From 4a03e801811c53235f43eb915ac42ec86c2a4fe4 Mon Sep 17 00:00:00 2001
From: lorenzocerrone <lorenzocerrone@users.noreply.github.com>
Date: Thu, 7 Nov 2024 15:14:22 +0000
Subject: [PATCH] deploy: 40f2440b66b56b97aa234792183f637068338508

---
 api/core/index.html              |  12 +-
 getting-started/index.html       |   4 +-
 index.html                       |   4 +-
 notebooks/basic_usage/index.html |   4 +-
 notebooks/image/index.html       | 431 ++++++++++++++-----------------
 notebooks/processing/index.html  | 211 +++++++--------
 search/search_index.json         |   2 +-
 7 files changed, 318 insertions(+), 350 deletions(-)

diff --git a/api/core/index.html b/api/core/index.html
index 6f2d7f8..cbe7bd0 100644
--- a/api/core/index.html
+++ b/api/core/index.html
@@ -1195,7 +1195,7 @@ <h3 id="ngio.core.NgffImage" class="doc doc-heading">
 
         <span class="n">default_kwargs</span> <span class="o">=</span> <span class="p">{</span>
             <span class="s2">&quot;store&quot;</span><span class="p">:</span> <span class="n">store</span><span class="p">,</span>
-            <span class="s2">&quot;on_disk_shape&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">on_disk_shape</span><span class="p">,</span>
+            <span class="s2">&quot;shape&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">on_disk_shape</span><span class="p">,</span>
             <span class="s2">&quot;chunks&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">on_disk_array</span><span class="o">.</span><span class="n">chunks</span><span class="p">,</span>
             <span class="s2">&quot;dtype&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">on_disk_array</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
             <span class="s2">&quot;on_disk_axis&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">on_disk_axes_names</span><span class="p">,</span>
@@ -1204,7 +1204,7 @@ <h3 id="ngio.core.NgffImage" class="doc doc-heading">
             <span class="s2">&quot;z_scaling_factor&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_meta</span><span class="o">.</span><span class="n">z_scaling_factor</span><span class="p">,</span>
             <span class="s2">&quot;time_spacing&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">time_spacing</span><span class="p">,</span>
             <span class="s2">&quot;time_units&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">time_axis_unit</span><span class="p">,</span>
-            <span class="s2">&quot;levels&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_levels</span><span class="p">,</span>
+            <span class="s2">&quot;num_levels&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_levels</span><span class="p">,</span>
             <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="n">name</span><span class="p">,</span>
             <span class="s2">&quot;channel_labels&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">channel_labels</span><span class="p">,</span>
             <span class="s2">&quot;channel_wavelengths&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">ch</span><span class="o">.</span><span class="n">wavelength_id</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="n">channels</span><span class="p">],</span>
@@ -1515,7 +1515,7 @@ <h4 id="ngio.core.NgffImage.derive_new_image" class="doc doc-heading">
 
     <span class="n">default_kwargs</span> <span class="o">=</span> <span class="p">{</span>
         <span class="s2">&quot;store&quot;</span><span class="p">:</span> <span class="n">store</span><span class="p">,</span>
-        <span class="s2">&quot;on_disk_shape&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">on_disk_shape</span><span class="p">,</span>
+        <span class="s2">&quot;shape&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">on_disk_shape</span><span class="p">,</span>
         <span class="s2">&quot;chunks&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">on_disk_array</span><span class="o">.</span><span class="n">chunks</span><span class="p">,</span>
         <span class="s2">&quot;dtype&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">on_disk_array</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
         <span class="s2">&quot;on_disk_axis&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">on_disk_axes_names</span><span class="p">,</span>
@@ -1524,7 +1524,7 @@ <h4 id="ngio.core.NgffImage.derive_new_image" class="doc doc-heading">
         <span class="s2">&quot;z_scaling_factor&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_meta</span><span class="o">.</span><span class="n">z_scaling_factor</span><span class="p">,</span>
         <span class="s2">&quot;time_spacing&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">time_spacing</span><span class="p">,</span>
         <span class="s2">&quot;time_units&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">time_axis_unit</span><span class="p">,</span>
-        <span class="s2">&quot;levels&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_levels</span><span class="p">,</span>
+        <span class="s2">&quot;num_levels&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_levels</span><span class="p">,</span>
         <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="n">name</span><span class="p">,</span>
         <span class="s2">&quot;channel_labels&quot;</span><span class="p">:</span> <span class="n">image_0</span><span class="o">.</span><span class="n">channel_labels</span><span class="p">,</span>
         <span class="s2">&quot;channel_wavelengths&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">ch</span><span class="o">.</span><span class="n">wavelength_id</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="n">channels</span><span class="p">],</span>
@@ -1870,7 +1870,7 @@ <h4 id="ngio.core.NgffImage.update_omero_window" class="doc doc-heading">
     <span class="md-icon" title="Last update">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1zM12.5 7v5.2l4 2.4-1 1L11 13V7zM11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
@@ -1880,7 +1880,7 @@ <h4 id="ngio.core.NgffImage.update_omero_window" class="doc doc-heading">
     <span class="md-icon" title="Created">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M14.47 15.08 11 13V7h1.5v5.25l3.08 1.83c-.41.28-.79.62-1.11 1m-1.39 4.84c-.36.05-.71.08-1.08.08-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8c0 .37-.03.72-.08 1.08.69.1 1.33.32 1.92.64.1-.56.16-1.13.16-1.72 0-5.5-4.5-10-10-10S2 6.5 2 12s4.47 10 10 10c.59 0 1.16-.06 1.72-.16-.32-.59-.54-1.23-.64-1.92M18 15v3h-3v2h3v3h2v-3h3v-2h-3v-3z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
diff --git a/getting-started/index.html b/getting-started/index.html
index ca80921..8dec45a 100644
--- a/getting-started/index.html
+++ b/getting-started/index.html
@@ -676,7 +676,7 @@ <h2 id="example-usage">Example Usage</h2>
     <span class="md-icon" title="Last update">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1zM12.5 7v5.2l4 2.4-1 1L11 13V7zM11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
@@ -686,7 +686,7 @@ <h2 id="example-usage">Example Usage</h2>
     <span class="md-icon" title="Created">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M14.47 15.08 11 13V7h1.5v5.25l3.08 1.83c-.41.28-.79.62-1.11 1m-1.39 4.84c-.36.05-.71.08-1.08.08-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8c0 .37-.03.72-.08 1.08.69.1 1.33.32 1.92.64.1-.56.16-1.13.16-1.72 0-5.5-4.5-10-10-10S2 6.5 2 12s4.47 10 10 10c.59 0 1.16-.06 1.72-.16-.32-.59-.54-1.23-.64-1.92M18 15v3h-3v2h3v3h2v-3h3v-2h-3v-3z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
diff --git a/index.html b/index.html
index ba1c3a6..9eb442c 100644
--- a/index.html
+++ b/index.html
@@ -793,7 +793,7 @@ <h2 id="license">License</h2>
     <span class="md-icon" title="Last update">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1zM12.5 7v5.2l4 2.4-1 1L11 13V7zM11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
@@ -803,7 +803,7 @@ <h2 id="license">License</h2>
     <span class="md-icon" title="Created">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M14.47 15.08 11 13V7h1.5v5.25l3.08 1.83c-.41.28-.79.62-1.11 1m-1.39 4.84c-.36.05-.71.08-1.08.08-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8c0 .37-.03.72-.08 1.08.69.1 1.33.32 1.92.64.1-.56.16-1.13.16-1.72 0-5.5-4.5-10-10-10S2 6.5 2 12s4.47 10 10 10c.59 0 1.16-.06 1.72-.16-.32-.59-.54-1.23-.64-1.92M18 15v3h-3v2h3v3h2v-3h3v-2h-3v-3z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
diff --git a/notebooks/basic_usage/index.html b/notebooks/basic_usage/index.html
index 530d71c..f8993bf 100644
--- a/notebooks/basic_usage/index.html
+++ b/notebooks/basic_usage/index.html
@@ -1836,7 +1836,7 @@ <h2 id="derive-a-new-ngffimage">Derive a new NgffImage<a class="anchor-link" hre
     <span class="md-icon" title="Last update">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1zM12.5 7v5.2l4 2.4-1 1L11 13V7zM11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
@@ -1846,7 +1846,7 @@ <h2 id="derive-a-new-ngffimage">Derive a new NgffImage<a class="anchor-link" hre
     <span class="md-icon" title="Created">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M14.47 15.08 11 13V7h1.5v5.25l3.08 1.83c-.41.28-.79.62-1.11 1m-1.39 4.84c-.36.05-.71.08-1.08.08-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8c0 .37-.03.72-.08 1.08.69.1 1.33.32 1.92.64.1-.56.16-1.13.16-1.72 0-5.5-4.5-10-10-10S2 6.5 2 12s4.47 10 10 10c.59 0 1.16-.06 1.72-.16-.32-.59-.54-1.23-.64-1.92M18 15v3h-3v2h3v3h2v-3h3v-2h-3v-3z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
diff --git a/notebooks/image/index.html b/notebooks/image/index.html
index 15cbfa5..f4c4078 100644
--- a/notebooks/image/index.html
+++ b/notebooks/image/index.html
@@ -1440,79 +1440,87 @@ <h2 id="roitableimage-interaction">RoiTable/Image Interaction<a class="anchor-li
 </pre>
 </div>
 </div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">AttributeError</span>                            Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[5], line 5</span>
-<span class="ansi-green-intense-fg ansi-bold">      2</span> roi <span style="color: rgb(98,98,98)">=</span> roi_table<span style="color: rgb(98,98,98)">.</span>get_roi(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">FOV_1</span><span style="color: rgb(175,0,0)">"</span>)
-<span class="ansi-green-intense-fg ansi-bold">      3</span> <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>roi<span class="ansi-bold" style="color: rgb(175,95,135)">=}</span><span style="color: rgb(175,0,0)">"</span>)
-<span class="ansi-green-fg">----&gt; 5</span> image_roi_1 <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">image</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">get_array_from_roi</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">0</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">preserve_dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">dask</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">      6</span> image_roi_1
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_handler.py:105</span>, in <span class="ansi-cyan-fg">Image.get_array_from_roi</span><span class="ansi-blue-fg">(self, roi, c, t, mode, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    102</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">isinstance</span>(c, <span style="color: rgb(0,135,0)">str</span>):
-<span class="ansi-green-intense-fg ansi-bold">    103</span>     c <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>get_channel_idx(label<span style="color: rgb(98,98,98)">=</span>c)
-<span class="ansi-green-fg">--&gt; 105</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_get_array_from_roi</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    106</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">preserve_dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">preserve_dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">    107</span> <span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:344</span>, in <span class="ansi-cyan-fg">ImageLike._get_array_from_roi</span><span class="ansi-blue-fg">(self, roi, t, c, mode, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    327</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">_get_array_from_roi</span>(
-<span class="ansi-green-intense-fg ansi-bold">    328</span>     <span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">    329</span>     roi: WorldCooROI,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    333</span>     preserve_dimensions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">    334</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ArrayLike <span style="color: rgb(98,98,98)">|</span> <span style="color: rgb(0,135,0)">tuple</span>[ArrayLike, DataTransformPipe]:
-<span class="ansi-green-intense-fg ansi-bold">    335</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Return the image data from a region of interest (ROI).</span>
-<span class="ansi-green-intense-fg ansi-bold">    336</span> 
-<span class="ansi-green-intense-fg ansi-bold">    337</span> <span style="color: rgb(175,0,0)">    Args:</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    342</span> <span style="color: rgb(175,0,0)">        preserve_dimensions (bool): Whether to preserve the dimensions of the data.</span>
-<span class="ansi-green-intense-fg ansi-bold">    343</span> <span style="color: rgb(175,0,0)">    """</span>
-<span class="ansi-green-fg">--&gt; 344</span>     data_pipe <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_build_roi_pipe</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    345</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">preserve_dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">preserve_dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">    346</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    347</span>     return_array <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_get_pipe(data_pipe<span style="color: rgb(98,98,98)">=</span>data_pipe, mode<span style="color: rgb(98,98,98)">=</span>mode)
-<span class="ansi-green-intense-fg ansi-bold">    348</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> return_array
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:291</span>, in <span class="ansi-cyan-fg">ImageLike._build_roi_pipe</span><span class="ansi-blue-fg">(self, roi, t, c, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    283</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">_build_roi_pipe</span>(
-<span class="ansi-green-intense-fg ansi-bold">    284</span>     <span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">    285</span>     roi: WorldCooROI,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    288</span>     preserve_dimensions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">    289</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> DataTransformPipe:
-<span class="ansi-green-intense-fg ansi-bold">    290</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Build the data transform pipe for a region of interest (ROI)."""</span>
-<span class="ansi-green-fg">--&gt; 291</span>     roi_coo <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">to_raster_coo</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    292</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">pixel_size</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">dataset</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">pixel_size</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">    293</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    295</span>     slicer <span style="color: rgb(98,98,98)">=</span> RoiSlicer(
-<span class="ansi-green-intense-fg ansi-bold">    296</span>         on_disk_axes_name<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>dataset<span style="color: rgb(98,98,98)">.</span>on_disk_axes_names,
-<span class="ansi-green-intense-fg ansi-bold">    297</span>         axes_order<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>dataset<span style="color: rgb(98,98,98)">.</span>axes_order,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    301</span>         preserve_dimensions<span style="color: rgb(98,98,98)">=</span>preserve_dimensions,
-<span class="ansi-green-intense-fg ansi-bold">    302</span>     )
-<span class="ansi-green-intense-fg ansi-bold">    303</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> DataTransformPipe(slicer<span style="color: rgb(98,98,98)">=</span>slicer)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/roi.py:44</span>, in <span class="ansi-cyan-fg">WorldCooROI.to_raster_coo</span><span class="ansi-blue-fg">(self, pixel_size, dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">     39</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">to_raster_coo</span>(
-<span class="ansi-green-intense-fg ansi-bold">     40</span>     <span style="color: rgb(0,135,0)">self</span>, pixel_size: PixelSize, dimensions: Dimensions
-<span class="ansi-green-intense-fg ansi-bold">     41</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">RasterCooROI</span><span style="color: rgb(175,0,0)">"</span>:
-<span class="ansi-green-intense-fg ansi-bold">     42</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Convert to raster coordinates."""</span>
-<span class="ansi-green-intense-fg ansi-bold">     43</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> RasterCooROI(
-<span class="ansi-green-fg">---&gt; 44</span>         x<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>x, pixel_size<span style="color: rgb(98,98,98)">.</span>x, <span class="ansi-yellow-bg">dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">x</span>),
-<span class="ansi-green-intense-fg ansi-bold">     45</span>         y<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>y, pixel_size<span style="color: rgb(98,98,98)">.</span>y, dimensions<span style="color: rgb(98,98,98)">.</span>y),
-<span class="ansi-green-intense-fg ansi-bold">     46</span>         z<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>z, pixel_size<span style="color: rgb(98,98,98)">.</span>z, dimensions<span style="color: rgb(98,98,98)">.</span>z),
-<span class="ansi-green-intense-fg ansi-bold">     47</span>         x_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>x_length, pixel_size<span style="color: rgb(98,98,98)">.</span>x, dimensions<span style="color: rgb(98,98,98)">.</span>x),
-<span class="ansi-green-intense-fg ansi-bold">     48</span>         y_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>y_length, pixel_size<span style="color: rgb(98,98,98)">.</span>y, dimensions<span style="color: rgb(98,98,98)">.</span>y),
-<span class="ansi-green-intense-fg ansi-bold">     49</span>         z_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>z_length, pixel_size<span style="color: rgb(98,98,98)">.</span>z, dimensions<span style="color: rgb(98,98,98)">.</span>z),
-<span class="ansi-green-intense-fg ansi-bold">     50</span>         original_roi<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">     51</span>     )
-
-<span class="ansi-red-fg">AttributeError</span>: 'Dimensions' object has no attribute 'x'</pre>
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
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@@ -1599,78 +1607,8 @@ <h2 id="roitableimage-interaction">RoiTable/Image Interaction<a class="anchor-li
 </div>
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">AttributeError</span>                            Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[6], line 7</span>
-<span class="ansi-green-intense-fg ansi-bold">      4</span> <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>image_2<span style="color: rgb(98,98,98)">.</span>pixel_size<span class="ansi-bold" style="color: rgb(175,95,135)">=}</span><span style="color: rgb(175,0,0)">"</span>)
-<span class="ansi-green-intense-fg ansi-bold">      6</span> <span style="color: rgb(95,135,135)"># Get roi for higher resolution image</span>
-<span class="ansi-green-fg">----&gt; 7</span> image_1_roi_1 <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">image</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">get_array_from_roi</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">0</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">preserve_dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">False</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">      9</span> <span style="color: rgb(95,135,135)"># Get roi for lower resolution image</span>
-<span class="ansi-green-intense-fg ansi-bold">     10</span> image_2_roi_1 <span style="color: rgb(98,98,98)">=</span> image_2<span style="color: rgb(98,98,98)">.</span>get_array_from_roi(roi<span style="color: rgb(98,98,98)">=</span>roi, c<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">0</span>, preserve_dimensions<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_handler.py:105</span>, in <span class="ansi-cyan-fg">Image.get_array_from_roi</span><span class="ansi-blue-fg">(self, roi, c, t, mode, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    102</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">isinstance</span>(c, <span style="color: rgb(0,135,0)">str</span>):
-<span class="ansi-green-intense-fg ansi-bold">    103</span>     c <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>get_channel_idx(label<span style="color: rgb(98,98,98)">=</span>c)
-<span class="ansi-green-fg">--&gt; 105</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_get_array_from_roi</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    106</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">preserve_dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">preserve_dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">    107</span> <span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:344</span>, in <span class="ansi-cyan-fg">ImageLike._get_array_from_roi</span><span class="ansi-blue-fg">(self, roi, t, c, mode, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    327</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">_get_array_from_roi</span>(
-<span class="ansi-green-intense-fg ansi-bold">    328</span>     <span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">    329</span>     roi: WorldCooROI,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    333</span>     preserve_dimensions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">    334</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ArrayLike <span style="color: rgb(98,98,98)">|</span> <span style="color: rgb(0,135,0)">tuple</span>[ArrayLike, DataTransformPipe]:
-<span class="ansi-green-intense-fg ansi-bold">    335</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Return the image data from a region of interest (ROI).</span>
-<span class="ansi-green-intense-fg ansi-bold">    336</span> 
-<span class="ansi-green-intense-fg ansi-bold">    337</span> <span style="color: rgb(175,0,0)">    Args:</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    342</span> <span style="color: rgb(175,0,0)">        preserve_dimensions (bool): Whether to preserve the dimensions of the data.</span>
-<span class="ansi-green-intense-fg ansi-bold">    343</span> <span style="color: rgb(175,0,0)">    """</span>
-<span class="ansi-green-fg">--&gt; 344</span>     data_pipe <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_build_roi_pipe</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    345</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">preserve_dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">preserve_dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">    346</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    347</span>     return_array <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_get_pipe(data_pipe<span style="color: rgb(98,98,98)">=</span>data_pipe, mode<span style="color: rgb(98,98,98)">=</span>mode)
-<span class="ansi-green-intense-fg ansi-bold">    348</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> return_array
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:291</span>, in <span class="ansi-cyan-fg">ImageLike._build_roi_pipe</span><span class="ansi-blue-fg">(self, roi, t, c, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    283</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">_build_roi_pipe</span>(
-<span class="ansi-green-intense-fg ansi-bold">    284</span>     <span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">    285</span>     roi: WorldCooROI,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    288</span>     preserve_dimensions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">    289</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> DataTransformPipe:
-<span class="ansi-green-intense-fg ansi-bold">    290</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Build the data transform pipe for a region of interest (ROI)."""</span>
-<span class="ansi-green-fg">--&gt; 291</span>     roi_coo <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">to_raster_coo</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    292</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">pixel_size</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">dataset</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">pixel_size</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">    293</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    295</span>     slicer <span style="color: rgb(98,98,98)">=</span> RoiSlicer(
-<span class="ansi-green-intense-fg ansi-bold">    296</span>         on_disk_axes_name<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>dataset<span style="color: rgb(98,98,98)">.</span>on_disk_axes_names,
-<span class="ansi-green-intense-fg ansi-bold">    297</span>         axes_order<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>dataset<span style="color: rgb(98,98,98)">.</span>axes_order,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    301</span>         preserve_dimensions<span style="color: rgb(98,98,98)">=</span>preserve_dimensions,
-<span class="ansi-green-intense-fg ansi-bold">    302</span>     )
-<span class="ansi-green-intense-fg ansi-bold">    303</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> DataTransformPipe(slicer<span style="color: rgb(98,98,98)">=</span>slicer)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/roi.py:44</span>, in <span class="ansi-cyan-fg">WorldCooROI.to_raster_coo</span><span class="ansi-blue-fg">(self, pixel_size, dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">     39</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">to_raster_coo</span>(
-<span class="ansi-green-intense-fg ansi-bold">     40</span>     <span style="color: rgb(0,135,0)">self</span>, pixel_size: PixelSize, dimensions: Dimensions
-<span class="ansi-green-intense-fg ansi-bold">     41</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">RasterCooROI</span><span style="color: rgb(175,0,0)">"</span>:
-<span class="ansi-green-intense-fg ansi-bold">     42</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Convert to raster coordinates."""</span>
-<span class="ansi-green-intense-fg ansi-bold">     43</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> RasterCooROI(
-<span class="ansi-green-fg">---&gt; 44</span>         x<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>x, pixel_size<span style="color: rgb(98,98,98)">.</span>x, <span class="ansi-yellow-bg">dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">x</span>),
-<span class="ansi-green-intense-fg ansi-bold">     45</span>         y<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>y, pixel_size<span style="color: rgb(98,98,98)">.</span>y, dimensions<span style="color: rgb(98,98,98)">.</span>y),
-<span class="ansi-green-intense-fg ansi-bold">     46</span>         z<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>z, pixel_size<span style="color: rgb(98,98,98)">.</span>z, dimensions<span style="color: rgb(98,98,98)">.</span>z),
-<span class="ansi-green-intense-fg ansi-bold">     47</span>         x_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>x_length, pixel_size<span style="color: rgb(98,98,98)">.</span>x, dimensions<span style="color: rgb(98,98,98)">.</span>x),
-<span class="ansi-green-intense-fg ansi-bold">     48</span>         y_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>y_length, pixel_size<span style="color: rgb(98,98,98)">.</span>y, dimensions<span style="color: rgb(98,98,98)">.</span>y),
-<span class="ansi-green-intense-fg ansi-bold">     49</span>         z_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>z_length, pixel_size<span style="color: rgb(98,98,98)">.</span>z, dimensions<span style="color: rgb(98,98,98)">.</span>z),
-<span class="ansi-green-intense-fg ansi-bold">     50</span>         original_roi<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">     51</span>     )
-
-<span class="ansi-red-fg">AttributeError</span>: 'Dimensions' object has no attribute 'x'</pre>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
 </div>
 </div>
 </div>
@@ -1993,7 +1931,7 @@ <h2 id="creating-a-new-table">Creating a new table<a class="anchor-link" href="#
 
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"New list of feature table: </span><span class="si">{</span><span class="n">ngff_image</span><span class="o">.</span><span class="n">table</span><span class="o">.</span><span class="n">list</span><span class="p">(</span><span class="n">table_type</span><span class="o">=</span><span class="s1">'feature_table'</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
 <span class="n">feat_table</span><span class="o">.</span><span class="n">set_table</span><span class="p">(</span><span class="n">feat_df</span><span class="p">)</span>
-<span class="n">feat_table</span><span class="o">.</span><span class="n">consolidate</span><span class="p">()</span>
+<span class="n">feat_table</span><span class="o">.</span><span class="n">write</span><span class="p">()</span>
 
 <span class="n">feat_table</span><span class="o">.</span><span class="n">table</span>
 </pre></div>
@@ -2027,7 +1965,7 @@ <h2 id="creating-a-new-table">Creating a new table<a class="anchor-link" href="#
 
 print(f"New list of feature table: {ngff_image.table.list(table_type='feature_table')}")
 feat_table.set_table(feat_df)
-feat_table.consolidate()
+feat_table.write()
 
 feat_table.table</div>
 </div>
@@ -2042,98 +1980,121 @@ <h2 id="creating-a-new-table">Creating a new table<a class="anchor-link" href="#
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
 <pre>List of feature table: ['regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4']
+New list of feature table: ['regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4', 'new_feature_table']
 </pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">AttributeError</span>                            Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[10], line 12</span>
-<span class="ansi-green-intense-fg ansi-bold">     10</span> list_of_records <span style="color: rgb(98,98,98)">=</span> []
-<span class="ansi-green-intense-fg ansi-bold">     11</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> roi <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> roi_table<span style="color: rgb(98,98,98)">.</span>rois:
-<span class="ansi-green-fg">---&gt; 12</span>     nuclei_in_roi <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">nuclei</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">get_array_from_roi</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">numpy</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     13</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> nuclei_id <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> np<span style="color: rgb(98,98,98)">.</span>unique(nuclei_in_roi)[<span style="color: rgb(98,98,98)">1</span>:]:
-<span class="ansi-green-intense-fg ansi-bold">     14</span>         list_of_records<span style="color: rgb(98,98,98)">.</span>append(
-<span class="ansi-green-intense-fg ansi-bold">     15</span>             {<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">label</span><span style="color: rgb(175,0,0)">"</span>: nuclei_id,
-<span class="ansi-green-intense-fg ansi-bold">     16</span>              <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">feat1</span><span style="color: rgb(175,0,0)">"</span>: np<span style="color: rgb(98,98,98)">.</span>random<span style="color: rgb(98,98,98)">.</span>rand(),
-<span class="ansi-green-intense-fg ansi-bold">     17</span>              <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">feat2</span><span style="color: rgb(175,0,0)">"</span>: np<span style="color: rgb(98,98,98)">.</span>random<span style="color: rgb(98,98,98)">.</span>rand(),
-<span class="ansi-green-intense-fg ansi-bold">     18</span>          <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">ROI</span><span style="color: rgb(175,0,0)">"</span>: roi<span style="color: rgb(98,98,98)">.</span>infos<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">FieldIndex</span><span style="color: rgb(175,0,0)">"</span>)}
-<span class="ansi-green-intense-fg ansi-bold">     19</span>     )
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/label_handler.py:89</span>, in <span class="ansi-cyan-fg">Label.get_array_from_roi</span><span class="ansi-blue-fg">(self, roi, t, mode, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">     74</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">get_array_from_roi</span>(
-<span class="ansi-green-intense-fg ansi-bold">     75</span>     <span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">     76</span>     roi: WorldCooROI,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">     79</span>     preserve_dimensions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">     80</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ArrayLike:
-<span class="ansi-green-intense-fg ansi-bold">     81</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Return the label data from a region of interest (ROI).</span>
-<span class="ansi-green-intense-fg ansi-bold">     82</span> 
-<span class="ansi-green-intense-fg ansi-bold">     83</span> <span style="color: rgb(175,0,0)">    Args:</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">     87</span> <span style="color: rgb(175,0,0)">        preserve_dimensions (bool): Whether to preserve the dimensions of the data.</span>
-<span class="ansi-green-intense-fg ansi-bold">     88</span> <span style="color: rgb(175,0,0)">    """</span>
-<span class="ansi-green-fg">---&gt; 89</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_get_array_from_roi</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">     90</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">None</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">preserve_dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">preserve_dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">     91</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:344</span>, in <span class="ansi-cyan-fg">ImageLike._get_array_from_roi</span><span class="ansi-blue-fg">(self, roi, t, c, mode, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    327</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">_get_array_from_roi</span>(
-<span class="ansi-green-intense-fg ansi-bold">    328</span>     <span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">    329</span>     roi: WorldCooROI,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    333</span>     preserve_dimensions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">    334</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> ArrayLike <span style="color: rgb(98,98,98)">|</span> <span style="color: rgb(0,135,0)">tuple</span>[ArrayLike, DataTransformPipe]:
-<span class="ansi-green-intense-fg ansi-bold">    335</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Return the image data from a region of interest (ROI).</span>
-<span class="ansi-green-intense-fg ansi-bold">    336</span> 
-<span class="ansi-green-intense-fg ansi-bold">    337</span> <span style="color: rgb(175,0,0)">    Args:</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    342</span> <span style="color: rgb(175,0,0)">        preserve_dimensions (bool): Whether to preserve the dimensions of the data.</span>
-<span class="ansi-green-intense-fg ansi-bold">    343</span> <span style="color: rgb(175,0,0)">    """</span>
-<span class="ansi-green-fg">--&gt; 344</span>     data_pipe <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_build_roi_pipe</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    345</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">c</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">preserve_dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">preserve_dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">    346</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    347</span>     return_array <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_get_pipe(data_pipe<span style="color: rgb(98,98,98)">=</span>data_pipe, mode<span style="color: rgb(98,98,98)">=</span>mode)
-<span class="ansi-green-intense-fg ansi-bold">    348</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> return_array
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:291</span>, in <span class="ansi-cyan-fg">ImageLike._build_roi_pipe</span><span class="ansi-blue-fg">(self, roi, t, c, preserve_dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">    283</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">_build_roi_pipe</span>(
-<span class="ansi-green-intense-fg ansi-bold">    284</span>     <span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">    285</span>     roi: WorldCooROI,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    288</span>     preserve_dimensions: <span style="color: rgb(0,135,0)">bool</span> <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>,
-<span class="ansi-green-intense-fg ansi-bold">    289</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> DataTransformPipe:
-<span class="ansi-green-intense-fg ansi-bold">    290</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Build the data transform pipe for a region of interest (ROI)."""</span>
-<span class="ansi-green-fg">--&gt; 291</span>     roi_coo <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">roi</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">to_raster_coo</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    292</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">pixel_size</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">dataset</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">pixel_size</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">dimensions</span>
-<span class="ansi-green-intense-fg ansi-bold">    293</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    295</span>     slicer <span style="color: rgb(98,98,98)">=</span> RoiSlicer(
-<span class="ansi-green-intense-fg ansi-bold">    296</span>         on_disk_axes_name<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>dataset<span style="color: rgb(98,98,98)">.</span>on_disk_axes_names,
-<span class="ansi-green-intense-fg ansi-bold">    297</span>         axes_order<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>dataset<span style="color: rgb(98,98,98)">.</span>axes_order,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    301</span>         preserve_dimensions<span style="color: rgb(98,98,98)">=</span>preserve_dimensions,
-<span class="ansi-green-intense-fg ansi-bold">    302</span>     )
-<span class="ansi-green-intense-fg ansi-bold">    303</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> DataTransformPipe(slicer<span style="color: rgb(98,98,98)">=</span>slicer)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/roi.py:44</span>, in <span class="ansi-cyan-fg">WorldCooROI.to_raster_coo</span><span class="ansi-blue-fg">(self, pixel_size, dimensions)</span>
-<span class="ansi-green-intense-fg ansi-bold">     39</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">to_raster_coo</span>(
-<span class="ansi-green-intense-fg ansi-bold">     40</span>     <span style="color: rgb(0,135,0)">self</span>, pixel_size: PixelSize, dimensions: Dimensions
-<span class="ansi-green-intense-fg ansi-bold">     41</span> ) <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">&gt;</span> <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">RasterCooROI</span><span style="color: rgb(175,0,0)">"</span>:
-<span class="ansi-green-intense-fg ansi-bold">     42</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">"""Convert to raster coordinates."""</span>
-<span class="ansi-green-intense-fg ansi-bold">     43</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> RasterCooROI(
-<span class="ansi-green-fg">---&gt; 44</span>         x<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>x, pixel_size<span style="color: rgb(98,98,98)">.</span>x, <span class="ansi-yellow-bg">dimensions</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">x</span>),
-<span class="ansi-green-intense-fg ansi-bold">     45</span>         y<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>y, pixel_size<span style="color: rgb(98,98,98)">.</span>y, dimensions<span style="color: rgb(98,98,98)">.</span>y),
-<span class="ansi-green-intense-fg ansi-bold">     46</span>         z<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>z, pixel_size<span style="color: rgb(98,98,98)">.</span>z, dimensions<span style="color: rgb(98,98,98)">.</span>z),
-<span class="ansi-green-intense-fg ansi-bold">     47</span>         x_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>x_length, pixel_size<span style="color: rgb(98,98,98)">.</span>x, dimensions<span style="color: rgb(98,98,98)">.</span>x),
-<span class="ansi-green-intense-fg ansi-bold">     48</span>         y_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>y_length, pixel_size<span style="color: rgb(98,98,98)">.</span>y, dimensions<span style="color: rgb(98,98,98)">.</span>y),
-<span class="ansi-green-intense-fg ansi-bold">     49</span>         z_length<span style="color: rgb(98,98,98)">=</span>_to_raster(<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>z_length, pixel_size<span style="color: rgb(98,98,98)">.</span>z, dimensions<span style="color: rgb(98,98,98)">.</span>z),
-<span class="ansi-green-intense-fg ansi-bold">     50</span>         original_roi<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span>,
-<span class="ansi-green-intense-fg ansi-bold">     51</span>     )
-
-<span class="ansi-red-fg">AttributeError</span>: 'Dimensions' object has no attribute 'x'</pre>
+<pre>/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/anndata/_core/anndata.py:1758: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
+  utils.warn_names_duplicates("obs")
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+<div>
+<style scoped="">
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>feat1</th>
+<th>feat2</th>
+<th>ROI</th>
+</tr>
+<tr>
+<th>label</th>
+<th></th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>1</th>
+<td>0.361989</td>
+<td>0.747412</td>
+<td>FOV_1</td>
+</tr>
+<tr>
+<th>2</th>
+<td>0.928323</td>
+<td>0.369813</td>
+<td>FOV_1</td>
+</tr>
+<tr>
+<th>3</th>
+<td>0.569211</td>
+<td>0.392189</td>
+<td>FOV_1</td>
+</tr>
+<tr>
+<th>4</th>
+<td>0.129771</td>
+<td>0.803985</td>
+<td>FOV_1</td>
+</tr>
+<tr>
+<th>5</th>
+<td>0.050885</td>
+<td>0.932590</td>
+<td>FOV_1</td>
+</tr>
+<tr>
+<th>...</th>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+</tr>
+<tr>
+<th>2987</th>
+<td>0.708020</td>
+<td>0.141566</td>
+<td>FOV_4</td>
+</tr>
+<tr>
+<th>2991</th>
+<td>0.132859</td>
+<td>0.310013</td>
+<td>FOV_4</td>
+</tr>
+<tr>
+<th>2993</th>
+<td>0.137844</td>
+<td>0.735762</td>
+<td>FOV_4</td>
+</tr>
+<tr>
+<th>2995</th>
+<td>0.910480</td>
+<td>0.083270</td>
+<td>FOV_4</td>
+</tr>
+<tr>
+<th>2996</th>
+<td>0.797235</td>
+<td>0.146990</td>
+<td>FOV_4</td>
+</tr>
+</tbody>
+</table>
+<p>3091 rows × 3 columns</p>
+</div>
 </div>
 </div>
 </div>
@@ -2169,7 +2130,7 @@ <h2 id="creating-a-new-table">Creating a new table<a class="anchor-link" href="#
     <span class="md-icon" title="Last update">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1zM12.5 7v5.2l4 2.4-1 1L11 13V7zM11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
@@ -2179,7 +2140,7 @@ <h2 id="creating-a-new-table">Creating a new table<a class="anchor-link" href="#
     <span class="md-icon" title="Created">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M14.47 15.08 11 13V7h1.5v5.25l3.08 1.83c-.41.28-.79.62-1.11 1m-1.39 4.84c-.36.05-.71.08-1.08.08-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8c0 .37-.03.72-.08 1.08.69.1 1.33.32 1.92.64.1-.56.16-1.13.16-1.72 0-5.5-4.5-10-10-10S2 6.5 2 12s4.47 10 10 10c.59 0 1.16-.06 1.72-.16-.32-.59-.54-1.23-.64-1.92M18 15v3h-3v2h3v3h2v-3h3v-2h-3v-3z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
diff --git a/notebooks/processing/index.html b/notebooks/processing/index.html
index ad70c69..fd9aca4 100644
--- a/notebooks/processing/index.html
+++ b/notebooks/processing/index.html
@@ -1272,8 +1272,8 @@ <h3 id="step-2-create-a-new-ngff-image-to-store-the-mip">step 2: Create a new ng
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
 <div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
@@ -1303,39 +1303,6 @@ <h3 id="step-2-create-a-new-ngff-image-to-store-the-mip">step 2: Create a new ng
 </div>
 </div>
 </div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">TypeError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[2], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> mip_ngff <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">ngff_image</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">derive_new_image</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0_mip</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">      2</span> <span class="ansi-yellow-bg">                                       </span><span class="ansi-yellow-bg">name</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">MIP</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">      3</span> <span class="ansi-yellow-bg">                                       </span><span class="ansi-yellow-bg">shape</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">1</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">1</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">2160</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">5120</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/ngff_image.py:202</span>, in <span class="ansi-cyan-fg">NgffImage.derive_new_image</span><span class="ansi-blue-fg">(self, store, name, overwrite, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">    179</span> default_kwargs <span style="color: rgb(98,98,98)">=</span> {
-<span class="ansi-green-intense-fg ansi-bold">    180</span>     <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">store</span><span style="color: rgb(175,0,0)">"</span>: store,
-<span class="ansi-green-intense-fg ansi-bold">    181</span>     <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">on_disk_shape</span><span style="color: rgb(175,0,0)">"</span>: image_0<span style="color: rgb(98,98,98)">.</span>on_disk_shape,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    197</span>     <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">version</span><span style="color: rgb(175,0,0)">"</span>: <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>image_meta<span style="color: rgb(98,98,98)">.</span>version,
-<span class="ansi-green-intense-fg ansi-bold">    198</span> }
-<span class="ansi-green-intense-fg ansi-bold">    200</span> default_kwargs<span style="color: rgb(98,98,98)">.</span>update(kwargs)
-<span class="ansi-green-fg">--&gt; 202</span> <span class="ansi-yellow-bg">create_empty_ome_zarr_image</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    203</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">default_kwargs</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    204</span> <span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    205</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> NgffImage(store<span style="color: rgb(98,98,98)">=</span>store)
-
-<span class="ansi-red-fg">TypeError</span>: create_empty_ome_zarr_image() got an unexpected keyword argument 'shape'</pre>
-</div>
-</div>
-</div>
-</div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
@@ -1441,23 +1408,18 @@ <h3 id="step-3-run-the-workflow">step 3: Run the workflow<a class="anchor-link"
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
 <pre>Source image loaded with shape: (1, 2, 2160, 5120)
+MIP image loaded with shape: (1, 1, 2160, 5120)
+ROI table loaded with 2 ROIs
+ - Processing ROI None
+ - Processing ROI None
+MIP image saved
 </pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[3], line 6</span>
-<span class="ansi-green-intense-fg ansi-bold">      3</span> <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">Source image loaded with shape:</span><span style="color: rgb(175,0,0)">"</span>, source_image<span style="color: rgb(98,98,98)">.</span>shape)
-<span class="ansi-green-intense-fg ansi-bold">      5</span> <span style="color: rgb(95,135,135)"># Get the MIP image</span>
-<span class="ansi-green-fg">----&gt; 6</span> mip_image <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">mip_ngff</span><span style="color: rgb(98,98,98)">.</span>get_image()
-<span class="ansi-green-intense-fg ansi-bold">      7</span> <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">MIP image loaded with shape:</span><span style="color: rgb(175,0,0)">"</span>, mip_image<span style="color: rgb(98,98,98)">.</span>shape)
-<span class="ansi-green-intense-fg ansi-bold">      9</span> <span style="color: rgb(95,135,135)"># Get a ROI table</span>
-
-<span class="ansi-red-fg">NameError</span>: name 'mip_ngff' is not defined</pre>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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/FY2WxWgUDA4FCgq3A4rN3dXXMGcBqZS2/QWq2WRqOR4vG4JJnj0Ov1bP49kkNqAEc1FAoZQsA+5G/2kL8OhgV5Bu3y84ch55rIIw7YcDj8hOxzvUWHEqPOPCA/t7e3mkwmhkAAJyKvkUhkLiLFgUIuFnVEu93WaDQyCJqBcw9KAQLDXLdaLU0mE3OykAnScegPDDkDh4f3wwljPnjO0Wg0p9SRgbcxzotj8bt/Wlj9Tb6PMfteH4sIlB+k3kgFeX3Od1hPZM3bIPaKtx2fdj+frvVBrDSby2q1asa61+tZmvBt1vKNDTVRpYfuPESFwPN/L7QMFAIKBCXilcWiF4cnTE4RIzwej7WysqJEImELQW6c5+W7/jlRAolEwvJT7XZbjUbD8rDS/AZDefX7/TnFy7+5B1DhZDJRJpMxw8z9iRDC4bDW19dt8VqtlmKxmFKplMG9FxcXGg6Hikajun//viKRiF68eKHRaKS1tTWLWKvVqqEH3kASOZJXzWQyFoEOh0M1Gg2VSqXXenWdTkfFYtGgbAwE3iK5f7zEXq+nvb09xWIxyw9ihLPZrL7yla/od3/3dy0Pz5oTbZIDBH5C6ZVKJVWrVXtuSdrf37fn5GdcC04DDlcikTAnxvMQJpOJrq6uLFLd3t62uQ+FQur1ehYFj8djxWIxbW9vG9oQj8f1+PFj3dzc2Eb3KaB2u23GPhQK6eDgwN6p3W6rVqtJkkFhKysrJheTycQMzMrKismTjwD4PaiGdJca8gYEx8zfi9SP/7c0M078nGiff/v8nd9/fpDqwAiypjiE7FEGziMQPQ6uJOOXeJQNeWcOMY5ct9/vG9cAx61er9s1yGcD+ePgMAekQmKxmD27lzHe0cPo/p3eJpf8Jgb6s77/tt99m/v9eR6LqIWXVT8H/B4HnT1PGms6nSoej2tvb8/0/vX1te07j9qyb5GHwWCgSqViKUFJajQaajQan7j/24w3NtShUMgUkc/3sXkYTIwn0fgojxdAsUgyT8bD6X7SMWaRSMQiV7xgPPm1tTWtrKyY8eD+fJYIgRwj3hKLQnTjJ9JvEqJ47ucjdSLoXC5nECWLxvvE43HV63U1Gg2lUint7e1Z3gRiEtFLPB5XIpFQrVbTZDLR7e2tWq2W+v2+crmc6vW6wZs3NzcWdTMfvV5P1WpVwWBQOzs7SiQSisfjZqRHo5FWV1ftHovvO5lMVCqVVK/XzYHAGeFZR6ORrq+vFQgEjGiIUeHZptOpMpmMDg4O9P7772ttbU3pdNqME4aJa5N3ZKCc/TySf8Z54LuQ4aQZvNtsNk0hIzM4aRjQbrdr8GcqlZpT9BhMyFzA0Jubm+ZEAKX5NAAODc+RTCbnYNdUKqWrqyv1ej0lEgnt7+9bWoe8/+3treXDSQ8Nh0PFYjGLJpFruAEeFmdNQS5YW/YaaAlKiLVAvokM2MfkwSFzARN2u11zXqPRqJG3WCvPDwiFQrZW8BTgEZDjB2YkIED5YSC9E5PJZEzmB4OBWq3WXKQCP4A8+Hg8niMY8UzsYWQWRIRnCAQCxrEgv+9l6XX8msXxpzWU3w42/7TxnTDS34v56s96p8UUDDr+dUhHs9lUsVjU5uam6dnJZGLpJk9wzufzllIpFApmoKU7xAU9wD2QYQLRP808vrGh9hv+dZt5MZKVZB62dAdxE/4zESgCJsXnu7kPG5wNhFJ8nbfE8/X7fcuvooRwEoh8MV5EuNLM+2GD8lzeu0aR+GeLRCLa29szIxYMBtVqtXR9fW2ElHa7rVKppFgsps3NTTM2wCp4+OSGUqmUKeXT01N1u10lEgmVy2Xd3Nwom80qlUqp0WioXC6rVqsZOQhjnM/n7WdEZKwFUG+z2ZSkuciZCIeIqtlsKpFIaH193X7e6XSUTCaVy+UUj8ftHkCkrPXHH3+sx48f6/b2VtlsVuPx2HgAi9EujMvFFApjMpkYbI1z1Ww2lcvlLCfZ6/UUCoWUy+XmGMTegHrHEnmo1WpGXMNZGQ6HyufzSqVSxtKuVCoWAUuzSLVYLEq6yy3zLJKMpEVKgA2eSCSMnYqC4LlWV1fnctKSDBrmZ+Vy2SJXBmQYjKEfPI9HRnAoF/c2yAIRo490IWiBIFQqlbn7obAgkA2HQ9uLOPqZTMZyu8xRJBLR1dWVOVFE6MwxzstgMDAiIvoDxyOTycylEcjjs98x+ChukC7SGKw5RhkHqdvt2vohB3yWaoVFQ70ou39SY/tZ13zT7/j7vi6y/Hbffdtn/7My7sFgUCsrK0qn0ybTrOft7e2cU01g12q11O12bd9nMhnTcfF43Pg+o9FIGxsb5oginwQBXJsUbDqdNr10e3urRqMxl55lnt50vLGhljSnTPz/+cPwEJI3FN64IfgYXKJ0oDMP2fnf4RzAnJtMJqYYiRiBwIG3MexEUf1+3+BjL8AszNrammKxmDqdjprNppU58TlICjD3NjY2lM1mNRwODZIbDodGigJaiUQiOjw8tOgZY8XCIxxEp+vr67q+vrZcy+rqqvL5vLa3tw3NiMViuri4MAPrn9MLK++JwkRZr62taWNjw6K66XSqcrk8x2QsFosajUZqNBoaDAa6vb1VKpXS5z//eTOEGGBgY4hhICHSLHJ6+PChzs7O1Ol0TOmTn76+vtbV1dUcxHx7eztXgjcYDHR5eWnzFQjMyqbwdrPZrDqdjiKRiKUGgLUDgYBtWAwWkKmH5HlWn8/EcUNxN5tNMzTtdlsnJydGQqKcibUEEYhEImZsgNKRX4wdjgY5bXLyGPt+v696vW5RMwjR5uamOYnsOQ/1+/nDEEkzMtSiM+SNs48+k8mkyQ6GmnKT8XisRqOhdrtt0YlnnLNOGxsbZmB5PvQF5EcccP7NnEPoYr5YH3TL6urqXK45GAxqbW1NmUzGFDOpGvRIIBBQu922NeJePifOe3hiHbrE81r8fP1px+uu8ye57md957OM+Kddx8Ppr3NEXvf/133+TzoW7++difX1dRUKBUmaq2gAwet2u3OIJraI4JF1RDZBnficj6IlGQKK3m2326pUKsaPSaVSlu6kBLFUKtn+Izh8k/HGhtrDAEySD+kXFw1jy4bg50ysJ35Jd4xgSre8EUdJ8ByLOWl+5qPJQCBgBpwFzefzVtsG5OwHuQciNmBoDAO53pWVFQ2HQ8sxZ7NZSTIl5olrjUbD7n9wcDAHUZJOIM/tYXVppkRbrZbBfQhiLBazyAGD5okzDAQI+JQIR7pDPu7du2dkrVgsZvehlI3PttttI91JM6NDNL6+vm5RBxH/zc2NfZd17na7Ojs7m0MwSFO0Wi2VSiWNx2Pt7e0pm80qEolYSUWpVJpLQyAXQNmgLZ7YCCxZr9ctigPmREkDdyMrOBy1Ws3mg+jdlwhdX1/PkW7YfGx4DDUIQzgc1u3trUqlkpGRQIrId5HHRgFgCEipjMdjM9LsKSBxolL2GfNA5Oh/hgz6qJHI4HVcEYwyTgvz6+8NyQvnw0f27OtEImE/4/oe7WKfDAYDQ5Mmk1nNNM4ATHuegfVYJNMFg0Hbr8hFLBZTOp1WpVKxnPVkMjHOgM93s3eYP2QVXeMJbswln2e8zvB5hvpnjdf9/m2iYf+5TzPAi8bubZ7lbb6/GJl/1jW/3Vg0zoxYLKZsNmu8AxzISCRiupJqjlwuZwYXQunq6qqho+wDT0qsVCra2tqyWmj2KQ5xvV63gAZUGCdSukOkqRBIJBJm095kvLGhJgJBebPB/YSzGYjafG4Q5bQ4eBFPxkGpAFMBhaF8PXzFpPocHN9BKfgSFhYLSCydTlv+EGPpiSZAaigajDOTD5kN4QgGgwbt+qiGiB3nBTarh5K5ryep8OzUfUp3wkoezRPtGORgVlZWtLGxMUcyIkpFWJgrlFAgENDa2ppKpZJB4vwunU5bvo9n9CkC0AcP5ycSibki/y996Uu2NpSCSLLIGCMdDoeN+UyOHsW5trY2V6MozRSpb9ABHJXL5ZTP5y3HGI/HVSqVjI1OMxQIcrVaTf1+35yyXC5nstxut1WtVufk1ysPGs4Eg0FzZHi2UqlkhMVWq2UNZTzzmXkh7wq8z7qsrKwok8nYfCJnHqHy+w2n0+fmcVT4nm8wQmoH5w0ymXeAMPCj0cj4AJDuyBl7XgHO72KpIM/Z7/ctlxwMBi2qRb94x4HP867sY0kWzQcCAZNtr9SR8WQyqVqtZpF0MBg0GV0kwS7++3WNLxYNFdDq2tqayQM6402M9KJsfdrvXveMn/Vd/zPWBX3E3HxaLnUxvfjtnvfbRdDfaXicoAs9j/xjVxhU4MTjcdsP/X7fjCrplmazqZubG2uABW8HXe/Lt4rFosrlsqF60WhU0nylFBH6cDhUuVy2iP9Nx1uVZ+FxeI/ZL8zKyoqSyaQ9nPd0FwWB32NsWTg2M3knSCt+EYC/gDLZXF4pcC1vMPGEqZulvAYngE2Ph49TAIQP+YaoAmMEfOkXBzhvY2ND4/HYIl7fJIMoodfrGSksnU7PGXGiNk84QmmiTF8HB62trVlB/8rKijqdjnVbqtfrSqVSyufzFs1KMlKeNGsAAYnPR2cYYpRaNBo1SBhlS7czIigiLTr0cC0McaPRMFITRprrIENbW1umxK+vr60+WpJxDTyB8Pr6WtVqVfl83hAP3pFuVScnJ4YEUA7modpOp6Pz83MFg0E9ePBAjUbD1ssbycV9cn19rdFoZM0U+v2+yuXyHNO60WhYnTdyxT4ZDAbWfIW5grewvb1txCzk5vb2Vslk0iJ0z6UA9sfRgihDcxGMIo5pLBazRjKgW8gH6QCMJugDCoo0Ezk7UAjW0TvUKDMQLn7nGbVe4XkmOmgWcz+ZTAxpYI1x3tjf/J85xWmEIMizNJvNuXdkeIPv03isD7+LxWImc0Re7XZb9Xpd1Wp1ji3/acZq0Sh+u8+8zeAZM5mM8WR4B/QUZZ9vcq/Pgrw/a3jn9m0N9uuiauwMumo4HJpx9EgcfCGa11AqiP64urqyclP0szTTMdvb29rc3LQ9xV7DEd7a2jLiLfYCxyAQCKjb7erq6sqqEnAK3mS8VY7aC5n3vAKBgNXM+sYg/B7FgRcuySImP4meNLZ4XzZPNBo1qI17o0joRoSnCARNrmERBsQIEm10Oh2DEH0ugijfb3pgTkmmkDwMPxqNtL6+bvk05oHFDQQCBrE1m02LcsivEu34DYORJv/K95kvnJVMJqOdnR1znhgXFxdWJgAMPBwODfZhHmCZc00E3MOk5XLZmkfE43FzfIh0fUcglO/NzY05AjSbYc5QfJD8MF5E+5lMxuBivxY842g0snwrBgxHic5zyA0O5/r6+lzbzMXoiXdqtVpqNpsW+TFvyN+iohkOhwavEn1iEHCuaOzS6/WMUe7RgfX1dUUiEVWrVYPCIaegHFAWlUpFzWbTurvhQHvozcO0RL58DrIXOV6P5lBf6lETn74iIvW5W08MxWD5fDNRN3PKfONgemfBK0PaxpKrBqEibx+Px5XP5+c4LpBFST34NBDkONCyw8NDnZycmFMNosEg8t7Y2JB0V3bjIVAPtwcCAcvrE/FRKcE7f7vh04Wvk1H/e54RwwEisgjH47wzfOkdskGK5m2Gtwes/+ucHj7j//02zsjrHGQCH09e9gEVJauBwKxfBpUwdLAcje5KTT1nQZrtA1one2cUmeTnPqi7vb21Rk8QJy8uLky2cPbfdLxVeZZXjigrBBSIlP974pmvZQbqY2Pzfw+lswi8KCxSImzpzjOkDGQymVj+ADjYR/E+6Y/3jrHDSHiCCs+Awcc4Mwce1hiNZh3JYAFSR0eDEIzWYDCwz3nvlZIgSXNtOf1otVoqFArm3HiUgnlGmdOAg3ki70JJmnQX0dKpiqim1+upXC7PwYhcB/iVcqbhcGj5e2CdXC4357RQ0sM8MXfIC01ARqNZOQ1RnCcO9vt9SzsgB76sjGv6OcFJwcjwzkSVEP88JMtcwTUA9r29vdWLFy/m2MMwjF/XHAJ5Yh2z2aw2NjZMedPCslwuq9lsGgLhITqYqxhi3h8+AnLY7XYVDod1fX1tdfNeXsmRY0RZR+6FQ0gEzt5mnaV5Ehx6wCNWyMVinpg+BdwbI045E/vQoxj0kMchZ2+AvgQCATUaDbVaLdNBKMitrS1D9CaTiTlFXheRI4QcFI/HVSgULD0UiUTM+DabTZ2fn5vDuLGxoXv37hnkSUUJlQrIDikd9jT6KJPJzEWriwZq0XgBn4MswBz28+VHKBTS/v6+8vn8XBrn7OxsLrXgnWHSLsjSeDxWOp22qHJxfT5rYKATiYTJr+957WVjEbp/k0j8swz6eDy2cwU8kQynHNkjmr28vNT+/r6Rcr2u87wD5iuXy5lTh83xTYAWeUKkJc/Pz3VxcTF3Xd7Zp9C+3XhjQ83G8dElbGZPUvH5VR7IK1F+5iNbNjLGz5fIQG5ptVpzOVqUGN/BaLIpvFGq1+tzzw1suEiakWTs1el0ahEfG9orPwhMxWLRFOfq6qoJx8HBgXUZ8yzbcrk8x0D2EbN0h05kMhklk0ljJXc6HZsDDBNeIDm7fD6vjY0N69Ym3ZHGfE/kQOCO6UrP5WAwOMfYHg6Hqtfrc/CiNCsBeuedd/Tee+/p+vpav/M7vzPHrPSIA4YZhGF1dVWdTmcuJ4RRv7y8VKfT0dXV1VxkVqlUJMnY3Dw/80/nMJpUADX3ej3l83mTF0nmJXunC9mFsY0zFI/HrezN93j28zgczrq0fVbD/ng8bmgAUBvMayB46jJBhyaTiZVo4fz5xhwYp9c5KigdUAbvFKNIQTO4Jo6FV8j+2v4QDRwu4NPr6+s5mB3nWtInmhF5UhYRj69l9vwX9p83uqyV7waIk4Xjh15CkUYiEZVKJVPmnpw5nU6tMgRWOU4L/JNEImFON3sLZIbIn8gRBU5LUmTbp/O47yJ0uyg36XTayivZJ97Bf913kF/Pr5FmRuTJkydzjXKQTeZOujuohbSjRxP8fRivy4HncjkVCgX7HGzn6+trczJed83FaJy9gux4QvLivXmfbrer8/PzuXp+DsVAZ9frddPx0oxY5q9BescjFZPJXccyb5OocphOpxaZg0xKdyTexRTJp83fZ423qqP2D47yYAMg4N579sQPvueJC977QTl5SMZDnV7pS3fKGuiXKMx74nx2dXXV2k9ijCjRYCwKAL2uYQXjmfuaSukOFoc0gtJiMfEqgU15Xu/Q4BDwHvv7+waf3d7eGtEI0hMRB/mW0WhkOWcMPM9WrVZNWZMTJYq6uLjQxsaGPQc1rhh27yABoeVyOW1vb8+Vow0GA4PTms2mwX2sEe8Iuebx48fa2tpSPB5Xs9lUuVy2jcNJUuR5W62Wtre3LbL1aMfV1ZXleYD8RqORbUYcMlADojnaneKsYaSpmSY68rlaXyHg0zhAjBCIcHggvvkSRV8ChHPDaVyfFlmRC0fWPUmLXJjPGXvHypcAsk+5PwREjDnvhVHAMGO8PeIEPMqeAPXgYAuu6QluvBcQK8qX/2O40Q2UX/oueMglhpLDRHw5FvsPwwlxkiqPeDxuyAzPBQMc6Beo0s8Pkbr0yTp8Uig8O/KQSqU+keLiupFIZK5JU7vdNp3kCaw4UYHAjOi0sbHxqe1/QWVwVjAcq6urOjg40LNnz4z3gmOLrmL9WZfXGWE/XmdkIpFZ737WGacuEonM6RB0BesOYZKgaHNz07gqcF+urq5sL7zuOZDdwWCgUqk0F9QgpxyUwWg0Gur1elaBQ1560RmBF0OPfC9T/Dk8PLRgDjuFkwDE7W0G/37T8daHcvDwnrTilQL/9gxUFIGHUXw+l0X2EbN01wIR4UfhsIGALXyJhoeNgXbC4bBtNP6PwVwUQHI4+/v71oayVqtZUwPgZaCN1dVVra2tzeWrgHGZA37G/YBPiDJSqZRGo5GazaYZCyIUyGfkCsvlsl2nWCyqWq1qOp1an3AEAiPk12Vra8ueBcfh2bNndngIxgunB2Y3UJ9014UHVrNvuQf81G63rbEL3yFqGY/HdrAH5DAcvFwuZ/AmgxrrZrM5105TkkX9q6ur5kXz3FdXV0YWYs6BEzEmRK+ZTEa7u7tWEYBDhkLz7T25P4oSRbSzs2NtOFEK19fXrzWi/r3S6bRB4oslSjTTIJLudDoWaXMNHBJJ9hmiaV92RfTp2+DyXD6n7KsU+D1OB382Nja0ubmpx48f23v5SgfmpdVqGfyNvuAdF3OXfI9o3+cIud5i1O+RG9YD+UZ3kC6Du4JeyWazBkPTwtc3LOLnnU5njlgHWsO/0+m01tbWNJlM5o5DZH/xzpDXiHwzmYyR5jBeGBjmzO8DdBzGAg6JHziYpBt8ZEhzIo6xJY0ynU6tWoKI0qMAfn1epyt9JOwrVnD0iSohGyYSCdOfOFpEpxcXFzo8PFQ6nVYqlbKAamVlRffu3dOTJ09eG+UvPiPyyCEYPNMi94l1ovOeR668LE+nU11dXWk0GhkBt91uW7ke7wDyJsmIp5z1gHNcKBQMBXyb8caG2pdieVh60cPBMLHpIT75F+caKBJvfJlsNhm/I7fgc8REGUTuvg2gJ6z43Lokg6zwHhcNA2UzLHIul7PcVqFQsM1AJARk5L1RTllCMIFAfd9XivBHo5EODg50eXlpSg2YRpLNw8rKiq6vr/XRRx99Yk1QVJPJxK5PH21+j+FFkQPv4lECz/KZUCikzc3NuX7NRIGSbFNPp1Nls1nrfb23t6fDw0MdHh7q61//+lwOiD7Nt7e3VvvIHAD1IR8YJX+OtYc/gS4fPHhg0KdXVCcnJ6pUKgaBe34FzkI0GtX+/v5cCVaj0VCxWLQoDEMAqsP8QU5KpVLWvAWDlMlk5owtMDcGCoemVqtZugYlD4pD3pln5pxwT9j0hCw4A6wJEQiy4UsskRtPEMSoEJnye77HPqF0jDVhL7N/6KpHRMzPcRAxOpQzghSwx71MTyYTi1DgNID8UDHgOSCTycSiNRS9J5hyr2g0OpfPZw8TCbVaLZ2dnc3BwMwzUS6OO7Kdz+ctiq9UKga3YqhR5uwf5Bzkjf4F/nheiEk+dcf8eqQCnUMaBSdOumPYp1IplUoljUYjKxEC+SLlKMkIkz4wW8yfvy5fzP1AkWgs5Uug2GfoYAiTcD6o0PApCHRgLpezHv2flq/2z7roEH7ad/jsoo3y6QnSnOVy2X5OqgEdUSwWDZEslUoWXDKvdKREh31ayux1460O5fBkr0ViAD/z8LjPTb8ux4BhRnEgLIuem78OZ/L6CIAN7VnYRKKdTsfgLEk2cb5kxA8gGww4wswzv07JeYjRz0k6ndbW1pZBURgnhJcNSiMG31CB58ARgRWMN4YxZZBz9vOI0lhdXVUmk5lbF6BGCCOeKzCdTo0AwrvQQADouNVqWTphMBjogw8+sC5nm5ub+spXvqLLy0u9evXKnrvRaGh1dVU//dM/rV/7tV+zCAEEQpLVtaOgpTvWJNBZtVq1iBzhZy54fjY1h63AaJbu6sglqVAoWG04Dh8RDjlBIFfpDlGAKby+vm6GEIUP5J7NZtVsNo3zgEz64/Y4yziTyZiThKL3JDhp5niVy2Xrk418Qogj8vZROXvSO19ebsiXexTGOwKeZY/z6h0O5ANCKakhIlucPxwRjqdEJmkK02g0VK/X53KBsMWHw6EZsUajoXg8rmw2awjSZDLrL48BBkrudDpKJBI2rz6vD5LAWnpyXqPR0OXlpUVk6BbeX5J1vJNkEC2/h7wJnwDUD5lZ1DXMH3C1J16iH4iWiUJ95Od1QLVatcAA+YErgENDkHN1dWUd7XDGcQzILXe7XXOK4cGAABJ0IAce2YATgTNDagj5om+BT0NARIW0CYcB2QV1+LScL2PRWC86Gf5zHhHw6wJ5E50Og54URzQa1ebmpgUIwPc4mr7nA868b+QVCATmZOHbjTc21ECvPneFgl8kgeBlYxQwpEy879yFR4lXTvcjXpAcBpGuZ1Yi3NJddMf9UFiemeeN4+sWEWMFZMoGicfjxuCmvhgBRCkRBbPwmUxG+XzevEsWW7orGWEuhsOhnj9/bnAfysWTgdhMr/P8pJnxefbsmfVBJiIH1iU65B1ZR78ePrK6uLgwghiQFYrgN37jN5RIJKx0yCu/RCKhr3zlK9YHGoX9V/7KX1Eul9Ov/MqvqF6vG8ycTCZtPugCtEjyY+7Pz8/VbDZtnmH4wyjFGLC5+T9ED5QOzlkoFDIjj7GjxMaTWCAM8h1quIm2/GZcXV21Ric4IvV63Uhxi1497+lLApFXomNGKBTS+vq6Re/MWyQSsc+iCDDKPB9//L70qBj7zNefemKPdFe9QdrIO5XIt3cEgCCZS75HmsZD+N6I+ICAHCuRNxHxYDDQ5uampWA85M99yZkimzyzdIeqkCdmPpvNpi4vL9VoNCyXHAqFrBUwaJu/DjqI/G+v17PWoh6qp2IAlIu1wJCxV3gHnGx0LOik7y/gx3g8qwPGcQCVonoBZII1p8JjfX3d9gfpkYODA0MlIVWiS7g3qUrq10HzfB/3170fvQNYA+TSc5HokU1dMu+9aHRfZ6w/LZ+++PNP+0w2m7W+DSAZ2JJGo6Hr62ubR9YLG0W5KggPvA1sD/YRuX/T8caGmmhAmu8djPeBV+uNL4Yco0odKPkb4Bu/sb0x4l7T6XSOeMFiYswglPio0JMkuAYkD/J3TBwTnMvlDB5G+ZN7pTSFzlIeqidnQcSH4uEePm/e6/UsJ4aHRdMIvMtgMGgRHXlwSHA0ZPACxrXJy3gugD9pCsWDguZ9qtWqzQ1lKZAsaIiBISIfX6/X53KX5H/v3bunYDCoRqOharWqyWSiZDKpv/pX/6pyuZx++Zd/WV/72tc0HA61vb09V+YQDodVLBaNVEe+EjmTNAc/8mze8aL/NHlyOnmxUer1uuX5J5OJisXiXB6PDcScApE+efJEoVBIOzs7Wltb+0Q+y0OvcApwdDx7nufkHdbW1ubgM0lzZVBeieVyOW1tbZmiHg6H1g8cSD6fz9u8U1dNWsjvzddFJfzeM8SRGdYHBIU0BzJKfhND5VE2omivjL2OkOYJWp6lDvHv9PTUCEfsVaJ3/qAXIFQuRs9E6zDZ4SpEo1ErP/NOWSKR0PHxsQKBgJ48eaKbmxvbC7DB0XHA+pIMDfHrzOd4V5x8ZBfHGmeEdfMByXQ6tVI5j1B6HdDv93V1dWVBAqkMgolFY9Xr9XR1dTVnSDY3Ny0CDoVm3Rvb7ba2trbmoP5YLKa9vb25zmvodZAyvzfhUMAl4f4YwXa7bSWVnBuALODokdvnsCDvyHo7wtz48bro+nWGnmge5xHHR7oLLGq1mjH/cYpyuZx2dnbsXfl8oVCwclJvO70e/3bjrRqe8HKejCLdHc+Hx8bDYIxQxEBVEBnwVL3QQQbCY+FlfKtO6a73uP+ePzqRBcYrR/jJQWFUUdCQqfB6A4GAORR4c0SMwI4cCo7B8AvQ6XSsuQfPR8SItwxUClwCWxlnhAiN56WOcnE9FqEeL3x8x5eUMD/UMAeDQZVKpU98n+sCvTPv+XxeuVxOp6enpoSZj8FgoFQqpT/4gz8w41Cr1fSbv/mbevfddw2+BG5GafvyP7qEcfjJYtoDlGE4HJrBANrn5zRjgeiDnGJYeM9Go6FOp6Ojo6O5em9aysZiMWOxgy5QyUDuypPtPBnLQ5c4SCA/zJnnEFAtQUToKw/a7bblQHEEPQmM3PDOzo7xMnze3DvMnmy02FTHp01wFvgeRhaDhAHhdyhfX0bHQLkT9aITfISPIXrvvfc0nU7tNC0cNl9uFI/HValULFrESVtfX5+rcuBIWJzWnZ0dSbPo1/+7Xq8bKgEs+YM/+IM6PDxUrVaz/KQvjeIACPbKaDSrOKDzVSKRUK/XM3iYzzBf3nGiSQ+pDy9LfA/H2B/jyzyHQiFtbW2pUCgYuYlUA2goaJX/nnSHNkh3/B3kg6CHSNF/hzLSVCplqavJZFb3j8yzz9j3VHXs7+/bnkDeOREun8+bPiWYAxr35WdnZ2d6/vz5XB+ERSO9CHX7d190Sv2zMB/sH8r12LPValXFYlHZbNbeFWjfpz2m06mhkb6jIimRNx1vbKgRFs++lO7YnWxwFB3RgieC8NIIVjKZnIvEMeaeus8i+G5KwG8+L0Q5E8+KkUeZwsjGoHjKPN1mWEBgWHIPvCcsPrxKvFsP/7Lw5Lp8yc7l5aVtdI5WI58Uj8et1jCbzRoBCiVE5zQfhbwJpNPtdvX06VPt7u4qn8/bNYLBWRvW+/fvW/0yDHKuCcyIAvZkrGg0qkQiYT3KUSAwgT/66CM1m00dHR2pUqnov/7X/6q1tTVLFUAq4toQdnztMAqHc4VTqdTcmd+DwezwhsvLS7VaLW1tbUm661IEgcs3vZhMJta9io5jGHKiN9AGavGJhnxOyaMW5Pd8PpsoKx6P6+HDh2bkWq2WNe9nH/i5ZX6oU5Zk+X3QBYwb5TdEMPv7+wb5+yMicYaAnHGSh8NZS1nIQ8gvz7TIGQkEAtati/2IvHuYUJLtS4wSSNPa2tpcFOX3Fo4ateyhUEgnJyfa3NxUMpm0wxEajYZWVlb0Iz/yI3rx4oV6vZ5Bszg+zGcoFLIDVg4PD63XAc+GzohEIoa0RKNRvfPOO7YHPeLD/9vttorFojlPIFPtdtu6WPG9Vqul8/Nzg0DRHzgo7XZbNzc3pp+Gw9nRqel02oIP1guD7kcgENDW1pbu379v6GY2m1Wv19OjR490eXlpRo/9jWMHd4ZgIZlMGgeBefLoHzKInEmzqNE374AISbkYUT6ORq1W02AwO0GONB3kzWKxaMEQ+XIcBqJtHO79/X1J0tOnTz/Bgfo0A+0Hxhf2O84QewqnGVIk+xUHk8AkHo8rnU6bkcfRwskBLYX7QCXJdyWiJr/kjTSTAImD/tsYMQR7Mcol9+GVDRg/ZQJMCMrPw9YYff88PrLkGcghZrNZW4TF4/eku1N4EDyiHhTt5eWlVldXrZSGHE2z2ZwjqMTjca2vr5uSpqMViAMLSMkIGxmPFE8e6AmiF5/P5/Mql8vmia2srBijFTLDIpMQA/rixQtTLPv7+1pbWzO4HiUG7M0gQsDwYJARUhyRd999V9PpVJVKRTc3N/rt3/5t21x+Y6dSKe3v76tWq+n8/NwOBsHj9BsSdj3vwDnQXA8li5ODscZ47O3tKZlMGnxG9BiPx7W1taXRaKR0Om2GykeJIB0oWSIBno+5CQQCdg44jiIKlXXOZrO2d4ARh8NZY37kFWeFvYRy9POH4cVI46jyLJzDS4RPbg+HBs+eOcURwmAsdqKT7siSpH+8rHso1xsdX27H2uBcHh8f6969e/r6179u0SPOE4zgbrerjz76SHt7e/rCF75gqTL0y97enl69eqV+v69kMqnt7W1dX1/b+eg4eTi4yAPEJfLoMK9BhHgnonq61z158kTlclnRaNQi8Eqlona7bZ3lcAYxPlSJgPKRYjo7O7PaXYw17w+ihGHinGSeG7iew2n8wOH3KQrprlPZ+fn5HBIQCoV0cHBgemY0Gun8/FzFYtHKWFl39BiyD/Gu1+sZKcw3tmHA+vc8HK+nO52Onj9/Pue08L0XL15oZ2fH7t9qtZTP581I+3Tk4eGhqtWqrq+v9WnjdRB3KBQyIp10VxYI8ujbf/IMzK9vpzsa3XXaq1Qq1oMeHUKwB7fKp32/K72+fSTNJl3ctGwONjwbhtHv940RiUB5Y+0HOWaMAyQunAE8TwQXb90rFE6GwkEYj8fWYxlhRzmi9HAUUJ7D4XCOEMY8cBqSV7Y0HOFza2trKhaLtuheULkewoeC9gQbnhvlI8mYsOvr63rw4IFFakSe5+fnczWQPsKZTqcqFApWCgFJCOgzl8sZTMjgPWGlcy0Og6A9JrBjq9XSo0ePDMb/1re+pXg8rvv37yufzyuZTBpM9PLlS8Xjcd27d8+MBYPIA15CoVAwiNw3GCHP1+v1VCwWLY+FvBG94VhR282aeadpMpkYmcrnx8nB4Vhg9CGXoDT4HTCdN7Zs5kBgVkdbqVSMOUuEi0LAScGLxzFi9Hq9OacVqM1zMnwvA/YH0D5KkZ8ByeM8A896OcAx2djYsBIfnBbk2jdk8YoqFAoZ3FetVo38RRRODTOREs4T8gCRDCcKh/nFixcql8vGAfC6gXng/x7xIOcZCAQMYaIlKY1viO5IUaHXpBlRlO/R8EKSVQFwb2TVE+6oXmA/xmIxq62lXLJcLttnqdf1e9jvbe+MgG6k02nLB4fDs7Ivv7c43AVUTZL29/fnuqpBkuOasOqRfSB6dLlHGWm3GQqFjKvih0cEX1evzdkHzDvVJBh+IlUcQRrOkF7kcziMrLufO9A2v494B/gxIBeevEytPRUOXLPX6+n8/FzHx8f2HswN1/OpjGg0agjgm4w3NtQYEh8pA4V74gh/E8l4g8SJQXguPupiYtkMbCiviFqtltWsAm/i3QCnIFh0vvIeKwOF4qFLFhIvFm+RfBARPKQLctOS5shfRG3c2zP7fLTS7Xa1sbFhTPZKpWI5Y8g4HmrzeeNUKqXj42NzlvAwM5mM1Qe/Du5BGXoIDiWLgUKwGUTtvI/36gOBWVcmcvLSLG9Xr9fnujBFo1Ftb28bWQ0UIR6P69WrV3YQQ6/XU6VSsbXHA8VRIaXhUyoYOQheyBqQHte4ublRo9GwdUIOmBfeDZiLZ+10OqpUKgbZwcTN5/NKJBJWG1+tVk3B7uzsmAPmSUCgSr6khRO6gACDwVndPqgRZUHsNRQMTiswbLfbtVI45IhIrt/vW3kIMsT7sz987tGXx2GAQadAq7xDzNrwPekufz2ZzMiEP/7jP65Xr17p937v98x5gjRILhOFWigU9IM/+IP65je/adUK7GHgdRwkn/v2ShluB/sWoiUBALltyFuVSmXuJC1Y3+x9HDDfRW6RmIRjxR/SVTiCi3oyELg7IIOOablcznTLYu7UD/QcOsynBEFwcPBYN+aIFA5yhV7nUBtqyjHK6FnOKWDduR5Objwe19HRkZWtwZloNBp68uTJXJMWnABkzuscnm0xePP5ZGwLuvGLX/yitra2NJlM9PTp07nujZeXlzo/P7fnJgDwuoJgBb1Ciub8/Fyrq6vm3JACSCQSKhaLqlQqpoe63a6ePXumzc1NOwURe8F1kRWfnnqT8VY5aiI+YEHyt54M4oVmNBqZN+JhMZjVePwILRuH3CACh1LgOwiPVxJ4xcFg0CYUuAEjggfnNxjkBRRgIBAwhU2EQy9vFhOl7r1ilCBeGuN1rUp5F6KpWq2mWq1m80bkiwAQDcCohVkIvBaPzxrCw1RcHB6B4P8oMeBAyA8evlpZWbGOXXiIGNHr6+u5hiXk7v27M8+Qh3zTD1CG29tbnZ2d6eHDh3ORJ41AvLFF8QOl0bUIKI3R6XRUKpUMObi+vjYlUSwWNZ3O2NGhUMiiDtaq0WhofX3dCGJXV1cWzbI22WzWlNFwODTS1+Xl5ZznDGEEL51UwtXV1dw8w1pGfnAQcIKBsEejWb355uamKXCi0/F4djoP8uXrkf0eQdlxTRSijziQcz5L2qjT6Zizvba2Ngej83nkRLrrQX57e2tHinqD6vPEzNl4PDsf/Fd+5Vc0mUzm+hD41BYlRYlEQqlUSufn56rX69ZQg70DEjedTnV+fm4Me/QLee9arab19XV7PhzlVCo1V1aJ04eu89GhV/J+Pvjcw4cP7ftE6nAGFpEij8It6g9SQ6S8cM5Jd+AceXQBw+h1mD+0xEfFOMnsRf4ul8uWqiIlB6FzOp3VObPHSR8MBgOrBqGzWCaT0d7e3hzD++TkZK46wA/SPKCco9FoTidXKhUVCgXdu3dPa2trevjwoekN5saPRYcJZG04HJosTiYT66bYbre1s7Nj+i0ajRpRVZLJoDQ7y4Ea/Hw+b3wcqgxYj36//4nn+qzxxoaaTYVxJY/MZDG5nvUNLIl368tXeEEMP0oZSMXnLnwHMbxEvxmBxtgQ5AWIgFlcDLA/+g0liwCTY4AYwiESKCxKnbzRg+GJQ8FGl+5qaVGeXtl0Oh07WcWnDji/OZfLmVPCcYvkGmFnMrdsnEWIJxwOG+QM6QaBxziQP/eReCBwd0Qf84hHyJzjyGDYyL361pncp9ls6uDgwKIbvg9kh8H70pe+ZPl0zjcnmqLuk0MupFkNZ6lUsjpaaabsUb4+FYMHfnZ2Zrk1P4BlafW4SJwk2oXo4pGlWGx2DjH15369kfl6va7z8/NPbYPojSL5btIEOL70QKfFYqlUsv3E3xhlPyAM4QTzbjwn6xUIzMpuQDx8Q5JGo2GGyBtqhnded3d39fDhQ52cnKhUKun999+3e/qcpEeK+D2liKFQyA6MITigZhfjJsmik7OzM0tveD2zsbGh6+trq+vHiCG/kFmJ3n1zHD83w+HQzglfNNK8PzA/+iiXy6ler2tjY8OiNHgKzIMPWFiDxWtj9CORiAqFgra3t+376XTa9gD6i/ImnMXd3V1DPTxxFiQHpxCHFoQNYhX7+NWrV1pfXzd9R5Ah3Z0YRW6eGm7IVFtbW6pUKjo6OlImk1EgMOtUyJ76+OOP545+9Ebt5OTE8tboO07BajQaevnypRFNkVvIYsfHxyoWi/acHnmT7k6GBK3xOXrSqRw7S2CG3aNckvUn6Ds7O7MDfYLB4Fz5Ljrj8vLytXrgdeOtDuVYbF+IAKG0MKS8MFi+7yzFRPmTlsLhsDWlQLiApxdzS+12W9FoVOvr6wqHw6pUKgb5SHenXyGQbH5+5w/kwLj5fDmRjDR/Hi0dzmhdSMMPfxAHzGUfqRweHioYnDVloL4ym81aHpWaZQ9BbmxsWB1kJDI7dg8lQ8SOkfUpA5ScdNdF7f79++YNsoGJVlkLmjzQqhNF4Tu4Icw4FPF43GBh8l2tVkvPnz+3DcEYjUZ69eqVEomEksmkOR/UZu7v7xu8jRPy8uXLuRrpcDhsjGnq0AeDgSkCZBHyiocMfcQmyRj4NLUBbUDpkVb5tGgG6H08nj/zFoemXq/PPTeOCqQSDxEvDtIvOD9+70HQuby8tKh8Mfp4XcoDJMn3PvcOHYoLI53L5UxBIZOQamBFAy36CA35iEQieuedd3RwcKB+v69isWhOAbKKo0XNONcGLUGfVKtVczAwvOSBUa7oFVJSg8HAFCbrhfNPNEgtOOvs9/giekYgQPOK182xpDk4nRQKTj18GdaDOUWBo4dw8vw9OON9PB7bdX1AgH7FmG1vb5seBZqlmxjlk76PP0ERhDdSEuhS9DMo1uXl5SdIYMgQyNF4PDZyK++Gzs5kMnMoWC6XsxTko0ePNB7PymB3d3etsqZYLOrRo0dzzV5AA6bTqUqlkn7rt37LyKJE+BxlGY1G5zpFEiCQEvGON5A5CAfvxudwXsfjsaECyWTS9gtVRAR6PkXh0dy3GW9lqFkYNjp/iFbY/Px/a2tLiURCpVLJYC+gHbxhDMKigZDu8mXSzFvDmLPJifJ8LgNF6DcvA+H35TPdblepVMoWDiVCa8iLiwszerQ85LN03+p0OnZwPe/EhsIw4d0FAgHt7++b50aegggwmUzq+PjYIEHg+O3tbcuz4KGORrNzsImAODuX9yRHNBqNrAMXDg0NA4D2/fdQpkRIPs2BYUPp1Go1I4yBKORyOfPouWaz2dTXv/51myOUx9bWliEBKI61tTUdHR0ZgtBqtVSv162RB1GQF/pwOKy9vT3F47Mj/bwhDIfDevjwodrttk5PT+39IBBtbW1Zj2EUNDlnj4JAfELOcFaQV+QK8hl5KmSaBiQrKyvWMMEP5sq3bURRwOrn/z6v59nXfk68skcRkoNE+XoyDfwMjBXET2QAohf7kvtyb5+7fvHihW5vb/XkyRNTVp7TwjMhz5SR4ajiGFBdQBtWGo2QDwahAeqE+Y1+GY1Gtg9xAplPqk3Ys9Pp1Mo4+T7Og2f6vm5+pZnTgQPIOuLsY6T8d+FfgPQt8kPIYe/t7ZnceaiaBj3dblfZbNa6E5ZKJdODw+FQu7u7hkqgx0l5kYvGiZlOZxUWcIfW19e1vb0tSYaYcUAF74Hh86lQdCnwuj8VkWfDsQb9TKfTlq764R/+YR0eHto8PX36VF//+tdfe4IW60yFCg5tMBjU48ePFY/PzmEAGgdZAImBxU8UjhyjQ+hjQLS+iJjys52dHetG5mXq02TGy8O3G28FfTNpPoIjYsFge6Pre+1imDA+KHZpJuAoPgyvj8xDoVnNNeU5Ps/FtbzHglf7undIpVJWghAOh61BgYf2cTpQqpBQECw+MxqN7Mzo09NTi6qDwaA2NjaMtQi6AMxEXoSFlmTHpLFhiGjC4bARUtLptK6vr3Vzc2NlWaAOlUpFp6enc5A8c09kjhfqc4hA+16wGHRCI/8iyWBJvylRFKlUyhR1v9+32lGuS6TKmjJHeMjMCc+TSCTUaDT04sWLT3ALpPk6cjaVb0nIwJAeHR0pEAjo7OzM5mlzc1M7Ozu2QUm/AKMRYXjnBdiOqIU0BN8FeWFtfQ4XIspkMjHHY3EMh0NdXFxYJz/2CHXkzBdNag4ODhQMBu24RtABojn//MicL7nzSh+Z9fwO9ht5exjTyLGHKGkS0+12dXp6akgYTh/rhq4AncLpXFtbUzKZtH0FhHp1dWVOAnBuvV63+uXRaNaakvdivjFENM4BNZBmaa96vW5RN/MMAuJJU17eXjeYD1IT5Gc958E3UwKdBMHw/ALmLJfLWWMQqk+YY1JBkqxXQz6fV61Wm+uHwEE3IB7oJ/YFTG+ieoxoKBTSO++8YzqQeYCMCmqWSqWszIse/Mw1ndFGo5F1fgPqphkPcwcakcvldHt7a2k/aVaaeHR0pGfPnn0CrVtcF08UDgQCKpVKVo7nkTD2WTQatZpzmOaeOAwK0el0rNQOo+1TTPADaOji7c+i3LBfFtHpzxpvbKh9/tPXxfFivpyIh6GhBALoI2/PpEapsJHxvD1Zi25mwG0YTU9gQlF65eeVO2w/DCJK5urqyhQoxg9hxUEhsvXeNooYWBnP2Hdgk+4UNB687yPMhoUgU6vVjJBEPkaSwY0QuSTZJqZhiWdVIhzeKwS6iUajhiSgfBfnanV11TYq601US5MXFM7u7q5WV1etKQVCjgJ4naJDTkajkeWcgYTX1tasPhW5WYwW/ZoCMZ6fn1v/Z+kOBep0Onr69Kn29/etmQJrjIFCieL0DYdDZbNZ1et1y3WTZwY2xCnFG8cYkdZg3/Ccfn8Qzb6uOxEOMSQZoDS+j3wkEgnl83nrkLW9va12u21y0O12Va/XLRplz2BUkAX2G88KTC1pjpTmOSZcg7WmQgIDQ1ldIpFQKBQyAhUyEA6HzUjjoDHQJxiYeDyuQqFgitD39iYy5joYAI/E+QYUlBTxfjRTgXwqyeDo8/PzOT4Ozs6nGWwQwUajYeRQ8p3A1qBGvokIwYWkuZwuhEfWAAISewUYGJga1MbrAeaf+/v9jyzg+PjmOESXwWBwrkFHKBTS7u6uGo2GYrGYdnd3jYwlzciayCa8JGS22WyqWCxqZ2fHHFzSEHwfVjlwNQYRG7BIskMHwTfBIfCfgYCKbiG9iW6Mx+PWRQ5HSbpL58AHkDRXOUGlADojHJ41ykJffNZgL77peGNDzaJ6L5OJQoEgEEBaGGE8HDagNIt8qJ8kUuGaXnB4KQwm3qZvEoGgeEIR0QLRpO9HK905HrTUpPMTimg6nT9BykfrsI1xGBYJXGxsrxQlmSAUi0WLzGgWgFcJGQZDQoSEwLNJS6WSscaB+hfzRb6fOs4AJCWGnxM/d3ig1IEWCgWL4k9OTsxQU5eNQgNiZ2N4eNTPERARUBfQHM6LjwZhWZLX9gxWYE48WA6/yGazdhIX0Rocga2trbmWqTghvuUoSMra2pp58UCj3JMNDXwHZAjsh1JkD3iSEA7u6ww1Y3V11Yy0VyzIFdEn79DpdCwKwDAjW/xhD+HcLjq2KElkiv0G3O15IxA0MUZAuR5hQ8nitDHC4bBB7P6wBXL5GInF+YFnQpSJ4USmmWfmycOLBAg+tcZRm9Fo1E5vwpCR7yZlJcnOnF7c86HQrKPaxsaGIXqsCX8jCzhHcELojuedOjgq3CMSiahUKunm5sbSNMgFpWqLJEV0M3OAXpZmqR32GnLC0YzSnSONIxwOh63kMp1OWymb7/HA/n358qXG47Hl5UlfnZ2dqdvt6uXLlzo8PLTnQqbIGVerVWskxPtzFrvXp4HArCfBvXv3LAdfq9WswsAPctGgD+hv9i7omLczgUBAe3t7ti849W5lZUW9Xk+np6dKJpN6+PChpeMggPJ95v1149N4Kq8bb2yoWXQ2KEaVTe0jJDwYb2gZPDS5JbxMz670EbU0v5AoOO+R8P1A4K5XOBELih0DzIksKFMUEfCGfycUlSRrwpBOpxUOhy2CqNVqcw4Cz+8Jdp1Ox2pbOZWFGj16v06nU52cnBj8imNAdMPP2WgoKCJZHBuUKfN7dnam9fV1E0LfKerq6uoTAu2VHN4q848yCofDevbs2RzpB2NJ/mc8Hltv5VgsZt60z1vTp3tra8veA94AkcVoNNK9e/eMwHF5eWmQcSAwY9wjj5D0qtWqnXJEpLy6uqqbmxvF43H92I/9mD766CPLbVIqw7ojc8D0fuBoAY8Hg7P2kZSAeQIYco6RA8HByfgsj5o59WiEd4qRBZ/fR54XCY2LOTKiDC9LfNa3W/VysLgf/P5eJMtQ5hIIBMxgeSiQ9/C6gOdY7L/AvvfpsHQ6rVQqpdvbW1tDFDlzS2BAzTpOI2Qi5onyPBx2DDmpJklmYH3KiBQA71MoFOy8AIwU+5c+z6PRyBwWdBwBxMbGhqF6Po+P88X+ge/i6/LRNxDevMzUajU7d5v1hPDYbDatv0Gj0TCCokde4FywH5GdTCZjiKGvB6ZV5vvvv28pPOYAFIVufzSO8fukUqmo2Wzqgw8+0Oc+9zmlUik72tcTPHm2jY0NS1PBCwI+XwwMGo2GrQvkOFAZev4TjPKOODI4Zzc3N7aW6Ljp9K6daDKZVD6f19nZmaS79IsnwPH8b0Mse6s6ag+NMLwB8QYRz9Q/EAqFjUkE1mg05iJCNiTfxyODrYjhx7hz6gy5LaCXcDhsSovjByEu4BHDYAbCGI1mh6pDWvDKp1QqzeWHUBKL0fRoNNLV1ZXlw6rVqkqlklZXV7WxsWF5oJubG9VqNZXLZVv4QCCgV69eaWtryyB2In6f04xEIrp3754ymYwJkScHcX1Y0pBpUFrknz9NWG5vb+00Gzxb5p7uTRcXFzafnu2JcO7u7trz4oU/efLEFDkoCIx4vFHWHWYxnvnt7a3effdd3dzcGCscBjjICs4Qm5sIi0YnoVBIFxcXlkqhXA/jR0MTNhYwop+n4XCoq6srNZtNS8ckk0mLdNgr/Fu6qymG7PJZG5U5BhXAGej1ehbt0YKUwxJQ/DSjwSn1jhFrzx/vEGOI+RloABERBpx9Ql01RsUjTux15tC3aPW6AMca+eIZMNSe9ImCSyQS2tnZUTqdVqVSsT4Et7e3KpfLBgdTLYHDHA6HVSgUDKFiL3IcJeuKQ8sc8HzMI0qXNSYttr6+bmuK3DLvPCPET18eubKyYk1WSJutrq6qVqsZdwamNY2QyNWDFiJnRHysBQgQ/bOlO/SnVCoZHE0ABtKyvb2tnZ0dQ8pYa2SItWWfU60jzVI6KysrajablnYJh8N67733tLu7ayjSaHR3hC5RNGcdTCazdpwfffSRnflMb3Uc9Gw2aycqsm6sHSjJIpG4UqmY88Ca8k6QwdDtnHs9Go2MuAuacHNzMyf3rBtlrswNa7S3t2ccIeSUPfqm440NNUaRTYWQ8qJEHxhZ4EEPUxCNE/0CmS2SNkKhu7On+R1QqT8rGhgM5i2bCG8cZdloNGyxgsHgXBOWXq9nyg7lkM/njc3N94gqyuWyReP+3fz16alMPomo1Z/cBCuehfdQLCQZFCWsUw+VrK2tGWHJE+xQBERwg8HAzqjlrGoECGKSPxDArzeChdfoy0voBFatVq2zGF5vp9PRzs6OkQe5bjabVS6XU7PZ1HvvvWekvlQqpRcvXujp06cWtXBgiFe8IAb0Qz87OzOkYzKZtTWl57FnRONFs160Am02m3P1ykQL1JtTl7s4IKeQQwRV8DJcrVbnImbqniF8ESXi3DKvkOwgAUFg4Z2q1aqVLEnSy5cvDSIlX8tnPSrFPvXpGqJU79BA6MQYeRSHfvfIHJGSj+D6/b7lL9kzrMMixH57e2tOGHsRY+SJmPAraLADsxvjDSnKo2Lck8jYn8LmSZ3AmD7yh+XLPiVlx9zQ7Y2B7sDQoYdI3fDHN70Ih8Pa3Nw0IpY30jwLjrQ/6IEoHSjX70nez8s6USpkMd7Rl7OyvoHArKMj9cqS7PTDVCpljU84txpdzvxhAzyCKs0O7djc3LQ0WjKZNBgdMhsnlHkuys3NzVz7UXQunAUcOQ7TWVtbM8fdE8LQ9e12W+fn57p3754ZdoKmzc1N0wWJRELpdFpnZ2fmuOKAhUIha1kqyRoY3b9/3zpD+qCTmm5y9nSAW+y//u3GW5VnIRCvy8vyO+kuN+QZdFyDzcuCEJH5BSJC8fcBPvUENTxXIAhYlCzqZDKraWPyGNzPR9seRkeYiKoRPuBZIHXfClK6O3R8f39/jmEJtMJgfogq2UwoehTeYhTiHQOeibwQnjLKnqiacie8Ov5AigM69ifyTKdTe3cMCZ4wMCGe+2Aw0MuXL1Uul03hHB4e2nqjfJrNpjURmExm9co4Cv60nI8//tiUKA4IzwpcihH3SAfrDUTOMzNHmUzGIjOuy/xUKhVz9CRZ/eTGxoYkzcGJkgze4l14T+YfiBND+Tpj3O/37Yi8drttbPVsNmtwpHTXhQlWKichBQIBy6n72lKMNVEy+8tHsh5GxnhDRMI5hCBDrnYymXxibzG/i5EBDkgkErF9DFKFTE8mE4vmQGpwuJAtDB7vtLm5qWw2q3A4rIuLC9tbvK93lHxppE85gL4gyxhPvk/OF7KlNDP4a2trVtVAqs07KKBK6BFP3kJmIpGIarWaXdsfpIFDBuJCyScGmOfwRtjnUv27e320tram7e1tI41WKpVPlGIyYrGYNjY2LD1GuR7pFiB/2vySfvDNWyTN5cqxFzjtML+5H45LKpWyvznBi7KpRcQSh4mfg8LwOxwY+DPSzKBWKhXV63U9e/ZM29vbxl2SZMgVqQ9KEWlig3M1Ho+t2Y/nv7AH2HOkAnHSeF/04sHBgZ48eaI3HW9sqBHw4XBoeY7PCt15OX/wAw/tSWl+YBR9ZyDpzotkYcmNEtX5E3Dw5nxe20eKwBp47AgFXiWQlyddhUKzk1YKhYLlZWDlXlxcWOQeDoftYA7voIzHY/PC2NAYSw+vL87HZw06TbG58ea80uI5U6mUQWTkIXlPrxC9Z003IKL+Xq9n716v13V9fT2HqAAR4zF6shLQZ6fTsXz9+++/r4ODA+3s7Nj6PnjwQK1WSy9evDAYySMrrAt/b21taTAYaH9/X8+fPzc2vXeeiPzy+bzS6bQ++ugjTSazFqV445waROMW3wRnOr3r8sTceHIj3rqHmYla+D0KjogiELgrncMhkWaR/uI5vTyPbz8ozdAJTmrDALG3/Hzh9PnSQRAw3ol0A1AqBiAajRqLGnSI37HXYNbzrvQ7ICVA9IrDSISNDuGUKGQamcF4JpNJ20841p6oukhuZbz33ns6ODjQ8+fP59INRPo46kSm5CGJFtkvoGy0yCUS8xE1zgLz55Uz8gHcGo/H7Vxiz9Yml8s7YqCYVx85Mz6LrBQMBpVOp3VwcGC1wzj1/vpeptAD5MU9iRUEpFQqqVarmQxTNosDSHc/nstzdJLJpB3ug/ND+gnDSvMjHPjHjx/POUXS3dG/GOzxeDx3wl2xWJxDECeTiTVBqVarajQac9ylw8NDm2NkGeY9FUG+CYpHbUCdQILy+bzeffddQ+xKpZKGw6G1ePa5f/b9m4y3Zn2jDPjjI2Hv2fEgbGYWGzjCj0WBw+MFNiXvyWdRTgjceDw2KMZ7sR6m8lAQjELq51A2XuF5+IKcLAdUkCOiQxK1tihVBAnoDRQBZVMsFueMNUQLSZ8QSsYizD4YzJorHB0dmfcJFAVUc3t7q2q1ajWJeMooaFIPi7lSDzmSy8pkMkaI417+s/67PEM2mzUIFVSD8gnQCBQuCj2bzdpa7+zsKJfL6Wtf+5qSyaR2dna0urqq+/fv2wEURH7vvfeeHj16ZMadiA5OweXlpXZ2drSzs2MQmzRzOvDEeQYMqiRDaVDM0+nUjjjM5XIGq/sD7uv1ulqtljX2gVHKGkJSos4UOJ/8LwqS06Ag5/HMkHLYkxCQ2Kesv3c6SInw+0hkdh43cC2KiDlAHgOBwCcaPXh0zSMHHuHxpYcYf9+LwB9ZSgcrntNH+Rh7L39UI3hH3OsOjEOz2bQT3dAp3iGfTqdW80rkenBwYO02fYMh9MxoNJprkYu8V6tVrayszDkbPAdO+2g0stQa6TGMPAYF/eDXM5fLGSH06upqzmn0usEPUii++Qs5Vgiw6FHvaDHvrNXt7a0uLy+t3Mzr0YuLC+P3wJ2gHavfO5FIxNJMOEHktsPhsAVeEP1wLJPJpJ00xwBFwykEMQqHw9YmdDQaGYrA3p1OZ21tA4GAIQI4Ux4J8wRZEKVOp2Mcm+l0arD94qArGkgJJbWXl5e2b3hnSXbe+ZuMt85R4zkhiGwUhMYr38VchWeZSrLaOCbL5659WzcmEcgUwedkK/JzeMGQkehqxH39IN+HgiLCgaiD4vXQ3Os2RKFQUKvVUrVaNQOPd0x9JoaIzeFJFpHI7Mxi+mDTsMBHhd5I+2dotVoGmYfDYVNORCRE2BA3yJlBOMPp8huQ+6RSKW1sbJiyRQkBCdMMZHFORqNZh6BsNmvlCvycaEG6K7ljY3gDiTxtbm4qkUio1WqZAb+9vdXNzY3xICCZ0XLw4uLC2Nconkqlom63a3kh6orH47GKxaKkO7IX84VzuRg9xWIxY7J78lQsFlOj0VC5XDYnD6jfQ/j0BsY4ehQDeNLDyjiFEMVwAH1uHKY8e8TzGXCU2WvsC3KPnvyCo+HXzOeW/T73//doFg4SigpHjSgKw0+TEZ7v5ubGcoPT6dTWBR1CZQT7kpSXJ8f5cXl5qfX1de3s7JgjsLu7q2w2q3K5rEBg1joX/TCdzk6le/jwoSSZ03hycmJO03g8q6OPRCI6ODhQo9EweBpCG/lz3/CGtSbdgIz5iovBYGDVDg8ePNDt7a1OT08VCoWMHEneGiNGNEcJGMbW54l9eRtzFgjMur/BaiYt4QfPKmlu3/qgDD3j0R70QS6X0+bmpqWTkAkMrU9H4jCCxBG5Mu/IiE/V+HIq3iuVSlkpJbKYyWQMPUCWIRUzqtWqOU7oAEmGBBLcLAarNJMiWCCQ89wqOBhE4ciG78fxJuONDbWHFb1QMIH8DgOA8pTmz1EFOgqHw8rlcnOG17fpo87al4CQ50EoYJ8GAgE7XAMPfDQaGSSOgVs0dCgOBIZGBYs5HA+JTSYTywsSufgj0/CeIA+grKn1RoniXRcKBStLgpSC8Pu8uecEALEcHR2Zo+MjV98ZhxQFKMJiHbVXflyb9IOPKn0UBave1y/7cXNzo93dXYVCIVPQOFO8I8SqQqFgBx3E43FzeCTpF3/xFw05mUzujkk9Ozuz/sYovWq1anCuR3noR06JC3lHoLxMJmN1vrxXMBi0cg1qr5kbWKOeDwHsRSkYczkejw3ul2T38BEyn2XfEPUiXz6tsrGxofX1dfs+ji2dknAskRfkHUMNWsRegGzIcwArIzcoVdjdKEpvzJkX/2+ftvI5cY9asVelOyMMAoGTS/TMnLJXX716pdXVVVOki44JexYC0cOHDxUIBPQX/+Jf1Je+9CX9+q//un73d39X5XJ5bl/hPLIO0WhUrVZLtVrNnBnWAKVLtA261+12tbm5aWgbzp4vi+KIWxCucDhskej+/r6l+Yj0fK0z8kdJFLIKsdLrCK+v+Rl6Gd2MfBQKBV1dXdl+ppc+z8sao2c874HrMKLRqA4ODkzfAJ97lBTexWg0q08mRw+C5A0rDrs0czwo82TfoeNwLrA3kIy980/w5+WVPDOfhftQq9VMFxEs8p10Om3pC75HGRxBJvdMJBJ6/vy59vf3DUVDxt50vLGhXjykgJf2JB+/cLe3t5aXkO4Uo4evUEreA8ezJLfJBkmn06YIGQhcJBIx0gBKCcOHokdYmfSVlRVr7Uf0Ra/t1dVV6wM9nU4tAqNDDdE7TofPH3Eu8NbWlkFoeH/9ft/qbcl7+85YHpbMZDIGjXBKko+oMpmMGV2gotXVVWvIz3GOk8nE2JPUGWN42+22arXaJ0qQgHcWoXvprlEMG5W19xsVJIPe7MzPxsaGlZ1dXl6qWCwqHo9bk/4XL17o/PzcruMNZLfbNZgZQ0YqYTAYqFar2cEiiURCf/kv/2U9fvzYYESiD2QOWB30xXdeQkkBfTJAGnzEyZyxWUF1XjcwWr7tJoqUPsWU0EGiwmjR9J9cKoQjn3cOBoNWOiLJYEaiDu8gekcbNINcONE6zi5sVc+7YL0x/t7YE1niYGFEMQLIus/XcbgBz876ouyIniKRiEGuOJgoQ+B0Ds/41re+pc9//vP6vu/7PhWLRX3jG9/QvXv3tLu7a8x5DA/7lBaYGOLDw0O9ePFCvV7P+Cd0sYK8iIFkb8KlIdDgd6VSSdfX159wZmq1mhKJhJVuYmggy/r1wNBkMhnTlfF4XNvb23r58qXJJHoK1IJ922q1DD3k+UFWKMmU7sq4gPmRfVjq5XJ5jgDrA6JoNGrd+zBw6DY6i3EwUK83O+eaFsrItyTrAohB89UHwNHT6dSca/g/nhwMB4DUh09/sA5EwQSdGH0CvkXUMBaLqVAozDXq8SldnG72ukfO0P04BG863qqOetFQc0OGVxhEOXTkYWOC87MxMPZsVL4bjUaNKY1X7XPIGGOgBU8s8Eb50+A6n9gHxvB1sFdXV8aEJqqoVqsGXdDq7nWeZSgUMu8KQQFloNwF9jhEEoSTrme0uyRXHIvFjFjh8/8oY3J2eMhe4FizTqdj3n6r1TIPOhgMWoka3mSr1dL19bU5Er6pAYL/hS98wfLY3vscj8d68eKFRqOR5V7xIDH6GMYnT57MbapFCJM5rVQqliciwqIf83g81vX1tTGnYfL/yI/8iAaDgbErJ5NZaQm9hqW71qHkjTFO29vbury8/ER1A2jQZDIxBY0M4XRSirWYcgE6xQHDkPmaWRQP5K18Pm/Gn7+JAi4uLpRKpcyJRa44l539AMwKhEhekLlGvn0+0+eYYdJixD2BEYe1Wq3aucTsB94PJuwi6dMbKz7P86H04TuwV6LR6Fw6Ym1tTevr65bPJ8I5PT3VaDQ7te2Xf/mXLTj4zd/8TUNwWBPmdDAY6PHjx9Zog0N46EJFoyRSbrwHMoqMgQ5JspKxxZO36DIGiREo278fMgWrGdmDfc6+xCBCeAM9ur6+thz3cDg7ohMdij5FXnDwOKWPhiigjbQLBcHb29szJ87vU5wE5AlZwlCHQiHVajVdXV3N6SaaEeH4NBoNa3/KwFmJx+Mql8uq1+taW1szhy0Wi2lvb0+j0azqggNcsDPYpMXudd4ZwnnBuQHe9yOVStlccyaDL2/EjnkWP3IBk549+KbjrQz1Yh5iMZJajKqlOzKK7xokyUhe0WjUmNzSXS5z0bgSxfn8IR7RaDQyQgafAV4jKl98Fwg7nmjBtYgKvBOCMvP5RumuGQnPaRP7x5G9JEMKOPCdWlqMDN+lBpXe1z5awkvEQ8cBWIRHyW96FjcDpvTl5aVtyGBwVugPlM1ctNtt6wLW7XbtJDSEGBKJP8P34uLC3qfb7erZs2fKZrM6PDw0hUK0ihNDxOXzna/jAnQ6Hb148cLaq3I9//4Y20ajoV/6pV/Sj/7oj2pvb09Pnz79xPrzLKwDG49NyTt5wslkMlG5XFYoFLJzsjFGGBIirtFoNEf84b60jeTeGEBkqVarWZppe3tb0+mMqRqLzc67BkJcWVnRxsaG5ciIhrk3+8PXt2LIkB+fUvHd5HgHn5PzxMJsNmulazgXm5ubFj15dMgz/nkuFDbPwfU9L4DDYoiQiPzYk6FQyJ7D59dxVCUZ3I8zGwqF9PjxYzsDHufGE4PoPJXL5eaehz0KAoAB97LL8Om/6+trW18v13wvl8uZQQExGY1GBrfDa+G7iy1aeXbmA8cqGo3q6upKr169UrFYNKft/v37VgpFh7DRaGSNkzqdjkql0lxAJt0Zp1QqpVgsZjwUb8T4bL/fN7Klh5phhZfL5TkCm6RPlNByNC8BHARHnEacc5xSyiWxH4spKEnG2/HrgD2hPSk17J6f5M+hYA+R8gSZYP+xFiB3HiFuNpvKZDIaj8dz6dU3GW/VQtTnqKS7ekqUgl+scDhshBGUO4ONhbfjf4bi9EYGZYz3jjFmgyPMRNqcbuQ37cXFxRyxot/vW14IL42NS3cnroFCgagl3Rl2Wt55wea5vJDhYUl39YP+/fG4ptOpld3w/263ax6oL3ejMxbeOzlRBG8xf0xTe08GLBQKlnPDUNMKj+5PRBScTkSOnqg+Ho9b4whIKeRx9vb2bP4Q3Ol0OheBcw1+70ubvDC3223L+VNPyzWI8G5ubtRoNNRsNvUbv/EbhnDgZIA8QLADeeAPqYxgMKjj42M9efJkztgOBgOdnp5a1BcKhSzFwcaGeEJZG2tObhsiJA0kFgdOBPMNnIljJsnKTyRZrpnnhtxCSon0gC+xxLlddOZ8LhtHETQENAzkiWedTCamtHDMMV7Arbwn8kzOMRAIqF6vG2pEzhlHvlarGXNeksHV6+vrSqfTJiOgTHAuMpmMQdg4MkQ+vkc1ZZY0qAmHw1ZiFA6HdXJyYnX6nh1MROSh0XA4rP39fctP4wxdXV19giVM9EWqAz0F0XQRcfB7BUIZMDE6EeIVnbTK5bJF9KwLMsw1MW402anX63r69OlcNQipBd/YBQefunD/XpJULpct/YWzA5qI7vHOsD8WF/0NcgRkzRrz/ETuBCbYJ05co/titVo1oqbfy1wDeUQvTCaz8kVKCrExDNAynE9kyzPQfSCH4wPpD/QHdOJNxltF1J4UhsCirBaVKgcKYKA9O9MLN7/HE/IePr/n/3hJ5PCGw6F11ZpOp9ra2jKomGfCu0GZ8ewIB977ysqKRbKvO/OU9+egDvIzeEuLn+OIT+muj60vPeHZe72ezRNdeHz03Ov1dHZ2pmAwqHv37llE1Ww259oRQtxi0y9Gp7DnJ5NZU41arWbnMDNHzCN5S66Ns4Kh4TOevdxqtZTJZObqq2lbCkKCcN/c3MyR3fb29rSzs2PM8idPnljaYTGtEIvF5vr1wg6FCSvNGL84I5wu5Q/SuLi4MPIIKQi8YXgH9+/fn1PQi7LARoQj4Q1xtVpVr9czVMRHqOwbyHAwZn10xMDoIj+gQ1yHOY7H42q1WqbMfLkhe4/5RvlTp+6Jd4vsb+TBRxOkfTwJzjveRFDIIXLH++AslEqlucYzKGCcVwxFp9OxE5lQ9rCDcSZQ1CAWrVZL9+7dU6VSsb0gyY6L9Ws2Hs/KLK+urszBzefzFjXS3leSOdfMN6d4MRf0WvDtdGEdk5v1Mk0HNPpVT6dTa1+M7uC9O52OEcyYN0iu6DVSiPF43Iy9j/bJEfsKDuQEOdvf31exWJzrCAaK4FEXf2APn0kmk7bX2u221Swv8jwkGRqCjoEfQH8D7kk6ycukr9PmmjhZOBHsg2AwaM1jXof2wl2BEMh9SNMRGPi0RaPRsM6Q3AdkGAeJNMlihc/FxYVub28NLXvT8Scy1B4C9zk8n1sin+ajY+8dcRgFE8yE+U5K+Xze8HwiEBQNSp8NTc2aJ2r4qJTF5/nxzjKZjLLZrJ4+fap6va5weFbHh/FahG5gcvuj5Gwyw2HrPwvb0TPaKQ1i0YH8gXKIiq6urpTL5YwUFo/HrWsU1yNHCdmI6BavkvdmMN+842QyMbYtECXfR/kusiNZX6J5ohWY2jhgzHGtVjM4Ds7B+fm5keKi0dmJRQ8ePDBvfzqd6vj42DqZ+XtOp9O5U8vwZmFS+l7K0WhUR0dHlr8lciZn9+GHH2o0Gmlzc9O6ll1dXalYLOr7vu/7lM/nVSwWX9tCdHFO/EDxMfeZTMZKddgrcAqi0VmPaOTm/PzciIvIt2+hisPQbrdfGx0zMFreKURmQYrYl7FYzAhyEChxdJl7D1ejgNgPvDMkUfYMCg8EJRwOWz90IlmanQDBe4cBfgZGahF58GkHjBbkJ0ibOJwoYSBez0lhHx8fH1tzIJTudDo1R244HJrC5ezhcrk8l3bC2KKHiLw/bSCzR0dHZvwhV3omP+Wf6XTaaouRqXa7bWWSOF2edOp19XQ6495UKhUFAgG9++67hiAtdm9bHMgZZMtgMGhNfKRZ2ukLX/iC6eL19XWr6IAv4OWTVrCgjKQ38vm8HRbkAztJRpZcRApHo9Hc6YfeccRhZw684+KdAFqPMgcYfFAR5oUoGFmAHOsD2EajYXOMzikUChYs0kfiu9JClIdgYOhQCiyk97QxjChxPPRUKmUbgk0KfEopAP15IYdg+FhAj/H7ukLvIEgyWMjnggOBgO7du6ef+Imf0O/8zu+Y8uI5g8HZKTuwkT1prtvt6uTkZC7KAsbZ2NiwTk7AL8xDuVzW6empCRmR1vr6ukVXlUpFlUpFz549M6+cDlQobw9vnZ2d6fT0VDs7O3a8XqlUMuIKzwYciHJKJpOq1+tzJTzBYNCaaFDXCKTlCSLSXVkcmwGHA+H0dezPnz/XxsaGQYx0iOKeu7u7yuVyBlUR8dNedHd3V7FYTMViUaenp3PlEEQBlUrFyvJQUnt7eyoUCgZHR6NRK8dBRiCkAOnyu4uLC/3n//yfdXNzM7epfTN/FDlQOhsQqBqnkzwdjhTGbTQa6XOf+5yOjo5s7+RyOT158kSlUknj8Vinp6caDod23jQ5z36/r+Pj4zkjAoJEJMc1ibhQ5F5uMS44w94Z8FGYRz9QbL7PAQcNSPMGFCMJdOqbRsA8pv6cPUi0RqQ8nU6t7z6Ktt/vm8PGc6CsR6OR8vm89vb2NBwO9dFHH5n8FgqFOVY1ERCGB6UaiUTMOSD6xtBXKhUrdWROPCfGt5eU7px7H+CgS9FFQKg4o+jUarVq7zWdTpVOp80wkApJJBI6Ojoy2UYWcQp9KhInH4a/dHc4SqvVshwwOss/a6PRsPmD98ApUqBihULBIGx/NjnpKK5FZEzku7a2ZpFrt9tVJpOxHvYebkeefD02Dgi6B3SSvUljJZ/GxL7s7+8bqsKBIBAopbtKJ2Bwmkuxj09OTkzXYF/QBx4d8/0BkOlF3tS3G2+Vo/b/Bk5GwP2LIXyLUC9G2ues8b59QxXyn0BaeIYrKyuvbWCCkvFEE08yIspkoCjoNkTzdQZeK4JECRTn/3LknIf0tra2VCgU7B2YD0q5qBGlj/TOzo5FuRg+aWYELy8vLa8Eq9B7Y9wjmUzOwWY0W6HG0AsLJT2hUMiujwfL2gA5opA8/CXN8mftdnuOHUwE9uzZM7VaLa2vr+vg4MDyzWdnZzo5OTEo30PynJ6Dx4qTRP41m81aPhRlenNzYxsPZQTMDKsao0qEl06nje3JvViL4XBoZ+viGYPEwB4OhULa3t6eOyT+5ubGWPOVSsXQH5TJzc2NQW+e+OadxlwuZ1AsjSt8BNTv93V2dmawHdArkZRPrWBgQW6QB5yrRXYyssZehLDIMYU+X8d1QW8wCKx/uVw2Jcd+BoZmTdPptNLp9BwCx1wupi/oP48R4cxyv69arZYpcuSdNEKv19OHH35osuzfH5QGxIL8KXsd2QAar9frppuQW7gXsVhMnU5HJycnkmSkIhw1jO1nsXuRe98NDqNaLBZtzxweHiqdTs/pNNYYdjF1yThknhl+fHxsc8XxvPAC2KvM4+Lzspbn5+d2tGulUrET3NifMJ991Qkys8hTuL6+Nnljz+MkIV+tVmsu6qQ+m6iWkwd5Bpj03ph6rpGX+4cPHyqVSs11xDs5OTH2OdHzZDKxfguk1vyzUN2STCatY+Ei1A//xiOWi4TpbzfeKqL2BC2Ulo/KgNiY+HA4bAQgvi/dweGenEY0A/EDYYWQJd0J8KKh9sxHIgtfK9put83D5Xfn5+fmyfJ9Pu+74eAkeAh70RvypDkcAJ4lHA6bd46n6iFAhIoBNEvDhn6/bxD1InFvb2/PInc2ei6XU6FQ0B/8wR984sg7ohyg9nK5bJuVzktE7osRgDRTkC9evDDWsTSrCywWi6YwNjc3LTJvNBo6ODjQ06dP58gYrMPa2ppFqZQwMOdA2qxLMBjUwcGBNXTxJDCis3K5bLltDDdKiWvjZeOcpNNpa6jgmdCUd0AKo980Mru+vm5EoXq9bo4PsCFyLMmISZ6RWq/X9eGHH6pUKmlzc9OYuJ6YiGz7A13YP2dnZ+r1enOkKHKh5OLJWfMeIFHsBeQYQ43zFQwGDYFBdjhmU5I5jxg73zAHsiFQN6d9kRP160blhU8JUA/N+qBw0TU4duxpnBvuhx4CwvRpCAyoPx5Skin39fV1S+usra2p2+3agQvMQywW0/Hxsckcp5zRnx7nIBKJWAcsH2EvDiogYKdjHLyjBALJXmDdSDfQvhNOBhEyjsPh4aGhgxgnelETUWJkPZzN/o9EIob8TSYTO7bXOy84ZOwpGj41Go25wIy0QywWs8oT1obKIC/vrF00GtXDhw+NNQ1cz8lx6HlQO69n/N+SrB6ePDNQ9N7enlXkkJKSZox3AiqCANaMg3IuLy8tYvcD4qtHNnjeT5OJ1423Zn0jGNTIQezy0HI2m9WDBw80HA51enpqcCQwGp433isLAkwN3MH92MjeeLA5ib6pXUSZYLB7vZ5FCEAjh4eHBi2fnJx8ohMZsA2RNMqOXNdijgQoCs8bJcDpMoFAQNfX13P5LKBWoBKgZHJgRK+UA5Hr5fM4BkCJ5Lp9bbGfJ5QvQozDJckYuzw3Si8QCBhj0SvsZ8+ezfEAFtMK/Jtc8dbWll6+fPlahjPPAd+A662srFi03+l0zItOpVJ6+fKlgsGgCoWCEomERd3b29taWVmxg1KIzICeiRh8KQhQpSdKYrhevXplDGwMGaxO8us+15TL5exZPDpAvamHFlFiFxcXWl1dVTabtc5Wi5vd/5/57ff7uri4sDn3eV6MHLle1golSVSFsWQfMffhcNgiLq6HfAJjIr/eQG9tbVn5kGfhokAbjcYc8oWcAs37FBnKHfQAEhvKvNls6tWrV1ah4FnJXAMnh/XxTgHO6Gg0ssgVVMETiGq12hxMTZMkugq2220VCgVLWyFLfJ4UWrFY/IQBwTDSoQ/UiZ/5iBh9xSD69LnzbDZrpFAg4HA4bM/MOoEiNRoNOxoW5wA95dHJd955Rzs7O5pOp0ZUY8/mcjlbQ48IEPD4tsArKys6ODgwVFW6g955Z/74KDgcDuvBgwfa3d21dClrubm5aboDSBydBacCNJOT4GB4Y9NwXkOhkDnk7HVP2PXERdbCnw7n9ylztbe394nWqN7ReNPxxobad1fxRpvJZmPwe16QfrKQWYAG2SRALuQ6IXBg/H0ulJcMh8NG0llZWZkjbNAVCzIAp0xJMyW/vb1tfaP7/b7y+bwdFMEEEyVSKwqcSM3rYtTjczKecY6XOhwOVSwW55QE7EmiCuYSoxCLxawG9ezsTNfX15ar56xnSVbbzX1Qqt6pSSQSun//vk5OTuZ64a6vr1uzCGBin16AYOIHkflkMjuRZmNjQ5PJ7KB3NjlRAt5uLpfT6enpJ9IPvmGCh8OZJ7r4ALUD95JK4KhAyD3eq3/27JkhHEQbgcCM4Oab/DPvOEE4Uy9fvjQHAYgZBYajBcu22+1qe3vblC3OK7AqLFhSLSAsXKfRaMzl8Bg+TcDvQqHZYQugLPV63dIBvAfv4KN6j8iw3p4Nzh5nHkEjFok3yK93bJAnH8F6Mg9GO5vNWkoJJ5Q9wlwRyfsuZiBF0l1/glAoZP21cVrRBRgN0iODwcDOD+e0J2p6q9WqlQp+/PHHRmID8sTQ8K7A7URZvGMmk9Hjx4/V7XbNyQTGRVYx1BzywzzQix5Ux583ziCvzPsD9QN349j4xjQYGypL4AAEg0FruVsul61mm73rnW3SU/RLIG9/fHys0Whk53yTwsBYIkMghisrK/riF79op2dBjIPEihElUme+CfxoSexLDpEPOvSBYEKKxCmAzCrJ+BCS5tADHDw4Rh7tnE6n5kyypjh2PAO63ztpVMWAxOCkgrK8jpD8aeONDfXa2pp5lxTGY5CAssj7kc/gpREiPBeEi+/63AXCi5DhaWMsx+OxGVu8FJQZedvxePyJumnaAeL9MImUKqG82aQIMM+BI7IY7UgytikQHc/FJq5Wq3PlGQiHj74xAgijj4I8scs3YuD9iKA4f5cUQa/XsyL7YDBo9aO8KwYIiNOjERgcSF6wPTHCm5ub2tvbswgKzxKnC0cBslM6nbYuQjC66YgUjUZtQ/GutJsF8ut2u/YcHI9J/hknEqcO6O+b3/ymnTwUDofVbDbtPGc2YLVaVTgcNniY/Cf5ed/tic+trq4akQbvG1nHmBGBA4XSgAJS09bWlnEzLi4u5tjlbHRa5FIGRL52d3fXlCZGzTPpcT58isZHJ6w5eUz2Fw4YjjjrR0UF0SKQM/fxTvfq6qo5W+zzUCike/fuWZRHFB8KhYwIiGMzGs36LuMQsf7Iqq9KSKfTisVmZygjux7SxoCR10SmieIGg4EqlYrtQ2BgX/JEsBEMBg0WxckESgclhJuDTK6trRm5Ch5FLpezQ136/b7laXHGV1ZWzPmEVQ6RjncDEWq1WmYY0YE+/QcK+ezZM9uDoG/0oCfd6PUSMijdnYewiKCm02nbb6CgBFrICPD9YDDQxsaGka5wrFh3qnnu3btnaTQ/eD6cCJ4F40qUjWMRi8W0vr5uvBJPcOa8COQfxyeZTKpWq1mNs7dZzBN6jbVeXV1VuVzWl7/8ZUsLUlkhyRwpeEPIDsTcy8tLvel4qxw1Udx4PLZmG+SCIaYQVZVKJVNkbEIWnUgE0gMTgCBgkMkJkNtjYilH4rosBHkDoA5vMID2UAZAniyeP7EKchdGieFzEGwOBJqIm2diQalz3dnZsdpLFCubDPSA51uMdDzBiCiU3ubUFUajd0cREoUkk0nt7e3pi1/8oj7++GO9fPnSDmb3kWyv17OSNE/8Q2HF43GDlSEPHRwcWC6a3G+hULDojsiGtMEP/MAPGBR2dnamJ0+eaDAY6NmzZ+r1eioUCgoEZj29z87OrPbYOx2LUNRwOGuLiMcPsnF5eWlKg5IrZCsQmDE+gQPb7baurq7s3FlyUBB3/AlPbFDQH98IwaczGJ4khkMTj8f1zjvvWCSOkfjwww/NgKAEfuzHfkwbGxt6//339f7771v5EE5mt9s1CN6fnoWseOavj3QhSzFHGFr2q3QXQWOgiWIhQ3qHAJnFGUV5k7+m1ahHeihZ8blk9gGOM88Ju500F+kk2LQ4ABwX63ksOFBE9ugN3gOEiioUWOikxICuuRZG0nd8wzhGIhGrw/aH2uBEptNpbWxs2HugSyGpttttc0DRATDez87ODOGjjwJrz54FXZPuDlGiLAjyF4aLdsjHx8eGsKC3vS4EpaMc0OfSiWxBERuNhp2pLt11n6zX65bygeWNEeRnvrRqUe8RBKJXWD8IZ+w/HBnONAAN8/LtWeQgdfS0mEwmZjum06n10282m4Z+Int0cvPH8pK6wUkiGInH47a+nHhIZc2bjjc21CgRvDUWcHV11TaSNKsbpabNTzjRMo1FgI3ITfi6X8o//BF8fJ/fE4lztFir1bJo+NOeH+9GkilPDAk5TE/2gtnNexNx4N3SVlOaebmUzkgzY18oFAwWIQ9Zq9V0fn6ucrlsSsh3MANyo6k/h75Dyuv3+3r58qXNIXAKyokoyitgIntPMALO4foYGqIEjgAcj8d2wpd0d84t3clQUrzjeDy2Iwvb7baxt+kSFQqFdHR0pGazaeVqL1680MnJiaEHPHsmk7FNQtoD0haMYaJ47u/Zwq+DZ/f393V8fGwyCYz86NEjTadTHR4e2rvd3t6acUWZoByQP5y529tbe39kEFiP/TIejy0HxnuSXojH43NGf3t7247E5H2JRHE2ce4gR+HQ8s79fn8OfsNR8fPic4ugQChtkB2MKU4ZypUcOO9AugIoGYKQV9I4p8gsihNDQI8F2Mf+Hal88M9DfpO1QMlCdMOIs1fgsRAx7+7u2kE2pFl4x6urK0sfTCYTi0jhwzB3OD3kg3k20BQcKPQNzgRwOIcXMZc4TIlEwgwIqBxO0XQ61f7+vulZ1oBKk16vp62tLcsHw5W4uLiwLmw426BewWBQ29vb5gjAWSDSD4VCBtGTphoMBsZ+x6GgiUu73VaxWLR6eVBWjBfoLM4xcDwOFU4x7O98Pm8dEEE9fW6aNAUOE84beoI1IVUHsZAmM0dHR+bsSzOk9Orqyvg/7XbbDn65ubnR6empMpmMVTMkk0mrdpJkASGVDKDCPP/bjLdqeMLGYAGB3lD8oVDIoi4EkkEOgWjYG3KE2UevRBVsHD4HYYoIBfiQxWVhFwlfdGKCSQ5Mj2B4T47aRGBR4DqUayg0qxskbwYxAOHB82Re8OgDgdkJNOQsLy4ulM/nzdMLhUKWH08mkwbZAluVSiXz5m9ubgz6Y+OjwImMYQe/evXKjBcb+OjoyJQwEDARNF4vDVIWGYsoE4wUa4rBYYMeHByYEYEMg5KD5exlANmKRCKq1+u6uLiwdwLqv7i4MCXH+crMNY7F7u6uer3eJ3oWw1FYW1uziDidTmt7e9tKPSCg+NwVig55wRP2EOn5+bl52iBNQMTNZtPaSLJWPHetVrMSIKLpTCajo6OjubyXT3NwAhIKm9wi+5R3JmXDz5FxX8Zze3trz0XJjy9bQz6IdsLhsDnKKysrVteLbMA+B1HDkYURT/4S44bs+4gKI0Aelj3tU0s4S97AI5OgcpRfSTI59BUZlUplrmEFDjO6hWvj8BBZ9vt9JZPJufIv6ph9bhyj4FnxRMrhcNhQOOaJ50Ke4Qj4AIY1Zz8A1TKv6MfJZDLnBBFtUjoJIYy14LmpaMBB5L1fvHhhKY39/X1Np9O5qJqg4uDgQNVqVd/61rdMTpCfDz/8UPfu3TO9if6YTqfWEwKUBJng8KDnz5/PoauXl5d6+fKltra2dHBwYL3hkWn2Dc41QQUBG0ib122cYjYej62CwncsPD091fb2tmKxmBlznKpwOKzNzU0rbyQNRL8MbJ8k65j3XYmofbTmoV0MBILkIw8+y9/kEBZ/D0HAsxrX1tYsIl0ksdESDmjUQ22wUhdrAYGIEVhgcU8A4xlOTk50fX2tvb09e0cEBFIbJAoILJSOoHR573A4bIaB+eMZer2ezs/PrZ0icBBGGjIW5RR4ymx6NvEi65zNv9joHtYqNcN4mOScLi4ulEgktL+/bx7v+vr6XGSIoqq788LxvlHkKOd0Om2lHT4CbjQaNhcYd2BHSZZvLZVKqlQqBhUSLYRCIZ2cnOjBgweWG5PueiuTC6QJh393ck1sXOQvmUzq6upKzWZTW1tbtgE9jwJncjgcWuOL9fV1I0iBDnnFCOno+PhYFxcXppzJ15+fn8+dwR0Ohw1uvb291eXlpc7OzkwJ+V7qKHsiJIw0+xMD5lMnnvfBe4EkeaOMPPncMdE6c8C6YQS5D7JVKpXsM94YIKc4bZ6tDaTJnvIGhQgTRMtHs81m0+ByoE8UpE+boT+AlGkCRCkg8Dwd/DCAIHFwTnAaRqPZiUzX19dzUSLXajabqtVqc3MLAoQzTZTs2fLe+WJ4p1OSqtWqoT/kwjloA71MkIPRTyaT2tnZsXnmuXAOIpGIDg4OVCqVzCHmwB1Outvb2zN0knVHV21sbCgUCllZm5dVGt5gLElz1Wo1Sw8iCzxfOp1Wo9HQ5eWlEWqRv+l0qvPzc4OsOXULufLsbt/9kbRmIpGw+4xGI2uawsE01IwXi0XVajU7TSwSiWhvb28OsSAIgudAEEAPgGQyqZubGwu26BXxpuOtoG8fIfsIFHgYY04EyuaAmedrI8k7SpojhaFofSMGDATwRiAQ0MXFxVxjEiCkRVgBoZ5MJlZWgvBRzuB711IXS2TkoSrpjtiA8vB1gDgpeNpAhQgqcKWPfnwaQZJBJWzWQCBgdamLBLObmxvl8/k51jjRBzWWfgSDQRMOctoojlQqpd3dXfPAfcMQ/+y+sT490oEn/bsTIcBep+wIhwjY9Pb2VhsbG9YjORyelX28//77FvkRKfD99fV1K93ypVXMczAYnDtAhXWD8Eak4LsI4bhBJMM5wHnCy6a94Wg00u7urkGdGERQDPpNQ0TCofrggw/0+PFjxeNxk1ecUVCber2ubrercrms58+fW56XPCSEG8p2IKZR94ss+x4G7C/2mM9nIxs+esMgs16Lhp15Y2+RR0QGstnsXD0uqADQMR2mPDOd/Y7sS3c8BCoAxuPx3NGfKF7SQuSBMYTsm1KpZDoIp0eSHQXpIe9araZqtarNzU3TfcDrfL9cLtvzQhLC0UA38p1MJvOJc739H8izODCkQNB96DgcDtZnMBjoxYsXc/wD9gHwLg4PnB/WcBHGZr59qg4HazKZdQssFAoql8tGnvPVFMgKf6N3Qcm2traUz+dtDuEYbGxs6Pb2VgcHB3NnrvNOlMT6tWasra1pZ2dH6XR6zpHu9/tz535j0Hu9nvb29pTP57Wzs2NtqelKRpdEggdPWnz58qXOzs4UCs1O2Lu6utJ7771nMukrfTY2Noz9j4NLAFitVhUMzipuPKr87caf6JhLNjTlU0S6XmDwWoj2mGBP4mLBEGyfT2Rj4xWhLD3LEE8Y5buysmIHMrzu2SHBJRIJO6aQaCwYnJ3NmkwmrX2dz+mRC4bQwP8RZIT78PBQz58/lySDuCjdGo1mNZueyOWfkVycd4jwDjEmdMhhTs/OzixXxoZ7HaTi4Uu8PjYF3iDH1pFfhBkZCNyd302TDRRoLpczJ0q667uLpz+dTu2AgEQioeFwqKurK0WjUR0eHqpWq2lvb2+OVZ3JZKyxCmuAbGSzWT18+FCJRMLYo5ylK80MUr1e/0RXNZoseEXiIWCinmazqRcvXhjbmN7to9Gs2X61WtV0OjVmLtGIl1/yfD6l4ktrKpWK5frv3btnUcKLFy/UaDTspDQagFDDOh6PLQKnjleS3YscJ88CxOnz9sizj+BhsqN0RqORkR4xNp7DwP4iKmKPswY8OygIc0A6oFKpaDAY6ODgwJwwb6x9dzVJc1UP7Pder2c92ok+g8FZZQNyB68kGp319F9EmNijZ2dnZqB8lMueSaVSWllZUbPZnHNW/ZqzDtVq1Qhs2WzWeojf3t7aqVzkNEEB6/W6nTKHLqOhy/7+vvb39y1dViqVdHp6amsAHO71XCgUMsOIbBIMcbIcrTXJ5eIkeGfJO2NAt8DQNB/CEfH6jn3r9x7pMXQH6Bi5f77n9R8ljTjpHlFgrZPJpJW0HhwcaGNjQ/V6XcViUS9fvrTrjkYj7e/va3Nz03QRDns8Hle9XjfIPZ1OWwqX0wY3NjbU6XRMD6M3sGGewOiP9EWOcYpAghf5M99u/IlY395r4+F9FyMiFZ9b4/sYbDYDOViibYQPGAGPBIEcDmfF/MCYPI8kI5YBjzG8QQyHw3OHJJCL8zmy7e1tYxP6/A9Ci5LwZQEsVLlcNqNQKpW0vr5usNnNzc1czSsD+MhvChSuhzjJsQINkdMmQgNW9lCnH9PpVJeXl/a+oVDImh2gcImIUdiLiAZ5OjzL58+fa3t726D5ly9fWv33zc2NMpmMdT7r9Xp6+vSpisWijo+PLb9N9E2aAW+UekjeIxgMGpENuImuYzgE6XRaR0dHisVi+v3f/32TP5QHpBrWZDSalQMB77PZYrGYzs/PVSwWDRal/hmPW7o7GW1ra0uDwcAamxQKBTUaDauXRcHz3LlcTu+9956CwaARG/f29ow04wl8jFarZWduc/IQMuM5I57h2u/3X9tEBTnDOQUR4d3j8bhFT8yhL/diTkGffD5cktUHTyYTU4qU5q2urloNP8eoIl/VavUTBy+gpH0awjsOkH1Q4ESCHj4GCiUi9fuDKN/ricV9zT1BKdg/OC6eTDeZTPTw4UOr+Oj3Z72iYW0Xi0Vzln13M+5FJEn/A3RtPH7X+vJb3/qWptOpNTwC2pVmzuz+/r45/jj1GB7q18n/M2dUfzB36HsQGow5p2Ktrq7qvffes89jYIF22ZfIDesBYRVIGsfRr3kkElGtVtPV1dVc6pMBj4bUBU4PrO1oNGoVJdKd3seGRCIRKxmlbajn+IRCITWbTdMLnAzGPE8ms+Mr6SPhUQQIYxDxQHE9OvC2440NNYYSj0u6y3ktnt/si8EXFQQ5A6I5v3hsNH7uX4zJ8aduEemh7DBePgJbHDgHGGrPeOVZ4vG4wZ8McoDch9wcsCWbGUEHWi6VSraAvgzNX/fdd9+VJD158sSEhSg5FAoZXHJ4eGjOCXl9WNs4J0R/HP/JGjAnjUZDz549s2tByqHWD/YxpRWS7DoYJ5pzYHCYA5AOIqnz83NVKhWD2BuNhtWEPn36VLFYzCBvHDCe9+joSLVazRSXl0Ou4xsNXF9fa3NzU9vb24pEImo0Glb3zdxfXV1pY2PD5BanBGYnaYaf+Imf0MrKit5//32dnZ19wvHjesgx5BXWfTAYGDJTKpXMQwdOJ+JhD6RSKfV6PWM2U/KBXLVaLVPIsN4xIhhm6j1ZK9YOB2kRZcH5wunBOWQfptNpM8T+pCPel6i43++rWq3OEYSazaaur69NSTJXRF8PHz60lEo+n9fz5891eXlpRncRkie6xXmEJEbNNgQxDK43Kj7aBwHjPcjXcx9kj78pK2JPgTJRxx4Ozw4kAdUiIoZjAIqSSqVM7jOZjK6urizy3t7eNnQPx+Pk5ESdTkdf/vKXrWYYvRQMzioXaFnJ0b7NZlP1el2tVsvqtJkvHDDmgPcmqkWGCJCq1aoyf3yeNymBer1u/RdAGsg5B4NBIxKHQiE9fPhQu7u7dlyt74HgHZJAIGA98UG6yO9Cql000sgt+4Hgj4OB6GOAbFMyyPNBFqMNKHuQQGE0GlkfchrUsNY4OuPxWDs7O+r1evaM5P+RG1BUSiB5N59SWgzYPmu8VY4ar4mFYngCBf/33s8ikcKH/Vx3PB6bl4PgcE4oRjqfzxts4L00IEz+7ZXqIlwynU7tzFOgH/JweJggAaVSaa5ODtZvp9PRy5cvtb29rVQqZV4Y8AvQ+GAwOwqPJht+LvzzXV9fW+QJUY1Ntb6+bmdH+5+jrNvttrLZrB2fGQgEDBYFQl3MPfrmK3ihGFy8WH8IBYqKz9MKEqNCqYokfeELX1Cn09GjR4+Uy+V0eXmpUqkk6e7saZTu/fv3LV8Ii9b3137w4IEePXpksjWdzk7JAfYmon727JnVwlLb7POzuVxO+/v7tn7A8yAgfj440SyXyymdTutrX/vaXDtHortGo2HPgVMJmcgbA0rpqEMnKqYGnfyoJztBdiN1hFxQX8x7Ig88E04oUPh0Op1zSv0gikK2vdFiD4XDYevoheInkiHFQMtUuvW1223VarU5fcC1+S6K0Ts3QNt+b3gCkiTrQ9BoNAzyRGbC4bC1z/XoCfsc5Gxra8ty7hw848l06Jher6darWakKvQEyhiDA2yKEYPlK8m4Lx5pwcH3Bz2AjEA6An0g54kzR+R6e3urnZ0dy22TYsGgEFRB8orH4yanGEf2wfX1tc01xDgakGxtbRkETGDgdQlIHKgJfBUQN8qd6Iedz+ctAkXv8Z5wTwaD2dGfIGXobbgn6XTaSiipqiCogatE6sPbIOwJ/AUQBPqxo/PZNwyqPa6vr3V+fm7BYLvd1r1791StVvXkyRPTq9izxft7Yh26+rvC+sYTWMyr+n97r2fxu/xNfaT/HZtqY2PDDADklMFgYGU3RHTkMYD1gPZgbOI1Eamzsba3t605Pd1m8GKZNOosIRrgOfIdYI3pdKqTkxNrKEApGffD88vn88bEXFw8BBA4VrqrvfXCRgcwoCwPE3kFSBQM3LZ4P5/ngZ2ezWa1ublpRoNNWiqVlPnjs7qpQW02m9ZFa2VlRffu3TPvNpFIqFqtWtOJe/fuzTXdZ4NjfI6OjrS3t2eKA3gOYsZ4PDZl7JEIFGsul7NNLWnuRC+igslkVmry3nvvKZvNGkR6dXWlm5sbyzmCDITDYeM5tNttnZyc6PT0dE5+PR+DM48pY/LpGc8UpxIBZchxeZI+0e0LohLRoB/sDXoMXF1dmdzxjJFIZI5451MHi8MTy5g3ZJI9idPhqy9wcAeDge0hTxBiTX3ay5M6MT6S5kop/XMi0xg7HFD2KkRECGM4AETxsJiJsAOBgDY3N814sW7pdFqnp6fq9Xra3d01yHgwGCifz+v29la5XM6iIEiWPr20GJTA+yD/Tr8CX39PdI+j6ud9Y2PDDObOzo4hN0T17HGiNp4ByJdoG7IhjsdoNDLyFvPS7XbnyiBHoxnDfWtry77Dun7uc5/Ty5cvLfCgBO/4+Nj0MQGJb9uM01wqlazyxtey43DX63WTJWQgHo9brTL6CIIuTgJrTv00nAZf3jaZTHRycqJo9O7wlcUS3ouLC4u26Q0CWlepVExXQPhsNpsmE/AsyEvjlGE3FvcB+vlNx1vlqL3X4TH5txkYRj/IAeARcjqPF17a96EkESy/4DC1gaAhcECVX19fN2MKgQivzTMEfX2jhzG84Zfujvg7OjoyCBGYz0c22Wz2Ey0imYvhcGit5OLxuHZ3d81wQz77/u//fps3kITpdGqnMjWbTZ2dnanT6VhxPY6BH4vrRUkH+SbIPUB+V1dXKpfLSiaTcwQjf13mEmeIze1zuNLdCTx4spRVNZtNO3fXnzzmCYesC9eCne8JZEB/i+0riShABpAP3gfI6vj4WLFYTPv7+0omk3r06JG+9a1vGWQpzRzEvb09i/K4BnsDrgXcAU848mx4Im9PKJJkxBVQG3KURA+h0OzQgCdPnqharRqL1udScSJxGojOIcdRm4zTROqHdfGpIa5LlMC9UOjIJOgOv49Go0okEtZy2O99WsbiaPkGE15OgSoh/LCW6+vrhoZ4sh5prGAwqLOzM8v7gso9fPjQnAo4B3wemd/f37doezqdqlAozPWbl2QONJEtMDswMYYhEAjYSWZUP5A2wJEiaKGDoZfJWCymy8tLra+vW4QOvIsD5tOQnoBKAym4ONVq1ZCL29tbM1ZXV1c2dzjxGGUQQvgTODc0K8LJZV+Mx2NrgOJlEKPKtYnCR6NZX210diAQsINNeB/IwpRAUVpaq9VMHkE4OGoUdKbZbM7xE+jzjoHFCWWfkDZIp9OWHoDrkMvlrDQL+RwOh3r06NHcWfaBwIyhT20+e6hSqZiOBzH2lT5vMt7q9Cwmn0l/XbnWonHAc5Tu+sSieCl494afzYPXjgJaWVmxOjeUDgzG8Xisvb09pVIpiwqJAmAZQqoAsiLHSb6V5+r1enMsYpT+4lzYBP5xFMaGg2Dmc+WeUcu9YJX7ekb601I6AUP261//uj73uc/NbUwipdvbW11cXKjVaikYDFqJDnWuXsEC63jIhWtAktjZ2dHe3p5F1OHw7Ag//3lJtiG9MueQDPoQY6x93o5c6P3793V5eWn1iUSPnF2L8nznnXesXGY4HOrBgwfK5/PGgL64uNBoNNKrV68s7yfJiEw0SPkLf+EvzEFpzEs8HteXv/xl3bt3T+vr6wqFQlazSatVUIvDw8M5dAPY9eTkxMh+kix9sJh2YA/ggFD/ilPp+82HQiHLXyJLGCaa3xBloWTpDjUYDMwJiEajBvexR+v1+hw8jaygVD2TmOclrYKRIJrlD3sYHeH7D3AUq4dXYR0T/SzqDZw18vpEncD9PJ/XO+Sti8XiXA398fGxfQ7HHx1EaoEIioiJfe8dAZw8b6grlYqd4saexgFlXtmX/X5/riql0WhYhEjaDT4B6ZWnT5/q4cOH1imPCBqHh3fhGVl75AAyI04EKCH/h6BJAyHfkhMDLN01rMLJxqHgVL/hcDh3ShWGi+crFAra2dmx6JM8MI4h3SCJhDOZjJXhUZ4KJwgnnrRbJBLRs2fP5g7b8fr54cOHKhQKhoaMRiO1222dnp5akDCdzjq9QaT0JYLx+Kxf/Pn5uckPbG5SPplMxuQMJ4dnC4fDRvKlD/xiZdK3G29sqD1rDU8aJQQ0hREg6gqFQnNn7bLgKBZgFzad987JL0kyhu7l5aUuLi6MSYcxhQHL/3k2jL4kg2WI2L2C8WUFi/m1xSgUB8AzzmmZR9TNInvKPhs8kUhob2/PcrIcpbd4bisNBPL5vC4uLvT06VNtb29bjisQCFgET8SIp4bH6Vurbm1tmeEulUpGlPDMzFarZY1EqONFCbDpeGeiffJsPrrBgONZsxaBQMAO+SCKB3KmhvKDDz6w6BTZYi4ePnyod955xwR9e3tbm5ub+sY3vjHXiIKoldwTx0lyhjC1k17JYbiBTNvttpUmIf/r6+sW7XhHaHV1VR9++KFFLdPp1EpOUArsidvbWxWLRVtrIkp/NjAEIEpaOErQ5wM5FKRcLlsvc59nBrqm77RnLlOJQBSBcvfd9CBFQlgD4eJ5gXFRmh7GREZYb/YnOXHSCp8Gy/MzIntfUYISBYrE8ZbuSrswtOxXZNHntYmQUKqkWIDlMdTsQeYPlKLf71uuHOIdcwA3AQ4DUfv19bWdXifJauKB4XEkveyVSiX1+33t7u6ao16tVtVqtXRwcGAOimdTe93FXPiggXwqcr2xsWGRLSRHEDJfr44cg8gQhJGuIvgCtQHhOTg4sJ7i6MCNjQ1VKhWTMxA2ZBUyHgQw8uMEQeTNkY9Pg5HhDEA4xsYkEgnt7u5aMIQTwHszB6BZ6XRa5XJZn/vc59RqtfTBBx/MyXmhULC0KnICt0SS5e198PJdIZPhXXkFj4EDUgbSIJrhYG6i1cX8LNEBXWy4HguAAfFGm42CsUeBwpxkkcfjsUXLk8nE+s36WlpyvoeHh7Yx19bWrEPV4ohGo9rd3TXyCouEsUXIgFXweMk5wvYFAuaz1A6jSKlfBhHodrs6PT1VrVbT/v6+sTrx/ujiJs0aOJBrocPXu+++a5FuMDirIXz27JmxZ+mExiau1Wp2HjHvTdmUz8lByOH7/DydTqtarapSqZjgUlsNc//i4sLWbjqdtRA8Pz83g4CzBTRPeQsbEuY9uXDaCT58+NAQkxcvXqhcLqvRaOib3/ymksmk9RRGqbZaLf3BH/yBrXEoFNLHH3+sUGh2Du83v/lN3d7eWmtD3p91Bx1BKdLRCAWGsoK4RH7rww8/NBSBlq60uKTBB0bQO3Cwvr/v+77PoqKnT5+a44F8828MGWRJSSYzfBbCpo/YiIpRMEDerVbLojV4BRhKol4Mxng8tqgHY0KKi1JABoaE+aUCg6gb58xDknQ+4136/f5reRHULgPfJpNJq8aYTCYqFot2GhSQJe9zfX1tUHGv17OOZfyNLvBpjkAgYM1sQMxwiBbRFSI6EA2iZRwNPkNpH0xtFD6OBj9nz/Ju/f7shK5WqzXnOBLseH2LvKA7SElgVMbjseXO/fzA/1h0kEhXwsngSFKeeXt72+YRGeJ52BseXvf2A4e10+l8Irjyg4CQ9cG5Zu/u7u7OnZPudQ9IKDl0HNTT01N7VgIdgljkAZY7jj2yzdp5XsmbjDc21MBQnrWNkhqNRuYR3tzczFH1IXsQYQINsAB4Mb6Wsl6vm3GHeMDPOKZsUbH4BgC+/y+bHdYsXiCLvra2pvX1dcsPeiLFYmS9vb1tXhg5PZyFfr+v09NTWywgIHKlDx48sBNrvIGG/LO7u2s1mN7LRBEGg0E7HhEIJRKJmGcci8V0dXVlDf5RlvF43MhikH/wALvdrgqFgg4ODizagRSCV+/zqwgWhJx79+4Z3AsJjbWRpPX1dcvxQu6i17Ykq9OFHLO3t6fz83NDCSSZ0qLmEeeEqK/b7Wpvb0+NRsMUAr9ns/sNAdGDvBOb9vHjx3YowOXlpTVO8JAqMgNDG4NCDTpdkhb70BMF8j7M6XA46+HuW7rSdOHq6sqaA1GXSv4QQ8c8v/vuu8Zg9UQnqiCoYyev6eVqdXXV4FCf7+aUNQwITieNOIjSID/6yAvmPbKCISI37AlsKDJ4KSg8DBTzgnHmnVGG9FIHiYMM5cl8nFZUKBS0v79vxigcDuvk5MT2EAgGBofvXl9fq1AomK7DiadEB+LbysqKre/nP/95K1mjX/XFxcWco8u51UDqPC/tidFpyWTS2vqC+kGw9SxlDKN3pEulkq0/kPjl5eUn8sjoaPgxdL8rFAqmgyuVigUB3KNWq9m5AxhrIN9YLGZIoySTXdCCdDptcoI8gEj6QMijKcPh0JxV9iJte2HTe14JDh/6liZFoIIgMDSeWV9fNxkj0PFo1kcffWSpPJj0BDYck4n8Ywdo8ILx57nfZrxVjppIEVjAM07L5bJByZyLLMmwfL6HgsOAoiTwcHAAPIMT9h4Ma7wzjBwbB6gOBQ5Mx8LgOQElSrJNG4lErOxFmkFCRITSPOENxUSOXZLBuJwCBQsWwTw4ONDq6qp2d3fnyn0QILzXer1uGxXlyLziaTKPdCoiv8YxeZA6zs7OTPGSM8HZolPTvXv3zLEi78p9fS91GiSgzImUMIZ8BoWDQaAHL4qXSGxzc9NkwcNXNPanld/JyYldl43FvEmyPuMgEqRWfE39+vq6vvjFL5qTSM7w4uJCr169ssjA99w+Pz+XJJMN+khvbW2ZPEDUOT8/19bWlgqFwtw84EQiL8y5N1LZbHZO3oF7s9mskQN9WoR5JdUDZH5wcKCPP/5YkiyfCeuYufVK2Z8n7fPowWBQ2WzWGgr53HYwGDQSGDlmUg7MO5EXjhIIkW8BDOQpzRxl3zCH3uXdblfD4dB6CHAKFvPkGwfhWODIgW746LHXmx3k0O/3jf3NKX/vvPOOXQt5kmQ1wOgnP7a3t+d0Fam18Xhs3fpisZg5ERsbG3On64EmeqKjj0iz2azOz8+tFIvPweReWVmx9yJtRzllo9GwsiccRxBH7lUqlYzbwvnfq6urJp++p/p0OiuL9HoLuRkOh3r27JlFn7QahQwWDofNEUFnEIFCxPOpBnTMZDKZOxUOpA/dRjDC9+/du2fPdHp6aijmZDLR5eWlBSM+GKIG3hvmXC5njYYoBSQIYf9ubW0pnU6bnUsmk7q+vtbLly/V6XTsjAj2VblctvUA1VjkCn278VblWRgIogXgCkgGKFrP/sWoNhoNYy/i6VMXKWnOaPtICIPHSVIoaJ/HBj7iWEC8LiImabaZ8/m8HZVGU4/JZGLECK5LXmSx0YVnE1MDiicKzNdsNvV3/+7fNYF58uSJHelIveXW1pblWYg2iBo4/nI0Gtm7lUolU/zSzEjRIrNQKBjMRF23NIN49vf3jWzF9aWZIs9kMtrb27MzokkH4NwgiGyGQCCg8/NzEzhgMvJOXJeGKSinTCajly9f2slUe3t72t7eNsjbH985HA4tciD6euedd3R7e2tKk1wiqAQkQwwQaAdGMhKJ6HOf+5z1ygYl2NjYsCYoRI/k4Shd451isZhBh6lUSpeXl9rZ2dFkMtHTp0/NIfMEGiD8xeY/Xp7IWflKCj7HmiJjeP4eDZJk0S3zJckiC1IOIF7cyx/jt0jkgiHOe3sCIsoTY+AHz+8dY0+6Au6E2YsDg7EChcAZvr6+tpIcWkQiA75xDnlV0Cl0gicLSnelNxicWCxmvdhxLICsi8WiIRR+7RikPSDK4YDQic/LIkSrxSoZHFT+gKBRowtZCiIssuI/DyEqGAxqZ2fHcrGZTEaXl5dGyiI65XAgaYZ24ZhyhKNvYBOPx+1QCu90eiY/DhTX7Xa72t7eNiSPiB/nCceJ8kJY78g0/wdiR5+jJ2guQn13o9GwChWf793c3DRy3u7urjKZjDUc4iAcUns4q+SsQY3o2Efb15WVFWWzWSNnImegMDTewfmhiVWtVrNgAAidah2QxTcZb2yoWWCfO6Bsg81IGYWHUvDCPBMYT/x1XYgWhRmjIN1Fv0DvHkZiwyPI5EOlmRI5Pj7WwcGBwZTpdNrq4yAaQfzgWfyYTCbmjASDd20Ko9Go9aQlenjw4IG2trZ0fX2tH/qhH9LFxYWeP39uz9vpdHR0dGTCi+fY6/WsOxcL7glClBXgVaLYiR7X19ctFw5pA4cIcoYkI0AROROd1et1Y8zjWfoOXOSnyPtId40EWAdgK1IAnikcjUa1vb39iQhldXXV5AeolbxZMBi0LmPFYtGYpTgVEJN8tAsc2e/3tbOzY9AyfbbJFQYCAW1sbFitKgjN1dWVLi8vjUULwYha8kePHtmJVrC9MWa+/h354105p9nLO+9IH3RkgtI/un6h/O7fv690Om31/Mxfp9MxD97voeFwaCcKeWYz84RjDNy8uAcXUx7sP54/Hr/rjYyDQWkgihkZxciitIgwkA/uD69lbW3N9qQPDDhKFqeaPxgwDBMEvlQqZW0zMR7U8bNvyaFzqpnvNe7hVwZIBmk8IvzDw0NLIUFE8hUSPu2HzAH5oz+YF79WQPsY7EBg1pyFQ0UymYwFSegM6qlxJGC3Iys4RUTzVJCEQiE9ePBA0l2dP2krnFkIqoeHh6YT6WPN/5Gpdrtt1TfwG8rlslqtlvb29oxU6NtzctY0Osg3pqF/wGAwsCoMT2oj+oU57p1Yz7dBNkkrUrPPOo/HY9OpILSgcQQaOK/sk/v37+v09NTOqn716pXpZ+ZxOp1aE6Q3HW8NfWOIIWj5XJSvMeRn1DljWPE+iIzwfJhcNhpwyOvIKkwkG55DNrh3JDLrcLa1tWVHSFL2RHQCK3o0Gn2iRIu8A5PKgLzCvaivxmDSaOPnf/7nlUwmbaNwbRaz0Wjo0aNHOjw8VKFQMIND0T/Hry2WUTUaDet/jTH2J4YBi+Odo0grlYpBrKwFRujZs2fWYKFeryuXy2lnZ8fuifG8ubmZM661Wm3O+BNNspnYcESXHGyAAuN7NH04Pj623A5OBN41UCvOy/379y0SajabyuVyqtfr6vf7til7vVlLTioSQA6QG4g3kOgYHqb0xJhMJmNsW06rAmHCCSWiG43mDzOQZEflSXfHxKKE2fCBQGBO7uEqoMAajcZcT3bPuibNQTTNfuU5MVo4QMiIbwAET4N1hhDDOwA7o+iJuoA9+/2+lfhg8G9vb+152E/+vXEEaTsLIoJxhPBJ2RCd+miSgyxDBIVghOyxTzyhLZPJmBMJzIlj54+jXdR/PDMyRHTOehcKBQtgeH/y+4PBQO+88451uarVatYV0FemsI9wsAaDgUV06D8MOHwdUgK8J2gfZyOvrKxYVQUwLvchcuS9IELiUOCU4UjCO1lZWbFugKQyM5mMAoGABUmkYJ49e6ZMJqONjQ31+32VSiU9f/7c8ul0m6S8Ckd8sW0p60AdN7wo9gKyChq5srJiR8rCI8Lpf/fdd/WHf/iHZl983wfgeXhVhUJBNzc3FqScnJwYGoRDSiVRIpHQV77yFT158sQ+v7GxYcFIuVxWOp02FvybjrdifVOmId2V4AAd4NEiBF5ZMWAnE3H5jkTkYDDUCIqHRMjDsOGJBNnUbCY88HQ6bc8nzfLlbAyUEcbj+vraojpyEwgGymU4HOr8/NxICXhnkqxcqdfr6dGjR0ZKSyaTyufzlkundKrf7+v58+cqlUpG9AEynkwmlpeq1+sW3dGN6uDgwBQaZWZ4maQIpJmRoGMYEBqeLeUpl5eXxi8IBoPWKAJ4ORwOq1wu6+rqyoxAOBzW/fv3rbyHEjyOGvUMW7xqjE6v17N8Is4Higa+Ax6qz91y7iwpEp+z397eVqFQmIt+w+FZdzRysbyvN9bdbtdydyBDPu0CXJnP55XNZucOUqDVK8ozmUyag4H3jQPmu9ktRqs4ltRjYnipYy8UCubgRSIRXV5e6tGjRxZNA/Ourq7qc5/7nCTp7OzMIGO6t1HHC4KDUcMpJjrknG0UHwPnq/7HJ1DBeIYM6lMrHLQSCMy6R9X/+JQlb3wxAKSfUOz8HsPT7XYtNYFTj9KeTqc2z6BDtITEiUIGSRFBfoMlzUlWOEj8G0IpqTmenffg/r4BEBwJ9BcGFWOdSqWsfHAwGFg6hbJBImugfWS1VCoZtO+DIeTcB0a+FDAej+vy8tIIlkTWODagbqB49T8+QUqapUAIROBJkG4CXUL/8Tfy7klToK7FYlHFYtE60dEgZjqd2jntk8lEu7u7hoBxkt3rThuUZKgs6RbIXZVKxb6DQ0pZJnowEpl1MSPlVqvVrAQO+WRPUNolyQinFxcXc0El+3lra0vvvvuuYrGYfuM3fsPSfQRJtDGlKdCbjjc21ECHKHDpk6dpEVFIMi+HAYkLmAVj6+n4eNY+kkaJ0iGo2+2aQoQs4+vf2IBsjGg0qqurq7ke2mwgvC5pFkmdnJzMweq8G/ltGIx0jiLXQFTu0wPb29va29szmIUSpkgkYs0wyCHiFBDNUq6Fd7y2tqYXL15oNJo17O92u1Z/3Gq1jNUKuQ9eQDAYtB7UlUrFBAtIknwTGx14e3d31/KC19fXBt+wsSGaZbNZfeMb3zAy0XA4tKPnkAlYqiABhULBjCdRB0oIpYv3jvxwQhtePIqGnBxy4ln6GPhoNKp6vW79tCWZ8UIBomBRfr1ez46WpLyFeWNjYlxSqZQSiYTJdqfTUSwWs6gE2NlHBMjYZHLXkIUoguqEcrmsnZ0da4kIM54oGYURCAS0s7Ojo6Mjc+Zg+XvS38nJiX0eQ8I8sfeAfT2EjFNJFQSywvvxTkTfksxRk2QKigYv5CPJiXa7XVWrVSMzRqNRKzWbTCb2/jyHj8IICCCcETEzvz4XD2s/l8sZhInDASEI3fOFL3zBotGnT5/aPvHBAArcByTITywWs5ax5POJynznMYyybx6DI5BKpYxwifHB+WLw/qPR7Axt7ywR6Xq0BRItUS1BFfOLPCAD/Mw30SElALJCUOCPp6T6gB4ARNL9fl8fffSRNjc3tbm5aQhrr9fT8+fPNR6PrVSWcshQKKQPP/zQnNVFR7dYLOrhw4eSZPJ5dXVl8sNhQ76rGLqHFMF0OrX5OD4+tnvghCO7jUbD5qTZbBonIRgMGkryzjvvaHd31+bI8208URf05k3HW7UQ9RsFzJ2XZzOz2F6Q8Lj5mVd0foP5Y/2AxbgnrF9yJOQkiLQRDq9A8Kin06mdgQwEhrLCyHvl6Qf5i4ODg7kSJ6L56+trUywMX6Qfj8fnair5HdR/RigUUqFQMMZtKBSyUoPt7W2Nx2O9ePHCBLFYLFq0z73b7bY1RtnZ2bHNlslkLHomYjs/P7fyI587q9Vq+vjjj411TK6IzxH5feMb39Bf+kt/ST/8wz+s3/7t37bI4+bmxshIzFcikdDx8bFBrEC3PoqGoEdUxN90f8LD9blUngVZYt6QO9jNtVpNH330keWEKpWKisWi5cLYSBhryE2sNXLr84vcgzI5lD/zxLutr6/PtVtEMaAk2+323DGlGCTg+UajYUYBecewk3fc39+fU7Q4QDwPSu/6+tpkfzqdmjPHHvbcC/YOuWGebTFfzfepHIDwx/4CIucwBQzJkydPVCwWNR7PSoiYa7p64djiLIJ4kNIgGun3+6rX6zo7O7OIHOeePYXDToqNMh+gWYzXYDCws6ThbtATgPeORCK2T1nPm5sbXV9fq9Pp6OzszEhJ0+nUnFiMM4gWqBxRdr1et9rmfD5vzY0kmQ45Pz83nTIej1UsFs055YhXImdysMgt/wYlIADgHWiw02w2LU2ztbVlc46O5dx2omRSgZR1STNEJ5lM6t69e3ONbtbW1izapD8EpXlUUHA2AlU4kUhkroTO24dAYMbcf/HihenIYrFofbevrq7mkFrmLhAI6Pr6eq51bTQaVTabtTp8IHRSTqlUStls1tJstVpNl5eX1njHp2x8IJtKpeYaJ0l3Dp1He7/deKuIGuXnc51EXj5nykv6dnhAFHgrRNV4dCgtJgflDTvQGzVfxoDQVCoVO6GI+xO5wW4lomOTo1ylu57ViyMej1sbPl/OIt2dcUrUZJP6xxud+wPhY9zpGOY9RBjF0szgovSYc1r1Edl6CE66y+F4g4xywJC9evXKFCwOFKiCb6ZAyYUnRPn7sJ7Pnj3T8fGxKQ4QDmAyohTYxuTzqbvMZDKmIPGCgd4k2SllPu9NjTVohM/J8l1IIrDqORsZNifrAuzWaDSskQ1NZoC4MZREorw7EUUqlTID5nO/RONsfpwiabaBt7e3ValULJryjfsZlA/BNViUU0+oQiaIuDHYOMWZTMZy5Bhlroej5vkWyDoRAIiEXwveEfmFRQshkOtSY350dGTfodyKNeWENQb9qokgx+OxwYU+4kcWfOkT+4I94Ht7E9WDZJBnrFarBtkD4fr343r37t2zDmc4yuR/K5WKPvroIyMS7e7uznVjQ190Oh0dHx9bRDWdzvqK8zlSZrxLODyr+6/Vanr69KnV6k4mswM8IIyenZ0pl8uZI1IsFpXNZrW7uzvH+3j16pXB0C9evNDDhw8NBiaPS7QPJO+RVOYXo+65AtgAZB0CI8ZpY2PD1hJiLJE4Op/e3kD96XTaDDUyj4GHB/Ho0SPTTblczipgvKONQ1atVnVycjKXNiUdR32632t7e3tzaa9cLqerqyudn5/PGfvRaKRf//Vf18nJyRxvAuIve/Tm5sZSbm863iqiZpEwsGwi/g+bmxpq6a7XN5EcXrw30sCHnjTGZz0EioB4IgWGi1xYKBQyb81H31/60pdUKpWsX6tvakJLSYQA4wJBywsU7FRfr4tSQjiJiPDMyNf5+QGR8FAapWX8H3TAE7BQlNKd0HqDTU5kZ2dHo9HICvg5xMILB/nXw8NDc17oEBaJRCwXDklOunOwJFlO/t1339XLly/n+muvra3pc5/7nMGJeNQYuVevXplC5+AEnBkUDZ4878fnEHpy15ubm0ZigrSBA4cy4ZkfPnyo4XCob3zjG3ORK4qUdQKxub29tcM+EomEGf5qtWr8CUp0otGodXvDeAMBAr0Cz52fn5tiwaChVNg3cA84UGY6nRp5CVnjHcn5e5li3jFqGFjydD6/CJtVujPOXhaJrLk2kCsOKnNBPTvvhQNHVHhycmKRmU8xvW6srq5+oi0j0R+GfjAY2LssKj7WioMQcGKkmZP+wQcf2HWIknle4EmfH93Z2dHGxoZdn7IkHKFKpWJpMAhVGFpSZqwzsDvRL2x3UnY4Ajja7A3KoOLxuPVmGI1GevHihZUSgdYEg0EdHR1ZFInDt7OzY522yOdSckQaDcTFV3TgyPnjb9PptJU8Qlbj3hj3WCxmvdC73a6VKMHIRsfh8LD25JSxCzzH2tqa9vf3ja80GAx0eXlppVr09fYVA8hyvV7X8+fPDbYnTROJRKwccnt7W/F4XGdnZ4bGENSBOtECFWeL2vZWq6Xf+q3fMgcOpyMUCtn7UKmyu7v7qbK/ON7YUBPVAjV7sgZ5IQyrh4lg1SKo/EHh04xdki3KIhmDOmJGIpHQ5uamGWSUfb1et2gU77lYLJoHSjeulZUV7e/v63//7/899yyS5mo3gTPY9ETq/B8jhieJMOHhskF5Hl/MDzzJwDBBgCMni0PEnEt3BnpR0eEZkkvHOPR6PeXzeWtsQBTYbDbNe6c5CTndRCKhH/qhH1IsFjOYB6Xy8uVL64D2Qz/0Q9rc3FQmk9Hv/d7vGRHJpz9CodBcBzPPHl4coAgccehzgJCsKPnDaer3+3rw4IGhGNPprO6y2Wzq8vLS4Pb33nvPZCabzVqPbBwz8qQoI/797rvvql6v6/3335+rvZRmLVt3dnYM9qKhDDLV6/V0eHhoaEqtVrN8HAqYGk5/vCHRO0xzDioBrUBBDIez09d2d3fncvooN/YUsofzQeQP5N7rzc5f9mx8ruf/TYQKzBoMBq1TFvIn3TnbrCcM5tPTU0tZ+D+LDmcymTQSjjTjFZDXxeiy7zyhZxFlIndLTS48FtYGTgSojz8iFwcKYhZGem1tba4qAHnjMxhayIYYZhAS7kVZn0caSVdgENBPOB0gN9SOU66Vy+VsrokU6cEPv8jPF2QxjBWGirVhLpFD3uPhw4cW5LBnVldXdXZ2pnq9bofFEEFTUuoNf6vVMri71WpZZQlGjFOu0Lc+xRoIBOy8+EAgYBUGwPTML5A8uhb5y2Qyun//vj7++GOrDGKNcRaCwaAhefv7+5YGGY/H9i6sL21RIf8Gg0E9efLEasXZ495ZyOfzVtb7puONDTUEKE984QX9JDJQQHgk/vcY7clkYspRujuwY21tba6Dj9+AXBcHgWgTxXR1dWVKw0cQ77//vgl2IpHQ2dmZeXy8Qzgcnut57UlnRO6gAER3CACDKIqzin1kTRRIuY0fw+FQZ2dnc+0Puff19fXcsW2fupjhsMFtzBd53ZWVFeuoA7RbrVaNcYryoz97oVDQw4cPDaaH5DEez8pQfu/3fk+RSMSigGQyObf+wOhEAsBO5Lk9mYN1TafT2tjYMAh7Op11RIIMQw6a08FQ8BDq+HetVjOOAIqew1DoPler1VQqlUwJxWIxW2cUXTab1fHxsQqFgpWHvXr1StLdYQ+tVkulUslquJlbHC1qgiGwrK+vq1KpmMMyHo+1sbGhbDZrzhgkGnpUd7tdgzxRvslk0hAHmgnBtEUBs3doQoPCI99ISiYYDKrVaqndbs8xwX1FAw5TLpeze+GwcKyqdGegeTcYzI8ePbJI73VGmmdFFiBT4pBiuGKxmMrlsj07cuL3EwYGw8z7kmp5XZ00OXBK4EA0fNDB4EATyqA4b/zo6MhIiOx58s++gxjrw3zgdPjuf6TqqI6ZTqc2x3AfCCK8DgQFRH8wZ1SW4OjBCfjiF7+ohw8fWgBRKpX0wQcfWDXH/v6+BoOBXrx4oel0dsLUgwcPdHV1ZRUm5Np/+7d/25wO38wENIX059XVle7fv2/8AvpajMezw0y+9KUvGSpFStTrFpARmNvoRY7A9ORSUDscaKB10hzIjicHdjodO50MRwVHm94WOEg49awnuoGGUjR4Qf9BEORzbzreKqJms/iHQpgW2dI+d+YFHSXmlQDXJpL2Ea6vwfY5L3KVwIncI5lMzrUXRAFMJhPzvmjIgKAzUBjk24ANeQaMA4aaYzZfl2vglCS8dd6/1+vNteLzc4YC2tvbM5gV4Xj16tUnUgBsUGAVD5sBc1HqNJlMLHqGR0CRPwoEYp2P/iRZREdHIKD0YrFozQx8Lh7v2Z8LTJTd7Xb1/d///bq5udHv//7v27snEgkdHR0pn89LkpX0UGcejUb1hS98QeFwWF//+tftLF3WuFwumze/tbVl7PNCoaA/+qM/stw587W1taVnz55Z+gOSDIzwQqGgXC5nR+9dXFxYFEsUwtyxD2ASt9ttg/WAy5hTHMB+v698Pm8RL0rEk7BQEjDOc7mcGVLSTnTYowSMlA1rTKUEjUaQORwSDAPoBEoRyJC/qUclB0rXularZToBhAnHhDI1z3plD/l8fSAQsHwjDrzv2cDeJtWFEkb50hMcHQTk6Elk3W5XxWLRjBXRDTqJz4xGI/sd+xuHH5QPuJ2KBshTnjlO+ZUki+opr0yn09rb27N7smcajYaur6/NwSNFBocDtAgnyAc/sVjM9j8dxegANxqNzDnmWr1eT7lcTkdHR+bMD4dD7e7uKh6P64/+6I90dHRkhohSxwcPHmhjY0Ph8KxPPCQ1KlSm06khnu1228hXyMLFxYXOzs4MtiZFKcnyvo1GQ3t7e+ZYgRjgXIIyUOaGzmataTVKmtX3HcAWwM7HuYX0hu1JpVLWS4MoHYcJ1IOgJZvNKpvNGro0GAz08OFD/fW//tf19a9/XS9fvrR12NzcNAf3u1ZHDdHC58dQPh6i9Lle6u6kO0MIG3k6nVrhOsoepYXi8vAAG5yF9fcDyvL5YohgeEvBYFCXl5eWH/IRHdfD2ySPt7KyYo1IEAgYyQip3zA+UqCswZfVoKx8Tpu5IYJst9vWaQjyA3kgBsaG96tUKgZbYTR4Ht8iFCPARiVn1mq1LEcTj8f1+PFjK/XhHSHs4W0DlRERwhrlOclF5XI5HRwcGKGNTYOCIFLzXAauATkE+YNfwDGPHNYxGs0OUNjb27NzZYkyODFrOr2rU15fX9fW1pY5QMVi0RQ4kOnm5qYeP36sp0+f6uzszGDX/f19m3d/Ms9gMNDFxYVWV1ctb43BxRBi3CaTibU+hd28SADLZrPWHhfnGNiUuQEl4gS3crmsfD5v0D+w2+sYpjicyB97mgoKH5mMRiPt7u7aXgKKR8GjwJrNpvr9vr3H8fGxKdTJZHZSFa0rPcL24MEDbW9v68MPP7S6WnKH7EtKYiQZe5qcfTabtRIbGprgVAJJttttawuME8660TUR2et0Orq4uLC9SqMS+rmPx2NrDUwdO3MKEYz93263dXZ2ZqRb8vq0VI7FYqrX69Y97cWLFxYJojN8Nz3W0lfSkJrDSMdiMeXz+U/U1FNGBJxLQx3mg3nf2Ngw4iX7czSa9QBfXV1Vs9lUqVRSsVg0tBDi5f379y2d2el0zEnkYJLhcKjHjx8byhGJzE5COz09neOF4KhC5m00Grq6ujJYmTlAxtF9yAGll/QQwLhymAvlsjjvVFx4FPDk5ESHh4eWCsFJKpfLVhONI8292DMffvihtSrFAfZNonBC32S8VWcyhMJvMnooAzkAWXioQtJc1OlzrD6vjRIIh8PWPMOzLiXZhqULDpOP04BBgyCCcs/n85bjosRCkrWy4xkxYEwizzca3dVrejgOb9vPkzcyGKV4PG7ebiAQMOO/SO5KJpNWEC/J2L7k3/kchhEGJblXukPRe9bnVOkoBDGIKBqvmevjIEAQY45RBhgVlBCQrWe+e5LcO++8o4cPHxoZ6uTkRFtbW/rKV75ihBaMNFFvJpMxtIXr43wdHR2p0Wjo5uZGDx48UKfT0enpqVKplB0XybxR4jYajayHN5EzBg9Z2d/f12g0q1V//PixOSK+YcfGxsacU0HEgxKGfATsBwTI74n62eAoHyLw1dVVFQoFazDz3nvvmXPDfWC8Ez0DNQPdgTx5hOPb7W0PPU+nU9sT/B8GNXPHCIfD1uOZ52KP4dDwveFwqL29PUNAmHucqc9//vMGvzabTRUKhTmnlxImFCmcgHA4bKVeoEkYU8hMGHBynzSwYQ3K5bKdeISsIM/I/9XVlaEZ1WpV19fXpsB9/h9dQlBCiRdoBKmxYrFoKBbODQGE78Hu036kz1hz5IHIFA4KUf/KyopVcBAJ8gcuDak+H3Rg8GnPSX371772Ne3v79teRiY9LwdYHkLcq1ev7Jz5TCZjJXA3NzfWvZFIFmQNtjwIKmu5sbGhy8tLk0/kHhgdJjw5e8jDlKLVajVDFEBNvdyAWqDXX716NZcS8kgq64mRJ7Acj8dWxRCJRLS1tWWoVrlcNuRlEdH9rPHGhpqciTdCKysr+spXvqJHjx4ZSYu8FEqDKDf0x80cKCvAU8QrD4fD5sWhALxy8goH0kcul7Nout/vGwuPZyP/ClGC6G19fd2gDiA6PDJaY/q2ktwbA8fRb2xOzin15QssNikAFANdcmhpSTtG6a5RCt4o5C2QATYkuWFO0kFxplIpdTod69Ll8y8Qeep/fDoXUCHoAM37UQA0vSfPzPwwSB0Mh0NdXFyoUqnMMYB5p0QiYcxzFEC/37cTk4AoaeBB5yVP4qP+t1qt2ob60pe+pLOzMz179swOGUmlUtrZ2THvu9vtWiu/yWR2RB193UGBgsFZ56X79++bPAWDQX3rW9+yfDTvi8OHtw37FegZpVKtVq0DFkoxHA4bBPjgwYO5fOL5+bnOz88VjUb17rvvGnL15MkTbW1tGeTru7iB/ODJs1cKhYLV7a+trZlB49hS3sXLNlEFzg0RJHlFSQaLI8PcF2ibMhT4H2tra7aXKXuDzFgoFCyqXl1d1Xvvvafj42MNBgNzMjm/GTngsBS4B5JMaaLkfXqKaCwYDFrfAvKp8XjcFC//nkwm1kaXOVoMNuAK4HTDJ8FAE+1yX9Z4MpnYoRnMJ+feX11dGTrGyWScXY4THwqF5kqRKL0C7u52u0a4RSfREyCdTuvq6krBYFAPHz5UIpFQuVzW2dmZcTtg1hPx9ft9Q2NoDwtS8PLlS0sd4nyCqpDeQuYJIpCDbDare/fuGQqUzWZ1fX1txhpS2ObmptVlI+/YEUn2/j5Kpox3f39fX/jCF4zklUgkDG7nOZnXSqWi9fV1SbK+DwQiyBHNo7BR5JwJLL0TS98DasDR6dLdedr1el0XFxdzbVvfZLxVRO0hPhbjD//wD8340qBEkp1YQltMIF/+8NnpdNY0f3t7W6urq9bnGiN7cnIyB38Tda6vr8+V7kwmEyM3nJ+fG/PP5wXxqj0hzpddSXdMUTwymIq3t7dWD0oDEZpjUGLgT9viufypKwg8hhXmJd8BxvXlNERICNfZ2ZkpAd+Qg/dDWEAc2Linp6e6vr7Wzc2NCTBKNhQK6fu///s/EaGjBHE8Ft8N8sfz58+txvTZs2fWPW48Hpvh5BCDyWSig4MDraysWDQBvFkqlcyQh8Nh1Wo1SwXQEAFYORqNWtchynharZa1ge31ejo7O1OxWLTnhigSjUZ1fHysTCajDz74QGtra3b0YTAYNDIX8s73UYT8DETFE1UoP+EzKDq6UmWzWYucMXaUfwCH4wDAEMfAdjqdub7Z3APHiLKScrls+Tff4MezZ3GA4XXA+Od9JpNZRQanr00mE2vhieMAJE75oSTLXyPHKKpoNGrOKc6BJB0e/v9oe7PmxtLrSnthIAmOmEGAM5M51aSSZEu2QvbncHdH9F/oX9X3/TP6yt3hcLslW4MlSyXVkMxMzhiIiQBIcMKM7wJ6NjeolM3qCJ+IiqrKJIFz3vO+e1h77bW39fnnnxu3pFqt2lrA5QAKhbuBXCvQs+cr9Pt909D2NgOD73vlOVusAZDwhy72wOLiol6/fq1EImFcgHK5bARB/tzXQIF+sXX9/mSIEUMusJec+2w2ayTSYHDSc7+7u2t9/RBm3717Z9C1J5HBZKcvmEl429vbZmfC4bCOjo50eHhojhF0gXnU2CBJpu4FaskeZO+xbsPhRDRpZ2fHiI5wTNLptGKxmPWhewJXqVSytjuQM7hMaBD0ej2bwoeQDSW5u7s7pdNpPX/+3PTaQQ+3t7fVbrdNPY+E5O7uTvl83royKFPBNSGZIKDDfhcKBROCIpEZj8cm3APhkvOF7aDNl5IigfFTrm815lKS9S+Px2PrFeQQsPDD4dDYcbDr+PNqtWovnMgPUpPPUnkwGKt8B1EbvZnD4aR3j1o4mTbBAH/GgeeFUNsAHvLQz/X1tfL5vOntAr2urq5avzEb25MPyGQ8wQMYnWfhd4jsvPPDsELI8f8Mh0Pr1eSepAdGLlC0JJ2cnNgoSYyfj4IZL+q5BETotJJ4LoJ30pLMmCSTSZs33Wg0zBkAG+EAIWYAL7InMCqsRaVSUaPR0Pr6uh1EjP3l5aUODg7U6/WsNY96GhldqVTSF198oUQioWq1qmq1OoVyrK+vmzPb2tqy7I53AwLkM08MEWtXq9VsjB57HGGGarVqKnUwgakjjkYjPXv2zOqudCzgMCORyBTLntr3/Py80um0wb7+eZBo5MDjYHke75Co4wMpEtAR+EC64e95z/QGSzK2vCdeEfhiAKWHdjeCU0kmZkSww94nKz4+PtbJyYnZB0mmOw6ngEyMd7GysqIXL16o0+nY8AZJBv2iTeBJQMCgICEEhcC0rO3jCyO+t7dnWtSBQMBG5xaLRUNgksmkvefRaKRWq2V8HUm25nd3d1pbWzMZZEpoc3NzymazJj360UcfmaNgPxwfH+v9+/eWmTL7mSDW64rzfdIDcY4afrvdNrSBNYCljL4FiUo+n7ckjfftWfzs82KxaKUc6trsBZwopad+vz81mESadIyQ9NClMzMzo3q9brD55uam2R70JxCjYa4AyCMoI+1zZOuFQkGNRsNIYSSJ1OFx0tgg0DyItnQXzc3N2Zn1mhtA8h6dCYcn4jV+MNFTrm+VUROZ+eiSheWmiF4XFhYse4DINDs7awtH5s2mIrMjwgUqJ1OmbktfKb+PkfcwEz1qwMY4bRyZb1+SpP/v//v/dHx8rLdv39rn+rqo9CAHR5RGsOJZ72RYrNfi4qL1KAJd4dipH5NNEpFS4wdC8vXPmZkZra6uWm2MbAVHQhZIL3MkErGAw/+cv/iO3//+98aerVQqfyTZx+/Oz89rZ2fHiGiwP1+/fq3RaKSvv/7aoFPUkIBtMabUVqkN0WKBAAAZN4FZOp3WxcWFDg8PTcACo/v69WsNh0Otr6/r5uZG79+/N+IOmdJoNGm/evnypbVRAWPV63XFYjHV63XL8Bmc4iF8jG69Xtf+/r6y2ewU4tFqtXR4eGj/T+DnjS8OjH27tLSk8/Nz9fuTOdyw6iORiMrlsv05LGbpYcTj4uKiXrx4YSM6GRQwGo0sqyPDYq1WVlbsvymZEHB6B8o+9TBhMBg0ONuPFWS/4njZTzgEGO88syRrNRyPxzo9PZ2SCPVZGudLkmXSwIy+Xz0ajU7pcTcaDVtvX3/3gSR7w0OalEFACKhjIjPq1bBgZVPXzmazOjs7Uz6ft8CCKV+9Xk9ra2vGVqatiJosPeL8m30zMzNjXSOeNAUvYW5uzrI+kg9g8larZckNZDmvD+BnJGCXCVZJtEqlktl59AvYe9vb20Y+K5VKU4qJjUZD+/v7SiaTurq60vn5uQ3hgMwJIxthE/xIpVKxPc8aEXAhRUxGT5mMdSQYpaTCXu/3J4qUOzs7lk1T/tvf31ez2TSOlTRBNwhA2Su+04L/9wEiIixA/aCloGV+fThTHyJ4/qnryY6aSEF6mMpEjZLsgCwMXVQiIRR5+HuITz7S8AxS3xICXIJTYkNXq1Vr/SHrBuamFQCVHrRtqdvx+UT7jC08Pj62TQOszv3xOzA8MSJE2o+ZymQ8ZFtkq9wLNW1P4AA+JQMCIiWDnp2dtTaJi4sL26gEChijly9fqtPp6PDw0A6jV1gC+WDdWev379//ERmOgIHNuLW1ZSxSvw5MHCO7hwAEk/Xw8FArKyv2/PzM7Oysta/kcjmD7AnS6LHEmKJhDksVIs4vf/lL/ehHP9LCwoJ+97vfaWFhQXt7e1PlD0Z93t/fq9FoqFQqmSAOKlRXV1cqFouWUTDt7eXLlxoMBjo4ONDZ2Zmurq5MEe329lblctmcD5f/735/MliGKH00mrBgvUAEhnx+fl65XM5gT/bpysqKEeKy2azdG++V9QLy5N2zv8iYfW2bfwi0CSwgxhCER6NRU3HCAbC21OhxggR5vobK2YZQg1PBiXgUCr4BnwXTGxsEJ6Xb7Vqgn06nDQ0LhULmqHgPOAhms+PAvPjJ+vq6JSIgWNJDqyU/hwIWpDPgWkkmCFQoFCzBoN0KQur9/b0FCdgb2Pxwfbjvi4sL7e3tTe2harVqfcxegpNAEFvH/TYaDUUik+Eg7D9qsNFoVHt7e1OtQoPBQN98843G47F9H44GkiDiL+l0WnNzc3r37t2U4wE6p2vBi1nB0/GICvsOxjb3K8lEoHC8/D5BG7bafw9DL0BQ8RXss/v7e2WzWV1fX+vo6MhsOz3QkCBBF0gGCUA9gRh+FaJR8DhmZx+UCiVZ7Z9y3X8ImYzIjjobcJevW5NRU+8iWpUeWgkglAFhIUaASg/Oiv45WJhEsRgiItdoNGrfhXGhtoVMnid5UNSn+b7T6eif/umfLMPlwrhBLvDEFJTOgM7Y3P7l4UCB6GmR4h7S6bT1BWI4cMhEzzhvMp+7uzsdHR2Z4Ec+n1cikTDIE8gMuBNm+NnZmdWRaaUiOry6urJ+aGmane9Rh3A4PDXNieAE48L9kY1JMqEZnEgkEtGPfvQjFYtF1ev1qdY57oe9RIaDQcfAcgii0ahtej7jX/7lX5RMJk0SFWa/JGOnBwIBNZtNVatVnZ2dWV8pLUkvX77U0tKSvvnmG62tremHP/yhDWQhu0eVjcMoTcQWSqXSB5nvrCutLAsLC7q+vrbBADgYBptQAoE1DuQGtBkIBKbaX+hzRyCFgAGHyvcDx3EOPeudTI797J3vwsKCGSACdN9NAKRONkVLId9DEA+LHiWnQCBg5Q5pQjYiuOI+0QVAZY997uFNSbY3KA9heMvlsmVnOIXRaDJOEdtEiSKdTk+pgbHHyeTL5bLq9bq2tram6qbYDk9uikaj2tzctACfEbvcX71etx5w9LrJwtg34/FEme6rr77S7u6uqtWqISrYQc40rWoQztCy9l0rpVLJ6tEE5Gtra1NypXz23t6eoXTwBarVqiKRyFSZstvtKhaLGbPdd8VUKhUtLy9rfX3d2q3YR+zHw8ND6yxhLxJsSDK7Alrjy1LsV3wP3Bz2J/aTIBU+hSd4JhIJg/S5PErEWSLAITHw/CgQL8qXdMAQONGm6s/U0dHRlM37964nO+q1tTXTi+bAUJO9vb01OBtYwKf5tK0Q5QEvSTIxeRaQugXEMNh+GAUy1GAwaOMTyVBwjCw2Cw6ETW3O12IhZ0iyjECSsbsXFhZ0fn5uDslHhX6+sTdcfAbPTL8wRubs7MzgcOqibC4viUpEx+aAaIVho2eQrP7Vq1eWObDOZJ6zs7Pa2NiwLIyaJAzWd+/eTTHQCVJgj+IgPdMTWDGVSun29nZK7YnDAOqAuMflH4bBc4ioieK8CEJgxs/NzalQKKhcLmt1dVXdbtf0hQmg/ESyer2uRCKhzz//3CJkH823Wi29f//e6tfj8aSXv9/va21tzUhms7OzVsOCrVuv11UsFq1O5ttHFhYWtLy8bExbtLc5jMPhZGgDGue04fmRjmdnZ0aqxNihM0Apg84K0KxGo2FkGowcinOcRw/9knXADeB8ADHzTNTmIPbwOexpgnA09ske6Lrodruq1WqmP8D3wPx9LF26tLRkTpqzjARnJpOZSgQotwQCAXOSvBPOEVrsOMqLiwszxgTyELtubm5s8AIdDwQYELVw5JVKxXgB/D51WJwC3AdfI/clrXA4rNPTU0MaGL9LEMf7wFn4fbi5uflHZxOCpP8OdKW5aDtEFAd7DbwMzwM+CFPfQGCQLKWVkpIiztwT4VKplDqdjs7PzzUajSwgp6UKdT2QHI+ssvdw5P4skFAkk0lLyrDFSAjTTYBTvL6+1tbWlgXWIJHYP79GfA991qwbiRY+r1gsTvGL8B34AWwzyJUfyuOJrf8hGTXR8OzsrJLJpDk5YAAPpRGtsPhsPFJ+MloMwdXV1dQoOeoBZBDUwxDBHwwGev78uUqlklqt1lSvNbU4hPiBp2jPApaD4chB9FcwGNTq6qoymYyRhy4uLuywIFCBY76+vjaylieH3d/fq1arWQ2Fw0ONDrUtGKPz8/NqNpuGWFCDw1g1m0391V/9lV6+fKmf//znJoEJJIVzIJAiYILlDWeAQ4WxBTr0DPTFxUVtbGzYgYQRSxaCXjSRtQ+meP5AYKL97Msc79690/X1tdLptDluDm4wGDQin1dYKpVKikajRiaihOJbHz755BNzTkCiIBPAXzh3DAGZgR+neXJyot3dXSPz/PznP9fh4aG10l1fX2tzc9NgMO6fQCAajWp9fd24GWQYZDFXV1cmU0sNEj7G0dGRkePYJx7mI1hqtVo6Pj42FjDEQKBmxBV4Pzgr3lur1bJ+T0mGDKVSKa2trZkDIEMmoPLwIOWudrttrTw+K2IPeoQhEonYqFFKIziobDZrbUI4EzJ90ClKaGT/OGoCJaQqQTrQ3cZ+VatVcy6QqNhHgUDASKm+5Yo9RhBULpd1dHSkXq9nJKt2u238APY+NoxAjcAHrouHnxG+IVkhyMGJQBjLZrNWzpAemMTA+H6PMbCj3W6rWCxaaaVYLNp+8ERCEgNfB4eM5ZEXTzikFkzJK5vNamNjw84BmXE0GrVzSOkRn5DL5aY4McPh0Jyxb/u7uLjQ2dmZ5ubmlEwmDXHlfPhEaX9/33grJD3Ly8s2I5yAzqOom5ubU9MLLy4uTH1QmqAu1NAfjyhGLGppacnOPIHIzc2NicY8JimCMj/lerKjRlCAMWV3d3dTNybJ6me0p7CpYJjyktjAHA6izj+6ufDDGDvpQeyE2pmX34Pstr6+bg6LTU8ESKN7o9GwTMtHc9ID+5raWiAQMMYgkBJOliyLjMzXtLkvZEtXVlasXsv/r66uamNjw2qK0qRGd39/b5EhxjSZTKrZbOrLL7/U9773Pa2vr08NBmB9/MB6EAqyIlqFMILAyhCbOEA06dMn6oMrsgsifkkGE1JrHI/HNlrv9PTU4KtPP/1UpVLJ1JHISFgj4PGNjQ01Gg29e/dO4/GDJCQoCffe7/enGPtkU7xr4DyMD9n6YDDQxsaGfvCDH+ju7k6/+MUv1Gq1dHt7a33Z1OHy+bwODg5sjeFeYGiphUmy1jCCTP9uIM9gwDDcQLIQ0crlsmUKDCzY3d21AQC0SPnxghgNjBVaBrQ50hqFEfRqalyxWExra2vGGqasEY/Hlc/npxwihpr6MmedgEOSGUbvvFDbkx7UBAkqWFMcoydqkuV5Bj08DxCMYDBorUCDwcDY2QT2oBxHR0dTPAqCGVAX3w9MsAPxkedEbIdgHbvHNR6PbS47e5Pgy6MRrAsBEF0A9XrdMrBQKKS1tTUjCFIakx6Y9L5nd3Z2VltbW7YngfMPDw8tiQJh5JkoV41GD6N5QdcIpHA8XuEsEolYdwNKaOx90DzKShDx/MQxAiHWwr93+BY+AEwmk6rVajo7O7O9hO8hAaG1Fk5FOBy2Ns1kMmn2mSlh5XJZ6+vrev36tdlw7PrJyYkWFhYM2QmFQkqlUpZc+uz4/Pzc2kTxWXBeQIL8xdo+9Xqyo0Y44+bmRsVi0WpCbDwcHi8UqIDNxd/xc0SCCwsLU9m0P8DLy8tThXsOPdkAG46aHUxEIjmi2WAwqHg8bg41lUrZyEUiHrK+aDSqTz75xA5HqVQy8lyz2dSbN29soAf36qMrXgLrgFFpt9taX19Xr9czQQxqYUhdAvNjwHFogUDAsudisaj9/f0p8g7vB1UynDHGAZiLd4FBhHTh36GkqVokBxXYmANHLQ81oIuLC9u83qlLsqDnX/7lXzQajSw4IaLlsKHKRQ344ODAMrazszODl1lbZiR7wQGvsAYPAZja10o/+eQTffbZZxYMSA+ERlTBMPI8B0QTWjTYF5QveN88P2xdyD23t7cmh0i2jxgLGQRKaJyFwWBg2sg4J/bc432HkfPEJoIwnArPRPvJcDg0wRvOGIEX6APnnrIJ5xgBFHgbODrkbFkLyk607ODUCLwlmcAKpRtfHwV5abfbKpfL9jPLy8tKpVLmTIHzgaXZiz5zrNfrur6+tho53AUCZdaOgAEInMAkFAopk8kYTI/BLRQKOjk5sXdzcXGhSCRinQye8OSFjpaXly0QoxQ2Nzeno6Mj68JIJBJ2Pvy7Hw4nEpdLS0um70AGSJA+Gk3EViqVigUlBDQ4JNrNPJcmn88rl8uZgIdnzHu4GuQnkUiYJCf/xONxm0KFRgNBE4qBDIPhmpubswE17A0CeLQfmHqXy+VMwCkajVrQiO3CDq+srOiLL74weV3aqUAykbmlxs95pKRCQAD3J5VK6c2bN5as0ibc6XRUrVbNThHUQozDj7HHfF3837u+VR81ztHXvCQZkSIUCln9mR5rapmercfvELVS7OfzR6ORRWEQAMgwmIEsPWh9A1XzIu/v76f6nRFm4MCS0ZBlQuqhBWVlZcU23fPnz+2eYXD+5Cc/mWJR82+f3XNfwHoYDox9o9EwoocnpBHVSZOs5O7ubopg4hELvpdDWSqVtL29bVATc5Pz+bwZCT/6ks3V6XRM0vFxrQiVKD+Agl5I2m7QycaIUNc6Ojqy7wViTyQShlAcHx+bkyBDJmDCQFH3JkDc29uzqJ6aIAeI0gzEL+6XsY/dbteCLODb8Xhsg0AgibCfz87OplrbuNjXiDZwFnK5nG5ublSpVEzwIBQKWZ+5r8V7CJM9g6H0rYsgRDigubnJ6NYPdRl86AJi9FkLtUQifwIdntNzDegfpYeV9wkcDirC/WO0kPr0Z4G/I6OCxAOpDLSNAAfUBoEPMj4+LxqNWhDA+bm6urLRhThbprFhcyA40W9/cHBgz4zzpbUG1MbPTd7Y2Jja19JkdOH19bWdI+wIzo0gvN/v2+jVYDCora0tZbNZq2eOx2M7J9T3R6ORyfoSJIJUgtBRJgoEAhaM++wZRT0YypAnuV+UD6VJCyC9xL6/G9iYmjbrBcIC90KSEWUha1J+u76+tjLf4uLi1F4GAQMN5AyzD/gOkD9q3AQ/OHTeHQgF+58Mu1QqTSm/EXSDtFD+AD0mAPeKZMlkUslkUtFodKoezTsngB+NRqaDcHZ2NoW8PC65/lvXkx01RgeSz2g0ssgMCIqDCuz3uBZKRoeBA0JYWFhQIpFQq9WyHmAi816vZ7AzBpsF8DUxb/g87Mgh7vV6U+IrvGSCA+7p5uZG7969Mwc5Nzen1dVV7ezsTNWcPTOTfxPB8fm8MFTaPMt7dnZW+Xze1MEkWRZ8dXVlYgEcPn/5Ojj/jWErFouq1WpG1EGKEYKJH6PpFaACgYDW1tYsA7+9vbX7JBrkvVMzGo8ner++h5Uo2+unS9Lu7q5FmTzT8+fPrb8UyJXna7VatpfIEiuVikXv0sQYPHv2TPF43AzSYDDQzMyMarWaWq2Wtd7RdkUrGAYTiVMMBo7j6upqCiHgWZrNpu1lT4bx+gK8VzL4+/t7xeNxm1kLSYl6L0GGh4ohJBFYgW7Qske2wT54/N/wRFCCYl9i3NlzOAEyJYJtgkYCEZAGAiGyN4w3gbQZlj/UXBH26HQ6pgE+MzNjet7cK208TE3CWJ6cnEypy/kzAPJye3urZrOpcrls/a84XIJYShQE2Gg4v3r1amqgCdwZzhROg7IDMCbvhHuZnZ0obxGQ5XI5Y4uTSZ6cnKjVapktY79iO8ksH3dAsE6cP4Id4Hf2oEd++F1JU8I2QNLpdNoyWoZrkLGzX+/u7oyYCDyO5jpSo9hPL/SEzgMjZ7mP9fV1C6wJmiGnAQNDAMtmsxaMEGjzPaAbIGg40V6vZzK9iUTCsuqbmxttb29rc3NT8/PzWl1d1a9//WtDUUgAPD8E23t8fKyNjQ3zf/gmHLEkI+vCwfC91ZSqYrGYBfH/L9eTHbV3eGS0ZKpsfpzdY5lADBaZiKeuj0YjI375qF56gMYODw+1uLiov/zLv7Q6sz+44/HYWoQkWeQuyRxVq9WylhbPWAwGg4rFYmb8eem//vWvLSCgxzcYDBpszX16p7m4uGjtUbAOOfjAqhxQshEkEyGQQYTybQc8o7/8WsX+MD8a2Mu3hdGnDBM2FotNRYmw2WllgvXqh577lizfb+nH7nmHKj2gHWQeRLgYbyJdGKIYUQI4T0zk+am9Ql6Kx+N69eqVLi4uDHrlcBcKBdXrdZ2dndk9cK9kfMViUc+ePbN6P2QVr0jk5WPZN3QJLC8vG0ube7y/vzdCDVwDFOqA0PwzMvrQk3mkSSaLDgH370lhfI7nMeCYgUzJonAoZBxkgbxT2MIgDZ557REfDBjwIfcDRA35st+fCEwwe5d2LaZijcdjG+nKBcwOHM+fAdHDf2BfcLZPTk4sc4LJjTGl1i/J2mOePXumy8tL6/Ntt9taXl421TeCc96Fb+cEficA9MgiJST2GGRBbNLs7Kw2NzeNWwOjmcCQaWDYRwhLtFXd3NyYqhaCJGR49JpjQzxx7eLiwrooPF+DAJAyG3XcTCajFy9eWLDAVCtKBxBw6/W67QlEVkCmKMEUi0WrlZ+fn+vq6sqEie7v71WtVs2WRiIRZbNZzc3N6fb2VvV6fSoQjEajtkfoSb+8vJwaMkQZs1arWb14MJj0rtNpQAKEJoE0CWROT0+1t7dnaCBlGMpuvG+v+00SylnwXRIgz14nY3l52UqY3/Z6sqNOp9N2ADCUZCC+7YB/Sw8Ow0MHkvSjH/1IhULBmv/j8bhFwNQUlpaWbBJPv9/X7OysarWa9vb2rD7ga4tMtuHlEEGziSGb0XdLkMEmoBYlPYibYEz6/b5OT08tcwcGAzYejyctQmR31CQwmsD4PJ8nEgyHE2lS1oc/B+ImU5T+WNoQqCiTyUwZODI+WMCzs7Mql8uq1WoGKZNRV6tVraysWBROtnRzc2N1cMh+ZKLSA/xL60a32/0jrWSf3XnSCAgGESebm6ETBBC+rsPn+DW4vb3VP/zDP+j29lYff/yx1cIpfTAb2l/sz/Pzc7XbbR0fHxvJKpfLWQ3Z71uMGiIX9XpdhULBnGkwGLQAi0EprAMGA3hwNJq0eJyfnxvqhBQrLPKLiwtzAjhZMvRebzK4grnBNzc3RnBcXl5WLBYz1IHyD4aMfUSAgBMnO8Uh+x5rBs7wPkHVyFphP0P+kWT3AVsd+I/n9gGYLxcxQ5w9T/CdyWSMCBQMBm3cJUEE9w0SQvsU77HZbBrDeDQaaXV1VaHQZDTs73//e8voFxYW7B5865tHRrxGuA9mLi4urG2HDJrMl97hbrdrPc8EeAjwIOaCeA7ZMkECdoQsHaY1e+Po6Eibm5uWSFFuWVhYsGlXfp2o/XMmUOzb3NycEuWBkId9hUfC75PVotdwfHyspaUlc8K8W/69s7Nj6pToiofDk9ZSzh7nAM16smu4RtS8m82misWi0um0OWnKJBCK4c7wPvygGa7RaDKwB1tMufTy8tJQAVoIh8OhoaTYUPxIv9+f6kxizXHc/y8OmuvJjpq6J72+kDAwSD7zoZUE3F56GI/Z6/VMxQbnSMa1u7tryjyLi4u6vr7W27dvjexFnRN2rzfit7e3Ojs7m5Jtg6jAQgId8nvcGwYQWI9atRcgmJmZ0dramuLxuEXKt7e3Oj4+1t3dndUSkT8FGqMfkVYbYE9PGrq6urKedCJfDtu/9XKpl/jaPhEcUTWwGkMPCoWCqtWqORccBRsKY7i0tGR1ewwrhhpHS71vZ2dH33zzjbUJ+b0gyQh0q6urBqtzf7C0QU98mxY1OU/S4h1hKBgn9/vf/95KKOl0WtlsVsvLy9rf37esJZlM2vQeMoXT01MTeIjH45bFEKBEIhHFYjFrYYpEIoY+PBanIAgkmwbVQDCEbgmIU8PhUM+fP9eLFy/M8UBOwUGw9mgR9Pt9ra+vW28xpYTT01ODJz1Ll73IPofIRPAA9I5jurm5meoY8P24vgTAOnGmCZZnZmZM3344HJogkUdLePe+rRP4FngZCNtrVHMuGWhQLBanmLOUxkqlku0V6qGMJgUNfP78udrttkGRdDrgpGiJKpVKxsCenZ0oAxKM8XOtVstkUEF+cDLBYHCqF3tmZsba91gDyk04PqZJ0R5EnZgSAnaXs0owXqlUrA7K2aOsgaPDWfOc7Jf5+XmTwPS16IWFBWWzWYOP/bCTUqlkrGaGmrTbbV1cXEwhaqB+2KNUKqWDgwM7+77li72LVDEJFa1dvsefGeRffPGFPQv7CTtHwEnSQfmn3W4bJwL4vFAoGPJ6f3+vUqlktoPEJRQKmaiQr3FTR2+32zadkZIjnQSP7fl/SHsWdWKMKpEyzkGSZRU4CuAgfobNSi2YfyOWwaQpDmU0GrXRjcBnSDcCr+CwibaQiYzH40qlUsbIbLfbxvpj42OkcNJAf2QZQMDBYFCbm5s2SxrDgQBBrVYzqcfHxpIXBX3fk0B4aZVKxXSU+Xt/fxw0nCUBC8aNzcK/PVxNJuzhT+Ao3itBDMiFj7iJGvku39e6sLBgU8zW1taM7MfvSzJNZQ4RBEH+32ckZKlra2tKpVJKp9OmYrawsKBcLqd0Om2ztlutlgqFghHDkP4jkyNw6vf7WllZMdUxMhlaiCDwkC1JMkOMVjvkHpxmJpPR2dmZKV/xLDh31laS1d8hbiEfG4/H9eLFC1sjlP8IiCVNIUD0k/NO2CPpdFrdbtdanTxfA3Y0wSjwLqRKzhGOAunNx8EW/83+8YG4L+sAuWIAPcQoyRTjCDB6vZ6KxaI5ofF4PNX2yfnD7pDBwwL+ELGSSWWs60cffTQVZPBzfAccA2qxvg0RMiV7Ynl5WcvLy0Zk63a7ptN+eHio8/Nzg4bD4bCJffj2JzJHYGPqx9TpA4HJJDTqrOwh2lzr9br1Bu/t7VkSxbNTf2f/sKexcbRXkRSATNB9QlAIxwiVNTSsWR/qsd1u19owPSPc24Fer2fqfXz39773PeumGQweJtvRJ48/oP2t1+sZo5sgB1vkAzbsM3YuEAioVqvp2bNnGgwGqlQq9h4gIZMc0LIFmun3PvsHroUv5XIeUUyED0SQSukFe4Q631OvbzWUAxYkBgcDCaTt6ztsCA8Pwa5jo/F3iCqgl4qBg8JPdtDpdPRXf/VXGg6H+vLLL43wBUWfOjKfgZGbn5/XmzdvDE6khQvnvbKyYv1xsLPR86UWCgscR+ZrYpKMmeuZ7LxgygPI6D0m//D5OGHPZmw2mwaDEdWjt83GYW14Xg+vs5m96pg3vvwZTFsCDZjP5XLZaoTATtyf5xZ0Oh2b1kUkmU6ntbGxYVkVtR9654mgEY0AamPwPHUfSQZJetIZ5YdcLmc9jxg/DC6/jxGi/sqUHSJ9nBB1SboWCHJwitzTYDBQOp3WwcGBGadoNGqBJmUCAhrq24j7MC4RB7S4uGitiuxTfm4wGJiwD2IMvGuvMkX2gDGhvsr74L4DgYDVLjnDcCzIOPg5Xxdn7xAQYFAJuoDlCXKA+QlKkfgkm6MmHQwGdXR0pFarZSMyPUEJDgB7k/cDCZLPoYXt6urKMhw0m/06UGv1xDDWiozNQ8V898rKiu1B3jHnKxQKaXV11VqbhsOhsbsROCFgSSaTpjJIMA0ZCulc5rSDCkiaWofBYNIrToIA+57S3Lt37wzJa7VaFrhjUzqdjnZ3d6d4CO122xwXI2SxOfT/z83NWZDSbreNk7O6umrfOTMzGR40Pz9vE9Du7u50fHxs9oved/7tnZ400StgT2GvfOJHuYbW3g/ZNr9nW62Wfvvb3yqZTKpcLms8njDhM5mMnTeelYSJkqm3k8DpBNtk8OFwWB9//LGNNMZmVCoVFYtFLSwsWNmj3W4rnU4bZ+op17difRM18//0M/sIlVqPJ8GwmRlVJ00ifdiWGCAiDlob6EOklnpzc6OjoyMzmGSDCwsLlvkAu3vIPRwOmxoUESGGKZFIWD0ZsREffYfDEylRom4yI3R9pYnjB4ZcXFy0g0C9hSwBZuj6+roODg5M+xlngRHh+bh3dGypJXGvHnrxHAAySrJpom8gW3oIgd18rcpHkZKmGLessa9VQnKCQJNIJExc4NWrV0bCGg6H1joBw3ptbU2rq6tKJBLKZDL2XKAHGEPgY5y0Z/mHQiF98cUXNsQkmUxOKQ6xlrwnDhsZBG0d1MW4X4Ibno/PgzXbbDbNaMPypBcaMiDOkywQtjxZKefncdubJymRweVyOdVqNZO8jUajSiQSU2UcIv1+v28EOFT0hsOhwZqgNvA26EPF6bXbbeuvXVpaMgEOgkIMKnAkTGCyG0lT64pdADXg37QnLi8vKx6Pq1ar6fT01DLq6+trU5eCqEpwBixPVolzxxGhxoXNgvBEggAUSjDGvvNlH0ovKN55u+XRLlAKnJ70kN0zDhF7QhcFSQABCe8EUhv3SFZG0IkjQcdfekBsmES1tLSkV69e6Te/+Y3xa5DjHQwGU8pacHrIJn0iAeIBIhWLxewc0J2yvLxsJUb2MUJO0oPDRRVNkokpEfRRV+e9MygHFrcvNXjSHiRZT+rlfHMRiI3Hk64R37VDQAonxiOqsVjsT84nB8bmuwhambtAgBuJRLS5uTnFY0JBjnt46vVkR10qlcw5wEb+Uw9BVMjBRxqQxnpgHzJziDXAQ5FIxHreMGT8LHVTMhY+j00CdMJB9JBKJBKZasgfDAYGn7VaLXNkRErMG/VEHDY5tbZer6dGo2H1E2rL3qjFYjEjKcBWlGSZH5/NM+C0MdrLy8tWv2XUJhAqhLFgMKjz83NzDhCxlpaWrFSAYaa2WqlU7F0uLi7q7u5O0WjUSE5exEOSDVTg3vkujDF1PqRWQVA8Y5k+zsFgoNXVVetDnJmZzCUH1uQ7YXoTzXvDi5EEhmNPUNfsdDq2jziIc3NzJmvqa1pkqmTE7PVms2mZGPcDd4H3wzMgFoGDQDOYvQ2iks1m7fCSSYEGsAfoc4bNDjT79u1bNZtNm7Gby+VULpdNv9o7VNYNRixoBpwQ1LrIIqUHZ0K2BspxenpqZCkIPhglsnqIfDjQv/mbv9HPfvYzI4UieEIgT3CPVjva4Dg2SQYh+z2E4tN4PNazZ89s5Ch8FDo4qC8j80qAyfnlH38OPR+D1jYCtUajoUajYXaMNkDWbTQaGU9DenAS3k4C9VNvx0bQukN/db1eVy6X0/39vWXLlPhAQGJ/GMeIzcG+AfmCyrHPGQqCdn2r1TJNdJ67UCgYV2I8HqtarVpAenl5aQEaaw03gfPW7/dt6A9Oj/dHEIazIvAAxkb3ATIkanmQgnkWAtFOp2Nqend3d4bMcn3IEfrE8vz83AI4AjbQwg+hSVwfQiR5ByCFvhd8dXVVrVZryvfAKn/q9WRHzchINuSHHoCDgCPmYYF3MBAYcBjIDCbgwjjx+7RQUQcgiyCTJfIlUgcCCwQmyk/FYlGJRMKMsvSQaVGLLZfLuru7M4iUaI2Xx32zGQkaYFdeX19bBkLEBfuYyBfyFnNqYfKiMSw9ZMOSDK70US7GhOCEjYcThBDkyW8QG8hwyH4xJAjV+9ooWZB/LwyyQG9XkkHwBF4EKb4EwLqTDYxGI6stEoBwbxghDBiazD778ogDRv34+NjU5CBKSbKggHsBevIcAiBMOBAbGxs2/ABNe2q6kkwdDMZrIpFQpzMZLoG4A5DjaDTSu3fvFI1GTUULcsxwOPygnO1oNGFJI3hDQDQ7O6sXL14oGAyaQWVgCbC3Z5d2u12dn5+boeTvIEf6YAdjiQPxTNtwOKwXL17o5uZGV1dXSqVShh6BLrFnQWsk6d27dzZOFH4Gxp1zS6bq2728o0NNEGibIJ4sC+e0tLSkubm5KTUrtJmPj4+tPUeSlWB8Hd7vweFwaLwDng3Yu1KpqFAoaHd317pcSF5KpZJlWnzuY/sIUnN0dDTVh+1ryQSI3W5XKysrVsYgsGO+M86BvngYzdgU3mcmkzGImoCFkZcHBwd2PpDvZS46NohhGNSoede0WHU6HZ2cnNh75GyRHFWrVZt+R1ACg552OtaqVqsp9odRt7Ozs4YozM7O6uuvvza+wMzMjPb29ozgNhwOtb+/r/fv339w7f3FGej1ejo7OzObwdqHQiE744+z9cf/L8l4BqiSYZchqeHDQAhZaybMPeV6sqMGVsGQ8GXULzHwGGTPpCYaOjo6UiqVUiwWM/Y07Tl+IaSHKSTeWc/Ozhq5RHoQNpmZmbEsHpYlETCbjEMKNE7bBFKeOMPBYGA1QCLui4sL642m75DvoFaxuLhoGRDCAiAFGCiezWveQmzC8Pm1mJmZUaPRsDoMjpBme7/2GHo2IS0ytJYA2ZP5UfthA1UqFZv4Q+vK4/YKWNYM8QCORkoSh7q6ujpVy2btIaqg1873Mege5039kmEHZAQnJyfmYKj5zs3NTfUtA23hGKj3eRII7GlQGjgJ1FghX0kywwbkBlMT6M0jDJFIRPV63UYR8nOQRyTZetHO0Ww2TVWKcoQn7rBHbm9v7ftjsZgqlYqJt/gWtMdGBAfNvgUJwSGxjqz57e2t4vG4ZWOeMOqJdTyvJPs8vg+InlGLvCfOOnVMIEPOs3fSPAs/Q8Dr5UCxQz4A8QRQHGu73dabN2+sbQsiIkEo3AECfezJYDCw0hq1/KWlJe3v76tWqymTyVjtGXXD2dlZC47D4bANbPClMv9c1I4hM/l6Kx0SPqnwz4q4B0Ex+6DVauns7MxKbmtra+bwPI+FIB8ewIsXLxT7wyxw2hszmYzG47GOj4+VTqfNZpMUMMkP2+eDL0nG4m80GvqzP/szff311xqNRpbQgLaQrRNwMqwGmxaNRq3FLxAImD43iCJqf4/9yJ86F6xhv983qWaCdgKvDyWj/rOxL2tra9YCB8oLouXRUv5+PJ6QLkul0gc//0PXt2J9e/IFBzuVSll07CPh1dVVU6ghOsJREEECk2UyGWPGkbXSyuAL/WRDEHAgkXB/t7e3Bs9imOLxuG5vb02nmkxyOJyIXiCNSHvCzs6Ojo6O9NVXX9kz1et1BYNBmyZFTYuJWqFQyOoT1B+oycMepD5BhgIBaTiczDjG0QNj02Neq9XM8HAvEHAe0/sh+YzHD6MJ+TyyJg+N87PAkZC6PAP3Q5szHA7b/bLpQqGQ6eZiEDzL2DsEMklKAo1GQ4PBwLI/JEXL5fJUSwNZkjQxAGtra/r000+tfYr9RPZNwAgBEPIgUrOgA0zOkmTCJJDfCP5AJXBCZBh8thf54b9fvHhhhKqVlRWDu1A/Igs6PDw0eJUeXnprQ6GQTanDgFQqFctcPEz8oYugjfYkygdA+gSxlLIIOldWVmyqGEZ9ZmbGsiKPmJEhc1GDZ38FAhNJThSpMIBeTx7InnvmnRPEstewHUDwoGCQR/k7nBu/Q3bNHvbXaDSy95pIJKb0yH3LYiQSsQEytVrNZFK571gsZkQp7CPfvb+/b+Nd4/H41AS28XisWCxmPf3sQbQJ7u/vp7JOaULcPTk50dramiGAEHvhqvC+yUxp1SQ4J4DvdrvWzuh5HNTj19bW1Gg0LLBDI5t7WlhYsFn12OBUKqX19XUVi0X1ej2lUim9evVqyvb0ej0dHx+r0+kokUhoe3vbGOfYSHyAJxVKMnQONjgtVz5Z4fpT2fXjYJr/fuzkP5RF8+fz8/PG7CeAJOAnSJFktXZ8j6Sp7pt/73qyo8bw0eeJ06Wuwo17thuZkI/aJJnjW1pasuZxiv3n5+dGLCCiwnkTgfGiE4mEGTBfw8Rxkn2SxTDmjoNYq9U0Pz9vPbSff/65Zmdn1Wg0jLjAhTHHsRFFA1/7zINIC9jat/wAp/KSUI9iPcm+5ubmdHx8bFO0tra21Gq1rNaTTCaNaENUCDJAzyVZoy9FRCIPU8SSyaQ2Nja0sLBgMM35+bnBR483O//PfVI3u7+/N1Z4MBi0GjqHFjUgiG3Hx8e6vb01dajRaGQ1VvaO3+SP74X/hpRCEEiARBBFkAfc5JnnQFtAcdTeYn8YuoFxXllZMWdO5oisI0aPgDUajerw8NCyeso9ngcQjUYt88EZb21tKRgM6vLyUs1mc2pSlC9vsAfZh5KszxZURXoQ7JEmGTx1PE8SymQyU6ISGM5MJjMVuPD9kN8oPdA54adxscacORAqGNYMMcAY93o9Q0K63cmYzcf6DBg2An7fQlgul3VxcaG1tTUbPIKTpi3GZ0U+kfiQs6bMQeY4Ho9NMQ94/bFaIJ8fDoe1tbWlZDKplZWVKVGShYUF7e7uWh15Z2fHHCfkVtqgstms4vG4laGy2awNp3lc/kLMAz7F/Py8nj9/bkkBAbckK0kxtIZM+L/9t/+mv//7v7dkhaCMRAD+QjKZtP3lUaVAIGBkRBKSy8tLFYtF+26C8i+//NJkcmGVc+ZyudzUZ4BwkEBwTn3gBdGN/udSqWT35Eta/M6fujxq6PfJv7Vv+G8fDJIU0P5GC22j0VC5XFY6nbbZ4xB8n3o92VHTp0qtiCiGg8wi0pvndX89iYgIiWyHzTc/P28KV+PxWK9fv9a7d+/skPJw1LgeQ6r8m3uAOAMUz6Erl8tTtTDITjc3Nzo5OVEkEtHJyYnBdIFAQJlMRltbW+ZUiNqXl5eNzVur1axmxz2wRjc3N8pms0bqgcHuXzZBDEaw1+spnU5ba0M4HNYnn3xiENnx8bHp85JJUMuhbYZWH9qoyCoGg4E+++wzkxykrsK65PN5i8o/dKGtTJZzfX1tkBGbmP++vLw0IlGv1zPjKsna9NbX17W2tqbt7W3LTqvVqs7OzsxokDH5w8ReQfOZLKHdbpt4DJk0cpw4ddr6QDXYp7w/Ah/qzbwbshImScXjcf3oRz/S/v6+6vW6dnZ2tLy8bJ89Ho+n9NXJgAnUNjY2LCtlD0CC8m1OQO+sQTabNclGMnBKNvF43LJwgjAkISFnjsdjc2ZotQOL095EVsr7xlky5IILciK6Al4bmkCbASysI0E8qNhHH31k63J0dGRZWTqdtnKWFzDivzk/sP4Jlj0ZlP0CHE5rHmUv78zv7++ta8HbFxKJUqk0lX2x34Hn4cUwhIKzgAOPRqMWyEqyfd1qtbS5uWm2lXcWCEx6qnHGXpGMdw/n4eXLl1MaE5T2AoGAWq2WDfmA9xEOh/WrX/3Kgk6vBcHf53I506tIJBL68z//c3U6Hf393/+9EYyp78J8XllZUT6f19dff2212lwuZ6xqv38ymYyVHCgPNRoNLS0t6Xe/+51ifxgbib1gzcvlsk3j84IyoGSpVErhcNj2jSeZPb58EuATqw/9PRf34dtB+Q5KRp3OZNgR88u73a6dRyY4PvX6Vn3U9AwDCcb+MFScrIHeW5h5tFWg6ETE5FVkMJ5Ebowb++d//mc1Gg2L4LvdrkHpLNT19bVSqZQZaO6LKFV6YOMB1/v2GWBSpjy9f//eHAUvYnZ21vojh8Oh9ZpigDh08Xh8qlZH+wn1WDJKMgbW1G8gIH1IdwsLC1pfXzdRBT8Gb2FhQV988YUxiefn56feRyQSMUiZ9iDa0D755BPLxFlzCF5AnKwxaIi/hsOhjo+P7btDoZC2traspxkIGG7C2dmZtWb4ACgcDmt7e9tqzKx7KDTRVkcqErY7kfzMzIwpuxFckAWjp9vpdAzCJRADnkLc43F03O/3TY4TRSfWjvfCe+QszM3N6csvvzTCUygUsvJEJBLR9va2er2eqtWqaUVTT4xGo/ZZsM1XVlb01VdfaTweK5fL2WGmRbFSqRg3BOdLrT2RSNiZCQQCNiCCMybJWPP9ft8MBlkOe4CgmP15e3ur7373u1pbW9M//uM/Wh3y5ubGSHPz8/NKp9N2TwQpBLS5XE7v37+33lQCl3g8bsaaoGx7e1vX19dKp9MKBB5kd+lq4F7Zm5AemRVOkPqYgR2Px7W3t2fOcDAYKJ/Pm/4yNsqLyvR6PSOWgpCwv4G42X+UgbB/EC4J2rzwDeiWR058exVT1oLBidgSQXW1WtXh4eEf1fIRe8IG8vnsndnZWRMn4XdGo4l0JvshlUpZlkeNfTQa6fT01Oa77+/vG7nOlzWDwckEr62tLb1580aZTEbSJOikvQz7SQshZDPKlwTX2B/KYdgwX17pdrsmYrK+vm5+5uDgwLJ07Decm8cOmH3h/5v36/cN6/WhC8Lm69evLaAGiQClwReBAIA4+WT237ue7KhxpNSGPPSGcgwvnwjVk0Soi5E14KyDwaDBGEjxAe/SQsBnPN6ciDZgNH32QeBQq9UsM4JNTvQLu4/s8U9BHKAAwH1e2IS+0NgfBpkDz0mTjZbL5TQcDk1uj8/l92npoc3Jk31Go5EJAoA++P7oZDKp+/t7qwvz90BbaN/SYrG6ump92JIMfqSfG6fkiXxkVRh4LpxPMBg0dS2en8yT52PD+jWWJjVKZDtp0+D3qPH79p1sNms/A6ROO5dvX4lGo1bf9nVQslFq2d7Y853oDyM4sbS0ZEpD1Iylifb9q1evdH5+bjA7BhqiHqRDAiLY2pyhXC5ncqKgTtLESKZSKXt+Ai/0mCGj+fYyTzLy7VZwQWgL8a1OvruCdwOLGFIdgcn6+rpmZmb0/e9/X8vLyyoUCvrd736nUGiiAkjmQ6AH3wJDNTMzY6I+ZPvhcNiIaZ4gmk6nzVlChuMeCYTJYn3dmrV9DHUS/BFI+Pfz7Nkzg49h5XMvkmxfemGNmZkZvX792to5uTcY/fAyaIfi/z1pD1IW2WoqlVK327UAj3XnHXC2UMTzZSEugiscaSQSsSDDs/wf22Z+96uvvtLGxob9LvdGnRpUolqtajQa6cWLF5IepnMNBgNj+MdiMUWjUUPNLi8vbX2wjcViUWtra5qbm5sSMuI9v3z50tpG9/f3Da7n/nd2drS+vm5wOX3YNzc39lmUUVHA9Hb9cQnk34O+/eXPTLVaVSgUMkQEe4ns6Id+l+D/qde3mp51dXVlyisYCQwTNzAcDpXNZnV1dTXF6IaFzaHFYbNREMLwkCsO2I85w4F5hh41OyJUGIT0ykKmIPqj/hSNRjUYDKZq7P6leCNO9sNBBDJFRYtNzDMBYUsyApGfuvX4okbsa9zSA6zvn5u2kbm5OaXTaRtZx89B2iEb9b2UQOOwTQmwqDNC/sB50qYEdIYh9EQhnte3uLBnaJPzsDhrRAkEJ++jZsoHr1+/1suXLw3+p3WDrKlcLiuXy1nmhyOlvx02qvQAE0KM+VC0zH9nMhltbGyYgMrBwYFqtZrBtaenp1OweDgcNuYvmQzfCZs/FoupWq2a6IYkY3ezRhhGXwfGKPd6Peupp+zEPico4kyCTjCJSJqQnfy7oWyDI/PQYS6XM4W/5eVl6/89PT21boLV1VVjOC8vL1srEbVXzmin0zH1PwIBpDYhxXEP7E+fkft6pL/IUkFOzs/PP0i8InihNBUOPwwUIftCQpZ3BjxLW6q/crmcnj17NgXtswc4S/S0855AHhhogdogBh34l3ISAS48AHg1H7IdnM92u23CNTDGHw9QerzX+Yffef/+vfEaksmk1YnZm1988YWSyaQpj7GO2J77+3trh11YWNDr16/t3rjoDhmPx9rc3LT9y9pJDxrlBIyw5ynvEWihMeCTA941olOURvy5+xBPgT/3duDxPuKzmAxXLBZNX77VallwT7mG9j72COuNH3nq9a3as6j/SQ+EFWqQZIM+IqS+zA2S0VCr8EV/X2sgU93Z2TE2N6xiWiu8/B5OEzjrMa0eo0FWTEbB/3OxYf2FOg4Rvmf1Uo/nv6UHx4qxZF14LpxTt9u1vljq9ZBVYMDTioDR5sBAWgAeZzOMx2MzBihZAa+gcsT6YwxhqdMLCUwXCASMDY4zhZxC8JJIJBSPxw1KBNqilgrZzvcR+o1PX643xigoofO7tbVlvcNAxpQbGo2GkaGQNYSYxCHwwyDok4Yc5I0swZE3tul0Ws+ePdPs7KxevnyparVqa/2rX/1Kh4eHxvS8v5/MB2Yqkg/aCDivrq4syyRw4uBC/Gs2m1Z/BRYnMIGUB4fhMWLFvHPkIDkH1NtxmMCEvCPf7jYajcwRQ6ACacDg1+t1pVIp06z2+uZAp61Wa6q84tXpqEfyM7RyEixQFkEalT1H3Vd6kBcmA0YYhfPwuCbJZ3OvsHN9Cxa2gD3A/vDciEBgMrcdmUnq+ZBBIbziYBGaOTo6UqfTUT6fN6b2YDDRnR6NRlpbW7PaPXaD7J9A7TFygm1jSAliKZQ6SGwo6bDXcVIfQhAJftgDlBGxGSgJ0k5KJwDBCcRX7DW92zc3N1MDckisPApLkEndFxicn4nH49Yhg/+hm4DSRLVaNXIl60YwRmnvQ5n0hzJsLuwC6o5ocoAanZ+fq16vW3C0vLxsw1soETzWDfhQ186/dT3ZUXtm7bt37wxOZDNjuGmW5zD6yVA4b+9IOZQ4MzIpYE6yZGqOFOPr9bqi0agxxq+urgyS+dBCk/XxInDoBBI4E/R1fV2jWCwakxzIjss3sXsxeTIAjJDvW+b7kOWj3n59fa2FhQWb7vT+/XsjJxFgYCzJSIHGgP1RUsKQexYnKk0o+UBCqtVqU7NjeS9ErLSo0CcpyTajh6vJUAgYkLqEeMWfsY8QmmEP4ByoaV1dXZkWe7fbNTIg5BQChy+//FKbm5uKRqMmsZnL5UyfneAG0uPm5qbW19ct0i+Xy1ZnJytsNps6ODiwICqVSunly5fWs9lqtfSzn/3MnDKZM/s1EolY4FmpVKyOR2A5Go1s/Knfj5VKZaq/msCYvc/6eq5CJBJRPp+3oAjkigsEBuflzwEZmHeI/kzCL+H8MCmNjAanh6ElECUwAtEC+oS7AVGJKWnsHYJJD5ESxNL6NBxOxhW+f//eslZKRChAfchRA/3DfPbfgdPGpuEk4HZwraysTOn6Yw8I4q+vr1UsFqdqwQSxgUDAEhvYwfw+QRbGm4CM3mpfviGDRHyG8hpOmXIe6Bv91ojuEETQ+vnYORF08Z3YVJASIH1PfAMpYX0pAVLWiEajNpqScw7ilslkrPRGsMOexxn3+31jhmNH7+7u1Gw2Tbfg6upKlUrFUB6egYDTZ//sKWDoD5HN4H2AtFCqQ2yG+0+lUlMjiSk9+BY/pkGiSsl5eur1ZEctyR4IJqgko6BLskiRBYKw4vusffRCdI+jY0P5eZ84KLJYMi7qKPwuIiCPnTV9mCwKhwFj4B319va2Pv/8c2MmVioVvXnzRmdnZzZGMBaLWUZEfRHnyOIDSQJ/XVxcGKRD9kjfNhEwWdN4PDZnymdSf6ZGw2dcXV0pl8vZn3HoIILgYIF/aX1CqANIk7Ft+Xx+KptGxpLPkR6iTzY+f+YhVupBi4uLev78uTkjIGgYzYlEQqenp5qbmzNNYDgJNzc3KpfLKhQKRvCitrm+vq79/X1jwM7OTmaVHxwcqNvtGoscwgbRuY/IO52Otre3tba2pv/9v/+3RqOR/uzP/swMRbPZ1Lt371QsFg2Cf/HihSmrkd3U63WDCoHfyMg4F0xTKpVKVkccjUYmP0kGQC0cA1Cv1xWLxazVyWek7AuCEJw6sJ8ny9FVAKzsyYI4ZGQgQRMIXPkdrwroOwT4DN8CyDP50hPlDWwH30sQKcmCPhwCDhNNfowrpYHV1VXrG0YnIRQKWXDjSzScdV8H5V5gODOtivPGzHvuC3tCkAw8zfpw7wQ93gFi9DkDcGekiQM8OjrS3t6eZmdnbVbz1taWnWVf0oBAS/mBxAAkiSDRn+dMJqO1tTVrY4VXc3Z2NjWBDBvJd+Fo+/3JhLtUKmUoy/X1tX03nS8MQcF+9ft9c+Lo+UOM86gXe75cLhtjG1nafD5v+4U6s29BY1Z2NBrV+vq68ZMoZ7AX8RWRyGSGBGcZ1BR+FO+Z6Vrsb5Azgi4EgDgb+DccMpMasVF0MKDr8aEyxp+6nuyoOXSQc9ik9MKh/gR8SVaBYySqgmwSDoenNFYhuzSbTctoOSA4MhSjBoOBCeUTNY5GI62vr1uEzSHx0o/Az9IkokJVjP//6KOP9PHHH1vt7uXLl7q4uFA+n1ev17M+PSBIWsBwtjwLMHyxWDTFGxigKExhoHyEB1xFMAQxjM0HJBYOh43pCrMVKb7Z2VlrR8Kg/df/+l/VaDR0eHhoU3B4B0CXzGfl/dDrSU/r46gbliuZxXA4nBJnCYfDJoIARESgMxwO9fr1a4uuGbuIchjkE9rgaB9inVAr4+DzPFdXVzZInkzYlz2A9IDybm5u9P79exu9+PHHH1uLUyaTUbPZ1Js3b4wAVa1WjdXbaDTM0NK6d3R0pNFopGw2a7VI5GLJmHC89PSfn59L0pRT3draMv1qerY5d0DQODP/Dn3Q6omLXOx/HPXjfUtp5u7uTrVaTYlEwgwt57harZpeOAaYtQW+Zg8WCgVrh8IGAJWCsGCACUTIoMlavFTp1dWVwf4E+xAj6/W6ZVK+zsuFeEgkEjEhF5jDIDO3t7emE+5r5YFAwNqyrq6u1Gg0rAxFxifJdCU+JBaUzWa1vb1t9mJ+fl6Hh4dGGuv3+3r79q3993g8tnYpkI1Op2NSopFIxGq2dJ9gC0EuUECkc4VgCTgZuP+rr76ayij9YCI6VfyYRpA9AqzxeKxisahGo2F7hklylHtAfyC2kW3z7mD003FAiZG213g8bueW1NJz1gAAyDJJREFUcwFxbH5+3rpNBoOBEdvgcuCoUa+kNQr0hIltwWDQziNwOYler9ez8iO69BB2H0stgxSSPGKDvHgLHUdPvZ7sqMmKgKnYgD4qB/qSJtCojzSkiWFA0N73YZOFApvwezhEDi/RLFGpJzaRJRPd+hqOJzrhmH2NS5pEkQxsh00ejUZVLBYNihkOh8rn82ZYotGodnZ2JMl6IVkPHxDkcjllMhnLCsvlstVTdnZ2LDDhuQOBgNVYEElAZhKS1OXlpTkd1ggHeXp6quvraxtDt7S0pNPT0ykxBFqBqAPyjNIDvAccGQ6HTQaRd07bGUYB483FpuZgEh1DUPvkk0/0k5/8RNlsVj/4wQ+0s7Ojn/70pyqXy4ZW8J55X7w/2PSZTMYCAYwbLT2eLQ6aA5x/e3tryMhoNFK5XDaNatiY9NMCU19eXurk5ESj0YOeOfs6n8+bKIonHHJ4QTNubm5MpvHy8tIgYPqs7+7ulEgklMvlDBEBtYGhTyDI+eO/qfXzLiBOcj9An5Kmsgv+PhwO25hZoNharWZZgyQbHEH25AdzwESnL/js7MwyfNaQ4L3b7ery8lKHh4fa2NiwDA8Yc21tzQINYHhKZ61Wyzo2MLDU89Eox76cnZ1NqZEVCgVzVAxxqFardrZpmcPWYEdIPDhrR0dHtr/Ya51Ox9Czx/VfgifWiqAHYSNQsZubGxWLRXMgiLnwvb1ez2qeIJkECaA4iD4NBgOztV7Fi2AeNHI0GpnQDE75+vraaunY01wuZz3aJE7MX8AekWRgu8hUfdDCmSRAx76RqUqaaq1bXl62mfecqfn5ebXbbQsanz17ZoNxQBUpu6KDD+Lh+RRMfiPzZyoZ9g+uFV0Q3rHSIprP56dKI7xvxIS8sh1rw3f/h7RncTgwoOFw2BieRE2+kZ8bon6D8WUT+VYFDy0gVnJ5eWn1KgguHCiyAA9r8buPLzIINrIkyxI3NjYMtq5Wq9rf39fZ2ZkdPk/k8IxESD60y/hgBWgKR0fvMhObIGUAixeLRYPxPdmrUCgYocTrOhPt7uzsKJVK6fr62t4N7VVsOuRUv/rqKwt8IHj49eF7uHwjPocPJ8YaMguWHnPKFL5Nj3cK0gJqsL+/b0HNaDTSb3/7W3W7XT179kylUmkqKkfl6fr6WvV6Xe12W7lczqJdDj41by/VSQTNfVNvBa3xgcbbt291cXFh+5VMlssTqjzxjIzKl4IwkEBe1Kfv7+/1zTffTHUJZLNZC+L8LGrIhv6c4FzJEthfOMNkMqlkMqlKpWL8DVCaVqularVqGRbEReDsTCZjgiEYclokyVRxDufn51ZrwwlRd8RJI05BgMb8aLgGh4eHBjWura0ZsQp29PLy8tS8YE9S7PcnalSdTsf69+PxuDY2NqYywc3NTQuIyIzoHgAC9ZoLS0tLhjiNx2MLhGFoz8xMpsPlcjkTOzo/PzeSIe/UByc+8wQdZO/xvrgvxpYCWZ+fnxthFG18nDy1fUiMjOKFRT4ajexc4MB9RwQQ++3trWkWgBbCYEYd7PXr14ZQkV1Ho1E1Gg3ToSCTh3PDXHqCRVAZr4vh+UqcK84PaBTIFwH/6uqqCcfAzQA5YG9IMn+AypnvdmDwEkEjugrdblerq6sG30O6BEFgPgW2ASftyX2SDLFAjYw18EHL4zbAf+/6VoInfIEkyyDYCJeXl0YsIZr3maj00ETPghFt8Pc+I2MQhs8KMAZsAMQNOCTU0MkUIAp4B4MRzuVyVmvx9dJ8Pj/FrqQtgiiXe6c21Ok8DGpASQyjyobwz+jZ2tQspEnNitac29tbzc/P6+XLl0akqtVq1u9M7zFtAZCcqtWq7u7utL6+boQkSTo/P9erV6+mDgXQEI7UtywR2OCEIKEhrcjvwoCmTse0p/F4bA4eh00/Mn3K2WxWd3d3RtjDSbZaLYOOgZ/m5+cVjUaVy+VMmo+6M4EN19zcnE5OTqyVy2cjRPixWEzX19daXFy0/th+v69SqTR14B5D/mRK8/PzWl1dNSLf9fW1PReQKNkvjs+TkPysYwiFBEs4Xp9Fsn98f7sky+IYpEHQhJPz53VxcVHv37+3DBIoOplMWs3QM94JvFOplNX6ZmYeRD6Qt4VPEfvDSEjePecU7sP6+rr1uVarVcs8YeLze9KEMQ73A5vgSzRMY+p2uxZArq+vG4JD0EbdGYIRyoqofKGpfXBwYCNjfdcGrHUCo+fPn2tnZ8ecL604fAbqY+HwRLyHCWd83nj8MFKTPcF4U2wOLXwE9YeHh4YI0i6FrYS0iWMFmWm329re3raggKDB22P2PFk+e4X1xRYwmtSXRk5PTyXJxKnoMAgGJ5O2+B1sPGx3znK329Xy8rKhB74d9JNPPlEul7OA4OrqSqenpwoGg2bnCNCRu6UES0mBf7PfsFEkDuwnnh8OVLPZ1O7u7pTCHRk45V4/k4KhSJ64GQgETNUOgSI6FihzsI7/IY4a6IHaJC+dflzITJAZcKxEQ9LEUXOY2Dw4axaaCKbT6ejw8NAm1ODgcM5+TizfTe0ymUxaTQ9nDQOVe6a1hAMpyaJzatGSpkhwvAhUgKjHwezm+zjcDG3gmcikqdcQeEiTAeuj0UhnZ2eSpO3tbcuUfUbGWp6cnBgU/pvf/MbEP+LxuLUNLS4u2kzbVqs11QvuYapKpTLFRqRlxEPe1KyBdhCoYaNLD+URH9lHo1Hr9aVOyAjHdDpt+wOnxCEDYWANcFzAhBg9MnvEKviso6OjKedwcXFhDpF6E4ecvfKYAfu4xitNHNzGxobN3MWILy8vG/OTdSTr5nngF/T7fV1eXur4+NgCWn4ergf7hpocM8/pyZcmcPfl5aWWlpb0ne98x+qYsFk5cwQyZBAYIQJaAh6cBnt+eXlZmUzGkB6QAMiejUbDAhecCOd/bm5O5+fn6vV6ur29VaFQsCCGDIcsykP5nDXanNDs574gfm1vb6vb7apYLBqJDWSLc3h7e6tUKqW7uztDOQhQ+O+FhQVtbm7q/fv3VksEugX5I5tNJpMajR7a7UBckE5FvIXgnBIa5E/uD54DmS8QOt9JTZV95NcD7WsQs5ubG+tUAILFJsEdAWb2f+YdGFDx3Nyc/Rz2G/1+7sV3XzwmQ9EGdnl5qefPnysSiRgLvlAo6PLy0hjenU7H0Ay08cmSsZUQxAhAQQIhRHJO6J6IRCLGBeLe4vG4SqWSlpeXTUqZ9aZchx6Fb+2DsIldonS0srJiZ8V3SXj7AGEONBQ9C3wD5NH/kD7qTudBonM4HFp9zWefTCWiBgYjD+KUh0SpN2KI+A4IKuPxpO+a4d7SpL6xu7trIwxR9aJv9PLyUtls1mpnRHQ4QMg+8Xjc6oZ8NvdAszr3+iEiFY6Z+pWvn/McfC/wObVFNpd30rQ4DAYDy1gWFxctcqQ2HI/HdXZ2ZpKHbFycDvDn7e2tHQrqWEA+1OW73a5OT0+n2OJcsLYx5Lu7uzZAxetPR6NRNZtNY2x78g7kMli2RJPIRVIm+S//5b8oHA7rm2++0fv3782A8PccVgwexoI9Qv0I9IAMi3dOWxb1q1wuZ2syGk2GAxwdHdka+72A0/BrQ2aH02I/QHZstVpmPMnsJFlAQ9tGv983mUVIOtwrRBnqdpeXl9Yfmsvl7GwQHCYSCStDeXTBEzo9U5u9OBwODQUgW+UZmfiFc+edg7gsLS0ZqQomMGQn9g0OGScH6a9arRpyhYP90NozPAaCGCx3BE4ikYgN+CGLo72PMhDrSpLgHSbnmLOG7cIxUif2088oexHwYB8gyLEX+E6yTQiqjUbDYGh/Xgjq/BpwsQcJ4n3P+/r6ugXxZ2dnVveE7EdiEAwGLVhFBYz6MOcVQSaeiayPZ6KdEVsPAxt7yXV1daU3b95YEIrzHw6H+uqrr7S+vm52hnIHF2cbIaBer2eiUgRY/AxBfT6f18zMZD41a8c7rdfrBpcTpBFMlUolQx9mZ2ctEIY7BTJCUMk9e5W5bDZr2T+lrKWlJeVyORuuArPedzV4lbSnXE921D7CBIqBlMGGK5VKisViRvsnC+eCREWWTL0aMgpTdnz2ChOUzeQJaGzgXq9nkB4zZ8l2MAa5XE4XFxeWOeJYOZSehMFmfWxE+D6cExtJelC94t5xUMDjGBB+BmNBhOVbMOjNJYgg4OAfjDSw+8rKiprNpkWMsHPJGj7//HNzMLAUz8/PDWJ/9eqVzs7OppifBCorKytKJpP2uZBSeKbxeGxCBmxWoJ2bmxsdHR2ZMMzz588t2+Pdjsdj1Wo1lctlM5LA7GT+ZPUQlorFopGO0DeGlQprc319XTs7OxqNRva8wPi8n9vbWzUaDZ2fn5tx5b3zPny2CwSIcAElBPYmPazMxsUwbG9v2++iKUDNntoeB98zrIHjkBglMJVkhB72Fb8nPfQeh8NhC6jhLiwuLmpra0uFQsHOHFk4RpusiUAC1CscDuvq6socPsGJJMsQMMgEBBsbG3beke6FPMSZ9efLOyna5Jhh7wOM6+trg4rr9boFybwH9iBG0csXSzJY3pPE6EJgj1arVZtbTTANQsEztttt9ft9C0JwbDgzZowD1YMq+KljPBP39aGeXkp7JycnyuVyphxHEiBJq6urtg8gu2HLCE7L5bI5sUQiYXV9nC1ESe4FvgB2OBgMKpfLaTAYGOSOI4OrQJLGWUOqdW5uTo1GQ2/fvrWz7m09wSbJTzKZtFIMLbYkN76NigDIlyUoDVL7Z63oUgkGg4rH48YMB23jHRHwYBPgvVA2ARVlf6Hv8fnnnxuaSTJGO2o4HDYRKljgT72+laMGsqbQDgtvZmZmiiTgp5VA2kCuU5pMRyHSBSIdjyeSnkRM9Al7JZvBYCLSzpQladKTS38qZBBE7X2NDyNF2w8ZozQxMqiAXV5eWk0BBu3jgwPDGwYx7QcYEuqWvk4OXO8HTWDoyKCur6/VaDSmhpD7EZHz8/P6y7/8S3U6Hf3iF78wYwmRqdfr6eDgQP1+XxsbG3YIB4PBlEFNJBLW8wyJCUPhozw2/WAwUDweN4IcDptM482bNxbAIUrAYaU/GGftYUcOw9dff61yuWw1u8FgIvO5trYmSdYrCyuedifqijh9DMbm5qYx3Hn2XC6nX/3qV3r//r1lZ4PBwxzkRCJhYhD9fl+xWEyFQkGlUskytq2tLcsg+AyMmx++MhpNhGdoq/NwK+tMVM8QDElGXkFDncDUGzN4HtQ52eNkeI+5GCh5MTgjkUhod3dXzWbTzh1Q7/z8vJaXlw1qJoj0ZCj2Eyxz6WGgBPfgSY+rq6t2lijVjEYjpdPpKQfrn9FzOoCmPTERG0QGOBwOLSAEOsWuwP0oFAqSZEQhfm84HKpcLts7KJfLlrl7dIJ3CzSOOiLOFgQNVjVnl/cIQZQM+Pz8XNfX14pEIlOOkPYu9gtlJtYJkiAXw4BA2LCfKBFSG6UkRA2b/Ua/sSQjbPrgE0UznjuVShmimUql1O9PdPcpB1L+glRIdwlBE6WsSqUypYMNQprP5/Xq1StTu+TZOdsEtJyLVqtlqGq9Xrc2K/goaE0Q2HpUCQ7G9fW1ISIEcyAIBGVoWKC/z1mmk2Jzc1MvXrxQIBCwoAJxp0wmY0ItS0tLpodOYPyU61uxvslQyUKJojKZzNQEFB8pExnRxE4GXC6XrdHeM2V9VpNKpXRycjLlKNvtts7Ozozl1263jW0Y+8MsYUgk9Lpy32SzGGhPOsPhMZKRYeAQth5nm/V6XbOzs/ZzGDQkBcm6JRkrFEPsMx8/yANj4GFkIEvUbmCAZjIZgyRBC6iJ4Vh8vc+3VHS7XaXTaTNQCG+wkQOBB21uMjHgcQ4sCm6eTbm6umoSmsDrCB0A+3JgYRZLk+x2d3fXImICnUqlYrAURgYHw3vw7yUQCGh9fV2bm5tT2Q0CG+l0Wqenp1OwPkZ/ZWVFf/EXf2Fa4vRjoiRFJujr5UDPGGlYneFwWB9//LHxHbz2AHU/iIPtdlvNZtPul8/ymvcExpw91p0aZ7vdNkPjSYuSrJ+fslA6nVa1Wv0jUZBgMKiPPvpI6+vrOjg40PHxsU5PT430RF3Vj6Eka1lZWTHHDMMaGwB86BE4ngEnBjJAZwLKg9IDr4X949EDzu1oNGmZOz09tdIVATHywziHy8vLKVJWvV63s4yuNdknWbA0CRYZV+hrlyQT0gNxj24CgjaCEASdCMLH47EODw/NsGOrwuGw1tfXzTZwFlgDTxDDnnjbCSrFFDWfPHnUotFoGNqCHYB3QHLU7XaND0PJkPUBLu73+9ZSyJ5i9jooBfwceEnpdHpKMhqCba/X08nJiVZXV6ec9OHhoXZ2dqYGb7RaLWufhZjKXj47O7NxuthoSmr00AOf4w/YewSFkUjEhvuMx2Pj7hB4U2OH57KwsGDkPiD0QqFgATY2YGNjw9bpqdeTHTW0e6IMsoaNjY2pQxeJRAyOpAbie5UxImiv0vKAcAWQFGSh3d1dyxK52u22ic2vrKxobW3NarAc3MvLS4MrqB1iTIg4ifLJAGq1msLhsDE3YViThUvTIxHz+bxubm60uroqSbaBaTHxBC1J9gwYmLu7O7snXw+lf5mNzQG8urrSN998o88++0yvX7/W8fGxIpGIiXTw2RCjIFAAH3EY2CR8L1coFNLr16+1srKiUqlk0OvFxYVl/hhe9gCDKuhhxZF5trEXMqBmSZaF1vTm5qbdF2zc09NT/eu//qu1njDmdHV1Vc1mc6q2zgHNZDKWifBuk8mkZd9+/T30Rh0tmUyq0Wio0+komUyaQYF4wu+SVXAI+f+ZmRlzxNTbqZcSKAKXYbj5cw/bPb74rLu7OyMeEnyiDQ7rV3rIzq+vr60OC2GILN6vXSaTsU6I7e1tXV5eqlKp6Pe//73Vg/l59hDGnwyQTJ9hGyji8R2sXzgcNkiYZ+bew+HwVM0ScRKgb/YUkCU16mQyOUWC4l1C5qP0cHd3ZzVyglgU66hxs6700vd6PQuE7u/v7ZkuLi4MWQgGJ2N/QRpvb29VqVSMn8Pe9loEfD92koACEh9BTyKRUCAQUKVSMWeIbaDlUJp0jmBjBoOJJjtjdT0CwgVyhePy8739GeH3KClKD6UD9vvs7KwpHM7OzlpwS0800tKUGeH33N3dKZvNGvfIM7DhOpEZ07EBO519ArGYoBckirX3g3sgP0JOhkuBWAsInO+j9l0dtIMhLQqiTGD49u1bsx8gt3t7e/Zeq9WqnaX/EMGTwWBg2QbRLkxbelG9cg0EIIgqnmwFIQQc34sqAEmw0DSh853+mp2dNS1saPLUT3H20uSwM+VEmsD4pVJpajg8mr6rq6tTAv1sWklT/8ZokAET8QLJcgh5JgIID/U9Jo1wOKhJEmnT0pPP59VqtfTFF1/o+fPn+uu//mtFIhH94z/+o5FVMEygCLCSH/dZ05bkv5u1n5ub03e+8x2Dj/b391UqlWzDUbOk7CDJyBoYHti31FthnjJyDrZ3Pp83OUPKCGzkVCqlcrmsb775RtLEQP/1X/+1ksmkDg4O9NVXXxl8zlp6gg4oCdmK74Pm8tnOl19+qdPTUzUajSkt6E8//dTKKgR6GBtqpb1ez9q2yOJhA9M6I8n2KbrEnsMB6zudTiufz1sLl8+ggOBwHBg2Sif+2T3ZLZlMWtuJb8nhHH/ve9+zKWx+ChktUED/6M53Oh01m00dHR3p/PzcEIfYH8a98g6Bd6nLsma1Wk3b29uSpGKxqNvbW+NQ+DXpdDr66quv9OLFC6uZc1/lctmGshB0eziRzgraA+EZzM3NKZ/PW4sNokVAxwQVs7MPo1dpYcPpgrZRw6YcQ1AN9I2ELggIUwUJWiRNIY6QzzzXBXgVuVOYzTiJ4XAi1kTGR78xQbXnxfCdMzMTSVMUy2DmeziawJ49jiNmYA7BFoFLu922gN6ThOEIJBIJY2wTIIVCIevOoFzJfPbf/va3Nsce9Obx/WUymal3Q+mIIJ4ulmAwaKxykMlAIGABzMXFhRqNhmleEFAzeKjf7+v4+FiNRsO4RPALJKlQKOjg4MAGEXmkkeCb5PL/5fpWYy6BeCXZ4aDFgQ1MVIyDJgpj0xMlBgIBq69gVLwwAZuKOht9l1zAgWTyQIe9Xk/X19cWHV1dXaler1vtCccH0cC3m9F75y8gSm/4gsGgSRsuLi4qlUopm82qVCrp+Ph4avRhPB634RA3NzfWsuKzGf+5ZMNHR0fKZrO28Y+OjkyO9PLyUr/97W91fX2tjz/+eIrZS/YsydpJut2u1ao8yWN1ddUyE94HKMTq6qpN5alUKqa3u76+bka3WCzaASEzWl5eNuOAkSMyZsJMIpFQtzsZwwlfgTWmfQwIeXNzU19//bVtfNZ8Y2PDDiQ1tHA4bII1qVTK2N+0hdXrdZvm42t2kEh+85vfGKwVj8f14sUL67UHrh2Px1MiB3w/07YQEuF7eSY06Ml4yHT9fqZ3WXqY9gQR5v7+Xul0WqVSyZTB2J/AqP7y+2tmZsZa4sjmCRjZf2TDo9HIiIYgXLOzszYxCvRpZmZGm5ubGgwGOjg4sGCE+ilyv2Q+MzMzyufz9hxkvRsbG7q6ujIyoL9vfwb39/dVqVQM+mTOMgEt+0+SdVZQ7iGzwgFD7BkMBiY6gmOhHEK9PZFITLU28d5nZmaUyWRs9juGnfc7Pz+vzz77TKlUyoIrMuW7uzuVSiUbzkELHDwOVML8ZDbKe/l83vY3AkZIcMJlWVtbMyVD5GdDoZCVGufm5qx/HscJN+Ps7Mxm19MKWSqVVCgUDHbn7FBGJLMkmEUAC24LNV4CJl+Gozfft2ZJk8Af3QTfyucvOBXLy8sWiDWbTa2trVmSIcmIbzc3N3r16pUlKnARIpGIqfKBCFELB5kisPEiLJIs6CBwBSZ/bNOxhZT+PJP8Kde3EjwBHgF2YRGYwYtj9RGc9DCn1DPdOEw3NzdmpMH8fQ2cg/ohOJDPwsgBExcKBR0dHdk9BwIBm6/KotZqNTWbTYtsx+Ox1RFABySZwQLS8iQXWNj8dzabNbJGt9u1KU1kymQEOBOIQZ5V/PLlSyOrHB4eTjk7oFXY0icnJ6YXjCPZ2NiwyJpeYiCm29tb4xQgitLpdCwS7Pf7evXqlTY2NmzjQwChToTedTab1d7engaDgaEVEHw8rEdtiNahw8NDJZNJvXz5UltbW9rb27P94PcLhnVpacl0wDFwsFbX19c1GAyUz+ftQGDUd3Z2LIsB3gqHw9rZ2bEWKfbX1dWVTZ8iI9nc3DRyGUgE5Dnq72TJOD7YwhgpMjxIhECN1DAf72naPYBb+bNoNGoqeLSiEezhOGjjo4bmHTGtcoi6wKPgur+/t/d/f3+vhYUFZbNZ9fsTzWL6d+GpAB+GQiEjDvL+gGZ5fvambwsD7i8UCuZwP2S0cJrUKZvNprXhefvCxfcBfxPgUEbj7FCbxOHicPhOj+o8LsfxuayDZ9/DAr6/vzeBm1AopGw2a1nbcDg0xIHpbDhKvg9iGY7o4uLC0DJJJm2bTqclPdTxyXZBNXj3t7e3pmfQ6XSUzWatk8OjECRODAdhH6fTaes+AGnB2fjRjnSxkEVubm4ar+Ddu3dGKqXlzrfGQiCGx4ENYM98yP7zPuDNYAPG44nCGTwK7AtEMkSFgO4Jjr1NZA97IhuoDTVtkGISPvbC43slAIZYx8Chx0nhv3V9K0dN4RwDxSHyKkwYZIgRnU5nSpKS3kXad9rttqlNSZPDRoQK9t/pdGwTYUylByY62S3sZ8ZEsmAbGxtaXV21e8TBA0P6lyk9qAgRgXs1ICCx5eVlJRIJbW1taXZ21up1BCQQzTBq1CoTiYTBxaurq9re3jZy1fb2til2/f3f/73VMFKplG0OygrZbNa0kdlItL8kEgkjbhWLRUNBvvvd7xqiALogTcRVqL1xyKiTE12yPu122wIQoKpcLqdKpWKiAJQAuB+yVlp79vb2TC4RWJRakG/PQP3O8wJ+//vfa25uTplMRltbW/auZ2Zm9Nlnn+nrr7+2eyaAokcYxw0Eh9HlHugrJ1PGuZJF+hYe1gi2KapcrJOvS47Hkz5beAKFQsGILv4Cvm6320qn0zZ9B0cwGAy0sbGhlZUVYw1LsowZuUwCiUAgYBnH9fW16R4/zlqRNl1fX9fGxoay2axlSiA0kJwg0XDWPQubsw90CIkMAwzSQAkikUjo+fPnev/+/dR9cW84pn7/Ybyib+tEvevu7k5XV1dTWVkoFLIACs4E75kkAfIoTgHnA2s7EAhYEEFvtncclHSi0ahevHhhnw/qgRMGkmVIA2gCJFBJBjGTIPD+fE2aYAOkDSRhPH7QT4BQy3oiHATSyDvxNWVY49g2v34+uapUKqZRMR6Pp4JOkDT2wsLCgpGzQqGQXr16pf39/alpg5wn+AHsV1Arj7B42J7EB9VJat+cuT/V4kbgAvrC+ed9ErB0Oh2z+dg+Sob4iGazqVgsZoOH2PeBQGAK/YVzRCCB3ymXy1bCeMr1ZEeNpCeRNmxDDDFOm0NE1H51dWXykmSnONhut2tZC8adyJtDEgwGTQ6Pfjmyg/v7eyOFeTY1kRTOHiIKEAb1UzI9XiK1M1i61Ck5WGQHsBY3NzetzajRaKhQKKhcLuv+/t7qgAsLC4rH41ZDxkFy4D777DMtLy8b9OSFPRg1GY1G7aADPyKrubKyopOTE/X7fWWzWcuOCIYgm2xtbVmEPhqNbIwn9WSCKVicy8vLOjs7m2oto48ZJ0uJw8Oy3W7Xsk6MAHUjmKgox0kyBSvuFWiczIvDyHVzc6Mvv/xS3//+943oRSAA2zKZTGpjY8OE9Dudjo1EpIeRuh+9uPArOODwHIj8eT/39/cW/AyHkzGXtHdwMDngweBEOCQejyuVSqnb7do7YkIYe0+SMV5Ho5GpG3GuiOghaIXDYb179872TLPZVCAQsEErZMAYHBw+NVUgfhzT7u6uka+A3b0TrlQqmpmZMWdJy9zV1ZUhGwhi4ND5DM52OBzWq1evVK/XjWz5zTffWGuf/75YLGatTLSLUWIDvbu9vdX5+bna7bZJ0WI0OWvonYdCoSnI1hvcarVq8rj9ft86QmBCI/NKBkp2xB7Z29szpT2IdLVazSB/T0wlsOCscWYuLy8tcPYIIkE23wnRib3L/sFGkWXSHUKgMR6PzZ6Cenqte+wta8jQCI92eFa3r7EDqbOGrVZLL1++tISAAGh1dVX7+/vWCoijx2mR2VKL551y4cgRhaL8Btl1e3vbAgZabv1aEgjBsxiNRmbfeJfcKwgn7wuCst+jIAipVMqSGAI5OEAEHZw/7BTqbk+9nuyoSdshRhBlQTyhuE4N9P7+3qIV1GeACjAoz549m6qt9vsTAXygHQwcmxVoEDJIv9+fYh0DvVIT8puKWhaRnCd2seHoqfQtKWx8NgzPDTJwdXWl8/Nzg9KBLIlgqTl5aDAUmszSffbsmcGwb9++VbVaVa1WM1SBIEaSsZfJjkAGyMhPTk6s3YpaEHVSsnBgN4w7kSWQDVEsMDclgx/96EdqtVr66U9/OsXa9FnDwsKCMpmM9SmTXUAoAgkhY/ekGzb09fW1jo6OTMwBsg7BFAeu2WzqZz/7mSkWYcTQBN7a2rJ9Bdtyfn5ezWbTamS0aIF4eJi2359IjgJR+ovWwru7O4O0iKKTyaTVvfv9vrGlgbx9AEpw4Ek3rAOlinfv3unzzz83o8j5YA0XFhaMD/G4fIRUovQACVPfhbjF3mfYwM3NjdVJcWa+pgzfgzVPJpN6/fq16Q3U63VTl4vH40Yw4zl/+MMf6sc//rEKhYL+1//6X6pUKqZfAMzN+2K2PW2JwPrsV9Srtra2LCNvNBpTJElqlxhrshuCN1qIEEzCEbGeFxcXBsX6gJ8gjG4Q5gH4drONjQ2bqgYK4VX6QMhw0kDfOF/QQd4FiQR9wtgEgkk+k4QAp09mjLwoiAxZNtko9095j/o5iGkkErEAGudNjR4ZXwLP8XistbU1bW9vT9X+o9Go2Qjpodx1fn5ufJRgcNLiivNHda/X6ykejyuRSFjWHYvFdHFxofPzcyNKUoLLZDIajUY6OTkx293vT8bKovlO0sLeKpfLth6gPiAdsLp97RkUhsQMwRfOGDYPEabZ2VljsOO0n3o92VETxeCkYe+y8RuNhhl55oMC9dTrdSOMRCIRk6TEwRJ1Q+Jpt9sGJbDREVS4u7uz6SkMkqCwz0EDBifTqlQqZrh8JMjloUA2IiQL6UH+kp+9u7tToVBQPp+3LO2x4yfCoo4EdAXsVqvV9ObNGyNEVSoV67cG6qSO5MlMvq2AaD2TyahUKhnD9vnz54pGo+awT09PdXl5aWQIAiMY4Z1OxzKkf/mXfzHSEY4KHVzIK5IsIMFgSxNyBxEuQQAGmDUmWibqZxb1YDAZzEE/bDabNWdPFsP6886B5nwNixY51p+6cTA46fPGMK6srCiRSNgQFM8exygeHh6q1WppdXXVSCVHR0dTjNJIJKJcLqdEIqH5+XkjzOHgCQ5BShqNhrUjzszM6ODgwPbh3NycXr9+bRktdUEQF+B7DB8EMC9q4ZEU2KnsEwgyPouCZU0bJe+Xd4VBYm2kCUN+d3fXWuEIcBC9aDabWllZsWx+fn7eyGzs652dHdN45oxBWoMfwfrRIwx6Rma/sLBgM8h9zZ59gl2g3ZL2KsSKdnd3jcmP0SyVSjZ/nmduNpuKRCJTA3fQcKC+SgYaCoUsOIhEIvr9739vU/ru7+8NiQGGfvHihX0emuo43YuLCwue2KfZbNacNaRY2t1ALghIeeeIi+Ck/XmCPErigXgPaASBVjQaNS13EBnKU3S5eJ4Jw4JAMbGByWTSAgJvK09PT1UsFi35gLRKRlutVg2JA90ieaRbhHMD+Q9y2+XlpeLx+JQoF6UcyjiQzdbW1gzB5PxdXFzYNMP5+XnbZ/gtSVO6B8wooBQA8sN5yWQylsw+9Xqyo+ZFoqLjhR4whmdnZwqFQsZ+9QQvMH5GFGJcyQI4LNDnqT17shYvAChybm5O6+vrpl+L0eFQM6rt8vLSDDr1GeTqHl9sNCJpDJH0IFuHY8Op84ze4ePQ2WgQ8HAaOIKDgwPLCDEWnhAE5M0mDAQC1hvd6/WmFOJCoZC2trZsOABRXrvd1tdff21OAMfI/V9cXFg9iKwcZjTGDvYu7wxomsNLhspsW+Q9Wb9yuaxKpWKGA4iSoOj8/HxKoOBHP/qRsVwPDw/NeLK2/NsfeA4Qe4zghv2KQ7m5uVEulzNRDmkS7a6vr1vPLnuh3W7riy++0MzMzBQ3odVq6fb2Vi9fvrRgczQaaXd3V+/evTNlNeqyBHo4FtYUQwUc7xnIa2tr5lRwwqA7wLjUB6n/YxwwIhBgqBmzZrTSgEKRNflyFa1PQMLUO6PRqPEv4GMAp7IHGaeJw8Nw/cM//IMNbiAo4nPokOCsUt/0QRqZLYgO755/E4jgpCAhsg+A2SkFhcOTKWu0ZyJde3Z2ZvuJEbOUIsgod3d3jRxFAAUKAbQMExghIIiNoVBIe3t71hlTq9WmMmvsE1mqpKlsXtJUW+zV1ZW961qtplarNQVnh0IP869B9sbjsU0WG41GKhaLur6+VqFQ0NbWlgWXoKME/qBO4/HYyG7sA0lWEqpWq0ZiwyF67gbwMl0fcHgYDUnG74cbgX5hO+BM3d7emq0EYeD7YrGYaTd0Oh0bKhMOh20c59zcnC4vL1UoFHR1dWUILYGLNOEUYRu8Uh2B89zcnOr1umlYNBoN25OIgnk5VDo3nnI92VFzYMjauHkf4fMP7GlPvkK+E7gGhwEr1ztFD60DdfH3nc7DWMn19XXriZNksJ0nEgHpIi7hlYdACRBV8NE42SYvzGe21F6l6b5dDAqRJbUzsmoOKVCcZwvipH20RuSJESVb8iPkqA0Sre7u7ppTIbNbWlpSPp/XF198odPTU0M7vPTg5uam9YICIdIq5WE6DDu1P1ozgBNHo5GeP3+uXC5nrXZArTDyaQGBQHh4eKjj42MLnMgGR6PJABVKJBjPpaUlY0EzJpEgaDQaTbWweHIQsFq73db5+bn1HxPZDgYDra+vWymAnk7v3JPJpAKBgPUcgyZQSkCdjJYwoGXquohQcK9cwWDQugLY7yAP3tDNz8/r8vLStMslmegHrWGgB5eXl1M1Tv7NP77N7OTkxBwPtdnLy0utrq5OBUZk8EC5iFqAdNzd3Wl7e9uIOgT4nG3qdRsbG+p0JkN3gO0JOgg46DLh2SmzUPdDNIZaK1k8nIvhcKiLiwvVajVbq/F4bAEs5xRbBPoQDE4kPj2JEuU/zgv90WTrzWZTjUbD9gN98jgVRkNKk6TnO9/5jgUIwLrFYtFmX4NseKQolUqZ9gNoArZqfX3dBs6wx0ul0hSbfWZmxoJ92NE4ZpwvQdLV1ZWVrK6vr03WmH2MfQQ9e/XqlYkWsc8LhYJloJRGGdIkSZlMxjplJJmKH6UNH1BQQvRn1aNE8GAIzglm+/2+oYv4FlAaPp99TLcNCR7vHlsC7E7gDdcEG8u6SzKUIRAITHGeHmf9T72e/JPAaxhtr4lKDxnEJd9KQGTPwsCoozbEgmLIyYSj0egUWxqYkcx0OJwMu/dwl++lw7Gurq6aUaBhPRwOa29vz0gAg8GkF5SXw31TPwMe4/5AB/xB+lAvK86Fmh11PLJqNo4nKHCxLj6bQn3KQ3Xlctk0ZSEc+eiVgAdZVGprtVpNqVTKIEQyhmq1alk1mYWHtMbjsfL5vL2bQqFgoje93mQ4xs7OjsH+EF/u7u6Uy+XsEHz55ZcmPcmmZg3a7bZ+8Ytf6Pnz59rY2DBWLVn99va2MXzT6bQKhYIajYYZ8l6vZ3WwbDY7hQBIEzj1+PjY1mZtbU0vXrzQycmJZXn8HO8OEhzCLBhpAlicJMYNZno6nZ5ip+IIdnZ2dHV1ZYYOVSYCRn6u05mM7GR2OVKJsHfZZxh3sq5yuWyfw3NCApVkwRwse5xGoVAwRTOY5rx/2OS0OM3NzVngGQgEDMb3tVYQLEh6sT/MrQa6DQaDNtUO1j/rj1OGHLm1tWVnwzuZYDCodDo9JQIDI5rgwMOMtDt6IRHUFanvs4/m5uZs+hJywmR9kgzJWFpa0vv373V8fGyBwNrammV7HoKmX963iA0Gk57u/f39KZuGjQVJwAYSxJVKJUUiEW1vb5uEJUjjwsKC3r59a/aX/QKXggSEgJ06MQ739vZ2ytmTnVOXJwNnqiEBFn3ctVpNv//97617ALKu9BB4IM40Nzen7e3tqR5ySdZ5MR6PjczllfIgb/l590DciI9QguEc0ILIPpAmLW6rq6tmN716GAmPb88iCSEY4LuCwaDdB+8OUtpg8DDq2NvUp1xPdtQ4x5mZGdVqNd3c3FjLFH2fZAleBxa2N4fv6urKGL9E+dS+B4OJwle3251S2pJkxmF+ft6cH9ETCz07O2usS+6Vl0ZNiVoHho8NvLm5OTWBiMNCbZvaMH13yMo9dtj+wlFCtpiZmbH6qX9RweDDEHo2B1KkBDjAYIyunJub0+npqdXsiNJYx2AwaE4yFovZsAz+7re//a1lRysrKwYjsi4cUF+i4CJiZs2RcuTdeYiNWh0BXDQatZY7jDdBhv8OIvlWq6VEImFa7mRPvDuCsVwuZ7XMQCCgt2/f2iAGOBMYp48++ki9Xk+//OUvTXik35+oEHE/7GOMBAgC+xxmKAFkv9+3wRzj8ViffvqpksmkqYvRU4szgkUvPYj3kOXd39+bDClKdwSNHm2Zm5uzzMPXeqUHpKfValnGwDrPzc1NDXagM4FyjyfVeea7JCPa4bTZG/l8XhcXF0ZY4/3jVDhLnltB1wekspubGx0eHpo6YK/X08XFhekCdDodEwe5uroyUh9nx3cucH7J6rmwUZBGCe4JnuHcQEbc29vT6uqqEcTS6bS9QwSayN5h9lP79uJQrAm6BxBNh8PhFJpHRusvIGZKDwRsIE+cX+wN54IA03cXeBY9LXN8N5PyCAYYLHNwcKDLy0tj4UciEdNI9z3gOCP2EGN5QbjgO1CK8exqOgWwp6jU+dp3t9tVoVCwUiD15HA4bO8F6Vv2sCTjNnCfoJC+rAmfxkuNBoNBWz9fBqC8IsnmK+CMfQeCt2cgySDCEGmfej0999ZD9gwk5acF+ZsiS+VmfV0CuEp60I4FMvQC6DAVWRQiYgr7QFUU+IlkfL0XYwC0DszCISJAIABIJBJTPXCLi4tTZDCiPHoQl5aWjGSAEf3Qmi0uLlo2CSPQH8x4PK7V1VWr9/Jc9En7tq27uzvd3NxYKwJZ8tzcnA4ODgyWpm4uyeA43kk8HtfOzs7UCD4Mru/NBBJ7vOGeP3+u3d1dc/oYGz+OEVgedbBYLKbLy0uDlbl8pu5rjYlEwjKGQCBgbXnD4VCFQkGbm5sGVWJYcOLxeFzr6+t68+aNBSD5fN6GG1QqFa2srFhkPDs7q3q9rmg0qs8++0yHh4e6urqy7OH+/l7r6+tT8GGn07FSDsxaYLPNzU0jjkkyAg9M1m63OzU7WJIxS8nskMisVCp2pvguHAwdAwhiEED4ej1nYTwea3t72wK8zc1NC7RweMCZ3C9kGs5qr9eztipKN6A34fBEZYyWSd4jRtjXcMlUMdqUYSircCbJ4HzJKRqNGgrEJCI/OIZ7IZiDf+HPIsQ/WmQgHcGERj0wGo2azCVrTZ2SAIZ9hL2JRqOGNlIiJMiB1wMSAIpIUMX8bm8buEBjfPuQ9EBWlGTw/9zcnAXasPvZr9SjCUYIAKUJwzqRSFhAyu+Ox2MTNSKZiEaj+t73vqebmxtVq1U1Gg2l02nbU5wRnCwKjqAcKNEhaATyw7nBjsApoPQGojszM2NDfODc0JsuyXggkqZaBuk44T17ci4oGWgK7yCVStmIZAI7bPHd3Z1psBNUPM6WKf1438SzPPV6sqPmi29vb+0lcyCpe3AjRPf+8LAZR6ORqtWqEVaA6sgGOZSDwcBasLyIBhADrEhqCzw00SnRLAaADcHB8y1LsDW99Bx1HKJtghSiLeoSDFBfWVkxaIZDw+GnRgETkfXDwRLFUquTZBE0xoTsZTgc2lAFiEuZTEaffvqppEm9EjYz9w0069sUbm9vTdqT+w0EAub8GNxOxsi7j0Qi2tzctAgS6A+9Xohfz58/lyTboIPBwGrxQNbRaNQyJp+5UzIgUKDFAhSFzHVhYcFYn2TFIAsYnbOzM9sLwWDQ9izvCjjrxYsX+uyzz/TrX//agkJ60mGoc8DIQprNpsrlsrWLUJ8iq/HOiYwQ6IyfxaliGAaDgU5OThSNRq2tj3tHm5mpbDi1cDj8R3VYfgd2O87j6urKpHKB7vm7UqlktbelpSVzykDUsGXH47GhavAdQHbQNKB26MVPJBkvgaBxPB4rk8nou9/9rm5vb5XJZDQcDvW73/3O6rrsC6R6OePLy8va3d21AJ/WQfY4+s1kqJFIRKlUymwBdoA6tzQR9SCgmp2dNaQDpjnBnQ+IeMegKWTZPvjhu3ieZrOpr776yhzr/f291fohFvqyEfVYEh+PRnryKSWyQCBgqmCSLMClbRC0AhIpzh77jvPjmYbDh/Gyn376qc2kRtQEhJFaLQEuEP3m5qaRqUajh1ZZz2+SZGhloVAwAhYoISUCHCJnyAvLSA/Biz9/FxcX1iIM7wMWPtwQz4NgP3BeIbySHME3Ijnh3XjeCZ/pu0A4mwQWT72e/JNXV1e6ubmxg/v4ZrgJoFsOPwaKF87lhSx8NsXP0PYAk9xDIDwogzVoRcAhIOLB1BmELAgucJiwCIlUFxYWlEqljKQBexNjLsl6Onl5QCDU8HhhDFfwQgQvXrzQ/f299vf3bYoKtWOCC4w6z0iU7Kf0SNLr16/16tUrExI5PDxUPB63LIzImZ7E73//+xoOJ+L9b9++VT6ft+CE74V8h2GLRqNaW1szBnWz2bSpQ17yMBKJWB1oZ2dHR0dHWllZMYUrH4Dt7u6asVxcXNT29rb29/d1dHSk8XhsyEa/3zeI0RsToFs4DOPx2Awpw9sJUCg1sB92dnY0Ho9tahuDEBqNhvL5vPb29iwI8C1giPLjXNgX1KGBIglSa7WaSbdCvoFxjOFmmEAgEDAiEnVq7v3169dqNptqtVq6u7uz8Z3SA4kzHA5beQlyIhBiJDLRMIYlHQ6HrTuiWq2aIAUkJIiLfF84/DCiNhQKmVY9CMz8/Lw5aPY+TqPf75ujJaP+6KOPDDplHw+HQ21uburjjz+WNAmsv/76a+tr9vwN3w9NCQoS3tu3by2gIOhkfZl7zUAO9iRITCQS0enpqSFykqbgWZ6R4IOgBccUCEyEZGq1mp49e2YtQGTwlDy88hiIhFfe45+lpSWtr6+bIYesBjoIQtjv9017Gh4NASL8Bs40yVKhUJiyldhdAg2GYxBkgKhgdxcWFiyxIADj/s/OzkwYptPpWOtpLBazThTpgXjJ+rPWoLUEnogpwegmcMGeXlxcWFmG9ZA0FeB6n8Pnc/GdDJ2h9IGvwXbjVHH+KN6BgPjP8xf/TxtZLBbTwsKC9X1/m+vJjvr09PSPSA4fukFgax/dUk8heuKhiQD978LC46WSKX3oeweDhx5tomCIV+l02qQig8GgCRhUKhVTEYJZSksZEp0+GMB4EV2zMQgygDJ8sz8QlzSptTCNi2BhdXV1qj2M++Pl4Qg8NMvn+ZaD9fV1Y4v6rJkg6fT0VGdnZ0b+WFxc1Js3b4yNCqmK8ZTd7sNUqHa7rWQyaQ5xNBpZzbVer1urCvAw0FE6nValUtHh4aE6nY5NWINNvru7a3KBfNfOzo6pk+3t7VnGIz0Msye7npmZMbY/zhS4KhAIGFqB0fZ1XbgTmUxGr1+/NiN6dHSkw8ND/fSnP9XW1paRqThUaIz7rgRkXWGQEt1TwsH5EahS7w6Hw1a/ps4bj8e1srKiu7s7ra6u6vXr19ZDz3ACDjdnhyCJZ+a/pYc2HmrBODr2E6zmQqFgegRk3uPxRCeZKU0gJaPRyAQr9vf3TfzBIx3e+PsMTZIRMUFpGB9LYLm+vq5oNKpWq6XT01ODTX3WigIgn/HYFgCj068tyTgq1WrVPouz1Ov1LMggE2Rt+XuCLP4bZbDZ2YkULEQtWkMhfJLJ0pNbLpdtcIZPRgig+HPfJZBKpSw4yWazev/+vf15OBy2XlwQJIIX+AGXl5dW3uKCHItIjXc29OwTKMKsx257KJ0gYnl5WbVaTcvLy4YwYQd5R5xVj05SQqKMwp7FTxAc4lgjkYfJbOxzRvBKsj8HBftTZUjsLc9DGQA+FAENZ3U8Hk8lX0Dfc3NzisfjKpVKpj/vL49qJZNJ7e3taXt7W4HApGMEdO6p17cik7GRHtctP/SzH8qYGV8GC3A4HJoBx7BQ5yBbBeK+uLiwF/+4Hu6JSVy8LEgzHHJIP/QdAt8AhfB7GLZ6va61tTUzQDDQJVm9+PGmwHnDZgSCarfbRiT65JNP1O129e7dO4PXpAeWLJuOjR2Px7W4uGi1tf39fa2srKhYLBp0l06njTD1n//zf9bbt2/tIPz617+eyrT29vZMwYtMm+EqBDUMk4AQFQqFlEqlrE/TkzH4HAwULHvgVKJKiBcQQoCg5+fnlUqlpuT9QqHQVEsEQg4rKytTTsFPK+P5MQze8F5eXur169e2/tRd6WMlqyBLhWFar9eN7cv6oScO5EgwRZ1/Z2dnqg7Nnt3a2jJyEX32BIHSQ8vK9fW1BR2BQEDZbNYCZeqUBEjeWXImpIc2N/YVQSyfQ5Z3fn5uxBzEcVqtlp4/f24tTzx3p9PR9va2ZfDcA+UnEKGbm5upbAtWN+RDgtS7uzuVy2X96le/0t7ent6/f29ERbgZPBOsevQbcDAoAhJUBgIBu+der6dkMmnzy9lr1E3JjhH4oE6N/Wq1WhaU4dgJ+nx7H2taLpcN8sdBQQ6kTZQrGJyo6BGkoZdPQIYT921IBPBzc3PG46jX6yoWiybuwVodHx9PORGcxO7urp2V09NTGyFMHVuawMmtVstGt4IaEZjXarUpZAS7OBqNpkqayLmSfLBO2F7OEQkVMDdnCTIvPBf2cCQS0cbGho0wRnoXf+Of2f8/dgbUAN4QaoCgi/ybPUiJyItO9Xo9mxv+2P9wUdZqtVqGvnEf2MqnXN+aTOb/PfVBjgnni+YsElObvKwhC0tdGLYwEGYsFjNSD1NafKCAU+MzfORIVkVWgcoWc2iPj4/V7XaVTCYtQ2YuLhdRKXU0nAMRth/U7i8CFTImP16OtXv58qWWl5d1cnJiLWOIuXjCCN8JJAWTE0NLFgL5Y2VlRYVCQf/zf/5P+15J5tQSiYRev35tspHUX0OhkH75y18qnU5b/ZjokvYHoNNudzKjOJvNWh2fA0wmRB0VqIhDR42I+hWHhPYajD6tM5BtFhcXdX5+brViT2QiiKQ+z/7zjM6NjQ09e/ZMu7u7U+8I+BAmeKvVMnIRWXUgELDsAgM/OztruvdAzuzb29tb/d3f/Z05i7m5ObvfTCZjhgjUCIMALP04S6YODbmSSJ8zhhP071qaQIy0w6DRTdaCcwEWrVQq+vnPfy7pQWWJzBMOCg52YWFBa2tr1m9PRwOSnaA+1Ds5s81m0+BozjMSwO/evVM+nzelNzo4fL84LU4EtsPh0NC0lZUV7ezs2PoQwINwEVTCbaBFjCACUifZdTgcniIeptNpCzRJLp49e6aFhQVdXl6aIA+MbuZBUyYgswJJo9Pk2bNnBqtnMhl9+eWXFkzwrrAFqVRKkiyoAOYPhUI6PT3Vr3/9awtUCKK8kwqFQjZ/2geDlDMhsCLEA7mSPTg/P2+MZkoIBD/X19daWlrSy5cvLUBpt9sqFArmpOnEAfHDuddqNYO1cd6bm5tTQYqXYuWZQPHW19dNylR6aGeDG8Q7C4VCpngIKkl/M2cKshm+C8SXv0M/wEPi8XjcOhMeX/iQUqmk5eVlm+P9oXr2v3V9K2Wyx0aC6G52dtY0ufkzSda/CCTFpsJAsEg4L7ISIma0p4lQvQEB84doBowGnJBMJvXixQvL8pjrOxo9TLfJ5/Oq1+tTGc6H0ALug3o2jsJHUY/r7Nw3ERRROYbjm2++sfoymwbxEJw/cB9ZP5kpG+b6+toIcExzISotl8taWFhQOp02gsloNFI6ndbW1pY5YUbg4SxzudzUYcDggTBIkwNydnZmcpA8I4cMMQgi+1KpZHKIROUEWkTWtMBQ/iCDI1ihpxYCHMaYOiuQ6fn5uWnE01dbKpUUi8W0t7dna7S/v69SqaRKpaKLiwutra0pl8vZ83KR1TOvFmd+cXFhMKNvTeSAA2n7uhrwLM/iIViMK04E5jVZKQYN+M2zzx8zm4GMITxiUOF98D7Zq7xn2m5wJGSQkqxMFIlMNJ+9bGSlUlEikTDded7rwsKCDg4O7Eze39/r8PDQMlqycBAFWvCAF+lJJ/gDVYFdDfSO6EggEFCxWLT1IXAfDAZ6+fKl1U7JwHHW3AdIHuz+bDard+/e6d27d7q7u7PhOjc3N9rd3bUAN51OKx6P65tvvtHKyopevHhhOuz+HcO/oK3LiwKhhpVKpVSv1w3eBgVbWFiw4JU6MfVb2mAh2tJmFQ6H1Wq1jPDEIJ9QKGRDXMgSsU2+YwanxhkDor66ujLeBl08nU5HP/jBD4zpToKRzWaNMY6EJvaFtkXqw8ynJ+gHZaKs5ZEuOoHQ2UbCFeIwZ1+S9Z+D6oJS0brHO+I7PM8Du04gDccE/hDnmKTsQ9dwODQEQpp0Lp2entr7fMr1ZEeNSL6n7N/d3VnRH6bd45eOofHwDTqrOHUiCw4WCynJshG+O5FIWFAAsQJoeDgcqlgs2iFg0fksNnin07EIiMieF+XrfGROOAXk5D4UCT127sCRRPO+dkVU6RmZFxcXxsKdmZnR3NxkNCMzpMm2ib7R5R0MBsrlcjbEAUeTzWZtDu7MzIyazaYKhcIU454M8fLyUpeXl8bgXVpaskwHGM5LYQKNMloQEhTtEdTDcVy8f6aNUTP39SAcAgIMEBIh3BSLRctWaM8CBiYQAEEhe4zFYjbYhEMyPz+v4+NjffXVVzYApd+fyGYiAIOmPO+V6W0zM5Ph8RhsnAlCIxjxcDistbW1KWiQw0xPJ47K7xsIhpR8CGolWS8+RtJriENKpAzDmaFUQs0YZ0Sm4cUsPGvZZ9m+/9wTmSgLSBMnBA8Dw0cbJHsDpw5MyD3xD0El5xy7AGJDgIqEKyxfgtfb21sdHBwYXE2PcqfTseyWnt1AYNJnD0TOfpRkdV72fiQyGQ7z/v1726MfffSR9TTDcVlbW7NSH1kciQ2ZLYNKgsGgnUGyU56TqXZv377V1taWSTZTNhiPJyRXdN9B6aixZzIZbWxsmGogpcODgwNDPT1rnT3+uHw3OztrJEVIewQV+XzeODHX19caDoc2w9rfC+sZCoWsJICDl2RjObGD+I5yuazLy0sjYpJRcx/4IHwLiRPnjPo2Z344nKiRsQf83qETwycEtNCxLpxZ7DplP+RNISV7R825Z10JdBEr4mw+9XqyoyYb9QQVolEPGXARoXv4FoNNlkHEQusRfaRkj0RKkHE2NjZsOASwId/p4cRQ6EHPmo2C4SNboSdXmt6oZHRAsUR2vl5On583so/rIkhfLiwsaG5uzoZuAIch9J9Opy3YgORFhFwoFKzm4w0ogQ5rCeHFb1YISjwjPa5AnMDMMzMzOj091f39vb7//e9bDZGaXCKRsOkyPLdvSYCkMz8/r729PTMCQE0YqO3tbet7xUDxfrwSFlkSQ1+q1aopIFHfYoQg5YDLy0uD/j/++GMjlsDs39zcVKlU0r/+679qZmZG+XxejUbDonBpYlD+4i/+QsViUd98842tMQar0+lY+WQ4HNqEp/Pzc1WrVQsoRqOR1cAes0Zx5rSFsPeB31utlhHw6BWWZIphBH7ValWxWExbW1u2L8makTVl+MhwOLTsmKARzgEMYc/J4OdHo5FarZZOTk700UcfSXoYxFKr1SwjxS5wtnGOQOtra2va29sz1AfVO0+korPBE9AoZ/h2TzNaf+jawHjCbpYmBErYzThijChoFvuDfQbKg/GGiIhzAI3imePxuAU4CPhQHkM7GocAykSWDzfDE5sod7COIAe0E0ky5S+CYLgktAuORiNtbW1pbW3Ngkr23uLiojKZjPU7sz9hdXvNdMoBMKB5p9iDwWCgbrer9+/f2zN8+umnlvUjEEKgxN5CRpXEDiQERI7vTaVSuvzDhCmyWPYr59D/PAEmJTXvkCnV0XVEMEmggJ/AloGUYfdBdUEjeOegQJBvvWoZyBA+DsLe/f29qtWq3S9+7KnXkx01G4zF4SVFIhFjS/sMgGI8hhupOf+wREMQXPr9yRg/YGBpEokgI8dm9+0g1M4eZ+cYRl4qG85n2P7y0SUbiGxleXl5SqQBpiVRKw3ykIPm5+f1/Plz+7tut6tMJqNkMqnT01OT/WPoOM6JNoHLy0ul02mtrq6qVqspFouZ4fVav2Q2PkDinXDoyITIMFF34t4ajYZKpZJlR48lLEEQIOVxECORiPU/S7IIlloXLWVk68lkUldXVyYFyXpjvBqNhs7OzrS2tmZC/8Vi0XTM/Xsiq0AJCLgV5wakh/NcWlrS1taWfvnLXxpbGSfK57Xbbf3f//t/rRWN7K/ZbJoYjZ8gx2QpRu9xHxxcjD6kE+mhI4HWOJwRP4/c5P39vY36xDgjuILISjKZNAUuanKsJ72tkUjEeAqedOZJSp5x7MlfXGdnZ6rVambU/tN/+k9qtVrWjsdagvBwngnCCK5hKQ8GAwvuuC+fNXN+CRq8ktN4PLahJrRBeag9k8mo2WyqXq+r3W5bqxD7ARuGZOjs7KzN2fYBsNen9lORcPScLdCxQCBguu5oKkgPegXSJMjBHlGKY2/jGH1wxe8TQIAu+JIFHA/poc2NuiowNwEhJRpUHZnVXiwWrbcaFIxOG8pMoIr+vfhzz0UwBpqAxoUnXSKURb2YIBY7jpjU3d3dVGmNudWgi6w7+4/583yXJ+N5UST8EfcLysVzEcyQNHQ6Hftvgjt4M+xXgrJoNKqNjQ3zGZFIRI1GQ61Wy7gcZ2dnCofDU+1qT7me7KhZRB/d8kXUyYCYgDDIbsfjselcezgLxxr7w2xeXiojM4la/HdDEvMMWIyN75+DaAXMgCOHgOKfwxf2MZiekEA9nTofGT2bGyMJMYONwSYha/IzZrPZrLUVSZND+ed//ufmnAhsqN1Qy2bUHhnIwsKCfvjDH6pWq5kjYPMQ6VGCoKWLaJe6dCqVUrFYNKfG31GrxSDRzhQKhQzCgiVP1BqLxSyLIIBKpVImJ4ihQcKRoIw+fSA6sqnHkJwkq5V5dmg4HNaLFy+MDxGLxaaY3ETrGEd4DTgAL3QDtPrNN9/YPb1+/dqibIKRUqlk0TaZmaQpQ4KT8HVsfj+bzZrh4fCXy2UFAhO5yscEF7IcsqXH/aMgXMlk0oJk3gtBJQ4VNr/PevyZgNRFQMa0LSBN4EFJ5lBPTk6M7Hd/f2/EPowbU7dAgEBqPCTJuWafra6uamlpaUqcA6lInAd7lZKRr8+j5w+8jKb06empqtWqZf8E4byzQCCgQqEwVRbjnPJzEEZZw3a7bYgB6MloNNLOzo59BoprweBENa1erxsvhHnf7EnvlGu1mtWX0QaA01Or1cw2YTMHg4HV4mEucxYYJ0tGTssj3QxwVyqViqEzOCkkfcnokbhlwhUlNfgMvv8f2VHPUfIdQKB5lAVzuZx1gSwvL+vu7k5HR0eanZ3Vs2fPzAfRxsXPkCVjy7x9BvL3hFQCafYjpRx+H0TXl7EIbrG5PC88JlBYVOw84sV79QnIv3d9K9Y3mS9ROw6I7IOFI2okumGz4Ui9ZBy1NhjezWbzj2rHfDetEiw+Eaf0IMGIwUG1jEVDbKJWq00Nl8fIE616VSXqs5BQhsOJ+AGtXUCv/nN48aAOvFAPs0CYCYVCFgiQ/QF5SQ9BQzKZtL5l5qP2ej1zsDgcMhvqgL1ez6JM74Cov7HRlpeXlcvlbPwegZRHRXC8wHo4JyRXB4OJotby8rIWFhb06aefTkHwoVBI3/ve9/Tu3TszgARj0WjURGYkTTHpcUocIn4GY4jBQGAjm81qfn7e2h8ymYw6nY6JnuDgOSyoNBFsNptN7e7uWp/9zs6Okc84eC9evDAyCyIKwHMEq0TbrB9OEGUmJnH5Wn4wGLQWO088xGGTrV9eXhoMSCDmSYgQNKlhVqtVO5eBQMDkGznHrDk1tmg0qufPn1v70vz8vPb399VqtUz0KBAImKJWODwZ49fv902hit5e3hfPvbm5qXq9rlarZfdMMA6cCZkJJ+i12snYms2mZS28T7Kh8Xgicbm6umrkLdCEXq+nf/7nf7agiz/nn/v7e1PWghuD1rw0Ma6VSsVmLfuM8eTkZKo7BaY2NVJs3t3dnfU4z81Nxivye166kgANKPzk5MS4D7Ozs8aVIAG5vr6eKiHQzjYajUz+kvGzIB8LCwu2lqw59ufi4kKHh4cW/PT7faXTaatHIwgEM35mZsZ4L7SjEoiyBrw7EiEgeA/7k8ABZXOGZmdnjThI0MqZkGQ+aDQa2fkgAMN+QNrz5wU/4hMpkEvvfwiGsDs3NzdGdmy32/YsBA4EiOwFhhCBCOI7nnJ9KwlRskmMlIdBILYAA7PwRDBEykTTkI5w5JAWFhYWzNB4ctdoNDJWN20KPlonO6lUKqaRjQHhgJ2fn9t4Q+kB5vY1RYZh4HzIUjA0vg6M4eNQYTRgj5KRwlInomL6C5sIliMqVFw0yi8tLdmGBB6rVqv63ve+p5/85Cc6Pz+3ZyFAYtYypDsOhidFoLp1f3+vxcVF5fN5iz75ee4PkpTP3oiC2Q+wy8kggGaB7v7qr/7KYOPDw0Nb41ar9cGSxPz8vD799FOl02kTauEwkqkC7W1ublowhTOBvMIgBIJKCE/hcFg/+MEPtLi4qP/zf/6POa92u63PP/9cW1tbBm1/9dVXJljx13/91+p0Ovr1r39tvafj8VhbW1smZtNsNg0CZd/Q1kNNlgOPYwa18CUc9iCtjdSyeb+0wbHHpYdWRe+sJRns2O12LdukJ308Hhtys7e3Z/cHQrG7u6u3b9/qpz/9qQVpZERMKKO/FYSEYIqABagW44VNIFi/vLw0rgKSk/1+39Ak5tQjC8oUN2BcBnRA1oMrQSB0d3dn2d/u7q4uLi60v79v59QrVwUCASNk+rUg62NcLIYaJ81zAcXDNyFIGAwGNlb35cuX9r46nY6KxaI5HzQFotGo7u/vlc/nVa1WrcVta2tL4fCkzTEajWpmZsZqzvSCk3QQCKDv7vlEaANgv/luOBx0auCgOHMQFmlZOzk5mdrzg8HAxGYI7rGD2Djq8/wMvCSQwMs/aJOzl7G1ZL0kHB6K73YnQ6I+/vjjKRSWYJbOiVgsNlXHp8VVepjzTTLqeSY4YrL8cHiibAg6xeRHXyIDnofkSnZNQPWU68mO2sOQZLFEGZIM+vFtEb4R3BthT6Zg8TFYkUhkSg2HKIsoCnEP2H2ZTMacCxHYeDzp2SwWi6pWq7axPMwnTeruBAxA47BCkQfEmXDfHDgiXe4Np8vmIENATAHIkUMFu5fPAXr1cpmZTMY2MTAMbN50Oq1/+Id/UK/X09ramrVN+IiU9xEKhYzc5YMParpk3IjKkD3QjlStVo30hhEkMnz16pVF5ScnJ4rFYorH4+bIgaP6/b6+/vpr3d3d6fj4WKPRSNvb2zax6uLiYmoGMESg7e1tvXr1SslkUr/4xS/MwQeDQX300UdKp9P6zW9+o9FopNXVVYuOQT3q9br1fjIKM5vN2iGLRCLa2trS1taWTk9PbZrTT37yEyUSCdXrdWP2Yry73a6VCuAVeG30UChkvb30eILYYJSBLTmsZAwEE6AhnA9mTgOTAwmS/SF+0W63rWRycXFh5DT2P+parAHGl3+zP8hkgfBRSiOL4qwyMWphYcEchDQxwrVaTY1GwzgefBbBFShBPB63M0rQw7kbj8dTqAMEQC/jCDJBNwh2Am1mScYT2dnZmWpJ4r16MikJyevXrw0V4EwlEgnLfn/2s5/ZeQ6Hw/r44491d3dnXQk8FzVw5Ffn5+e1tbVlimYQb1OplHE4QAsYnxoOh23oDoEx8CpOgK6DUqlk5wAeRyQSsZo15UfWhaCcM4czATnhfbAv2+22OSNJ5izz+bzZwE8++cQY7tg/3+dM6QNeBigpqCPI0vn5udVz6fro9/vWUvoYDWk0Gup0Onrz5o2ePXumeDxuTp2Ei9IJ54R+cp7TI2SeI0AwTSbNWU+n01NlBfTAOe+cJe/0Z2dnTeTqKdeTHTUygx7OBarBSBHVEUmQGcPGxhn6eg8EDg8P+iyahcAhQyqgngoEjmMMBALKZDLGZhwMBlMR6PX1tUqlkq6urkwDmYgHZw8JB6iXrJSeQaJ++miBAYG4MYjIGZLlXVxcqFQq6cWLF0omk1NsyqWlJeXzeWOnsm4YU1+DGgwmA0X8+ErgtPfv39s6ApHRxoVTYW09GkKUVygUVK/XbRMjMYiYCkan2+1qY2PDIt1EIqFGo2HOh4gRhGF+ft40te/v77Wzs2MwtaSp9+fr0hgEDgqHRpK1hyG52O127eepww4GE3W56+trdbtdq3sxHIV6HQa4Wq1auwnzcs/Pz/XNN9+Y0T8+Ptbt7e1UbS8YDJpBoa+YITLA8KxfoVBQu93WRx99ZO9NkpGtMFw4ZdSdKJFQdxwOh0qn0/r6668tECOromaGwAVCJGRalG0g2EB+k2T7FwMO05eAjgCVTJN7wQmApqG2RaBLZu+hfd4/XJZsNmsiGfw5gbg0SRI2NzftvcKyZV+yH4H8S6WSpEmJ47vf/a4JF93c3Ojs7GyqDc/XyXHgGFNEieAi0FZERk/wCPkpHo8basM5Bw34/ve/b9AoyQDn3vf/s16UwHK5nI22lR7mC5DdwZSOxWJ69+6draEkQ7voWcZB393dqdlsamFhwdBHkiLeGzZRmkyi8hOmCOY8rwRIm7UZDCYtV5Q0edcQ+TxHAH0IgolarWaaEJSwxuOxDg8PTTXPn3dq3EiaEhSz9uw7PzqWC7sLIonML3aHn2V9sMUE/8wbp7uH0hFJKCh0LBazPvinXk921EBLHGjq1dQ9PUGM6IbaM9kjGbiP3nHgZN58LszWbDZroh2DwcC+r1KpmFMkGuR3US5qtVpKJpNaW1szVSwgHjYMcAbEF6J9ImCi73A4bNNSxuOJChb1EpypNDEszNblJePkS6WS1aIJaNDOJqpjbB4sWdjMiMoggNLv923wA89PjzltXbS28V0XFxd69+6dBS25XM4CGtp/JP2RChyHiuzv8vLSGOsYaV+34dnJBKj5393dWZsdqmZE29TMHxvNVqulXC5nkTz3NBqNVCgU7GCCHHg2MWQZoEYCHRTwWLf5+XnLOuLxuBqNht69e6dyuazFxUWDYYniOdQ4UrKTm5ubqWCRvc7fQ7JDnej9+/dWzvBci2azafVashIg01qtZs94f3+vWq2mdDpthoqecFCtwWBg7ykcDhuZi+CHZyE4IKiGBImTBJXg/fAOyHJh7VIGwkbA9AdZubu7m2pv5AzgXGmzKZfLJjIDaZKfwzgTeHNfvE8cAlkP64Cc5mAwsDGIH3/8ser1uvX0ksnd3NwYApNMJs0ps5ckGbSNqAzPg80jqOSMptNp28O0VbFvsWWM4JQeRKYoKfrsHb0Dzvft7a05KPY4rbOQbs/Ozux9wU+h1S6fz0t6GM0JoXVpaUkbGxsmDtNqtaZUuEi+PIxLsEYGCwG415vIvCYSCdvPaNgz4he+CWuysLCgSqUyNRpYktW6KZtRvkFrg3IKQRnDdh5f/jM9eY9gAbU6CKd0tGBjCDRCoZBpGbRaLYVCIW1sbFgt3XdFgFj+h0DfRBoemkwmk7bAfCmHikgGXJ4DiAHiRaNdTX2IF5JMJvX8+XPbeJLMmc/NzRmjm8gGww6hgfYc4GOi91AopIuLiz+C1clgfQsBh8AbJqKuXq+nRqOhm5sbg7Ew+svLy6bHTT8dLFtJOjg4MGMM8hCNRvXd737XGuivrq5MdxmlLzIugg3PPAWi29zcNAgZKMi3kAHfMFCAwII6IAaUDezX/uTkxDJHIE7W0ctxHh0d2T1jECAccvBBKdjoj2vUGNtKpaLPPvvMekE54N7Ik7kA4bKv5ufndXh4aOseDAaVy+WUSCR0eXlpDozgKxqNGnKANnO/P9Fm/+ijj0zCk8EMZGuQcnypwxMUB4OBBY8Y+XA4bG1xwJIYPWRhgW4h8wHPMY2IfcuzUdeDBe3JnhhxtLKBt4GrMZI4V1SzIIGen5/bfX+oFs7ZxPHwmfBSPN8E2B4Y0/8u5ZJyuWw1b9p4fBCP/QA+JfCAMwBHAURiZmbG2NGLi4taXV3V+vq6IQO1Ws3gfdAPAgrPwWHfQ6LzgSroFcRS/oyaPUTTer2uTCZjSYInfCH6AskU9IEaK+9jcXHRSF+sM++Zd4uT5rq/v9fJyYklLLFYTNvb2zal6u7uzgaiQBJdX183JAmyJKUf3iNOCNvIO0in05JkiAHlERCb2dlZnZ+fWxDoOxlwjp7P8BhtYy+AZJHY0JpImQlSF9oMvrsBX+bXjM/2o4DxFfAPksmk/R49/dw77xiIniSFcZfYKfbSU64nO2pugmgJeANjD/aO44NwQt0OB87mRGSALBsnjRGAzUomACzCQmMQcKwQEHgJZNo+wucFsCGCwaBJEeKgeDmSjMnqAwjY2yy0z7yj0ag9D7Vc6ptEywQx9EvyvWh/U2NPJpMaDAZ6//699vf39fLlS/szyFJA4zAjIeYgUuJ7oMfjsUGs1WrV2o52dnaUyWSUSqWUz+dVLBbtUDA9SXoYaoJT4zDRgwgxbW5uznozMTps3JubG0MBPKwGSxKDg0G/vr5WtVpVr9fTs2fPdHZ2ptPTUwUCk+ECCwsLyuVyCgQC5ngxHGTzdBHw7k9PT42ww35FupOaZyKRUD6ft35PoE54E5KMVUqmBb+Bcg0tSt1uV6urq5bVcOjRiW+1WiZcgqDJ+vq6YrGYzs7OLAtkXUAFOIfseYJVWMa0xlDC4PM9CUeSlTH6/Yf5zQQesVjMlOB8sMPv9fsT7W32g0cpaCOklYfnxvCCiHgpXt9CRfkE8k4ymTS0CRSF9wU6xvtcWVnRy5cv7X0UCgVls1nlcjlThAKV4vNYM8472TroFO8UtnQ+nzdOBJrwXAy5gAwHU531Z42Btr0MJ8abBIZzTVnDw8ze9hKoEmz73nS/fzzPBQibViTeg2+VpawJ6RAk7/z83JAzH8xwX4eHhxac8O9sNmu2mWdHG7/T6Zi8L2UEWPX+8lkvyBVo6Pz8vCEE+CESH4JOnDe/v7S0ZB01ZN5+zXxSyXVxcaGZmRlr/cPHYMs5H6g3EhTRGstzen3+f+96sqNmDjPsts3NTXtxjwlXGNy5uTlzTDhconkmlxAlsxhsQg4fGYmvVbF5UXiSHiAYeuXI/PkzHCuGjpqLFxWgltxut23zQ+jgZxkRRyQlTZSa8vm89b5C/Nrb29NwOLRhEc+fP7eD1ul0lM/nTTN6cXHRDo80OSygCoeHh8rn8zo5ObH1hxQDUkFUC6kKSJ16DqxKIHC0hBEfAebyrNWNjQ0jey0uLmptbU3Hx8fW84xD5J90Oq3FxUXV63VtbGxYlIskJ3uEdybJnuexQ8WoMBkIgQtqQggcFAoF/eVf/qV+9atfmdYxEGwsFptCgMbjyXSju7s768+l04CM/+7uzoIVAlOCEchv9XrdDDwzfHH4nkXq37U0zcrGOTBgBYIhfIKFhQWD4Vk3ov1Op2POhozBG3QMHRk9WS3Zc71e1+rq6lQPK0TG+fl5gzrZD9vb25qbm1O5XLapUKwbvfSBQMDW+/LyUrVazYR1cO4gXpyleDxuxLBgcCJqAenGG/9ut6tEIqHd3V07M41GQ5VKxZwfBnJhYcHY+qzLy5cvDR0BPgfpohcYGWRQMlpFa7Wa9f+TQbFnCWZ8YNloNHR+fm5ZHIxqaVLn3tzctOBydnZWV1dX1kJaLBZVqVSs35/3vrS0ZGUNWgMhHnpG93A4NHIhSOeHzhV2FlSL/UcAR9ZLDZnBRb4UQkCMUhsMdKaUdTodFQoFO+dM4MJ+E7QuLi5OIYWPIfUPTSfkPMO9ICiJRCJqNpuGOPCcoHX8NxflP+wnASQIx+MMnrXzDH3s4+3t7VQZhvXGluZyual+bhj9T72+lYQoovn5fH6q9QijymLTNI/x8KxqnLInHxCRLC8v6+XLl3YYz8/PTQwA4ha1ql5vMr2KWieRLkbw+vrasmacGVEpi07vIVJwZBYIQwBZepYhrFNeCGSmb775RpeXl1a3ghXLi6btAYiYlhfqkGRJQNDU/RA52Nvb0/r6un72s59NKaLx4vv9vrVQQYRjEhbEJ4xCLpez2i9r59vFqM34Ga8YKmQSS6XSlJAMxCFJVmeDeAMxCgdGBgssXq1W7XcfXzB48/m83r9/b1BrNptVMBjUwcGB3r9/b6QsWngoK3BQ6PkFxUBAg3oZme3Kyorevn2rRCKhzc1NHR0dGRGOGt/d3Z1NbRqNJi0f1JKRNMVYE7yQUV1fX6tSqVjmBGw/Go1sYhmBLXwJ9iv/hhzjsxkCWtAmzgMiNNKk/FKtVi2ggzx0d3en73//+zbUBKPKd0myrJ9xmxh6zvLBwYG1CcJd8S1onAEMOiULEAvpoe+XrAZDt7i4aOWPt2/fWgmC8+0dES1uQKdkTTik8XhsIh3xeNwgyUwmYwE+ErXYL7ImHD/XwcGB6YGHQiHd3NxYUlCpVOws53I5C5B3dnbsmXAOy8vLVi6rVqs6OjqywJT3dnJyotXV1SkhJYIj1Np8t4b0MPaV2uyHWsWwC5QRPQLpIXh64wlqycS3t7fN+YGqMK6TvQuqQ6bLGfRlIIIl9ptfzz91kUyxD+AJ0P6FEiN+AfvORTsYcD3vhN/xCBAlDq5+v2+wPb6JEs9j+5XNZqcIaPTq/4f0Uc/Pz2ttbU3f+c53dH5+rkqlYmxW32I0Go0s+mNRMJa8LIwK9U4g9KWlJaXTaRPWZyg4k5WAuskA2GDJZNKgG0ggvoWKuhpQDUQjDjLRPMxVNgEbmcXnO6hb0PIyHA71+vVra6nh9/kuFHCoCQIjxmIxvXz50urV1JwwNDMzE4lPSgJ/+7d/q1arpXQ6rf39fduY1PqA7lEhg0BCL+ivfvUrI9JRV0MIAWPjszHqcMBUoB/j8VitVsug9McRL4xKDjPD6EOhkIrFot69e2dD1C8vL1UsFs0AAMVyKEajkd68eWPKdjiJeDxukoJHR0cWkOGwB4OB1R1pdyF729nZ0RdffKFSqaRyuWx133A4rC+++MLWFEQFaHIwGFiZo9vtmhBHu902Yt7t7a1lFSjuHR8fa3Fx0aCwx22C7GfWkmf3tVhJBpu9fPlSFxcXqlarBuv5s0V2RCmCPdtoNPSjH/1It7e3+qd/+ifd399rfX3dSgLA1bTbsI89IzsajZriG7wIapf01QJhe+Ie7HDOPueO8wwBk3cuPaAg19fX+slPfqLPP//cMjxkOE9OTky+kzXodDrWEQIMnEgkbJ757e2tIYHVatUG0GSzWcvIcNK8E58pcdEu5CcALi4uGrImybLzRqMx5SQIyICsPWs+n89bP263OxmTS2Dq6/E4TDJwkiJPqKT9KZVKaW9vz9YUeV9KN74kSTbMPvXqbF7DHz0AbH00GrUgAMQoHo9beyXOjq4FzjqJV7VaVTQaNT4AgiUEUI/tDJk5nSUEtnS4oHvPu4B1zbPAg/FaE9i7UGgiRoXOfb1eN2SS34eTBKJAdn15eWmMc4+ScuH4vaDXv3d9K2WyQqGgcrlsdaPr62vbPGRVXl2nXq9P9QwT6VGPZCKX71P7zW9+Y9keGXClUrEXSB81DMLxeMLArlar9pJ4oUQ9UOX5bpSxCCw44MAn/pBKD3A8BK1QaCJtmE6nbRTfZ599ZgQtNhcbGzKGz6Y5aES4QGm0GFBTRtji6OhI//2//3eVSiUThVlfX7eMmVYq3k88Hle9Xlc0GlUymTTFptFoMhWJ3lSM4mg0sp+hrCA9RJ0gD4VCQaFQyNrlHk8U88aNgwpEBApweHioRqNhLWYvXrywdee9lMtlXV1dGVuTzMi3q3388cf627/9W/3d3/2d1eeHw6EKhYJOTk4UiUyGnqB7DEIBQQd4i7oYh5b60q9+9Svt7u6q2WxO1c+9wdza2tLXX3+tjY0NVatVvXnzxkhI9Ff/67/+q6rV6lTmwdpT4qAswfcDHxLgQDiDTc79Y/QgLiHdCQyP4Atn6sc//rGq1aq++OIL7e7uWiACgz4cDltHAwaf/YlBDYfD1vHA+YlGo8rlclpbW7P6PL+HgyaohTjkkanHxCf+zGfNBO70FodCIXMOmUzGzhLzsanB4iD9Hkd05+TkxOB75GV95vQY/vSX3zs4Lnp3faKC4T87O1OxWNTa2prtBciRd3d3RuDCkSLXOjs7q9XVVQvc+BlsDFkx8D2ZOBD58vKyjXhlb2WzWbObkOgkmYMB8WJ6nx896pnvZOKRSMQEUrD3nIHZ2VlDWXlm9kW9XrcEAKQlGJy0t8FxkWRiQQTq3nFzLnH8/DkMczgasVhMR0dH5p9AIam/43xpXVxeXtaLFy80Ozurr7/+2jpj/OdvbGxMBbbYy6WlJZXLZUNA2P8EA2T4T72e7KiJgjHKRGAXFxcGNbCJOOBkiKlUypwyUCktAr5nj++gb/PFixcGWTI3uN/vK5FIWBbIgae2xAZlYdrttt6+fWuRPUo7EAs8vZ9+RLJLnhs4n0MzPz+ZK0vEGYvF1G63jdDkoTBeHoYGWDgWi+n09FTB4ERxqVQqKZlMmuQh/d6sLTUzYJrxeKyTkxOVSqWpuh8GvFwuWxsLikY+O2O9IfTV63XVajXbhMiUptNp6wOsVquan5/Xs2fPDBIaDAYqFAo6Pz83Y8WEMmqpXq0Ihy3J2uYwxhw26uOwkMPhyYhMpkqxHjMzM7r8w9D7dDpt73x3d9dIHGQYEFg4jLFYTJubm4pGo7q4uNDm5qbevXtnsPBoNNJnn32m3d1di5Dn5uaUy+XMAQD3t9tt/eQnP7H7p6a4v79v5wI1JA/TUaLo9/u2lzlX1PswxMvLywblEWhQ24TshLFh/zebTevzZtDJ//gf/8MyFe9M/ExgAhVINmQAMMYxPL6UIMnU0yBqIY6CWI5v5alWq4Zm+MuThTg7yFCSORMcIBrB3GRarCiLhUIhy/hYn6+++srQpnK5PBVEPCYNfejyjhsHwD0BgaKxcHNzY8EKWWuz2bR+f5xfu93WxcXFlOoeawD8ShAKSRaiLOTXm5sbFYtFuyeyy05nMp/aw8r04kejUZVKJWOhg1T4d0IS4BMbiJAkSQguEaD5RIf3LsnWhn1BVu3XlVIgYjHBYHCqg4I9T02Y9wbPxZMeaWcj4KMuDNnPI2cEBASSMP8JqJBs5bzi0CE/UjINhULW3x+Px617CBY69sxzV55yPdlRQy33jFNfZ2ZzUeDnd8iIPIOPBWQBfO2YrO/y8lKlUskiQq8wxAbHEPOyacNgw9GeRMb8oVnSHmrs9/tTDpnAAqYmUAcZLbAGhwdHSYAAcYVeYVSX9vb2tLa2ZuItwNDlclmFQsEOL2PR2CysjT9ItPP4yLXdbluQRE3ysUHs9XrGKqYs4CO84XBo8DZZQyqV0sbGhhkJNmg6nbZs4NmzZ0YkYx3ZxLDngU3JUtlT9JqORiPLJiD+0I5E+wVwKWUF5D/5HeBAT2TiXfF7kqyv+OjoSLE/zK/u9yeDYZrNprWYRCIRm0VNRrewsGB1Wfr6o9GoyT5S24/H49rb25uqh41Gk1aaWq2mi4uLKXiu3+8b05ZeXlo8OOQENBhzzxgm4BoOh7aHMIRHR0dTPZ7Us3HYd3d3pnP+/v17bW1tSZqgYyBbj/vnMeQQmgjQwuGJFgBtXMDYpVLJWvV868pjVEZ6GGMLzMlzYMSz2axlb+PxWCsrKxY00/oGO5/e4Gw2q08++UQff/yxfvOb39iZ8dC0tw8f+n+ySM90xwZ6fg4OjMBqdXXV7AbIDNK/Z2dnUxC7RzRwHqAw2Jtut6tGo2EBMWREHGKz2dTGxoYRDylHgRCS8CSTScsMQYsoJbbbbWtHAk0lOCmVStbdwz7yHRwkJwTx/r3idH09OxB4kELGxmPnSDyYkNftdk27AEETb999uxX/DU+Cfms4EwQeiJdQNgJFPDs7MxIp90mCR+2boIIzhTIiQS/nFoIzAcNTrm+l9Q3zGsNNoz/9pECLEMyIgqntSbKMikzaH2rIJNKEZc4G8L2evFxaKfh5hpwTaaO7TetRr9ezARZ/ikUIo5MXgMOkfi3JjOzp6akymYxevnyp4+Nj7e/vT5EvYEjyIhGraDQa2tjY0O3t7VSbxmeffWbEKOraRJAc9MfwYCgU0s7OjmWmtJVA5ALO9EQkP/4OPoHvLfwQy9HXwiCGEKSxTmQvHEoOK06GtfB1Iu7B9yBDLsQY4HBAX8hkySow5GSpHrUhugdJGQ6H5mQRt/jyyy+VTCZtShBkpFAopMPDQ83OzurHP/6x/umf/sl4FmSFkkwYhHUiSyaIgZgHUY/n9nKdb9++NV4BvbbSA2OVAIU/C4fDxoZ9+fKlVlZWzJiQrTEdCSUrUASv2OcD7FAoZPXZL7/8UuFwWBcXFxbw0YWBNCulk0ajoWq1OpUtexYu50qSrdPc3GSCFXuJfnlq31zsA29sed88RzqdtholwcrFxYWur69Vq9XM+ZTLZeXzecvMbm5ulM1mbQb5wcGBGo3GlL3zZ+CxzfC20Lfn0C/su0k4/+vr68pms7q9vbXEg1ouzglbhv2i3bNSqWhzc1OSLDPDEQcCAdOcoJeemeucRS7OH2cFHkYg8KAgSSICysW78wRaCJXdbleHh4cmU0tyxHtstVoql8taW1uzM+LLTh8iS/pz4tv3/NmmnEk5xJPm+J5yuWzPR9cJw3nS6bQFiiBgqC0yNa7X6+n9+/dWhvDrSMDEOcbOwQnBt3W7XZOOpeRJCenbXE921L7OAxECx0BPKo6ASDMcDluERwZOpuEzYQgGvvYBjOTrP54RSKYFcUGSNZOvrKxYvRp5QV9HpybCBUwCXd6zQh9DYtSnZ2cn4+S+/PJLI3GxWYBJce6lUkmVSsUy7F//+tc2EIFstlgs6vr6Wru7uwbJcC8408cZdSwW087Ojm0SID7gSZyGJDPU29vbxg4Nh8O2kfL5/FQrE/+mxxL4zde1MSJwBi4vL3V0dGR14WAwaFkl5JBWq2XIhXcUwFfUZFk7PoPWHQw00BQBIVkMAR+iNYwR5JC/fPlSxWLRhGqi0ajVwnC6HOZYLGYtQAxlIcikdENmiQNA0CUSmYwfrNfrU4xP9hOHFYdJSQaIlvYX3jl7T5LBaezxlZUVIwqdn5/r2bNnKhQK9vm93kQ/end3V+/evbOyBi2T7Ce4AJDCQLbINNbX17W0tGSiLt4WUC8na4LzwVn1/0+QBJ+DYATxHy6PdkHURPnp+vpaiUTCiIrsJzgKg8FEthLEiHUOh8OmwsU78fbNl+78/z+uVfszwv6lF923IUFsuru7UzabtRIMBFKCORx2Npu1ejQXQQdIEagQEwIRbsGBLS8va2try+ypD848aYz30ul0LLijjZWzLT30g/f7fSsl0P3CfkWtzAf8rBt7CKEg9h6wvV/b0WjSDolgFWWd6+trQ1/hJ0ASZi4DUDPnBpnYRCIxpYU+OztrCBvcnpWVFV1cXFiwDrxOi9hj4td4PDamOtwrr9XBmWY9GFLDvZC4PPV6sqMG/iVqQuEnEolofX1d3W53im7PohMVMQACI4VDoT7kSWjSA+EAWAH4mKEDHq4jsyeT4eVCViJQoFZHe4c3ICsrKyZYAkSHupH0cCA96WcwGOj09NQcPGtDvRk42LOYgYM40BjilZUV/fmf/7k2NjbsENO7fnV1pWq1ak6Omi9yqhwsmLbUXajPAr/ncjml02kjl6Gxu7GxoW63O4WMgJjs7e1Jemjt4PeoHQaDwSljOBhMet5hmlPH7PUmQ+Xr9bptYsopGBG+k3rd7u7uVBmEljMif4wJQQr7h/00Go2MAJJMJqfmBSeTSRvrSfsOF9H+J598ooODA7vn6+tr65tF8x29YDJ3AiFQne3tbbsHMi+kVjGiZDPsa18mCoVCtk7sechHsPrRPH727Jk5L+BML/cI+XNra8u6JnBe7FOehQDZB4WcM84YZyMajer8/FytVkvLy8tTpEAkGHFIw+FEn5zaHsEgoiaPZR59qyV1Zd4zLUN8ZygU0unpqb17r71AItHvT3ScWXsITLFYTMvLy9Y+yBnwTttfnsiEAwRJA4khgCCwQXue+j4cDwSMKC15bgX7KhqN6uDgwKR8O52OMpmMtre3rV0PJ3B+fm77DFIikqzSQ1DS6XQskxyPJ4xxgtzBYGAKgj5ZImjt9XpTffI4a9/KOTc3p/X1dXPuOL9ms6mrqyvNz89re3vbkD3sCCgsCUiv1zMf4stOZMvU+6XJxEFPjKStFu1xHDjoHsEDXRo+2SKxYJ89fu/YZo+6UIP2ZMBAYCLQRAkCG/mhToI/dX0r1jeiAbRVUd/w/7BxiXiAh4BG2YAILBCZkO0Mh0MTlKfdhN5VSTb9BNo9RgbHSuvR3NycsWk9yYnMkv5rNgiHCugnEAiY5KC/er2escZxhtRrg8HJPGFe0Hg8Vi6X03g8VqlUmjrQvuVgZmbGhkD44AECw+rqqqLRqL755huDoPluNiSGzI+48xEbAhoI0PioFLLD7OysIRBXV1daXV3V6uqqoSccfqDnwWAwNT87lUppfX19ijkNiQ8RDIxEt9tVtVrV5uamSYgCV6VSKVPzCoVCyuVyKhaLOjs7U7vdtnYrnpOBCQRl9XrdGKcoPjG6FAJRKpVStVq1edWsHeUGkCKcLu+erIGIfH5+3oY7IO2ZzWbte2CzEkj4EgC1+FQqZevP4aUtj150ar3eiYLkwCjf2Ngw9TrgfL7v9vZWX331lUGjQJsEfqFQSC9evFAwGLS53Nx/MPgg7kO2QTmC84U8Jb3gnviF+AiQL4iOJCOeISxE0CU9BMd+upH/O4I/7MvFxYW9e8pwH3/8sa3z0dGRZWkEaVtbW0qlUhbwNptNvXnzZkpExcOzj7NqH8ADV9NeykWbGkE5aEg0GjWeCEMxcCoEjpSu0Pl/+/atxuOJciGMY34HG8a58FkqpUeG8kgyG0vwwjUej6fIjWTrkGqRqaVmjOPs9/sW7GML0Z7A+dKdAHEWBTYQSnhEkEE9ySwYDJrmOGchGo1aTR07jBoZiRoy1HBj+Fl8lUeJGo2Gfv7zn9sZAl19jKxQK8cv+nuFyY/9XV5e1s7Ojun6w9nwycG/d32rMZc8HDdJVOzT+JmZGcsGMOxE9xTU+/2+stmsRSsQrH74wx/q+PhY+XzeInLgTWorRDxE2UBrGL7BYKB8Pq+dnR0Fg0HrUfWEk9XVVXsGngOdWA4WJBk2rr/I7H/84x+rVCqpUChMsVFRB6LlwUd21D/5DEZLNhoNy0KRmLv8wzxWSHmQZTCWXoebyFB6gOEIMjy/gIPPiDgMlyTlcjnFYrEp+Vd4AUDb0uSwY7jZrKVSSTs7O2aEaVHgYFN/rVarUxnq4eHhFGfBSz/6f1KplOr1ukW61Mtvb29tMs/y8rJlpTxHMBjUq1evDG0gMESUgTGJPA9BHmx5H9nzLqijsbcht9EWBVwuPSgowZ8APYCpm0wmDZF5TB6C5ESk7h0D7/X29tay/vF4rHg8rmazaUYwnU4baYUAg2xckmVy1EzhPZyenk5JHNKTyt5lj7H/cMSeTEq9mGCy0+mYYAqOBAcAU3pzc9MIlRg+EKPHF8Ei9ebHw3HYv7lcTvF4XOfn51OltU8++cQG3lBbnJubMwIQBvVDWTXogO8k8GRHni8cDmt7e1vJZNLWhPPF7PJWq2WiTnwGAii+XYw1CQQCNnkOcSVPtlteXrZSChe8CLQvIHguLy/r/Pzc9hn7kJIJjpgEwwt8EFCwV0ELsDdA07xb1gR0FTuBXffkMc69d5AEFTzPaDQyW8k//p0Q+IIWDAaTvveVlRWDwD0fiT2FTwIFegznRyIRC655p5xV9im/A2LEHsaH+rLOU65vJXiytbVlm90bFQ8REd1yszgpsh0WdDyetDXd3NzYJj04ONDvfvc7qw0wWYXNUK/XTebQqy89boYn+iKSvby8tE0PXAskxsZhowAXkdnwIvlZbwCANHmuwWBgYxJBHVqtlv7mb/7GoFKfUcMgprXp/PxcyWRSvV7PYMb5+XlVq1UVi0W1Wi1rqgdmwxGQ2dC6c3V1ZWIHbBIYm0iPcvAgtKVSKXM69B2TeVOm8JAo5YW9vT0rc3Ag2Pz0VNL8v7Ozo3B4MpDiMbFiZ2fH3rcn03AAYfQi88k+wMhyUGi/qdfrSqfTU7A8gwE+++wzxWIxvX371upfjx0QLFFJFgFj6GjtQ2CHfcVnSLJuhl6vp0qlYhO42N9Ah+wHLtaDPcX58uUav9eBC1FUKxaLymQyWl1d1eXlpQ4ODqwG6pnNfObOzo4kGeQ7Go2Uy+V0cXFhZ2Y8HqtQKFhPrYfLEUohmwQpi0Qiev369VTWws/gZNmP+XzezvD29rZl/MhsMmudn4nH4/qzP/szzczMmGIcc9Bpybm9vVWtVtPGxoZl2ewvSKY+QxqNJi1W3/3ud601ElEZ1pqLmvzc3MPEL37Ga5szzpUzQD0exIEAy6NfEJX8szQajakODPgFnBGQCfbR+fn51D5aW1sztIN1xQZHIhEdHR3ZsBv2+/n5uaEN4/HY4GoCBEoJ/DwoC8/DmeO+CXwgHIMs0U+PA2Q/g0zCVPeSuJwzAnMvqMN7woaDlKLzDoJLcgQvxpNEfTDJ/xMwYLcfB3DBYNCQA38fnU5nahohgdB/SI2aIQRIWpJVEFkSQfGCvLMmQyUD5AEw5NQOEOLAqEAGAPMfDCZqS34i1nA4VK1WM2UeLhrz+TOyRxjjRINs+IWFBTWbTYPreelEn57olE6n1Wg0dHx8rI8//th6OCEj+c26vb1tdSKCFF4wmWS73TYxGZzz9va2IpGIEVF4/pubG62urto6k5lSoya6hfyDpjpECp6JzIponJJEKBQy2Nz3zPIPawOaAewFMQ5ykxea4D1jUGBPPw6uyAKoFdHiAETr74UhC+zBRqOhQqGglZUVy9qWlpYsOiYLILijv3RtbU1HR0c2N1p66Pccj8e2Z1lryGD8s7KyYmMSIZdQOwPmRBSH9ep0Onr27JmVGYD+JVn24gNEgl2Y1l5kxhvtdrutq6srM360fbGWHjr2695sNqf0A6iT8//A1ff391PjEIG8YV8DUVNmgsQZj8f19u1bQ2SeP38+lSVSSkFcplQqGWIyHk/kSJGZ9c7AB0Kss4cf2fOSjORKKS4ej1tACOci9ofhNcPhULlcziQ7Dw8PzdH7PesFkB6TgyAeMp+a84S+AhkpQQ3ER5w098n+Pzk50d3dnV68eGE2grYzVPdIWnhmOkF4BwwOgvhKgJJMJk1hzT/f3d2d8vn8FLwfj8clyVASatGQPvk5HDOlMklTZUl+xjvZ3d1dO+uQf0ejkQmy8D2084GYse4+SWBNIYwh2CQ99EozXIPP8agon+GRHf4OhJHSAKUH2j4JEHzphqQGRNn3kD/lerKjJmuTpsliPsrHKHid6263a33Q8/PzVufmc5LJpBFa2Ez0f47HDxOfpEnLFjNqyYhZOOqo8Xjc6peodLFYCCIQfdKqhNH1RBYyGWm6RjUajUxycDgc6ujoaKp9IJ1Oq1KpaHV1VR999NEUuQ0HyiFh03jW4Orqqtrtto6Pj3Vzc6NarWYtHr62zyQhnMHp6akkWf2d793a2jJC1tnZmTY3N419PxgMLKP1MoLBYNDGY25sbEjSFEkEZ0+kHAgETI94Z2fHoD6MiSdrkbFi+FhfPuPy8tIyH54NZ4YeeCaTmTrUQNDX19daW1tTLBYzyO3m5sZKIdfX11Zbw1Hs7OwYw5mMnPUl8KQVEOgYxwA5jmepVqtWMgBW80I8vpOBbAiOAs/KGnlWfCKRMCibAKBcLltgxzvxvcgEL5xVDCOBNRdEGoaD+I4IygV7e3u6u7szlKdSqVgNlnfYbDYNdQHtubm5UaVSsX5z3u1gMLCWQn6f9w/fgeloCGMkk0kTgeE8v3v3zoI6Jq1xLglKWq2Wfv3rXxscL01ahhABwRGyXzzzvtfrKZlMqlgsfpD1y7nwjoKzBqoGcsDeoaSFLVleXlYymbT9J8lIt+wzfv758+dqtVra3t62xIYhM7Ozsyb6A7ciEonY+vvgGKgf7ggcBGBu3ydNUMQzgwiQyGAreeb19XVzSJx1OjA8zyCVStm9MHSFRIF9ikAQZxHuCD6Gz+L8EJxRGpMeOhkoeXFfrG2pVDL1Mp6fIJCEAztDokGA5wmsrCnnzV/D4dBaH+fn55VKpUwh8anXt+qj5iLq/lDqT4TNBr26upqqt/gognozbSvUHWCA0kbAIaK/DbiTzQTEja4zcAia5FD2IR1wcEKhkLGGuf7/9t7kuc0rO+M+GAkOAEkQAAFwkCiRkmy30ymnUtkk2Web/zVZJKnKwpXuSjvxJMuUZIkjABIjQZDEPHwL+PfwgO3+ml7k+3qBW+WyLZHA+957z/Sc55xDxBcIBGYUJ4dH1MqfUdJAhM8l3d7e1kXykE6r1ZKyxbNCOfT7ffvd734nRwYPlnmx9MXlO9kXH7mmUilbX1/Xe4TD0x60dPI5OjoSqYcmARgB4KRwOKxGMSAF5DUxujTViMfj1mg05E3Sa5zIiss9HE57b5fLZU2nMTMJJ6xmyCj+nlUqFTs6OhIMTuSDgQeeJgKDJY0A1+t1i0Qi6lqFYSU/DykGqNnfUZAdzseXU4EUIRswZjGENzc3QnoCgYBY4sD6RCUeqjUzfTa5TvpWE60DTXN+PlLz6QnuOIoVRjF9nnFCQDsw4kSkw+FQuWs+j3w2JVD8HU00PAQbCoXs3bt39u7dO+0RDlWlUpE8ky5rt9sqZ0MuiXApO0KWeSdkLZ/PWzQatZOTkxmEgpxzsVhUu1GIjJBIicx5Zpws0DSacjzUg95Y80wYDBboE7oRpwKZ3t3dnUFRQNcgIHJXaX4TCoWsWCxarVZTANRsNm1ra2tGJ1AfvLQ0nYV9c3NjuVxuJndO1A/bmmAJpj6wOnXp4/FYbYm536Byw+HQ9vf31bCItBYGkDrkQCAgngl6MJPJCO3yv0tahDvq8+XoQeqzIfeCVoBWUd1AMORldTKZzMDuZiad4MdfoltJFyDfPAcBD3vm7wgLfU8zLp+Pf8z6VYaaBzG7jziBCzGAUNl9XSMX1Od9Q6GQlJj3cogqMZTNZtMuLi5EhPAEMk/4ojSGGaG3t7eWy+VsdXXVPn78qJIyIE+cBISz2+2qng4SBAob4wz0i1PhI2O6UdXrdfvkk09URuBz98w5pqxqMBjY9fW1ra+vWz6ft2azqWb4sHh3dnbEYD89PbVarSaWIQrkYXMC/o6cEN4eXuTZ2dkMZAQJh0iCudN42sFg0EqlkgyiZ/kD/8HcfPbsmSJJcrxczHg8rk5snNn29rZls1l99+XlpbqEra2tWbPZtHq9rj1BYP3Zj0bTblgwtD1/gX1kpGUoFJppPcgZ+HaFvubUEz9gxnpOBhEvUfrTp081uILe4SjHXq9nqVRKFQekGciDIh948UBtPprAmTCbtmBFsfh56eTa4CxwZqlUSqVzhULBKpWKOtLx/sjWxcWFdTodW1paUq6avwM5QifgsCEPOJ6+fn08Hluj0bBsNiuyHU4E7wiHgDvNd3kuCvL45MkTlf5g2EHtQE5g7tIFinI0otfDw0NNCoNwt7e3J2gU2JbcN1GjR9i4C8gYaAhOz93dnR0cHFihUJDBHI3uu3+BsmCQvRLnv1dWVlTV8vTp0xloFSSDwIEgA0eQf79588Y6nY5KtPh87k6lUrEnT57IMSN9A7+AwTmQe+/u7jTu1uy+fBOHj7QH/RMSiYTY08DSTPvDgQiHwyovNbMZ7ojPnRO5djodq9VqSi9Q5kYkTtBBpJ3L5WZY5qHQ/Rhe0p8Ye/bAI1K+PSl6BueRiqOHVUIsSGj0+EDHPHY92lDTkhPvgaT55uamYLNarTYzAQcj3u121TKQXCjtMzOZjAUCAR2qL2qPxWK2vr4u6OehsgKmZSMZIE4+kMtGnnY4nDZBMDNNc0GwisWiXV9fy1mAzEG5S/Pn6Ul+egyRP0QZLh1kLSBrs/vJW3Rh4qCZ+Uxp1HA47fQWi01nmHLI9Evu9XpWqVTkARP1pNNpNUZJJBIz6AHGzezeo/TwJ+UhKCNygVdXVzNNEHCW+H0gIaJthBjyGmfG+RAZtFoti0SmAwwguWDIyBleXFwoagc5YS+Av1Aq1EWi4ICXR6ORZTIZ/bknp5FLGo+npUQwcVGO5Pr5LioN/DMQsWPg1tfXla+ilzhdojgvyIAoHBwpJjvl83l1hlpZWVHqZzwey3nypXW0jez1elJgRIWQdcjz4ajARE4kEhoGgVIKh8N2cnJiFxcXUsiUjuEMEoky89h3iGM6VTwet1evXtnW1pacCO4ExNFGo6FcMRGOT6HxrKS16J3Nz5vd557J06J/IMbR9hHEbTAYqCMgsDGDayjdATIPBKalnPv7+9bv961UKina9YhONBrVfHMUffPnkbdnZ2e2tbWlJieNRkPseRyyfr+viBCdAwI3HA4tlUrZH/7wB7u+vrYff/zR9vb2FGk2m02VMhJQgETCb2GVSiWdJ45gpzMduYhj7PO1pI4WFhbs+fPn0nnIMt/j01igNNwX9Azy651c5KD58xQviLnUfVMGRn+Kh5D7ZDIl4BIUcpYM/0H3RqPRGQIkz5bNZhUJw0cg0ucMyXFj9Eejke49ck8Fj2/VjO3BaQIhAOXinj92/SpDzReT16xWq2rL5/NbFOj77kUIkM/vwdSFzYfAA2f6qB32MvAOXg21jyg8ZtkSWeAwcGl6vZ56FiP8KAUIJUBMFNiPx2MVtoMA8H5bW1vqYFatVgWhcAnJ/ZJ/fXipPMyEwJP3BJqC0MS+3t7eKh+6u7srKDiRSOjichm41Cg2FARKAO/26Ohops4caGk4nHbTicVidn19baVSSSVkcAgoj/LGjX1AsXJmCDkQKuVi5OXMTF2VYMlDNiFKIRKu1WqqGwd2xagzK5p9i0ajEiTuDQ4Jd9JH1Tg5nJVvXkC6BecLZyidTttgMLCTkxMRd3DsMMzULOPZm02jXVAk7jjPiTFFiZuZ5AR4Gu4F74myXFhYsOvra6tUKor4ydUSeZpNu99Vq1U5UmYm5jxRgu+vTLMNomkcW86IXObDnDeKant7W005IOOZmdAT8pV3d3ciRPm+C8PhdKAJOXPeCfQApY/MY/w4n+XlZU32475h7En3MLYTkmY4HBaJ9ePHj4JxQYpIP4zHYyFgpJv+8z//U8NfiGBx8KjhNzNFdTjDoBNMzGu3p5PWqDnHAYFYhXwwaarValkwGFRjFPRDt9tVUxDOitGcQOaeVEqkiXGBzIZeCgQC4g8wOYzUFwuHAX3Eu/p+DARwyKLv4QAyhZ7xbHNav+JAe/kFFeB7gsGgUmQEiJ5wjF2hIonqGY+uojNwdGgQw17SO4LvRh7Rwegwbyv+3Hq0oQaCISLJ5XI2GAxkcFmE+MAYLHKUMP8QaD8wHHjLD1IPh6eNJ5hPTWRpNlXANzc3dnFxYcvLy9ZoNBSJo/gpOeAQIeZwKVqtliWTSTkS1FgDS2EQOMx4PG5//dd/bX/1V39lP/zwg4wDz0NUziB6DvXm5sYKhYI6CWGIuHzesEaj025u9XrdlpeXdXlQ3MFg0M7Pz21jY0P1iChMYDIUNsYUgwvZo9PpWKlUEpy7tramTkTAw+fn5xaNRm17e1sGvFAo2OXlpUg9wLqbm5uWzWZtaWlJgy6oTffdeyClUW/t8+vcB38OT548sXB4Ws5C0wavAHCcer2enZ2dCfaiIQlVCj6f6kf0AWV2Oh2rVCqqj8QoYhj8/SZfy2zx3d1d++yzz8zM1FUNIcT481k+J47zCY+B78JY46zBUF1bW7Pl5WUbjUbqw00kFo/HlSc2u3fIksmkHBHP3u12u4KmFxcX1cQH1j1IFAoKJYZzQroAWSOSvLy8FPL01VdfzeR7fQqk1+tZPp+3eDxub968MTOTrNdqNTnv3BmqEzgDcrnAqnAAPOcBZ4ioDuc4Ho/LiEHKxImHTNjv93VX+T3mDtDxjdw8U91INaCE6/W6nCkPiTJWlHylj9Dot0BHRrgjGxsbNplMLJ1OqxNbIBAQ6nJ0dCRjQM4cKHxnZ0eIKAZrb29PRFjuHZA1jiIGjrQZcuC/AzIwzwBfyPeooOUri0EkHloG9WS0ZCQSUYRbKpXkOKCbYO5DNMUJJyIHCQQdI3fvI30f7eKgemc3HA6r+Y+/x+grfoZgIhKJ6H5hF3xqFlvAZ9H34jHrV3Um49JzYEtLS39UFkVk/LCYHIPijTqfVa1W9edbW1sqOWITgJmKxaI1Gg1FsyiMwWA6nKNarYrxa2bKoTJOL51OWyqVUjRoNjVQ1WpVUey7d+/U0QkFz2eSC47FYppdy0VAkSFk//3f/63LMJlMJ08x/7ff72tIPQKMsiPiHo/HVigULBwOa34t5A0MO8xVhIxI7/z8XMMBgHh3d3dtaWnJksmkfhdWdq/XU1tHzmxhYcE2NzfV6Wc4HKrWmjaLrGQyqVF3RAEocD8YAGfLzCQgnC8kI4hA8Xhck4bMTNEOteuQ9jxRy2w6OSoajdr+/r5a3IJosE++A5JntBK94A17eI+f8+RFiCzlcln5UhwDIMWLiwu9ZzqdFqGJ9ABIDsQbIPtQKKT6cBy3Wq1m9XpdZ4oXT5oFNID7wfNyR1D+ROgQ0GDOMu+bz6MLHh0IV1ZWFLFB/Priiy800CQcDqvFJznISCQifcA5l0olu729tbdv39p4PFYLVJTpxcWF+i7TzYlIxjtcpDkwFKFQyLa2tuzo6Mgmk4kiQxSqmQlqZkwqzjjoB84Kn212PwCEZ/AIHXoNhQxJDAcSefKQL84RqZvz83O7vb213/72t9InwP7kYvldHGlfAYBj5FEf0Ct+jj1bXV3VHrC3OKrsB3oaB5gmJxh5erJnMhk5gZw5UT1BFneUYCkej2uEJc+PTltfX7eDg4MZ1ApE1XNkzEzlq+xvoVAQ1A8KCkLFnQHN6/f7QoZAgggYPTcAOad3CKk+Tz7lPZBfUrvoVuwFyGKr1bK1tTU56o9dv3ooBxeNHrYPl89FmZmMNBAwhBFaJJqZyBoeigPKQakRjUBUW1xcVB5wMpnWgn78+NF2d3flJZPDIecLbASMgzBks1n1iDW7J/MQ3Zrd9xPv9/tWLpdl4PDauPDkLSHPRKNR29vbk7FF0GAAwnImoobtaTYVlp9++slKpZKew+dZ+B6+E+IG0CBw0sHBgQw0wsY5bG5uKh8ZCASUQgA5AXJiX8xmJwrh2XK5EXqUP4RA0AuiIvJSCA4KAjiP1n8YkvF42i6QUaCwoMmlV6tVW1lZUVnZQ3iYASWh0LR3Nqxn3gdnEMUAs/UheYj93NjYsOPjY41HPD8/t3A4rLIQnBkvN6PRtGMac3OBaDnrTqejOlUz+yP5wtA+rLhAEUHIfJj78oQufo/e7oycDAaD6qOOM0mkjuNCv4B8Pm/Hx8dCULi/ICCHh4dSwp4Vm0qldB5mU54IOVeqOEajkUaLlkol7VM2m7Xr62shLjgjwOK1Wk1TytiTRqMhx+fi4sLS6bTd3d3Z1dWVKiiWlpYsl8uJ+FUsFpWvhZzYarWsVCrJGececG4ofvQFyFAoFLJ8Pm+dTkdENOrFNzc3latOpVJWLBYFheMUQDhFBoH7MShwFCCneacfYwX8Sivim5sbW15etna7Lbk3myJB+XxeTg3po2q1qiY2GNRkMink6ebmRpF/IBDQbHBPEIR9bWYagEMA52uKcSI8qYxqHfLAlAuurKyo6QqBD4EM/A4z00wBeETorH6/r9kJnBcROTYCZ5XnROdBlONzSF9A3gXVYZwznBh6g+Mw/Sni2S+tRxtq4IPJZCIPBKHx0C+GBEo8yh6vMBy+L/2BELGzszPT0ALvjUiCfAAbFQ6HbWtrS54NcGKhULCPHz+q05mZ6RIlEgmVLXHgmUzGwuGw/d3f/Z0NBgP7j//4DysWizOQBReIiAtm8vv37zWqbzSadnyiJGAymXY7q9frlsvlLJfLKU9pZoqqgI99X+irqyvb2dmxXq8nIkwqlVLfXC41AgKDEUUcDAal+HyOJRaLCTb1xuNhHglv1sM0QEOUXXkD5+FUnACUA0q0WCzORK5mpucCAkfZRaNREUiIanBAiBKurq7shx9+0H50Op2Z+da8IzXctGpldCpCWKlUlHvlXi8vL6uX+mQy0cQjoFIIN0xmYj9OTk4sHA4L+qWXdK/Xs8XFRU1c+8Mf/mDD4VAKi+en697NzY3F43GVZOHowJj1ERoLJ82zsnmn4XAoZjfpnUAgIHSDPUXB4BB6di3yDxEJueaMcG4Y5ccEMdqHghDB4SBq5P5j3JAdUDlPGq3Vavb8+XM7OjoSwkHeGriZXDescEh5p6enatbiI00IfvAKiAIxoMjkwsKC5fN5Oei8k5mpfBE5QE+urq4qYJhMpjXPpVLJ1tfXNWIRZ2E8HtvOzo5Vq1UhAMDGwLhHR0eq+wW1gdlOWgEonMBnMBhYLpfTLGnOA6cKeQfx+u6776xcLs/IEk4BJZgbGxtCP9AjnBVtos1M5EaM1GQymZl5TUSLDiFw4nu5p8Df7C+wOo4M9wWOAXcFeJyAgcAD5JZ7w/OS8vCMfxwN7xRj+zyhDh4H6d2VlRXb2dmxZDIpxxonhJJNBkM9dj36J09PTwUlwSpNJpPW6/VmGNLANdQLkvdZXV210WiknHYmk7FWq2WZTEZGkAjDt2cjypx56J8JD0BHOBDZbFYMXYgXzHCFpUuhP5tLXikQCNj29rY8N0a7bWxs2P7+vn311VdyJvD0cUYmk4mY70QjQOcIFZGHh6qJhFH6jKeDGY3HmsvlBBv5rmDAizhD5IuI9HFO8PxwIFDm7XZb+Z2rqyuV3GAMSD8gLETLODIYbF8viYPDJXxoVMymAkju3+y+ZzPRCrAvwsU/lIFxZggdAhiNRu3Vq1d2cnKifcLIAafjUEAA3Nra0veiuIB6adawsbEheBYlDfT+7t07M7OZHBzKz8wEx0I2ikajSrUgG3jiZiblTq0tioD9Nbuf08vylQ6ci0cSbm9vLRaLCYnwCBA/h7xxtkS3wLBA25z55uam8ut0v+J3tra27Pj4eKYFo5nJaeCZkR9INjhZvuQwlUpZNDptrnN8fKy0jGcbwzHBsC4uLtr6+rpG10KoM5v2qX/y5IkIQkS7yCCdBLn3fA/G7Pr6Wmky3gmkCIdkc3NTCCK6MJlMWqPRUHOdfr+vDl0YE8ZSrq+vW6/Xs5ubGyuVSlYsFoU+knLACIGgQfDCICD/tC9tNptyjJvNpn3//fdCbLiv3EWY1RhNT8pkXoPP0QKFExQxBIPPJNjqdmfnH4DqMWUPcjGBFvJPCRX3hiCAlBxGHkQNeVhaWlKEz+/CYyKH7v/u5uZGDjaNljzZDz3E3SVY8oEkCAElo+gxnp87BX/psetXkclQdkCE1A7iefPS5CdXVlas0WiozAdlOB6P1XELr3llZUWF/BBBKGXyESCb6vNV/D95KcpDstmselZjTMxMUAqkK0pFyNv5XBL5z36/b1tbWzNRM+8G3DkYDGQYHrKsIQxxcREwhL7X69ne3p6UC8Qd2vqRC9HB/Uy8YSDIJ598IriRv8cwYNTYfw9P05q0UqkoT0nun5Ih2keiEPb29iwUCok8ksvl7NNPP7XBYKBe55A+6GAF2Y2cIp42DS58Pmpzc3OGNTqZTERuY19ZVAigHM2myuzo6EjGmI5l7DPOQC6XE3TI3eD7fNSUzWYVab17905RIVOrfglC904HaAaKjAgFCNYjBxhtHEY+18zUd5ko3BtBDDJIFF29bm5uNDoVw2Rmyh1jhHCWE4mE9fvTASyTyUQlgkCf9BAHakTZUqXR7/ctmUyq+uDhvrC//Hm/37dKpTLDsqaMaX193XZ3dy0QCFgmk5ECpgENRC4cb2qlQZ3YV0bOhkIh1bAThdEMCBQK6JlBCrC06/W6nZ6eSgn7xTPj5JF6oYkP0SdwvU9BYGQ8Wa7f79vZ2ZnGRnryFa1hqR7gz2D+w03AGaesDAes2+1apVIRmvHQiUZGCQwwij4/jAyBspAbBt3zrGvPBaGOnWjYdyxkaAqlopTKTSYT1fFzDzGAGDz04sLCgtIbwP1ExrznysrKzHOyIHcx9pN3JGL3pEHfMtXvJfv+kChHYOYZ/b92/aoWoj6/Sh4Tijs5wfF4PFMq4Hug+pcBsgEqoNXiYDCw77//XpGnj+AQdggWfB+eEZBFKBQSOQoo3cwkgNTrYagw8LVazY6OjuS10W+WwRWnp6d2fHxsCwsL9uzZM82JbjQatr29rdpHvHSzKbmJg8PjxxlgaEQ6nZbwjcdjlR2x3xhZIkguJ+/ApSBSxXhB9mNhsIkWgVrNpk7G2dmZ2PKTyUTKgO8n9/Py5UuVq4TDYTUSAGKCcUkJDmeXTqdtd3dXwlatVhWlYkhg5PsoMx6P28nJiYTH7F7BkONLpVKWyWSsVCrNoAUsCG4+KoUcRA4WsgdGG3LIP/7jP1q9XrfDw0Prdrt2eXmp7l78nCfz+ZpNn4PzZWpAdRgDYNjFxcWZqJOfH43u2+ry3txbjDfRN4YWOLtUKun7SGVEIhE1H6nX65IFM5NDls1m9S5XV1dCwJBxnGyf1oJM6VMipCh8isE7N76nNJEIsCufTQ049wFCKGcNwx0Z4xlp2ILjhOHt9/ty5KLRqHKunGnz51n0ENUoayJv/7AvNu9CVO0dfJAdSK9E86QsQBL39/enSvlnA5tMJvUcoIHtdtsqlYqdn59rfChdyoCpeQ76FSD7IEY0EPLPz3/f3NwoavZ179xnxtBGo1E5m+glIlaie9CqWq0mHU6feuTE984gzYF+gLXv5Rgkjhwvuo6foZMjhMlwOCw9TlMkT9ijrz+68Zf2JBgMqk6eGRHcT2wZdoTfI9AiRw965tNSv2Y92lBT+O7Zl55dOhwOZyIj6nDJjz1c5MPo1eu9EiAY+iaTw+FgPvnkE5EcMCbAMMA+KDBfykVHLzym0WjanjIcDmu2q9mUhXl+fm6h0HTKzs7OjrpnQUwjquF7uLQoiU8//dQ+fPggRjs5deDI6+tre/bsmepRC4WCIjAfTeI1o+AwXh5uIUfGP0RwRPOFQkFeLA4QZQ+e+Af8Q+MNlDSRf61Ws52dHeV6d3Z27Pb2VqgFRqler4sXkEwm1RuaLmS0GgWCHg6HdnR0pH64OCpE5X/7t39rS0tLVi6X7X//939nYOaNjQ1bW1uzQCAgZncmk1GZFAsiDo6mmQnhQEh9y1MMADAXta+VSkVOBArZEyc9q5UIEyGNx+OKTIGbEdyVlRVLp9P2D//wD/bll1/a0dGR0AwzkyG9u7sTkYXf411QbDg/5NKAEkkxUPdMPTD95T3569WrV/bJJ58olxaPxwU9g66gXCFZmd2Xs2EU9vf3LZvNyhFHEfvPMLOZXDZ7SnrI5yJxtDzZBw4M9eh+ljbyCKx+d3cnA+dL4CAIcS7Hx8fqLe/TN5FIxF68eGHff//9TD6VnwPWpisd6Yhut2ubm5u2tramoAWFHwjclyviHJIuxLHC2Q2FQqrxPT4+FtpGugYDiyPearVUejSZTESk5H56bgJ76ysf2Gf4JtyPYDCowIrzikQiKkuNxWIqIYQ8lclk7P379zPEMfS2z/MT7BDgmJk6upmZZpCDotENk73wOWWcLd4TpxYbxBmCdHqWOosSOhw77iwpT4aaoI+5y/T3AF2Bz8Iz/Rpj/WhD3ev1Zlp54q0AR1HfSx6KnANwxy8tOlChlNk4NpEN29ra0nQecjPHx8ciA2QyGZFgIGewwRCiuOTX19dWq9UE2+DlAF1RL1mr1axYLFo6nVakazb15n/zm9/Y8+fPRcpBcRaLRY0YxJkhIm82m4I3R6OR7e/vy0jj1eOxe8YxypWLg4JjrxA2YCAMPN9DfWIgENDw8tvbWzs/P5dzRPs/cqN09wHuwoHgHHu93gzrMhCYNjx4//69vX37VnNuzUwwFsoOqLTVamn8KXkhhPLk5ETNDu7u7jTJaGdnxxqNhr1580bPAtsc4QB6CgaDVigUpPiJhIHUiPp9swnesVAoKI3Q6XTs3//930XG8WQ6ls/hUUaGfGAgUHLsAf9eWVnR/Q6FQirnQgnxWSgblApMX77DcwRoWIMRZbQnNadErTgO5FDZS/qR+7aksVjMtre37fLyUhwMuoEVCgX9DLIJ6SwQCGiWNXvkm6mA6vjohT1tNpuKEjlvnw6B9U/0THkRFRQ4N3TyMzP1RqcnBIEH+7i6uqrcqEc/KJ+8vr6277//Xp/H/ffQPoYPQ9ztdhWF4gzAIl9aWrJ0Oj1Ty4sTgU6AUU3k6u8W//j0IOc+mUxmcv2UatFEh0oMGhh5bghGHgPJeUOMhZfE/UEW8/m8EMuzszMZUAw7TlEikZCz4Fv6mpnuGmQ5nDEaxpiZumASHCLX3CFkGR0Jvwdkgy6EOFY4kL+0sC2MWqXzGvuOzeJeQl5FfnE2uZvoKxCsx6xHG2paeRJJoqRRePrAn3MmeM2+lpiF94+CJ+9Is47xeDzDvmUqjNl903RIEcDSHES1WrV+v2/n5+e2u7ur78Tbvbq6UlRkNp2eRN00hKtAIGCbm5tmZlIUQFh3d3f25ZdfKm+BIo7H47a1tTVT+0muxEebeM9E6MAuTAkCDaBuOxKJCLoEduECvHv3TmQHet6Ox2Pl6clFdzodTdQi1722tiYn4/nz50oJwMbk3chPwbAvlUrq7HRwcCCUAxRiMpmorIX3o8FFq9USA53IHkMwHo/ts88+s3g8bj/99JNSEDSz8PfQK+7r62tF/mb3XbswZGam3upEyv1+X4JEowoULrAd+S3SIET7KArIOhC72AcMCUaYdqbkXXHMUKw0mtnb27Pj42P74YcfBMdjOHFiGLDgyVcPOy/5KB3DDeuX+0cpGGdLlzgUIobAw/IY3bW1Nbu8vFTEy3588803epbb21uV/gEPDgYDNQzxzTdIY5nZH33mzc2NXV5eKg9tZuIcgKJgsFD+pJpKpZL9/ve/t1AopDItM1MOMh6PWz6fV9lipVKZMbAYaAiZEBHhSjC6MJlM2pMnT9RLm1QAji/ON0Rb3nFhYcGKxaI9ffpU74YzS+MWzsJHgfwcd4A7W6/X5eCD5FBTvL29LccJ55M9hksEcsn7kxrirGixieHj82KxmFIy/X7fjo+PdU9jsZhY1SBtZvdz1jFwODPc42w2q/3jmaneIK2ytramuQ48J86pRzqQefRgu922ZDIpZ56Ahrx8rVb7RU4U6A+6Ck4ButvMhEIQIMKBisVils1mZ/acvXvserShXl1dnamZ293dtUwmYz/++KM89NPTU11yYG2UKvmalZUVeZdcAlo5Xl9fK7f62WefWSwWs5OTE7Vow3sKh8P2N3/zN/b27Vu7vLwUwWs0Gik/DrtvZWVF3X4gfXCQwWBQbPRYLDYjEOPxdFIQfXDJbXW70/Z733zzjSBg8i+0+Dw6OtL4TIyMz61Go/cdxjqdjl1dXdn79+8tHo+rC5gXRgrud3Z2VAbTaDTUC5fSE8p+aI86Ho9Vc84/eJF+HCfGMxaLiRhINAG8SY7s+vra3r59a8vLy1YsFsXINTM1PYHMxD5DOsTA4wkT1eCgMFLzyZMn6s1OVcFDRw3jBbkIghf1ozg+9KKHJIenTR0lOXmiu0QioQgHAR2NRqoy4F3pD41QtlotazQaIvWY3Xdh4xw96YiaarMpgkD0RNmi2X10SbSBIifV4Muq+Hny0JlMRh67H9DC/SbCRVGTJiJ1wnMQnZGLDIfDclh8VzzOiefg94kofJ4ZQ8We4JQDscKHINJGxtkj+iwwrpJyx6urKwuHw2Kk//DDDzO5cPYJwhiKEuIYQQZG4+DgQHlJ9Acw5ocPH9RalUELoCSXl5dCT4iY0ZsYUZ+m4xy9M0+Ex900u+8fTcrNkwqpF/ZcHu6p5+nAaYEUzPcQleJsmN03jCK94gMJnDif7jMz6YqtrS0hrkDcwWDQLi8vbTweK3+OriNaxV6Q/vSthXHGKUGDRMgdB3Xid31qAxtCLh8H1EP5BHv0v6Cih/2FEMpnYrPobgmyC8JQLpc1JIQ99eTi/5PyrEqlIiHHgyUi7vWmc6BRqIlEYsaIUWPMEA5gA4TYj8EcjUYiV7FQsEDHt7e39u2339p4PJ237BmWTHQhJ3Zzc2P1et2ePHliBwcHtry8bK9fvxZx4O7uTqxtIhKi5EBgOkUIT7jdbmugBsw/BA0hxvCXy2V5/A+hf3Jl5G4rlYpmIV9dXYl0YWaaltXr9ezdu3ciT+3s7KiLW6VS0cSrcrlsHz9+VBvWhYUF29nZEZTpczXxeFwRDqVAkDPos+zLQFAC5Pjr9bqUHxGf7+qGcBNR4gx4Nju1rvQZxxjAwO10OnZ4eKhyFvJn5NDpSNf8ea4ueTX20TMveR4YpziWDIYYjUaKsBAo0jg+egPC5A7j9cOAHQ6nte84cBhcGpb4RX78u+++E7QN85oFHwR4n5wtf4ZB9Ln/SqUiXgA8AZQgnZHMTJA3PBJkAGVDhIyhpFwSQs/y8vJM8xiWTwPgIKI8OXtPviO9RJRNx0DIPtlsVk5Ts9kUqgcKBAIAjOvRCPYVYw0qhYEyM0XLjUbD2u227e7uKpIGwSKKSiaTtr29bYeHh3Z6eqqJd6x+f9oUCVkFVcHR55mHw6EasSwuLoonEIlErFwu208//aSIHnSRyWC7u7uWTCbt+PhY5EP21KcNIY9GIhGhMcDvOAe0OsV4kDLr9/sikCJXfrCL778OsW80Glk+n5eewWHmjCBWUb5LOoZ3QDfiBOKk9vt9lX6RpkLvwAInnYnh55m4mx6ZRV4elqBxXym/JEDEEfQkQPQgPfxBXtE7flgKes0z0D0S/efWryKTsQaDgR0dHdnbt2/1AETInsLuR2BGIhG17wwEArqoKGvY4ZPJxI6OjuTpp1KpGZgHsg+wyEOGH1BbJBJRK00IXL1eT+zihYUFy+VyIhmhRFAk5C9vbm7s9evXYmbCHMRgQbIjB8F4PqIEIDPfQanRaNjvfvc7vT8H1+127fXr13I+bm9vrVKpSDAhWWxtbVkymRS0DVGNFpODwUAQMc0BPLkJ7xOyhq9xpHb7+PhYkTa5Hs/cZfn/RjHjyFEq40lMjB2E50D0lE6nVTaHAPD55XJZzN2lpSXb3d211dXVmdzT2dmZ8kOepIO37KsHbm9vhbgwpYc8HkQw8pYgNT5FAzqwsrKiqASSCDA4LS0ZAMB9RYEBQSI73G+ze7gOyA6lgIHDIeYetlotKRNaIUKWvL29tUKhYMFgUHAkbFSgfyJMZBWFhbHx5UM4SjiTwMoP7wRySgTykDxDtMa74kQ8JBbFYjH1EeC5UqmUffjwQblvz+WoVCr28eNHu76+Fgek2+3OwK7b29u2vb1t7XZ7xlkLBAIyWjQHwnnlWakOwFnCqeezuRtmJsPMe6E7ycuy9999952qVEAD0VP9ft92dnbkQKGnaBxCKeNDR4lzqNfrImzyjDiM3Am4OkSNBB6+htgbLPQviBORMGWc3GvaaMIF4Pu5H8xhePj86BkieSDrRCIhiBoDzDNBWubO+XI3M9OZQMI0M6G/nDPv5fkeIIpm97PpcWI9wRC0g3OfTCYiiHrelK94+D+JqM3u4SO8IKKlwWAw03qPfBZCh5fhvVO8GOAeLgSKDPiCRgUIxcPJMz5/CeROBIOwra6uioxGCZWZ2eeff27ffPONFQqFmRnBMPuq1arIUDgStI08PDxU+QqeabvdtvPzc1tcXLTnz5+rwxZowN3dnZVKJXUZ8kQUVqvVsjdv3sjbymaz9urVKzVugFENY5lSD/a8Wq3qe9k32Nw4SBhDH+0AW6KAgJ3JXRYKhT/Z8g4DCDuS3+fdcHhOT0813SwcnpZ1wUJ98eKFWOnv3r37o0oB7gX5Kz7bK3qgb5rMVCoVIUEe7gJtYW+o8YeRS/0yd5LxiewRiApGgPvCM6Cs+RmiCSAwFBxtBbmPGDOEHAVB9BEMBqXciYhQsigcDDnsYIhlxWJRfA8mLnmkhO9C4QyH03Gr9XpdHaX4npcvXwoK9131eDcz04xj8uO+eQ6saDObUaakLTB+DO1AnlH2QM4gQTg+nqSzvLwsOWAvgVvz+bzt7e3JIOOYm01nDbx9+1YpL7gOlLThhEFMDAQCIszCAObZcLTh3HA2g8HAfvOb3+j8zs7O1J8ceWLhGBApe2cWEp3PJw+H96VCyGOn05FzhSEDiQI6x+hhCEmBYLhIoyFvZvf11Pw9OubHH3+UnPKcsL8xVMPhUGlM8uOk23xPeZx59gBZwVATdEFspPqGe8ResmdU1ZA+804lfQf4He4bOsJzih4ytj2DHp4KepnBOdgvIvVfs35VwxMegMPlQNl4IB4Mntn9EA68Xh7e52DJeXn2JF4fUNMXX3xhz549s//5n//RRlPoT1RC9IPQUkdJ20dyURjtn376ySaTyUwPYjzBSqUiwhSNJhYWFjTByZcB1Ot1q9Vq6nb2+eefi7WMd8oFzOVydn19rVyNF0oOH0UVjUbt2bNnIk14+BVj5y8A4/bYDy6WmamWEwTBbErEAvalexFs7Wg0ai9fvrTJZKJ66PX1dXm3Dz14H835PE4gEFDODsNJZBwOh8XehDhHzt57q5DzfJqBvBvpkkgkotaMnpxTLBZnWoU+rJfkHk8mE8G5dO/CmIF8AK9x583u2eJE3OFw2NbX12WoJ5OJGMnhcFiEGO9c4DRwd31/YmBI7i85QPKTKBmcQaIOIOXxeKxOesgkcodhw7n2EQXNaDjvTCaj6gbK42KxmD19+lRyzRhU9uz09NSq1aplMhnJoCcQ4TiijEmJwVvAccOphZwFkoIeQRaCwft+5egdziiZTFq73VaJIN9PJA4KtLi4aMlk0orFohxC0nC+M+PLly+l3HFg0+m06nh5n3a7bScnJ4rkgsGgbW1taUY1aR2/HqITOK04w0DgODCkL4gaQ6FpP4CdnR29A2fdaDTEnl77eRrb7e2t+ndDqIVYCJpUr9flrDBBDKSPqhZ0MAxsX5ffbrdn7i9OGAaYShTOCx0EbwJyGXvCPeh2u0JVPLnTI8CevAp6xnkSSJEmxTm/uroSukbA2el0pCuRXaJ4dAr6CjmDVNZut8XDwMbBd3nMerShxrsEeoGgg3dGZMElw6NBaFE8eGYYr9XVVbu6utKlwGPyip7PJclP5x9gGzwfYFAWJSnkNiA6UX7y4cMH29/ft62tLcFgTNrCyyYK8PnAXC5nmUzGBoP78YRHR0fK51A2wt/hyCBwa2trVi6X9Ww+MvblbMlk0nZ3d6Xgyb/7Hr+UHHD5EDQP1wwG06ERwOI4BxDRuDj+gt3e3tqHDx9keGgmQZcmjCnfN5lM1CUNWHM0mtYSsqd8L4L5448/yuB9/fXXQkCIaMPhsFALhAxDQtWA2dTRy+VyckDYF7OpoaeenygLg81382ydTsdOTk5UqsLZk7dfWFiwSqUioeT3POmLlA8RLnlYD1nj5PF3wWBwZmDJeDwtTbq7u1OpHGdDL21Kjyg1o9yEfCfwXa/Xs1evXtm33347w0JmEe3g3JpNHTiIdnj/TGrDAOIM0OAEzsNoNFJvd+90pNNpVTpgUHgH3wAE5QeblvnGwOFE7USo6AjIWtwboiX+TcSJTBLpjsdj1VVjsFOplF1eXtrHjx/V0nU8Hosgxb7wvNS709jFIxzsNagL+4bMM9Gv2WzqLnFHzEzVI/SG50xJUZXLZVtbW9PdpzcBDqJPMYEs0VoznU4LrfG8GyJw9hteQigUEvub96KC5N27d2K7kzah5n88Hs/wM9Z+HriDk8SftVqtmcoKbAn6cDKZKGWAA0Hak1RgODwdVAL8vLi4aPl8Xkaf9r3IOMEmjHkGdVDWhsONgR6Px+LhkHum8ohn9WkGdDaOOIguLUgfux5tqPFIufgQUvA26OuNUca4MWgBD4eXZ7Oz2axVq1U1HgE+8FELRoN6XC4JEYtvIkEECYxCJIywYyxhHuNp0YYSeBmiEAYRoU+n0xogz8W7u7ub8XphEwK3UkdHpEBUzxQY2JT9ft+KxaIdHx+LlOfhopubG7u5ubF0Oi3YjAsciUTs/fv3Eohms2mrq6vWaDSsUCjo4vIeKHyejf3EKMGq3d/fF7mP76ELG2McuZg00VhdXVWE5hsVkB9l/32Z0dXVleq9Wb7pAp4tRpx8Hq0uV1ZWZDSAlNn7UGjaLpSIkOdi6Ilf5KCYDEWUALRNBzvKOIDCgDTx9v29IuLGSMLcx9Bg0H2u10d8QJogBTi65EzN7tmkOCQYXfLcuVxO7Q7p2cxdOT8/12cRKeDkkh8nQl9ZWVHNPo637/y3sbEhJqw/SwwhqaWFhQVN0ENe2EszUzXI0tKSmMsou0ajIUXLPj1kTCO/8AaIhOGLIFe0k+T5kReQDNjI3NtQKDRTlkmzjXQ6LRh2OBzKAfalYZFIRH3jeVYIn8gkhg/SFHcZOSIXi+NOmSB3hvwwjpXZPVsbIwIXg/wx+4S80OkLpIsGMk+ePBFJkjMl0iTKZ3jHZDJR2gMnlzJaOp9RYcJdZmwsC7lB3s1MUwHRv+w5xLVwOKzSYTObKRXlDHBKuCOe3R4KhaxcLsvJRF+wP1QzcV9JdVF6ilPE2bEXBAK0Q8Xxeex6tKEGWvKQLwoUZddsNq3RaMwcJl4Fc3SBIff29tQrtV6vz8CRwWDQstms9ft9wQMYMrygbrerDd/Y2LCLi4uZFpg0jeCCYjjwWr3XyLN6gpuZKcfYbrfVvo/SMox0pVKx4+Nj/T+kH3rOcpAoCcp4GK2JYFB7Go1GrVwuSxkT2ZyentrR0ZEEGg+diOz8/Ny63a5g/WazaSsrK+qzzmWBiUqDDS46AweIaPv9vuVyOSkgeuSGw2GlCsxMTRN4ns3NTdUpmk0V7unpqd3d3dmzZ8+Ehtzd3Yls4+eRcxYIEcIMqYOWkCgGpk2Z3RsDjyrQjAfHkueGYINT5hfs/UQiIQIYSoNUA140xC0GudBQhu+pVqtqNuHTOjgqPi/tnwMjzTuZTZ3leDyuunpq4f3nYPB9Gmc8Hlsul1N0W6/XLZ/Py+Ggz72POilnu729laMFAY2IlnPCiSLSSyQSqjPGcb2+vrZEIqHZ1Z1OR8gI06F8CRZdnQ4PDy2Xy9nS0pLyj0S4GE8MKgYY+JtnNruPdIC0Q6GQXV5ezpCMzExpGhzSq6srIVZE7zhm6BzISNTeY/y4I+ggcsYY5+FwWp9frVYVvFDm4x1mEJLr62vNrk4kErazs6NgyaMD6FJY8KFQSOWH/X5fc+bN7uFZECbuMHljoj4MFg1YuKPeoPscbC6Xk27CkQYZw6liTzBulIdxN1hwKTy/w6cL0HfwYzxfhO5uvKMnauKMgebyu96AQhbG7iDv2CR0A84G34vzOJlMhOSxH/z+/wmZjPpFWkbitQP/UOjNdCyaUPB75BHJLw8GA/UZRvl5A7m1taXLxc8jYEQPbCheF94fpAufV/ceO4p1Y2NDrexoGuIHIZC7oyvb7e2tpVIp5S5PTk7EzsTj7/V6dnZ2Zmb3NYxEX3RFow4SJjK/BzrAXhSLRUVhNGmJRCJ2dXWlBg+U5JBTNjOVfjEYwRsAYDqUOAzw4XBox8fHupCQkUA+6Dt8e3uryU/UbJdKJTs/P1cjF+By9gWkgp9HOGjgsrKyYoVCQagLBpWz4kLjDfsevxh9ImvqQMnJLi4uKm3DgqvA73t+g2++QToH4eO/ITs2Gg0Rd+g/TX4URcN7kMJ5GMH79MHDhfLFSUbw+Q4ieowNzz0YDKQUeF64GjhWr1+/VpkcDi/QJ8oQhYji9wrTw9C+8gOnyjcswgGmuoJ/aIiB0wapLxKJqLey2f2QCiDafD4vqJkzIsqHtElAQb0vzjv9HBKJhF1dXdnTp08V0RcKBet2u7a6uirnjyE58XhcVRLD4dDK5bKdnZ3JYOAog96ZTYeowEkh/bO4uGgbGxsyfDim1GqT4wbt8Aqf1ODS0pI9efJE8LF3Hj3hkyg8HA5Lbn00C8IHX8hH/kTlOP61Ws2q1eoMGxuHpdlszhhO9Ae610ekVBBgrHDcYZv7JiDIMPen3++rJzsdF0EcaFgD4gMXit9lnwOBgK2vr6vHBzwJngdnkz4DRMXwYrjjXm59upb/9pB6MplUygWEFsTksevRP9lqtbQBvsEED4qC9xAtD7ywsGC7u7szjUl4YOArRjuiEFqtlt3d3alv88XFhZigQAsINQQhlII3fGwgCh/F8pBU1Gw2rVKpzECPV1dX6m9MfpzcdSQSsefPnysKIBeLZ+gZoghxKBQSaYOoHkMDAYKJOWYmZ4ZFTTQ5KKAZcsheuA8ODqxcLqsfsJmphAIDBWzpy5Sy2ayZmXLDzWZTJBlaCt7e3tr29rZSCQjz+vq6PEfeF3LFQ2aw92iJ8qml915xuVy2nZ2dmdKd4XAomBwPtVAoaJ84g83NTcF6RDoobrxfoEgMVL/fV4Tv2Zv8m39YpGoQYGSB76MXMZGnN9REYqPRSEYHJAFnk5/zFRDceRjByWTSksmk0JONjQ3l+piO5slr5IC73a69evVK+wmjmEiJyJ+7OhqNNOt3b2/PNjc3dZY8D2U3RI44Sw+7x6EYPTzO/9My1gcB6+vrgqQpG2QBp9LWlrahGBzeB8JVvz/tXDgej+38/FwozXA4nQxHYyFfq+uHtcRiMZFFC4WC9A0cDbNp0x7qdmniBITMe7VaLQ0jIeIE0qV8isgThx+kizvtu3FBDPP12qVSSfediBunkfdDH6+trSkXzgqHw3ZwcKC9uri4mNHh6ErOzqcocIJpNzoajVQtQLMfPoc7QSBGtO1bUXPG7GcsFrOLiwu1McVZhyzH/vnnJJVGqofvQh56vZ6QTtI74fC0xTQVGsgTe+rJmegMiKUPJ2nxHA8d7D+3Hm2oGWnH5lILy2FSP/qQDUx+ECHCuwMeWVxclHECVsMDIi+Gk1CpVGx9fV2wKsofw+2jJohP5HkhekCeAeK9uLiwhYUF9Q73kQ0GlM3tdrv29u1by2Qytr+/r1IMoB2U1ebmpg4nFotpYHsqlVIrT0h1CC8XIRgMzkBA/nkYOk/uCfh+c3NTF8jnC5PJpCZ8mZmU3srKipQvxh3DSitSIgqavGDkgJe4lMBZwH6eYEQpXSAQUF0rhpK0AzlQSDaDwUCTsGAfQ9JjQb4hwjW7N6awwVFm5NGIdhDKwWAgtMDMJMDkG/HQV1dX5TxyZ1CcDxcplmAwOFNHDEpDbgsIkkYfa2trtrS0ZJVKRXwHolKIcOy1R7F4djgPKEKUHnk9WLeQPjc2NuzFixf28eNH5QV9igk5oaENzUh8rSmyCIQKe/309FRpHBxDFBnOvDdCDwk1jHR8eC4QCxmsw3tRTpjP55UqIaUBuhYOTye9kVP3yJQfppNKpezg4ED3mAjQp8ZA9+DpVCqVmaAAfQRqB7K3uLhoqVTKbm5u7OuvvxZJrdVqqbGKr1rB4eb3KQ9kT+GWLCws2NOnT+3777/XUJqLiwvt5cNomRw3ESFROoilNyjoc4iogUBASCcTsR4u0i83NzczjXt4Jz/GEnn3A1qQLWwMiNR4PJauBSEaj6cthpFtmiT5oAUHn/IsInKCCPQmZZl8Dn+OzfLog3e2SWHxTrDTacqDM0Rg4Duh/Zr1aENNvpNNb7fbyrVgFIkMOHg8mEQiIbatVzIQAICtGKF3eXlpJycnls/nVZqSy+VUJoGn02w2pTxQfpA9yOlQR4kSrtfrmgHd6XTs7du3MwSUX7p4fqVSKTUZKBQKM6xwoE8MBJ4XOR8aMKBw+XvyIDDp+/2+OjOBMtAvFgIcAughPv4bZQtZBYarmYlpDvoAAz6bzWpwPe+NYQ8GgzNsXRi4PEe73VatKWeMwE8m0zIEUhzPnj0TZAYDlZxuJDJtPZjL5ZQXgoGOM0GE50lBZveRGdE0nASeA6MJIcfMlH/mTsIaBQI+OzubKV0CauPZMITdblf5a0hffFe73RY5iBUMBi2RSMwoW9IL8XhczViq1aqMGqxuIn2cAb7n97//vXrx/9d//ZcNBtP2qfRahicSDk+HvtTrdQ2Y+Kd/+id78+aN1Wo1DZQhb03kQqRAyqLf78tpDQQCmgjHmQyH913EPBmTUhg+w1dpcCYYSqoq+HPgTE96A31g70FdvOH0ZYvsP0REs/spVqlUSgRLHHM/MAgnwzuIKHGvJ8jVoqgxCtHotN0uqTHSQuyvr6DwFQFAqpD8yFnTEwGewvX1tWYd0Lnw/fv3QmJAWOB1ECGSngSF4N0CgelEunq9bpHItDtcKpUS8YqpaMgWsDGVCtfX1zobIt1YbDoD3BMt0YGcBfdsPB7PcCjQPTj5BAvoNNIxVCAQPIJ48By8H70vqB7y5E7O0fOW+HwfSVN5Qa8Nnm1jY0NEQgw778Dv+hKyP7cebaiZwUrUS4SLRx2LxQR7DodDXZhgMCh2YSwWk7L3uS3gxGq1asFgUAbg8vJShh5vrt/va4A73trV1ZVadmYyGeUXgAw/fvwo7wxolfXQQHMgDxeRGjOYgd09nI5RNrvPy/DuNHsBIkXJ87NEBHzuaDSy9fV1QZjxeHyGJAO0gkOAw8L+4wh4oUMogT656OFwWKxpck4YR0hLy8vLViqVVCtNnsWzt1GkKJ3V1VV5u0tLSxpgzwQe9obLv7S0ZOvr67o3nAPKudPpiAPhc8mcH86EF1i+BwULgQcnwey+XpOz8IrYEydRtl988YXycggdESspD5xHfo98Mov8vL8r/mcghWGUQJxQYCgZFHm5XJbRI4d/d3cneWVP6QXga5//5V/+RSkW6uC9g0F+DcdvaWnJjo+Pxe4GVufuwqqnnhZUBeeR84HrgQIjioKwBDzIcBucFh/VYqw9xG1mlslk1MDCTxmjyqLZbFosFrOdnR05hIwHJYIjoCAniiz6u+mRHK9DiDrNTDC6d2C413S3Q4Y6nY4Vi0VLJpO2sbGhAISoDUIv6bzb21srl8v24sULu7y8VI49n8/bq1evZuqmzabGqVgsWi6XU+QLeZTqEHoJcEdBp4bDaTOVTqdjz58/txcvXtjr16+VJ767u7OnT58q9cPsaVCHYDCoqVg+R47+ajabQiPNpunWarUqZxp4Hr0OWQwHETSGQAR9By+gVqvJYGMHgsFpIy6QMpxLuBbeOaPShugfR8dXQHl7gQMCAuwhct9Y6THr0YZ6YWFBBA6+DDg8k8mo3pScFB2TEC4OnfpIhmOwCXgva2tr9vnnn5uZ2eHhocViMbu8vLREImH5fF6HhaeHYE8mE0smk+r/C3uVDTw6OpJnQ/SEUP2/LZSTj2J8mQcK1eweYkQJmplYkRgF2L8YSn6OQeiw2fkclIQ3JtQ1Qp4hQsXzA+YhMnqYd19fX5cR80IPzN9ut+3Dhw+2ubkp5isCjaIlbzSZTGZqLvl7HAVqN/P5vH348EEzsMmF4cXS3hL4Cz4El597hULwbH3yv5999tlMrhRFixODsvRMZf8dOC7cJw+X+vzb+fm5UBEMazAYVGRD9Izx8Ll07iDPgvASGXCfQFWAS1FGNN9ptVozziGK1DuaKBacDgy1N0S+OcTi4qImUHH3/fJwHc4MRCvedTweK7puNBozM4XD4Wl/aFqakg+Es9Fut61YLM4YOBRlv9+fadSBrKEkiUCRJSIqIiUcXJy1Xq9nOzs7ciZubm4EmwONU2IHq5izCwSmvQJoS8yee12yvLwsdGIymSi9Byrp99Tf5dvbW1VskOtHN5Iyq9fr0nPLy8vWbrft8PBQbVJTqZS9fPlSDorXZWamBhyeoIgTwdx5puqBXuEog8gEAgEhNMgI+WOcG2DnQCBgZ2dnM2lLghFQBLgFFxcXqmrBmcK2gM6gb5BLkET0IK1xGSQFD4Qghr4BsVjMXrx4IQLfmzdvFHSBZuBU+d4JnrBHehYn3qN9ZqZ7w9wLAiV09GPXr2ohCiTIYXFJYRBjyDgIcjXMlcUgeCYmZBifk4WFu7+/L8NGjhJGZDg8bYcITBiNRmWAvPHgYnKoweC09MuPM/PKDaHBgAaD025HW1tbFo1GrVAo2GAwUBccD/Hzd+T1YAZDYCFdQHSLMvBRPRfXR3B4rETCPO94PJ5xiMxMpQ3BYNDq9fofdb+5urqyUqmkxhhcmKurK7VEvLu7E4wGIQinACHwKQvfqa5Wq82MtgPm7ff7MzlXIGTuCHAcXq73ooF7PULA94F07O7u2j//8z/bV199ZYeHh3ZxcaEzA9kgiqM8yE9zAvHhbLxyB7oFGcA4E0niQLE3fJ+PHDHCvBNKjjttdl/DzX7SthD0otfr2eXl5UyrTVJOyCVIAkY7HA7bp59+qn7pyCVKlNIUnMKdnR1FrBjg4XA407Oe94hGoxq+43O58EFA1ehi1e12lYc3M3E2uOc4OCcnJ2Z2P7EPBO/i4kLETpQmzUe485wh/7+6uqrvw/Az0GNlZUWRGE6zd3JgvN/c3AilQC8wTOehw8/vr66u2t///d/b69ev7aeffrLLy0uRiMgxQwhF7iGT8mw4036WQbPZtIuLCzs4OJCOeAgXM7aW5h2kufx66OiSyuKMQRnYz4dIAH8Py94jIzg8PM/a2ppSbJ5Iiv73BFLPLYCcyjv6VAopVsi95KopFcOxgMcDUkSZJ0TPtbU1BYEfPnxQPwSzexgeeWD5s8bR8kgc74KsN5tNRc/cn4cM9z+3Hm2oUaB4JcBcRHJmNhNJ+pesVCqKhIE6fc6IjWaK1JdffqlcMy8MRES+B6g0Go2quQjQkJmJdDIejzUBC4Xb6/U0fQlWNuxpDgBFurm5aS9fvpRRSyaT9u2336rmkXGWnU5HEBIGiDK1ZDJp3W5XfbqBZogiIByxj0TP5K7NTA0OMBJcksFgoDpu2oECiQHnsFDQtHn87W9/K4h7fX1dhg1lgMHgWfFmOX8gUgx2IBCY6ZWLU8L9WV5etv39fdWkn52dzUxJo6xsY2Njpm4eCAkEByUXDoc1WWgwmA7mALWBqQk7F+EcDAaC5OkZDaEFb7der2vfMHi+VhJBxXHhuXhHzg8FyRxqjCKcDhQV0YRvgoJc4KWTOwdtIErwFRQ0G+GZMMbhcFjRkJmJHeydRsrsisWi8o/0lAd+Rb4x1kQ7ICOMmwQO7HQ6qkUHRULhRaNR297elk6AsEfqA3Jcu922crmslpl8DpULOC5Er+gWcpl8H+kV+BsQk8xsppa2UChIV3FHKHsiSgS29HfhocPW7XbtX//1Xy0Wi8mxIDUH3wD9iPOAYfWd5fhs0ic4foeHh5ZIJOzVq1dTRR4OWz6ft5OTExsOh1ar1ezrr7+2o6MjBRP8XCqV0uCfd+/eWa1Wk9OEXJvZTFBASSAlqgQm/DmIBDoZeQN9TCaTGgPrBydhzHwQwt3nboG+IAP0kYcdz8+ACpDbBjWYTO57rOPgERlDesMBR/54dnQq+uSX0qR8Hnefv/OlsR55pdXvryGUPdpQ89JcIA4zEomoU4wvc/H5OMo6GIcH05VNxciSI+VQIpGI1et15ajJ/ZE/4JCIwL3AUOR+e3trT58+FVRJDWU2mxVTko3sdDpSUNfX17a1tWUbGxtSSN4j433I5zSbTXmtjUZD7EwUJVDWycmJZbNZeVOUUlCf7tmmvvCeuu3d3V2R07iEiUTCSqWSOppxUfx6CM3hLXuyFZ5hLDadWHRxcSGCCLlfDA3GEBiIJiR0/CHqwyhFo1F1Nmq1Wra2tqYGOSir0WgkhimKg30nB/zixQshGZwzd/Hf/u3fRCAB4sKJQ/lBVKIO00OK3vEIhUJSzhBlWq3WzBxdBNs3PuAuco4YS5/mIYLgPEiNoBQCgYBIKjhqZibDz5Q2HDsMO8af6B+D/ubNGzkPwITknqkUgHWPkiLK5/1IpUwmE01Fg6TpIwMQLyBBX3uMUwspimgY2QUdAtkxmxr0dDqtnCZnQgpgOBzKycJxQuZQniBu/Hej0RAKQF4a1ANHBz4AECoNh/y5sYLBoG1ubmqkpU+5+FprnF3OwsOxweC0VBDdiUOEjgwEAhqLi5EB9kYfcLbffPON5fN5GWm/VlZW7MWLF/b06VOliCitJHLl/SKRaac7zhu9lM1mJb+kzOCbVKtVy2azQhZx4CeTaftP7lUoFFK+F1kcj8dKRZDaIxDjPoRCIQULo9FIUxZ5XsiOOJj0w4dfQD8NPg8DjjPk9SR3mDuFrvYLZw0oH3mE64CO9lE4OsjzlP7cerShBiLy0EUikVA0wPxfIl/vQfDwHB5GEyXIAmZBwIBt6emKZ25mgjrIn/L7KDYiI7pwgQjQheubb76x4+PjGar93d2dHR8fm9nU60mn0zoMoBrydxsbG+q+wwUulUrybFdXV2UAgN6CwaCgPxq+oySAE3Fg+DtqAz3L0efklpeXVZ51cnLyJ3Pu7CskByJ2HCzOh3fxSo5cJ04RuSw+h/GaNNXAqSNqwenwdbU+f2V2750i2LFYTN2OcNz4fPaK6MZDchcXFzMsXBjpPCuCxV1hfCawJkZvNBoJ+gd6JWXDXSQSwAEkivCCTq6ee8Yz83ugOJwlysnnoclvITPUOnMPcAqpq4WJzVANT9qr1+sWDAZV/sZZQwzFMCAD5Ew5Jwhk/L03tEQ+yDtOPb3pB4OBGk2grKiXJ9eI0vMlXeFwWIhbOp0WWRSD7KsXPNpCPwdKMIPBoOaL89zwashvxuNxDacgOmw2m5ZKpez09FRsZRQzqMHe3p4ca+RmeXnZnj59ah8+fFCpE9+Ng4Mx5H1AQ/zAin6/L2JgIpFQFQXwON/JuTEdznNGkK/V1VXNcSfn79FPvydAuaPRSEhZJBKxi4sLoSMY+oWFBTUzok0tn0nKo9/vCwFhHgJBSiRyP8yDfwgIQCjD4bB4NOgT9tDD7j6lxH3kTrGfLFIr6DZvqEm78vegB/5n0I2k0UCbfP99fgaE0qNgj12PNtQwMPkSM5MnTT0xuT8zU36NAnu8Mp/HYvNReJ584yEFDjiZTNra2pqg4l6vJyYfOVQ26fLycqbHrM9Z8+zeC2Jx2TFcGBu8slarZbu7u7a9vW2hUEgHTV9tcm2eNczhoMSJ2tjXzc1N1d8B8eIJ8nyrq6vqRkQen4vCBfulFQqFpMSJSomoUDoImNk9MxFPGngYBINnKpfL1uv1LJfLKf2BUqeFLMYBcglkME/oogc130t+f2lpSQYWBRIKheS88b4IKQQzTxQ0MzkMCL6Hln0+0i/y6p7M5J3PTqcjwiL15xDiSNOw90CwIBJAxJBMuLe+TSFoB9EissFejMdjGXi8dq+wMMAMjsGJKhaLYpb7yJ735/MpsyQHXiqVpPQ9XIdRh7BGisLsvkGSh0+BM2Ex43gS5RAho8RwSFCAwMYYXVIZKFD2i33FQPi+456Pwnn6ch6id//5S0tLundLS0sz1RflclkpAm/ggKr9NK7JZKK+CZQoYWDhamD4BoOBbWxsWDgctnq9bslkUpEdTha5YpAIUjfRaFSNjbhz7EsqlRJTnOCCVBn6lxJIM5OB8Wx7ys64q4PBQOgXZwUZFI4STjflZL5sKhAIyIagg3Ag0Yf0nkdekT+e2ZMpvTzjkBF4eNkHGSWN+vz5cz0rziP3CseT5e8QJFJSU+PxWGflyXXNZlPkNp8qecx6tKEml4ChRtkC+UEU4ELf3NxYJpOxg4MDi0ajVq/X7cOHD/L0PDkBASJqJ0d5d3dn1WrVJpOJmkLgFZHTQ4kOBgONY+QwNjc3ZyB4FBSXDNiL78djxKtEYUJKaLfbKi/wUaLP03tYmM+Kx+N2dnZmjUZjZk+DwaA6GAGLhkLTlponJyfaH6A1z/xGkWFAarWaxeNxi0ajM8KVz+ctmUzKgDEAhWgPD943BIEAFI1G7fj42AqFgm1ubs5cboQHw8FzFItFtQulRGN9fd1Go5HG3gE1QeIBMvOQG/+gfMiJAv0CDwaDQSEScAS8UvbeK+dL7vFhe1Wz+9axIBso5OXlZQ0cAD71pWc+h25mYm1T/mE2C5MlEgm1lfROqlcGdLOi4sCXKHlUAoWAocNYc15mU+IWBEogZPYIwh6kMdAnM1N6gFy6Z3djWIiekKVms6k0F0NCiHK4q+SDnzx5MgMT8hx030MuQVnMTMoQefaomOcnVKvVGTTAw+kYLIyCT6Nh3HAggFtDoZDaJ7MH6XR6Jg0BYkT+lHfmPPf29gTtj0ajmY6PGEEzE3TP/lJtw9/5Mjpf4REOh5Uewxn270Hah5+npzpIIek3csbklHGwIJFyP+BVcI/YA+4OjYkeLoIZygJBVEBQSD/xHl6Po8swvNx1CJHce84WBMGnb9j/er1uHz9+tO3t7RmnCZnjs7E7OEhE7uh7nMFYbNp+1BOvkTOqoQhqfmlf/tQKTP4UVjpf8zVf8zVf8zVf/7+vX9fHbL7ma77ma77ma77+P11zQz1f8zVf8zVf8/UXvOaGer7ma77ma77m6y94zQ31fM3XfM3XfM3XX/CaG+r5mq/5mq/5mq+/4DU31PM1X/M1X/M1X3/Ba26o52u+5mu+5mu+/oLX3FDP13zN13zN13z9Ba+5oZ6v+Zqv+Zqv+foLXv8PvOJrM+jksIcAAAAASUVORK5CYII="/>
 </div>
 </div>
 </div>
@@ -1545,17 +1507,8 @@ <h3 id="step-4-consolidate-the-results-important">step 4: Consolidate the result
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[4], line 2</span>
-<span class="ansi-green-intense-fg ansi-bold">      1</span> <span style="color: rgb(95,135,135)"># Get the MIP image at a lower resolution</span>
-<span class="ansi-green-fg">----&gt; 2</span> mip_image_2 <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">mip_ngff</span><span style="color: rgb(98,98,98)">.</span>get_image(path<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">2</span><span style="color: rgb(175,0,0)">"</span>)
-<span class="ansi-green-intense-fg ansi-bold">      4</span> image_before_consolidation <span style="color: rgb(98,98,98)">=</span> mip_image_2<span style="color: rgb(98,98,98)">.</span>get_array(c<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">0</span>, z<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">0</span>)
-<span class="ansi-green-intense-fg ansi-bold">      6</span> <span style="color: rgb(95,135,135)"># Consolidate the pyramid</span>
-
-<span class="ansi-red-fg">NameError</span>: name 'mip_ngff' is not defined</pre>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
 </div>
 </div>
 </div>
@@ -1601,7 +1554,7 @@ <h3 id="step-5-create-a-new-roi-table">step 5: Create a new ROI table<a class="a
     <span class="n">roi_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
 
 <span class="n">mip_roi_table</span><span class="o">.</span><span class="n">set_rois</span><span class="p">(</span><span class="n">roi_list</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">mip_roi_table</span><span class="o">.</span><span class="n">consolidate</span><span class="p">()</span>
+<span class="n">mip_roi_table</span><span class="o">.</span><span class="n">write</span><span class="p">()</span>
 
 <span class="n">mip_roi_table</span><span class="o">.</span><span class="n">table</span>
 </pre></div>
@@ -1614,7 +1567,7 @@ <h3 id="step-5-create-a-new-roi-table">step 5: Create a new ROI table<a class="a
     roi_list.append(roi)
 
 mip_roi_table.set_rois(roi_list, overwrite=True)
-mip_roi_table.consolidate()
+mip_roi_table.write()
 
 mip_roi_table.table</div>
 </div>
@@ -1627,16 +1580,80 @@ <h3 id="step-5-create-a-new-roi-table">step 5: Create a new ROI table<a class="a
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[5], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> mip_roi_table <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">mip_ngff</span><span style="color: rgb(98,98,98)">.</span>table<span style="color: rgb(98,98,98)">.</span>new(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">FOV_ROI_table</span><span style="color: rgb(175,0,0)">"</span>, overwrite<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>)
-<span class="ansi-green-intense-fg ansi-bold">      3</span> roi_list <span style="color: rgb(98,98,98)">=</span> []
-<span class="ansi-green-intense-fg ansi-bold">      4</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> roi <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> roi_table<span style="color: rgb(98,98,98)">.</span>rois:
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre> - Processing ROI None
+ - Processing ROI None
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+<div>
+<style scoped="">
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
 
-<span class="ansi-red-fg">NameError</span>: name 'mip_ngff' is not defined</pre>
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>x_micrometer</th>
+<th>y_micrometer</th>
+<th>z_micrometer</th>
+<th>len_x_micrometer</th>
+<th>len_y_micrometer</th>
+<th>len_z_micrometer</th>
+<th>x_micrometer_original</th>
+<th>y_micrometer_original</th>
+</tr>
+<tr>
+<th>FieldIndex</th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>FOV_1</th>
+<td>0.0</td>
+<td>0.0</td>
+<td>0.0</td>
+<td>416.0</td>
+<td>351.0</td>
+<td>1</td>
+<td>-1448.300049</td>
+<td>-1517.699951</td>
+</tr>
+<tr>
+<th>FOV_2</th>
+<td>416.0</td>
+<td>0.0</td>
+<td>0.0</td>
+<td>416.0</td>
+<td>351.0</td>
+<td>1</td>
+<td>-1032.300049</td>
+<td>-1517.699951</td>
+</tr>
+</tbody>
+</table>
+</div>
 </div>
 </div>
 </div>
@@ -1729,8 +1746,8 @@ <h3 id="step-1-derive-a-new-label-image-from-the-mip-image">step 1: Derive a new
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
 <div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
@@ -1755,24 +1772,6 @@ <h3 id="step-1-derive-a-new-label-image-from-the-mip-image">step 1: Derive a new
 </div>
 </div>
 </div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[7], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> nuclei_image <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">mip_ngff</span><span style="color: rgb(98,98,98)">.</span>label<span style="color: rgb(98,98,98)">.</span>derive(name<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">nuclei</span><span style="color: rgb(175,0,0)">"</span>, overwrite<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>)
-
-<span class="ansi-red-fg">NameError</span>: name 'mip_ngff' is not defined</pre>
-</div>
-</div>
-</div>
-</div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
@@ -1894,17 +1893,25 @@ <h3 id="step-2-run-the-workflow">step 2: Run the workflow<a class="anchor-link"
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[8], line 2</span>
-<span class="ansi-green-intense-fg ansi-bold">      1</span> <span style="color: rgb(95,135,135)"># Get the source imag</span>
-<span class="ansi-green-fg">----&gt; 2</span> source_image <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">mip_ngff</span><span style="color: rgb(98,98,98)">.</span>get_image()
-<span class="ansi-green-intense-fg ansi-bold">      3</span> <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">Source image loaded with shape:</span><span style="color: rgb(175,0,0)">"</span>, source_image<span style="color: rgb(98,98,98)">.</span>shape)
-<span class="ansi-green-intense-fg ansi-bold">      5</span> <span style="color: rgb(95,135,135)"># Get a ROI table</span>
-
-<span class="ansi-red-fg">NameError</span>: name 'mip_ngff' is not defined</pre>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Source image loaded with shape: (1, 1, 2160, 5120)
+ROI table loaded with 2 ROIs
+ - Processing ROI None
+ - Processing ROI None
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Segmentation image saved
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
 </div>
 </div>
 </div>
@@ -1967,7 +1974,7 @@ <h3 id="step-2-run-the-workflow">step 2: Run the workflow<a class="anchor-link"
     <span class="md-icon" title="Last update">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1zM12.5 7v5.2l4 2.4-1 1L11 13V7zM11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2z"/></svg>
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-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
@@ -1977,7 +1984,7 @@ <h3 id="step-2-run-the-workflow">step 2: Run the workflow<a class="anchor-link"
     <span class="md-icon" title="Created">
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M14.47 15.08 11 13V7h1.5v5.25l3.08 1.83c-.41.28-.79.62-1.11 1m-1.39 4.84c-.36.05-.71.08-1.08.08-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8c0 .37-.03.72-.08 1.08.69.1 1.33.32 1.92.64.1-.56.16-1.13.16-1.72 0-5.5-4.5-10-10-10S2 6.5 2 12s4.47 10 10 10c.59 0 1.16-.06 1.72-.16-.32-.59-.54-1.23-.64-1.92M18 15v3h-3v2h3v3h2v-3h3v-2h-3v-3z"/></svg>
     </span>
-    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 7, 2024</span>
+    <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">November 6, 2024</span>
   </span>
 
     
diff --git a/search/search_index.json b/search/search_index.json
index 2ce4ec6..22e9dc0 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 NGIO","text":"<p>NGIO is a Python library to streamline OME-Zarr image analysis workflows.</p> <p>Main Goals:</p> <ul> <li>Abstract object base API for handling OME-Zarr files</li> <li>Powefull iterators for processing data using common access patterns</li> <li>Tight integration with Fractal's Table Fractal</li> <li>Validate OME-Zarr files</li> </ul> <p>To get started, check out the Getting Started guide.</p>"},{"location":"#ngio-is-under-active-development","title":"\ud83d\udea7 Ngio is Under active Development \ud83d\udea7","text":""},{"location":"#roadmap","title":"Roadmap","text":"Feature Status ETA Description Metadata Handling \u2705 Read, Write, Validate OME-Zarr Metadata (0.4 supported, 0.5 ready) OME-Zarr Validation \u2705 Validate OME-Zarr files for compliance with the OME-Zarr Specification + Compliance between Metadata and Data Base Image Handling \u2705 Load data from OME-Zarr files, retrieve basic metadata, and write data ROI Handling \u2705 Common ROI models Label Handling \u2705 Mid-September Based on Image Handling Table Validation \u2705 Mid-September Validate Table fractal V1 + Compliance between Metadata and Data Table Handling \u2705 Mid-September Read, Write ROI, Features, and Masked Tables Basic Iterators Ongoing End-September Read and Write Iterators for common access patterns Base Documentation \u2705 End-September API Documentation and Examples Beta Ready Testing \u2705 End-September Beta Testing; Library is ready for testing, but the API is not stable Mask Iterators Ongoing October Iterators over Masked Tables Advanced Iterators Not started October Iterators for advanced access patterns Parallel Iterators Not started End of the Year Concurrent Iterators for parallel read and write Full Documentation Not started End of the Year Complete Documentation Release 1.0 (Commitment to API) Not started End of the Year API is stable; breaking changes will be avoided"},{"location":"#contributors","title":"Contributors","text":"<p><code>ngio</code> is developed at the BioVisionCenter at the University of Zurich. The main contributors are: @lorenzocerrone, @jluethi.</p>"},{"location":"#license","title":"License","text":"<p><code>ngio</code> is released according to the BSD-3-Clause License. See LICENSE</p>"},{"location":"getting-started/","title":"Getting Started","text":"<p>Warning</p> <p>The library is still in development and is not yet stable. The API is subject to change, bugs and breaking changes are expected.</p> <p>Warning</p> <p>The documentation is still under development. It is not yet complete and may contain errors and inaccuracies.</p>"},{"location":"getting-started/#installation","title":"Installation","text":"<p>The library can be installed from PyPI using pip:</p> <pre><code>pip install \"ngio[core]\"\n</code></pre> <p>The <code>core</code> extra installs the the <code>zarr-python</code> dependency. As of now, <code>zarr-python</code> is required to be installed separately, due to the transition to the new <code>zarr-v3</code> library.</p>"},{"location":"getting-started/#ngio-api-overview","title":"<code>ngio</code> API Overview","text":"<p><code>ngio</code> implements an abstract object base API for handling OME-Zarr files. The three main objects are <code>NgffImage</code>, <code>Image</code> (<code>Label</code>), and <code>ROITables</code>.</p> <ul> <li><code>NgffImage</code> is the main entry point to the library. It is used to open an OME-Zarr Image and manage its metadata. This object can not be used to access the data directly.   but it can be used to access and create the <code>Image</code>, <code>Label</code>, and <code>Tables</code> objects. Moreover it can be used to derive a new <code>Ngff</code> images based on the current one.</li> <li><code>Image</code> and <code>Label</code> are used to access \"ImageLike\" objects. They are the main objects to access the data in the OME-Zarr file, manage the metadata, and write data.</li> <li><code>ROITables</code> can be used to access specific region of interest in the image. They are tightly integrated with the <code>Image</code> and <code>Label</code> objects.</li> </ul>"},{"location":"getting-started/#example-usage","title":"Example Usage","text":"<p>Currently, the library is not yet stable. However, you can see some example usage in our demo notebooks:</p> <ul> <li>Basic Usage</li> <li>Image/Label/Tables</li> <li>Processing</li> </ul>"},{"location":"api/core/","title":"ngio.core","text":""},{"location":"api/core/#ngio.core","title":"<code>ngio.core</code>","text":"<p>Core classes for the ngio library.</p>"},{"location":"api/core/#ngffimage","title":"NGFFImage","text":""},{"location":"api/core/#ngio.core.NgffImage","title":"<code>ngio.core.NgffImage</code>","text":"<p>A class to handle OME-NGFF images.</p> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>class NgffImage:\n    \"\"\"A class to handle OME-NGFF images.\"\"\"\n\n    def __init__(\n        self, store: StoreLike, cache: bool = False, mode: AccessModeLiteral = \"r+\"\n    ) -&gt; None:\n        \"\"\"Initialize the NGFFImage in read mode.\"\"\"\n        self.store = store\n        self._mode = mode\n        self.group = open_group_wrapper(store=store, mode=self._mode)\n\n        if self.group.read_only:\n            self._mode = \"r\"\n\n        self._image_meta = get_ngff_image_meta_handler(\n            self.group, meta_mode=\"image\", cache=cache\n        )\n        self._metadata_cache = cache\n        self.table = TableGroup(self.group, mode=self._mode)\n        self.label = LabelGroup(self.group, image_ref=self.get_image(), mode=self._mode)\n\n        ngio_logger.info(f\"Opened image located in store: {store}\")\n        ngio_logger.info(f\"- Image number of levels: {self.num_levels}\")\n\n    @property\n    def image_meta(self) -&gt; ImageMeta:\n        \"\"\"Get the image metadata.\"\"\"\n        meta = self._image_meta.load_meta()\n        assert isinstance(meta, ImageMeta)\n        return meta\n\n    @property\n    def num_levels(self) -&gt; int:\n        \"\"\"Get the number of levels in the image.\"\"\"\n        return self.image_meta.num_levels\n\n    @property\n    def levels_paths(self) -&gt; list[str]:\n        \"\"\"Get the paths of the levels in the image.\"\"\"\n        return self.image_meta.levels_paths\n\n    def get_image(\n        self,\n        *,\n        path: str | None = None,\n        pixel_size: PixelSize | None = None,\n        highest_resolution: bool = True,\n    ) -&gt; Image:\n        \"\"\"Get an image handler for the given level.\n\n        Args:\n            path (str | None, optional): The path to the level.\n            pixel_size (tuple[float, ...] | list[float] | None, optional): The pixel\n                size of the level.\n            highest_resolution (bool, optional): Whether to get the highest\n                resolution level\n\n        Returns:\n            ImageHandler: The image handler.\n        \"\"\"\n        if path is not None or pixel_size is not None:\n            highest_resolution = False\n\n        image = Image(\n            store=self.group,\n            path=path,\n            pixel_size=pixel_size,\n            highest_resolution=highest_resolution,\n            label_group=LabelGroup(self.group, image_ref=None, mode=self._mode),\n            cache=self._metadata_cache,\n            mode=self._mode,\n        )\n        ngio_logger.info(f\"Opened image at path: {image.path}\")\n        ngio_logger.info(f\"- {image.dimensions}\")\n        ngio_logger.info(f\"- {image.pixel_size}\")\n        return image\n\n    def update_omero_window(\n        self, start_percentile: int = 5, end_percentile: int = 95\n    ) -&gt; None:\n        \"\"\"Update the OMERO window.\n\n        This will setup percentiles based values for the window of each channel.\n\n        Args:\n            start_percentile (int): The start percentile.\n            end_percentile (int): The end percentile\n\n        \"\"\"\n        meta = self.image_meta\n\n        lowest_res_image = self.get_image(highest_resolution=True)\n        lowest_res_shape = lowest_res_image.shape\n        for path in self.levels_paths:\n            image = self.get_image(path=path)\n            if np.prod(image.shape) &lt; np.prod(lowest_res_shape):\n                lowest_res_shape = image.shape\n                lowest_res_image = image\n\n        max_dtype = np.iinfo(image.on_disk_array.dtype).max\n        num_c = lowest_res_image.dimensions.get(\"c\", 1)\n\n        if meta.omero is None:\n            raise NotImplementedError(\n                \"OMERO metadata not found. \" \" Please add OMERO metadata to the image.\"\n            )\n\n        channel_list = meta.omero.channels\n        if len(channel_list) != num_c:\n            raise ValueError(\"The number of channels does not match the image.\")\n\n        for c, channel in enumerate(channel_list):\n            data = image.get_array(c=c, mode=\"dask\").ravel()\n            _start_percentile = da.percentile(\n                data, start_percentile, method=\"nearest\"\n            ).compute()\n            _end_percentile = da.percentile(\n                data, end_percentile, method=\"nearest\"\n            ).compute()\n            channel.extra_fields[\"window\"] = {\n                \"start\": _start_percentile,\n                \"end\": _end_percentile,\n                \"min\": 0,\n                \"max\": max_dtype,\n            }\n            ngio_logger.info(\n                f\"Updated window for channel {channel.label}. \"\n                f\"Start: {start_percentile}, End: {end_percentile}\"\n            )\n            meta.omero.channels[c] = channel\n\n        self._image_meta.write_meta(meta)\n\n    def derive_new_image(\n        self,\n        store: StoreLike,\n        name: str,\n        overwrite: bool = True,\n        **kwargs: dict,\n    ) -&gt; \"NgffImage\":\n        \"\"\"Derive a new image from the current image.\n\n        Args:\n            store (StoreLike): The store to create the new image in.\n            name (str): The name of the new image.\n            overwrite (bool): Whether to overwrite the image if it exists\n            **kwargs: Additional keyword arguments.\n                Follow the same signature as `create_empty_ome_zarr_image`.\n\n        Returns:\n            NgffImage: The new image.\n        \"\"\"\n        image_0 = self.get_image(highest_resolution=True)\n\n        # Get the channel information if it exists\n        omero = self.image_meta.omero\n        if omero is not None:\n            channels = omero.channels\n            omero_kwargs = omero.extra_fields\n        else:\n            channels = []\n            omero_kwargs = {}\n\n        default_kwargs = {\n            \"store\": store,\n            \"on_disk_shape\": image_0.on_disk_shape,\n            \"chunks\": image_0.on_disk_array.chunks,\n            \"dtype\": image_0.on_disk_array.dtype,\n            \"on_disk_axis\": image_0.dataset.on_disk_axes_names,\n            \"pixel_sizes\": image_0.pixel_size,\n            \"xy_scaling_factor\": self.image_meta.xy_scaling_factor,\n            \"z_scaling_factor\": self.image_meta.z_scaling_factor,\n            \"time_spacing\": image_0.dataset.time_spacing,\n            \"time_units\": image_0.dataset.time_axis_unit,\n            \"levels\": self.num_levels,\n            \"name\": name,\n            \"channel_labels\": image_0.channel_labels,\n            \"channel_wavelengths\": [ch.wavelength_id for ch in channels],\n            \"channel_kwargs\": [ch.extra_fields for ch in channels],\n            \"omero_kwargs\": omero_kwargs,\n            \"overwrite\": overwrite,\n            \"version\": self.image_meta.version,\n        }\n\n        default_kwargs.update(kwargs)\n\n        create_empty_ome_zarr_image(\n            **default_kwargs,\n        )\n        return NgffImage(store=store)\n</code></pre>"},{"location":"api/core/#ngio.core.NgffImage.image_meta","title":"<code>image_meta: ImageMeta</code>  <code>property</code>","text":"<p>Get the image metadata.</p>"},{"location":"api/core/#ngio.core.NgffImage.levels_paths","title":"<code>levels_paths: list[str]</code>  <code>property</code>","text":"<p>Get the paths of the levels in the image.</p>"},{"location":"api/core/#ngio.core.NgffImage.num_levels","title":"<code>num_levels: int</code>  <code>property</code>","text":"<p>Get the number of levels in the image.</p>"},{"location":"api/core/#ngio.core.NgffImage.__init__","title":"<code>__init__(store: StoreLike, cache: bool = False, mode: AccessModeLiteral = 'r+') -&gt; None</code>","text":"<p>Initialize the NGFFImage in read mode.</p> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>def __init__(\n    self, store: StoreLike, cache: bool = False, mode: AccessModeLiteral = \"r+\"\n) -&gt; None:\n    \"\"\"Initialize the NGFFImage in read mode.\"\"\"\n    self.store = store\n    self._mode = mode\n    self.group = open_group_wrapper(store=store, mode=self._mode)\n\n    if self.group.read_only:\n        self._mode = \"r\"\n\n    self._image_meta = get_ngff_image_meta_handler(\n        self.group, meta_mode=\"image\", cache=cache\n    )\n    self._metadata_cache = cache\n    self.table = TableGroup(self.group, mode=self._mode)\n    self.label = LabelGroup(self.group, image_ref=self.get_image(), mode=self._mode)\n\n    ngio_logger.info(f\"Opened image located in store: {store}\")\n    ngio_logger.info(f\"- Image number of levels: {self.num_levels}\")\n</code></pre>"},{"location":"api/core/#ngio.core.NgffImage.derive_new_image","title":"<code>derive_new_image(store: StoreLike, name: str, overwrite: bool = True, **kwargs: dict) -&gt; NgffImage</code>","text":"<p>Derive a new image from the current image.</p> <p>Parameters:</p> <ul> <li> <code>store</code>               (<code>StoreLike</code>)           \u2013            <p>The store to create the new image in.</p> </li> <li> <code>name</code>               (<code>str</code>)           \u2013            <p>The name of the new image.</p> </li> <li> <code>overwrite</code>               (<code>bool</code>, default:                   <code>True</code> )           \u2013            <p>Whether to overwrite the image if it exists</p> </li> <li> <code>**kwargs</code>               (<code>dict</code>, default:                   <code>{}</code> )           \u2013            <p>Additional keyword arguments. Follow the same signature as <code>create_empty_ome_zarr_image</code>.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NgffImage</code> (              <code>NgffImage</code> )          \u2013            <p>The new image.</p> </li> </ul> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>def derive_new_image(\n    self,\n    store: StoreLike,\n    name: str,\n    overwrite: bool = True,\n    **kwargs: dict,\n) -&gt; \"NgffImage\":\n    \"\"\"Derive a new image from the current image.\n\n    Args:\n        store (StoreLike): The store to create the new image in.\n        name (str): The name of the new image.\n        overwrite (bool): Whether to overwrite the image if it exists\n        **kwargs: Additional keyword arguments.\n            Follow the same signature as `create_empty_ome_zarr_image`.\n\n    Returns:\n        NgffImage: The new image.\n    \"\"\"\n    image_0 = self.get_image(highest_resolution=True)\n\n    # Get the channel information if it exists\n    omero = self.image_meta.omero\n    if omero is not None:\n        channels = omero.channels\n        omero_kwargs = omero.extra_fields\n    else:\n        channels = []\n        omero_kwargs = {}\n\n    default_kwargs = {\n        \"store\": store,\n        \"on_disk_shape\": image_0.on_disk_shape,\n        \"chunks\": image_0.on_disk_array.chunks,\n        \"dtype\": image_0.on_disk_array.dtype,\n        \"on_disk_axis\": image_0.dataset.on_disk_axes_names,\n        \"pixel_sizes\": image_0.pixel_size,\n        \"xy_scaling_factor\": self.image_meta.xy_scaling_factor,\n        \"z_scaling_factor\": self.image_meta.z_scaling_factor,\n        \"time_spacing\": image_0.dataset.time_spacing,\n        \"time_units\": image_0.dataset.time_axis_unit,\n        \"levels\": self.num_levels,\n        \"name\": name,\n        \"channel_labels\": image_0.channel_labels,\n        \"channel_wavelengths\": [ch.wavelength_id for ch in channels],\n        \"channel_kwargs\": [ch.extra_fields for ch in channels],\n        \"omero_kwargs\": omero_kwargs,\n        \"overwrite\": overwrite,\n        \"version\": self.image_meta.version,\n    }\n\n    default_kwargs.update(kwargs)\n\n    create_empty_ome_zarr_image(\n        **default_kwargs,\n    )\n    return NgffImage(store=store)\n</code></pre>"},{"location":"api/core/#ngio.core.NgffImage.get_image","title":"<code>get_image(*, path: str | None = None, pixel_size: PixelSize | None = None, highest_resolution: bool = True) -&gt; Image</code>","text":"<p>Get an image handler for the given level.</p> <p>Parameters:</p> <ul> <li> <code>path</code>               (<code>str | None</code>, default:                   <code>None</code> )           \u2013            <p>The path to the level.</p> </li> <li> <code>pixel_size</code>               (<code>tuple[float, ...] | list[float] | None</code>, default:                   <code>None</code> )           \u2013            <p>The pixel size of the level.</p> </li> <li> <code>highest_resolution</code>               (<code>bool</code>, default:                   <code>True</code> )           \u2013            <p>Whether to get the highest resolution level</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>ImageHandler</code> (              <code>Image</code> )          \u2013            <p>The image handler.</p> </li> </ul> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>def get_image(\n    self,\n    *,\n    path: str | None = None,\n    pixel_size: PixelSize | None = None,\n    highest_resolution: bool = True,\n) -&gt; Image:\n    \"\"\"Get an image handler for the given level.\n\n    Args:\n        path (str | None, optional): The path to the level.\n        pixel_size (tuple[float, ...] | list[float] | None, optional): The pixel\n            size of the level.\n        highest_resolution (bool, optional): Whether to get the highest\n            resolution level\n\n    Returns:\n        ImageHandler: The image handler.\n    \"\"\"\n    if path is not None or pixel_size is not None:\n        highest_resolution = False\n\n    image = Image(\n        store=self.group,\n        path=path,\n        pixel_size=pixel_size,\n        highest_resolution=highest_resolution,\n        label_group=LabelGroup(self.group, image_ref=None, mode=self._mode),\n        cache=self._metadata_cache,\n        mode=self._mode,\n    )\n    ngio_logger.info(f\"Opened image at path: {image.path}\")\n    ngio_logger.info(f\"- {image.dimensions}\")\n    ngio_logger.info(f\"- {image.pixel_size}\")\n    return image\n</code></pre>"},{"location":"api/core/#ngio.core.NgffImage.update_omero_window","title":"<code>update_omero_window(start_percentile: int = 5, end_percentile: int = 95) -&gt; None</code>","text":"<p>Update the OMERO window.</p> <p>This will setup percentiles based values for the window of each channel.</p> <p>Parameters:</p> <ul> <li> <code>start_percentile</code>               (<code>int</code>, default:                   <code>5</code> )           \u2013            <p>The start percentile.</p> </li> <li> <code>end_percentile</code>               (<code>int</code>, default:                   <code>95</code> )           \u2013            <p>The end percentile</p> </li> </ul> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>def update_omero_window(\n    self, start_percentile: int = 5, end_percentile: int = 95\n) -&gt; None:\n    \"\"\"Update the OMERO window.\n\n    This will setup percentiles based values for the window of each channel.\n\n    Args:\n        start_percentile (int): The start percentile.\n        end_percentile (int): The end percentile\n\n    \"\"\"\n    meta = self.image_meta\n\n    lowest_res_image = self.get_image(highest_resolution=True)\n    lowest_res_shape = lowest_res_image.shape\n    for path in self.levels_paths:\n        image = self.get_image(path=path)\n        if np.prod(image.shape) &lt; np.prod(lowest_res_shape):\n            lowest_res_shape = image.shape\n            lowest_res_image = image\n\n    max_dtype = np.iinfo(image.on_disk_array.dtype).max\n    num_c = lowest_res_image.dimensions.get(\"c\", 1)\n\n    if meta.omero is None:\n        raise NotImplementedError(\n            \"OMERO metadata not found. \" \" Please add OMERO metadata to the image.\"\n        )\n\n    channel_list = meta.omero.channels\n    if len(channel_list) != num_c:\n        raise ValueError(\"The number of channels does not match the image.\")\n\n    for c, channel in enumerate(channel_list):\n        data = image.get_array(c=c, mode=\"dask\").ravel()\n        _start_percentile = da.percentile(\n            data, start_percentile, method=\"nearest\"\n        ).compute()\n        _end_percentile = da.percentile(\n            data, end_percentile, method=\"nearest\"\n        ).compute()\n        channel.extra_fields[\"window\"] = {\n            \"start\": _start_percentile,\n            \"end\": _end_percentile,\n            \"min\": 0,\n            \"max\": max_dtype,\n        }\n        ngio_logger.info(\n            f\"Updated window for channel {channel.label}. \"\n            f\"Start: {start_percentile}, End: {end_percentile}\"\n        )\n        meta.omero.channels[c] = channel\n\n    self._image_meta.write_meta(meta)\n</code></pre>"},{"location":"notebooks/basic_usage/","title":"Basic Usage","text":"In\u00a0[1]: Copied! <pre>from ngio.core import NgffImage\nngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr/B/03/0\")\n</pre> from ngio.core import NgffImage ngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr/B/03/0\") <p>The <code>ngff_image</code> contains several attributes and methods to interact with the <code>OME-NGFF</code> (<code>OME-Zarr</code>) file on the storage.</p> In\u00a0[2]: Copied! <pre># Explore object metadata\nprint(\"Levels: \", ngff_image.levels_paths)\nprint(\"Num Levels: \", ngff_image.num_levels)\n</pre> # Explore object metadata print(\"Levels: \", ngff_image.levels_paths) print(\"Num Levels: \", ngff_image.num_levels) <pre>Levels:  ['0', '1', '2', '3', '4']\nNum Levels:  5\n</pre> <p>Get a single <code>level</code> of the image pyramid as <code>Image</code> (to know more about the <code>Image</code> class, please refer to the Image notebook The <code>Image</code> object is the main object to interact with the image. It contains methods to interact with the image data and metadata.</p> In\u00a0[3]: Copied! <pre>from ngio.ngff_meta import PixelSize\n\n# 1. Get image from highest resolution (default)\nimage = ngff_image.get_image()\nprint(f\"{image.shape=}\")\nprint(f\"{image.pixel_size=}\")\n\n# 2. Get image from a specific level using the path keyword\nprint(f\"{image.shape=}\")\nprint(f\"{image.pixel_size=}\")\n\n# 3. Get image from a specific pixel size using the pixel_size keyword\nimage = ngff_image.get_image(pixel_size=PixelSize(x=0.65, y=0.65, z=1))\nprint(f\"{image.shape=}\")\nprint(f\"{image.pixel_size=}\")\n</pre> from ngio.ngff_meta import PixelSize  # 1. Get image from highest resolution (default) image = ngff_image.get_image() print(f\"{image.shape=}\") print(f\"{image.pixel_size=}\")  # 2. Get image from a specific level using the path keyword print(f\"{image.shape=}\") print(f\"{image.pixel_size=}\")  # 3. Get image from a specific pixel size using the pixel_size keyword image = ngff_image.get_image(pixel_size=PixelSize(x=0.65, y=0.65, z=1)) print(f\"{image.shape=}\") print(f\"{image.pixel_size=}\") <pre>image.shape=(3, 1, 4320, 5120)\nimage.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\nimage.shape=(3, 1, 4320, 5120)\nimage.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\nimage.shape=(3, 1, 1080, 1280)\nimage.pixel_size=PixelSize(x=0.65, y=0.65, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=True)\n</pre> In\u00a0[4]: Copied! <pre>print(\"List of Labels: \", ngff_image.label.list())\n\nlabel_nuclei = ngff_image.label.get_label(\"nuclei\", path=\"0\")\nprint(f\"{label_nuclei.shape=}\")\nprint(f\"{label_nuclei.pixel_size=}\")\n</pre> print(\"List of Labels: \", ngff_image.label.list())  label_nuclei = ngff_image.label.get_label(\"nuclei\", path=\"0\") print(f\"{label_nuclei.shape=}\") print(f\"{label_nuclei.pixel_size=}\") <pre>List of Labels:  ['nuclei', 'wf_2_labels', 'wf_3_labels', 'wf_4_labels']\nlabel_nuclei.shape=(1, 4320, 5120)\nlabel_nuclei.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\n</pre> In\u00a0[5]: Copied! <pre>print(\"List of Tables: \", ngff_image.table.list())\nprint(\" - Feature tables: \", ngff_image.table.list(table_type='feature_table'))\nprint(\" - Roi tables: \", ngff_image.table.list(table_type='roi_table'))\nprint(\" - Masking Roi tables: \", ngff_image.table.list(table_type='masking_roi_table'))\n</pre> print(\"List of Tables: \", ngff_image.table.list()) print(\" - Feature tables: \", ngff_image.table.list(table_type='feature_table')) print(\" - Roi tables: \", ngff_image.table.list(table_type='roi_table')) print(\" - Masking Roi tables: \", ngff_image.table.list(table_type='masking_roi_table')) <pre>List of Tables:  ['FOV_ROI_table', 'nuclei_ROI_table', 'well_ROI_table', 'regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4']\n - Feature tables:  ['regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4']\n - Roi tables:  ['FOV_ROI_table', 'well_ROI_table']\n - Masking Roi tables:  ['nuclei_ROI_table']\n</pre> In\u00a0[6]: Copied! <pre># Loading a table\nfeature_table = ngff_image.table.get_table(\"regionprops_DAPI\")\nfeature_table.table\n</pre> # Loading a table feature_table = ngff_image.table.get_table(\"regionprops_DAPI\") feature_table.table <pre>/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n</pre> Out[6]: area bbox_area equivalent_diameter max_intensity mean_intensity min_intensity standard_deviation_intensity label 1 2120.0 2655.0 15.938437 476.0 278.635864 86.0 54.343792 2 327.0 456.0 8.547709 604.0 324.162079 118.0 90.847092 3 1381.0 1749.0 13.816510 386.0 212.682114 60.0 50.169601 4 2566.0 3588.0 16.985800 497.0 251.731491 61.0 53.307186 5 4201.0 5472.0 20.019413 466.0 223.862885 51.0 56.719025 ... ... ... ... ... ... ... ... 3002 1026.0 1288.0 12.513618 589.0 308.404480 132.0 64.681778 3003 859.0 1080.0 11.794101 400.0 270.349243 107.0 49.040470 3004 508.0 660.0 9.899693 314.0 205.043304 82.0 33.249981 3005 369.0 440.0 8.899028 376.0 217.970184 82.0 50.978519 3006 278.0 330.0 8.097459 339.0 217.996399 100.0 38.510067 <p>3006 rows \u00d7 7 columns</p> In\u00a0[7]: Copied! <pre># Loading a roi table\nroi_table = ngff_image.table.get_table(\"FOV_ROI_table\")\n\nprint(f\"{roi_table.field_indexes=}\")\nprint(f\"{roi_table.get_roi('FOV_1')=}\")\n\nroi_table.table\n</pre> # Loading a roi table roi_table = ngff_image.table.get_table(\"FOV_ROI_table\")  print(f\"{roi_table.field_indexes=}\") print(f\"{roi_table.get_roi('FOV_1')=}\")  roi_table.table <pre>roi_table.field_indexes=['FOV_1', 'FOV_2', 'FOV_3', 'FOV_4']\nroi_table.get_roi('FOV_1')=WorldCooROI(x_length=416.0, y_length=351.0, z_length=1.0, x=0.0, y=0.0, z=0.0)\n</pre> Out[7]: x_micrometer y_micrometer z_micrometer len_x_micrometer len_y_micrometer len_z_micrometer x_micrometer_original y_micrometer_original FieldIndex FOV_1 0.0 0.0 0.0 416.0 351.0 1.0 -1448.300049 -1517.699951 FOV_2 416.0 0.0 0.0 416.0 351.0 1.0 -1032.300049 -1517.699951 FOV_3 0.0 351.0 0.0 416.0 351.0 1.0 -1448.300049 -1166.699951 FOV_4 416.0 351.0 0.0 416.0 351.0 1.0 -1032.300049 -1166.699951 In\u00a0[8]: Copied! <pre>new_ngff_image = ngff_image.derive_new_image(\"../../data/new_ome.zarr\", name=\"new_image\")\n\nprint(f\"{new_ngff_image.store=}\")\nprint(f\"{new_ngff_image.levels_paths=}\")\nprint(f\"{new_ngff_image.num_levels=}\")\n</pre> new_ngff_image = ngff_image.derive_new_image(\"../../data/new_ome.zarr\", name=\"new_image\")  print(f\"{new_ngff_image.store=}\") print(f\"{new_ngff_image.levels_paths=}\") print(f\"{new_ngff_image.num_levels=}\") <pre>new_ngff_image.store='../../data/new_ome.zarr'\nnew_ngff_image.levels_paths=['0', '1', '2', '3', '4']\nnew_ngff_image.num_levels=5\n</pre>"},{"location":"notebooks/basic_usage/#basic-usage","title":"Basic Usage\u00b6","text":"<p>In this notebook we will show how to use the 'ngffImage' class to manage a OME-NGFF image.</p> <p>For this example we will use a small example image that can be downloaded from the following link: example ome-zarr</p> <p>You can download the example image (on Linux and Mac os) by running the following command:</p> <pre>bash setup_data.sh\n</pre> <p>from the root of the repository.</p>"},{"location":"notebooks/basic_usage/#ngffimage","title":"NgffImage\u00b6","text":"<p>The <code>NgffImage</code> provides a high-level interface to read, write and manipulate NGFF images. A <code>NgffImage</code> can be created from a storelike object (e.g. a path to a directory or a zarr store) or from a <code>zarr.Group</code> object.</p>"},{"location":"notebooks/basic_usage/#labels","title":"Labels\u00b6","text":"<p>The <code>NgffImage</code> can also be used to load labels from a <code>OME-NGFF</code> file.</p>"},{"location":"notebooks/basic_usage/#tables","title":"Tables\u00b6","text":"<p>The <code>NgffImage</code> can also be used to load tables from a <code>OME-NGFF</code> file.</p> <p><code>ngio</code> supports three types of tables:</p> <ul> <li><code>features table</code> A simple table to store features associated with a label.</li> <li><code>roi table</code> A table to store regions of interest.</li> <li><code>masking roi tables</code> A table to store single objects bounding boxes associated with a label.</li> </ul>"},{"location":"notebooks/basic_usage/#derive-a-new-ngffimage","title":"Derive a new NgffImage\u00b6","text":"<p>When processing an image, it is often useful to derive a new image from the original image. The <code>NgffImage</code> class provides a method to derive a new image from the original image. When deriving a new image, a new <code>NgffImage</code> object is created with the same metadata as the original image. Optionally the user can specify different metadata for the new image(.e.g. different channels names).</p>"},{"location":"notebooks/image/","title":"Images/Labels/Tables","text":"In\u00a0[1]: Copied! <pre>import matplotlib.pyplot as plt\n\nfrom ngio.core.ngff_image import NgffImage\n\nngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr/B/03/0\")\n</pre> import matplotlib.pyplot as plt  from ngio.core.ngff_image import NgffImage  ngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr/B/03/0\") In\u00a0[2]: Copied! <pre>image = ngff_image.get_image()\n\nprint(\"Image information:\")\nprint(f\"{image.shape=}\")\nprint(f\"{image.axes_names=}\")\nprint(f\"{image.pixel_size=}\")\nprint(f\"{image.channel_labels=}\")\nprint(f\"{image.dimensions=}\")\n</pre> image = ngff_image.get_image()  print(\"Image information:\") print(f\"{image.shape=}\") print(f\"{image.axes_names=}\") print(f\"{image.pixel_size=}\") print(f\"{image.channel_labels=}\") print(f\"{image.dimensions=}\") <pre>Image information:\nimage.shape=(3, 1, 4320, 5120)\nimage.axes_names=['c', 'z', 'y', 'x']\nimage.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\nimage.channel_labels=['DAPI', 'nanog', 'Lamin B1']\nimage.dimensions=Dimensions(c=3, z=1, y=4320, x=5120)\n</pre> <p>The <code>Image</code> object created is a lazy object, meaning that the image is not loaded into memory until it is needed. To get the image data from disk we can use the <code>.array</code> attribute or we can get it as a <code>dask.array</code> object using the <code>.dask_array</code> attribute.</p> In\u00a0[3]: Copied! <pre># Get image as a dask array\ndask_array = image.on_disk_dask_array\ndask_array\n</pre> # Get image as a dask array dask_array = image.on_disk_dask_array dask_array Out[3]:  Array   Chunk   Bytes   126.56 MiB   10.55 MiB   Shape   (3, 1, 4320, 5120)   (1, 1, 2160, 2560)   Dask graph   12 chunks in 2 graph layers   Data type   uint16 numpy.ndarray  3 1 5120 4320 1 <p>Note, directly accessing the <code>.on_disk_array</code> or <code>.on_disk_dask_array</code> attributes will load the image as stored in the file.</p> <p>Since in principle the images can have different axes order. A safer way to access the image data is to use the <code>.get_array()</code> method, which will return the image data in canonical order (TCZYX).</p> In\u00a0[4]: Copied! <pre>image_numpy = image.get_array(c=0, x=slice(0, 250), y=slice(0, 250), preserve_dimensions=False, mode=\"numpy\")\n\nprint(f\"{image_numpy.shape=}\")\n</pre> image_numpy = image.get_array(c=0, x=slice(0, 250), y=slice(0, 250), preserve_dimensions=False, mode=\"numpy\")  print(f\"{image_numpy.shape=}\") <pre>image_numpy.shape=(1, 250, 250)\n</pre> In\u00a0[5]: Copied! <pre>roi_table = ngff_image.table.get_table(\"FOV_ROI_table\")\nroi = roi_table.get_roi(\"FOV_1\")\nprint(f\"{roi=}\")\n\nimage_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=True, mode=\"dask\")\nimage_roi_1\n</pre> roi_table = ngff_image.table.get_table(\"FOV_ROI_table\") roi = roi_table.get_roi(\"FOV_1\") print(f\"{roi=}\")  image_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=True, mode=\"dask\") image_roi_1 <pre>roi=WorldCooROI(x_length=416.0, y_length=351.0, z_length=1.0, x=0.0, y=0.0, z=0.0)\n</pre> <pre>\n---------------------------------------------------------------------------\nAttributeError                            Traceback (most recent call last)\nCell In[5], line 5\n      2 roi = roi_table.get_roi(\"FOV_1\")\n      3 print(f\"{roi=}\")\n----&gt; 5 image_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=True, mode=\"dask\")\n      6 image_roi_1\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_handler.py:105, in Image.get_array_from_roi(self, roi, c, t, mode, preserve_dimensions)\n    102 if isinstance(c, str):\n    103     c = self.get_channel_idx(label=c)\n--&gt; 105 return self._get_array_from_roi(\n    106     roi=roi, t=t, c=c, mode=mode, preserve_dimensions=preserve_dimensions\n    107 )\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:344, in ImageLike._get_array_from_roi(self, roi, t, c, mode, preserve_dimensions)\n    327 def _get_array_from_roi(\n    328     self,\n    329     roi: WorldCooROI,\n   (...)\n    333     preserve_dimensions: bool = False,\n    334 ) -&gt; ArrayLike | tuple[ArrayLike, DataTransformPipe]:\n    335     \"\"\"Return the image data from a region of interest (ROI).\n    336 \n    337     Args:\n   (...)\n    342         preserve_dimensions (bool): Whether to preserve the dimensions of the data.\n    343     \"\"\"\n--&gt; 344     data_pipe = self._build_roi_pipe(\n    345         roi=roi, t=t, c=c, preserve_dimensions=preserve_dimensions\n    346     )\n    347     return_array = self._get_pipe(data_pipe=data_pipe, mode=mode)\n    348     return return_array\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:291, in ImageLike._build_roi_pipe(self, roi, t, c, preserve_dimensions)\n    283 def _build_roi_pipe(\n    284     self,\n    285     roi: WorldCooROI,\n   (...)\n    288     preserve_dimensions: bool = False,\n    289 ) -&gt; DataTransformPipe:\n    290     \"\"\"Build the data transform pipe for a region of interest (ROI).\"\"\"\n--&gt; 291     roi_coo = roi.to_raster_coo(\n    292         pixel_size=self.dataset.pixel_size, dimensions=self.dimensions\n    293     )\n    295     slicer = RoiSlicer(\n    296         on_disk_axes_name=self.dataset.on_disk_axes_names,\n    297         axes_order=self.dataset.axes_order,\n   (...)\n    301         preserve_dimensions=preserve_dimensions,\n    302     )\n    303     return DataTransformPipe(slicer=slicer)\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/roi.py:44, in WorldCooROI.to_raster_coo(self, pixel_size, dimensions)\n     39 def to_raster_coo(\n     40     self, pixel_size: PixelSize, dimensions: Dimensions\n     41 ) -&gt; \"RasterCooROI\":\n     42     \"\"\"Convert to raster coordinates.\"\"\"\n     43     return RasterCooROI(\n---&gt; 44         x=_to_raster(self.x, pixel_size.x, dimensions.x),\n     45         y=_to_raster(self.y, pixel_size.y, dimensions.y),\n     46         z=_to_raster(self.z, pixel_size.z, dimensions.z),\n     47         x_length=_to_raster(self.x_length, pixel_size.x, dimensions.x),\n     48         y_length=_to_raster(self.y_length, pixel_size.y, dimensions.y),\n     49         z_length=_to_raster(self.z_length, pixel_size.z, dimensions.z),\n     50         original_roi=self,\n     51     )\n\nAttributeError: 'Dimensions' object has no attribute 'x'</pre> <p>The roi object can is defined in physical coordinates, and can be used to extract the region of interest from the image or label at any resolution.</p> In\u00a0[6]: Copied! <pre>image_2 = ngff_image.get_image(path=\"2\")\n# Two images at different resolutions\nprint(f\"{image.pixel_size=}\")\nprint(f\"{image_2.pixel_size=}\")\n\n# Get roi for higher resolution image\nimage_1_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)\n\n# Get roi for lower resolution image\nimage_2_roi_1 = image_2.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)\n\n# Plot the two images side by side\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(image_1_roi_1[0], cmap=\"gray\")\naxs[1].imshow(image_2_roi_1[0], cmap=\"gray\")\nplt.show()\n</pre> image_2 = ngff_image.get_image(path=\"2\") # Two images at different resolutions print(f\"{image.pixel_size=}\") print(f\"{image_2.pixel_size=}\")  # Get roi for higher resolution image image_1_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)  # Get roi for lower resolution image image_2_roi_1 = image_2.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)  # Plot the two images side by side fig, axs = plt.subplots(1, 2, figsize=(10, 5)) axs[0].imshow(image_1_roi_1[0], cmap=\"gray\") axs[1].imshow(image_2_roi_1[0], cmap=\"gray\") plt.show() <pre>image.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\nimage_2.pixel_size=PixelSize(x=0.65, y=0.65, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\n</pre> <pre>\n---------------------------------------------------------------------------\nAttributeError                            Traceback (most recent call last)\nCell In[6], line 7\n      4 print(f\"{image_2.pixel_size=}\")\n      6 # Get roi for higher resolution image\n----&gt; 7 image_1_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)\n      9 # Get roi for lower resolution image\n     10 image_2_roi_1 = image_2.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_handler.py:105, in Image.get_array_from_roi(self, roi, c, t, mode, preserve_dimensions)\n    102 if isinstance(c, str):\n    103     c = self.get_channel_idx(label=c)\n--&gt; 105 return self._get_array_from_roi(\n    106     roi=roi, t=t, c=c, mode=mode, preserve_dimensions=preserve_dimensions\n    107 )\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:344, in ImageLike._get_array_from_roi(self, roi, t, c, mode, preserve_dimensions)\n    327 def _get_array_from_roi(\n    328     self,\n    329     roi: WorldCooROI,\n   (...)\n    333     preserve_dimensions: bool = False,\n    334 ) -&gt; ArrayLike | tuple[ArrayLike, DataTransformPipe]:\n    335     \"\"\"Return the image data from a region of interest (ROI).\n    336 \n    337     Args:\n   (...)\n    342         preserve_dimensions (bool): Whether to preserve the dimensions of the data.\n    343     \"\"\"\n--&gt; 344     data_pipe = self._build_roi_pipe(\n    345         roi=roi, t=t, c=c, preserve_dimensions=preserve_dimensions\n    346     )\n    347     return_array = self._get_pipe(data_pipe=data_pipe, mode=mode)\n    348     return return_array\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:291, in ImageLike._build_roi_pipe(self, roi, t, c, preserve_dimensions)\n    283 def _build_roi_pipe(\n    284     self,\n    285     roi: WorldCooROI,\n   (...)\n    288     preserve_dimensions: bool = False,\n    289 ) -&gt; DataTransformPipe:\n    290     \"\"\"Build the data transform pipe for a region of interest (ROI).\"\"\"\n--&gt; 291     roi_coo = roi.to_raster_coo(\n    292         pixel_size=self.dataset.pixel_size, dimensions=self.dimensions\n    293     )\n    295     slicer = RoiSlicer(\n    296         on_disk_axes_name=self.dataset.on_disk_axes_names,\n    297         axes_order=self.dataset.axes_order,\n   (...)\n    301         preserve_dimensions=preserve_dimensions,\n    302     )\n    303     return DataTransformPipe(slicer=slicer)\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/roi.py:44, in WorldCooROI.to_raster_coo(self, pixel_size, dimensions)\n     39 def to_raster_coo(\n     40     self, pixel_size: PixelSize, dimensions: Dimensions\n     41 ) -&gt; \"RasterCooROI\":\n     42     \"\"\"Convert to raster coordinates.\"\"\"\n     43     return RasterCooROI(\n---&gt; 44         x=_to_raster(self.x, pixel_size.x, dimensions.x),\n     45         y=_to_raster(self.y, pixel_size.y, dimensions.y),\n     46         z=_to_raster(self.z, pixel_size.z, dimensions.z),\n     47         x_length=_to_raster(self.x_length, pixel_size.x, dimensions.x),\n     48         y_length=_to_raster(self.y_length, pixel_size.y, dimensions.y),\n     49         z_length=_to_raster(self.z_length, pixel_size.z, dimensions.z),\n     50         original_roi=self,\n     51     )\n\nAttributeError: 'Dimensions' object has no attribute 'x'</pre> In\u00a0[7]: Copied! <pre>import numpy as np\n\n# Get a small slice of the image\nsmall_slice = image.get_array(x=slice(1000, 2000), y=slice(1000, 2000))\n\n# Set the sample slice to zeros\nzeros_slice = np.zeros_like(small_slice)\nimage.set_array(patch=zeros_slice, x=slice(1000, 2000), y=slice(1000, 2000))\n\n\n# Load the image from disk and show the edited image\nnuclei = ngff_image.label.get_label(\"nuclei\")\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(image.on_disk_array[0, 0], cmap=\"gray\")\naxs[1].imshow(nuclei.on_disk_array[0])\nfor ax in axs:\n    ax.axis(\"off\")\nplt.tight_layout()\nplt.show()\n\n# Add back the original slice to the image\nimage.set_array(patch=small_slice, x=slice(1000, 2000), y=slice(1000, 2000))\n</pre> import numpy as np  # Get a small slice of the image small_slice = image.get_array(x=slice(1000, 2000), y=slice(1000, 2000))  # Set the sample slice to zeros zeros_slice = np.zeros_like(small_slice) image.set_array(patch=zeros_slice, x=slice(1000, 2000), y=slice(1000, 2000))   # Load the image from disk and show the edited image nuclei = ngff_image.label.get_label(\"nuclei\") fig, axs = plt.subplots(1, 2, figsize=(10, 5)) axs[0].imshow(image.on_disk_array[0, 0], cmap=\"gray\") axs[1].imshow(nuclei.on_disk_array[0]) for ax in axs:     ax.axis(\"off\") plt.tight_layout() plt.show()  # Add back the original slice to the image image.set_array(patch=small_slice, x=slice(1000, 2000), y=slice(1000, 2000)) In\u00a0[8]: Copied! <pre># Create a a new label object and set it to a simple segmentation\nnew_label = ngff_image.label.derive(\"new_label\", overwrite=True)\n\nsimple_segmentation = image.on_disk_array[0] &gt; 100\nnew_label.on_disk_array[...] = simple_segmentation\n\n# make a subplot with two image show side by side\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(image.on_disk_array[0, 0], cmap=\"gray\")\naxs[1].imshow(new_label.on_disk_array[0], cmap=\"gray\")\nfor ax in axs:\n    ax.axis(\"off\")\nplt.tight_layout()\nplt.show()\n</pre> # Create a a new label object and set it to a simple segmentation new_label = ngff_image.label.derive(\"new_label\", overwrite=True)  simple_segmentation = image.on_disk_array[0] &gt; 100 new_label.on_disk_array[...] = simple_segmentation  # make a subplot with two image show side by side fig, axs = plt.subplots(1, 2, figsize=(10, 5)) axs[0].imshow(image.on_disk_array[0, 0], cmap=\"gray\") axs[1].imshow(new_label.on_disk_array[0], cmap=\"gray\") for ax in axs:     ax.axis(\"off\") plt.tight_layout() plt.show() In\u00a0[9]: Copied! <pre>label_0 = ngff_image.label.get_label(\"new_label\", path=\"0\")\nlabel_2 = ngff_image.label.get_label(\"new_label\", path=\"2\")\n\nlabel_before_consolidation = label_2.on_disk_array[...]\n\n# Consolidate the label\nlabel_0.consolidate()\n\nlabel_after_consolidation = label_2.on_disk_array[...]\n\n\n# make a subplot with two image show side by side\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(label_before_consolidation[0], cmap=\"gray\")\naxs[1].imshow(label_after_consolidation[0], cmap=\"gray\")\nfor ax in axs:\n    ax.axis(\"off\")\nplt.tight_layout()\nplt.show()\n</pre> label_0 = ngff_image.label.get_label(\"new_label\", path=\"0\") label_2 = ngff_image.label.get_label(\"new_label\", path=\"2\")  label_before_consolidation = label_2.on_disk_array[...]  # Consolidate the label label_0.consolidate()  label_after_consolidation = label_2.on_disk_array[...]   # make a subplot with two image show side by side fig, axs = plt.subplots(1, 2, figsize=(10, 5)) axs[0].imshow(label_before_consolidation[0], cmap=\"gray\") axs[1].imshow(label_after_consolidation[0], cmap=\"gray\") for ax in axs:     ax.axis(\"off\") plt.tight_layout() plt.show() In\u00a0[10]: Copied! <pre>import numpy as np\nimport pandas as pd\n\nprint(f\"List of feature table: {ngff_image.table.list(table_type='feature_table')}\")\n\n\nnuclei = ngff_image.label.get_label('nuclei')\n\n# Create a table with random features for each nuclei in each ROI\nlist_of_records = []\nfor roi in roi_table.rois:\n    nuclei_in_roi = nuclei.get_array_from_roi(roi, mode='numpy')\n    for nuclei_id in np.unique(nuclei_in_roi)[1:]:\n        list_of_records.append(\n            {\"label\": nuclei_id,\n             \"feat1\": np.random.rand(),\n             \"feat2\": np.random.rand(),\n         \"ROI\": roi.infos.get(\"FieldIndex\")}\n    )\n\nfeat_df = pd.DataFrame.from_records(list_of_records)\n\n# Create a new feature table\nfeat_table = ngff_image.table.new(name='new_feature_table',\n                     label_image='../nuclei',\n                     table_type='feature_table',\n                     overwrite=True)\n\nprint(f\"New list of feature table: {ngff_image.table.list(table_type='feature_table')}\")\nfeat_table.set_table(feat_df)\nfeat_table.consolidate()\n\nfeat_table.table\n</pre> import numpy as np import pandas as pd  print(f\"List of feature table: {ngff_image.table.list(table_type='feature_table')}\")   nuclei = ngff_image.label.get_label('nuclei')  # Create a table with random features for each nuclei in each ROI list_of_records = [] for roi in roi_table.rois:     nuclei_in_roi = nuclei.get_array_from_roi(roi, mode='numpy')     for nuclei_id in np.unique(nuclei_in_roi)[1:]:         list_of_records.append(             {\"label\": nuclei_id,              \"feat1\": np.random.rand(),              \"feat2\": np.random.rand(),          \"ROI\": roi.infos.get(\"FieldIndex\")}     )  feat_df = pd.DataFrame.from_records(list_of_records)  # Create a new feature table feat_table = ngff_image.table.new(name='new_feature_table',                      label_image='../nuclei',                      table_type='feature_table',                      overwrite=True)  print(f\"New list of feature table: {ngff_image.table.list(table_type='feature_table')}\") feat_table.set_table(feat_df) feat_table.consolidate()  feat_table.table <pre>List of feature table: ['regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4']\n</pre> <pre>\n---------------------------------------------------------------------------\nAttributeError                            Traceback (most recent call last)\nCell In[10], line 12\n     10 list_of_records = []\n     11 for roi in roi_table.rois:\n---&gt; 12     nuclei_in_roi = nuclei.get_array_from_roi(roi, mode='numpy')\n     13     for nuclei_id in np.unique(nuclei_in_roi)[1:]:\n     14         list_of_records.append(\n     15             {\"label\": nuclei_id,\n     16              \"feat1\": np.random.rand(),\n     17              \"feat2\": np.random.rand(),\n     18          \"ROI\": roi.infos.get(\"FieldIndex\")}\n     19     )\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/label_handler.py:89, in Label.get_array_from_roi(self, roi, t, mode, preserve_dimensions)\n     74 def get_array_from_roi(\n     75     self,\n     76     roi: WorldCooROI,\n   (...)\n     79     preserve_dimensions: bool = False,\n     80 ) -&gt; ArrayLike:\n     81     \"\"\"Return the label data from a region of interest (ROI).\n     82 \n     83     Args:\n   (...)\n     87         preserve_dimensions (bool): Whether to preserve the dimensions of the data.\n     88     \"\"\"\n---&gt; 89     return self._get_array_from_roi(\n     90         roi=roi, t=t, c=None, mode=mode, preserve_dimensions=preserve_dimensions\n     91     )\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:344, in ImageLike._get_array_from_roi(self, roi, t, c, mode, preserve_dimensions)\n    327 def _get_array_from_roi(\n    328     self,\n    329     roi: WorldCooROI,\n   (...)\n    333     preserve_dimensions: bool = False,\n    334 ) -&gt; ArrayLike | tuple[ArrayLike, DataTransformPipe]:\n    335     \"\"\"Return the image data from a region of interest (ROI).\n    336 \n    337     Args:\n   (...)\n    342         preserve_dimensions (bool): Whether to preserve the dimensions of the data.\n    343     \"\"\"\n--&gt; 344     data_pipe = self._build_roi_pipe(\n    345         roi=roi, t=t, c=c, preserve_dimensions=preserve_dimensions\n    346     )\n    347     return_array = self._get_pipe(data_pipe=data_pipe, mode=mode)\n    348     return return_array\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/image_like_handler.py:291, in ImageLike._build_roi_pipe(self, roi, t, c, preserve_dimensions)\n    283 def _build_roi_pipe(\n    284     self,\n    285     roi: WorldCooROI,\n   (...)\n    288     preserve_dimensions: bool = False,\n    289 ) -&gt; DataTransformPipe:\n    290     \"\"\"Build the data transform pipe for a region of interest (ROI).\"\"\"\n--&gt; 291     roi_coo = roi.to_raster_coo(\n    292         pixel_size=self.dataset.pixel_size, dimensions=self.dimensions\n    293     )\n    295     slicer = RoiSlicer(\n    296         on_disk_axes_name=self.dataset.on_disk_axes_names,\n    297         axes_order=self.dataset.axes_order,\n   (...)\n    301         preserve_dimensions=preserve_dimensions,\n    302     )\n    303     return DataTransformPipe(slicer=slicer)\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/roi.py:44, in WorldCooROI.to_raster_coo(self, pixel_size, dimensions)\n     39 def to_raster_coo(\n     40     self, pixel_size: PixelSize, dimensions: Dimensions\n     41 ) -&gt; \"RasterCooROI\":\n     42     \"\"\"Convert to raster coordinates.\"\"\"\n     43     return RasterCooROI(\n---&gt; 44         x=_to_raster(self.x, pixel_size.x, dimensions.x),\n     45         y=_to_raster(self.y, pixel_size.y, dimensions.y),\n     46         z=_to_raster(self.z, pixel_size.z, dimensions.z),\n     47         x_length=_to_raster(self.x_length, pixel_size.x, dimensions.x),\n     48         y_length=_to_raster(self.y_length, pixel_size.y, dimensions.y),\n     49         z_length=_to_raster(self.z_length, pixel_size.z, dimensions.z),\n     50         original_roi=self,\n     51     )\n\nAttributeError: 'Dimensions' object has no attribute 'x'</pre>"},{"location":"notebooks/image/#imageslabelstables","title":"Images/Labels/Tables\u00b6","text":"<p>In this notebook we will show how to use the <code>Image</code>, <code>Label</code> and <code>Table</code> objects.</p>"},{"location":"notebooks/image/#images","title":"Images\u00b6","text":"<p>Images can be loaded from a <code>NgffImage</code> object.</p>"},{"location":"notebooks/image/#data-loading","title":"Data Loading\u00b6","text":""},{"location":"notebooks/image/#roitableimage-interaction","title":"RoiTable/Image Interaction\u00b6","text":"<p><code>roi</code> objects from a <code>roi_table</code> can be used to extract a region of interest from an image or a label.</p>"},{"location":"notebooks/image/#writing-images","title":"Writing Images\u00b6","text":"<p>Similarly to the <code>.array()</code>  we can use the <code>.set_array()</code> (or <code>set_array_from_roi</code>) method to write part of an image to disk.</p>"},{"location":"notebooks/image/#deriving-a-new-label","title":"Deriving a new label\u00b6","text":"<p>When doing image analysis, we often need to create new labels or tables. The <code>ngff_image</code> allows us to simply create new labels and tables.</p>"},{"location":"notebooks/image/#image-consolidation","title":"Image Consolidation\u00b6","text":"<p>Every time we modify a label or a image, we are modifying the on-disk data on one layer only. This means that if we have the image saved in multiple resolutions, we need to consolidate the changes to all resolutions. To do so, we can use the <code>.consolidate()</code> method.</p>"},{"location":"notebooks/image/#creating-a-new-table","title":"Creating a new table\u00b6","text":"<p>We can simply create a new table by create a new <code>Table</code> object from a pandas dataframe. For a simple feature table the only reuiremnt is to have a integer column named <code>label</code> that will be used to link the table to the objects in the image.</p>"},{"location":"notebooks/processing/","title":"Processing","text":"In\u00a0[1]: Copied! <pre>import matplotlib.pyplot as plt\n\nfrom ngio.core import NgffImage\n\nngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0\")\n</pre> import matplotlib.pyplot as plt  from ngio.core import NgffImage  ngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0\") In\u00a0[2]: Copied! <pre>mip_ngff = ngff_image.derive_new_image(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0_mip\",\n                                       name=\"MIP\",\n                                       shape=(1, 1, 2160, 5120))\n</pre>  mip_ngff = ngff_image.derive_new_image(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0_mip\",                                        name=\"MIP\",                                        shape=(1, 1, 2160, 5120)) <pre>\n---------------------------------------------------------------------------\nTypeError                                 Traceback (most recent call last)\nCell In[2], line 1\n----&gt; 1 mip_ngff = ngff_image.derive_new_image(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0_mip\",\n      2                                        name=\"MIP\",\n      3                                        shape=(1, 1, 2160, 5120))\n\nFile /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/ngio/core/ngff_image.py:202, in NgffImage.derive_new_image(self, store, name, overwrite, **kwargs)\n    179 default_kwargs = {\n    180     \"store\": store,\n    181     \"on_disk_shape\": image_0.on_disk_shape,\n   (...)\n    197     \"version\": self.image_meta.version,\n    198 }\n    200 default_kwargs.update(kwargs)\n--&gt; 202 create_empty_ome_zarr_image(\n    203     **default_kwargs,\n    204 )\n    205 return NgffImage(store=store)\n\nTypeError: create_empty_ome_zarr_image() got an unexpected keyword argument 'shape'</pre> In\u00a0[3]: Copied! <pre># Get the source imag\nsource_image = ngff_image.get_image()\nprint(\"Source image loaded with shape:\", source_image.shape)\n\n# Get the MIP image\nmip_image = mip_ngff.get_image()\nprint(\"MIP image loaded with shape:\", mip_image.shape)\n\n# Get a ROI table\nroi_table = ngff_image.table.get_table(\"FOV_ROI_table\")\nprint(\"ROI table loaded with\", len(roi_table.rois), \"ROIs\")\n\n# For each ROI in the table\n# - get the data from the source image\n# - calculate the MIP\n# - set the data in the MIP image\nfor roi in roi_table.rois:\n    print(f\" - Processing ROI {roi.infos.get('field_index')}\")\n    patch = source_image.get_array_from_roi(roi)\n    mip_patch = patch.max(axis=1, keepdims=True)\n    mip_image.set_array_from_roi(patch=mip_patch, roi=roi)\n    \nprint(\"MIP image saved\")\n\nplt.figure(figsize=(5, 5))\nplt.title(\"Mip\")\nplt.imshow(mip_image.on_disk_array[0, 0, :, :], cmap=\"gray\")\nplt.axis('off')\nplt.tight_layout()\nplt.show()\n</pre> # Get the source imag source_image = ngff_image.get_image() print(\"Source image loaded with shape:\", source_image.shape)  # Get the MIP image mip_image = mip_ngff.get_image() print(\"MIP image loaded with shape:\", mip_image.shape)  # Get a ROI table roi_table = ngff_image.table.get_table(\"FOV_ROI_table\") print(\"ROI table loaded with\", len(roi_table.rois), \"ROIs\")  # For each ROI in the table # - get the data from the source image # - calculate the MIP # - set the data in the MIP image for roi in roi_table.rois:     print(f\" - Processing ROI {roi.infos.get('field_index')}\")     patch = source_image.get_array_from_roi(roi)     mip_patch = patch.max(axis=1, keepdims=True)     mip_image.set_array_from_roi(patch=mip_patch, roi=roi)      print(\"MIP image saved\")  plt.figure(figsize=(5, 5)) plt.title(\"Mip\") plt.imshow(mip_image.on_disk_array[0, 0, :, :], cmap=\"gray\") plt.axis('off') plt.tight_layout() plt.show()  <pre>Source image loaded with shape: (1, 2, 2160, 5120)\n</pre> <pre>\n---------------------------------------------------------------------------\nNameError                                 Traceback (most recent call last)\nCell In[3], line 6\n      3 print(\"Source image loaded with shape:\", source_image.shape)\n      5 # Get the MIP image\n----&gt; 6 mip_image = mip_ngff.get_image()\n      7 print(\"MIP image loaded with shape:\", mip_image.shape)\n      9 # Get a ROI table\n\nNameError: name 'mip_ngff' is not defined</pre> In\u00a0[4]: Copied! <pre># Get the MIP image at a lower resolution\nmip_image_2 = mip_ngff.get_image(path=\"2\")\n\nimage_before_consolidation = mip_image_2.get_array(c=0, z=0)\n\n# Consolidate the pyramid\nmip_image.consolidate()\n\nimage_after_consolidation = mip_image_2.get_array(c=0, z=0)\n\nfig, axs = plt.subplots(2, 1, figsize=(10, 5))\naxs[0].set_title(\"Before consolidation\")\naxs[0].imshow(image_before_consolidation, cmap=\"gray\")\naxs[1].set_title(\"After consolidation\")\naxs[1].imshow(image_after_consolidation, cmap=\"gray\")\nfor ax in axs:\n    ax.axis('off')\nplt.tight_layout()\nplt.show()\n</pre> # Get the MIP image at a lower resolution mip_image_2 = mip_ngff.get_image(path=\"2\")  image_before_consolidation = mip_image_2.get_array(c=0, z=0)  # Consolidate the pyramid mip_image.consolidate()  image_after_consolidation = mip_image_2.get_array(c=0, z=0)  fig, axs = plt.subplots(2, 1, figsize=(10, 5)) axs[0].set_title(\"Before consolidation\") axs[0].imshow(image_before_consolidation, cmap=\"gray\") axs[1].set_title(\"After consolidation\") axs[1].imshow(image_after_consolidation, cmap=\"gray\") for ax in axs:     ax.axis('off') plt.tight_layout() plt.show()  <pre>\n---------------------------------------------------------------------------\nNameError                                 Traceback (most recent call last)\nCell In[4], line 2\n      1 # Get the MIP image at a lower resolution\n----&gt; 2 mip_image_2 = mip_ngff.get_image(path=\"2\")\n      4 image_before_consolidation = mip_image_2.get_array(c=0, z=0)\n      6 # Consolidate the pyramid\n\nNameError: name 'mip_ngff' is not defined</pre> In\u00a0[5]: Copied! <pre>mip_roi_table = mip_ngff.table.new(\"FOV_ROI_table\", overwrite=True)\n\nroi_list = []\nfor roi in roi_table.rois:\n    print(f\" - Processing ROI {roi.infos.get('field_index')}\")\n    roi.z_length = 1 # In the MIP image, the z dimension is 1\n    roi_list.append(roi)\n\nmip_roi_table.set_rois(roi_list, overwrite=True)\nmip_roi_table.consolidate()\n\nmip_roi_table.table\n</pre> mip_roi_table = mip_ngff.table.new(\"FOV_ROI_table\", overwrite=True)  roi_list = [] for roi in roi_table.rois:     print(f\" - Processing ROI {roi.infos.get('field_index')}\")     roi.z_length = 1 # In the MIP image, the z dimension is 1     roi_list.append(roi)  mip_roi_table.set_rois(roi_list, overwrite=True) mip_roi_table.consolidate()  mip_roi_table.table <pre>\n---------------------------------------------------------------------------\nNameError                                 Traceback (most recent call last)\nCell In[5], line 1\n----&gt; 1 mip_roi_table = mip_ngff.table.new(\"FOV_ROI_table\", overwrite=True)\n      3 roi_list = []\n      4 for roi in roi_table.rois:\n\nNameError: name 'mip_ngff' is not defined</pre> In\u00a0[6]: Copied! <pre># Setup a simple segmentation function\n\nimport numpy as np\nfrom matplotlib.colors import ListedColormap\nfrom skimage.filters import threshold_otsu\nfrom skimage.measure import label\n\nrand_cmap = np.random.rand(1000, 3)\nrand_cmap[0] = 0\nrand_cmap = ListedColormap(rand_cmap)\n\n\ndef otsu_threshold_segmentation(image: np.ndarray, max_label:int) -&gt; np.ndarray:\n    \"\"\"Simple segmentation using Otsu thresholding.\"\"\"\n    threshold = threshold_otsu(image)\n    binary = image &gt; threshold\n    label_image = label(binary)\n    label_image += max_label\n    label_image = np.where(binary, label_image, 0)\n    return label_image\n</pre> # Setup a simple segmentation function  import numpy as np from matplotlib.colors import ListedColormap from skimage.filters import threshold_otsu from skimage.measure import label  rand_cmap = np.random.rand(1000, 3) rand_cmap[0] = 0 rand_cmap = ListedColormap(rand_cmap)   def otsu_threshold_segmentation(image: np.ndarray, max_label:int) -&gt; np.ndarray:     \"\"\"Simple segmentation using Otsu thresholding.\"\"\"     threshold = threshold_otsu(image)     binary = image &gt; threshold     label_image = label(binary)     label_image += max_label     label_image = np.where(binary, label_image, 0)     return label_image In\u00a0[7]: Copied! <pre>nuclei_image = mip_ngff.label.derive(name=\"nuclei\", overwrite=True)\n</pre> nuclei_image = mip_ngff.label.derive(name=\"nuclei\", overwrite=True) <pre>\n---------------------------------------------------------------------------\nNameError                                 Traceback (most recent call last)\nCell In[7], line 1\n----&gt; 1 nuclei_image = mip_ngff.label.derive(name=\"nuclei\", overwrite=True)\n\nNameError: name 'mip_ngff' is not defined</pre> In\u00a0[8]: Copied! <pre># Get the source imag\nsource_image = mip_ngff.get_image()\nprint(\"Source image loaded with shape:\", source_image.shape)\n\n# Get a ROI table\nroi_table = mip_ngff.table.get_table(\"FOV_ROI_table\")\nprint(\"ROI table loaded with\", len(roi_table.rois), \"ROIs\")\n\n# Find the DAPI channel\ndapi_idx = source_image.get_channel_idx(label=\"DAPI\")\n\n# For each ROI in the table\n# - get the data from the source image\n# - calculate the Segmentation\n# - set the data in segmentation image\nmax_label = 0\nfor roi in roi_table.rois:\n    print(f\" - Processing ROI {roi.infos.get('field_index')}\")\n    patch = source_image.get_array_from_roi(roi, c=dapi_idx)\n    segmentation = otsu_threshold_segmentation(patch, max_label)\n\n    # Add the max label of the previous segmentation to avoid overlapping labels\n    max_label = segmentation.max()\n\n    nuclei_image.set_array_from_roi(patch=segmentation, roi=roi)\n\n# Consolidate the segmentation image\nnuclei_image.consolidate()\n\nprint(\"Segmentation image saved\")\nfig, axs = plt.subplots(2, 1, figsize=(10, 5))\naxs[0].set_title(\"MIP\")\naxs[0].imshow(source_image.on_disk_array[0, 0], cmap=\"gray\")\naxs[1].set_title(\"Nuclei segmentation\")\naxs[1].imshow(nuclei_image.on_disk_array[0], cmap=rand_cmap, interpolation='nearest')\nfor ax in axs:\n    ax.axis('off')\nplt.tight_layout()\nplt.show()\n</pre> # Get the source imag source_image = mip_ngff.get_image() print(\"Source image loaded with shape:\", source_image.shape)  # Get a ROI table roi_table = mip_ngff.table.get_table(\"FOV_ROI_table\") print(\"ROI table loaded with\", len(roi_table.rois), \"ROIs\")  # Find the DAPI channel dapi_idx = source_image.get_channel_idx(label=\"DAPI\")  # For each ROI in the table # - get the data from the source image # - calculate the Segmentation # - set the data in segmentation image max_label = 0 for roi in roi_table.rois:     print(f\" - Processing ROI {roi.infos.get('field_index')}\")     patch = source_image.get_array_from_roi(roi, c=dapi_idx)     segmentation = otsu_threshold_segmentation(patch, max_label)      # Add the max label of the previous segmentation to avoid overlapping labels     max_label = segmentation.max()      nuclei_image.set_array_from_roi(patch=segmentation, roi=roi)  # Consolidate the segmentation image nuclei_image.consolidate()  print(\"Segmentation image saved\") fig, axs = plt.subplots(2, 1, figsize=(10, 5)) axs[0].set_title(\"MIP\") axs[0].imshow(source_image.on_disk_array[0, 0], cmap=\"gray\") axs[1].set_title(\"Nuclei segmentation\") axs[1].imshow(nuclei_image.on_disk_array[0], cmap=rand_cmap, interpolation='nearest') for ax in axs:     ax.axis('off') plt.tight_layout() plt.show()  <pre>\n---------------------------------------------------------------------------\nNameError                                 Traceback (most recent call last)\nCell In[8], line 2\n      1 # Get the source imag\n----&gt; 2 source_image = mip_ngff.get_image()\n      3 print(\"Source image loaded with shape:\", source_image.shape)\n      5 # Get a ROI table\n\nNameError: name 'mip_ngff' is not defined</pre> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"notebooks/processing/#processing","title":"Processing\u00b6","text":"<p>In this notebook we will implement a couple of mock image analysis workflows using <code>ngio</code>.</p>"},{"location":"notebooks/processing/#maximum-intensity-projection","title":"Maximum intensity projection\u00b6","text":"<p>In this workflow we will read a volumetric image and create a maximum intensity projection (MIP) along the z-axis.</p>"},{"location":"notebooks/processing/#step-1-create-a-ngff-image","title":"step 1: Create a ngff image\u00b6","text":"<p>For this example we will use the following publicly available image</p>"},{"location":"notebooks/processing/#step-2-create-a-new-ngff-image-to-store-the-mip","title":"step 2: Create a new ngff image to store the MIP\u00b6","text":""},{"location":"notebooks/processing/#step-3-run-the-workflow","title":"step 3: Run the workflow\u00b6","text":"<p>For each roi in the image, create a MIP and store it in the new image</p>"},{"location":"notebooks/processing/#step-4-consolidate-the-results-important","title":"step 4: Consolidate the results (!!! Important)\u00b6","text":"<p>In this we wrote the mip image to a single level of the image pyramid. To truly consolidate the results we would need to write the mip to all levels of the image pyramid. We can do this by calling the <code>.consolidate()</code> method on the image.</p>"},{"location":"notebooks/processing/#step-5-create-a-new-roi-table","title":"step 5: Create a new ROI table\u00b6","text":"<p>As a final step we will create a new ROI table that contains the MIPs as ROIs. Where we correct the <code>z</code> bounds of the ROIs to reflect the MIP.</p>"},{"location":"notebooks/processing/#image-segmentation","title":"Image segmentation\u00b6","text":"<p>Now we can use the MIP image to segment the image using a simple thresholding algorithm.</p>"},{"location":"notebooks/processing/#step-1-derive-a-new-label-image-from-the-mip-image","title":"step 1: Derive a new label image from the MIP image\u00b6","text":""},{"location":"notebooks/processing/#step-2-run-the-workflow","title":"step 2: Run the workflow\u00b6","text":""}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to NGIO","text":"<p>NGIO is a Python library to streamline OME-Zarr image analysis workflows.</p> <p>Main Goals:</p> <ul> <li>Abstract object base API for handling OME-Zarr files</li> <li>Powefull iterators for processing data using common access patterns</li> <li>Tight integration with Fractal's Table Fractal</li> <li>Validate OME-Zarr files</li> </ul> <p>To get started, check out the Getting Started guide.</p>"},{"location":"#ngio-is-under-active-development","title":"\ud83d\udea7 Ngio is Under active Development \ud83d\udea7","text":""},{"location":"#roadmap","title":"Roadmap","text":"Feature Status ETA Description Metadata Handling \u2705 Read, Write, Validate OME-Zarr Metadata (0.4 supported, 0.5 ready) OME-Zarr Validation \u2705 Validate OME-Zarr files for compliance with the OME-Zarr Specification + Compliance between Metadata and Data Base Image Handling \u2705 Load data from OME-Zarr files, retrieve basic metadata, and write data ROI Handling \u2705 Common ROI models Label Handling \u2705 Mid-September Based on Image Handling Table Validation \u2705 Mid-September Validate Table fractal V1 + Compliance between Metadata and Data Table Handling \u2705 Mid-September Read, Write ROI, Features, and Masked Tables Basic Iterators Ongoing End-September Read and Write Iterators for common access patterns Base Documentation \u2705 End-September API Documentation and Examples Beta Ready Testing \u2705 End-September Beta Testing; Library is ready for testing, but the API is not stable Mask Iterators Ongoing October Iterators over Masked Tables Advanced Iterators Not started October Iterators for advanced access patterns Parallel Iterators Not started End of the Year Concurrent Iterators for parallel read and write Full Documentation Not started End of the Year Complete Documentation Release 1.0 (Commitment to API) Not started End of the Year API is stable; breaking changes will be avoided"},{"location":"#contributors","title":"Contributors","text":"<p><code>ngio</code> is developed at the BioVisionCenter at the University of Zurich. The main contributors are: @lorenzocerrone, @jluethi.</p>"},{"location":"#license","title":"License","text":"<p><code>ngio</code> is released according to the BSD-3-Clause License. See LICENSE</p>"},{"location":"getting-started/","title":"Getting Started","text":"<p>Warning</p> <p>The library is still in development and is not yet stable. The API is subject to change, bugs and breaking changes are expected.</p> <p>Warning</p> <p>The documentation is still under development. It is not yet complete and may contain errors and inaccuracies.</p>"},{"location":"getting-started/#installation","title":"Installation","text":"<p>The library can be installed from PyPI using pip:</p> <pre><code>pip install \"ngio[core]\"\n</code></pre> <p>The <code>core</code> extra installs the the <code>zarr-python</code> dependency. As of now, <code>zarr-python</code> is required to be installed separately, due to the transition to the new <code>zarr-v3</code> library.</p>"},{"location":"getting-started/#ngio-api-overview","title":"<code>ngio</code> API Overview","text":"<p><code>ngio</code> implements an abstract object base API for handling OME-Zarr files. The three main objects are <code>NgffImage</code>, <code>Image</code> (<code>Label</code>), and <code>ROITables</code>.</p> <ul> <li><code>NgffImage</code> is the main entry point to the library. It is used to open an OME-Zarr Image and manage its metadata. This object can not be used to access the data directly.   but it can be used to access and create the <code>Image</code>, <code>Label</code>, and <code>Tables</code> objects. Moreover it can be used to derive a new <code>Ngff</code> images based on the current one.</li> <li><code>Image</code> and <code>Label</code> are used to access \"ImageLike\" objects. They are the main objects to access the data in the OME-Zarr file, manage the metadata, and write data.</li> <li><code>ROITables</code> can be used to access specific region of interest in the image. They are tightly integrated with the <code>Image</code> and <code>Label</code> objects.</li> </ul>"},{"location":"getting-started/#example-usage","title":"Example Usage","text":"<p>Currently, the library is not yet stable. However, you can see some example usage in our demo notebooks:</p> <ul> <li>Basic Usage</li> <li>Image/Label/Tables</li> <li>Processing</li> </ul>"},{"location":"api/core/","title":"ngio.core","text":""},{"location":"api/core/#ngio.core","title":"<code>ngio.core</code>","text":"<p>Core classes for the ngio library.</p>"},{"location":"api/core/#ngffimage","title":"NGFFImage","text":""},{"location":"api/core/#ngio.core.NgffImage","title":"<code>ngio.core.NgffImage</code>","text":"<p>A class to handle OME-NGFF images.</p> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>class NgffImage:\n    \"\"\"A class to handle OME-NGFF images.\"\"\"\n\n    def __init__(\n        self, store: StoreLike, cache: bool = False, mode: AccessModeLiteral = \"r+\"\n    ) -&gt; None:\n        \"\"\"Initialize the NGFFImage in read mode.\"\"\"\n        self.store = store\n        self._mode = mode\n        self.group = open_group_wrapper(store=store, mode=self._mode)\n\n        if self.group.read_only:\n            self._mode = \"r\"\n\n        self._image_meta = get_ngff_image_meta_handler(\n            self.group, meta_mode=\"image\", cache=cache\n        )\n        self._metadata_cache = cache\n        self.table = TableGroup(self.group, mode=self._mode)\n        self.label = LabelGroup(self.group, image_ref=self.get_image(), mode=self._mode)\n\n        ngio_logger.info(f\"Opened image located in store: {store}\")\n        ngio_logger.info(f\"- Image number of levels: {self.num_levels}\")\n\n    @property\n    def image_meta(self) -&gt; ImageMeta:\n        \"\"\"Get the image metadata.\"\"\"\n        meta = self._image_meta.load_meta()\n        assert isinstance(meta, ImageMeta)\n        return meta\n\n    @property\n    def num_levels(self) -&gt; int:\n        \"\"\"Get the number of levels in the image.\"\"\"\n        return self.image_meta.num_levels\n\n    @property\n    def levels_paths(self) -&gt; list[str]:\n        \"\"\"Get the paths of the levels in the image.\"\"\"\n        return self.image_meta.levels_paths\n\n    def get_image(\n        self,\n        *,\n        path: str | None = None,\n        pixel_size: PixelSize | None = None,\n        highest_resolution: bool = True,\n    ) -&gt; Image:\n        \"\"\"Get an image handler for the given level.\n\n        Args:\n            path (str | None, optional): The path to the level.\n            pixel_size (tuple[float, ...] | list[float] | None, optional): The pixel\n                size of the level.\n            highest_resolution (bool, optional): Whether to get the highest\n                resolution level\n\n        Returns:\n            ImageHandler: The image handler.\n        \"\"\"\n        if path is not None or pixel_size is not None:\n            highest_resolution = False\n\n        image = Image(\n            store=self.group,\n            path=path,\n            pixel_size=pixel_size,\n            highest_resolution=highest_resolution,\n            label_group=LabelGroup(self.group, image_ref=None, mode=self._mode),\n            cache=self._metadata_cache,\n            mode=self._mode,\n        )\n        ngio_logger.info(f\"Opened image at path: {image.path}\")\n        ngio_logger.info(f\"- {image.dimensions}\")\n        ngio_logger.info(f\"- {image.pixel_size}\")\n        return image\n\n    def update_omero_window(\n        self, start_percentile: int = 5, end_percentile: int = 95\n    ) -&gt; None:\n        \"\"\"Update the OMERO window.\n\n        This will setup percentiles based values for the window of each channel.\n\n        Args:\n            start_percentile (int): The start percentile.\n            end_percentile (int): The end percentile\n\n        \"\"\"\n        meta = self.image_meta\n\n        lowest_res_image = self.get_image(highest_resolution=True)\n        lowest_res_shape = lowest_res_image.shape\n        for path in self.levels_paths:\n            image = self.get_image(path=path)\n            if np.prod(image.shape) &lt; np.prod(lowest_res_shape):\n                lowest_res_shape = image.shape\n                lowest_res_image = image\n\n        max_dtype = np.iinfo(image.on_disk_array.dtype).max\n        num_c = lowest_res_image.dimensions.get(\"c\", 1)\n\n        if meta.omero is None:\n            raise NotImplementedError(\n                \"OMERO metadata not found. \" \" Please add OMERO metadata to the image.\"\n            )\n\n        channel_list = meta.omero.channels\n        if len(channel_list) != num_c:\n            raise ValueError(\"The number of channels does not match the image.\")\n\n        for c, channel in enumerate(channel_list):\n            data = image.get_array(c=c, mode=\"dask\").ravel()\n            _start_percentile = da.percentile(\n                data, start_percentile, method=\"nearest\"\n            ).compute()\n            _end_percentile = da.percentile(\n                data, end_percentile, method=\"nearest\"\n            ).compute()\n            channel.extra_fields[\"window\"] = {\n                \"start\": _start_percentile,\n                \"end\": _end_percentile,\n                \"min\": 0,\n                \"max\": max_dtype,\n            }\n            ngio_logger.info(\n                f\"Updated window for channel {channel.label}. \"\n                f\"Start: {start_percentile}, End: {end_percentile}\"\n            )\n            meta.omero.channels[c] = channel\n\n        self._image_meta.write_meta(meta)\n\n    def derive_new_image(\n        self,\n        store: StoreLike,\n        name: str,\n        overwrite: bool = True,\n        **kwargs: dict,\n    ) -&gt; \"NgffImage\":\n        \"\"\"Derive a new image from the current image.\n\n        Args:\n            store (StoreLike): The store to create the new image in.\n            name (str): The name of the new image.\n            overwrite (bool): Whether to overwrite the image if it exists\n            **kwargs: Additional keyword arguments.\n                Follow the same signature as `create_empty_ome_zarr_image`.\n\n        Returns:\n            NgffImage: The new image.\n        \"\"\"\n        image_0 = self.get_image(highest_resolution=True)\n\n        # Get the channel information if it exists\n        omero = self.image_meta.omero\n        if omero is not None:\n            channels = omero.channels\n            omero_kwargs = omero.extra_fields\n        else:\n            channels = []\n            omero_kwargs = {}\n\n        default_kwargs = {\n            \"store\": store,\n            \"shape\": image_0.on_disk_shape,\n            \"chunks\": image_0.on_disk_array.chunks,\n            \"dtype\": image_0.on_disk_array.dtype,\n            \"on_disk_axis\": image_0.dataset.on_disk_axes_names,\n            \"pixel_sizes\": image_0.pixel_size,\n            \"xy_scaling_factor\": self.image_meta.xy_scaling_factor,\n            \"z_scaling_factor\": self.image_meta.z_scaling_factor,\n            \"time_spacing\": image_0.dataset.time_spacing,\n            \"time_units\": image_0.dataset.time_axis_unit,\n            \"num_levels\": self.num_levels,\n            \"name\": name,\n            \"channel_labels\": image_0.channel_labels,\n            \"channel_wavelengths\": [ch.wavelength_id for ch in channels],\n            \"channel_kwargs\": [ch.extra_fields for ch in channels],\n            \"omero_kwargs\": omero_kwargs,\n            \"overwrite\": overwrite,\n            \"version\": self.image_meta.version,\n        }\n\n        default_kwargs.update(kwargs)\n\n        create_empty_ome_zarr_image(\n            **default_kwargs,\n        )\n        return NgffImage(store=store)\n</code></pre>"},{"location":"api/core/#ngio.core.NgffImage.image_meta","title":"<code>image_meta: ImageMeta</code>  <code>property</code>","text":"<p>Get the image metadata.</p>"},{"location":"api/core/#ngio.core.NgffImage.levels_paths","title":"<code>levels_paths: list[str]</code>  <code>property</code>","text":"<p>Get the paths of the levels in the image.</p>"},{"location":"api/core/#ngio.core.NgffImage.num_levels","title":"<code>num_levels: int</code>  <code>property</code>","text":"<p>Get the number of levels in the image.</p>"},{"location":"api/core/#ngio.core.NgffImage.__init__","title":"<code>__init__(store: StoreLike, cache: bool = False, mode: AccessModeLiteral = 'r+') -&gt; None</code>","text":"<p>Initialize the NGFFImage in read mode.</p> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>def __init__(\n    self, store: StoreLike, cache: bool = False, mode: AccessModeLiteral = \"r+\"\n) -&gt; None:\n    \"\"\"Initialize the NGFFImage in read mode.\"\"\"\n    self.store = store\n    self._mode = mode\n    self.group = open_group_wrapper(store=store, mode=self._mode)\n\n    if self.group.read_only:\n        self._mode = \"r\"\n\n    self._image_meta = get_ngff_image_meta_handler(\n        self.group, meta_mode=\"image\", cache=cache\n    )\n    self._metadata_cache = cache\n    self.table = TableGroup(self.group, mode=self._mode)\n    self.label = LabelGroup(self.group, image_ref=self.get_image(), mode=self._mode)\n\n    ngio_logger.info(f\"Opened image located in store: {store}\")\n    ngio_logger.info(f\"- Image number of levels: {self.num_levels}\")\n</code></pre>"},{"location":"api/core/#ngio.core.NgffImage.derive_new_image","title":"<code>derive_new_image(store: StoreLike, name: str, overwrite: bool = True, **kwargs: dict) -&gt; NgffImage</code>","text":"<p>Derive a new image from the current image.</p> <p>Parameters:</p> <ul> <li> <code>store</code>               (<code>StoreLike</code>)           \u2013            <p>The store to create the new image in.</p> </li> <li> <code>name</code>               (<code>str</code>)           \u2013            <p>The name of the new image.</p> </li> <li> <code>overwrite</code>               (<code>bool</code>, default:                   <code>True</code> )           \u2013            <p>Whether to overwrite the image if it exists</p> </li> <li> <code>**kwargs</code>               (<code>dict</code>, default:                   <code>{}</code> )           \u2013            <p>Additional keyword arguments. Follow the same signature as <code>create_empty_ome_zarr_image</code>.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NgffImage</code> (              <code>NgffImage</code> )          \u2013            <p>The new image.</p> </li> </ul> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>def derive_new_image(\n    self,\n    store: StoreLike,\n    name: str,\n    overwrite: bool = True,\n    **kwargs: dict,\n) -&gt; \"NgffImage\":\n    \"\"\"Derive a new image from the current image.\n\n    Args:\n        store (StoreLike): The store to create the new image in.\n        name (str): The name of the new image.\n        overwrite (bool): Whether to overwrite the image if it exists\n        **kwargs: Additional keyword arguments.\n            Follow the same signature as `create_empty_ome_zarr_image`.\n\n    Returns:\n        NgffImage: The new image.\n    \"\"\"\n    image_0 = self.get_image(highest_resolution=True)\n\n    # Get the channel information if it exists\n    omero = self.image_meta.omero\n    if omero is not None:\n        channels = omero.channels\n        omero_kwargs = omero.extra_fields\n    else:\n        channels = []\n        omero_kwargs = {}\n\n    default_kwargs = {\n        \"store\": store,\n        \"shape\": image_0.on_disk_shape,\n        \"chunks\": image_0.on_disk_array.chunks,\n        \"dtype\": image_0.on_disk_array.dtype,\n        \"on_disk_axis\": image_0.dataset.on_disk_axes_names,\n        \"pixel_sizes\": image_0.pixel_size,\n        \"xy_scaling_factor\": self.image_meta.xy_scaling_factor,\n        \"z_scaling_factor\": self.image_meta.z_scaling_factor,\n        \"time_spacing\": image_0.dataset.time_spacing,\n        \"time_units\": image_0.dataset.time_axis_unit,\n        \"num_levels\": self.num_levels,\n        \"name\": name,\n        \"channel_labels\": image_0.channel_labels,\n        \"channel_wavelengths\": [ch.wavelength_id for ch in channels],\n        \"channel_kwargs\": [ch.extra_fields for ch in channels],\n        \"omero_kwargs\": omero_kwargs,\n        \"overwrite\": overwrite,\n        \"version\": self.image_meta.version,\n    }\n\n    default_kwargs.update(kwargs)\n\n    create_empty_ome_zarr_image(\n        **default_kwargs,\n    )\n    return NgffImage(store=store)\n</code></pre>"},{"location":"api/core/#ngio.core.NgffImage.get_image","title":"<code>get_image(*, path: str | None = None, pixel_size: PixelSize | None = None, highest_resolution: bool = True) -&gt; Image</code>","text":"<p>Get an image handler for the given level.</p> <p>Parameters:</p> <ul> <li> <code>path</code>               (<code>str | None</code>, default:                   <code>None</code> )           \u2013            <p>The path to the level.</p> </li> <li> <code>pixel_size</code>               (<code>tuple[float, ...] | list[float] | None</code>, default:                   <code>None</code> )           \u2013            <p>The pixel size of the level.</p> </li> <li> <code>highest_resolution</code>               (<code>bool</code>, default:                   <code>True</code> )           \u2013            <p>Whether to get the highest resolution level</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>ImageHandler</code> (              <code>Image</code> )          \u2013            <p>The image handler.</p> </li> </ul> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>def get_image(\n    self,\n    *,\n    path: str | None = None,\n    pixel_size: PixelSize | None = None,\n    highest_resolution: bool = True,\n) -&gt; Image:\n    \"\"\"Get an image handler for the given level.\n\n    Args:\n        path (str | None, optional): The path to the level.\n        pixel_size (tuple[float, ...] | list[float] | None, optional): The pixel\n            size of the level.\n        highest_resolution (bool, optional): Whether to get the highest\n            resolution level\n\n    Returns:\n        ImageHandler: The image handler.\n    \"\"\"\n    if path is not None or pixel_size is not None:\n        highest_resolution = False\n\n    image = Image(\n        store=self.group,\n        path=path,\n        pixel_size=pixel_size,\n        highest_resolution=highest_resolution,\n        label_group=LabelGroup(self.group, image_ref=None, mode=self._mode),\n        cache=self._metadata_cache,\n        mode=self._mode,\n    )\n    ngio_logger.info(f\"Opened image at path: {image.path}\")\n    ngio_logger.info(f\"- {image.dimensions}\")\n    ngio_logger.info(f\"- {image.pixel_size}\")\n    return image\n</code></pre>"},{"location":"api/core/#ngio.core.NgffImage.update_omero_window","title":"<code>update_omero_window(start_percentile: int = 5, end_percentile: int = 95) -&gt; None</code>","text":"<p>Update the OMERO window.</p> <p>This will setup percentiles based values for the window of each channel.</p> <p>Parameters:</p> <ul> <li> <code>start_percentile</code>               (<code>int</code>, default:                   <code>5</code> )           \u2013            <p>The start percentile.</p> </li> <li> <code>end_percentile</code>               (<code>int</code>, default:                   <code>95</code> )           \u2013            <p>The end percentile</p> </li> </ul> Source code in <code>ngio/core/ngff_image.py</code> <pre><code>def update_omero_window(\n    self, start_percentile: int = 5, end_percentile: int = 95\n) -&gt; None:\n    \"\"\"Update the OMERO window.\n\n    This will setup percentiles based values for the window of each channel.\n\n    Args:\n        start_percentile (int): The start percentile.\n        end_percentile (int): The end percentile\n\n    \"\"\"\n    meta = self.image_meta\n\n    lowest_res_image = self.get_image(highest_resolution=True)\n    lowest_res_shape = lowest_res_image.shape\n    for path in self.levels_paths:\n        image = self.get_image(path=path)\n        if np.prod(image.shape) &lt; np.prod(lowest_res_shape):\n            lowest_res_shape = image.shape\n            lowest_res_image = image\n\n    max_dtype = np.iinfo(image.on_disk_array.dtype).max\n    num_c = lowest_res_image.dimensions.get(\"c\", 1)\n\n    if meta.omero is None:\n        raise NotImplementedError(\n            \"OMERO metadata not found. \" \" Please add OMERO metadata to the image.\"\n        )\n\n    channel_list = meta.omero.channels\n    if len(channel_list) != num_c:\n        raise ValueError(\"The number of channels does not match the image.\")\n\n    for c, channel in enumerate(channel_list):\n        data = image.get_array(c=c, mode=\"dask\").ravel()\n        _start_percentile = da.percentile(\n            data, start_percentile, method=\"nearest\"\n        ).compute()\n        _end_percentile = da.percentile(\n            data, end_percentile, method=\"nearest\"\n        ).compute()\n        channel.extra_fields[\"window\"] = {\n            \"start\": _start_percentile,\n            \"end\": _end_percentile,\n            \"min\": 0,\n            \"max\": max_dtype,\n        }\n        ngio_logger.info(\n            f\"Updated window for channel {channel.label}. \"\n            f\"Start: {start_percentile}, End: {end_percentile}\"\n        )\n        meta.omero.channels[c] = channel\n\n    self._image_meta.write_meta(meta)\n</code></pre>"},{"location":"notebooks/basic_usage/","title":"Basic Usage","text":"In\u00a0[1]: Copied! <pre>from ngio.core import NgffImage\nngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr/B/03/0\")\n</pre> from ngio.core import NgffImage ngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr/B/03/0\") <p>The <code>ngff_image</code> contains several attributes and methods to interact with the <code>OME-NGFF</code> (<code>OME-Zarr</code>) file on the storage.</p> In\u00a0[2]: Copied! <pre># Explore object metadata\nprint(\"Levels: \", ngff_image.levels_paths)\nprint(\"Num Levels: \", ngff_image.num_levels)\n</pre> # Explore object metadata print(\"Levels: \", ngff_image.levels_paths) print(\"Num Levels: \", ngff_image.num_levels) <pre>Levels:  ['0', '1', '2', '3', '4']\nNum Levels:  5\n</pre> <p>Get a single <code>level</code> of the image pyramid as <code>Image</code> (to know more about the <code>Image</code> class, please refer to the Image notebook The <code>Image</code> object is the main object to interact with the image. It contains methods to interact with the image data and metadata.</p> In\u00a0[3]: Copied! <pre>from ngio.ngff_meta import PixelSize\n\n# 1. Get image from highest resolution (default)\nimage = ngff_image.get_image()\nprint(f\"{image.shape=}\")\nprint(f\"{image.pixel_size=}\")\n\n# 2. Get image from a specific level using the path keyword\nprint(f\"{image.shape=}\")\nprint(f\"{image.pixel_size=}\")\n\n# 3. Get image from a specific pixel size using the pixel_size keyword\nimage = ngff_image.get_image(pixel_size=PixelSize(x=0.65, y=0.65, z=1))\nprint(f\"{image.shape=}\")\nprint(f\"{image.pixel_size=}\")\n</pre> from ngio.ngff_meta import PixelSize  # 1. Get image from highest resolution (default) image = ngff_image.get_image() print(f\"{image.shape=}\") print(f\"{image.pixel_size=}\")  # 2. Get image from a specific level using the path keyword print(f\"{image.shape=}\") print(f\"{image.pixel_size=}\")  # 3. Get image from a specific pixel size using the pixel_size keyword image = ngff_image.get_image(pixel_size=PixelSize(x=0.65, y=0.65, z=1)) print(f\"{image.shape=}\") print(f\"{image.pixel_size=}\") <pre>image.shape=(3, 1, 4320, 5120)\nimage.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\nimage.shape=(3, 1, 4320, 5120)\nimage.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\nimage.shape=(3, 1, 1080, 1280)\nimage.pixel_size=PixelSize(x=0.65, y=0.65, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=True)\n</pre> In\u00a0[4]: Copied! <pre>print(\"List of Labels: \", ngff_image.label.list())\n\nlabel_nuclei = ngff_image.label.get_label(\"nuclei\", path=\"0\")\nprint(f\"{label_nuclei.shape=}\")\nprint(f\"{label_nuclei.pixel_size=}\")\n</pre> print(\"List of Labels: \", ngff_image.label.list())  label_nuclei = ngff_image.label.get_label(\"nuclei\", path=\"0\") print(f\"{label_nuclei.shape=}\") print(f\"{label_nuclei.pixel_size=}\") <pre>List of Labels:  ['nuclei', 'wf_2_labels', 'wf_3_labels', 'wf_4_labels']\nlabel_nuclei.shape=(1, 4320, 5120)\nlabel_nuclei.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\n</pre> In\u00a0[5]: Copied! <pre>print(\"List of Tables: \", ngff_image.table.list())\nprint(\" - Feature tables: \", ngff_image.table.list(table_type='feature_table'))\nprint(\" - Roi tables: \", ngff_image.table.list(table_type='roi_table'))\nprint(\" - Masking Roi tables: \", ngff_image.table.list(table_type='masking_roi_table'))\n</pre> print(\"List of Tables: \", ngff_image.table.list()) print(\" - Feature tables: \", ngff_image.table.list(table_type='feature_table')) print(\" - Roi tables: \", ngff_image.table.list(table_type='roi_table')) print(\" - Masking Roi tables: \", ngff_image.table.list(table_type='masking_roi_table')) <pre>List of Tables:  ['FOV_ROI_table', 'nuclei_ROI_table', 'well_ROI_table', 'regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4']\n - Feature tables:  ['regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4']\n - Roi tables:  ['FOV_ROI_table', 'well_ROI_table']\n - Masking Roi tables:  ['nuclei_ROI_table']\n</pre> In\u00a0[6]: Copied! <pre># Loading a table\nfeature_table = ngff_image.table.get_table(\"regionprops_DAPI\")\nfeature_table.table\n</pre> # Loading a table feature_table = ngff_image.table.get_table(\"regionprops_DAPI\") feature_table.table <pre>/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n</pre> Out[6]: area bbox_area equivalent_diameter max_intensity mean_intensity min_intensity standard_deviation_intensity label 1 2120.0 2655.0 15.938437 476.0 278.635864 86.0 54.343792 2 327.0 456.0 8.547709 604.0 324.162079 118.0 90.847092 3 1381.0 1749.0 13.816510 386.0 212.682114 60.0 50.169601 4 2566.0 3588.0 16.985800 497.0 251.731491 61.0 53.307186 5 4201.0 5472.0 20.019413 466.0 223.862885 51.0 56.719025 ... ... ... ... ... ... ... ... 3002 1026.0 1288.0 12.513618 589.0 308.404480 132.0 64.681778 3003 859.0 1080.0 11.794101 400.0 270.349243 107.0 49.040470 3004 508.0 660.0 9.899693 314.0 205.043304 82.0 33.249981 3005 369.0 440.0 8.899028 376.0 217.970184 82.0 50.978519 3006 278.0 330.0 8.097459 339.0 217.996399 100.0 38.510067 <p>3006 rows \u00d7 7 columns</p> In\u00a0[7]: Copied! <pre># Loading a roi table\nroi_table = ngff_image.table.get_table(\"FOV_ROI_table\")\n\nprint(f\"{roi_table.field_indexes=}\")\nprint(f\"{roi_table.get_roi('FOV_1')=}\")\n\nroi_table.table\n</pre> # Loading a roi table roi_table = ngff_image.table.get_table(\"FOV_ROI_table\")  print(f\"{roi_table.field_indexes=}\") print(f\"{roi_table.get_roi('FOV_1')=}\")  roi_table.table <pre>roi_table.field_indexes=['FOV_1', 'FOV_2', 'FOV_3', 'FOV_4']\nroi_table.get_roi('FOV_1')=WorldCooROI(x_length=416.0, y_length=351.0, z_length=1.0, x=0.0, y=0.0, z=0.0)\n</pre> Out[7]: x_micrometer y_micrometer z_micrometer len_x_micrometer len_y_micrometer len_z_micrometer x_micrometer_original y_micrometer_original FieldIndex FOV_1 0.0 0.0 0.0 416.0 351.0 1.0 -1448.300049 -1517.699951 FOV_2 416.0 0.0 0.0 416.0 351.0 1.0 -1032.300049 -1517.699951 FOV_3 0.0 351.0 0.0 416.0 351.0 1.0 -1448.300049 -1166.699951 FOV_4 416.0 351.0 0.0 416.0 351.0 1.0 -1032.300049 -1166.699951 In\u00a0[8]: Copied! <pre>new_ngff_image = ngff_image.derive_new_image(\"../../data/new_ome.zarr\", name=\"new_image\")\n\nprint(f\"{new_ngff_image.store=}\")\nprint(f\"{new_ngff_image.levels_paths=}\")\nprint(f\"{new_ngff_image.num_levels=}\")\n</pre> new_ngff_image = ngff_image.derive_new_image(\"../../data/new_ome.zarr\", name=\"new_image\")  print(f\"{new_ngff_image.store=}\") print(f\"{new_ngff_image.levels_paths=}\") print(f\"{new_ngff_image.num_levels=}\") <pre>new_ngff_image.store='../../data/new_ome.zarr'\nnew_ngff_image.levels_paths=['0', '1', '2', '3', '4']\nnew_ngff_image.num_levels=5\n</pre>"},{"location":"notebooks/basic_usage/#basic-usage","title":"Basic Usage\u00b6","text":"<p>In this notebook we will show how to use the 'ngffImage' class to manage a OME-NGFF image.</p> <p>For this example we will use a small example image that can be downloaded from the following link: example ome-zarr</p> <p>You can download the example image (on Linux and Mac os) by running the following command:</p> <pre>bash setup_data.sh\n</pre> <p>from the root of the repository.</p>"},{"location":"notebooks/basic_usage/#ngffimage","title":"NgffImage\u00b6","text":"<p>The <code>NgffImage</code> provides a high-level interface to read, write and manipulate NGFF images. A <code>NgffImage</code> can be created from a storelike object (e.g. a path to a directory or a zarr store) or from a <code>zarr.Group</code> object.</p>"},{"location":"notebooks/basic_usage/#labels","title":"Labels\u00b6","text":"<p>The <code>NgffImage</code> can also be used to load labels from a <code>OME-NGFF</code> file.</p>"},{"location":"notebooks/basic_usage/#tables","title":"Tables\u00b6","text":"<p>The <code>NgffImage</code> can also be used to load tables from a <code>OME-NGFF</code> file.</p> <p><code>ngio</code> supports three types of tables:</p> <ul> <li><code>features table</code> A simple table to store features associated with a label.</li> <li><code>roi table</code> A table to store regions of interest.</li> <li><code>masking roi tables</code> A table to store single objects bounding boxes associated with a label.</li> </ul>"},{"location":"notebooks/basic_usage/#derive-a-new-ngffimage","title":"Derive a new NgffImage\u00b6","text":"<p>When processing an image, it is often useful to derive a new image from the original image. The <code>NgffImage</code> class provides a method to derive a new image from the original image. When deriving a new image, a new <code>NgffImage</code> object is created with the same metadata as the original image. Optionally the user can specify different metadata for the new image(.e.g. different channels names).</p>"},{"location":"notebooks/image/","title":"Images/Labels/Tables","text":"In\u00a0[1]: Copied! <pre>import matplotlib.pyplot as plt\n\nfrom ngio.core.ngff_image import NgffImage\n\nngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr/B/03/0\")\n</pre> import matplotlib.pyplot as plt  from ngio.core.ngff_image import NgffImage  ngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr/B/03/0\") In\u00a0[2]: Copied! <pre>image = ngff_image.get_image()\n\nprint(\"Image information:\")\nprint(f\"{image.shape=}\")\nprint(f\"{image.axes_names=}\")\nprint(f\"{image.pixel_size=}\")\nprint(f\"{image.channel_labels=}\")\nprint(f\"{image.dimensions=}\")\n</pre> image = ngff_image.get_image()  print(\"Image information:\") print(f\"{image.shape=}\") print(f\"{image.axes_names=}\") print(f\"{image.pixel_size=}\") print(f\"{image.channel_labels=}\") print(f\"{image.dimensions=}\") <pre>Image information:\nimage.shape=(3, 1, 4320, 5120)\nimage.axes_names=['c', 'z', 'y', 'x']\nimage.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\nimage.channel_labels=['DAPI', 'nanog', 'Lamin B1']\nimage.dimensions=Dimensions(c=3, z=1, y=4320, x=5120)\n</pre> <p>The <code>Image</code> object created is a lazy object, meaning that the image is not loaded into memory until it is needed. To get the image data from disk we can use the <code>.array</code> attribute or we can get it as a <code>dask.array</code> object using the <code>.dask_array</code> attribute.</p> In\u00a0[3]: Copied! <pre># Get image as a dask array\ndask_array = image.on_disk_dask_array\ndask_array\n</pre> # Get image as a dask array dask_array = image.on_disk_dask_array dask_array Out[3]:  Array   Chunk   Bytes   126.56 MiB   10.55 MiB   Shape   (3, 1, 4320, 5120)   (1, 1, 2160, 2560)   Dask graph   12 chunks in 2 graph layers   Data type   uint16 numpy.ndarray  3 1 5120 4320 1 <p>Note, directly accessing the <code>.on_disk_array</code> or <code>.on_disk_dask_array</code> attributes will load the image as stored in the file.</p> <p>Since in principle the images can have different axes order. A safer way to access the image data is to use the <code>.get_array()</code> method, which will return the image data in canonical order (TCZYX).</p> In\u00a0[4]: Copied! <pre>image_numpy = image.get_array(c=0, x=slice(0, 250), y=slice(0, 250), preserve_dimensions=False, mode=\"numpy\")\n\nprint(f\"{image_numpy.shape=}\")\n</pre> image_numpy = image.get_array(c=0, x=slice(0, 250), y=slice(0, 250), preserve_dimensions=False, mode=\"numpy\")  print(f\"{image_numpy.shape=}\") <pre>image_numpy.shape=(1, 250, 250)\n</pre> In\u00a0[5]: Copied! <pre>roi_table = ngff_image.table.get_table(\"FOV_ROI_table\")\nroi = roi_table.get_roi(\"FOV_1\")\nprint(f\"{roi=}\")\n\nimage_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=True, mode=\"dask\")\nimage_roi_1\n</pre> roi_table = ngff_image.table.get_table(\"FOV_ROI_table\") roi = roi_table.get_roi(\"FOV_1\") print(f\"{roi=}\")  image_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=True, mode=\"dask\") image_roi_1 <pre>roi=WorldCooROI(x_length=416.0, y_length=351.0, z_length=1.0, x=0.0, y=0.0, z=0.0)\n</pre> Out[5]:  Array   Chunk   Bytes   10.55 MiB   10.55 MiB   Shape   (1, 1, 2160, 2560)   (1, 1, 2160, 2560)   Dask graph   1 chunks in 3 graph layers   Data type   uint16 numpy.ndarray  1 1 2560 2160 1 <p>The roi object can is defined in physical coordinates, and can be used to extract the region of interest from the image or label at any resolution.</p> In\u00a0[6]: Copied! <pre>image_2 = ngff_image.get_image(path=\"2\")\n# Two images at different resolutions\nprint(f\"{image.pixel_size=}\")\nprint(f\"{image_2.pixel_size=}\")\n\n# Get roi for higher resolution image\nimage_1_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)\n\n# Get roi for lower resolution image\nimage_2_roi_1 = image_2.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)\n\n# Plot the two images side by side\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(image_1_roi_1[0], cmap=\"gray\")\naxs[1].imshow(image_2_roi_1[0], cmap=\"gray\")\nplt.show()\n</pre> image_2 = ngff_image.get_image(path=\"2\") # Two images at different resolutions print(f\"{image.pixel_size=}\") print(f\"{image_2.pixel_size=}\")  # Get roi for higher resolution image image_1_roi_1 = image.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)  # Get roi for lower resolution image image_2_roi_1 = image_2.get_array_from_roi(roi=roi, c=0, preserve_dimensions=False)  # Plot the two images side by side fig, axs = plt.subplots(1, 2, figsize=(10, 5)) axs[0].imshow(image_1_roi_1[0], cmap=\"gray\") axs[1].imshow(image_2_roi_1[0], cmap=\"gray\") plt.show() <pre>image.pixel_size=PixelSize(x=0.1625, y=0.1625, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\nimage_2.pixel_size=PixelSize(x=0.65, y=0.65, z=1.0, unit=&lt;SpaceUnits.micrometer: 'micrometer'&gt;, virtual=False)\n</pre> In\u00a0[7]: Copied! <pre>import numpy as np\n\n# Get a small slice of the image\nsmall_slice = image.get_array(x=slice(1000, 2000), y=slice(1000, 2000))\n\n# Set the sample slice to zeros\nzeros_slice = np.zeros_like(small_slice)\nimage.set_array(patch=zeros_slice, x=slice(1000, 2000), y=slice(1000, 2000))\n\n\n# Load the image from disk and show the edited image\nnuclei = ngff_image.label.get_label(\"nuclei\")\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(image.on_disk_array[0, 0], cmap=\"gray\")\naxs[1].imshow(nuclei.on_disk_array[0])\nfor ax in axs:\n    ax.axis(\"off\")\nplt.tight_layout()\nplt.show()\n\n# Add back the original slice to the image\nimage.set_array(patch=small_slice, x=slice(1000, 2000), y=slice(1000, 2000))\n</pre> import numpy as np  # Get a small slice of the image small_slice = image.get_array(x=slice(1000, 2000), y=slice(1000, 2000))  # Set the sample slice to zeros zeros_slice = np.zeros_like(small_slice) image.set_array(patch=zeros_slice, x=slice(1000, 2000), y=slice(1000, 2000))   # Load the image from disk and show the edited image nuclei = ngff_image.label.get_label(\"nuclei\") fig, axs = plt.subplots(1, 2, figsize=(10, 5)) axs[0].imshow(image.on_disk_array[0, 0], cmap=\"gray\") axs[1].imshow(nuclei.on_disk_array[0]) for ax in axs:     ax.axis(\"off\") plt.tight_layout() plt.show()  # Add back the original slice to the image image.set_array(patch=small_slice, x=slice(1000, 2000), y=slice(1000, 2000)) In\u00a0[8]: Copied! <pre># Create a a new label object and set it to a simple segmentation\nnew_label = ngff_image.label.derive(\"new_label\", overwrite=True)\n\nsimple_segmentation = image.on_disk_array[0] &gt; 100\nnew_label.on_disk_array[...] = simple_segmentation\n\n# make a subplot with two image show side by side\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(image.on_disk_array[0, 0], cmap=\"gray\")\naxs[1].imshow(new_label.on_disk_array[0], cmap=\"gray\")\nfor ax in axs:\n    ax.axis(\"off\")\nplt.tight_layout()\nplt.show()\n</pre> # Create a a new label object and set it to a simple segmentation new_label = ngff_image.label.derive(\"new_label\", overwrite=True)  simple_segmentation = image.on_disk_array[0] &gt; 100 new_label.on_disk_array[...] = simple_segmentation  # make a subplot with two image show side by side fig, axs = plt.subplots(1, 2, figsize=(10, 5)) axs[0].imshow(image.on_disk_array[0, 0], cmap=\"gray\") axs[1].imshow(new_label.on_disk_array[0], cmap=\"gray\") for ax in axs:     ax.axis(\"off\") plt.tight_layout() plt.show() In\u00a0[9]: Copied! <pre>label_0 = ngff_image.label.get_label(\"new_label\", path=\"0\")\nlabel_2 = ngff_image.label.get_label(\"new_label\", path=\"2\")\n\nlabel_before_consolidation = label_2.on_disk_array[...]\n\n# Consolidate the label\nlabel_0.consolidate()\n\nlabel_after_consolidation = label_2.on_disk_array[...]\n\n\n# make a subplot with two image show side by side\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(label_before_consolidation[0], cmap=\"gray\")\naxs[1].imshow(label_after_consolidation[0], cmap=\"gray\")\nfor ax in axs:\n    ax.axis(\"off\")\nplt.tight_layout()\nplt.show()\n</pre> label_0 = ngff_image.label.get_label(\"new_label\", path=\"0\") label_2 = ngff_image.label.get_label(\"new_label\", path=\"2\")  label_before_consolidation = label_2.on_disk_array[...]  # Consolidate the label label_0.consolidate()  label_after_consolidation = label_2.on_disk_array[...]   # make a subplot with two image show side by side fig, axs = plt.subplots(1, 2, figsize=(10, 5)) axs[0].imshow(label_before_consolidation[0], cmap=\"gray\") axs[1].imshow(label_after_consolidation[0], cmap=\"gray\") for ax in axs:     ax.axis(\"off\") plt.tight_layout() plt.show() In\u00a0[10]: Copied! <pre>import numpy as np\nimport pandas as pd\n\nprint(f\"List of feature table: {ngff_image.table.list(table_type='feature_table')}\")\n\n\nnuclei = ngff_image.label.get_label('nuclei')\n\n# Create a table with random features for each nuclei in each ROI\nlist_of_records = []\nfor roi in roi_table.rois:\n    nuclei_in_roi = nuclei.get_array_from_roi(roi, mode='numpy')\n    for nuclei_id in np.unique(nuclei_in_roi)[1:]:\n        list_of_records.append(\n            {\"label\": nuclei_id,\n             \"feat1\": np.random.rand(),\n             \"feat2\": np.random.rand(),\n         \"ROI\": roi.infos.get(\"FieldIndex\")}\n    )\n\nfeat_df = pd.DataFrame.from_records(list_of_records)\n\n# Create a new feature table\nfeat_table = ngff_image.table.new(name='new_feature_table',\n                     label_image='../nuclei',\n                     table_type='feature_table',\n                     overwrite=True)\n\nprint(f\"New list of feature table: {ngff_image.table.list(table_type='feature_table')}\")\nfeat_table.set_table(feat_df)\nfeat_table.write()\n\nfeat_table.table\n</pre> import numpy as np import pandas as pd  print(f\"List of feature table: {ngff_image.table.list(table_type='feature_table')}\")   nuclei = ngff_image.label.get_label('nuclei')  # Create a table with random features for each nuclei in each ROI list_of_records = [] for roi in roi_table.rois:     nuclei_in_roi = nuclei.get_array_from_roi(roi, mode='numpy')     for nuclei_id in np.unique(nuclei_in_roi)[1:]:         list_of_records.append(             {\"label\": nuclei_id,              \"feat1\": np.random.rand(),              \"feat2\": np.random.rand(),          \"ROI\": roi.infos.get(\"FieldIndex\")}     )  feat_df = pd.DataFrame.from_records(list_of_records)  # Create a new feature table feat_table = ngff_image.table.new(name='new_feature_table',                      label_image='../nuclei',                      table_type='feature_table',                      overwrite=True)  print(f\"New list of feature table: {ngff_image.table.list(table_type='feature_table')}\") feat_table.set_table(feat_df) feat_table.write()  feat_table.table <pre>List of feature table: ['regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4']\nNew list of feature table: ['regionprops_DAPI', 'nuclei_measurements_wf3', 'nuclei_measurements_wf4', 'nuclei_lamin_measurements_wf4', 'new_feature_table']\n</pre> <pre>/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/anndata/_core/anndata.py:1758: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n  utils.warn_names_duplicates(\"obs\")\n</pre> Out[10]: feat1 feat2 ROI label 1 0.361989 0.747412 FOV_1 2 0.928323 0.369813 FOV_1 3 0.569211 0.392189 FOV_1 4 0.129771 0.803985 FOV_1 5 0.050885 0.932590 FOV_1 ... ... ... ... 2987 0.708020 0.141566 FOV_4 2991 0.132859 0.310013 FOV_4 2993 0.137844 0.735762 FOV_4 2995 0.910480 0.083270 FOV_4 2996 0.797235 0.146990 FOV_4 <p>3091 rows \u00d7 3 columns</p>"},{"location":"notebooks/image/#imageslabelstables","title":"Images/Labels/Tables\u00b6","text":"<p>In this notebook we will show how to use the <code>Image</code>, <code>Label</code> and <code>Table</code> objects.</p>"},{"location":"notebooks/image/#images","title":"Images\u00b6","text":"<p>Images can be loaded from a <code>NgffImage</code> object.</p>"},{"location":"notebooks/image/#data-loading","title":"Data Loading\u00b6","text":""},{"location":"notebooks/image/#roitableimage-interaction","title":"RoiTable/Image Interaction\u00b6","text":"<p><code>roi</code> objects from a <code>roi_table</code> can be used to extract a region of interest from an image or a label.</p>"},{"location":"notebooks/image/#writing-images","title":"Writing Images\u00b6","text":"<p>Similarly to the <code>.array()</code>  we can use the <code>.set_array()</code> (or <code>set_array_from_roi</code>) method to write part of an image to disk.</p>"},{"location":"notebooks/image/#deriving-a-new-label","title":"Deriving a new label\u00b6","text":"<p>When doing image analysis, we often need to create new labels or tables. The <code>ngff_image</code> allows us to simply create new labels and tables.</p>"},{"location":"notebooks/image/#image-consolidation","title":"Image Consolidation\u00b6","text":"<p>Every time we modify a label or a image, we are modifying the on-disk data on one layer only. This means that if we have the image saved in multiple resolutions, we need to consolidate the changes to all resolutions. To do so, we can use the <code>.consolidate()</code> method.</p>"},{"location":"notebooks/image/#creating-a-new-table","title":"Creating a new table\u00b6","text":"<p>We can simply create a new table by create a new <code>Table</code> object from a pandas dataframe. For a simple feature table the only reuiremnt is to have a integer column named <code>label</code> that will be used to link the table to the objects in the image.</p>"},{"location":"notebooks/processing/","title":"Processing","text":"In\u00a0[1]: Copied! <pre>import matplotlib.pyplot as plt\n\nfrom ngio.core import NgffImage\n\nngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0\")\n</pre> import matplotlib.pyplot as plt  from ngio.core import NgffImage  ngff_image = NgffImage(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0\") In\u00a0[2]: Copied! <pre>mip_ngff = ngff_image.derive_new_image(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0_mip\",\n                                       name=\"MIP\",\n                                       shape=(1, 1, 2160, 5120))\n</pre>  mip_ngff = ngff_image.derive_new_image(\"../../data/20200812-CardiomyocyteDifferentiation14-Cycle1.zarr/B/03/0_mip\",                                        name=\"MIP\",                                        shape=(1, 1, 2160, 5120)) In\u00a0[3]: Copied! <pre># Get the source imag\nsource_image = ngff_image.get_image()\nprint(\"Source image loaded with shape:\", source_image.shape)\n\n# Get the MIP image\nmip_image = mip_ngff.get_image()\nprint(\"MIP image loaded with shape:\", mip_image.shape)\n\n# Get a ROI table\nroi_table = ngff_image.table.get_table(\"FOV_ROI_table\")\nprint(\"ROI table loaded with\", len(roi_table.rois), \"ROIs\")\n\n# For each ROI in the table\n# - get the data from the source image\n# - calculate the MIP\n# - set the data in the MIP image\nfor roi in roi_table.rois:\n    print(f\" - Processing ROI {roi.infos.get('field_index')}\")\n    patch = source_image.get_array_from_roi(roi)\n    mip_patch = patch.max(axis=1, keepdims=True)\n    mip_image.set_array_from_roi(patch=mip_patch, roi=roi)\n    \nprint(\"MIP image saved\")\n\nplt.figure(figsize=(5, 5))\nplt.title(\"Mip\")\nplt.imshow(mip_image.on_disk_array[0, 0, :, :], cmap=\"gray\")\nplt.axis('off')\nplt.tight_layout()\nplt.show()\n</pre> # Get the source imag source_image = ngff_image.get_image() print(\"Source image loaded with shape:\", source_image.shape)  # Get the MIP image mip_image = mip_ngff.get_image() print(\"MIP image loaded with shape:\", mip_image.shape)  # Get a ROI table roi_table = ngff_image.table.get_table(\"FOV_ROI_table\") print(\"ROI table loaded with\", len(roi_table.rois), \"ROIs\")  # For each ROI in the table # - get the data from the source image # - calculate the MIP # - set the data in the MIP image for roi in roi_table.rois:     print(f\" - Processing ROI {roi.infos.get('field_index')}\")     patch = source_image.get_array_from_roi(roi)     mip_patch = patch.max(axis=1, keepdims=True)     mip_image.set_array_from_roi(patch=mip_patch, roi=roi)      print(\"MIP image saved\")  plt.figure(figsize=(5, 5)) plt.title(\"Mip\") plt.imshow(mip_image.on_disk_array[0, 0, :, :], cmap=\"gray\") plt.axis('off') plt.tight_layout() plt.show()  <pre>Source image loaded with shape: (1, 2, 2160, 5120)\nMIP image loaded with shape: (1, 1, 2160, 5120)\nROI table loaded with 2 ROIs\n - Processing ROI None\n - Processing ROI None\nMIP image saved\n</pre> In\u00a0[4]: Copied! <pre># Get the MIP image at a lower resolution\nmip_image_2 = mip_ngff.get_image(path=\"2\")\n\nimage_before_consolidation = mip_image_2.get_array(c=0, z=0)\n\n# Consolidate the pyramid\nmip_image.consolidate()\n\nimage_after_consolidation = mip_image_2.get_array(c=0, z=0)\n\nfig, axs = plt.subplots(2, 1, figsize=(10, 5))\naxs[0].set_title(\"Before consolidation\")\naxs[0].imshow(image_before_consolidation, cmap=\"gray\")\naxs[1].set_title(\"After consolidation\")\naxs[1].imshow(image_after_consolidation, cmap=\"gray\")\nfor ax in axs:\n    ax.axis('off')\nplt.tight_layout()\nplt.show()\n</pre> # Get the MIP image at a lower resolution mip_image_2 = mip_ngff.get_image(path=\"2\")  image_before_consolidation = mip_image_2.get_array(c=0, z=0)  # Consolidate the pyramid mip_image.consolidate()  image_after_consolidation = mip_image_2.get_array(c=0, z=0)  fig, axs = plt.subplots(2, 1, figsize=(10, 5)) axs[0].set_title(\"Before consolidation\") axs[0].imshow(image_before_consolidation, cmap=\"gray\") axs[1].set_title(\"After consolidation\") axs[1].imshow(image_after_consolidation, cmap=\"gray\") for ax in axs:     ax.axis('off') plt.tight_layout() plt.show()  In\u00a0[5]: Copied! <pre>mip_roi_table = mip_ngff.table.new(\"FOV_ROI_table\", overwrite=True)\n\nroi_list = []\nfor roi in roi_table.rois:\n    print(f\" - Processing ROI {roi.infos.get('field_index')}\")\n    roi.z_length = 1 # In the MIP image, the z dimension is 1\n    roi_list.append(roi)\n\nmip_roi_table.set_rois(roi_list, overwrite=True)\nmip_roi_table.write()\n\nmip_roi_table.table\n</pre> mip_roi_table = mip_ngff.table.new(\"FOV_ROI_table\", overwrite=True)  roi_list = [] for roi in roi_table.rois:     print(f\" - Processing ROI {roi.infos.get('field_index')}\")     roi.z_length = 1 # In the MIP image, the z dimension is 1     roi_list.append(roi)  mip_roi_table.set_rois(roi_list, overwrite=True) mip_roi_table.write()  mip_roi_table.table <pre> - Processing ROI None\n - Processing ROI None\n</pre> Out[5]: x_micrometer y_micrometer z_micrometer len_x_micrometer len_y_micrometer len_z_micrometer x_micrometer_original y_micrometer_original FieldIndex FOV_1 0.0 0.0 0.0 416.0 351.0 1 -1448.300049 -1517.699951 FOV_2 416.0 0.0 0.0 416.0 351.0 1 -1032.300049 -1517.699951 In\u00a0[6]: Copied! <pre># Setup a simple segmentation function\n\nimport numpy as np\nfrom matplotlib.colors import ListedColormap\nfrom skimage.filters import threshold_otsu\nfrom skimage.measure import label\n\nrand_cmap = np.random.rand(1000, 3)\nrand_cmap[0] = 0\nrand_cmap = ListedColormap(rand_cmap)\n\n\ndef otsu_threshold_segmentation(image: np.ndarray, max_label:int) -&gt; np.ndarray:\n    \"\"\"Simple segmentation using Otsu thresholding.\"\"\"\n    threshold = threshold_otsu(image)\n    binary = image &gt; threshold\n    label_image = label(binary)\n    label_image += max_label\n    label_image = np.where(binary, label_image, 0)\n    return label_image\n</pre> # Setup a simple segmentation function  import numpy as np from matplotlib.colors import ListedColormap from skimage.filters import threshold_otsu from skimage.measure import label  rand_cmap = np.random.rand(1000, 3) rand_cmap[0] = 0 rand_cmap = ListedColormap(rand_cmap)   def otsu_threshold_segmentation(image: np.ndarray, max_label:int) -&gt; np.ndarray:     \"\"\"Simple segmentation using Otsu thresholding.\"\"\"     threshold = threshold_otsu(image)     binary = image &gt; threshold     label_image = label(binary)     label_image += max_label     label_image = np.where(binary, label_image, 0)     return label_image In\u00a0[7]: Copied! <pre>nuclei_image = mip_ngff.label.derive(name=\"nuclei\", overwrite=True)\n</pre> nuclei_image = mip_ngff.label.derive(name=\"nuclei\", overwrite=True) In\u00a0[8]: Copied! <pre># Get the source imag\nsource_image = mip_ngff.get_image()\nprint(\"Source image loaded with shape:\", source_image.shape)\n\n# Get a ROI table\nroi_table = mip_ngff.table.get_table(\"FOV_ROI_table\")\nprint(\"ROI table loaded with\", len(roi_table.rois), \"ROIs\")\n\n# Find the DAPI channel\ndapi_idx = source_image.get_channel_idx(label=\"DAPI\")\n\n# For each ROI in the table\n# - get the data from the source image\n# - calculate the Segmentation\n# - set the data in segmentation image\nmax_label = 0\nfor roi in roi_table.rois:\n    print(f\" - Processing ROI {roi.infos.get('field_index')}\")\n    patch = source_image.get_array_from_roi(roi, c=dapi_idx)\n    segmentation = otsu_threshold_segmentation(patch, max_label)\n\n    # Add the max label of the previous segmentation to avoid overlapping labels\n    max_label = segmentation.max()\n\n    nuclei_image.set_array_from_roi(patch=segmentation, roi=roi)\n\n# Consolidate the segmentation image\nnuclei_image.consolidate()\n\nprint(\"Segmentation image saved\")\nfig, axs = plt.subplots(2, 1, figsize=(10, 5))\naxs[0].set_title(\"MIP\")\naxs[0].imshow(source_image.on_disk_array[0, 0], cmap=\"gray\")\naxs[1].set_title(\"Nuclei segmentation\")\naxs[1].imshow(nuclei_image.on_disk_array[0], cmap=rand_cmap, interpolation='nearest')\nfor ax in axs:\n    ax.axis('off')\nplt.tight_layout()\nplt.show()\n</pre> # Get the source imag source_image = mip_ngff.get_image() print(\"Source image loaded with shape:\", source_image.shape)  # Get a ROI table roi_table = mip_ngff.table.get_table(\"FOV_ROI_table\") print(\"ROI table loaded with\", len(roi_table.rois), \"ROIs\")  # Find the DAPI channel dapi_idx = source_image.get_channel_idx(label=\"DAPI\")  # For each ROI in the table # - get the data from the source image # - calculate the Segmentation # - set the data in segmentation image max_label = 0 for roi in roi_table.rois:     print(f\" - Processing ROI {roi.infos.get('field_index')}\")     patch = source_image.get_array_from_roi(roi, c=dapi_idx)     segmentation = otsu_threshold_segmentation(patch, max_label)      # Add the max label of the previous segmentation to avoid overlapping labels     max_label = segmentation.max()      nuclei_image.set_array_from_roi(patch=segmentation, roi=roi)  # Consolidate the segmentation image nuclei_image.consolidate()  print(\"Segmentation image saved\") fig, axs = plt.subplots(2, 1, figsize=(10, 5)) axs[0].set_title(\"MIP\") axs[0].imshow(source_image.on_disk_array[0, 0], cmap=\"gray\") axs[1].set_title(\"Nuclei segmentation\") axs[1].imshow(nuclei_image.on_disk_array[0], cmap=rand_cmap, interpolation='nearest') for ax in axs:     ax.axis('off') plt.tight_layout() plt.show()  <pre>Source image loaded with shape: (1, 1, 2160, 5120)\nROI table loaded with 2 ROIs\n - Processing ROI None\n - Processing ROI None\n</pre> <pre>Segmentation image saved\n</pre> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"notebooks/processing/#processing","title":"Processing\u00b6","text":"<p>In this notebook we will implement a couple of mock image analysis workflows using <code>ngio</code>.</p>"},{"location":"notebooks/processing/#maximum-intensity-projection","title":"Maximum intensity projection\u00b6","text":"<p>In this workflow we will read a volumetric image and create a maximum intensity projection (MIP) along the z-axis.</p>"},{"location":"notebooks/processing/#step-1-create-a-ngff-image","title":"step 1: Create a ngff image\u00b6","text":"<p>For this example we will use the following publicly available image</p>"},{"location":"notebooks/processing/#step-2-create-a-new-ngff-image-to-store-the-mip","title":"step 2: Create a new ngff image to store the MIP\u00b6","text":""},{"location":"notebooks/processing/#step-3-run-the-workflow","title":"step 3: Run the workflow\u00b6","text":"<p>For each roi in the image, create a MIP and store it in the new image</p>"},{"location":"notebooks/processing/#step-4-consolidate-the-results-important","title":"step 4: Consolidate the results (!!! Important)\u00b6","text":"<p>In this we wrote the mip image to a single level of the image pyramid. To truly consolidate the results we would need to write the mip to all levels of the image pyramid. We can do this by calling the <code>.consolidate()</code> method on the image.</p>"},{"location":"notebooks/processing/#step-5-create-a-new-roi-table","title":"step 5: Create a new ROI table\u00b6","text":"<p>As a final step we will create a new ROI table that contains the MIPs as ROIs. Where we correct the <code>z</code> bounds of the ROIs to reflect the MIP.</p>"},{"location":"notebooks/processing/#image-segmentation","title":"Image segmentation\u00b6","text":"<p>Now we can use the MIP image to segment the image using a simple thresholding algorithm.</p>"},{"location":"notebooks/processing/#step-1-derive-a-new-label-image-from-the-mip-image","title":"step 1: Derive a new label image from the MIP image\u00b6","text":""},{"location":"notebooks/processing/#step-2-run-the-workflow","title":"step 2: Run the workflow\u00b6","text":""}]}
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