{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a802ca09-12d2-46a6-b530-70c8be0448e6",
   "metadata": {},
   "source": [
    "# NAIP Inference and Similarity Search with Clay v1\n",
    "\n",
    "This notebook walks through Clay model v1 inference on [NAIP (National Agriculture Imagery Program) data](https://naip-usdaonline.hub.arcgis.com/) and similarity search. The workflow includes loading and preprocessing data from STAC, tiling the images and encoding metadata, generating embeddings and querying across them for similar representations. The NAIP data comes in annual composites. We are using data from one year within a sampled region in San Francisco, California.\n",
    "\n",
    "The workflow includes the following steps:\n",
    "\n",
    "1. **Loading and Preprocessing Data**:\n",
    "    - Connect to a STAC (SpatioTemporal Asset Catalog) to query and download NAIP imagery for a specified region and time period.\n",
    "    - Preprocess the downloaded imagery, including tiling the images and extracting metadata.\n",
    "\n",
    "2. **Generating Embeddings**:\n",
    "    - Use a pretrained Clay model to generate embeddings for the preprocessed image tiles.\n",
    "\n",
    "3. **Saving Embeddings**:\n",
    "    - Save the generated embeddings along with the associated image data and select metadata in parquet format.\n",
    "\n",
    "4. **Similarity Search**:\n",
    "    - Load the saved embeddings into a LanceDB database.\n",
    "    - Perform a similarity search by querying the database with a randomly selected embedding.\n",
    "    - Retrieve and display the top similar images based on the similarity search."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb77ee7",
   "metadata": {},
   "source": [
    "Install the [stacchip](https://github.com/Clay-foundation/stacchip) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "195b282d-8ec2-48a1-9a54-9ee9442d80da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages/lancedb/__init__.py:220: UserWarning: lance is not fork-safe. If you are using multiprocessing, use spawn instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: stacchip==0.1.33 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (0.1.33)\n",
      "Requirement already satisfied: boto3>=1.29.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (1.38.14)\n",
      "Requirement already satisfied: geoarrow-pyarrow>=0.1.2 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (0.1.2)\n",
      "Requirement already satisfied: geopandas>=0.14.1 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (1.0.1)\n",
      "Requirement already satisfied: numpy>=1.26.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (2.2.5)\n",
      "Requirement already satisfied: planetary-computer>=1.0.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (1.0.0)\n",
      "Requirement already satisfied: pyarrow>=14.0.1 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (20.0.0)\n",
      "Requirement already satisfied: pystac-client>=0.7.5 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (0.8.6)\n",
      "Requirement already satisfied: pystac>=1.9.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (1.13.0)\n",
      "Requirement already satisfied: rasterio>=1.3.9 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (1.4.3)\n",
      "Requirement already satisfied: rio-stac>=0.9.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from stacchip==0.1.33) (0.11.0)\n",
      "Requirement already satisfied: botocore<1.39.0,>=1.38.14 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from boto3>=1.29.0->stacchip==0.1.33) (1.38.14)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from boto3>=1.29.0->stacchip==0.1.33) (1.0.1)\n",
      "Requirement already satisfied: s3transfer<0.13.0,>=0.12.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from boto3>=1.29.0->stacchip==0.1.33) (0.12.0)\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from botocore<1.39.0,>=1.38.14->boto3>=1.29.0->stacchip==0.1.33) (2.9.0.post0)\n",
      "Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from botocore<1.39.0,>=1.38.14->boto3>=1.29.0->stacchip==0.1.33) (2.4.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.39.0,>=1.38.14->boto3>=1.29.0->stacchip==0.1.33) (1.17.0)\n",
      "Requirement already satisfied: geoarrow-c in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from geoarrow-pyarrow>=0.1.2->stacchip==0.1.33) (0.1.2)\n",
      "Requirement already satisfied: pyarrow-hotfix in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from geoarrow-pyarrow>=0.1.2->stacchip==0.1.33) (0.7)\n",
      "Requirement already satisfied: pyogrio>=0.7.2 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from geopandas>=0.14.1->stacchip==0.1.33) (0.11.0)\n",
      "Requirement already satisfied: packaging in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from geopandas>=0.14.1->stacchip==0.1.33) (24.2)\n",
      "Requirement already satisfied: pandas>=1.4.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from geopandas>=0.14.1->stacchip==0.1.33) (2.2.3)\n",
      "Requirement already satisfied: pyproj>=3.3.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from geopandas>=0.14.1->stacchip==0.1.33) (3.7.1)\n",
      "Requirement already satisfied: shapely>=2.0.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from geopandas>=0.14.1->stacchip==0.1.33) (2.1.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from pandas>=1.4.0->geopandas>=0.14.1->stacchip==0.1.33) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from pandas>=1.4.0->geopandas>=0.14.1->stacchip==0.1.33) (2025.2)\n",
      "Requirement already satisfied: click>=7.1 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from planetary-computer>=1.0.0->stacchip==0.1.33) (8.2.0)\n",
      "Requirement already satisfied: pydantic>=1.7.3 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from planetary-computer>=1.0.0->stacchip==0.1.33) (2.11.4)\n",
      "Requirement already satisfied: requests>=2.25.1 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from planetary-computer>=1.0.0->stacchip==0.1.33) (2.32.3)\n",
      "Requirement already satisfied: python-dotenv in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from planetary-computer>=1.0.0->stacchip==0.1.33) (1.1.0)\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from pydantic>=1.7.3->planetary-computer>=1.0.0->stacchip==0.1.33) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.33.2 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from pydantic>=1.7.3->planetary-computer>=1.0.0->stacchip==0.1.33) (2.33.2)\n",
      "Requirement already satisfied: typing-extensions>=4.12.2 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from pydantic>=1.7.3->planetary-computer>=1.0.0->stacchip==0.1.33) (4.13.2)\n",
      "Requirement already satisfied: typing-inspection>=0.4.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from pydantic>=1.7.3->planetary-computer>=1.0.0->stacchip==0.1.33) (0.4.0)\n",
      "Requirement already satisfied: certifi in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from pyogrio>=0.7.2->geopandas>=0.14.1->stacchip==0.1.33) (2025.4.26)\n",
      "Requirement already satisfied: jsonschema~=4.18 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from pystac[validation]>=1.10.0->pystac-client>=0.7.5->stacchip==0.1.33) (4.23.0)\n",
      "Requirement already satisfied: attrs>=22.2.0 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from jsonschema~=4.18->pystac[validation]>=1.10.0->pystac-client>=0.7.5->stacchip==0.1.33) (25.3.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from jsonschema~=4.18->pystac[validation]>=1.10.0->pystac-client>=0.7.5->stacchip==0.1.33) (2025.4.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from jsonschema~=4.18->pystac[validation]>=1.10.0->pystac-client>=0.7.5->stacchip==0.1.33) (0.36.2)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from jsonschema~=4.18->pystac[validation]>=1.10.0->pystac-client>=0.7.5->stacchip==0.1.33) (0.24.0)\n",
      "Requirement already satisfied: affine in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from rasterio>=1.3.9->stacchip==0.1.33) (2.4.0)\n",
      "Requirement already satisfied: cligj>=0.5 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from rasterio>=1.3.9->stacchip==0.1.33) (0.7.2)\n",
      "Requirement already satisfied: click-plugins in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from rasterio>=1.3.9->stacchip==0.1.33) (1.1.1)\n",
      "Requirement already satisfied: pyparsing in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from rasterio>=1.3.9->stacchip==0.1.33) (3.2.3)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from requests>=2.25.1->planetary-computer>=1.0.0->stacchip==0.1.33) (3.4.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/brunosan/anaconda3/envs/claymodel/lib/python3.11/site-packages (from requests>=2.25.1->planetary-computer>=1.0.0->stacchip==0.1.33) (3.10)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install stacchip==0.1.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0c141b9-4038-4542-832c-f71e04bd93c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(\"../../\")  # Model src\n",
    "\n",
    "# If the pip install for stacchip doesn't work above,\n",
    "# git clone the repo and comment out the following with the path\n",
    "sys.path.append(\"../../../stacchip/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48002199-2fab-4d85-aeba-25f651865f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import glob\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "\n",
    "import geopandas as gpd\n",
    "import lancedb\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pystac_client\n",
    "import requests\n",
    "import shapely\n",
    "import torch\n",
    "import yaml\n",
    "from box import Box\n",
    "from pyproj import Transformer\n",
    "from rasterio.io import MemoryFile\n",
    "from shapely.geometry import box\n",
    "from stacchip.chipper import Chipper\n",
    "from stacchip.indexer import NoStatsChipIndexer\n",
    "from stacchip.processors.prechip import normalize_timestamp\n",
    "from torchvision.transforms import v2\n",
    "\n",
    "from src.module import ClayMAEModule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ce6a21b7-cfe8-44f7-86ce-ce7852f963eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the platform name and year for the NAIP data\n",
    "PLATFORM_NAME = \"naip\"\n",
    "YEAR = 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "71742792-0bf4-429e-ab00-122643a9fafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query STAC catalog for NAIP data\n",
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "279e0cdf-da24-411e-9a90-1b42327cc766",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform a search on the STAC catalog,\n",
    "# specifying the collection to search within (NAIP data),\n",
    "# defining the bounding box for the search area (San Francisco region), and\n",
    "# setting the date range for the search (entire year 2020).\n",
    "# Also limit the search to a maximum of 100 items.\n",
    "items = catalog.search(\n",
    "    collections=[PLATFORM_NAME],\n",
    "    bbox=[-122.6, 37.6, -122.35, 37.85],\n",
    "    datetime=f\"{YEAR}-01-01T00:00:00Z/{YEAR+1}-01-01T00:00:00Z\",\n",
    "    max_items=100,\n",
    ")\n",
    "\n",
    "# Convert the search results to an item collection\n",
    "items = items.item_collection()\n",
    "\n",
    "# Convert the item collection to a list for easier manipulation\n",
    "items_list = list(items)\n",
    "\n",
    "# Randomly shuffle the list of items to ensure random sampling\n",
    "random.shuffle(items_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2b1520d-4977-48b3-a270-b786ed69ae28",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_bounds_centroid(url: str):\n",
    "    \"\"\"\n",
    "    Retrieve the bounds and centroid of an image from its URL.\n",
    "\n",
    "    Parameters:\n",
    "    url (str): The URL of the image.\n",
    "\n",
    "    Returns:\n",
    "    tuple: Bounds coordinates and centroid coordinates.\n",
    "    \"\"\"\n",
    "    response = requests.get(url)\n",
    "    response.raise_for_status()\n",
    "\n",
    "    with MemoryFile(response.content) as memfile:\n",
    "        with memfile.open() as src:\n",
    "            bounds = src.bounds\n",
    "            transformer = Transformer.from_crs(src.crs, 4326)\n",
    "            # Calculate centroid\n",
    "            centroid_x = (bounds.left + bounds.right) / 2\n",
    "            centroid_y = (bounds.top + bounds.bottom) / 2\n",
    "            centroid_x, centroid_y = transformer.transform(centroid_x, centroid_y)\n",
    "            bounds_b, bounds_l = transformer.transform(bounds.left, bounds.bottom)\n",
    "            bounds_t, bounds_r = transformer.transform(bounds.right, bounds.top)\n",
    "            return [bounds_b, bounds_l, bounds_t, bounds_r], centroid_x, centroid_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ccb089e6-36d7-41a4-8297-f5517aa42065",
   "metadata": {},
   "outputs": [],
   "source": [
    "chip_images = []  # List to hold chip pixels\n",
    "chip_bounds = []  # List to hold chip bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "292aaae1-e5fd-4c94-bc93-fa8c06191a52",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for item in items_list[:2]:\n",
    "    print(f\"Working on {item}\")\n",
    "\n",
    "    # Index the chips in the item\n",
    "    indexer = NoStatsChipIndexer(item)\n",
    "\n",
    "    # Obtain the item bounds and centroid\n",
    "    bounds, centroid_x, centroid_y = get_bounds_centroid(item.assets[\"image\"].href)\n",
    "    print(\n",
    "        f\"Bbox coordinates: {bounds}, centroid coordinates: {centroid_x}, {centroid_y}\"\n",
    "    )\n",
    "\n",
    "    # Instantiate the chipper\n",
    "    chipper = Chipper(indexer, asset_blacklist=[\"metadata\"])\n",
    "\n",
    "    # Get 5 randomly sampled chips from the total\n",
    "    # number of chips within this item's entire image\n",
    "    for chip_id in random.sample(range(0, len(chipper)), 5):\n",
    "        chip_images.append(chipper[chip_id][\"image\"])\n",
    "        chip_bounds.append(bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61aaad7",
   "metadata": {},
   "source": [
    "Visualize a generated image chip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1fabb53d-0132-4fba-befb-d16be8116428",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mIndexError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m fig, ax = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m, gridspec_kw={\u001b[33m\"\u001b[39m\u001b[33mwspace\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m0.01\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mhspace\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m0.01\u001b[39m}, squeeze=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m chip = \u001b[43mchip_images\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# Visualize the data\u001b[39;00m\n\u001b[32m      6\u001b[39m ax.imshow(chip[:\u001b[32m3\u001b[39m].swapaxes(\u001b[32m0\u001b[39m, \u001b[32m1\u001b[39m).swapaxes(\u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m))\n",
      "\u001b[31mIndexError\u001b[39m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, gridspec_kw={\"wspace\": 0.01, \"hspace\": 0.01}, squeeze=True)\n",
    "\n",
    "chip = chip_images[0]\n",
    "\n",
    "# Visualize the data\n",
    "ax.imshow(chip[:3].swapaxes(0, 1).swapaxes(1, 2))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "6f605ecc-2809-4c91-abc0-14e238d97d9a.jpeg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "id": "e6052e63-209b-4ac3-a69e-5ededdeb3c61",
   "metadata": {},
   "source": [
    "![Screenshot 2024-07-22 at 3.00.56 PM Small.jpeg](attachment:6f605ecc-2809-4c91-abc0-14e238d97d9a.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589611d4",
   "metadata": {},
   "source": [
    "Below are some functions we will rely on to prepare the data cubes, generate embeddings, and plot subsets of the chipped images for visualization purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a0112ed-7bbc-434f-8914-7160a3a2c239",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_rgb(stack):\n",
    "    \"\"\"\n",
    "    Plot the RGB bands of the given stack.\n",
    "\n",
    "    Parameters:\n",
    "    stack (xarray.DataArray): The input data array containing band information.\n",
    "    \"\"\"\n",
    "    stack.sel(band=[1, 2, 3]).plot.imshow(rgb=\"band\", vmin=0, vmax=2000, col_wrap=6)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e23b0258-2b41-4c45-a0df-0d2e62809067",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_latlon(lat, lon):\n",
    "    \"\"\"\n",
    "    Normalize latitude and longitude to a range between -1 and 1.\n",
    "\n",
    "    Parameters:\n",
    "    lat (float): Latitude value.\n",
    "    lon (float): Longitude value.\n",
    "\n",
    "    Returns:\n",
    "    tuple: Normalized latitude and longitude values.\n",
    "    \"\"\"\n",
    "    lat = lat * np.pi / 180\n",
    "    lon = lon * np.pi / 180\n",
    "\n",
    "    return (math.sin(lat), math.cos(lat)), (math.sin(lon), math.cos(lon))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fba068e-ee25-43c0-976f-c2beac2d39ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(ckpt, device=\"cuda\"):\n",
    "    \"\"\"\n",
    "    Load a pretrained Clay model from a checkpoint.\n",
    "\n",
    "    Parameters:\n",
    "    ckpt (str): Path to the model checkpoint.\n",
    "    device (str): Device to load the model onto (default is 'cuda').\n",
    "\n",
    "    Returns:\n",
    "    model: Loaded model.\n",
    "    \"\"\"\n",
    "    torch.set_default_device(device)\n",
    "    model = ClayMAEModule.load_from_checkpoint(\n",
    "        ckpt, metadata_path=\"../configs/metadata.yaml\", shuffle=False, mask_ratio=0\n",
    "    )\n",
    "    model.eval()\n",
    "    return model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "029e3fb5-c6eb-4b8b-9430-d3e2a6f0b3a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prep_datacube(image, lat, lon, date, gsd, device):\n",
    "    \"\"\"\n",
    "    Prepare a data cube for model input.\n",
    "\n",
    "    Parameters:\n",
    "    image (np.array): The input image array.\n",
    "    lat (float): Latitude value for the location.\n",
    "    lon (float): Longitude value for the location.\n",
    "    device (str): Device to load the data onto.\n",
    "\n",
    "    Returns:\n",
    "    dict: Prepared data cube with normalized values and embeddings.\n",
    "    \"\"\"\n",
    "    platform = \"naip\"\n",
    "\n",
    "    # Extract mean, std, and wavelengths from metadata\n",
    "    metadata = Box(yaml.safe_load(open(\"../configs/metadata.yaml\")))\n",
    "    mean = []\n",
    "    std = []\n",
    "    waves = []\n",
    "    bands = [\"red\", \"green\", \"blue\", \"nir\"]\n",
    "    for band_name in bands:\n",
    "        mean.append(metadata[platform].bands.mean[band_name])\n",
    "        std.append(metadata[platform].bands.std[band_name])\n",
    "        waves.append(metadata[platform].bands.wavelength[band_name])\n",
    "\n",
    "    transform = v2.Compose(\n",
    "        [\n",
    "            v2.Normalize(mean=mean, std=std),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # Prep datetimes embedding\n",
    "    times = normalize_timestamp(date)\n",
    "    week_norm = times[0]\n",
    "    hour_norm = times[1]\n",
    "\n",
    "    # Prep lat/lon embedding\n",
    "    latlons = normalize_latlon(lat, lon)\n",
    "    lat_norm = latlons[0]\n",
    "    lon_norm = latlons[1]\n",
    "\n",
    "    # Prep pixels\n",
    "    pixels = torch.from_numpy(image.astype(np.float32))\n",
    "    pixels = transform(pixels)\n",
    "    pixels = pixels.unsqueeze(0)\n",
    "\n",
    "    # Prepare additional information\n",
    "    return {\n",
    "        \"pixels\": pixels.to(device),\n",
    "        \"time\": torch.tensor(\n",
    "            np.hstack((week_norm, hour_norm)),\n",
    "            dtype=torch.float32,\n",
    "            device=device,\n",
    "        ).unsqueeze(0),\n",
    "        \"latlon\": torch.tensor(\n",
    "            np.hstack((lat_norm, lon_norm)), dtype=torch.float32, device=device\n",
    "        ).unsqueeze(0),\n",
    "        \"gsd\": torch.tensor(gsd, device=device),\n",
    "        \"waves\": torch.tensor(waves, device=device),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bba433de-df3a-4e8e-880a-653352b9d4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_embeddings(model, datacube):\n",
    "    \"\"\"\n",
    "    Generate embeddings from the model.\n",
    "\n",
    "    Parameters:\n",
    "    model (ClayMAEModule): The pretrained model.\n",
    "    datacube (dict): Prepared data cube.\n",
    "\n",
    "    Returns:\n",
    "    numpy.ndarray: Generated embeddings.\n",
    "    \"\"\"\n",
    "    with torch.no_grad():\n",
    "        unmsk_patch, unmsk_idx, msk_idx, msk_matrix = model.model.encoder(datacube)\n",
    "\n",
    "    # The first embedding is the class token, which is the\n",
    "    # overall single embedding.\n",
    "    return unmsk_patch[:, 0, :].cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "419e0d80-7250-4397-b383-db027813536a",
   "metadata": {},
   "outputs": [],
   "source": [
    "outdir_embeddings = \"../data/embeddings/\"\n",
    "os.makedirs(outdir_embeddings, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f01975",
   "metadata": {},
   "source": [
    "### Load the trained Clay v1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59d0bdbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the pretrained model from\n",
    "# https://huggingface.co/made-with-clay/Clay/blob/main/mae_v1.5.0_epoch-07_val-loss-0.1718.ckpt\n",
    "# and put it in a checkpoints folder.\n",
    "model = load_model(\n",
    "    ckpt=\"../../checkpoints/mae_v1.5.0_epoch-07_val-loss-0.1718.ckpt\",\n",
    "    device=torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ae07f7",
   "metadata": {},
   "source": [
    "### Generate embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05409114-2bba-4497-8cbb-a7303a8d5be6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "embeddings = []\n",
    "i = 0\n",
    "for tile, bounding_box in zip(chip_images, chip_bounds):\n",
    "    date = datetime.datetime.strptime(f\"{YEAR}-06-01\", \"%Y-%m-%d\")\n",
    "    gsd = 0.6\n",
    "\n",
    "    lon, lat = box(\n",
    "        bounding_box[0], bounding_box[1], bounding_box[2], bounding_box[3]\n",
    "    ).centroid.coords[0]\n",
    "\n",
    "    datacube = prep_datacube(\n",
    "        np.array(tile), lat, lon, pd.to_datetime(f\"{YEAR}-06-01\"), gsd, model.device\n",
    "    )\n",
    "    embeddings_ = generate_embeddings(model, datacube)\n",
    "    embeddings.append(embeddings_)\n",
    "\n",
    "    data = {\n",
    "        \"source_url\": str(i),\n",
    "        \"date\": pd.to_datetime(arg=date, format=\"%Y-%m-%d\"),\n",
    "        \"embeddings\": [np.ascontiguousarray(embeddings_.squeeze())],\n",
    "        \"image\": [np.ascontiguousarray(np.array(tile.transpose(1, 2, 0)).flatten())],\n",
    "    }\n",
    "\n",
    "    # Create the GeoDataFrame\n",
    "    gdf = gpd.GeoDataFrame(data, geometry=[bounding_box], crs=\"EPSG:4326\")\n",
    "\n",
    "    outpath = f\"{outdir_embeddings}/{i}.gpq\"\n",
    "    gdf.to_parquet(path=outpath, compression=\"ZSTD\", schema_version=\"1.0.0\")\n",
    "    print(\n",
    "        f\"Saved {len(gdf)} rows of embeddings of \"\n",
    "        f\"shape {gdf.embeddings.iloc[0].shape} to {outpath}\"\n",
    "    )\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ec8c89b-38fe-44e3-9780-e1df8a3f41c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Created {len(embeddings)} embeddings of shape {embeddings[0].shape[1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9cf665b",
   "metadata": {},
   "source": [
    "### Run a similarity search to identify similar embeddings\n",
    "We will select a random index to search with and plot the corresponding RGB images from the search results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99132892-c6bd-4c01-a92c-f54e1cba1921",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to the embeddings database\n",
    "db = lancedb.connect(outdir_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf872d0-6b90-4102-9dc3-c726527c19dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data for DB table\n",
    "data = []\n",
    "# Dataframe to find overlaps within\n",
    "gdfs = []\n",
    "idx = 0\n",
    "for emb in glob.glob(f\"{outdir_embeddings}/*.gpq\"):\n",
    "    gdf = gpd.read_parquet(emb)\n",
    "    gdf[\"year\"] = gdf.date.dt.year\n",
    "    gdf[\"tile\"] = gdf[\"source_url\"]\n",
    "    gdf[\"idx\"] = idx\n",
    "    gdf[\"box\"] = [shapely.geometry.box(*geom.bounds) for geom in gdf.geometry]\n",
    "    gdfs.append(gdf)\n",
    "\n",
    "    for _, row in gdf.iterrows():\n",
    "        data.append(\n",
    "            {\n",
    "                \"vector\": row[\"embeddings\"],\n",
    "                \"path\": row[\"source_url\"],\n",
    "                \"tile\": row[\"tile\"],\n",
    "                \"date\": row[\"date\"],\n",
    "                \"year\": int(row[\"year\"]),\n",
    "                \"idx\": row[\"idx\"],\n",
    "                \"box\": row[\"box\"].bounds,\n",
    "                \"image\": row[\"image\"],\n",
    "            }\n",
    "        )\n",
    "    idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfab482d-56e7-4d2b-af72-e4ddce555667",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the geodataframes into one\n",
    "embeddings_gdf = pd.concat(gdfs, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41e8633a-4db6-4487-958b-48ed955426cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop existing table if any\n",
    "try:\n",
    "    db.drop_table(\"clay-v001\")\n",
    "except FileNotFoundError:\n",
    "    pass\n",
    "db.table_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19fde8ff-9970-44f9-9798-3d701961621f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new table with the embeddings data\n",
    "tbl = db.create_table(\"clay-v001\", data=data, mode=\"overwrite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5e4fe11-87c8-4830-bb44-4c63ac981329",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a random embedding for the search query\n",
    "idx = random.randint(0, len(embeddings_gdf))\n",
    "v = tbl.to_pandas().iloc[idx][\"vector\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3428bb77-9c85-4d28-9675-ad1718dbff53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform the search\n",
    "search_x_images = 6\n",
    "result = tbl.search(query=v).limit(search_x_images).to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5529e840-052b-4a43-b4b7-41ed3062db62",
   "metadata": {},
   "outputs": [],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f347667-ed4d-48e6-9f79-855b866073dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(df, cols=4, save=False):\n",
    "    \"\"\"\n",
    "    Plot the top similar images.\n",
    "\n",
    "    Parameters:\n",
    "    df (pandas.DataFrame): DataFrame containing the search results.\n",
    "    cols (int): Number of columns to display in the plot.\n",
    "    \"\"\"\n",
    "    fig, axs = plt.subplots(1, cols, figsize=(20, 10))\n",
    "    i = 0\n",
    "    for ax, (_, row) in zip(axs.flatten(), df.iterrows()):\n",
    "        # row = df.iloc[i]\n",
    "        chip = np.array(row[\"image\"]).reshape(256, 256, 4)\n",
    "        chip = chip[:, :, :3]\n",
    "        ax.imshow(chip)\n",
    "        ax.set_title(f\"{row['idx']}\")\n",
    "        i += 1\n",
    "    plt.tight_layout()\n",
    "    if save:\n",
    "        fig.savefig(\"similar.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc343525-ebbd-447d-a00b-0ae7582e88c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the top similar images\n",
    "plot(result, search_x_images)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "4be43ecc-c893-483c-addc-0e48dacd4cee",
   "metadata": {},
   "source": [
    "![nnaip_ss_results.jpeg](attachment:251a5bb0-fbb0-493f-a6a5-e6ba5f279621.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf91604b-0bfd-4a71-ad12-dc13b6eba660",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "claymodel",
   "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": "3.11.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}