5, R4, L3, R5, L1, L3, R4, L1, L3, " \
      "L2, R1, R3, R2, R5, L3, L1, L1, R5, L4, L5, R5, R2, L5, R2, L1, L5, L3, L5, L5, L1, R1, L4, L3, L1, " \
      "R2, R5, L1, L3, R4, R5, L4, L1, R5, L1, R5, R5, R5, R2, R1, R2, L5, L5, L5, R4, L5, L4, L4, R5, L2, R1, " \
      "R5, L1, L5, R4, L3, R4, L2, R3, R3, R3, L2, L2, L2, L1, L4, R3, L4, L2, R2, R5, L1, R2".split(", ")

instructions = [[x[0], int(x[1:])] for x in instructions]

plallin

file = open("recommendations.txt")

recommendation = {}
for line in file:
    # print(line)
    if line == "":
        pass
    else:
        if line in recommendation:
            recommendation[line] += 1

plallin

"""
The account was scraped using the following Twitter library: https://github.com/sixohsix/twitter
I prefer this library over Tweepy as it is much "closer" to the API (less abstraction).

The way the following script works is basically as follows:
- First, I get all of the user's friend (I get max 200 friend per call) and store their the friends' names in a list
- Once I have the list of all the user's friends, I iterate through it to get the friends of the friends
- In a second script, I count the number of times a particular username occurs, and then return the sorted list of
friends (the most followed ones).
- That's it, more or less

plallin

from PIL import Image

'''
Based on a script by BigglesZX: https://gist.github.com/BigglesZX/4016539

BigglesZX was adapted as follows:
    - Updated to be compatible with Python 3.
    - The original function 'processImage' was renamed 'extract_and_resize_frames' and was adapted as follows:
        - It resizes each frames as it extracts them
        - It saves all the frames to an array

plallin

import functools

number = '''73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776