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‎_sources/notebooks/xarray_mosaic_and_clip.ipynb‎

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"\n",
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"**Data Credit**:\n",
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"- NASA Shuttle Radar Topography Mission Global 1 arc second provided by The Land Processes Distributed Active Archive Center (LP DAAC). Downloaded using the [30-Meter SRTM Tile Downloader](https://dwtkns.com/srtm30m/).\n",
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"- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com."
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"- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com.\n",
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"\n",
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"\n",
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"**Watch Video Walkthrough** <a href='https://www.youtube.com/watch?v=Ff9ctKNlku0' target='_blank'><img src='https://github.com/spatialthoughts/geopython-tutorials/blob/main/images/yt_logo.png?raw=1'></a>"
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},
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"source": [
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"clipped = merged.rio.clip(filtered_gdf.geometry)\n"
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{
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"cell_type": "markdown",
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"Our clipped data has -32768 as nodata. We mask these and [encode the correct nodata](https://corteva.github.io/rioxarray/stable/getting_started/nodata_management.html#Manually-masking-data) value."
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"clipped = clipped.where(clipped != clipped.rio.nodata)\n",
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"clipped.rio.write_nodata(clipped.rio.nodata, encoded=True, inplace=True)\n",
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"clipped"
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"clipped = merged.rio.clip(filtered_gdf.geometry)"
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‎notebooks/xarray_mosaic_and_clip.html‎

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<li><p>NASA Shuttle Radar Topography Mission Global 1 arc second provided by The Land Processes Distributed Active Archive Center (LP DAAC). Downloaded using the <a class="reference external" href="https://dwtkns.com/srtm30m/">30-Meter SRTM Tile Downloader</a>.</p></li>
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<li><p>Made with Natural Earth. Free vector and raster map data &#64; naturalearthdata.com.</p></li>
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</ul>
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<p><strong>Watch Video Walkthrough</strong> <a href='https://www.youtube.com/watch?v=Ff9ctKNlku0' target='_blank'><img alt="https://github.com/spatialthoughts/geopython-tutorials/blob/main/images/yt_logo.png?raw=1" src="https://github.com/spatialthoughts/geopython-tutorials/blob/main/images/yt_logo.png?raw=1" /></a></p>
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</section>
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<section id="setup-and-data-download">
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<h2>Setup and Data Download<a class="headerlink" href="#setup-and-data-download" title="Link to this heading">#</a></h2>
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<p>Our clipped data has -32768 as nodata. We mask these and <a class="reference external" href="https://corteva.github.io/rioxarray/stable/getting_started/nodata_management.html#Manually-masking-data">encode the correct nodata</a> value.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">clipped</span> <span class="o">=</span> <span class="n">clipped</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">clipped</span> <span class="o">!=</span> <span class="n">clipped</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span><span class="p">)</span>
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<span class="n">clipped</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">write_nodata</span><span class="p">(</span><span class="n">clipped</span><span class="o">.</span><span class="n">rio</span><span class="o">.</span><span class="n">nodata</span><span class="p">,</span> <span class="n">encoded</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="n">clipped</span>
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</pre></div>
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<p>Last step is to save the results to disk as GeoTiff files. We use <a class="reference external" href="https://gdal.org/drivers/raster/cog.html">Cloud-Optimized GeoTIFF (COG)</a> driver and specify additional GDAL <a class="reference external" href="https://rasterio.readthedocs.io/en/stable/topics/image_options.html#creation-options">compression options</a>.</p>
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‎searchindex.js‎

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