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Co-authored-by: Jaladh Singhal <[email protected]> Co-authored-by: Brigitta Sipőcz <[email protected]>
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tutorials/parquet-catalog-demos/euclid-hats-parquet.md

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@@ -53,7 +53,7 @@ especially for large-scale analyses and/or parallel processing.
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It does this by adapting the HEALPix order at which data is partitioned in a given catalog based
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on the on-sky density of the rows it contains.
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In other words, data from dense regions of sky will be partitioned at a higher order
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(i.e., higher resolution; smaller pixel size) than data in sparse regions.
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(i.e., higher resolution; more pixels/tiles with smaller area) than data in sparse regions.
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HATS-aware python packages are being developed to take full advantage of the partitioning.
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In this notebook, we will use the [hats](https://hats.readthedocs.io/) library to visualize the
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catalog and access the schema, and [lsdb](https://docs.lsdb.io/) to do a query for all likely stars.
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## Installs and imports
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```{important}
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We rely on ``hast``, ``lsdb``, ``numpy``, and ``pyerfa`` features that have been recently added, so please make sure you have the respective versions v0.5, v0.5, v2.0, and v2.0.1.3 or newer installed.
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```
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```{code-cell}
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# # Uncomment the next line to install dependencies if needed.
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# !pip install 'hats>=0.5' 'lsdb>=0.5' matplotlib 'numpy>=2.0' 'pyerfa>=2.0.1.3' s3fs
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hats.inspection.plot_pixels(euclid_hats)
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```
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## 3. CMD of stars in Euclid Q1
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## 3. CMD of ALL stars in Euclid Q1
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# Set up the query for likely stars.
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star_cuts = "FLUX_VIS_PSF > 0 & FLUX_Y_TEMPLFIT > 0 & FLUX_J_TEMPLFIT > 0 & FLUX_H_TEMPLFIT > 0 & POINT_LIKE_FLAG == 1"
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euclid_stars = euclid_lsdb.query(star_cuts)
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euclid_stars
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```
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```{code-cell}
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client.close()
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```
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## 4. Schema
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## 4. Inspecting MER Catalog's Parquet Schema
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