paulopperman

# an inelegant script to melt state data into files for SUMO model

import pandas as pd


def build_od_list(od_file):
	od = pd.read_csv(od_file, header=None, index_col=0)
	od.columns = od.index

	# clean matrix

paulopperman

;; extending random walk 2d example into 3d
turtles-own
[
  xc  ; next x coordinate
  yc  ; next y coordinate
  zc  ; next z coordinate
]

to setup
  clear-all

paulopperman

import requests
import pandas as pd

# API documentation is at https://www.ncdc.noaa.gov/cdo-web/webservices/v2

NOAA_API_TOKEN = '<from NOAA>'

headers = {'token':NOAA_API_TOKEN}

# get stations in Newport, RI

paulopperman

import requests
from PIL import Image
from io import BytesIO
import os
import pandas as pd

# get crosswalk dataset
newport_crossings_url = 'https://raw.githubusercontent.com/NewportDataPortal/sidewalk-map/development/npt-sidewalk-street-intersections.geojson'
crossing_response = requests.get(newport_crossings_url)
crossing_json = crossing_response.json()

paulopperman

import requests
from PIL import Image
from io import BytesIO
import json

url = 'https://maps.googleapis.com/maps/api/streetview'

key = STREETVIEW_API_KEY

out_file = 'streetviewimg.jpg'

paulopperman

import shapefile
import geopandas as gp

def convert_shapefile(filepath, crs=None):
    # Function to read an ESRI shapefile and return a geopandas GeoDataFrame
    reader = shapefile.Reader(filepath)

    # Extract the column names for the data
    fields = reader.fields[1:]
    field_names = [field[0] for field in fields]

paulopperman

# snippet from github.com/geopy/geopy/blob/master/README.md
from geopy.geocoders import Nominatim
geolocator = Nominatim()
location = geolocator.geocode("175 5th Avenue NYC")
print(location.address)
print((location.latitude, location.longitude))