Nate-Wessel

// https://en.wikipedia.org/wiki/Gaussian_function
const [height,center,bandwidth] = [1,0,1]
function kernel(x){
	return height*Math.exp( - ( ((x-center)**2)/(2*bandwidth**2) ) )
}

Nate-Wessel

Nate-Wessel

// identify 3-way intersections of identically named ways
// thus to identify/located divided/split roadways

// get nodes of all named ways
way[highway][name]({{bbox}})->.namedways;
// foreach way 
foreach .namedways -> .thisway(
  node(w.thisway)->.childnodes;
  foreach.childnodes -> .thisnode(
  	// select named parent ways of this node with the same name

Nate-Wessel

way[highway][highway!~"footway|service|path|cycleway|steps|pedestrian"]({{bbox}});
node(w);
way(bn)[highway=footway][footway=sidewalk];
out body;
>;
out skel qt;

Nate-Wessel

[timeout:600];

// Hamilton County area by id
rel(104784); map_to_area->.ham;
// get older buildings inside the county 
way(area.ham)[building](if: timestamp() < "2014-08-25")->.buildings;

// iterate over buildings, checking the dates on their nodes
foreach .buildings->.way {
  // only add ways to the result set if nodes are also 

Nate-Wessel

import os
from_path = '/home/nate/Dropbox/muni-17477/'
to_path = '/home/nate/Dropbox/muni-17477-fixed/'
old_files = os.listdir(from_path)
for old_file in old_files:
	old_fpath = from_path+old_file
	with open(old_fpath,'r') as fp:
		old_content = fp.read()
		old_hour = old_file[11:13]
		new_hour =str(int(old_hour)-3)

Nate-Wessel

=(cell-25569)*86400 - hour_offset*3600

Nate-Wessel

def inner_angle_plane(point1,point2,point3):
	"""Given three points,                     p1
		calculate the smaller of the two angles   \    a
		formed by the sequence.                    \  
		Returns degrees.                            p2------p3
    Input is three (x,y) tuples
	"""
	# be sure there IS an angle to measure
	assert point1 != point2 and point2 != point3
	# get coordinate values

Nate-Wessel

import requests, json, psycopg2, multiprocessing, time, os
from random import shuffle
from datetime import datetime, timedelta

fileroot = 'itins/jv/sched-all-stops/'

# define the start time
start_time = datetime( year=2017, month=11, day=10 )
print 'from ', start_time

Nate-Wessel

# this script takes a set of points, in this case bus stops
# and does a centroidal voronoi tesselation weighted by 
# those points from an initial uniform random seeding. The effect 
# is an even-ish distribution of sample points accross the space.
# My goal is to use these as representative ODs for a travel time 
# matrix that effectively samples a whole transit network.

library('deldir')	# voronoi stuff
library('rgdal')	# projections
library('sp') # spatial data types