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<!DOCTYPE html>
<html lang="en">
<head>
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<meta charset="utf-8"/>
<title>EarthML — EarthML 0.2.1 documentation</title>
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<li class="nav-link doc-head"><a href="#">Introduction</a></li>
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<div class="toc" style="width:15%; margin-right:20px;">
<ul class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorial/index.html">Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="topics/index.html">Topics</a></li>
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<div class="content"> <!-- style="max-width:80%;margin-left:auto;margin-right: auto;">-->
<div class="section" id="earthml">
<h1>EarthML<a class="headerlink" href="#earthml" title="Permalink to this headline">¶</a></h1>
<div style="max-width: 1200; float:left"><p><strong>Machine learning and visualization in Python for Earth science</strong></p>
<p>Python offers a wide variety of open-source libraries covering a huge
range of functionality, but it can be difficult to work out which
libraries are suitable for which tasks. The EarthML project helps to:</p>
<blockquote>
<div><ul class="simple">
<li><p>Demonstrate how to use Python tools for machine learning and analysis in the earth sciences</p></li>
<li><p>Identify libraries suitable for working with earth-science data</p></li>
<li><p>Make improvements to these libraries as needed to help improve earth-science workflows</p></li>
</ul>
</div></blockquote>
<p>EarthML contains no code of its own, only tutorials and examples
showing how to use packages like:</p>
<ul class="simple">
<li><p><strong>Data libraries:</strong></p>
<ul>
<li><p><a class="reference external" href="https://intake.readthedocs.io">Intake</a>: Cleanly loading data from various sources.</p></li>
<li><p><a class="reference external" href="http://xarray.pydata.org">XArray</a>: Processing gridded (n-dimensional) data structures.</p></li>
<li><p><a class="reference external" href="http://pandas.pydata.org">Pandas</a>: Processing columnar (tabular) data structures.</p></li>
<li><p><a class="reference external" href="http://dask.pydata.org">Dask</a>: Parallelism and performance at scale.</p></li>
</ul>
</li>
<li><p><strong>Visualization libraries:</strong></p>
<ul>
<li><p><a class="reference external" href="http://hvplot.pyviz.org">hvPlot</a>: Simple data-centric API for plotting, building on:</p>
<ul>
<li><p><a class="reference external" href="http://bokeh.pydata.org">Bokeh</a>: Interactive browser-based plotting.</p></li>
<li><p><a class="reference external" href="http://holoviews.org">HoloViews</a>: Easy construction of Bokeh plots for datasets.</p></li>
<li><p><a class="reference external" href="http://geoviews.org">GeoViews</a>: HoloViews with earth-specific projections.</p></li>
<li><p><a class="reference external" href="https://github.com/bokeh/datashader">Datashader</a>: Rendering large datasets into images for display in browsers.</p></li>
</ul>
</li>
<li><p><a class="reference external" href="https://panel.pyviz.org">Panel</a>: Dashboards, apps, and widgets for any library’s plots.</p></li>
</ul>
</li>
<li><p><strong>Other tools:</strong></p>
<ul>
<li><p><a class="reference external" href="http://jupyter.org/">Jupyter</a>: Reproducible notebooks (source code for all examples on this site).</p></li>
<li><p><a class="reference external" href="https://scitools.org.uk/cartopy">Cartopy</a>: Geographic coordinate reference systems.</p></li>
<li><p><a class="reference external" href="http://dask.pydata.org">Dask</a>: Parallelism and performance at scale.</p></li>
</ul>
</li>
<li><p><strong>ML tools: (representative only – use any you like!)</strong></p>
<ul>
<li><p><a class="reference external" href="https://scikit-learn.org">Scikit-learn</a>: General ML for numeric data.</p></li>
<li><p><a class="reference external" href="https://keras.io">Keras</a>/ <a class="reference external" href="https://www.tensorflow.org">TensorFlow</a>/ <a class="reference external" href="https://pytorch.org">PyTorch</a>: Deep learning, images.</p></li>
</ul>
</li>
</ul>
<p>The EarthML <a class="reference external" href="tutorial">Tutorial</a> offers a general-purpose overview
of the concepts and tools involved, and the <a class="reference external" href="topics">Topics</a> section
shows examples of how these tools may be used to perform machine
learning and related tasks in the Earth sciences, such as:</p>
<style>table {border-spacing: 15px} td { border: 1px solid black; vertical-align: top} </style>
<table>
<tr>
<td border="1">
<a href="https://examples.pyviz.org/carbon_flux/"><b>Carbon Flux</b></a><br/>
<a href="https://examples.pyviz.org/carbon_flux/"><img src="_static/collage/carbon.png"/></a>
</td>
<td border="1">
<a href="https://examples.pyviz.org/heat_and_trees/Heat_and_Trees.html"><b>Heat and Trees</b></a><br/>
<a href="https://examples.pyviz.org/heat_and_trees/Heat_and_Trees.html"><img src="_static/collage/trees.png"/></a>
</td>
<td border="1">
<a href="https://examples.pyviz.org/walker_lake/Walker_Lake.html"><b>Walker Lake</b></a><br/>
<a href="https://examples.pyviz.org/walker_lake/Walker_Lake.html"><img src="_static/collage/walker.png"/></a>
</td>
</tr>
<tr>
<td border="1">
<a href="https://examples.pyviz.org/landuse_classification/Image_Classification.html"><b>Classification</b></a><br/>
<a href="https://examples.pyviz.org/landuse_classification/Image_Classification.html"><img src="_static/collage/classifier.png"/></a>
</td>
<td border="1">
<a href="https://examples.pyviz.org/landsat_clustering/landuse_clustering.html"><b>Clustering</b></a><br/>
<a href="https://examples.pyviz.org/landsat_clustering/landuse_clustering.html"><img src="_static/collage/cluster.png"/></a>
</td>
<td border="1">
<a href="https://examples.pyviz.org/seattle_lidar/Seattle_Lidar.html"><b>Lidar</b></a><br/>
<a href="https://examples.pyviz.org/seattle_lidar/Seattle_Lidar.html"><img src="_static/collage/lidar.png"/></a>
</td>
<tr>
</tr></tr></table><p>Please feel free to report <a class="reference external" href="https://github.com/pyviz-topics/EarthML/issues">issues</a> or <a class="reference external" href="https://github.com/pyviz-topics/EarthML/">contribute code</a>.</p>
<div class="section" id="running-the-earthml-projects">
<h2>Running the EarthML projects<a class="headerlink" href="#running-the-earthml-projects" title="Permalink to this headline">¶</a></h2>
<p>The EarthML topic examples are included among the many examples
available on <a class="reference external" href="https://examples.pyviz.org/">examples.pyviz.org</a>. Each
project is encapsulated in a reproducible project that includes an
environment specification, an optional <a class="reference external" href="https://intake.readthedocs.io/en/latest/">intake</a> data catalog as well as
notebooks and any other necessary assets.</p>
<p>Instructions for running these projects with <a class="reference external" href="https://github.com/Anaconda-Platform/anaconda-project">anaconda-project</a> can found in
the <a class="reference external" href="https://examples.pyviz.org/user_guide.html">examples.pyviz User Guide</a>.</p>
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