JohnnyFang

GnuPG 2.2.x Build Instructions

Below are my build instructions for GnuPG 2.2.10, released on August 30th, 2018. These instructions are built for a headless Ubuntu 18.04 LTS server (and have also been tested on Ubuntu 14.04/16.04).

If you prefer, you may use the below install script to install GnuPG 2.2.x by running the following commands:

curl -OL "https://gist.githubusercontent.com/JohnnyFang/ca2f243a9adb179186e5932c9c1445d0/raw/a2d4ca7056f9908d0f856acff7d2b466a99306ff/install-gnupg22.sh" && sudo -H bash ./install-gnupg22.sh

Install the needed dependencies

JohnnyFang

path ="datasets/"
files = ["file1.json", "file2.json", "file3.json", "file4.json", "file5.json"]
column_names = ["restaurant.id", "restaurant.name", "restaurant.cuisines", "restaurant.location.city",
                "restaurant.location.address", "restaurant.location.latitude",
                "restaurant.location.longitude", "restaurant.user_rating.rating_text"]
new_col_names = ["id", "name", "cuisines", "city", "address", "latitude", "longitude", "rating_text"]
dataset = pd.DataFrame()

for file in files:
    file_path = path + file

JohnnyFang

column_names = ["restaurant.id", "restaurant.name", "restaurant.cuisines", "restaurant.location.city",
                "restaurant.location.address", "restaurant.location.latitude",
                "restaurant.location.longitude", "restaurant.user_rating.rating_text"]
new_col_names = ["id", "name", "cuisines", "city", "address", "latitude", "longitude", "rating_text"]         


if 'restaurants' in el and len(el['restaurants']):
    df = pd.concat([pd.DataFrame(json_normalize(restaurant)) for restaurant in el['restaurants']])
    restaurants_df = df[[col for col in column_names]]
    restaurants_df.rename(columns=dict(zip(column_names, new_col_names)), inplace=True)

JohnnyFang

path = "datasets/"
files = ["file1.json", "file2.json", "file3.json", "file4.json", "file5.json"]
for file in files:
    file_path = path + file
    with open(file_path) as f:
        json_data = json.load(f)
        for el in json_data:
            # we check that 'restaurants' exists and it contains data
            if 'restaurants' in el and len(el['restaurants']):
                # Do something with our restaurant data

JohnnyFang

import datetime
import itertools
import random

class Sensor:
  def __iter__(self):
    return self
  
  def __next__(self):
    return random.random()

JohnnyFang

//*******************************************************************
// Welcome to CompileJava!
// If you experience any issues, please contact us ('More Info')  -->
// Also, sorry that the "Paste" feature no longer works! GitHub broke
// this (so we'll switch to a new provider): https://blog.github.com\
// /2018-02-18-deprecation-notice-removing-anonymous-gist-creation/
//*******************************************************************

import java.lang.Math; // headers MUST be above the first class

JohnnyFang

# app/todo_api/views.py 
from flask import request, jsonify, Response

from app import db
from app.models import Todo, TodoSchema
from . import todo_api

todo_schema = TodoSchema()
todos_schema = TodoSchema(many=True)

JohnnyFang

# app/webapp/views.py  
from flask import render_template

from app.models import Todo
from . import webapp


@webapp.route('/')
def index():
   all_todos = Todo.query.all()

JohnnyFang

# app/todo_api/__init__.py
from flask import Blueprint

todo_api = Blueprint('todo_api', __name__)

from . import views

JohnnyFang

# app/webapp/__init__.py
from flask import Blueprint

webapp = Blueprint('webapp', __name__)

from . import views