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astro-catalog-plotting-cache Use when turning astronomy catalog data into reproducible cached products and publication-ready plots, especially CMDs, RA/Dec maps, Galactic projections, hexbin density plots, Datashader outputs, and provenance-backed figure deliverables.
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Zip 다운로드 다운로드 중... name astro-catalog-plotting-cache description Use when turning astronomy catalog data into reproducible cached products and publication-ready plots, especially CMDs, RA/Dec maps, Galactic projections, hexbin density plots, Datashader outputs, and provenance-backed figure deliverables. version 1.0.0 author AstroAgent / AIP license MIT metadata {"hermes":{"tags":["astronomy","plotting","cmd","hexbin","datashader","cache","provenance"],"related_skills":["astro-data-access-umbrella","s3-parquet-astro-access","tap-pyvo-adql-access","data-visualization-umbrella"]}}
Astro Catalog Plotting and Cache
Overview
This is the canonical plotting layer for astronomy catalog outputs. Use it after data access has produced a dataframe, Parquet cache, or small preview table. It encodes the preferred publication-ready conventions: white backgrounds, hexbin/density for large samples, explicit NaN filtering, magnitude-axis inversion, and provenance files.
When to Use
Use this skill when the user asks to:
plot a color-magnitude diagram (CMD);
visualize Gaia/RAVE/SHBoost/StarHorse catalog data;
make RA/Dec or Galactic projections;
handle dense scatter plots without overplotting;
cache data before plotting;
produce publication-ready PNGs from catalog tables.
Core Rules
Filter before plotting. Drop NaNs and invalid numeric values in plotted columns.Use density, not scatter, for large stellar catalogs. Prefer hexbin or Datashader.White background. Publication-style figures should be readable in papers and slides.Invert magnitude axes. Lower magnitude is brighter.Save provenance. Figure without source/filter metadata is not reproducible.Read back outputs. Verify the PNG and cache exist before reporting success.
CMD Hexbin Template import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg" )
import matplotlib.pyplot as plt
xcol = "bprp0"
ycol = "mg0"
df = pd.read_parquet("data.parquet" , columns=[xcol, ycol])
mask = np.isfinite(df[xcol]) & np.isfinite(df[ycol])
plot = df.loc[mask, [xcol, ycol]]
plt.style.use("default" )
fig, ax = plt.subplots(figsize=(7.2 , 8.0 ), facecolor="white" )
hb = ax.hexbin(
plot[xcol], plot[ycol],
gridsize=320 ,
mincnt=1 ,
bins="log" ,
cmap="viridis" ,
linewidths=0 ,
)
ax.invert_yaxis()
ax.set_xlabel("BP - RP" )
ax.set_ylabel(r"$M_G$" )
ax.set_title("Colour-Magnitude Diagram" )
cb = fig.colorbar(hb, ax=ax)
cb.set_label("log10(count)" )
fig.tight_layout()
fig.savefig("cmd.png" , dpi=300 , bbox_inches="tight" )
RA/Dec Plot fig, ax = plt.subplots(figsize=(10 , 5 ), facecolor="white" )
sc = ax.scatter(df["ra" ], df["dec" ], c=df.get("parallax" ), s=8 , cmap="plasma" , alpha=0.8 )
ax.set_xlabel("RA [deg]" )
ax.set_ylabel("Dec [deg]" )
fig.colorbar(sc, ax=ax, label="Parallax [mas]" )
fig.tight_layout()
fig.savefig("radec.png" , dpi=300 , bbox_inches="tight" )
For large samples use Datashader or hexbin in projected coordinates.
Galactic XY Projection import numpy as np
l = np.deg2rad(df["l" ].to_numpy())
b = np.deg2rad(df["b" ].to_numpy())
x = np.cos(b) * np.cos(l)
y = np.cos(b) * np.sin(l)
fig, ax = plt.subplots(figsize=(7 , 7 ), facecolor="white" )
hb = ax.hexbin(x, y, gridsize=250 , mincnt=1 , bins="log" , cmap="magma" )
ax.scatter([0 ], [0 ], c="gold" , edgecolors="orange" , s=160 , label="Sun" )
ax.set_aspect("equal" )
ax.set_xlabel("cos(b) cos(l)" )
ax.set_ylabel("cos(b) sin(l)" )
ax.legend(frameon=False )
fig.colorbar(hb, ax=ax, label="log10(count)" )
fig.tight_layout()
fig.savefig("galactic_xy.png" , dpi=300 , bbox_inches="tight" )
Datashader for Very Large Data Use Datashader when millions of points would make hexbin slow or memory-heavy. Keep the data pipeline Dask-backed as long as possible.
import dask.dataframe as dd
import datashader as ds
import datashader.transfer_functions as tf
ddf = dd.read_parquet("data/*.parquet" , columns=["bprp0" , "mg0" ])
ddf = ddf.dropna()
canvas = ds.Canvas(plot_width=1200 , plot_height=1000 ,
x_range=(-1 , 5 ), y_range=(15 , -5 ))
agg = canvas.points(ddf, "bprp0" , "mg0" )
img = tf.shade(agg, cmap=["#f7fbff" , "#6baed6" , "#08306b" ], how="log" )
img.to_pil().save("cmd_datashader.png" )
Provenance figure: cmd.png
source_cache: data.parquet
columns: [bprp0 , mg0 ]
filters:
finite: [bprp0 , mg0 ]
plot_type: hexbin
style: white_background
created_utc: "YYYY-MM-DDTHH:MM:SSZ"
REANA Use If the plot is the requested deliverable, package the plotting script and run it with reana-operator task:
python reana-workflows/reana-operator/scripts/reana_operator.py task \
--project /tmp/catalog-plot \
--task "make CMD plot" \
--script plot_cmd.py \
--output cmd.png \
--output provenance.yaml \
--environment-profile astro-ml \
--run --timestamp
Common Pitfalls
Scatter for huge catalogs. Dense scatter hides structure and is slow.NaNs in plotted columns. Matplotlib may silently omit or distort results.Forgetting magnitude inversion. CMDs should have brighter stars upward.Dark backgrounds for publication plots. Use white unless the user requests talk visuals.Missing labels/units. Axes need physical meaning.No cache readback. Confirm files exist and can be loaded.Datashader API drift. Check the installed Datashader version for exact methods.
Verification Checklist 