atrawog

cd ~
sudo apt-get update
sudo apt-get install openjdk-7-jre -y

wget https://github.com/downloads/elasticsearch/elasticsearch/elasticsearch-0.19.0.tar.gz -O elasticsearch.tar.gz
tar -xf elasticsearch.tar.gz
rm elasticsearch.tar.gz
sudo mv elasticsearch-* elasticsearch
sudo mv elasticsearch /usr/local/share

atrawog

L.TileLayer.TileJSON = L.TileLayer.Canvas.extend({
    options: {
        debug: false
    },

    tileSize: 256,

    initialize: function (options) {
        L.Util.setOptions(this, options);

atrawog

About

This package contains a set of tools, utilities and stylesheets for working with the Geoscience Australia National Dynamic Land Cover Dataset.

Using the Scripts

You can run the scripts individually as described in the sections below, or just as,

./scripts/00-all.sh

Downloading the Dataset

atrawog

"""
Likely not useful to anyone else, but just putting it out there.

This script will take a directory of GeoTIFFs and merge them together without issues.
This script simply decompresses the files, runs nearblack to remove pseudo-black borders caused by compression, and then uses gdalwarp to stitch the files together.

The script is designed to use the minimal amount of disk space possible -- it cleans up each file after decompression and continually merges with a master image.
"""

import os

atrawog

#!/usr/bin/env python
"""
A script to grab timeseries from MODIS data using GDAL and python
Author: J Gomez-Dans/NCEO & UCL
"""
__author__: J Gómez-Dans

import numpy as np
import matplotlib.pyplot as plt
from osgeo import gdal

atrawog

This script processes VIIRS Nighttime Lights 2012 GeoTIFFs from the Earth Observation Group at NOAA National Geophysical Data Center to prepare them for rendering in TileMill and uploading to MapBox Hosting.

Read Lights of the Night on MapBox to learn more about NPP the functions of this script.

atrawog

server {

    listen   80;
    server_name mydomain.org;

    root /var/www;

    access_log off;
    error_log /home/user/geonode/log/nginx_error.log;

atrawog

import numpy as np
import matplotlib

def make_cmap_guassianHSV( num_segs     = 100, #number of segments
                           bandwidth    = 0.25,
                           red_center   = 1.00,
                           green_center = 0.75,
                           blue_center  = 0.50,   
                           name = "gaussianHSV"             
                         ):