fscottfoti

from monary import Monary
import numpy as np, pandas as pd
import time
mon = Monary()
columns = ['x','y','shape_area','zone_id','county_id','parcel_id']
t1 = time.time()
numpy_arrays = mon.query('bayarea','parcels',{},columns,['float32']*len(columns))
df = np.matrix(numpy_arrays).transpose()
df = pd.DataFrame(df, columns=columns)
print time.time()-t1

fscottfoti

{
 "metadata": {
  "name": "",
  "signature": "sha256:0cf6cb9492bf54a87341cd63b1eaa77ad1a76608073d08f68c5c2d41d7568686"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [

fscottfoti

{
 "metadata": {
  "name": "",
  "signature": "sha256:3687bfe6e588cba4b1013e657e1f62b803fc49a204d980923fc3bdb31ced8c99"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [

fscottfoti

import pandas as pd
import cPickle
import sys

args = sys.argv[1:]
assert len(args) == 2, "go.py <infile.jar> <outfile.h5>"

d = cPickle.load(open(args[0]))

nodes = pd.DataFrame(d['nodes'], index=d['nodeids'])

fscottfoti

{
 "metadata": {
  "name": "",
  "signature": "sha256:1cb19ce6c787115025feeb23df2dc82b4547224d32a0d8f2dfc941418962d417"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [

fscottfoti

import models
import time
import urbansim.sim.simulation as sim
import sys

print "Started", time.ctime()
print "Run number", sim.get_injectable("run_number")
in_year, out_year = 2010, 2040

try:

fscottfoti

description	filter	expression	other	coeff
Drive alone - Unavailable		sovAvailable==0		-999
Drive alone - Unavailable for zero auto households	autos==0	1		-999
Drive alone - Unavailable for persons less than 16	age<16	1		-999
Drive alone - Unavailable for joint tours	tourCategoryJoint==1	1		-999
Drive alone - Unavailable if didn't drive to work

fscottfoti

import pyproj
import pandas as pd

s = pd.HDFStore('osm_bayarea.h5', "r")
s2 = pd.HDFStore('osm_bayarea4326.h5', "w")
s2["edges"] = s["edges"]

po = s["nodes"]

print po.describe()

fscottfoti

def make_key(row):
  return "cx="+(row.centroidx/10).round(0).astype('str') + \
    ", cy=" + (row.centroidy/10).round(0).astype('str') + \
    ", ar=" + (row.area/25).round(0).astype('str') + \
    ", l=" + (row.length/10).round(0).astype('str') + \
    ", bbminx=" + (row.minx/2).round(0).astype('str') + \
    ", bbmaxy=" + (row.maxy/2).round(0).astype('str') 

df["new_geom_id"] = df.apply(make_key, axis=1)

fscottfoti

from sklearn.neighbors import KDTree

def nearest_neighbor(df1, df2):
    kdt = KDTree(df1.as_matrix())
    distances, indexes = kdt.query(df2.as_matrix(), k=1, return_distance=True)
    return pd.Series(distances.flatten(), index=df1.index.values[indexes.flatten()])


import sys
import pandas as pd