rave-dr6-3d-public-animation

name	rave-dr6-3d-public-animation
description	Generate a public‑talk‑ready 3‑D animation of the 100 nearest RAVE DR6 stars using matplotlib.
category	astronomy

Purpose

When to Use

Generate a public‑talk‑ready 3‑D animation of the 100 nearest RAVE DR6 stars using matplotlib.

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
• 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

• 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.

Create a smooth, dark‑background MP4 animation that visualises the spatial distribution of the 100 nearest stars from the RAVE DR6 catalog, with the Sun at the centre. The animation should be suitable for presentations, social media, or outreach.

Prerequisites

• matplotlib with ffmpeg writer (pip install matplotlib ffmpeg).
• pandas, numpy for data handling.
• astroquery for TAP access (pip install astroquery).
• Network access to the RAVE DR6 TAP service (https://rave-survey.org/tap).

WARNING: Do NOT use Manim for 3D star animations

Manim v0.20.1 is impractical for this use case:

• 3D rendering with Dot3D is extremely slow (~5+ minutes for 30s of output).
• APIs are broken: set_background_color() removed, lerp_color() removed, Vector() no longer works as a coordinate constructor.
• Use matplotlib 3D + FuncAnimation instead — same visual result, seconds vs minutes.

Workflow

1. Query RAVE DR6 (via Vizier VOTable — most reliable)

import pandas as pd

# Most reliable Vizier endpoint for RAVE DR6 nearest stars
url = "https://vizier.cds.unistra.fr/viz-bin/VOTable/-2/2/-1?_outAll=1&-c=185.34603190%2B33.19542000&-c.eq=J2000&-c.u=arcsec&-c.r=500&-c.geom=enc"
# Better: query the whole catalogue and filter by parallax

Alternative: query the RAVE DR6 × Gaia DR2 cross-match from Vizier:

from astroquery.vizier import Vizier

vizier = Vizier(columns=["*", "RADECODE"])
result = vizier.get_catalogs("I/358/gaiadr3")[0]  # or use TAP endpoint

Simpler approach — query TAP directly:

from astroquery.utils.tap.core import TapPlus

tap = TapPlus(url="https://rave-survey.org/tap")
query = """
SELECT source_id, ra, dec, l, b, parallax, pmra, pmdec,
       radial_velocity, phot_g_mean_mag, bp_rp
FROM gaiadr2.x gaiadr2 dr6_x_gaiadr2
WHERE parallax IS NOT NULL AND parallax > 0
ORDER BY parallax DESC
LIMIT 100
"""
# Note: RAVE TAP JOINs may return 500 errors; use cross-match table directly
result = tap.launch_job(query).get_results()
df = result.to_pandas()

2. Convert to Cartesian coordinates (parsecs)

import numpy as np

# distance = 1/parallax (mas to pc)
df['distance'] = 1000.0 / df['parallax']

# Convert spherical (l, b) to Cartesian (x, y, z) in parsecs
l_rad = np.deg2rad(df['l'].values)
b_rad = np.deg2rad(df['b'].values)
d = df['distance'].values

x = d * np.cos(b_rad) * np.cos(l_rad)  # cos(b)*cos(l)
y = d * np.cos(b_rad) * np.sin(l_rad)  # cos(b)*sin(l)
z = d * np.sin(b_rad)                  # sin(b)

3. Create the matplotlib 3D animation

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import matplotlib.colors as mcolors
import matplotlib.cm as cm

rv = df['radial_velocity'].values
rv_min, rv_max = rv.min(), rv.max()
norm = mcolors.Normalize(vmin=rv_min, vmax=rv_max)
colors = [cm.coolwarm(norm(r)) for r in rv]

fig = plt.figure(figsize=(10, 10), dpi=100)
fig.patch.set_facecolor('#0D1117')
ax = fig.add_subplot(111, projection='3d')
ax.set_facecolor('#0D1117')

# Plot stars
scatter = ax.scatter(x, y, z, c=colors, s=60, edgecolors='white',
                     linewidths=0.3, alpha=0.9)

# Sun at origin
ax.scatter([0], [0], [0], c='gold', s=200, marker='*',
           edgecolors='orange', linewidths=1.5, label='Sun')

# Labels and styling
ax.set_xlabel('cos(b)·cos(l)  [pc]', color='white', fontsize=10)
ax.set_ylabel('cos(b)·sin(l)  [pc]', color='white', fontsize=10)
ax.set_zlabel('sin(b)  [pc]', color='white', fontsize=10)
ax.set_title('100 Nearest RAVE DR6 Stars — 3D View',
             color=, fontsize=, pad=)
ax.tick_params(colors=, labelsize=)


 axis  [ax.xaxis, ax.yaxis, ax.zaxis]:
    axis.pane.fill = 
    axis.pane.set_edgecolor()


cbar = fig.colorbar(scatter, ax=ax, shrink=, pad=)
cbar.set_label(, color=, fontsize=)
cbar.ax.tick_params(colors=, labelsize=)


 ():
    ax.view_init(elev=, azim=frame)
     []

ani = FuncAnimation(fig, animate, frames=, interval=,
                    blit=, repeat=)
ani.save(, writer=, fps=)
plt.close()

4. Render parameters

• frames=360, interval=30 → 12s total, one full rotation
• dpi=100, figsize=(10,10) → 1000×1000 output, good quality-to-size ratio
• Adjust interval for faster/slower rotation
• Adjust elev for initial viewing angle (0 = equatorial, 90 = pole-on)

Verification

• Open the MP4; you should see a dark canvas with a golden star at the centre (the Sun) and a rotating 3D cloud of coloured points.
• Stars are coloured by radial velocity: blue (approaching, negative RV), white (~0 km/s), red (receding, positive RV).
• The animation runs ~12s, completing a full 360° rotation.
• File size typically 2–4 MB for 1000×1000 at 30fps.

Pitfalls & Tips

• TAP JOINs fail with 500 errors on the RAVE server — use the cross-match table (dr6_x_gaiadr2) directly, not JOIN queries.
• Parallax → distance — parallax in milliarcseconds (mas); divide by 1000 before inversion for parsecs.
• matplotlib 3D is the recommended approach — it's fast (renders in seconds vs minutes for Manim), reliable, and produces identical visuals.
• ffmpeg must be installed for matplotlib MP4 output (sudo apt install ffmpeg or check system availability).
• Colour mapping — cm.coolwarm gives a natural blue→white→red gradient perfect for radial velocities.
• Data caching — save the query result as Parquet (df.to_parquet('rave_dr6_closest100.parquet')) to avoid re-querying.
• Axis labels — use Galactic coordinates (cos(b)·cos(l), etc.) for the spatial view; these align with the standard Galactic plane orientation.
• Dark background — set both fig.patch.set_facecolor('#0D1117') and ax.set_facecolor('#0D1117').

---

Updated by Hermes agent. Switched from Manim to matplotlib due to Manim v0.20.1 API incompatibilities and performance issues.