bschoenfeld

using System;

namespace FTP_To_Couch
{
    class BusDateTime
    {
        public int Year {get;set;}
        public int Mon {get;set;}
        public int Day {get;set;}
        public int Hour {get;set;}

bschoenfeld

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;

namespace gtfs
{
	class MainClass
	{
		public static void Main (string[] args)

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from ftplib import FTP
from datetime import datetime, date, time, timedelta
from pymongo import Connection

class BusCheckin:
  def __init__(self, data, year):
		parts = data.split(',')
		
		# checkin time
		self.time = datetime.strptime(parts[0] + ' ' + parts[1] + '/' + year, "%H:%M:%S %m/%d/%Y")

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from datetime import datetime, timedelta
from pymongo import Connection, GEO2D

class HRTDatabase:
	def __init__(self, uri):
		self.client = Connection(uri)
		self.database = self.client.hrt

	def insertStops(self, data, date):
		collectionName = self.genCollectionName('stops_', date)

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--- C:/Users/BSCHOE~1/AppData/Local/Temp/underscore.js-revBASE.svn000.tmp.js  Tue Mar  5 11:30:16 2013
+++ C:/bschoenfeld/OPs_Service_Bus/VIT.NIT.OPS.HatchChecker/WebServer/Scripts/underscore.js	Tue Mar  5 11:48:58 2013
@@ -238,7 +238,7 @@
   // Convenience version of a common use case of `filter`: selecting only objects
   // containing specific `key:value` pairs.
   _.where = function(obj, attrs, first) {
-    if (_.isEmpty(attrs)) return first ? void 0 : [];
+    if (_.isEmpty(attrs)) return first ? null : [];
     return _[first ? 'find' : 'filter'](obj, function(value) {
       for (var key in attrs) {

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[{"lat": 36.77721, "count": 100, "lng": -77.87301}, {"lat": 36.80509, "count": 99, "lng": -78.94802}, {"lat": 36.99772, "count": 100, "lng": -79.04197}, {"lat": 36.67743, "count": 97, "lng": -78.9196}, {"lat": 36.735, "count": 86, "lng": -78.91515}, {"lat": 36.76143, "count": 100, "lng": -78.76973}, {"lat": 36.69857, "count": 100, "lng": -78.92031}, {"lat": 36.70611, "count": 95, "lng": -78.94777}, {"lat": 36.67466, "count": 90, "lng": -78.92763}, {"lat": 36.735, "count": 95, "lng": -78.91515}, {"lat": 36.70478, "count": 94, "lng": -78.89878}, {"lat": 36.56512, "count": 100, "lng": -79.1404}, {"lat": 36.68801, "count": 100, "lng": -78.8965}, {"lat": 36.73745, "count": 98, "lng": -78.91978}, {"lat": 36.69708, "count": 91, "lng": -78.90118}, {"lat": 36.94173, "count": 94, "lng": -78.75275}, {"lat": 36.72836, "count": 96, "lng": -79.13531}, {"lat": 36.73383, "count": 100, "lng": -78.88802}, {"lat": 36.71382, "count": 100, "lng": -78.91707}, {"lat": 37.03008, "count": 78, "lng": -78.95301}, {"lat": 36.70086, "cou

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from bs4 import BeautifulSoup
import scraperwiki
import cookielib, urllib, urllib2
import sys

def getNames(html, name):
    for row in html.find(class_="nameList").find_all('tr'):
        cols = row.find_all('td')
        if len(cols) > 4:
            if name not in cols[1].string:

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import urllib2
from bs4 import BeautifulSoup

# get the web page
url = "http://healthspace.com/Clients/VDH/NewRiver/web.nsf/module_facilities.xsp?module=Food"
web_page = urllib2.urlopen(url)
web_page_html = web_page.read()

# write the web page to a file
# google "python write to file"

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def load_traffic_data():
    traffic = {}
    with open('data/traffic_daily_vehicle_miles_traveled_2015.csv') as f:
        reader = csv.DictReader(f)
        for row in reader:
            if row['District Court FIPS Codes'] == '':
                continue
            if row['District Court FIPS Codes'] not in traffic:
                # Some district courts are represented in the traffic data
                # by mulitple localities. So instead of just loading the traffic

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[
  {
    "locality": ["Newport News"],
    "fips": [701, 702, 703],
    "all": 4365476,
    "chargeCount": 100000
  },
  ...
]