boyboi86

Animated Weather Icons

Based on dribbble shot https://dribbble.com/shots/2097042-Widget-Weather by kylor

A Pen by Josh Bader on CodePen.

License.

boyboi86

/*
 * Inspired by: http://stackoverflow.com/questions/4360060/video-streaming-with-html-5-via-node-js
 */

var http = require('http'),
    fs = require('fs'),
    util = require('util');

http.createServer(function (req, res) {
  var path = 'video.mp4';

boyboi86

class EventEmitter {
  constructor(){
    this.events = {};
  }
  on(eventName, cb){
    if(this.events[eventName]){
      this.events[eventName].push(cb);
    } else {
      this.events[eventName] = [cb];
    }

boyboi86

export default function (ComposedComponent) {
  class Authentication extends Component{

/*Defined contextTypes to access context */
    static contextTypes = {
        router: React.PropTypes.object
    }

/*Made used of context to define route*/
    componentWillMount(){

boyboi86

'use strict';

const http = require('http');
const url = require('url');
const qs = require('querystring');
const port = 3000;

/*Pure nodejs way of routing, an obj to provide methods as main key then routes with deep clone*/

let routes = {

boyboi86

//... 
Model.add({
	content: { 
		type: Types.Html, wysiwyg: true, height: 400
	},

	hash : {
		type : String,
		hidden : true
	},

boyboi86

function getDistanceFromLatLonInKm(lat1,lon1,lat2,lon2) {
  var R = 6371; // Radius of the earth in km
  var dLat = deg2rad(lat2-lat1);  // deg2rad below
  var dLon = deg2rad(lon2-lon1); 
  var a = 
    Math.sin(dLat/2) * Math.sin(dLat/2) +
    Math.cos(deg2rad(lat1)) * Math.cos(deg2rad(lat2)) * 
    Math.sin(dLon/2) * Math.sin(dLon/2)
    ; 
  var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a)); 

boyboi86

Option Explicit 
 
Sub AllSectionsToSubDoc() 
     
    Dim x               As Long 
    Dim Sections        As Long 
    Dim Doc             As Document 
     
    Application.ScreenUpdating = False 
    Application.DisplayAlerts = False 

boyboi86

import pandas as pd
import datetime as dt
import pandas_datareader.data as pdr

p = print

# =============================================================================
# return dataframe
# ============================================================================= 

boyboi86

import numpy as np
import pandas as pd
import datetime as dt

from sklearn.datasets import make_classification

def create_price_data(start_price: float = 1000.00, mu: float = .0, var: float = 1.0, n_samples: int = 1000000):
    
    i = np.random.normal(mu, var, n_samples)
    df0 = pd.date_range(periods=n_samples, freq=pd.tseries.offsets.Minute(), end=dt.datetime.today())