colobas

from skopt.space import Real, Integer
from skopt.utils import use_named_args
from skopt import gp_minimize

from xgboost import XGBClassifier

from sklearn.model_selection import cross_val_score

cls = XGBClassifier()

colobas

import umap
import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import axes3d

def draw_umap(data, n_neighbors=15, min_dist=0.1, n_components=2, metric='euclidean', title='', color=None):
    fit = umap.UMAP(
        n_neighbors=n_neighbors,
        min_dist=min_dist,
        n_components=n_components,

colobas

import time, sys
while True:
	blah="⡿⣟⣯⣷⣾⣽⣻⢿"
	for l in blah:
		_ = sys.stdout.write(l)
		_ = sys.stdout.flush()
		_ = sys.stdout.write('\b')
		time.sleep(0.2)

colobas

from selenium import webdriver
import pyperclip

driver = webdriver.Chrome()
driver.get("http://192.168.1.218")

input("AUTHENTICATE AND GO TO PASSMAN PAGE")

table = driver.find_elements_by_class_name("credential-table")[0]
rows = table.find_elements_by_tag_name("tr")

colobas

# credits to: https://stackoverflow.com/a/26209900
def pretty(value, htchar='\t', lfchar='\n', indent=0):
    nlch = lfchar + htchar * (indent + 1)
    if type(value) is dict:
        items = [
            nlch + repr(key) + ': ' + pretty(value[key], htchar, lfchar, indent + 1)
            for key in value
        ]
        return '{%s}' % (','.join(items) + lfchar + htchar * indent)
    elif type(value) is list:

colobas

[
	(7035, 0.145),
	(20100-7035, 0.285),
	(40200-20100, 0.37),
	(80000-40200, 0.45),
	("inf", 0.48)
]

colobas

words="$(echo | k2pdfopt nickel2016.pdf -h 20000 -w 1200 -pl 100 -pr 100 | grep -Eo "[0-9]* words" | grep -Eo "[0-9]*")"
let "reading_time = $words * 1.1 / 275"                               
reading_mins="$(printf "%.0f" $reading_time)"
if [[ $reading_mins -le 60 ]]; then                            
	echo "approx reading time: $reading_mins mins"
else                                        
	let "hours = $reading_mins / 60"
	let "mins = $reading_mins % 60"
	echo "approx reading time: $hours h $mins mins"
fi

colobas

#! /usr/bin/env python2
import ctypes, re, sys

## Partial interface to ptrace(2), only for PTRACE_ATTACH and PTRACE_DETACH.
c_ptrace = ctypes.CDLL("libc.so.6").ptrace
c_pid_t = ctypes.c_int32 # This assumes pid_t is int32_t
c_ptrace.argtypes = [ctypes.c_int, c_pid_t, ctypes.c_void_p, ctypes.c_void_p]
def ptrace(attach, pid):
    op = ctypes.c_int(16 if attach else 17) #PTRACE_ATTACH or PTRACE_DETACH
    c_pid = c_pid_t(pid)

colobas

def retirement(s,y,n,r):
  """
  Considering:
    - You have 0 debt
    - You make y per year (yearly income)
    - You can save a fraction s per year
    - You can invest at a r-1 yearly interest
    
  This function returns the amount you have accumulated after n years.
  This means you can live with (1-s)*y per year.

colobas

import numpy as np
from sklearn.metrics import mutual_info_score

def calc_MI(x, y, bins):
    c_xy = np.histogram2d(x, y, bins)[0]
    mi = mutual_info_score(None, None, contingency=c_xy)
    return mi

def calc_MI_matrix(A, bins)
    n = A.shape[1]