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alexgleith/aca_export.ipynb

Created May 7, 2025 22:38
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aca_export.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "37bc5ab7",
"metadata": {},
"source": [
"# Export tiles as Xarray"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "197a581b",
"metadata": {},
"outputs": [],
"source": [
"import ee\n",
"import xarray as xr\n",
"import numpy as np\n",
"import odc.geo.xr # noqa: F401"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d333b35d",
"metadata": {},
"outputs": [],
"source": [
"# Authenticate and initialize\n",
"# ee.Authenticate()\n",
"ee.Initialize(opt_url=\"https://earthengine-highvolume.googleapis.com\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "43ef4e2e",
"metadata": {},
"outputs": [],
"source": [
"dataset = \"ACA/reef_habitat/v2_0\"\n",
"ic = ee.ImageCollection(ee.Image(dataset))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "29acaf93",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/homebrew/lib/python3.11/site-packages/xee/ext.py:688: UserWarning: Unable to retrieve 'system:time_start' values from an ImageCollection due to: No 'system:time_start' values found in the 'ImageCollection'.\n",
" warnings.warn(\n",
"/var/folders/0h/8_60bs_52tx9q2mdgszb37gw0000gn/T/ipykernel_48612/491851988.py:11: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 10000. This could degrade performance. Instead, consider rechunking after loading.\n",
" ds = xr.open_dataset(\n",
"/var/folders/0h/8_60bs_52tx9q2mdgszb37gw0000gn/T/ipykernel_48612/491851988.py:11: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 10000. This could degrade performance. Instead, consider rechunking after loading.\n",
" ds = xr.open_dataset(\n"
]
},
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;reef_mask&#x27; (lon: 40000, lat: 40000)&gt; Size: 2GB\n",
"dask.array&lt;astype, shape=(40000, 40000), dtype=uint8, chunksize=(10000, 10000), chunktype=numpy.ndarray&gt;\n",
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" * lon (lon) float64 320kB 140.0 140.0 140.0 140.0 ... 142.0 142.0 142.0\n",
" * lat (lat) float64 320kB -10.0 -10.0 -10.0 -10.0 ... -12.0 -12.0 -12.0\n",
"Attributes:\n",
" id: reef_mask\n",
" data_type: {&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;int&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;...\n",
" dimensions: [8015004, 1437907]\n",
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" crs_transform: [4.491576420597607e-05, 0, -180.00001488697754, 0, -4.491...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'reef_mask'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 40000</li><li><span class='xr-has-index'>lat</span>: 40000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ed02cea5-cb59-4819-b276-6587f77c4c77' class='xr-array-in' type='checkbox' checked><label for='section-ed02cea5-cb59-4819-b276-6587f77c4c77' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(10000, 10000), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
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" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.49 GiB </td>\n",
" <td> 95.37 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (40000, 40000) </td>\n",
" <td> (10000, 10000) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 16 chunks in 4 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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" </td>\n",
" </tr>\n",
"</table></div></div></li><li class='xr-section-item'><input id='section-1aac9e17-cc5b-4b7c-bec9-a8e9a622cd0a' class='xr-section-summary-in' type='checkbox' checked><label for='section-1aac9e17-cc5b-4b7c-bec9-a8e9a622cd0a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ebee7baf-85bb-4d30-be5a-75bb521577b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebee7baf-85bb-4d30-be5a-75bb521577b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b53fa533-2020-4117-8c83-ccc43d696548' class='xr-var-data-in' type='checkbox'><label for='data-b53fa533-2020-4117-8c83-ccc43d696548' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>140.0 140.0 140.0 ... 142.0 142.0</div><input id='attrs-42ec0f4e-13e5-4d8d-850f-e23d8f4da5ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42ec0f4e-13e5-4d8d-850f-e23d8f4da5ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7bf5f1c-9745-4e77-a2a6-1b3719118a52' class='xr-var-data-in' type='checkbox'><label for='data-b7bf5f1c-9745-4e77-a2a6-1b3719118a52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([140.000025, 140.000075, 140.000125, ..., 141.999875, 141.999925,\n",
" 141.999975], shape=(40000,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -10.0 -10.0 ... -12.0 -12.0</div><input id='attrs-8b65f303-a2fb-4f89-9743-d99537637d92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b65f303-a2fb-4f89-9743-d99537637d92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b80c2602-dbe6-4278-80d3-54b284a32311' class='xr-var-data-in' type='checkbox'><label for='data-b80c2602-dbe6-4278-80d3-54b284a32311' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-10.000025, -10.000075, -10.000125, ..., -11.999875, -11.999925,\n",
" -11.999975], shape=(40000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8528812-ec04-4253-8d22-f360f6e6bf03' class='xr-section-summary-in' type='checkbox' ><label for='section-d8528812-ec04-4253-8d22-f360f6e6bf03' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-013cc990-2677-44da-b969-47d7e235deee' class='xr-index-data-in' type='checkbox'/><label for='index-013cc990-2677-44da-b969-47d7e235deee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 140.000025, 140.000075, 140.000125,\n",
" 140.000175, 140.000225, 140.000275,\n",
" 140.000325, 140.000375, 140.000425,\n",
" 140.000475,\n",
" ...\n",
" 141.999525, 141.99957500000002, 141.999625,\n",
" 141.999675, 141.999725, 141.999775,\n",
" 141.99982500000002, 141.999875, 141.99992500000002,\n",
" 141.999975],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=40000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d8a52bc7-b8e2-4709-ab4e-0eff123d9f99' class='xr-index-data-in' type='checkbox'/><label for='index-d8a52bc7-b8e2-4709-ab4e-0eff123d9f99' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -10.000025, -10.000075, -10.000125,\n",
" -10.000175, -10.000225, -10.000275,\n",
" -10.000325, -10.000375, -10.000425,\n",
" -10.000475,\n",
" ...\n",
" -11.999525, -11.999575, -11.999625,\n",
" -11.999675, -11.999725, -11.999775,\n",
" -11.999825, -11.999875000000001, -11.999925000000001,\n",
" -11.999975000000001],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=40000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e748385b-1359-4868-9ed8-17c886c1c9a6' class='xr-section-summary-in' type='checkbox' checked><label for='section-e748385b-1359-4868-9ed8-17c886c1c9a6' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>reef_mask</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;int&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 255}</dd><dt><span>dimensions :</span></dt><dd>[8015004, 1437907]</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd><dt><span>crs_transform :</span></dt><dd>[4.491576420597607e-05, 0, -180.00001488697754, 0, -4.491576420597607e-05, 32.85956460933639]</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'reef_mask' (lon: 40000, lat: 40000)> Size: 2GB\n",
"dask.array<astype, shape=(40000, 40000), dtype=uint8, chunksize=(10000, 10000), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" time int64 8B 0\n",
" * lon (lon) float64 320kB 140.0 140.0 140.0 140.0 ... 142.0 142.0 142.0\n",
" * lat (lat) float64 320kB -10.0 -10.0 -10.0 -10.0 ... -12.0 -12.0 -12.0\n",
"Attributes:\n",
" id: reef_mask\n",
" data_type: {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max'...\n",
" dimensions: [8015004, 1437907]\n",
" crs: EPSG:4326\n",
" crs_transform: [4.491576420597607e-05, 0, -180.00001488697754, 0, -4.491..."
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Region of interest. Eventually, we need to do all of -180 to 180 and -32 to 32\n",
"left = 140.0\n",
"bottom = -12.0\n",
"right = 142.0\n",
"top = -10.0\n",
"\n",
"# Close to full resolution\n",
"res = 0.00005\n",
"transform = [res, 0, left, 0, -res, top]\n",
"\n",
"ds = xr.open_dataset(\n",
" ic,\n",
" engine='ee',\n",
" geometry=[left, bottom, right, top],\n",
" projection=ee.Projection(\n",
" crs=\"epsg:4326\", transform=transform\n",
" ),\n",
" chunks={\"time\": 1, \"lon\": 10000, \"lat\": 10000},\n",
").squeeze()\n",
"\n",
"reef_mask = ds.reef_mask.astype(\"uint8\")\n",
"reef_mask"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b80bf2ff",
"metadata": {},
"outputs": [],
"source": [
"reef_mask.odc.nodata = 0\n",
"reef_mask.astype(np.uint8).odc.write_cog(\"test.tif\", overwrite=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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