cschmidli

import * as React from 'react';
import * as hash from 'object-hash';
import { 
    TableProps, 
    Table,
    TableBody,
    TableCell, 
    TableFooter,
    TableHeaderCell,

jbaxleyiii

import { Component } from "react";
import { print } from "graphql-tag/printer";
import { graphql } from "react-apollo";
import { MockedProvider } from "react-apollo/test-utils";
import { addTypenameToDocument } from "apollo-client/queries/queryTransform";

import { mount } from "enzyme";

import { withSavedPayments, SAVED_ACCTS_QUERY } from "../";

matthewriley

Install Git 2.9.0 from source on RHEL 7.x

These are the Terminal commands I recently used (June 2016) to install Git 2.9.0 from source on RHEL 7.x. I ran this in a VirtualBox VM after a fresh install from the ISO.

You mileage will vary as the yum packages are updated over time. The yum install line below should include all the dependencies, at least it did for me. Depending upon how often you use yum update you may need to run yum --enablerepo=base clean metadata as su before you run the following commands.

cd ~/Downloads
su
yum install autoconf cpio curl-devel expat-devel gcc gettext-devel make openssl-devel perl-ExtUtils-MakeMaker zlib-devel
wget -O v2.9.0.tar.gz https://github.com/git/git/archive/v2.9.0.tar.gz

brightredchilli

References:

• https://github.com/kubo/fix_oralib_osx
• https://gist.github.com/thom-nic/6011715
• oracle/node-oracledb#149 (comment)

This was tested on El Capitan 10.11.4 (15E65) with python 2.7, which was installed via pyenv.

Download the sdk and client from Oracle. This guide uses 11.2.0.4.0

• instantclient-basic-$VERSION-macosx-x64.zip
• instantclient-sdk-$VERSION-macosx-x64.zip

yakneens

def set_up_dag_run(context, dag_run_obj):
    dag_run_obj.payload = {"config": context["config"]}
    dag_run_obj.run_id = str(uuid4())
    print context
    return dag_run_obj
    
def launch_workflow_command(args):

    config_location = args.config_location
    analysis_id = args.analysis_id

learncodeacademy

var debug = process.env.NODE_ENV !== "production";
var webpack = require('webpack');

module.exports = {
  context: __dirname,
  devtool: debug ? "inline-sourcemap" : null,
  entry: "./js/scripts.js",
  output: {
    path: __dirname + "/js",
    filename: "scripts.min.js"

diegoconcha

###Redux Egghead Video Notes###

####Introduction:#### Managing state in an application is critical, and is often done haphazardly. Redux provides a state container for JavaScript applications that will help your applications behave consistently.

Redux is an evolution of the ideas presented by Facebook's Flux, avoiding the complexity found in Flux by looking to how applications are built with the Elm language.

####1st principle of Redux:#### Everything that changes in your application including the data and ui options is contained in a single object called the state tree

seanmavley

from django import forms
from django.contrib.auth.models import User


class UserProfileForm(forms.ModelForm):
    class Meta:
        model = User
        fields = ['first_name', 'last_name', 'email']
        

Dr-Nikson

This is an auth example (WIP)

react + redux + RR

It uses https://gist.github.com/iNikNik/3c1b870f63dc0de67c38 for stores and actions.

1) create redux

const redux = createRedux(state);

2) get requireAccess func => bindCheckAuth to redux

non

What is the appeal of dynamically-typed languages?

Kris Nuttycombe asks:

I genuinely wish I understood the appeal of unityped languages better. Can someone who really knows both well-typed and unityped explain?

I think the terms well-typed and unityped are a bit of question-begging here (you might as well say good-typed versus bad-typed), so instead I will say statically-typed and dynamically-typed.

I'm going to approach this article using Scala to stand-in for static typing and Python for dynamic typing. I feel like I am credibly proficient both languages: I don't currently write a lot of Python, but I still have affection for the language, and have probably written hundreds of thousands of lines of Python code over the years.