rave-dr6-nearest-100-plot

name	rave-dr6-nearest-100-plot
description	Query the 100 nearest RAVE DR6 stars and generate two clear PNG plots: Galactic projection and RA/Dec scatter, with reproducible local parquet output.
version	1.0.0
author	AstroAgent / AIP
license	MIT
metadata	{"hermes":{"tags":["astronomy","rave-dr6","plotting","nearest-stars","pyvo","parquet"],"category":"astronomy","related_skills":["rave-dr6","rave-dr6-public-talk-visualizations","gaiadr3-aip-de-adql"]}}

RAVE DR6 Nearest 100 Plot

When to Use

Use this skill when the task is specifically to retrieve the nearest 100 RAVE DR6 stars and create straightforward scientific PNG plots rather than general TAP exploration.

Procedure

1. Query the nearest 100 stars

Use pyvo and the public RAVE TAP service:

import pyvo

service = pyvo.dal.TAPService('https://www.rave-survey.org/tap')
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 = service.run_sync(query)
df = result.to_table().to_pandas()

2. Save a reproducible local table

df.to_parquet('rave_dr6_nearest100.parquet', index=False)

3. Compute Galactic projection coordinates

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)

4. Plot Galactic projection

import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 7), dpi=180)
scatter = 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=250, marker='o', edgecolors='orange', linewidths=2, zorder=10)
ax.set_xlabel('xgal = cos(b) cos(l)')
ax.set_ylabel('ygal = cos(b) sin(l)')
ax.set_aspect('equal')
fig.colorbar(scatter, ax=ax, shrink=0.8).set_label('Parallax [mas]', rotation=270, labelpad=12)
fig.tight_layout()
fig.savefig('rave_dr6_nearest100_xgal_ygal.png', dpi=180)

5. Plot RA/Dec

fig, ax = plt.subplots(figsize=(14, 5), dpi=180)
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]')
ax.set_ylabel('Dec [deg]')
ax.set_title('RA vs Dec — 100 nearest RAVE DR6 stars')
fig.colorbar(scatter, ax=ax, shrink=0.8).set_label('Parallax [mas]', rotation=270, labelpad=14)
fig.tight_layout()
fig.savefig('rave_dr6_nearest100_ra_dec.png', dpi=180)

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
• astro-catalog-plotting-cache

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

Pitfalls

• Use run_sync() with pyvo; avoid old async/query patterns that fail on this TAP service.
• Filter on parallax > 0 before ordering by parallax.
• Keep the query lightweight with TOP 100.
• This skill is for clean scientific plots; if the user wants polished presentation graphics, switch to rave-dr6-public-talk-visualizations.

Verification

• Query returns 100 rows.
• rave_dr6_nearest100.parquet is written.
• Both PNG outputs exist and open correctly.
• Galactic projection has the Sun at the origin and RA/Dec covers the sky footprint.