talaikis

import { saveAs } from 'file-saver'

const base64Data = ''
const imgBuff = Buffer.from(base64Data.replace(/^data:image\/\w+;base64,/, ''), 'base64')
const blob = new Blob([new Uint8Array(imgBuff).buffer])
saveAs(blob)

talaikis

const child = require('child_process')
const { cpus } = require('os')
const transform = require('stream-transform')

class Pool {
  constructor (file, maxPool, done) {
    this.pool = []
    this.active = []
    this.waiting = []
    this.maxPool = maxPool

talaikis

const { join } = require('path')
const { writeFile, unlink } = require('fs')

const functions = [
  { p: join(__dirname, 'handlers', 'user', 'confirm', 'index.js'), o: join(__dirname, 'build', 'userConfirm.js') },
  { p: join(__dirname, 'handlers', 'user', 'delete', 'index.js'), o: join(__dirname, 'build', 'userDelete.js') },
  { p: join(__dirname, 'handlers', 'user', 'edit', 'index.js'), o: join(__dirname, 'build', 'userEdit.js') },
  { p: join(__dirname, 'handlers', 'user', 'get', 'index.js'), o: join(__dirname, 'build', 'userGet.js') },
  { p: join(__dirname, 'handlers', 'user', 'signin', 'index.js'), o: join(__dirname, 'build', 'signin.js') },
  { p: join(__dirname, 'handlers', 'user', 'signup', 'index.js'), o: join(__dirname, 'build', 'signup.js') },

talaikis

talaikis

"""
This idea first used for bitcoin. Data file format is MT4's.
Similar strategy(-ies) is used by market makers.
It's definitely betetr than any moving average based strategy.
ETH donations accepted: 0x007F11363140F2edE5f6d4F1a19A352861e013e0
"""

from os.path import join

from pandas import read_csv, to_datetime, DataFrame, read_pickle

talaikis

"""
Recycle bin sniffer for Windows.
Tested on Python 3.6, Windows 8.1.
"""

from os import listdir
from os.path import isdir
import optparse
from winreg import (OpenKey, QueryValueEx)
from shutil import  move

talaikis

from os.path import dirname, join

from numpy import where, shape
from pandas import read_csv
from sklearn.metrics import accuracy_score
from sklearn.cluster import DBSCAN, MiniBatchKMeans, SpectralClustering, KMeans
from sklearn.decomposition import FastICA, PCA, NMF
from sklearn.linear_model import LogisticRegression, RidgeClassifier
from sklearn.ensemble import GradientBoostingClassifier, BaggingClassifier, RandomForestClassifier, VotingClassifier, AdaBoostClassifier
from sklearn.preprocessing import PolynomialFeatures, scale