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ran and cleared example noe notebooks
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docs/source/user_guide/classification.ipynb

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docs/source/user_guide/focal.ipynb

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docs/source/user_guide/local.ipynb

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docs/source/user_guide/multispectral.ipynb

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docs/source/user_guide/pathfinding.ipynb

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docs/source/user_guide/proximity.ipynb

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docs/source/user_guide/surface.ipynb

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docs/source/user_guide/zonal.ipynb

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examples/cloudless-mosaic-sentinel2.ipynb

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"from datashader import Canvas\n",
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"\n",
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"import stackstac\n",
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"from satsearch import Search\n",
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"import pystac_client\n",
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"\n",
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"import xrspatial.multispectral as ms"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"items = Search(\n",
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" url='https://earth-search.aws.element84.com/v0',\n",
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"catalog = pystac_client.Client.open('https://earth-search.aws.element84.com/v1')\n",
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"\n",
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"search = catalog.search(\n",
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" bbox=[-96.185642, 28.569157, -95.117574, 29.500710],\n",
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" collections=['sentinel-s2-l2a-cogs'],\n",
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" collections=['sentinel-2-l2a'],\n",
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" query={'eo:cloud_cover': {'lt': 25}},\n",
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" datetime='2019-07-01/2020-06-30'\n",
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").items()\n",
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")\n",
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"\n",
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"items = search.item_collection()\n",
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"len(items)"
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]
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},
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"outputs": [],
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"source": [
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"stack_ds = stackstac.stack(\n",
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" items, epsg=32613, resolution=100, assets=['B04', 'B03', 'B02'], chunksize=4096\n",
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" items, epsg=32613, resolution=100, assets=['red', 'green', 'blue'], chunksize=4096\n",
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")\n",
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"\n",
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"stack_ds"
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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 (ipykernel)",
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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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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.2"
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}
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},
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"nbformat": 4,

examples/housing_price_feature_engineering.ipynb

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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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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-01-23T14:26:33.627412Z",
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"iopub.status.busy": "2026-01-23T14:26:33.627252Z",
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"iopub.status.idle": "2026-01-23T14:26:34.752109Z",
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"shell.execute_reply": "2026-01-23T14:26:34.751375Z"
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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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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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-01-23T14:26:34.754220Z",
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"iopub.status.busy": "2026-01-23T14:26:34.753759Z",
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"iopub.status.idle": "2026-01-23T14:26:34.757293Z",
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"shell.execute_reply": "2026-01-23T14:26:34.756767Z"
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}
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},
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"outputs": [],
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"source": [
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"# assume the data contain lat lon coords with some additional values\n",
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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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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-01-23T14:26:34.758688Z",
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"iopub.status.busy": "2026-01-23T14:26:34.758576Z",
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"iopub.status.idle": "2026-01-23T14:26:36.327629Z",
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"shell.execute_reply": "2026-01-23T14:26:36.327038Z"
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}
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},
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"outputs": [],
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"source": [
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"# bounding box of the raster\n",
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],
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"metadata": {
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"language_info": {
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"name": "python"
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.2"
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}
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},
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"nbformat": 4,

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