diff --git a/pr-preview/pr-139/notebooks/quickstarts/downsample-zarr.html b/pr-preview/pr-139/notebooks/quickstarts/downsample-zarr.html index 43018611..1827cdfb 100644 --- a/pr-preview/pr-139/notebooks/quickstarts/downsample-zarr.html +++ b/pr-preview/pr-139/notebooks/quickstarts/downsample-zarr.html @@ -5120,7 +5120,7 @@

Reproject before plotting

Strategy 2: Coarsen spatial resolution of the data

Below, we coarsen the spatial resolution of the data by a factor of 4 in the x and 2 in the y. These values were chosen because they can be used with the exact boundary argument as the dimensions size is a multiple of these values.

-

You can also coarsen by datetime, using the same strategy as below but replacing easting_m and northing_m with datetime. If {datetime: n} is the value given to the dim argument, this would create a mean of the soil moisture average for n days.

+

You can also coarsen by datetime, using the same strategy as below but replacing easting_m and northing_m with datetime. If {datetime: n} is the value given to the dim argument, this would create a mean of the soil moisture average for n days.

Once the data has been coarsened, again it is reprojected for map visualization and then visualized.

coarsened = soil_moisture.coarsen(dim={"easting_m": 4, "northing_m": 2}).mean()
diff --git a/pr-preview/pr-139/sitemap.xml b/pr-preview/pr-139/sitemap.xml
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     https://nasa-impact.github.io/veda-docs/index.html
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     https://nasa-impact.github.io/veda-docs/contributing/dashboard-configuration/dataset-configuration.html
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     https://nasa-impact.github.io/veda-docs/contributing/dataset-ingestion/index.html
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