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import pyproj
from shapely.geometry import shape
from shapely.ops import transform

geom = {'type': 'Polygon',
        'coordinates': [[[-122., 37.], [-125., 37.],
	                     [-125., 38.], [-122., 38.],
                         [-122., 37.]]]}

s = shape(geom)

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# Compile/install GEOS. Taken from:
# http://grasswiki.osgeo.org/wiki/Compile_and_Install_Ubuntu#GEOS_2

cd /tmp
wget http://download.osgeo.org/geos/geos-3.4.2.tar.bz2
bunzip2 geos-3.4.2.tar.bz2
tar xvf  geos-3.4.2.tar

cd geos-3.4.2

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minikube stop; minikube delete &&
docker stop $(docker ps -aq) &&
rm -rf ~/.kube ~/.minikube &&
sudo rm -rf /usr/local/bin/localkube /usr/local/bin/minikube &&
launchctl stop '*kubelet*.mount' &&
launchctl stop localkube.service &&
launchctl disable localkube.service &&
sudo rm -rf /etc/kubernetes/ &&
docker system prune -af --volumes

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import sys

import boto


# based on http://www.quora.com/Amazon-S3/What-is-the-fastest-way-to-measure-the-total-size-of-an-S3-bucket

# assumes you've already configured your access id & secret key
s3 = boto.connect_s3()

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# install dependencies
sudo apt-get update
sudo apt-get install -y build-essential
sudo apt-get install -y cmake
sudo apt-get install -y libgtk2.0-dev
sudo apt-get install -y pkg-config
sudo apt-get install -y python-numpy python-dev
sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install -y libjpeg-dev libpng-dev libtiff-dev libjasper-dev

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sudo add-apt-repository ppa:ubuntugis/ppa
sudo apt-get update
sudo apt-get install gdal-bin
sudo apt-get -y install python-gdal

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#!/usr/bin/env python

import argparse
import os
import requests
import json
import sys
import logging
import datetime

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from osgeo import gdal
import os

layer_dict = {"reli":"reliability", "qual":"Quality", "ndvi":"NDVI", "evi":"EVI"}

def print_data(layer, data):
    print data
    print "data:", layer
    print "type:", data.dtype
    print "mean:", data.mean()

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"Request to deploy app with id 5c777df946e0fb0008ada92c"
"Deploying app with id 5c777df946e0fb0008ada92c"
"Deployment created: run-5c777df946e0fb0008ada92c"
"Pod created: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"
"Scaled up replica set run-5c777df946e0fb0008ada92c-6794d486b8 to 1"
"Successfully assigned run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4 to ip-10-0-175-133.us-west-2.compute.internal"
"Pod updated: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"
"Deployment updated: run-5c777df946e0fb0008ada92c"
"Deployment updated: run-5c777df946e0fb0008ada92c"
"Pod updated: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"

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Set up for update

1. Go to fires download page - https://firms.modaps.eosdis.nasa.gov/download/
2. Zoom to Sumatra. It doesn't have to be perfect. We do some screening in EE to make sure there are no fires in Malaysia used.
3. Submit the download request (2013-03-30 to present) - csv is easiest - then wait for it to complete (usually < 30 minutes)
4. Upload the CSV file to Fusion Tables. Go here, then just click "new table" under the File menu and follow the instructions.
5. Get the docid from the url. For example, for docid=1SzJl73nj5IPVEOGqhGc8uv5Vkwb504uqK_YTnVGh just grab 1SzJl73nj5IPVEOGqhGc8uv5Vkwb504uqK_YTnVGh
6. Update dates in this script when you run it on EE - the second date in the POST variable may need to be extended.
7. Update the FIRES variable to equal to "ft:". You'll see how it is in the code.

Run + export