Open and visualize COGs

Demonstrates how to visualize COGs using pystac_client, rioxarray, stackstac, and hvplot

Run this notebook

You can launch this notebook in VEDA JupyterHub by clicking the link below.

Launch in VEDA JupyterHub (requires access)

Learn more

Inside the Hub

This notebook was written on the VEDA JupyterHub and as such is designed to be run on a jupyterhub which is associated with an AWS IAM role which has been granted permissions to the VEDA data store via its bucket policy. The instance used provided 16GB of RAM.

See (VEDA Analytics JupyterHub Access)[https://nasa-impact.github.io/veda-docs/veda-jh-access.html] for information about how to gain access.

Outside the Hub

The data is in a protected bucket. Please request access by emailng aimee@developmentseed.org or alexandra@developmentseed.org and providing your affiliation, interest in or expected use of the dataset and an AWS IAM role or user Amazon Resource Name (ARN). The team will help you configure the cognito client.

You should then run:

%run -i 'cognito_login.py'

Approach

1. Use pystac_client to open the STAC catalog and retrieve the items in the collection
2. Use stackstac to create an xarray dataset containing all the items
3. Use rioxarray to crop data to AOI
4. Use hvplot to render the COG at every timestep

import requests
from pystac_client import Client
import pandas as pd
import stackstac

import rioxarray  # noqa
import hvplot.xarray  # noqa

Declare your collection of interest

You can discover available collections the following ways:

• Programmatically: see example in the list-collections.ipynb notebook
• JSON API: https://staging-stac.delta-backend.com/collections
• STAC Browser: http://veda-staging-stac-browser.s3-website-us-west-2.amazonaws.com

STAC_API_URL = "https://openveda.cloud/api/stac"
collection_id = "no2-monthly"

Discover items in collection for region and time of interest

Use pystac_client to search the STAC collection for a particular area of interest within specified datetime bounds.

catalog = Client.open(STAC_API_URL)
search = catalog.search(collections=[collection_id], sortby="start_datetime")

item_collection = search.item_collection()
print(f"Found {len(item_collection)} items")

Found 93 items

Define an AOI

We can fetch GeoJSON for metropolitan France and Corsica (excluding overseas territories) from an authoritative online source (https://gadm.org/download_country.html).

response = requests.get(
    "https://geodata.ucdavis.edu/gadm/gadm4.1/json/gadm41_FRA_0.json"
)

# If anything goes wrong with this request output error contents
assert response.ok, response.text

result = response.json()
print(f"There are {len(result['features'])} features in this collection")

There are 1 features in this collection

That is the geojson for a feature collection, but since there is only one feature in it we can grab just that.

france_aoi = result["features"][0]

Read data

Create an xarray.DataArray using stackstac

da = stackstac.stack(item_collection, epsg=4326)
da = da.assign_coords({"time": da.start_datetime}).squeeze()
da

/srv/conda/envs/notebook/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  times = pd.to_datetime(

<xarray.DataArray 'stackstac-63f208ae3e2c01863c43588cc3899b3c' (time: 93,
                                                                y: 1800, x: 3600)> Size: 5GB
dask.array<getitem, shape=(93, 1800, 3600), dtype=float64, chunksize=(1, 1024, 1024), chunktype=numpy.ndarray>
Coordinates: (12/17)
    id              (time) <U37 14kB 'OMI_trno2_0.10x0.10_201601_Col3_V4.nc' ...
    band            <U11 44B 'cog_default'
  * x               (x) float64 29kB -180.0 -179.9 -179.8 ... 179.7 179.8 179.9
  * y               (y) float64 14kB 90.0 89.9 89.8 89.7 ... -89.7 -89.8 -89.9
    start_datetime  (time) <U25 9kB '2016-01-01T00:00:00+00:00' ... '2023-09-...
    end_datetime    (time) <U25 9kB '2016-01-31T00:00:00+00:00' ... '2023-09-...
    ...              ...
    proj:geometry   object 8B {'type': 'Polygon', 'coordinates': [[[-180.0, -...
    description     <U47 188B 'Cloud optimized default layer to display on map'
    title           <U17 68B 'Default COG Layer'
    proj:epsg       int64 8B 4326
    epsg            int64 8B 4326
  * time            (time) <U25 9kB '2016-01-01T00:00:00+00:00' ... '2023-09-...
Attributes:
    spec:        RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), r...
    crs:         epsg:4326
    transform:   | 0.10, 0.00,-180.00|\n| 0.00,-0.10, 90.00|\n| 0.00, 0.00, 1...
    resolution:  0.1

xarray.DataArray

'stackstac-63f208ae3e2c01863c43588cc3899b3c'

• time: 93
• y: 1800
• x: 3600

• dask.array<chunksize=(1, 1024, 1024), meta=np.ndarray>

  Array Chunk Bytes 4.49 GiB 8.00 MiB Shape (93, 1800, 3600) (1, 1024, 1024) Dask graph 744 chunks in 4 graph layers Data type float64 numpy.ndarray

• Coordinates: (17)
  • id
    (time)
    <U37
    'OMI_trno2_0.10x0.10_201601_Col3...

    array(['OMI_trno2_0.10x0.10_201601_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201602_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201603_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201604_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201605_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201606_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201607_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201608_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201609_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201610_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201611_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201612_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201701_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201702_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201703_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201704_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201705_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201706_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201707_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201708_Col3_V4.nc',
    ...
           'OMI_trno2_0.10x0.10_202202_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202203_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202204_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202205_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202206_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202207_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202208_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202209_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202210_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202211_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202212_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202301_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202302_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202303_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202304_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202305_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202306_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202307_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202308_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202309_Col3_V4.nc'], dtype='<U37')

  • band
    ()
    <U11
    'cog_default'

    array('cog_default', dtype='<U11')

  • x
    (x)
    float64
    -180.0 -179.9 ... 179.8 179.9

    array([-180. , -179.9, -179.8, ...,  179.7,  179.8,  179.9])

  • y
    (y)
    float64
    90.0 89.9 89.8 ... -89.8 -89.9

    array([ 90. ,  89.9,  89.8, ..., -89.7, -89.8, -89.9])

  • start_datetime
    (time)
    <U25
    '2016-01-01T00:00:00+00:00' ... ...

    array(['2016-01-01T00:00:00+00:00', '2016-02-01T00:00:00+00:00',
           '2016-03-01T00:00:00+00:00', '2016-04-01T00:00:00+00:00',
           '2016-05-01T00:00:00+00:00', '2016-06-01T00:00:00+00:00',
           '2016-07-01T00:00:00+00:00', '2016-08-01T00:00:00+00:00',
           '2016-09-01T00:00:00+00:00', '2016-10-01T00:00:00+00:00',
           '2016-11-01T00:00:00+00:00', '2016-12-01T00:00:00+00:00',
           '2017-01-01T00:00:00+00:00', '2017-02-01T00:00:00+00:00',
           '2017-03-01T00:00:00+00:00', '2017-04-01T00:00:00+00:00',
           '2017-05-01T00:00:00+00:00', '2017-06-01T00:00:00+00:00',
           '2017-07-01T00:00:00+00:00', '2017-08-01T00:00:00+00:00',
           '2017-09-01T00:00:00+00:00', '2017-10-01T00:00:00+00:00',
           '2017-11-01T00:00:00+00:00', '2017-12-01T00:00:00+00:00',
           '2018-01-01T00:00:00+00:00', '2018-02-01T00:00:00+00:00',
           '2018-03-01T00:00:00+00:00', '2018-04-01T00:00:00+00:00',
           '2018-05-01T00:00:00+00:00', '2018-06-01T00:00:00+00:00',
           '2018-07-01T00:00:00+00:00', '2018-08-01T00:00:00+00:00',
           '2018-09-01T00:00:00+00:00', '2018-10-01T00:00:00+00:00',
           '2018-11-01T00:00:00+00:00', '2018-12-01T00:00:00+00:00',
           '2019-01-01T00:00:00+00:00', '2019-02-01T00:00:00+00:00',
           '2019-03-01T00:00:00+00:00', '2019-04-01T00:00:00+00:00',
    ...
           '2020-07-01T00:00:00+00:00', '2020-08-01T00:00:00+00:00',
           '2020-09-01T00:00:00+00:00', '2020-10-01T00:00:00+00:00',
           '2020-11-01T00:00:00+00:00', '2020-12-01T00:00:00+00:00',
           '2021-01-01T00:00:00+00:00', '2021-02-01T00:00:00+00:00',
           '2021-03-01T00:00:00+00:00', '2021-04-01T00:00:00+00:00',
           '2021-05-01T00:00:00+00:00', '2021-06-01T00:00:00+00:00',
           '2021-07-01T00:00:00+00:00', '2021-08-01T00:00:00+00:00',
           '2021-09-01T00:00:00+00:00', '2021-10-01T00:00:00+00:00',
           '2021-11-01T00:00:00+00:00', '2021-12-01T00:00:00+00:00',
           '2022-01-01T00:00:00+00:00', '2022-02-01T00:00:00+00:00',
           '2022-03-01T00:00:00+00:00', '2022-04-01T00:00:00+00:00',
           '2022-05-01T00:00:00+00:00', '2022-06-01T00:00:00+00:00',
           '2022-07-01T00:00:00+00:00', '2022-08-01T00:00:00+00:00',
           '2022-09-01T00:00:00+00:00', '2022-10-01T00:00:00+00:00',
           '2022-11-01T00:00:00+00:00', '2022-12-01T00:00:00+00:00',
           '2023-01-01T00:00:00+00:00', '2023-02-01T00:00:00+00:00',
           '2023-03-01T00:00:00+00:00', '2023-04-01T00:00:00+00:00',
           '2023-05-01T00:00:00+00:00', '2023-06-01T00:00:00+00:00',
           '2023-07-01T00:00:00+00:00', '2023-08-01T00:00:00+00:00',
           '2023-09-01T00:00:00+00:00'], dtype='<U25')

  • end_datetime
    (time)
    <U25
    '2016-01-31T00:00:00+00:00' ... ...

    array(['2016-01-31T00:00:00+00:00', '2016-02-29T00:00:00+00:00',
           '2016-03-31T00:00:00+00:00', '2016-04-30T00:00:00+00:00',
           '2016-05-31T00:00:00+00:00', '2016-06-30T00:00:00+00:00',
           '2016-07-31T00:00:00+00:00', '2016-08-31T00:00:00+00:00',
           '2016-09-30T00:00:00+00:00', '2016-10-31T00:00:00+00:00',
           '2016-11-30T00:00:00+00:00', '2016-12-31T00:00:00+00:00',
           '2017-01-31T00:00:00+00:00', '2017-02-28T00:00:00+00:00',
           '2017-03-31T00:00:00+00:00', '2017-04-30T00:00:00+00:00',
           '2017-05-31T00:00:00+00:00', '2017-06-30T00:00:00+00:00',
           '2017-07-31T00:00:00+00:00', '2017-08-31T00:00:00+00:00',
           '2017-09-30T00:00:00+00:00', '2017-10-31T00:00:00+00:00',
           '2017-11-30T00:00:00+00:00', '2017-12-31T00:00:00+00:00',
           '2018-01-31T00:00:00+00:00', '2018-02-28T00:00:00+00:00',
           '2018-03-31T00:00:00+00:00', '2018-04-30T00:00:00+00:00',
           '2018-05-31T00:00:00+00:00', '2018-06-30T00:00:00+00:00',
           '2018-07-31T00:00:00+00:00', '2018-08-31T00:00:00+00:00',
           '2018-09-30T00:00:00+00:00', '2018-10-31T00:00:00+00:00',
           '2018-11-30T00:00:00+00:00', '2018-12-31T00:00:00+00:00',
           '2019-01-31T00:00:00+00:00', '2019-02-28T00:00:00+00:00',
           '2019-03-31T00:00:00+00:00', '2019-04-30T00:00:00+00:00',
    ...
           '2020-07-31T00:00:00+00:00', '2020-08-31T00:00:00+00:00',
           '2020-09-30T00:00:00+00:00', '2020-10-31T00:00:00+00:00',
           '2020-11-30T00:00:00+00:00', '2020-12-31T00:00:00+00:00',
           '2021-01-31T00:00:00+00:00', '2021-02-28T00:00:00+00:00',
           '2021-03-31T00:00:00+00:00', '2021-04-30T00:00:00+00:00',
           '2021-05-31T00:00:00+00:00', '2021-06-30T00:00:00+00:00',
           '2021-07-31T00:00:00+00:00', '2021-08-31T00:00:00+00:00',
           '2021-09-30T00:00:00+00:00', '2021-10-31T00:00:00+00:00',
           '2021-11-30T00:00:00+00:00', '2021-12-31T00:00:00+00:00',
           '2022-01-31T00:00:00+00:00', '2022-02-28T00:00:00+00:00',
           '2022-03-31T00:00:00+00:00', '2022-04-30T00:00:00+00:00',
           '2022-05-31T00:00:00+00:00', '2022-06-30T00:00:00+00:00',
           '2022-07-31T00:00:00+00:00', '2022-08-31T00:00:00+00:00',
           '2022-09-30T00:00:00+00:00', '2022-10-31T00:00:00+00:00',
           '2022-11-30T00:00:00+00:00', '2022-12-31T00:00:00+00:00',
           '2023-01-31T00:00:00+00:00', '2023-02-28T00:00:00+00:00',
           '2023-03-31T00:00:00+00:00', '2023-04-30T00:00:00+00:00',
           '2023-05-31T00:00:00+00:00', '2023-06-30T00:00:00+00:00',
           '2023-07-31T00:00:00+00:00', '2023-08-31T00:00:00+00:00',
           '2023-09-30T00:00:00+00:00'], dtype='<U25')

  • proj:bbox
    ()
    object
    {90.0, 180.0, -90.0, -180.0}

    array({90.0, 180.0, -90.0, -180.0}, dtype=object)

  • proj:projjson
    ()
    object
    {'id': {'code': 4326, 'authority...

    array({'id': {'code': 4326, 'authority': 'EPSG'}, 'name': 'WGS 84', 'type': 'GeographicCRS', 'datum': {'name': 'World Geodetic System 1984', 'type': 'GeodeticReferenceFrame', 'ellipsoid': {'name': 'WGS 84', 'semi_major_axis': 6378137, 'inverse_flattening': 298.257223563}}, '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json', 'coordinate_system': {'axis': [{'name': 'Geodetic latitude', 'unit': 'degree', 'direction': 'north', 'abbreviation': 'Lat'}, {'name': 'Geodetic longitude', 'unit': 'degree', 'direction': 'east', 'abbreviation': 'Lon'}], 'subtype': 'ellipsoidal'}},
          dtype=object)

  • proj:shape
    ()
    object
    {1800, 3600}

    array({1800, 3600}, dtype=object)

  • proj:transform
    ()
    object
    {0.1, 0.0, 1.0, -0.1, -180.0, 90.0}

    array({0.1, 0.0, 1.0, -0.1, -180.0, 90.0}, dtype=object)

  • proj:wkt2
    ()
    <U277
    'GEOGCS["WGS 84",DATUM["WGS_1984...

    array('GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]',
          dtype='<U277')

  • proj:geometry
    ()
    object
    {'type': 'Polygon', 'coordinates...

    array({'type': 'Polygon', 'coordinates': [[[-180.0, -90.0], [180.0, -90.0], [180.0, 90.0], [-180.0, 90.0], [-180.0, -90.0]]]},
          dtype=object)

  • description
    ()
    <U47
    'Cloud optimized default layer t...

    array('Cloud optimized default layer to display on map', dtype='<U47')

  • title
    ()
    <U17
    'Default COG Layer'

    array('Default COG Layer', dtype='<U17')

  • proj:epsg
    ()
    int64
    4326

    array(4326)

  • epsg
    ()
    int64
    4326

    array(4326)

  • time
    (time)
    <U25
    '2016-01-01T00:00:00+00:00' ... ...

    array(['2016-01-01T00:00:00+00:00', '2016-02-01T00:00:00+00:00',
           '2016-03-01T00:00:00+00:00', '2016-04-01T00:00:00+00:00',
           '2016-05-01T00:00:00+00:00', '2016-06-01T00:00:00+00:00',
           '2016-07-01T00:00:00+00:00', '2016-08-01T00:00:00+00:00',
           '2016-09-01T00:00:00+00:00', '2016-10-01T00:00:00+00:00',
           '2016-11-01T00:00:00+00:00', '2016-12-01T00:00:00+00:00',
           '2017-01-01T00:00:00+00:00', '2017-02-01T00:00:00+00:00',
           '2017-03-01T00:00:00+00:00', '2017-04-01T00:00:00+00:00',
           '2017-05-01T00:00:00+00:00', '2017-06-01T00:00:00+00:00',
           '2017-07-01T00:00:00+00:00', '2017-08-01T00:00:00+00:00',
           '2017-09-01T00:00:00+00:00', '2017-10-01T00:00:00+00:00',
           '2017-11-01T00:00:00+00:00', '2017-12-01T00:00:00+00:00',
           '2018-01-01T00:00:00+00:00', '2018-02-01T00:00:00+00:00',
           '2018-03-01T00:00:00+00:00', '2018-04-01T00:00:00+00:00',
           '2018-05-01T00:00:00+00:00', '2018-06-01T00:00:00+00:00',
           '2018-07-01T00:00:00+00:00', '2018-08-01T00:00:00+00:00',
           '2018-09-01T00:00:00+00:00', '2018-10-01T00:00:00+00:00',
           '2018-11-01T00:00:00+00:00', '2018-12-01T00:00:00+00:00',
           '2019-01-01T00:00:00+00:00', '2019-02-01T00:00:00+00:00',
           '2019-03-01T00:00:00+00:00', '2019-04-01T00:00:00+00:00',
           '2019-05-01T00:00:00+00:00', '2019-06-01T00:00:00+00:00',
           '2019-07-01T00:00:00+00:00', '2019-08-01T00:00:00+00:00',
           '2019-09-01T00:00:00+00:00', '2019-10-01T00:00:00+00:00',
           '2019-11-01T00:00:00+00:00', '2019-12-01T00:00:00+00:00',
           '2020-01-01T00:00:00+00:00', '2020-02-01T00:00:00+00:00',
           '2020-03-01T00:00:00+00:00', '2020-04-01T00:00:00+00:00',
           '2020-05-01T00:00:00+00:00', '2020-06-01T00:00:00+00:00',
           '2020-07-01T00:00:00+00:00', '2020-08-01T00:00:00+00:00',
           '2020-09-01T00:00:00+00:00', '2020-10-01T00:00:00+00:00',
           '2020-11-01T00:00:00+00:00', '2020-12-01T00:00:00+00:00',
           '2021-01-01T00:00:00+00:00', '2021-02-01T00:00:00+00:00',
           '2021-03-01T00:00:00+00:00', '2021-04-01T00:00:00+00:00',
           '2021-05-01T00:00:00+00:00', '2021-06-01T00:00:00+00:00',
           '2021-07-01T00:00:00+00:00', '2021-08-01T00:00:00+00:00',
           '2021-09-01T00:00:00+00:00', '2021-10-01T00:00:00+00:00',
           '2021-11-01T00:00:00+00:00', '2021-12-01T00:00:00+00:00',
           '2022-01-01T00:00:00+00:00', '2022-02-01T00:00:00+00:00',
           '2022-03-01T00:00:00+00:00', '2022-04-01T00:00:00+00:00',
           '2022-05-01T00:00:00+00:00', '2022-06-01T00:00:00+00:00',
           '2022-07-01T00:00:00+00:00', '2022-08-01T00:00:00+00:00',
           '2022-09-01T00:00:00+00:00', '2022-10-01T00:00:00+00:00',
           '2022-11-01T00:00:00+00:00', '2022-12-01T00:00:00+00:00',
           '2023-01-01T00:00:00+00:00', '2023-02-01T00:00:00+00:00',
           '2023-03-01T00:00:00+00:00', '2023-04-01T00:00:00+00:00',
           '2023-05-01T00:00:00+00:00', '2023-06-01T00:00:00+00:00',
           '2023-07-01T00:00:00+00:00', '2023-08-01T00:00:00+00:00',
           '2023-09-01T00:00:00+00:00'], dtype='<U25')

• Indexes: (3)
  • x
    PandasIndex

    PandasIndex(Index([            -180.0,             -179.9,             -179.8,
                       -179.7,             -179.6,             -179.5,
                       -179.4,             -179.3,             -179.2,
                       -179.1,
           ...
                        179.0, 179.10000000000002, 179.20000000000005,
                        179.3, 179.40000000000003,              179.5,
           179.60000000000002, 179.70000000000005,              179.8,
           179.90000000000003],
          dtype='float64', name='x', length=3600))

  • y
    PandasIndex

    PandasIndex(Index([              90.0,               89.9,               89.8,
                         89.7,               89.6,               89.5,
                         89.4,               89.3,               89.2,
                         89.1,
           ...
                        -89.0, -89.10000000000002, -89.20000000000002,
           -89.30000000000001,              -89.4,              -89.5,
           -89.60000000000002, -89.70000000000002, -89.80000000000001,
                        -89.9],
          dtype='float64', name='y', length=1800))

  • time
    PandasIndex

    PandasIndex(Index(['2016-01-01T00:00:00+00:00', '2016-02-01T00:00:00+00:00',
           '2016-03-01T00:00:00+00:00', '2016-04-01T00:00:00+00:00',
           '2016-05-01T00:00:00+00:00', '2016-06-01T00:00:00+00:00',
           '2016-07-01T00:00:00+00:00', '2016-08-01T00:00:00+00:00',
           '2016-09-01T00:00:00+00:00', '2016-10-01T00:00:00+00:00',
           '2016-11-01T00:00:00+00:00', '2016-12-01T00:00:00+00:00',
           '2017-01-01T00:00:00+00:00', '2017-02-01T00:00:00+00:00',
           '2017-03-01T00:00:00+00:00', '2017-04-01T00:00:00+00:00',
           '2017-05-01T00:00:00+00:00', '2017-06-01T00:00:00+00:00',
           '2017-07-01T00:00:00+00:00', '2017-08-01T00:00:00+00:00',
           '2017-09-01T00:00:00+00:00', '2017-10-01T00:00:00+00:00',
           '2017-11-01T00:00:00+00:00', '2017-12-01T00:00:00+00:00',
           '2018-01-01T00:00:00+00:00', '2018-02-01T00:00:00+00:00',
           '2018-03-01T00:00:00+00:00', '2018-04-01T00:00:00+00:00',
           '2018-05-01T00:00:00+00:00', '2018-06-01T00:00:00+00:00',
           '2018-07-01T00:00:00+00:00', '2018-08-01T00:00:00+00:00',
           '2018-09-01T00:00:00+00:00', '2018-10-01T00:00:00+00:00',
           '2018-11-01T00:00:00+00:00', '2018-12-01T00:00:00+00:00',
           '2019-01-01T00:00:00+00:00', '2019-02-01T00:00:00+00:00',
           '2019-03-01T00:00:00+00:00', '2019-04-01T00:00:00+00:00',
           '2019-05-01T00:00:00+00:00', '2019-06-01T00:00:00+00:00',
           '2019-07-01T00:00:00+00:00', '2019-08-01T00:00:00+00:00',
           '2019-09-01T00:00:00+00:00', '2019-10-01T00:00:00+00:00',
           '2019-11-01T00:00:00+00:00', '2019-12-01T00:00:00+00:00',
           '2020-01-01T00:00:00+00:00', '2020-02-01T00:00:00+00:00',
           '2020-03-01T00:00:00+00:00', '2020-04-01T00:00:00+00:00',
           '2020-05-01T00:00:00+00:00', '2020-06-01T00:00:00+00:00',
           '2020-07-01T00:00:00+00:00', '2020-08-01T00:00:00+00:00',
           '2020-09-01T00:00:00+00:00', '2020-10-01T00:00:00+00:00',
           '2020-11-01T00:00:00+00:00', '2020-12-01T00:00:00+00:00',
           '2021-01-01T00:00:00+00:00', '2021-02-01T00:00:00+00:00',
           '2021-03-01T00:00:00+00:00', '2021-04-01T00:00:00+00:00',
           '2021-05-01T00:00:00+00:00', '2021-06-01T00:00:00+00:00',
           '2021-07-01T00:00:00+00:00', '2021-08-01T00:00:00+00:00',
           '2021-09-01T00:00:00+00:00', '2021-10-01T00:00:00+00:00',
           '2021-11-01T00:00:00+00:00', '2021-12-01T00:00:00+00:00',
           '2022-01-01T00:00:00+00:00', '2022-02-01T00:00:00+00:00',
           '2022-03-01T00:00:00+00:00', '2022-04-01T00:00:00+00:00',
           '2022-05-01T00:00:00+00:00', '2022-06-01T00:00:00+00:00',
           '2022-07-01T00:00:00+00:00', '2022-08-01T00:00:00+00:00',
           '2022-09-01T00:00:00+00:00', '2022-10-01T00:00:00+00:00',
           '2022-11-01T00:00:00+00:00', '2022-12-01T00:00:00+00:00',
           '2023-01-01T00:00:00+00:00', '2023-02-01T00:00:00+00:00',
           '2023-03-01T00:00:00+00:00', '2023-04-01T00:00:00+00:00',
           '2023-05-01T00:00:00+00:00', '2023-06-01T00:00:00+00:00',
           '2023-07-01T00:00:00+00:00', '2023-08-01T00:00:00+00:00',
           '2023-09-01T00:00:00+00:00'],
          dtype='object', name='time'))

• Attributes: (4)

  spec :

  RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), resolutions_xy=(0.1, 0.1))

  crs :

  epsg:4326

  transform :

  | 0.10, 0.00,-180.00| | 0.00,-0.10, 90.00| | 0.00, 0.00, 1.00|

  resolution :

  0.1

Clip the data to AOI

subset = da.rio.clip([france_aoi["geometry"]])
subset

<xarray.DataArray 'stackstac-63f208ae3e2c01863c43588cc3899b3c' (time: 93,
                                                                y: 97, x: 143)> Size: 10MB
dask.array<getitem, shape=(93, 97, 143), dtype=float64, chunksize=(1, 97, 143), chunktype=numpy.ndarray>
Coordinates: (12/18)
    id              (time) <U37 14kB 'OMI_trno2_0.10x0.10_201601_Col3_V4.nc' ...
    band            <U11 44B 'cog_default'
  * x               (x) float64 1kB -4.7 -4.6 -4.5 -4.4 -4.3 ... 9.2 9.3 9.4 9.5
  * y               (y) float64 776B 51.0 50.9 50.8 50.7 ... 41.7 41.6 41.5 41.4
    start_datetime  (time) <U25 9kB '2016-01-01T00:00:00+00:00' ... '2023-09-...
    end_datetime    (time) <U25 9kB '2016-01-31T00:00:00+00:00' ... '2023-09-...
    ...              ...
    description     <U47 188B 'Cloud optimized default layer to display on map'
    title           <U17 68B 'Default COG Layer'
    proj:epsg       int64 8B 4326
    epsg            int64 8B 4326
  * time            (time) <U25 9kB '2016-01-01T00:00:00+00:00' ... '2023-09-...
    spatial_ref     int64 8B 0
Attributes:
    spec:        RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), r...
    resolution:  0.1

xarray.DataArray

'stackstac-63f208ae3e2c01863c43588cc3899b3c'

• time: 93
• y: 97
• x: 143

• dask.array<chunksize=(1, 97, 143), meta=np.ndarray>

  Array Chunk Bytes 9.84 MiB 108.37 kiB Shape (93, 97, 143) (1, 97, 143) Dask graph 93 chunks in 8 graph layers Data type float64 numpy.ndarray

• Coordinates: (18)
  • id
    (time)
    <U37
    'OMI_trno2_0.10x0.10_201601_Col3...

    array(['OMI_trno2_0.10x0.10_201601_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201602_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201603_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201604_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201605_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201606_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201607_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201608_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201609_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201610_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201611_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201612_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201701_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201702_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201703_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201704_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201705_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201706_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201707_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201708_Col3_V4.nc',
    ...
           'OMI_trno2_0.10x0.10_202202_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202203_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202204_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202205_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202206_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202207_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202208_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202209_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202210_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202211_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202212_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202301_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202302_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202303_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202304_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202305_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202306_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202307_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202308_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202309_Col3_V4.nc'], dtype='<U37')

  • band
    ()
    <U11
    'cog_default'

    array('cog_default', dtype='<U11')

  • x
    (x)
    float64
    -4.7 -4.6 -4.5 -4.4 ... 9.3 9.4 9.5

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-4.7, -4.6, -4.5, -4.4, -4.3, -4.2, -4.1, -4. , -3.9, -3.8, -3.7, -3.6,
           -3.5, -3.4, -3.3, -3.2, -3.1, -3. , -2.9, -2.8, -2.7, -2.6, -2.5, -2.4,
           -2.3, -2.2, -2.1, -2. , -1.9, -1.8, -1.7, -1.6, -1.5, -1.4, -1.3, -1.2,
           -1.1, -1. , -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1,  0. ,
            0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,  1.2,
            1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,  2.3,  2.4,
            2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,  3.4,  3.5,  3.6,
            3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,  4.8,
            4.9,  5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,  6. ,
            6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,
            7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,
            8.5,  8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5])

  • y
    (y)
    float64
    51.0 50.9 50.8 ... 41.6 41.5 41.4

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([51. , 50.9, 50.8, 50.7, 50.6, 50.5, 50.4, 50.3, 50.2, 50.1, 50. , 49.9,
           49.8, 49.7, 49.6, 49.5, 49.4, 49.3, 49.2, 49.1, 49. , 48.9, 48.8, 48.7,
           48.6, 48.5, 48.4, 48.3, 48.2, 48.1, 48. , 47.9, 47.8, 47.7, 47.6, 47.5,
           47.4, 47.3, 47.2, 47.1, 47. , 46.9, 46.8, 46.7, 46.6, 46.5, 46.4, 46.3,
           46.2, 46.1, 46. , 45.9, 45.8, 45.7, 45.6, 45.5, 45.4, 45.3, 45.2, 45.1,
           45. , 44.9, 44.8, 44.7, 44.6, 44.5, 44.4, 44.3, 44.2, 44.1, 44. , 43.9,
           43.8, 43.7, 43.6, 43.5, 43.4, 43.3, 43.2, 43.1, 43. , 42.9, 42.8, 42.7,
           42.6, 42.5, 42.4, 42.3, 42.2, 42.1, 42. , 41.9, 41.8, 41.7, 41.6, 41.5,
           41.4])

  • start_datetime
    (time)
    <U25
    '2016-01-01T00:00:00+00:00' ... ...

    array(['2016-01-01T00:00:00+00:00', '2016-02-01T00:00:00+00:00',
           '2016-03-01T00:00:00+00:00', '2016-04-01T00:00:00+00:00',
           '2016-05-01T00:00:00+00:00', '2016-06-01T00:00:00+00:00',
           '2016-07-01T00:00:00+00:00', '2016-08-01T00:00:00+00:00',
           '2016-09-01T00:00:00+00:00', '2016-10-01T00:00:00+00:00',
           '2016-11-01T00:00:00+00:00', '2016-12-01T00:00:00+00:00',
           '2017-01-01T00:00:00+00:00', '2017-02-01T00:00:00+00:00',
           '2017-03-01T00:00:00+00:00', '2017-04-01T00:00:00+00:00',
           '2017-05-01T00:00:00+00:00', '2017-06-01T00:00:00+00:00',
           '2017-07-01T00:00:00+00:00', '2017-08-01T00:00:00+00:00',
           '2017-09-01T00:00:00+00:00', '2017-10-01T00:00:00+00:00',
           '2017-11-01T00:00:00+00:00', '2017-12-01T00:00:00+00:00',
           '2018-01-01T00:00:00+00:00', '2018-02-01T00:00:00+00:00',
           '2018-03-01T00:00:00+00:00', '2018-04-01T00:00:00+00:00',
           '2018-05-01T00:00:00+00:00', '2018-06-01T00:00:00+00:00',
           '2018-07-01T00:00:00+00:00', '2018-08-01T00:00:00+00:00',
           '2018-09-01T00:00:00+00:00', '2018-10-01T00:00:00+00:00',
           '2018-11-01T00:00:00+00:00', '2018-12-01T00:00:00+00:00',
           '2019-01-01T00:00:00+00:00', '2019-02-01T00:00:00+00:00',
           '2019-03-01T00:00:00+00:00', '2019-04-01T00:00:00+00:00',
    ...
           '2020-07-01T00:00:00+00:00', '2020-08-01T00:00:00+00:00',
           '2020-09-01T00:00:00+00:00', '2020-10-01T00:00:00+00:00',
           '2020-11-01T00:00:00+00:00', '2020-12-01T00:00:00+00:00',
           '2021-01-01T00:00:00+00:00', '2021-02-01T00:00:00+00:00',
           '2021-03-01T00:00:00+00:00', '2021-04-01T00:00:00+00:00',
           '2021-05-01T00:00:00+00:00', '2021-06-01T00:00:00+00:00',
           '2021-07-01T00:00:00+00:00', '2021-08-01T00:00:00+00:00',
           '2021-09-01T00:00:00+00:00', '2021-10-01T00:00:00+00:00',
           '2021-11-01T00:00:00+00:00', '2021-12-01T00:00:00+00:00',
           '2022-01-01T00:00:00+00:00', '2022-02-01T00:00:00+00:00',
           '2022-03-01T00:00:00+00:00', '2022-04-01T00:00:00+00:00',
           '2022-05-01T00:00:00+00:00', '2022-06-01T00:00:00+00:00',
           '2022-07-01T00:00:00+00:00', '2022-08-01T00:00:00+00:00',
           '2022-09-01T00:00:00+00:00', '2022-10-01T00:00:00+00:00',
           '2022-11-01T00:00:00+00:00', '2022-12-01T00:00:00+00:00',
           '2023-01-01T00:00:00+00:00', '2023-02-01T00:00:00+00:00',
           '2023-03-01T00:00:00+00:00', '2023-04-01T00:00:00+00:00',
           '2023-05-01T00:00:00+00:00', '2023-06-01T00:00:00+00:00',
           '2023-07-01T00:00:00+00:00', '2023-08-01T00:00:00+00:00',
           '2023-09-01T00:00:00+00:00'], dtype='<U25')

  • end_datetime
    (time)
    <U25
    '2016-01-31T00:00:00+00:00' ... ...

    array(['2016-01-31T00:00:00+00:00', '2016-02-29T00:00:00+00:00',
           '2016-03-31T00:00:00+00:00', '2016-04-30T00:00:00+00:00',
           '2016-05-31T00:00:00+00:00', '2016-06-30T00:00:00+00:00',
           '2016-07-31T00:00:00+00:00', '2016-08-31T00:00:00+00:00',
           '2016-09-30T00:00:00+00:00', '2016-10-31T00:00:00+00:00',
           '2016-11-30T00:00:00+00:00', '2016-12-31T00:00:00+00:00',
           '2017-01-31T00:00:00+00:00', '2017-02-28T00:00:00+00:00',
           '2017-03-31T00:00:00+00:00', '2017-04-30T00:00:00+00:00',
           '2017-05-31T00:00:00+00:00', '2017-06-30T00:00:00+00:00',
           '2017-07-31T00:00:00+00:00', '2017-08-31T00:00:00+00:00',
           '2017-09-30T00:00:00+00:00', '2017-10-31T00:00:00+00:00',
           '2017-11-30T00:00:00+00:00', '2017-12-31T00:00:00+00:00',
           '2018-01-31T00:00:00+00:00', '2018-02-28T00:00:00+00:00',
           '2018-03-31T00:00:00+00:00', '2018-04-30T00:00:00+00:00',
           '2018-05-31T00:00:00+00:00', '2018-06-30T00:00:00+00:00',
           '2018-07-31T00:00:00+00:00', '2018-08-31T00:00:00+00:00',
           '2018-09-30T00:00:00+00:00', '2018-10-31T00:00:00+00:00',
           '2018-11-30T00:00:00+00:00', '2018-12-31T00:00:00+00:00',
           '2019-01-31T00:00:00+00:00', '2019-02-28T00:00:00+00:00',
           '2019-03-31T00:00:00+00:00', '2019-04-30T00:00:00+00:00',
    ...
           '2020-07-31T00:00:00+00:00', '2020-08-31T00:00:00+00:00',
           '2020-09-30T00:00:00+00:00', '2020-10-31T00:00:00+00:00',
           '2020-11-30T00:00:00+00:00', '2020-12-31T00:00:00+00:00',
           '2021-01-31T00:00:00+00:00', '2021-02-28T00:00:00+00:00',
           '2021-03-31T00:00:00+00:00', '2021-04-30T00:00:00+00:00',
           '2021-05-31T00:00:00+00:00', '2021-06-30T00:00:00+00:00',
           '2021-07-31T00:00:00+00:00', '2021-08-31T00:00:00+00:00',
           '2021-09-30T00:00:00+00:00', '2021-10-31T00:00:00+00:00',
           '2021-11-30T00:00:00+00:00', '2021-12-31T00:00:00+00:00',
           '2022-01-31T00:00:00+00:00', '2022-02-28T00:00:00+00:00',
           '2022-03-31T00:00:00+00:00', '2022-04-30T00:00:00+00:00',
           '2022-05-31T00:00:00+00:00', '2022-06-30T00:00:00+00:00',
           '2022-07-31T00:00:00+00:00', '2022-08-31T00:00:00+00:00',
           '2022-09-30T00:00:00+00:00', '2022-10-31T00:00:00+00:00',
           '2022-11-30T00:00:00+00:00', '2022-12-31T00:00:00+00:00',
           '2023-01-31T00:00:00+00:00', '2023-02-28T00:00:00+00:00',
           '2023-03-31T00:00:00+00:00', '2023-04-30T00:00:00+00:00',
           '2023-05-31T00:00:00+00:00', '2023-06-30T00:00:00+00:00',
           '2023-07-31T00:00:00+00:00', '2023-08-31T00:00:00+00:00',
           '2023-09-30T00:00:00+00:00'], dtype='<U25')

  • proj:bbox
    ()
    object
    {90.0, 180.0, -90.0, -180.0}

    array({90.0, 180.0, -90.0, -180.0}, dtype=object)

  • proj:projjson
    ()
    object
    {'id': {'code': 4326, 'authority...

    array({'id': {'code': 4326, 'authority': 'EPSG'}, 'name': 'WGS 84', 'type': 'GeographicCRS', 'datum': {'name': 'World Geodetic System 1984', 'type': 'GeodeticReferenceFrame', 'ellipsoid': {'name': 'WGS 84', 'semi_major_axis': 6378137, 'inverse_flattening': 298.257223563}}, '$schema': 'https://proj.org/schemas/v0.4/projjson.schema.json', 'coordinate_system': {'axis': [{'name': 'Geodetic latitude', 'unit': 'degree', 'direction': 'north', 'abbreviation': 'Lat'}, {'name': 'Geodetic longitude', 'unit': 'degree', 'direction': 'east', 'abbreviation': 'Lon'}], 'subtype': 'ellipsoidal'}},
          dtype=object)

  • proj:shape
    ()
    object
    {1800, 3600}

    array({1800, 3600}, dtype=object)

  • proj:transform
    ()
    object
    {0.1, 0.0, 1.0, -0.1, -180.0, 90.0}

    array({0.1, 0.0, 1.0, -0.1, -180.0, 90.0}, dtype=object)

  • proj:wkt2
    ()
    <U277
    'GEOGCS["WGS 84",DATUM["WGS_1984...

    array('GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]',
          dtype='<U277')

  • proj:geometry
    ()
    object
    {'type': 'Polygon', 'coordinates...

    array({'type': 'Polygon', 'coordinates': [[[-180.0, -90.0], [180.0, -90.0], [180.0, 90.0], [-180.0, 90.0], [-180.0, -90.0]]]},
          dtype=object)

  • description
    ()
    <U47
    'Cloud optimized default layer t...

    array('Cloud optimized default layer to display on map', dtype='<U47')

  • title
    ()
    <U17
    'Default COG Layer'

    array('Default COG Layer', dtype='<U17')

  • proj:epsg
    ()
    int64
    4326

    array(4326)

  • epsg
    ()
    int64
    4326

    array(4326)

  • time
    (time)
    <U25
    '2016-01-01T00:00:00+00:00' ... ...

    array(['2016-01-01T00:00:00+00:00', '2016-02-01T00:00:00+00:00',
           '2016-03-01T00:00:00+00:00', '2016-04-01T00:00:00+00:00',
           '2016-05-01T00:00:00+00:00', '2016-06-01T00:00:00+00:00',
           '2016-07-01T00:00:00+00:00', '2016-08-01T00:00:00+00:00',
           '2016-09-01T00:00:00+00:00', '2016-10-01T00:00:00+00:00',
           '2016-11-01T00:00:00+00:00', '2016-12-01T00:00:00+00:00',
           '2017-01-01T00:00:00+00:00', '2017-02-01T00:00:00+00:00',
           '2017-03-01T00:00:00+00:00', '2017-04-01T00:00:00+00:00',
           '2017-05-01T00:00:00+00:00', '2017-06-01T00:00:00+00:00',
           '2017-07-01T00:00:00+00:00', '2017-08-01T00:00:00+00:00',
           '2017-09-01T00:00:00+00:00', '2017-10-01T00:00:00+00:00',
           '2017-11-01T00:00:00+00:00', '2017-12-01T00:00:00+00:00',
           '2018-01-01T00:00:00+00:00', '2018-02-01T00:00:00+00:00',
           '2018-03-01T00:00:00+00:00', '2018-04-01T00:00:00+00:00',
           '2018-05-01T00:00:00+00:00', '2018-06-01T00:00:00+00:00',
           '2018-07-01T00:00:00+00:00', '2018-08-01T00:00:00+00:00',
           '2018-09-01T00:00:00+00:00', '2018-10-01T00:00:00+00:00',
           '2018-11-01T00:00:00+00:00', '2018-12-01T00:00:00+00:00',
           '2019-01-01T00:00:00+00:00', '2019-02-01T00:00:00+00:00',
           '2019-03-01T00:00:00+00:00', '2019-04-01T00:00:00+00:00',
           '2019-05-01T00:00:00+00:00', '2019-06-01T00:00:00+00:00',
           '2019-07-01T00:00:00+00:00', '2019-08-01T00:00:00+00:00',
           '2019-09-01T00:00:00+00:00', '2019-10-01T00:00:00+00:00',
           '2019-11-01T00:00:00+00:00', '2019-12-01T00:00:00+00:00',
           '2020-01-01T00:00:00+00:00', '2020-02-01T00:00:00+00:00',
           '2020-03-01T00:00:00+00:00', '2020-04-01T00:00:00+00:00',
           '2020-05-01T00:00:00+00:00', '2020-06-01T00:00:00+00:00',
           '2020-07-01T00:00:00+00:00', '2020-08-01T00:00:00+00:00',
           '2020-09-01T00:00:00+00:00', '2020-10-01T00:00:00+00:00',
           '2020-11-01T00:00:00+00:00', '2020-12-01T00:00:00+00:00',
           '2021-01-01T00:00:00+00:00', '2021-02-01T00:00:00+00:00',
           '2021-03-01T00:00:00+00:00', '2021-04-01T00:00:00+00:00',
           '2021-05-01T00:00:00+00:00', '2021-06-01T00:00:00+00:00',
           '2021-07-01T00:00:00+00:00', '2021-08-01T00:00:00+00:00',
           '2021-09-01T00:00:00+00:00', '2021-10-01T00:00:00+00:00',
           '2021-11-01T00:00:00+00:00', '2021-12-01T00:00:00+00:00',
           '2022-01-01T00:00:00+00:00', '2022-02-01T00:00:00+00:00',
           '2022-03-01T00:00:00+00:00', '2022-04-01T00:00:00+00:00',
           '2022-05-01T00:00:00+00:00', '2022-06-01T00:00:00+00:00',
           '2022-07-01T00:00:00+00:00', '2022-08-01T00:00:00+00:00',
           '2022-09-01T00:00:00+00:00', '2022-10-01T00:00:00+00:00',
           '2022-11-01T00:00:00+00:00', '2022-12-01T00:00:00+00:00',
           '2023-01-01T00:00:00+00:00', '2023-02-01T00:00:00+00:00',
           '2023-03-01T00:00:00+00:00', '2023-04-01T00:00:00+00:00',
           '2023-05-01T00:00:00+00:00', '2023-06-01T00:00:00+00:00',
           '2023-07-01T00:00:00+00:00', '2023-08-01T00:00:00+00:00',
           '2023-09-01T00:00:00+00:00'], dtype='<U25')

  • 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 :

    -4.7499999999999885 0.09999999999999992 0.0 51.05 0.0 -0.10000000000000002

    array(0)

• Indexes: (3)
  • x
    PandasIndex

    PandasIndex(Index([ -4.699999999999989,  -4.599999999999994,                -4.5,
            -4.399999999999977,  -4.299999999999983,  -4.199999999999989,
            -4.099999999999994,                -4.0, -3.8999999999999773,
            -3.799999999999983,
           ...
             8.600000000000023,   8.700000000000017,   8.800000000000011,
             8.900000000000006,                 9.0,   9.100000000000023,
             9.200000000000017,   9.300000000000011,   9.400000000000006,
                           9.5],
          dtype='float64', name='x', length=143))

  • y
    PandasIndex

    PandasIndex(Index([              51.0,               50.9,               50.8,
           50.699999999999996, 50.599999999999994,               50.5,
                         50.4,               50.3, 50.199999999999996,
           50.099999999999994,               50.0,               49.9,
                         49.8, 49.699999999999996, 49.599999999999994,
                         49.5,               49.4,               49.3,
           49.199999999999996, 49.099999999999994,               49.0,
                         48.9,               48.8, 48.699999999999996,
           48.599999999999994,               48.5,               48.4,
                         48.3, 48.199999999999996, 48.099999999999994,
                         48.0,               47.9,               47.8,
           47.699999999999996, 47.599999999999994,               47.5,
                         47.4,               47.3, 47.199999999999996,
           47.099999999999994,               47.0,               46.9,
                         46.8, 46.699999999999996, 46.599999999999994,
                         46.5,               46.4,               46.3,
           46.199999999999996, 46.099999999999994,               46.0,
                         45.9,               45.8, 45.699999999999996,
           45.599999999999994,               45.5,               45.4,
                         45.3, 45.199999999999996, 45.099999999999994,
                         45.0,               44.9,               44.8,
           44.699999999999996, 44.599999999999994,               44.5,
                         44.4,               44.3, 44.199999999999996,
           44.099999999999994,               44.0,               43.9,
                         43.8, 43.699999999999996, 43.599999999999994,
                         43.5,               43.4,               43.3,
           43.199999999999996, 43.099999999999994,               43.0,
                         42.9,               42.8, 42.699999999999996,
           42.599999999999994,               42.5,               42.4,
                         42.3, 42.199999999999996, 42.099999999999994,
                         42.0,               41.9,               41.8,
           41.699999999999996, 41.599999999999994,               41.5,
                         41.4],
          dtype='float64', name='y'))

  • time
    PandasIndex

    PandasIndex(Index(['2016-01-01T00:00:00+00:00', '2016-02-01T00:00:00+00:00',
           '2016-03-01T00:00:00+00:00', '2016-04-01T00:00:00+00:00',
           '2016-05-01T00:00:00+00:00', '2016-06-01T00:00:00+00:00',
           '2016-07-01T00:00:00+00:00', '2016-08-01T00:00:00+00:00',
           '2016-09-01T00:00:00+00:00', '2016-10-01T00:00:00+00:00',
           '2016-11-01T00:00:00+00:00', '2016-12-01T00:00:00+00:00',
           '2017-01-01T00:00:00+00:00', '2017-02-01T00:00:00+00:00',
           '2017-03-01T00:00:00+00:00', '2017-04-01T00:00:00+00:00',
           '2017-05-01T00:00:00+00:00', '2017-06-01T00:00:00+00:00',
           '2017-07-01T00:00:00+00:00', '2017-08-01T00:00:00+00:00',
           '2017-09-01T00:00:00+00:00', '2017-10-01T00:00:00+00:00',
           '2017-11-01T00:00:00+00:00', '2017-12-01T00:00:00+00:00',
           '2018-01-01T00:00:00+00:00', '2018-02-01T00:00:00+00:00',
           '2018-03-01T00:00:00+00:00', '2018-04-01T00:00:00+00:00',
           '2018-05-01T00:00:00+00:00', '2018-06-01T00:00:00+00:00',
           '2018-07-01T00:00:00+00:00', '2018-08-01T00:00:00+00:00',
           '2018-09-01T00:00:00+00:00', '2018-10-01T00:00:00+00:00',
           '2018-11-01T00:00:00+00:00', '2018-12-01T00:00:00+00:00',
           '2019-01-01T00:00:00+00:00', '2019-02-01T00:00:00+00:00',
           '2019-03-01T00:00:00+00:00', '2019-04-01T00:00:00+00:00',
           '2019-05-01T00:00:00+00:00', '2019-06-01T00:00:00+00:00',
           '2019-07-01T00:00:00+00:00', '2019-08-01T00:00:00+00:00',
           '2019-09-01T00:00:00+00:00', '2019-10-01T00:00:00+00:00',
           '2019-11-01T00:00:00+00:00', '2019-12-01T00:00:00+00:00',
           '2020-01-01T00:00:00+00:00', '2020-02-01T00:00:00+00:00',
           '2020-03-01T00:00:00+00:00', '2020-04-01T00:00:00+00:00',
           '2020-05-01T00:00:00+00:00', '2020-06-01T00:00:00+00:00',
           '2020-07-01T00:00:00+00:00', '2020-08-01T00:00:00+00:00',
           '2020-09-01T00:00:00+00:00', '2020-10-01T00:00:00+00:00',
           '2020-11-01T00:00:00+00:00', '2020-12-01T00:00:00+00:00',
           '2021-01-01T00:00:00+00:00', '2021-02-01T00:00:00+00:00',
           '2021-03-01T00:00:00+00:00', '2021-04-01T00:00:00+00:00',
           '2021-05-01T00:00:00+00:00', '2021-06-01T00:00:00+00:00',
           '2021-07-01T00:00:00+00:00', '2021-08-01T00:00:00+00:00',
           '2021-09-01T00:00:00+00:00', '2021-10-01T00:00:00+00:00',
           '2021-11-01T00:00:00+00:00', '2021-12-01T00:00:00+00:00',
           '2022-01-01T00:00:00+00:00', '2022-02-01T00:00:00+00:00',
           '2022-03-01T00:00:00+00:00', '2022-04-01T00:00:00+00:00',
           '2022-05-01T00:00:00+00:00', '2022-06-01T00:00:00+00:00',
           '2022-07-01T00:00:00+00:00', '2022-08-01T00:00:00+00:00',
           '2022-09-01T00:00:00+00:00', '2022-10-01T00:00:00+00:00',
           '2022-11-01T00:00:00+00:00', '2022-12-01T00:00:00+00:00',
           '2023-01-01T00:00:00+00:00', '2023-02-01T00:00:00+00:00',
           '2023-03-01T00:00:00+00:00', '2023-04-01T00:00:00+00:00',
           '2023-05-01T00:00:00+00:00', '2023-06-01T00:00:00+00:00',
           '2023-07-01T00:00:00+00:00', '2023-08-01T00:00:00+00:00',
           '2023-09-01T00:00:00+00:00'],
          dtype='object', name='time'))

• Attributes: (2)

  spec :

  RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), resolutions_xy=(0.1, 0.1))

  resolution :

  0.1

Compute and plot

So far we have just been setting up a calculation lazily in Dask. Now we can trigger computation using .compute().

%%time

image_stack = subset.compute()

CPU times: user 2.96 s, sys: 658 ms, total: 3.62 s
Wall time: 8.35 s

# get the 2% and 98% percentiles for min and max bounds of color
vmin, vmax = image_stack.quantile(0.02).item(), image_stack.quantile(0.98).item()

image_stack.hvplot(
    groupby="time",
    tiles=True,
    colorbar=False,
    clim=(vmin, vmax),
    cmap="viridis",
    alpha=0.8,
    frame_height=512,
    widget_location="bottom",
)