Update schema with new tags: internet, owner

.github/ISSUE_TEMPLATE/community_submission.yaml

@@ -14,7 +14,7 @@ body:
         - Each object must include **at least one** of:
           - `registration_number`
           - `transponder_code_hex` (6 uppercase hex chars, e.g., `ABC123`)
-          - `openairframes_id`
+          - `planequery_airframe_id`
         - Your contributor name (entered below) will be applied to all objects.
         - `contributor_uuid` is derived from your GitHub account automatically.
         - `creation_timestamp` is created by the system (you may omit it).

.github/workflows/historical-adsb.yaml

@@ -48,7 +48,7 @@ jobs:
       matrix:
         chunk: ${{ fromJson(needs.generate-matrix.outputs.chunks) }}
       max-parallel: 3
-      fail-fast: true
+      fail-fast: false
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -220,11 +220,11 @@ jobs:
           END_DATE: ${{ needs.generate-matrix.outputs.global_end }}
         run: |
           python -m src.adsb.combine_chunks_to_csv --chunks-dir data/output/adsb_chunks --start-date "$START_DATE" --end-date "$END_DATE" --skip-base --stream
-          ls -lah data/openairframes/
+          ls -lah data/planequery_aircraft/
 
       - name: Upload final artifact
         uses: actions/upload-artifact@v4
         with:
-          name: openairframes_adsb-${{ needs.generate-matrix.outputs.global_start }}-${{ needs.generate-matrix.outputs.global_end }}
+          name: planequery_aircraft_adsb-${{ needs.generate-matrix.outputs.global_start }}-${{ needs.generate-matrix.outputs.global_end }}
-          path: data/openairframes/*.csv
+          path: data/planequery_aircraft/*.csv
           retention-days: 30

.github/workflows/planequery-aircraft-daily-release.yaml

@@ -1,4 +1,4 @@
-name: OpenAirframes Daily Release
+name: planequery-aircraft Daily Release
 
 on:
   schedule:
@@ -22,7 +22,7 @@ jobs:
             await github.rest.actions.createWorkflowDispatch({
               owner: context.repo.owner,
               repo: context.repo.repo,
-              workflow_id: 'openairframes-daily-release.yaml',
+              workflow_id: 'planequery-aircraft-daily-release.yaml',
               ref: 'main'
             });
       
@@ -33,7 +33,7 @@ jobs:
             await github.rest.actions.createWorkflowDispatch({
               owner: context.repo.owner,
               repo: context.repo.repo,
-              workflow_id: 'openairframes-daily-release.yaml',
+              workflow_id: 'planequery-aircraft-daily-release.yaml',
               ref: 'develop'
             });
 
@@ -58,16 +58,16 @@ jobs:
 
       - name: Run FAA release script
         run: |
-          python src/create_daily_faa_release.py
+          python src/create_daily_planequery_aircraft_release.py
           ls -lah data/faa_releasable
-          ls -lah data/openairframes
+          ls -lah data/planequery_aircraft
 
       - name: Upload FAA artifacts
         uses: actions/upload-artifact@v4
         with:
           name: faa-release
           path: |
-            data/openairframes/openairframes_faa_*.csv
+            data/planequery_aircraft/planequery_aircraft_faa_*.csv
             data/faa_releasable/ReleasableAircraft_*.zip
           retention-days: 1
 
@@ -214,13 +214,13 @@ jobs:
           mkdir -p data/output/adsb_chunks
           ls -lah data/output/adsb_chunks/ || echo "Directory empty or does not exist"
           python -m src.adsb.combine_chunks_to_csv --chunks-dir data/output/adsb_chunks
-          ls -lah data/openairframes/
+          ls -lah data/planequery_aircraft/
 
       - name: Upload ADS-B artifacts
         uses: actions/upload-artifact@v4
         with:
           name: adsb-release
-          path: data/openairframes/openairframes_adsb_*.csv
+          path: data/planequery_aircraft/planequery_aircraft_adsb_*.csv
           retention-days: 1
 
   build-community:
@@ -245,13 +245,13 @@ jobs:
       - name: Run Community release script
         run: |
           python -m src.contributions.create_daily_community_release
-          ls -lah data/openairframes
+          ls -lah data/planequery_aircraft
 
       - name: Upload Community artifacts
         uses: actions/upload-artifact@v4
         with:
           name: community-release
-          path: data/openairframes/openairframes_community_*.csv
+          path: data/planequery_aircraft/planequery_aircraft_community_*.csv
           retention-days: 1
 
   create-release:
@@ -297,16 +297,16 @@ jobs:
           elif [ "$BRANCH_NAME" = "develop" ]; then
             BRANCH_SUFFIX="-develop"
           fi
-          TAG="openairframes-${DATE}${BRANCH_SUFFIX}"
+          TAG="planequery-aircraft-${DATE}${BRANCH_SUFFIX}"
           
-          # Find files from artifacts using find (handles nested structures)
+          # Find files from artifacts
-          CSV_FILE_FAA=$(find artifacts/faa -name "openairframes_faa_*.csv" | head -1)
+          CSV_FILE_FAA=$(ls artifacts/faa/data/planequery_aircraft/planequery_aircraft_faa_*.csv | head -1)
           CSV_BASENAME_FAA=$(basename "$CSV_FILE_FAA")
-          CSV_FILE_ADSB=$(find artifacts/adsb -name "openairframes_adsb_*.csv" | head -1)
+          CSV_FILE_ADSB=$(ls artifacts/adsb/planequery_aircraft_adsb_*.csv | head -1)
           CSV_BASENAME_ADSB=$(basename "$CSV_FILE_ADSB")
-          CSV_FILE_COMMUNITY=$(find artifacts/community -name "openairframes_community_*.csv" 2>/dev/null | head -1 || echo "")
+          CSV_FILE_COMMUNITY=$(ls artifacts/community/planequery_aircraft_community_*.csv 2>/dev/null | head -1 || echo "")
           CSV_BASENAME_COMMUNITY=$(basename "$CSV_FILE_COMMUNITY" 2>/dev/null || echo "")
-          ZIP_FILE=$(find artifacts/faa -name "ReleasableAircraft_*.zip" | head -1)
+          ZIP_FILE=$(ls artifacts/faa/data/faa_releasable/ReleasableAircraft_*.zip | head -1)
           ZIP_BASENAME=$(basename "$ZIP_FILE")
           
           echo "date=$DATE" >> "$GITHUB_OUTPUT"
@@ -319,15 +319,12 @@ jobs:
           echo "csv_basename_community=$CSV_BASENAME_COMMUNITY" >> "$GITHUB_OUTPUT"
           echo "zip_file=$ZIP_FILE" >> "$GITHUB_OUTPUT"
           echo "zip_basename=$ZIP_BASENAME" >> "$GITHUB_OUTPUT"
-          echo "name=OpenAirframes snapshot ($DATE)${BRANCH_SUFFIX}" >> "$GITHUB_OUTPUT"
+          echo "name=planequery-aircraft snapshot ($DATE)${BRANCH_SUFFIX}" >> "$GITHUB_OUTPUT"
-
-      - name: Checkout for gh CLI
-        uses: actions/checkout@v4
 
       - name: Delete existing release if exists
         run: |
-          echo "Attempting to delete release: ${{ steps.meta.outputs.tag }}"
+          gh release delete "${{ steps.meta.outputs.tag }}" --yes 2>/dev/null || true
-          gh release delete "${{ steps.meta.outputs.tag }}" --yes --cleanup-tag || echo "No existing release to delete"
+          git push --delete origin "refs/tags/${{ steps.meta.outputs.tag }}" 2>/dev/null || true
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 

.github/workflows/process-historical-faa.yaml

@@ -1,171 +0,0 @@
-name: Process Historical FAA Data
-
-on:
-  workflow_dispatch:  # Manual trigger
-
-jobs:
-  generate-matrix:
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    steps:
-      - name: Generate date ranges
-        id: set-matrix
-        run: |
-          python3 << 'EOF'
-          import json
-          from datetime import datetime, timedelta
-          
-          start = datetime(2023, 8, 16)
-          end = datetime(2026, 1, 1)
-          
-          ranges = []
-          current = start
-          
-          # Process in 4-day chunks
-          while current < end:
-            chunk_end = current + timedelta(days=4)
-            # Don't go past the end date
-            if chunk_end > end:
-              chunk_end = end
-            
-            ranges.append({
-              "since": current.strftime("%Y-%m-%d"),
-              "until": chunk_end.strftime("%Y-%m-%d")
-            })
-            
-            current = chunk_end
-          
-          print(f"::set-output name=matrix::{json.dumps(ranges)}")
-          EOF
-
-  clone-faa-repo:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Cache FAA repository
-        id: cache-faa-repo
-        uses: actions/cache@v4
-        with:
-          path: data/scrape-faa-releasable-aircraft
-          key: faa-repo-v1
-          
-      - name: Clone FAA repository
-        if: steps.cache-faa-repo.outputs.cache-hit != 'true'
-        run: |
-          mkdir -p data
-          git clone https://github.com/simonw/scrape-faa-releasable-aircraft data/scrape-faa-releasable-aircraft
-          echo "Repository cloned successfully"
-
-  process-chunk:
-    needs: [generate-matrix, clone-faa-repo]
-    runs-on: ubuntu-latest
-    strategy:
-      max-parallel: 5  # Process 5 chunks at a time
-      matrix:
-        range: ${{ fromJson(needs.generate-matrix.outputs.matrix) }}
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-      
-      - name: Restore FAA repository cache
-        uses: actions/cache/restore@v4
-        with:
-          path: data/scrape-faa-releasable-aircraft
-          key: faa-repo-v1
-          fail-on-cache-miss: true
-      
-      - name: Install dependencies
-        run: |
-          pip install -r requirements.txt
-      
-      - name: Process chunk ${{ matrix.range.since }} to ${{ matrix.range.until }}
-        run: |
-          python src/get_historical_faa.py "${{ matrix.range.since }}" "${{ matrix.range.until }}"
-      
-      - name: Upload CSV artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: csv-${{ matrix.range.since }}-to-${{ matrix.range.until }}
-          path: data/faa_releasable_historical/*.csv
-          retention-days: 1
-
-  create-release:
-    needs: process-chunk
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - name: Download all artifacts
-        uses: actions/download-artifact@v4
-        with:
-          path: artifacts
-      
-      - name: Prepare release files
-        run: |
-          mkdir -p release-files
-          find artifacts -name "*.csv" -exec cp {} release-files/ \;
-          ls -lh release-files/
-      
-      - name: Create Release
-        uses: softprops/action-gh-release@v1
-        with:
-          tag_name: historical-faa-${{ github.run_number }}
-          name: Historical FAA Data Release ${{ github.run_number }}
-          body: |
-            Automated release of historical FAA aircraft data
-            Processing period: 2023-08-16 to 2026-01-01
-            Generated: ${{ github.event.repository.updated_at }}
-          files: release-files/*.csv
-          draft: false
-          prerelease: false
-
-  concatenate-and-release:
-    needs: process-chunk
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-      
-      - name: Install dependencies
-        run: |
-          pip install -r requirements.txt
-      
-      - name: Download all artifacts
-        uses: actions/download-artifact@v4
-        with:
-          path: artifacts
-      
-      - name: Prepare CSVs for concatenation
-        run: |
-          mkdir -p data/faa_releasable_historical
-          find artifacts -name "*.csv" -exec cp {} data/faa_releasable_historical/ \;
-          ls -lh data/faa_releasable_historical/
-      
-      - name: Concatenate all CSVs
-        run: |
-          python scripts/concat_csvs.py
-      
-      - name: Create Combined Release
-        uses: softprops/action-gh-release@v1
-        with:
-          tag_name: historical-faa-combined-${{ github.run_number }}
-          name: Historical FAA Data Combined Release ${{ github.run_number }}
-          body: |
-            Combined historical FAA aircraft data (all chunks concatenated)
-            Processing period: 2023-08-16 to 2026-01-01
-            Generated: ${{ github.event.repository.updated_at }}
-          files: data/openairframes/*.csv
-          draft: false
-          prerelease: false

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2026 OpenAirframes
+Copyright (c) 2026 PlaneQuery
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

community/2026-02-12/ggman12_2026-02-12_87250f4c.json

@@ -0,0 +1,19 @@
+[
+  {
+    "contributor_uuid": "2981c3ee-8712-5f96-84bf-732eda515a3f",
+    "creation_timestamp": "2026-02-12T20:52:47.207684+00:00",
+    "registration_number": "N12345",
+    "tags": {
+      "internet": "starlink"
+    }
+  },
+  {
+    "contributor_uuid": "2981c3ee-8712-5f96-84bf-732eda515a3f",
+    "creation_timestamp": "2026-02-12T20:52:47.207684+00:00",
+    "tags": {
+      "internet": "viasat",
+      "owner": "John Doe"
+    },
+    "transponder_code_hex": "ABC123"
+  }
+]

infra/stack.py

@@ -23,7 +23,7 @@ class AdsbProcessingStack(Stack):
         # --- S3 bucket for intermediate and final results ---
         bucket = s3.Bucket(
             self, "ResultsBucket",
-            bucket_name="openairframes-dev",
+            bucket_name="planequery-aircraft-dev",
             removal_policy=RemovalPolicy.DESTROY,
             auto_delete_objects=True,
             lifecycle_rules=[

notebooks/planequery_adsb_read.ipynb

@@ -0,0 +1,640 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06ae0319",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import clickhouse_connect\n",
+    "client = clickhouse_connect.get_client(\n",
+    "    host=os.environ[\"CLICKHOUSE_HOST\"],\n",
+    "    username=os.environ[\"CLICKHOUSE_USERNAME\"],\n",
+    "    password=os.environ[\"CLICKHOUSE_PASSWORD\"],\n",
+    "    secure=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "779710f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = client.query_df(\"SELECT time, icao,r,t,dbFlags,ownOp,year,desc,aircraft FROM adsb_messages Where time > '2024-01-01 00:00:00' AND time < '2024-01-02 00:00:00'\")\n",
+    "df_copy = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf024da8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -- military = dbFlags & 1; interesting = dbFlags & 2; PIA = dbFlags & 4; LADD = dbFlags & 8;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "270607b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = load_raw_adsb_for_day(datetime(2024,1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac06a30e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['aircraft']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91edab3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "COLUMNS = ['dbFlags', 'ownOp', 'year', 'desc', 'aircraft_category', 'r', 't']\n",
+    "def compress_df(df):\n",
+    "    icao = df.name\n",
+    "    df[\"_signature\"] = df[COLUMNS].astype(str).agg('|'.join, axis=1)\n",
+    "    original_df = df.copy()\n",
+    "    df = df.groupby(\"_signature\", as_index=False).last() # check if it works with both last and first.\n",
+    "    # For each row, create a dict of non-empty column values. This is using sets and subsets...\n",
+    "    def get_non_empty_dict(row):\n",
+    "        return {col: row[col] for col in COLUMNS if row[col] != ''}\n",
+    "    \n",
+    "    df['_non_empty_dict'] = df.apply(get_non_empty_dict, axis=1)\n",
+    "    df['_non_empty_count'] = df['_non_empty_dict'].apply(len)\n",
+    "    \n",
+    "    # Check if row i's non-empty values are a subset of row j's non-empty values\n",
+    "    def is_subset_of_any(idx):\n",
+    "        row_dict = df.loc[idx, '_non_empty_dict']\n",
+    "        row_count = df.loc[idx, '_non_empty_count']\n",
+    "        \n",
+    "        for other_idx in df.index:\n",
+    "            if idx == other_idx:\n",
+    "                continue\n",
+    "            other_dict = df.loc[other_idx, '_non_empty_dict']\n",
+    "            other_count = df.loc[other_idx, '_non_empty_count']\n",
+    "            \n",
+    "            # Check if all non-empty values in current row match those in other row\n",
+    "            if all(row_dict.get(k) == other_dict.get(k) for k in row_dict.keys()):\n",
+    "                # If they match and other has more defined columns, current row is redundant\n",
+    "                if other_count > row_count:\n",
+    "                    return True\n",
+    "        return False\n",
+    "    \n",
+    "    # Keep rows that are not subsets of any other row\n",
+    "    keep_mask = ~df.index.to_series().apply(is_subset_of_any)\n",
+    "    df = df[keep_mask]\n",
+    "\n",
+    "    if len(df) > 1:\n",
+    "        original_df = original_df[original_df['_signature'].isin(df['_signature'])]\n",
+    "        value_counts = original_df[\"_signature\"].value_counts()\n",
+    "        max_signature = value_counts.idxmax()\n",
+    "        df = df[df['_signature'] == max_signature]\n",
+    "\n",
+    "    df['icao'] = icao\n",
+    "    df = df.drop(columns=['_non_empty_dict', '_non_empty_count', '_signature'])\n",
+    "    return df\n",
+    "\n",
+    "# df = df_copy\n",
+    "# df = df_copy.iloc[0:100000]\n",
+    "# df = df[df['r'] == \"N4131T\"]\n",
+    "# df = df[(df['icao'] == \"008081\")]\n",
+    "# df = df.iloc[0:500]\n",
+    "df['aircraft_category'] = df['aircraft'].apply(lambda x: x.get('category') if isinstance(x, dict) else None)\n",
+    "df = df.drop(columns=['aircraft'])\n",
+    "df = df.sort_values(['icao', 'time'])\n",
+    "df[COLUMNS] = df[COLUMNS].fillna('')\n",
+    "ORIGINAL_COLUMNS = df.columns.tolist()\n",
+    "df_compressed = df.groupby('icao',group_keys=False).apply(compress_df)\n",
+    "cols = df_compressed.columns.tolist()\n",
+    "cols.remove(\"icao\")\n",
+    "cols.insert(1, \"icao\")\n",
+    "df_compressed = df_compressed[cols]\n",
+    "df_compressed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efdfcb2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['aircraft_category'] = df['aircraft'].apply(lambda x: x.get('category') if isinstance(x, dict) else None)\n",
+    "df[~df['aircraft_category'].isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "495c5025",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SOME KIND OF MAP REDUCE SYSTEM\n",
+    "import os\n",
+    "\n",
+    "COLUMNS = ['dbFlags', 'ownOp', 'year', 'desc', 'aircraft_category', 'r', 't']\n",
+    "def compress_df(df):\n",
+    "    icao = df.name\n",
+    "    df[\"_signature\"] = df[COLUMNS].astype(str).agg('|'.join, axis=1)\n",
+    "    \n",
+    "    # Compute signature counts before grouping (avoid copy)\n",
+    "    signature_counts = df[\"_signature\"].value_counts()\n",
+    "    \n",
+    "    df = df.groupby(\"_signature\", as_index=False).first() # check if it works with both last and first.\n",
+    "    # For each row, create a dict of non-empty column values. This is using sets and subsets...\n",
+    "    def get_non_empty_dict(row):\n",
+    "        return {col: row[col] for col in COLUMNS if row[col] != ''}\n",
+    "    \n",
+    "    df['_non_empty_dict'] = df.apply(get_non_empty_dict, axis=1)\n",
+    "    df['_non_empty_count'] = df['_non_empty_dict'].apply(len)\n",
+    "    \n",
+    "    # Check if row i's non-empty values are a subset of row j's non-empty values\n",
+    "    def is_subset_of_any(idx):\n",
+    "        row_dict = df.loc[idx, '_non_empty_dict']\n",
+    "        row_count = df.loc[idx, '_non_empty_count']\n",
+    "        \n",
+    "        for other_idx in df.index:\n",
+    "            if idx == other_idx:\n",
+    "                continue\n",
+    "            other_dict = df.loc[other_idx, '_non_empty_dict']\n",
+    "            other_count = df.loc[other_idx, '_non_empty_count']\n",
+    "            \n",
+    "            # Check if all non-empty values in current row match those in other row\n",
+    "            if all(row_dict.get(k) == other_dict.get(k) for k in row_dict.keys()):\n",
+    "                # If they match and other has more defined columns, current row is redundant\n",
+    "                if other_count > row_count:\n",
+    "                    return True\n",
+    "        return False\n",
+    "    \n",
+    "    # Keep rows that are not subsets of any other row\n",
+    "    keep_mask = ~df.index.to_series().apply(is_subset_of_any)\n",
+    "    df = df[keep_mask]\n",
+    "\n",
+    "    if len(df) > 1:\n",
+    "        # Use pre-computed signature counts instead of original_df\n",
+    "        remaining_sigs = df['_signature']\n",
+    "        sig_counts = signature_counts[remaining_sigs]\n",
+    "        max_signature = sig_counts.idxmax()\n",
+    "        df = df[df['_signature'] == max_signature]\n",
+    "\n",
+    "    df['icao'] = icao\n",
+    "    df = df.drop(columns=['_non_empty_dict', '_non_empty_count', '_signature'])\n",
+    "    return df\n",
+    "\n",
+    "# names of releases something like\n",
+    "# planequery_aircraft_adsb_2024-06-01T00-00-00Z.csv.gz\n",
+    "\n",
+    "# Let's build historical first. \n",
+    "\n",
+    "_ch_client = None\n",
+    "\n",
+    "def _get_clickhouse_client():\n",
+    "    \"\"\"Return a reusable ClickHouse client, with retry/backoff for transient DNS or connection errors.\"\"\"\n",
+    "    global _ch_client\n",
+    "    if _ch_client is not None:\n",
+    "        return _ch_client\n",
+    "\n",
+    "    import clickhouse_connect\n",
+    "    import time\n",
+    "\n",
+    "    max_retries = 5\n",
+    "    for attempt in range(1, max_retries + 1):\n",
+    "        try:\n",
+    "            _ch_client = clickhouse_connect.get_client(\n",
+    "                host=os.environ[\"CLICKHOUSE_HOST\"],\n",
+    "                username=os.environ[\"CLICKHOUSE_USERNAME\"],\n",
+    "                password=os.environ[\"CLICKHOUSE_PASSWORD\"],\n",
+    "                secure=True,\n",
+    "            )\n",
+    "            return _ch_client\n",
+    "        except Exception as e:\n",
+    "            wait = min(2 ** attempt, 30)\n",
+    "            print(f\"  ClickHouse connect attempt {attempt}/{max_retries} failed: {e}\")\n",
+    "            if attempt == max_retries:\n",
+    "                raise\n",
+    "            print(f\"  Retrying in {wait}s...\")\n",
+    "            time.sleep(wait)\n",
+    "\n",
+    "\n",
+    "def load_raw_adsb_for_day(day):\n",
+    "    \"\"\"Load raw ADS-B data for a day from cache or ClickHouse.\"\"\"\n",
+    "    from datetime import timedelta\n",
+    "    from pathlib import Path\n",
+    "    import pandas as pd\n",
+    "    import time\n",
+    "    \n",
+    "    start_time = day.replace(hour=0, minute=0, second=0, microsecond=0)\n",
+    "    end_time = start_time + timedelta(days=1)\n",
+    "    \n",
+    "    # Set up caching\n",
+    "    cache_dir = Path(\"data/adsb\")\n",
+    "    cache_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    cache_file = cache_dir / f\"adsb_raw_{start_time.strftime('%Y-%m-%d')}.csv.zst\"\n",
+    "    \n",
+    "    # Check if cache exists\n",
+    "    if cache_file.exists():\n",
+    "        print(f\"  Loading from cache: {cache_file}\")\n",
+    "        df = pd.read_csv(cache_file, compression='zstd')\n",
+    "        df['time'] = pd.to_datetime(df['time'])\n",
+    "    else:\n",
+    "        # Format dates for the query\n",
+    "        start_str = start_time.strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        end_str = end_time.strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        \n",
+    "        max_retries = 3\n",
+    "        for attempt in range(1, max_retries + 1):\n",
+    "            try:\n",
+    "                client = _get_clickhouse_client()\n",
+    "                print(f\"  Querying ClickHouse for {start_time.strftime('%Y-%m-%d')}\")\n",
+    "                df = client.query_df(f\"SELECT time, icao,r,t,dbFlags,ownOp,year,desc,aircraft FROM adsb_messages Where time > '{start_str}' AND time < '{end_str}'\")\n",
+    "                break\n",
+    "            except Exception as e:\n",
+    "                wait = min(2 ** attempt, 30)\n",
+    "                print(f\"  Query attempt {attempt}/{max_retries} failed: {e}\")\n",
+    "                if attempt == max_retries:\n",
+    "                    raise\n",
+    "                # Reset client in case connection is stale\n",
+    "                global _ch_client\n",
+    "                _ch_client = None\n",
+    "                print(f\"  Retrying in {wait}s...\")\n",
+    "                time.sleep(wait)\n",
+    "        \n",
+    "        # Save to cache\n",
+    "        df.to_csv(cache_file, index=False, compression='zstd')\n",
+    "        print(f\"  Saved to cache: {cache_file}\")\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def load_historical_for_day(day):\n",
+    "    from pathlib import Path\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    df = load_raw_adsb_for_day(day)\n",
+    "    print(df)\n",
+    "    df['aircraft_category'] = df['aircraft'].apply(lambda x: x.get('category') if isinstance(x, dict) else None)\n",
+    "    df = df.drop(columns=['aircraft'])\n",
+    "    df = df.sort_values(['icao', 'time'])\n",
+    "    df[COLUMNS] = df[COLUMNS].fillna('')\n",
+    "    df_compressed = df.groupby('icao',group_keys=False).apply(compress_df)\n",
+    "    cols = df_compressed.columns.tolist()\n",
+    "    cols.remove('time')\n",
+    "    cols.insert(0, 'time')\n",
+    "    cols.remove(\"icao\")\n",
+    "    cols.insert(1, \"icao\")\n",
+    "    df_compressed = df_compressed[cols]\n",
+    "    return df_compressed\n",
+    "\n",
+    "\n",
+    "def concat_compressed_dfs(df_base, df_new):\n",
+    "    \"\"\"Concatenate base and new compressed dataframes, keeping the most informative row per ICAO.\"\"\"\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    # Combine both dataframes\n",
+    "    df_combined = pd.concat([df_base, df_new], ignore_index=True)\n",
+    "    \n",
+    "    # Sort by ICAO and time\n",
+    "    df_combined = df_combined.sort_values(['icao', 'time'])\n",
+    "    \n",
+    "    # Fill NaN values\n",
+    "    df_combined[COLUMNS] = df_combined[COLUMNS].fillna('')\n",
+    "    \n",
+    "    # Apply compression logic per ICAO to get the best row\n",
+    "    df_compressed = df_combined.groupby('icao', group_keys=False).apply(compress_df)\n",
+    "    \n",
+    "    # Sort by time\n",
+    "    df_compressed = df_compressed.sort_values('time')\n",
+    "    \n",
+    "    return df_compressed\n",
+    "\n",
+    "\n",
+    "def get_latest_aircraft_adsb_csv_df():\n",
+    "    \"\"\"Download and load the latest ADS-B CSV from GitHub releases.\"\"\"\n",
+    "    from get_latest_planequery_aircraft_release import download_latest_aircraft_adsb_csv\n",
+    "    \n",
+    "    import pandas as pd\n",
+    "    import re\n",
+    "    \n",
+    "    csv_path = download_latest_aircraft_adsb_csv()\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    df = df.fillna(\"\")\n",
+    "    \n",
+    "    # Extract start date from filename pattern: planequery_aircraft_adsb_{start_date}_{end_date}.csv\n",
+    "    match = re.search(r\"planequery_aircraft_adsb_(\\d{4}-\\d{2}-\\d{2})_\", str(csv_path))\n",
+    "    if not match:\n",
+    "        raise ValueError(f\"Could not extract date from filename: {csv_path.name}\")\n",
+    "    \n",
+    "    date_str = match.group(1)\n",
+    "    return df, date_str\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f66acf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SOME KIND OF MAP REDUCE SYSTEM\n",
+    "\n",
+    "\n",
+    "COLUMNS = ['dbFlags', 'ownOp', 'year', 'desc', 'aircraft_category', 'r', 't']\n",
+    "def compress_df(df):\n",
+    "    icao = df.name\n",
+    "    df[\"_signature\"] = df[COLUMNS].astype(str).agg('|'.join, axis=1)\n",
+    "    original_df = df.copy()\n",
+    "    df = df.groupby(\"_signature\", as_index=False).first() # check if it works with both last and first.\n",
+    "    # For each row, create a dict of non-empty column values. This is using sets and subsets...\n",
+    "    def get_non_empty_dict(row):\n",
+    "        return {col: row[col] for col in COLUMNS if row[col] != ''}\n",
+    "    \n",
+    "    df['_non_empty_dict'] = df.apply(get_non_empty_dict, axis=1)\n",
+    "    df['_non_empty_count'] = df['_non_empty_dict'].apply(len)\n",
+    "    \n",
+    "    # Check if row i's non-empty values are a subset of row j's non-empty values\n",
+    "    def is_subset_of_any(idx):\n",
+    "        row_dict = df.loc[idx, '_non_empty_dict']\n",
+    "        row_count = df.loc[idx, '_non_empty_count']\n",
+    "        \n",
+    "        for other_idx in df.index:\n",
+    "            if idx == other_idx:\n",
+    "                continue\n",
+    "            other_dict = df.loc[other_idx, '_non_empty_dict']\n",
+    "            other_count = df.loc[other_idx, '_non_empty_count']\n",
+    "            \n",
+    "            # Check if all non-empty values in current row match those in other row\n",
+    "            if all(row_dict.get(k) == other_dict.get(k) for k in row_dict.keys()):\n",
+    "                # If they match and other has more defined columns, current row is redundant\n",
+    "                if other_count > row_count:\n",
+    "                    return True\n",
+    "        return False\n",
+    "    \n",
+    "    # Keep rows that are not subsets of any other row\n",
+    "    keep_mask = ~df.index.to_series().apply(is_subset_of_any)\n",
+    "    df = df[keep_mask]\n",
+    "\n",
+    "    if len(df) > 1:\n",
+    "        original_df = original_df[original_df['_signature'].isin(df['_signature'])]\n",
+    "        value_counts = original_df[\"_signature\"].value_counts()\n",
+    "        max_signature = value_counts.idxmax()\n",
+    "        df = df[df['_signature'] == max_signature]\n",
+    "\n",
+    "    df['icao'] = icao\n",
+    "    df = df.drop(columns=['_non_empty_dict', '_non_empty_count', '_signature'])\n",
+    "    return df\n",
+    "\n",
+    "# names of releases something like\n",
+    "# planequery_aircraft_adsb_2024-06-01T00-00-00Z.csv.gz\n",
+    "\n",
+    "# Let's build historical first. \n",
+    "\n",
+    "def load_raw_adsb_for_day(day):\n",
+    "    \"\"\"Load raw ADS-B data for a day from cache or ClickHouse.\"\"\"\n",
+    "    from datetime import timedelta\n",
+    "    import clickhouse_connect\n",
+    "    from pathlib import Path\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    start_time = day.replace(hour=0, minute=0, second=0, microsecond=0)\n",
+    "    end_time = start_time + timedelta(days=1)\n",
+    "    \n",
+    "    # Set up caching\n",
+    "    cache_dir = Path(\"data/adsb\")\n",
+    "    cache_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    cache_file = cache_dir / f\"adsb_raw_{start_time.strftime('%Y-%m-%d')}.csv.zst\"\n",
+    "    \n",
+    "    # Check if cache exists\n",
+    "    if cache_file.exists():\n",
+    "        print(f\"  Loading from cache: {cache_file}\")\n",
+    "        df = pd.read_csv(cache_file, compression='zstd')\n",
+    "        df['time'] = pd.to_datetime(df['time'])\n",
+    "    else:\n",
+    "        # Format dates for the query\n",
+    "        start_str = start_time.strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        end_str = end_time.strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        \n",
+    "        client = clickhouse_connect.get_client(\n",
+    "            host=os.environ[\"CLICKHOUSE_HOST\"],\n",
+    "            username=os.environ[\"CLICKHOUSE_USERNAME\"],\n",
+    "            password=os.environ[\"CLICKHOUSE_PASSWORD\"],\n",
+    "            secure=True,\n",
+    "        )\n",
+    "        print(f\"  Querying ClickHouse for {start_time.strftime('%Y-%m-%d')}\")\n",
+    "        df = client.query_df(f\"SELECT time, icao,r,t,dbFlags,ownOp,year,desc,aircraft FROM adsb_messages Where time > '{start_str}' AND time < '{end_str}'\")\n",
+    "        \n",
+    "        # Save to cache\n",
+    "        df.to_csv(cache_file, index=False, compression='zstd')\n",
+    "        print(f\"  Saved to cache: {cache_file}\")\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def load_historical_for_day(day):\n",
+    "    from pathlib import Path\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    df = load_raw_adsb_for_day(day)\n",
+    "    \n",
+    "    df['aircraft_category'] = df['aircraft'].apply(lambda x: x.get('category') if isinstance(x, dict) else None)\n",
+    "    df = df.drop(columns=['aircraft'])\n",
+    "    df = df.sort_values(['icao', 'time'])\n",
+    "    df[COLUMNS] = df[COLUMNS].fillna('')\n",
+    "    df_compressed = df.groupby('icao',group_keys=False).apply(compress_df)\n",
+    "    cols = df_compressed.columns.tolist()\n",
+    "    cols.remove('time')\n",
+    "    cols.insert(0, 'time')\n",
+    "    cols.remove(\"icao\")\n",
+    "    cols.insert(1, \"icao\")\n",
+    "    df_compressed = df_compressed[cols]\n",
+    "    return df_compressed\n",
+    "\n",
+    "\n",
+    "def concat_compressed_dfs(df_base, df_new):\n",
+    "    \"\"\"Concatenate base and new compressed dataframes, keeping the most informative row per ICAO.\"\"\"\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    # Combine both dataframes\n",
+    "    df_combined = pd.concat([df_base, df_new], ignore_index=True)\n",
+    "    \n",
+    "    # Sort by ICAO and time\n",
+    "    df_combined = df_combined.sort_values(['icao', 'time'])\n",
+    "    \n",
+    "    # Fill NaN values\n",
+    "    df_combined[COLUMNS] = df_combined[COLUMNS].fillna('')\n",
+    "    \n",
+    "    # Apply compression logic per ICAO to get the best row\n",
+    "    df_compressed = df_combined.groupby('icao', group_keys=False).apply(compress_df)\n",
+    "    \n",
+    "    # Sort by time\n",
+    "    df_compressed = df_compressed.sort_values('time')\n",
+    "    \n",
+    "    return df_compressed\n",
+    "\n",
+    "\n",
+    "def get_latest_aircraft_adsb_csv_df():\n",
+    "    \"\"\"Download and load the latest ADS-B CSV from GitHub releases.\"\"\"\n",
+    "    from get_latest_planequery_aircraft_release import download_latest_aircraft_adsb_csv\n",
+    "    \n",
+    "    import pandas as pd\n",
+    "    import re\n",
+    "    \n",
+    "    csv_path = download_latest_aircraft_adsb_csv()\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    df = df.fillna(\"\")\n",
+    "    \n",
+    "    # Extract start date from filename pattern: planequery_aircraft_adsb_{start_date}_{end_date}.csv\n",
+    "    match = re.search(r\"planequery_aircraft_adsb_(\\d{4}-\\d{2}-\\d{2})_\", str(csv_path))\n",
+    "    if not match:\n",
+    "        raise ValueError(f\"Could not extract date from filename: {csv_path.name}\")\n",
+    "    \n",
+    "    date_str = match.group(1)\n",
+    "    return df, date_str\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e14c8363",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "df = load_historical_for_day(datetime(2024,1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3874ba4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcae50ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[(df['icao'] == \"008081\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50921c86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[df['icao'] == \"a4e1d2\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8194d9aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[df['r'] == \"N4131T\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e3b7aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_compressed[df_compressed['icao'].duplicated(keep=False)]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40613bc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "\n",
+    "path = \"/Users/jonahgoode/Downloads/test_extract/traces/fb/trace_full_acbbfb.json\"\n",
+    "\n",
+    "with gzip.open(path, \"rt\", encoding=\"utf-8\") as f:\n",
+    "    data = json.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "# use `data` here\n",
+    "import json\n",
+    "print(json.dumps(data, indent=2)[:2000])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "320109b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, load the JSON to inspect its structure\n",
+    "import json\n",
+    "with open(\"/Users/jonahgoode/Documents/PlaneQuery/Other-Code/readsb-protobuf/webapp/src/db/aircrafts.json\", 'r') as f:\n",
+    "    data = json.load(f)\n",
+    "\n",
+    "# Check the structure\n",
+    "print(type(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "590134f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['AC97E3']"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

schemas/community_submission.v1.schema.json

@@ -1,6 +1,6 @@
 {
   "$schema": "https://json-schema.org/draft/2020-12/schema",
-  "title": "OpenAirframes Community Submission (v1)",
+  "title": "PlaneQuery Aircraft Community Submission (v1)",
   "type": "object",
   "additionalProperties": false,
   "properties": {
@@ -12,7 +12,7 @@
       "type": "string",
       "pattern": "^[0-9A-F]{6}$"
     },
-    "openairframes_id": {
+    "planequery_airframe_id": {
       "type": "string",
       "minLength": 1
     },
@@ -46,15 +46,22 @@
     },
     "tags": {
       "type": "object",
-      "description": "Additional community-defined tags as key/value pairs (values may be scalar, array, or object).",
+      "description": "Community-defined tags. New tags can be added, but must use consistent types.",
       "propertyNames": {
         "type": "string",
         "pattern": "^[a-z][a-z0-9_]{0,63}$"
       },
+      "properties": {
+        "internet": {
+          "type": "string"
+        },
+        "owner": {
+          "type": "string"
+        }
+      },
       "additionalProperties": {
         "$ref": "#/$defs/tagValue"
-      },
+      }
-      "properties": {}
     }
   },
   "allOf": [
@@ -72,7 +79,7 @@
         },
         {
           "required": [
-            "openairframes_id"
+            "planequery_airframe_id"
           ]
         }
       ]

src/adsb/combine_chunks_to_csv.py

@@ -27,7 +27,7 @@ from src.adsb.compress_adsb_to_aircraft_data import compress_multi_icao_df, COLU
 
 
 DEFAULT_CHUNK_DIR = os.path.join(OUTPUT_DIR, "adsb_chunks")
-FINAL_OUTPUT_DIR = "./data/openairframes"
+FINAL_OUTPUT_DIR = "./data/planequery_aircraft"
 os.makedirs(FINAL_OUTPUT_DIR, exist_ok=True)
 
 
@@ -85,12 +85,12 @@ def combine_compressed_chunks(compressed_dfs: list[pl.DataFrame]) -> pl.DataFram
 
 def download_and_merge_base_release(compressed_df: pl.DataFrame) -> pl.DataFrame:
     """Download base release and merge with new data."""
-    from src.get_latest_release import download_latest_aircraft_adsb_csv
+    from src.get_latest_planequery_aircraft_release import download_latest_aircraft_adsb_csv
     
     print("Downloading base ADS-B release...")
     try:
         base_path = download_latest_aircraft_adsb_csv(
-            output_dir="./data/openairframes_base"
+            output_dir="./data/planequery_aircraft_base"
         )
         print(f"Download returned: {base_path}")
         
@@ -176,10 +176,10 @@ def main():
     if args.start_date and args.end_date:
         # Historical mode
         output_id = f"{args.start_date}_{args.end_date}"
-        output_filename = f"openairframes_adsb_{args.start_date}_{args.end_date}.csv"
+        output_filename = f"planequery_aircraft_adsb_{args.start_date}_{args.end_date}.csv"
         print(f"Combining chunks for date range: {args.start_date} to {args.end_date}")
     else:
-        # Daily mode - use same date for start and end
+        # Daily mode
         if args.date:
             target_day = datetime.strptime(args.date, "%Y-%m-%d")
         else:
@@ -187,7 +187,7 @@ def main():
         
         date_str = target_day.strftime("%Y-%m-%d")
         output_id = date_str
-        output_filename = f"openairframes_adsb_{date_str}_{date_str}.csv"
+        output_filename = f"planequery_aircraft_adsb_{date_str}.csv"
         print(f"Combining chunks for {date_str}")
     
     chunks_dir = args.chunks_dir

src/adsb/compress_adsb_to_aircraft_data.py

@@ -253,7 +253,7 @@ def concat_compressed_dfs(df_base, df_new):
 
 def get_latest_aircraft_adsb_csv_df():
     """Download and load the latest ADS-B CSV from GitHub releases."""
-    from get_latest_release import download_latest_aircraft_adsb_csv
+    from get_latest_planequery_aircraft_release import download_latest_aircraft_adsb_csv
     import re
     
     csv_path = download_latest_aircraft_adsb_csv()
@@ -264,8 +264,8 @@ def get_latest_aircraft_adsb_csv_df():
         if df[col].dtype == pl.Utf8:
             df = df.with_columns(pl.col(col).fill_null(""))
     
-    # Extract start date from filename pattern: openairframes_adsb_{start_date}_{end_date}.csv
+    # Extract start date from filename pattern: planequery_aircraft_adsb_{start_date}_{end_date}.csv
-    match = re.search(r"openairframes_adsb_(\d{4}-\d{2}-\d{2})_", str(csv_path))
+    match = re.search(r"planequery_aircraft_adsb_(\d{4}-\d{2}-\d{2})_", str(csv_path))
     if not match:
         raise ValueError(f"Could not extract date from filename: {csv_path.name}")
     

src/adsb/download_adsb_data_to_parquet.py

@@ -82,8 +82,7 @@ def fetch_releases(version_date: str) -> list:
     if version_date == "v2024.12.31":
         year = "2025"
     BASE_URL = f"https://api.github.com/repos/adsblol/globe_history_{year}/releases"
-    # Match exact release name, exclude tmp releases
+    PATTERN = f"{version_date}-planes-readsb-prod-0"
-    PATTERN = rf"^{re.escape(version_date)}-planes-readsb-prod-\d+$"
     releases = []
     page = 1
     
@@ -188,23 +187,19 @@ def extract_split_archive(file_paths: list, extract_dir: str) -> bool:
         cat_proc = subprocess.Popen(
             ["cat"] + file_paths,
             stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE
+            stderr=subprocess.DEVNULL
         )
         tar_cmd = ["tar", "xf", "-", "-C", extract_dir, "--strip-components=1"]
-        result = subprocess.run(
+        subprocess.run(
             tar_cmd,
             stdin=cat_proc.stdout,
-            stdout=subprocess.PIPE,
+            stdout=subprocess.DEVNULL,
-            stderr=subprocess.PIPE,
+            stderr=subprocess.DEVNULL,
             check=True
         )
         cat_proc.stdout.close()
-        cat_stderr = cat_proc.stderr.read().decode() if cat_proc.stderr else ""
         cat_proc.wait()
         
-        if cat_stderr:
-            print(f"cat stderr: {cat_stderr}")
-        
         print(f"Successfully extracted archive to {extract_dir}")
         
         # Delete tar files immediately after extraction
@@ -222,10 +217,7 @@ def extract_split_archive(file_paths: list, extract_dir: str) -> bool:
         
         return True
     except subprocess.CalledProcessError as e:
-        stderr_output = e.stderr.decode() if e.stderr else ""
         print(f"Failed to extract split archive: {e}")
-        if stderr_output:
-            print(f"tar stderr: {stderr_output}")
         return False
 
 

src/adsb/reducer.py

@@ -76,8 +76,8 @@ def main():
     print(f"After dedup: {df_accumulated.height} rows")
 
     # Write and upload final result
-    output_name = f"openairframes_adsb_{global_start}_{global_end}.csv.gz"
+    output_name = f"planequery_aircraft_adsb_{global_start}_{global_end}.csv.gz"
-    csv_output = Path(f"/tmp/openairframes_adsb_{global_start}_{global_end}.csv")
+    csv_output = Path(f"/tmp/planequery_aircraft_adsb_{global_start}_{global_end}.csv")
     gz_output = Path(f"/tmp/{output_name}")
     
     df_accumulated.write_csv(csv_output)

src/contributions/create_daily_community_release.py

@@ -17,7 +17,7 @@ import pandas as pd
 
 
 COMMUNITY_DIR = Path(__file__).parent.parent.parent / "community"
-OUT_ROOT = Path("data/openairframes")
+OUT_ROOT = Path("data/planequery_aircraft")
 
 
 def read_all_submissions(community_dir: Path) -> list[dict]:
@@ -47,7 +47,7 @@ def submissions_to_dataframe(submissions: list[dict]) -> pd.DataFrame:
     - creation_timestamp (first)
     - transponder_code_hex
     - registration_number  
-    - openairframes_id
+    - planequery_airframe_id
     - contributor_name
     - [other columns alphabetically]
     - contributor_uuid (last)
@@ -62,7 +62,7 @@ def submissions_to_dataframe(submissions: list[dict]) -> pd.DataFrame:
         "creation_timestamp",
         "transponder_code_hex",
         "registration_number",
-        "openairframes_id",
+        "planequery_airframe_id",
         "contributor_name",
         "contributor_uuid",
     ]
@@ -78,7 +78,7 @@ def submissions_to_dataframe(submissions: list[dict]) -> pd.DataFrame:
         "creation_timestamp",
         "transponder_code_hex",
         "registration_number",
-        "openairframes_id",
+        "planequery_airframe_id",
         "contributor_name",
     ]
     last_cols = ["contributor_uuid"]
@@ -108,7 +108,7 @@ def main():
             "creation_timestamp",
             "transponder_code_hex",
             "registration_number",
-            "openairframes_id",
+            "planequery_airframe_id",
             "contributor_name",
             "tags",
             "contributor_uuid",
@@ -127,7 +127,7 @@ def main():
     
     # Output
     OUT_ROOT.mkdir(parents=True, exist_ok=True)
-    output_file = OUT_ROOT / f"openairframes_community_{start_date_str}_{date_str}.csv"
+    output_file = OUT_ROOT / f"planequery_aircraft_community_{start_date_str}_{date_str}.csv"
     
     df.to_csv(output_file, index=False)
     

src/contributions/read_community_data.py

@@ -112,8 +112,8 @@ def group_by_identifier(submissions: list[dict]) -> dict[str, list[dict]]:
             key = f"reg:{submission['registration_number']}"
         elif "transponder_code_hex" in submission:
             key = f"icao:{submission['transponder_code_hex']}"
-        elif "openairframes_id" in submission:
+        elif "planequery_airframe_id" in submission:
-            key = f"id:{submission['openairframes_id']}"
+            key = f"id:{submission['planequery_airframe_id']}"
         else:
             key = "_unknown"
         

src/contributions/schema.py

@@ -111,7 +111,7 @@ def download_github_attachment(url: str) -> str | None:
     import urllib.error
     
     try:
-        req = urllib.request.Request(url, headers={"User-Agent": "OpenAirframes-Bot"})
+        req = urllib.request.Request(url, headers={"User-Agent": "PlaneQuery-Bot"})
         with urllib.request.urlopen(req, timeout=30) as response:
             return response.read().decode("utf-8")
     except (urllib.error.URLError, urllib.error.HTTPError, UnicodeDecodeError) as e:

src/contributions/update_schema.py

@@ -58,9 +58,18 @@ def generate_updated_schema(base_schema: dict, tag_registry: dict[str, str]) ->
     for tag_name, type_name in sorted(tag_registry.items()):
         tag_properties[tag_name] = type_name_to_json_schema(type_name)
     
-    # Only add/update the properties key within tags, preserve everything else
+    # Update tags definition
-    if "properties" in schema and "tags" in schema["properties"]:
+    schema["properties"]["tags"] = {
-        schema["properties"]["tags"]["properties"] = tag_properties
+        "type": "object",
+        "description": "Community-defined tags. New tags can be added, but must use consistent types.",
+        "propertyNames": {
+            "type": "string",
+            "pattern": "^[a-z][a-z0-9_]{0,63}$"
+        },
+        "properties": tag_properties,
+        # Still allow additional properties for new tags
+        "additionalProperties": {"$ref": "#/$defs/tagValue"}
+    }
     
     return schema
 

src/create_daily_planequery_aircraft_adsb_release.py

@@ -74,10 +74,10 @@ if __name__ == '__main__':
             )
 
     # Save the result
-    OUT_ROOT = Path("data/openairframes")
+    OUT_ROOT = Path("data/planequery_aircraft")
     OUT_ROOT.mkdir(parents=True, exist_ok=True)
 
-    output_file = OUT_ROOT / f"openairframes_adsb_{start_date_str}_{date_str}.csv"
+    output_file = OUT_ROOT / f"planequery_aircraft_adsb_{start_date_str}_{date_str}.csv"
     df_combined.write_csv(output_file)
 
     print(f"Saved: {output_file}")

src/create_daily_planequery_aircraft_release.py

@@ -22,19 +22,12 @@ if not zip_path.exists():
         body = r.read()
         zip_path.write_bytes(body)
 
-OUT_ROOT = Path("data/openairframes")
+OUT_ROOT = Path("data/planequery_aircraft")
 OUT_ROOT.mkdir(parents=True, exist_ok=True)
 from derive_from_faa_master_txt import convert_faa_master_txt_to_df, concat_faa_historical_df
-from get_latest_release import get_latest_aircraft_faa_csv_df
+from get_latest_planequery_aircraft_release import get_latest_aircraft_faa_csv_df
 df_new = convert_faa_master_txt_to_df(zip_path, date_str)
-
+df_base, start_date_str = get_latest_aircraft_faa_csv_df()
-try:
+df_base = concat_faa_historical_df(df_base, df_new)
-    df_base, start_date_str = get_latest_aircraft_faa_csv_df()
+assert df_base['download_date'].is_monotonic_increasing, "download_date is not monotonic increasing"
-    df_base = concat_faa_historical_df(df_base, df_new)
+df_base.to_csv(OUT_ROOT / f"planequery_aircraft_faa_{start_date_str}_{date_str}.csv", index=False)
-    assert df_base['download_date'].is_monotonic_increasing, "download_date is not monotonic increasing"
-except Exception as e:
-    print(f"No existing FAA release found, using only new data: {e}")
-    df_base = df_new
-    start_date_str = date_str
-
-df_base.to_csv(OUT_ROOT / f"openairframes_faa_{start_date_str}_{date_str}.csv", index=False)

src/derive_from_faa_master_txt.py

@@ -29,8 +29,8 @@ def convert_faa_master_txt_to_df(zip_path: Path, date: str):
     certification = pd.json_normalize(df["certification"].where(df["certification"].notna(), {})).add_prefix("certificate_")
     df = df.drop(columns="certification").join(certification)
     
-    # Create openairframes_id
+    # Create planequery_airframe_id
-    df["openairframes_id"] = (
+    df["planequery_airframe_id"] = (
         normalize(df["aircraft_manufacturer"])
         + "|"
         + normalize(df["aircraft_model"])
@@ -38,11 +38,11 @@ def convert_faa_master_txt_to_df(zip_path: Path, date: str):
         + normalize(df["serial_number"])
     )
     
-    # Move openairframes_id to come after registration_number
+    # Move planequery_airframe_id to come after registration_number
     cols = df.columns.tolist()
-    cols.remove("openairframes_id")
+    cols.remove("planequery_airframe_id")
     reg_idx = cols.index("registration_number")
-    cols.insert(reg_idx + 1, "openairframes_id")
+    cols.insert(reg_idx + 1, "planequery_airframe_id")
     df = df[cols]
     
     # Convert all NaN to empty strings

src/get_latest_planequery_aircraft_release.py

@@ -9,7 +9,7 @@ import urllib.error
 import json
 
 
-REPO = "PlaneQuery/openairframes"
+REPO = "PlaneQuery/planequery-aircraft"
 LATEST_RELEASE_URL = f"https://api.github.com/repos/{REPO}/releases/latest"
 
 
@@ -31,7 +31,7 @@ def get_latest_release_assets(repo: str = REPO, github_token: Optional[str] = No
     url = f"https://api.github.com/repos/{repo}/releases/latest"
     headers = {
         "Accept": "application/vnd.github+json",
-        "User-Agent": "openairframes-downloader/1.0",
+        "User-Agent": "planequery-aircraft-downloader/1.0",
     }
     if github_token:
         headers["Authorization"] = f"Bearer {github_token}"
@@ -80,7 +80,7 @@ def download_asset(asset: ReleaseAsset, out_path: Path, github_token: Optional[s
     out_path.parent.mkdir(parents=True, exist_ok=True)
 
     headers = {
-        "User-Agent": "openairframes-downloader/1.0",
+        "User-Agent": "planequery-aircraft-downloader/1.0",
         "Accept": "application/octet-stream",
     }
     if github_token:
@@ -109,7 +109,7 @@ def download_latest_aircraft_csv(
     repo: str = REPO,
 ) -> Path:
     """
-    Download the latest openairframes_faa_*.csv file from the latest GitHub release.
+    Download the latest planequery_aircraft_faa_*.csv file from the latest GitHub release.
 
     Args:
         output_dir: Directory to save the downloaded file (default: "downloads")
@@ -121,10 +121,10 @@ def download_latest_aircraft_csv(
     """
     assets = get_latest_release_assets(repo, github_token=github_token)
     try:
-        asset = pick_asset(assets, name_regex=r"^openairframes_faa_.*\.csv$")
+        asset = pick_asset(assets, name_regex=r"^planequery_aircraft_faa_.*\.csv$")
     except FileNotFoundError:
         # Fallback to old naming pattern
-        asset = pick_asset(assets, name_regex=r"^openairframes_\d{4}-\d{2}-\d{2}_.*\.csv$")
+        asset = pick_asset(assets, name_regex=r"^planequery_aircraft_\d{4}-\d{2}-\d{2}_.*\.csv$")
     saved_to = download_asset(asset, output_dir / asset.name, github_token=github_token)
     print(f"Downloaded: {asset.name} ({asset.size} bytes) -> {saved_to}")
     return saved_to
@@ -136,11 +136,11 @@ def get_latest_aircraft_faa_csv_df():
            'unique_regulatory_id': str, 
            'registrant_county': str})
     df = df.fillna("")
-    # Extract start date from filename pattern: openairframes_faa_{start_date}_{end_date}.csv
+    # Extract start date from filename pattern: planequery_aircraft_faa_{start_date}_{end_date}.csv
-    match = re.search(r"openairframes_faa_(\d{4}-\d{2}-\d{2})_", str(csv_path))
+    match = re.search(r"planequery_aircraft_faa_(\d{4}-\d{2}-\d{2})_", str(csv_path))
     if not match:
-        # Fallback to old naming pattern: openairframes_{start_date}_{end_date}.csv
+        # Fallback to old naming pattern: planequery_aircraft_{start_date}_{end_date}.csv
-        match = re.search(r"openairframes_(\d{4}-\d{2}-\d{2})_", str(csv_path))
+        match = re.search(r"planequery_aircraft_(\d{4}-\d{2}-\d{2})_", str(csv_path))
     if not match:
         raise ValueError(f"Could not extract date from filename: {csv_path.name}")
     
@@ -154,7 +154,7 @@ def download_latest_aircraft_adsb_csv(
     repo: str = REPO,
 ) -> Path:
     """
-    Download the latest openairframes_adsb_*.csv file from the latest GitHub release.
+    Download the latest planequery_aircraft_adsb_*.csv file from the latest GitHub release.
 
     Args:
         output_dir: Directory to save the downloaded file (default: "downloads")
@@ -165,7 +165,7 @@ def download_latest_aircraft_adsb_csv(
         Path to the downloaded file
     """
     assets = get_latest_release_assets(repo, github_token=github_token)
-    asset = pick_asset(assets, name_regex=r"^openairframes_adsb_.*\.csv$")
+    asset = pick_asset(assets, name_regex=r"^planequery_aircraft_adsb_.*\.csv$")
     saved_to = download_asset(asset, output_dir / asset.name, github_token=github_token)
     print(f"Downloaded: {asset.name} ({asset.size} bytes) -> {saved_to}")
     return saved_to
@@ -176,8 +176,8 @@ def get_latest_aircraft_adsb_csv_df():
     import pandas as pd
     df = pd.read_csv(csv_path)
     df = df.fillna("")
-    # Extract start date from filename pattern: openairframes_adsb_{start_date}_{end_date}.csv
+    # Extract start date from filename pattern: planequery_aircraft_adsb_{start_date}_{end_date}.csv
-    match = re.search(r"openairframes_adsb_(\d{4}-\d{2}-\d{2})_", str(csv_path))
+    match = re.search(r"planequery_aircraft_adsb_(\d{4}-\d{2}-\d{2})_", str(csv_path))
     if not match:
         raise ValueError(f"Could not extract date from filename: {csv_path.name}")