rave-dr6-tap-query

name	rave-dr6-tap-query
title	Query RAVE DR6 Catalog via TAP/pyvo
author	Hermes Assistant
version	1
description	Query the RAVE DR6 catalog hosted at https://www.rave-survey.org/tap/ using pyvo (TAPService.run_sync). Useful for accessing stellar parameters, Gaia cross-matches, distances, and Galactic coordinates (l, b). Includes galactic projection (xgal/ygal) and equirectangular (RA/Dec) plotting recipes.

Overview

When to Use

Query the RAVE DR6 catalog hosted at https://www.rave-survey.org/tap/ using pyvo (TAPService.run_sync). Useful for accessing stellar parameters, Gaia cross-matches, distances, and Galactic coordinates (l, b). Includes galactic projection (xgal/ygal) and equirectangular (RA/Dec) plotting recipes.

Overview

This skill contains a reusable operational workflow. Follow the existing task-specific steps and examples in the sections below.

Canonical Routing

This is a specialized or legacy example skill. For new work, start with astro-data-access-umbrella and route through:

• rave-dr6-data-access
• tap-pyvo-adql-access

Keep this skill for dataset-specific examples, but prefer the canonical skills for new implementations, live probes, REANA execution, and plotting/cache conventions.

Pitfalls

• Do not hardcode credentials, tokens, or personal secrets.
• Verify external service URLs, paths, and permissions before making changes.
• Keep generated outputs reproducible and record input assumptions.

Verification

• Confirm required inputs and credentials are available.
• Run the smallest safe command or example before scaling up.
• Check produced files, API responses, or plots before reporting success.

Query the RAVE (RAdial Velocity Experiment) 6th Data Release via its Table Access Protocol (TAP) endpoint using the pyvo library. This avoids browser-based queries and lets you pull data directly into Python/pandas for analysis or plotting.

The RAVE TAP service is at https://www.rave-survey.org/tap/ and hosts ~30 tables including the main DR6 tables, Gaia DR2/EDR3 cross-matches, seismic data, and more.

Prerequisites

pip install --quiet pyvo pandas matplotlib seaborn

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
ravedr6.dr6_x_gaiadr2	Gaia DR2 cross-match
ravedr6.dr6_orbits	Orbital parameters
ravedr6.dr6_seismic	Seismic data
ravedr6.dr6_madera	Asteroseismic parameters

Steps

1. Connect to the TAP service

import pyvo, pandas as pd, numpy as np, warnings
warnings.filterwarnings('ignore')

tap = pyvo.dal.TAPService('https://www.rave-survey.org/tap/')

2. Run a synchronous query (including radial velocity)

query = """
SELECT TOP 100
    source_id, ra, dec, l, b, parallax, dr2_radial_velocity
FROM ravedr6.dr6_x_gaiaedr3
WHERE parallax > 0
ORDER BY parallax DESC
"""
result = tap.run_sync(query)
df = result.to_table().to_pandas()
# If radial velocity is missing, fall back to NaN
if 'dr2_radial_velocity' not in df.columns:
    df['dr2_radial_velocity'] = np.nan
rv_col = 'dr2_radial_velocity'

3. Save locally as Parquet (requires pyarrow)

# Ensure Parquet engine is available
import pandas as pd
# `pip install pyarrow` may be required beforehand
df.to_parquet('rave_dr6_closest100.parquet', index=False)

4. Galactic projection (xgal vs ygal) – colour by RV

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))
scatter = ax.scatter(df['xgal'], df['ygal'], c=df[rv_col], 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')
cb = fig.colorbar(scatter, ax=ax, shrink=0.8)
cb.set_label('Radial velocity [km/s]')
fig.tight_layout()
fig.savefig('rave_dr6_xgal_ygal.png', dpi=180)

5. Plot RA vs Dec – colour by RV

fig2, ax2 = plt.subplots(figsize=(12,5))
scatter2 = ax2.scatter(df['ra'], df['dec'], c=df[rv_col], cmap='plasma', s=60, edgecolors='white', linewidths=0.4, alpha=0.9)
ax2.set_xlabel('RA [deg]')
ax2.set_ylabel('Dec [deg]')
ax2.set_title(f'RA vs Dec — {len(df)} RAVE DR6 Stars')
cb2 = fig2.colorbar(scatter2, ax=ax2, shrink=0.8)
cb2.set_label('Radial velocity [km/s]')
fig2.tight_layout()
fig2.savefig('rave_dr6_ra_dec.png', dpi=180)

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 all available tables

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

3. Check 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 bare submit_job() (those either don't exist or use async and fail with 400 errors). Note that l (Galactic longitude) and b (Galactic latitude) are also available in dr6_x_gaiaedr3:

query = \"\"\"
SELECT TOP 100
    source_id, ra, dec, l, b, parallax, 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_closest100.parquet', index=False)

6. Galactic projection (xgal vs ygal)

The dr6_x_gaiaedr3 table also contains l (Galactic longitude) and b (Galactic latitude). Project onto the Galactic plane:

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)

Plot with the Sun at the origin:

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style('whitegrid')
fig, ax = plt.subplots(figsize=(8, 7))
scatter = ax.scatter(df['xgal'], df['ygal'], c=df['parallax'], cmap='plasma',
                     s=70, edgecolors='white', linewidths=0.5, alpha=0.9)
# Sun at Galactic centre
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)
cb = fig.colorbar(scatter, ax=ax, shrink=0.8)
cb.set_label('Parallax [mas]', rotation=270, labelpad=12)
fig.tight_layout()
fig.savefig(, dpi=)

7. Plot RA vs Dec (equirectangular projection)

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style('whitegrid')
fig, ax = plt.subplots(figsize=(12, 5))
scatter = ax.scatter(df['ra'], df['dec'], c=df['parallax'], cmap='plasma',
                     s=60, edgecolors='white', linewidths=0.4, alpha=0.9)
ax.set_xlabel('RA [deg]')
ax.set_ylabel('Dec [deg]')
ax.set_title(f'RA vs Dec — {len(df)} RAVE DR6 Stars')
cb = fig.colorbar(scatter, ax=ax, shrink=0.8)
cb.set_label('Parallax [mas]', rotation=270, labelpad=12)
fig.tight_layout()
fig.savefig('rave_dr6_ra_dec.png', dpi=180)

Pitfalls & Tips

• run_sync() is the correct method — submit_job() uses async and returns 400 Bad Request on this server.
• query() does not exist on TAPService — always use run_sync() for synchronous queries.
• The ravedr6.dr6_x_gaiaedr3 table is the best source for distances (parallax), coordinates (ra, dec), AND Galactic coordinates (l, b) in one table.
• Filter parallax > 0 to exclude negative parallax entries (which correspond to stars with poorly constrained distances).
• If you need only a few columns, select them explicitly to reduce data transfer.
• The TAP service may time out on very large queries; use TOP N or WHERE clauses to limit results.
• pyvo may not be pre-installed — install with pip install --quiet pyvo before use.
• For the 100 closest stars, parallax > 0 AND ORDER BY parallax DESC gives the nearest stars first (parallax in mas → distance in pc ≈ 1000/parallax).
• When using curl to hit the TAP sync endpoint, the response is a VOTable XML, not a plain CSV. Use a POST request with appropriate parameters, or prefer the pyvo library which handles parsing automatically. If you must use curl, you’ll need to parse the VOTable (e.g., with astropy.io.votable).
• run_sync() is the correct method — submit_job() uses async and returns 400 Bad Request on this server.
• query() does not exist on TAPService — always use run_sync() for synchronous queries.
• The ravedr6.dr6_x_gaiaedr3 table is the best source for distances (parallax), coordinates (ra, dec), AND Galactic coordinates (l, b) in one table.
• Filter parallax > 0 to exclude negative parallax entries (which correspond to stars with poorly constrained distances).
• If you need only a few columns, select them explicitly to reduce data transfer.
• The TAP service may time out on very large queries; use TOP N or WHERE clauses to limit results.
• pyvo may not be pre-installed — install with pip install --quiet pyvo before use.