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import pandas as pd
import datacube

dc = datacube.Datacube()

# Get table of dataset counts
product_dict = {
    product: {maturity: dc.index.datasets.count(product=product, dataset_maturity=maturity) 
              for maturity in ["final", "nrt", "interim"]}
    for product in ["ga_s2am_ard_3", "ga_s2bm_ard_3", "ga_s2cm_ard_3"]

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AEO 2024 Digital Earth Australia workshops

To download these notebooks, please click "Download ZIP" to the top right:

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# Loads cloud-friendly Zarr format Analysis-Ready, Cloud Optimized ERA5 data from Google Cloud.
# Available: https://github.com/google-research/arco-era5


import xarray
import gcsfs
import odc.geo.xr
from odc.geo.geom import BoundingBox

def load_era5(

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import momepy
import geopandas as gpd

# Read data and convert to projected CRS
gdf = gpd.read_file(
    "/gdata1/projects/coastal/cem/sediment_compartments/compartments_checklist_final.geojson"
).to_crs("EPSG:3577")
col = "ID_Seconda"

# Create a limit for tessellation (join all polygons and buffer that)

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import numpy as np

def weighted_median(values, weights):
    """
    Compute the weighted median of an array of values.
    
    This implementation sorts values and computes the cumulative
    sum of the weights. The weighted median is the smallest value for
    which the cumulative sum is greater than or equal to half of the
    total sum of weights.

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"""
This code demonstrates how to load Digital Earth Australia Sentinel-2 Analysis Ready Data into R.
It uses `rstac` to search for available data for a time and location using DEA's STAC endpoint,
and `gdalcubes` to load and analyse the data.

Functionality includes:
    * Creating a custom pixel grid to reproject data into
    * Apply a cloud mask using the "s2cloudless" cloud mask
    * Combine data into seasonal composites
    * Create an RGB animation

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from pystac import Item
from odc.stac import load
import rasterio
import odc.geo.xr

path = 'https://dea-public-data-dev.s3-ap-southeast-2.amazonaws.com/derivative/ga_s2ls_intertidal_cyear_3/1-0-0/x128/y165/2016--P1Y/ga_s2ls_intertidal_cyear_3_x128y165_2016--P1Y_final.stac-item.json'
# path = 'https://dea-public-data-dev.s3-ap-southeast-2.amazonaws.com/derivative/ga_s2ls_intertidal_cyear_3/1-0-0/x128/y165/2017--P1Y/ga_s2ls_intertidal_cyear_3_x128y165_2017--P1Y_final.stac-item.json'

items = [Item.from_file(path)]
data = load(items, chunks={})

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import s3fs
import pandas as pd

s3 = s3fs.S3FileSystem(anon=True)
file_list = s3.glob(
    "dea-public-data-dev/derivative/ga_ls8cls9c_gm_cyear_3/4-0-0/*/*/2023--P1Y/ga_ls8cls9c_gm_cyear_3_*_2023--P1Y_final_nbart_red.tif"
)

pd.DataFrame(file_list).iloc[:, 0].str.replace(
    "dea-public-data-dev/",