ara | ARA Records Ansible and makes it easier to understand and troubleshoot | REST library

create-tftf \
  --type s2fw \
  --header-size 0x200 \
  --name "NuttX S3FW-as-S2FW" \
  --elf ~/nuttx/nuttx/nuttx \
  --out ~/nuttx/nuttx/nuttx.tftf

create-ffff \
  --header-size 0x1000 \
  --flash-capacity 0x40000 \
  --image-length 0x40000 \
  --e  

server := &http.Server{
    Addr: "127.0.0.1:80",
    Handler: http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        w.Write([]byte("Alo?"))
    })
}
go server.ListenAndServe()
client := ara.NewClient(ara.NewCustomResolver(map[s  

create-tftf \
  --header-size 0x200 \
  --type s2fw \
  --name "NuttX S3FW-as-S2FW" \
  --code ~/nuttx-es2-debug-apbridgea.text \
    --load 0x10000000 \
  --start 0x10000ae4 \
  --data ~/nuttx-es2-debug-apbridgea.data \
    --load 0x1001e6e8 \
  --o  

import re

regex = re.compile(r'(\d{2})\s*([A-Z]{3})\s*(\d{2})\s*-\s*(\d{2})\s*([A-Z]{3})\s*(\d{2})')
text = ['04 GOR23- 02 OER 23',
'04 ORO 21-02 RRO 24',
'04 DRE25- 12 RIS21']
[regex.sub(r'\1 \2 \3 - \4 \5 \6', t) for t in text]
<

#if there are unique due_amount_quantiles, P2P_Status tuples
df = df.pivot('due_amount_quantiles','P2P_Status','Unique User Id')

#if there ara duplicated due_amount_quantiles, P2P_Status tuples is necessary aggregate, here by `sum`
df = d

def merge(obj, /):
    result = {}
    for key, val in sorted(obj.items()):
        if isinstance(val, dict):
            val = merge(val)
            if not val:
                continue
            if len(val) == 1:
                k1, v

from itertools import tee


def pairwise(iterable):
    # pairwise('ABCDEFG') --> AB BC CD DE EF FG
    a, b = tee(iterable)
    next(b, None)
    return zip(a, b)


data = [' Ye', 'oks', 'am', '-', 'd', 'ong', ' Gang', 'nam', '-', 'gu 

cols = ["COD", "TEC", "SET"]
df_cut = (
    df[df['AZIM'].eq(
        df.groupby(cols)['AZIM'].transform(lambda x: x.mode().max())
    )].drop_duplicates(cols).reset_index(drop=True)
)

          COD STATE CITY  AZI

import tkinter as tk
import random

window = tk.Tk()
window.title(" Magical Name Generator ")

window.geometry("400x400")


# Functions.
def name_gen():
    """
    This function randomly concatenates items from
    two lists and combines 

def scorer(row):
  date_scores = []
  for col in row:
    if 'Top' in col:
      date_scores.append(get_sentiment_score(row[col]))
  sentiment_daily_mean = date_scores.mean()
  return sentiment_daily_mean
    
df['date_score'] = df.apply(s