Robbi Bishop-Taylor robbibt

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/cocosislandz47.zip" 

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/christmasislandz48.zip"

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/waz50.zip"

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/waz51.zip"

"https://elevation-direct-downloads.s3-ap-southeast-2.amazonaws.com/5m-dem/national_utm_mosaics/ntz52.zip"

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	metadata:
	generateName: dea-coastlines-
	namespace: processing
	labels:
	workflows.argoproj.io/workflow-template: dea-coastlines
	spec:
	arguments:
	parameters:
	- name: image-tag
	value: 1.2.3

	import os
	import fire
	import h5py
	import glob
	import tqdm
	import numpy as np
	import pandas as pd
	# requires h5py, tqdm, fire, numpy, and pandas to run

	import geopandas as gpd
	import rasterio
	import fiona
	from rasterio import features
	import os
	from datetime import datetime as dt

	def Rasterize(shapefile, inras, outras, meta, field):
	with rasterio.open(inras) as src:
	kwargs = src.meta.copy()

	import xarray as xr
	import numpy as np

	# create an example dataset
	da = xr.DataArray(np.random.rand(10,30,40), dims=['time', 'lat', 'lon'])

	# define a function to compute a linear trend of a timeseries
	def linear_trend(x):
	pf = np.polyfit(x.time, x, 1)
	# we need to return a dataarray or else xarray's groupby won't be happy

	from __future__ import division
	import numpy as np
	import matplotlib.pyplot as plt
	from skimage import io, img_as_float


	def median_filter(A, filter_size):
	rows = np.shape(A)[0]
	columns = np.shape(A)[1]
	half_length = int((filter_size-1)/2)