danhammer

#! /usr/bin/env python

"""
This script updates our MODIS data archive stored on S3 based on what's
available on NASA's MODIS data FTP server (e4ftl01u.ecs.nasa.gov)

The MODIS FTP server is organized by product then date, in the format
/MODIS_Composites/MOLT/MOD13A3.005/2000-02-01. This corresponds to the Terra
(MOLT) vegetation indices product at 1000m resolution, 32-day composites,
for the month of February 2000.

danhammer

[ Launch: uk geo hackery ] 5827283 by danhammer[ Launch: uk geo hackery ] 5825795 by robinkraft

danhammer

import datetime
import urllib
import csv
from shapely.geometry import Point
from shapely.prepared import prep
import ee
import os
import ee.mapclient
import requests, zipfile, StringIO
import Image

danhammer

import pandas as pd
import forma
import ee

from bbox_tiler import bbox
from globalmaptiles import GlobalMercator

def coordsToTiles(lon, lat):
    # Convert the supplied latitude and longitude coordinates to
    # mercator tiles at zoom level 16, ready for upload to CartoDB.

danhammer

import requests

def construct_query(iso, fmt = 'shp'):
    # Builds the API call to download FORMA in various formats Other
    # acceptable formats are 'csv', 'GeoJSON', 'JSON', 'kml', or 'svg'
    base = "http://wri-01.cartodb.com/api/v2"
    var = "x,y,date_array"
    sql = "SELECT %s FROM gfw2_forma WHERE iso = '{%s}'" % (var, iso)
    query = "%s/sql?format=%s&q=%s" % (base, fmt, sql)
    

danhammer

library(zoo)
library(stats)
library(dtw)
library(timeDate)
library(lubridate)
library(reshape)
library(randomForest)

clean.pixels <- function(filename, dir) {
  ## accepts output from Pavel's script and returns a data frame with

danhammer

def formatPolygon(coords):
    """Accepts a set of coordinates, in sequence, and checks that they
    are formatted counter-clockwise; returns an Earth Engine polygon.

    Works for non-convex polygons."""
    def _edger(c0, c1):
        # anonymous function to generate the edge value between
        # coordinate tuples `c0` and `c1`
        x0, y0 = c0
        x1, y1 = c1

danhammer

import os
import requests
import json
import pandas as pd

# Go from JSON to a measurement of UMD deforestation by year.  If you
# have a shapefile, you will have to convert and simplify the
# polygons, preserving topology.  The Earth Engine API does not
# support long, long requests.  You can convert the shapefile using
# OGR within the data subdirectory:

danhammer

import requests
import pandas
from BeautifulSoup import BeautifulSoup

def _grab_data():
    url = 'http://www.bettermap.org/json'
    x = requests.get(url)
    return x.json()['features']

def _process_entry(entry):

danhammer

from bs4 import BeautifulSoup
import json
import pandas as pd

# Process information on Caltrans projects.  Convert this file to
# GeoJSON:

# wget https://dot.ca.gov/hq/construc/cons.kml
# ogr2ogr -f GeoJSON cons.json cons.kml