robbibt

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)

robbibt

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.

robbibt

"""
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

robbibt

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={})

robbibt

robbibt

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/",

robbibt

import glob
import warnings
import datetime
from odc.geo.geom import BoundingBox



def _load_gauge_metadata(metadata_path):
    
    # Load metadata

robbibt

robbibt

import otps
import pytz

def otps_tides(lats, lons, times, timezone=None):
    """
    Model tide heights for one or more locations and times using the
    OTPS TPXO8 tidal model.
    Parameters:
    -----------
    lats, lons : numeric or list of numeric values

robbibt

import numpy as np
import xarray as xr


def load_ls_s2(
    x=None,
    y=None,
    geom=None,
    start_date="2020",
    end_date="2021",