From d2e3ff8b8e72623d037187f2d5624d1ef98f2972 Mon Sep 17 00:00:00 2001 From: Ian McGinnis <67600557+ian-noaa@users.noreply.github.com> Date: Mon, 30 Sep 2024 14:10:49 -0600 Subject: [PATCH 1/3] Add sample input data to git LFS If anyone wants to do some software archaeology or revive the project, hopefully these data will be useful. --- .gitattributes | 2 ++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170000.HRRR.nc4.gz | 3 +++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170100.HRRR.nc4.gz | 3 +++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170200.HRRR.nc4.gz | 3 +++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170300.HRRR.nc4.gz | 3 +++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170400.HRRR.nc4.gz | 3 +++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170500.HRRR.nc4.gz | 3 +++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170600.HRRR.nc4.gz | 3 +++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170700.HRRR.nc4.gz | 3 +++ .../WCOSS/CONUS/REALTIME/diag_ps_anl.202409170800.HRRR.nc4.gz | 3 +++ 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...001760e535744f958328694b4b0a68b3-0.parquet | 3 + data_samples/app_data/rtma-vis.sql | Bin 0 -> 230805 bytes ...g_zarr_inventory.2023-04-24T15:00.full.txt | 112 ++++++++++++++++++ .../s3_diag_zarr_inventory.overview.txt | 16 +++ 7 files changed, 243 insertions(+) create mode 100644 data_samples/README.md create mode 100644 data_samples/app_data/RTMA_HRRR_WCOSS_CONUS_REALTIME/t/loop=anl/0019cd0014f94635aad5b82c4c4b7cb4-0.parquet create mode 100644 data_samples/app_data/RTMA_HRRR_WCOSS_CONUS_REALTIME/t/loop=ges/001760e535744f958328694b4b0a68b3-0.parquet create mode 100644 data_samples/app_data/rtma-vis.sql create mode 100644 data_samples/app_data/s3_diag_zarr_inventory.2023-04-24T15:00.full.txt create mode 100644 data_samples/app_data/s3_diag_zarr_inventory.overview.txt diff --git a/.prettierignore b/.prettierignore index 0dbe12f1..05d464b3 100644 --- a/.prettierignore +++ b/.prettierignore @@ -1,6 +1,9 @@ tmp/ .github/ +# Ignore the data_samples +data_samples/ + # FIXME: Maybe we should lint/format the k8s files? kubernetes/ diff --git a/data_samples/README.md b/data_samples/README.md new file mode 100644 index 00000000..69bdecdc --- /dev/null +++ b/data_samples/README.md @@ -0,0 +1,106 @@ +# Data Sample + +## Input Data + +The input data directory mimics the structure of our input S3 bucket. The application was designed to consume NetCDF file output from [Gridpoint Statistical Interpoliation (GSI) diagnostic files](https://web.archive.org/web/20240521023824/https://dtcenter.org/sites/default/files/community-code/gsi/docs/users-guide/html_v3.7/gsi_ch3.html#gsi-analysis-result-files-in-run-directory). + +## App Data + +This directory contains outlines of the Zarr data structure, a dump from our SQL database, and the outlines of the Parquet data structure. + +- `app_data/s3_diag_zarr_inventory.*.txt` - a few files showing the bucket layout of the Zarr array in S3. One focus is on the general group structure, the other file is focused on data for a particular day. The full array was to big to preserve and it was difficult to extract a subset. +- `app_data/*.sql` - the SQL DB dump +- `app_data/HRRR_RTMA_.../...` - the parquet store with sample data + +## Parquet Data Structure + +We attempted to use Parquet tables to get around some limitations we were finding with Zarr arrays. The jury is still out if a tabular or array form is better for accessing the data. There's a chance our Zarr array was just too large (we only had one for all data) and we could have benefitted by creating numerous Zarr arrays with consolidated metadata. + +```python +>>> import pyarrow.parquet as pq +>>> import pandas as pd +>>> parquet_table = pq.read_table('RTMA_HRRR_WCOSS_CONUS_REALTIME/t/loop=anl/0004ee89f24c4f4784f76d1384e23588-0.parquet') +>>> df = parquet_table.to_pandas() +>>> print(df) +nobs observation forecast_unadjusted forecast_adjusted obs_minus_forecast_unadjusted obs_minus_forecast_adjusted latitude longitude is_used initialization_time +0 226.350006 225.855835 225.855835 0.494174 0.494174 32.733002 -125.932999 False 2024-03-07 09:00:00 +1 225.949997 226.027313 226.027313 -0.077320 -0.077320 32.910000 -125.223007 False 2024-03-07 09:00:00 +2 226.149994 225.890976 225.890976 0.259025 0.259025 32.813000 -125.639999 False 2024-03-07 09:00:00 +3 226.350006 225.801147 225.801147 0.548863 0.548863 32.715000 -126.057999 False 2024-03-07 09:00:00 +4 226.550003 226.019058 226.019058 0.530942 0.530942 32.615002 -126.477005 False 2024-03-07 09:00:00 +... ... ... ... ... ... ... ... ... ... +73958 273.750000 273.555115 273.586578 0.194870 0.163422 46.250832 -63.334167 True 2024-03-07 09:00:00 +73959 273.750000 273.594238 273.586578 0.155760 0.163422 46.250832 -63.334167 True 2024-03-07 09:00:00 +73960 272.549988 273.157928 273.146973 -0.607946 -0.596997 46.299500 -63.176331 True 2024-03-07 09:00:00 +73961 273.149994 273.147095 273.147003 0.002895 0.003003 46.299500 -63.176331 True 2024-03-07 09:00:00 +73962 273.149994 273.060333 273.147003 0.089654 0.003003 46.299500 -63.176331 True 2024-03-07 09:00:00 + +[73963 rows x 9 columns] +>>> parquet_table = pq.read_table('RTMA_HRRR_WCOSS_CONUS_REALTIME/t/loop=anl/0019cd0014f94635aad5b82c4c4b7cb4-0.parquet') +>>> df = parquet_table.to_pandas() +>>> print(df) +nobs observation forecast_unadjusted forecast_adjusted obs_minus_forecast_unadjusted obs_minus_forecast_adjusted latitude longitude is_used initialization_time +0 226.449997 226.298874 226.298874 0.151119 0.151119 31.202000 -123.207993 True 2023-11-13 20:00:00 +1 226.149994 226.220901 226.220901 -0.070908 -0.070908 31.150000 -123.460999 True 2023-11-13 20:00:00 +2 225.949997 226.098465 226.098465 -0.148462 -0.148462 31.118999 -123.718002 False 2023-11-13 20:00:00 +3 225.949997 226.062836 226.062836 -0.112842 -0.112842 31.118000 -123.742004 True 2023-11-13 20:00:00 +4 226.149994 225.935959 225.935959 0.214042 0.214042 31.098000 -124.001999 True 2023-11-13 20:00:00 +... ... ... ... ... ... ... ... ... ... +92779 275.350006 275.182159 275.144073 0.167840 0.205931 46.250832 -63.334167 True 2023-11-13 20:00:00 +92780 275.350006 275.256195 275.408539 0.093802 -0.058537 46.299500 -63.176331 False 2023-11-13 20:00:00 +92781 275.350006 275.271393 275.408539 0.078619 -0.058537 46.299500 -63.176331 False 2023-11-13 20:00:00 +92782 275.350006 275.271393 275.408539 0.078619 -0.058537 46.299500 -63.176331 True 2023-11-13 20:00:00 +92783 274.850006 275.256195 275.408539 -0.406198 -0.558537 46.299500 -63.176331 True 2023-11-13 20:00:00 +>>> parquet_table = pq.read_table('RTMA_HRRR_WCOSS_CONUS_REALTIME/t/loop=ges/001760e535744f958328694b4b0a68b3-0.parquet') +>>> pd = parquet_table.to_pandas() +>>> print(pd) +nobs observation forecast_unadjusted forecast_adjusted obs_minus_forecast_unadjusted obs_minus_forecast_adjusted latitude longitude is_used initialization_time +0 221.149994 220.868866 220.868866 0.281134 0.281134 35.980000 -125.253006 False 2024-04-23 14:00:00 +1 220.149994 220.890320 220.890320 -0.740329 -0.740329 36.037998 -125.147003 False 2024-04-23 14:00:00 +2 282.850006 282.224060 282.342255 0.625944 0.507754 46.143330 -131.089996 False 2024-04-23 14:00:00 +3 282.950012 282.224060 282.342255 0.725944 0.607754 46.143330 -131.089996 False 2024-04-23 14:00:00 +4 282.950012 282.224060 282.342255 0.725944 0.607754 46.143330 -131.089996 False 2024-04-23 14:00:00 +... ... ... ... ... ... ... ... ... ... +96622 278.750000 278.530273 279.071014 0.219722 -0.321001 46.250832 -63.334167 False 2024-04-23 14:00:00 +96623 279.250000 278.215424 278.905365 1.034572 0.344643 46.299500 -63.176331 False 2024-04-23 14:00:00 +96624 279.250000 278.215424 278.905365 1.034572 0.344643 46.299500 -63.176331 False 2024-04-23 14:00:00 +96625 279.250000 278.197266 278.905365 1.052734 0.344643 46.299500 -63.176331 False 2024-04-23 14:00:00 +96626 279.250000 278.215424 278.905365 1.034572 0.344643 46.299500 -63.176331 False 2024-04-23 14:00:00 + +[96627 rows x 9 columns] +``` + +## Zarr Data Structure + +To give an idea of the shape of the data in a Zarr array: + +```python +>>> import xarray +>>> import zarr +>>> ds = xarray.open_dataset("s3://osti-modeling-dev-rtma-vis-prod/diagnostics.zarr/RTMA/WCOSS/CONUS/HRRR/REALTIME/t/2024-09-18T00:00/anl/", engine="zarr") +>>> print(ds) + Size: 3MB +Dimensions: (nobs: 116004) +Coordinates: + is_used (nobs) bool 116kB ... + latitude (nobs) float32 464kB ... + longitude (nobs) float32 464kB ... +Dimensions without coordinates: nobs +Data variables: + forecast_adjusted (nobs) float32 464kB ... + forecast_unadjusted (nobs) float32 464kB ... + obs_minus_forecast_adjusted (nobs) float32 464kB ... + obs_minus_forecast_unadjusted (nobs) float32 464kB ... + observation (nobs) float32 464kB ... +Attributes: + background: HRRR + domain: CONUS + frequency: REALTIME + initialization_time: 2024-09-18T00:00 + loop: anl + model: RTMA + name: t + system: WCOSS +``` + +The "first guess" (ges) file has a similar structure. diff --git a/data_samples/app_data/RTMA_HRRR_WCOSS_CONUS_REALTIME/t/loop=anl/0019cd0014f94635aad5b82c4c4b7cb4-0.parquet b/data_samples/app_data/RTMA_HRRR_WCOSS_CONUS_REALTIME/t/loop=anl/0019cd0014f94635aad5b82c4c4b7cb4-0.parquet new file mode 100644 index 00000000..6ad22d87 --- /dev/null +++ b/data_samples/app_data/RTMA_HRRR_WCOSS_CONUS_REALTIME/t/loop=anl/0019cd0014f94635aad5b82c4c4b7cb4-0.parquet @@ -0,0 +1,3 @@ +version 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