Input data QA notebooks - Githubissues

linea-it / pz-compute

Pipeline to compute photo-zs using public codes for large volumes of data using the Brazilian's LSST IDAC infrastructure.

https://www.linea.org.br/idac-2

MIT License

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Input data QA notebooks #3

Closed gschwend closed 1 week ago

gschwend commented 1 year ago

Add quality assessment notebooks to characterize PZ Compute input data.

Content suggestion for the notebook:

Local properties - 1 sample file randomy selected from `/lustre/t1/cl/lsst/dp0.2` (temporary: `/home/julia/photoz/qa-pz-compute/skinny_tables`): (as an exercise, using DES DR2 data)

[x] Summary statistics of selected columns (e.g. using Pandas Dataframe method)
[x] Spatial distribution (density)
[x] 6-band magnitude distributions
[x] 6-band magnitude error
[x] 6-band magnitude versus error (scatter or density)
[x] CMDs
[x] color-color diagrams
[x] redshift plots (in case of simulations)

Global properties: (using LSST DP0.2 to test)

(loop over the files, extract info as arrays, combine arrays to make global plots)

[x] Summary statistics of selected columns (e.g. using Pandas Dataframe method)
[x] Spatial distribution (density)
[x] 6-band magnitude distributions
[x] 6-band magnitude error
[x] 6-band magnitude versus error (scatter or density)
[x] CMDs
[x] color-color diagrams
[x] redshift plots (in case of simulations)

Region-dependent properties (maps):

TBD (on a new issue)

luigilcsilva commented 3 weeks ago

The first part of this issue is done (Local Properties). The second part (Global Properties) is still in progress. When I finish the "QA report LSST DP0.2 full #82" issue, this issue will also be finished.