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Added a new documentation page for faster GRIB aggregations #495
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Added a new page for faster aggregations
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updated the description
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Aggregation special cases | ||
============================= | ||
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As we have already seen in this `page <https://fsspec.github.io/kerchunk/test_example.html#multi-file-jsons>`_, | ||
that the main purpose of ``kerchunk`` it to generate references, to view whole archive | ||
of files like GRIB2, NetCDF etc. allowing us for direct access to the data. In | ||
this part of the documentation, we will see some other efficient ways of | ||
combining references. | ||
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GRIB Aggregations | ||
----------------- | ||
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This reference aggregation method of GRIB files, developed by **Camus Energy**, functions if | ||
accompanying ``.idx`` files are present. | ||
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**But this procedure has some certain restrictions:** | ||
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- GRIB files must paired with their ``.idx`` files | ||
- The ``.idx`` file must be of *text* type. | ||
- Only specialised for time-series data, where GRIB files | ||
have *identical* structure. | ||
- Aggregation only works for a specific **forecast horizon** files. | ||
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Utilizing this method can significantly reduce the time required to combine | ||
references, cutting it down to a fraction of the previous duration. The original | ||
idea was showcased in this `talk <https://discourse.pangeo.io/t/pangeo-showcase-optimizations-for-kerchunk-aggregation-and-zarr-i-o-at-scale-for-machine-learning/4074>`_. | ||
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*How is it faster* | ||
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The ``.idx`` file otherwise known as an *index* file contains the key | ||
metadata of the messages in the GRIB files. These metadata include `index`, `offset`, `datetime`, | ||
`variable` and `forecast time` for their respective messages stored in the files. | ||
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**It follows three step approach:** | ||
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1. Extract and persist metadata directly from a few arbitrary grib | ||
files for a given product such as HRRR SUBH, GEFS, GFS etc. | ||
2. Use the metadata mapping to build an index table of every grib | ||
message from the ``.idx`` files | ||
3. Combine the index data with the metadata to build any FMRC | ||
slice (Horizon, RunTime, ValidTime, BestAvailable) | ||
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.. tip:: | ||
To confirm the indexing of messages, see this `notebook <https://gist.github.com/Anu-Ra-g/efa01ad1c274c1bd1c14ee01666caa77>`_. | ||
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These metadata will be used to build an k_index for every GRIB message that we will be | ||
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indexing. Indexing process primarily involves the `pandas <https://pandas.pydata.org/>`_ library. | ||
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.. note:: | ||
The index in ``.idx`` file indexes the GRIB messages where as the ``k_index`` (kerchunk index) | ||
we build as part of this workflow index the variables in those messages. | ||
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.. list-table:: k_index for a single GRIB file | ||
:header-rows: 1 | ||
:widths: 5 10 15 10 20 15 10 20 20 30 10 10 10 | ||
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* - | ||
- varname | ||
- typeOfLevel | ||
- stepType | ||
- name | ||
- step | ||
- level | ||
- time | ||
- valid_time | ||
- uri | ||
- offset | ||
- length | ||
- inline_value | ||
* - 0 | ||
- gh | ||
- isobaricInhPa | ||
- instant | ||
- Geopotential height | ||
- 0 days 06:00:00 | ||
- 0.0 | ||
- 2017-01-01 06:00:00 | ||
- 2017-01-01 12:00:00 | ||
- s3://noaa-gefs-pds/gefs.20170101/06/gec00.t06z... | ||
- 0 | ||
- 47493 | ||
- None | ||
* - 1 | ||
- t | ||
- isobaricInhPa | ||
- instant | ||
- Temperature | ||
- 0 days 06:00:00 | ||
- 0.0 | ||
- 2017-01-01 06:00:00 | ||
- 2017-01-01 12:00:00 | ||
- s3://noaa-gefs-pds/gefs.20170101/06/gec00.t06z... | ||
- 47493 | ||
- 19438 | ||
- None | ||
* - 2 | ||
- r | ||
- isobaricInhPa | ||
- instant | ||
- Relative humidity | ||
- 0 days 06:00:00 | ||
- 0.0 | ||
- 2017-01-01 06:00:00 | ||
- 2017-01-01 12:00:00 | ||
- s3://noaa-gefs-pds/gefs.20170101/06/gec00.t06z... | ||
- 66931 | ||
- 10835 | ||
- None | ||
* - 3 | ||
- u | ||
- isobaricInhPa | ||
- instant | ||
- U component of wind | ||
- 0 days 06:00:00 | ||
- 0.0 | ||
- 2017-01-01 06:00:00 | ||
- 2017-01-01 12:00:00 | ||
- s3://noaa-gefs-pds/gefs.20170101/06/gec00.t06z... | ||
- 77766 | ||
- 22625 | ||
- None | ||
* - 4 | ||
- v | ||
- isobaricInhPa | ||
- instant | ||
- V component of wind | ||
- 0 days 06:00:00 | ||
- 0.0 | ||
- 2017-01-01 06:00:00 | ||
- 2017-01-01 12:00:00 | ||
- s3://noaa-gefs-pds/gefs.20170101/06/gec00.t06z... | ||
- 100391 | ||
- 20488 | ||
- None | ||
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*What now* | ||
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After creating the k_index as per the desired duration, we will use the ``DataTree`` model | ||
from the `xarray-datatree <https://xarray-datatree.readthedocs.io/en/latest/>`_ to view a | ||
part of the aggregation or the whole. Below is a tree model made from an aggregation of | ||
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GRIB files produced from **GEFS** model hosted in AWS S3 bucket. | ||
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.. code-block:: bash | ||
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DataTree('None', parent=None) | ||
├── DataTree('prmsl') | ||
│ │ Dimensions: () | ||
│ │ Data variables: | ||
│ │ *empty* | ||
│ │ Attributes: | ||
│ │ name: Pressure reduced to MSL | ||
│ └── DataTree('instant') | ||
│ │ Dimensions: () | ||
│ │ Data variables: | ||
│ │ *empty* | ||
│ │ Attributes: | ||
│ │ stepType: instant | ||
│ └── DataTree('meanSea') | ||
│ Dimensions: (latitude: 181, longitude: 360, time: 1, step: 1, | ||
│ model_horizons: 1, valid_times: 237) | ||
│ Coordinates: | ||
│ * latitude (latitude) float64 1kB 90.0 89.0 88.0 87.0 ... -88.0 -89.0 -90.0 | ||
│ * longitude (longitude) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0 | ||
│ meanSea float64 8B ... | ||
│ number (time, step) int64 8B ... | ||
│ step (model_horizons, valid_times) timedelta64[ns] 2kB ... | ||
│ time (model_horizons, valid_times) datetime64[ns] 2kB ... | ||
│ valid_time (model_horizons, valid_times) datetime64[ns] 2kB ... | ||
│ Dimensions without coordinates: model_horizons, valid_times | ||
│ Data variables: | ||
│ prmsl (model_horizons, valid_times, latitude, longitude) float64 124MB ... | ||
│ Attributes: | ||
│ typeOfLevel: meanSea | ||
└── DataTree('ulwrf') | ||
│ Dimensions: () | ||
│ Data variables: | ||
│ *empty* | ||
│ Attributes: | ||
│ name: Upward long-wave radiation flux | ||
└── DataTree('avg') | ||
│ Dimensions: () | ||
│ Data variables: | ||
│ *empty* | ||
│ Attributes: | ||
│ stepType: avg | ||
└── DataTree('nominalTop') | ||
Dimensions: (latitude: 181, longitude: 360, time: 1, step: 1, | ||
model_horizons: 1, valid_times: 237) | ||
Coordinates: | ||
* latitude (latitude) float64 1kB 90.0 89.0 88.0 87.0 ... -88.0 -89.0 -90.0 | ||
* longitude (longitude) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0 | ||
nominalTop float64 8B ... | ||
number (time, step) int64 8B ... | ||
step (model_horizons, valid_times) timedelta64[ns] 2kB ... | ||
time (model_horizons, valid_times) datetime64[ns] 2kB ... | ||
valid_time (model_horizons, valid_times) datetime64[ns] 2kB ... | ||
Dimensions without coordinates: model_horizons, valid_times | ||
Data variables: | ||
ulwrf (model_horizons, valid_times, latitude, longitude) float64 124MB ... | ||
Attributes: | ||
typeOfLevel: nominalTop | ||
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.. tip:: | ||
For a full tutorial on this workflow, refer this `kerchunk cookbook <https://projectpythia.org/kerchunk-cookbook/README.html>`_ | ||
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in `Project Pythia <https://projectpythia.org/>`_. | ||
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.. raw:: html | ||
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<script data-goatcounter="https://kerchunk.goatcounter.com/count" | ||
async src="//gc.zgo.at/count.js"></script> |
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The reference index can be combined across many horizons but each horizon must be indexed separately.
Looking forward to seeing what you make of the reinflate api... there you can see all of the FMRC slices are supported against a collection of indexed data from many horizons, runtimes and valid times.
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ooh, what is this?
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The method to turn the k_index and the metadata back into a ref_spec you can use in zarr/xarray
https://github.com/asascience-open/nextgen-dmac/blob/main/grib_index_aggregation/dynamic_zarr_store.py#L198
I think @Anu-Ra-g is already working on adding it into kerchunk?
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Oh, in that case I suspect it works already, right @Anu-Ra-g : but you can only work on one set of horizons OR one set of timepoints, not both at once? Something like that.
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I think you can return an array with multiple dimensions.
I didn't have a strong use for this so struggled to do something general and practical.
For instance if you request by Horizon, you can provide multiple horizon axis and you dimensions should include 'horizon' and 'valid_time". Similarly you can request multiple runtimes and then your dimensions should include 'runtime' and 'step'.
Honestly not sure if this is helpful or over complicated.
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@martindurant I tried it out with one set of horizons with the original code. Actually, I'm still figuring out the reinflating part of the code, aggregation types and the new indexes.
I noticed that for reinflating can also work with a
grib_tree
model made from a single grib file.@emfdavid can you confirm this in this notebook that I made?