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Stargazing

Calculate the altitude, rise, and set times of celestial objects for any location on Earth. Analyze light pollution to enhance your stargazing experience and make informed observations of the night sky.

Installation

Installing for Claude Desktop

Option 1: One-Command Installation

npx mcpbar@latest install StarGazer1995/mcp-stargazing -c claude

This command will automatically install and configure the Stargazing MCP server for your selected client.

Option 2: Manual Configuration

Run the command below to open your configuration file:

npx mcpbar@latest edit -c claude

After opening your configuration file, copy and paste this configuration:

View JSON configuration
{
  "mcpServers": {
    "Stargazing": {
      "command": "uvx",
      "args": [
        "mcp-stargazing"
      ],
      "env": {}
    }
  }
}

mcp-stargazing

Calculate the altitude, rise, and set times of celestial objects (Sun, Moon, planets, stars, and deep-space objects) for any location on Earth, with optional light pollution analysis.

Features

  • Altitude/Azimuth Calculation: Get elevation and compass direction for any celestial object.
  • Rise/Set Times: Determine when objects appear/disappear above the horizon.
  • Light Pollution Analysis: Load and analyze light pollution maps (GeoTIFF format).
  • Supports:
    • Solar system objects (Sun, Moon, planets)
    • Stars (e.g., "sirius")
    • Deep-space objects (e.g., "andromeda", "orion_nebula")
  • Time Zone Aware: Works with local or UTC times.

Installation

pip install astropy pytz numpy astroquery rasterio geopy

Usage

Calculate Altitude/Azimuth

from src.celestial import celestial_pos
from astropy.coordinates import EarthLocation
import pytz
from datetime import datetime

# Observer location (New York)
location = EarthLocation(lat=40.7128, lon=-74.0060)

# Time (local timezone-aware)
local_time = pytz.timezone("America/New_York").localize(datetime(2023, 10, 1, 12, 0))
altitude, azimuth = celestial_pos("sun", location, local_time)
print(f"Sun Position: Altitude={altitude:.1f}°, Azimuth={azimuth:.1f}°")

Calculate Rise/Set Times

from src.celestial import celestial_rise_set

rise, set_ = celestial_rise_set("andromeda", location, local_time.date())
print(f"Andromeda: Rise={rise.iso}, Set={set_.iso}")

Load Light Pollution Map

from src.light_pollution import load_map

# Load a GeoTIFF light pollution map
vriis_data, bounds, crs, transform = load_map("path/to/map.tif")
print(f"Map Bounds: {bounds}")

API Reference

celestial_pos(celestial_object, observer_location, time) (src/celestial.py)

  • Inputs:
    • celestial_object: Name (e.g., "sun", "andromeda").
    • observer_location: EarthLocation object.
    • time: datetime (timezone-aware) or Astropy Time.
  • Returns: (altitude_degrees, azimuth_degrees).

celestial_rise_set(celestial_object, observer_location, date, horizon=0.0) (src/celestial.py)

  • Inputs:
    • date: Timezone-aware datetime.
    • horizon: Horizon elevation (default: 0°).
  • Returns: (rise_time, set_time) as UTC Time objects.

load_map(map_path) (src/light_pollution.py)

  • Inputs:
    • map_path: Path to GeoTIFF file.
  • Returns: Tuple (vriis_data, bounds, crs, transform) for light pollution analysis.

Testing

Run tests with:

pytest tests/

Key Test Cases (tests/test_celestial.py)

def test_calculate_altitude_deepspace():
    """Test deep-space object resolution."""
    altitude, _ = celestial_pos("andromeda", NYC, Time.now())
    assert -90 <= altitude <= 90

def test_calculate_rise_set_sun():
    """Validate Sun rise/set times."""
    rise, set_ = celestial_rise_set("sun", NYC, datetime(2023, 10, 1))
    assert rise < set_

Project Structure

.
├── src/
│   ├── celestial.py          # Core celestial calculations
│   ├── light_pollution.py    # Light pollution map utilities
│   ├── utils.py              # Time/location helpers
│   └── main.py               # CLI entry point
├── tests/
│   ├── test_celestial.py
│   └── test_utils.py
└── README.md

Future Work

  • Add support for comets/asteroids.
  • Optimize SIMBAD queries for offline use.
  • Integrate light pollution data into visibility predictions.

Key Updates:

  1. Light Pollution: Added light_pollution.py to features and API reference.
  2. Dependencies: Added rasterio and geopy to installation instructions.
  3. Project Structure: Clarified file roles and test coverage.
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Stargazing: MCP Server – MCP.Bar