Step 1)
Create a new Github repo. Name it: koop-provider-remote-geojson
Step 2)
| COPY ( | |
| WITH a AS ( | |
| SELECT h3_cell_to_parent(h3_string_to_h3(SUBSTR(id, 0, 17)), 2) h3_2, | |
| COUNT(*) num_recs | |
| FROM read_parquet('s3://overturemaps-us-west-2/release/2024-05-16-beta.0/theme=places/type=place/*.parquet', | |
| filename=true, | |
| hive_partitioning=1) | |
| GROUP BY 1 | |
| ) | |
| SELECT h3_cell_to_boundary_wkt(h3_2), |
| __doc__ = """ | |
| # Overview | |
| This example shows how to open High Resolution Rapid Refresh (HRRR) meteorology | |
| "surfaces." These surfaces are slices in vertical space to create single level | |
| maps of variables like temperature, wind speed components, pressure, etc. Each | |
| surface is opened using the Zarr archive described in Amazon's opendata registry | |
| described at https://registry.opendata.aws/noaa-hrrr-pds/. | |
| # Examples |
| -- Snap the points to their closest lines, found in the subquery below | |
| SELECT | |
| point_id, | |
| line_id, | |
| ST_LINE_INTERPOLATE_POINT(line_geom, | |
| ST_Line_Locate_Point(line_geom, point_geom)) AS snapped_points --Create the snapped points | |
| FROM | |
| --Subquery to find the closest line to each point (within a pre-defined raidus) | |
| ( |
| import gdal | |
| from osgeo import gdal_array | |
| import osr | |
| import numpy | |
| def get_dst_dataset(dst_img, cols, rows, layers, dtype, proj, gt): | |
| """ | |
| Create a GDAL data set in Cloud Optimized GeoTIFF (COG) format | |
| :param dst_img: Output filenane full path |
Step 1)
Create a new Github repo. Name it: koop-provider-remote-geojson
Step 2)
| worker_processes 1; | |
| events { | |
| worker_connections 1024; | |
| } | |
| http { | |
| include mime.types; | |
| default_type application/octet-stream; |