kayibal

def test_get_price():
    exchange_gw = UniswapGateway()
    usdt = Token(address="0xdAC17F958D2ee523a2206206994597C13D831ec7")
    wbtc = Token(address="0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599")
    order = Order(
        inp_token=usdt,
        out_token=wbtc,
        amount=1
    )

kayibal

#!/Users/kayibal/miniconda3/bin/python
import boto3
import click
from urllib.parse import urlparse, urlunparse

def pypi_url(env):
    ssm = boto3.client("ssm")
    url = ssm.get_parameter(Name=f"/CodeBuild/PyPi/{env}/url", WithDecryption=True)[
        "Parameter"
    ]["Value"]

kayibal

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('whitegrid')
sns.set_palette(sns.dark_palette((0.6, 0.6, 0.6), reverse=True))
plt.rcParams["figure.figsize"] = (15, 7)

kayibal

(╯°□°）╯︵ ┻━┻

kayibal

import numpy as np

def compute_overlap(x1, x2, y1, y2):
    if x2 >= y1 and y2 >= x1:
        if x1 < y1 < x2 and x2 < y2:
            return x2 - y1
        elif y1 < x1 < y2 and y2 < x2:
            return y2 - x1
        elif x1 < y1 and y2 < x2:
            return y2 - y1

kayibal

"""Recommendation module.

This module deals with using lightFM models in production and includes a
LightFm subclass which provides a predict_online method to use in API or
similar scenarios.
"""
import operator
from logging import getLogger

import numpy as np

kayibal

# export PROJECT=is24_user_scoring
# export PROD_SETTINGS=is24.settings.production
# export VERSION=v2.0.1
sleep 5m
cd /home/ec2-user
sudo pip install awscli
if [ ! -d "$PROJECT" ]; then 
  git clone git@github.com:datarevenue-berlin/$PROJECT.git 
  ln -s $PROJECT bin; 
fi

kayibal

#!/usr/bin/env bash

git clone --recursive https://github.com/dmlc/xgboost
cd xgboost
sudo make -j4
cd python-package
python3 setup.py install
rm -rf xgboost

kayibal

import re

import nltk
from nltk.tokenize.regexp import RegexpTokenizer
from nltk.corpus import stopwords

from sklearn.svm import SVC
from sklearn.naive_bayes import BernoulliNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.feature_selection import SelectPercentile

kayibal

#!/usr/bin/env bash

MIN_LOAD=1
ADMIN_NUMBERS=( "+49NUMBER1" "+49NUMBER2" )

if ! who | grep -q *; then
  echo "users logged in"
  exit 0
fi