prl900

import xarray as xr
import numpy as np

tp = xr.open_dataset("EU_TP_ERAI.nc").tp[:].data.astype(np.float32)
print(tp.shape)
np.savez("/g/data/fj4/scratch/tp", tp[:, :, :])

z = xr.open_dataset("EU_Z_ERAI.nc").z[:].data.astype(np.float32)
print(z.shape)
np.savez("/g/data/fj4/scratch/z", z[:, :, :, :])

prl900

package main

import (
	"fmt"
	"image"
	"image/color"
	"image/png"
	"os"
)

prl900

from google.cloud import storage
from glob import glob
from os.path import basename, join

gsbucket = "nsw_water_compliance"

def upload_files(gsbucket, dst_path, src_path, wildcard="*"):
    storage_client = storage.Client()
    bucket = storage_client.bucket(gsbucket)
    file_paths = glob("{}/{}".format(src_path, wildcard))

prl900

Flask==1.0.2
numpy==1.15.4
numexpr==2.6.9
matplotlib==3.0.2
gdal==2.2.2
google-cloud-storage

prl900

runtime: python37

service: pycloud-wms

prl900

prl900

{
    "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

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]

prl900

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