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<h1><a class="reference internal" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F_images%2Ficon.svg"><img alt="icon" src="proxy.php?url=https%3A%2F%2Fgithub.com%2F_images%2Ficon.svg" width="30" /></a> BoxKit<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23icon-boxkit" title="Permalink to this headline"></a></h1>
<p><a class="reference external" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://github.com/psf/black"><img">https://github.com/psf/black"><img alt="Code style: black" src="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://img.shields.io/badge/code%20style-black-000000.svg" rel="nofollow">https://img.shields.io/badge/code%20style-black-000000.svg" /></a></p>
<p><img alt="FlashX" src="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://github.com/Box-Tools/BoxKit/workflows/FlashX/badge.svg">https://github.com/Box-Tools/BoxKit/workflows/FlashX/badge.svg" /> <img alt="FlowX" src="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://github.com/Box-Tools/BoxKit/workflows/FlowX/badge.svg">https://github.com/Box-Tools/BoxKit/workflows/FlowX/badge.svg" /> <img alt="Minimal" src="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://github.com/Box-Tools/BoxKit/workflows/Minimal/badge.svg">https://github.com/Box-Tools/BoxKit/workflows/Minimal/badge.svg" /> <img alt="Publish" src="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://github.com/Box-Tools/BoxKit/workflows/Publish/badge.svg">https://github.com/Box-Tools/BoxKit/workflows/Publish/badge.svg" /> <img alt="Linting" src="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://github.com/Box-Tools/BoxKit/workflows/Linting/badge.svg">https://github.com/Box-Tools/BoxKit/workflows/Linting/badge.svg" /></p>
<div class="section" id="overview">
<h2>Overview<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23overview" title="Permalink to this headline"></a></h2>
<p>BoxKit is a library that provides building blocks to parallelize and
scale data science, statistical analysis, and machine learning
applications for block-structured simulation datasets. Spatial data from
simulations can be accessed and managed using tools available in this
library to interface with packages like SciKit, PyTorch, and OpticalFlow
for post-processing and analysis.</p>
<p>The library provides a Python interface to efficiently access Adaptive
Mesh Refinement (AMR) data typical of simulation outputs, and leverages
multiprocessing libraries like JobLib and Dask to scale analysis on
Non-Uniform Memory Access (NUMA) and distributed computing
architectures.</p>
</div>
<div class="section" id="installation">
<h2>Installation<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23installation" title="Permalink to this headline"></a></h2>
<p>Stable releases of BoxKit are hosted on Python Package Index website
(<a class="reference external" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://pypi.org/project/BoxKit/">https://pypi.org/project/BoxKit/</a" rel="nofollow">https://pypi.org/project/BoxKit/">https://pypi.org/project/BoxKit/</a>) and can be installed by executing,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">BoxKit</span> <span class="o">--</span><span class="n">user</span>
</pre></div>
</div>
<p>Note that <code class="docutils literal notranslate"><span class="pre">pip</span></code> should point to <code class="docutils literal notranslate"><span class="pre">python3+</span></code> installation package
<code class="docutils literal notranslate"><span class="pre">pip3</span></code>.</p>
<p>Upgrading and uninstallation is easily managed through this interface
using,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">--</span><span class="n">upgrade</span> <span class="n">BoxKit</span> <span class="o">--</span><span class="n">user</span>
<span class="n">pip</span> <span class="n">uninstall</span> <span class="n">BoxKit</span>
</pre></div>
</div>
<p>Pre-release version can be installed directly from the git reposity by
executing,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">ssh</span><span class="p">:</span><span class="o">//</span><span class="n">git</span><span class="nd">@github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">Box</span><span class="o">-</span><span class="n">Tools</span><span class="o">/</span><span class="n">BoxKit</span><span class="o">.</span><span class="n">git</span> <span class="o">--</span><span class="n">user</span>
</pre></div>
</div>
<p>BoxKit provides various installation options that can be used to
configure the library with desired features. Following is a list of
options,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span><span class="o">-</span><span class="n">cbox</span> <span class="o">-</span> <span class="n">With</span> <span class="n">C</span><span class="o">++</span> <span class="n">backend</span>
<span class="k">with</span><span class="o">-</span><span class="n">pyarrow</span> <span class="o">-</span> <span class="n">With</span> <span class="n">Apache</span> <span class="n">Arrow</span> <span class="n">data</span> <span class="n">backend</span>
<span class="k">with</span><span class="o">-</span><span class="n">zarr</span> <span class="o">-</span> <span class="n">With</span> <span class="n">Zarr</span> <span class="n">data</span> <span class="n">backend</span>
<span class="k">with</span><span class="o">-</span><span class="n">dask</span> <span class="o">-</span> <span class="n">With</span> <span class="n">Dask</span> <span class="n">data</span><span class="o">/</span><span class="n">parallel</span> <span class="n">backend</span>
<span class="n">enable</span><span class="o">-</span><span class="n">testing</span> <span class="o">-</span> <span class="n">Enabling</span> <span class="n">testing</span> <span class="n">mode</span> <span class="k">for</span> <span class="n">development</span>
</pre></div>
</div>
<p>Correspondingly, the installation command can be modified to include
necessary options as follows,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>export CXX=$(CPP_COMPILER)
pip install BoxKit --user --install-option="--enable-testing" --install-option="--with-cbox"
</pre></div>
</div>
<p>There maybe situations where users may want to install BoxKit in
development mode $\textemdash$ to design new features, debug, or
customize classes/methods to their needs. This can be easily
accomplished using the <code class="docutils literal notranslate"><span class="pre">setup</span></code> script located in the project root
directory and executing,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">setup</span> <span class="n">develop</span>
</pre></div>
</div>
<p>Development mode enables testing of features/updates directly from the
source code and is an effective method for debugging. Note that the
<code class="docutils literal notranslate"><span class="pre">setup</span></code> script relies on <code class="docutils literal notranslate"><span class="pre">click</span></code>, which can be installed using,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">click</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">setup</span></code> command acts a wrapper over <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> to provide a
developer friendly interface. The <code class="docutils literal notranslate"><span class="pre">--help</span></code> option provides
instructions on how to configure installation with different options,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">setup</span> <span class="o">--</span><span class="n">help</span>
<span class="o">./</span><span class="n">setup</span> <span class="n">develop</span> <span class="o">--</span><span class="n">help</span>
</pre></div>
</div>
</div>
<div class="section" id="usage">
<h2>Usage<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23usage" title="Permalink to this headline"></a></h2>
<p>After <code class="docutils literal notranslate"><span class="pre">pip</span></code> installation, BoxKit can be imported inside Python
environment by adding the following to iPython notebooks and scripts,</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">boxkit</span>
</pre></div>
</div>
<p>Once the library is imported in the environment, simulation datasets can
be read by executing,</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Read dataset from a Flash-X simulation</span>
<span class="n">dset</span> <span class="o">=</span> <span class="n">boxkit</span><span class="o">.</span><span class="n">read_dataset</span><span class="p">(</span><span class="n">path_to_hdf5_file</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="s2">"flash"</span><span class="p">)</span>
</pre></div>
</div>
<p>New datasets can be created using the <code class="docutils literal notranslate"><span class="pre">create_dataset</span></code> method</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create a dataset using custom attributes</span>
<span class="n">dset</span> <span class="o">=</span> <span class="n">boxkit</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="o">**</span><span class="n">attributes</span><span class="p">)</span>
</pre></div>
</div>
<p>Following is an example on how to create a block-structured dataset in
BoxKit and use its interface. Similar functionality exists for datasets
that are read from a simulation source like Flash-X
(<a class="reference external" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://flash-x.org">https://flash-x.org</a>)</p" rel="nofollow">https://flash-x.org">https://flash-x.org</a>)</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create a two-dimensional dataset with 25 blocks of size 4x4</span>
<span class="n">dset</span> <span class="o">=</span> <span class="n">boxkit</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">xmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">xmax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">ymax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">nxb</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span><span class="n">nyb</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span><span class="n">nblockx</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span><span class="n">nblocky</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>print(dset)
Dataset:
- type : <class 'boxkit.library._dataset.Dataset'>
- file : None
- keys : []
- dtype : []
- bound(z-y-x) : [0.0, 1.0] x [0.0, 0.8] x [0.0, 1.6]
- shape(z-y-x) : 1 x 4 x 4
- guard(z-y-x) : 0 x 0 x 0
- nblocks : 25
- dtype : {}
</pre></div>
</div>
<p>Next add a solution variable using,</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Add a solution variable to the dataset</span>
<span class="n">dset</span><span class="o">.</span><span class="n">addvar</span><span class="p">(</span><span class="s2">"soln"</span><span class="p">)</span>
</pre></div>
</div>
<p>This creates a numpy memmap for solution variable and stores it on disk.
The data can be accessed directly using <code class="docutils literal notranslate"><span class="pre">dset["soln"]</span></code>. When dataset
is read from HDF5 source using <code class="docutils literal notranslate"><span class="pre">read_dataset</span></code>, like Flash-X
simulations, then its representation on the disk is in the form of
<code class="docutils literal notranslate"><span class="pre">h5py</span></code> objects.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">dset</span><span class="p">[</span><span class="s2">"soln"</span><span class="p">])</span>
<span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
<p>The example dataset here contains 25 blocks that are arranged using a
space-filling morton order as below,</p>
<p><a class="reference internal" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F_images%2Fmorton.png"><img alt="morton" src="proxy.php?url=https%3A%2F%2Fgithub.com%2F_images%2Fmorton.png" style="width: 150px;" /></a></p>
<p>Solution data local to individual blocks can be accessed by looping over
a dataset’s <code class="docutils literal notranslate"><span class="pre">blocklist</span></code></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">dset</span><span class="o">.</span><span class="n">blocklist</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">block</span><span class="p">[</span><span class="s2">"soln"</span><span class="p">])</span>
</pre></div>
</div>
<p>BoxKit also offers wrappers to scale the process of deploying workflows
on NUMA and distributed computing architectures by providing decorators
that can parallelize Python operations over a single data structure to
operate over a list,</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">boxkit.library</span> <span class="kn">import</span> <span class="n">Action</span>
<span class="c1"># Decorate function on a block with desired configuration for parallelization</span>
<span class="nd">@Action</span><span class="p">(</span><span class="n">num_procs</span><span class="p">,</span> <span class="n">parallel_backend</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">operation_on_block</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="k">pass</span>
<span class="c1"># Call the function with list of blocks as the first argument</span>
<span class="n">operation_on_block</span><span class="p">((</span><span class="n">block</span> <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">list_of_blocks</span><span class="p">),</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">Action</span></code> wrapper converts the function, <code class="docutils literal notranslate"><span class="pre">operation_on_block</span></code>,
into a parallel method which can be deployed on a multinode cluster with
the desired backend (JobLib/Dask). BoxKit does not interfere with
parallelization schema of target applications like SciKit, OpticalFlow,
and PyTorch which function independently using available resources.</p>
<p>Detailed information on full functionality is availabe in documentation
(<a class="reference external" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://box-tools.github.io/BoxKit/">https://box-tools.github.io/BoxKit/</a>).</p" rel="nofollow">https://box-tools.github.io/BoxKit/">https://box-tools.github.io/BoxKit/</a>).</p>
</div>
<div class="section" id="contribution">
<h2>Contribution<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23contribution" title="Permalink to this headline"></a></h2>
<p>Developers are encouraged to fork the repository and contribute to the
source code in the form of pull requests to the <code class="docutils literal notranslate"><span class="pre">development</span></code> branch.
Please read documentation (<a class="reference external" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%3Ca+href%3D"https://box-tools.github.io/BoxKit/">https://box-tools.github.io/BoxKit/</a" rel="nofollow">https://box-tools.github.io/BoxKit/">https://box-tools.github.io/BoxKit/</a>) for an
overview of software design and developer guide</p>
</div>
<div class="section" id="testing">
<h2>Testing<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23testing" title="Permalink to this headline"></a></h2>
<p>Testing for BoxKit is performed across different hardware platforms
where high-fidelity simulation data can reside. The sites $\textemdash$
acadia and sedona refer to a Mac and Ubuntu operating systems
respectively where regular testing takes place.</p>
<p>For lightweight testing during pull requests and merger, new tests can
be added to <code class="docutils literal notranslate"><span class="pre">tests/container</span></code>. Each test should be accompanied with a
coresspoding addition to YAML files located under <code class="docutils literal notranslate"><span class="pre">.github/workflows</span></code>.
See <code class="docutils literal notranslate"><span class="pre">tests/container/heater.py</span></code> and <code class="docutils literal notranslate"><span class="pre">.github/workflows/flashx.yaml</span></code>
for an example.</p>
</div>
<div class="section" id="citation">
<h2>Citation<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23citation" title="Permalink to this headline"></a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@software</span><span class="p">{</span><span class="n">akash_dhruv_2023_8063195</span><span class="p">,</span>
<span class="n">author</span> <span class="o">=</span> <span class="p">{</span><span class="n">Akash</span> <span class="n">Dhruv</span><span class="p">},</span>
<span class="n">title</span> <span class="o">=</span> <span class="p">{</span><span class="n">akashdhruv</span><span class="o">/</span><span class="n">BoxKit</span><span class="p">:</span> <span class="n">June</span> <span class="mi">2023</span><span class="p">},</span>
<span class="n">month</span> <span class="o">=</span> <span class="n">jun</span><span class="p">,</span>
<span class="n">year</span> <span class="o">=</span> <span class="mi">2023</span><span class="p">,</span>
<span class="n">publisher</span> <span class="o">=</span> <span class="p">{</span><span class="n">Zenodo</span><span class="p">},</span>
<span class="n">version</span> <span class="o">=</span> <span class="p">{</span><span class="mf">2023.06</span><span class="p">},</span>
<span class="n">doi</span> <span class="o">=</span> <span class="p">{</span><span class="mf">10.5281</span><span class="o">/</span><span class="n">zenodo</span><span class="mf">.8063195</span><span class="p">},</span>
<span class="n">url</span> <span class="o">=</span> <span class="p">{</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">doi</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="mf">10.5281</span><span class="o">/</span><span class="n">zenodo</span><span class="mf">.8063195</span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="help-support">
<h2>Help & Support<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23help-support" title="Permalink to this headline"></a></h2>
<p>Please file an issue on the repository page to report bugs, request
features, and ask questions about usage</p>
</div>
<div class="section" id="tutorials">
<h2>Tutorials<a class="headerlink" href="proxy.php?url=https%3A%2F%2Fgithub.com%2F%23tutorials" title="Permalink to this headline"></a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="proxy.php?url=https%3A%2F%2Fgithub.com%2Ftutorials%2Fastrophysics_example_01%2Fastrophysics_example_01.html">Astrophysics Example 1</a></li>
<li class="toctree-l1"><a class="reference internal" href="proxy.php?url=https%3A%2F%2Fgithub.com%2Ftutorials%2Fpool_boiling_gravity%2Fpool_boiling_gravity.html">Pool Boiling Gravity</a></li>
</ul>
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