rave-dr6

name	rave-dr6
description	Query the RAVE DR6 catalog at https://www.rave-survey.org/tap/ using pyvo (TAPService.run_sync). Access stellar parameters, Gaia cross-matches, distances, and Galactic coordinates (l, b). Includes galactic and equirectangular projection plotting recipes.
version	1.1.0
author	AstroAgent / AIP
license	MIT
prerequisites	{"python":["pyvo","pandas","pyarrow","matplotlib","seaborn","scipy"]}
metadata	{"hermes":{"tags":["astronomy","rave-dr6","tap","stellar-parameters","galactic-coordinates","pyvo"],"category":"astronomy","related_skills":["rave-dr6-nearest-100-plot","rave-dr6-public-talk-visualizations","gaia-dr3-tap-query","shboost24-cmd","starhorse-access"]}}

RAVE DR6 TAP Query

When to Use

Use this skill when querying the RAVE (RAdial Velocity Experiment) 6th Data Release for stellar parameters, distances (parallax), or Galactic coordinates. Ideal for: generic TAP exploration, finding the nearest 100 stars, plotting RA/Dec sky distributions, galactic plane projections, or cross-matching with Gaia.

Skill taxonomy

Use the public RAVE skills with this split:

• rave-dr6 — canonical query + table/column discovery skill
• rave-dr6-nearest-100-plot — focused workflow for the nearest-100 PNG plots
• rave-dr6-public-talk-visualizations — polished public-talk-ready visual outputs

Start with rave-dr6 for general querying and branch to the more specialized skill when the task is specifically plotting or presentation oriented.

Procedure

Dependencies (pyvo, pandas, pyarrow, matplotlib, seaborn) are declared in prerequisites.python and pre-installed in this skill's dedicated venv. Run all Python commands below with {{SKILL_PYTHON}} — do NOT pip install anything.

1. Connect to the TAP service

import pyvo, pandas as pd, warnings
warnings.filterwarnings('ignore')
tap = pyvo.dal.TAPService("https://www.rave-survey.org/tap/")

2. List available tables

for t in tap.tables:
    print(t.name)

3. Inspect columns of a table

t = tap.tables['ravedr6.dr6_x_gaiaedr3']
for c in t.columns:
    print(c.name)

4. Run a synchronous query

Use run_sync() — not query() or submit_job() (those use async and return 400 errors).

query = """
SELECT TOP 100
    rave_obs_id, ra, dec, l, b, parallax, parallax_error,
    phot_g_mean_mag, bp_rp
FROM ravedr6.dr6_x_gaiaedr3
WHERE parallax > 0
ORDER BY parallax DESC
"""
result = tap.run_sync(query)
df = result.to_table().to_pandas()

5. Save locally as Parquet

df.to_parquet('rave_dr6_subset.parquet', index=False)

6. Galactic projection (xgal vs ygal)

Project onto the Galactic plane with the Sun at origin:

import numpy as np
l_rad = np.radians(df['l'].values)
b_rad = np.radians(df['b'].values)
df['xgal'] = np.cos(b_rad) * np.cos(l_rad)
df['ygal'] = np.cos(b_rad) * np.sin(l_rad)

import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt, seaborn as sns
sns.set_style('whitegrid')
fig, ax = plt.subplots(figsize=(8, 7))
ax.scatter(df['xgal'], df['ygal'], c=df['parallax'], cmap='plasma',
           s=70, edgecolors='white', linewidths=0.5, alpha=0.9)
ax.scatter(0, 0, c='gold', s=300, marker='o', edgecolors='orange', linewidths=2, zorder=10)
ax.annotate('Sun', xy=(0, 0), xytext=(0.07, 0.07), fontsize=12, color='darkorange',
            fontweight='bold', arrowprops=dict(arrowstyle='->', color='darkorange', lw=1.5))
ax.set_xlabel('xgal = cos(b) cos(l)')
ax.set_ylabel('ygal = cos(b) sin(l)')
ax.set_aspect('equal')
ax.set_xlim(-1.1, 1.1); ax.set_ylim(-1.1, 1.1)
fig.colorbar(ax.collections[0], ax=ax, shrink=0.8).set_label('Parallax [mas]', rotation=270, labelpad=12)
fig.tight_layout()
fig.savefig('rave_dr6_xgal_ygal.png', dpi=)

7. Plot RA vs Dec (equirectangular)

fig, ax = plt.subplots(figsize=(14, 5))
scatter = ax.scatter(df['ra'], df['dec'], c=df['parallax'], cmap='plasma',
                     s=100, edgecolors='white', linewidths=0.6, alpha=0.9)
ax.set_xlabel('RA [deg]', fontsize=13)
ax.set_ylabel('Dec [deg]', fontsize=13)
ax.set_title('RA vs Dec — RAVE DR6 Stars', fontsize=14, fontweight='bold')
fig.colorbar(scatter, ax=ax, shrink=0.8).set_label('Parallax [mas]', rotation=270, labelpad=14)
fig.tight_layout()
fig.savefig('rave_dr6_ra_dec.png', dpi=180)

Key Tables

Table	Description
ravedr6.dr6_sparv	Master file + classification + obs diagnostics
ravedr6.dr6_x_gaiaedr3	Gaia EDR3 cross-match — has ra, dec, parallax, phot_g_mean_mag, bp_rp, l, b
ravedr6.dr6_x_gaiadr2	Gaia DR2 cross-match
ravedr6.dr6_orbits	Orbital parameters
ravedr6.dr6_seismic	Seismic data
ravedr6.dr6_madera	Asteroseismic parameters

Pitfalls

• run_sync() is the only correct method — query() does not exist on TAPService, submit_job() uses async and fails with 400.
• Filter parallax > 0 — negative parallax entries correspond to stars with poorly constrained distances.
• Use TOP N or WHERE clauses to limit results — the TAP service times out on very large queries.
• Distance estimation: parallax in mas → distance in pc ≈ 1000/parallax.
• For nearest stars: parallax > 0 ORDER BY parallax DESC.
• Keep the query in the foreground. Run it in a single foreground script — never as a background process (terminal background=true / notify_on_complete). RAVE TAP is sync-only (run_sync) anyway; just bound the result with TOP N / a WHERE clause.
• Anchor selections to literature values. When isolating a known object's members (e.g. an open cluster or stellar stream), set your parallax / proper-motion / distance cuts from its published values — not from whatever maximizes the star count.

Verification

• Query returns 100 rows for the TOP 100 example.
• Parquet file is saved with non-zero size.
• RA covers 0–360°, Dec covers southern sky (RAVE observes from Siding Spring, Australia).
• Galactic projection places the Sun at origin and shows asymmetric stellar distribution.