diff --git a/404.html b/404.html
index f79aa400..a3b634ab 100644
--- a/404.html
+++ b/404.html
@@ -989,6 +989,8 @@ <h1>404 - Not found</h1>
       
         <script src="/assets/js/google-analytics.js"></script>
       
+        <script src="/js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/assets/img/Celldega_Broad-Retreat-2024.png b/assets/img/Celldega_Broad-Retreat-2024.png
new file mode 100644
index 00000000..8e42ee2f
Binary files /dev/null and b/assets/img/Celldega_Broad-Retreat-2024.png differ
diff --git a/examples/index.html b/examples/index.html
index 3dbea4f5..5efbbbcc 100644
--- a/examples/index.html
+++ b/examples/index.html
@@ -1018,6 +1018,8 @@ <h1 id="jupyter-notebook-examples">Jupyter Notebook Examples</h1>
       
         <script src="../assets/js/google-analytics.js"></script>
       
+        <script src="../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/examples/short_notebooks/Landscape_View_Xenium/index.html b/examples/short_notebooks/Landscape_View_Xenium/index.html
index 7b19ac7a..6dff45bf 100644
--- a/examples/short_notebooks/Landscape_View_Xenium/index.html
+++ b/examples/short_notebooks/Landscape_View_Xenium/index.html
@@ -4848,6 +4848,8 @@ <h1 id="landscape-view-xenium">Landscape View Xenium<a class="anchor-link" href=
       
         <script src="../../../assets/js/google-analytics.js"></script>
       
+        <script src="../../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/gallery/gallery_xenium/index.html b/gallery/gallery_xenium/index.html
index e008c5e4..109c5f2a 100644
--- a/gallery/gallery_xenium/index.html
+++ b/gallery/gallery_xenium/index.html
@@ -1070,6 +1070,8 @@ <h2 id="bone-marrow">Bone Marrow</h2>
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/gallery/gallery_xenium_mouse_brain/index.html b/gallery/gallery_xenium_mouse_brain/index.html
index 90cd24d8..9525b94a 100644
--- a/gallery/gallery_xenium_mouse_brain/index.html
+++ b/gallery/gallery_xenium_mouse_brain/index.html
@@ -1032,6 +1032,8 @@ <h1 id="xenium-prime-mouse-brain-coronal-ff">Xenium Prime Mouse Brain Coronal FF
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/gallery/gallery_xenium_multi/index.html b/gallery/gallery_xenium_multi/index.html
index c764feef..957b6721 100644
--- a/gallery/gallery_xenium_multi/index.html
+++ b/gallery/gallery_xenium_multi/index.html
@@ -1149,6 +1149,8 @@ <h2 id="bone-marrow">Bone Marrow</h2>
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/gallery/gallery_xenium_skin_cancer/index.html b/gallery/gallery_xenium_skin_cancer/index.html
index b2b14c3d..4a47bb22 100644
--- a/gallery/gallery_xenium_skin_cancer/index.html
+++ b/gallery/gallery_xenium_skin_cancer/index.html
@@ -1035,6 +1035,8 @@ <h1 id="xenium-prime-human-skin-ffpe-outs">Xenium Prime Human Skin FFPE outs</h1
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/gallery/index.html b/gallery/index.html
index fd6b504c..7896aae0 100644
--- a/gallery/index.html
+++ b/gallery/index.html
@@ -1084,6 +1084,8 @@ <h3 id="visium-hd">Visium HD</h3>
       
         <script src="../assets/js/google-analytics.js"></script>
       
+        <script src="../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/index.html b/index.html
index 156f98f1..8186ae5b 100644
--- a/index.html
+++ b/index.html
@@ -236,6 +236,15 @@
     </label>
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
+        <li class="md-nav__item">
+  <a href="#whats-new" class="md-nav__link">
+    <span class="md-ellipsis">
+      What's New
+    </span>
+  </a>
+  
+</li>
+      
         <li class="md-nav__item">
   <a href="#about" class="md-nav__link">
     <span class="md-ellipsis">
@@ -969,6 +978,15 @@
     </label>
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
+        <li class="md-nav__item">
+  <a href="#whats-new" class="md-nav__link">
+    <span class="md-ellipsis">
+      What's New
+    </span>
+  </a>
+  
+</li>
+      
         <li class="md-nav__item">
   <a href="#about" class="md-nav__link">
     <span class="md-ellipsis">
@@ -1002,14 +1020,20 @@ <h1 id="welcome-to-celldegas-documentation">Welcome to Celldega's Documentation<
     Celldega Landscape visualization of a human skin cancer Xenium dataset obtained from <a href='https://www.10xgenomics.com/datasets' target='_blank'>10X Genomics</a>.
 </div>
 
-<p>Celldega is a spatial analysis and visualization library that is being developed by the <a href='https://www.broadinstitute.org/spatial-technology-platform' target='_blank'>Spatial Technology Platform</a> at the <a href='https://www.broadinstitute.org/spatial-technology-platform' target='_blank'>Broad Institute of MIT and Harvard</a>. This project enables researchers to easily visualize large ST datasets (e.g., datasets with &gt;100M transcripts) alongside single-cell and spatial analysis notebook workflows (e.g., <a href='https://scverse.org/' target='_blank'>sverse</a> tools and novel spatial analysis approaches).</p>
+<p>Celldega is a spatial analysis and visualization library that is being developed by the <a href="https://www.broadinstitute.org/spatial-technology-platform">Spatial Technology Platform</a> at the <a href="https://www.broadinstitute.org/spatial-technology-platform">Broad Institute of MIT and Harvard</a>. This project enables researchers to easily visualize large ST datasets (e.g., datasets with &gt;100M transcripts) alongside single-cell and spatial analysis notebook workflows (e.g., <a href="https://scverse.org/">sverse</a> tools and novel spatial analysis approaches).</p>
 <ul>
 <li><a href="overview/getting_started/">Getting Started</a></li>
 <li><a href="overview/installation/">Installation</a></li>
 <li><a href="overview/usage/">Usage</a></li>
 </ul>
+<h2 id="whats-new">What's New</h2>
+<p>The project was recently presented at the Broad Retreat Data Visualization Breakout Session -</p>
+<p><a href="https://broadinstitute.github.io/Celldega-Broad-Retreat-2024/" target="_blank" rel="noopener noreferrer">
+    <img src="assets/img/Celldega_Broad-Retreat-2024.png" alt="Celldega Broad Retreat 2024" style="display:block; margin:0 auto; width:75%; border:1px solid lightgray; border-radius:5px;">
+</a></p>
 <h2 id="about">About</h2>
 <p>Celldega is named after a bodega, a small shop with all the essentials that is part of the fabric of a neighborhood.</p>
+<p><a href="https://www.nytimes.com/2025/01/03/sports/dukes-mayo-bowl.html">checking link</a></p>
 
 
 
@@ -1095,6 +1119,8 @@ <h2 id="about">About</h2>
       
         <script src="assets/js/google-analytics.js"></script>
       
+        <script src="js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/index.md b/index.md
index bd5e0163..ff23d7b3 100644
--- a/index.md
+++ b/index.md
@@ -8,14 +8,22 @@
     Celldega Landscape visualization of a human skin cancer Xenium dataset obtained from <a href='https://www.10xgenomics.com/datasets' target='_blank'>10X Genomics</a>.
 </div>
 
-Celldega is a spatial analysis and visualization library that is being developed by the <a href='https://www.broadinstitute.org/spatial-technology-platform' target='_blank'>Spatial Technology Platform</a> at the <a href='https://www.broadinstitute.org/spatial-technology-platform' target='_blank'>Broad Institute of MIT and Harvard</a>. This project enables researchers to easily visualize large ST datasets (e.g., datasets with >100M transcripts) alongside single-cell and spatial analysis notebook workflows (e.g., <a href='https://scverse.org/' target='_blank'>sverse</a> tools and novel spatial analysis approaches).
-
-
-
+Celldega is a spatial analysis and visualization library that is being developed by the [Spatial Technology Platform](https://www.broadinstitute.org/spatial-technology-platform) at the [Broad Institute of MIT and Harvard](https://www.broadinstitute.org/spatial-technology-platform). This project enables researchers to easily visualize large ST datasets (e.g., datasets with >100M transcripts) alongside single-cell and spatial analysis notebook workflows (e.g., [sverse](https://scverse.org/) tools and novel spatial analysis approaches).
 
 - [Getting Started](overview/getting_started.md)
 - [Installation](overview/installation.md)
 - [Usage](overview/usage.md)
 
+## What's New
+The project was recently presented at the Broad Retreat Data Visualization Breakout Session -
+
+<a href="https://broadinstitute.github.io/Celldega-Broad-Retreat-2024/" target="_blank" rel="noopener noreferrer">
+    <img src="assets/img/Celldega_Broad-Retreat-2024.png" alt="Celldega Broad Retreat 2024" style="display:block; margin:0 auto; width:75%; border:1px solid lightgray; border-radius:5px;">
+</a>
+
+
+
 ## About
-Celldega is named after a bodega, a small shop with all the essentials that is part of the fabric of a neighborhood.
\ No newline at end of file
+Celldega is named after a bodega, a small shop with all the essentials that is part of the fabric of a neighborhood.
+
+[checking link](https://www.nytimes.com/2025/01/03/sports/dukes-mayo-bowl.html)
\ No newline at end of file
diff --git a/javascript/api/index.html b/javascript/api/index.html
index 05015d51..3547a784 100644
--- a/javascript/api/index.html
+++ b/javascript/api/index.html
@@ -1797,6 +1797,8 @@ <h3 id="example-usage">Example Usage</h3>
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/javascript/index.html b/javascript/index.html
index 7b4a4827..2e052c3c 100644
--- a/javascript/index.html
+++ b/javascript/index.html
@@ -1018,6 +1018,8 @@ <h1 id="javascript-api-overview">JavaScript API Overview</h1>
       
         <script src="../assets/js/google-analytics.js"></script>
       
+        <script src="../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/js/open_in_new_tab.js b/js/open_in_new_tab.js
new file mode 100644
index 00000000..9bd05506
--- /dev/null
+++ b/js/open_in_new_tab.js
@@ -0,0 +1,51 @@
+// Description: Open external links in a new tab and PDF links in a new tab
+// Based on: https://jekyllcodex.org/without-plugin/new-window-fix/
+
+// Open external links in a new window
+function external_new_window() {
+    for(let c = document.getElementsByTagName("a"), a = 0; a < c.length; a++) {
+        let b = c[a];
+        if(b.getAttribute("href") && b.host !== location.host) {
+            b.target = "_blank";
+            b.rel = "noopener";
+        }
+    }
+}
+
+// Open PDF links in a new window
+function pdf_new_window() {
+    if (!document.getElementsByTagName) {
+        return false;
+    }
+
+    const extensions = ['.pdf', '.doc', '.docx', '.json', '.xls', '.xlsx', '.ppt', '.pptx', '.zip', '.rar', '.tar', '.gz', '.7z', '.bz2', '.xz', '.tgz', '.tar.gz'];
+    let links = document.getElementsByTagName("a");
+
+    for (let eleLink = 0; eleLink < links.length; eleLink++) {
+        let href = links[eleLink].href.toLowerCase(); // Convert href to lowercase for case-insensitive matching
+
+        if (extensions.some(ext => href.endsWith(ext))) {
+            links[eleLink].onclick = function() {
+                window.open(this.href);
+                return false;
+            }
+        }
+    }
+}
+
+function apply_rules() {
+    external_new_window();
+    pdf_new_window();
+}
+
+if (typeof document$ !== "undefined") {
+    // Compatibility with mkdocs-material's instant loading feature
+    document$.subscribe(function() {
+        apply_rules();
+    });
+} else {
+    // For browsers without mkdocs-material's instant loading feature
+    document.addEventListener("DOMContentLoaded", function() {
+        apply_rules();
+    });
+}
diff --git a/objects.inv b/objects.inv
index 091be81c..cd8dff01 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/overview/file_formats/index.html b/overview/file_formats/index.html
index ddfaa47f..1f7adec0 100644
--- a/overview/file_formats/index.html
+++ b/overview/file_formats/index.html
@@ -1085,6 +1085,8 @@ <h2 id="landscapefiles">LandscapeFiles</h2>
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/overview/getting_started/index.html b/overview/getting_started/index.html
index 419d15b3..88f76be3 100644
--- a/overview/getting_started/index.html
+++ b/overview/getting_started/index.html
@@ -1033,6 +1033,8 @@ <h1 id="getting-started">Getting Started</h1>
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/overview/index.html b/overview/index.html
index 79b9c436..1e1a86be 100644
--- a/overview/index.html
+++ b/overview/index.html
@@ -1024,6 +1024,8 @@ <h1 id="overview">Overview</h1>
       
         <script src="../assets/js/google-analytics.js"></script>
       
+        <script src="../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/overview/installation/index.html b/overview/installation/index.html
index 351edaac..a3956b5b 100644
--- a/overview/installation/index.html
+++ b/overview/installation/index.html
@@ -1111,6 +1111,8 @@ <h2 id="javascript">JavaScript</h2>
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/overview/usage/index.html b/overview/usage/index.html
index 1049d6ca..550d81f9 100644
--- a/overview/usage/index.html
+++ b/overview/usage/index.html
@@ -1085,6 +1085,8 @@ <h2 id="terrabio">Terra.bio</h2>
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/python/api/api.md b/python/api/api.md
index fe13c227..f2151d09 100644
--- a/python/api/api.md
+++ b/python/api/api.md
@@ -1,5 +1,6 @@
 # Python API Reference
 
+::: celldega.nbhd
 ::: celldega.pre
 ::: celldega.viz
     handler: python
diff --git a/python/api/index.html b/python/api/index.html
index 35e2440c..30f28e6f 100644
--- a/python/api/index.html
+++ b/python/api/index.html
@@ -918,6 +918,24 @@
     </label>
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
+        <li class="md-nav__item">
+  <a href="#celldega.nbhd" class="md-nav__link">
+    <span class="md-ellipsis">
+      nbhd
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#celldega.nbhd.alpha_shape_cell_clusters" class="md-nav__link">
+    <span class="md-ellipsis">
+      alpha_shape_cell_clusters
+    </span>
+  </a>
+  
+</li>
+      
         <li class="md-nav__item">
   <a href="#celldega.pre" class="md-nav__link">
     <span class="md-ellipsis">
@@ -1318,6 +1336,157 @@ <h1 id="python-api-reference">Python API Reference</h1>
 
 
 
+<a id="celldega.nbhd"></a>
+    <div class="doc doc-contents first">
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+<div class="doc doc-object doc-function">
+
+
+<h2 id="celldega.nbhd.alpha_shape_cell_clusters" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">alpha_shape_cell_clusters</span><span class="p">(</span><span class="n">meta_cell</span><span class="p">,</span> <span class="n">cat</span><span class="o">=</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="n">alphas</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">350</span><span class="p">])</span></code>
+
+</h2>
+
+
+    <div class="doc doc-contents ">
+
+      <p>Compute alpha shapes for each cluster in the cell metadata.</p>
+<p>Parameters:
+- meta_cell: GeoDataFrame of cell metadata.
+- cat: Column name in meta_cell containing the cluster labels.
+- alphas: List of alpha values to compute shapes for.</p>
+<p>Returns:
+- GeoDataFrame of alpha shapes.</p>
+
+            <details class="quote">
+              <summary>Source code in <code>src/celldega/nbhd/__init__.py</code></summary>
+              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">126</span>
+<span class="normal">127</span>
+<span class="normal">128</span>
+<span class="normal">129</span>
+<span class="normal">130</span>
+<span class="normal">131</span>
+<span class="normal">132</span>
+<span class="normal">133</span>
+<span class="normal">134</span>
+<span class="normal">135</span>
+<span class="normal">136</span>
+<span class="normal">137</span>
+<span class="normal">138</span>
+<span class="normal">139</span>
+<span class="normal">140</span>
+<span class="normal">141</span>
+<span class="normal">142</span>
+<span class="normal">143</span>
+<span class="normal">144</span>
+<span class="normal">145</span>
+<span class="normal">146</span>
+<span class="normal">147</span>
+<span class="normal">148</span>
+<span class="normal">149</span>
+<span class="normal">150</span>
+<span class="normal">151</span>
+<span class="normal">152</span>
+<span class="normal">153</span>
+<span class="normal">154</span>
+<span class="normal">155</span>
+<span class="normal">156</span>
+<span class="normal">157</span>
+<span class="normal">158</span>
+<span class="normal">159</span>
+<span class="normal">160</span>
+<span class="normal">161</span>
+<span class="normal">162</span>
+<span class="normal">163</span>
+<span class="normal">164</span>
+<span class="normal">165</span>
+<span class="normal">166</span>
+<span class="normal">167</span>
+<span class="normal">168</span>
+<span class="normal">169</span>
+<span class="normal">170</span>
+<span class="normal">171</span>
+<span class="normal">172</span>
+<span class="normal">173</span>
+<span class="normal">174</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">alpha_shape_cell_clusters</span><span class="p">(</span><span class="n">meta_cell</span><span class="p">,</span> <span class="n">cat</span><span class="o">=</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="n">alphas</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">350</span><span class="p">]):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Compute alpha shapes for each cluster in the cell metadata.</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    - meta_cell: GeoDataFrame of cell metadata.</span>
+<span class="sd">    - cat: Column name in meta_cell containing the cluster labels.</span>
+<span class="sd">    - alphas: List of alpha values to compute shapes for.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    - GeoDataFrame of alpha shapes.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">gdf_alpha</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">inv_alpha</span> <span class="ow">in</span> <span class="n">alphas</span><span class="p">:</span>
+
+        <span class="k">for</span> <span class="n">inst_cluster</span> <span class="ow">in</span> <span class="n">meta_cell</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+
+            <span class="n">inst_clust</span> <span class="o">=</span> <span class="n">meta_cell</span><span class="p">[</span><span class="n">meta_cell</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span> <span class="o">==</span> <span class="n">inst_cluster</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">inst_clust</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
+
+                <span class="n">nested_array</span> <span class="o">=</span> <span class="n">inst_clust</span><span class="p">[</span><span class="s1">&#39;geometry&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+                <span class="c1"># Convert to a 2D NumPy array</span>
+                <span class="n">flat_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">nested_array</span><span class="p">)</span>
+
+                <span class="n">inst_shape</span> <span class="o">=</span> <span class="n">alpha_shape</span><span class="p">(</span><span class="n">flat_array</span><span class="p">,</span> <span class="n">inv_alpha</span><span class="p">)</span>
+
+                <span class="n">inst_name</span> <span class="o">=</span> <span class="n">inst_cluster</span> <span class="o">+</span> <span class="s1">&#39;_&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">inv_alpha</span><span class="p">)</span>
+
+                <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">inst_name</span><span class="p">,</span> <span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst_name</span>
+
+                <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">inst_name</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst_cluster</span>
+
+                <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">inst_name</span><span class="p">,</span> <span class="s1">&#39;geometry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst_shape</span>
+
+                <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">inst_name</span><span class="p">,</span> <span class="s1">&#39;inv_alpha&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">inv_alpha</span><span class="p">)</span>
+
+    <span class="n">gdf_alpha</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gdf_alpha</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">geom</span><span class="p">:</span> <span class="n">_round_coordinates</span><span class="p">(</span><span class="n">geom</span><span class="p">,</span> <span class="n">precision</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
+
+    <span class="n">gdf_alpha</span><span class="p">[</span><span class="s1">&#39;area&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">area</span>
+
+    <span class="n">gdf_alpha</span> <span class="o">=</span> <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">gdf_alpha</span><span class="o">.</span><span class="n">area</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
+
+    <span class="k">return</span> <span class="n">gdf_alpha</span>
+</code></pre></div></td></tr></table></div>
+            </details>
+    </div>
+
+</div>
+
+
+
+  </div>
+
+    </div>
+
+</div>
+
+<div class="doc doc-object doc-module">
+
+
+
 <a id="celldega.pre"></a>
     <div class="doc doc-contents first">
 
@@ -3674,7 +3843,8 @@ <h2 id="celldega.viz.Landscape" class="doc doc-heading">
 <span class="normal">60</span>
 <span class="normal">61</span>
 <span class="normal">62</span>
-<span class="normal">63</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">Landscape</span><span class="p">(</span><span class="n">anywidget</span><span class="o">.</span><span class="n">AnyWidget</span><span class="p">):</span>
+<span class="normal">63</span>
+<span class="normal">64</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">Landscape</span><span class="p">(</span><span class="n">anywidget</span><span class="o">.</span><span class="n">AnyWidget</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    A widget for interactive visualization of spatial omics data. This widget</span>
 <span class="sd">    currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)</span>
@@ -3717,6 +3887,7 @@ <h2 id="celldega.viz.Landscape" class="doc doc-heading">
     <span class="n">square_tile_size</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Float</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">dataset_name</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Unicode</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">region</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Dict</span><span class="p">({})</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">nbhd</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Dict</span><span class="p">({})</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
     <span class="n">update_trigger</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Dict</span><span class="p">()</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">cell_clusters</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Dict</span><span class="p">()</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -3915,8 +4086,7 @@ <h2 id="celldega.viz.Matrix" class="doc doc-heading">
 
               <details class="quote">
                 <summary>Source code in <code>src/celldega/viz/widget.py</code></summary>
-                <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">66</span>
-<span class="normal">67</span>
+                <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">67</span>
 <span class="normal">68</span>
 <span class="normal">69</span>
 <span class="normal">70</span>
@@ -3939,7 +4109,8 @@ <h2 id="celldega.viz.Matrix" class="doc doc-heading">
 <span class="normal">87</span>
 <span class="normal">88</span>
 <span class="normal">89</span>
-<span class="normal">90</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">Matrix</span><span class="p">(</span><span class="n">anywidget</span><span class="o">.</span><span class="n">AnyWidget</span><span class="p">):</span>
+<span class="normal">90</span>
+<span class="normal">91</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">Matrix</span><span class="p">(</span><span class="n">anywidget</span><span class="o">.</span><span class="n">AnyWidget</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    A widget for interactive visualization of a hierarchically clustered matrix.</span>
 
@@ -4080,6 +4251,8 @@ <h2 id="celldega.viz.Matrix" class="doc doc-heading">
       
         <script src="../../assets/js/google-analytics.js"></script>
       
+        <script src="../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/python/index.html b/python/index.html
index 738c97d0..bdc16d07 100644
--- a/python/index.html
+++ b/python/index.html
@@ -924,6 +924,15 @@
     </label>
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
+        <li class="md-nav__item">
+  <a href="#neighborhood-overview" class="md-nav__link">
+    <span class="md-ellipsis">
+      Neighborhood Overview
+    </span>
+  </a>
+  
+</li>
+      
         <li class="md-nav__item">
   <a href="#pre-module-overview" class="md-nav__link">
     <span class="md-ellipsis">
@@ -958,6 +967,8 @@
 
 
 <h1 id="python-api-overview">Python API Overview</h1>
+<h2 id="neighborhood-overview"><a href="nbhd/api">Neighborhood Overview</a></h2>
+<p>The <code>nbhd</code> module contains methods for calculating tissue neighborhoods.</p>
 <h2 id="pre-module-overview"><a href="pre/api">Pre Module Overview</a></h2>
 <p>The <code>pre</code> module contains methods for pre-processing LandscapeFiles.</p>
 <h2 id="viz-module-overview"><a href="viz/api">Viz Module Overview</a></h2>
@@ -1047,6 +1058,8 @@ <h2 id="viz-module-overview"><a href="viz/api">Viz Module Overview</a></h2>
       
         <script src="../assets/js/google-analytics.js"></script>
       
+        <script src="../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/python/index.md b/python/index.md
index f386f772..0d76eee1 100644
--- a/python/index.md
+++ b/python/index.md
@@ -1,5 +1,8 @@
 # Python API Overview
 
+## [Neighborhood Overview](nbhd/api)
+The `nbhd` module contains methods for calculating tissue neighborhoods.
+
 ## [Pre Module Overview](pre/api)
 
 The `pre` module contains methods for pre-processing LandscapeFiles.
diff --git a/python/nbhd/api/api.md b/python/nbhd/api/api.md
new file mode 100644
index 00000000..f738d069
--- /dev/null
+++ b/python/nbhd/api/api.md
@@ -0,0 +1,5 @@
+# Neighborhood Module API Reference
+
+<!-- ::: celldega.pre.landscape -->
+<!-- ::: celldega.pre -->
+::: celldega.nbhd
\ No newline at end of file
diff --git a/python/nbhd/api/index.html b/python/nbhd/api/index.html
new file mode 100644
index 00000000..3e5d1654
--- /dev/null
+++ b/python/nbhd/api/index.html
@@ -0,0 +1,1198 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
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+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.30">
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+    </span>
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+      
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+  <a href="#celldega.nbhd.alpha_shape_cell_clusters" class="md-nav__link">
+    <span class="md-ellipsis">
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+
+
+<h1 id="neighborhood-module-api-reference">Neighborhood Module API Reference</h1>
+<!-- ::: celldega.pre.landscape -->
+<!-- ::: celldega.pre -->
+
+
+<div class="doc doc-object doc-module">
+
+
+
+<a id="celldega.nbhd"></a>
+    <div class="doc doc-contents first">
+
+
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+<div class="doc doc-object doc-function">
+
+
+<h2 id="celldega.nbhd.alpha_shape_cell_clusters" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">alpha_shape_cell_clusters</span><span class="p">(</span><span class="n">meta_cell</span><span class="p">,</span> <span class="n">cat</span><span class="o">=</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="n">alphas</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">350</span><span class="p">])</span></code>
+
+</h2>
+
+
+    <div class="doc doc-contents ">
+
+      <p>Compute alpha shapes for each cluster in the cell metadata.</p>
+<p>Parameters:
+- meta_cell: GeoDataFrame of cell metadata.
+- cat: Column name in meta_cell containing the cluster labels.
+- alphas: List of alpha values to compute shapes for.</p>
+<p>Returns:
+- GeoDataFrame of alpha shapes.</p>
+
+            <details class="quote">
+              <summary>Source code in <code>src/celldega/nbhd/__init__.py</code></summary>
+              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">126</span>
+<span class="normal">127</span>
+<span class="normal">128</span>
+<span class="normal">129</span>
+<span class="normal">130</span>
+<span class="normal">131</span>
+<span class="normal">132</span>
+<span class="normal">133</span>
+<span class="normal">134</span>
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+<span class="normal">141</span>
+<span class="normal">142</span>
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+<span class="normal">144</span>
+<span class="normal">145</span>
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+<span class="normal">147</span>
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+<span class="normal">149</span>
+<span class="normal">150</span>
+<span class="normal">151</span>
+<span class="normal">152</span>
+<span class="normal">153</span>
+<span class="normal">154</span>
+<span class="normal">155</span>
+<span class="normal">156</span>
+<span class="normal">157</span>
+<span class="normal">158</span>
+<span class="normal">159</span>
+<span class="normal">160</span>
+<span class="normal">161</span>
+<span class="normal">162</span>
+<span class="normal">163</span>
+<span class="normal">164</span>
+<span class="normal">165</span>
+<span class="normal">166</span>
+<span class="normal">167</span>
+<span class="normal">168</span>
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+<span class="normal">171</span>
+<span class="normal">172</span>
+<span class="normal">173</span>
+<span class="normal">174</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">alpha_shape_cell_clusters</span><span class="p">(</span><span class="n">meta_cell</span><span class="p">,</span> <span class="n">cat</span><span class="o">=</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="n">alphas</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">350</span><span class="p">]):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Compute alpha shapes for each cluster in the cell metadata.</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    - meta_cell: GeoDataFrame of cell metadata.</span>
+<span class="sd">    - cat: Column name in meta_cell containing the cluster labels.</span>
+<span class="sd">    - alphas: List of alpha values to compute shapes for.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    - GeoDataFrame of alpha shapes.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">gdf_alpha</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">inv_alpha</span> <span class="ow">in</span> <span class="n">alphas</span><span class="p">:</span>
+
+        <span class="k">for</span> <span class="n">inst_cluster</span> <span class="ow">in</span> <span class="n">meta_cell</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+
+            <span class="n">inst_clust</span> <span class="o">=</span> <span class="n">meta_cell</span><span class="p">[</span><span class="n">meta_cell</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span> <span class="o">==</span> <span class="n">inst_cluster</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">inst_clust</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
+
+                <span class="n">nested_array</span> <span class="o">=</span> <span class="n">inst_clust</span><span class="p">[</span><span class="s1">&#39;geometry&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+                <span class="c1"># Convert to a 2D NumPy array</span>
+                <span class="n">flat_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">nested_array</span><span class="p">)</span>
+
+                <span class="n">inst_shape</span> <span class="o">=</span> <span class="n">alpha_shape</span><span class="p">(</span><span class="n">flat_array</span><span class="p">,</span> <span class="n">inv_alpha</span><span class="p">)</span>
+
+                <span class="n">inst_name</span> <span class="o">=</span> <span class="n">inst_cluster</span> <span class="o">+</span> <span class="s1">&#39;_&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">inv_alpha</span><span class="p">)</span>
+
+                <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">inst_name</span><span class="p">,</span> <span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst_name</span>
+
+                <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">inst_name</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst_cluster</span>
+
+                <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">inst_name</span><span class="p">,</span> <span class="s1">&#39;geometry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst_shape</span>
+
+                <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">inst_name</span><span class="p">,</span> <span class="s1">&#39;inv_alpha&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">inv_alpha</span><span class="p">)</span>
+
+    <span class="n">gdf_alpha</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gdf_alpha</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">geom</span><span class="p">:</span> <span class="n">_round_coordinates</span><span class="p">(</span><span class="n">geom</span><span class="p">,</span> <span class="n">precision</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
+
+    <span class="n">gdf_alpha</span><span class="p">[</span><span class="s1">&#39;area&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">area</span>
+
+    <span class="n">gdf_alpha</span> <span class="o">=</span> <span class="n">gdf_alpha</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">gdf_alpha</span><span class="o">.</span><span class="n">area</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
+
+    <span class="k">return</span> <span class="n">gdf_alpha</span>
+</code></pre></div></td></tr></table></div>
+            </details>
+    </div>
+
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+    <script id="__config" type="application/json">{"base": "../../..", "features": ["navigation.indexes"], "search": "../../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    
+    
+      <script src="../../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
+      
+        <script src="../../../assets/js/index.js" type="module"></script>
+      
+        <script src="../../../assets/js/gallery_mouse_brain.js" type="module"></script>
+      
+        <script src="../../../assets/js/gallery_skin_cancer.js" type="module"></script>
+      
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+</html>
\ No newline at end of file
diff --git a/python/pre/api/index.html b/python/pre/api/index.html
index d508c763..7815c8a6 100644
--- a/python/pre/api/index.html
+++ b/python/pre/api/index.html
@@ -3775,6 +3775,8 @@ <h2 id="celldega.pre.save_landscape_parameters" class="doc doc-heading">
       
         <script src="../../../assets/js/google-analytics.js"></script>
       
+        <script src="../../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/python/viz/api/index.html b/python/viz/api/index.html
index 9093adb9..e11cd15b 100644
--- a/python/viz/api/index.html
+++ b/python/viz/api/index.html
@@ -1407,7 +1407,8 @@ <h2 id="celldega.viz.widget.Landscape" class="doc doc-heading">
 <span class="normal">60</span>
 <span class="normal">61</span>
 <span class="normal">62</span>
-<span class="normal">63</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">Landscape</span><span class="p">(</span><span class="n">anywidget</span><span class="o">.</span><span class="n">AnyWidget</span><span class="p">):</span>
+<span class="normal">63</span>
+<span class="normal">64</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">Landscape</span><span class="p">(</span><span class="n">anywidget</span><span class="o">.</span><span class="n">AnyWidget</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    A widget for interactive visualization of spatial omics data. This widget</span>
 <span class="sd">    currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)</span>
@@ -1450,6 +1451,7 @@ <h2 id="celldega.viz.widget.Landscape" class="doc doc-heading">
     <span class="n">square_tile_size</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Float</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">dataset_name</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Unicode</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">region</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Dict</span><span class="p">({})</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">nbhd</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Dict</span><span class="p">({})</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
     <span class="n">update_trigger</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Dict</span><span class="p">()</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">cell_clusters</span> <span class="o">=</span> <span class="n">traitlets</span><span class="o">.</span><span class="n">Dict</span><span class="p">()</span><span class="o">.</span><span class="n">tag</span><span class="p">(</span><span class="n">sync</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -1648,8 +1650,7 @@ <h2 id="celldega.viz.widget.Matrix" class="doc doc-heading">
 
               <details class="quote">
                 <summary>Source code in <code>src/celldega/viz/widget.py</code></summary>
-                <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">66</span>
-<span class="normal">67</span>
+                <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">67</span>
 <span class="normal">68</span>
 <span class="normal">69</span>
 <span class="normal">70</span>
@@ -1672,7 +1673,8 @@ <h2 id="celldega.viz.widget.Matrix" class="doc doc-heading">
 <span class="normal">87</span>
 <span class="normal">88</span>
 <span class="normal">89</span>
-<span class="normal">90</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">Matrix</span><span class="p">(</span><span class="n">anywidget</span><span class="o">.</span><span class="n">AnyWidget</span><span class="p">):</span>
+<span class="normal">90</span>
+<span class="normal">91</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">Matrix</span><span class="p">(</span><span class="n">anywidget</span><span class="o">.</span><span class="n">AnyWidget</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    A widget for interactive visualization of a hierarchically clustered matrix.</span>
 
@@ -1813,6 +1815,8 @@ <h2 id="celldega.viz.widget.Matrix" class="doc doc-heading">
       
         <script src="../../../assets/js/google-analytics.js"></script>
       
+        <script src="../../../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
\ No newline at end of file
diff --git a/search/search_index.json b/search/search_index.json
index 3d626c25..8fb6c060 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to Celldega's Documentation","text":"Celldega Landscape visualization of a human skin cancer Xenium dataset obtained from 10X Genomics.  <p>Celldega is a spatial analysis and visualization library that is being developed by the Spatial Technology Platform at the Broad Institute of MIT and Harvard. This project enables researchers to easily visualize large ST datasets (e.g., datasets with &gt;100M transcripts) alongside single-cell and spatial analysis notebook workflows (e.g., sverse tools and novel spatial analysis approaches).</p> <ul> <li>Getting Started</li> <li>Installation</li> <li>Usage</li> </ul>"},{"location":"#about","title":"About","text":"<p>Celldega is named after a bodega, a small shop with all the essentials that is part of the fabric of a neighborhood.</p>"},{"location":"examples/","title":"Jupyter Notebook Examples","text":"<p>Landscape View Xenium</p>"},{"location":"examples/short_notebooks/Landscape_View_Xenium/","title":"Landscape View Xenium","text":"In\u00a0[2]: Copied! <pre># %load_ext autoreload\n# %autoreload 2\n# %env ANYWIDGET_HMR=1\n</pre> # %load_ext autoreload # %autoreload 2 # %env ANYWIDGET_HMR=1 In\u00a0[3]: Copied! <pre>import celldega as dega\ndega.__version__\n</pre> import celldega as dega dega.__version__ Out[3]: <pre>'0.0.0'</pre> In\u00a0[6]: Copied! <pre>from observable_jupyter import embed\n</pre> from observable_jupyter import embed In\u00a0[11]: Copied! <pre>base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Mouse_Brain_Coronal_FF_outs/main/Xenium_Prime_Mouse_Brain_Coronal_FF_outs'\n</pre> base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Mouse_Brain_Coronal_FF_outs/main/Xenium_Prime_Mouse_Brain_Coronal_FF_outs' In\u00a0[14]: Copied! <pre>embed('@cornhundred/celldega-landscape-ist', inputs={'base_url': base_url}, cells=['landscape_container'], display_logo=False)\n</pre> embed('@cornhundred/celldega-landscape-ist', inputs={'base_url': base_url}, cells=['landscape_container'], display_logo=False) In\u00a0[13]: Copied! <pre># base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Human_Skin_FFPE_outs/main/Xenium_Prime_Human_Skin_FFPE_outs'\n\n# landscape_ist = dega.viz.Landscape(\n#     technology='Xenium',\n#     ini_zoom = -4.5,\n#     ini_x=6000,\n#     ini_y=8000,\n#     base_url = base_url,\n\n# )\n\n# landscape_ist\n</pre> # base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Human_Skin_FFPE_outs/main/Xenium_Prime_Human_Skin_FFPE_outs'  # landscape_ist = dega.viz.Landscape( #     technology='Xenium', #     ini_zoom = -4.5, #     ini_x=6000, #     ini_y=8000, #     base_url = base_url,  # )  # landscape_ist In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/short_notebooks/Landscape_View_Xenium/#landscape-view-xenium","title":"Landscape View Xenium\u00b6","text":""},{"location":"gallery/","title":"Celldega Gallery","text":"<p>This page includes links to visualizations that are made with the stand-alone Celldega JavaScript library.</p>"},{"location":"gallery/#imaging-spatial-transcriptomics","title":"Imaging Spatial Transcriptomics","text":""},{"location":"gallery/#xenium","title":"Xenium","text":"<ul> <li>Xenium Mouse Brain </li> <li>Xenium Human Skin Cancer </li> </ul>"},{"location":"gallery/#sequencing-spatial-transcriptomics","title":"Sequencing Spatial Transcriptomics","text":""},{"location":"gallery/#visium-hd","title":"Visium HD","text":""},{"location":"gallery/gallery_xenium/","title":"Celldega Xenium Gallery","text":""},{"location":"gallery/gallery_xenium/#xenium-prime-mouse-brain-coronal-ff","title":"Xenium Prime Mouse Brain Coronal FF","text":""},{"location":"gallery/gallery_xenium/#xenium-prime-human-skin-ffpe-outs","title":"Xenium Prime Human Skin FFPE outs","text":""},{"location":"gallery/gallery_xenium/#xenium-human-pancreas-ffpe","title":"Xenium Human Pancreas FFPE","text":""},{"location":"gallery/gallery_xenium/#bone-marrow","title":"Bone Marrow","text":""},{"location":"gallery/gallery_xenium_mouse_brain/","title":"Xenium Prime Mouse Brain Coronal FF","text":""},{"location":"gallery/gallery_xenium_multi/","title":"Xenium Multi Dataset","text":""},{"location":"gallery/gallery_xenium_multi/#xenium-prime-mouse-brain-coronal-ff","title":"Xenium Prime Mouse Brain Coronal FF","text":""},{"location":"gallery/gallery_xenium_multi/#xenium-prime-human-skin-ffpe-outs","title":"Xenium Prime Human Skin FFPE outs","text":""},{"location":"gallery/gallery_xenium_multi/#xenium-human-pancreas-ffpe","title":"Xenium Human Pancreas FFPE","text":""},{"location":"gallery/gallery_xenium_multi/#bone-marrow","title":"Bone Marrow","text":""},{"location":"gallery/gallery_xenium_skin_cancer/","title":"Xenium Prime Human Skin FFPE outs","text":""},{"location":"javascript/","title":"JavaScript API Overview","text":"<p>Celldega's visualization methods can be used as a stand-alone JavaScript library outside the context of a Jupyter notebook. This can be used to create showcase visualizations with publicly hosted data.</p>"},{"location":"javascript/api/","title":"Celldega JavaScript API Documentation","text":"<p>The JavaScript component of Celldega is used within the Jupyter Widgets framework to provide interactive visualization in the context of a Jupyter notebook but can also be used as a standalone JavaScript library.</p>"},{"location":"javascript/api/#landscape_ist-api-documentation","title":"<code>landscape_ist</code> API Documentation","text":"<p>The <code>landscape_ist</code> function initializes and renders an interactive spatial transcriptomics (IST) landscape visualization. This API is designed to work with Deck.gl and includes customizable visualization options, dynamic data updates, and UI interactions.</p>"},{"location":"javascript/api/#parameters","title":"Parameters","text":"<ul> <li><code>el</code> (<code>HTMLElement</code>): The root DOM element where the visualization is rendered.</li> <li><code>ini_model</code> (<code>Object</code>): The initial data model containing configuration and state.</li> <li><code>token</code> (<code>string</code>): Authentication token for accessing data.</li> <li><code>ini_x</code>, <code>ini_y</code>, <code>ini_z</code> (<code>number</code>): Initial spatial coordinates for the view.</li> <li><code>ini_zoom</code> (<code>number</code>): Initial zoom level for the visualization.</li> <li><code>base_url</code> (<code>string</code>): Base URL for accessing data files.</li> <li><code>dataset_name</code> (<code>string</code>, optional): Name of the dataset being visualized.</li> <li><code>trx_radius</code> (<code>number</code>, optional): Initial radius for transcript points. Default: <code>0.25</code>.</li> <li><code>width</code> (<code>number|string</code>, optional): Width of the visualization. Default: <code>100%</code>.</li> <li><code>height</code> (<code>number</code>, optional): Height of the visualization. Default: <code>800</code>.</li> <li><code>view_change_custom_callback</code> (<code>Function</code>, optional): Custom callback triggered on view changes.</li> </ul>"},{"location":"javascript/api/#public-api","title":"Public API","text":"<p>The <code>landscape_ist</code> function returns an object (<code>landscape</code>) with several methods for interacting with the visualization.</p>"},{"location":"javascript/api/#update_matrix_gene","title":"<code>update_matrix_gene</code>","text":"<p>Updates the visualization to highlight data for a specific gene.</p>"},{"location":"javascript/api/#parameters_1","title":"Parameters","text":"<ul> <li><code>inst_gene</code> (<code>string</code>): The gene to highlight.</li> </ul>"},{"location":"javascript/api/#behavior","title":"Behavior","text":"<ul> <li>Updates the transcript layer to show data for the specified gene.</li> <li>Scrolls the bar graph to bring the selected gene into view.</li> <li>Toggles visibility of image layers and controls based on the selected gene.</li> </ul>"},{"location":"javascript/api/#update_matrix_col","title":"<code>update_matrix_col</code>","text":"<p>Updates the visualization to highlight data for a specific column (e.g., cluster).</p>"},{"location":"javascript/api/#parameters_2","title":"Parameters","text":"<ul> <li><code>inst_col</code> (<code>string</code>): The column to highlight.</li> </ul>"},{"location":"javascript/api/#behavior_1","title":"Behavior","text":"<ul> <li>Highlights the bar graph corresponding to the selected column.</li> <li>Updates cell and path layers to reflect the selected column.</li> <li>Toggles visibility of layers based on the column selection.</li> </ul>"},{"location":"javascript/api/#update_matrix_dendro_col","title":"<code>update_matrix_dendro_col</code>","text":"<p>Updates the visualization based on a dendrogram selection of columns.</p>"},{"location":"javascript/api/#parameters_3","title":"Parameters","text":"<ul> <li><code>selected_cols</code> (<code>Array&lt;string&gt;</code>): The list of selected column names.</li> </ul>"},{"location":"javascript/api/#behavior_2","title":"Behavior","text":"<ul> <li>Highlights the selected columns in the bar graph.</li> <li>Updates layers to reflect the selection.</li> </ul>"},{"location":"javascript/api/#update_view_state","title":"<code>update_view_state</code>","text":"<p>Updates the view state of the Deck.gl visualization.</p>"},{"location":"javascript/api/#parameters_4","title":"Parameters","text":"<ul> <li><code>new_view_state</code> (<code>Object</code>): The new view state configuration.</li> <li><code>close_up</code> (<code>boolean</code>): Whether the view should zoom in closely.</li> <li><code>trx_layer</code> (<code>Object</code>): The transcript layer to update.</li> </ul>"},{"location":"javascript/api/#behavior_3","title":"Behavior","text":"<ul> <li>Adjusts the viewport and reconfigures layers based on the new view state.</li> </ul>"},{"location":"javascript/api/#update_layers","title":"<code>update_layers</code>","text":"<p>Updates all visualization layers.</p>"},{"location":"javascript/api/#behavior_4","title":"Behavior","text":"<ul> <li>Refreshes the Deck.gl layers with the current visualization state.</li> </ul>"},{"location":"javascript/api/#finalize","title":"<code>finalize</code>","text":"<p>Finalizes the Deck.gl instance and cleans up resources.</p>"},{"location":"javascript/api/#behavior_5","title":"Behavior","text":"<ul> <li>Disposes of all Deck.gl resources and event listeners to prevent memory leaks.</li> </ul>"},{"location":"javascript/api/#usage-example","title":"Usage Example","text":"<pre><code>\njavascript\nimport { landscape_ist } from 'path/to/landscape_ist';\n\nconst rootElement = document.getElementById('visualization-container');\nconst model = { /* Model containing visualization data */ };\n\nconst visualization = await landscape_ist(\n    rootElement,\n    model,\n    'example-token',\n    100,\n    200,\n    0,\n    -5,\n    'https://example.com/data',\n    'Example Dataset'\n);\n\n// Update the visualization with a specific gene.\nvisualization.update_matrix_gene('TP53');\n\n// Update the visualization with a specific column.\nvisualization.update_matrix_col('Cluster 1');\n\n// Finalize the visualization when done.\nvisualization.finalize();\n\n</code></pre>"},{"location":"javascript/api/#matrix_viz-api-documentation","title":"<code>matrix_viz</code> API Documentation","text":"<p>The <code>matrix_viz</code> function initializes and renders a matrix visualization. This API is built using approaches and code adaptations from the Clustergrammer-GL library, and it integrates tightly with Deck.gl to provide interactive and dynamic visualizations.</p>"},{"location":"javascript/api/#parameters_5","title":"Parameters","text":"<ul> <li><code>model</code> (<code>Object</code>): The model object containing configuration data for the visualization.</li> <li><code>el</code> (<code>HTMLElement</code>): The root DOM element where the visualization is rendered.</li> <li><code>network</code> (<code>Object</code>): The network object containing the matrix data to visualize.</li> <li><code>width</code> (<code>string|number</code>, optional): The width of the visualization. Default: <code>'800'</code>.</li> <li><code>height</code> (<code>string|number</code>, optional): The height of the visualization. Default: <code>'800'</code>.</li> <li><code>row_label_callback</code> (<code>Function</code>, optional): A callback function triggered on row label interactions.</li> <li><code>col_label_callback</code> (<code>Function</code>, optional): A callback function triggered on column label interactions.</li> <li><code>col_dendro_callback</code> (<code>Function</code>, optional): A callback function triggered on dendrogram column interactions.</li> </ul>"},{"location":"javascript/api/#internal-behavior","title":"Internal Behavior","text":"<p>The function performs the following setup: 1. Deck.gl Integration:    - Initializes a Deck.gl instance for the matrix visualization.    - Sets properties for interactivity, including tooltips, view state changes, and layer filtering.</p> <ol> <li>Matrix Data Setup:</li> <li>Parses and structures the matrix data from the <code>network</code> object.</li> <li> <p>Configures labels, categories, and dendrograms for both rows and columns.</p> </li> <li> <p>Layer Initialization:</p> </li> <li>Creates layers for:<ul> <li>Matrix cells.</li> <li>Row and column labels.</li> <li>Row and column categories.</li> <li>Row and column dendrograms.</li> </ul> </li> <li> <p>Attaches interactions (e.g., click events) to these layers.</p> </li> <li> <p>UI Setup:</p> </li> <li>Creates a container for the visualization and appends it to the root DOM element.</li> </ol>"},{"location":"javascript/api/#example-usage","title":"Example Usage","text":"<pre><code>import { matrix_viz } from 'path/to/matrix_viz';\n\nconst rootElement = document.getElementById('matrix-container');\nconst model = { /* Model containing visualization data */ };\nconst network = { /* Network object representing the matrix data */ };\n\n// Callback functions\nconst rowLabelCallback = (row) =&gt; {\n    console.log('Row label clicked:', row);\n};\n\nconst colLabelCallback = (col) =&gt; {\n    console.log('Column label clicked:', col);\n};\n\nconst colDendroCallback = (dendro) =&gt; {\n    console.log('Column dendrogram clicked:', dendro);\n};\n\n// Initialize the matrix visualization\nawait matrix_viz(\n    model,\n    rootElement,\n    network,\n    800,\n    800,\n    rowLabelCallback,\n    colLabelCallback,\n    colDendroCallback\n);\n</code></pre>"},{"location":"overview/","title":"Overview","text":"<p>The Celldega library is being developed to help researchers easily visualize and analyze high-dimensional spatial-omics data in the context of a notebook workflow. Initial development has been focused on spatial transcriptomics visualization.</p> <p>Celldega can be used as a Python library in a Jupyter notebook environment or as a stand-alone JavaScript library for creating visualizations.</p> <ul> <li>Getting Started</li> <li>Installation</li> <li>Usage</li> </ul>"},{"location":"overview/file_formats/","title":"File Formats","text":""},{"location":"overview/file_formats/#landscapefiles","title":"LandscapeFiles","text":"<p>This format is small enough that we can store an entire Xenium dataset on GitHub as a repo for the purposes of building a public gallery.</p>"},{"location":"overview/getting_started/","title":"Getting Started","text":"<p>Celldega is a spatial analysis and visualization library that is being developed by the Spatial Technology Platform at the Broad Institute of MIT and Harvard. Celldega can be used as a Jupyter Widget in Python as well as a stand-alone JavaScript library.</p> <p>Please see examples notebooks below to try out Celldega in a Jupyter notebook or ObservableHQ JavaScript notebook:</p> <ul> <li>Celldega_Xenium_Landscape_Visualizations_Colab.ipynb</li> <li>Celldega Landscape Xenium ObservableHQ</li> </ul>"},{"location":"overview/installation/","title":"Installation","text":""},{"location":"overview/installation/#python","title":"Python","text":"<p>The Celldega library can be installed using pip</p> <p>Celldega can be installed using pip:</p> <pre><code>pip install celldega\n</code></pre> <p>Celldega can also be installed with the optional pre-processing requirements (e.g., vips for image pre-processing) using:</p> <pre><code>pip install celldega[pre]\n</code></pre>"},{"location":"overview/installation/#javascript","title":"JavaScript","text":"<p>Celldega can be used in a JavaScript environment such as ObservableHQ by importing it as a module from content delivery networks like esm.sh:</p> <pre><code>celldega = await import('https://esm.sh/celldega@latest')\n</code></pre> <p>Or by importing it from a local file</p> <pre><code>import celldega from 'js/widget.js'\n</code></pre>"},{"location":"overview/usage/","title":"Celldega Usage","text":""},{"location":"overview/usage/#terrabio","title":"Terra.bio","text":"<p>** Coming soon **</p>"},{"location":"python/","title":"Python API Overview","text":""},{"location":"python/#pre-module-overview","title":"Pre Module Overview","text":"<p>The <code>pre</code> module contains methods for pre-processing LandscapeFiles.</p>"},{"location":"python/#viz-module-overview","title":"Viz Module Overview","text":"<p>The <code>viz</code> module contains functions and classes for data visualization.</p>"},{"location":"python/api/","title":"Python API Reference","text":"<p>Module for pre-processing to generate LandscapeFiles from ST data.</p> <p>Module for visualization</p>"},{"location":"python/api/#celldega.pre.convert_long_id_to_short","title":"<code>convert_long_id_to_short(df)</code>","text":"<p>Converts a column of long integer cell IDs in a DataFrame to a shorter, hash-based representation.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>The DataFrame containing the EntityID.</p> required <p>Returns:     pd.DataFrame: The original DataFrame with an additional column named <code>cell_id</code>                   containing the shortened cell IDs.</p> <p>The function applies a SHA-256 hash to each cell ID, encodes the hash using base64, and truncates it to create a shorter identifier that is added as a new column to the DataFrame.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_long_id_to_short(df):\n    \"\"\"\n    Converts a column of long integer cell IDs in a DataFrame to a shorter, hash-based representation.\n\n    Args:\n        df (pd.DataFrame): The DataFrame containing the EntityID.\n    Returns:\n        pd.DataFrame: The original DataFrame with an additional column named `cell_id`\n                      containing the shortened cell IDs.\n\n    The function applies a SHA-256 hash to each cell ID, encodes the hash using base64, and truncates\n    it to create a shorter identifier that is added as a new column to the DataFrame.\n    \"\"\"\n    # Function to hash and encode the cell ID\n    def hash_and_shorten_id(cell_id):\n        # Create a hash of the cell ID\n        cell_id_bytes = str(cell_id).encode('utf-8')\n        hash_object = hashlib.sha256(cell_id_bytes)\n        hash_digest = hash_object.digest()\n\n        # Encode the hash to a base64 string to mix letters and numbers, truncate to 9 characters\n        short_id = base64.urlsafe_b64encode(hash_digest).decode('utf-8')[:9]\n        return short_id\n\n    # Apply the hash_and_shorten_id function to each cell ID in the specified column\n    df['cell_id'] = df['EntityID'].apply(hash_and_shorten_id)\n\n    return df\n</code></pre>"},{"location":"python/api/#celldega.pre.convert_to_jpeg","title":"<code>convert_to_jpeg(image_path, quality=80)</code>","text":"<p>Convert a TIFF image to a JPEG image with a quality of score</p>"},{"location":"python/api/#celldega.pre.convert_to_jpeg--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=80)     Quality score for the JPEG image</p>"},{"location":"python/api/#celldega.pre.convert_to_jpeg--returns","title":"Returns","text":"<p>new_image_path : str     Path to the JPEG image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_jpeg(image_path, quality=80):\n    \"\"\"\n    Convert a TIFF image to a JPEG image with a quality of score\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=80)\n        Quality score for the JPEG image\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the JPEG image file\n\n    \"\"\"\n\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a JPEG with a quality of 80\n    new_image_path = image_path.replace(\".tif\", \".jpeg\")\n    image.jpegsave(new_image_path, Q=quality)\n\n    return new_image_path\n</code></pre>"},{"location":"python/api/#celldega.pre.convert_to_png","title":"<code>convert_to_png(image_path)</code>","text":"<p>Convert a TIFF image to a JPEG image with a quality of score</p>"},{"location":"python/api/#celldega.pre.convert_to_png--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=80)     Quality score for the JPEG image</p>"},{"location":"python/api/#celldega.pre.convert_to_png--returns","title":"Returns","text":"<p>new_image_path : str     Path to the JPEG image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_png(image_path):\n    \"\"\"\n    Convert a TIFF image to a JPEG image with a quality of score\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=80)\n        Quality score for the JPEG image\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the JPEG image file\n\n    \"\"\"\n\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a JPEG with a quality of 80\n    new_image_path = image_path.replace(\".tif\", \".png\")\n    image.pngsave(new_image_path)\n\n    return new_image_path\n</code></pre>"},{"location":"python/api/#celldega.pre.convert_to_webp","title":"<code>convert_to_webp(image_path, quality=100)</code>","text":"<p>Convert a TIFF image to a WEBP image with a specified quality score.</p>"},{"location":"python/api/#celldega.pre.convert_to_webp--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=100)     Quality score for the WEBP image (higher is better quality)</p>"},{"location":"python/api/#celldega.pre.convert_to_webp--returns","title":"Returns","text":"<p>new_image_path : str     Path to the WEBP image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_webp(image_path, quality=100):\n    \"\"\"\n    Convert a TIFF image to a WEBP image with a specified quality score.\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=100)\n        Quality score for the WEBP image (higher is better quality)\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the WEBP image file\n    \"\"\"\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a WEBP with specified quality\n    new_image_path = image_path.replace(\".tif\", \".webp\")\n    image.webpsave(new_image_path, Q=quality)\n\n    return new_image_path\n</code></pre>"},{"location":"python/api/#celldega.pre.get_max_zoom_level","title":"<code>get_max_zoom_level(path_image_pyramid)</code>","text":"<p>Returns the maximum zoom level based on the highest-numbered directory in the specified path_image_pyramid.</p> <p>Parameters:</p> Name Type Description Default <code>path_image_pyramid</code> <code>str</code> <p>The path to the directory containing zoom level directories.</p> required <p>Returns:</p> Name Type Description <code>max_pyramid_zoom</code> <code>int</code> <p>The maximum zoom level.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def get_max_zoom_level(path_image_pyramid):\n    \"\"\"\n    Returns the maximum zoom level based on the highest-numbered directory\n    in the specified path_image_pyramid.\n\n    Parameters:\n        path_image_pyramid (str): The path to the directory containing zoom level directories.\n\n    Returns:\n        max_pyramid_zoom (int): The maximum zoom level.\n    \"\"\"\n    # List all entries in the path_image_pyramid that are directories and can be converted to integers\n    zoom_levels = [\n        entry\n        for entry in os.listdir(path_image_pyramid)\n        if os.path.isdir(os.path.join(path_image_pyramid, entry)) and entry.isdigit()\n    ]\n\n    # Convert to integer and find the maximum value\n    max_pyramid_zoom = max(map(int, zoom_levels)) if zoom_levels else None\n\n    return max_pyramid_zoom\n</code></pre>"},{"location":"python/api/#celldega.pre.make_cell_boundary_tiles","title":"<code>make_cell_boundary_tiles(technology, path_cell_boundaries, path_meta_cell_micron, path_transformation_matrix, path_output, coarse_tile_factor=20, tile_size=250, tile_bounds=None, image_scale=1, max_workers=8)</code>","text":"<p>Processes cell boundary data and divides it into spatial tiles based on the provided technology. Reads cell boundary data, applies affine transformations, and divides the data into coarse and fine tiles. The resulting tiles are saved as Parquet files, each containing the geometries of cells in that tile.</p>"},{"location":"python/api/#celldega.pre.make_cell_boundary_tiles--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the cell boundary data, e.g., \"MERSCOPE\", \"Xenium\", or \"custom\". path_cell_boundaries : str     Path to the file containing the cell boundaries (Parquet format). path_meta_cell_micron : str     Path to the file containing cell metadata (CSV format). path_transformation_matrix : str     Path to the file containing the transformation matrix (CSV format). path_output : str     Directory path where the output files (Parquet files) for each tile will be saved. coarse_tile_factor  : int, optional, default=20.     scaling factor of each coarse-grain tile comparing to the fine tile size. tile_size : int, optional, default=500     Size of each fine-grain tile in microns. tile_bounds : dict, optional     Dictionary containing the minimum and maximum bounds for x and y coordinates. image_scale : float, optional, default=1     Scale factor to apply to the geometry data. max_workers : int, optional, default=8     Maximum number of parallel workers for processing tiles.</p>"},{"location":"python/api/#celldega.pre.make_cell_boundary_tiles--returns","title":"Returns","text":"<p>None</p> Source code in <code>src/celldega/pre/boundary_tile.py</code> <pre><code>def make_cell_boundary_tiles(\n    technology,\n    path_cell_boundaries,\n    path_meta_cell_micron,\n    path_transformation_matrix,\n    path_output,\n    coarse_tile_factor=20,\n    tile_size=250,\n    tile_bounds=None,\n    image_scale=1,\n    max_workers=8\n):\n\n\n    \"\"\"\n    Processes cell boundary data and divides it into spatial tiles based on the provided technology.\n    Reads cell boundary data, applies affine transformations, and divides the data into coarse and fine tiles.\n    The resulting tiles are saved as Parquet files, each containing the geometries of cells in that tile.\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the cell boundary data, e.g., \"MERSCOPE\", \"Xenium\", or \"custom\".\n    path_cell_boundaries : str\n        Path to the file containing the cell boundaries (Parquet format).\n    path_meta_cell_micron : str\n        Path to the file containing cell metadata (CSV format).\n    path_transformation_matrix : str\n        Path to the file containing the transformation matrix (CSV format).\n    path_output : str\n        Directory path where the output files (Parquet files) for each tile will be saved.\n    coarse_tile_factor  : int, optional, default=20.\n        scaling factor of each coarse-grain tile comparing to the fine tile size.\n    tile_size : int, optional, default=500\n        Size of each fine-grain tile in microns.\n    tile_bounds : dict, optional\n        Dictionary containing the minimum and maximum bounds for x and y coordinates.\n    image_scale : float, optional, default=1\n        Scale factor to apply to the geometry data.\n    max_workers : int, optional, default=8\n        Maximum number of parallel workers for processing tiles.\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    def numpy_affine_transform(coords, matrix):\n        \"\"\"Apply affine transformation to numpy coordinates.\"\"\"\n        # Homogeneous coordinates for affine transformation\n        coords = np.hstack([coords, np.ones((coords.shape[0], 1))])\n        transformed_coords = coords @ matrix.T\n        return transformed_coords[:, :2]  # Drop the homogeneous coordinate\n\n    def batch_transform_geometries(geometries, transformation_matrix, scale):\n        \"\"\"\n        Batch transform geometries using numpy for optimized performance.\n        \"\"\"\n        # Extract affine transformation parameters into a 3x3 matrix for numpy\n        affine_matrix = np.array([\n            [transformation_matrix[0, 0], transformation_matrix[0, 1], transformation_matrix[0, 2]],\n            [transformation_matrix[1, 0], transformation_matrix[1, 1], transformation_matrix[1, 2]],\n            [0, 0, 1]\n        ])\n\n        transformed_geometries = []\n\n        for polygon in geometries:\n            # Extract coordinates and transform them\n            if isinstance(polygon, MultiPolygon):\n                polygon = next(polygon.geoms)  # Use the first geometry\n\n            # Transform the exterior of the polygon\n            exterior_coords = np.array(polygon.exterior.coords)\n\n            # Apply the affine transformation and scale\n            transformed_coords = numpy_affine_transform(exterior_coords, affine_matrix) / scale\n\n            # Append the result to the transformed_geometries list\n            transformed_geometries.append([transformed_coords.tolist()])\n\n        return transformed_geometries\n\n\n    def filter_and_save_fine_boundary(coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output):\n        cell_ids = coarse_tile.index.values\n\n        tile_filter = (\n            (coarse_tile[\"center_x\"] &gt;= fine_tile_x_min) &amp; (coarse_tile[\"center_x\"] &lt; fine_tile_x_max) &amp;\n            (coarse_tile[\"center_y\"] &gt;= fine_tile_y_min) &amp; (coarse_tile[\"center_y\"] &lt; fine_tile_y_max)\n        )\n        filtered_indices = np.where(tile_filter)[0]\n\n        keep_cells = cell_ids[filtered_indices]\n        fine_tile_cells = coarse_tile.loc[keep_cells, [\"GEOMETRY\"]]\n        fine_tile_cells = fine_tile_cells.assign(name=fine_tile_cells.index)\n\n        if not fine_tile_cells.empty:\n            filename = f\"{path_output}/cell_tile_{fine_i}_{fine_j}.parquet\"\n            fine_tile_cells.to_parquet(filename)\n\n    def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y):\n        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n            futures = []\n            for fine_i in range(n_fine_tiles_x):\n                fine_tile_x_min = x_min + fine_i * tile_size\n                fine_tile_x_max = fine_tile_x_min + tile_size\n\n                if not (fine_tile_x_min &gt;= coarse_tile_x_min and fine_tile_x_max &lt;= coarse_tile_x_max):\n                    continue\n\n                for fine_j in range(n_fine_tiles_y):\n                    fine_tile_y_min = y_min + fine_j * tile_size\n                    fine_tile_y_max = fine_tile_y_min + tile_size\n\n                    if not (fine_tile_y_min &gt;= coarse_tile_y_min and fine_tile_y_max &lt;= coarse_tile_y_max):\n                        continue\n\n                    futures.append(executor.submit(\n                        filter_and_save_fine_boundary, coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output\n                    ))\n\n            for future in futures:\n                future.result()\n\n    tile_size_x = tile_size\n    tile_size_y = tile_size\n\n    transformation_matrix = pd.read_csv(path_transformation_matrix, header=None, sep=\" \").values\n\n    # Load cell boundary data based on the technology\n    if technology == \"MERSCOPE\":\n        df_meta = pd.read_parquet(f\"{path_output.replace('cell_segmentation','cell_metadata.parquet')}\")\n        entity_to_cell_id_dict = pd.Series(df_meta.index.values, index=df_meta.EntityID).to_dict()\n        cells_orig = gpd.read_parquet(path_cell_boundaries)\n        cells_orig['cell_id'] = cells_orig['EntityID'].map(entity_to_cell_id_dict)\n        cells_orig = cells_orig[cells_orig[\"ZIndex\"] == 1]\n\n        # Correct cell_id issues with meta_cell\n        meta_cell = pd.read_csv(path_meta_cell_micron)\n        meta_cell['cell_id'] = meta_cell['EntityID'].map(entity_to_cell_id_dict)\n        cells_orig.index = meta_cell[meta_cell[\"cell_id\"].isin(cells_orig['cell_id'])].index\n\n        # Correct 'MultiPolygon' to 'Polygon'\n        cells_orig[\"geometry\"] = cells_orig[\"Geometry\"].apply(\n            lambda x: list(x.geoms)[0] if isinstance(x, MultiPolygon) else x\n        )\n\n        cells_orig.set_index('cell_id', inplace=True)\n\n    elif technology == \"Xenium\":\n        xenium_cells = pd.read_parquet(path_cell_boundaries)\n        grouped = xenium_cells.groupby(\"cell_id\")[[\"vertex_x\", \"vertex_y\"]].agg(lambda x: x.tolist())\n        grouped[\"geometry\"] = grouped.apply(lambda row: Polygon(zip(row[\"vertex_x\"], row[\"vertex_y\"])), axis=1)\n        cells_orig = gpd.GeoDataFrame(grouped, geometry=\"geometry\")[[\"geometry\"]]\n\n    elif technology == \"custom\":\n        cells_orig = gpd.read_parquet(path_cell_boundaries)\n\n    # Transform geometries\n    cells_orig[\"GEOMETRY\"] = batch_transform_geometries(cells_orig[\"geometry\"], transformation_matrix, image_scale)\n\n    # Convert transformed geometries to polygons and calculate centroids\n    cells_orig[\"polygon\"] = cells_orig[\"GEOMETRY\"].apply(lambda x: Polygon(x[0]))\n    gdf_cells = gpd.GeoDataFrame(geometry=cells_orig[\"polygon\"])\n    gdf_cells[\"center_x\"] = gdf_cells.geometry.centroid.x\n    gdf_cells[\"center_y\"] = gdf_cells.geometry.centroid.y\n    gdf_cells[\"GEOMETRY\"] = cells_orig[\"GEOMETRY\"]\n\n    # Ensure the output directory exists\n    if not os.path.exists(path_output):\n        os.makedirs(path_output)\n\n    # Calculate tile bounds and fine/coarse tiles\n    x_min, x_max = tile_bounds[\"x_min\"], tile_bounds[\"x_max\"]\n    y_min, y_max = tile_bounds[\"y_min\"], tile_bounds[\"y_max\"]\n    n_fine_tiles_x = int(np.ceil((x_max - x_min) / tile_size))\n    n_fine_tiles_y = int(np.ceil((y_max - y_min) / tile_size))\n    n_coarse_tiles_x = int(np.ceil((x_max - x_min) / (coarse_tile_factor * tile_size)))\n    n_coarse_tiles_y = int(np.ceil((y_max - y_min) / (coarse_tile_factor * tile_size)))\n\n    # Process coarse tiles in parallel\n    for i in tqdm(range(n_coarse_tiles_x), desc=\"Processing coarse tiles\"):\n        coarse_tile_x_min = x_min + i * (coarse_tile_factor * tile_size)\n        coarse_tile_x_max = coarse_tile_x_min + (coarse_tile_factor * tile_size)\n\n        for j in range(n_coarse_tiles_y):\n            coarse_tile_y_min = y_min + j * (coarse_tile_factor * tile_size)\n            coarse_tile_y_max = coarse_tile_y_min + (coarse_tile_factor * tile_size)\n\n            coarse_tile = gdf_cells[\n                (gdf_cells[\"center_x\"] &gt;= coarse_tile_x_min) &amp; (gdf_cells[\"center_x\"] &lt; coarse_tile_x_max) &amp;\n                (gdf_cells[\"center_y\"] &gt;= coarse_tile_y_min) &amp; (gdf_cells[\"center_y\"] &lt; coarse_tile_y_max)\n            ]\n            if not coarse_tile.empty:\n                process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y)\n</code></pre>"},{"location":"python/api/#celldega.pre.make_deepzoom_pyramid","title":"<code>make_deepzoom_pyramid(image_path, output_path, pyramid_name, tile_size=512, overlap=0, suffix='.jpeg')</code>","text":"<p>Create a DeepZoom image pyramid from a JPEG image</p>"},{"location":"python/api/#celldega.pre.make_deepzoom_pyramid--parameters","title":"Parameters","text":"<p>image_path : str     Path to the JPEG image file tile_size : int (default=512)     Tile size for the DeepZoom pyramid overlap : int (default=0)     Overlap size for the DeepZoom pyramid suffix : str (default='jpeg')     Suffix for the DeepZoom pyramid tiles</p>"},{"location":"python/api/#celldega.pre.make_deepzoom_pyramid--returns","title":"Returns","text":"<p>None</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_deepzoom_pyramid(\n    image_path, output_path, pyramid_name, tile_size=512, overlap=0, suffix=\".jpeg\"\n):\n    \"\"\"\n    Create a DeepZoom image pyramid from a JPEG image\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the JPEG image file\n    tile_size : int (default=512)\n        Tile size for the DeepZoom pyramid\n    overlap : int (default=0)\n        Overlap size for the DeepZoom pyramid\n    suffix : str (default='jpeg')\n        Suffix for the DeepZoom pyramid tiles\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n\n    # Define the output path\n    output_path = Path(output_path)\n\n    # Load the JPEG image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # check if the output path exists and create it if it does not\n    output_path.mkdir(parents=True, exist_ok=True)\n\n    # append the pyramid name to the output path\n    output_path = output_path / pyramid_name\n\n    # Save the image as a DeepZoom image pyramid\n    image.dzsave(output_path, tile_size=tile_size, overlap=overlap, suffix=suffix)\n</code></pre>"},{"location":"python/api/#celldega.pre.make_meta_cell_image_coord","title":"<code>make_meta_cell_image_coord(technology, path_transformation_matrix, path_meta_cell_micron, path_meta_cell_image, image_scale)</code>","text":"<p>Apply an affine transformation to the cell coordinates in microns and save the transformed coordinates in pixels</p>"},{"location":"python/api/#celldega.pre.make_meta_cell_image_coord--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the data, Xenium and MERSCOPE are supported. path_transformation_matrix : str     Path to the transformation matrix file path_meta_cell_micron : str     Path to the meta cell file with coordinates in microns path_meta_cell_image : str     Path to save the meta cell file with coordinates in pixels</p>"},{"location":"python/api/#celldega.pre.make_meta_cell_image_coord--returns","title":"Returns","text":"<p>None</p>"},{"location":"python/api/#celldega.pre.make_meta_cell_image_coord--examples","title":"Examples","text":"<p>make_meta_cell_image_coord( ...     technology='Xenium', ...     path_transformation_matrix='data/transformation_matrix.txt', ...     path_meta_cell_micron='data/meta_cell_micron.csv', ...     path_meta_cell_image='data/meta_cell_image.parquet' ... )</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_meta_cell_image_coord(\n    technology,\n    path_transformation_matrix,\n    path_meta_cell_micron,\n    path_meta_cell_image,\n    image_scale\n):\n    \"\"\"\n    Apply an affine transformation to the cell coordinates in microns and save\n    the transformed coordinates in pixels\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the data, Xenium and MERSCOPE are supported.\n    path_transformation_matrix : str\n        Path to the transformation matrix file\n    path_meta_cell_micron : str\n        Path to the meta cell file with coordinates in microns\n    path_meta_cell_image : str\n        Path to save the meta cell file with coordinates in pixels\n\n    Returns\n    -------\n    None\n\n    Examples\n    --------\n    &gt;&gt;&gt; make_meta_cell_image_coord(\n    ...     technology='Xenium',\n    ...     path_transformation_matrix='data/transformation_matrix.txt',\n    ...     path_meta_cell_micron='data/meta_cell_micron.csv',\n    ...     path_meta_cell_image='data/meta_cell_image.parquet'\n    ... )\n\n    \"\"\"\n\n    transformation_matrix = pd.read_csv(\n        path_transformation_matrix, header=None, sep=\" \"\n    ).values\n\n    if technology == \"MERSCOPE\":\n        meta_cell = pd.read_csv(path_meta_cell_micron, usecols=[\"EntityID\", \"center_x\", \"center_y\"])\n        meta_cell = convert_long_id_to_short(meta_cell)\n        meta_cell[\"name\"] =  meta_cell[\"cell_id\"]\n        meta_cell = meta_cell.set_index('cell_id')\n    elif technology == \"Xenium\":\n        usecols = [\"cell_id\", \"x_centroid\", \"y_centroid\"]\n        meta_cell = pd.read_csv(path_meta_cell_micron, index_col=0, usecols=usecols)\n        meta_cell.columns = [\"center_x\", \"center_y\"]\n        meta_cell[\"name\"] = pd.Series(meta_cell.index, index=meta_cell.index)\n\n    # Adding a ones column to accommodate for affine transformation\n    meta_cell[\"ones\"] = 1\n\n    # Preparing the data for matrix multiplication\n    points = meta_cell[[\"center_x\", \"center_y\", \"ones\"]].values\n\n    # Applying the transformation matrix\n    transformed_points = np.dot(transformation_matrix, points.T).T\n\n    # Updating the DataFrame with transformed coordinates\n    meta_cell[\"center_x\"] = transformed_points[:, 0]\n    meta_cell[\"center_y\"] = transformed_points[:, 1]\n\n    # Dropping the ones column as it's no longer needed\n    meta_cell.drop(columns=[\"ones\"], inplace=True)\n\n    meta_cell[\"center_x\"] = meta_cell[\"center_x\"] / image_scale\n    meta_cell[\"center_y\"] = meta_cell[\"center_y\"] / image_scale\n\n    meta_cell[\"geometry\"] = meta_cell.apply(\n        lambda row: [row[\"center_x\"], row[\"center_y\"]], axis=1\n    )\n\n    if technology == \"MERSCOPE\":\n        meta_cell = meta_cell[[\"name\", \"geometry\", \"EntityID\"]]\n    else:\n        meta_cell = meta_cell[[\"name\", \"geometry\"]]\n\n\n    meta_cell.to_parquet(path_meta_cell_image)\n</code></pre>"},{"location":"python/api/#celldega.pre.make_meta_gene","title":"<code>make_meta_gene(technology, path_cbg, path_output)</code>","text":"<p>Create a DataFrame with genes and their assigned colors</p>"},{"location":"python/api/#celldega.pre.make_meta_gene--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the data, Xenium and MERSCOPE are supported. path_cbg : str     Path to the cell-by-gene matrix data (the data format can vary based on technology) path_output : str     Path to save the meta gene file</p>"},{"location":"python/api/#celldega.pre.make_meta_gene--returns","title":"Returns","text":"<p>None</p>"},{"location":"python/api/#celldega.pre.make_meta_gene--examples","title":"Examples","text":"<p>make_meta_gene( ...     technology='Xenium', ...     path_cbg='data/', ...     path_output='data/meta_gene.parquet' ... )</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_meta_gene(technology, path_cbg, path_output):\n    \"\"\"\n    Create a DataFrame with genes and their assigned colors\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the data, Xenium and MERSCOPE are supported.\n    path_cbg : str\n        Path to the cell-by-gene matrix data (the data format can vary based on technology)\n    path_output : str\n        Path to save the meta gene file\n\n    Returns\n    -------\n    None\n\n    Examples\n    --------\n    &gt;&gt;&gt; make_meta_gene(\n    ...     technology='Xenium',\n    ...     path_cbg='data/',\n    ...     path_output='data/meta_gene.parquet'\n    ... )\n    \"\"\"\n\n    if technology == \"MERSCOPE\":\n        cbg = pd.read_csv(path_cbg, index_col=0)\n        genes = cbg.columns.tolist()\n    elif technology == \"Xenium\":\n        # genes = pd.read_csv(path_cbg + 'features.tsv.gz', sep='\\t', header=None)[1].values.tolist()\n        cbg = read_cbg_mtx(path_cbg)\n        genes = cbg.columns.tolist()\n\n    # Get all categorical color palettes from Matplotlib and flatten them into a single list of colors\n    palettes = [plt.get_cmap(name).colors for name in plt.colormaps() if \"tab\" in name]\n    flat_colors = [color for palette in palettes for color in palette]\n\n    # Convert RGB tuples to hex codes\n    flat_colors_hex = [to_hex(color) for color in flat_colors]\n\n    # Use modular arithmetic to assign a color to each gene, white for genes with \"Blank\"\n    colors = [\n        flat_colors_hex[i % len(flat_colors_hex)] if \"Blank\" not in gene else \"#FFFFFF\"\n        for i, gene in enumerate(genes)\n    ]\n\n    # Create a DataFrame with genes and their assigned colors\n    ser_color = pd.Series(colors, index=genes)\n\n    # calculate gene expression metadata\n    meta_gene = calc_meta_gene_data(cbg)\n    meta_gene['color'] = ser_color\n\n    # Identify sparse columns\n    sparse_cols = [col for col in meta_gene.columns if pd.api.types.is_sparse(meta_gene[col])]\n\n    # Convert sparse columns to dense\n    for col in sparse_cols:\n        meta_gene[col] = meta_gene[col].sparse.to_dense()\n\n    meta_gene.to_parquet(path_output)\n</code></pre>"},{"location":"python/api/#celldega.pre.make_trx_tiles","title":"<code>make_trx_tiles(technology, path_trx, path_transformation_matrix, path_trx_tiles, coarse_tile_factor=10, tile_size=250, chunk_size=1000000, verbose=False, image_scale=1, max_workers=8)</code>","text":"<p>Processes transcript data by dividing it into coarse-grain and fine-grain tiles, applying transformations, and saving the results in a parallelized manner.</p>"},{"location":"python/api/#celldega.pre.make_trx_tiles--parameters","title":"Parameters","text":"<p>technology : str     The technology used for generating the transcript data (e.g., \"MERSCOPE\" or \"Xenium\"). path_trx : str     Path to the file containing the transcript data. path_transformation_matrix : str     Path to the file containing the transformation matrix (CSV file). path_trx_tiles : str     Directory path where the output files (Parquet files) for each tile will be saved. coarse_tile_factor : int, optional     Scaling factor of each coarse-grain tile comparing to the fine tile size. tile_size : int, optional     Size of each fine-grain tile in microns (default is 250). chunk_size : int, optional     Number of rows to process per chunk for memory efficiency (default is 1000000). verbose : bool, optional     Flag to enable verbose output (default is False). image_scale : float, optional     Scale factor to apply to the transcript coordinates (default is 0.5). max_workers : int, optional     Maximum number of parallel workers for processing tiles (default is 8).</p>"},{"location":"python/api/#celldega.pre.make_trx_tiles--returns","title":"Returns","text":"<p>dict     A dictionary containing the bounds of the processed data in both x and y directions.</p> Source code in <code>src/celldega/pre/trx_tile.py</code> <pre><code>def make_trx_tiles(\n    technology,\n    path_trx,\n    path_transformation_matrix,\n    path_trx_tiles,\n    coarse_tile_factor=10,\n    tile_size=250,\n    chunk_size=1000000,\n    verbose=False,\n    image_scale=1,\n    max_workers=8\n):\n    \"\"\"\n    Processes transcript data by dividing it into coarse-grain and fine-grain tiles,\n    applying transformations, and saving the results in a parallelized manner.\n\n    Parameters\n    ----------\n    technology : str\n        The technology used for generating the transcript data (e.g., \"MERSCOPE\" or \"Xenium\").\n    path_trx : str\n        Path to the file containing the transcript data.\n    path_transformation_matrix : str\n        Path to the file containing the transformation matrix (CSV file).\n    path_trx_tiles : str\n        Directory path where the output files (Parquet files) for each tile will be saved.\n    coarse_tile_factor : int, optional\n        Scaling factor of each coarse-grain tile comparing to the fine tile size.\n    tile_size : int, optional\n        Size of each fine-grain tile in microns (default is 250).\n    chunk_size : int, optional\n        Number of rows to process per chunk for memory efficiency (default is 1000000).\n    verbose : bool, optional\n        Flag to enable verbose output (default is False).\n    image_scale : float, optional\n        Scale factor to apply to the transcript coordinates (default is 0.5).\n    max_workers : int, optional\n        Maximum number of parallel workers for processing tiles (default is 8).\n\n    Returns\n    -------\n    dict\n        A dictionary containing the bounds of the processed data in both x and y directions.\n    \"\"\"\n\n    def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers):\n        # Filter the entire dataset for the current coarse tile\n        coarse_tile = trx.filter(\n            (pl.col(\"transformed_x\") &gt;= coarse_tile_x_min) &amp; (pl.col(\"transformed_x\") &lt; coarse_tile_x_max) &amp;\n            (pl.col(\"transformed_y\") &gt;= coarse_tile_y_min) &amp; (pl.col(\"transformed_y\") &lt; coarse_tile_y_max)\n        )\n\n        if not coarse_tile.is_empty():\n            # Now process fine tiles using global fine tile indices\n            process_fine_tiles(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers)   \n\n\n    def process_fine_tiles(coarse_tile, coarse_i, coarse_j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=8):\n\n        # Use ThreadPoolExecutor for parallel processing of fine-grain tiles within the coarse tile\n        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n            futures = []\n\n            # Iterate over fine-grain tiles within the global bounds\n            for fine_i in range(n_fine_tiles_x):\n                fine_tile_x_min = x_min + fine_i * tile_size\n                fine_tile_x_max = fine_tile_x_min + tile_size\n\n                # Process only if the fine tile falls within the current coarse tile's bounds\n                if not (fine_tile_x_min &gt;= coarse_tile_x_min and fine_tile_x_max &lt;= coarse_tile_x_max):\n                    continue\n\n                for fine_j in range(n_fine_tiles_y):\n                    fine_tile_y_min = y_min + fine_j * tile_size\n                    fine_tile_y_max = fine_tile_y_min + tile_size\n\n                    # Process only if the fine tile falls within the current coarse tile's bounds\n                    if not (fine_tile_y_min &gt;= coarse_tile_y_min and fine_tile_y_max &lt;= coarse_tile_y_max):\n                        continue\n\n                    # Submit the task for each fine tile to process in parallel\n                    futures.append(executor.submit(\n                        filter_and_save_fine_tile, coarse_tile, coarse_i, coarse_j, fine_i, fine_j, \n                        fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles\n                    ))\n\n            # Wait for all futures to complete\n            for future in concurrent.futures.as_completed(futures):\n                future.result()  # Raise exceptions if any occurred during execution\n\n\n    def filter_and_save_fine_tile(coarse_tile, coarse_i, coarse_j, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles):\n\n        # Filter the coarse tile for the current fine tile's boundaries\n        fine_tile_trx = coarse_tile.filter(\n            (pl.col(\"transformed_x\") &gt;= fine_tile_x_min) &amp; (pl.col(\"transformed_x\") &lt; fine_tile_x_max) &amp;\n            (pl.col(\"transformed_y\") &gt;= fine_tile_y_min) &amp; (pl.col(\"transformed_y\") &lt; fine_tile_y_max)\n        )\n\n        if not fine_tile_trx.is_empty():\n            # Add geometry column as a list of [x, y] pairs\n            fine_tile_trx = fine_tile_trx.with_columns(\n                pl.concat_list([pl.col(\"transformed_x\"), pl.col(\"transformed_y\")]).alias(\"geometry\")\n            ).drop(['transformed_x', 'transformed_y'])\n\n            # Define the filename based on fine tile coordinates\n            filename = f\"{path_trx_tiles}/transcripts_tile_{fine_i}_{fine_j}.parquet\"\n\n            # Save the filtered DataFrame to a Parquet file\n            fine_tile_trx.to_pandas().to_parquet(filename)\n\n\n    # Load transformation matrix\n    transformation_matrix = np.loadtxt(path_transformation_matrix)\n\n    # Load the transcript data based on the technology using Polars\n    if technology == \"MERSCOPE\":\n        trx_ini = pl.read_csv(path_trx, columns=[\"gene\", \"global_x\", \"global_y\"])\n        trx_ini = trx_ini.with_columns([\n            pl.col(\"global_x\").alias(\"x\"),\n            pl.col(\"global_y\").alias(\"y\"),\n            pl.col(\"gene\").alias(\"name\")\n        ]).select([\"name\", \"x\", \"y\"])\n\n    elif technology == \"Xenium\":\n        trx_ini = pl.read_parquet(path_trx).select([\n            pl.col(\"feature_name\").alias(\"name\"),\n            pl.col(\"x_location\").alias(\"x\"),\n            pl.col(\"y_location\").alias(\"y\")\n        ])\n\n    # Process the data in chunks and apply transformations\n    all_chunks = []\n\n    for start_row in tqdm(range(0, trx_ini.height, chunk_size), desc=\"Processing chunks\"):\n        chunk = trx_ini.slice(start_row, chunk_size)\n\n        # Apply transformation matrix to the coordinates\n        points = np.hstack([chunk.select([\"x\", \"y\"]).to_numpy(), np.ones((chunk.height, 1))])\n        transformed_points = np.dot(points, transformation_matrix.T)[:, :2]\n\n        # Create new transformed columns and drop original x, y columns\n        transformed_chunk = chunk.with_columns([\n            (pl.Series(transformed_points[:, 0]) * image_scale).round(2).alias(\"transformed_x\"),\n            (pl.Series(transformed_points[:, 1]) * image_scale).round(2).alias(\"transformed_y\")\n        ]).drop([\"x\", \"y\"])\n        all_chunks.append(transformed_chunk)\n\n    # Concatenate all chunks after processing\n    trx = pl.concat(all_chunks)\n\n    # Ensure the output directory exists\n    if not os.path.exists(path_trx_tiles):\n        os.makedirs(path_trx_tiles)\n\n    # Get min and max x, y values\n    x_min, x_max = trx.select([\n        pl.col(\"transformed_x\").min().alias(\"x_min\"),\n        pl.col(\"transformed_x\").max().alias(\"x_max\")\n    ]).row(0)\n\n    y_min, y_max = trx.select([\n        pl.col(\"transformed_y\").min().alias(\"y_min\"),\n        pl.col(\"transformed_y\").max().alias(\"y_max\")\n    ]).row(0)\n\n    # Calculate the number of fine-grain tiles globally\n    n_fine_tiles_x = int(np.ceil((x_max - x_min) / tile_size))\n    n_fine_tiles_y = int(np.ceil((y_max - y_min) / tile_size))\n\n    # Calculate the number of coarse-grain tiles\n    n_coarse_tiles_x = int(np.ceil((x_max - x_min) / (coarse_tile_factor * tile_size)))\n    n_coarse_tiles_y = int(np.ceil((y_max - y_min) / (coarse_tile_factor * tile_size)))\n\n    # Use ThreadPoolExecutor for parallel processing of coarse-grain tiles\n    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n        futures = []\n        for i in range(n_coarse_tiles_x):\n            coarse_tile_x_min = x_min + i * (coarse_tile_factor * tile_size)\n            coarse_tile_x_max = coarse_tile_x_min + (coarse_tile_factor * tile_size)\n\n            for j in range(n_coarse_tiles_y):\n                coarse_tile_y_min = y_min + j * (coarse_tile_factor * tile_size)\n                coarse_tile_y_max = coarse_tile_y_min + (coarse_tile_factor * tile_size)\n\n                # Submit each coarse tile for parallel processing\n                futures.append(executor.submit(\n                    process_coarse_tile, trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers\n                ))\n\n        # Wait for all coarse tiles to complete\n        for future in tqdm(concurrent.futures.as_completed(futures), desc=\"Processing coarse tiles\", unit=\"tile\"):\n            future.result()  # Raise exceptions if any occurred during execution\n\n    # Return the tile bounds\n    tile_bounds = {\n        \"x_min\": x_min,\n        \"x_max\": x_max,\n        \"y_min\": y_min,\n        \"y_max\": y_max,\n    }\n\n    return tile_bounds\n</code></pre>"},{"location":"python/api/#celldega.pre.reduce_image_size","title":"<code>reduce_image_size(image_path, scale_image=0.5, path_landscape_files='')</code>","text":""},{"location":"python/api/#celldega.pre.reduce_image_size--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file scale_image : float (default=0.5)     Scale factor for the image resize</p>"},{"location":"python/api/#celldega.pre.reduce_image_size--returns","title":"Returns","text":"<p>new_image_path : str     Path to the resized image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def reduce_image_size(image_path, scale_image=0.5, path_landscape_files=\"\"):\n    \"\"\"\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    scale_image : float (default=0.5)\n        Scale factor for the image resize\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the resized image file\n    \"\"\"\n\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    resized_image = image.resize(scale_image)\n\n    new_image_name = image_path.split(\"/\")[-1].replace(\".tif\", \"_downsize.tif\")\n    new_image_path = f\"{path_landscape_files}/{new_image_name}\"\n    resized_image.write_to_file(new_image_path)\n\n    return new_image_path\n</code></pre>"},{"location":"python/api/#celldega.pre.save_landscape_parameters","title":"<code>save_landscape_parameters(technology, path_landscape_files, image_name='dapi_files', tile_size=1000, image_info={}, image_format='.webp')</code>","text":"<p>Save the landscape parameters to a JSON file.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def save_landscape_parameters(\n    technology, path_landscape_files, image_name=\"dapi_files\", tile_size=1000, image_info={}, image_format='.webp'\n):\n    \"\"\"\n    Save the landscape parameters to a JSON file.\n    \"\"\"\n\n    path_image_pyramid = f\"{path_landscape_files}/pyramid_images/{image_name}\"\n\n    print(path_image_pyramid)\n\n    max_pyramid_zoom = get_max_zoom_level(path_image_pyramid)\n\n    landscape_parameters = {\n        \"technology\": technology,\n        \"max_pyramid_zoom\": max_pyramid_zoom,\n        \"tile_size\": tile_size,\n        \"image_info\": image_info,\n        \"image_format\": image_format\n    }\n\n    path_landscape_parameters = f\"{path_landscape_files}/landscape_parameters.json\"\n\n    with open(path_landscape_parameters, \"w\") as file:\n        json.dump(landscape_parameters, file, indent=4)\n</code></pre>"},{"location":"python/api/#celldega.viz.Landscape","title":"<code>Landscape</code>","text":"<p>               Bases: <code>AnyWidget</code></p> <p>A widget for interactive visualization of spatial omics data. This widget currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)</p> <p>Parameters:</p> Name Type Description Default <code>ini_x</code> <code>float</code> <p>The initial x-coordinate of the view.</p> required <code>ini_y</code> <code>float</code> <p>The initial y-coordinate of the view.</p> required <code>ini_zoom</code> <code>float</code> <p>The initial zoom level of the view.</p> required <code>token</code> <code>str</code> <p>The token traitlet.</p> required <code>base_url</code> <code>str</code> <p>The base URL for the widget.</p> required <code>dataset_name</code> <code>str</code> <p>The name of the dataset to visualize. This will show up in the user interface bar.</p> required <p>Attributes:</p> Name Type Description <code>component</code> <code>str</code> <p>The name of the component.</p> <code>technology</code> <code>str</code> <p>The technology used.</p> <code>base_url</code> <code>str</code> <p>The base URL for the widget.</p> <code>token</code> <code>str</code> <p>The token traitlet.</p> <code>ini_x</code> <code>float</code> <p>The initial x-coordinate of the view.</p> <code>ini_y</code> <code>float</code> <p>The initial y-coordinate of the view.</p> <code>ini_z</code> <code>float</code> <p>The initial z-coordinate of the view.</p> <code>ini_zoom</code> <code>float</code> <p>The initial zoom level of the view.</p> <code>dataset_name</code> <code>str</code> <p>The name of the dataset to visualize.</p> <code>update_trigger</code> <code>dict</code> <p>The dictionary to trigger updates.</p> <code>cell_clusters</code> <code>dict</code> <p>The dictionary containing cell cluster information.</p> <p>Returns:</p> Name Type Description <code>Landscape</code> <p>A widget for visualizing a 'landscape' view of spatial omics data.</p> Source code in <code>src/celldega/viz/widget.py</code> <pre><code>class Landscape(anywidget.AnyWidget):\n    \"\"\"\n    A widget for interactive visualization of spatial omics data. This widget\n    currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)\n\n    Args:\n        ini_x (float): The initial x-coordinate of the view.\n        ini_y (float): The initial y-coordinate of the view.\n        ini_zoom (float): The initial zoom level of the view.\n        token (str): The token traitlet.\n        base_url (str): The base URL for the widget.\n        dataset_name (str, optional): The name of the dataset to visualize. This will show up in the user interface bar.\n\n    Attributes:\n        component (str): The name of the component.\n        technology (str): The technology used.\n        base_url (str): The base URL for the widget.\n        token (str): The token traitlet.\n        ini_x (float): The initial x-coordinate of the view.\n        ini_y (float): The initial y-coordinate of the view.\n        ini_z (float): The initial z-coordinate of the view.\n        ini_zoom (float): The initial zoom level of the view.\n        dataset_name (str): The name of the dataset to visualize.\n        update_trigger (dict): The dictionary to trigger updates.\n        cell_clusters (dict): The dictionary containing cell cluster information.\n\n    Returns:\n        Landscape: A widget for visualizing a 'landscape' view of spatial omics data.\n    \"\"\"\n    _esm = pathlib.Path(__file__).parent / \"../static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"../static\" / \"widget.css\"\n    component = traitlets.Unicode(\"Landscape\").tag(sync=True)\n\n    technology = traitlets.Unicode(\"sst\").tag(sync=True)\n    base_url = traitlets.Unicode(\"\").tag(sync=True)\n    token = traitlets.Unicode(\"\").tag(sync=True)\n    ini_x = traitlets.Float(1000).tag(sync=True)\n    ini_y = traitlets.Float(1000).tag(sync=True)\n    ini_z = traitlets.Float(0).tag(sync=True)\n    ini_zoom = traitlets.Float(0).tag(sync=True)\n    square_tile_size = traitlets.Float(1.4).tag(sync=True)\n    dataset_name = traitlets.Unicode(\"\").tag(sync=True)\n    region = traitlets.Dict({}).tag(sync=True)\n\n    update_trigger = traitlets.Dict().tag(sync=True)\n    cell_clusters = traitlets.Dict().tag(sync=True)\n\n    width = traitlets.Int(0).tag(sync=True)\n    height = traitlets.Int(800).tag(sync=True)\n\n    def trigger_update(self, new_value):\n        # This method updates the update_trigger traitlet with a new value\n        # You can pass any information necessary for the update, or just a timestamp\n        self.update_trigger = new_value\n\n    def update_cell_clusters(self, new_clusters):\n        # Convert the new_clusters to a JSON serializable format if necessary\n        self.cell_clusters = new_clusters\n</code></pre>"},{"location":"python/api/#celldega.viz.Matrix","title":"<code>Matrix</code>","text":"<p>               Bases: <code>AnyWidget</code></p> <p>A widget for interactive visualization of a hierarchically clustered matrix.</p> <p>Parameters:</p> Name Type Description Default <code>value</code> <code>int</code> <p>The value traitlet.</p> required <code>component</code> <code>str</code> <p>The component traitlet.</p> required <code>network</code> <code>dict</code> <p>The network traitlet.</p> required <code>click_info</code> <code>dict</code> <p>The click_info traitlet.</p> required <p>Attributes:</p> Name Type Description <code>component</code> <code>str</code> <p>The name of the component.</p> <code>network</code> <code>dict</code> <p>The network dictionary.</p> <code>click_info</code> <code>dict</code> <p>The click_info dictionary.</p> <p>Returns:</p> Name Type Description <code>Matrix</code> <p>A widget for visualizing a hierarchically clustered matrix.</p> Source code in <code>src/celldega/viz/widget.py</code> <pre><code>class Matrix(anywidget.AnyWidget):\n    \"\"\"\n    A widget for interactive visualization of a hierarchically clustered matrix.\n\n    Args:\n        value (int): The value traitlet.\n        component (str): The component traitlet.\n        network (dict): The network traitlet.\n        click_info (dict): The click_info traitlet.\n\n    Attributes:\n        component (str): The name of the component.\n        network (dict): The network dictionary.\n        click_info (dict): The click_info dictionary.\n\n    Returns:\n        Matrix: A widget for visualizing a hierarchically clustered matrix.\n    \"\"\"\n    _esm = pathlib.Path(__file__).parent / \"../static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"../static\" / \"widget.css\"\n    value = traitlets.Int(0).tag(sync=True)\n    component = traitlets.Unicode(\"Matrix\").tag(sync=True)\n\n    network = traitlets.Dict({}).tag(sync=True)\n    click_info = traitlets.Dict({}).tag(sync=True)\n</code></pre>"},{"location":"python/pre/api/","title":"Pre Module API Reference","text":"<p>Module for pre-processing to generate LandscapeFiles from ST data.</p>"},{"location":"python/pre/api/#celldega.pre.convert_long_id_to_short","title":"<code>convert_long_id_to_short(df)</code>","text":"<p>Converts a column of long integer cell IDs in a DataFrame to a shorter, hash-based representation.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>The DataFrame containing the EntityID.</p> required <p>Returns:     pd.DataFrame: The original DataFrame with an additional column named <code>cell_id</code>                   containing the shortened cell IDs.</p> <p>The function applies a SHA-256 hash to each cell ID, encodes the hash using base64, and truncates it to create a shorter identifier that is added as a new column to the DataFrame.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_long_id_to_short(df):\n    \"\"\"\n    Converts a column of long integer cell IDs in a DataFrame to a shorter, hash-based representation.\n\n    Args:\n        df (pd.DataFrame): The DataFrame containing the EntityID.\n    Returns:\n        pd.DataFrame: The original DataFrame with an additional column named `cell_id`\n                      containing the shortened cell IDs.\n\n    The function applies a SHA-256 hash to each cell ID, encodes the hash using base64, and truncates\n    it to create a shorter identifier that is added as a new column to the DataFrame.\n    \"\"\"\n    # Function to hash and encode the cell ID\n    def hash_and_shorten_id(cell_id):\n        # Create a hash of the cell ID\n        cell_id_bytes = str(cell_id).encode('utf-8')\n        hash_object = hashlib.sha256(cell_id_bytes)\n        hash_digest = hash_object.digest()\n\n        # Encode the hash to a base64 string to mix letters and numbers, truncate to 9 characters\n        short_id = base64.urlsafe_b64encode(hash_digest).decode('utf-8')[:9]\n        return short_id\n\n    # Apply the hash_and_shorten_id function to each cell ID in the specified column\n    df['cell_id'] = df['EntityID'].apply(hash_and_shorten_id)\n\n    return df\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.convert_to_jpeg","title":"<code>convert_to_jpeg(image_path, quality=80)</code>","text":"<p>Convert a TIFF image to a JPEG image with a quality of score</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_jpeg--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=80)     Quality score for the JPEG image</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_jpeg--returns","title":"Returns","text":"<p>new_image_path : str     Path to the JPEG image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_jpeg(image_path, quality=80):\n    \"\"\"\n    Convert a TIFF image to a JPEG image with a quality of score\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=80)\n        Quality score for the JPEG image\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the JPEG image file\n\n    \"\"\"\n\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a JPEG with a quality of 80\n    new_image_path = image_path.replace(\".tif\", \".jpeg\")\n    image.jpegsave(new_image_path, Q=quality)\n\n    return new_image_path\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.convert_to_png","title":"<code>convert_to_png(image_path)</code>","text":"<p>Convert a TIFF image to a JPEG image with a quality of score</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_png--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=80)     Quality score for the JPEG image</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_png--returns","title":"Returns","text":"<p>new_image_path : str     Path to the JPEG image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_png(image_path):\n    \"\"\"\n    Convert a TIFF image to a JPEG image with a quality of score\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=80)\n        Quality score for the JPEG image\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the JPEG image file\n\n    \"\"\"\n\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a JPEG with a quality of 80\n    new_image_path = image_path.replace(\".tif\", \".png\")\n    image.pngsave(new_image_path)\n\n    return new_image_path\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.convert_to_webp","title":"<code>convert_to_webp(image_path, quality=100)</code>","text":"<p>Convert a TIFF image to a WEBP image with a specified quality score.</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_webp--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=100)     Quality score for the WEBP image (higher is better quality)</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_webp--returns","title":"Returns","text":"<p>new_image_path : str     Path to the WEBP image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_webp(image_path, quality=100):\n    \"\"\"\n    Convert a TIFF image to a WEBP image with a specified quality score.\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=100)\n        Quality score for the WEBP image (higher is better quality)\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the WEBP image file\n    \"\"\"\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a WEBP with specified quality\n    new_image_path = image_path.replace(\".tif\", \".webp\")\n    image.webpsave(new_image_path, Q=quality)\n\n    return new_image_path\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.get_max_zoom_level","title":"<code>get_max_zoom_level(path_image_pyramid)</code>","text":"<p>Returns the maximum zoom level based on the highest-numbered directory in the specified path_image_pyramid.</p> <p>Parameters:</p> Name Type Description Default <code>path_image_pyramid</code> <code>str</code> <p>The path to the directory containing zoom level directories.</p> required <p>Returns:</p> Name Type Description <code>max_pyramid_zoom</code> <code>int</code> <p>The maximum zoom level.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def get_max_zoom_level(path_image_pyramid):\n    \"\"\"\n    Returns the maximum zoom level based on the highest-numbered directory\n    in the specified path_image_pyramid.\n\n    Parameters:\n        path_image_pyramid (str): The path to the directory containing zoom level directories.\n\n    Returns:\n        max_pyramid_zoom (int): The maximum zoom level.\n    \"\"\"\n    # List all entries in the path_image_pyramid that are directories and can be converted to integers\n    zoom_levels = [\n        entry\n        for entry in os.listdir(path_image_pyramid)\n        if os.path.isdir(os.path.join(path_image_pyramid, entry)) and entry.isdigit()\n    ]\n\n    # Convert to integer and find the maximum value\n    max_pyramid_zoom = max(map(int, zoom_levels)) if zoom_levels else None\n\n    return max_pyramid_zoom\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_cell_boundary_tiles","title":"<code>make_cell_boundary_tiles(technology, path_cell_boundaries, path_meta_cell_micron, path_transformation_matrix, path_output, coarse_tile_factor=20, tile_size=250, tile_bounds=None, image_scale=1, max_workers=8)</code>","text":"<p>Processes cell boundary data and divides it into spatial tiles based on the provided technology. Reads cell boundary data, applies affine transformations, and divides the data into coarse and fine tiles. The resulting tiles are saved as Parquet files, each containing the geometries of cells in that tile.</p>"},{"location":"python/pre/api/#celldega.pre.make_cell_boundary_tiles--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the cell boundary data, e.g., \"MERSCOPE\", \"Xenium\", or \"custom\". path_cell_boundaries : str     Path to the file containing the cell boundaries (Parquet format). path_meta_cell_micron : str     Path to the file containing cell metadata (CSV format). path_transformation_matrix : str     Path to the file containing the transformation matrix (CSV format). path_output : str     Directory path where the output files (Parquet files) for each tile will be saved. coarse_tile_factor  : int, optional, default=20.     scaling factor of each coarse-grain tile comparing to the fine tile size. tile_size : int, optional, default=500     Size of each fine-grain tile in microns. tile_bounds : dict, optional     Dictionary containing the minimum and maximum bounds for x and y coordinates. image_scale : float, optional, default=1     Scale factor to apply to the geometry data. max_workers : int, optional, default=8     Maximum number of parallel workers for processing tiles.</p>"},{"location":"python/pre/api/#celldega.pre.make_cell_boundary_tiles--returns","title":"Returns","text":"<p>None</p> Source code in <code>src/celldega/pre/boundary_tile.py</code> <pre><code>def make_cell_boundary_tiles(\n    technology,\n    path_cell_boundaries,\n    path_meta_cell_micron,\n    path_transformation_matrix,\n    path_output,\n    coarse_tile_factor=20,\n    tile_size=250,\n    tile_bounds=None,\n    image_scale=1,\n    max_workers=8\n):\n\n\n    \"\"\"\n    Processes cell boundary data and divides it into spatial tiles based on the provided technology.\n    Reads cell boundary data, applies affine transformations, and divides the data into coarse and fine tiles.\n    The resulting tiles are saved as Parquet files, each containing the geometries of cells in that tile.\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the cell boundary data, e.g., \"MERSCOPE\", \"Xenium\", or \"custom\".\n    path_cell_boundaries : str\n        Path to the file containing the cell boundaries (Parquet format).\n    path_meta_cell_micron : str\n        Path to the file containing cell metadata (CSV format).\n    path_transformation_matrix : str\n        Path to the file containing the transformation matrix (CSV format).\n    path_output : str\n        Directory path where the output files (Parquet files) for each tile will be saved.\n    coarse_tile_factor  : int, optional, default=20.\n        scaling factor of each coarse-grain tile comparing to the fine tile size.\n    tile_size : int, optional, default=500\n        Size of each fine-grain tile in microns.\n    tile_bounds : dict, optional\n        Dictionary containing the minimum and maximum bounds for x and y coordinates.\n    image_scale : float, optional, default=1\n        Scale factor to apply to the geometry data.\n    max_workers : int, optional, default=8\n        Maximum number of parallel workers for processing tiles.\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    def numpy_affine_transform(coords, matrix):\n        \"\"\"Apply affine transformation to numpy coordinates.\"\"\"\n        # Homogeneous coordinates for affine transformation\n        coords = np.hstack([coords, np.ones((coords.shape[0], 1))])\n        transformed_coords = coords @ matrix.T\n        return transformed_coords[:, :2]  # Drop the homogeneous coordinate\n\n    def batch_transform_geometries(geometries, transformation_matrix, scale):\n        \"\"\"\n        Batch transform geometries using numpy for optimized performance.\n        \"\"\"\n        # Extract affine transformation parameters into a 3x3 matrix for numpy\n        affine_matrix = np.array([\n            [transformation_matrix[0, 0], transformation_matrix[0, 1], transformation_matrix[0, 2]],\n            [transformation_matrix[1, 0], transformation_matrix[1, 1], transformation_matrix[1, 2]],\n            [0, 0, 1]\n        ])\n\n        transformed_geometries = []\n\n        for polygon in geometries:\n            # Extract coordinates and transform them\n            if isinstance(polygon, MultiPolygon):\n                polygon = next(polygon.geoms)  # Use the first geometry\n\n            # Transform the exterior of the polygon\n            exterior_coords = np.array(polygon.exterior.coords)\n\n            # Apply the affine transformation and scale\n            transformed_coords = numpy_affine_transform(exterior_coords, affine_matrix) / scale\n\n            # Append the result to the transformed_geometries list\n            transformed_geometries.append([transformed_coords.tolist()])\n\n        return transformed_geometries\n\n\n    def filter_and_save_fine_boundary(coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output):\n        cell_ids = coarse_tile.index.values\n\n        tile_filter = (\n            (coarse_tile[\"center_x\"] &gt;= fine_tile_x_min) &amp; (coarse_tile[\"center_x\"] &lt; fine_tile_x_max) &amp;\n            (coarse_tile[\"center_y\"] &gt;= fine_tile_y_min) &amp; (coarse_tile[\"center_y\"] &lt; fine_tile_y_max)\n        )\n        filtered_indices = np.where(tile_filter)[0]\n\n        keep_cells = cell_ids[filtered_indices]\n        fine_tile_cells = coarse_tile.loc[keep_cells, [\"GEOMETRY\"]]\n        fine_tile_cells = fine_tile_cells.assign(name=fine_tile_cells.index)\n\n        if not fine_tile_cells.empty:\n            filename = f\"{path_output}/cell_tile_{fine_i}_{fine_j}.parquet\"\n            fine_tile_cells.to_parquet(filename)\n\n    def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y):\n        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n            futures = []\n            for fine_i in range(n_fine_tiles_x):\n                fine_tile_x_min = x_min + fine_i * tile_size\n                fine_tile_x_max = fine_tile_x_min + tile_size\n\n                if not (fine_tile_x_min &gt;= coarse_tile_x_min and fine_tile_x_max &lt;= coarse_tile_x_max):\n                    continue\n\n                for fine_j in range(n_fine_tiles_y):\n                    fine_tile_y_min = y_min + fine_j * tile_size\n                    fine_tile_y_max = fine_tile_y_min + tile_size\n\n                    if not (fine_tile_y_min &gt;= coarse_tile_y_min and fine_tile_y_max &lt;= coarse_tile_y_max):\n                        continue\n\n                    futures.append(executor.submit(\n                        filter_and_save_fine_boundary, coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output\n                    ))\n\n            for future in futures:\n                future.result()\n\n    tile_size_x = tile_size\n    tile_size_y = tile_size\n\n    transformation_matrix = pd.read_csv(path_transformation_matrix, header=None, sep=\" \").values\n\n    # Load cell boundary data based on the technology\n    if technology == \"MERSCOPE\":\n        df_meta = pd.read_parquet(f\"{path_output.replace('cell_segmentation','cell_metadata.parquet')}\")\n        entity_to_cell_id_dict = pd.Series(df_meta.index.values, index=df_meta.EntityID).to_dict()\n        cells_orig = gpd.read_parquet(path_cell_boundaries)\n        cells_orig['cell_id'] = cells_orig['EntityID'].map(entity_to_cell_id_dict)\n        cells_orig = cells_orig[cells_orig[\"ZIndex\"] == 1]\n\n        # Correct cell_id issues with meta_cell\n        meta_cell = pd.read_csv(path_meta_cell_micron)\n        meta_cell['cell_id'] = meta_cell['EntityID'].map(entity_to_cell_id_dict)\n        cells_orig.index = meta_cell[meta_cell[\"cell_id\"].isin(cells_orig['cell_id'])].index\n\n        # Correct 'MultiPolygon' to 'Polygon'\n        cells_orig[\"geometry\"] = cells_orig[\"Geometry\"].apply(\n            lambda x: list(x.geoms)[0] if isinstance(x, MultiPolygon) else x\n        )\n\n        cells_orig.set_index('cell_id', inplace=True)\n\n    elif technology == \"Xenium\":\n        xenium_cells = pd.read_parquet(path_cell_boundaries)\n        grouped = xenium_cells.groupby(\"cell_id\")[[\"vertex_x\", \"vertex_y\"]].agg(lambda x: x.tolist())\n        grouped[\"geometry\"] = grouped.apply(lambda row: Polygon(zip(row[\"vertex_x\"], row[\"vertex_y\"])), axis=1)\n        cells_orig = gpd.GeoDataFrame(grouped, geometry=\"geometry\")[[\"geometry\"]]\n\n    elif technology == \"custom\":\n        cells_orig = gpd.read_parquet(path_cell_boundaries)\n\n    # Transform geometries\n    cells_orig[\"GEOMETRY\"] = batch_transform_geometries(cells_orig[\"geometry\"], transformation_matrix, image_scale)\n\n    # Convert transformed geometries to polygons and calculate centroids\n    cells_orig[\"polygon\"] = cells_orig[\"GEOMETRY\"].apply(lambda x: Polygon(x[0]))\n    gdf_cells = gpd.GeoDataFrame(geometry=cells_orig[\"polygon\"])\n    gdf_cells[\"center_x\"] = gdf_cells.geometry.centroid.x\n    gdf_cells[\"center_y\"] = gdf_cells.geometry.centroid.y\n    gdf_cells[\"GEOMETRY\"] = cells_orig[\"GEOMETRY\"]\n\n    # Ensure the output directory exists\n    if not os.path.exists(path_output):\n        os.makedirs(path_output)\n\n    # Calculate tile bounds and fine/coarse tiles\n    x_min, x_max = tile_bounds[\"x_min\"], tile_bounds[\"x_max\"]\n    y_min, y_max = tile_bounds[\"y_min\"], tile_bounds[\"y_max\"]\n    n_fine_tiles_x = int(np.ceil((x_max - x_min) / tile_size))\n    n_fine_tiles_y = int(np.ceil((y_max - y_min) / tile_size))\n    n_coarse_tiles_x = int(np.ceil((x_max - x_min) / (coarse_tile_factor * tile_size)))\n    n_coarse_tiles_y = int(np.ceil((y_max - y_min) / (coarse_tile_factor * tile_size)))\n\n    # Process coarse tiles in parallel\n    for i in tqdm(range(n_coarse_tiles_x), desc=\"Processing coarse tiles\"):\n        coarse_tile_x_min = x_min + i * (coarse_tile_factor * tile_size)\n        coarse_tile_x_max = coarse_tile_x_min + (coarse_tile_factor * tile_size)\n\n        for j in range(n_coarse_tiles_y):\n            coarse_tile_y_min = y_min + j * (coarse_tile_factor * tile_size)\n            coarse_tile_y_max = coarse_tile_y_min + (coarse_tile_factor * tile_size)\n\n            coarse_tile = gdf_cells[\n                (gdf_cells[\"center_x\"] &gt;= coarse_tile_x_min) &amp; (gdf_cells[\"center_x\"] &lt; coarse_tile_x_max) &amp;\n                (gdf_cells[\"center_y\"] &gt;= coarse_tile_y_min) &amp; (gdf_cells[\"center_y\"] &lt; coarse_tile_y_max)\n            ]\n            if not coarse_tile.empty:\n                process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y)\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_deepzoom_pyramid","title":"<code>make_deepzoom_pyramid(image_path, output_path, pyramid_name, tile_size=512, overlap=0, suffix='.jpeg')</code>","text":"<p>Create a DeepZoom image pyramid from a JPEG image</p>"},{"location":"python/pre/api/#celldega.pre.make_deepzoom_pyramid--parameters","title":"Parameters","text":"<p>image_path : str     Path to the JPEG image file tile_size : int (default=512)     Tile size for the DeepZoom pyramid overlap : int (default=0)     Overlap size for the DeepZoom pyramid suffix : str (default='jpeg')     Suffix for the DeepZoom pyramid tiles</p>"},{"location":"python/pre/api/#celldega.pre.make_deepzoom_pyramid--returns","title":"Returns","text":"<p>None</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_deepzoom_pyramid(\n    image_path, output_path, pyramid_name, tile_size=512, overlap=0, suffix=\".jpeg\"\n):\n    \"\"\"\n    Create a DeepZoom image pyramid from a JPEG image\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the JPEG image file\n    tile_size : int (default=512)\n        Tile size for the DeepZoom pyramid\n    overlap : int (default=0)\n        Overlap size for the DeepZoom pyramid\n    suffix : str (default='jpeg')\n        Suffix for the DeepZoom pyramid tiles\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n\n    # Define the output path\n    output_path = Path(output_path)\n\n    # Load the JPEG image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # check if the output path exists and create it if it does not\n    output_path.mkdir(parents=True, exist_ok=True)\n\n    # append the pyramid name to the output path\n    output_path = output_path / pyramid_name\n\n    # Save the image as a DeepZoom image pyramid\n    image.dzsave(output_path, tile_size=tile_size, overlap=overlap, suffix=suffix)\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_meta_cell_image_coord","title":"<code>make_meta_cell_image_coord(technology, path_transformation_matrix, path_meta_cell_micron, path_meta_cell_image, image_scale)</code>","text":"<p>Apply an affine transformation to the cell coordinates in microns and save the transformed coordinates in pixels</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_cell_image_coord--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the data, Xenium and MERSCOPE are supported. path_transformation_matrix : str     Path to the transformation matrix file path_meta_cell_micron : str     Path to the meta cell file with coordinates in microns path_meta_cell_image : str     Path to save the meta cell file with coordinates in pixels</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_cell_image_coord--returns","title":"Returns","text":"<p>None</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_cell_image_coord--examples","title":"Examples","text":"<p>make_meta_cell_image_coord( ...     technology='Xenium', ...     path_transformation_matrix='data/transformation_matrix.txt', ...     path_meta_cell_micron='data/meta_cell_micron.csv', ...     path_meta_cell_image='data/meta_cell_image.parquet' ... )</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_meta_cell_image_coord(\n    technology,\n    path_transformation_matrix,\n    path_meta_cell_micron,\n    path_meta_cell_image,\n    image_scale\n):\n    \"\"\"\n    Apply an affine transformation to the cell coordinates in microns and save\n    the transformed coordinates in pixels\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the data, Xenium and MERSCOPE are supported.\n    path_transformation_matrix : str\n        Path to the transformation matrix file\n    path_meta_cell_micron : str\n        Path to the meta cell file with coordinates in microns\n    path_meta_cell_image : str\n        Path to save the meta cell file with coordinates in pixels\n\n    Returns\n    -------\n    None\n\n    Examples\n    --------\n    &gt;&gt;&gt; make_meta_cell_image_coord(\n    ...     technology='Xenium',\n    ...     path_transformation_matrix='data/transformation_matrix.txt',\n    ...     path_meta_cell_micron='data/meta_cell_micron.csv',\n    ...     path_meta_cell_image='data/meta_cell_image.parquet'\n    ... )\n\n    \"\"\"\n\n    transformation_matrix = pd.read_csv(\n        path_transformation_matrix, header=None, sep=\" \"\n    ).values\n\n    if technology == \"MERSCOPE\":\n        meta_cell = pd.read_csv(path_meta_cell_micron, usecols=[\"EntityID\", \"center_x\", \"center_y\"])\n        meta_cell = convert_long_id_to_short(meta_cell)\n        meta_cell[\"name\"] =  meta_cell[\"cell_id\"]\n        meta_cell = meta_cell.set_index('cell_id')\n    elif technology == \"Xenium\":\n        usecols = [\"cell_id\", \"x_centroid\", \"y_centroid\"]\n        meta_cell = pd.read_csv(path_meta_cell_micron, index_col=0, usecols=usecols)\n        meta_cell.columns = [\"center_x\", \"center_y\"]\n        meta_cell[\"name\"] = pd.Series(meta_cell.index, index=meta_cell.index)\n\n    # Adding a ones column to accommodate for affine transformation\n    meta_cell[\"ones\"] = 1\n\n    # Preparing the data for matrix multiplication\n    points = meta_cell[[\"center_x\", \"center_y\", \"ones\"]].values\n\n    # Applying the transformation matrix\n    transformed_points = np.dot(transformation_matrix, points.T).T\n\n    # Updating the DataFrame with transformed coordinates\n    meta_cell[\"center_x\"] = transformed_points[:, 0]\n    meta_cell[\"center_y\"] = transformed_points[:, 1]\n\n    # Dropping the ones column as it's no longer needed\n    meta_cell.drop(columns=[\"ones\"], inplace=True)\n\n    meta_cell[\"center_x\"] = meta_cell[\"center_x\"] / image_scale\n    meta_cell[\"center_y\"] = meta_cell[\"center_y\"] / image_scale\n\n    meta_cell[\"geometry\"] = meta_cell.apply(\n        lambda row: [row[\"center_x\"], row[\"center_y\"]], axis=1\n    )\n\n    if technology == \"MERSCOPE\":\n        meta_cell = meta_cell[[\"name\", \"geometry\", \"EntityID\"]]\n    else:\n        meta_cell = meta_cell[[\"name\", \"geometry\"]]\n\n\n    meta_cell.to_parquet(path_meta_cell_image)\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_meta_gene","title":"<code>make_meta_gene(technology, path_cbg, path_output)</code>","text":"<p>Create a DataFrame with genes and their assigned colors</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_gene--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the data, Xenium and MERSCOPE are supported. path_cbg : str     Path to the cell-by-gene matrix data (the data format can vary based on technology) path_output : str     Path to save the meta gene file</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_gene--returns","title":"Returns","text":"<p>None</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_gene--examples","title":"Examples","text":"<p>make_meta_gene( ...     technology='Xenium', ...     path_cbg='data/', ...     path_output='data/meta_gene.parquet' ... )</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_meta_gene(technology, path_cbg, path_output):\n    \"\"\"\n    Create a DataFrame with genes and their assigned colors\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the data, Xenium and MERSCOPE are supported.\n    path_cbg : str\n        Path to the cell-by-gene matrix data (the data format can vary based on technology)\n    path_output : str\n        Path to save the meta gene file\n\n    Returns\n    -------\n    None\n\n    Examples\n    --------\n    &gt;&gt;&gt; make_meta_gene(\n    ...     technology='Xenium',\n    ...     path_cbg='data/',\n    ...     path_output='data/meta_gene.parquet'\n    ... )\n    \"\"\"\n\n    if technology == \"MERSCOPE\":\n        cbg = pd.read_csv(path_cbg, index_col=0)\n        genes = cbg.columns.tolist()\n    elif technology == \"Xenium\":\n        # genes = pd.read_csv(path_cbg + 'features.tsv.gz', sep='\\t', header=None)[1].values.tolist()\n        cbg = read_cbg_mtx(path_cbg)\n        genes = cbg.columns.tolist()\n\n    # Get all categorical color palettes from Matplotlib and flatten them into a single list of colors\n    palettes = [plt.get_cmap(name).colors for name in plt.colormaps() if \"tab\" in name]\n    flat_colors = [color for palette in palettes for color in palette]\n\n    # Convert RGB tuples to hex codes\n    flat_colors_hex = [to_hex(color) for color in flat_colors]\n\n    # Use modular arithmetic to assign a color to each gene, white for genes with \"Blank\"\n    colors = [\n        flat_colors_hex[i % len(flat_colors_hex)] if \"Blank\" not in gene else \"#FFFFFF\"\n        for i, gene in enumerate(genes)\n    ]\n\n    # Create a DataFrame with genes and their assigned colors\n    ser_color = pd.Series(colors, index=genes)\n\n    # calculate gene expression metadata\n    meta_gene = calc_meta_gene_data(cbg)\n    meta_gene['color'] = ser_color\n\n    # Identify sparse columns\n    sparse_cols = [col for col in meta_gene.columns if pd.api.types.is_sparse(meta_gene[col])]\n\n    # Convert sparse columns to dense\n    for col in sparse_cols:\n        meta_gene[col] = meta_gene[col].sparse.to_dense()\n\n    meta_gene.to_parquet(path_output)\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_trx_tiles","title":"<code>make_trx_tiles(technology, path_trx, path_transformation_matrix, path_trx_tiles, coarse_tile_factor=10, tile_size=250, chunk_size=1000000, verbose=False, image_scale=1, max_workers=8)</code>","text":"<p>Processes transcript data by dividing it into coarse-grain and fine-grain tiles, applying transformations, and saving the results in a parallelized manner.</p>"},{"location":"python/pre/api/#celldega.pre.make_trx_tiles--parameters","title":"Parameters","text":"<p>technology : str     The technology used for generating the transcript data (e.g., \"MERSCOPE\" or \"Xenium\"). path_trx : str     Path to the file containing the transcript data. path_transformation_matrix : str     Path to the file containing the transformation matrix (CSV file). path_trx_tiles : str     Directory path where the output files (Parquet files) for each tile will be saved. coarse_tile_factor : int, optional     Scaling factor of each coarse-grain tile comparing to the fine tile size. tile_size : int, optional     Size of each fine-grain tile in microns (default is 250). chunk_size : int, optional     Number of rows to process per chunk for memory efficiency (default is 1000000). verbose : bool, optional     Flag to enable verbose output (default is False). image_scale : float, optional     Scale factor to apply to the transcript coordinates (default is 0.5). max_workers : int, optional     Maximum number of parallel workers for processing tiles (default is 8).</p>"},{"location":"python/pre/api/#celldega.pre.make_trx_tiles--returns","title":"Returns","text":"<p>dict     A dictionary containing the bounds of the processed data in both x and y directions.</p> Source code in <code>src/celldega/pre/trx_tile.py</code> <pre><code>def make_trx_tiles(\n    technology,\n    path_trx,\n    path_transformation_matrix,\n    path_trx_tiles,\n    coarse_tile_factor=10,\n    tile_size=250,\n    chunk_size=1000000,\n    verbose=False,\n    image_scale=1,\n    max_workers=8\n):\n    \"\"\"\n    Processes transcript data by dividing it into coarse-grain and fine-grain tiles,\n    applying transformations, and saving the results in a parallelized manner.\n\n    Parameters\n    ----------\n    technology : str\n        The technology used for generating the transcript data (e.g., \"MERSCOPE\" or \"Xenium\").\n    path_trx : str\n        Path to the file containing the transcript data.\n    path_transformation_matrix : str\n        Path to the file containing the transformation matrix (CSV file).\n    path_trx_tiles : str\n        Directory path where the output files (Parquet files) for each tile will be saved.\n    coarse_tile_factor : int, optional\n        Scaling factor of each coarse-grain tile comparing to the fine tile size.\n    tile_size : int, optional\n        Size of each fine-grain tile in microns (default is 250).\n    chunk_size : int, optional\n        Number of rows to process per chunk for memory efficiency (default is 1000000).\n    verbose : bool, optional\n        Flag to enable verbose output (default is False).\n    image_scale : float, optional\n        Scale factor to apply to the transcript coordinates (default is 0.5).\n    max_workers : int, optional\n        Maximum number of parallel workers for processing tiles (default is 8).\n\n    Returns\n    -------\n    dict\n        A dictionary containing the bounds of the processed data in both x and y directions.\n    \"\"\"\n\n    def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers):\n        # Filter the entire dataset for the current coarse tile\n        coarse_tile = trx.filter(\n            (pl.col(\"transformed_x\") &gt;= coarse_tile_x_min) &amp; (pl.col(\"transformed_x\") &lt; coarse_tile_x_max) &amp;\n            (pl.col(\"transformed_y\") &gt;= coarse_tile_y_min) &amp; (pl.col(\"transformed_y\") &lt; coarse_tile_y_max)\n        )\n\n        if not coarse_tile.is_empty():\n            # Now process fine tiles using global fine tile indices\n            process_fine_tiles(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers)   \n\n\n    def process_fine_tiles(coarse_tile, coarse_i, coarse_j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=8):\n\n        # Use ThreadPoolExecutor for parallel processing of fine-grain tiles within the coarse tile\n        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n            futures = []\n\n            # Iterate over fine-grain tiles within the global bounds\n            for fine_i in range(n_fine_tiles_x):\n                fine_tile_x_min = x_min + fine_i * tile_size\n                fine_tile_x_max = fine_tile_x_min + tile_size\n\n                # Process only if the fine tile falls within the current coarse tile's bounds\n                if not (fine_tile_x_min &gt;= coarse_tile_x_min and fine_tile_x_max &lt;= coarse_tile_x_max):\n                    continue\n\n                for fine_j in range(n_fine_tiles_y):\n                    fine_tile_y_min = y_min + fine_j * tile_size\n                    fine_tile_y_max = fine_tile_y_min + tile_size\n\n                    # Process only if the fine tile falls within the current coarse tile's bounds\n                    if not (fine_tile_y_min &gt;= coarse_tile_y_min and fine_tile_y_max &lt;= coarse_tile_y_max):\n                        continue\n\n                    # Submit the task for each fine tile to process in parallel\n                    futures.append(executor.submit(\n                        filter_and_save_fine_tile, coarse_tile, coarse_i, coarse_j, fine_i, fine_j, \n                        fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles\n                    ))\n\n            # Wait for all futures to complete\n            for future in concurrent.futures.as_completed(futures):\n                future.result()  # Raise exceptions if any occurred during execution\n\n\n    def filter_and_save_fine_tile(coarse_tile, coarse_i, coarse_j, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles):\n\n        # Filter the coarse tile for the current fine tile's boundaries\n        fine_tile_trx = coarse_tile.filter(\n            (pl.col(\"transformed_x\") &gt;= fine_tile_x_min) &amp; (pl.col(\"transformed_x\") &lt; fine_tile_x_max) &amp;\n            (pl.col(\"transformed_y\") &gt;= fine_tile_y_min) &amp; (pl.col(\"transformed_y\") &lt; fine_tile_y_max)\n        )\n\n        if not fine_tile_trx.is_empty():\n            # Add geometry column as a list of [x, y] pairs\n            fine_tile_trx = fine_tile_trx.with_columns(\n                pl.concat_list([pl.col(\"transformed_x\"), pl.col(\"transformed_y\")]).alias(\"geometry\")\n            ).drop(['transformed_x', 'transformed_y'])\n\n            # Define the filename based on fine tile coordinates\n            filename = f\"{path_trx_tiles}/transcripts_tile_{fine_i}_{fine_j}.parquet\"\n\n            # Save the filtered DataFrame to a Parquet file\n            fine_tile_trx.to_pandas().to_parquet(filename)\n\n\n    # Load transformation matrix\n    transformation_matrix = np.loadtxt(path_transformation_matrix)\n\n    # Load the transcript data based on the technology using Polars\n    if technology == \"MERSCOPE\":\n        trx_ini = pl.read_csv(path_trx, columns=[\"gene\", \"global_x\", \"global_y\"])\n        trx_ini = trx_ini.with_columns([\n            pl.col(\"global_x\").alias(\"x\"),\n            pl.col(\"global_y\").alias(\"y\"),\n            pl.col(\"gene\").alias(\"name\")\n        ]).select([\"name\", \"x\", \"y\"])\n\n    elif technology == \"Xenium\":\n        trx_ini = pl.read_parquet(path_trx).select([\n            pl.col(\"feature_name\").alias(\"name\"),\n            pl.col(\"x_location\").alias(\"x\"),\n            pl.col(\"y_location\").alias(\"y\")\n        ])\n\n    # Process the data in chunks and apply transformations\n    all_chunks = []\n\n    for start_row in tqdm(range(0, trx_ini.height, chunk_size), desc=\"Processing chunks\"):\n        chunk = trx_ini.slice(start_row, chunk_size)\n\n        # Apply transformation matrix to the coordinates\n        points = np.hstack([chunk.select([\"x\", \"y\"]).to_numpy(), np.ones((chunk.height, 1))])\n        transformed_points = np.dot(points, transformation_matrix.T)[:, :2]\n\n        # Create new transformed columns and drop original x, y columns\n        transformed_chunk = chunk.with_columns([\n            (pl.Series(transformed_points[:, 0]) * image_scale).round(2).alias(\"transformed_x\"),\n            (pl.Series(transformed_points[:, 1]) * image_scale).round(2).alias(\"transformed_y\")\n        ]).drop([\"x\", \"y\"])\n        all_chunks.append(transformed_chunk)\n\n    # Concatenate all chunks after processing\n    trx = pl.concat(all_chunks)\n\n    # Ensure the output directory exists\n    if not os.path.exists(path_trx_tiles):\n        os.makedirs(path_trx_tiles)\n\n    # Get min and max x, y values\n    x_min, x_max = trx.select([\n        pl.col(\"transformed_x\").min().alias(\"x_min\"),\n        pl.col(\"transformed_x\").max().alias(\"x_max\")\n    ]).row(0)\n\n    y_min, y_max = trx.select([\n        pl.col(\"transformed_y\").min().alias(\"y_min\"),\n        pl.col(\"transformed_y\").max().alias(\"y_max\")\n    ]).row(0)\n\n    # Calculate the number of fine-grain tiles globally\n    n_fine_tiles_x = int(np.ceil((x_max - x_min) / tile_size))\n    n_fine_tiles_y = int(np.ceil((y_max - y_min) / tile_size))\n\n    # Calculate the number of coarse-grain tiles\n    n_coarse_tiles_x = int(np.ceil((x_max - x_min) / (coarse_tile_factor * tile_size)))\n    n_coarse_tiles_y = int(np.ceil((y_max - y_min) / (coarse_tile_factor * tile_size)))\n\n    # Use ThreadPoolExecutor for parallel processing of coarse-grain tiles\n    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n        futures = []\n        for i in range(n_coarse_tiles_x):\n            coarse_tile_x_min = x_min + i * (coarse_tile_factor * tile_size)\n            coarse_tile_x_max = coarse_tile_x_min + (coarse_tile_factor * tile_size)\n\n            for j in range(n_coarse_tiles_y):\n                coarse_tile_y_min = y_min + j * (coarse_tile_factor * tile_size)\n                coarse_tile_y_max = coarse_tile_y_min + (coarse_tile_factor * tile_size)\n\n                # Submit each coarse tile for parallel processing\n                futures.append(executor.submit(\n                    process_coarse_tile, trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers\n                ))\n\n        # Wait for all coarse tiles to complete\n        for future in tqdm(concurrent.futures.as_completed(futures), desc=\"Processing coarse tiles\", unit=\"tile\"):\n            future.result()  # Raise exceptions if any occurred during execution\n\n    # Return the tile bounds\n    tile_bounds = {\n        \"x_min\": x_min,\n        \"x_max\": x_max,\n        \"y_min\": y_min,\n        \"y_max\": y_max,\n    }\n\n    return tile_bounds\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.reduce_image_size","title":"<code>reduce_image_size(image_path, scale_image=0.5, path_landscape_files='')</code>","text":""},{"location":"python/pre/api/#celldega.pre.reduce_image_size--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file scale_image : float (default=0.5)     Scale factor for the image resize</p>"},{"location":"python/pre/api/#celldega.pre.reduce_image_size--returns","title":"Returns","text":"<p>new_image_path : str     Path to the resized image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def reduce_image_size(image_path, scale_image=0.5, path_landscape_files=\"\"):\n    \"\"\"\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    scale_image : float (default=0.5)\n        Scale factor for the image resize\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the resized image file\n    \"\"\"\n\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    resized_image = image.resize(scale_image)\n\n    new_image_name = image_path.split(\"/\")[-1].replace(\".tif\", \"_downsize.tif\")\n    new_image_path = f\"{path_landscape_files}/{new_image_name}\"\n    resized_image.write_to_file(new_image_path)\n\n    return new_image_path\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.save_landscape_parameters","title":"<code>save_landscape_parameters(technology, path_landscape_files, image_name='dapi_files', tile_size=1000, image_info={}, image_format='.webp')</code>","text":"<p>Save the landscape parameters to a JSON file.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def save_landscape_parameters(\n    technology, path_landscape_files, image_name=\"dapi_files\", tile_size=1000, image_info={}, image_format='.webp'\n):\n    \"\"\"\n    Save the landscape parameters to a JSON file.\n    \"\"\"\n\n    path_image_pyramid = f\"{path_landscape_files}/pyramid_images/{image_name}\"\n\n    print(path_image_pyramid)\n\n    max_pyramid_zoom = get_max_zoom_level(path_image_pyramid)\n\n    landscape_parameters = {\n        \"technology\": technology,\n        \"max_pyramid_zoom\": max_pyramid_zoom,\n        \"tile_size\": tile_size,\n        \"image_info\": image_info,\n        \"image_format\": image_format\n    }\n\n    path_landscape_parameters = f\"{path_landscape_files}/landscape_parameters.json\"\n\n    with open(path_landscape_parameters, \"w\") as file:\n        json.dump(landscape_parameters, file, indent=4)\n</code></pre>"},{"location":"python/viz/api/","title":"Viz Module API Reference","text":""},{"location":"python/viz/api/#widget-classes","title":"Widget Classes","text":""},{"location":"python/viz/api/#celldega.viz.widget.Landscape","title":"<code>Landscape</code>","text":"<p>               Bases: <code>AnyWidget</code></p> <p>A widget for interactive visualization of spatial omics data. This widget currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)</p> <p>Parameters:</p> Name Type Description Default <code>ini_x</code> <code>float</code> <p>The initial x-coordinate of the view.</p> required <code>ini_y</code> <code>float</code> <p>The initial y-coordinate of the view.</p> required <code>ini_zoom</code> <code>float</code> <p>The initial zoom level of the view.</p> required <code>token</code> <code>str</code> <p>The token traitlet.</p> required <code>base_url</code> <code>str</code> <p>The base URL for the widget.</p> required <code>dataset_name</code> <code>str</code> <p>The name of the dataset to visualize. This will show up in the user interface bar.</p> required <p>Attributes:</p> Name Type Description <code>component</code> <code>str</code> <p>The name of the component.</p> <code>technology</code> <code>str</code> <p>The technology used.</p> <code>base_url</code> <code>str</code> <p>The base URL for the widget.</p> <code>token</code> <code>str</code> <p>The token traitlet.</p> <code>ini_x</code> <code>float</code> <p>The initial x-coordinate of the view.</p> <code>ini_y</code> <code>float</code> <p>The initial y-coordinate of the view.</p> <code>ini_z</code> <code>float</code> <p>The initial z-coordinate of the view.</p> <code>ini_zoom</code> <code>float</code> <p>The initial zoom level of the view.</p> <code>dataset_name</code> <code>str</code> <p>The name of the dataset to visualize.</p> <code>update_trigger</code> <code>dict</code> <p>The dictionary to trigger updates.</p> <code>cell_clusters</code> <code>dict</code> <p>The dictionary containing cell cluster information.</p> <p>Returns:</p> Name Type Description <code>Landscape</code> <p>A widget for visualizing a 'landscape' view of spatial omics data.</p> Source code in <code>src/celldega/viz/widget.py</code> <pre><code>class Landscape(anywidget.AnyWidget):\n    \"\"\"\n    A widget for interactive visualization of spatial omics data. This widget\n    currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)\n\n    Args:\n        ini_x (float): The initial x-coordinate of the view.\n        ini_y (float): The initial y-coordinate of the view.\n        ini_zoom (float): The initial zoom level of the view.\n        token (str): The token traitlet.\n        base_url (str): The base URL for the widget.\n        dataset_name (str, optional): The name of the dataset to visualize. This will show up in the user interface bar.\n\n    Attributes:\n        component (str): The name of the component.\n        technology (str): The technology used.\n        base_url (str): The base URL for the widget.\n        token (str): The token traitlet.\n        ini_x (float): The initial x-coordinate of the view.\n        ini_y (float): The initial y-coordinate of the view.\n        ini_z (float): The initial z-coordinate of the view.\n        ini_zoom (float): The initial zoom level of the view.\n        dataset_name (str): The name of the dataset to visualize.\n        update_trigger (dict): The dictionary to trigger updates.\n        cell_clusters (dict): The dictionary containing cell cluster information.\n\n    Returns:\n        Landscape: A widget for visualizing a 'landscape' view of spatial omics data.\n    \"\"\"\n    _esm = pathlib.Path(__file__).parent / \"../static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"../static\" / \"widget.css\"\n    component = traitlets.Unicode(\"Landscape\").tag(sync=True)\n\n    technology = traitlets.Unicode(\"sst\").tag(sync=True)\n    base_url = traitlets.Unicode(\"\").tag(sync=True)\n    token = traitlets.Unicode(\"\").tag(sync=True)\n    ini_x = traitlets.Float(1000).tag(sync=True)\n    ini_y = traitlets.Float(1000).tag(sync=True)\n    ini_z = traitlets.Float(0).tag(sync=True)\n    ini_zoom = traitlets.Float(0).tag(sync=True)\n    square_tile_size = traitlets.Float(1.4).tag(sync=True)\n    dataset_name = traitlets.Unicode(\"\").tag(sync=True)\n    region = traitlets.Dict({}).tag(sync=True)\n\n    update_trigger = traitlets.Dict().tag(sync=True)\n    cell_clusters = traitlets.Dict().tag(sync=True)\n\n    width = traitlets.Int(0).tag(sync=True)\n    height = traitlets.Int(800).tag(sync=True)\n\n    def trigger_update(self, new_value):\n        # This method updates the update_trigger traitlet with a new value\n        # You can pass any information necessary for the update, or just a timestamp\n        self.update_trigger = new_value\n\n    def update_cell_clusters(self, new_clusters):\n        # Convert the new_clusters to a JSON serializable format if necessary\n        self.cell_clusters = new_clusters\n</code></pre>"},{"location":"python/viz/api/#celldega.viz.widget.Matrix","title":"<code>Matrix</code>","text":"<p>               Bases: <code>AnyWidget</code></p> <p>A widget for interactive visualization of a hierarchically clustered matrix.</p> <p>Parameters:</p> Name Type Description Default <code>value</code> <code>int</code> <p>The value traitlet.</p> required <code>component</code> <code>str</code> <p>The component traitlet.</p> required <code>network</code> <code>dict</code> <p>The network traitlet.</p> required <code>click_info</code> <code>dict</code> <p>The click_info traitlet.</p> required <p>Attributes:</p> Name Type Description <code>component</code> <code>str</code> <p>The name of the component.</p> <code>network</code> <code>dict</code> <p>The network dictionary.</p> <code>click_info</code> <code>dict</code> <p>The click_info dictionary.</p> <p>Returns:</p> Name Type Description <code>Matrix</code> <p>A widget for visualizing a hierarchically clustered matrix.</p> Source code in <code>src/celldega/viz/widget.py</code> <pre><code>class Matrix(anywidget.AnyWidget):\n    \"\"\"\n    A widget for interactive visualization of a hierarchically clustered matrix.\n\n    Args:\n        value (int): The value traitlet.\n        component (str): The component traitlet.\n        network (dict): The network traitlet.\n        click_info (dict): The click_info traitlet.\n\n    Attributes:\n        component (str): The name of the component.\n        network (dict): The network dictionary.\n        click_info (dict): The click_info dictionary.\n\n    Returns:\n        Matrix: A widget for visualizing a hierarchically clustered matrix.\n    \"\"\"\n    _esm = pathlib.Path(__file__).parent / \"../static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"../static\" / \"widget.css\"\n    value = traitlets.Int(0).tag(sync=True)\n    component = traitlets.Unicode(\"Matrix\").tag(sync=True)\n\n    network = traitlets.Dict({}).tag(sync=True)\n    click_info = traitlets.Dict({}).tag(sync=True)\n</code></pre>"},{"location":"technologies/","title":"Technologies","text":"<p>Celldega utilizes a suite of complementary technologies to develop an efficient web-based spatial-omics analysis and visualization toolkit.</p>"},{"location":"technologies/#visualization-technologies","title":"Visualization Technologies","text":"<p>Spatial transcriptomics (ST) datasets can be very large and difficult for researchers to analyze and visualize collaboratively. Additionally, visualization that is linked to analysis is key to extracting biological insights. To address these issues, we built the Celldega <code>viz</code> module to help researchers interactively visualize large ST datasets within notebook-based workflows on the cloud (e.g., Terra.bio).</p> <p>The Celldega Landscape visualization method (see Gallery) utilizes novel vector tiling approaches to enable interactive visualization of large ST datasets in a notebook environment or as a stand-alone webpage. This approach allows Celldega to visualize larger datasets than currently available open-source tools (e.g., datasets with hundreds of millions of transcripts). We also utilize modern web image data formats (WebP) to reduce the data storage burden for interactive visualization. The resulting LandscapeFiles data format serves as a compact and highly performant visualization-specific data format.</p>"},{"location":"technologies/#terrabio","title":"Terra.bio","text":"<p>Terra.bio is a cloud-based compute and data storage platform that is being developed by the Broad Institute of MIT and Harvard. We are utilizing Terra.bio to help Spatial Technology Platform clients access, analyze, and visualize their ST data.</p>"},{"location":"technologies/#jupyter-widget","title":"Jupyter Widget","text":"<p>We utilize the Jupyter Widget ecosystem to build interactive spatial and data visualizations that enable users to perform two way communication between JavaScript (front-end) and Python (back-end). We are utilizing the AnyWidget implementation to build our custom widgets.</p>"},{"location":"technologies/#deckgl","title":"Deck.gl","text":"<p>Celldega uses the GPU-powered data visualization library deck.gl to create high-performance spatial- and data-visualizations.</p>"},{"location":"technologies/#apache-parquet","title":"Apache Parquet","text":"<p>Celldega uses the Apache Parquet file format for storing vectorized spatial data and metadata. This file format in combination with the JavaScript library ParquetWASM and Apache Arrow in memory representation is used to build Celldega's high-performance vector tiling spatial visualization functionality (see GeoArrow and GeoParquet in deck.gl).</p>"},{"location":"technologies/#parquetwasm-and-apache-arrow","title":"ParquetWASM and Apache Arrow","text":"<p>ParquetWASM is a JavaScript library for reading Parquet files into Apache Arrow memory and utilizes Web Assembly (WASM) to run Rust in a browser environment. The Apache Arrow in-memory format is a columnar in-memory format that is used for storing data from Apache Parquet files and efficiently passing to deck.gl. For more information please see GeoArrow and GeoParquet in deck.gl.</p>"},{"location":"technologies/#webp","title":"WebP","text":"<p>A modern image format developed by Google, offering efficient lossless compression and designed specifically for the web.</p>"},{"location":"technologies/#deep-zoom","title":"Deep Zoom","text":"<p>We utilize the Deep Zoom image schema, developed by Microsoft, to enable efficient visualization of large multi-channel microscopy images. Deep Zoom tile images are stored using the WebP image format.</p>"},{"location":"technologies/#clustergrammer-visualization-approaches","title":"Clustergrammer Visualization Approaches","text":"<p>The Celldega Matrix visualization builds upon the visualization approaches developed in the Clustergrammer project. This enables users to interactively explore high-dimensional datasets (e.g., single-cell gene expression data) alongside spatial data (e.g., cell distributions within a tissue).</p>"},{"location":"technologies/#data-analysis-technologies","title":"Data Analysis Technologies","text":""},{"location":"technologies/#scanpy-and-squidpy","title":"Scanpy and Squidpy","text":"<p>Celldega is built to interface with the AnnData and SpatialData objects, which enables users to easily import analysis results from Scanpy and Squidpy, respectively, into Celldega for downstream analysis and/or visuaization.</p>"},{"location":"technologies/#geopandas","title":"GeoPandas","text":"<p>Celldega uses GeoPandas for efficient spatial operations and storing collections of spatial objects (e.g., neighborhood multi-polygons) as GeoDataFrames.</p>"},{"location":"technologies/#libpysal-python-spatial-analysis-library-core","title":"LibPySal: Python Spatial Analysis Library Core","text":"<p>Celldega uses the Python Spatial Analysis Library (libpysal) for spatial analysis - namely for calculating alpha shape cell type neighborhoods.</p>"},{"location":"technologies/#clustergrammer-data-analysis-approaches","title":"Clustergrammer Data Analysis Approaches","text":"<p>The Celldega Cluster module build upon the hierarchical clustering approaches developed in the Clustergrammer project. This enables users to perform hierarchical clustering on observations (e.g., single cells) and measurements (e.g., genes) and easily visualize these two orthogonal clustering results interactively using Celldega's Matrix visualization method.</p>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to Celldega's Documentation","text":"Celldega Landscape visualization of a human skin cancer Xenium dataset obtained from 10X Genomics.  <p>Celldega is a spatial analysis and visualization library that is being developed by the Spatial Technology Platform at the Broad Institute of MIT and Harvard. This project enables researchers to easily visualize large ST datasets (e.g., datasets with &gt;100M transcripts) alongside single-cell and spatial analysis notebook workflows (e.g., sverse tools and novel spatial analysis approaches).</p> <ul> <li>Getting Started</li> <li>Installation</li> <li>Usage</li> </ul>"},{"location":"#whats-new","title":"What's New","text":"<p>The project was recently presented at the Broad Retreat Data Visualization Breakout Session -</p> <p> </p>"},{"location":"#about","title":"About","text":"<p>Celldega is named after a bodega, a small shop with all the essentials that is part of the fabric of a neighborhood.</p> <p>checking link</p>"},{"location":"examples/","title":"Jupyter Notebook Examples","text":"<p>Landscape View Xenium</p>"},{"location":"examples/short_notebooks/Landscape_View_Xenium/","title":"Landscape View Xenium","text":"In\u00a0[2]: Copied! <pre># %load_ext autoreload\n# %autoreload 2\n# %env ANYWIDGET_HMR=1\n</pre> # %load_ext autoreload # %autoreload 2 # %env ANYWIDGET_HMR=1 In\u00a0[3]: Copied! <pre>import celldega as dega\ndega.__version__\n</pre> import celldega as dega dega.__version__ Out[3]: <pre>'0.0.0'</pre> In\u00a0[6]: Copied! <pre>from observable_jupyter import embed\n</pre> from observable_jupyter import embed In\u00a0[11]: Copied! <pre>base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Mouse_Brain_Coronal_FF_outs/main/Xenium_Prime_Mouse_Brain_Coronal_FF_outs'\n</pre> base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Mouse_Brain_Coronal_FF_outs/main/Xenium_Prime_Mouse_Brain_Coronal_FF_outs' In\u00a0[14]: Copied! <pre>embed('@cornhundred/celldega-landscape-ist', inputs={'base_url': base_url}, cells=['landscape_container'], display_logo=False)\n</pre> embed('@cornhundred/celldega-landscape-ist', inputs={'base_url': base_url}, cells=['landscape_container'], display_logo=False) In\u00a0[13]: Copied! <pre># base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Human_Skin_FFPE_outs/main/Xenium_Prime_Human_Skin_FFPE_outs'\n\n# landscape_ist = dega.viz.Landscape(\n#     technology='Xenium',\n#     ini_zoom = -4.5,\n#     ini_x=6000,\n#     ini_y=8000,\n#     base_url = base_url,\n\n# )\n\n# landscape_ist\n</pre> # base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Human_Skin_FFPE_outs/main/Xenium_Prime_Human_Skin_FFPE_outs'  # landscape_ist = dega.viz.Landscape( #     technology='Xenium', #     ini_zoom = -4.5, #     ini_x=6000, #     ini_y=8000, #     base_url = base_url,  # )  # landscape_ist In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/short_notebooks/Landscape_View_Xenium/#landscape-view-xenium","title":"Landscape View Xenium\u00b6","text":""},{"location":"gallery/","title":"Celldega Gallery","text":"<p>This page includes links to visualizations that are made with the stand-alone Celldega JavaScript library.</p>"},{"location":"gallery/#imaging-spatial-transcriptomics","title":"Imaging Spatial Transcriptomics","text":""},{"location":"gallery/#xenium","title":"Xenium","text":"<ul> <li>Xenium Mouse Brain </li> <li>Xenium Human Skin Cancer </li> </ul>"},{"location":"gallery/#sequencing-spatial-transcriptomics","title":"Sequencing Spatial Transcriptomics","text":""},{"location":"gallery/#visium-hd","title":"Visium HD","text":""},{"location":"gallery/gallery_xenium/","title":"Celldega Xenium Gallery","text":""},{"location":"gallery/gallery_xenium/#xenium-prime-mouse-brain-coronal-ff","title":"Xenium Prime Mouse Brain Coronal FF","text":""},{"location":"gallery/gallery_xenium/#xenium-prime-human-skin-ffpe-outs","title":"Xenium Prime Human Skin FFPE outs","text":""},{"location":"gallery/gallery_xenium/#xenium-human-pancreas-ffpe","title":"Xenium Human Pancreas FFPE","text":""},{"location":"gallery/gallery_xenium/#bone-marrow","title":"Bone Marrow","text":""},{"location":"gallery/gallery_xenium_mouse_brain/","title":"Xenium Prime Mouse Brain Coronal FF","text":""},{"location":"gallery/gallery_xenium_multi/","title":"Xenium Multi Dataset","text":""},{"location":"gallery/gallery_xenium_multi/#xenium-prime-mouse-brain-coronal-ff","title":"Xenium Prime Mouse Brain Coronal FF","text":""},{"location":"gallery/gallery_xenium_multi/#xenium-prime-human-skin-ffpe-outs","title":"Xenium Prime Human Skin FFPE outs","text":""},{"location":"gallery/gallery_xenium_multi/#xenium-human-pancreas-ffpe","title":"Xenium Human Pancreas FFPE","text":""},{"location":"gallery/gallery_xenium_multi/#bone-marrow","title":"Bone Marrow","text":""},{"location":"gallery/gallery_xenium_skin_cancer/","title":"Xenium Prime Human Skin FFPE outs","text":""},{"location":"javascript/","title":"JavaScript API Overview","text":"<p>Celldega's visualization methods can be used as a stand-alone JavaScript library outside the context of a Jupyter notebook. This can be used to create showcase visualizations with publicly hosted data.</p>"},{"location":"javascript/api/","title":"Celldega JavaScript API Documentation","text":"<p>The JavaScript component of Celldega is used within the Jupyter Widgets framework to provide interactive visualization in the context of a Jupyter notebook but can also be used as a standalone JavaScript library.</p>"},{"location":"javascript/api/#landscape_ist-api-documentation","title":"<code>landscape_ist</code> API Documentation","text":"<p>The <code>landscape_ist</code> function initializes and renders an interactive spatial transcriptomics (IST) landscape visualization. This API is designed to work with Deck.gl and includes customizable visualization options, dynamic data updates, and UI interactions.</p>"},{"location":"javascript/api/#parameters","title":"Parameters","text":"<ul> <li><code>el</code> (<code>HTMLElement</code>): The root DOM element where the visualization is rendered.</li> <li><code>ini_model</code> (<code>Object</code>): The initial data model containing configuration and state.</li> <li><code>token</code> (<code>string</code>): Authentication token for accessing data.</li> <li><code>ini_x</code>, <code>ini_y</code>, <code>ini_z</code> (<code>number</code>): Initial spatial coordinates for the view.</li> <li><code>ini_zoom</code> (<code>number</code>): Initial zoom level for the visualization.</li> <li><code>base_url</code> (<code>string</code>): Base URL for accessing data files.</li> <li><code>dataset_name</code> (<code>string</code>, optional): Name of the dataset being visualized.</li> <li><code>trx_radius</code> (<code>number</code>, optional): Initial radius for transcript points. Default: <code>0.25</code>.</li> <li><code>width</code> (<code>number|string</code>, optional): Width of the visualization. Default: <code>100%</code>.</li> <li><code>height</code> (<code>number</code>, optional): Height of the visualization. Default: <code>800</code>.</li> <li><code>view_change_custom_callback</code> (<code>Function</code>, optional): Custom callback triggered on view changes.</li> </ul>"},{"location":"javascript/api/#public-api","title":"Public API","text":"<p>The <code>landscape_ist</code> function returns an object (<code>landscape</code>) with several methods for interacting with the visualization.</p>"},{"location":"javascript/api/#update_matrix_gene","title":"<code>update_matrix_gene</code>","text":"<p>Updates the visualization to highlight data for a specific gene.</p>"},{"location":"javascript/api/#parameters_1","title":"Parameters","text":"<ul> <li><code>inst_gene</code> (<code>string</code>): The gene to highlight.</li> </ul>"},{"location":"javascript/api/#behavior","title":"Behavior","text":"<ul> <li>Updates the transcript layer to show data for the specified gene.</li> <li>Scrolls the bar graph to bring the selected gene into view.</li> <li>Toggles visibility of image layers and controls based on the selected gene.</li> </ul>"},{"location":"javascript/api/#update_matrix_col","title":"<code>update_matrix_col</code>","text":"<p>Updates the visualization to highlight data for a specific column (e.g., cluster).</p>"},{"location":"javascript/api/#parameters_2","title":"Parameters","text":"<ul> <li><code>inst_col</code> (<code>string</code>): The column to highlight.</li> </ul>"},{"location":"javascript/api/#behavior_1","title":"Behavior","text":"<ul> <li>Highlights the bar graph corresponding to the selected column.</li> <li>Updates cell and path layers to reflect the selected column.</li> <li>Toggles visibility of layers based on the column selection.</li> </ul>"},{"location":"javascript/api/#update_matrix_dendro_col","title":"<code>update_matrix_dendro_col</code>","text":"<p>Updates the visualization based on a dendrogram selection of columns.</p>"},{"location":"javascript/api/#parameters_3","title":"Parameters","text":"<ul> <li><code>selected_cols</code> (<code>Array&lt;string&gt;</code>): The list of selected column names.</li> </ul>"},{"location":"javascript/api/#behavior_2","title":"Behavior","text":"<ul> <li>Highlights the selected columns in the bar graph.</li> <li>Updates layers to reflect the selection.</li> </ul>"},{"location":"javascript/api/#update_view_state","title":"<code>update_view_state</code>","text":"<p>Updates the view state of the Deck.gl visualization.</p>"},{"location":"javascript/api/#parameters_4","title":"Parameters","text":"<ul> <li><code>new_view_state</code> (<code>Object</code>): The new view state configuration.</li> <li><code>close_up</code> (<code>boolean</code>): Whether the view should zoom in closely.</li> <li><code>trx_layer</code> (<code>Object</code>): The transcript layer to update.</li> </ul>"},{"location":"javascript/api/#behavior_3","title":"Behavior","text":"<ul> <li>Adjusts the viewport and reconfigures layers based on the new view state.</li> </ul>"},{"location":"javascript/api/#update_layers","title":"<code>update_layers</code>","text":"<p>Updates all visualization layers.</p>"},{"location":"javascript/api/#behavior_4","title":"Behavior","text":"<ul> <li>Refreshes the Deck.gl layers with the current visualization state.</li> </ul>"},{"location":"javascript/api/#finalize","title":"<code>finalize</code>","text":"<p>Finalizes the Deck.gl instance and cleans up resources.</p>"},{"location":"javascript/api/#behavior_5","title":"Behavior","text":"<ul> <li>Disposes of all Deck.gl resources and event listeners to prevent memory leaks.</li> </ul>"},{"location":"javascript/api/#usage-example","title":"Usage Example","text":"<pre><code>\njavascript\nimport { landscape_ist } from 'path/to/landscape_ist';\n\nconst rootElement = document.getElementById('visualization-container');\nconst model = { /* Model containing visualization data */ };\n\nconst visualization = await landscape_ist(\n    rootElement,\n    model,\n    'example-token',\n    100,\n    200,\n    0,\n    -5,\n    'https://example.com/data',\n    'Example Dataset'\n);\n\n// Update the visualization with a specific gene.\nvisualization.update_matrix_gene('TP53');\n\n// Update the visualization with a specific column.\nvisualization.update_matrix_col('Cluster 1');\n\n// Finalize the visualization when done.\nvisualization.finalize();\n\n</code></pre>"},{"location":"javascript/api/#matrix_viz-api-documentation","title":"<code>matrix_viz</code> API Documentation","text":"<p>The <code>matrix_viz</code> function initializes and renders a matrix visualization. This API is built using approaches and code adaptations from the Clustergrammer-GL library, and it integrates tightly with Deck.gl to provide interactive and dynamic visualizations.</p>"},{"location":"javascript/api/#parameters_5","title":"Parameters","text":"<ul> <li><code>model</code> (<code>Object</code>): The model object containing configuration data for the visualization.</li> <li><code>el</code> (<code>HTMLElement</code>): The root DOM element where the visualization is rendered.</li> <li><code>network</code> (<code>Object</code>): The network object containing the matrix data to visualize.</li> <li><code>width</code> (<code>string|number</code>, optional): The width of the visualization. Default: <code>'800'</code>.</li> <li><code>height</code> (<code>string|number</code>, optional): The height of the visualization. Default: <code>'800'</code>.</li> <li><code>row_label_callback</code> (<code>Function</code>, optional): A callback function triggered on row label interactions.</li> <li><code>col_label_callback</code> (<code>Function</code>, optional): A callback function triggered on column label interactions.</li> <li><code>col_dendro_callback</code> (<code>Function</code>, optional): A callback function triggered on dendrogram column interactions.</li> </ul>"},{"location":"javascript/api/#internal-behavior","title":"Internal Behavior","text":"<p>The function performs the following setup: 1. Deck.gl Integration:    - Initializes a Deck.gl instance for the matrix visualization.    - Sets properties for interactivity, including tooltips, view state changes, and layer filtering.</p> <ol> <li>Matrix Data Setup:</li> <li>Parses and structures the matrix data from the <code>network</code> object.</li> <li> <p>Configures labels, categories, and dendrograms for both rows and columns.</p> </li> <li> <p>Layer Initialization:</p> </li> <li>Creates layers for:<ul> <li>Matrix cells.</li> <li>Row and column labels.</li> <li>Row and column categories.</li> <li>Row and column dendrograms.</li> </ul> </li> <li> <p>Attaches interactions (e.g., click events) to these layers.</p> </li> <li> <p>UI Setup:</p> </li> <li>Creates a container for the visualization and appends it to the root DOM element.</li> </ol>"},{"location":"javascript/api/#example-usage","title":"Example Usage","text":"<pre><code>import { matrix_viz } from 'path/to/matrix_viz';\n\nconst rootElement = document.getElementById('matrix-container');\nconst model = { /* Model containing visualization data */ };\nconst network = { /* Network object representing the matrix data */ };\n\n// Callback functions\nconst rowLabelCallback = (row) =&gt; {\n    console.log('Row label clicked:', row);\n};\n\nconst colLabelCallback = (col) =&gt; {\n    console.log('Column label clicked:', col);\n};\n\nconst colDendroCallback = (dendro) =&gt; {\n    console.log('Column dendrogram clicked:', dendro);\n};\n\n// Initialize the matrix visualization\nawait matrix_viz(\n    model,\n    rootElement,\n    network,\n    800,\n    800,\n    rowLabelCallback,\n    colLabelCallback,\n    colDendroCallback\n);\n</code></pre>"},{"location":"overview/","title":"Overview","text":"<p>The Celldega library is being developed to help researchers easily visualize and analyze high-dimensional spatial-omics data in the context of a notebook workflow. Initial development has been focused on spatial transcriptomics visualization.</p> <p>Celldega can be used as a Python library in a Jupyter notebook environment or as a stand-alone JavaScript library for creating visualizations.</p> <ul> <li>Getting Started</li> <li>Installation</li> <li>Usage</li> </ul>"},{"location":"overview/file_formats/","title":"File Formats","text":""},{"location":"overview/file_formats/#landscapefiles","title":"LandscapeFiles","text":"<p>This format is small enough that we can store an entire Xenium dataset on GitHub as a repo for the purposes of building a public gallery.</p>"},{"location":"overview/getting_started/","title":"Getting Started","text":"<p>Celldega is a spatial analysis and visualization library that is being developed by the Spatial Technology Platform at the Broad Institute of MIT and Harvard. Celldega can be used as a Jupyter Widget in Python as well as a stand-alone JavaScript library.</p> <p>Please see examples notebooks below to try out Celldega in a Jupyter notebook or ObservableHQ JavaScript notebook:</p> <ul> <li>Celldega_Xenium_Landscape_Visualizations_Colab.ipynb</li> <li>Celldega Landscape Xenium ObservableHQ</li> </ul>"},{"location":"overview/installation/","title":"Installation","text":""},{"location":"overview/installation/#python","title":"Python","text":"<p>The Celldega library can be installed using pip</p> <p>Celldega can be installed using pip:</p> <pre><code>pip install celldega\n</code></pre> <p>Celldega can also be installed with the optional pre-processing requirements (e.g., vips for image pre-processing) using:</p> <pre><code>pip install celldega[pre]\n</code></pre>"},{"location":"overview/installation/#javascript","title":"JavaScript","text":"<p>Celldega can be used in a JavaScript environment such as ObservableHQ by importing it as a module from content delivery networks like esm.sh:</p> <pre><code>celldega = await import('https://esm.sh/celldega@latest')\n</code></pre> <p>Or by importing it from a local file</p> <pre><code>import celldega from 'js/widget.js'\n</code></pre>"},{"location":"overview/usage/","title":"Celldega Usage","text":""},{"location":"overview/usage/#terrabio","title":"Terra.bio","text":"<p>** Coming soon **</p>"},{"location":"python/","title":"Python API Overview","text":""},{"location":"python/#neighborhood-overview","title":"Neighborhood Overview","text":"<p>The <code>nbhd</code> module contains methods for calculating tissue neighborhoods.</p>"},{"location":"python/#pre-module-overview","title":"Pre Module Overview","text":"<p>The <code>pre</code> module contains methods for pre-processing LandscapeFiles.</p>"},{"location":"python/#viz-module-overview","title":"Viz Module Overview","text":"<p>The <code>viz</code> module contains functions and classes for data visualization.</p>"},{"location":"python/api/","title":"Python API Reference","text":"<p>Module for pre-processing to generate LandscapeFiles from ST data.</p> <p>Module for visualization</p>"},{"location":"python/api/#celldega.nbhd.alpha_shape_cell_clusters","title":"<code>alpha_shape_cell_clusters(meta_cell, cat='cluster', alphas=[100, 150, 200, 250, 300, 350])</code>","text":"<p>Compute alpha shapes for each cluster in the cell metadata.</p> <p>Parameters: - meta_cell: GeoDataFrame of cell metadata. - cat: Column name in meta_cell containing the cluster labels. - alphas: List of alpha values to compute shapes for.</p> <p>Returns: - GeoDataFrame of alpha shapes.</p> Source code in <code>src/celldega/nbhd/__init__.py</code> <pre><code>def alpha_shape_cell_clusters(meta_cell, cat='cluster', alphas=[100, 150, 200, 250, 300, 350]):\n\n    \"\"\"\n    Compute alpha shapes for each cluster in the cell metadata.\n\n    Parameters:\n    - meta_cell: GeoDataFrame of cell metadata.\n    - cat: Column name in meta_cell containing the cluster labels.\n    - alphas: List of alpha values to compute shapes for.\n\n    Returns:\n    - GeoDataFrame of alpha shapes.\n\n    \"\"\"\n\n    gdf_alpha = gpd.GeoDataFrame()\n\n    for inv_alpha in alphas:\n\n        for inst_cluster in meta_cell[cat].unique():\n\n            inst_clust = meta_cell[meta_cell[cat] == inst_cluster]\n\n            if inst_clust.shape[0]&gt; 3:\n\n                nested_array = inst_clust['geometry'].values\n\n                # Convert to a 2D NumPy array\n                flat_array = np.vstack(nested_array)\n\n                inst_shape = alpha_shape(flat_array, inv_alpha)\n\n                inst_name = inst_cluster + '_' + str(inv_alpha)\n\n                gdf_alpha.loc[inst_name, 'name'] = inst_name\n\n                gdf_alpha.loc[inst_name, 'cat'] = inst_cluster\n\n                gdf_alpha.loc[inst_name, 'geometry'] = inst_shape\n\n                gdf_alpha.loc[inst_name, 'inv_alpha'] = int(inv_alpha)\n\n    gdf_alpha[\"geometry\"] = gdf_alpha[\"geometry\"].apply(lambda geom: _round_coordinates(geom, precision=2))\n\n    gdf_alpha['area'] = gdf_alpha.area\n\n    gdf_alpha = gdf_alpha.loc[gdf_alpha.area.sort_values(ascending=False).index.tolist()]\n\n    return gdf_alpha\n</code></pre>"},{"location":"python/api/#celldega.pre.convert_long_id_to_short","title":"<code>convert_long_id_to_short(df)</code>","text":"<p>Converts a column of long integer cell IDs in a DataFrame to a shorter, hash-based representation.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>The DataFrame containing the EntityID.</p> required <p>Returns:     pd.DataFrame: The original DataFrame with an additional column named <code>cell_id</code>                   containing the shortened cell IDs.</p> <p>The function applies a SHA-256 hash to each cell ID, encodes the hash using base64, and truncates it to create a shorter identifier that is added as a new column to the DataFrame.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_long_id_to_short(df):\n    \"\"\"\n    Converts a column of long integer cell IDs in a DataFrame to a shorter, hash-based representation.\n\n    Args:\n        df (pd.DataFrame): The DataFrame containing the EntityID.\n    Returns:\n        pd.DataFrame: The original DataFrame with an additional column named `cell_id`\n                      containing the shortened cell IDs.\n\n    The function applies a SHA-256 hash to each cell ID, encodes the hash using base64, and truncates\n    it to create a shorter identifier that is added as a new column to the DataFrame.\n    \"\"\"\n    # Function to hash and encode the cell ID\n    def hash_and_shorten_id(cell_id):\n        # Create a hash of the cell ID\n        cell_id_bytes = str(cell_id).encode('utf-8')\n        hash_object = hashlib.sha256(cell_id_bytes)\n        hash_digest = hash_object.digest()\n\n        # Encode the hash to a base64 string to mix letters and numbers, truncate to 9 characters\n        short_id = base64.urlsafe_b64encode(hash_digest).decode('utf-8')[:9]\n        return short_id\n\n    # Apply the hash_and_shorten_id function to each cell ID in the specified column\n    df['cell_id'] = df['EntityID'].apply(hash_and_shorten_id)\n\n    return df\n</code></pre>"},{"location":"python/api/#celldega.pre.convert_to_jpeg","title":"<code>convert_to_jpeg(image_path, quality=80)</code>","text":"<p>Convert a TIFF image to a JPEG image with a quality of score</p>"},{"location":"python/api/#celldega.pre.convert_to_jpeg--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=80)     Quality score for the JPEG image</p>"},{"location":"python/api/#celldega.pre.convert_to_jpeg--returns","title":"Returns","text":"<p>new_image_path : str     Path to the JPEG image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_jpeg(image_path, quality=80):\n    \"\"\"\n    Convert a TIFF image to a JPEG image with a quality of score\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=80)\n        Quality score for the JPEG image\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the JPEG image file\n\n    \"\"\"\n\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a JPEG with a quality of 80\n    new_image_path = image_path.replace(\".tif\", \".jpeg\")\n    image.jpegsave(new_image_path, Q=quality)\n\n    return new_image_path\n</code></pre>"},{"location":"python/api/#celldega.pre.convert_to_png","title":"<code>convert_to_png(image_path)</code>","text":"<p>Convert a TIFF image to a JPEG image with a quality of score</p>"},{"location":"python/api/#celldega.pre.convert_to_png--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=80)     Quality score for the JPEG image</p>"},{"location":"python/api/#celldega.pre.convert_to_png--returns","title":"Returns","text":"<p>new_image_path : str     Path to the JPEG image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_png(image_path):\n    \"\"\"\n    Convert a TIFF image to a JPEG image with a quality of score\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=80)\n        Quality score for the JPEG image\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the JPEG image file\n\n    \"\"\"\n\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a JPEG with a quality of 80\n    new_image_path = image_path.replace(\".tif\", \".png\")\n    image.pngsave(new_image_path)\n\n    return new_image_path\n</code></pre>"},{"location":"python/api/#celldega.pre.convert_to_webp","title":"<code>convert_to_webp(image_path, quality=100)</code>","text":"<p>Convert a TIFF image to a WEBP image with a specified quality score.</p>"},{"location":"python/api/#celldega.pre.convert_to_webp--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=100)     Quality score for the WEBP image (higher is better quality)</p>"},{"location":"python/api/#celldega.pre.convert_to_webp--returns","title":"Returns","text":"<p>new_image_path : str     Path to the WEBP image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_webp(image_path, quality=100):\n    \"\"\"\n    Convert a TIFF image to a WEBP image with a specified quality score.\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=100)\n        Quality score for the WEBP image (higher is better quality)\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the WEBP image file\n    \"\"\"\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a WEBP with specified quality\n    new_image_path = image_path.replace(\".tif\", \".webp\")\n    image.webpsave(new_image_path, Q=quality)\n\n    return new_image_path\n</code></pre>"},{"location":"python/api/#celldega.pre.get_max_zoom_level","title":"<code>get_max_zoom_level(path_image_pyramid)</code>","text":"<p>Returns the maximum zoom level based on the highest-numbered directory in the specified path_image_pyramid.</p> <p>Parameters:</p> Name Type Description Default <code>path_image_pyramid</code> <code>str</code> <p>The path to the directory containing zoom level directories.</p> required <p>Returns:</p> Name Type Description <code>max_pyramid_zoom</code> <code>int</code> <p>The maximum zoom level.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def get_max_zoom_level(path_image_pyramid):\n    \"\"\"\n    Returns the maximum zoom level based on the highest-numbered directory\n    in the specified path_image_pyramid.\n\n    Parameters:\n        path_image_pyramid (str): The path to the directory containing zoom level directories.\n\n    Returns:\n        max_pyramid_zoom (int): The maximum zoom level.\n    \"\"\"\n    # List all entries in the path_image_pyramid that are directories and can be converted to integers\n    zoom_levels = [\n        entry\n        for entry in os.listdir(path_image_pyramid)\n        if os.path.isdir(os.path.join(path_image_pyramid, entry)) and entry.isdigit()\n    ]\n\n    # Convert to integer and find the maximum value\n    max_pyramid_zoom = max(map(int, zoom_levels)) if zoom_levels else None\n\n    return max_pyramid_zoom\n</code></pre>"},{"location":"python/api/#celldega.pre.make_cell_boundary_tiles","title":"<code>make_cell_boundary_tiles(technology, path_cell_boundaries, path_meta_cell_micron, path_transformation_matrix, path_output, coarse_tile_factor=20, tile_size=250, tile_bounds=None, image_scale=1, max_workers=8)</code>","text":"<p>Processes cell boundary data and divides it into spatial tiles based on the provided technology. Reads cell boundary data, applies affine transformations, and divides the data into coarse and fine tiles. The resulting tiles are saved as Parquet files, each containing the geometries of cells in that tile.</p>"},{"location":"python/api/#celldega.pre.make_cell_boundary_tiles--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the cell boundary data, e.g., \"MERSCOPE\", \"Xenium\", or \"custom\". path_cell_boundaries : str     Path to the file containing the cell boundaries (Parquet format). path_meta_cell_micron : str     Path to the file containing cell metadata (CSV format). path_transformation_matrix : str     Path to the file containing the transformation matrix (CSV format). path_output : str     Directory path where the output files (Parquet files) for each tile will be saved. coarse_tile_factor  : int, optional, default=20.     scaling factor of each coarse-grain tile comparing to the fine tile size. tile_size : int, optional, default=500     Size of each fine-grain tile in microns. tile_bounds : dict, optional     Dictionary containing the minimum and maximum bounds for x and y coordinates. image_scale : float, optional, default=1     Scale factor to apply to the geometry data. max_workers : int, optional, default=8     Maximum number of parallel workers for processing tiles.</p>"},{"location":"python/api/#celldega.pre.make_cell_boundary_tiles--returns","title":"Returns","text":"<p>None</p> Source code in <code>src/celldega/pre/boundary_tile.py</code> <pre><code>def make_cell_boundary_tiles(\n    technology,\n    path_cell_boundaries,\n    path_meta_cell_micron,\n    path_transformation_matrix,\n    path_output,\n    coarse_tile_factor=20,\n    tile_size=250,\n    tile_bounds=None,\n    image_scale=1,\n    max_workers=8\n):\n\n\n    \"\"\"\n    Processes cell boundary data and divides it into spatial tiles based on the provided technology.\n    Reads cell boundary data, applies affine transformations, and divides the data into coarse and fine tiles.\n    The resulting tiles are saved as Parquet files, each containing the geometries of cells in that tile.\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the cell boundary data, e.g., \"MERSCOPE\", \"Xenium\", or \"custom\".\n    path_cell_boundaries : str\n        Path to the file containing the cell boundaries (Parquet format).\n    path_meta_cell_micron : str\n        Path to the file containing cell metadata (CSV format).\n    path_transformation_matrix : str\n        Path to the file containing the transformation matrix (CSV format).\n    path_output : str\n        Directory path where the output files (Parquet files) for each tile will be saved.\n    coarse_tile_factor  : int, optional, default=20.\n        scaling factor of each coarse-grain tile comparing to the fine tile size.\n    tile_size : int, optional, default=500\n        Size of each fine-grain tile in microns.\n    tile_bounds : dict, optional\n        Dictionary containing the minimum and maximum bounds for x and y coordinates.\n    image_scale : float, optional, default=1\n        Scale factor to apply to the geometry data.\n    max_workers : int, optional, default=8\n        Maximum number of parallel workers for processing tiles.\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    def numpy_affine_transform(coords, matrix):\n        \"\"\"Apply affine transformation to numpy coordinates.\"\"\"\n        # Homogeneous coordinates for affine transformation\n        coords = np.hstack([coords, np.ones((coords.shape[0], 1))])\n        transformed_coords = coords @ matrix.T\n        return transformed_coords[:, :2]  # Drop the homogeneous coordinate\n\n    def batch_transform_geometries(geometries, transformation_matrix, scale):\n        \"\"\"\n        Batch transform geometries using numpy for optimized performance.\n        \"\"\"\n        # Extract affine transformation parameters into a 3x3 matrix for numpy\n        affine_matrix = np.array([\n            [transformation_matrix[0, 0], transformation_matrix[0, 1], transformation_matrix[0, 2]],\n            [transformation_matrix[1, 0], transformation_matrix[1, 1], transformation_matrix[1, 2]],\n            [0, 0, 1]\n        ])\n\n        transformed_geometries = []\n\n        for polygon in geometries:\n            # Extract coordinates and transform them\n            if isinstance(polygon, MultiPolygon):\n                polygon = next(polygon.geoms)  # Use the first geometry\n\n            # Transform the exterior of the polygon\n            exterior_coords = np.array(polygon.exterior.coords)\n\n            # Apply the affine transformation and scale\n            transformed_coords = numpy_affine_transform(exterior_coords, affine_matrix) / scale\n\n            # Append the result to the transformed_geometries list\n            transformed_geometries.append([transformed_coords.tolist()])\n\n        return transformed_geometries\n\n\n    def filter_and_save_fine_boundary(coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output):\n        cell_ids = coarse_tile.index.values\n\n        tile_filter = (\n            (coarse_tile[\"center_x\"] &gt;= fine_tile_x_min) &amp; (coarse_tile[\"center_x\"] &lt; fine_tile_x_max) &amp;\n            (coarse_tile[\"center_y\"] &gt;= fine_tile_y_min) &amp; (coarse_tile[\"center_y\"] &lt; fine_tile_y_max)\n        )\n        filtered_indices = np.where(tile_filter)[0]\n\n        keep_cells = cell_ids[filtered_indices]\n        fine_tile_cells = coarse_tile.loc[keep_cells, [\"GEOMETRY\"]]\n        fine_tile_cells = fine_tile_cells.assign(name=fine_tile_cells.index)\n\n        if not fine_tile_cells.empty:\n            filename = f\"{path_output}/cell_tile_{fine_i}_{fine_j}.parquet\"\n            fine_tile_cells.to_parquet(filename)\n\n    def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y):\n        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n            futures = []\n            for fine_i in range(n_fine_tiles_x):\n                fine_tile_x_min = x_min + fine_i * tile_size\n                fine_tile_x_max = fine_tile_x_min + tile_size\n\n                if not (fine_tile_x_min &gt;= coarse_tile_x_min and fine_tile_x_max &lt;= coarse_tile_x_max):\n                    continue\n\n                for fine_j in range(n_fine_tiles_y):\n                    fine_tile_y_min = y_min + fine_j * tile_size\n                    fine_tile_y_max = fine_tile_y_min + tile_size\n\n                    if not (fine_tile_y_min &gt;= coarse_tile_y_min and fine_tile_y_max &lt;= coarse_tile_y_max):\n                        continue\n\n                    futures.append(executor.submit(\n                        filter_and_save_fine_boundary, coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output\n                    ))\n\n            for future in futures:\n                future.result()\n\n    tile_size_x = tile_size\n    tile_size_y = tile_size\n\n    transformation_matrix = pd.read_csv(path_transformation_matrix, header=None, sep=\" \").values\n\n    # Load cell boundary data based on the technology\n    if technology == \"MERSCOPE\":\n        df_meta = pd.read_parquet(f\"{path_output.replace('cell_segmentation','cell_metadata.parquet')}\")\n        entity_to_cell_id_dict = pd.Series(df_meta.index.values, index=df_meta.EntityID).to_dict()\n        cells_orig = gpd.read_parquet(path_cell_boundaries)\n        cells_orig['cell_id'] = cells_orig['EntityID'].map(entity_to_cell_id_dict)\n        cells_orig = cells_orig[cells_orig[\"ZIndex\"] == 1]\n\n        # Correct cell_id issues with meta_cell\n        meta_cell = pd.read_csv(path_meta_cell_micron)\n        meta_cell['cell_id'] = meta_cell['EntityID'].map(entity_to_cell_id_dict)\n        cells_orig.index = meta_cell[meta_cell[\"cell_id\"].isin(cells_orig['cell_id'])].index\n\n        # Correct 'MultiPolygon' to 'Polygon'\n        cells_orig[\"geometry\"] = cells_orig[\"Geometry\"].apply(\n            lambda x: list(x.geoms)[0] if isinstance(x, MultiPolygon) else x\n        )\n\n        cells_orig.set_index('cell_id', inplace=True)\n\n    elif technology == \"Xenium\":\n        xenium_cells = pd.read_parquet(path_cell_boundaries)\n        grouped = xenium_cells.groupby(\"cell_id\")[[\"vertex_x\", \"vertex_y\"]].agg(lambda x: x.tolist())\n        grouped[\"geometry\"] = grouped.apply(lambda row: Polygon(zip(row[\"vertex_x\"], row[\"vertex_y\"])), axis=1)\n        cells_orig = gpd.GeoDataFrame(grouped, geometry=\"geometry\")[[\"geometry\"]]\n\n    elif technology == \"custom\":\n        cells_orig = gpd.read_parquet(path_cell_boundaries)\n\n    # Transform geometries\n    cells_orig[\"GEOMETRY\"] = batch_transform_geometries(cells_orig[\"geometry\"], transformation_matrix, image_scale)\n\n    # Convert transformed geometries to polygons and calculate centroids\n    cells_orig[\"polygon\"] = cells_orig[\"GEOMETRY\"].apply(lambda x: Polygon(x[0]))\n    gdf_cells = gpd.GeoDataFrame(geometry=cells_orig[\"polygon\"])\n    gdf_cells[\"center_x\"] = gdf_cells.geometry.centroid.x\n    gdf_cells[\"center_y\"] = gdf_cells.geometry.centroid.y\n    gdf_cells[\"GEOMETRY\"] = cells_orig[\"GEOMETRY\"]\n\n    # Ensure the output directory exists\n    if not os.path.exists(path_output):\n        os.makedirs(path_output)\n\n    # Calculate tile bounds and fine/coarse tiles\n    x_min, x_max = tile_bounds[\"x_min\"], tile_bounds[\"x_max\"]\n    y_min, y_max = tile_bounds[\"y_min\"], tile_bounds[\"y_max\"]\n    n_fine_tiles_x = int(np.ceil((x_max - x_min) / tile_size))\n    n_fine_tiles_y = int(np.ceil((y_max - y_min) / tile_size))\n    n_coarse_tiles_x = int(np.ceil((x_max - x_min) / (coarse_tile_factor * tile_size)))\n    n_coarse_tiles_y = int(np.ceil((y_max - y_min) / (coarse_tile_factor * tile_size)))\n\n    # Process coarse tiles in parallel\n    for i in tqdm(range(n_coarse_tiles_x), desc=\"Processing coarse tiles\"):\n        coarse_tile_x_min = x_min + i * (coarse_tile_factor * tile_size)\n        coarse_tile_x_max = coarse_tile_x_min + (coarse_tile_factor * tile_size)\n\n        for j in range(n_coarse_tiles_y):\n            coarse_tile_y_min = y_min + j * (coarse_tile_factor * tile_size)\n            coarse_tile_y_max = coarse_tile_y_min + (coarse_tile_factor * tile_size)\n\n            coarse_tile = gdf_cells[\n                (gdf_cells[\"center_x\"] &gt;= coarse_tile_x_min) &amp; (gdf_cells[\"center_x\"] &lt; coarse_tile_x_max) &amp;\n                (gdf_cells[\"center_y\"] &gt;= coarse_tile_y_min) &amp; (gdf_cells[\"center_y\"] &lt; coarse_tile_y_max)\n            ]\n            if not coarse_tile.empty:\n                process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y)\n</code></pre>"},{"location":"python/api/#celldega.pre.make_deepzoom_pyramid","title":"<code>make_deepzoom_pyramid(image_path, output_path, pyramid_name, tile_size=512, overlap=0, suffix='.jpeg')</code>","text":"<p>Create a DeepZoom image pyramid from a JPEG image</p>"},{"location":"python/api/#celldega.pre.make_deepzoom_pyramid--parameters","title":"Parameters","text":"<p>image_path : str     Path to the JPEG image file tile_size : int (default=512)     Tile size for the DeepZoom pyramid overlap : int (default=0)     Overlap size for the DeepZoom pyramid suffix : str (default='jpeg')     Suffix for the DeepZoom pyramid tiles</p>"},{"location":"python/api/#celldega.pre.make_deepzoom_pyramid--returns","title":"Returns","text":"<p>None</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_deepzoom_pyramid(\n    image_path, output_path, pyramid_name, tile_size=512, overlap=0, suffix=\".jpeg\"\n):\n    \"\"\"\n    Create a DeepZoom image pyramid from a JPEG image\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the JPEG image file\n    tile_size : int (default=512)\n        Tile size for the DeepZoom pyramid\n    overlap : int (default=0)\n        Overlap size for the DeepZoom pyramid\n    suffix : str (default='jpeg')\n        Suffix for the DeepZoom pyramid tiles\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n\n    # Define the output path\n    output_path = Path(output_path)\n\n    # Load the JPEG image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # check if the output path exists and create it if it does not\n    output_path.mkdir(parents=True, exist_ok=True)\n\n    # append the pyramid name to the output path\n    output_path = output_path / pyramid_name\n\n    # Save the image as a DeepZoom image pyramid\n    image.dzsave(output_path, tile_size=tile_size, overlap=overlap, suffix=suffix)\n</code></pre>"},{"location":"python/api/#celldega.pre.make_meta_cell_image_coord","title":"<code>make_meta_cell_image_coord(technology, path_transformation_matrix, path_meta_cell_micron, path_meta_cell_image, image_scale)</code>","text":"<p>Apply an affine transformation to the cell coordinates in microns and save the transformed coordinates in pixels</p>"},{"location":"python/api/#celldega.pre.make_meta_cell_image_coord--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the data, Xenium and MERSCOPE are supported. path_transformation_matrix : str     Path to the transformation matrix file path_meta_cell_micron : str     Path to the meta cell file with coordinates in microns path_meta_cell_image : str     Path to save the meta cell file with coordinates in pixels</p>"},{"location":"python/api/#celldega.pre.make_meta_cell_image_coord--returns","title":"Returns","text":"<p>None</p>"},{"location":"python/api/#celldega.pre.make_meta_cell_image_coord--examples","title":"Examples","text":"<p>make_meta_cell_image_coord( ...     technology='Xenium', ...     path_transformation_matrix='data/transformation_matrix.txt', ...     path_meta_cell_micron='data/meta_cell_micron.csv', ...     path_meta_cell_image='data/meta_cell_image.parquet' ... )</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_meta_cell_image_coord(\n    technology,\n    path_transformation_matrix,\n    path_meta_cell_micron,\n    path_meta_cell_image,\n    image_scale\n):\n    \"\"\"\n    Apply an affine transformation to the cell coordinates in microns and save\n    the transformed coordinates in pixels\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the data, Xenium and MERSCOPE are supported.\n    path_transformation_matrix : str\n        Path to the transformation matrix file\n    path_meta_cell_micron : str\n        Path to the meta cell file with coordinates in microns\n    path_meta_cell_image : str\n        Path to save the meta cell file with coordinates in pixels\n\n    Returns\n    -------\n    None\n\n    Examples\n    --------\n    &gt;&gt;&gt; make_meta_cell_image_coord(\n    ...     technology='Xenium',\n    ...     path_transformation_matrix='data/transformation_matrix.txt',\n    ...     path_meta_cell_micron='data/meta_cell_micron.csv',\n    ...     path_meta_cell_image='data/meta_cell_image.parquet'\n    ... )\n\n    \"\"\"\n\n    transformation_matrix = pd.read_csv(\n        path_transformation_matrix, header=None, sep=\" \"\n    ).values\n\n    if technology == \"MERSCOPE\":\n        meta_cell = pd.read_csv(path_meta_cell_micron, usecols=[\"EntityID\", \"center_x\", \"center_y\"])\n        meta_cell = convert_long_id_to_short(meta_cell)\n        meta_cell[\"name\"] =  meta_cell[\"cell_id\"]\n        meta_cell = meta_cell.set_index('cell_id')\n    elif technology == \"Xenium\":\n        usecols = [\"cell_id\", \"x_centroid\", \"y_centroid\"]\n        meta_cell = pd.read_csv(path_meta_cell_micron, index_col=0, usecols=usecols)\n        meta_cell.columns = [\"center_x\", \"center_y\"]\n        meta_cell[\"name\"] = pd.Series(meta_cell.index, index=meta_cell.index)\n\n    # Adding a ones column to accommodate for affine transformation\n    meta_cell[\"ones\"] = 1\n\n    # Preparing the data for matrix multiplication\n    points = meta_cell[[\"center_x\", \"center_y\", \"ones\"]].values\n\n    # Applying the transformation matrix\n    transformed_points = np.dot(transformation_matrix, points.T).T\n\n    # Updating the DataFrame with transformed coordinates\n    meta_cell[\"center_x\"] = transformed_points[:, 0]\n    meta_cell[\"center_y\"] = transformed_points[:, 1]\n\n    # Dropping the ones column as it's no longer needed\n    meta_cell.drop(columns=[\"ones\"], inplace=True)\n\n    meta_cell[\"center_x\"] = meta_cell[\"center_x\"] / image_scale\n    meta_cell[\"center_y\"] = meta_cell[\"center_y\"] / image_scale\n\n    meta_cell[\"geometry\"] = meta_cell.apply(\n        lambda row: [row[\"center_x\"], row[\"center_y\"]], axis=1\n    )\n\n    if technology == \"MERSCOPE\":\n        meta_cell = meta_cell[[\"name\", \"geometry\", \"EntityID\"]]\n    else:\n        meta_cell = meta_cell[[\"name\", \"geometry\"]]\n\n\n    meta_cell.to_parquet(path_meta_cell_image)\n</code></pre>"},{"location":"python/api/#celldega.pre.make_meta_gene","title":"<code>make_meta_gene(technology, path_cbg, path_output)</code>","text":"<p>Create a DataFrame with genes and their assigned colors</p>"},{"location":"python/api/#celldega.pre.make_meta_gene--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the data, Xenium and MERSCOPE are supported. path_cbg : str     Path to the cell-by-gene matrix data (the data format can vary based on technology) path_output : str     Path to save the meta gene file</p>"},{"location":"python/api/#celldega.pre.make_meta_gene--returns","title":"Returns","text":"<p>None</p>"},{"location":"python/api/#celldega.pre.make_meta_gene--examples","title":"Examples","text":"<p>make_meta_gene( ...     technology='Xenium', ...     path_cbg='data/', ...     path_output='data/meta_gene.parquet' ... )</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_meta_gene(technology, path_cbg, path_output):\n    \"\"\"\n    Create a DataFrame with genes and their assigned colors\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the data, Xenium and MERSCOPE are supported.\n    path_cbg : str\n        Path to the cell-by-gene matrix data (the data format can vary based on technology)\n    path_output : str\n        Path to save the meta gene file\n\n    Returns\n    -------\n    None\n\n    Examples\n    --------\n    &gt;&gt;&gt; make_meta_gene(\n    ...     technology='Xenium',\n    ...     path_cbg='data/',\n    ...     path_output='data/meta_gene.parquet'\n    ... )\n    \"\"\"\n\n    if technology == \"MERSCOPE\":\n        cbg = pd.read_csv(path_cbg, index_col=0)\n        genes = cbg.columns.tolist()\n    elif technology == \"Xenium\":\n        # genes = pd.read_csv(path_cbg + 'features.tsv.gz', sep='\\t', header=None)[1].values.tolist()\n        cbg = read_cbg_mtx(path_cbg)\n        genes = cbg.columns.tolist()\n\n    # Get all categorical color palettes from Matplotlib and flatten them into a single list of colors\n    palettes = [plt.get_cmap(name).colors for name in plt.colormaps() if \"tab\" in name]\n    flat_colors = [color for palette in palettes for color in palette]\n\n    # Convert RGB tuples to hex codes\n    flat_colors_hex = [to_hex(color) for color in flat_colors]\n\n    # Use modular arithmetic to assign a color to each gene, white for genes with \"Blank\"\n    colors = [\n        flat_colors_hex[i % len(flat_colors_hex)] if \"Blank\" not in gene else \"#FFFFFF\"\n        for i, gene in enumerate(genes)\n    ]\n\n    # Create a DataFrame with genes and their assigned colors\n    ser_color = pd.Series(colors, index=genes)\n\n    # calculate gene expression metadata\n    meta_gene = calc_meta_gene_data(cbg)\n    meta_gene['color'] = ser_color\n\n    # Identify sparse columns\n    sparse_cols = [col for col in meta_gene.columns if pd.api.types.is_sparse(meta_gene[col])]\n\n    # Convert sparse columns to dense\n    for col in sparse_cols:\n        meta_gene[col] = meta_gene[col].sparse.to_dense()\n\n    meta_gene.to_parquet(path_output)\n</code></pre>"},{"location":"python/api/#celldega.pre.make_trx_tiles","title":"<code>make_trx_tiles(technology, path_trx, path_transformation_matrix, path_trx_tiles, coarse_tile_factor=10, tile_size=250, chunk_size=1000000, verbose=False, image_scale=1, max_workers=8)</code>","text":"<p>Processes transcript data by dividing it into coarse-grain and fine-grain tiles, applying transformations, and saving the results in a parallelized manner.</p>"},{"location":"python/api/#celldega.pre.make_trx_tiles--parameters","title":"Parameters","text":"<p>technology : str     The technology used for generating the transcript data (e.g., \"MERSCOPE\" or \"Xenium\"). path_trx : str     Path to the file containing the transcript data. path_transformation_matrix : str     Path to the file containing the transformation matrix (CSV file). path_trx_tiles : str     Directory path where the output files (Parquet files) for each tile will be saved. coarse_tile_factor : int, optional     Scaling factor of each coarse-grain tile comparing to the fine tile size. tile_size : int, optional     Size of each fine-grain tile in microns (default is 250). chunk_size : int, optional     Number of rows to process per chunk for memory efficiency (default is 1000000). verbose : bool, optional     Flag to enable verbose output (default is False). image_scale : float, optional     Scale factor to apply to the transcript coordinates (default is 0.5). max_workers : int, optional     Maximum number of parallel workers for processing tiles (default is 8).</p>"},{"location":"python/api/#celldega.pre.make_trx_tiles--returns","title":"Returns","text":"<p>dict     A dictionary containing the bounds of the processed data in both x and y directions.</p> Source code in <code>src/celldega/pre/trx_tile.py</code> <pre><code>def make_trx_tiles(\n    technology,\n    path_trx,\n    path_transformation_matrix,\n    path_trx_tiles,\n    coarse_tile_factor=10,\n    tile_size=250,\n    chunk_size=1000000,\n    verbose=False,\n    image_scale=1,\n    max_workers=8\n):\n    \"\"\"\n    Processes transcript data by dividing it into coarse-grain and fine-grain tiles,\n    applying transformations, and saving the results in a parallelized manner.\n\n    Parameters\n    ----------\n    technology : str\n        The technology used for generating the transcript data (e.g., \"MERSCOPE\" or \"Xenium\").\n    path_trx : str\n        Path to the file containing the transcript data.\n    path_transformation_matrix : str\n        Path to the file containing the transformation matrix (CSV file).\n    path_trx_tiles : str\n        Directory path where the output files (Parquet files) for each tile will be saved.\n    coarse_tile_factor : int, optional\n        Scaling factor of each coarse-grain tile comparing to the fine tile size.\n    tile_size : int, optional\n        Size of each fine-grain tile in microns (default is 250).\n    chunk_size : int, optional\n        Number of rows to process per chunk for memory efficiency (default is 1000000).\n    verbose : bool, optional\n        Flag to enable verbose output (default is False).\n    image_scale : float, optional\n        Scale factor to apply to the transcript coordinates (default is 0.5).\n    max_workers : int, optional\n        Maximum number of parallel workers for processing tiles (default is 8).\n\n    Returns\n    -------\n    dict\n        A dictionary containing the bounds of the processed data in both x and y directions.\n    \"\"\"\n\n    def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers):\n        # Filter the entire dataset for the current coarse tile\n        coarse_tile = trx.filter(\n            (pl.col(\"transformed_x\") &gt;= coarse_tile_x_min) &amp; (pl.col(\"transformed_x\") &lt; coarse_tile_x_max) &amp;\n            (pl.col(\"transformed_y\") &gt;= coarse_tile_y_min) &amp; (pl.col(\"transformed_y\") &lt; coarse_tile_y_max)\n        )\n\n        if not coarse_tile.is_empty():\n            # Now process fine tiles using global fine tile indices\n            process_fine_tiles(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers)   \n\n\n    def process_fine_tiles(coarse_tile, coarse_i, coarse_j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=8):\n\n        # Use ThreadPoolExecutor for parallel processing of fine-grain tiles within the coarse tile\n        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n            futures = []\n\n            # Iterate over fine-grain tiles within the global bounds\n            for fine_i in range(n_fine_tiles_x):\n                fine_tile_x_min = x_min + fine_i * tile_size\n                fine_tile_x_max = fine_tile_x_min + tile_size\n\n                # Process only if the fine tile falls within the current coarse tile's bounds\n                if not (fine_tile_x_min &gt;= coarse_tile_x_min and fine_tile_x_max &lt;= coarse_tile_x_max):\n                    continue\n\n                for fine_j in range(n_fine_tiles_y):\n                    fine_tile_y_min = y_min + fine_j * tile_size\n                    fine_tile_y_max = fine_tile_y_min + tile_size\n\n                    # Process only if the fine tile falls within the current coarse tile's bounds\n                    if not (fine_tile_y_min &gt;= coarse_tile_y_min and fine_tile_y_max &lt;= coarse_tile_y_max):\n                        continue\n\n                    # Submit the task for each fine tile to process in parallel\n                    futures.append(executor.submit(\n                        filter_and_save_fine_tile, coarse_tile, coarse_i, coarse_j, fine_i, fine_j, \n                        fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles\n                    ))\n\n            # Wait for all futures to complete\n            for future in concurrent.futures.as_completed(futures):\n                future.result()  # Raise exceptions if any occurred during execution\n\n\n    def filter_and_save_fine_tile(coarse_tile, coarse_i, coarse_j, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles):\n\n        # Filter the coarse tile for the current fine tile's boundaries\n        fine_tile_trx = coarse_tile.filter(\n            (pl.col(\"transformed_x\") &gt;= fine_tile_x_min) &amp; (pl.col(\"transformed_x\") &lt; fine_tile_x_max) &amp;\n            (pl.col(\"transformed_y\") &gt;= fine_tile_y_min) &amp; (pl.col(\"transformed_y\") &lt; fine_tile_y_max)\n        )\n\n        if not fine_tile_trx.is_empty():\n            # Add geometry column as a list of [x, y] pairs\n            fine_tile_trx = fine_tile_trx.with_columns(\n                pl.concat_list([pl.col(\"transformed_x\"), pl.col(\"transformed_y\")]).alias(\"geometry\")\n            ).drop(['transformed_x', 'transformed_y'])\n\n            # Define the filename based on fine tile coordinates\n            filename = f\"{path_trx_tiles}/transcripts_tile_{fine_i}_{fine_j}.parquet\"\n\n            # Save the filtered DataFrame to a Parquet file\n            fine_tile_trx.to_pandas().to_parquet(filename)\n\n\n    # Load transformation matrix\n    transformation_matrix = np.loadtxt(path_transformation_matrix)\n\n    # Load the transcript data based on the technology using Polars\n    if technology == \"MERSCOPE\":\n        trx_ini = pl.read_csv(path_trx, columns=[\"gene\", \"global_x\", \"global_y\"])\n        trx_ini = trx_ini.with_columns([\n            pl.col(\"global_x\").alias(\"x\"),\n            pl.col(\"global_y\").alias(\"y\"),\n            pl.col(\"gene\").alias(\"name\")\n        ]).select([\"name\", \"x\", \"y\"])\n\n    elif technology == \"Xenium\":\n        trx_ini = pl.read_parquet(path_trx).select([\n            pl.col(\"feature_name\").alias(\"name\"),\n            pl.col(\"x_location\").alias(\"x\"),\n            pl.col(\"y_location\").alias(\"y\")\n        ])\n\n    # Process the data in chunks and apply transformations\n    all_chunks = []\n\n    for start_row in tqdm(range(0, trx_ini.height, chunk_size), desc=\"Processing chunks\"):\n        chunk = trx_ini.slice(start_row, chunk_size)\n\n        # Apply transformation matrix to the coordinates\n        points = np.hstack([chunk.select([\"x\", \"y\"]).to_numpy(), np.ones((chunk.height, 1))])\n        transformed_points = np.dot(points, transformation_matrix.T)[:, :2]\n\n        # Create new transformed columns and drop original x, y columns\n        transformed_chunk = chunk.with_columns([\n            (pl.Series(transformed_points[:, 0]) * image_scale).round(2).alias(\"transformed_x\"),\n            (pl.Series(transformed_points[:, 1]) * image_scale).round(2).alias(\"transformed_y\")\n        ]).drop([\"x\", \"y\"])\n        all_chunks.append(transformed_chunk)\n\n    # Concatenate all chunks after processing\n    trx = pl.concat(all_chunks)\n\n    # Ensure the output directory exists\n    if not os.path.exists(path_trx_tiles):\n        os.makedirs(path_trx_tiles)\n\n    # Get min and max x, y values\n    x_min, x_max = trx.select([\n        pl.col(\"transformed_x\").min().alias(\"x_min\"),\n        pl.col(\"transformed_x\").max().alias(\"x_max\")\n    ]).row(0)\n\n    y_min, y_max = trx.select([\n        pl.col(\"transformed_y\").min().alias(\"y_min\"),\n        pl.col(\"transformed_y\").max().alias(\"y_max\")\n    ]).row(0)\n\n    # Calculate the number of fine-grain tiles globally\n    n_fine_tiles_x = int(np.ceil((x_max - x_min) / tile_size))\n    n_fine_tiles_y = int(np.ceil((y_max - y_min) / tile_size))\n\n    # Calculate the number of coarse-grain tiles\n    n_coarse_tiles_x = int(np.ceil((x_max - x_min) / (coarse_tile_factor * tile_size)))\n    n_coarse_tiles_y = int(np.ceil((y_max - y_min) / (coarse_tile_factor * tile_size)))\n\n    # Use ThreadPoolExecutor for parallel processing of coarse-grain tiles\n    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n        futures = []\n        for i in range(n_coarse_tiles_x):\n            coarse_tile_x_min = x_min + i * (coarse_tile_factor * tile_size)\n            coarse_tile_x_max = coarse_tile_x_min + (coarse_tile_factor * tile_size)\n\n            for j in range(n_coarse_tiles_y):\n                coarse_tile_y_min = y_min + j * (coarse_tile_factor * tile_size)\n                coarse_tile_y_max = coarse_tile_y_min + (coarse_tile_factor * tile_size)\n\n                # Submit each coarse tile for parallel processing\n                futures.append(executor.submit(\n                    process_coarse_tile, trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers\n                ))\n\n        # Wait for all coarse tiles to complete\n        for future in tqdm(concurrent.futures.as_completed(futures), desc=\"Processing coarse tiles\", unit=\"tile\"):\n            future.result()  # Raise exceptions if any occurred during execution\n\n    # Return the tile bounds\n    tile_bounds = {\n        \"x_min\": x_min,\n        \"x_max\": x_max,\n        \"y_min\": y_min,\n        \"y_max\": y_max,\n    }\n\n    return tile_bounds\n</code></pre>"},{"location":"python/api/#celldega.pre.reduce_image_size","title":"<code>reduce_image_size(image_path, scale_image=0.5, path_landscape_files='')</code>","text":""},{"location":"python/api/#celldega.pre.reduce_image_size--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file scale_image : float (default=0.5)     Scale factor for the image resize</p>"},{"location":"python/api/#celldega.pre.reduce_image_size--returns","title":"Returns","text":"<p>new_image_path : str     Path to the resized image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def reduce_image_size(image_path, scale_image=0.5, path_landscape_files=\"\"):\n    \"\"\"\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    scale_image : float (default=0.5)\n        Scale factor for the image resize\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the resized image file\n    \"\"\"\n\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    resized_image = image.resize(scale_image)\n\n    new_image_name = image_path.split(\"/\")[-1].replace(\".tif\", \"_downsize.tif\")\n    new_image_path = f\"{path_landscape_files}/{new_image_name}\"\n    resized_image.write_to_file(new_image_path)\n\n    return new_image_path\n</code></pre>"},{"location":"python/api/#celldega.pre.save_landscape_parameters","title":"<code>save_landscape_parameters(technology, path_landscape_files, image_name='dapi_files', tile_size=1000, image_info={}, image_format='.webp')</code>","text":"<p>Save the landscape parameters to a JSON file.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def save_landscape_parameters(\n    technology, path_landscape_files, image_name=\"dapi_files\", tile_size=1000, image_info={}, image_format='.webp'\n):\n    \"\"\"\n    Save the landscape parameters to a JSON file.\n    \"\"\"\n\n    path_image_pyramid = f\"{path_landscape_files}/pyramid_images/{image_name}\"\n\n    print(path_image_pyramid)\n\n    max_pyramid_zoom = get_max_zoom_level(path_image_pyramid)\n\n    landscape_parameters = {\n        \"technology\": technology,\n        \"max_pyramid_zoom\": max_pyramid_zoom,\n        \"tile_size\": tile_size,\n        \"image_info\": image_info,\n        \"image_format\": image_format\n    }\n\n    path_landscape_parameters = f\"{path_landscape_files}/landscape_parameters.json\"\n\n    with open(path_landscape_parameters, \"w\") as file:\n        json.dump(landscape_parameters, file, indent=4)\n</code></pre>"},{"location":"python/api/#celldega.viz.Landscape","title":"<code>Landscape</code>","text":"<p>               Bases: <code>AnyWidget</code></p> <p>A widget for interactive visualization of spatial omics data. This widget currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)</p> <p>Parameters:</p> Name Type Description Default <code>ini_x</code> <code>float</code> <p>The initial x-coordinate of the view.</p> required <code>ini_y</code> <code>float</code> <p>The initial y-coordinate of the view.</p> required <code>ini_zoom</code> <code>float</code> <p>The initial zoom level of the view.</p> required <code>token</code> <code>str</code> <p>The token traitlet.</p> required <code>base_url</code> <code>str</code> <p>The base URL for the widget.</p> required <code>dataset_name</code> <code>str</code> <p>The name of the dataset to visualize. This will show up in the user interface bar.</p> required <p>Attributes:</p> Name Type Description <code>component</code> <code>str</code> <p>The name of the component.</p> <code>technology</code> <code>str</code> <p>The technology used.</p> <code>base_url</code> <code>str</code> <p>The base URL for the widget.</p> <code>token</code> <code>str</code> <p>The token traitlet.</p> <code>ini_x</code> <code>float</code> <p>The initial x-coordinate of the view.</p> <code>ini_y</code> <code>float</code> <p>The initial y-coordinate of the view.</p> <code>ini_z</code> <code>float</code> <p>The initial z-coordinate of the view.</p> <code>ini_zoom</code> <code>float</code> <p>The initial zoom level of the view.</p> <code>dataset_name</code> <code>str</code> <p>The name of the dataset to visualize.</p> <code>update_trigger</code> <code>dict</code> <p>The dictionary to trigger updates.</p> <code>cell_clusters</code> <code>dict</code> <p>The dictionary containing cell cluster information.</p> <p>Returns:</p> Name Type Description <code>Landscape</code> <p>A widget for visualizing a 'landscape' view of spatial omics data.</p> Source code in <code>src/celldega/viz/widget.py</code> <pre><code>class Landscape(anywidget.AnyWidget):\n    \"\"\"\n    A widget for interactive visualization of spatial omics data. This widget\n    currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)\n\n    Args:\n        ini_x (float): The initial x-coordinate of the view.\n        ini_y (float): The initial y-coordinate of the view.\n        ini_zoom (float): The initial zoom level of the view.\n        token (str): The token traitlet.\n        base_url (str): The base URL for the widget.\n        dataset_name (str, optional): The name of the dataset to visualize. This will show up in the user interface bar.\n\n    Attributes:\n        component (str): The name of the component.\n        technology (str): The technology used.\n        base_url (str): The base URL for the widget.\n        token (str): The token traitlet.\n        ini_x (float): The initial x-coordinate of the view.\n        ini_y (float): The initial y-coordinate of the view.\n        ini_z (float): The initial z-coordinate of the view.\n        ini_zoom (float): The initial zoom level of the view.\n        dataset_name (str): The name of the dataset to visualize.\n        update_trigger (dict): The dictionary to trigger updates.\n        cell_clusters (dict): The dictionary containing cell cluster information.\n\n    Returns:\n        Landscape: A widget for visualizing a 'landscape' view of spatial omics data.\n    \"\"\"\n    _esm = pathlib.Path(__file__).parent / \"../static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"../static\" / \"widget.css\"\n    component = traitlets.Unicode(\"Landscape\").tag(sync=True)\n\n    technology = traitlets.Unicode(\"sst\").tag(sync=True)\n    base_url = traitlets.Unicode(\"\").tag(sync=True)\n    token = traitlets.Unicode(\"\").tag(sync=True)\n    ini_x = traitlets.Float(1000).tag(sync=True)\n    ini_y = traitlets.Float(1000).tag(sync=True)\n    ini_z = traitlets.Float(0).tag(sync=True)\n    ini_zoom = traitlets.Float(0).tag(sync=True)\n    square_tile_size = traitlets.Float(1.4).tag(sync=True)\n    dataset_name = traitlets.Unicode(\"\").tag(sync=True)\n    region = traitlets.Dict({}).tag(sync=True)\n    nbhd = traitlets.Dict({}).tag(sync=True)\n\n    update_trigger = traitlets.Dict().tag(sync=True)\n    cell_clusters = traitlets.Dict().tag(sync=True)\n\n    width = traitlets.Int(0).tag(sync=True)\n    height = traitlets.Int(800).tag(sync=True)\n\n    def trigger_update(self, new_value):\n        # This method updates the update_trigger traitlet with a new value\n        # You can pass any information necessary for the update, or just a timestamp\n        self.update_trigger = new_value\n\n    def update_cell_clusters(self, new_clusters):\n        # Convert the new_clusters to a JSON serializable format if necessary\n        self.cell_clusters = new_clusters\n</code></pre>"},{"location":"python/api/#celldega.viz.Matrix","title":"<code>Matrix</code>","text":"<p>               Bases: <code>AnyWidget</code></p> <p>A widget for interactive visualization of a hierarchically clustered matrix.</p> <p>Parameters:</p> Name Type Description Default <code>value</code> <code>int</code> <p>The value traitlet.</p> required <code>component</code> <code>str</code> <p>The component traitlet.</p> required <code>network</code> <code>dict</code> <p>The network traitlet.</p> required <code>click_info</code> <code>dict</code> <p>The click_info traitlet.</p> required <p>Attributes:</p> Name Type Description <code>component</code> <code>str</code> <p>The name of the component.</p> <code>network</code> <code>dict</code> <p>The network dictionary.</p> <code>click_info</code> <code>dict</code> <p>The click_info dictionary.</p> <p>Returns:</p> Name Type Description <code>Matrix</code> <p>A widget for visualizing a hierarchically clustered matrix.</p> Source code in <code>src/celldega/viz/widget.py</code> <pre><code>class Matrix(anywidget.AnyWidget):\n    \"\"\"\n    A widget for interactive visualization of a hierarchically clustered matrix.\n\n    Args:\n        value (int): The value traitlet.\n        component (str): The component traitlet.\n        network (dict): The network traitlet.\n        click_info (dict): The click_info traitlet.\n\n    Attributes:\n        component (str): The name of the component.\n        network (dict): The network dictionary.\n        click_info (dict): The click_info dictionary.\n\n    Returns:\n        Matrix: A widget for visualizing a hierarchically clustered matrix.\n    \"\"\"\n    _esm = pathlib.Path(__file__).parent / \"../static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"../static\" / \"widget.css\"\n    value = traitlets.Int(0).tag(sync=True)\n    component = traitlets.Unicode(\"Matrix\").tag(sync=True)\n\n    network = traitlets.Dict({}).tag(sync=True)\n    click_info = traitlets.Dict({}).tag(sync=True)\n</code></pre>"},{"location":"python/nbhd/api/","title":"Neighborhood Module API Reference","text":""},{"location":"python/nbhd/api/#celldega.nbhd.alpha_shape_cell_clusters","title":"<code>alpha_shape_cell_clusters(meta_cell, cat='cluster', alphas=[100, 150, 200, 250, 300, 350])</code>","text":"<p>Compute alpha shapes for each cluster in the cell metadata.</p> <p>Parameters: - meta_cell: GeoDataFrame of cell metadata. - cat: Column name in meta_cell containing the cluster labels. - alphas: List of alpha values to compute shapes for.</p> <p>Returns: - GeoDataFrame of alpha shapes.</p> Source code in <code>src/celldega/nbhd/__init__.py</code> <pre><code>def alpha_shape_cell_clusters(meta_cell, cat='cluster', alphas=[100, 150, 200, 250, 300, 350]):\n\n    \"\"\"\n    Compute alpha shapes for each cluster in the cell metadata.\n\n    Parameters:\n    - meta_cell: GeoDataFrame of cell metadata.\n    - cat: Column name in meta_cell containing the cluster labels.\n    - alphas: List of alpha values to compute shapes for.\n\n    Returns:\n    - GeoDataFrame of alpha shapes.\n\n    \"\"\"\n\n    gdf_alpha = gpd.GeoDataFrame()\n\n    for inv_alpha in alphas:\n\n        for inst_cluster in meta_cell[cat].unique():\n\n            inst_clust = meta_cell[meta_cell[cat] == inst_cluster]\n\n            if inst_clust.shape[0]&gt; 3:\n\n                nested_array = inst_clust['geometry'].values\n\n                # Convert to a 2D NumPy array\n                flat_array = np.vstack(nested_array)\n\n                inst_shape = alpha_shape(flat_array, inv_alpha)\n\n                inst_name = inst_cluster + '_' + str(inv_alpha)\n\n                gdf_alpha.loc[inst_name, 'name'] = inst_name\n\n                gdf_alpha.loc[inst_name, 'cat'] = inst_cluster\n\n                gdf_alpha.loc[inst_name, 'geometry'] = inst_shape\n\n                gdf_alpha.loc[inst_name, 'inv_alpha'] = int(inv_alpha)\n\n    gdf_alpha[\"geometry\"] = gdf_alpha[\"geometry\"].apply(lambda geom: _round_coordinates(geom, precision=2))\n\n    gdf_alpha['area'] = gdf_alpha.area\n\n    gdf_alpha = gdf_alpha.loc[gdf_alpha.area.sort_values(ascending=False).index.tolist()]\n\n    return gdf_alpha\n</code></pre>"},{"location":"python/pre/api/","title":"Pre Module API Reference","text":"<p>Module for pre-processing to generate LandscapeFiles from ST data.</p>"},{"location":"python/pre/api/#celldega.pre.convert_long_id_to_short","title":"<code>convert_long_id_to_short(df)</code>","text":"<p>Converts a column of long integer cell IDs in a DataFrame to a shorter, hash-based representation.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>The DataFrame containing the EntityID.</p> required <p>Returns:     pd.DataFrame: The original DataFrame with an additional column named <code>cell_id</code>                   containing the shortened cell IDs.</p> <p>The function applies a SHA-256 hash to each cell ID, encodes the hash using base64, and truncates it to create a shorter identifier that is added as a new column to the DataFrame.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_long_id_to_short(df):\n    \"\"\"\n    Converts a column of long integer cell IDs in a DataFrame to a shorter, hash-based representation.\n\n    Args:\n        df (pd.DataFrame): The DataFrame containing the EntityID.\n    Returns:\n        pd.DataFrame: The original DataFrame with an additional column named `cell_id`\n                      containing the shortened cell IDs.\n\n    The function applies a SHA-256 hash to each cell ID, encodes the hash using base64, and truncates\n    it to create a shorter identifier that is added as a new column to the DataFrame.\n    \"\"\"\n    # Function to hash and encode the cell ID\n    def hash_and_shorten_id(cell_id):\n        # Create a hash of the cell ID\n        cell_id_bytes = str(cell_id).encode('utf-8')\n        hash_object = hashlib.sha256(cell_id_bytes)\n        hash_digest = hash_object.digest()\n\n        # Encode the hash to a base64 string to mix letters and numbers, truncate to 9 characters\n        short_id = base64.urlsafe_b64encode(hash_digest).decode('utf-8')[:9]\n        return short_id\n\n    # Apply the hash_and_shorten_id function to each cell ID in the specified column\n    df['cell_id'] = df['EntityID'].apply(hash_and_shorten_id)\n\n    return df\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.convert_to_jpeg","title":"<code>convert_to_jpeg(image_path, quality=80)</code>","text":"<p>Convert a TIFF image to a JPEG image with a quality of score</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_jpeg--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=80)     Quality score for the JPEG image</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_jpeg--returns","title":"Returns","text":"<p>new_image_path : str     Path to the JPEG image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_jpeg(image_path, quality=80):\n    \"\"\"\n    Convert a TIFF image to a JPEG image with a quality of score\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=80)\n        Quality score for the JPEG image\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the JPEG image file\n\n    \"\"\"\n\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a JPEG with a quality of 80\n    new_image_path = image_path.replace(\".tif\", \".jpeg\")\n    image.jpegsave(new_image_path, Q=quality)\n\n    return new_image_path\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.convert_to_png","title":"<code>convert_to_png(image_path)</code>","text":"<p>Convert a TIFF image to a JPEG image with a quality of score</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_png--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=80)     Quality score for the JPEG image</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_png--returns","title":"Returns","text":"<p>new_image_path : str     Path to the JPEG image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_png(image_path):\n    \"\"\"\n    Convert a TIFF image to a JPEG image with a quality of score\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=80)\n        Quality score for the JPEG image\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the JPEG image file\n\n    \"\"\"\n\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a JPEG with a quality of 80\n    new_image_path = image_path.replace(\".tif\", \".png\")\n    image.pngsave(new_image_path)\n\n    return new_image_path\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.convert_to_webp","title":"<code>convert_to_webp(image_path, quality=100)</code>","text":"<p>Convert a TIFF image to a WEBP image with a specified quality score.</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_webp--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file quality : int (default=100)     Quality score for the WEBP image (higher is better quality)</p>"},{"location":"python/pre/api/#celldega.pre.convert_to_webp--returns","title":"Returns","text":"<p>new_image_path : str     Path to the WEBP image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def convert_to_webp(image_path, quality=100):\n    \"\"\"\n    Convert a TIFF image to a WEBP image with a specified quality score.\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    quality : int (default=100)\n        Quality score for the WEBP image (higher is better quality)\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the WEBP image file\n    \"\"\"\n    # Load the TIFF image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # Save the image as a WEBP with specified quality\n    new_image_path = image_path.replace(\".tif\", \".webp\")\n    image.webpsave(new_image_path, Q=quality)\n\n    return new_image_path\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.get_max_zoom_level","title":"<code>get_max_zoom_level(path_image_pyramid)</code>","text":"<p>Returns the maximum zoom level based on the highest-numbered directory in the specified path_image_pyramid.</p> <p>Parameters:</p> Name Type Description Default <code>path_image_pyramid</code> <code>str</code> <p>The path to the directory containing zoom level directories.</p> required <p>Returns:</p> Name Type Description <code>max_pyramid_zoom</code> <code>int</code> <p>The maximum zoom level.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def get_max_zoom_level(path_image_pyramid):\n    \"\"\"\n    Returns the maximum zoom level based on the highest-numbered directory\n    in the specified path_image_pyramid.\n\n    Parameters:\n        path_image_pyramid (str): The path to the directory containing zoom level directories.\n\n    Returns:\n        max_pyramid_zoom (int): The maximum zoom level.\n    \"\"\"\n    # List all entries in the path_image_pyramid that are directories and can be converted to integers\n    zoom_levels = [\n        entry\n        for entry in os.listdir(path_image_pyramid)\n        if os.path.isdir(os.path.join(path_image_pyramid, entry)) and entry.isdigit()\n    ]\n\n    # Convert to integer and find the maximum value\n    max_pyramid_zoom = max(map(int, zoom_levels)) if zoom_levels else None\n\n    return max_pyramid_zoom\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_cell_boundary_tiles","title":"<code>make_cell_boundary_tiles(technology, path_cell_boundaries, path_meta_cell_micron, path_transformation_matrix, path_output, coarse_tile_factor=20, tile_size=250, tile_bounds=None, image_scale=1, max_workers=8)</code>","text":"<p>Processes cell boundary data and divides it into spatial tiles based on the provided technology. Reads cell boundary data, applies affine transformations, and divides the data into coarse and fine tiles. The resulting tiles are saved as Parquet files, each containing the geometries of cells in that tile.</p>"},{"location":"python/pre/api/#celldega.pre.make_cell_boundary_tiles--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the cell boundary data, e.g., \"MERSCOPE\", \"Xenium\", or \"custom\". path_cell_boundaries : str     Path to the file containing the cell boundaries (Parquet format). path_meta_cell_micron : str     Path to the file containing cell metadata (CSV format). path_transformation_matrix : str     Path to the file containing the transformation matrix (CSV format). path_output : str     Directory path where the output files (Parquet files) for each tile will be saved. coarse_tile_factor  : int, optional, default=20.     scaling factor of each coarse-grain tile comparing to the fine tile size. tile_size : int, optional, default=500     Size of each fine-grain tile in microns. tile_bounds : dict, optional     Dictionary containing the minimum and maximum bounds for x and y coordinates. image_scale : float, optional, default=1     Scale factor to apply to the geometry data. max_workers : int, optional, default=8     Maximum number of parallel workers for processing tiles.</p>"},{"location":"python/pre/api/#celldega.pre.make_cell_boundary_tiles--returns","title":"Returns","text":"<p>None</p> Source code in <code>src/celldega/pre/boundary_tile.py</code> <pre><code>def make_cell_boundary_tiles(\n    technology,\n    path_cell_boundaries,\n    path_meta_cell_micron,\n    path_transformation_matrix,\n    path_output,\n    coarse_tile_factor=20,\n    tile_size=250,\n    tile_bounds=None,\n    image_scale=1,\n    max_workers=8\n):\n\n\n    \"\"\"\n    Processes cell boundary data and divides it into spatial tiles based on the provided technology.\n    Reads cell boundary data, applies affine transformations, and divides the data into coarse and fine tiles.\n    The resulting tiles are saved as Parquet files, each containing the geometries of cells in that tile.\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the cell boundary data, e.g., \"MERSCOPE\", \"Xenium\", or \"custom\".\n    path_cell_boundaries : str\n        Path to the file containing the cell boundaries (Parquet format).\n    path_meta_cell_micron : str\n        Path to the file containing cell metadata (CSV format).\n    path_transformation_matrix : str\n        Path to the file containing the transformation matrix (CSV format).\n    path_output : str\n        Directory path where the output files (Parquet files) for each tile will be saved.\n    coarse_tile_factor  : int, optional, default=20.\n        scaling factor of each coarse-grain tile comparing to the fine tile size.\n    tile_size : int, optional, default=500\n        Size of each fine-grain tile in microns.\n    tile_bounds : dict, optional\n        Dictionary containing the minimum and maximum bounds for x and y coordinates.\n    image_scale : float, optional, default=1\n        Scale factor to apply to the geometry data.\n    max_workers : int, optional, default=8\n        Maximum number of parallel workers for processing tiles.\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    def numpy_affine_transform(coords, matrix):\n        \"\"\"Apply affine transformation to numpy coordinates.\"\"\"\n        # Homogeneous coordinates for affine transformation\n        coords = np.hstack([coords, np.ones((coords.shape[0], 1))])\n        transformed_coords = coords @ matrix.T\n        return transformed_coords[:, :2]  # Drop the homogeneous coordinate\n\n    def batch_transform_geometries(geometries, transformation_matrix, scale):\n        \"\"\"\n        Batch transform geometries using numpy for optimized performance.\n        \"\"\"\n        # Extract affine transformation parameters into a 3x3 matrix for numpy\n        affine_matrix = np.array([\n            [transformation_matrix[0, 0], transformation_matrix[0, 1], transformation_matrix[0, 2]],\n            [transformation_matrix[1, 0], transformation_matrix[1, 1], transformation_matrix[1, 2]],\n            [0, 0, 1]\n        ])\n\n        transformed_geometries = []\n\n        for polygon in geometries:\n            # Extract coordinates and transform them\n            if isinstance(polygon, MultiPolygon):\n                polygon = next(polygon.geoms)  # Use the first geometry\n\n            # Transform the exterior of the polygon\n            exterior_coords = np.array(polygon.exterior.coords)\n\n            # Apply the affine transformation and scale\n            transformed_coords = numpy_affine_transform(exterior_coords, affine_matrix) / scale\n\n            # Append the result to the transformed_geometries list\n            transformed_geometries.append([transformed_coords.tolist()])\n\n        return transformed_geometries\n\n\n    def filter_and_save_fine_boundary(coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output):\n        cell_ids = coarse_tile.index.values\n\n        tile_filter = (\n            (coarse_tile[\"center_x\"] &gt;= fine_tile_x_min) &amp; (coarse_tile[\"center_x\"] &lt; fine_tile_x_max) &amp;\n            (coarse_tile[\"center_y\"] &gt;= fine_tile_y_min) &amp; (coarse_tile[\"center_y\"] &lt; fine_tile_y_max)\n        )\n        filtered_indices = np.where(tile_filter)[0]\n\n        keep_cells = cell_ids[filtered_indices]\n        fine_tile_cells = coarse_tile.loc[keep_cells, [\"GEOMETRY\"]]\n        fine_tile_cells = fine_tile_cells.assign(name=fine_tile_cells.index)\n\n        if not fine_tile_cells.empty:\n            filename = f\"{path_output}/cell_tile_{fine_i}_{fine_j}.parquet\"\n            fine_tile_cells.to_parquet(filename)\n\n    def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y):\n        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n            futures = []\n            for fine_i in range(n_fine_tiles_x):\n                fine_tile_x_min = x_min + fine_i * tile_size\n                fine_tile_x_max = fine_tile_x_min + tile_size\n\n                if not (fine_tile_x_min &gt;= coarse_tile_x_min and fine_tile_x_max &lt;= coarse_tile_x_max):\n                    continue\n\n                for fine_j in range(n_fine_tiles_y):\n                    fine_tile_y_min = y_min + fine_j * tile_size\n                    fine_tile_y_max = fine_tile_y_min + tile_size\n\n                    if not (fine_tile_y_min &gt;= coarse_tile_y_min and fine_tile_y_max &lt;= coarse_tile_y_max):\n                        continue\n\n                    futures.append(executor.submit(\n                        filter_and_save_fine_boundary, coarse_tile, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_output\n                    ))\n\n            for future in futures:\n                future.result()\n\n    tile_size_x = tile_size\n    tile_size_y = tile_size\n\n    transformation_matrix = pd.read_csv(path_transformation_matrix, header=None, sep=\" \").values\n\n    # Load cell boundary data based on the technology\n    if technology == \"MERSCOPE\":\n        df_meta = pd.read_parquet(f\"{path_output.replace('cell_segmentation','cell_metadata.parquet')}\")\n        entity_to_cell_id_dict = pd.Series(df_meta.index.values, index=df_meta.EntityID).to_dict()\n        cells_orig = gpd.read_parquet(path_cell_boundaries)\n        cells_orig['cell_id'] = cells_orig['EntityID'].map(entity_to_cell_id_dict)\n        cells_orig = cells_orig[cells_orig[\"ZIndex\"] == 1]\n\n        # Correct cell_id issues with meta_cell\n        meta_cell = pd.read_csv(path_meta_cell_micron)\n        meta_cell['cell_id'] = meta_cell['EntityID'].map(entity_to_cell_id_dict)\n        cells_orig.index = meta_cell[meta_cell[\"cell_id\"].isin(cells_orig['cell_id'])].index\n\n        # Correct 'MultiPolygon' to 'Polygon'\n        cells_orig[\"geometry\"] = cells_orig[\"Geometry\"].apply(\n            lambda x: list(x.geoms)[0] if isinstance(x, MultiPolygon) else x\n        )\n\n        cells_orig.set_index('cell_id', inplace=True)\n\n    elif technology == \"Xenium\":\n        xenium_cells = pd.read_parquet(path_cell_boundaries)\n        grouped = xenium_cells.groupby(\"cell_id\")[[\"vertex_x\", \"vertex_y\"]].agg(lambda x: x.tolist())\n        grouped[\"geometry\"] = grouped.apply(lambda row: Polygon(zip(row[\"vertex_x\"], row[\"vertex_y\"])), axis=1)\n        cells_orig = gpd.GeoDataFrame(grouped, geometry=\"geometry\")[[\"geometry\"]]\n\n    elif technology == \"custom\":\n        cells_orig = gpd.read_parquet(path_cell_boundaries)\n\n    # Transform geometries\n    cells_orig[\"GEOMETRY\"] = batch_transform_geometries(cells_orig[\"geometry\"], transformation_matrix, image_scale)\n\n    # Convert transformed geometries to polygons and calculate centroids\n    cells_orig[\"polygon\"] = cells_orig[\"GEOMETRY\"].apply(lambda x: Polygon(x[0]))\n    gdf_cells = gpd.GeoDataFrame(geometry=cells_orig[\"polygon\"])\n    gdf_cells[\"center_x\"] = gdf_cells.geometry.centroid.x\n    gdf_cells[\"center_y\"] = gdf_cells.geometry.centroid.y\n    gdf_cells[\"GEOMETRY\"] = cells_orig[\"GEOMETRY\"]\n\n    # Ensure the output directory exists\n    if not os.path.exists(path_output):\n        os.makedirs(path_output)\n\n    # Calculate tile bounds and fine/coarse tiles\n    x_min, x_max = tile_bounds[\"x_min\"], tile_bounds[\"x_max\"]\n    y_min, y_max = tile_bounds[\"y_min\"], tile_bounds[\"y_max\"]\n    n_fine_tiles_x = int(np.ceil((x_max - x_min) / tile_size))\n    n_fine_tiles_y = int(np.ceil((y_max - y_min) / tile_size))\n    n_coarse_tiles_x = int(np.ceil((x_max - x_min) / (coarse_tile_factor * tile_size)))\n    n_coarse_tiles_y = int(np.ceil((y_max - y_min) / (coarse_tile_factor * tile_size)))\n\n    # Process coarse tiles in parallel\n    for i in tqdm(range(n_coarse_tiles_x), desc=\"Processing coarse tiles\"):\n        coarse_tile_x_min = x_min + i * (coarse_tile_factor * tile_size)\n        coarse_tile_x_max = coarse_tile_x_min + (coarse_tile_factor * tile_size)\n\n        for j in range(n_coarse_tiles_y):\n            coarse_tile_y_min = y_min + j * (coarse_tile_factor * tile_size)\n            coarse_tile_y_max = coarse_tile_y_min + (coarse_tile_factor * tile_size)\n\n            coarse_tile = gdf_cells[\n                (gdf_cells[\"center_x\"] &gt;= coarse_tile_x_min) &amp; (gdf_cells[\"center_x\"] &lt; coarse_tile_x_max) &amp;\n                (gdf_cells[\"center_y\"] &gt;= coarse_tile_y_min) &amp; (gdf_cells[\"center_y\"] &lt; coarse_tile_y_max)\n            ]\n            if not coarse_tile.empty:\n                process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y)\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_deepzoom_pyramid","title":"<code>make_deepzoom_pyramid(image_path, output_path, pyramid_name, tile_size=512, overlap=0, suffix='.jpeg')</code>","text":"<p>Create a DeepZoom image pyramid from a JPEG image</p>"},{"location":"python/pre/api/#celldega.pre.make_deepzoom_pyramid--parameters","title":"Parameters","text":"<p>image_path : str     Path to the JPEG image file tile_size : int (default=512)     Tile size for the DeepZoom pyramid overlap : int (default=0)     Overlap size for the DeepZoom pyramid suffix : str (default='jpeg')     Suffix for the DeepZoom pyramid tiles</p>"},{"location":"python/pre/api/#celldega.pre.make_deepzoom_pyramid--returns","title":"Returns","text":"<p>None</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_deepzoom_pyramid(\n    image_path, output_path, pyramid_name, tile_size=512, overlap=0, suffix=\".jpeg\"\n):\n    \"\"\"\n    Create a DeepZoom image pyramid from a JPEG image\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the JPEG image file\n    tile_size : int (default=512)\n        Tile size for the DeepZoom pyramid\n    overlap : int (default=0)\n        Overlap size for the DeepZoom pyramid\n    suffix : str (default='jpeg')\n        Suffix for the DeepZoom pyramid tiles\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n\n    # Define the output path\n    output_path = Path(output_path)\n\n    # Load the JPEG image\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    # check if the output path exists and create it if it does not\n    output_path.mkdir(parents=True, exist_ok=True)\n\n    # append the pyramid name to the output path\n    output_path = output_path / pyramid_name\n\n    # Save the image as a DeepZoom image pyramid\n    image.dzsave(output_path, tile_size=tile_size, overlap=overlap, suffix=suffix)\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_meta_cell_image_coord","title":"<code>make_meta_cell_image_coord(technology, path_transformation_matrix, path_meta_cell_micron, path_meta_cell_image, image_scale)</code>","text":"<p>Apply an affine transformation to the cell coordinates in microns and save the transformed coordinates in pixels</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_cell_image_coord--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the data, Xenium and MERSCOPE are supported. path_transformation_matrix : str     Path to the transformation matrix file path_meta_cell_micron : str     Path to the meta cell file with coordinates in microns path_meta_cell_image : str     Path to save the meta cell file with coordinates in pixels</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_cell_image_coord--returns","title":"Returns","text":"<p>None</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_cell_image_coord--examples","title":"Examples","text":"<p>make_meta_cell_image_coord( ...     technology='Xenium', ...     path_transformation_matrix='data/transformation_matrix.txt', ...     path_meta_cell_micron='data/meta_cell_micron.csv', ...     path_meta_cell_image='data/meta_cell_image.parquet' ... )</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_meta_cell_image_coord(\n    technology,\n    path_transformation_matrix,\n    path_meta_cell_micron,\n    path_meta_cell_image,\n    image_scale\n):\n    \"\"\"\n    Apply an affine transformation to the cell coordinates in microns and save\n    the transformed coordinates in pixels\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the data, Xenium and MERSCOPE are supported.\n    path_transformation_matrix : str\n        Path to the transformation matrix file\n    path_meta_cell_micron : str\n        Path to the meta cell file with coordinates in microns\n    path_meta_cell_image : str\n        Path to save the meta cell file with coordinates in pixels\n\n    Returns\n    -------\n    None\n\n    Examples\n    --------\n    &gt;&gt;&gt; make_meta_cell_image_coord(\n    ...     technology='Xenium',\n    ...     path_transformation_matrix='data/transformation_matrix.txt',\n    ...     path_meta_cell_micron='data/meta_cell_micron.csv',\n    ...     path_meta_cell_image='data/meta_cell_image.parquet'\n    ... )\n\n    \"\"\"\n\n    transformation_matrix = pd.read_csv(\n        path_transformation_matrix, header=None, sep=\" \"\n    ).values\n\n    if technology == \"MERSCOPE\":\n        meta_cell = pd.read_csv(path_meta_cell_micron, usecols=[\"EntityID\", \"center_x\", \"center_y\"])\n        meta_cell = convert_long_id_to_short(meta_cell)\n        meta_cell[\"name\"] =  meta_cell[\"cell_id\"]\n        meta_cell = meta_cell.set_index('cell_id')\n    elif technology == \"Xenium\":\n        usecols = [\"cell_id\", \"x_centroid\", \"y_centroid\"]\n        meta_cell = pd.read_csv(path_meta_cell_micron, index_col=0, usecols=usecols)\n        meta_cell.columns = [\"center_x\", \"center_y\"]\n        meta_cell[\"name\"] = pd.Series(meta_cell.index, index=meta_cell.index)\n\n    # Adding a ones column to accommodate for affine transformation\n    meta_cell[\"ones\"] = 1\n\n    # Preparing the data for matrix multiplication\n    points = meta_cell[[\"center_x\", \"center_y\", \"ones\"]].values\n\n    # Applying the transformation matrix\n    transformed_points = np.dot(transformation_matrix, points.T).T\n\n    # Updating the DataFrame with transformed coordinates\n    meta_cell[\"center_x\"] = transformed_points[:, 0]\n    meta_cell[\"center_y\"] = transformed_points[:, 1]\n\n    # Dropping the ones column as it's no longer needed\n    meta_cell.drop(columns=[\"ones\"], inplace=True)\n\n    meta_cell[\"center_x\"] = meta_cell[\"center_x\"] / image_scale\n    meta_cell[\"center_y\"] = meta_cell[\"center_y\"] / image_scale\n\n    meta_cell[\"geometry\"] = meta_cell.apply(\n        lambda row: [row[\"center_x\"], row[\"center_y\"]], axis=1\n    )\n\n    if technology == \"MERSCOPE\":\n        meta_cell = meta_cell[[\"name\", \"geometry\", \"EntityID\"]]\n    else:\n        meta_cell = meta_cell[[\"name\", \"geometry\"]]\n\n\n    meta_cell.to_parquet(path_meta_cell_image)\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_meta_gene","title":"<code>make_meta_gene(technology, path_cbg, path_output)</code>","text":"<p>Create a DataFrame with genes and their assigned colors</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_gene--parameters","title":"Parameters","text":"<p>technology : str     The technology used to generate the data, Xenium and MERSCOPE are supported. path_cbg : str     Path to the cell-by-gene matrix data (the data format can vary based on technology) path_output : str     Path to save the meta gene file</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_gene--returns","title":"Returns","text":"<p>None</p>"},{"location":"python/pre/api/#celldega.pre.make_meta_gene--examples","title":"Examples","text":"<p>make_meta_gene( ...     technology='Xenium', ...     path_cbg='data/', ...     path_output='data/meta_gene.parquet' ... )</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def make_meta_gene(technology, path_cbg, path_output):\n    \"\"\"\n    Create a DataFrame with genes and their assigned colors\n\n    Parameters\n    ----------\n    technology : str\n        The technology used to generate the data, Xenium and MERSCOPE are supported.\n    path_cbg : str\n        Path to the cell-by-gene matrix data (the data format can vary based on technology)\n    path_output : str\n        Path to save the meta gene file\n\n    Returns\n    -------\n    None\n\n    Examples\n    --------\n    &gt;&gt;&gt; make_meta_gene(\n    ...     technology='Xenium',\n    ...     path_cbg='data/',\n    ...     path_output='data/meta_gene.parquet'\n    ... )\n    \"\"\"\n\n    if technology == \"MERSCOPE\":\n        cbg = pd.read_csv(path_cbg, index_col=0)\n        genes = cbg.columns.tolist()\n    elif technology == \"Xenium\":\n        # genes = pd.read_csv(path_cbg + 'features.tsv.gz', sep='\\t', header=None)[1].values.tolist()\n        cbg = read_cbg_mtx(path_cbg)\n        genes = cbg.columns.tolist()\n\n    # Get all categorical color palettes from Matplotlib and flatten them into a single list of colors\n    palettes = [plt.get_cmap(name).colors for name in plt.colormaps() if \"tab\" in name]\n    flat_colors = [color for palette in palettes for color in palette]\n\n    # Convert RGB tuples to hex codes\n    flat_colors_hex = [to_hex(color) for color in flat_colors]\n\n    # Use modular arithmetic to assign a color to each gene, white for genes with \"Blank\"\n    colors = [\n        flat_colors_hex[i % len(flat_colors_hex)] if \"Blank\" not in gene else \"#FFFFFF\"\n        for i, gene in enumerate(genes)\n    ]\n\n    # Create a DataFrame with genes and their assigned colors\n    ser_color = pd.Series(colors, index=genes)\n\n    # calculate gene expression metadata\n    meta_gene = calc_meta_gene_data(cbg)\n    meta_gene['color'] = ser_color\n\n    # Identify sparse columns\n    sparse_cols = [col for col in meta_gene.columns if pd.api.types.is_sparse(meta_gene[col])]\n\n    # Convert sparse columns to dense\n    for col in sparse_cols:\n        meta_gene[col] = meta_gene[col].sparse.to_dense()\n\n    meta_gene.to_parquet(path_output)\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.make_trx_tiles","title":"<code>make_trx_tiles(technology, path_trx, path_transformation_matrix, path_trx_tiles, coarse_tile_factor=10, tile_size=250, chunk_size=1000000, verbose=False, image_scale=1, max_workers=8)</code>","text":"<p>Processes transcript data by dividing it into coarse-grain and fine-grain tiles, applying transformations, and saving the results in a parallelized manner.</p>"},{"location":"python/pre/api/#celldega.pre.make_trx_tiles--parameters","title":"Parameters","text":"<p>technology : str     The technology used for generating the transcript data (e.g., \"MERSCOPE\" or \"Xenium\"). path_trx : str     Path to the file containing the transcript data. path_transformation_matrix : str     Path to the file containing the transformation matrix (CSV file). path_trx_tiles : str     Directory path where the output files (Parquet files) for each tile will be saved. coarse_tile_factor : int, optional     Scaling factor of each coarse-grain tile comparing to the fine tile size. tile_size : int, optional     Size of each fine-grain tile in microns (default is 250). chunk_size : int, optional     Number of rows to process per chunk for memory efficiency (default is 1000000). verbose : bool, optional     Flag to enable verbose output (default is False). image_scale : float, optional     Scale factor to apply to the transcript coordinates (default is 0.5). max_workers : int, optional     Maximum number of parallel workers for processing tiles (default is 8).</p>"},{"location":"python/pre/api/#celldega.pre.make_trx_tiles--returns","title":"Returns","text":"<p>dict     A dictionary containing the bounds of the processed data in both x and y directions.</p> Source code in <code>src/celldega/pre/trx_tile.py</code> <pre><code>def make_trx_tiles(\n    technology,\n    path_trx,\n    path_transformation_matrix,\n    path_trx_tiles,\n    coarse_tile_factor=10,\n    tile_size=250,\n    chunk_size=1000000,\n    verbose=False,\n    image_scale=1,\n    max_workers=8\n):\n    \"\"\"\n    Processes transcript data by dividing it into coarse-grain and fine-grain tiles,\n    applying transformations, and saving the results in a parallelized manner.\n\n    Parameters\n    ----------\n    technology : str\n        The technology used for generating the transcript data (e.g., \"MERSCOPE\" or \"Xenium\").\n    path_trx : str\n        Path to the file containing the transcript data.\n    path_transformation_matrix : str\n        Path to the file containing the transformation matrix (CSV file).\n    path_trx_tiles : str\n        Directory path where the output files (Parquet files) for each tile will be saved.\n    coarse_tile_factor : int, optional\n        Scaling factor of each coarse-grain tile comparing to the fine tile size.\n    tile_size : int, optional\n        Size of each fine-grain tile in microns (default is 250).\n    chunk_size : int, optional\n        Number of rows to process per chunk for memory efficiency (default is 1000000).\n    verbose : bool, optional\n        Flag to enable verbose output (default is False).\n    image_scale : float, optional\n        Scale factor to apply to the transcript coordinates (default is 0.5).\n    max_workers : int, optional\n        Maximum number of parallel workers for processing tiles (default is 8).\n\n    Returns\n    -------\n    dict\n        A dictionary containing the bounds of the processed data in both x and y directions.\n    \"\"\"\n\n    def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers):\n        # Filter the entire dataset for the current coarse tile\n        coarse_tile = trx.filter(\n            (pl.col(\"transformed_x\") &gt;= coarse_tile_x_min) &amp; (pl.col(\"transformed_x\") &lt; coarse_tile_x_max) &amp;\n            (pl.col(\"transformed_y\") &gt;= coarse_tile_y_min) &amp; (pl.col(\"transformed_y\") &lt; coarse_tile_y_max)\n        )\n\n        if not coarse_tile.is_empty():\n            # Now process fine tiles using global fine tile indices\n            process_fine_tiles(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers)   \n\n\n    def process_fine_tiles(coarse_tile, coarse_i, coarse_j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=8):\n\n        # Use ThreadPoolExecutor for parallel processing of fine-grain tiles within the coarse tile\n        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n            futures = []\n\n            # Iterate over fine-grain tiles within the global bounds\n            for fine_i in range(n_fine_tiles_x):\n                fine_tile_x_min = x_min + fine_i * tile_size\n                fine_tile_x_max = fine_tile_x_min + tile_size\n\n                # Process only if the fine tile falls within the current coarse tile's bounds\n                if not (fine_tile_x_min &gt;= coarse_tile_x_min and fine_tile_x_max &lt;= coarse_tile_x_max):\n                    continue\n\n                for fine_j in range(n_fine_tiles_y):\n                    fine_tile_y_min = y_min + fine_j * tile_size\n                    fine_tile_y_max = fine_tile_y_min + tile_size\n\n                    # Process only if the fine tile falls within the current coarse tile's bounds\n                    if not (fine_tile_y_min &gt;= coarse_tile_y_min and fine_tile_y_max &lt;= coarse_tile_y_max):\n                        continue\n\n                    # Submit the task for each fine tile to process in parallel\n                    futures.append(executor.submit(\n                        filter_and_save_fine_tile, coarse_tile, coarse_i, coarse_j, fine_i, fine_j, \n                        fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles\n                    ))\n\n            # Wait for all futures to complete\n            for future in concurrent.futures.as_completed(futures):\n                future.result()  # Raise exceptions if any occurred during execution\n\n\n    def filter_and_save_fine_tile(coarse_tile, coarse_i, coarse_j, fine_i, fine_j, fine_tile_x_min, fine_tile_x_max, fine_tile_y_min, fine_tile_y_max, path_trx_tiles):\n\n        # Filter the coarse tile for the current fine tile's boundaries\n        fine_tile_trx = coarse_tile.filter(\n            (pl.col(\"transformed_x\") &gt;= fine_tile_x_min) &amp; (pl.col(\"transformed_x\") &lt; fine_tile_x_max) &amp;\n            (pl.col(\"transformed_y\") &gt;= fine_tile_y_min) &amp; (pl.col(\"transformed_y\") &lt; fine_tile_y_max)\n        )\n\n        if not fine_tile_trx.is_empty():\n            # Add geometry column as a list of [x, y] pairs\n            fine_tile_trx = fine_tile_trx.with_columns(\n                pl.concat_list([pl.col(\"transformed_x\"), pl.col(\"transformed_y\")]).alias(\"geometry\")\n            ).drop(['transformed_x', 'transformed_y'])\n\n            # Define the filename based on fine tile coordinates\n            filename = f\"{path_trx_tiles}/transcripts_tile_{fine_i}_{fine_j}.parquet\"\n\n            # Save the filtered DataFrame to a Parquet file\n            fine_tile_trx.to_pandas().to_parquet(filename)\n\n\n    # Load transformation matrix\n    transformation_matrix = np.loadtxt(path_transformation_matrix)\n\n    # Load the transcript data based on the technology using Polars\n    if technology == \"MERSCOPE\":\n        trx_ini = pl.read_csv(path_trx, columns=[\"gene\", \"global_x\", \"global_y\"])\n        trx_ini = trx_ini.with_columns([\n            pl.col(\"global_x\").alias(\"x\"),\n            pl.col(\"global_y\").alias(\"y\"),\n            pl.col(\"gene\").alias(\"name\")\n        ]).select([\"name\", \"x\", \"y\"])\n\n    elif technology == \"Xenium\":\n        trx_ini = pl.read_parquet(path_trx).select([\n            pl.col(\"feature_name\").alias(\"name\"),\n            pl.col(\"x_location\").alias(\"x\"),\n            pl.col(\"y_location\").alias(\"y\")\n        ])\n\n    # Process the data in chunks and apply transformations\n    all_chunks = []\n\n    for start_row in tqdm(range(0, trx_ini.height, chunk_size), desc=\"Processing chunks\"):\n        chunk = trx_ini.slice(start_row, chunk_size)\n\n        # Apply transformation matrix to the coordinates\n        points = np.hstack([chunk.select([\"x\", \"y\"]).to_numpy(), np.ones((chunk.height, 1))])\n        transformed_points = np.dot(points, transformation_matrix.T)[:, :2]\n\n        # Create new transformed columns and drop original x, y columns\n        transformed_chunk = chunk.with_columns([\n            (pl.Series(transformed_points[:, 0]) * image_scale).round(2).alias(\"transformed_x\"),\n            (pl.Series(transformed_points[:, 1]) * image_scale).round(2).alias(\"transformed_y\")\n        ]).drop([\"x\", \"y\"])\n        all_chunks.append(transformed_chunk)\n\n    # Concatenate all chunks after processing\n    trx = pl.concat(all_chunks)\n\n    # Ensure the output directory exists\n    if not os.path.exists(path_trx_tiles):\n        os.makedirs(path_trx_tiles)\n\n    # Get min and max x, y values\n    x_min, x_max = trx.select([\n        pl.col(\"transformed_x\").min().alias(\"x_min\"),\n        pl.col(\"transformed_x\").max().alias(\"x_max\")\n    ]).row(0)\n\n    y_min, y_max = trx.select([\n        pl.col(\"transformed_y\").min().alias(\"y_min\"),\n        pl.col(\"transformed_y\").max().alias(\"y_max\")\n    ]).row(0)\n\n    # Calculate the number of fine-grain tiles globally\n    n_fine_tiles_x = int(np.ceil((x_max - x_min) / tile_size))\n    n_fine_tiles_y = int(np.ceil((y_max - y_min) / tile_size))\n\n    # Calculate the number of coarse-grain tiles\n    n_coarse_tiles_x = int(np.ceil((x_max - x_min) / (coarse_tile_factor * tile_size)))\n    n_coarse_tiles_y = int(np.ceil((y_max - y_min) / (coarse_tile_factor * tile_size)))\n\n    # Use ThreadPoolExecutor for parallel processing of coarse-grain tiles\n    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n        futures = []\n        for i in range(n_coarse_tiles_x):\n            coarse_tile_x_min = x_min + i * (coarse_tile_factor * tile_size)\n            coarse_tile_x_max = coarse_tile_x_min + (coarse_tile_factor * tile_size)\n\n            for j in range(n_coarse_tiles_y):\n                coarse_tile_y_min = y_min + j * (coarse_tile_factor * tile_size)\n                coarse_tile_y_max = coarse_tile_y_min + (coarse_tile_factor * tile_size)\n\n                # Submit each coarse tile for parallel processing\n                futures.append(executor.submit(\n                    process_coarse_tile, trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers\n                ))\n\n        # Wait for all coarse tiles to complete\n        for future in tqdm(concurrent.futures.as_completed(futures), desc=\"Processing coarse tiles\", unit=\"tile\"):\n            future.result()  # Raise exceptions if any occurred during execution\n\n    # Return the tile bounds\n    tile_bounds = {\n        \"x_min\": x_min,\n        \"x_max\": x_max,\n        \"y_min\": y_min,\n        \"y_max\": y_max,\n    }\n\n    return tile_bounds\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.reduce_image_size","title":"<code>reduce_image_size(image_path, scale_image=0.5, path_landscape_files='')</code>","text":""},{"location":"python/pre/api/#celldega.pre.reduce_image_size--parameters","title":"Parameters","text":"<p>image_path : str     Path to the image file scale_image : float (default=0.5)     Scale factor for the image resize</p>"},{"location":"python/pre/api/#celldega.pre.reduce_image_size--returns","title":"Returns","text":"<p>new_image_path : str     Path to the resized image file</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def reduce_image_size(image_path, scale_image=0.5, path_landscape_files=\"\"):\n    \"\"\"\n\n    Parameters\n    ----------\n    image_path : str\n        Path to the image file\n    scale_image : float (default=0.5)\n        Scale factor for the image resize\n\n    Returns\n    -------\n    new_image_path : str\n        Path to the resized image file\n    \"\"\"\n\n    image = pyvips.Image.new_from_file(image_path, access=\"sequential\")\n\n    resized_image = image.resize(scale_image)\n\n    new_image_name = image_path.split(\"/\")[-1].replace(\".tif\", \"_downsize.tif\")\n    new_image_path = f\"{path_landscape_files}/{new_image_name}\"\n    resized_image.write_to_file(new_image_path)\n\n    return new_image_path\n</code></pre>"},{"location":"python/pre/api/#celldega.pre.save_landscape_parameters","title":"<code>save_landscape_parameters(technology, path_landscape_files, image_name='dapi_files', tile_size=1000, image_info={}, image_format='.webp')</code>","text":"<p>Save the landscape parameters to a JSON file.</p> Source code in <code>src/celldega/pre/__init__.py</code> <pre><code>def save_landscape_parameters(\n    technology, path_landscape_files, image_name=\"dapi_files\", tile_size=1000, image_info={}, image_format='.webp'\n):\n    \"\"\"\n    Save the landscape parameters to a JSON file.\n    \"\"\"\n\n    path_image_pyramid = f\"{path_landscape_files}/pyramid_images/{image_name}\"\n\n    print(path_image_pyramid)\n\n    max_pyramid_zoom = get_max_zoom_level(path_image_pyramid)\n\n    landscape_parameters = {\n        \"technology\": technology,\n        \"max_pyramid_zoom\": max_pyramid_zoom,\n        \"tile_size\": tile_size,\n        \"image_info\": image_info,\n        \"image_format\": image_format\n    }\n\n    path_landscape_parameters = f\"{path_landscape_files}/landscape_parameters.json\"\n\n    with open(path_landscape_parameters, \"w\") as file:\n        json.dump(landscape_parameters, file, indent=4)\n</code></pre>"},{"location":"python/viz/api/","title":"Viz Module API Reference","text":""},{"location":"python/viz/api/#widget-classes","title":"Widget Classes","text":""},{"location":"python/viz/api/#celldega.viz.widget.Landscape","title":"<code>Landscape</code>","text":"<p>               Bases: <code>AnyWidget</code></p> <p>A widget for interactive visualization of spatial omics data. This widget currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)</p> <p>Parameters:</p> Name Type Description Default <code>ini_x</code> <code>float</code> <p>The initial x-coordinate of the view.</p> required <code>ini_y</code> <code>float</code> <p>The initial y-coordinate of the view.</p> required <code>ini_zoom</code> <code>float</code> <p>The initial zoom level of the view.</p> required <code>token</code> <code>str</code> <p>The token traitlet.</p> required <code>base_url</code> <code>str</code> <p>The base URL for the widget.</p> required <code>dataset_name</code> <code>str</code> <p>The name of the dataset to visualize. This will show up in the user interface bar.</p> required <p>Attributes:</p> Name Type Description <code>component</code> <code>str</code> <p>The name of the component.</p> <code>technology</code> <code>str</code> <p>The technology used.</p> <code>base_url</code> <code>str</code> <p>The base URL for the widget.</p> <code>token</code> <code>str</code> <p>The token traitlet.</p> <code>ini_x</code> <code>float</code> <p>The initial x-coordinate of the view.</p> <code>ini_y</code> <code>float</code> <p>The initial y-coordinate of the view.</p> <code>ini_z</code> <code>float</code> <p>The initial z-coordinate of the view.</p> <code>ini_zoom</code> <code>float</code> <p>The initial zoom level of the view.</p> <code>dataset_name</code> <code>str</code> <p>The name of the dataset to visualize.</p> <code>update_trigger</code> <code>dict</code> <p>The dictionary to trigger updates.</p> <code>cell_clusters</code> <code>dict</code> <p>The dictionary containing cell cluster information.</p> <p>Returns:</p> Name Type Description <code>Landscape</code> <p>A widget for visualizing a 'landscape' view of spatial omics data.</p> Source code in <code>src/celldega/viz/widget.py</code> <pre><code>class Landscape(anywidget.AnyWidget):\n    \"\"\"\n    A widget for interactive visualization of spatial omics data. This widget\n    currently supports iST (Xenium and MERSCOPE) and sST (Visium HD data)\n\n    Args:\n        ini_x (float): The initial x-coordinate of the view.\n        ini_y (float): The initial y-coordinate of the view.\n        ini_zoom (float): The initial zoom level of the view.\n        token (str): The token traitlet.\n        base_url (str): The base URL for the widget.\n        dataset_name (str, optional): The name of the dataset to visualize. This will show up in the user interface bar.\n\n    Attributes:\n        component (str): The name of the component.\n        technology (str): The technology used.\n        base_url (str): The base URL for the widget.\n        token (str): The token traitlet.\n        ini_x (float): The initial x-coordinate of the view.\n        ini_y (float): The initial y-coordinate of the view.\n        ini_z (float): The initial z-coordinate of the view.\n        ini_zoom (float): The initial zoom level of the view.\n        dataset_name (str): The name of the dataset to visualize.\n        update_trigger (dict): The dictionary to trigger updates.\n        cell_clusters (dict): The dictionary containing cell cluster information.\n\n    Returns:\n        Landscape: A widget for visualizing a 'landscape' view of spatial omics data.\n    \"\"\"\n    _esm = pathlib.Path(__file__).parent / \"../static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"../static\" / \"widget.css\"\n    component = traitlets.Unicode(\"Landscape\").tag(sync=True)\n\n    technology = traitlets.Unicode(\"sst\").tag(sync=True)\n    base_url = traitlets.Unicode(\"\").tag(sync=True)\n    token = traitlets.Unicode(\"\").tag(sync=True)\n    ini_x = traitlets.Float(1000).tag(sync=True)\n    ini_y = traitlets.Float(1000).tag(sync=True)\n    ini_z = traitlets.Float(0).tag(sync=True)\n    ini_zoom = traitlets.Float(0).tag(sync=True)\n    square_tile_size = traitlets.Float(1.4).tag(sync=True)\n    dataset_name = traitlets.Unicode(\"\").tag(sync=True)\n    region = traitlets.Dict({}).tag(sync=True)\n    nbhd = traitlets.Dict({}).tag(sync=True)\n\n    update_trigger = traitlets.Dict().tag(sync=True)\n    cell_clusters = traitlets.Dict().tag(sync=True)\n\n    width = traitlets.Int(0).tag(sync=True)\n    height = traitlets.Int(800).tag(sync=True)\n\n    def trigger_update(self, new_value):\n        # This method updates the update_trigger traitlet with a new value\n        # You can pass any information necessary for the update, or just a timestamp\n        self.update_trigger = new_value\n\n    def update_cell_clusters(self, new_clusters):\n        # Convert the new_clusters to a JSON serializable format if necessary\n        self.cell_clusters = new_clusters\n</code></pre>"},{"location":"python/viz/api/#celldega.viz.widget.Matrix","title":"<code>Matrix</code>","text":"<p>               Bases: <code>AnyWidget</code></p> <p>A widget for interactive visualization of a hierarchically clustered matrix.</p> <p>Parameters:</p> Name Type Description Default <code>value</code> <code>int</code> <p>The value traitlet.</p> required <code>component</code> <code>str</code> <p>The component traitlet.</p> required <code>network</code> <code>dict</code> <p>The network traitlet.</p> required <code>click_info</code> <code>dict</code> <p>The click_info traitlet.</p> required <p>Attributes:</p> Name Type Description <code>component</code> <code>str</code> <p>The name of the component.</p> <code>network</code> <code>dict</code> <p>The network dictionary.</p> <code>click_info</code> <code>dict</code> <p>The click_info dictionary.</p> <p>Returns:</p> Name Type Description <code>Matrix</code> <p>A widget for visualizing a hierarchically clustered matrix.</p> Source code in <code>src/celldega/viz/widget.py</code> <pre><code>class Matrix(anywidget.AnyWidget):\n    \"\"\"\n    A widget for interactive visualization of a hierarchically clustered matrix.\n\n    Args:\n        value (int): The value traitlet.\n        component (str): The component traitlet.\n        network (dict): The network traitlet.\n        click_info (dict): The click_info traitlet.\n\n    Attributes:\n        component (str): The name of the component.\n        network (dict): The network dictionary.\n        click_info (dict): The click_info dictionary.\n\n    Returns:\n        Matrix: A widget for visualizing a hierarchically clustered matrix.\n    \"\"\"\n    _esm = pathlib.Path(__file__).parent / \"../static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"../static\" / \"widget.css\"\n    value = traitlets.Int(0).tag(sync=True)\n    component = traitlets.Unicode(\"Matrix\").tag(sync=True)\n\n    network = traitlets.Dict({}).tag(sync=True)\n    click_info = traitlets.Dict({}).tag(sync=True)\n</code></pre>"},{"location":"technologies/","title":"Technologies","text":"<p>Celldega utilizes a suite of complementary technologies to develop an efficient web-based spatial-omics analysis and visualization toolkit.</p>"},{"location":"technologies/#visualization-technologies","title":"Visualization Technologies","text":"<p>Spatial transcriptomics (ST) datasets can be very large and difficult for researchers to analyze and visualize collaboratively. Additionally, visualization that is linked to analysis is key to extracting biological insights. To address these issues, we built the Celldega <code>viz</code> module to help researchers interactively visualize large ST datasets within notebook-based workflows on the cloud (e.g., Terra.bio).</p> <p>The Celldega Landscape visualization method (see Gallery) utilizes novel vector tiling approaches to enable interactive visualization of large ST datasets in a notebook environment or as a stand-alone webpage. This approach allows Celldega to visualize larger datasets than currently available open-source tools (e.g., datasets with hundreds of millions of transcripts). We also utilize modern web image data formats (WebP) to reduce the data storage burden for interactive visualization. The resulting LandscapeFiles data format serves as a compact and highly performant visualization-specific data format.</p>"},{"location":"technologies/#terrabio","title":"Terra.bio","text":"<p>Terra.bio is a cloud-based compute and data storage platform that is being developed by the Broad Institute of MIT and Harvard. We are utilizing Terra.bio to help Spatial Technology Platform clients access, analyze, and visualize their ST data.</p>"},{"location":"technologies/#jupyter-widget","title":"Jupyter Widget","text":"<p>We utilize the Jupyter Widget ecosystem to build interactive spatial and data visualizations that enable users to perform two way communication between JavaScript (front-end) and Python (back-end). We are utilizing the AnyWidget implementation to build our custom widgets.</p>"},{"location":"technologies/#deckgl","title":"Deck.gl","text":"<p>Celldega uses the GPU-powered data visualization library deck.gl to create high-performance spatial- and data-visualizations.</p>"},{"location":"technologies/#apache-parquet","title":"Apache Parquet","text":"<p>Celldega uses the Apache Parquet file format for storing vectorized spatial data and metadata. This file format in combination with the JavaScript library ParquetWASM and Apache Arrow in memory representation is used to build Celldega's high-performance vector tiling spatial visualization functionality (see GeoArrow and GeoParquet in deck.gl).</p>"},{"location":"technologies/#parquetwasm-and-apache-arrow","title":"ParquetWASM and Apache Arrow","text":"<p>ParquetWASM is a JavaScript library for reading Parquet files into Apache Arrow memory and utilizes Web Assembly (WASM) to run Rust in a browser environment. The Apache Arrow in-memory format is a columnar in-memory format that is used for storing data from Apache Parquet files and efficiently passing to deck.gl. For more information please see GeoArrow and GeoParquet in deck.gl.</p>"},{"location":"technologies/#webp","title":"WebP","text":"<p>A modern image format developed by Google, offering efficient lossless compression and designed specifically for the web.</p>"},{"location":"technologies/#deep-zoom","title":"Deep Zoom","text":"<p>We utilize the Deep Zoom image schema, developed by Microsoft, to enable efficient visualization of large multi-channel microscopy images. Deep Zoom tile images are stored using the WebP image format.</p>"},{"location":"technologies/#clustergrammer-visualization-approaches","title":"Clustergrammer Visualization Approaches","text":"<p>The Celldega Matrix visualization builds upon the visualization approaches developed in the Clustergrammer project. This enables users to interactively explore high-dimensional datasets (e.g., single-cell gene expression data) alongside spatial data (e.g., cell distributions within a tissue).</p>"},{"location":"technologies/#data-analysis-technologies","title":"Data Analysis Technologies","text":""},{"location":"technologies/#scanpy-and-squidpy","title":"Scanpy and Squidpy","text":"<p>Celldega is built to interface with the AnnData and SpatialData objects, which enables users to easily import analysis results from Scanpy and Squidpy, respectively, into Celldega for downstream analysis and/or visuaization.</p>"},{"location":"technologies/#geopandas","title":"GeoPandas","text":"<p>Celldega uses GeoPandas for efficient spatial operations and storing collections of spatial objects (e.g., neighborhood multi-polygons) as GeoDataFrames.</p>"},{"location":"technologies/#libpysal-python-spatial-analysis-library-core","title":"LibPySal: Python Spatial Analysis Library Core","text":"<p>Celldega uses the Python Spatial Analysis Library (libpysal) for spatial analysis - namely for calculating alpha shape cell type neighborhoods.</p>"},{"location":"technologies/#clustergrammer-data-analysis-approaches","title":"Clustergrammer Data Analysis Approaches","text":"<p>The Celldega Cluster module build upon the hierarchical clustering approaches developed in the Clustergrammer project. This enables users to perform hierarchical clustering on observations (e.g., single cells) and measurements (e.g., genes) and easily visualize these two orthogonal clustering results interactively using Celldega's Matrix visualization method.</p>"}]}
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diff --git a/technologies/index.html b/technologies/index.html
index febbc552..98cc8dd4 100644
--- a/technologies/index.html
+++ b/technologies/index.html
@@ -1192,6 +1192,8 @@ <h3 id="clustergrammer-data-analysis-approaches">Clustergrammer Data Analysis Ap
       
         <script src="../assets/js/google-analytics.js"></script>
       
+        <script src="../js/open_in_new_tab.js"></script>
+      
     
   </body>
 </html>
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