136 lines
56 KiB
Plaintext
136 lines
56 KiB
Plaintext
|
{
|
||
|
"cells": [
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 1,
|
||
|
"id": "8784196f",
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"import datacube"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 2,
|
||
|
"id": "3c0adfbd",
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"dc = datacube.Datacube(config='/home/user/.config/datacube/datacube.conf')"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 3,
|
||
|
"id": "a0118381",
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"ds = dc.load(product=\"esa_worldcover_2021\")"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 4,
|
||
|
"id": "7921c073",
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"name": "stdout",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"<xarray.Dataset>\n",
|
||
|
"Dimensions: (time: 1, latitude: 12874, longitude: 8912)\n",
|
||
|
"Coordinates:\n",
|
||
|
" * time (time) datetime64[ns] 2021-07-02\n",
|
||
|
" * latitude (latitude) float64 43.17 43.17 43.17 ... 42.1 42.1 42.1\n",
|
||
|
" * longitude (longitude) float64 20.22 20.22 20.22 ... 20.96 20.96 20.96\n",
|
||
|
" spatial_ref int32 4326\n",
|
||
|
"Data variables:\n",
|
||
|
" classification (time, latitude, longitude) uint8 0 0 0 0 0 0 ... 0 0 0 0 0\n",
|
||
|
"Attributes:\n",
|
||
|
" crs: EPSG:4326\n",
|
||
|
" grid_mapping: spatial_ref\n"
|
||
|
]
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"print(ds)"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 4,
|
||
|
"id": "68ff4906",
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"<matplotlib.collections.QuadMesh at 0x7f3c7aa39180>"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 4,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"image/png": "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
|
||
|
"text/plain": [
|
||
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {
|
||
|
"needs_background": "light"
|
||
|
},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"ds.classification.plot()"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"id": "68cc04b9",
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"dc."
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"id": "8902b8fb",
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": []
|
||
|
}
|
||
|
],
|
||
|
"metadata": {
|
||
|
"kernelspec": {
|
||
|
"display_name": "Python 3 (ipykernel)",
|
||
|
"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": "3.10.6"
|
||
|
}
|
||
|
},
|
||
|
"nbformat": 4,
|
||
|
"nbformat_minor": 5
|
||
|
}
|