Xarray + rio-tiler

In [ ]:

from urllib.parse import urlparse

import matplotlib.pyplot as plt
import xarray

import obstore
from zarr.storage import ObjectStore

from rio_tiler.io.xarray import XarrayReader

from urllib.parse import urlparse import matplotlib.pyplot as plt import xarray import obstore from zarr.storage import ObjectStore from rio_tiler.io.xarray import XarrayReader

MUR SST¶

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src_path = "https://mur-sst.s3.us-west-2.amazonaws.com/zarr-v1"

parsed = urlparse(src_path)
store = obstore.store.from_url(src_path, skip_signature=True)
zarr_store = ObjectStore(store=store, read_only=True)
ds = xarray.open_dataset(
    zarr_store,
    decode_times=True,
    decode_coords="all",
    consolidated=True,
    engine="zarr",
)

ds

src_path = "https://mur-sst.s3.us-west-2.amazonaws.com/zarr-v1" parsed = urlparse(src_path) store = obstore.store.from_url(src_path, skip_signature=True) zarr_store = ObjectStore(store=store, read_only=True) ds = xarray.open_dataset( zarr_store, decode_times=True, decode_coords="all", consolidated=True, engine="zarr", ) ds

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da = ds["analysed_sst"]
da

da = ds["analysed_sst"] da

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da = ds["analysed_sst"]

# Make sure we have a valid CRS
crs = da.rio.crs or "epsg:4326"
da.rio.write_crs(crs, inplace=True)

# Select the first time stamp
da = da.isel(time=0)

with XarrayReader(da) as dst:
    print(dst.info())
    print(dst.minzoom, dst.maxzoom)

da = ds["analysed_sst"] # Make sure we have a valid CRS crs = da.rio.crs or "epsg:4326" da.rio.write_crs(crs, inplace=True) # Select the first time stamp da = da.isel(time=0) with XarrayReader(da) as dst: print(dst.info()) print(dst.minzoom, dst.maxzoom)

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with XarrayReader(da) as dst:
    img = dst.tile(31, 22, 6)

plt.imshow(img.data_as_image())

with XarrayReader(da) as dst: img = dst.tile(31, 22, 6) plt.imshow(img.data_as_image())

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with XarrayReader(da) as dst:
    img = dst.part(
        [-5, 45, 0.0, 49],
        max_size=1024,
    )

plt.imshow(img.data_as_image())

with XarrayReader(da) as dst: img = dst.part( [-5, 45, 0.0, 49], max_size=1024, ) plt.imshow(img.data_as_image())

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geojson = {
    "type": "Feature",
    "properties": {},
    "geometry": {
        "coordinates": [
            [
                [-4.262517145703015, 49.58852771128011],
                [-6.121754821536115, 48.07366436953052],
                [-5.679508966604999, 47.26534294147169],
                [-2.890652452854056, 46.8101748147248],
                [-1.8527423498805433, 47.28371740880311],
                [-1.6541805026549241, 49.26565692911578],
                [-3.0440846882388257, 49.565118360747164],
                [-4.262517145703015, 49.58852771128011],
            ]
        ],
        "type": "Polygon",
    },
}

with XarrayReader(da) as dst:
    img = dst.feature(
        geojson,
        max_size=1024,
    )

plt.imshow(img.data_as_image())

geojson = { "type": "Feature", "properties": {}, "geometry": { "coordinates": [ [ [-4.262517145703015, 49.58852771128011], [-6.121754821536115, 48.07366436953052], [-5.679508966604999, 47.26534294147169], [-2.890652452854056, 46.8101748147248], [-1.8527423498805433, 47.28371740880311], [-1.6541805026549241, 49.26565692911578], [-3.0440846882388257, 49.565118360747164], [-4.262517145703015, 49.58852771128011], ] ], "type": "Polygon", }, } with XarrayReader(da) as dst: img = dst.feature( geojson, max_size=1024, ) plt.imshow(img.data_as_image())

NetCDF¶

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import fsspec

filesystem = fsspec.filesystem("https")


fp = filesystem.open(
    "https://dap.ceda.ac.uk/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRCDR/L3S/0.01/v2.00/monthly/2020/11/ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-20201101000000-fv2.00.nc"
)
ds = xarray.open_dataset(
    fp,
    engine="h5netcdf",
    decode_coords="all",
)
ds

import fsspec filesystem = fsspec.filesystem("https") fp = filesystem.open( "https://dap.ceda.ac.uk/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRCDR/L3S/0.01/v2.00/monthly/2020/11/ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-20201101000000-fv2.00.nc" ) ds = xarray.open_dataset( fp, engine="h5netcdf", decode_coords="all", ) ds

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da = ds["lst"]

# # Make sure we have a valid CRS
crs = da.rio.crs or "epsg:4326"
da.rio.write_crs(crs, inplace=True)

# Select the first time stamp
# da = da.isel(time=0)
da

da = ds["lst"] # # Make sure we have a valid CRS crs = da.rio.crs or "epsg:4326" da.rio.write_crs(crs, inplace=True) # Select the first time stamp # da = da.isel(time=0) da

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with XarrayReader(da) as dst:
    print(dst.info())
    print(dst.minzoom, dst.maxzoom)

with XarrayReader(da) as dst: print(dst.info()) print(dst.minzoom, dst.maxzoom)

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with XarrayReader(da) as dst:
    img = dst.tile(31, 22, 6)

plt.imshow(img.data_as_image())

with XarrayReader(da) as dst: img = dst.tile(31, 22, 6) plt.imshow(img.data_as_image())

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