sergiolucero

import glob, os, pptx, scipy.misc
from PIL import Image

size = 800,600
prs = pptx.Presentation()

for fn in glob.glob('*.jpg'):
	img = Image.open(fn)
	img.thumbnail(size, Image.ANTIALIAS)
	img.save(fn)

sergiolucero

import folium, random, pandas as pd
from urllib.request import urlopen

colors=['red','blue','green','orange','yellow']
URL = 'http://sergiolucero.carto.com/api/v2/sql?format=csv&q=SELECT%20*,ST_AsText(the_geom)%20AS%20g2%20FROM%20'
ZOOM_LEVEL = 5;TILES='cartodbdark_matter'
CARTO_DATABASE = 'tcw'

response = urlopen(URL+CARTO_DATABASE)
data = pd.read_csv(response)

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

import folium
from util import zload
import pandas as pd

cluster_data = zload('Labels_16.il')
loc_data = pd.read_excel('georef.xlsx')
loc_dict = loc_data.T.to_dict()		# transformed

centroid = (loc_data.lat.median(),loc_data.lon.median())
bikemap = folium.Map(location=centroid,tiles='cartodbpositron',zoom_start=14)

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):

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

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])