aplz

import cv2 # opencv
import numpy as np

font_scale = 1.5
font = cv2.FONT_HERSHEY_PLAIN

# set the rectangle background to white
rectangle_bgr = (255, 255, 255)
# make a black image
img = np.zeros((500, 500))

aplz

import argparse
import os

import fasttext
from sklearn.base import BaseEstimator
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score, StratifiedKFold


def read_data(data_dir):

aplz

import pytest
from typing import List

def method_under_test(values: List[int]) -> int:
    """dummy method"""
    if 0 in values:
        raise(ValueError("Zeros are not supported"))
    return sum(values)

@pytest.mark.parametrize(

aplz

"""How to create a sparse vector for dlib from a numpy array.

dlib: http://dlib.net/
"""
import dlib
import numpy

vector = [0,2,0]
sv = dlib.sparse_vector()
for i in numpy.nonzero(vector)[0]:

aplz

GET _analyze
{
  "text": "Quick fox",
  "tokenizer": {
    "type": "ngram",
    "min_gram": 3,
    "max_gram": 4,
    "token_chars": [
      "letter",
      "digit"

aplz

tui,label
T001,Organism
T002,Plant
T004,Fungus
T005,Virus
T007,Bacterium
T008,Animal
T010,Vertebrate
T011,Amphibian
T012,Bird

aplz

es = Elasticsearch()
tokens = es.indices.analyze(
  body={"analyzer": "german",
        "text": "Die junge Informatikerin Katie Bouman machte die "
                "historische Aufnahme eines schwarzen Lochs "
                "möglich."})['tokens']
for token in tokens:
  print(token)
  

aplz

# coding:utf-8

from elasticsearch import Elasticsearch
import json

# Define config
host = "127.0.0.1"
port = 9200
timeout = 1000
index = "index"

aplz

place the line below above the offensive code part
# noinspection SpellCheckingInspection 

for IDEA, the analogon is
@SuppressWarnings("SpellCheckingInspection")

aplz

# copy your cron jobs
crontab -l > cron.txt 
# copy the exported config to otherHost
scp cron.txt otherHost:/your_directory
# login to otherHost 
ssh otherHost 
# set the cronjobs to your old config
crontab cron.txt