liquidgenius

#! /bin/bash

# License:    MIT; https://opensource.org/licenses/MIT
# Version:    0.0.1
# Maintainer: https://gist.github.com/liquidgenius

# Given a script main.py on an ubuntu machine in /home/ubuntu/app/src directory with the interpreter 
# in /home/ubuntu/app/.venv directory in project. This script, run.sh, would be placed in the
# /home/ubuntu/app directory.

liquidgenius

#! /bin/bash

# License:    MIT; https://opensource.org/licenses/MIT
# Version:    0.0.1
# Maintainer: https://gist.github.com/liquidgenius

#*******************************************************************************
# Pycharmer; A Pycharm Python project creator
#*******************************************************************************

liquidgenius

#! /bin/bash

# License:    MIT; https://opensource.org/licenses/MIT
# Version:    0.0.1
# Maintainer: https://gist.github.com/liquidgenius
# Gist:       https://gist.github.com/liquidgenius/56167cfedc7270752d207377cf618fcf


#*******************************************************************************
# Masonite 3 Project Scaffolding

liquidgenius

#!/usr/bin/env python
# -*- coding: utf-8 -*-
__license__ = "MIT"
__version__ = "0.1"
__py_version__ = "3.8.7"
__status__ = "Development"

import json
from pathlib import Path
from time import sleep

liquidgenius

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# https://orm.masoniteproject.com/
# app.helpers.columns.py

from config.database import DB
from masoniteorm.collection import Collection


def columns(model):

liquidgenius

# Ensure we have the latest pip and pipenv
pip3 install --upgrade pip pipenv
# Setup new Python 3.8 project
pipenv --python 3.8
# Install aws-lambda-powertools
pipenv install boto3 aws-lambda-powertools
# Install dev dependencies
pipenv install black pytest --dev --pre

liquidgenius

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

from random import choice

import httpx
import fake_useragent


def Proxymesh(url, proxy_choices=None, headers=None, country='US'):

liquidgenius

from dataclasses import dataclass, fields as datafields
from ujson import dumps, loads
# Note: ujson seamlessly serializes dataclasses, unlike stdlib's json

@dataclass
class Point:
    x: float
    y: float

# Shallow dataclass can be rebuilt from dict/json:

liquidgenius

"""
* requires usaddress https://github.com/datamade/usaddress
* check out the website! https://parserator.datamade.us/usaddress

The usaddress package is pretty great for normalizing inconsistent address data,
especially when if you have a lot to process and can't rely on using a geocoding api.
The results are really granular, probably moreso than you'll need, and definitely
more than most CRM systems if integrating these addresses is your goal. 

This is just a simple wrapper around the usaddress.tag() function that I feel will 

liquidgenius

-- Function: makegrid_epsg3857(text, text, text, numeric)

-- DROP FUNCTION makegrid_epsg3857(text, text, text, numeric);

CREATE OR REPLACE FUNCTION makegrid_epsg3857(schemaname text, boundingbox text, gridtable text, halfwidth numeric)
  RETURNS text AS
$BODY$
    DECLARE
    tbl_cnt int;
    XMIN numeric;