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@giswqs
Last active July 1, 2020 18:16
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xarray-leaflet-geotiff
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{
"cells": [
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import requests\nimport os\nfrom tqdm import tqdm\nimport numpy as np\nimport zipfile\nimport rioxarray\nimport xarray as xr\nimport xarray_leaflet\nfrom ipyleaflet import Map, basemaps\nfrom matplotlib import pyplot as plt",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The example SRTM (10 MB) can be downloaded at the link below. Place the downloaded DEM under the same directory of this notebook.\n\nhttps://drive.google.com/file/d/1Cpcpe5XW5Kjfw43vE3aOe5sGSzKYaQRl/view?usp=sharing"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# tif_file = os.path.join(os.getcwd(), 'srtm90.tif')\ntif_file = '/media/hdd/Google-Drive/export/landsat.tif'",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "da = xr.open_rasterio(tif_file)\nda",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "crs = da.rio.crs\nnan = da.attrs['nodatavals'][0]\nda = da.sel(band=[5, 4, 3])\nda = xr.where(da==nan, np.nan, da)\nda = da.rio.write_crs(crs)",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "da",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"scrolled": false
},
"cell_type": "code",
"source": "# import geemap\n# Map = geemap.Map()\n# Map",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"scrolled": false,
"trusted": true
},
"cell_type": "code",
"source": "m = Map(center=[40, -100], zoom=4, scroll_wheel_zoom = True)\nm",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "l = da.leaflet.plot(m, x_dim='x', y_dim='y', rgb_dim='band')\nl.interact(opacity=(0.0,1.0,0.1))",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# l = da.leaflet.plot(Map, x_dim='x', y_dim='y', colormap=plt.cm.gray)\n# l.interact(opacity=(0.0,1.0,0.1))",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# import ee",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318').select(['B5', 'B4', 'B3'])",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# geemap.ee_export_image_to_drive(image, 'landsat', scale=90)",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/60cba5e22c0b850ee039f4faec795334"
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"gist": {
"id": "60cba5e22c0b850ee039f4faec795334",
"data": {
"description": "xarray-leaflet-geotiff",
"public": true
}
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.8.2",
"mimetype": "text/x-python",
"codemirror_mode": {
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"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
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"toc": {
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": true,
"base_numbering": 1,
"title_cell": "Table of Contents",
"title_sidebar": "Table of Contents",
"toc_cell": false,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "270px"
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"toc_window_display": true
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