d4hines

/*
	Downloads Saved View/Advanced Find via Web API
	Expand All Columns to access the returned results. Columns prefixed "OData.Community.Display.V1.FormattedValue" show the properly formatted values.
*/
(OrgUrl as text, QueryName as text, LogicalCollectionName as text, UserView as logical) =>
let 

	GetQueryByName =
		(OrgUrl as text, QueryName as text, UserView as logical) => 
			let

d4hines

import { Router } from  '@angular/router';
import { TestBed } from '@angular/core/testing';

import { EffectsModule } from '@ngrx/effects';
import { provideMockActions } from '@ngrx/effects/testing';

import { Observable } from 'rxjs/Observable';
import { ReplaySubject } from 'rxjs/ReplaySubject';

import { AuthEffects } from './auth.effects';

d4hines

import 'rxjs/add/operator/catch';
import 'rxjs/add/operator/do';
import 'rxjs/add/operator/exhaustMap';
import 'rxjs/add/operator/map';
import 'rxjs/add/operator/take';

import { Observable } from 'rxjs/Observable';
import 'rxjs/add/observable/timer';

import { Injectable } from '@angular/core';

d4hines

We are looking to hire a skilled Clojure/ClojureScript developer to help build a new platform for medical knowledge. By applying Machine Learning and Natural Language Processing to medical literature, we aim to give experts the insights they need to make informed decisions.

The project is part of Doctor Evidence, a company based in Santa Monica, CA. At this moment a small team is working remotely (GMT+2, EU timezone) on this project as a full-time Research & Development effort.

You will work in a small team that will architect and build the next-generation of data science tools for Evidence Based Medicine and Health Technology Assessment. A good fit would be someone who can work quickly while still following best practices, who thrives on new technology, and has an avid interest in medical informatics and machine learning.

What we offer

• Flexible work hours from the comfort of your home

d4hines

{:known-macros [[fulcro.client.primitives/defsc [name args]]
                [fulcro.client.mutations/defmutation [name args]]
                [fulcro.server/defmutation [name args]]
                fulcro.client.primitives/defui
                [fulcro.client.routing/defrouter [name args]]
                [fulcro-spec.suite/def-test-suite [name]]
                [fulcro.client.cards/defcard-fulcro [name args]]
                fulcro-spec.core/assertions]
 :ignored-unused-namespaces [devcards.core]
 :rules {:if-without-else true}

d4hines

import requests
from requests_ntlm import HttpNtlmAuth

import json

session = requests.Session()
session.auth = HttpNtlmAuth('DOMAIN\\username', 'password')
url = "https://recruitercrm.regent.edu/CRMRECRUIT/api/data/v8.0/contacts?$filter=datatel_erpid%20eq%20%27B06892407%27"

response = session.get(url)

d4hines

;; -*- 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

d4hines

function anagrams

• given an anagram key ak, an initial set of partial angrams pas

  e.g ak = "adeor", pas = #{"a", "are", "dare" ...}

  • For each partial anagram pa in pas,

    • Let key-diff be the multiset difference between pa and ak

      e.g pa = "a", key-diff = "deaor"

d4hines

Focus
  Unfocused
    focus -> Focused
  Focused*
    unfocus -> Unfocused

Tabs
  Tab1 Selected*
    click tab 2 -> Tab1 Selected
    click tab 1 -> Tab1 Selected

d4hines

(ns constrained.core
  (:require [reagent.core :as reagent]
            [goog.dom :as dom]))

(set! *warn-on-infer* true)

(defn collision? [x y w h other-rectangles]
  (some
    (fn a-collision? [{:keys [left top width height]}]
      (and