{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["# Witnessed Meteorite Falls - NASA Catalog\n\nExplore **1,097 meteorites** observed falling to Earth from 1688 to 2013.\n\n**Dataset Highlights:**\n- Witnessed falls only (not discovered finds)\n- 325 years of documented observations\n- Pristine samples for scientific study\n- ~3% of all known meteorites"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["import json\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport warnings\nwarnings.filterwarnings('ignore')\n\nplt.style.use('seaborn-v0_8-whitegrid')\nprint('Libraries loaded')"]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["## 1. Load Dataset"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["with open('witnessed_meteorite_falls.json') as f:\n    data = json.load(f)\n\ndf = pd.DataFrame(data)\nprint(f'Total witnessed falls: {len(df):,}')\ndf.head()"]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["## 2. Why Witnessed Falls Matter"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["print('Witnessed Falls vs Discovered Finds:')\nprint('=' * 50)\nprint(f'Witnessed Falls in this dataset: {len(df):,}')\nprint(f'Total NASA catalog: ~38,400')\nprint(f'Percentage witnessed: {len(df)/38400*100:.1f}%')\nprint()\nprint('Benefits of witnessed falls:')\nprint('- Exact fall date known')\nprint('- Often trajectory documented')\nprint('- Minimal terrestrial contamination')\nprint('- Highest scientific value')"]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["## 3. Classification Distribution"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["# Extract main classification\ndf['main_class'] = df['meteorite_class'].str.split('[,\\s]').str[0]\nclass_counts = df['main_class'].value_counts().head(15)\n\nprint('Top Meteorite Classifications:')\nprint('=' * 40)\nfor cls, count in class_counts.items():\n    pct = count / len(df) * 100\n    print(f'{cls:15s} {count:5,} ({pct:5.1f}%)')"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["fig, ax = plt.subplots(figsize=(10, 6))\nclass_counts.plot(kind='barh', ax=ax, color='darkblue')\nax.set_xlabel('Number of Falls')\nax.set_title('Witnessed Falls by Classification', fontweight='bold')\nplt.tight_layout()\nplt.show()"]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["## 4. Temporal Distribution"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["df['year'] = pd.to_datetime(df['date'], errors='coerce').dt.year\ndf['century'] = (df['year'] // 100) * 100\n\ncentury_counts = df.groupby('century').size()\nprint('Falls by Century:')\nprint('=' * 40)\nfor century, count in century_counts.items():\n    if century >= 1600:\n        print(f'{int(century)}s: {count:5,}')"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["recent = df[df['year'] >= 1800]\nyearly = recent.groupby('year').size()\n\nfig, ax = plt.subplots(figsize=(14, 5))\nyearly.plot(ax=ax, color='darkblue')\nax.set_xlabel('Year')\nax.set_ylabel('Number of Witnessed Falls')\nax.set_title('Witnessed Meteorite Falls by Year (1800+)', fontweight='bold')\nplt.tight_layout()\nplt.show()"]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["## 5. Mass Distribution"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["with_mass = df[df['mass_g'].notna() & (df['mass_g'] > 0)]\nprint(f'Falls with mass data: {len(with_mass):,}')\nprint(f'\\nMass Statistics (grams):')\nprint(f'Minimum: {with_mass[\"mass_g\"].min():,.0f} g')\nprint(f'Median: {with_mass[\"mass_g\"].median():,.0f} g')\nprint(f'Maximum: {with_mass[\"mass_g\"].max():,.0f} g')"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["heaviest = with_mass.nlargest(10, 'mass_g')[['name', 'date', 'mass_g', 'meteorite_class']]\nprint('\\nLargest Witnessed Falls:')\nprint('=' * 70)\nfor i, row in heaviest.iterrows():\n    mass_kg = row['mass_g'] / 1000\n    print(f\"{row['name']:25s} {mass_kg:10,.0f} kg  {row['meteorite_class']}\")"]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["## 6. Geographic Distribution"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["valid_coords = df[(df['latitude'].notna()) & (df['longitude'].notna())]\nprint(f'Falls with coordinates: {len(valid_coords):,}')\n\nfig, ax = plt.subplots(figsize=(14, 7))\nax.scatter(valid_coords['longitude'], valid_coords['latitude'], \n           alpha=0.6, s=20, c='darkblue')\nax.set_xlabel('Longitude')\nax.set_ylabel('Latitude')\nax.set_title('Witnessed Meteorite Fall Locations', fontweight='bold')\nax.set_xlim(-180, 180)\nax.set_ylim(-90, 90)\nax.grid(alpha=0.3)\nplt.tight_layout()\nplt.show()"]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["## 7. Famous Witnessed Falls"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": ["famous_names = ['Murchison', 'Allende', 'Peekskill', 'Chelyabinsk', 'Sikhote-Alin', 'Tunguska']\n\nprint('Famous Witnessed Falls in Dataset:')\nprint('=' * 60)\nfor name in famous_names:\n    matches = df[df['name'].str.contains(name, case=False, na=False)]\n    if len(matches) > 0:\n        for i, row in matches.iterrows():\n            print(f\"{row['name']:25s} {row['date'][:10]}  {row['meteorite_class']}\")"]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": ["## Conclusion\n\nThis notebook demonstrated:\n- Loading 1,097 witnessed meteorite falls\n- Scientific value of witnessed vs found meteorites\n- Classification distribution (L chondrites most common)\n- 325 years of documented observations\n- Geographic patterns of fall locations\n\n**Source**: NASA Meteoritical Bulletin\n\n**Author**: Luke Steuber | @lukesteuber.com (Bluesky)"]
    }
  ]
}