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release notes for v4.3.1
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# Release v4.3.x

2024-03-08

Version description of the v4.3.1 release of ICESat-2 SlideRule.

## New Features

* [eb1085d](https://github.com/ICESat2-SlideRule/sliderule/commit/eb1085da18a3cd63939f340f811ccadae1c22715) - GEDI APIs for L1B and L2A datasets now point to LP DAAC S3 bucket.

* GeoParquet output now supports ancillary fields

* Added support for staging GeoParquet files in public S3 bucket via setting the output asset to "sliderule-public".

* ICESat-2 APIs support filtering data by beam (e.g. gt1l, gt1r, gt2l, gt2r, gt3l, gt3r)

* The Python client `sliderule.toregion` function now supports shapely Polygons

* Added `region` to output of `atl06p` and `atl03sp` APIs; makes it easier to determine direction of travel for each measurement in addition to easily reconstructing which granule a measurement comes from

* [878a48b](https://github.com/ICESat2-SlideRule/sliderule/commit/878a48b4474e6a6a7b598e4abdddcceb8b438697) - Python client can now be configured to rethrow exceptions for applications that want to manage exceptions themselves

* SlideRule web APIs now support CORS as well as JavaScript clients that can only provide data payloads on POST requests

## Issues Resolved

* [24a6c79](https://github.com/ICESat2-SlideRule/sliderule/commit/24a6c798059a13bc81cd9feae09e96ab5a78461a) - fixed hanging issue when terminator is dropped due to queue being full

* [25640d1](https://github.com/ICESat2-SlideRule/sliderule/commit/25640d123e4b9f015edad683f141f3ecba45ec69) - fixed corner-case bug in ATL08 processing when no ancillary data is available.

* [599f4d7](https://github.com/ICESat2-SlideRule/sliderule/commit/599f4d778d726a12887b2a63af5fc59aec288026) - fixed memory leak with atl08 ancillary fields

* [407da7d](https://github.com/ICESat2-SlideRule/sliderule/commit/407da7d6bedf27d898bc0f5939321af6686b3005) - fixed bug in ABoVE classifier code that was causing out of bounds memory access

* [e94161a](https://github.com/ICESat2-SlideRule/sliderule/commit/e94161ae0506df39e66001341fe293e8dde1270a) - fixed bug in GeoParquet geometry code where only the first latitude,longitude of each batch was being used for the batch; it caused the data to stack up on top of eacho ther and look decimated

* [368](https://github.com/ICESat2-SlideRule/sliderule/issues/368) - fixed bug in PhoREAL implementation ancillary field interpolation

## Development Updates

* [e9ec583](https://github.com/ICESat2-SlideRule/sliderule/commit/e9ec583e0b136f66d429281bf6492dd12f1880dc) - reordered build and deploy action so that temporary token is still valid when uploading terraform

* [381](https://github.com/ICESat2-SlideRule/sliderule/pull/381) - static website move from ECS to CloudFront

* GeoParquet output no longer includes latitude and longitude columns that duplicate what is provided in the geometry column

* [e055bc6](https://github.com/ICESat2-SlideRule/sliderule/commit/e055bc6e419f13c0f95ab0ba60ada8897bfa2aa9) - meta data for each record (e.g. coordinates, index, etc) is now indicated in the definition of the record and no longer provided in each endpoint lua file

* [00ecdb3](https://github.com/ICESat2-SlideRule/sliderule/commit/00ecdb34d3cefca4ca35b232c149e32abac05567) - server.lua script waits to authenticate to DAACs until the IAM role credentials have been established; this prevents errors at startup when trying to fetch the netrc file

* [08b56f7](https://github.com/ICESat2-SlideRule/sliderule/commit/08b56f70880d9b86ace32f318805357a5d603193) - fixed disk usage metric display in Grafana

* [0b00277](https://github.com/ICESat2-SlideRule/sliderule/commit/0b00277df7c040cc4fe06abf7eadd4a665d0d76f) - fixed prometheus endpoint to return valid table

* [373](https://github.com/ICESat2-SlideRule/sliderule/issues/373) - removed `maxi` from all examples

## Getting This Release

[https://github.com/ICESat2-SlideRule/sliderule/releases/tag/v4.3.1](https://github.com/ICESat2-SlideRule/sliderule/releases/tag/v4.3.1)

## Benchmarks

```
atl06_aoi
output: 625445 x 16 elements
total: 26.377888 secs
gdf2poly: 0.000519 secs
toregion: 0.163608 secs
__parse_native: 21.453290 secs
todataframe: 0.406155 secs
merge: 0.000003 secs
flatten: 2.858293 secs
atl06p: 26.116402 secs
atl06_ancillary
output: 1180 x 17 elements
total: 2.853569 secs
gdf2poly: 0.000519 secs
toregion: 0.163608 secs
__parse_native: 0.733802 secs
todataframe: 0.006865 secs
merge: 0.016739 secs
flatten: 0.035448 secs
atl06p: 2.649313 secs
atl03_ancillary
output: 1180 x 17 elements
total: 2.746311 secs
gdf2poly: 0.000519 secs
toregion: 0.163608 secs
__parse_native: 0.159768 secs
todataframe: 0.007559 secs
merge: 0.017402 secs
flatten: 0.037174 secs
atl06p: 2.744864 secs
atl06_parquet
output: 1577 x 17 elements
total: 2.649226 secs
gdf2poly: 0.000519 secs
toregion: 0.163608 secs
__parse_native: 0.210186 secs
todataframe: 0.007559 secs
merge: 0.017402 secs
flatten: 0.041889 secs
atl06p: 2.648749 secs
atl03_parquet
output: 22833 x 22 elements
total: 1.534764 secs
gdf2poly: 0.000519 secs
toregion: 0.163608 secs
__parse_native: 0.011159 secs
todataframe: 0.007559 secs
merge: 0.017402 secs
flatten: 0.041889 secs
atl06p: 2.648749 secs
atl03sp: 1.507323 secs
atl06_sample_landsat
output: 914 x 20 elements
total: 6.381781 secs
gdf2poly: 0.000519 secs
toregion: 0.163608 secs
__parse_native: 0.373254 secs
todataframe: 0.008152 secs
merge: 0.015304 secs
flatten: 0.037495 secs
atl06p: 4.657887 secs
atl03sp: 1.507323 secs
atl06_sample_zonal_arcticdem
output: 1651 x 27 elements
total: 5.275135 secs
gdf2poly: 0.000451 secs
toregion: 0.006655 secs
__parse_native: 0.326858 secs
todataframe: 0.007310 secs
merge: 0.023985 secs
flatten: 0.050125 secs
atl06p: 5.266107 secs
atl03sp: 1.507323 secs
atl06_sample_nn_arcticdem
output: 1651 x 20 elements
total: 4.773236 secs
gdf2poly: 0.000421 secs
toregion: 0.006201 secs
__parse_native: 0.092055 secs
todataframe: 0.008839 secs
merge: 0.026527 secs
flatten: 0.051369 secs
atl06p: 4.764687 secs
atl03sp: 1.507323 secs
atl06_msample_nn_arcticdem
output: 1596050 x 20 elements
total: 125.249892 secs
gdf2poly: 0.000456 secs
toregion: 0.007406 secs
__parse_native: 89.351120 secs
todataframe: 1.128149 secs
merge: 15.596906 secs
flatten: 29.527919 secs
atl06p: 124.077317 secs
atl03sp: 1.507323 secs
atl06_no_sample_arcticdem
output: 1596050 x 16 elements
total: 64.487345 secs
gdf2poly: 0.000436 secs
toregion: 0.007185 secs
__parse_native: 53.242468 secs
todataframe: 0.967663 secs
merge: 0.000003 secs
flatten: 7.009740 secs
atl06p: 63.322487 secs
atl03sp: 1.507323 secs
atl03_rasterized_subset
output: 51968 x 22 elements
total: 3.796079 secs
gdf2poly: 0.000436 secs
toregion: 0.007185 secs
__parse_native: 1.657236 secs
todataframe: 0.038700 secs
merge: 0.000003 secs
flatten: 0.321405 secs
atl06p: 63.322487 secs
atl03sp: 3.282703 secs
atl03_polygon_subset
output: 50799 x 22 elements
total: 3.085772 secs
gdf2poly: 0.000436 secs
toregion: 0.007185 secs
__parse_native: 1.390404 secs
todataframe: 0.041605 secs
merge: 0.000003 secs
flatten: 0.349935 secs
atl06p: 63.322487 secs
atl03sp: 3.056956 secs
```

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