sergiolucero

import folium
import geopandas as gp
import requests

data = requests.get('http://sergiolucerovera.pythonanywhere.com/static/chile.shp')
chile = gp.GeoDataFrame(data.json())
elihc = None # use geopandas.read_file??

centroid = [(cc[1],cc[0]) for cc in chile.centroid]
cmap = folium(location=centroid, zoom=15, tiles='Staro_Pramen')

sergiolucero

import openaq

parameters = ['co', 'no2', 'o3', 'pm10', 'pm25', 'so2']
api = openaq.OpenAQ()

country_list = api.countries(df=True)
print('MONITORING %d locations worldwide' %(country_list.locations.sum()))

for cid, cdata in country_list.iterrows():
    ctry = cdata.name

sergiolucero

import geopandas as gp
import folium
# Fuentes SHAPEFILES... http://www.ide.cl/descarga/capas.html
# src: https://ocefpaf.github.io/python4oceanographers/blog/2015/02/02/cartopy_folium_shapefile/

sergiolucero

import pandas as pd
from pandasqlutils import *     # to easily cross tables

bikes = pd.read_url('http://quant.cl/db/bikes')   # serving from quant via sqlite
airquality = pd.read_url('s3://amazonxyz.aws.com/sdakjldajds')   # using an S3 bucket
weather = pd.read_url('quant.cl/postgres/weather'     # running on a docker (Rad?)
                      
airvsclimate = cross(airquality, weather)
print(airvsclimate.head())

sergiolucero

import wikipedia

CITIES=['Paris','Barcelona','Tokyo', 'New York City','Amsterdam','Copenhaguen','San Francisco']
AMBIGUOUS_CITIES = ['Santiago']

for city in CITIES:
    citywiki = wikipedia.page(city)
    cwsum = citywiki.summary
    poploc = cwsum.index('population')  # first and only? use re!
    print(city, cwsum[poploc:poploc+30])

sergiolucero

import googlemaps

GMAPS_API = 'AIzaSyBjB7GbQ2TL2SUW989uu7ZoP6S0bxsv3t8'    # EKHOS July 2017
gmaps = googlemaps.Client(key=GMAPS_API)
gref = gmaps.geocode('Emilio Vaisse 564, Providencia')
print(gref[0]['geometry'])

sergiolucero

library(tm);library(wordcloud);library(memoise)

books <<- list("A Mid Summer Night's Dream" = "summer",
              "Glamorama" = "Glamorama1")     # The list of valid books

getTermMatrix <- memoise(function(book) { # Using "memoise" to automatically cache the results
  if (!(book %in% books))  stop("Unknown book")
  text <- readLines(sprintf("./%s.txt.gz", book), encoding="UTF-8")

  myCorpus = Corpus(VectorSource(text))

sergiolucero

import json
import pandas as pd
import urllib.request

ori='75+9th+Ave+New+York,+NY'
des='MetLife+Stadium+1+MetLife+Stadium+Dr+East+Rutherford,+NJ+07073'
dep=int(datetime.datetime.now().timestamp())

URL='https://maps.googleapis.com/maps/api/directions/json?origin=%s&destination=%s&departure_time=%d' %(ori,des,dep)
URL+='&traffic_model=best_guess&key=AIzaSyB_ZS04dfON0PZVBRwhKTkeChK3rlYUgSk'

sergiolucero

from forestry import ForestryLinearProgram      # this is project-specific
from PuLP.solvers import CBC            # open-source LinearProgramming library and solver
from creds import BASE_URL, API_KEY     # these are used to id with IBM
from docloud.job import JobClient

my_problem = ForestryLinearProgram('forestry_test.xlsx')    # input file contains all relevant tree data

#my_problem.solve(CBC)     # this is how we used to work, until our problems grew too big for open-source

my_problem.save_to_MPS('forestry_LP.mps')     # here we export to a format that IBM CPLEX can recognize

sergiolucero

from selenium import webdriver
url='http://consultawebvehiculos.carabineros.cl/index.php'
fd = webdriver.Firefox()
plates = ['CZJB81','BDPW78']
sections = ['txtLetras','txtNumeros1','txtNumeros2']

def check_robo(patente):
 	fd.get(url)   # point to the starting page again
	patsplit = [patente[:2],patente[2:4], patente[4:]]
	for ix, sec in enumerate(sections):