prl900

#!/g/data/v10/public/modules/dea-env/20181015/bin/python

import argparse
import os

locations = """'Yanco',-34.9878,146.2908,2012,2017
'WombatStateForest',-37.4222,144.0944,2010,2017
'Whroo',-36.6732,145.0294,2011,2017
'Warra',-43.09502,146.65452,2015,2017
'WallabyCreek',-34.00206,140.58912,2005,2013

prl900

#!/g/data/xc0/software/python/miniconda2/bin/python
###############################################################################
# $Id: gdal_merge.py 33790 2016-03-26 12:42:12Z goatbar $
#
# Project:  InSAR Peppers
# Purpose:  Module to extract data from many rasters into one output.
# Author:   Frank Warmerdam, [email protected]
#
###############################################################################
# Copyright (c) 2000, Atlantis Scientific Inc. (www.atlsci.com)

prl900

'''This script demonstrates how to build a variational autoencoder with Keras.
Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
'''
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from scipy.stats import norm

from keras.layers import Input, Dense, Lambda

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package main

import (
	"fmt"
	"image"
	"io/ioutil"
	"log"
	"os"
	"unsafe"

prl900

#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
from datetime import timedelta, date

def monthly_range(start_date, end_date):
    start_date = start_date.replace(day=1)

    while start_date < end_date:
        last_day_month = start_date + timedelta(days=33)
        last_day_month = last_day_month.replace(day=1)

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from keras.models import Sequential
from keras.layers import BatchNormalization, Conv2D, Conv2DTranspose
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np

def GetModel():
    """ Encoder-Decoder Approach """
    """
    model = Sequential()

prl900

import glob
import os
from datetime import datetime 
import numpy as np
import xarray as xr
import netCDF4
import argparse

def extract_time(file_name):
    fname = os.path.splitext(os.path.basename(file_name))[0]

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{
    "Conventions": "CF-1.6, ACDD-1.3",
    "title": "Live Fuel Moisture Content (LFMC), Australia Coverage.",
    "summary": "Live Fuel moisture content (LFMC), the mass of water contained within live vegetation in relation to the dry mass, is a critical variable affecting fire interactions with fuel. LFMC is one of the primary variables in many fire behavior prediction models and fire danger indices, as it affects ignition, combustion, the amount of available fuel, fire severity and spread, and smoke generation and composition (Text from Yebra, M., Chuvieco, E., Danson, M., Dennison, P., Hunt, E.R, Jurdao, S., Riano, D., Zylstra, P, 2013.  A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products. Remote sensing of environment.136, 455-468.).",
    "license": "Creative Commons with Attribution (https://creativecommons.org/licenses/by/3.0/au/deed.en)",
    "publisher_name": "ANU/Fenner School of Environment & Society",
    "publisher_emai

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