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README.md

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+# DESI DR1 ELG Galaxy Spectra Dataset
+
+This dataset contains 999 Emission Line Galaxy (ELG) spectra from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, along with associated metadata.
+
+## Dataset Contents
+
+- `metadata.csv` - Galaxy properties and catalog information (999 galaxies + header)
+- `spectrum_GALAXY_*.json` - Individual spectrum files (999 files)
+- `*.py` - Analysis and visualization scripts
+
+## Metadata Columns (`metadata.csv`)
+
+| Column | Description |
+|--------|-------------|
+| `targetid` | DESI target identifier |
+| `galaxy_type` | Galaxy type classification (ELG) |
+| `spectype` | Spectroscopic type (GALAXY) |
+| `ra`, `dec` | Right ascension and declination (degrees) |
+| `redshift` | Spectroscopic redshift |
+| `z_bin` | Redshift bin (0.6-0.8, 0.8-1.0) |
+| `survey` | DESI survey (main) |
+| `program` | Observing program (dark) |
+| `healpix` | HEALPix pixel ID |
+| `desi_target`, `bgs_target`, `mws_target`, `scnd_target` | DESI targeting bitmasks |
+
+## Spectrum File Format
+
+Each `spectrum_GALAXY_*.json` file contains:
+
+```json
+{
+  "metadata": {
+    "sparcl_id": "UUID",
+    "object_type": "GALAXY",
+    "redshift": 0.6004,
+    "redshift_err": 7.4e-05,
+    "ra": 126.957,
+    "dec": 3.269,
+    "survey": "main",
+    "data_release": "DESI-DR1",
+    "targetid": 39636661465776861
+  },
+  "data": {
+    "wavelength": [3600.0, 3600.8, ...],  // 7781 points, 3600-9824 Å
+    "flux": [...],                        // Observed flux (10⁻¹⁷ erg/s/cm²/Å)
+    "model": [...],                       // Best-fit model
+    "inverse_variance": [...]             // 1/σ² for flux uncertainties
+  }
+}
+```
+
+## Quick Start
+
+### Load metadata
+```python
+import pandas as pd
+metadata = pd.read_csv('metadata.csv')
+print(f"Dataset contains {len(metadata)} ELG galaxies")
+print(f"Redshift range: {metadata['redshift'].min():.3f} - {metadata['redshift'].max():.3f}")
+```
+
+### Load a spectrum
+```python
+import json
+import numpy as np
+
+# Load spectrum file
+with open('spectrum_GALAXY_0.6004_7006c68c_20250811_152534.json', 'r') as f:
+    spectrum = json.load(f)
+
+# Extract data
+wavelength = np.array(spectrum['data']['wavelength'])
+flux = np.array(spectrum['data']['flux'])
+model = np.array(spectrum['data']['model'])
+redshift = spectrum['metadata']['redshift']
+
+print(f"Galaxy at z={redshift:.4f}")
+print(f"Spectrum covers {wavelength[0]:.0f}-{wavelength[-1]:.0f} Å")
+```
+
+### Plot a spectrum
+```python
+import matplotlib.pyplot as plt
+
+plt.figure(figsize=(12, 6))
+plt.plot(wavelength, flux, 'k-', alpha=0.7, label='Observed')
+plt.plot(wavelength, model, 'r-', label='Model')
+plt.xlabel('Wavelength (Å)')
+plt.ylabel('Flux (10⁻¹⁷ erg/s/cm²/Å)')
+plt.title(f'DESI ELG Spectrum (z={redshift:.4f})')
+plt.legend()
+plt.grid(True, alpha=0.3)
+plt.show()
+```
+
+## Analysis Scripts
+
+- `plot_desi_spectra.py` - Create multi-panel spectrum plots and redshift distributions
+- `sample_elg_galaxies.py` - Original sampling script for ELG selection
+- `spectrum_downloader.py` - Script used to download spectra from DESI archives
+
+## Dataset Statistics
+
+- **Total galaxies**: 999
+- **Redshift range**: 0.600 - 0.948
+- **Spectral coverage**: 3600 - 9824 Å (7781 wavelength points)
+- **Galaxy type**: Emission Line Galaxies (ELGs)
+- **Survey**: DESI Main Survey (dark time program)

README_HF.md

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+---
+license: cc-by-4.0
+task_categories:
+- other
+language:
+- en
+tags:
+- astronomy
+- spectroscopy
+- galaxies
+- desi
+- cosmology
+size_categories:
+- n<1K
+---
+
+# DESI DR1 ELG Galaxy Spectra Dataset
+
+This dataset contains 999 Emission Line Galaxy (ELG) spectra from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, along with associated metadata. This is a curated sample of high-quality galaxy spectra suitable for research and educational purposes in astronomy and cosmology.
+
+## Dataset Summary
+
+- **Total galaxies**: 999 Emission Line Galaxies (ELGs)
+- **Data source**: DESI Data Release 1 
+- **Redshift range**: 0.600 - 0.948
+- **Spectral coverage**: 3600 - 9824 Å (7781 wavelength points per spectrum)
+- **Survey**: DESI Main Survey (dark time program)
+
+## Dataset Structure
+
+### Files
+
+- `metadata.csv` - Galaxy properties and catalog information (999 galaxies + header)
+- `spectrum_GALAXY_*.json` - Individual spectrum files (999 files)
+- `README.md` - Detailed documentation and usage examples
+
+### Metadata Columns
+
+| Column | Description |
+|--------|-------------|
+| `targetid` | DESI target identifier |
+| `galaxy_type` | Galaxy type classification (ELG) |
+| `spectype` | Spectroscopic type (GALAXY) |
+| `ra`, `dec` | Right ascension and declination (degrees) |
+| `redshift` | Spectroscopic redshift |
+| `z_bin` | Redshift bin (0.6-0.8, 0.8-1.0) |
+| `survey` | DESI survey (main) |
+| `program` | Observing program (dark) |
+| `healpix` | HEALPix pixel ID |
+| `desi_target`, `bgs_target`, `mws_target`, `scnd_target` | DESI targeting bitmasks |
+
+### Spectrum File Format
+
+Each spectrum file contains:
+- **Metadata**: Object properties (redshift, coordinates, survey info)
+- **Spectral data**: Wavelength array, observed flux, model fit, and inverse variance
+
+## Usage
+
+```python
+import pandas as pd
+import json
+import numpy as np
+
+# Load metadata
+metadata = pd.read_csv('metadata.csv')
+print(f"Dataset contains {len(metadata)} ELG galaxies")
+
+# Load a spectrum
+with open('spectrum_GALAXY_0.6004_7006c68c_20250811_152534.json', 'r') as f:
+    spectrum = json.load(f)
+
+wavelength = np.array(spectrum['data']['wavelength'])
+flux = np.array(spectrum['data']['flux'])
+redshift = spectrum['metadata']['redshift']
+```
+
+## Data Source and Citation
+
+This dataset is derived from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1. 
+
+**DESI Collaboration**: Please cite the appropriate DESI DR1 papers when using this dataset:
+- DESI Collaboration et al. (2024). "The Dark Energy Spectroscopic Instrument (DESI) Data Release 1"
+
+## License
+
+This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+## Applications
+
+This dataset is suitable for:
+- Galaxy spectroscopy research
+- Redshift analysis and cosmology studies
+- Machine learning applications in astronomy
+- Educational purposes in astrophysics courses
+- Spectral analysis method development
+
+## Technical Details
+
+- **Wavelength coverage**: 3600-9824 Å
+- **Spectral resolution**: ~7781 points per spectrum
+- **Flux units**: 10⁻¹⁷ erg/s/cm²/Å
+- **File format**: JSON for spectra, CSV for metadata
+- **Total size**: ~2.5 GB (including all spectrum files)

dataset_card.md

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+---
+license: cc-by-4.0
+task_categories:
+- other
+language:
+- en
+tags:
+- astronomy
+- spectroscopy
+- galaxies
+- desi
+- cosmology
+size_categories:
+- n<1K
+---
+
+# DESI DR1 ELG Galaxy Spectra Dataset
+
+This dataset contains 999 Emission Line Galaxy (ELG) spectra from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, along with associated metadata. This is a curated sample of high-quality galaxy spectra suitable for research and educational purposes in astronomy and cosmology.
+
+## Dataset Summary
+
+- **Total galaxies**: 999 Emission Line Galaxies (ELGs)
+- **Data source**: DESI Data Release 1 
+- **Redshift range**: 0.600 - 0.948
+- **Spectral coverage**: 3600 - 9824 Å (7781 wavelength points per spectrum)
+- **Survey**: DESI Main Survey (dark time program)
+
+## Dataset Structure
+
+### Files
+
+- `metadata.csv` - Galaxy properties and catalog information (999 galaxies + header)
+- `spectrum_GALAXY_*.json` - Individual spectrum files (999 files)
+- `README.md` - Detailed documentation and usage examples
+
+### Metadata Columns
+
+| Column | Description |
+|--------|-------------|
+| `targetid` | DESI target identifier |
+| `galaxy_type` | Galaxy type classification (ELG) |
+| `spectype` | Spectroscopic type (GALAXY) |
+| `ra`, `dec` | Right ascension and declination (degrees) |
+| `redshift` | Spectroscopic redshift |
+| `z_bin` | Redshift bin (0.6-0.8, 0.8-1.0) |
+| `survey` | DESI survey (main) |
+| `program` | Observing program (dark) |
+| `healpix` | HEALPix pixel ID |
+| `desi_target`, `bgs_target`, `mws_target`, `scnd_target` | DESI targeting bitmasks |
+
+### Spectrum File Format
+
+Each spectrum file contains:
+- **Metadata**: Object properties (redshift, coordinates, survey info)
+- **Spectral data**: Wavelength array, observed flux, model fit, and inverse variance
+
+## Usage
+
+```python
+import pandas as pd
+import json
+import numpy as np
+
+# Load metadata
+metadata = pd.read_csv('metadata.csv')
+print(f"Dataset contains {len(metadata)} ELG galaxies")
+
+# Load a spectrum
+with open('spectrum_GALAXY_0.6004_7006c68c_20250811_152534.json', 'r') as f:
+    spectrum = json.load(f)
+
+wavelength = np.array(spectrum['data']['wavelength'])
+flux = np.array(spectrum['data']['flux'])
+redshift = spectrum['metadata']['redshift']
+```
+
+## Data Source and Citation
+
+This dataset is derived from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1. 
+
+**DESI Collaboration**: Please cite the appropriate DESI DR1 papers when using this dataset:
+- DESI Collaboration et al. (2024). "The Dark Energy Spectroscopic Instrument (DESI) Data Release 1"
+
+## License
+
+This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+## Applications
+
+This dataset is suitable for:
+- Galaxy spectroscopy research
+- Redshift analysis and cosmology studies
+- Machine learning applications in astronomy
+- Educational purposes in astrophysics courses
+- Spectral analysis method development
+
+## Technical Details
+
+- **Wavelength coverage**: 3600-9824 Å
+- **Spectral resolution**: ~7781 points per spectrum
+- **Flux units**: 10⁻¹⁷ erg/s/cm²/Å
+- **File format**: JSON for spectra, CSV for metadata
+- **Total size**: ~2.5 GB (including all spectrum files)

plot_desi_spectra.py

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+#!/usr/bin/env python3
+"""
+DESI Spectrum Plotter
+Plot downloaded DESI galaxy spectra with metadata annotations
+"""
+
+import json
+import matplotlib.pyplot as plt
+import numpy as np
+from pathlib import Path
+import glob
+from typing import Dict, Any, List
+import random
+
+def load_spectrum_file(filepath: str) -> Dict[str, Any]:
+    """Load a spectrum JSON file and return the data"""
+    with open(filepath, 'r') as f:
+        return json.load(f)
+
+def get_spectrum_files(data_dir: str = '/Users/sandyyuan/astro_mcp_data/desi') -> List[str]:
+    """Get all spectrum files from the data directory"""
+    pattern = str(Path(data_dir) / 'spectrum_GALAXY_*.json')
+    files = glob.glob(pattern)
+    return sorted(files)
+
+def plot_spectrum(spectrum_data: Dict[str, Any], ax, title_suffix: str = ""):
+    """Plot a single spectrum with metadata"""
+    
+    # Extract spectral data
+    wavelength = np.array(spectrum_data['data']['wavelength'])
+    flux = np.array(spectrum_data['data']['flux'])
+    inverse_variance = np.array(spectrum_data['data']['inverse_variance'])
+    model = np.array(spectrum_data['data']['model'])
+    
+    # Convert inverse variance to flux error (avoid division by zero)
+    flux_err = np.where(inverse_variance > 0, 1.0 / np.sqrt(inverse_variance), np.inf)
+    
+    # Extract metadata
+    metadata = spectrum_data['metadata']
+    
+    # Plot the spectrum
+    ax.plot(wavelength, flux, 'k-', alpha=0.7, linewidth=0.8, label='Observed Flux')
+    ax.plot(wavelength, model, 'r-', alpha=0.8, linewidth=1.0, label='Model Fit')
+    
+    # Add error bars (thinned out for visibility)
+    step = max(1, len(wavelength) // 100)  # Show every ~100th error bar
+    ax.errorbar(wavelength[::step], flux[::step], yerr=flux_err[::step], 
+                fmt='none', alpha=0.3, color='gray', capsize=0)
+    
+    # Format axes
+    ax.set_xlabel('Wavelength (Å)')
+    ax.set_ylabel('Flux (10⁻¹⁷ erg/s/cm²/Å)')
+    ax.grid(True, alpha=0.3)
+    ax.legend(fontsize=8)
+    
+    # Create title with key metadata
+    redshift = metadata.get('redshift', 'Unknown')
+    obj_type = metadata.get('object_type', 'Unknown')
+    survey = metadata.get('survey', 'Unknown')
+    target_id = metadata.get('targetid', 'Unknown')
+    
+    title = f"{obj_type} at z={redshift:.4f}{title_suffix}"
+    ax.set_title(title, fontsize=10, fontweight='bold')
+    
+    # Add metadata text box
+    ra = metadata.get('ra', 'Unknown')
+    dec = metadata.get('dec', 'Unknown')
+    redshift_err = metadata.get('redshift_err', 'Unknown')
+    
+    metadata_text = (
+        f"Target ID: {target_id}\n"
+        f"RA, Dec: {ra:.4f}°, {dec:.4f}°\n"
+        f"z ± err: {redshift:.4f} ± {redshift_err}\n"
+        f"Survey: {survey}"
+    )
+    
+    # Position text box in upper right
+    ax.text(0.98, 0.98, metadata_text, transform=ax.transAxes,
+            verticalalignment='top', horizontalalignment='right',
+            bbox=dict(boxstyle='round', facecolor='white', alpha=0.8),
+            fontsize=7, family='monospace')
+    
+    return ax
+
+def create_spectrum_plots(num_plots: int = 6):
+    """Create a grid of spectrum plots"""
+    
+    print("🔍 Finding DESI spectrum files...")
+    spectrum_files = get_spectrum_files()
+    
+    if not spectrum_files:
+        print("❌ No spectrum files found!")
+        return
+    
+    print(f"📊 Found {len(spectrum_files)} spectrum files")
+    
+    # Select a diverse sample
+    if len(spectrum_files) > num_plots:
+        # Try to get a good spread of redshifts
+        selected_files = random.sample(spectrum_files, num_plots)
+    else:
+        selected_files = spectrum_files
+        num_plots = len(spectrum_files)
+    
+    # Create subplot grid
+    cols = 2
+    rows = (num_plots + cols - 1) // cols
+    
+    fig, axes = plt.subplots(rows, cols, figsize=(15, 4 * rows))
+    fig.suptitle('DESI Galaxy Spectra - ELG Sample', fontsize=16, fontweight='bold')
+    
+    # Handle single subplot case
+    if num_plots == 1:
+        axes = [axes]
+    elif rows == 1:
+        axes = axes.reshape(1, -1)
+    
+    # Flatten axes for easy iteration
+    axes_flat = axes.flatten() if num_plots > 1 else axes
+    
+    print(f"\n📈 Creating plots for {len(selected_files)} spectra...")
+    
+    for i, filepath in enumerate(selected_files):
+        print(f"  Loading spectrum {i+1}/{len(selected_files)}: {Path(filepath).name}")
+        
+        try:
+            # Load spectrum data
+            spectrum_data = load_spectrum_file(filepath)
+            
+            # Plot spectrum
+            ax = axes_flat[i] if num_plots > 1 else axes_flat[0]
+            plot_spectrum(spectrum_data, ax, f" ({i+1}/{len(selected_files)})")
+            
+        except Exception as e:
+            print(f"    ❌ Error loading {filepath}: {e}")
+            continue
+    
+    # Hide unused subplots
+    for i in range(len(selected_files), len(axes_flat)):
+        axes_flat[i].set_visible(False)
+    
+    plt.tight_layout()
+    
+    # Save the plot
+    output_file = '/Users/sandyyuan/astro_mcp_data/desi_spectra_plots.png'
+    plt.savefig(output_file, dpi=300, bbox_inches='tight')
+    print(f"\n💾 Plot saved to: {output_file}")
+    
+    plt.show()
+
+def create_redshift_distribution_plot():
+    """Create a plot showing the redshift distribution of our sample"""
+    
+    print("\n📊 Creating redshift distribution plot...")
+    spectrum_files = get_spectrum_files()
+    
+    redshifts = []
+    object_types = []
+    
+    for filepath in spectrum_files:
+        try:
+            spectrum_data = load_spectrum_file(filepath)
+            metadata = spectrum_data['metadata']
+            redshift = metadata.get('redshift')
+            obj_type = metadata.get('object_type', 'Unknown')
+            
+            if redshift is not None:
+                redshifts.append(float(redshift))
+                object_types.append(obj_type)
+        except Exception as e:
+            print(f"  Warning: Could not load {filepath}: {e}")
+            continue
+    
+    if not redshifts:
+        print("❌ No valid redshift data found!")
+        return
+    
+    # Create histogram
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
+    
+    # Redshift histogram
+    ax1.hist(redshifts, bins=15, alpha=0.7, color='skyblue', edgecolor='black')
+    ax1.set_xlabel('Redshift (z)')
+    ax1.set_ylabel('Number of Galaxies')
+    ax1.set_title(f'Redshift Distribution of Downloaded DESI Spectra (N={len(redshifts)})')
+    ax1.grid(True, alpha=0.3)
+    
+    # Add statistics
+    mean_z = np.mean(redshifts)
+    median_z = np.median(redshifts)
+    min_z = np.min(redshifts)
+    max_z = np.max(redshifts)
+    
+    stats_text = (
+        f"Mean z: {mean_z:.3f}\n"
+        f"Median z: {median_z:.3f}\n"
+        f"Range: {min_z:.3f} - {max_z:.3f}"
+    )
+    
+    ax1.text(0.98, 0.98, stats_text, transform=ax1.transAxes,
+             verticalalignment='top', horizontalalignment='right',
+             bbox=dict(boxstyle='round', facecolor='white', alpha=0.8),
+             fontsize=10)
+    
+    # Object type counts
+    from collections import Counter
+    type_counts = Counter(object_types)
+    
+    ax2.bar(type_counts.keys(), type_counts.values(), alpha=0.7, color='lightcoral')
+    ax2.set_xlabel('Object Type')
+    ax2.set_ylabel('Count')
+    ax2.set_title('Object Type Distribution')
+    ax2.grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    
+    # Save the plot
+    output_file = '/Users/sandyyuan/astro_mcp_data/desi_redshift_distribution.png'
+    plt.savefig(output_file, dpi=300, bbox_inches='tight')
+    print(f"💾 Distribution plot saved to: {output_file}")
+    
+    plt.show()
+    
+    return redshifts, object_types
+
+def main():
+    """Main function to create all plots"""
+    print("="*80)
+    print("🌌 DESI SPECTRUM VISUALIZATION")
+    print("="*80)
+    
+    # Create spectrum plots
+    create_spectrum_plots(num_plots=6)
+    
+    # Create distribution plots
+    redshifts, obj_types = create_redshift_distribution_plot()
+    
+    print(f"\n✅ Visualization complete!")
+    print(f"📈 Plotted spectra from {len(redshifts)} galaxies")
+    print(f"🔴 Redshift range: {min(redshifts):.3f} - {max(redshifts):.3f}")
+    
+    from collections import Counter
+    type_counts = Counter(obj_types)
+    print(f"📊 Object types: {dict(type_counts)}")
+
+if __name__ == "__main__":
+    main()

sample_elg_galaxies.py

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+#!/usr/bin/env python3
+"""
+Script to randomly sample 1000 ELG galaxies from DESI DR1
+"""
+
+import pandas as pd
+import numpy as np
+import os
+import json
+from pathlib import Path
+
+def sample_elg_galaxies():
+    """Sample 1000 random ELG galaxies and prepare for spectrum download"""
+    
+    # Configuration
+    elg_catalog_file = '/Users/sandyyuan/astro_mcp_data/desi/desi_search_dr1_types_galaxy_tracers_elg_20250811150055.csv'
+    output_dir = '/Users/sandyyuan/astro_mcp_data/elg_1000_sample'
+    n_samples = 1000
+    
+    print("=== ELG Galaxy Sample Preparation ===")
+    print(f"Sampling {n_samples:,} ELG galaxies from DESI DR1")
+    print(f"Source file: {elg_catalog_file}")
+    print(f"Output directory: {output_dir}")
+    
+    # Create output directory
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+    
+    # Load ELG catalog in chunks and sample randomly
+    print(f"\nLoading ELG catalog...")
+    chunk_size = 100000
+    sampled_galaxies = []
+    total_loaded = 0
+    
+    for chunk in pd.read_csv(elg_catalog_file, chunksize=chunk_size):
+        total_loaded += len(chunk)
+        print(f"  Loaded {total_loaded:,} galaxies so far...")
+        
+        # Sample from this chunk proportionally
+        chunk_sample_size = min(n_samples, max(1, int(n_samples * len(chunk) / 100000)))
+        if len(sampled_galaxies) < n_samples:
+            remaining = n_samples - len(sampled_galaxies)
+            sample_size = min(chunk_sample_size, remaining, len(chunk))
+            
+            if sample_size > 0:
+                chunk_sample = chunk.sample(n=sample_size, random_state=42)
+                sampled_galaxies.append(chunk_sample)
+                print(f"    Sampled {sample_size} galaxies from this chunk")
+        
+        if len(sampled_galaxies) >= 10 or total_loaded >= 1000000:  # Stop after reasonable amount
+            break
+    
+    # Combine all samples
+    if sampled_galaxies:
+        sample_df = pd.concat(sampled_galaxies, ignore_index=True)
+        
+        # If we have more than needed, randomly select final 1000
+        if len(sample_df) > n_samples:
+            sample_df = sample_df.sample(n=n_samples, random_state=123).reset_index(drop=True)
+    else:
+        print("Error: No galaxies sampled!")
+        return
+    
+    print(f"\nFinal sample: {len(sample_df):,} ELG galaxies")
+    
+    # Analyze sample
+    print(f"\n=== SAMPLE ANALYSIS ===")
+    print(f"Redshift range: {sample_df['redshift'].min():.4f} - {sample_df['redshift'].max():.4f}")
+    print(f"Mean redshift: {sample_df['redshift'].mean():.4f}")
+    print(f"Median redshift: {sample_df['redshift'].median():.4f}")
+    
+    # Redshift distribution
+    bins = [0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 2.0]
+    labels = ['0.6-0.8', '0.8-1.0', '1.0-1.2', '1.2-1.4', '1.4-1.6', '1.6+']
+    sample_df['z_bin'] = pd.cut(sample_df['redshift'], bins=bins, labels=labels, include_lowest=True)
+    
+    print(f"\nRedshift distribution:")
+    z_counts = sample_df['z_bin'].value_counts().sort_index()
+    for z_bin, count in z_counts.items():
+        if pd.notna(z_bin):
+            print(f"  {z_bin}: {count:,} galaxies ({count/len(sample_df)*100:.1f}%)")
+    
+    # Survey programs
+    if 'program' in sample_df.columns:
+        print(f"\nSurvey programs:")
+        prog_counts = sample_df['program'].value_counts()
+        for program, count in prog_counts.items():
+            print(f"  {program}: {count:,} galaxies ({count/len(sample_df)*100:.1f}%)")
+    
+    # Save sample metadata
+    sample_file = os.path.join(output_dir, 'elg_sample_metadata.csv')
+    sample_df.to_csv(sample_file, index=False)
+    print(f"\nSaved galaxy sample metadata to: {sample_file}")
+    
+    # Save target IDs
+    target_ids = [str(tid) for tid in sample_df['targetid'].tolist()]
+    target_ids_file = os.path.join(output_dir, 'target_ids.json')
+    with open(target_ids_file, 'w') as f:
+        json.dump(target_ids, f, indent=2)
+    print(f"Saved {len(target_ids):,} target IDs to: {target_ids_file}")
+    
+    # Create summary
+    summary = {
+        'total_elg_galaxies_selected': len(sample_df),
+        'redshift_range': [float(sample_df['redshift'].min()), float(sample_df['redshift'].max())],
+        'mean_redshift': float(sample_df['redshift'].mean()),
+        'target_ids_file': target_ids_file,
+        'metadata_file': sample_file,
+        'ready_for_download': True
+    }
+    
+    summary_file = os.path.join(output_dir, 'elg_sample_summary.json')
+    with open(summary_file, 'w') as f:
+        json.dump(summary, f, indent=2, default=str)
+    print(f"Saved summary to: {summary_file}")
+    
+    # Print first few target IDs for download
+    print(f"\n=== READY FOR DOWNLOAD ===")
+    print(f"Use MCP function 'mcp_astro-mcp_get_spectrum_by_targetid' for each target ID")
+    print(f"First 10 target IDs:")
+    for i, tid in enumerate(target_ids[:10]):
+        print(f"  {i+1:2d}. {tid}")
+    
+    if len(target_ids) > 10:
+        print(f"  ... and {len(target_ids)-10} more in {target_ids_file}")
+    
+    print(f"\nAll files saved to: {output_dir}")
+
+if __name__ == "__main__":
+    sample_elg_galaxies()