robbibt

from collections import Counter
import warnings

def mostcommon_utm(dc, product, query):    
    
    '''
    Takes a given query and returns the most common UTM zone for 
    observations returned for that spatial extent.

    Parameters

robbibt

!gdalbuildvrt /g/data/r78/rt1527/nidem/output_data/mosaics/NIDEM_uncertainty.vrt ../output_data/geotiff/nidem_uncertainty/NIDEM_uncertainty*.tif

!gdal_translate \
   -co COMPRESS=DEFLATE \
   -co ZLEVEL=9 \
   -co PREDICTOR=1 \
   -co TILED=YES \
   -co BLOCKXSIZE=1024 \
   -co BLOCKYSIZE=1024 \
   /g/data/r78/rt1527/nidem/output_data/mosaics/NIDEM.vrt /g/data/r78/rt1527/nidem/output_data/mosaics/NIDEM_mosaic.tif

robbibt

from datacube.testutils.io import rio_slurp
from datacube.storage import measurement_paths

ds = dc.find_datasets(product='ls8_nbar_scene')[0]
band_path = measurement_paths(ds)['3']
raster_meta = rio_slurp(band_path)
raster_meta[1].transform
raster_meta[1].crs

robbibt

# Author: Claire Krause

# Save the polygons to a shapefile
schema = {'geometry': 'Polygon','properties': {'area': 'str'}}

if os.path.isfile('test.shp'):
    with fiona.open('test.shp', "a", crs = from_epsg(3577), driver = 'ESRI Shapefile', schema = schema) as output:
        for ix, poly in MergedPolygonsGPD.iterrows():
                        output.write(({'properties': {'area': poly['area']},'geometry': mapping(shape(poly['geometry']))})) 
else:

robbibt

import pandas as pd
from otps import TimePoint
from otps import predict_tide

tidepost_lat, tidepost_lon =-12.213764, 131.824570  # pointstuart
tidepost_lat, tidepost_lon =-13.315045, 130.234212  # dalyriver
tidepost_lat, tidepost_lon =-14.957638244600385, 129.5448589323929  # josephbonapartegulf

# Use the OTPS tidal mode to compute tide heights for each observation:
date_range = pd.date_range("2013-01-01", "2019-05-05", freq="1h")

robbibt

import datacube
from datacube.utils import geometry
from datacube.utils.geometry import CRS

# Connect to datacube
dc = datacube.Datacube(app='Simple example')

# Set up analysis data query
query = {
    'x': (1754576.964742866, 1762576.964742866),

robbibt

# Author: Imam Alam

# imports
import numpy as np
import pandas as pd
import xarray as xr

# define an example volume, roughly MGA xy, + elevation
x = range(50000, 52000, 100)
y = range(800000, 802000, 100)

robbibt

import imageio
import numpy as np

from_array = imageio.imread('Visualisation/pilbara/pilbara_before.png')
to_array = imageio.imread('Visualisation/pilbara/pilbara_after.png')

stacked_from_to = np.stack([to_array, from_array], axis=3)

weights = [0.2, 0.4, 0.6, 0.8, 1.0, 0.8, 0.6, 0.4, 0.2, 0]
milliseconds = [50, 50, 50, 50, 3500, 50, 50, 50, 50, 3500]

robbibt

import glob
import xarray as xr
from datetime import datetime

# List all matching files
files = glob.glob('/g/data/r78/mc9153/tide_otps/L3_2008_nc3/*.L3m')

# Create list for 
individual_files = []

robbibt

# For anyone putting together notebooks that use Collection Upgrade/Collection 3 
# Landsat, the product names are:

# • ga_ls5t_ard_3 (Landsat 5 ARD)
# • ga_ls7e_ard_3 (Landsat 7 ARD)
# • ga_ls8c_ard_3 (Landsat 8 ARD)

# The Collection 3 samples on the NCI are stored in a different database, so for 
# now we can use the try/except below to connect to the datacube differently on 
# both the NCI and the Sandbox (we'll hopefully just be able to connect using