davidlenz

from svglib.svglib import svg2rlg
from reportlab.graphics import renderPDF

def svg_to_pdf(in_path, out_path):
    # svg to pdf
    drawing = svg2rlg(in_path)
    renderPDF.drawToFile(drawing, out_path)
      

davidlenz

import newsapi_v2
import findurls
import praw
import pandas as pd
import utils_func
import os
import time
import subreddit
import requests
from newspaper import fulltext

davidlenz

# uses the date_to_milliseconds and interval_to_milliseconds functions
# https://gist.github.com/sammchardy/3547cfab1faf78e385b3fcb83ad86395
# https://gist.github.com/sammchardy/fcbb2b836d1f694f39bddd569d1c16fe

from binance.client import Client
import time

def get_historical_klines(symbol, interval, start_str, end_str=None):
    """Get Historical Klines from Binance

davidlenz

import justext, time
import pandas as pd
import requests, urllib
import utils_func


def get_sources(key):
    """
    retrieve all sources from newsapi, filter the german and english speaking
    and return them as dataframe

davidlenz

#!/usr/bin/python
# -*- coding: utf-8 -*-

"""
url matching regex
http://daringfireball.net/2010/07/improved_regex_for_matching_urls
"""

"""
The regex patterns in this gist are intended to match any URLs,

davidlenz

import newsapi_v2
import findurls
import praw
import pandas as pd
import utils_func
import os
import subreddit
import requests
from newspaper import fulltext

davidlenz

from tweepy import Stream
from tweepy import OAuthHandler
from tweepy.streaming import StreamListener
import json
import sqlite3
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from unidecode import unidecode
import time

analyzer = SentimentIntensityAnalyzer()

davidlenz

# https://gist.github.com/azam-a/32b89944b98a3fd79d44ebfdac16b63d
import pandas as pd

import selenium
print('selenium.__version__: ', selenium.__version__)

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

davidlenz

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

davidlenz

import numpy as np
from scipy.stats import entropy

def js(p, q):
    p = np.asarray(p)
    q = np.asarray(q)
    # normalize
    p /= p.sum()
    q /= q.sum()
    m = (p + q) / 2