GeoTIFF

Generating virutal datasets from GeoTiff files

Overview

In this tutorial we will cover:

1. How to generate virtual datasets from GeoTIFFs.

2. Combining virtual datasets.

Prerequisites

Concepts	Importance	Notes
Basics of virtual Zarr stores	Required	Core
Multi-file virtual datasets with VirtualiZarr	Required	Core
Parallel virtual dataset creation with VirtualiZarr, Kerchunk, and Dask	Required	Core
Introduction to Xarray	Required	IO/Visualization

• Time to learn: 30 minutes

---

About the Dataset

The Finish Meterological Institute (FMI) Weather Radar Dataset is a collection of GeoTIFF files containing multiple radar specific variables, such as rainfall intensity, precipitation accumulation (in 1, 12 and 24 hour increments), radar reflectivity, radial velocity, rain classification and the cloud top height. It is available through the AWS public data portal and is updated frequently.

More details on this dataset can be found here.

import logging
from datetime import datetime

import dask
import fsspec
import rioxarray
import s3fs
import xarray as xr
from distributed import Client
from virtualizarr import open_virtual_dataset

Examining a Single GeoTIFF File

Before we use Kerchunk to create indices for multiple files, we can load a single GeoTiff file to examine it.

# URL pointing to a single GeoTIFF file
url = "s3://fmi-opendata-radar-geotiff/2023/07/01/FIN-ACRR-3067-1KM/202307010100_FIN-ACRR1H-3067-1KM.tif"

# Initialize a s3 filesystem
fs = s3fs.S3FileSystem(anon=True)

xds = rioxarray.open_rasterio(fs.open(url))

xds

<xarray.DataArray (band: 1, y: 1345, x: 850)> Size: 2MB
[1143250 values with dtype=uint16]
Coordinates:
  * band         (band) int64 8B 1
  * x            (x) float64 7kB -1.177e+05 -1.166e+05 ... 8.738e+05 8.75e+05
  * y            (y) float64 11kB 7.907e+06 7.906e+06 ... 6.337e+06 6.336e+06
    spatial_ref  int64 8B 0
Attributes:
    GDAL_METADATA:  <GDALMetadata>\n<Item name="Observation time" format="YYY...
    AREA_OR_POINT:  Area
    scale_factor:   1.0
    add_offset:     0.0

xarray.DataArray

• band: 1
• y: 1345
• x: 850

• ...

  [1143250 values with dtype=uint16]

• Coordinates: (4)
  • band
    (band)
    int64
    1

    array([1])

  • x
    (x)
    float64
    -1.177e+05 -1.166e+05 ... 8.75e+05

    array([-117746.71988 , -116577.426823, -115408.133766, ...,  872644.499264,
            873813.792321,  874983.085378])

  • y
    (y)
    float64
    7.907e+06 7.906e+06 ... 6.336e+06

    array([7907167.102182, 7905998.232018, 7904829.361854, ..., 6338543.342424,
           6337374.47226 , 6336205.602096])

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    PROJCS["ETRS89 / TM35FIN(E,N)",GEOGCS["ETRS89",DATUM["European_Terrestrial_Reference_System_1989",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6258"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4258"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",27],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","3067"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    inverse_flattening :

    298.257222101

    reference_ellipsoid_name :

    GRS 1980

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    ETRS89

    horizontal_datum_name :

    European Terrestrial Reference System 1989

    projected_crs_name :

    ETRS89 / TM35FIN(E,N)

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    27.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["ETRS89 / TM35FIN(E,N)",GEOGCS["ETRS89",DATUM["European_Terrestrial_Reference_System_1989",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6258"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4258"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",27],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","3067"]]

    GeoTransform :

    -118331.36640835612 1169.2930568410832 0.0 7907751.537263516 0.0 -1168.8701637541064

    array(0)

• Indexes: (3)
  • band
    PandasIndex

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

  • x
    PandasIndex

    PandasIndex(Index([-117746.71987993557, -116577.42682309449,  -115408.1337662534,
           -114238.84070941232, -113069.54765257123, -111900.25459573015,
           -110730.96153888907, -109561.66848204799,  -108392.3754252069,
           -107223.08236836582,
           ...
             864459.4478665743,   865628.7409234153,   866798.0339802564,
             867967.3270370975,   869136.6200939385,   870305.9131507797,
             871475.2062076208,   872644.4992644618,   873813.7923213029,
              874983.085378144],
          dtype='float64', name='x', length=850))

  • y
    PandasIndex

    PandasIndex(Index([7907167.1021816395,  7905998.232017885,  7904829.361854131,
            7903660.491690377,  7902491.621526623,  7901322.751362869,
            7900153.881199115,   7898985.01103536,  7897816.140871607,
            7896647.270707852,
           ...
            6346725.433569907,  6345556.563406154,  6344387.693242399,
            6343218.823078645,  6342049.952914891,  6340881.082751137,
            6339712.212587383,  6338543.342423629,  6337374.472259874,
             6336205.60209612],
          dtype='float64', name='y', length=1345))

• Attributes: (4)

  GDAL_METADATA :

  <GDALMetadata> <Item name="Observation time" format="YYYYMMDDhhmm">202307010100</Item> <Item name="Quantity" unit="mm">Precipitation accumulation</Item> <Item name="Gain">0.010000</Item> <Item name="Offset">0.000000</Item> <Item name="Nodata">65535</Item> <Item name="Undetect">0</Item> <Item name="Accumulation time" unit="h">1</Item> <Item name="Temporal type">Past</Item> <Item name="Z=AR^B relation parameter A">200.000000</Item> <Item name="Z=AR^B relation parameter B">1.600000</Item> </GDALMetadata>

  AREA_OR_POINT :

  Area

  scale_factor :

  1.0

  add_offset :

  0.0

xds.isel(band=0).where(xds < 2000).plot()

<matplotlib.collections.QuadMesh at 0x7fb938c334c0>

Create Input File List

Here we are using fsspec's glob functionality along with the * wildcard operator and some string slicing to grab a list of GeoTIFF files from a s3 fsspec filesystem.

# Initiate fsspec filesystems for reading
fs_read = fsspec.filesystem("s3", anon=True, skip_instance_cache=True)

files_paths = fs_read.glob(
    "s3://fmi-opendata-radar-geotiff/2023/01/01/FIN-ACRR-3067-1KM/*24H-3067-1KM.tif"
)
# Here we prepend the prefix 's3://', which points to AWS.
files_paths = sorted(["s3://" + f for f in files_paths])

Start a Dask Client

To parallelize the creation of our reference files, we will use Dask. For a detailed guide on how to use Dask and Kerchunk, see the Foundations notebook: Kerchunk and Dask.

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

Client

Client-d68bb06f-b0ee-11ef-8cdf-7c1e5222ecf8

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

Launch dashboard in JupyterLab

Cluster Info

LocalCluster

529afdab

Dashboard: http://127.0.0.1:8787/status	Workers: 8
Total threads: 8	Total memory: 15.61 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-38004c7e-36af-4c3a-90fa-8cdb7356d0ca

Comm: tcp://127.0.0.1:43443	Workers: 8
Dashboard: http://127.0.0.1:8787/status	Total threads: 8
Started: Just now	Total memory: 15.61 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:38243	Total threads: 1
Dashboard: http://127.0.0.1:40241/status	Memory: 1.95 GiB
Nanny: tcp://127.0.0.1:45345
Local directory: /tmp/dask-scratch-space/worker-0tnublw0

Worker: 1

Comm: tcp://127.0.0.1:36741	Total threads: 1
Dashboard: http://127.0.0.1:37629/status	Memory: 1.95 GiB
Nanny: tcp://127.0.0.1:34977
Local directory: /tmp/dask-scratch-space/worker-ahz0gtbr

Worker: 2

Comm: tcp://127.0.0.1:45587	Total threads: 1
Dashboard: http://127.0.0.1:34865/status	Memory: 1.95 GiB
Nanny: tcp://127.0.0.1:35621
Local directory: /tmp/dask-scratch-space/worker-imqszvku

Worker: 3

Comm: tcp://127.0.0.1:43483	Total threads: 1
Dashboard: http://127.0.0.1:46287/status	Memory: 1.95 GiB
Nanny: tcp://127.0.0.1:34117
Local directory: /tmp/dask-scratch-space/worker-jqoo9bst

Worker: 4

Comm: tcp://127.0.0.1:38623	Total threads: 1
Dashboard: http://127.0.0.1:34921/status	Memory: 1.95 GiB
Nanny: tcp://127.0.0.1:34439
Local directory: /tmp/dask-scratch-space/worker-9nn4gnve

Worker: 5

Comm: tcp://127.0.0.1:33911	Total threads: 1
Dashboard: http://127.0.0.1:43329/status	Memory: 1.95 GiB
Nanny: tcp://127.0.0.1:40101
Local directory: /tmp/dask-scratch-space/worker-rim13nh2

Worker: 6

Comm: tcp://127.0.0.1:43991	Total threads: 1
Dashboard: http://127.0.0.1:41865/status	Memory: 1.95 GiB
Nanny: tcp://127.0.0.1:37671
Local directory: /tmp/dask-scratch-space/worker-ocy8wg1v

Worker: 7

Comm: tcp://127.0.0.1:33143	Total threads: 1
Dashboard: http://127.0.0.1:33439/status	Memory: 1.95 GiB
Nanny: tcp://127.0.0.1:37755
Local directory: /tmp/dask-scratch-space/worker-_q1jxtoe

def generate_virtual_dataset(file):
    storage_options = dict(
        anon=True, default_fill_cache=False, default_cache_type="none"
    )
    vds = open_virtual_dataset(
        file,
        indexes={},
        filetype="tiff",
        reader_options={
            "remote_options": {"anon": True},
            "storage_options": storage_options,
        },
    )
    # Pre-process virtual datasets to extract time step information from the filename
    subst = file.split("/")[-1].split(".json")[0].split("_")[0]
    time_val = datetime.strptime(subst, "%Y%m%d%H%M")
    vds = vds.expand_dims(dim={"time": [time_val]})
    # Only include the raw data, not the overviews
    vds = vds[["0"]]
    return vds

# Generate Dask Delayed objects
tasks = [dask.delayed(generate_virtual_dataset)(file) for file in files_paths]

# Start parallel processing
import warnings

warnings.filterwarnings("ignore")
virtual_datasets = dask.compute(*tasks)

/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(

/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(

/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(

/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(

/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(
/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(

/home/runner/miniconda3/envs/kerchunk-cookbook/lib/python3.10/site-packages/virtualizarr/readers/tiff.py:41: UserWarning: storage_options have been dropped from reader_options as they are not supported by kerchunk.tiff.tiff_to_zarr
  warnings.warn(

Combine virtual datasets

combined_vds = xr.concat(virtual_datasets, dim="time")
combined_vds

<xarray.Dataset> Size: 53MB
Dimensions:  (time: 23, Y: 1345, X: 850)
Coordinates:
  * time     (time) datetime64[ns] 184B 2023-01-01T01:00:00 ... 2023-01-01T23...
Dimensions without coordinates: Y, X
Data variables:
    0        (time, Y, X) uint16 53MB ManifestArray<shape=(23, 1345, 850), dt...
Attributes: (12/15)
    multiscales:             [{'datasets': [{'path': '0'}, {'path': '1'}, {'p...
    GDAL_METADATA:           &lt;GDALMetadata&gt;\n&lt;Item name=&quot;Observ...
    KeyDirectoryVersion:     1
    KeyRevision:             1
    KeyRevisionMinor:        0
    GTModelTypeGeoKey:       1
    ...                      ...
    GeogAngularUnitsGeoKey:  9102
    GeogTOWGS84GeoKey:       [0.0, 0.0, 0.0]
    ProjectedCSTypeGeoKey:   3067
    ProjLinearUnitsGeoKey:   9001
    ModelPixelScale:         [1169.2930568410832, 1168.8701637541064, 0.0]
    ModelTiepoint:           [0.0, 0.0, 0.0, -118331.36640835612, 7907751.537...

xarray.Dataset

• Dimensions:
  • time: 23
  • Y: 1345
  • X: 850
• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2023-01-01T01:00:00 ... 2023-01-...

    array(['2023-01-01T01:00:00.000000000', '2023-01-01T02:00:00.000000000',
           '2023-01-01T03:00:00.000000000', '2023-01-01T04:00:00.000000000',
           '2023-01-01T05:00:00.000000000', '2023-01-01T06:00:00.000000000',
           '2023-01-01T07:00:00.000000000', '2023-01-01T08:00:00.000000000',
           '2023-01-01T09:00:00.000000000', '2023-01-01T10:00:00.000000000',
           '2023-01-01T11:00:00.000000000', '2023-01-01T12:00:00.000000000',
           '2023-01-01T13:00:00.000000000', '2023-01-01T14:00:00.000000000',
           '2023-01-01T15:00:00.000000000', '2023-01-01T16:00:00.000000000',
           '2023-01-01T17:00:00.000000000', '2023-01-01T18:00:00.000000000',
           '2023-01-01T19:00:00.000000000', '2023-01-01T20:00:00.000000000',
           '2023-01-01T21:00:00.000000000', '2023-01-01T22:00:00.000000000',
           '2023-01-01T23:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (1)
  • 0
    (time, Y, X)
    uint16
    ManifestArray<shape=(23, 1345, 8...

    ManifestArray<shape=(23, 1345, 850), dtype=uint16, chunks=(1, 256, 256)>

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 01:00:00', '2023-01-01 02:00:00',
                   '2023-01-01 03:00:00', '2023-01-01 04:00:00',
                   '2023-01-01 05:00:00', '2023-01-01 06:00:00',
                   '2023-01-01 07:00:00', '2023-01-01 08:00:00',
                   '2023-01-01 09:00:00', '2023-01-01 10:00:00',
                   '2023-01-01 11:00:00', '2023-01-01 12:00:00',
                   '2023-01-01 13:00:00', '2023-01-01 14:00:00',
                   '2023-01-01 15:00:00', '2023-01-01 16:00:00',
                   '2023-01-01 17:00:00', '2023-01-01 18:00:00',
                   '2023-01-01 19:00:00', '2023-01-01 20:00:00',
                   '2023-01-01 21:00:00', '2023-01-01 22:00:00',
                   '2023-01-01 23:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (15)

  multiscales :

  [{'datasets': [{'path': '0'}, {'path': '1'}, {'path': '2'}, {'path': '3'}, {'path': '4'}, {'path': '5'}], 'metadata': {}, 'name': '', 'version': '0.1'}]

  GDAL_METADATA :

  &lt;GDALMetadata&gt; &lt;Item name=&quot;Observation time&quot; format=&quot;YYYYMMDDhhmm&quot;&gt;202301010100&lt;/Item&gt; &lt;Item name=&quot;Quantity&quot; unit=&quot;mm&quot;&gt;Precipitation accumulation&lt;/Item&gt; &lt;Item name=&quot;Gain&quot;&gt;0.010000&lt;/Item&gt; &lt;Item name=&quot;Offset&quot;&gt;0.000000&lt;/Item&gt; &lt;Item name=&quot;Nodata&quot;&gt;65535&lt;/Item&gt; &lt;Item name=&quot;Undetect&quot;&gt;0&lt;/Item&gt; &lt;Item name=&quot;Accumulation time&quot; unit=&quot;h&quot;&gt;24&lt;/Item&gt; &lt;Item name=&quot;Temporal type&quot;&gt;Past&lt;/Item&gt; &lt;Item name=&quot;Z=AR^B relation parameter A&quot;&gt;200.000000&lt;/Item&gt; &lt;Item name=&quot;Z=AR^B relation parameter B&quot;&gt;1.600000&lt;/Item&gt; &lt;/GDALMetadata&gt;

  KeyDirectoryVersion :

  1

  KeyRevision :

  1

  KeyRevisionMinor :

  0

  GTModelTypeGeoKey :

  1

  GTRasterTypeGeoKey :

  1

  GTCitationGeoKey :

  ETRS89 / TM35FIN(E,N)

  GeogCitationGeoKey :

  ETRS89

  GeogAngularUnitsGeoKey :

  9102

  GeogTOWGS84GeoKey :

  [0.0, 0.0, 0.0]

  ProjectedCSTypeGeoKey :

  3067

  ProjLinearUnitsGeoKey :

  9001

  ModelPixelScale :

  [1169.2930568410832, 1168.8701637541064, 0.0]

  ModelTiepoint :

  [0.0, 0.0, 0.0, -118331.36640835612, 7907751.537263516, 0.0]

Shut down the Dask cluster

client.shutdown()