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+.logos {
+    height: 4em;
+    margin: 1em 0em;
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+.logos img {
+    height: 100%;
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+    float: none;
+    margin: 0 auto;
+}
+
+.team-avatar {
+    float: left;
+    max-width: 10em;
+    max-height: 10em;
+    margin: 0.5em 1em 0em 0em;
+}
+
+/* clearfix hack
+ * https://www.w3schools.com/css/css_float.asp */
+.clearfix, .team-member::after {
+    content: "";
+    clear: both;
+    display: table;
+}
+
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+    float: left;
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+---
+---
+
+{% include header.html %}
+
+<h1>Documentation</h1>
+
+<p>This page describes how to install, use, and contribute to COAL.</p>
+
+<h2>Contents</h2>
+
+<ul>
+  <li><a href="#dependencies">Dependencies</a></li>
+  <ul>
+    <li><a href="#numpy-spectral">Numpy and Spectral Python</a></li>
+    <li><a href="#gdal">GDAL</a></li>
+    <li><a href="#qgis">QGIS</a></li>
+  </ul>
+  <li><a href="#installation">Installation</a></li>
+  <li><a href="#downloading">Downloading Data</a></li>
+  <ul>
+    <li><a href="#udsl06">USGS Digital Spectral Library 06</a></li>
+    <li><a href="#aviris">AVIRIS</a></li>
+    <li><a href="#national-map">The National Map</a></li>
+  </ul>
+  <li><a href="#usage">Usage</a></li>
+  <ul>
+    <li><a href="#mineral">Mineral Classification</a></li>
+    <li><a href="#mining">Mining Identification</a></li>
+    <li><a href="#environment">Environmental Correlation</a></li>
+  </ul>
+  <li><a href="#development">Development</a></li>
+</ul>
+
+<h2 id="dependencies">Dependencies</h2>
+
+<p>COAL has been tested on x86_64 GNU/Linux and is expected to work without 
modification on any Unix-like system. COAL supports <a 
href="https://www.python.org/"; class="external">Python</a> versions 2.6+ and 
3.3+ Required dependencies include <a href="http://www.numpy.org/"; 
class="external">NumPy</a>, <a href="http://www.spectralpython.net/"; 
class="external">Spectral Python</a>, and <a href="http://gdal.org/"; 
class="external">GDAL</a> version 2.2.0+. The recommended geographic 
information system is <a href="http://qgis.org/en/site/"; 
class="external">QGIS</a>.
+
+<h3 id="numpy-spectral">Numpy and Spectral Python</h3>
+
+<p>The easiest way to install both Numpy and Spectral Python is from the <a 
href="https://pypi.python.org/pypi"; class="external">Python Package Index 
(PyPI)</a> with <code>pip</code>:</p>
+
+<pre>$ sudo pip install spectral</pre>
+
+<h3 id="gdal">GDAL</h3>
+
+<p>The command-line utilities from GDAL 2.2.0+ are required for correct 
processing of <a href="/2017/04/30/gdal-envi-rotation-bug-fix.html">rotated 
ENVI files</a> in the environmental correlation module. If necessary, GDAL can 
be installed from source following the instructions for <a 
href="https://trac.osgeo.org/gdal/wiki/BuildingOnUnix"; 
class="external">Building on Unix</a>.</p>
+
+<h4>Install from Source</h4>
+
+<p>Check out the source code:</p>
+
+<pre>$ git clone https://github.com/OSGeo/gdal.git
+$ cd gdal/gdal
+$ git checkout --track origin/2.2</pre>
+
+<p>Configure GDAL with the Python bindings. The default installation directory 
is <code>/usr/local</code>. Compile with <code>make</code> and install as root 
with either <code>make install</code> or <a 
href="https://wiki.debian.org/CheckInstall"; 
class="external"><code>checkinstall</code></a> to create a package that can be 
easily installed and uninstalled. GDAL does not include a <code>make 
uninstall</code> target.</p>
+
+<pre>$ ./configure --with-python
+$ make
+$ sudo checkinstall</pre>
+
+<p>After installation, the correct version can be verified by calling the 
command-line interface or Python API.</p>
+
+<pre>$ gdalwarp --version
+GDAL 2.2.0beta2, released 2017/04/23</pre>
+
+<pre>>>> import osgeo.gdal
+>>> print osgeo.gdal.__version__
+2.2.0beta2
+>>> print osgeo.gdal.VersionInfo()
+2020000
+</pre>
+
+<h3 id="qgis">QGIS</h3>
+
+<p>QGIS is the recommended geographic information system for viewing COAL data 
products. QGIS must be linked against GDAL 2.2.0+ to import rotated ENVI files 
correctly. If necessary, install QGIS from source to link to the correct 
version of GDAL.</p>
+
+<h4>Install from source</h4>
+
+<p>See the <code>INSTALL</code> file in the root of the QGIS source tree for 
more details. The following steps can be used to install QGIS from a Debian 
source package. We can verify that Debian 8 was used to build QGIS however we 
encourage feedback and use of newer Debian versions.</p>
+
+<p>Install the dependencies and download the Debian source package with 
<code>apt-get</code> or <code>aptitude</code>.</p>
+
+<pre>$ sudo apt-get update
+$ sudo apt-get upgrade
+$ sudo apt-get build-dep qgis
+$ sudo apt-get install bison cmake doxygen flex git graphviz grass-dev 
libexpat1-dev libfcgi-dev libgdal-dev libgeos-dev libgsl0-dev 
libopenscenegraph-dev libosgearth-dev libpq-dev libproj-dev libqt4-dev 
libqt4-opengl-dev libqtwebkit-dev libqwt-dev libspatialindex-dev 
libspatialite-dev libsqlite3-dev pkg-config pyqt4-dev-tools python-all 
python-all-dev python-qt4 python-qt4-dev python-sip python-sip-dev txt2tags 
xauth xfonts-100dpi xfonts-75dpi xfonts-base xfonts-scalable xvfb devscripts 
pkg-kde-tools checkinstall
+$ apt-get source qgis
+$ cd qgis-2.4.0</pre></code>
+
+<p>Create a build directory and configure QGIS with <code>cmake</code>.</p>
+
+<pre>$ mkdir build-master
+$ cd build-master
+$ cmake -Wno-dev -DCMAKE_INSTALL_PREFIX=/usr/local ..</pre>
+
+<p>Verify that the line <code>-- Found GDAL: /usr/local/lib/libgdal.so 
(2.2.0beta2)</code> appears in the output. Then compile and install as root.</p>
+
+<pre>$ make
+$ sudo make install</pre>
+
+<h2 id="installation">Installation</h2>
+
+<p>The COAL Python package <code>pycoal</code> can be installed from PyPI with 
<code>pip</code>.</p>
+
+<pre>$ sudo pip install pycoal</pre>
+
+<p>The latest development source may be obtained from <a 
href="https://github.com/capstone-coal/pycoal"; class="external">GitHub</a>.</p>
+
+<h2 id="downloading">Downloading Data</h2>
+
+<h3 id="udsl06">USGS Digital Spectral Library 06</h3>
+
+<p>COAL was developed using classifications from the <a 
href="https://speclab.cr.usgs.gov/spectral.lib06/"; class="external">USGS 
Digital Spectral Library 06</a>. The ENVI spectral library files <a 
href="ftp://ftpext.cr.usgs.gov/pub/cr/co/denver/speclab/pub/spectral.library/splib06.library/Convolved.libraries/s06av95a_envi.hdr";
 class="external"><code>s06av95a_envi.hdr</code></a> and <a 
href="ftp://ftpext.cr.usgs.gov/pub/cr/co/denver/speclab/pub/spectral.library/splib06.library/Convolved.libraries/s06av95a_envi.sli";
 class="external"><code>s06av95a_envi.sli</code></a> can be accessed via 
FTP.</p>
+
+<h3 id="aviris">AVIRIS</h3>
+
+<p>Imaging spectrometer data from the Jet Propulsion Laboratory can be 
downloaded or requested via the <a href="https://aviris.jpl.nasa.gov/"; 
class="external">AVIRIS</a> and <a href="https://avirisng.jpl.nasa.gov/"; 
class="external">AVIRIS-NG</a> websites.</p>
+
+<h3 id="national-map">The National Map</h3>
+
+<p><a href="https://nationalmap.gov/"; class="external">The National Map</a> 
from the United States Geological Survey (USGS) provides detailed hydrography, 
transportation, and elevation datasets.</p>
+
+<h2 id="usage">Usage</h2>
+
+<p>This section demonstrates basic usage of COAL. Refer to the <a 
class="external" href="https://pycoal.readthedocs.io/en/latest/";>API 
reference</a> for detailed documentation. The following images display several 
COAL data products.</p>
+
+<div class="row">
+  <div class="col-sm-6">
+    <figure class="figure">
+      <img src="/images/visible-light.png" class="figure-img img-responsive" 
alt="Visible-light image" />
+      <figcaption class="figure-caption"><b>Figure 1:</b> Visible-light 
image.</figcaption>
+    </figure>
+  </div>
+  <div class="col-sm-6">
+    <figure class="figure">
+      <img src="/images/mineral-classified.png" class="figure-img 
img-responsive" alt="Mineral classified image" />
+      <figcaption class="figure-caption"><b>Figure 2:</b> Mineral classified 
image.</figcaption>
+    </figure>
+  </div>
+</div>
+<div class="row">
+  <div class="col-sm-6">
+    <figure class="figure">
+      <img src="/images/mining-classified.png" class="figure-img 
img-responsive" alt="Mining classified image" />
+      <figcaption class="figure-caption"><b>Figure 3:</b> Mining classified 
image.</figcaption>
+    </figure>
+  </div>
+  <div class="col-sm-6">
+    <figure class="figure">
+      <img src="/images/environmental-correlation.png" class="figure-img 
img-responsive" alt="Environmental correlation image" />
+      <figcaption class="figure-caption"><b>Figure 4:</b> Environmental 
correlation image.</figcaption>
+    </figure>
+  </div>
+</div>
+
+<h3 id="mineral">Mineral Classification</h3>
+
+<p>The <a href="https://pycoal.readthedocs.io/en/latest/mineral.html"; 
class="external">Mineral Classification API</a> provides methods for generating 
visible-light (Figure 1) and mineral classified (Figure 2) images. Mineral 
classification can take hours to days depending on the size of the spectral 
library and the available computing resources, so running a script in the 
background is recommended.</p>
+
+<pre>#!/usr/bin/env python
+import pycoal
+
+# path to spectral library
+library_filename = "s06av95a_envi.hdr"
+
+# path to orthocorrected, scaled-reflectance image
+input_filename = "ang20150420t182050_corr_v1e_img.hdr"
+
+# path to save RGB image
+rgb_filename = "ang20150420t182050_corr_v1e_img_rgb.hdr"
+
+# path to save mineral classified image
+classified_filename = "ang20150420t182050_corr_v1e_img_class.hdr"
+
+# create a new mineral classification instance
+mineral_classification = pycoal.mineral.MineralClassification(input_filename)
+
+# generate a georeferenced visible-light image
+mineral_classification.to_rgb(input_filename, rgb_filename)
+
+# generate a mineral classified image
+mineral_classification.classify_image(input_filename, 
classified_filename)</pre>
+
+<h3 id="mining">Mining Identification</h3>
+
+<p>The <a href="https://pycoal.readthedocs.io/en/latest/mining.html"; 
class="external">Mining Identification API</a> filters mineral classified 
images to identify specific classes of interest (Figure 3), by default proxies 
for coal mining in the USGS Digital Spectral Library 06.</p>
+
+<pre>#!/usr/bin/env python
+import pycoal
+
+# path to mineral classified image
+mineral_filename = "ang20150420t182050_corr_v1e_img_class.hdr"
+
+# path to save mining classified image
+mining_filename = "ang20150420t182050_corr_v1e_img_class_mining.hdr"
+
+# create a new mining classification instance
+mining_classification = pycoal.mining.MiningClassification()
+
+# generate a mining classified image
+mining_classification.classify_image(mineral_filename, mining_filename)</pre>
+
+<h3 id="environment">Environmental Correlation</h3>
+
+<p>The <a href="https://pycoal.readthedocs.io/en/latest/environment.html"; 
class="external">Environmental Correlation API</a> finds pixels in a mining 
classified image that are within a certain number of meters from features in a 
vector layer (Figure 4) such as flow lines in the National Hydrography Dataset 
(NHD).</p>
+
+<pre>#!/usr/bin/env python
+import pycoal
+
+# path to mining classified image
+mining_filename = "ang20150420t182050_corr_v1e_img_class_mining.hdr"
+
+# path to hydrography data
+vector_filename = "NHDNM/Shape/NHDFlowline.shp"
+
+# path to save environmental correlation image
+correlation_filename = 
"ang20150420t182050_corr_v1e_img_class_mining_NHDFlowline_correlation.hdr"
+
+# create a new environmental correlation instance
+environmental_correlation = pycoal.environment.EnvironmentalCorrelation()
+
+# generate an environmental correlation image of mining pixels within 10 
meters of a stream
+environmental_correlation.intersect_proximity(mining_filename, 
vector_filename, 10.0, correlation_filename)</pre>
+
+<h2 id="development">Development</h2>
+
+<p>Contribute to the project through our <a class="external" 
href="https://github.com/capstone-coal";>GitHub Organization</a>, refer to the 
COAL <a class="external" 
href="https://github.com/capstone-coal/pycoal/wiki";>wiki</a> for development 
documentation, and contact the <a href="/team.html">team</a> to get 
involved.</p>
+
+{% include footer.html %}

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