tnez

# Add some syntactical sugar to compose Procs / lambdas like in other, more
# functional languages. Inspired by a feature request in upstream Ruby
# (https://bugs.ruby-lang.org/issues/6284).
class Proc
  def *(g)
    proc { |*args, &blk| call(g.to_proc.call(*args, &blk)) }
  end
end

# Decode the session cookie as copied straight from devtools (for ~> Rails 3.2)

tnez

import React from 'react'
import { connect } from 'react-readux'
import { PropTypes } from 'prop-types'
import SuperCoolSpinner from './SuperCoolSpinner'

const DataFetcher = ({ children, dispatch, fetchAction, shouldFetch }) => {
  // fetch data if needed
  if (shouldFetch) { dispatch(fetchAction) }

  // render the spinner if we should fetch (this should be true until data has

tnez

Salesforce Transition

Existing Functionality

Customer Communication

Trigger on job status changes and send email, SMS as specified by user.

tnez

;; -*- mode: emacs-lisp -*-
;; This file is loaded by Spacemacs at startup.
;; It must be stored in your home directory.

(defun dotspacemacs/layers ()
  "Configuration Layers declaration.
You should not put any user code in this function besides modifying the variable
values."
  (setq-default
   ;; Base distribution to use. This is a layer contained in the directory

tnez

;; -*- mode: emacs-lisp -*-
;; This file is loaded by Spacemacs at startup.
;; It must be stored in your home directory.

(defun dotspacemacs/layers ()
  "Configuration Layers declaration.
You should not put any user code in this function besides modifying the variable
values."
  (setq-default
   ;; Base distribution to use. This is a layer contained in the directory

tnez

# given a *D* dimensional array *I* where *N* regions, denoted by
# sequential integers starting at 1, return an NxD array *coords*
# containing the centers of mass for each region (in index space) and
# an NxN array *Dist* where the entry D[i,j] corresponds to the euclidean
# distance between the centers of regions i and j respectively.
import numpy as np
from scipy.ndimage.measurements import center_of_mass
 
D = I.ndim
N = I[:].max()