From 399a06502560d9be7b61881b3a9d8cf924cd4f9b Mon Sep 17 00:00:00 2001
From:  <>
Date: Thu, 20 Jun 2024 16:45:00 +0000
Subject: [PATCH] Deployed e8430c0 with MkDocs version: 1.6.0

---
 CHANGELOG/index.html     |  10 +--
 index.html               |   5 +-
 io/index.html            | 168 +++++++++++++++++++--------------------
 search/search_index.json |   2 +-
 4 files changed, 94 insertions(+), 91 deletions(-)

diff --git a/CHANGELOG/index.html b/CHANGELOG/index.html
index d547e94..24a905e 100644
--- a/CHANGELOG/index.html
+++ b/CHANGELOG/index.html
@@ -499,9 +499,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#v010-14062024" class="md-nav__link">
+  <a href="#v010-20062024" class="md-nav__link">
     <span class="md-ellipsis">
-      v0.1.0 (14/06/2024)
+      v0.1.0 (20/06/2024)
     </span>
   </a>
   
@@ -542,9 +542,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#v010-14062024" class="md-nav__link">
+  <a href="#v010-20062024" class="md-nav__link">
     <span class="md-ellipsis">
-      v0.1.0 (14/06/2024)
+      v0.1.0 (20/06/2024)
     </span>
   </a>
   
@@ -582,7 +582,7 @@
 
 
 <h1 id="versions-history">Versions History</h1>
-<h2 id="v010-14062024">v0.1.0 (14/06/2024)</h2>
+<h2 id="v010-20062024">v0.1.0 (20/06/2024)</h2>
 <ul>
 <li>First release of <code>fibretracker</code>!</li>
 </ul>
diff --git a/index.html b/index.html
index 5a9a150..80cd11d 100644
--- a/index.html
+++ b/index.html
@@ -645,8 +645,11 @@ <h2 id="getting-started">💻 Getting Started</h2>
 <p>Activate the environment by running</p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>conda activate fibretracker
 </span></code></pre></div>
+<p>To read .txm file, install <code>dxchange</code> using <code>conda</code> [install before fibretracker module to avoid version conflicts and related error]</p>
+<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>conda install -c conda-forge dxchange
+</span></code></pre></div>
 <p>Install the FibreTracker tool using <code>pip</code></p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>pip install fibretracker
+<div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>pip install fibretracker
 </span></code></pre></div>
 <p>Go to Example and run the notebook with <code>fibretracker</code> enviroment</p>
 <h2 id="data">Data</h2>
diff --git a/io/index.html b/io/index.html
index ecdddb8..a77b307 100644
--- a/io/index.html
+++ b/io/index.html
@@ -855,9 +855,7 @@ <h3 id="fibretracker.io.load" class="doc doc-heading">
 </details>
             <details class="quote">
               <summary>Source code in <code>fibretracker/io/read_file.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-443">443</a></span>
-<span class="normal"><a href="#__codelineno-0-444">444</a></span>
-<span class="normal"><a href="#__codelineno-0-445">445</a></span>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-445">445</a></span>
 <span class="normal"><a href="#__codelineno-0-446">446</a></span>
 <span class="normal"><a href="#__codelineno-0-447">447</a></span>
 <span class="normal"><a href="#__codelineno-0-448">448</a></span>
@@ -914,66 +912,68 @@ <h3 id="fibretracker.io.load" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-499">499</a></span>
 <span class="normal"><a href="#__codelineno-0-500">500</a></span>
 <span class="normal"><a href="#__codelineno-0-501">501</a></span>
-<span class="normal"><a href="#__codelineno-0-502">502</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-443"><a id="__codelineno-0-443" name="__codelineno-0-443"></a><span class="k">def</span> <span class="nf">load</span><span class="p">(</span>
-</span><span id="__span-0-444"><a id="__codelineno-0-444" name="__codelineno-0-444"></a>    <span class="n">path</span><span class="p">,</span>
-</span><span id="__span-0-445"><a id="__codelineno-0-445" name="__codelineno-0-445"></a>    <span class="n">dataset_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-</span><span id="__span-0-446"><a id="__codelineno-0-446" name="__codelineno-0-446"></a>    <span class="n">return_metadata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-</span><span id="__span-0-447"><a id="__codelineno-0-447" name="__codelineno-0-447"></a>    <span class="n">contains</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-</span><span id="__span-0-448"><a id="__codelineno-0-448" name="__codelineno-0-448"></a>    <span class="n">force_load</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-</span><span id="__span-0-449"><a id="__codelineno-0-449" name="__codelineno-0-449"></a>    <span class="n">dim_order</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-</span><span id="__span-0-450"><a id="__codelineno-0-450" name="__codelineno-0-450"></a>    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-</span><span id="__span-0-451"><a id="__codelineno-0-451" name="__codelineno-0-451"></a><span class="p">):</span>
-</span><span id="__span-0-452"><a id="__codelineno-0-452" name="__codelineno-0-452"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-</span><span id="__span-0-453"><a id="__codelineno-0-453" name="__codelineno-0-453"></a><span class="sd">    Load data from the specified file or directory.</span>
-</span><span id="__span-0-454"><a id="__codelineno-0-454" name="__codelineno-0-454"></a>
-</span><span id="__span-0-455"><a id="__codelineno-0-455" name="__codelineno-0-455"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-456"><a id="__codelineno-0-456" name="__codelineno-0-456"></a><span class="sd">        path (str or os.PathLike): The path to the file or directory.</span>
-</span><span id="__span-0-457"><a id="__codelineno-0-457" name="__codelineno-0-457"></a><span class="sd">        dataset_name (str, optional): Specifies the name of the dataset to be loaded</span>
-</span><span id="__span-0-458"><a id="__codelineno-0-458" name="__codelineno-0-458"></a><span class="sd">            in case multiple dataset exist within the same file. Default is None (only for HDF5 files)</span>
-</span><span id="__span-0-459"><a id="__codelineno-0-459" name="__codelineno-0-459"></a><span class="sd">        return_metadata (bool, optional): Specifies whether to return metadata or not. Default is False (only for HDF5 and TXRM/TXM/XRM files)</span>
-</span><span id="__span-0-460"><a id="__codelineno-0-460" name="__codelineno-0-460"></a><span class="sd">        contains (str, optional): Specifies a part of the name that is common for the TIFF file stack to be loaded (only for TIFF stacks).</span>
-</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a><span class="sd">            Default is None.</span>
-</span><span id="__span-0-462"><a id="__codelineno-0-462" name="__codelineno-0-462"></a><span class="sd">        force_load (bool, optional): If the file size exceeds available memory, a MemoryError is raised.</span>
-</span><span id="__span-0-463"><a id="__codelineno-0-463" name="__codelineno-0-463"></a><span class="sd">            If force_load is True, the error is changed to warning and the loader tries to load it anyway. Default is False.</span>
-</span><span id="__span-0-464"><a id="__codelineno-0-464" name="__codelineno-0-464"></a><span class="sd">        dim_order (tuple, optional): The order of the dimensions in the volume for .vol files. Default is (2,1,0) which corresponds to (z,y,x)</span>
-</span><span id="__span-0-465"><a id="__codelineno-0-465" name="__codelineno-0-465"></a><span class="sd">        **kwargs: Additional keyword arguments to be passed</span>
-</span><span id="__span-0-466"><a id="__codelineno-0-466" name="__codelineno-0-466"></a><span class="sd">        to the DataLoader constructor.</span>
-</span><span id="__span-0-467"><a id="__codelineno-0-467" name="__codelineno-0-467"></a>
-</span><span id="__span-0-468"><a id="__codelineno-0-468" name="__codelineno-0-468"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-469"><a id="__codelineno-0-469" name="__codelineno-0-469"></a><span class="sd">        vol (numpy.ndarray): The loaded volume</span>
-</span><span id="__span-0-470"><a id="__codelineno-0-470" name="__codelineno-0-470"></a><span class="sd">            If `return_metadata=True` and file format is either HDF5, NIfTI or TXRM/TXM/XRM, returns `tuple` (volume, metadata).</span>
-</span><span id="__span-0-471"><a id="__codelineno-0-471" name="__codelineno-0-471"></a>
-</span><span id="__span-0-472"><a id="__codelineno-0-472" name="__codelineno-0-472"></a><span class="sd">    Raises:</span>
-</span><span id="__span-0-473"><a id="__codelineno-0-473" name="__codelineno-0-473"></a><span class="sd">        MemoryError: if the given file size exceeds available memory</span>
-</span><span id="__span-0-474"><a id="__codelineno-0-474" name="__codelineno-0-474"></a>
-</span><span id="__span-0-475"><a id="__codelineno-0-475" name="__codelineno-0-475"></a><span class="sd">    Example:</span>
-</span><span id="__span-0-476"><a id="__codelineno-0-476" name="__codelineno-0-476"></a><span class="sd">        ```python</span>
-</span><span id="__span-0-477"><a id="__codelineno-0-477" name="__codelineno-0-477"></a><span class="sd">        # Load volume from a single file</span>
-</span><span id="__span-0-478"><a id="__codelineno-0-478" name="__codelineno-0-478"></a><span class="sd">        import fibretracker as ft</span>
-</span><span id="__span-0-479"><a id="__codelineno-0-479" name="__codelineno-0-479"></a>
-</span><span id="__span-0-480"><a id="__codelineno-0-480" name="__codelineno-0-480"></a><span class="sd">        vol = ft.io.load(data_path)</span>
-</span><span id="__span-0-481"><a id="__codelineno-0-481" name="__codelineno-0-481"></a><span class="sd">        ```</span>
-</span><span id="__span-0-482"><a id="__codelineno-0-482" name="__codelineno-0-482"></a><span class="sd">        ```python</span>
-</span><span id="__span-0-483"><a id="__codelineno-0-483" name="__codelineno-0-483"></a><span class="sd">        # Load a stack of TIFF files as a volume</span>
-</span><span id="__span-0-484"><a id="__codelineno-0-484" name="__codelineno-0-484"></a><span class="sd">        import fibretracker as ft</span>
-</span><span id="__span-0-485"><a id="__codelineno-0-485" name="__codelineno-0-485"></a>
-</span><span id="__span-0-486"><a id="__codelineno-0-486" name="__codelineno-0-486"></a><span class="sd">        vol = ft.io.load(data_path, contains=&#39;.tif&#39;)</span>
-</span><span id="__span-0-487"><a id="__codelineno-0-487" name="__codelineno-0-487"></a><span class="sd">        ```</span>
-</span><span id="__span-0-488"><a id="__codelineno-0-488" name="__codelineno-0-488"></a>
-</span><span id="__span-0-489"><a id="__codelineno-0-489" name="__codelineno-0-489"></a><span class="sd">    &quot;&quot;&quot;</span>
+<span class="normal"><a href="#__codelineno-0-502">502</a></span>
+<span class="normal"><a href="#__codelineno-0-503">503</a></span>
+<span class="normal"><a href="#__codelineno-0-504">504</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-445"><a id="__codelineno-0-445" name="__codelineno-0-445"></a><span class="k">def</span> <span class="nf">load</span><span class="p">(</span>
+</span><span id="__span-0-446"><a id="__codelineno-0-446" name="__codelineno-0-446"></a>    <span class="n">path</span><span class="p">,</span>
+</span><span id="__span-0-447"><a id="__codelineno-0-447" name="__codelineno-0-447"></a>    <span class="n">dataset_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+</span><span id="__span-0-448"><a id="__codelineno-0-448" name="__codelineno-0-448"></a>    <span class="n">return_metadata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+</span><span id="__span-0-449"><a id="__codelineno-0-449" name="__codelineno-0-449"></a>    <span class="n">contains</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+</span><span id="__span-0-450"><a id="__codelineno-0-450" name="__codelineno-0-450"></a>    <span class="n">force_load</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+</span><span id="__span-0-451"><a id="__codelineno-0-451" name="__codelineno-0-451"></a>    <span class="n">dim_order</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+</span><span id="__span-0-452"><a id="__codelineno-0-452" name="__codelineno-0-452"></a>    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+</span><span id="__span-0-453"><a id="__codelineno-0-453" name="__codelineno-0-453"></a><span class="p">):</span>
+</span><span id="__span-0-454"><a id="__codelineno-0-454" name="__codelineno-0-454"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+</span><span id="__span-0-455"><a id="__codelineno-0-455" name="__codelineno-0-455"></a><span class="sd">    Load data from the specified file or directory.</span>
+</span><span id="__span-0-456"><a id="__codelineno-0-456" name="__codelineno-0-456"></a>
+</span><span id="__span-0-457"><a id="__codelineno-0-457" name="__codelineno-0-457"></a><span class="sd">    Args:</span>
+</span><span id="__span-0-458"><a id="__codelineno-0-458" name="__codelineno-0-458"></a><span class="sd">        path (str or os.PathLike): The path to the file or directory.</span>
+</span><span id="__span-0-459"><a id="__codelineno-0-459" name="__codelineno-0-459"></a><span class="sd">        dataset_name (str, optional): Specifies the name of the dataset to be loaded</span>
+</span><span id="__span-0-460"><a id="__codelineno-0-460" name="__codelineno-0-460"></a><span class="sd">            in case multiple dataset exist within the same file. Default is None (only for HDF5 files)</span>
+</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a><span class="sd">        return_metadata (bool, optional): Specifies whether to return metadata or not. Default is False (only for HDF5 and TXRM/TXM/XRM files)</span>
+</span><span id="__span-0-462"><a id="__codelineno-0-462" name="__codelineno-0-462"></a><span class="sd">        contains (str, optional): Specifies a part of the name that is common for the TIFF file stack to be loaded (only for TIFF stacks).</span>
+</span><span id="__span-0-463"><a id="__codelineno-0-463" name="__codelineno-0-463"></a><span class="sd">            Default is None.</span>
+</span><span id="__span-0-464"><a id="__codelineno-0-464" name="__codelineno-0-464"></a><span class="sd">        force_load (bool, optional): If the file size exceeds available memory, a MemoryError is raised.</span>
+</span><span id="__span-0-465"><a id="__codelineno-0-465" name="__codelineno-0-465"></a><span class="sd">            If force_load is True, the error is changed to warning and the loader tries to load it anyway. Default is False.</span>
+</span><span id="__span-0-466"><a id="__codelineno-0-466" name="__codelineno-0-466"></a><span class="sd">        dim_order (tuple, optional): The order of the dimensions in the volume for .vol files. Default is (2,1,0) which corresponds to (z,y,x)</span>
+</span><span id="__span-0-467"><a id="__codelineno-0-467" name="__codelineno-0-467"></a><span class="sd">        **kwargs: Additional keyword arguments to be passed</span>
+</span><span id="__span-0-468"><a id="__codelineno-0-468" name="__codelineno-0-468"></a><span class="sd">        to the DataLoader constructor.</span>
+</span><span id="__span-0-469"><a id="__codelineno-0-469" name="__codelineno-0-469"></a>
+</span><span id="__span-0-470"><a id="__codelineno-0-470" name="__codelineno-0-470"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-471"><a id="__codelineno-0-471" name="__codelineno-0-471"></a><span class="sd">        vol (numpy.ndarray): The loaded volume</span>
+</span><span id="__span-0-472"><a id="__codelineno-0-472" name="__codelineno-0-472"></a><span class="sd">            If `return_metadata=True` and file format is either HDF5, NIfTI or TXRM/TXM/XRM, returns `tuple` (volume, metadata).</span>
+</span><span id="__span-0-473"><a id="__codelineno-0-473" name="__codelineno-0-473"></a>
+</span><span id="__span-0-474"><a id="__codelineno-0-474" name="__codelineno-0-474"></a><span class="sd">    Raises:</span>
+</span><span id="__span-0-475"><a id="__codelineno-0-475" name="__codelineno-0-475"></a><span class="sd">        MemoryError: if the given file size exceeds available memory</span>
+</span><span id="__span-0-476"><a id="__codelineno-0-476" name="__codelineno-0-476"></a>
+</span><span id="__span-0-477"><a id="__codelineno-0-477" name="__codelineno-0-477"></a><span class="sd">    Example:</span>
+</span><span id="__span-0-478"><a id="__codelineno-0-478" name="__codelineno-0-478"></a><span class="sd">        ```python</span>
+</span><span id="__span-0-479"><a id="__codelineno-0-479" name="__codelineno-0-479"></a><span class="sd">        # Load volume from a single file</span>
+</span><span id="__span-0-480"><a id="__codelineno-0-480" name="__codelineno-0-480"></a><span class="sd">        import fibretracker as ft</span>
+</span><span id="__span-0-481"><a id="__codelineno-0-481" name="__codelineno-0-481"></a>
+</span><span id="__span-0-482"><a id="__codelineno-0-482" name="__codelineno-0-482"></a><span class="sd">        vol = ft.io.load(data_path)</span>
+</span><span id="__span-0-483"><a id="__codelineno-0-483" name="__codelineno-0-483"></a><span class="sd">        ```</span>
+</span><span id="__span-0-484"><a id="__codelineno-0-484" name="__codelineno-0-484"></a><span class="sd">        ```python</span>
+</span><span id="__span-0-485"><a id="__codelineno-0-485" name="__codelineno-0-485"></a><span class="sd">        # Load a stack of TIFF files as a volume</span>
+</span><span id="__span-0-486"><a id="__codelineno-0-486" name="__codelineno-0-486"></a><span class="sd">        import fibretracker as ft</span>
+</span><span id="__span-0-487"><a id="__codelineno-0-487" name="__codelineno-0-487"></a>
+</span><span id="__span-0-488"><a id="__codelineno-0-488" name="__codelineno-0-488"></a><span class="sd">        vol = ft.io.load(data_path, contains=&#39;.tif&#39;)</span>
+</span><span id="__span-0-489"><a id="__codelineno-0-489" name="__codelineno-0-489"></a><span class="sd">        ```</span>
 </span><span id="__span-0-490"><a id="__codelineno-0-490" name="__codelineno-0-490"></a>
-</span><span id="__span-0-491"><a id="__codelineno-0-491" name="__codelineno-0-491"></a>    <span class="n">loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
-</span><span id="__span-0-492"><a id="__codelineno-0-492" name="__codelineno-0-492"></a>        <span class="n">dataset_name</span><span class="o">=</span><span class="n">dataset_name</span><span class="p">,</span>
-</span><span id="__span-0-493"><a id="__codelineno-0-493" name="__codelineno-0-493"></a>        <span class="n">return_metadata</span><span class="o">=</span><span class="n">return_metadata</span><span class="p">,</span>
-</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>        <span class="n">contains</span><span class="o">=</span><span class="n">contains</span><span class="p">,</span>
-</span><span id="__span-0-495"><a id="__codelineno-0-495" name="__codelineno-0-495"></a>        <span class="n">force_load</span><span class="o">=</span><span class="n">force_load</span><span class="p">,</span>
-</span><span id="__span-0-496"><a id="__codelineno-0-496" name="__codelineno-0-496"></a>        <span class="n">dim_order</span><span class="o">=</span><span class="n">dim_order</span><span class="p">,</span>
-</span><span id="__span-0-497"><a id="__codelineno-0-497" name="__codelineno-0-497"></a>        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-</span><span id="__span-0-498"><a id="__codelineno-0-498" name="__codelineno-0-498"></a>    <span class="p">)</span>
-</span><span id="__span-0-499"><a id="__codelineno-0-499" name="__codelineno-0-499"></a>
-</span><span id="__span-0-500"><a id="__codelineno-0-500" name="__codelineno-0-500"></a>    <span class="n">data</span> <span class="o">=</span> <span class="n">loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+</span><span id="__span-0-491"><a id="__codelineno-0-491" name="__codelineno-0-491"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-492"><a id="__codelineno-0-492" name="__codelineno-0-492"></a>
+</span><span id="__span-0-493"><a id="__codelineno-0-493" name="__codelineno-0-493"></a>    <span class="n">loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>        <span class="n">dataset_name</span><span class="o">=</span><span class="n">dataset_name</span><span class="p">,</span>
+</span><span id="__span-0-495"><a id="__codelineno-0-495" name="__codelineno-0-495"></a>        <span class="n">return_metadata</span><span class="o">=</span><span class="n">return_metadata</span><span class="p">,</span>
+</span><span id="__span-0-496"><a id="__codelineno-0-496" name="__codelineno-0-496"></a>        <span class="n">contains</span><span class="o">=</span><span class="n">contains</span><span class="p">,</span>
+</span><span id="__span-0-497"><a id="__codelineno-0-497" name="__codelineno-0-497"></a>        <span class="n">force_load</span><span class="o">=</span><span class="n">force_load</span><span class="p">,</span>
+</span><span id="__span-0-498"><a id="__codelineno-0-498" name="__codelineno-0-498"></a>        <span class="n">dim_order</span><span class="o">=</span><span class="n">dim_order</span><span class="p">,</span>
+</span><span id="__span-0-499"><a id="__codelineno-0-499" name="__codelineno-0-499"></a>        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+</span><span id="__span-0-500"><a id="__codelineno-0-500" name="__codelineno-0-500"></a>    <span class="p">)</span>
 </span><span id="__span-0-501"><a id="__codelineno-0-501" name="__codelineno-0-501"></a>
-</span><span id="__span-0-502"><a id="__codelineno-0-502" name="__codelineno-0-502"></a>    <span class="k">return</span> <span class="n">data</span>
+</span><span id="__span-0-502"><a id="__codelineno-0-502" name="__codelineno-0-502"></a>    <span class="n">data</span> <span class="o">=</span> <span class="n">loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+</span><span id="__span-0-503"><a id="__codelineno-0-503" name="__codelineno-0-503"></a>
+</span><span id="__span-0-504"><a id="__codelineno-0-504" name="__codelineno-0-504"></a>    <span class="k">return</span> <span class="n">data</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -1057,9 +1057,7 @@ <h3 id="fibretracker.io.normalize" class="doc doc-heading">
 </details>
             <details class="quote">
               <summary>Source code in <code>fibretracker/io/read_file.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-504">504</a></span>
-<span class="normal"><a href="#__codelineno-0-505">505</a></span>
-<span class="normal"><a href="#__codelineno-0-506">506</a></span>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-506">506</a></span>
 <span class="normal"><a href="#__codelineno-0-507">507</a></span>
 <span class="normal"><a href="#__codelineno-0-508">508</a></span>
 <span class="normal"><a href="#__codelineno-0-509">509</a></span>
@@ -1076,26 +1074,28 @@ <h3 id="fibretracker.io.normalize" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-520">520</a></span>
 <span class="normal"><a href="#__codelineno-0-521">521</a></span>
 <span class="normal"><a href="#__codelineno-0-522">522</a></span>
-<span class="normal"><a href="#__codelineno-0-523">523</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-504"><a id="__codelineno-0-504" name="__codelineno-0-504"></a><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span>
-</span><span id="__span-0-505"><a id="__codelineno-0-505" name="__codelineno-0-505"></a>        <span class="n">vol</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span>
-</span><span id="__span-0-506"><a id="__codelineno-0-506" name="__codelineno-0-506"></a>        <span class="p">):</span>
-</span><span id="__span-0-507"><a id="__codelineno-0-507" name="__codelineno-0-507"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Normalize the volume to the range [0, 1] using min-max scaling.</span>
-</span><span id="__span-0-508"><a id="__codelineno-0-508" name="__codelineno-0-508"></a>
-</span><span id="__span-0-509"><a id="__codelineno-0-509" name="__codelineno-0-509"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-510"><a id="__codelineno-0-510" name="__codelineno-0-510"></a><span class="sd">        vol (numpy.ndarray): The volume to normalize.</span>
-</span><span id="__span-0-511"><a id="__codelineno-0-511" name="__codelineno-0-511"></a>
-</span><span id="__span-0-512"><a id="__codelineno-0-512" name="__codelineno-0-512"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-513"><a id="__codelineno-0-513" name="__codelineno-0-513"></a><span class="sd">        norm_vol (numpy.ndarray): The normalized volume.</span>
-</span><span id="__span-0-514"><a id="__codelineno-0-514" name="__codelineno-0-514"></a>
-</span><span id="__span-0-515"><a id="__codelineno-0-515" name="__codelineno-0-515"></a><span class="sd">    Example:</span>
-</span><span id="__span-0-516"><a id="__codelineno-0-516" name="__codelineno-0-516"></a><span class="sd">        ```python</span>
-</span><span id="__span-0-517"><a id="__codelineno-0-517" name="__codelineno-0-517"></a><span class="sd">        import fibretracker as ft</span>
-</span><span id="__span-0-518"><a id="__codelineno-0-518" name="__codelineno-0-518"></a>
-</span><span id="__span-0-519"><a id="__codelineno-0-519" name="__codelineno-0-519"></a><span class="sd">        norm_vol = ft.io.normalize(vol)</span>
-</span><span id="__span-0-520"><a id="__codelineno-0-520" name="__codelineno-0-520"></a><span class="sd">        ```</span>
-</span><span id="__span-0-521"><a id="__codelineno-0-521" name="__codelineno-0-521"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-522"><a id="__codelineno-0-522" name="__codelineno-0-522"></a>    <span class="n">norm_vol</span> <span class="o">=</span> <span class="p">(</span><span class="n">vol</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">vol</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">vol</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">vol</span><span class="p">))</span>
-</span><span id="__span-0-523"><a id="__codelineno-0-523" name="__codelineno-0-523"></a>    <span class="k">return</span> <span class="n">norm_vol</span>
+<span class="normal"><a href="#__codelineno-0-523">523</a></span>
+<span class="normal"><a href="#__codelineno-0-524">524</a></span>
+<span class="normal"><a href="#__codelineno-0-525">525</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-506"><a id="__codelineno-0-506" name="__codelineno-0-506"></a><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span>
+</span><span id="__span-0-507"><a id="__codelineno-0-507" name="__codelineno-0-507"></a>        <span class="n">vol</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span>
+</span><span id="__span-0-508"><a id="__codelineno-0-508" name="__codelineno-0-508"></a>        <span class="p">):</span>
+</span><span id="__span-0-509"><a id="__codelineno-0-509" name="__codelineno-0-509"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Normalize the volume to the range [0, 1] using min-max scaling.</span>
+</span><span id="__span-0-510"><a id="__codelineno-0-510" name="__codelineno-0-510"></a>
+</span><span id="__span-0-511"><a id="__codelineno-0-511" name="__codelineno-0-511"></a><span class="sd">    Args:</span>
+</span><span id="__span-0-512"><a id="__codelineno-0-512" name="__codelineno-0-512"></a><span class="sd">        vol (numpy.ndarray): The volume to normalize.</span>
+</span><span id="__span-0-513"><a id="__codelineno-0-513" name="__codelineno-0-513"></a>
+</span><span id="__span-0-514"><a id="__codelineno-0-514" name="__codelineno-0-514"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-515"><a id="__codelineno-0-515" name="__codelineno-0-515"></a><span class="sd">        norm_vol (numpy.ndarray): The normalized volume.</span>
+</span><span id="__span-0-516"><a id="__codelineno-0-516" name="__codelineno-0-516"></a>
+</span><span id="__span-0-517"><a id="__codelineno-0-517" name="__codelineno-0-517"></a><span class="sd">    Example:</span>
+</span><span id="__span-0-518"><a id="__codelineno-0-518" name="__codelineno-0-518"></a><span class="sd">        ```python</span>
+</span><span id="__span-0-519"><a id="__codelineno-0-519" name="__codelineno-0-519"></a><span class="sd">        import fibretracker as ft</span>
+</span><span id="__span-0-520"><a id="__codelineno-0-520" name="__codelineno-0-520"></a>
+</span><span id="__span-0-521"><a id="__codelineno-0-521" name="__codelineno-0-521"></a><span class="sd">        norm_vol = ft.io.normalize(vol)</span>
+</span><span id="__span-0-522"><a id="__codelineno-0-522" name="__codelineno-0-522"></a><span class="sd">        ```</span>
+</span><span id="__span-0-523"><a id="__codelineno-0-523" name="__codelineno-0-523"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-524"><a id="__codelineno-0-524" name="__codelineno-0-524"></a>    <span class="n">norm_vol</span> <span class="o">=</span> <span class="p">(</span><span class="n">vol</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">vol</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">vol</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">vol</span><span class="p">))</span>
+</span><span id="__span-0-525"><a id="__codelineno-0-525" name="__codelineno-0-525"></a>    <span class="k">return</span> <span class="n">norm_vol</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
diff --git a/search/search_index.json b/search/search_index.json
index b475a43..5026e71 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"FibreTracker","text":"<p>A python library to track fibre in a volume</p>"},{"location":"#getting-started","title":"\ud83d\udcbb Getting Started","text":"<p>Create a new environment (highly recommended)</p> <pre><code>conda create -n fibretracker python=3.11\n</code></pre> <p>Activate the environment by running</p> <pre><code>conda activate fibretracker\n</code></pre> <p>Install the FibreTracker tool using <code>pip</code></p> <pre><code>pip install fibretracker\n</code></pre> <p>Go to Example and run the notebook with <code>fibretracker</code> enviroment</p>"},{"location":"#data","title":"Data","text":"<p>Following are the dataset on which fibre tracking is tested on 250 slices</p> <ul> <li>Mock and UD [link] -    <code>UD-01_FoV_2_B2_recon.txm</code> <code>Mock-01_FoV_2_B2_recon.txm</code></li> <li>GFRP [link] - <code>GFRP_Initial.zip</code></li> <li>XCT Low-Res [link] - <code>XCT_L.zip</code></li> </ul>"},{"location":"#license","title":"License","text":"<p><code>fibretracker</code> was created by Kumari Pooja. It is licensed under the terms of the MIT license.</p>"},{"location":"#credits","title":"Credits","text":"<p>This work is supported by the RELIANCE doctoral network via the Marie Sk\u0142odowska-Curie Actions HORIZON-MSCA-2021-DN- 01. Project no: 101073040 </p> <p> </p> <p>Project based on the cookiecutter data science project template. #cookiecutterdatascience</p>"},{"location":"CHANGELOG/","title":"Versions History","text":""},{"location":"CHANGELOG/#v010-14062024","title":"v0.1.0 (14/06/2024)","text":"<ul> <li>First release of <code>fibretracker</code>!</li> </ul>"},{"location":"detector/","title":"Documention for fibre detection","text":"<p>To detect fibre centre in a slice</p>"},{"location":"detector/#fibretracker.models","title":"fibretracker.models","text":""},{"location":"detector/#fibretracker.models.get_fibre_coords","title":"fibretracker.models.get_fibre_coords","text":"<pre><code>get_fibre_coords(vol, std=2.5, min_distance=3, threshold_abs=0.4, weighted_avg=False, window_size=10, apply_filter=False)\n</code></pre> <p>Get list of fibres centre coordinates in a volume using blob detector</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray</code> <p>input volume</p> required <code>std</code> <code>float</code> <p>standard deviation of the Gaussian filter</p> <code>2.5</code> <code>min_distance</code> <code>int</code> <p>minimum distance between fibres</p> <code>3</code> <code>threshold_abs</code> <code>float</code> <p>threshold value for the peak from the background</p> <code>0.4</code> <code>weighted_avg</code> <code>bool</code> <p>whether to apply weighted average to the detected coordinates</p> <code>False</code> <code>window_size</code> <code>int</code> <p>size of the neighbourhood window around the peak</p> <code>10</code> <code>apply_filter</code> <code>bool</code> <p>whether to apply Gaussian filter to the window</p> <code>False</code> <p>Returns:</p> Name Type Description <code>coords</code> <code>List(array)</code> <p>List of fibres centre coordinates in the volume</p> Example <pre><code>import fibretracker as ft\n\nvol = ft.detector.get_fib_coords(vol, std=2.5, min_distance=3, threshold_abs=0.4)\n</code></pre> Source code in <code>fibretracker/models/detector.py</code> <pre><code>def get_fibre_coords(\n        vol: np.ndarray, \n        std: float=2.5, \n        min_distance: int=3, \n        threshold_abs: float=0.4,\n        weighted_avg: bool=False,\n        window_size: int=10,\n        apply_filter: bool=False,\n        ):\n    ''' Get list of fibres centre coordinates in a volume using blob detector\n\n    Args:\n        vol: input volume\n        std: standard deviation of the Gaussian filter\n        min_distance: minimum distance between fibres\n        threshold_abs: threshold value for the peak from the background\n        weighted_avg: whether to apply weighted average to the detected coordinates\n        window_size: size of the neighbourhood window around the peak\n        apply_filter: whether to apply Gaussian filter to the window\n\n    Returns:\n        coords (List(nd.array)): List of fibres centre coordinates in the volume\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        vol = ft.detector.get_fib_coords(vol, std=2.5, min_distance=3, threshold_abs=0.4)\n        ```\n\n    '''\n    coords = []\n    for i, im in enumerate(vol):\n        coord = blob_centre_detector(im, std=std, min_distance=min_distance, threshold_abs=threshold_abs)\n        if weighted_avg:\n            coord = avg_fibre_coord(coord, im, window_size=window_size, apply_filter=apply_filter, std=std)\n        coords.append(np.stack([coord[:,1], coord[:,0], np.ones(len(coord)) * i], axis=1))\n        print(f'Detecting coordinates - slice: {i+1}/{len(vol)}', end='\\r')\n    print(' ' * len(f'Detecting coordinates - slice: {i+1}/{len(vol)}'), end='\\r')\n    return coords\n</code></pre>"},{"location":"detector/#fibretracker.models.blob_centre_detector","title":"fibretracker.models.blob_centre_detector","text":"<pre><code>blob_centre_detector(im, std=2.5, min_distance=3, threshold_abs=0.4)\n</code></pre> <p>Predict coordinates of fibres centre in a volume slice using blob detector</p> <p>Parameters:</p> Name Type Description Default <code>im</code> <code>ndarray</code> <p>input image</p> required <code>std</code> <code>float</code> <p>standard deviation of the Gaussian filter</p> <code>2.5</code> <code>min_distance</code> <code>int</code> <p>minimum distance between peaks</p> <code>3</code> <code>threshold_abs</code> <code>float</code> <p>threshold value for the peak from the background</p> <code>0.4</code> <p>Returns:</p> Name Type Description <code>pred_coords</code> <code>ndarray</code> <p>predicted coordinates of the fibre centre</p> Example <pre><code>import fibretracker as ft\n\nvol = ft.detector.blob_centre_detector(im, std=2.5, min_distance=3, threshold_abs=0.4)\n</code></pre> Source code in <code>fibretracker/models/detector.py</code> <pre><code>def blob_centre_detector(\n        im: np.ndarray, \n        std: float=2.5, \n        min_distance: int=3, \n        threshold_abs: float=0.4\n        ):\n    ''' Predict coordinates of fibres centre in a volume slice using blob detector\n\n    Args: \n        im: input image\n        std: standard deviation of the Gaussian filter\n        min_distance: minimum distance between peaks\n        threshold_abs: threshold value for the peak from the background\n\n    Returns: \n        pred_coords (np.ndarray): predicted coordinates of the fibre centre\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        vol = ft.detector.blob_centre_detector(im, std=2.5, min_distance=3, threshold_abs=0.4)\n        ```\n\n    '''\n    g = gauss_filter(std)[0]\n    im_g = scipy.ndimage.convolve(scipy.ndimage.convolve(im, g), g.T)\n    pred_coords = skimage.feature.peak_local_max(im_g, min_distance=min_distance, threshold_abs=threshold_abs)\n    return pred_coords\n</code></pre>"},{"location":"detector/#fibretracker.models.gauss_filter","title":"fibretracker.models.gauss_filter","text":"<pre><code>gauss_filter(std)\n</code></pre> <p>Generate a 1D Gaussian filter and its derivatives</p> <p>Parameters:</p> Name Type Description Default <code>std</code> <code>float</code> <p>standard deviation of the Gaussian filter</p> required <p>Returns:</p> Name Type Description <code>g</code> <code>ndarray</code> <p>1D Gaussian filter</p> <code>dg</code> <code>ndarray</code> <p>derivative of the Gaussian filter</p> <code>ddg</code> <code>ndarray</code> <p>second derivative of the Gaussian filter</p> Example <pre><code>import fibretracker as ft\n\nvol = ft.detector.gauss_filter(std=2.5)\n</code></pre> Source code in <code>fibretracker/models/detector.py</code> <pre><code>def gauss_filter(\n        std: float\n        ):\n    ''' Generate a 1D Gaussian filter and its derivatives\n\n    Args: \n        std: standard deviation of the Gaussian filter\n\n    Returns:\n        g (np.ndarray): 1D Gaussian filter\n        dg (np.ndarray): derivative of the Gaussian filter\n        ddg (np.ndarray): second derivative of the Gaussian filter\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        vol = ft.detector.gauss_filter(std=2.5)\n        ```\n\n    '''\n    x = np.arange(-np.ceil(5*std), np.ceil(5*std) + 1)[:,None]\n    g = np.exp(-x**2/(2*std**2))\n    g /= np.sum(g)\n    dg = -x/std**2 * g\n    ddg = -g/std**2 -x/std**2 * dg\n    return g, dg, ddg\n</code></pre>"},{"location":"detector/#fibretracker.models.avg_fibre_coord","title":"fibretracker.models.avg_fibre_coord","text":"<pre><code>avg_fibre_coord(pred_coord, im, window_size=10, apply_filter=False, std=None)\n</code></pre> <p>Recompute the fibre centre in a slice using weighted average of peak neighbourhood</p> <p>Parameters:</p> Name Type Description Default <code>pred_coord</code> <code>ndarray</code> <p>predicted coordinates of the peaks</p> required <code>im</code> <code>ndarray</code> <p>input image</p> required <code>window_size</code> <code>int</code> <p>size of the neighbourhood window around the peak</p> <code>10</code> <code>apply_filter</code> <code>bool</code> <p>whether to apply Gaussian filter to the window</p> <code>False</code> <code>std</code> <code>Optional[float]</code> <p>standard deviation of the Gaussian filter</p> <code>None</code> <p>Returns:</p> Name Type Description <code>coords</code> <code>ndarray</code> <p>recomputed fibre centre coordinates in the slice with weighted average</p> Example <pre><code>import fibretracker as ft\n\navg_coord = ft.detector.avg_fib_coord(pred_coord, im, window_size)\n</code></pre> Source code in <code>fibretracker/models/detector.py</code> <pre><code>def avg_fibre_coord(\n        pred_coord: np.ndarray, \n        im: np.ndarray, \n        window_size: int = 10,\n        apply_filter: bool = False,\n        std: Optional[float] = None\n        ):\n    ''' Recompute the fibre centre in a slice using weighted average of peak neighbourhood\n\n    Args: \n        pred_coord: predicted coordinates of the peaks\n        im: input image\n        window_size: size of the neighbourhood window around the peak\n        apply_filter: whether to apply Gaussian filter to the window\n        std: standard deviation of the Gaussian filter\n\n    Returns:\n        coords (np.ndarray): recomputed fibre centre coordinates in the slice with weighted average\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        avg_coord = ft.detector.avg_fib_coord(pred_coord, im, window_size)\n        ```\n\n    '''\n    coords = []\n    for coord in pred_coord:\n        x, y = coord\n        window = im[x-window_size//2:x+window_size//2+1, y-window_size//2:y+window_size//2+1]\n        # Apply Gaussian filter to the window\n        if apply_filter:\n            if std is not None:\n                g = gauss_filter(std)[0]\n            else:\n                g = gauss_filter(std=2.5)[0]\n            window = scipy.ndimage.convolve(scipy.ndimage.convolve(window, g), g.T)\n        x_coords, y_coords = np.meshgrid(range(x-window_size//2, x+window_size//2+1), range(y-window_size//2, y+window_size//2+1))\n        weighted_x = np.sum(window * x_coords) / np.sum(window)\n        weighted_y = np.sum(window * y_coords) / np.sum(window)\n        coords.append([weighted_x, weighted_y])\n    return np.array(coords)\n</code></pre>"},{"location":"faq/","title":"Frequently Asked Questions","text":""},{"location":"faq/#vizorthogonal-and-vizslicer-are-not-showing-output","title":"viz.orthogonal and viz.slicer are not showing output","text":"<p>Make sure to run %matplotlib widget alongwith other library</p>"},{"location":"faq/#how-modelstrack_fibres-works","title":"How models.track_fibres works ?","text":"<p>models.track_fibres either requires detected coordinates coords or normalized volume vol. It is suggested to use both if calculated coords available during smoothening to avoid re-compute.</p> <p> FAQ will be updated based on user experiences"},{"location":"io/","title":"Data input and output","text":"<p>Dealing with volumetric data can be done by <code>fibretracker</code> for the most common image formats available.</p> <p>Currently, it is possible to directly load <code>tiff</code>, <code>h5</code>, <code>nii</code>,<code>txm</code> and common <code>PIL</code> formats using one single function.</p>"},{"location":"io/#fibretracker.io","title":"fibretracker.io","text":""},{"location":"io/#fibretracker.io.load","title":"fibretracker.io.load","text":"<pre><code>load(path, dataset_name=None, return_metadata=False, contains=None, force_load=False, dim_order=(2, 1, 0), **kwargs)\n</code></pre> <p>Load data from the specified file or directory.</p> <p>Parameters:</p> Name Type Description Default <code>path</code> <code>str or PathLike</code> <p>The path to the file or directory.</p> required <code>dataset_name</code> <code>str</code> <p>Specifies the name of the dataset to be loaded in case multiple dataset exist within the same file. Default is None (only for HDF5 files)</p> <code>None</code> <code>return_metadata</code> <code>bool</code> <p>Specifies whether to return metadata or not. Default is False (only for HDF5 and TXRM/TXM/XRM files)</p> <code>False</code> <code>contains</code> <code>str</code> <p>Specifies a part of the name that is common for the TIFF file stack to be loaded (only for TIFF stacks). Default is None.</p> <code>None</code> <code>force_load</code> <code>bool</code> <p>If the file size exceeds available memory, a MemoryError is raised. If force_load is True, the error is changed to warning and the loader tries to load it anyway. Default is False.</p> <code>False</code> <code>dim_order</code> <code>tuple</code> <p>The order of the dimensions in the volume for .vol files. Default is (2,1,0) which corresponds to (z,y,x)</p> <code>(2, 1, 0)</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed</p> <code>{}</code> <p>Returns:</p> Name Type Description <code>vol</code> <code>ndarray</code> <p>The loaded volume If <code>return_metadata=True</code> and file format is either HDF5, NIfTI or TXRM/TXM/XRM, returns <code>tuple</code> (volume, metadata).</p> <p>Raises:</p> Type Description <code>MemoryError</code> <p>if the given file size exceeds available memory</p> Example <p><pre><code># Load volume from a single file\nimport fibretracker as ft\n\nvol = ft.io.load(data_path)\n</code></pre> <pre><code># Load a stack of TIFF files as a volume\nimport fibretracker as ft\n\nvol = ft.io.load(data_path, contains='.tif')\n</code></pre></p> Source code in <code>fibretracker/io/read_file.py</code> <pre><code>def load(\n    path,\n    dataset_name=None,\n    return_metadata=False,\n    contains=None,\n    force_load: bool = False,\n    dim_order=(2, 1, 0),\n    **kwargs,\n):\n    \"\"\"\n    Load data from the specified file or directory.\n\n    Args:\n        path (str or os.PathLike): The path to the file or directory.\n        dataset_name (str, optional): Specifies the name of the dataset to be loaded\n            in case multiple dataset exist within the same file. Default is None (only for HDF5 files)\n        return_metadata (bool, optional): Specifies whether to return metadata or not. Default is False (only for HDF5 and TXRM/TXM/XRM files)\n        contains (str, optional): Specifies a part of the name that is common for the TIFF file stack to be loaded (only for TIFF stacks).\n            Default is None.\n        force_load (bool, optional): If the file size exceeds available memory, a MemoryError is raised.\n            If force_load is True, the error is changed to warning and the loader tries to load it anyway. Default is False.\n        dim_order (tuple, optional): The order of the dimensions in the volume for .vol files. Default is (2,1,0) which corresponds to (z,y,x)\n        **kwargs: Additional keyword arguments to be passed\n        to the DataLoader constructor.\n\n    Returns:\n        vol (numpy.ndarray): The loaded volume\n            If `return_metadata=True` and file format is either HDF5, NIfTI or TXRM/TXM/XRM, returns `tuple` (volume, metadata).\n\n    Raises:\n        MemoryError: if the given file size exceeds available memory\n\n    Example:\n        ```python\n        # Load volume from a single file\n        import fibretracker as ft\n\n        vol = ft.io.load(data_path)\n        ```\n        ```python\n        # Load a stack of TIFF files as a volume\n        import fibretracker as ft\n\n        vol = ft.io.load(data_path, contains='.tif')\n        ```\n\n    \"\"\"\n\n    loader = DataLoader(\n        dataset_name=dataset_name,\n        return_metadata=return_metadata,\n        contains=contains,\n        force_load=force_load,\n        dim_order=dim_order,\n        **kwargs,\n    )\n\n    data = loader.load(path)\n\n    return data\n</code></pre>"},{"location":"io/#fibretracker.io.normalize","title":"fibretracker.io.normalize","text":"<pre><code>normalize(vol)\n</code></pre> <p>Normalize the volume to the range [0, 1] using min-max scaling.</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray</code> <p>The volume to normalize.</p> required <p>Returns:</p> Name Type Description <code>norm_vol</code> <code>ndarray</code> <p>The normalized volume.</p> Example <pre><code>import fibretracker as ft\n\nnorm_vol = ft.io.normalize(vol)\n</code></pre> Source code in <code>fibretracker/io/read_file.py</code> <pre><code>def normalize(\n        vol: np.ndarray\n        ):\n    \"\"\"Normalize the volume to the range [0, 1] using min-max scaling.\n\n    Args:\n        vol (numpy.ndarray): The volume to normalize.\n\n    Returns:\n        norm_vol (numpy.ndarray): The normalized volume.\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        norm_vol = ft.io.normalize(vol)\n        ```\n    \"\"\"\n    norm_vol = (vol - np.min(vol)) / (np.max(vol) - np.min(vol))\n    return norm_vol\n</code></pre>"},{"location":"tracker/","title":"For tracking file","text":"<p>To detect fibre centre in a slice</p>"},{"location":"tracker/#fibretracker.models","title":"fibretracker.models","text":""},{"location":"tracker/#fibretracker.models.track_fibres","title":"fibretracker.models.track_fibres","text":"<pre><code>track_fibres(vol=None, max_jump=5, max_skip=5, momentum=0.1, track_min_length=5, coords=None, std=2.5, min_distance=5, threshold_abs=0.5, weighted_avg=False, window_size=10, apply_filter=False, smoothtrack_gaussian=False, sigma=3, smoothtrack_watershed=False, threshold=None)\n</code></pre> <p>Tracks fibers throughout the volume</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray</code> <p>3D volume.</p> <code>None</code> <code>max_jump</code> <code>int</code> <p>Maximum distance between detected points in two consecutive frames. Threshold in pixels.</p> <code>5</code> <code>max_skip</code> <code>int</code> <p>Maximum number of frames along one track where no points are detected.</p> <code>5</code> <code>momentum</code> <code>float</code> <p>Parameter in the range [0;1] that gives momentum to the tracking direction.</p> <code>0.1</code> <code>track_min_length</code> <code>int</code> <p>Minimum number of points in a track.</p> <code>5</code> <code>coords</code> <code>list</code> <p>List of numpy arrays with row and column indices of detected points. One per slice, which                         means that z is gives as the index of the list.</p> <code>None</code> <code>std</code> <code>float</code> <p>Standard deviation of the Gaussian filter.</p> <code>2.5</code> <code>min_distance</code> <code>int</code> <p>Minimum distance between fibres.</p> <code>5</code> <code>threshold_abs</code> <code>float</code> <p>Threshold value for the peak from the background.</p> <code>0.5</code> <code>weighted_avg</code> <code>bool</code> <p>Whether to apply weighted average to the detected coordinates.</p> <code>False</code> <code>window_size</code> <code>int</code> <p>Size of the neighbourhood window around the peak.</p> <code>10</code> <code>apply_filter</code> <code>bool</code> <p>Whether to apply Gaussian filter to the window.</p> <code>False</code> <code>smoothtrack_gaussian</code> <code>bool</code> <p>Whether to smooth tracks using Gaussian.</p> <code>False</code> <code>sigma</code> <code>float</code> <p>Sigma value for Gaussian filter.</p> <code>3</code> <code>smoothtrack_watershed</code> <code>bool</code> <p>Whether to smooth tracks using watershed.</p> <code>False</code> <code>threshold</code> <code>float</code> <p>Threshold value for watershed.</p> <code>None</code> <p>Returns:</p> Name Type Description <code>tracks</code> <code>List[ndarray]</code> <p>List of arrays of shape (n_points, 3) - each list contains coordinates of tracked fibers.</p> Example <p><pre><code>import fibretracker as ft\n\nvol = ft.io.load(\"path/to/volume.txm\")\nv = ft.io.normalize(vol)\ncoords = ft.models.get_fibre_coords(v)\ntracks = ft.models.track_fibres(coords=coords)\n</code></pre> <pre><code>import fibretracker as ft\n\nvol = ft.io.load(\"path/to/volume.txm\")\nv = ft.io.normalize(vol)\ncoords = ft.models.get_fibre_coords(v)\ntracks = ft.models.track_fibres(vol=v, coords=coords, smoothtrack_gaussian=True, sigma=3) # Smoothened tracks using Gaussian\n</code></pre></p> Source code in <code>fibretracker/models/tracker.py</code> <pre><code>def track_fibres(\n        vol: Optional[np.ndarray]=None,\n        max_jump: int=5, \n        max_skip: int=5, \n        momentum: float=0.1, \n        track_min_length: int=5,\n        coords: Optional[List[np.ndarray]]=None,\n        std: float=2.5,\n        min_distance: int=5,\n        threshold_abs: float=0.5,\n        weighted_avg: bool=False,\n        window_size: int=10,\n        apply_filter: bool=False,\n        smoothtrack_gaussian: bool=False,\n        sigma: float=3,\n        smoothtrack_watershed: bool=False,\n        threshold: Optional[float]=None\n        ):\n    '''Tracks fibers throughout the volume\n\n        Args:\n            vol (np.ndarray, optional): 3D volume.\n            max_jump (int, optional): Maximum distance between detected points in two consecutive frames. Threshold in pixels.\n            max_skip (int, optional): Maximum number of frames along one track where no points are detected.\n            momentum (float, optional): Parameter in the range [0;1] that gives momentum to the tracking direction.\n            track_min_length (int, optional): Minimum number of points in a track.\n            coords (list, optional): List of numpy arrays with row and column indices of detected points. One per slice, which\n                                        means that z is gives as the index of the list.\n            std (float, optional): Standard deviation of the Gaussian filter.\n            min_distance (int, optional): Minimum distance between fibres.\n            threshold_abs (float, optional): Threshold value for the peak from the background.\n            weighted_avg (bool, optional): Whether to apply weighted average to the detected coordinates.\n            window_size (int, optional): Size of the neighbourhood window around the peak.\n            apply_filter (bool, optional): Whether to apply Gaussian filter to the window.\n            smoothtrack_gaussian (bool, optional): Whether to smooth tracks using Gaussian.\n            sigma (float, optional): Sigma value for Gaussian filter.\n            smoothtrack_watershed (bool, optional): Whether to smooth tracks using watershed.\n            threshold (float, optional): Threshold value for watershed.\n\n        Returns:\n            tracks (List[np.ndarray]): List of arrays of shape (n_points, 3) - each list contains coordinates of tracked fibers.\n\n        Example:\n            ```python\n            import fibretracker as ft\n\n            vol = ft.io.load(\"path/to/volume.txm\")\n            v = ft.io.normalize(vol)\n            coords = ft.models.get_fibre_coords(v)\n            tracks = ft.models.track_fibres(coords=coords)\n            ```\n            ```python\n            import fibretracker as ft\n\n            vol = ft.io.load(\"path/to/volume.txm\")\n            v = ft.io.normalize(vol)\n            coords = ft.models.get_fibre_coords(v)\n            tracks = ft.models.track_fibres(vol=v, coords=coords, smoothtrack_gaussian=True, sigma=3) # Smoothened tracks using Gaussian\n            ```\n\n\n    '''\n\n    fib_tracker = TrackPoints(max_jump=max_jump, max_skip=max_skip,\n                        momentum=momentum, track_min_length=track_min_length)\n\n    if coords is None:\n        coords = get_fibre_coords(vol, std=std, min_distance=min_distance, threshold_abs=threshold_abs, \n                                weighted_avg=weighted_avg, window_size=window_size, apply_filter=apply_filter)\n\n    tracks, _ = fib_tracker(coords)\n    if smoothtrack_gaussian:\n        print('Smoothing tracks using Gaussian')\n        tracks_smooth = []\n        for track in tracks:\n            tracks_smooth.append(gaussian_filter1d(track, sigma=sigma, axis=0))\n\n        return tracks_smooth\n\n    elif smoothtrack_watershed and vol is not None:\n        print('Smoothing tracks using watershed...')\n        tracks_filled = fib_tracker.fill_tracks(tracks)\n        if threshold is None:\n            threshold = threshold_otsu(vol[len(vol)//2])\n        V_thres = vol &gt; threshold\n        V_dist = -distance_transform_edt(V_thres)\n        V_coords = np.zeros(vol.shape)\n        for i, track in enumerate(tracks_filled):\n            for point in track:\n                V_coords[int(point[2]), int(point[1]), int(point[0])] = i + 1\n        V_ws = watershed(V_dist, markers=V_coords.astype(int))*V_thres\n        print('Watershed volume created.')\n        n_fibers = V_ws.max()\n        tracks_smooth = [[] for i in range(n_fibers)]\n\n        for i, v_ws in enumerate(V_ws):\n            props = regionprops(v_ws)\n            for prop in props:\n                tracks_smooth[prop.label-1].append(list(prop.centroid[::-1]) + [i])\n            print(f'Smoothing tracks - iteration: {i+1}/{len(V_ws)}', end='\\r')\n\n        for i in range(n_fibers):\n            tracks_smooth[i] = np.array(tracks_smooth[i])\n\n        return tracks_smooth\n\n    else:\n        return tracks\n</code></pre>"},{"location":"viz/","title":"Visualise detected centre and tracked fibre","text":"<p>To visualise volume accross the slice, detected centres and tracked fibre</p>"},{"location":"viz/#fibretracker.viz","title":"fibretracker.viz","text":""},{"location":"viz/#fibretracker.viz.orthogonal","title":"fibretracker.viz.orthogonal","text":"<pre><code>orthogonal(vol, cmap='gray', img_height=5, img_width=5)\n</code></pre> <p>Interactive widget for visualizing orthogonal slices of a 3D volume.</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray or Tensor</code> <p>The 3D volume to be sliced.</p> required <code>cmap</code> <code>str</code> <p>Specifies the color map for the image. Defaults to \"gray\".</p> <code>'gray'</code> <code>img_height(int,</code> <code>optional</code> <p>Height of the figure.</p> required <code>img_width(int,</code> <code>optional</code> <p>Width of the figure.</p> required <p>Returns:</p> Name Type Description <code>orthogonal_obj</code> <code>HBox</code> <p>The interactive widget for visualizing orthogonal slices of a 3D volume.</p> Example <p><pre><code>import fibretracker as ft\n\nvol = ft.io.load(\"path/to/volume.txm\")\nft.viz.orthogonal(vol, cmap=\"gray\")\n</code></pre> </p> Source code in <code>fibretracker/viz/visualize.py</code> <pre><code>def orthogonal(\n    vol: np.ndarray,\n    cmap: str = \"gray\",\n    img_height: int = 5,\n    img_width: int = 5\n)-&gt; widgets.HBox:\n    \"\"\"Interactive widget for visualizing orthogonal slices of a 3D volume.\n\n    Args:\n        vol (np.ndarray or torch.Tensor): The 3D volume to be sliced.\n        cmap (str, optional): Specifies the color map for the image. Defaults to \"gray\".\n        img_height(int, optional): Height of the figure.\n        img_width(int, optional): Width of the figure.\n\n    Returns:\n        orthogonal_obj (widgets.HBox): The interactive widget for visualizing orthogonal slices of a 3D volume.\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        vol = ft.io.load(\"path/to/volume.txm\")\n        ft.viz.orthogonal(vol, cmap=\"gray\")\n        ```\n        ![viz orthogonal](figures/viz-orthogonal.gif)\n    \"\"\"\n\n    fig, ax = plt.subplots(1, 3, figsize=(3*img_width, img_height))\n    ax[0].axis(\"off\")\n    ax[1].axis(\"off\")\n    ax[2].axis(\"off\")\n\n    def _slice(slice_idx_z, slice_idx_y, slice_idx_x):\n        slice_img_z = vol.take(slice_idx_z, axis=0)\n        slice_img_y = vol.take(slice_idx_y, axis=1)\n        slice_img_x = vol.take(slice_idx_x, axis=2)\n\n        ax[0].imshow(slice_img_z, cmap=cmap)\n        ax[0].set_aspect('equal')\n        ax[1].imshow(slice_img_y, cmap=cmap)\n        ax[1].set_aspect('equal')\n        ax[2].imshow(slice_img_x, cmap=cmap)\n        ax[2].set_aspect('equal')\n\n    slice_slider_z = widgets.IntSlider(\n        value=vol.shape[0] // 2,\n        min=0,\n        max=vol.shape[0] - 1,\n        description=\"Z\",\n        continuous_update=True,\n    )\n\n    slice_slider_y = widgets.IntSlider(\n        value=vol.shape[1] // 2,\n        min=0,\n        max=vol.shape[1] - 1,\n        description=\"Y\",\n        continuous_update=True,\n    )\n\n    slice_slider_x = widgets.IntSlider(\n        value=vol.shape[2] // 2,\n        min=0,\n        max=vol.shape[2] - 1,\n        description=\"X\",\n        continuous_update=True,\n    )\n\n    slicer_obj = interactive(_slice, slice_idx_z=slice_slider_z, slice_idx_y=slice_slider_y, slice_idx_x=slice_slider_x)\n\n    # Create a horizontal box for the sliders\n    hbox = widgets.HBox([slicer_obj.children[0], slicer_obj.children[1], slicer_obj.children[2]], layout=widgets.Layout(align_items=\"stretch\", justify_content=\"center\", align_content=\"center\", justify_items=\"center\", justify_self=\"center\", align_self=\"center\", width=\"100%\"))\n\n    # Replace the sliders in the interactive widget with the horizontal box\n    slicer_obj.children = (hbox,) + slicer_obj.children[3:]\n\n    return slicer_obj\n</code></pre>"},{"location":"viz/#fibretracker.viz.slicer","title":"fibretracker.viz.slicer","text":"<pre><code>slicer(vol, detect_coords=None, mark_size=None, axis=0, cmap='gray', img_height=5, img_width=5)\n</code></pre> <p>Interactive widget for visualizing slices of a 3D volume and fibres centre if provided.</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray</code> <p>The 3D volume to be sliced.</p> required <code>detect_coords</code> <code>list</code> <p>List of coordinates of detected fibres. Defaults to None.</p> <code>None</code> <code>mark_size</code> <code>int</code> <p>Size of the marker for detected fibres. Defaults to None.</p> <code>None</code> <code>axis</code> <code>int</code> <p>Specifies the axis, or dimension, along which to slice. Defaults to 0.</p> <code>0</code> <code>cmap</code> <code>str</code> <p>Specifies the color map for the image. Defaults to \"gray\".</p> <code>'gray'</code> <code>img_height</code> <code>int</code> <p>Height of the figure. Defaults to 5.</p> <code>5</code> <code>img_width</code> <code>int</code> <p>Width of the figure. Defaults to 5.</p> <code>5</code> <p>Returns:</p> Name Type Description <code>slicer_obj</code> <code>interactive</code> <p>The interactive widget for visualizing slices of a 3D volume.</p> Example <p><pre><code>import fibretracker as ft\n\n# Load the volume and visualize the slices\nvol = ft.io.load(\"path/to/volume.txm\")\nft.viz.slicer(vol)\n</code></pre> </p> <pre><code>import fibretracker as ft\n\n# Load the volume and detected coordinates\nvol = ft.io.load(\"path/to/volume.txm\")\nvol = ft.io.normalize(vol)\nvol = vol[100:350] # 250 slices along the z-axis\ndetect_coords = ft.models.get_fibre_coords(vol)\nft.viz.slicer(vol, detect_coords=detect_coords, mark_size=4)\n</code></pre> <p></p> Source code in <code>fibretracker/viz/visualize.py</code> <pre><code>def slicer(\n    vol: np.ndarray,\n    detect_coords: Optional[List[np.ndarray]] = None,\n    mark_size: Optional[int] = None,\n    axis: int = 0,\n    cmap: str = \"gray\",\n    img_height: int = 5,\n    img_width: int = 5, \n) -&gt; widgets.interactive:\n    \"\"\"Interactive widget for visualizing slices of a 3D volume and fibres centre if provided.\n\n    Args:\n        vol (np.ndarray): The 3D volume to be sliced.\n        detect_coords (list, optional): List of coordinates of detected fibres. Defaults to None.\n        mark_size (int, optional): Size of the marker for detected fibres. Defaults to None.\n        axis (int, optional): Specifies the axis, or dimension, along which to slice. Defaults to 0.\n        cmap (str, optional): Specifies the color map for the image. Defaults to \"gray\".\n        img_height (int, optional): Height of the figure. Defaults to 5.\n        img_width (int, optional): Width of the figure. Defaults to 5.\n\n    Returns:\n        slicer_obj (widgets.interactive): The interactive widget for visualizing slices of a 3D volume.\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        # Load the volume and visualize the slices\n        vol = ft.io.load(\"path/to/volume.txm\")\n        ft.viz.slicer(vol)\n        ```\n        ![viz slicer](figures/viz-slicer.gif)\n\n        ```python\n        import fibretracker as ft\n\n        # Load the volume and detected coordinates\n        vol = ft.io.load(\"path/to/volume.txm\")\n        vol = ft.io.normalize(vol)\n        vol = vol[100:350] # 250 slices along the z-axis\n        detect_coords = ft.models.get_fibre_coords(vol)\n        ft.viz.slicer(vol, detect_coords=detect_coords, mark_size=4)\n        ```\n\n        ![viz slicer](figures/viz-slicer_detector.gif)\n\n    \"\"\"\n\n    if detect_coords is None:\n        fig, ax = plt.subplots(figsize=(img_width, img_height))\n        ax.axis(\"off\")\n    else:\n        fig, ax = plt.subplots(1, 2, figsize=(2*img_width, img_height), sharex=True, sharey=True, gridspec_kw={'wspace': 0})\n        ax[0].axis(\"off\")\n        ax[1].axis(\"off\")\n\n    def _slice(slice_idx):\n        slice_img = vol.take(slice_idx, axis=axis)\n        if detect_coords is not None:\n            [l.remove() for l in ax[1].lines]\n            ax[0].imshow(slice_img, cmap=cmap)\n            ax[1].imshow(slice_img, cmap=cmap)\n            if mark_size is not None:\n                ax[1].plot(detect_coords[slice_idx][:, 0], detect_coords[slice_idx][:, 1], 'rx', markersize=mark_size)\n            else:\n                ax[1].plot(detect_coords[slice_idx][:, 0], detect_coords[slice_idx][:, 1], 'rx', markersize=3)\n        else:\n            ax.imshow(slice_img, cmap=cmap)\n\n    def on_release(event):\n        if detect_coords is not None:\n            xlim = ax[0].get_xlim()\n            ylim = ax[0].get_ylim()\n            ax[1].set_xlim(xlim)\n            ax[1].set_ylim(ylim)\n        else:\n            xlim = ax.get_xlim()\n            ylim = ax.get_ylim()\n\n    slice_slider = widgets.IntSlider(\n        value=vol.shape[axis] // 2,\n        min=0,\n        max=vol.shape[axis] - 1,\n        description=\"Slice\",\n        continuous_update=True,\n    )\n\n    slice_slider.style.description_width = 'middle'  # Set the width of the description to fit the larger font size\n    slice_slider.style.handle_color = 'blue'  # Optional: Change the handle color\n    slice_slider.style.font_size = '150px'  \n\n    fig.canvas.mpl_connect('button_release_event', on_release)\n    slicer_obj = interactive(_slice, slice_idx = slice_slider)\n\n    return slicer_obj\n</code></pre>"},{"location":"viz/#fibretracker.viz.plot_tracks","title":"fibretracker.viz.plot_tracks","text":"<pre><code>plot_tracks(tracks, grid=False)\n</code></pre> <p>Plot tracks of fibres detected in the volume</p> <p>Parameters:</p> Name Type Description Default <code>tracks</code> <code>List[ndarray]</code> <p>List of arrays of shape (n_points, 3)</p> required <code>grid</code> <code>bool</code> <p>Whether to show grid in the plot</p> <code>False</code> <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>matplotlib figure object</p> Example <pre><code>import fibretracker as ft\n\n# Load the volume and detected coordinates\nvol = ft.io.load(\"path/to/volume.txm\")\nvol = ft.io.normalize(vol)\nvol = vol[100:350] # 250 slices along the z-axis\ndetect_coords = ft.models.get_fibre_coords(vol)\ntracks_gauss = ft.models.track_fibres(coords=detect_coords, smoothtrack_gaussian=True)\nft.viz.plot_tracks(tracks_gauss)\n</code></pre> <p></p> Source code in <code>fibretracker/viz/plotting.py</code> <pre><code>def plot_tracks(\n        tracks: List[np.ndarray],\n        grid: bool = False,):\n\n    '''Plot tracks of fibres detected in the volume\n\n    Args:\n        tracks: List of arrays of shape (n_points, 3)\n        grid: Whether to show grid in the plot\n\n    Returns:\n        fig (matplotlib.figure.Figure): matplotlib figure object\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        # Load the volume and detected coordinates\n        vol = ft.io.load(\"path/to/volume.txm\")\n        vol = ft.io.normalize(vol)\n        vol = vol[100:350] # 250 slices along the z-axis\n        detect_coords = ft.models.get_fibre_coords(vol)\n        tracks_gauss = ft.models.track_fibres(coords=detect_coords, smoothtrack_gaussian=True)\n        ft.viz.plot_tracks(tracks_gauss)\n        ```\n\n        ![viz tracks](figures/tracks_gauss.gif)\n\n\n    '''\n    fig, ax = plt.subplots(figsize=(10, 10))\n    ax = fig.add_subplot(projection='3d')\n    for track in tracks:\n        ax.plot(track[:,0], track[:,1], track[:,2])\n    ax.grid(grid)\n    ax.set_aspect('equal')\n\n    plt.show()\n    return fig\n</code></pre>"},{"location":"notebooks/fibre_tracking/","title":"Example","text":"In\u00a0[1]: Copied! <pre>%matplotlib widget\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport fibretracker as ft\n</pre> %matplotlib widget import numpy as np import matplotlib.pyplot as plt import fibretracker as ft In\u00a0[2]: Copied! <pre>data_path = '/Users/pooja/Documents/PhD_work/ICPR_docs/data/Mock_UD_GFRP_data/UD-01_FoV_2_B2_recon.txm'\ndata_path1 = 'data/Mock_UD_GFRP_data/Mock-01_FoV_2_B2_recon.txm'\ndata_path2 = 'data/Mock_UD_GFRP_data/GFRP'\ndata_path3 = 'data/Mock_UD_GFRP_data/XCT_LR'\n\nV_UD = ft.io.load(data_path)\n# V_Mock = ft.io.load(data_path1)\n# V_GFRP = ft.io.load(data_path2, contains='.tif')\n# V_XCTLR = ft.io.load(data_path3, contains='.tif')\n\nV_norm = ft.io.normalize(V_UD).astype(float)\n</pre> data_path = '/Users/pooja/Documents/PhD_work/ICPR_docs/data/Mock_UD_GFRP_data/UD-01_FoV_2_B2_recon.txm' data_path1 = 'data/Mock_UD_GFRP_data/Mock-01_FoV_2_B2_recon.txm' data_path2 = 'data/Mock_UD_GFRP_data/GFRP' data_path3 = 'data/Mock_UD_GFRP_data/XCT_LR'  V_UD = ft.io.load(data_path) # V_Mock = ft.io.load(data_path1) # V_GFRP = ft.io.load(data_path2, contains='.tif') # V_XCTLR = ft.io.load(data_path3, contains='.tif')  V_norm = ft.io.normalize(V_UD).astype(float) <pre>astropy module not found\n</pre> In\u00a0[3]: Copied! <pre>ft.viz.slicer(V_norm, img_height=7, img_width=7, cmap='gray')\n</pre> ft.viz.slicer(V_norm, img_height=7, img_width=7, cmap='gray') Out[3]: <pre>interactive(children=(IntSlider(value=499, description='Slice', max=998, style=SliderStyle(description_width='\u2026</pre>                      Figure                  In\u00a0[4]: Copied! <pre>V_tmp = V_norm[100:350, V_norm.shape[1]//2 - 250:V_norm.shape[1]//2 + 250, V_norm.shape[2]//2 - 250:V_norm.shape[2]//2 + 250]\n</pre> V_tmp = V_norm[100:350, V_norm.shape[1]//2 - 250:V_norm.shape[1]//2 + 250, V_norm.shape[2]//2 - 250:V_norm.shape[2]//2 + 250] In\u00a0[5]: Copied! <pre>ft.viz.slicer(V_tmp, img_height=7, img_width=7, cmap='gray')\n</pre> ft.viz.slicer(V_tmp, img_height=7, img_width=7, cmap='gray') Out[5]: <pre>interactive(children=(IntSlider(value=125, description='Slice', max=249, style=SliderStyle(description_width='\u2026</pre>                      Figure                  In\u00a0[6]: Copied! <pre>ft.viz.orthogonal(V_tmp, img_height=7, img_width=7, cmap='gray')\n</pre> ft.viz.orthogonal(V_tmp, img_height=7, img_width=7, cmap='gray') Out[6]: <pre>interactive(children=(HBox(children=(IntSlider(value=125, description='Z', max=249), IntSlider(value=250, desc\u2026</pre>                      Figure                  In\u00a0[7]: Copied! <pre>n_bins = int(255*(V_tmp.max() - V_tmp.min()) + 1)\nhist, bins = np.histogram(V_tmp, bins=n_bins)\nbin_width = bins[1] - bins[0]\nfig, ax = plt.subplots(1, figsize=(5, 5))\nax.bar(bins[:-1], hist, width=bin_width)\nax.set_xlabel('Intensity')\nax.set_ylabel('Frequency')\nax.set_title('Histogram of UD section volume')\nplt.show()\n</pre> n_bins = int(255*(V_tmp.max() - V_tmp.min()) + 1) hist, bins = np.histogram(V_tmp, bins=n_bins) bin_width = bins[1] - bins[0] fig, ax = plt.subplots(1, figsize=(5, 5)) ax.bar(bins[:-1], hist, width=bin_width) ax.set_xlabel('Intensity') ax.set_ylabel('Frequency') ax.set_title('Histogram of UD section volume') plt.show()                      Figure                  In\u00a0[9]: Copied! <pre>coords = ft.models.get_fibre_coords(V_tmp, std=2.5, min_distance=3, threshold_abs=0.6)\n</pre> coords = ft.models.get_fibre_coords(V_tmp, std=2.5, min_distance=3, threshold_abs=0.6) <pre>                                      \r</pre> In\u00a0[10]: Copied! <pre>ft.viz.slicer(V_tmp, detect_coords=coords, mark_size=3, img_height=7, img_width=7, cmap='gray')\n</pre> ft.viz.slicer(V_tmp, detect_coords=coords, mark_size=3, img_height=7, img_width=7, cmap='gray') Out[10]: <pre>interactive(children=(IntSlider(value=125, description='Slice', max=249, style=SliderStyle(description_width='\u2026</pre>                      Figure                  In\u00a0[11]: Copied! <pre># fibre can be tracked either directly from the volume or from the detected coordinates, or both[to avoid coordinate detection again]\n\ntracks = ft.models.track_fibres(coords=coords)\ntracks_gauss = ft.models.track_fibres(V_tmp, coords=coords, smoothtrack_gaussian=True, sigma=2)\ntracks_ws = ft.models.track_fibres(V_tmp, coords=coords, smoothtrack_watershed=True, threshold=0.6)\n\n# tracks = ft.models.track_fibres(V_tmp, smoothtrack_watershed=True, threshold=0.5)\n# tracks = ft.models.track_fibres(V_tmp, smoothtrack_gaussian=True)\n# tracks = ft.models.track_fibres(coords=coords)\n# tracks = ft.models.track_fibres(coords=coords, smoothtrack_gaussian=True)\n</pre> # fibre can be tracked either directly from the volume or from the detected coordinates, or both[to avoid coordinate detection again]  tracks = ft.models.track_fibres(coords=coords) tracks_gauss = ft.models.track_fibres(V_tmp, coords=coords, smoothtrack_gaussian=True, sigma=2) tracks_ws = ft.models.track_fibres(V_tmp, coords=coords, smoothtrack_watershed=True, threshold=0.6)  # tracks = ft.models.track_fibres(V_tmp, smoothtrack_watershed=True, threshold=0.5) # tracks = ft.models.track_fibres(V_tmp, smoothtrack_gaussian=True) # tracks = ft.models.track_fibres(coords=coords) # tracks = ft.models.track_fibres(coords=coords, smoothtrack_gaussian=True) <pre>Smoothing tracks using Gaussian       \nSmoothing tracks using watershed...   \nWatershed volume created.\nSmoothing tracks - iteration: 250/250\r</pre> In\u00a0[12]: Copied! <pre>ft.viz.plot_tracks(tracks)\n</pre> ft.viz.plot_tracks(tracks)                      Figure                  Out[12]: <pre>(&lt;Figure size 1000x1000 with 2 Axes&gt;, &lt;Axes3D: &gt;)</pre> In\u00a0[13]: Copied! <pre>ft.viz.plot_tracks(tracks_gauss)\n</pre> ft.viz.plot_tracks(tracks_gauss)                      Figure                  Out[13]: <pre>(&lt;Figure size 1000x1000 with 2 Axes&gt;, &lt;Axes3D: &gt;)</pre> In\u00a0[14]: Copied! <pre>ft.viz.plot_tracks(tracks_ws)\n</pre> ft.viz.plot_tracks(tracks_ws)                      Figure                  Out[14]: <pre>(&lt;Figure size 1000x1000 with 2 Axes&gt;, &lt;Axes3D: &gt;)</pre>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"FibreTracker","text":"<p>A python library to track fibre in a volume</p>"},{"location":"#getting-started","title":"\ud83d\udcbb Getting Started","text":"<p>Create a new environment (highly recommended)</p> <pre><code>conda create -n fibretracker python=3.11\n</code></pre> <p>Activate the environment by running</p> <pre><code>conda activate fibretracker\n</code></pre> <p>To read .txm file, install <code>dxchange</code> using <code>conda</code> [install before fibretracker module to avoid version conflicts and related error]</p> <pre><code>conda install -c conda-forge dxchange\n</code></pre> <p>Install the FibreTracker tool using <code>pip</code></p> <pre><code>pip install fibretracker\n</code></pre> <p>Go to Example and run the notebook with <code>fibretracker</code> enviroment</p>"},{"location":"#data","title":"Data","text":"<p>Following are the dataset on which fibre tracking is tested on 250 slices</p> <ul> <li>Mock and UD [link] -    <code>UD-01_FoV_2_B2_recon.txm</code> <code>Mock-01_FoV_2_B2_recon.txm</code></li> <li>GFRP [link] - <code>GFRP_Initial.zip</code></li> <li>XCT Low-Res [link] - <code>XCT_L.zip</code></li> </ul>"},{"location":"#license","title":"License","text":"<p><code>fibretracker</code> was created by Kumari Pooja. It is licensed under the terms of the MIT license.</p>"},{"location":"#credits","title":"Credits","text":"<p>This work is supported by the RELIANCE doctoral network via the Marie Sk\u0142odowska-Curie Actions HORIZON-MSCA-2021-DN- 01. Project no: 101073040 </p> <p> </p> <p>Project based on the cookiecutter data science project template. #cookiecutterdatascience</p>"},{"location":"CHANGELOG/","title":"Versions History","text":""},{"location":"CHANGELOG/#v010-20062024","title":"v0.1.0 (20/06/2024)","text":"<ul> <li>First release of <code>fibretracker</code>!</li> </ul>"},{"location":"detector/","title":"Documention for fibre detection","text":"<p>To detect fibre centre in a slice</p>"},{"location":"detector/#fibretracker.models","title":"fibretracker.models","text":""},{"location":"detector/#fibretracker.models.get_fibre_coords","title":"fibretracker.models.get_fibre_coords","text":"<pre><code>get_fibre_coords(vol, std=2.5, min_distance=3, threshold_abs=0.4, weighted_avg=False, window_size=10, apply_filter=False)\n</code></pre> <p>Get list of fibres centre coordinates in a volume using blob detector</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray</code> <p>input volume</p> required <code>std</code> <code>float</code> <p>standard deviation of the Gaussian filter</p> <code>2.5</code> <code>min_distance</code> <code>int</code> <p>minimum distance between fibres</p> <code>3</code> <code>threshold_abs</code> <code>float</code> <p>threshold value for the peak from the background</p> <code>0.4</code> <code>weighted_avg</code> <code>bool</code> <p>whether to apply weighted average to the detected coordinates</p> <code>False</code> <code>window_size</code> <code>int</code> <p>size of the neighbourhood window around the peak</p> <code>10</code> <code>apply_filter</code> <code>bool</code> <p>whether to apply Gaussian filter to the window</p> <code>False</code> <p>Returns:</p> Name Type Description <code>coords</code> <code>List(array)</code> <p>List of fibres centre coordinates in the volume</p> Example <pre><code>import fibretracker as ft\n\nvol = ft.detector.get_fib_coords(vol, std=2.5, min_distance=3, threshold_abs=0.4)\n</code></pre> Source code in <code>fibretracker/models/detector.py</code> <pre><code>def get_fibre_coords(\n        vol: np.ndarray, \n        std: float=2.5, \n        min_distance: int=3, \n        threshold_abs: float=0.4,\n        weighted_avg: bool=False,\n        window_size: int=10,\n        apply_filter: bool=False,\n        ):\n    ''' Get list of fibres centre coordinates in a volume using blob detector\n\n    Args:\n        vol: input volume\n        std: standard deviation of the Gaussian filter\n        min_distance: minimum distance between fibres\n        threshold_abs: threshold value for the peak from the background\n        weighted_avg: whether to apply weighted average to the detected coordinates\n        window_size: size of the neighbourhood window around the peak\n        apply_filter: whether to apply Gaussian filter to the window\n\n    Returns:\n        coords (List(nd.array)): List of fibres centre coordinates in the volume\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        vol = ft.detector.get_fib_coords(vol, std=2.5, min_distance=3, threshold_abs=0.4)\n        ```\n\n    '''\n    coords = []\n    for i, im in enumerate(vol):\n        coord = blob_centre_detector(im, std=std, min_distance=min_distance, threshold_abs=threshold_abs)\n        if weighted_avg:\n            coord = avg_fibre_coord(coord, im, window_size=window_size, apply_filter=apply_filter, std=std)\n        coords.append(np.stack([coord[:,1], coord[:,0], np.ones(len(coord)) * i], axis=1))\n        print(f'Detecting coordinates - slice: {i+1}/{len(vol)}', end='\\r')\n    print(' ' * len(f'Detecting coordinates - slice: {i+1}/{len(vol)}'), end='\\r')\n    return coords\n</code></pre>"},{"location":"detector/#fibretracker.models.blob_centre_detector","title":"fibretracker.models.blob_centre_detector","text":"<pre><code>blob_centre_detector(im, std=2.5, min_distance=3, threshold_abs=0.4)\n</code></pre> <p>Predict coordinates of fibres centre in a volume slice using blob detector</p> <p>Parameters:</p> Name Type Description Default <code>im</code> <code>ndarray</code> <p>input image</p> required <code>std</code> <code>float</code> <p>standard deviation of the Gaussian filter</p> <code>2.5</code> <code>min_distance</code> <code>int</code> <p>minimum distance between peaks</p> <code>3</code> <code>threshold_abs</code> <code>float</code> <p>threshold value for the peak from the background</p> <code>0.4</code> <p>Returns:</p> Name Type Description <code>pred_coords</code> <code>ndarray</code> <p>predicted coordinates of the fibre centre</p> Example <pre><code>import fibretracker as ft\n\nvol = ft.detector.blob_centre_detector(im, std=2.5, min_distance=3, threshold_abs=0.4)\n</code></pre> Source code in <code>fibretracker/models/detector.py</code> <pre><code>def blob_centre_detector(\n        im: np.ndarray, \n        std: float=2.5, \n        min_distance: int=3, \n        threshold_abs: float=0.4\n        ):\n    ''' Predict coordinates of fibres centre in a volume slice using blob detector\n\n    Args: \n        im: input image\n        std: standard deviation of the Gaussian filter\n        min_distance: minimum distance between peaks\n        threshold_abs: threshold value for the peak from the background\n\n    Returns: \n        pred_coords (np.ndarray): predicted coordinates of the fibre centre\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        vol = ft.detector.blob_centre_detector(im, std=2.5, min_distance=3, threshold_abs=0.4)\n        ```\n\n    '''\n    g = gauss_filter(std)[0]\n    im_g = scipy.ndimage.convolve(scipy.ndimage.convolve(im, g), g.T)\n    pred_coords = skimage.feature.peak_local_max(im_g, min_distance=min_distance, threshold_abs=threshold_abs)\n    return pred_coords\n</code></pre>"},{"location":"detector/#fibretracker.models.gauss_filter","title":"fibretracker.models.gauss_filter","text":"<pre><code>gauss_filter(std)\n</code></pre> <p>Generate a 1D Gaussian filter and its derivatives</p> <p>Parameters:</p> Name Type Description Default <code>std</code> <code>float</code> <p>standard deviation of the Gaussian filter</p> required <p>Returns:</p> Name Type Description <code>g</code> <code>ndarray</code> <p>1D Gaussian filter</p> <code>dg</code> <code>ndarray</code> <p>derivative of the Gaussian filter</p> <code>ddg</code> <code>ndarray</code> <p>second derivative of the Gaussian filter</p> Example <pre><code>import fibretracker as ft\n\nvol = ft.detector.gauss_filter(std=2.5)\n</code></pre> Source code in <code>fibretracker/models/detector.py</code> <pre><code>def gauss_filter(\n        std: float\n        ):\n    ''' Generate a 1D Gaussian filter and its derivatives\n\n    Args: \n        std: standard deviation of the Gaussian filter\n\n    Returns:\n        g (np.ndarray): 1D Gaussian filter\n        dg (np.ndarray): derivative of the Gaussian filter\n        ddg (np.ndarray): second derivative of the Gaussian filter\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        vol = ft.detector.gauss_filter(std=2.5)\n        ```\n\n    '''\n    x = np.arange(-np.ceil(5*std), np.ceil(5*std) + 1)[:,None]\n    g = np.exp(-x**2/(2*std**2))\n    g /= np.sum(g)\n    dg = -x/std**2 * g\n    ddg = -g/std**2 -x/std**2 * dg\n    return g, dg, ddg\n</code></pre>"},{"location":"detector/#fibretracker.models.avg_fibre_coord","title":"fibretracker.models.avg_fibre_coord","text":"<pre><code>avg_fibre_coord(pred_coord, im, window_size=10, apply_filter=False, std=None)\n</code></pre> <p>Recompute the fibre centre in a slice using weighted average of peak neighbourhood</p> <p>Parameters:</p> Name Type Description Default <code>pred_coord</code> <code>ndarray</code> <p>predicted coordinates of the peaks</p> required <code>im</code> <code>ndarray</code> <p>input image</p> required <code>window_size</code> <code>int</code> <p>size of the neighbourhood window around the peak</p> <code>10</code> <code>apply_filter</code> <code>bool</code> <p>whether to apply Gaussian filter to the window</p> <code>False</code> <code>std</code> <code>Optional[float]</code> <p>standard deviation of the Gaussian filter</p> <code>None</code> <p>Returns:</p> Name Type Description <code>coords</code> <code>ndarray</code> <p>recomputed fibre centre coordinates in the slice with weighted average</p> Example <pre><code>import fibretracker as ft\n\navg_coord = ft.detector.avg_fib_coord(pred_coord, im, window_size)\n</code></pre> Source code in <code>fibretracker/models/detector.py</code> <pre><code>def avg_fibre_coord(\n        pred_coord: np.ndarray, \n        im: np.ndarray, \n        window_size: int = 10,\n        apply_filter: bool = False,\n        std: Optional[float] = None\n        ):\n    ''' Recompute the fibre centre in a slice using weighted average of peak neighbourhood\n\n    Args: \n        pred_coord: predicted coordinates of the peaks\n        im: input image\n        window_size: size of the neighbourhood window around the peak\n        apply_filter: whether to apply Gaussian filter to the window\n        std: standard deviation of the Gaussian filter\n\n    Returns:\n        coords (np.ndarray): recomputed fibre centre coordinates in the slice with weighted average\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        avg_coord = ft.detector.avg_fib_coord(pred_coord, im, window_size)\n        ```\n\n    '''\n    coords = []\n    for coord in pred_coord:\n        x, y = coord\n        window = im[x-window_size//2:x+window_size//2+1, y-window_size//2:y+window_size//2+1]\n        # Apply Gaussian filter to the window\n        if apply_filter:\n            if std is not None:\n                g = gauss_filter(std)[0]\n            else:\n                g = gauss_filter(std=2.5)[0]\n            window = scipy.ndimage.convolve(scipy.ndimage.convolve(window, g), g.T)\n        x_coords, y_coords = np.meshgrid(range(x-window_size//2, x+window_size//2+1), range(y-window_size//2, y+window_size//2+1))\n        weighted_x = np.sum(window * x_coords) / np.sum(window)\n        weighted_y = np.sum(window * y_coords) / np.sum(window)\n        coords.append([weighted_x, weighted_y])\n    return np.array(coords)\n</code></pre>"},{"location":"faq/","title":"Frequently Asked Questions","text":""},{"location":"faq/#vizorthogonal-and-vizslicer-are-not-showing-output","title":"viz.orthogonal and viz.slicer are not showing output","text":"<p>Make sure to run %matplotlib widget alongwith other library</p>"},{"location":"faq/#how-modelstrack_fibres-works","title":"How models.track_fibres works ?","text":"<p>models.track_fibres either requires detected coordinates coords or normalized volume vol. It is suggested to use both if calculated coords available during smoothening to avoid re-compute.</p> <p> FAQ will be updated based on user experiences"},{"location":"io/","title":"Data input and output","text":"<p>Dealing with volumetric data can be done by <code>fibretracker</code> for the most common image formats available.</p> <p>Currently, it is possible to directly load <code>tiff</code>, <code>h5</code>, <code>nii</code>,<code>txm</code> and common <code>PIL</code> formats using one single function.</p>"},{"location":"io/#fibretracker.io","title":"fibretracker.io","text":""},{"location":"io/#fibretracker.io.load","title":"fibretracker.io.load","text":"<pre><code>load(path, dataset_name=None, return_metadata=False, contains=None, force_load=False, dim_order=(2, 1, 0), **kwargs)\n</code></pre> <p>Load data from the specified file or directory.</p> <p>Parameters:</p> Name Type Description Default <code>path</code> <code>str or PathLike</code> <p>The path to the file or directory.</p> required <code>dataset_name</code> <code>str</code> <p>Specifies the name of the dataset to be loaded in case multiple dataset exist within the same file. Default is None (only for HDF5 files)</p> <code>None</code> <code>return_metadata</code> <code>bool</code> <p>Specifies whether to return metadata or not. Default is False (only for HDF5 and TXRM/TXM/XRM files)</p> <code>False</code> <code>contains</code> <code>str</code> <p>Specifies a part of the name that is common for the TIFF file stack to be loaded (only for TIFF stacks). Default is None.</p> <code>None</code> <code>force_load</code> <code>bool</code> <p>If the file size exceeds available memory, a MemoryError is raised. If force_load is True, the error is changed to warning and the loader tries to load it anyway. Default is False.</p> <code>False</code> <code>dim_order</code> <code>tuple</code> <p>The order of the dimensions in the volume for .vol files. Default is (2,1,0) which corresponds to (z,y,x)</p> <code>(2, 1, 0)</code> <code>**kwargs</code> <p>Additional keyword arguments to be passed</p> <code>{}</code> <p>Returns:</p> Name Type Description <code>vol</code> <code>ndarray</code> <p>The loaded volume If <code>return_metadata=True</code> and file format is either HDF5, NIfTI or TXRM/TXM/XRM, returns <code>tuple</code> (volume, metadata).</p> <p>Raises:</p> Type Description <code>MemoryError</code> <p>if the given file size exceeds available memory</p> Example <p><pre><code># Load volume from a single file\nimport fibretracker as ft\n\nvol = ft.io.load(data_path)\n</code></pre> <pre><code># Load a stack of TIFF files as a volume\nimport fibretracker as ft\n\nvol = ft.io.load(data_path, contains='.tif')\n</code></pre></p> Source code in <code>fibretracker/io/read_file.py</code> <pre><code>def load(\n    path,\n    dataset_name=None,\n    return_metadata=False,\n    contains=None,\n    force_load: bool = False,\n    dim_order=(2, 1, 0),\n    **kwargs,\n):\n    \"\"\"\n    Load data from the specified file or directory.\n\n    Args:\n        path (str or os.PathLike): The path to the file or directory.\n        dataset_name (str, optional): Specifies the name of the dataset to be loaded\n            in case multiple dataset exist within the same file. Default is None (only for HDF5 files)\n        return_metadata (bool, optional): Specifies whether to return metadata or not. Default is False (only for HDF5 and TXRM/TXM/XRM files)\n        contains (str, optional): Specifies a part of the name that is common for the TIFF file stack to be loaded (only for TIFF stacks).\n            Default is None.\n        force_load (bool, optional): If the file size exceeds available memory, a MemoryError is raised.\n            If force_load is True, the error is changed to warning and the loader tries to load it anyway. Default is False.\n        dim_order (tuple, optional): The order of the dimensions in the volume for .vol files. Default is (2,1,0) which corresponds to (z,y,x)\n        **kwargs: Additional keyword arguments to be passed\n        to the DataLoader constructor.\n\n    Returns:\n        vol (numpy.ndarray): The loaded volume\n            If `return_metadata=True` and file format is either HDF5, NIfTI or TXRM/TXM/XRM, returns `tuple` (volume, metadata).\n\n    Raises:\n        MemoryError: if the given file size exceeds available memory\n\n    Example:\n        ```python\n        # Load volume from a single file\n        import fibretracker as ft\n\n        vol = ft.io.load(data_path)\n        ```\n        ```python\n        # Load a stack of TIFF files as a volume\n        import fibretracker as ft\n\n        vol = ft.io.load(data_path, contains='.tif')\n        ```\n\n    \"\"\"\n\n    loader = DataLoader(\n        dataset_name=dataset_name,\n        return_metadata=return_metadata,\n        contains=contains,\n        force_load=force_load,\n        dim_order=dim_order,\n        **kwargs,\n    )\n\n    data = loader.load(path)\n\n    return data\n</code></pre>"},{"location":"io/#fibretracker.io.normalize","title":"fibretracker.io.normalize","text":"<pre><code>normalize(vol)\n</code></pre> <p>Normalize the volume to the range [0, 1] using min-max scaling.</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray</code> <p>The volume to normalize.</p> required <p>Returns:</p> Name Type Description <code>norm_vol</code> <code>ndarray</code> <p>The normalized volume.</p> Example <pre><code>import fibretracker as ft\n\nnorm_vol = ft.io.normalize(vol)\n</code></pre> Source code in <code>fibretracker/io/read_file.py</code> <pre><code>def normalize(\n        vol: np.ndarray\n        ):\n    \"\"\"Normalize the volume to the range [0, 1] using min-max scaling.\n\n    Args:\n        vol (numpy.ndarray): The volume to normalize.\n\n    Returns:\n        norm_vol (numpy.ndarray): The normalized volume.\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        norm_vol = ft.io.normalize(vol)\n        ```\n    \"\"\"\n    norm_vol = (vol - np.min(vol)) / (np.max(vol) - np.min(vol))\n    return norm_vol\n</code></pre>"},{"location":"tracker/","title":"For tracking file","text":"<p>To detect fibre centre in a slice</p>"},{"location":"tracker/#fibretracker.models","title":"fibretracker.models","text":""},{"location":"tracker/#fibretracker.models.track_fibres","title":"fibretracker.models.track_fibres","text":"<pre><code>track_fibres(vol=None, max_jump=5, max_skip=5, momentum=0.1, track_min_length=5, coords=None, std=2.5, min_distance=5, threshold_abs=0.5, weighted_avg=False, window_size=10, apply_filter=False, smoothtrack_gaussian=False, sigma=3, smoothtrack_watershed=False, threshold=None)\n</code></pre> <p>Tracks fibers throughout the volume</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray</code> <p>3D volume.</p> <code>None</code> <code>max_jump</code> <code>int</code> <p>Maximum distance between detected points in two consecutive frames. Threshold in pixels.</p> <code>5</code> <code>max_skip</code> <code>int</code> <p>Maximum number of frames along one track where no points are detected.</p> <code>5</code> <code>momentum</code> <code>float</code> <p>Parameter in the range [0;1] that gives momentum to the tracking direction.</p> <code>0.1</code> <code>track_min_length</code> <code>int</code> <p>Minimum number of points in a track.</p> <code>5</code> <code>coords</code> <code>list</code> <p>List of numpy arrays with row and column indices of detected points. One per slice, which                         means that z is gives as the index of the list.</p> <code>None</code> <code>std</code> <code>float</code> <p>Standard deviation of the Gaussian filter.</p> <code>2.5</code> <code>min_distance</code> <code>int</code> <p>Minimum distance between fibres.</p> <code>5</code> <code>threshold_abs</code> <code>float</code> <p>Threshold value for the peak from the background.</p> <code>0.5</code> <code>weighted_avg</code> <code>bool</code> <p>Whether to apply weighted average to the detected coordinates.</p> <code>False</code> <code>window_size</code> <code>int</code> <p>Size of the neighbourhood window around the peak.</p> <code>10</code> <code>apply_filter</code> <code>bool</code> <p>Whether to apply Gaussian filter to the window.</p> <code>False</code> <code>smoothtrack_gaussian</code> <code>bool</code> <p>Whether to smooth tracks using Gaussian.</p> <code>False</code> <code>sigma</code> <code>float</code> <p>Sigma value for Gaussian filter.</p> <code>3</code> <code>smoothtrack_watershed</code> <code>bool</code> <p>Whether to smooth tracks using watershed.</p> <code>False</code> <code>threshold</code> <code>float</code> <p>Threshold value for watershed.</p> <code>None</code> <p>Returns:</p> Name Type Description <code>tracks</code> <code>List[ndarray]</code> <p>List of arrays of shape (n_points, 3) - each list contains coordinates of tracked fibers.</p> Example <p><pre><code>import fibretracker as ft\n\nvol = ft.io.load(\"path/to/volume.txm\")\nv = ft.io.normalize(vol)\ncoords = ft.models.get_fibre_coords(v)\ntracks = ft.models.track_fibres(coords=coords)\n</code></pre> <pre><code>import fibretracker as ft\n\nvol = ft.io.load(\"path/to/volume.txm\")\nv = ft.io.normalize(vol)\ncoords = ft.models.get_fibre_coords(v)\ntracks = ft.models.track_fibres(vol=v, coords=coords, smoothtrack_gaussian=True, sigma=3) # Smoothened tracks using Gaussian\n</code></pre></p> Source code in <code>fibretracker/models/tracker.py</code> <pre><code>def track_fibres(\n        vol: Optional[np.ndarray]=None,\n        max_jump: int=5, \n        max_skip: int=5, \n        momentum: float=0.1, \n        track_min_length: int=5,\n        coords: Optional[List[np.ndarray]]=None,\n        std: float=2.5,\n        min_distance: int=5,\n        threshold_abs: float=0.5,\n        weighted_avg: bool=False,\n        window_size: int=10,\n        apply_filter: bool=False,\n        smoothtrack_gaussian: bool=False,\n        sigma: float=3,\n        smoothtrack_watershed: bool=False,\n        threshold: Optional[float]=None\n        ):\n    '''Tracks fibers throughout the volume\n\n        Args:\n            vol (np.ndarray, optional): 3D volume.\n            max_jump (int, optional): Maximum distance between detected points in two consecutive frames. Threshold in pixels.\n            max_skip (int, optional): Maximum number of frames along one track where no points are detected.\n            momentum (float, optional): Parameter in the range [0;1] that gives momentum to the tracking direction.\n            track_min_length (int, optional): Minimum number of points in a track.\n            coords (list, optional): List of numpy arrays with row and column indices of detected points. One per slice, which\n                                        means that z is gives as the index of the list.\n            std (float, optional): Standard deviation of the Gaussian filter.\n            min_distance (int, optional): Minimum distance between fibres.\n            threshold_abs (float, optional): Threshold value for the peak from the background.\n            weighted_avg (bool, optional): Whether to apply weighted average to the detected coordinates.\n            window_size (int, optional): Size of the neighbourhood window around the peak.\n            apply_filter (bool, optional): Whether to apply Gaussian filter to the window.\n            smoothtrack_gaussian (bool, optional): Whether to smooth tracks using Gaussian.\n            sigma (float, optional): Sigma value for Gaussian filter.\n            smoothtrack_watershed (bool, optional): Whether to smooth tracks using watershed.\n            threshold (float, optional): Threshold value for watershed.\n\n        Returns:\n            tracks (List[np.ndarray]): List of arrays of shape (n_points, 3) - each list contains coordinates of tracked fibers.\n\n        Example:\n            ```python\n            import fibretracker as ft\n\n            vol = ft.io.load(\"path/to/volume.txm\")\n            v = ft.io.normalize(vol)\n            coords = ft.models.get_fibre_coords(v)\n            tracks = ft.models.track_fibres(coords=coords)\n            ```\n            ```python\n            import fibretracker as ft\n\n            vol = ft.io.load(\"path/to/volume.txm\")\n            v = ft.io.normalize(vol)\n            coords = ft.models.get_fibre_coords(v)\n            tracks = ft.models.track_fibres(vol=v, coords=coords, smoothtrack_gaussian=True, sigma=3) # Smoothened tracks using Gaussian\n            ```\n\n\n    '''\n\n    fib_tracker = TrackPoints(max_jump=max_jump, max_skip=max_skip,\n                        momentum=momentum, track_min_length=track_min_length)\n\n    if coords is None:\n        coords = get_fibre_coords(vol, std=std, min_distance=min_distance, threshold_abs=threshold_abs, \n                                weighted_avg=weighted_avg, window_size=window_size, apply_filter=apply_filter)\n\n    tracks, _ = fib_tracker(coords)\n    if smoothtrack_gaussian:\n        print('Smoothing tracks using Gaussian')\n        tracks_smooth = []\n        for track in tracks:\n            tracks_smooth.append(gaussian_filter1d(track, sigma=sigma, axis=0))\n\n        return tracks_smooth\n\n    elif smoothtrack_watershed and vol is not None:\n        print('Smoothing tracks using watershed...')\n        tracks_filled = fib_tracker.fill_tracks(tracks)\n        if threshold is None:\n            threshold = threshold_otsu(vol[len(vol)//2])\n        V_thres = vol &gt; threshold\n        V_dist = -distance_transform_edt(V_thres)\n        V_coords = np.zeros(vol.shape)\n        for i, track in enumerate(tracks_filled):\n            for point in track:\n                V_coords[int(point[2]), int(point[1]), int(point[0])] = i + 1\n        V_ws = watershed(V_dist, markers=V_coords.astype(int))*V_thres\n        print('Watershed volume created.')\n        n_fibers = V_ws.max()\n        tracks_smooth = [[] for i in range(n_fibers)]\n\n        for i, v_ws in enumerate(V_ws):\n            props = regionprops(v_ws)\n            for prop in props:\n                tracks_smooth[prop.label-1].append(list(prop.centroid[::-1]) + [i])\n            print(f'Smoothing tracks - iteration: {i+1}/{len(V_ws)}', end='\\r')\n\n        for i in range(n_fibers):\n            tracks_smooth[i] = np.array(tracks_smooth[i])\n\n        return tracks_smooth\n\n    else:\n        return tracks\n</code></pre>"},{"location":"viz/","title":"Visualise detected centre and tracked fibre","text":"<p>To visualise volume accross the slice, detected centres and tracked fibre</p>"},{"location":"viz/#fibretracker.viz","title":"fibretracker.viz","text":""},{"location":"viz/#fibretracker.viz.orthogonal","title":"fibretracker.viz.orthogonal","text":"<pre><code>orthogonal(vol, cmap='gray', img_height=5, img_width=5)\n</code></pre> <p>Interactive widget for visualizing orthogonal slices of a 3D volume.</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray or Tensor</code> <p>The 3D volume to be sliced.</p> required <code>cmap</code> <code>str</code> <p>Specifies the color map for the image. Defaults to \"gray\".</p> <code>'gray'</code> <code>img_height(int,</code> <code>optional</code> <p>Height of the figure.</p> required <code>img_width(int,</code> <code>optional</code> <p>Width of the figure.</p> required <p>Returns:</p> Name Type Description <code>orthogonal_obj</code> <code>HBox</code> <p>The interactive widget for visualizing orthogonal slices of a 3D volume.</p> Example <p><pre><code>import fibretracker as ft\n\nvol = ft.io.load(\"path/to/volume.txm\")\nft.viz.orthogonal(vol, cmap=\"gray\")\n</code></pre> </p> Source code in <code>fibretracker/viz/visualize.py</code> <pre><code>def orthogonal(\n    vol: np.ndarray,\n    cmap: str = \"gray\",\n    img_height: int = 5,\n    img_width: int = 5\n)-&gt; widgets.HBox:\n    \"\"\"Interactive widget for visualizing orthogonal slices of a 3D volume.\n\n    Args:\n        vol (np.ndarray or torch.Tensor): The 3D volume to be sliced.\n        cmap (str, optional): Specifies the color map for the image. Defaults to \"gray\".\n        img_height(int, optional): Height of the figure.\n        img_width(int, optional): Width of the figure.\n\n    Returns:\n        orthogonal_obj (widgets.HBox): The interactive widget for visualizing orthogonal slices of a 3D volume.\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        vol = ft.io.load(\"path/to/volume.txm\")\n        ft.viz.orthogonal(vol, cmap=\"gray\")\n        ```\n        ![viz orthogonal](figures/viz-orthogonal.gif)\n    \"\"\"\n\n    fig, ax = plt.subplots(1, 3, figsize=(3*img_width, img_height))\n    ax[0].axis(\"off\")\n    ax[1].axis(\"off\")\n    ax[2].axis(\"off\")\n\n    def _slice(slice_idx_z, slice_idx_y, slice_idx_x):\n        slice_img_z = vol.take(slice_idx_z, axis=0)\n        slice_img_y = vol.take(slice_idx_y, axis=1)\n        slice_img_x = vol.take(slice_idx_x, axis=2)\n\n        ax[0].imshow(slice_img_z, cmap=cmap)\n        ax[0].set_aspect('equal')\n        ax[1].imshow(slice_img_y, cmap=cmap)\n        ax[1].set_aspect('equal')\n        ax[2].imshow(slice_img_x, cmap=cmap)\n        ax[2].set_aspect('equal')\n\n    slice_slider_z = widgets.IntSlider(\n        value=vol.shape[0] // 2,\n        min=0,\n        max=vol.shape[0] - 1,\n        description=\"Z\",\n        continuous_update=True,\n    )\n\n    slice_slider_y = widgets.IntSlider(\n        value=vol.shape[1] // 2,\n        min=0,\n        max=vol.shape[1] - 1,\n        description=\"Y\",\n        continuous_update=True,\n    )\n\n    slice_slider_x = widgets.IntSlider(\n        value=vol.shape[2] // 2,\n        min=0,\n        max=vol.shape[2] - 1,\n        description=\"X\",\n        continuous_update=True,\n    )\n\n    slicer_obj = interactive(_slice, slice_idx_z=slice_slider_z, slice_idx_y=slice_slider_y, slice_idx_x=slice_slider_x)\n\n    # Create a horizontal box for the sliders\n    hbox = widgets.HBox([slicer_obj.children[0], slicer_obj.children[1], slicer_obj.children[2]], layout=widgets.Layout(align_items=\"stretch\", justify_content=\"center\", align_content=\"center\", justify_items=\"center\", justify_self=\"center\", align_self=\"center\", width=\"100%\"))\n\n    # Replace the sliders in the interactive widget with the horizontal box\n    slicer_obj.children = (hbox,) + slicer_obj.children[3:]\n\n    return slicer_obj\n</code></pre>"},{"location":"viz/#fibretracker.viz.slicer","title":"fibretracker.viz.slicer","text":"<pre><code>slicer(vol, detect_coords=None, mark_size=None, axis=0, cmap='gray', img_height=5, img_width=5)\n</code></pre> <p>Interactive widget for visualizing slices of a 3D volume and fibres centre if provided.</p> <p>Parameters:</p> Name Type Description Default <code>vol</code> <code>ndarray</code> <p>The 3D volume to be sliced.</p> required <code>detect_coords</code> <code>list</code> <p>List of coordinates of detected fibres. Defaults to None.</p> <code>None</code> <code>mark_size</code> <code>int</code> <p>Size of the marker for detected fibres. Defaults to None.</p> <code>None</code> <code>axis</code> <code>int</code> <p>Specifies the axis, or dimension, along which to slice. Defaults to 0.</p> <code>0</code> <code>cmap</code> <code>str</code> <p>Specifies the color map for the image. Defaults to \"gray\".</p> <code>'gray'</code> <code>img_height</code> <code>int</code> <p>Height of the figure. Defaults to 5.</p> <code>5</code> <code>img_width</code> <code>int</code> <p>Width of the figure. Defaults to 5.</p> <code>5</code> <p>Returns:</p> Name Type Description <code>slicer_obj</code> <code>interactive</code> <p>The interactive widget for visualizing slices of a 3D volume.</p> Example <p><pre><code>import fibretracker as ft\n\n# Load the volume and visualize the slices\nvol = ft.io.load(\"path/to/volume.txm\")\nft.viz.slicer(vol)\n</code></pre> </p> <pre><code>import fibretracker as ft\n\n# Load the volume and detected coordinates\nvol = ft.io.load(\"path/to/volume.txm\")\nvol = ft.io.normalize(vol)\nvol = vol[100:350] # 250 slices along the z-axis\ndetect_coords = ft.models.get_fibre_coords(vol)\nft.viz.slicer(vol, detect_coords=detect_coords, mark_size=4)\n</code></pre> <p></p> Source code in <code>fibretracker/viz/visualize.py</code> <pre><code>def slicer(\n    vol: np.ndarray,\n    detect_coords: Optional[List[np.ndarray]] = None,\n    mark_size: Optional[int] = None,\n    axis: int = 0,\n    cmap: str = \"gray\",\n    img_height: int = 5,\n    img_width: int = 5, \n) -&gt; widgets.interactive:\n    \"\"\"Interactive widget for visualizing slices of a 3D volume and fibres centre if provided.\n\n    Args:\n        vol (np.ndarray): The 3D volume to be sliced.\n        detect_coords (list, optional): List of coordinates of detected fibres. Defaults to None.\n        mark_size (int, optional): Size of the marker for detected fibres. Defaults to None.\n        axis (int, optional): Specifies the axis, or dimension, along which to slice. Defaults to 0.\n        cmap (str, optional): Specifies the color map for the image. Defaults to \"gray\".\n        img_height (int, optional): Height of the figure. Defaults to 5.\n        img_width (int, optional): Width of the figure. Defaults to 5.\n\n    Returns:\n        slicer_obj (widgets.interactive): The interactive widget for visualizing slices of a 3D volume.\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        # Load the volume and visualize the slices\n        vol = ft.io.load(\"path/to/volume.txm\")\n        ft.viz.slicer(vol)\n        ```\n        ![viz slicer](figures/viz-slicer.gif)\n\n        ```python\n        import fibretracker as ft\n\n        # Load the volume and detected coordinates\n        vol = ft.io.load(\"path/to/volume.txm\")\n        vol = ft.io.normalize(vol)\n        vol = vol[100:350] # 250 slices along the z-axis\n        detect_coords = ft.models.get_fibre_coords(vol)\n        ft.viz.slicer(vol, detect_coords=detect_coords, mark_size=4)\n        ```\n\n        ![viz slicer](figures/viz-slicer_detector.gif)\n\n    \"\"\"\n\n    if detect_coords is None:\n        fig, ax = plt.subplots(figsize=(img_width, img_height))\n        ax.axis(\"off\")\n    else:\n        fig, ax = plt.subplots(1, 2, figsize=(2*img_width, img_height), sharex=True, sharey=True, gridspec_kw={'wspace': 0})\n        ax[0].axis(\"off\")\n        ax[1].axis(\"off\")\n\n    def _slice(slice_idx):\n        slice_img = vol.take(slice_idx, axis=axis)\n        if detect_coords is not None:\n            [l.remove() for l in ax[1].lines]\n            ax[0].imshow(slice_img, cmap=cmap)\n            ax[1].imshow(slice_img, cmap=cmap)\n            if mark_size is not None:\n                ax[1].plot(detect_coords[slice_idx][:, 0], detect_coords[slice_idx][:, 1], 'rx', markersize=mark_size)\n            else:\n                ax[1].plot(detect_coords[slice_idx][:, 0], detect_coords[slice_idx][:, 1], 'rx', markersize=3)\n        else:\n            ax.imshow(slice_img, cmap=cmap)\n\n    def on_release(event):\n        if detect_coords is not None:\n            xlim = ax[0].get_xlim()\n            ylim = ax[0].get_ylim()\n            ax[1].set_xlim(xlim)\n            ax[1].set_ylim(ylim)\n        else:\n            xlim = ax.get_xlim()\n            ylim = ax.get_ylim()\n\n    slice_slider = widgets.IntSlider(\n        value=vol.shape[axis] // 2,\n        min=0,\n        max=vol.shape[axis] - 1,\n        description=\"Slice\",\n        continuous_update=True,\n    )\n\n    slice_slider.style.description_width = 'middle'  # Set the width of the description to fit the larger font size\n    slice_slider.style.handle_color = 'blue'  # Optional: Change the handle color\n    slice_slider.style.font_size = '150px'  \n\n    fig.canvas.mpl_connect('button_release_event', on_release)\n    slicer_obj = interactive(_slice, slice_idx = slice_slider)\n\n    return slicer_obj\n</code></pre>"},{"location":"viz/#fibretracker.viz.plot_tracks","title":"fibretracker.viz.plot_tracks","text":"<pre><code>plot_tracks(tracks, grid=False)\n</code></pre> <p>Plot tracks of fibres detected in the volume</p> <p>Parameters:</p> Name Type Description Default <code>tracks</code> <code>List[ndarray]</code> <p>List of arrays of shape (n_points, 3)</p> required <code>grid</code> <code>bool</code> <p>Whether to show grid in the plot</p> <code>False</code> <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>matplotlib figure object</p> Example <pre><code>import fibretracker as ft\n\n# Load the volume and detected coordinates\nvol = ft.io.load(\"path/to/volume.txm\")\nvol = ft.io.normalize(vol)\nvol = vol[100:350] # 250 slices along the z-axis\ndetect_coords = ft.models.get_fibre_coords(vol)\ntracks_gauss = ft.models.track_fibres(coords=detect_coords, smoothtrack_gaussian=True)\nft.viz.plot_tracks(tracks_gauss)\n</code></pre> <p></p> Source code in <code>fibretracker/viz/plotting.py</code> <pre><code>def plot_tracks(\n        tracks: List[np.ndarray],\n        grid: bool = False,):\n\n    '''Plot tracks of fibres detected in the volume\n\n    Args:\n        tracks: List of arrays of shape (n_points, 3)\n        grid: Whether to show grid in the plot\n\n    Returns:\n        fig (matplotlib.figure.Figure): matplotlib figure object\n\n    Example:\n        ```python\n        import fibretracker as ft\n\n        # Load the volume and detected coordinates\n        vol = ft.io.load(\"path/to/volume.txm\")\n        vol = ft.io.normalize(vol)\n        vol = vol[100:350] # 250 slices along the z-axis\n        detect_coords = ft.models.get_fibre_coords(vol)\n        tracks_gauss = ft.models.track_fibres(coords=detect_coords, smoothtrack_gaussian=True)\n        ft.viz.plot_tracks(tracks_gauss)\n        ```\n\n        ![viz tracks](figures/tracks_gauss.gif)\n\n\n    '''\n    fig, ax = plt.subplots(figsize=(10, 10))\n    ax = fig.add_subplot(projection='3d')\n    for track in tracks:\n        ax.plot(track[:,0], track[:,1], track[:,2])\n    ax.grid(grid)\n    ax.set_aspect('equal')\n\n    plt.show()\n    return fig\n</code></pre>"},{"location":"notebooks/fibre_tracking/","title":"Example","text":"In\u00a0[1]: Copied! <pre>%matplotlib widget\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport fibretracker as ft\n</pre> %matplotlib widget import numpy as np import matplotlib.pyplot as plt import fibretracker as ft In\u00a0[2]: Copied! <pre>data_path = '/Users/pooja/Documents/PhD_work/ICPR_docs/data/Mock_UD_GFRP_data/UD-01_FoV_2_B2_recon.txm'\ndata_path1 = 'data/Mock_UD_GFRP_data/Mock-01_FoV_2_B2_recon.txm'\ndata_path2 = 'data/Mock_UD_GFRP_data/GFRP'\ndata_path3 = 'data/Mock_UD_GFRP_data/XCT_LR'\n\nV_UD = ft.io.load(data_path)\n# V_Mock = ft.io.load(data_path1)\n# V_GFRP = ft.io.load(data_path2, contains='.tif')\n# V_XCTLR = ft.io.load(data_path3, contains='.tif')\n\nV_norm = ft.io.normalize(V_UD).astype(float)\n</pre> data_path = '/Users/pooja/Documents/PhD_work/ICPR_docs/data/Mock_UD_GFRP_data/UD-01_FoV_2_B2_recon.txm' data_path1 = 'data/Mock_UD_GFRP_data/Mock-01_FoV_2_B2_recon.txm' data_path2 = 'data/Mock_UD_GFRP_data/GFRP' data_path3 = 'data/Mock_UD_GFRP_data/XCT_LR'  V_UD = ft.io.load(data_path) # V_Mock = ft.io.load(data_path1) # V_GFRP = ft.io.load(data_path2, contains='.tif') # V_XCTLR = ft.io.load(data_path3, contains='.tif')  V_norm = ft.io.normalize(V_UD).astype(float) <pre>astropy module not found\n</pre> In\u00a0[3]: Copied! <pre>ft.viz.slicer(V_norm, img_height=7, img_width=7, cmap='gray')\n</pre> ft.viz.slicer(V_norm, img_height=7, img_width=7, cmap='gray') Out[3]: <pre>interactive(children=(IntSlider(value=499, description='Slice', max=998, style=SliderStyle(description_width='\u2026</pre>                      Figure                  In\u00a0[4]: Copied! <pre>V_tmp = V_norm[100:350, V_norm.shape[1]//2 - 250:V_norm.shape[1]//2 + 250, V_norm.shape[2]//2 - 250:V_norm.shape[2]//2 + 250]\n</pre> V_tmp = V_norm[100:350, V_norm.shape[1]//2 - 250:V_norm.shape[1]//2 + 250, V_norm.shape[2]//2 - 250:V_norm.shape[2]//2 + 250] In\u00a0[5]: Copied! <pre>ft.viz.slicer(V_tmp, img_height=7, img_width=7, cmap='gray')\n</pre> ft.viz.slicer(V_tmp, img_height=7, img_width=7, cmap='gray') Out[5]: <pre>interactive(children=(IntSlider(value=125, description='Slice', max=249, style=SliderStyle(description_width='\u2026</pre>                      Figure                  In\u00a0[6]: Copied! <pre>ft.viz.orthogonal(V_tmp, img_height=7, img_width=7, cmap='gray')\n</pre> ft.viz.orthogonal(V_tmp, img_height=7, img_width=7, cmap='gray') Out[6]: <pre>interactive(children=(HBox(children=(IntSlider(value=125, description='Z', max=249), IntSlider(value=250, desc\u2026</pre>                      Figure                  In\u00a0[7]: Copied! <pre>n_bins = int(255*(V_tmp.max() - V_tmp.min()) + 1)\nhist, bins = np.histogram(V_tmp, bins=n_bins)\nbin_width = bins[1] - bins[0]\nfig, ax = plt.subplots(1, figsize=(5, 5))\nax.bar(bins[:-1], hist, width=bin_width)\nax.set_xlabel('Intensity')\nax.set_ylabel('Frequency')\nax.set_title('Histogram of UD section volume')\nplt.show()\n</pre> n_bins = int(255*(V_tmp.max() - V_tmp.min()) + 1) hist, bins = np.histogram(V_tmp, bins=n_bins) bin_width = bins[1] - bins[0] fig, ax = plt.subplots(1, figsize=(5, 5)) ax.bar(bins[:-1], hist, width=bin_width) ax.set_xlabel('Intensity') ax.set_ylabel('Frequency') ax.set_title('Histogram of UD section volume') plt.show()                      Figure                  In\u00a0[9]: Copied! <pre>coords = ft.models.get_fibre_coords(V_tmp, std=2.5, min_distance=3, threshold_abs=0.6)\n</pre> coords = ft.models.get_fibre_coords(V_tmp, std=2.5, min_distance=3, threshold_abs=0.6) <pre>                                      \r</pre> In\u00a0[10]: Copied! <pre>ft.viz.slicer(V_tmp, detect_coords=coords, mark_size=3, img_height=7, img_width=7, cmap='gray')\n</pre> ft.viz.slicer(V_tmp, detect_coords=coords, mark_size=3, img_height=7, img_width=7, cmap='gray') Out[10]: <pre>interactive(children=(IntSlider(value=125, description='Slice', max=249, style=SliderStyle(description_width='\u2026</pre>                      Figure                  In\u00a0[11]: Copied! <pre># fibre can be tracked either directly from the volume or from the detected coordinates, or both[to avoid coordinate detection again]\n\ntracks = ft.models.track_fibres(coords=coords)\ntracks_gauss = ft.models.track_fibres(V_tmp, coords=coords, smoothtrack_gaussian=True, sigma=2)\ntracks_ws = ft.models.track_fibres(V_tmp, coords=coords, smoothtrack_watershed=True, threshold=0.6)\n\n# tracks = ft.models.track_fibres(V_tmp, smoothtrack_watershed=True, threshold=0.5)\n# tracks = ft.models.track_fibres(V_tmp, smoothtrack_gaussian=True)\n# tracks = ft.models.track_fibres(coords=coords)\n# tracks = ft.models.track_fibres(coords=coords, smoothtrack_gaussian=True)\n</pre> # fibre can be tracked either directly from the volume or from the detected coordinates, or both[to avoid coordinate detection again]  tracks = ft.models.track_fibres(coords=coords) tracks_gauss = ft.models.track_fibres(V_tmp, coords=coords, smoothtrack_gaussian=True, sigma=2) tracks_ws = ft.models.track_fibres(V_tmp, coords=coords, smoothtrack_watershed=True, threshold=0.6)  # tracks = ft.models.track_fibres(V_tmp, smoothtrack_watershed=True, threshold=0.5) # tracks = ft.models.track_fibres(V_tmp, smoothtrack_gaussian=True) # tracks = ft.models.track_fibres(coords=coords) # tracks = ft.models.track_fibres(coords=coords, smoothtrack_gaussian=True) <pre>Smoothing tracks using Gaussian       \nSmoothing tracks using watershed...   \nWatershed volume created.\nSmoothing tracks - iteration: 250/250\r</pre> In\u00a0[12]: Copied! <pre>ft.viz.plot_tracks(tracks)\n</pre> ft.viz.plot_tracks(tracks)                      Figure                  Out[12]: <pre>(&lt;Figure size 1000x1000 with 2 Axes&gt;, &lt;Axes3D: &gt;)</pre> In\u00a0[13]: Copied! <pre>ft.viz.plot_tracks(tracks_gauss)\n</pre> ft.viz.plot_tracks(tracks_gauss)                      Figure                  Out[13]: <pre>(&lt;Figure size 1000x1000 with 2 Axes&gt;, &lt;Axes3D: &gt;)</pre> In\u00a0[14]: Copied! <pre>ft.viz.plot_tracks(tracks_ws)\n</pre> ft.viz.plot_tracks(tracks_ws)                      Figure                  Out[14]: <pre>(&lt;Figure size 1000x1000 with 2 Axes&gt;, &lt;Axes3D: &gt;)</pre>"}]}
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