deploy: a7e2527

Author: hweifluids

.doctrees/environment.pickle

@@ -1,37 +1,30 @@
 PyFTLE3D: Research Toolkit for 3D Lagrangian coherent structures with GUI
 ===================================================
 
-**Notice: The toolkit is undergoing an internal testing, and currently not publicly released. Who would like to use it in advance to release for non-profit use as early birds, please feel free contact me. The planned release time is 2025.06 - 2025.07 with high-freedom opensource license type, while the author still holds the right to change the release time. - Huanxia with Regards.**
+.. note::
+
+   Notice: The toolkit is undergoing an internal testing, and currently not publicly released. Who would like to use it in advance to release for non-profit use as early birds, please feel free contact me. The planned release time is 2025.06 - 2025.07 with high-freedom opensource license type, while the author still holds the right to change the release time. - Huanxia with Regards.
+
 
 PyFTLE3D is a Python-based research toolkit for computing and visualizing Lagrangian coherent structures (LCS) in three-dimensional fluid flows and other complex systems by computing Finite Time Lyapunov Exponent (FTLE) field.
 Coming with GUI and integrated runtime, it is designed to be user-friendly and accessible, allowing peers to easily analyze and visualize LCS in their data, with no need for researchers to have any programming knowledge, just like using postprocessing software such as ParaView or Tecplot.
 It provides multiple OOTB numerical methods, with various computation density, accuracy, and focus preferences.
-Certainly, it is also suitable for those with programming experience who want to customize their analysis.
+Certainly, it is also suitable for those with programming experience who want to customize their analysis and define more functions.
 
 The computation is accelerated by CUDA (Python Numba implement), achieving over 30x speedup compared to the CPU version.
 The author currently lacks the capability to develop with OpenCL, so for now only NVIDIA GPUs are supported for acceleration.
 As most computers have larger RAM than GPU memory, the toolkit also supports CPU mode for large datasets on CPU systems.
 Although, the new features would firstly be added to the GPU version, and the CPU version would be updated with a month-scale delay.
 
+For the start, it is strongly suggested to begin with reading the review by Prof. George Haller in regard to LCS `here<https://www.georgehaller.com/reprints/annurev-fluid-010313-141322.pdf>__`, published on Annual Review of Fluid Mechanics, instead of directly use this toolkit without any theoretical foundation.
+Some of the theories can be found in this section, it is more encouraged to learn LCS theories and numerical methods by reading the technical papers.
 
 
-This guide is intended to help new users get started with using nekRS, as well as serve as a
-reference for more advanced users. Because the :term:`Nek5000` code is somewhat of a predecessor to
-nekRS, some aspects of the current nekRS design are selected to enable faster translation of
-:term:`Nek5000` input files into nekRS input files. Throughout this documentation, all such
-:term:`Nek5000`-oriented settings will be referred to as "legacy" settings. Because these
-:term:`Nek5000`-oriented settings require proficiency in Fortran, structured text formats,
-and several additional input files, all new users are encouraged to adopt the nekRS-based problem setup.
-
 We recommend working through this user guide in the order below. At the very least, please
 read :ref:`The nekRS Input Files <input>` page before reading the :ref:`FAQs <detailed>`
 page, as some necessary concepts are introduced in this order.
 
-.. note::
 
-   This documentation is a work in progress, and will undergo big changes as more
-   features are added to nekRS. Please open issues to track any missing information
-   at the github repository `here <https://github.com/aprilnovak/nekRSDoc>`__.
 
 Table of Contents
 -----------------

.doctrees/index.doctree

@@ -83,31 +83,23 @@
 
   <section id="pyftle3d-research-toolkit-for-3d-lagrangian-coherent-structures-with-gui">
 <h1>PyFTLE3D: Research Toolkit for 3D Lagrangian coherent structures with GUI<a class="headerlink" href="#pyftle3d-research-toolkit-for-3d-lagrangian-coherent-structures-with-gui" title="Link to this heading"></a></h1>
-<p><strong>Notice: The toolkit is undergoing an internal testing, and currently not publicly released. Who would like to use it in advance to release for non-profit use as early birds, please feel free contact me. The planned release time is 2025.06 - 2025.07 with high-freedom opensource license type, while the author still holds the right to change the release time. - Huanxia with Regards.</strong></p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Notice: The toolkit is undergoing an internal testing, and currently not publicly released. Who would like to use it in advance to release for non-profit use as early birds, please feel free contact me. The planned release time is 2025.06 - 2025.07 with high-freedom opensource license type, while the author still holds the right to change the release time. - Huanxia with Regards.</p>
+</div>
 <p>PyFTLE3D is a Python-based research toolkit for computing and visualizing Lagrangian coherent structures (LCS) in three-dimensional fluid flows and other complex systems by computing Finite Time Lyapunov Exponent (FTLE) field.
 Coming with GUI and integrated runtime, it is designed to be user-friendly and accessible, allowing peers to easily analyze and visualize LCS in their data, with no need for researchers to have any programming knowledge, just like using postprocessing software such as ParaView or Tecplot.
 It provides multiple OOTB numerical methods, with various computation density, accuracy, and focus preferences.
-Certainly, it is also suitable for those with programming experience who want to customize their analysis.</p>
+Certainly, it is also suitable for those with programming experience who want to customize their analysis and define more functions.</p>
 <p>The computation is accelerated by CUDA (Python Numba implement), achieving over 30x speedup compared to the CPU version.
 The author currently lacks the capability to develop with OpenCL, so for now only NVIDIA GPUs are supported for acceleration.
 As most computers have larger RAM than GPU memory, the toolkit also supports CPU mode for large datasets on CPU systems.
 Although, the new features would firstly be added to the GPU version, and the CPU version would be updated with a month-scale delay.</p>
-<p>This guide is intended to help new users get started with using nekRS, as well as serve as a
-reference for more advanced users. Because the <a class="reference internal" href="glossary.html#term-Nek5000"><span class="xref std std-term">Nek5000</span></a> code is somewhat of a predecessor to
-nekRS, some aspects of the current nekRS design are selected to enable faster translation of
-<a class="reference internal" href="glossary.html#term-Nek5000"><span class="xref std std-term">Nek5000</span></a> input files into nekRS input files. Throughout this documentation, all such
-<a class="reference internal" href="glossary.html#term-Nek5000"><span class="xref std std-term">Nek5000</span></a>-oriented settings will be referred to as “legacy” settings. Because these
-<a class="reference internal" href="glossary.html#term-Nek5000"><span class="xref std std-term">Nek5000</span></a>-oriented settings require proficiency in Fortran, structured text formats,
-and several additional input files, all new users are encouraged to adopt the nekRS-based problem setup.</p>
+<p>For the start, it is strongly suggested to begin with reading the review by Prof. George Haller in regard to LCS <cite>here&lt;https://www.georgehaller.com/reprints/annurev-fluid-010313-141322.pdf&gt;__</cite>, published on Annual Review of Fluid Mechanics, instead of directly use this toolkit without any theoretical foundation.
+Some of the theories can be found in this section, it is more encouraged to learn LCS theories and numerical methods by reading the technical papers.</p>
 <p>We recommend working through this user guide in the order below. At the very least, please
 read <a class="reference internal" href="input_files.html#input"><span class="std std-ref">The nekRS Input Files</span></a> page before reading the <a class="reference internal" href="detailed_usage.html#detailed"><span class="std std-ref">FAQs</span></a>
 page, as some necessary concepts are introduced in this order.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This documentation is a work in progress, and will undergo big changes as more
-features are added to nekRS. Please open issues to track any missing information
-at the github repository <a class="reference external" href="https://github.com/aprilnovak/nekRSDoc">here</a>.</p>
-</div>
 <section id="table-of-contents">
 <h2>Table of Contents<a class="headerlink" href="#table-of-contents" title="Link to this heading"></a></h2>
 <div class="toctree-wrapper compound">