Private bucket to save and read data using s3fs

Discover data and create

• We show how to access s3 anonymous data in geotiff using Xarray, make a geographical area selection and store into zarr on s3 private object storage
• We do not address dask (chunking optimization, etc.)

import os
import logging
import s3fs
import rioxarray
import xarray as xr
from hvplot import xarray
from distributed import Client
import zipfile

client = Client(n_workers=8, silence_logs=logging.ERROR)
client

Client

Client-d21030db-331e-11ef-8d99-aa21e200a370

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Launch dashboard in JupyterLab

Cluster Info

LocalCluster

12a21ddf

Dashboard: http://127.0.0.1:8787/status	Workers: 8
Total threads: 8	Total memory: 32.00 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-b2c41524-2157-44bb-8588-da7c3f50b340

Comm: tcp://127.0.0.1:33885	Workers: 8
Dashboard: http://127.0.0.1:8787/status	Total threads: 8
Started: Just now	Total memory: 32.00 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:33261	Total threads: 1
Dashboard: http://127.0.0.1:33069/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:44633
Local directory: /tmp/dask-scratch-space/worker-j_5na5l1

Worker: 1

Comm: tcp://127.0.0.1:34991	Total threads: 1
Dashboard: http://127.0.0.1:37839/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:40869
Local directory: /tmp/dask-scratch-space/worker-gc_fpb4w

Worker: 2

Comm: tcp://127.0.0.1:43059	Total threads: 1
Dashboard: http://127.0.0.1:46119/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:36167
Local directory: /tmp/dask-scratch-space/worker-m4wggao9

Worker: 3

Comm: tcp://127.0.0.1:45355	Total threads: 1
Dashboard: http://127.0.0.1:41085/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:33831
Local directory: /tmp/dask-scratch-space/worker-190aj7ha

Worker: 4

Comm: tcp://127.0.0.1:43857	Total threads: 1
Dashboard: http://127.0.0.1:41811/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:39723
Local directory: /tmp/dask-scratch-space/worker-fpr0f2c8

Worker: 5

Comm: tcp://127.0.0.1:41847	Total threads: 1
Dashboard: http://127.0.0.1:33191/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:45719
Local directory: /tmp/dask-scratch-space/worker-p6ul74ju

Worker: 6

Comm: tcp://127.0.0.1:40223	Total threads: 1
Dashboard: http://127.0.0.1:37043/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:36475
Local directory: /tmp/dask-scratch-space/worker-4p22pew1

Worker: 7

Comm: tcp://127.0.0.1:40975	Total threads: 1
Dashboard: http://127.0.0.1:46691/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:38315
Local directory: /tmp/dask-scratch-space/worker-4eefz8ny

Access Data from OpenTopography

• OpenTopography facilitates efficient access to topography data, tools, and resources to advance our understanding of the Earth’s surface, vegetation, and built environment.

• The example below is taken from OpenTopography with some adjustments for the purpose of this training material. The original jupyter notebook OT_BulkAccessCOGs.ipynb has been developed by Matthew Beckley (UNAVCO, Inc.), Christopher Crosby (UNAVCO, Inc.), Viswanath Nandigam (San Diego Supercomputer Center, UC San Diego), Minh Phan (San Diego Supercomputer Center, UC San Diego). See OpenTopography/OT_BulkAccess_COGs for more information.

• Data is accessible via public S3-compatible object storage. See detailed information on OpenTopography data, which are publicly available in the Analysis Ready Cloud Optimized (ARCO) data format. See more info at https://opentopography.org/blog/programmatic-access-cloud-optimized-geotiff-cog-global-datasets

Connect to bucket (anonymous login for public data only)

fsd = s3fs.S3FileSystem(anon=True,
      client_kwargs={
         "endpoint_url": "https://opentopography.s3.sdsc.edu"
      })

List bucket content

fsd.ls("raster/SRTM_GL1/SRTM_GL1_srtm/")

Show code cell output Hide code cell output

['raster/SRTM_GL1/SRTM_GL1_srtm/N00E006.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E009.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E010.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E011.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E012.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E013.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E014.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E015.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E016.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E017.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E018.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E019.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E020.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E021.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E022.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E023.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E024.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E025.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E026.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E027.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E028.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E029.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E030.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E031.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E032.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E033.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E034.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E035.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E036.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E037.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E038.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E039.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E040.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E041.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E042.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E043.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E072.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E073.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E097.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E098.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E099.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E100.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E101.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E102.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E103.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E104.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E106.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E107.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E108.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E109.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E110.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E111.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E112.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E113.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E114.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E115.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E116.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E117.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E118.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E119.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E120.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E121.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E122.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E123.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E124.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E126.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E127.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E128.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E129.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E130.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E131.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E134.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E172.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00E173.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W050.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W051.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W052.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W053.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W054.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W055.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W056.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W057.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W058.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W059.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W060.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W061.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W062.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W063.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W064.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W065.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W066.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W067.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W068.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W069.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W070.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W071.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W072.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W073.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W074.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W075.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W076.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W077.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W078.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W079.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W080.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W081.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W090.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W091.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W092.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N00W177.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E007.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E009.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E010.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E011.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E012.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E013.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E014.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E015.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E016.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E017.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E018.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E019.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E020.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E021.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E022.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E023.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E024.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E025.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E026.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E027.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E028.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E029.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E030.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E031.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E032.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E033.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E034.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E035.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E036.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E037.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E038.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E039.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E040.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E041.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E042.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E043.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E044.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E045.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E073.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E097.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E098.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E099.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E100.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E101.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E102.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E103.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E104.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E106.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E107.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E108.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E109.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E110.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E111.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E112.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E113.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E114.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E115.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E116.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E117.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E118.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E119.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E120.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E121.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E122.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E124.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E125.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E126.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E127.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E128.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E131.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E154.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E172.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01E173.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W050.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W051.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W052.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W053.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W054.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W055.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W056.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W057.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W058.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W059.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W060.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W061.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W062.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W063.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W064.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W065.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W066.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W067.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W068.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W069.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W070.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W071.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W072.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W073.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W074.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W075.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W076.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W077.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W078.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W079.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N01W080.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N06W069.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N06W070.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N06W071.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N06W075.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N06W076.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N06W077.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N06W078.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N06W163.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E001.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E002.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E003.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E004.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E005.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E006.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E007.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E008.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E013.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E014.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E015.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E016.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E017.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E018.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E019.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E020.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E021.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E022.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E024.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E025.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E026.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E027.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E028.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E029.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E030.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E031.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E032.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E033.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E034.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E035.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E036.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E037.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E038.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E039.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E040.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E041.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E042.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E043.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E044.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E045.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E046.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E047.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E048.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E049.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E072.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E073.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E079.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E080.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E081.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E093.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E098.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E099.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E100.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E113.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E116.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E117.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E118.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E121.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E122.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E123.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E124.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E125.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E126.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E134.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E143.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E144.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E145.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E146.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E147.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E149.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E151.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E152.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E155.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E157.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E158.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07E168.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W007.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W009.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W010.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W012.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W013.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W014.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W060.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W062.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W063.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W064.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W065.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W066.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W067.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W068.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W069.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W070.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W071.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W072.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W073.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W074.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W075.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W076.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W077.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W078.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W079.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W080.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W081.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W082.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N07W083.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E000.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E001.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E002.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E003.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E004.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E005.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E007.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E008.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E009.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E010.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E011.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E012.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E013.tif',
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 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E015.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E016.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E017.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E018.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E019.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E020.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E021.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E022.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E023.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E024.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E025.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E026.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E027.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E028.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E029.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E030.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E031.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E032.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E033.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E034.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E035.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E036.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E037.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E038.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E039.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E040.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E041.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E042.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E043.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E044.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E045.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E046.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E047.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E048.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E049.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E050.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E073.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E076.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E077.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E078.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E079.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E080.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E081.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E092.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E093.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E097.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E098.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E099.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E100.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E104.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E105.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E106.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E111.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E116.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E117.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E118.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E122.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E123.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E124.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E125.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E126.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E134.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E137.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E140.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E144.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E146.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E147.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E149.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E150.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E151.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E152.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E154.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E165.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E166.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E167.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E168.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E170.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08E171.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W001.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W002.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W003.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W004.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W005.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W006.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W007.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W008.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W009.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W010.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W011.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W012.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W013.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W014.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W060.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W061.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W062.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W063.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W064.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W065.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W066.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W067.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W068.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W069.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W070.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W071.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W072.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W073.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W074.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W075.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W076.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N08W077.tif',
 ...]

Access data files

s3path = "s3://raster/SRTM_GL1/SRTM_GL1_srtm/N3[56]W11[234].tif"

remote_files = fsd.glob(s3path)

remote_files

['raster/SRTM_GL1/SRTM_GL1_srtm/N35W112.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N35W113.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N35W114.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N36W112.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N36W113.tif',
 'raster/SRTM_GL1/SRTM_GL1_srtm/N36W114.tif']

# Iterate through remote_files to create a fileset
fileset = [fsd.open(file) for file in remote_files]

# This works
dset = xr.open_mfdataset(fileset, combine='by_coords', engine="rasterio")

dset

<xarray.Dataset>
Dimensions:      (band: 1, x: 10803, y: 7202)
Coordinates:
  * band         (band) int64 1
  * x            (x) float64 -114.0 -114.0 -114.0 ... -111.0 -111.0 -111.0
  * y            (y) float64 37.0 37.0 37.0 37.0 37.0 ... 35.0 35.0 35.0 35.0
    spatial_ref  int64 0
Data variables:
    band_data    (band, y, x) float32 dask.array<chunksize=(1, 512, 512), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • band: 1
  • x: 10803
  • y: 7202
• Coordinates: (4)
  • band
    (band)
    int64
    1

    array([1])

  • x
    (x)
    float64
    -114.0 -114.0 ... -111.0 -111.0

    array([-114.      , -113.999722, -113.999444, ..., -111.000556, -111.000278,
           -111.      ])

  • y
    (y)
    float64
    37.0 37.0 37.0 ... 35.0 35.0 35.0

    array([37.      , 36.999722, 36.999444, ..., 35.000556, 35.000278, 35.      ])

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

    -114.00013888888888 0.0002777777777777778 0.0 37.00013888888889 0.0 -0.0002777777777777778

    array(0)

• Data variables: (1)
  • band_data
    (band, y, x)
    float32
    dask.array<chunksize=(1, 512, 512), meta=np.ndarray>

    AREA_OR_POINT :

    Point

    OVR_RESAMPLING_ALG :

    NEAREST

    Array Chunk Bytes 296.80 MiB 1.00 MiB Shape (1, 7202, 10803) (1, 512, 512) Dask graph 384 chunks in 15 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • band
    PandasIndex

    PandasIndex(Index([1], dtype='int64', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([             -114.0, -113.99972222222222, -113.99944444444445,
           -113.99916666666667, -113.99888888888889, -113.99861111111112,
           -113.99833333333333, -113.99805555555555, -113.99777777777778,
                     -113.9975,
           ...
                     -111.0025, -111.00222222222222, -111.00194444444445,
           -111.00166666666667, -111.00138888888888, -111.00111111111111,
           -111.00083333333333, -111.00055555555555, -111.00027777777778,
                        -111.0],
          dtype='float64', name='x', length=10803))

  • y
    PandasIndex

    PandasIndex(Index([              37.0, 36.999722222222225,  36.99944444444444,
            36.99916666666667,  36.99888888888889,  36.99861111111111,
           36.998333333333335,  36.99805555555555,  36.99777777777778,
                      36.9975,
           ...
                      35.0025,  35.00222222222222,  35.00194444444445,
           35.001666666666665,  35.00138888888889,  35.00111111111111,
            35.00083333333333,  35.00055555555556, 35.000277777777775,
                         35.0],
          dtype='float64', name='y', length=7202))

• Attributes: (0)

Coarsen to visualize data faster

• It is good practice to use coarsen to quickly visualize your data

• persist() triggers computation but keeps data as dask arrays.

dsmall = dset.band_data.coarsen(x=10, y=10, boundary='pad').mean().persist()

dsmall.hvplot(x = 'x', y = 'y',
           cmap='RdYlGn',  alpha=0.7,
           geo=True, tiles= 'CartoLight', width=700, height=700)

WARNING:param.Image01689: Image dimension(s) x and y are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01689: Image dimension(s) x and y are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Save the dataset into a local Zarr

dset.chunk(chunks="auto").to_zarr("SRTM_GL1_Colorado.zarr", mode="w", consolidated=True, compute=True, encoding={"x": {}, "y": {}})

<xarray.backends.zarr.ZarrStore at 0x7fa71a984b40>

Zip the local Zarr

def zipzarr(file):
    filename = file + os.path.extsep + "zip"
    with zipfile.ZipFile(
        filename, "w", compression=zipfile.ZIP_STORED, allowZip64=True) as fh:
        for root, _, filenames in os.walk(file):
            for each_filename in filenames:
                each_filename = os.path.join(root, each_filename)
                fh.write(each_filename, os.path.relpath(each_filename, file))

zipzarr("SRTM_GL1_Colorado.zarr")

Read local zarr

dc = xr.open_dataset("SRTM_GL1_Colorado.zarr.zip", engine="zarr", consolidated=True)

dc

<xarray.Dataset>
Dimensions:      (band: 1, y: 7202, x: 10803)
Coordinates:
  * band         (band) int64 1
  * x            (x) float64 -114.0 -114.0 -114.0 ... -111.0 -111.0 -111.0
  * y            (y) float64 37.0 37.0 37.0 37.0 37.0 ... 35.0 35.0 35.0 35.0
Data variables:
    band_data    (band, y, x) float32 ...
    spatial_ref  int64 ...

xarray.Dataset

• Dimensions:
  • band: 1
  • y: 7202
  • x: 10803
• Coordinates: (3)
  • band
    (band)
    int64
    1

    array([1])

  • x
    (x)
    float64
    -114.0 -114.0 ... -111.0 -111.0

    array([-114.      , -113.999722, -113.999444, ..., -111.000556, -111.000278,
           -111.      ])

  • y
    (y)
    float64
    37.0 37.0 37.0 ... 35.0 35.0 35.0

    array([37.      , 36.999722, 36.999444, ..., 35.000556, 35.000278, 35.      ])

• Data variables: (2)
  • band_data
    (band, y, x)
    float32
    ...

    AREA_OR_POINT :

    Point

    OVR_RESAMPLING_ALG :

    NEAREST

    grid_mapping :

    spatial_ref

    [77803206 values with dtype=float32]

  • spatial_ref
    ()
    int64
    ...

    GeoTransform :

    -114.00013888888888 0.0002777777777777778 0.0 37.00013888888889 0.0 -0.0002777777777777778

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    latitude_longitude

    horizontal_datum_name :

    World Geodetic System 1984

    inverse_flattening :

    298.257223563

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    reference_ellipsoid_name :

    WGS 84

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    [1 values with dtype=int64]

• Indexes: (3)
  • band
    PandasIndex

    PandasIndex(Index([1], dtype='int64', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([             -114.0, -113.99972222222222, -113.99944444444445,
           -113.99916666666667, -113.99888888888889, -113.99861111111112,
           -113.99833333333333, -113.99805555555555, -113.99777777777778,
                     -113.9975,
           ...
                     -111.0025, -111.00222222222222, -111.00194444444445,
           -111.00166666666667, -111.00138888888888, -111.00111111111111,
           -111.00083333333333, -111.00055555555555, -111.00027777777778,
                        -111.0],
          dtype='float64', name='x', length=10803))

  • y
    PandasIndex

    PandasIndex(Index([              37.0, 36.999722222222225,  36.99944444444444,
            36.99916666666667,  36.99888888888889,  36.99861111111111,
           36.998333333333335,  36.99805555555555,  36.99777777777778,
                      36.9975,
           ...
                      35.0025,  35.00222222222222,  35.00194444444445,
           35.001666666666665,  35.00138888888889,  35.00111111111111,
            35.00083333333333,  35.00055555555556, 35.000277777777775,
                         35.0],
          dtype='float64', name='y', length=7202))

• Attributes: (0)

dc.band_data.coarsen(x=10, y=10, boundary='pad').mean().persist().hvplot(x = 'x', y = 'y',
           cmap='RdYlGn',  alpha=0.7,
           geo=True, tiles= 'CartoLight', width=700, height=700)

WARNING:param.Image01892: Image dimension(s) x and y are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01892: Image dimension(s) x and y are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Put zarr into private bucket

Save results in zarr on private object storage for further analysis

• your credentials are in $HOME/.aws/credentials

• check with your instructor to get the secret access key (replace XXX by the right key)

[default]
aws_access_key_id=XXXXXXXXXXXX
aws_secret_access_key=YYYYYYYYYY
aws_endpoint_url=https://pangeo-eosc-minioapi.vm.fedcloud.eu/

Set the path to your group’s location (ask your instructor)

s3_prefix = os.environ['JUPYTERHUB_USER']
# s3_suffix below is what you need to customise to create your own buckets:
s3_suffix = 'work'
s3_bucket = s3_prefix + '-' + s3_suffix + '/'
s3_bucket

'afouilloux-work/'

fss = s3fs.S3FileSystem(anon=False,
      client_kwargs={
         "endpoint_url": "https://pangeo-eosc-minioapi.vm.fedcloud.eu/"
      })

fss.ls(s3_bucket)

['afouilloux-work/SRTM_GL1_srtm_Colorado.zarr',
 'afouilloux-work/SRTM_GL1_srtm_Colorado.zarr.zip']

try:
    fss.ls(s3_bucket + "SRTM_GL1_srtm_Colorado.zarr.zip")
    print("zarr is present online so we delete it")
    fss.rm(s3_bucket + "SRTM_GL1_srtm_Colorado.zarr.zip")
except:
    print("Zarr is not there; no need to delete it")

fss.put("SRTM_GL1_Colorado.zarr.zip", s3_bucket + "/SRTM_GL1_srtm_Colorado.zarr.zip")

zarr is present online so we delete it

[None]

Check what you have stored in s3

• we use https://pangeo-eosc-minioapi.vm.fedcloud.eu/ as an endpoint

• we need to authenticate to access data (anon=False)

Read zarr from private s3 bucket

import os
import s3fs
import zipfile
import fsspec
from fsspec.implementations.zip import ZipFileSystem
from fsspec.mapping import FSMap
import xarray as xr
from hvplot import xarray

fst = s3fs.S3FileSystem(anon=False,
      client_kwargs={
         "endpoint_url": "https://pangeo-eosc-minioapi.vm.fedcloud.eu/"
      })

s3_prefix = os.environ['JUPYTERHUB_USER']
# s3_suffix below is what you need to customise to create your own buckets:
s3_suffix = 'work'
s3_bucket = s3_prefix + '-' + s3_suffix + '/'
s3_bucket

'afouilloux-work/'

s3_path = "s3://" + s3_bucket + "SRTM_GL1_srtm_Colorado.zarr.zip"

f = fst.open(s3_path)
fs = ZipFileSystem(f, mode="r")
store = FSMap("", fs, check=False)

# Read Zarr file
ds = xr.open_zarr(store=store, consolidated=True)

ds

<xarray.Dataset>
Dimensions:      (band: 1, y: 7202, x: 10803)
Coordinates:
  * band         (band) int64 1
  * x            (x) float64 -114.0 -114.0 -114.0 ... -111.0 -111.0 -111.0
  * y            (y) float64 37.0 37.0 37.0 37.0 37.0 ... 35.0 35.0 35.0 35.0
Data variables:
    band_data    (band, y, x) float32 dask.array<chunksize=(1, 5632, 5632), meta=np.ndarray>
    spatial_ref  int64 ...

xarray.Dataset

• Dimensions:
  • band: 1
  • y: 7202
  • x: 10803
• Coordinates: (3)
  • band
    (band)
    int64
    1

    array([1])

  • x
    (x)
    float64
    -114.0 -114.0 ... -111.0 -111.0

    array([-114.      , -113.999722, -113.999444, ..., -111.000556, -111.000278,
           -111.      ])

  • y
    (y)
    float64
    37.0 37.0 37.0 ... 35.0 35.0 35.0

    array([37.      , 36.999722, 36.999444, ..., 35.000556, 35.000278, 35.      ])

• Data variables: (2)
  • band_data
    (band, y, x)
    float32
    dask.array<chunksize=(1, 5632, 5632), meta=np.ndarray>

    AREA_OR_POINT :

    Point

    OVR_RESAMPLING_ALG :

    NEAREST

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 296.80 MiB 121.00 MiB Shape (1, 7202, 10803) (1, 5632, 5632) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • spatial_ref
    ()
    int64
    ...

    GeoTransform :

    -114.00013888888888 0.0002777777777777778 0.0 37.00013888888889 0.0 -0.0002777777777777778

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    latitude_longitude

    horizontal_datum_name :

    World Geodetic System 1984

    inverse_flattening :

    298.257223563

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    reference_ellipsoid_name :

    WGS 84

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    [1 values with dtype=int64]

• Indexes: (3)
  • band
    PandasIndex

    PandasIndex(Index([1], dtype='int64', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([             -114.0, -113.99972222222222, -113.99944444444445,
           -113.99916666666667, -113.99888888888889, -113.99861111111112,
           -113.99833333333333, -113.99805555555555, -113.99777777777778,
                     -113.9975,
           ...
                     -111.0025, -111.00222222222222, -111.00194444444445,
           -111.00166666666667, -111.00138888888888, -111.00111111111111,
           -111.00083333333333, -111.00055555555555, -111.00027777777778,
                        -111.0],
          dtype='float64', name='x', length=10803))

  • y
    PandasIndex

    PandasIndex(Index([              37.0, 36.999722222222225,  36.99944444444444,
            36.99916666666667,  36.99888888888889,  36.99861111111111,
           36.998333333333335,  36.99805555555555,  36.99777777777778,
                      36.9975,
           ...
                      35.0025,  35.00222222222222,  35.00194444444445,
           35.001666666666665,  35.00138888888889,  35.00111111111111,
            35.00083333333333,  35.00055555555556, 35.000277777777775,
                         35.0],
          dtype='float64', name='y', length=7202))

• Attributes: (0)

Access and visualize

ds.band_data.coarsen(x=10, y=10, boundary='pad').mean().persist().hvplot(x = 'x', y = 'y',
           cmap='RdYlGn',  alpha=0.7,
           geo=True, tiles= 'CartoLight', width=700, height=700)

WARNING:param.Image02095: Image dimension(s) x and y are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image02095: Image dimension(s) x and y are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

client.close()