dixudx

import "bytes"

func StreamToByte(stream io.Reader) []byte {
  buf := new(bytes.Buffer)
	buf.ReadFrom(stream)
	return buf.Bytes()
}

func StreamToString(stream io.Reader) string {
	buf := new(bytes.Buffer)

jkluch

#!/usr/bin/env python3

import sys
import json
import requests
import subprocess
from os.path import expanduser
HOME = expanduser("~")
AUTH_EMAIL = "MODIFY THIS"
AUTH_KEY = "MODIFY THIS"

genomics-geek

Guide on how to create and set up a Dockerized web app using Django REST APIs and ReactJS

Hopefully this will answer "How do I setup or start a Django project using REST Framework and ReactJS?"

This is a guide to show you step by step how this can be setup. If you just want to get started, use the cookiecuter I set up cookiecutter-django-reactjs. It basically is a fork of pydanny's cookiecutter, just added the front-end stuff :).

I created this because it was SUCH a pain in the ass setting up a project using all the latest technologies. After some research, I figured it out and have it working. The repo that implements this is located here. Feel free to use it as a boilerplate ;)

Main features:

• Django REST APIs

odony

# Enable request logging in Odoo using one of the following:
#   * pass --log-level=debug_rpc
#   * or pass --log-handler=odoo.http.rpc.request:DEBUG (to only debug request times)
#   * or set the equivalent config option in the Odoo config file

                     
# Then save the following as a munin plugin to monitor the last 5 minutes of your Odoo config file
# Be sure to set the correct path for the Odoo log file, and adjust the variables as needed

#!/bin/bash

odony

# Setup an appropriate nginx log_format in the `http` section of your nginx config:

    log_format  main  '$remote_addr $time_iso8601 "$request" '
                      '$status $body_bytes_sent "$http_referer" '
                      '"$http_host" '
                      '"$http_user_agent" "$http_x_forwarded_for" '
                      '$request_time ';
                      
# Then save the following as a munin plugin to monitor the last 5 minutes of your nginx access.log file

vitorfs

from django.db import transaction

@transaction.atomic
def create_user_view(request):
    if request.method == 'POST':
        user_form = UserCreationForm(request.POST)
        profile_form = ProfileForm(request.POST)
        if user_form.is_valid() and profile_form.is_valid():
            user = user_form.save()
            user.refresh_from_db()  # This will load the Profile created by the Signal

yelizariev

# IMPORTS
# replace osv, orm
find . -type f -name '*.py' | xargs sed -i 's/from openerp.osv import orm$/from odoo import models/g'
find . -type f -name '*.py' | xargs sed -i 's/from openerp.models.orm import Model$/from odoo.models import Model/g'
find . -type f -name '*.py' | xargs sed -i 's/osv.osv_memory/models.TransientModel/g'
find . -type f -name '*.py' | xargs sed -i 's/osv.osv/models.Model/g'
find . -type f -name '*.py' | xargs sed -i 's/osv.except_osv/UserError/g'
find . -type f -name '*.py' | xargs sed -i 's/osv\./models./g'
find . -type f -name '*.py' | xargs sed -i 's/\<orm\./models./g'
find . -type f -name '*.py' | xargs sed -i 's/\(import .*\), osv/\1, models/g'

jasimmk

#!/usr/bin/env python

# Install fake factory at first. Helps us create 50k customers in odoo
# pip install fake-factory
# Documentation for odoo is listed here
# https://www.odoo.com/documentation/9.0/api_integration.html#calling-methods

# MIT License

# Copyright (c) 2016 Jasim Muhammed

achauve

import logging
import os
import time
import traceback


# pip install slackclient
from slackclient import SlackClient

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats