sergiolucero

import folium
import geopandas as gp
from fetch_airquality import fetch_data

df = fetch_data().rename(columns={'city': 'ciudad'})
df['medida'] = ['%s[%s]' %(p, u) for p, u in zip(df['parameter'], df['unit'])]
df = df.drop(['parameter', 'unit'], axis=1)

data = df.to_html(index=False, border=10, justify='right')
m = folium.Map(df[['lat','lng']].values)

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

from dask.distributed import Client, local_client
from dask import delayed, compute

@delayed
def dostand(stand):
    stand.compute_stand_values_by_area()
    stand.compute_stand_values_by_volume()
    stand.compute_NPV()

def standloop(standlist, option_dict):

sergiolucero

import folium
import pandas as pd

URL = 'https://www.bicilascondes.cl/availability_map/getJsonObject'
COLORS = ['red',  'darkred', 'lightred', 'yellow', 
	  'darkgreen', 'lightgreen', 'green']	# low is bad->red, high is good->green

bike_data = pd.read_json(URL)
bikemap = folium.Map(bike_data[['lat','lon']].mean().tolist(), # data centroid
					 tiles = 'Stamen Terrain', zoom_start = 15)	

sergiolucero

# deploy and time on Linode, LightSail, both my HPs, fpfi.cl, etc
import time
import multiprocessing
from dask import delayed, compute

nTasks = 100

def long_work():
  for i in range(100000):
    n = i*i*i*i*i*i #simulated long work time

sergiolucero

import openaq
api=openaq.OpenAQ()

api.cities(country='CL',limit=10)
api.latest(city='Antofagasta', df=True)
df_pm10 = api.measurements(city='Andacollo',location='Hospital',parameter='pm10',df=True,limit=10)

sergiolucero

# starting point is https://github.com/smouksassi/ggplotwithyourdata
from pyvirtualdisplay import Display
from selenium import webdriver
RPLOTTER_URL = 'http://50.116.4.6:3838/'

with Display():
    browser = webdriver.Firefox()
    try:
        browser.get('http://www.google.com')
        print browser.title #this should print "Google"

sergiolucero

library(shiny)
library(openxlsx)
library(lattice)
library(readxl)
library(ggplot2)
library(ggrepel)
setwd('C:/Users/Sergio/Sandbox/shiny_servers/excel_plotter')
#############################################################
read_excel_allsheets <- function(filename) {
  sheets <- readxl::excel_sheets(filename)

sergiolucero

from pflotran import Flow, Transport
from randomsoils import soil_generator
import pandas as pd

nYears = 30; time_step = 1/12 # allow change every month
grid, wells = pd.read_csv('basic_grid_and_wells.csv')

for ix in range(nRuns):   # run a few simulations
  modified_soil = soil_generator(grid)
  pumping_schedule = pd.random(wells, range(0,nYears,time_step))

sergiolucero

import wikipedia

CITIES=['Paris','Barcelona','Tokyo', 'New York City','Amsterdam','Copenhaguen','San Francisco']
AMBIGUOUS_CITIES = ['Santiago']     # need to dig deeper, 'population' is not contained in the summary

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