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Add COG overview generation user guide notebook (#1150)
Covers automatic and explicit overview levels, resampling methods, and round-trip verification. Self-contained with synthetic data.
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Cloud Optimized GeoTIFF: Overview (Pyramid) Generation\n",
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"\n",
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"Cloud Optimized GeoTIFFs (COGs) store internal overview images at reduced\n",
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"resolution, so map viewers and tiling servers can fetch the right zoom\n",
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"level without downloading the full file.\n",
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"\n",
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"xarray-spatial generates these overviews natively during `to_geotiff()`\n",
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"with `cog=True`. No GDAL or `gdaladdo` post-processing required.\n",
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"\n",
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"This notebook covers:\n",
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"- Writing a COG with automatic overviews\n",
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"- Choosing resampling methods\n",
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"- Specifying explicit overview levels\n",
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"- Verifying the result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import xarray as xr\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from xrspatial.geotiff import open_geotiff, to_geotiff\n",
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"from xrspatial.geotiff._header import parse_header, parse_all_ifds"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create synthetic terrain data\n",
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"\n",
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"A 256x256 elevation surface with some peaks, to give the overviews\n",
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"something visually meaningful to downsample."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rng = np.random.RandomState(42)\n",
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"y = np.linspace(45.0, 44.0, 256)\n",
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"x = np.linspace(-120.0, -119.0, 256)\n",
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"\n",
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"# Smooth terrain from Gaussian peaks\n",
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"xx, yy = np.meshgrid(x, y)\n",
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"terrain = (\n",
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" 800 * np.exp(-((xx + 119.5)**2 + (yy - 44.5)**2) / 0.02)\n",
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" + 600 * np.exp(-((xx + 119.3)**2 + (yy - 44.7)**2) / 0.05)\n",
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" + 100 * rng.rand(256, 256)\n",
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").astype(np.float32)\n",
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"\n",
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"da = xr.DataArray(\n",
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" terrain, dims=['y', 'x'],\n",
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" coords={'y': y, 'x': x},\n",
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" attrs={'crs': 4326},\n",
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" name='elevation',\n",
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")\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(6, 5))\n",
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"da.plot(ax=ax, cmap='terrain')\n",
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"ax.set_title('Synthetic elevation')\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Write a COG with automatic overviews\n",
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"\n",
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"Pass `cog=True` and xarray-spatial handles the rest: it halves the\n",
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"dimensions until the smallest overview fits within a single tile,\n",
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"then writes all levels into the file with IFDs at the start (the COG\n",
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"layout requirement for efficient HTTP range requests)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tempfile, os\n",
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"\n",
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"tmpdir = tempfile.mkdtemp(prefix='cog_demo_')\n",
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"cog_path = os.path.join(tmpdir, 'auto_overviews.tif')\n",
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"\n",
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"to_geotiff(da, cog_path, cog=True, compression='deflate')\n",
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"\n",
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"# Check how many IFDs (resolution levels) were written\n",
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"with open(cog_path, 'rb') as f:\n",
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" raw = f.read()\n",
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"\n",
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"header = parse_header(raw)\n",
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"ifds = parse_all_ifds(raw, header)\n",
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"for i, ifd in enumerate(ifds):\n",
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" label = 'Full resolution' if i == 0 else f'Overview {i}'\n",
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" print(f'{label}: {ifd.width} x {ifd.height}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Resampling methods\n",
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"\n",
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"Different data types need different resampling:\n",
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"- **mean** (default): continuous data like elevation or temperature\n",
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"- **nearest**: categorical or index data (land cover classes)\n",
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"- **mode**: majority-class downsampling for classified rasters\n",
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"- **min** / **max** / **median**: when you need conservative bounds"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"methods = ['mean', 'nearest', 'min', 'max']\n",
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"fig, axes = plt.subplots(1, len(methods), figsize=(14, 3))\n",
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"\n",
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"for ax, method in zip(axes, methods):\n",
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" path = os.path.join(tmpdir, f'cog_{method}.tif')\n",
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" to_geotiff(da, path, cog=True, compression='deflate',\n",
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" overview_resampling=method)\n",
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"\n",
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" # Read back the first overview level\n",
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" result = open_geotiff(path, overview_level=1)\n",
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" result.plot(ax=ax, cmap='terrain', add_colorbar=False)\n",
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" ax.set_title(f'{method} ({result.shape[0]}x{result.shape[1]})')\n",
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" ax.set_xlabel('')\n",
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" ax.set_ylabel('')\n",
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"\n",
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"plt.suptitle('Overview level 1 by resampling method', y=1.02)\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Explicit overview levels\n",
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"\n",
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"For fine-grained control, pass `overview_levels` as a list of\n",
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"decimation factors. Each factor halves the previous level once."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"explicit_path = os.path.join(tmpdir, 'explicit_levels.tif')\n",
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"to_geotiff(da, explicit_path, cog=True, compression='deflate',\n",
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" overview_levels=[2, 4, 8])\n",
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"\n",
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"with open(explicit_path, 'rb') as f:\n",
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" raw = f.read()\n",
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"\n",
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"header = parse_header(raw)\n",
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"ifds = parse_all_ifds(raw, header)\n",
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"for i, ifd in enumerate(ifds):\n",
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" label = 'Full resolution' if i == 0 else f'Overview {i}'\n",
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" print(f'{label}: {ifd.width} x {ifd.height}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Verify round-trip\n",
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"\n",
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"Full-resolution pixel values are preserved through the COG write."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"result = open_geotiff(cog_path)\n",
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"max_diff = float(np.max(np.abs(result.values - terrain)))\n",
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"print(f'Max pixel difference: {max_diff}')\n",
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"assert max_diff < 1e-5, 'Values should round-trip exactly'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Clean up\n",
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"import shutil\n",
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"shutil.rmtree(tmpdir)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.11.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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