diff --git a/articles/advanced.html b/articles/advanced.html
index d62f7847..b61739eb 100644
--- a/articles/advanced.html
+++ b/articles/advanced.html
@@ -66,7 +66,7 @@ <h4 data-toc-skip class="author"></h4>
 Chelsea Rochman, Sebastian Primpke, Jennifer Lynch, Hannah Hapich,
 Hannah De Frond, Keenan Munno, Bridget O’Donnell</p>
             
-            <h4 data-toc-skip class="date">2024-10-23</h4>
+            <h4 data-toc-skip class="date">2024-11-01</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/wincowgerDEV/OpenSpecy-package/blob/main/vignettes/advanced.Rmd" class="external-link"><code>vignettes/advanced.Rmd</code></a></small>
       <div class="d-none name"><code>advanced.Rmd</code></div>
diff --git a/articles/app.html b/articles/app.html
index d8453441..7350b026 100644
--- a/articles/app.html
+++ b/articles/app.html
@@ -64,7 +64,7 @@ <h4 data-toc-skip class="author"></h4>
 <p>Win Cowger,
 Zacharias Steinmetz</p>
             
-            <h4 data-toc-skip class="date">2024-10-23</h4>
+            <h4 data-toc-skip class="date">2024-11-01</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/wincowgerDEV/OpenSpecy-package/blob/main/vignettes/app.Rmd" class="external-link"><code>vignettes/app.Rmd</code></a></small>
       <div class="d-none name"><code>app.Rmd</code></div>
diff --git a/articles/sop.html b/articles/sop.html
index 1df8032c..3614c779 100644
--- a/articles/sop.html
+++ b/articles/sop.html
@@ -66,7 +66,7 @@ <h4 data-toc-skip class="author"></h4>
 <p>Win Cowger,
 Zacharias Steinmetz, Rachel Kozloski, Aleksandra Karapetrova</p>
             
-            <h4 data-toc-skip class="date">2024-10-23</h4>
+            <h4 data-toc-skip class="date">2024-11-01</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/wincowgerDEV/OpenSpecy-package/blob/main/vignettes/sop.Rmd" class="external-link"><code>vignettes/sop.Rmd</code></a></small>
       <div class="d-none name"><code>sop.Rmd</code></div>
@@ -317,7 +317,7 @@ <h3 id="spectra">Spectra<a class="anchor" aria-label="anchor" href="#spectra"></
 <span><span class="co"># changing your underlying data</span></span>
 <span><span class="fu"><a href="../reference/interactive_plots.html">plotly_spec</a></span><span class="op">(</span><span class="va">scratch_OpenSpecy</span>, <span class="va">json_example</span><span class="op">)</span></span></code></pre></div>
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 </div>
 <div class="section level3">
 <h3 id="maps">Maps<a class="anchor" aria-label="anchor" href="#maps"></a>
@@ -343,7 +343,7 @@ <h3 id="maps">Maps<a class="anchor" aria-label="anchor" href="#maps"></a>
 <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/interactive_plots.html">interactive_plot</a></span><span class="op">(</span><span class="va">spectral_map</span>, select <span class="op">=</span> <span class="fl">100</span>, z <span class="op">=</span> <span class="va">spectral_map</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">x</span><span class="op">)</span></span></code></pre></div>
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 </div>
 <div class="section level2">
@@ -404,7 +404,7 @@ <h2 id="filtering-openspecy-objects">Filtering OpenSpecy Objects<a class="anchor
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 <span><span class="co">#&gt;                                                           session_id</span></span>
 <span><span class="co">#&gt;                                                               &lt;char&gt;</span></span>
-<span><span class="co">#&gt; 1: d1d242baa8fa46d0ce56c12a010b2596/3989fcb78d9a044d32dcac2aed4c06db</span></span>
+<span><span class="co">#&gt; 1: 5a698732989a836ef6bdea187fd42e2c/8965151f3f9c317aa66aaff74253501e</span></span>
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 <span><span class="co">#&gt; 1: 215a0fd7e0dc3b1001799214618ce6eb</span></span></code></pre></div>
@@ -441,7 +441,7 @@ <h2 id="filtering-openspecy-objects">Filtering OpenSpecy Objects<a class="anchor
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 <span><span class="co">#&gt;                                                           session_id</span></span>
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@@ -583,7 +583,7 @@ <h3 id="threshold-signal-noise">Threshold Signal-Noise<a class="anchor" aria-lab
 <span></span>
 <span><span class="fu"><a href="../reference/interactive_plots.html">heatmap_spec</a></span><span class="op">(</span><span class="va">spectral_map_p</span>, sn <span class="op">=</span> <span class="va">spectral_map_p</span><span class="op">$</span><span class="va">metadata</span><span class="op">$</span><span class="va">sig_noise</span>, min_sn <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span></code></pre></div>
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 <div class="section level3">
 <h3 id="intensity-adjustment">Intensity Adjustment<a class="anchor" aria-label="anchor" href="#intensity-adjustment"></a>
@@ -607,7 +607,7 @@ <h3 id="intensity-adjustment">Intensity Adjustment<a class="anchor" aria-label="
 <span></span>
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 </div>
 <div class="section level3">
 <h3 id="conforming">Conforming<a class="anchor" aria-label="anchor" href="#conforming"></a>
diff --git a/articles/spectragryph.html b/articles/spectragryph.html
index 7e420b9a..fcfa0d58 100644
--- a/articles/spectragryph.html
+++ b/articles/spectragryph.html
@@ -66,7 +66,7 @@ <h4 data-toc-skip class="author"></h4>
 Chelsea Rochman, Sebastian Primpke, Jennifer Lynch, Hannah Hapich,
 Hannah De Frond, Keenan Munno, Bridget O’Donnell</p>
             
-            <h4 data-toc-skip class="date">2024-10-23</h4>
+            <h4 data-toc-skip class="date">2024-11-01</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/wincowgerDEV/OpenSpecy-package/blob/main/vignettes/spectragryph.Rmd" class="external-link"><code>vignettes/spectragryph.Rmd</code></a></small>
       <div class="d-none name"><code>spectragryph.Rmd</code></div>
diff --git a/pkgdown.yml b/pkgdown.yml
index 5eafc044..5b1105b8 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
   app: app.html
   sop: sop.html
   spectragryph: spectragryph.html
-last_built: 2024-10-23T22:02Z
+last_built: 2024-11-01T22:59Z
 urls:
   reference: http://wincowger.com/OpenSpecy-package/reference
   article: http://wincowger.com/OpenSpecy-package/articles
diff --git a/reference/def_features.html b/reference/def_features.html
index 401aa4aa..4beef1e5 100644
--- a/reference/def_features.html
+++ b/reference/def_features.html
@@ -257,8 +257,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2: 0.000025000, 0.000025000    0_1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/64639e89e58518f6b3eb1b73cdddb2ec</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2: d1d242baa8fa46d0ce56c12a010b2596/64639e89e58518f6b3eb1b73cdddb2ec</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/3e1ed1ca8fc59e615a9d978fcca5e984</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2: 5a698732989a836ef6bdea187fd42e2c/3e1ed1ca8fc59e615a9d978fcca5e984</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id centroid_x centroid_y first_x first_y</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;      &lt;num&gt;      &lt;num&gt;   &lt;num&gt;   &lt;num&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: 215a0fd7e0dc3b1001799214618ce6eb          0          6       0       0</span>
diff --git a/reference/human_ts.html b/reference/human_ts.html
index 673c24be..2ebfee3f 100644
--- a/reference/human_ts.html
+++ b/reference/human_ts.html
@@ -74,7 +74,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">human_ts</span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "20241023-220220"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "20241101-225933"</span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/interactive_plots.html b/reference/interactive_plots.html
index cfb1bd7f..477bb345 100644
--- a/reference/interactive_plots.html
+++ b/reference/interactive_plots.html
@@ -223,14 +223,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">tiny_map</span> <span class="op">&lt;-</span> <span class="fu"><a href="read_ext.html">read_extdata</a></span><span class="op">(</span><span class="st">"CA_tiny_map.zip"</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="read_multi.html">read_zip</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">plotly_spec</span><span class="op">(</span><span class="va">raman_hdpe</span><span class="op">)</span></span></span>
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 <span class="r-in"><span><span class="fu"><a href="manage_spec.html">sample_spec</a></span><span class="op">(</span><span class="va">tiny_map</span>, size <span class="op">=</span> <span class="fl">12</span><span class="op">)</span> <span class="op">|&gt;</span></span></span>
 <span class="r-in"><span>  <span class="fu">interactive_plot</span><span class="op">(</span>select <span class="op">=</span> <span class="fl">2</span>, x2 <span class="op">=</span> <span class="va">raman_hdpe</span><span class="op">)</span></span></span>
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 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
diff --git a/reference/io_spec.html b/reference/io_spec.html
index 176929c3..7ae7fc66 100644
--- a/reference/io_spec.html
+++ b/reference/io_spec.html
@@ -222,7 +222,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 raman_hdpe.csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: cb06ce2846b119d932fb6696479a445b</span>
diff --git a/reference/manage_lib.html b/reference/manage_lib.html
index 52d8363c..1b540fa1 100644
--- a/reference/manage_lib.html
+++ b/reference/manage_lib.html
@@ -65,7 +65,7 @@ <h1>Manage spectral libraries</h1>
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">check_lib</span><span class="op">(</span></span>
-<span>  type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"derivative"</span>, <span class="st">"nobaseline"</span>, <span class="st">"raw"</span>, <span class="st">"mediod"</span>, <span class="st">"model"</span><span class="op">)</span>,</span>
+<span>  type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"derivative"</span>, <span class="st">"nobaseline"</span>, <span class="st">"raw"</span>, <span class="st">"medoid"</span>, <span class="st">"model"</span><span class="op">)</span>,</span>
 <span>  path <span class="op">=</span> <span class="st">"system"</span>,</span>
 <span>  condition <span class="op">=</span> <span class="st">"warning"</span></span>
 <span><span class="op">)</span></span>
@@ -79,7 +79,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span><span class="fu">load_lib</span><span class="op">(</span><span class="va">type</span>, path <span class="op">=</span> <span class="st">"system"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">rm_lib</span><span class="op">(</span></span>
-<span>  type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"derivative"</span>, <span class="st">"nobaseline"</span>, <span class="st">"raw"</span>, <span class="st">"mediod"</span>, <span class="st">"model"</span><span class="op">)</span>,</span>
+<span>  type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"derivative"</span>, <span class="st">"nobaseline"</span>, <span class="st">"raw"</span>, <span class="st">"medoid"</span>, <span class="st">"model"</span><span class="op">)</span>,</span>
 <span>  path <span class="op">=</span> <span class="st">"system"</span></span>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
diff --git a/reference/read_ext.html b/reference/read_ext.html
index f43e15d6..ebda59f4 100644
--- a/reference/read_ext.html
+++ b/reference/read_ext.html
@@ -231,7 +231,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 raman_hdpe.csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: cb06ce2846b119d932fb6696479a445b</span>
@@ -256,7 +256,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 raman_atacamit.spc CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: 2693c0b0295ce36c16b10e1e660241f2</span>
@@ -281,7 +281,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 ftir_ldpe_soil.asp CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: a33700bad0376ddab156980f473bac92</span>
@@ -307,7 +307,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 testdata2 - Copy (2).csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: cb06ce2846b119d932fb6696479a445b</span>
@@ -333,7 +333,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 testdata2 - Copy.csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: cb06ce2846b119d932fb6696479a445b</span>
@@ -359,7 +359,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 testdata2.csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: cb06ce2846b119d932fb6696479a445b</span>
diff --git a/reference/read_multi.html b/reference/read_multi.html
index 1373c776..adf802d9 100644
--- a/reference/read_multi.html
+++ b/reference/read_multi.html
@@ -114,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 raman_hdpe.csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: cb06ce2846b119d932fb6696479a445b</span>
@@ -139,7 +139,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 ftir_ldpe_soil.asp CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: a33700bad0376ddab156980f473bac92</span>
@@ -166,7 +166,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 testdata2 - Copy (2).csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
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@@ -192,7 +192,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 testdata2 - Copy.csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
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@@ -218,7 +218,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:     1     1 testdata2.csv CC BY-NC intensity</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                               &lt;char&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                              &lt;char&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1: cb06ce2846b119d932fb6696479a445b</span>
@@ -737,17 +737,17 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 208: 0.000025000, 0.000025000  12_15</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                             session_id</span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span>   1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   3: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   5: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   5: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  ---                                                                  </span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span> 205: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 204: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 205: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 206: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 207: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 208: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                               file_id</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                &lt;char&gt;</span>
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diff --git a/reference/read_opus.html b/reference/read_opus.html
index 7f8a8835..2116c8bc 100644
--- a/reference/read_opus.html
+++ b/reference/read_opus.html
@@ -174,7 +174,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:            27.9          2         NA         NA         NA     V1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           session_id</span>
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 <span class="r-out co"><span class="r-pr">#&gt;</span>                             file_id</span>
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diff --git a/search.json b/search.json
index c84c0e5f..b2938d3b 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
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Creative Commons disclaims liability damages resulting use fullest extent possible.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"using-creative-commons-public-licenses","dir":"","previous_headings":"","what":"Using Creative Commons Public Licenses","title":"Attribution 4.0 International","text":"Creative Commons public licenses provide standard set terms conditions creators rights holders may use share original works authorship material subject copyright certain rights specified public license . following considerations informational purposes , exhaustive, form part licenses. Considerations licensors: public licenses intended use authorized give public permission use material ways otherwise restricted copyright certain rights. licenses irrevocable. Licensors read understand terms conditions license choose applying . Licensors also secure rights necessary applying licenses public can reuse material expected. 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Although required licenses, encouraged respect requests reasonable. considerations public: wiki.creativecommons.org/Considerations_for_licensees","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"creative-commons-attribution-40-international-public-license","dir":"","previous_headings":"","what":"Creative Commons Attribution 4.0 International Public License","title":"Attribution 4.0 International","text":"exercising Licensed Rights (defined ), accept agree bound terms conditions Creative Commons Attribution 4.0 International Public License (“Public License”). extent Public License may interpreted contract, granted Licensed Rights consideration acceptance terms conditions, Licensor grants rights consideration benefits Licensor receives making Licensed Material available terms conditions.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"section-1--definitions","dir":"","previous_headings":"","what":"Section 1 – Definitions.","title":"Attribution 4.0 International","text":"Adapted Material means material subject Copyright Similar Rights derived based upon Licensed Material Licensed Material translated, altered, arranged, transformed, otherwise modified manner requiring permission Copyright Similar Rights held Licensor. purposes Public License, Licensed Material musical work, performance, sound recording, Adapted Material always produced Licensed Material synched timed relation moving image. 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Licensed Rights means rights granted subject terms conditions Public License, limited Copyright Similar Rights apply use Licensed Material Licensor authority license. Licensor means individual(s) entity(ies) granting rights Public License. Share means provide material public means process requires permission Licensed Rights, reproduction, public display, public performance, distribution, dissemination, communication, importation, make material available public including ways members public may access material place time individually chosen . Sui Generis Database Rights means rights copyright resulting Directive 96/9/EC European Parliament Council 11 March 1996 legal protection databases, amended /succeeded, well essentially equivalent rights anywhere world. means individual entity exercising Licensed Rights Public License. corresponding meaning.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"section-2--scope","dir":"","previous_headings":"","what":"Section 2 – Scope.","title":"Attribution 4.0 International","text":"License grant. Subject terms conditions Public License, Licensor hereby grants worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license exercise Licensed Rights Licensed Material : reproduce Share Licensed Material, whole part; produce, reproduce, Share Adapted Material. Exceptions Limitations. avoidance doubt, Exceptions Limitations apply use, Public License apply, need comply terms conditions. Term. term Public License specified Section 6(). 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Patent trademark rights licensed Public License. extent possible, Licensor waives right collect royalties exercise Licensed Rights, whether directly collecting society voluntary waivable statutory compulsory licensing scheme. cases Licensor expressly reserves right collect royalties.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"section-3--license-conditions","dir":"","previous_headings":"","what":"Section 3 – License Conditions.","title":"Attribution 4.0 International","text":"exercise Licensed Rights expressly made subject following conditions. Attribution. Share Licensed Material (including modified form), must: retain following supplied Licensor Licensed Material: identification creator(s) Licensed Material others designated receive attribution, reasonable manner requested Licensor (including pseudonym designated); copyright notice; notice refers Public License; notice refers disclaimer warranties; URI hyperlink Licensed Material extent reasonably practicable; indicate modified Licensed Material retain indication previous modifications; indicate Licensed Material licensed Public License, include text , URI hyperlink , Public License. may satisfy conditions Section 3()(1) reasonable manner based medium, means, context Share Licensed Material. example, may reasonable satisfy conditions providing URI hyperlink resource includes required information. requested Licensor, must remove information required Section 3()(1)() extent reasonably practicable. Share Adapted Material produce, Adapter’s License apply must prevent recipients Adapted Material complying Public License.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"section-4--sui-generis-database-rights","dir":"","previous_headings":"","what":"Section 4 – Sui Generis Database Rights.","title":"Attribution 4.0 International","text":"Licensed Rights include Sui Generis Database Rights apply use Licensed Material: avoidance doubt, Section 2()(1) grants right extract, reuse, reproduce, Share substantial portion contents database; include substantial portion database contents database Sui Generis Database Rights, database Sui Generis Database Rights (individual contents) Adapted Material; must comply conditions Section 3() Share substantial portion contents database. avoidance doubt, Section 4 supplements replace obligations Public License Licensed Rights include Copyright Similar Rights.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"section-5--disclaimer-of-warranties-and-limitation-of-liability","dir":"","previous_headings":"","what":"Section 5 – Disclaimer of Warranties and Limitation of Liability.","title":"Attribution 4.0 International","text":"UNLESS OTHERWISE SEPARATELY UNDERTAKEN LICENSOR, EXTENT POSSIBLE, LICENSOR OFFERS LICENSED MATERIAL --AVAILABLE, MAKES REPRESENTATIONS WARRANTIES KIND CONCERNING LICENSED MATERIAL, WHETHER EXPRESS, IMPLIED, STATUTORY, . 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However, fail comply Public License, rights Public License terminate automatically. right use Licensed Material terminated Section 6(), reinstates: automatically date violation cured, provided cured within 30 days discovery violation; upon express reinstatement Licensor. avoidance doubt, Section 6(b) affect right Licensor may seek remedies violations Public License. avoidance doubt, Licensor may also offer Licensed Material separate terms conditions stop distributing Licensed Material time; however, terminate Public License. Sections 1, 5, 6, 7, 8 survive termination Public License.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"section-7--other-terms-and-conditions","dir":"","previous_headings":"","what":"Section 7 – Other Terms and Conditions.","title":"Attribution 4.0 International","text":"Licensor shall bound additional different terms conditions communicated unless expressly agreed. arrangements, understandings, agreements regarding Licensed Material stated herein separate independent terms conditions Public License.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/LICENSE.html","id":"section-8--interpretation","dir":"","previous_headings":"","what":"Section 8 – Interpretation.","title":"Attribution 4.0 International","text":"avoidance doubt, Public License , shall interpreted , reduce, limit, restrict, impose conditions use Licensed Material lawfully made without permission Public License. extent possible, provision Public License deemed unenforceable, shall automatically reformed minimum extent necessary make enforceable. provision reformed, shall severed Public License without affecting enforceability remaining terms conditions. term condition Public License waived failure comply consented unless expressly agreed Licensor. Nothing Public License constitutes may interpreted limitation upon, waiver , privileges immunities apply Licensor , including legal processes jurisdiction authority. Creative Commons party public licenses. Notwithstanding, Creative Commons may elect apply one public licenses material publishes instances considered “Licensor.” text Creative Commons public licenses dedicated public domain CC0 Public Domain Dedication. Except limited purpose indicating material shared Creative Commons public license otherwise permitted Creative Commons policies published creativecommons.org/policies, Creative Commons authorize use trademark “Creative Commons” trademark logo Creative Commons without prior written consent including, without limitation, connection unauthorized modifications public licenses arrangements, understandings, agreements concerning use licensed material. avoidance doubt, paragraph form part public licenses. Creative Commons may contacted creativecommons.org.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"how-to-interpret-the-reported-matches","dir":"Articles","previous_headings":"","what":"How to Interpret the Reported Matches","title":"Spectroscopy Advanced","text":"several important things consider interpreting spectral match including library source, Pearson’s r, metrics.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"the-library-source","dir":"Articles","previous_headings":"","what":"The Library Source","title":"Spectroscopy Advanced","text":"click spectrum, metadata Open Specy source displayed metadata window matches table. library different methodologies used develop . useful read library sources literature came . E.g. Chabuka et al. 2020 focuses weathered plastics, matching may suggest spectrum weathered polymer. Primpke et al. 2018 spectral range 2000, polymers may difficult differentiate . Make sure cite libraries use search publish results. authors kind enough make data open access used Open Specy return favor citing .","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"pearsons-r","dir":"Articles","previous_headings":"","what":"Pearson’s r","title":"Spectroscopy Advanced","text":"Correlation values used identify closest matches available current Open Specy spectral libraries improve material identification reduce sample processing times. Pearson’s r values range 0 - 1 0 completely different spectrum 1 exact match. general guidelines observed using Open Specy. matches > ~0.6 material may require additional processing may exist Open Specy library. Correlation values metric use assess spectra’s match material library, matches need make sense.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"things-to-consider-beyond-correlation","dir":"Articles","previous_headings":"","what":"Things to Consider beyond Correlation","title":"Spectroscopy Advanced","text":"Peak position height similarities important correlation need assessed manually. Peak position correlates specific bond types. Peak height correlates concentration compound. Therefore, peak height peak position match closely possible matched spectrum. peaks exist spectra trying interpret exist match, may additional materials identify. case, restrict processing range just unidentified peak try identify additional component (see also https://www.compoundchem.com/2015/02/05/irspectroscopy/). Also, check match metadata see match makes sense. Example: single fiber “cotton blend” since fibers make rest blend. Example: Cellophane degrade fibers, match fiber cellophane wouldn’t make sense. Example: analyzing particle room temperature, matched material liquid room temperature. material may component particle whole particle.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"how-specific-do-you-need-to-be-in-the-material-type-of-the-match","dir":"Articles","previous_headings":"","what":"How Specific Do You Need to be in the Material Type of the Match?","title":"Spectroscopy Advanced","text":"can choose specific classify substance (e.g. polyester, cellophane) general (e.g. synthetic, semi-synthetic, natural, etc.). choice depends research question. Using general groups can speed analysis time decrease information interpretation. identify materials generally, can often clump identities provided Open Specy suit needs. example, matches “polyester” “polypropylene” clumped category “plastic”.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"how-to-differentiate-between-similar-spectra","dir":"Articles","previous_headings":"","what":"How to Differentiate Between Similar Spectra?","title":"Spectroscopy Advanced","text":"One common challenge differentiating LDPE HDPE. , even low resolution instrument (MacroRAM, 2 cm-1 pixel-1), can still see differences. wide view, low, medium, high density PE samples look relatively similar (figures courtesy Bridget O'Donnell, Horiba Scientific):  , closer look 1450 cm-1 band reveals clear differences:  overlay , start see differences spectral regions :  , question , deal samples similar subtle differences? Usually, researchers use MVA techniques ’ve collected multiple reference spectra known samples (LDPE HDPE case). can develop models apply distinguish different types PE. reference database like Open Specy, complicated fact researchers measuring samples different instruments correspondingly different spectral responses spectral resolutions. makes even difficult accurately match definitively LDPE HDPE opposed generic ‘PE’. One possibility place emphasis (computational perspective) bands show difference (triplet 1450 cm-1) restricting range used match Open Specy. , much simpler option just match PE hit generic ‘PE’ specifically HDPE LDPE. Another challenge differentiating types nylons. , Raman pretty easy time distinguishing nylons. spectra recorded series nylons differences much distinguishable compared PE results (nylon 6, 6-6, 6-9, 6-10, 6-12 top bottom):  differences even pronounced overlay spectra:","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"what-to-do-when-matches-arent-making-sense","dir":"Articles","previous_headings":"","what":"What to Do When Matches Aren’t Making Sense","title":"Spectroscopy Advanced","text":"Double check baseline correction smoothing parameters result best processing data. Try reprocessing spectrum, limit specific peak regions higher signal noise ratio. Restrict spectral range include exclude questionable peaks peaks present previous matches. Restrict spectral range exclude things like CO2 (2200 cm-1) H2O (~1600 cm-1) spikes IR spectrum. nothing works determine quality match, may need measure spectrum material use another spectral analysis tool.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Spectroscopy Advanced","text":"Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy.” Applied Spectroscopy, 74(9), 1167–1183. doi: 10.1177/0003702820923993. Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi: 10.1177/0003702820929064. Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123. Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design Automated Analysis Microplastic Samples Based Fourier Transform Infrared (FTIR) Spectroscopy.” Analytical Bioanalytical Chemistry, 410(21), 5131–5141. doi: 10.1007/s00216-018-1156-x. Renner G, Schmidt TC, Schram J (2017). “New Chemometric Approach Automatic Identification Microplastics Environmental Compartments Based FT-IR Spectroscopy.” Analytical Chemistry, 89(22), 12045–12053. doi: 10.1021/acs.analchem.7b02472. Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639. Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi: 10.1366/000370207782597003.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"document-overview","dir":"Articles","previous_headings":"","what":"Document Overview","title":"Open Specy App Tutorial","text":"document outlines common workflow using Open Specy app highlights topics users often requesting tutorial . document followed sequentially beginning end, user better understanding every procedure involved using Open Specy R app tool interpreting spectra. takes approximately 45 minutes read follow along standard operating procedure first time. Afterward, knowledgeable users able thoroughly analyze spectra average speed 1 min-1 faster new batch automated procedures. looking documentation R package please see package vignette","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"running-the-app","dir":"Articles","previous_headings":"","what":"Running the App","title":"Open Specy App Tutorial","text":"get started Open Specy user interface, access https://openanalysis.org/openspecy/ start Shiny GUI directly computer R. using package, just need read library run command run_app().","code":"library(OpenSpecy) run_app()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"reading-data","dir":"Articles","previous_headings":"","what":"Reading Data","title":"Open Specy App Tutorial","text":"app open, data, click Browse top left hand corner Analyze Spectra tab.  uploading, indicate like share uploaded data using slider. selected, data uploaded tool automatically shared CC-4.0 license available researchers ventures use improve spectral analysis, build machine learning tools, etc. users may choose share need keep data private. switched , none uploaded data stored shared Open Specy.  Open Specy allows upload native Open Specy .y()ml, .json, .rds files. addition, .csv, .asp, .jdx, .0, .spa, .spc, .zip files can imported. .zip files can either contain multiple files individual spectra non-zip formats can contain .hdr .dat file form ENVI file spectral map. Open Specy .csv files always load correctly file types still development, though time files work perfectly. uploading .csv file, label column wavenumbers wavenumber name column intensities intensity. Wavenumber units must cm-1. columns used software. Always keep copy original file alteration preserve metadata raw data records. Sample data raman_hdpe best practice cross check files proprietary software came Open Specy use Open Specy. Due complexity proprietary file types, haven’t able make fully compatible yet. file working, please contact administrator share file can work integrating . specific steps converting instrument’s native files .csv can found software manual can check Spectragryph, supports many spectral file conversions see Spectragryph Tutorial. don’t data, can use test dataset.  .csv file HDPE Raman spectrum download computer. file can also used template formatting .csv data Open Specy accepted format.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"spectra","dir":"Articles","previous_headings":"Visualization","what":"Spectra","title":"Open Specy App Tutorial","text":"app spectral data uploaded, appear main window. plot selectable, zoomable, provides information hover. can also save .png file plot view using camera icon top right hover plot.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"maps","dir":"Articles","previous_headings":"Visualization","what":"Maps","title":"Open Specy App Tutorial","text":"Spectral maps can also visualized overlaid spectra addition spatial information can plotted heatmap. similar plot appear app upload multiple spectra spectral map. important note multiple spectra uploaded batch prescribed x y coordinates, can helpful visualizing summary statistics navigating vast amounts data shouldn’t confused data actually spatial coordinates. Hovering map reveal information signal noise correlation values. Clicking map provide selected spectrum underneath. app, colors heatmap either signal noise spectral identifications depending whether identification turned . Pixels black spectra pass signal-noise threshold /correlation threshold.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"processing","dir":"Articles","previous_headings":"","what":"Processing","title":"Open Specy App Tutorial","text":"goal processing increase signal noise ratio (S/N) without distorting shape relative size peaks. uploading data, can process data using intensity adjustment, baseline subtraction, smoothing, flattening, range selection save processed data. process button selected default processing initiate. absolute derivative transformation, kind magic, something similar smoothing, baseline subtraction, intensity correction simultaneously really quickly. clicking processing see spectrum update processed spectra.  view raw data , just deselect processing. Toggling processing can help make sure spectra processed correctly.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"threshold-signal-noise","dir":"Articles","previous_headings":"Processing","what":"Threshold Signal-Noise","title":"Open Specy App Tutorial","text":"Considering whether enough signal analyze spectra important. Classical spectroscopy recommend highest peak least 10 times baseline processed spectra begin analysis. spectra threshold, may want consider recollecting . practice, rarely able collect spectra good quality often use 5. Signal Noise technique searches spectra high low regions conducts division derive signal noise ratio. Signal Times Noise multiplies mean signal standard deviation signal Total Signal sums intensities. latter can really useful thresholding spectral maps identify particles discuss later.  analyzing spectra batch, recommend looking heatmap optimizing percent spectra signal noise threshold determine correct settings instead looking spectra individually. Good Signal tells percent data signal threshold.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"intensity-adjustment","dir":"Articles","previous_headings":"Processing","what":"Intensity Adjustment","title":"Open Specy App Tutorial","text":"Open Specy assumes intensity units absorbance units Open Specy can adjust reflectance transmittance spectra absorbance units using selection. transmittance adjustment uses log101/T\\log_{10} 1/T calculation correct system particle characteristics. reflectance adjustment use Kubelka-Munk equation (1−R)22R\\frac{(1-R)^2}{2R}. none selected, Open Specy assumes uploaded data absorbance spectrum apply adjustment.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"conforming","dir":"Articles","previous_headings":"Processing","what":"Conforming","title":"Open Specy App Tutorial","text":"Conforming spectra essential comparing reference library can useful summarizing data don’t need highly resolved spectrally. app, conforming happens behind scenes without user input make sure spectra user uploads library spectra compatible. set spectral resolution 8 tends pretty good lot applications 4 8 commonly used wavenumber resolutions.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"smoothing","dir":"Articles","previous_headings":"Processing","what":"Smoothing","title":"Open Specy App Tutorial","text":"value slider polynomial order Savitzky-Golay (SG) filter. Higher numbers lead wiggly fits thus less smooth, lower numbers lead smooth fits. SG filter fit moving window 11 data points default center point window replaced polynomial estimate. Larger windows produce smoother fits. derivative order set 1 default transforms spectra first derivative. zero order derivative derivative transformation. smoothing done well, peak shapes relative heights change. absolute value primarily useful first derivative spectra absolute value results absorbance-like spectrum set default.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"baseline-correction","dir":"Articles","previous_headings":"Processing","what":"Baseline Correction","title":"Open Specy App Tutorial","text":"goal baseline correction get non-peak regions spectra zero absorbance. higher polynomial order, wiggly fit baseline. baseline wiggly, wiggly fit remove peaks desired. baseline correction algorithm used Open Specy called “iModPolyfit” (Zhao et al. 2007). algorithm iteratively fits polynomial equations specified order whole spectrum. first fit iteration, peak regions often baseline fit. data peak region removed fit make sure baseline less likely fit peaks. iterative fitting terminates difference new previous fit small. example good baseline fit . OpenSpecy , notice app longer supports manual baseline correction, hard choice just didn’t make sense keep now moving toward high throughput automated methods. still exist R function though.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"range-selection","dir":"Articles","previous_headings":"Processing","what":"Range Selection","title":"Open Specy App Tutorial","text":"Sometimes instrument operates high noise ends spectrum , baseline fit produces distortions, regions interest analysis. Range selection accomplishes goals. look signal noise ratio specific instrument wavelength determine wavelength ranges use. Distortions due baseline fit can assessed looking process spectra. Additionally, can restrict range examine single peak subset peaks interests. maximum minimum wavenumbers specify range keep app.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"flattening-ranges","dir":"Articles","previous_headings":"Processing","what":"Flattening Ranges","title":"Open Specy App Tutorial","text":"Sometimes peaks really shouldn’t spectra can distort interpretation spectra don’t necessarily want remove regions analysis believe regions flat instead peak. One way deal replace peak values mean values around peak. purpose Flatten Range. default set flatten CO2 region FTIR spectra region often needs flattened atmospheric artifacts occur spectra.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"min-max-normalization","dir":"Articles","previous_headings":"Processing","what":"Min-Max Normalization","title":"Open Specy App Tutorial","text":"Min-Max normalization used throughout app one options user can specify. Often regard spectral intensities arbitrary min-max normalization allows us view spectra scale without drastically distorting shapes relative peak intensities. app, primarily used plotting processing step correlation analysis.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"downloading-processed-data","dir":"Articles","previous_headings":"Processing","what":"Downloading Processed Data","title":"Open Specy App Tutorial","text":"can download csv file processed data using Spectra option download. download version spectra whatever processing options selected time.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"identifying-spectra","dir":"Articles","previous_headings":"","what":"Identifying Spectra","title":"Open Specy App Tutorial","text":"uploading data processing (desired) can now identify spectrum. identify spectrum go Identification box. Pro tip: select Identification without uploading data app, ’ll able explore library .","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"reading-libraries","dir":"Articles","previous_headings":"Identifying Spectra","what":"Reading Libraries","title":"Open Specy App Tutorial","text":"options define strategy identification. ID Library inform library used. (default) search FTIR Raman libraries. Deriv search derivative transformed library. Baseline search baseline corrected library. line choose process spectra. Cor options use simple Pearson correlation search algorithm. AI currently experimental uses either multinomial model correlation mediod spectra library. Correlation thresholding set minimum value matching use ‘positive identification’ black pixels spectral map view lower threshold.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"matches","dir":"Articles","previous_headings":"Identifying Spectra","what":"Matches","title":"Open Specy App Tutorial","text":"Top matches app can assessed clicking cog right hand corner Spectra box. open side window matches sorted least similar. Clicking rows table add selected match spectra viewer. Using table’s filter options, can restrict range Pearson's r values search specific material types.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"selection-metadata","dir":"Articles","previous_headings":"Identifying Spectra","what":"Selection Metadata","title":"Open Specy App Tutorial","text":"Whatever match selected match table may additional metadata . metadata displayed plot. metadata may assist interpreting spectra. example, spectra metadata says liquid analyzing solid particle, spectrum may best match.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"match-plot","dir":"Articles","previous_headings":"Identifying Spectra","what":"Match Plot","title":"Open Specy App Tutorial","text":"plot dynamically updated selecting matches match table. red spectrum spectrum selected reference library white spectrum spectrum trying identify. Whenever new dataset uploaded, plot data table tab updated. plots can saved .png clicking camera button top plot.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"sharing-reference-data","dir":"Articles","previous_headings":"Identifying Spectra","what":"Sharing Reference Data","title":"Open Specy App Tutorial","text":"reference data AI models think useful sharing spectroscopy community OpenSpecy please contact website administrator discuss options collaborating.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"download-top-matches","dir":"Articles","previous_headings":"Identifying Spectra","what":"Download Top Matches","title":"Open Specy App Tutorial","text":"download top matches associated metadata, use top matches download option.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"characterizing-particles","dir":"Articles","previous_headings":"","what":"Characterizing Particles","title":"Open Specy App Tutorial","text":"Thresholding works shouldn’t used single particles. can threshold spectral maps using signal-noise values /correlation threshold. Whatever selection active inform spectra thresholded. get thresholded coincidence see spectral map. Pro tip: file gets downloaded OpenSpecy median spectra thresholded particles. can upload back app analyze independently.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"additional-app-features","dir":"Articles","previous_headings":"Characterizing Particles","what":"Additional App Features","title":"Open Specy App Tutorial","text":"Accessibility extremely important us making strives improve accessibility Open Specy spectroscopists. Please reach ideas improvement. added Google translate plugin pages app can easily translate app. know languages fully supported continue try improve translations.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Open Specy App Tutorial","text":"Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy.” Applied Spectroscopy, 74(9), 1167–1183. doi: 10.1177/0003702820923993. Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi: 10.1177/0003702820929064. Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123. Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design Automated Analysis Microplastic Samples Based Fourier Transform Infrared (FTIR) Spectroscopy.” Analytical Bioanalytical Chemistry, 410(21), 5131–5141. doi: 10.1007/s00216-018-1156-x. Renner G, Schmidt TC, Schram J (2017). “New Chemometric Approach Automatic Identification Microplastics Environmental Compartments Based FT-IR Spectroscopy.” Analytical Chemistry, 89(22), 12045–12053. doi: 10.1021/acs.analchem.7b02472. Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639. Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi: 10.1366/000370207782597003.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"document-overview","dir":"Articles","previous_headings":"","what":"Document Overview","title":"Open Specy Package Tutorial","text":"document outlines common workflow using Open Specy package highlights topics users often requesting tutorial . document followed sequentially beginning end, user better understanding every procedure involved using Open Specy R package tool interpreting spectra. takes approximately 45 minutes read follow along standard operating procedure first time. Afterward, knowledgeable users able thoroughly analyze spectra average speed 1 min-1 faster new batch automated procedures. Open Specy R package backbone Shiny app. choice start , use regular basis. tutorial talk R functions can use programatically analyze spectra. looking tutorial use app see tutorial.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Open Specy Package Tutorial","text":"installing R package, just need read library.","code":"library(OpenSpecy)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"running-the-app","dir":"Articles","previous_headings":"","what":"Running the App","title":"Open Specy Package Tutorial","text":"get started Open Specy user interface, access https://openanalysis.org/openspecy/ start Shiny GUI directly computer R. looking tutorial use app see tutorial.","code":"run_app()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"read-data","dir":"Articles","previous_headings":"","what":"Read Data","title":"Open Specy Package Tutorial","text":"following line code read data using package interprets reading function use based file extension. file type specific functions also read spectral data accordingly particular format mind. Open Specy allows upload native Open Specy .y()ml, .json, .rds files. addition, .csv, .asp, .jdx, .0, .spa, .spc, .zip files can imported. .zip files can either contain multiple files individual spectra non-zip formats can contain .hdr .dat file form ENVI file spectral map. Open Specy .csv files always load correctly file types still development, though time files work perfectly. uploading .csv file, label column wavenumbers wavenumber name column intensities intensity. Wavenumber units must cm-1. columns used software. Always keep copy original file alteration preserve metadata raw data records. best practice cross check files proprietary software came Open Specy use Open Specy. Due complexity proprietary file types, haven’t able make fully compatible yet. file working, please contact administrator share file can work integrating . specific steps converting instrument’s native files .csv can found software manual can check Spectragryph, supports many spectral file conversions. instructions, see Spectragryph Tutorial. don’t data, can use test dataset. also many onboard files can call test different formats: notice now R package reads files object class OpenSpecy. class created high throughput spectral analysis now also preserves spectral metadata. can even create scratch ’d like. Open Specy objects lists three components, wavenumber vector wavenumber values spectra corresponds rows spectra data.table column set spectral intensities. metadata data.table holds additional information spectra. row corresponds column spectra. generic functions built inspecting spectra:","code":"read_any(\"path/to/your/data\") read_text(\".csv\") read_asp(\".asp\") read_opus(\".0\") data(\"raman_hdpe\") spectral_map <- read_extdata(\"CA_tiny_map.zip\") |>    read_any() # preserves some metadata asp_example <- read_extdata(\"ftir_ldpe_soil.asp\") |>    read_any()  ps_example <- read_extdata(\"ftir_ps.0\") |>    read_any() # preserves some metadata csv_example <- read_extdata(\"raman_hdpe.csv\") |>    read_any() json_example <- read_extdata(\"raman_hdpe.json\") |>    read_any() # read in exactly as an OpenSpecy object scratch_OpenSpecy <- as_OpenSpecy(x = seq(1000,2000, by = 5),                                    spectra =  data.frame(runif(n = 201)),                                    metadata = list(file_name = \"fake_noise\")) # Access the wavenumbers scratch_OpenSpecy$wavenumber #>   [1] 1000 1005 1010 1015 1020 1025 1030 1035 1040 1045 1050 1055 1060 1065 1070 #>  [16] 1075 1080 1085 1090 1095 1100 1105 1110 1115 1120 1125 1130 1135 1140 1145 #>  [31] 1150 1155 1160 1165 1170 1175 1180 1185 1190 1195 1200 1205 1210 1215 1220 #>  [46] 1225 1230 1235 1240 1245 1250 1255 1260 1265 1270 1275 1280 1285 1290 1295 #>  [61] 1300 1305 1310 1315 1320 1325 1330 1335 1340 1345 1350 1355 1360 1365 1370 #>  [76] 1375 1380 1385 1390 1395 1400 1405 1410 1415 1420 1425 1430 1435 1440 1445 #>  [91] 1450 1455 1460 1465 1470 1475 1480 1485 1490 1495 1500 1505 1510 1515 1520 #> [106] 1525 1530 1535 1540 1545 1550 1555 1560 1565 1570 1575 1580 1585 1590 1595 #> [121] 1600 1605 1610 1615 1620 1625 1630 1635 1640 1645 1650 1655 1660 1665 1670 #> [136] 1675 1680 1685 1690 1695 1700 1705 1710 1715 1720 1725 1730 1735 1740 1745 #> [151] 1750 1755 1760 1765 1770 1775 1780 1785 1790 1795 1800 1805 1810 1815 1820 #> [166] 1825 1830 1835 1840 1845 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895 #> [181] 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 #> [196] 1975 1980 1985 1990 1995 2000 # Access the spectra scratch_OpenSpecy$spectra #>      runif.n...201. #>               <num> #>   1:    0.080750138 #>   2:    0.834333037 #>   3:    0.600760886 #>   4:    0.157208442 #>   5:    0.007399441 #>  ---                #> 197:    0.906051578 #> 198:    0.772730364 #> 199:    0.383370670 #> 200:    0.999652457 #> 201:    0.349299049 # Access the metadata scratch_OpenSpecy$metadata #>        x     y  file_name         col_id                          file_id #>    <int> <int>     <char>         <char>                           <char> #> 1:     1     1 fake_noise runif.n...201. 74944ff090b28e2fe0504f0e85d29ded # Performs checks to ensure that OpenSpecy objects are adhering to our standards; # returns TRUE if it passes.  check_OpenSpecy(scratch_OpenSpecy) #> [1] TRUE  # Checks only the object type to make sure it has OpenSpecy type is_OpenSpecy(scratch_OpenSpecy) #> [1] TRUE print(scratch_OpenSpecy) # shows the raw object #>      wavenumber runif.n...201. #>           <num>          <num> #>   1:       1000    0.080750138 #>   2:       1005    0.834333037 #>   3:       1010    0.600760886 #>   4:       1015    0.157208442 #>   5:       1020    0.007399441 #>  ---                           #> 197:       1980    0.906051578 #> 198:       1985    0.772730364 #> 199:       1990    0.383370670 #> 200:       1995    0.999652457 #> 201:       2000    0.349299049 #>  #> $metadata #>        x     y  file_name         col_id                          file_id #>    <int> <int>     <char>         <char>                           <char> #> 1:     1     1 fake_noise runif.n...201. 74944ff090b28e2fe0504f0e85d29ded summary(scratch_OpenSpecy) # summarizes the contents of the spectra #> $wavenumber #>  Length Min. Max.     Res. #>     201 1000 2000 4.975124 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1    0.004496308      0.9996525 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"         \"y\"         \"file_name\" \"col_id\"    \"file_id\" head(scratch_OpenSpecy) # shows the top wavenumbers and intensities #>    wavenumber runif.n...201. #>         <num>          <num> #> 1:       1000    0.080750138 #> 2:       1005    0.834333037 #> 3:       1010    0.600760886 #> 4:       1015    0.157208442 #> 5:       1020    0.007399441 #> 6:       1025    0.466393497"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"save-data","dir":"Articles","previous_headings":"","what":"Save Data","title":"Open Specy Package Tutorial","text":"Open Specy objects can saved accurately .rds, .yml, .json files. .rds reproducible native R file format floating point errors can happen .json .yml.","code":"write_spec(scratch_OpenSpecy, \"test_scratch_OpenSpecy.yml\", digits = 5) write_spec(scratch_OpenSpecy, \"test_scratch_OpenSpecy.json\", digits = 5)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"format-conversions","dir":"Articles","previous_headings":"","what":"Format Conversions","title":"Open Specy Package Tutorial","text":"Another great spectroscopy R package hyperSpec. actually depend functions several . currently making awesome new features want integrate well packages can easily used together. created as_hyperSpec function made sure as_OpenSpecy can convert hyperSpec objects.","code":"hyperspecy <- as_hyperSpec(scratch_OpenSpecy)"},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"spectra","dir":"Articles","previous_headings":"Visualization","what":"Spectra","title":"Open Specy Package Tutorial","text":"R, two ways visualize spectra, one quick efficient interactive. example quick efficient plotting.  example interactive plot. can plot two different datasets simultaneously compare.","code":"plot(scratch_OpenSpecy) # quick and efficient # This will min-max normalize your data even if it isn't already but are not # changing your underlying data plotly_spec(scratch_OpenSpecy, json_example)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"maps","dir":"Articles","previous_headings":"Visualization","what":"Maps","title":"Open Specy Package Tutorial","text":"Spectral maps can also visualized overlaid spectra addition spatial information can plotted heatmap. important note multiple spectra uploaded batch prescribed x y coordinates, can helpful visualizing summary statistics navigating vast amounts data shouldn’t confused data actually spatial coordinates. R user can control values form colors heatmap specifying z, handy put information want metadata reason. example just shows x values spectra. interactive plot heatmap spectra overlayed can generated interactive_plot() function. user can hover heatmap identify next row id interested update value select see spectrum.","code":"heatmap_spec(spectral_map,              z = spectral_map$metadata$x) interactive_plot(spectral_map, select = 100, z = spectral_map$metadata$x)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"combining-openspecy-objects","dir":"Articles","previous_headings":"","what":"Combining OpenSpecy Objects","title":"Open Specy Package Tutorial","text":"Sometimes several OpenSpecy objects want combine one. easiest way spectra exact format series wavenumbers. default settings c_spec assume case. different wavenumber ranges spectra want combine, can set range = \"common\" res equal wavenumber resolution want function collapse spectra whatever common range using linear interpolation.","code":"c_spec(list(asp_example, ps_example), range = \"common\", res = 8) |>    plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"filtering-openspecy-objects","dir":"Articles","previous_headings":"","what":"Filtering OpenSpecy Objects","title":"Open Specy Package Tutorial","text":"OpenSpecy objects can number spectra . create OpenSpecy subset spectra Open Specy object can use filter_spec function. Filtering allowed index number, name, using logical vector. Filtering update spectra metadata items OpenSpecy wavenumber.","code":"# Extract the 150th spectrum.  filter_spec(spectral_map, 150) #>      wavenumber         9_5 #>           <num>       <num> #>   1:     717.42  0.06159078 #>   2:     725.13  0.01614512 #>   3:     732.85 -0.02511934 #>   4:     740.56 -0.05653130 #>   5:     748.28 -0.07012609 #>  ---                        #> 423:    3972.81 -0.23276733 #> 424:    3980.52 -0.22747667 #> 425:    3988.24 -0.23046409 #> 426:    3995.95 -0.23281474 #> 427:    4003.67 -0.22850421 #>  #> $metadata #>        y     x       file_name  license #>    <num> <num>          <char>   <char> #> 1:     9     5 CA small UF.dat CC BY-NC #>                                                     description samples lines #>                                                          <char>   <num> <num> #> 1: Pixel 15, XPos=12549.72, YPos= 589.82, X=12362.22, Y= 614.82      16    13 #>    bands header offset data type interleave                  z plot titles #>    <num>         <num>     <num>     <char>                         <char> #> 1:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>                  pixel size col_id #>                      <char> <char> #> 1: 0.000025000, 0.000025000    9_5 #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/3989fcb78d9a044d32dcac2aed4c06db #>                             file_id #>                              <char> #> 1: 215a0fd7e0dc3b1001799214618ce6eb # Extract the spectrum with column name \"8_5\".  filter_spec(spectral_map, \"8_5\") |>    print() #>      wavenumber         8_5 #>           <num>       <num> #>   1:     717.42  0.15336494 #>   2:     725.13  0.10891031 #>   3:     732.85  0.06503499 #>   4:     740.56  0.03334329 #>   5:     748.28  0.01523896 #>  ---                        #> 423:    3972.81 -0.28495014 #> 424:    3980.52 -0.28366613 #> 425:    3988.24 -0.28505880 #> 426:    3995.95 -0.28465354 #> 427:    4003.67 -0.27912781 #>  #> $metadata #>        y     x       file_name  license #>    <num> <num>          <char>   <char> #> 1:     8     5 CA small UF.dat CC BY-NC #>                                                     description samples lines #>                                                          <char>   <num> <num> #> 1: Pixel 15, XPos=12549.72, YPos= 589.82, X=12362.22, Y= 614.82      16    13 #>    bands header offset data type interleave                  z plot titles #>    <num>         <num>     <num>     <char>                         <char> #> 1:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>                  pixel size col_id #>                      <char> <char> #> 1: 0.000025000, 0.000025000    8_5 #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/3989fcb78d9a044d32dcac2aed4c06db #>                             file_id #>                              <char> #> 1: 215a0fd7e0dc3b1001799214618ce6eb # Extract the spectra with a logical argument based on metadata filter_spec(spectral_map, spectral_map$metadata$y == 1) |>   plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"sampling-openspecy-objects","dir":"Articles","previous_headings":"","what":"Sampling OpenSpecy Objects","title":"Open Specy Package Tutorial","text":"Sometimes large suite examples spectra material want reduce number spectra run analysis simplicity running simulations procedures require first sample spectra contained OpenSpecy objects analysis. sample_spec function serves purpose.","code":"sample_spec(spectral_map, size = 5) |>   plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"processing","dir":"Articles","previous_headings":"","what":"Processing","title":"Open Specy Package Tutorial","text":"goal processing increase signal noise ratio (S/N) spectra without distorting shape, position, relative size peaks. loading data, can process data using intensity adjustment, baseline subtraction, smoothing, flattening, range selection. default settings absolute derivative transformation, kind magic, something similar smoothing, baseline subtraction, intensity correction simultaneously really quickly. process_spec() function monolithic function processing procedures optimized default result high signal noise cases, app. can compare processed unprocessed data overlay plot. want people use process_spec() function processing operations. processing functions can tuned using parameters single function set defaults nested processing functions way provides typically quality results. However, recognize nesting functions order operations can useful users control can also use individual functions operation ’d like. See explanations processing sub-function .","code":"processed <- process_spec(raman_hdpe,                           active = TRUE,                           adj_intens = FALSE,                           adj_intens_args = list(type = \"none\"),                           conform_spec = TRUE,                           conform_spec_args = list(range = NULL, res = 8,                                                    type = \"interp\"),                           restrict_range = FALSE,                           restrict_range_args = list(min = 0, max = 6000),                           flatten_range = FALSE,                           flatten_range_args = list(min = 2200, max = 2420),                           subtr_baseline = FALSE,                           subtr_baseline_args = list(type = \"polynomial\",                                                      degree = 8, raw = FALSE,                                                      baseline = NULL),                           smooth_intens = TRUE,                           smooth_intens_args = list(polynomial = 3, window = 11,                                                     derivative = 1, abs = TRUE),                           make_rel = TRUE) summary(processed) #> $wavenumber #>  Length Min. Max.     Res. #>     362  304 3192 7.977901 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1              0              1 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"                 \"y\"                 \"user_name\"         #> [4] \"spectrum_type\"     \"spectrum_identity\" \"organization\"      #> [7] \"license\"           \"session_id\"        \"file_id\" summary(raman_hdpe) #> $wavenumber #>  Length   Min.    Max.    Res. #>     964 301.04 3198.12 3.00527 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1             26            816 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"                 \"y\"                 \"user_name\"         #> [4] \"spectrum_type\"     \"spectrum_identity\" \"organization\"      #> [7] \"license\"           \"session_id\"        \"file_id\" plotly_spec(raman_hdpe, processed)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"threshold-signal-noise","dir":"Articles","previous_headings":"Processing","what":"Threshold Signal-Noise","title":"Open Specy Package Tutorial","text":"Considering whether enough signal analyze spectra important. Classical spectroscopy recommend highest peak least 10 times baseline processed spectra begin analysis. spectra threshold even processing, may want consider recollecting . practice, rarely able collect spectra good quality often use 5. “run_sig_over_noise” metric searches spectra high low regions conducts division derive signal noise ratio step specifies number intensities search. good way automatically calculate signal noise ratio. example can see signal noise ratio increased processing, goal processing generally maximize . “sig_times_noise” metric multiplies mean signal standard deviation signal “tot_sig” metric sums intensities. latter can really useful thresholding spectral maps identify particles discuss later. know signal region noise regions can specify sig_min, sig_max, noise_min, noise_max. analyzing spectra batch, recommend looking heatmap optimizing percent spectra signal noise threshold determine correct settings instead looking spectra individually. Setting min_sn threshold heatmap image color spectra sn value threshold.","code":"sig_noise(processed, metric = \"run_sig_over_noise\") >    sig_noise(raman_hdpe, metric = \"run_sig_over_noise\") spectral_map_p <- spectral_map |>   process_spec(flatten_range = T)  spectral_map_p$metadata$sig_noise <- sig_noise(spectral_map_p)  heatmap_spec(spectral_map_p, sn = spectral_map_p$metadata$sig_noise, min_sn = 5)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"intensity-adjustment","dir":"Articles","previous_headings":"Processing","what":"Intensity Adjustment","title":"Open Specy Package Tutorial","text":"Open Specy assumes intensity units absorbance units Open Specy can adjust reflectance transmittance spectra absorbance units. transmittance adjustment uses log101/T\\log_{10} 1/T calculation correct system particle characteristics. reflectance adjustment use Kubelka-Munk equation (1−R)22R\\frac{(1-R)^2}{2R}. respective R code scenario spectra doesn’t need intensity adjustment:","code":"trans_raman_hdpe <- raman_hdpe trans_raman_hdpe$spectra <- 2 - trans_raman_hdpe$spectra^2      rev_trans_raman_hdpe <- trans_raman_hdpe |>    adj_intens(type = \"transmittance\")  plotly_spec(trans_raman_hdpe, rev_trans_raman_hdpe)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"conforming","dir":"Articles","previous_headings":"Processing","what":"Conforming","title":"Open Specy Package Tutorial","text":"Conforming spectra essential comparing reference library can useful summarizing data don’t need highly resolved spectrally. set default spectral resolution 8 tends pretty good lot applications 4 8 commonly used wavenumber resolutions. several ways function can specified.","code":"conform_spec(raman_hdpe, res = 8) |> # convert res to 8 wavenumbers.   summary() #> $wavenumber #>  Length Min. Max.     Res. #>     362  304 3192 7.977901 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1       42.85152       602.4639 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"                 \"y\"                 \"user_name\"         #> [4] \"spectrum_type\"     \"spectrum_identity\" \"organization\"      #> [7] \"license\"           \"session_id\"        \"file_id\"  # Force one spectrum to have the exact same wavenumbers as another conform_spec(asp_example, range = ps_example$wavenumber, res = NULL) |>    summary() #> $wavenumber #>  Length     Min.     Max.     Res. #>    1736 651.8728 3998.025 1.927507 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1   0.0009781419      0.5156938 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"          \"y\"          \"file_name\"  \"license\"    \"col_id\"     #> [6] \"session_id\" \"file_id\"  # Specify the wavenumber resolution and use a rolling join instead of linear # approximation (faster for large datasets).  conform_spec(spectral_map, range = ps_example$wavenumber, res = 10,              type = \"roll\") |>    summary() #> $wavenumber #>  Length Min. Max.     Res. #>     329  720 4000 9.969605 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>     208      -1.317072       1.168225 #>  #> $metadata #>   Min. Max. #> x    0   15 #> y    0   12 #>  [1] \"y\"             \"x\"             \"file_name\"     \"license\"       #>  [5] \"description\"   \"samples\"       \"lines\"         \"bands\"         #>  [9] \"header offset\" \"data type\"     \"interleave\"    \"z plot titles\" #> [13] \"pixel size\"    \"col_id\"        \"session_id\"    \"file_id\""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"smoothing","dir":"Articles","previous_headings":"Processing","what":"Smoothing","title":"Open Specy Package Tutorial","text":"Savitzky-Golay filter used smoothing. Higher polynomial numbers lead wiggly fits thus less smoothing, lower numbers lead smooth fits. SG filter fit moving window 11 data points default center point window replaced polynomial estimate. Larger windows produce smoother fits. derivative order set 1 default transforms spectra first derivative. zero order derivative derivative transformation. smoothing done well, peak shapes relative heights change. absolute value primarily useful first derivative spectra absolute value results absorbance-like spectrum set default. ’ll notice new function using c_spec() used combine spectral objects one OpenSpecy object. Examples smoothing: Sample raman_hdpe spectrum different smoothing polynomials. Derivative transformation can happen function. Sample raman_hdpe spectrum different derivatives.","code":"none <- make_rel(raman_hdpe) p1 <- smooth_intens(raman_hdpe, polynomial = 1, derivative = 0, abs = F) p4 <- smooth_intens(raman_hdpe, polynomial = 4, derivative = 0, abs = F)  c_spec(list(none, p1, p4)) |>    plot() none <- make_rel(raman_hdpe) d1 <- smooth_intens(raman_hdpe, derivative = 1, abs = T) d2 <- smooth_intens(raman_hdpe, derivative = 2, abs = T)  c_spec(list(none, d1, d2)) |>    plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"baseline-correction","dir":"Articles","previous_headings":"Processing","what":"Baseline Correction","title":"Open Specy Package Tutorial","text":"goal baseline correction get non-peak regions spectra zero absorbance. higher polynomial order, wiggly fit baseline. baseline wiggly, wiggly fit remove peaks desired. baseline correction algorithm used Open Specy called “iModPolyfit” (Zhao et al. 2007). algorithm iteratively fits polynomial equations specified order whole spectrum. first fit iteration, peak regions often baseline fit. data peak region removed fit make sure baseline less likely fit peaks. iterative fitting terminates difference new previous fit small. example good baseline fit . Manual baseline correction can also specified providing baseline OpenSpecy object. Sample raman_hdpe spectrum different degrees background subtraction (Cowger et al., 2020).","code":"alternative_baseline <- smooth_intens(raman_hdpe, polynomial = 1, window = 51,                                       derivative = 0, abs = F, make_rel = F) |>   flatten_range(min = 2700, max = 3200, make_rel = F)  none <- make_rel(raman_hdpe) d2 <- subtr_baseline(raman_hdpe, type = \"manual\",                      baseline = alternative_baseline) d8 <- subtr_baseline(raman_hdpe, degree = 8)  c_spec(list(none, d2, d8)) |>    plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"range-selection","dir":"Articles","previous_headings":"Processing","what":"Range Selection","title":"Open Specy Package Tutorial","text":"Sometimes instrument operates high noise ends spectrum , baseline fit produces distortions, regions interest analysis. Range selection accomplishes goals. look signal noise ratio specific instrument wavelength determine wavelength ranges use. Distortions due baseline fit can assessed looking process spectra. Additionally, can restrict range examine single peak subset peaks interests. Multiple ranges can specified simultaneously. Sample raman_hdpe spectrum different degrees range restriction.","code":"none <- make_rel(raman_hdpe) r1 <- restrict_range(raman_hdpe, min = 1000, max = 2000) r2 <- restrict_range(raman_hdpe, min = c(1000, 1800), max = c(1200, 2000))  compare_ranges <- c_spec(list(none, r1, r2), range = \"common\") # Common argument crops the ranges to the most common range between the spectra # when joining.   plot(compare_ranges)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"flattening-ranges","dir":"Articles","previous_headings":"Processing","what":"Flattening Ranges","title":"Open Specy Package Tutorial","text":"Sometimes peaks really shouldn’t spectra can distort interpretation spectra don’t necessarily want remove regions analysis believe regions flat instead peak. One way deal replace peak values mean values around peak. purpose flatten_range function. default set flatten CO2 region FTIR spectra region often needs flattened atmospheric artifacts occur spectra. Like restrict_range, R function can accept multiple ranges. Sample raman_hdpe spectrum different degrees background subtraction (Cowger et al., 2020).","code":"single <- filter_spec(spectral_map, 120) # Function to filter spectra by index                                          # number or name or a logical vector.  none <- make_rel(single) f1 <- flatten_range(single) #default flattening the CO2 region.  f2 <- flatten_range(single, min = c(1000, 2500), max = c(1200, 3000))  compare_flats <- c_spec(list(none, f1, f2))  plot(compare_flats)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"min-max-normalization","dir":"Articles","previous_headings":"Processing","what":"Min-Max Normalization","title":"Open Specy Package Tutorial","text":"Often regard spectral intensities arbitrary min-max normalization allows us view spectra scale without drastically distorting shapes relative peak intensities. package, processing functions min-max transform spectra default specify otherwise. plot shows two plots nearly identical except min-max transformed spectrum y axis ranges 0-1.","code":"relative <- make_rel(raman_hdpe)"},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"reading-libraries","dir":"Articles","previous_headings":"Identifying Spectra","what":"Reading Libraries","title":"Open Specy Package Tutorial","text":"Reference libraries spectra known identities. Open Specy library now 10,000 spectra getting large fit within R package size limit 5 MB. host reference libraries OSF function pull libraries automatically. Running get_lib download libraries package director can specify libraries want want . download libraries can load active environment. load one time. Libraries also OpenSpecy objects can use Open Specy object library.","code":"get_lib(type = \"derivative\") lib <- load_lib(type = \"derivative\")"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"matches","dir":"Articles","previous_headings":"Identifying Spectra","what":"Matches","title":"Open Specy Package Tutorial","text":"attempting use reference library identify spectra really important understand format reference library . OpenSpecy reference libraries Absorbance units. derivative absolute first derivative transformed, nobaseline baseline corrected, raw rawest form reference spectra (recommended except advanced uses). previously mentioned libraries Raman FTIR spectra . mediod mediod compressed library version absolute first derivative FTIR, model exception multinomial regression approach FTIR identification absolute derivative spectra. example use data(\"test_lib\") subsampled version absolute derivative library data(\"raman_hdpe\") unprocessed Raman spectrum absorbance units HDPE plastic. single spectra easy look spectra batch, also refer Thresholding Signal-Noise section guidance making sure batch spectra processed quality specs. spectra processed similarly library specifications, can identify spectra using match_spec(). libraries wavenumbers 8 cm^-1 resolution. Whichever library choose, need get spectra similar enough format use comparison. means conforming wavenumbers values using conform = T. Alternatively, done conformation processing. add_library_metadata add_object_metadata options specify column name metadata want add metadata top_n specifies many matches want. example just identified single spectrum library can also send OpenSpecy object multiple spectra. output matches data.table least 3 columns, object_id tells column names spectra x, library_id tells column names library matched . match_val value Pearson correlation coefficient (default) correlation specified ... using model identification option match_val model confidence. output example returned correct material type, HDPE, top match. using Pearson correlation, 0.7 good threshold use positive ID. example, top match greater threshold disregard matches. matches threshold, proclaim spectrum unknown identity. ’ll also notice example matched library Raman FTIR spectra Raman spectra highest hits, rationale lazily matching library . want just match library FTIR Raman spectra, can first filter library using filter_spec() using SpectrumType.","code":"data(\"test_lib\") data(\"raman_hdpe\")  processed <- process_spec(x = raman_hdpe,                            conform_spec = F, #We will conform during matching.                            smooth_intens = T #Conducts the default derivative transformation.                            )  # Check to make sure that the signal to noise ratio of the processed spectra is # greater than 10. print(sig_noise(processed) > 10) plotly_spec(raman_hdpe, processed) matches <- match_spec(x = processed, library = test_lib, conform = T,                       add_library_metadata = \"sample_name\", top_n = 5) print(matches[,c(\"object_id\", \"library_id\", \"match_val\", \"SpectrumType\",                  \"SpectrumIdentity\")])"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"library-metadata","dir":"Articles","previous_headings":"Identifying Spectra","what":"Library Metadata","title":"Open Specy Package Tutorial","text":"libraries created 100 variables metadata can onerous read especially given many variables NA values. created get_metadata() remedy removing columns metadata blank values. function return metadata top match matches. Remember, similar filter_spec(), can specify logic one thing time.","code":"get_metadata(x = test_lib, logic = matches[[1,\"library_id\"]], rm_empty = T)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"plot-matches","dir":"Articles","previous_headings":"Identifying Spectra","what":"Plot Matches","title":"Open Specy Package Tutorial","text":"Overlaying unknown spectra best matches can extremely useful identify peaks don’t fit reference library may need investigation. example shows great correspondence best match unknown spectrum. major peaks accounted correct relative height. two small peaks unknown spectrum near 500 accounted investigated call positive id HDPE.","code":"plotly_spec(processed, filter_spec(test_lib, logic = matches[[1,\"library_id\"]]))"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"sharing-reference-data","dir":"Articles","previous_headings":"Identifying Spectra","what":"Sharing Reference Data","title":"Open Specy Package Tutorial","text":"reference data AI models think useful sharing spectroscopy community OpenSpecy please contact package administrator discuss options collaborating.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"characterizing-particles","dir":"Articles","previous_headings":"","what":"Characterizing Particles","title":"Open Specy Package Tutorial","text":"Sometimes spectroscopy task want perform identify particles spectral map. especially common microplastic analysis spectral map used image sample spectral information used differentiate microplastic particles nonplastic particles. addition material id, one often wants measure shape size particles. brute force technique, one first identify every spectrum map, use thresholding image analysis measure particles. However, often , particles well separated image surface background spectra quite different particle spectra therefore can use thresholding priori identify measure particles, pass exemplary spectrum particle identification routine. important note bleeding edge theory technique may updating functions near future.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"brute-force","dir":"Articles","previous_headings":"Characterizing Particles","what":"Brute Force","title":"Open Specy Package Tutorial","text":"","code":"data(\"test_lib\") test_map <- read_any(read_extdata(\"CA_tiny_map.zip\"))  test_map_processed <- process_spec(test_map, conform_spec_args = list(   range = test_lib$wavenumber, res = NULL)   )  identities <- match_spec(test_map_processed, test_lib, order = test_map,                          add_library_metadata = \"sample_name\", top_n = 1)  features <- ifelse(identities$match_val > 0.7,                    tolower(identities$polymer_class), \"unknown\")  id_map <- def_features(x = test_map_processed, features = features)  id_map$metadata$identities <- features # Also should probably be implemented automatically in the function when a # character value is provided.   # Collapses spectra to their median for each particle test_collapsed <- collapse_spec(id_map)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"a-priori-particle-thresholding","dir":"Articles","previous_headings":"Characterizing Particles","what":"A Priori Particle Thresholding","title":"Open Specy Package Tutorial","text":"","code":"data(\"test_lib\") test_map <- read_any(read_extdata(\"CA_tiny_map.zip\"))  # Characterize the total signal as a threshold value.  snr <- sig_noise(test_map,metric = \"log_tot_sig\")  # Use this to find your particles and the sig_noise value to use for # thresholding.  heatmap_spec(test_map, z = snr)  # Set define the feature regions based on the threshold. 400 appeared to be # where I suspected my particle to be in the previous heatmap.  id_map <- def_features(x = test_map, features = snr > 400)   # Check that the thresholding worked as expected. heatmap_spec(id_map, z = id_map$metadata$feature_id)  # Collapse the spectra to their medians based on the threshold. Important to # note here that the particles with id -88 are anything from the FALSE values # so they should be your background.  collapsed_id_map <- id_map |>   collapse_spec()  # Process the collapsed spectra.  id_map_processed <- process_spec(collapsed_id_map, conform_spec_args = list(   range = test_lib$wavenumber, res = NULL)   )  # Check the spectra. plot(id_map_processed)  # Get the matches of the collapsed spectra for the particles. matches <- match_spec(id_map_processed, test_lib,                       add_library_metadata = \"sample_name\", top_n = 1)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Open Specy Package Tutorial","text":"Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy.” Applied Spectroscopy, 74(9), 1167–1183. doi: 10.1177/0003702820923993. Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi: 10.1177/0003702820929064. Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123. Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design Automated Analysis Microplastic Samples Based Fourier Transform Infrared (FTIR) Spectroscopy.” Analytical Bioanalytical Chemistry, 410(21), 5131–5141. doi: 10.1007/s00216-018-1156-x. Renner G, Schmidt TC, Schram J (2017). “New Chemometric Approach Automatic Identification Microplastics Environmental Compartments Based FT-IR Spectroscopy.” Analytical Chemistry, 89(22), 12045–12053. doi: 10.1021/acs.analchem.7b02472. Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639. Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi: 10.1366/000370207782597003.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Win Cowger. Maintainer, author, data contributor. Zacharias Steinmetz. Author. Nick Leong. Author. Andrea Faltynkova. Author, data contributor. Hannah Sherrod. Author. Andrew B Gray. Contributor. Hannah Hapich. Contributor. Jennifer Lynch. Contributor, data contributor. Hannah De Frond. Contributor, data contributor. Keenan Munno. Contributor, data contributor. Chelsea Rochman. Contributor, data contributor. Sebastian Primpke. Contributor, data contributor. Orestis Herodotou. Contributor. Mary C Norris. Contributor. Christine M Knauss. Contributor. Aleksandra Karapetrova. Contributor, data contributor, reviewer. Vesna Teofilovic. Contributor. Laura . T. Markley. Contributor. Shreyas Patankar. Contributor, data contributor. Rachel Kozloski. Contributor, data contributor. Samiksha Singh. Contributor. Katherine Lasdin. Contributor. Cristiane Vidal. Contributor. Clare Murphy-Hagan. Contributor. Philipp Baumann. Contributor. Pierre Roudier. Contributor. National Renewable Energy Laboratory. Funder. Possibility Lab. Funder.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cowger W, Steinmetz Z, Leong N, Faltynkova , Sherrod H (2024). OpenSpecy: Analyze, Process, Identify, Share Raman (FT)IR Spectra. R package version 1.2.0, https://wincowger.com/OpenSpecy-package/, https://github.com/wincowgerDEV/OpenSpecy-package/.","code":"@Manual{,   title = {OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra},   author = {Win Cowger and Zacharias Steinmetz and Nick Leong and Andrea Faltynkova and Hannah Sherrod},   year = {2024},   note = {R package version 1.2.0,     https://wincowger.com/OpenSpecy-package/},   url = {https://github.com/wincowgerDEV/OpenSpecy-package/}, }"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"open-specy-10","dir":"","previous_headings":"","what":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"Analyze, Process, Identify, Share Raman (FT)IR Spectra Raman (FT)IR spectral analysis tool plastic particles environmental samples (Cowger et al. 2021, doi: 10.1021/acs.analchem.1c00123). read_any(), Open Specy provides single function reading individual, batch, map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction flattening, intensity conversions, wavenumber alignment, min-max normalization. Spectra can identified batch using onboard reference library (Cowger et al. 2020, doi: 10.1177/0003702820929064) using match_spec(). Shiny app available via run_app() online https://openanalysis.org/openspecy/.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"OpenSpecy available CRAN GitHub.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"install-from-cran-stable-version","dir":"","previous_headings":"Installation","what":"Install from CRAN (stable version)","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"can install latest release OpenSpecy CRAN :","code":"install.packages(\"OpenSpecy\")"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"install-from-github-development-version","dir":"","previous_headings":"Installation","what":"Install from GitHub (development version)","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"install development version package, paste following code R console (requires devtools):","code":"if (!require(devtools)) install.packages(\"devtools\") devtools::install_github(\"wincowgerDEV/OpenSpecy-package\")"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"","code":"library(OpenSpecy) run_app()"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"simple-workflow-for-single-spectral-identification","dir":"","previous_headings":"","what":"Simple workflow for single spectral identification","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"See package vignette detailed standard operating procedure.","code":"# Fetch current spectral library from https://osf.io/x7dpz/ get_lib(\"derivative\")  # Load library into global environment spec_lib <- load_lib(\"derivative\")  # Read sample spectrum raman_hdpe <- read_extdata(\"raman_hdpe.csv\") |>    read_any()  # Look at the spectrum plotly_spec(raman_hdpe)  # Process the spectra and conform it to the library format raman_proc <- raman_hdpe |>   process_spec(conform_spec_args = list(range = spec_lib$wavenumbers),                 smooth_intens = T, make_rel = T)  # Compare raw and processed spectra plotly_spec(raman_hdpe, raman_proc)  top_matches <- match_spec(raman_proc, library = spec_lib, na.rm = T, top_n = 5,                           add_library_metadata = \"sample_name\",                           add_object_metadata = \"col_id\")  # Print the top 5 results with relevant metadata top_matches[, c(\"object_id\", \"library_id\", \"match_val\", \"SpectrumType\",                 \"SpectrumIdentity\")]  # Get all metadata for the matches get_metadata(spec_lib, logic = top_matches$library_id)"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"citations","dir":"","previous_headings":"","what":"Citations","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123. Cowger W, Steinmetz Z, Leong N, Faltynkova , Sherrod H (2024). “OpenSpecy: Analyze, Process, Identify, Share Raman (FT)IR Spectra.” R package, 1.0.8. https://github.com/wincowgerDEV/OpenSpecy-package.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/OpenSpecy-package.html","id":null,"dir":"Reference","previous_headings":"","what":"OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra — OpenSpecy-package","title":"OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra — OpenSpecy-package","text":"Raman (FT)IR spectral analysis tool plastic particles environmental samples (Cowger et al. 2021, doi:10.1021/acs.analchem.1c00123 ). read_any(), Open Specy provides single function reading individual, batch, map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction flattening, intensity conversions, wavenumber alignment, min-max normalization. Spectra can identified batch using onboard reference library (Cowger et al. 2020, doi:10.1177/0003702820929064 ) using match_spec(). Shiny app available via run_app() online https://openanalysis.org/openspecy/.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/OpenSpecy-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra — OpenSpecy-package","text":"Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy.” Applied Spectroscopy, 74(9), 1167–1183. doi:10.1177/0003702820923993 . Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi:10.1177/0003702820929064 . Cowger, W (2023). “Library data.” OSF. doi:10.17605/OSF.IO/X7DPZ . Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi:10.1021/acs.analchem.1c00123 . Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design Automated Analysis Microplastic Samples Based Fourier Transform Infrared (FTIR) Spectroscopy.” Analytical Bioanalytical Chemistry, 410(21), 5131–5141. doi:10.1007/s00216-018-1156-x . Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639. Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi:10.1366/000370207782597003 .","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/OpenSpecy-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra — OpenSpecy-package","text":"Maintainer: Win Cowger wincowger@gmail.com (ORCID) [data contributor] Authors: Zacharias Steinmetz z.steinmetz@rptu.de (ORCID) Nick Leong (ORCID) Andrea Faltynkova (ORCID) [data contributor] Hannah Sherrod (ORCID) contributors: Andrew B Gray (ORCID) [contributor] Hannah Hapich (ORCID) [contributor] Jennifer Lynch (ORCID) [contributor, data contributor] Hannah De Frond (ORCID) [contributor, data contributor] Keenan Munno (ORCID) [contributor, data contributor] Chelsea Rochman (ORCID) [contributor, data contributor] Sebastian Primpke (ORCID) [contributor, data contributor] Orestis Herodotou [contributor] Mary C Norris [contributor] Christine M Knauss (ORCID) [contributor] Aleksandra Karapetrova (ORCID) [contributor, data contributor, reviewer] Vesna Teofilovic (ORCID) [contributor] Laura . T. Markley (ORCID) [contributor] Shreyas Patankar [contributor, data contributor] Rachel Kozloski (ORCID) [contributor, data contributor] Samiksha Singh [contributor] Katherine Lasdin [contributor] Cristiane Vidal (ORCID) [contributor] Clare Murphy-Hagan (ORCID) [contributor] Philipp Baumann info@spectral-cockpit.space (ORCID) [contributor] Pierre Roudier [contributor] National Renewable Energy Laboratory [funder] Possibility Lab [funder]","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust spectral intensities to standard absorbance units. — adj_intens","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"Converts reflectance transmittance intensity units absorbance units adjust log exp transformed units.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"","code":"adj_intens(x, ...)  # Default S3 method adj_intens(x, type = \"none\", make_rel = TRUE, log_exp = \"none\", ...)  # S3 method for class 'OpenSpecy' adj_intens(x, type = \"none\", make_rel = TRUE, log_exp = \"none\", ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"x list object class OpenSpecy. type character string specifying whether input spectrum absorbance units (\"none\", default) needs additional conversion \"reflectance\" \"transmittance\" data. make_rel logical; TRUE spectra automatically normalized make_rel(). log_exp character string specifying whether input needs log transformed \"log\", exp transformed \"exp\", (\"none\", default). ... arguments passed submethods; adj_neg() adj_intens() conform_res() conform_intens().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"adj_intens() returns data frame containing two columns named \"wavenumber\" \"intensity\".","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"Many Open Specy functions assume spectrum absorbance units. example, see subtr_baseline(). run functions properly, need first convert spectra transmittance reflectance absorbance using function. transmittance adjustment uses \\(log(1 / T)\\) calculation correct system particle characteristics. reflectance adjustment uses Kubelka-Munk equation \\((1 - R)^2 / 2R\\). assume reflectance intensity percent 1-100 first correct intensity dividing 100 fits form expected equation.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"","code":"data(\"raman_hdpe\")  adj_intens(raman_hdpe) #>      wavenumber  intensity #>           <num>      <num> #>   1:    301.040 0.00000000 #>   2:    304.632 0.03037975 #>   3:    308.221 0.02784810 #>   4:    311.810 0.02405063 #>   5:    315.398 0.02531646 #>  ---                       #> 960:   3187.990 0.05696203 #> 961:   3190.520 0.05696203 #> 962:   3193.060 0.06202532 #> 963:   3195.590 0.06202532 #> 964:   3198.120 0.05189873 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Range restriction and flattening for spectra — restrict_range","title":"Range restriction and flattening for spectra — restrict_range","text":"restrict_range() restricts wavenumber ranges user specified values. Multiple ranges can specified inputting series max min values order. flatten_range() flatten ranges spectra peaks. Multiple ranges can specified inputting series max min values order.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Range restriction and flattening for spectra — restrict_range","text":"","code":"restrict_range(x, ...)  # Default S3 method restrict_range(x, ...)  # S3 method for class 'OpenSpecy' restrict_range(x, min, max, make_rel = TRUE, ...)  flatten_range(x, ...)  # Default S3 method flatten_range(x, ...)  # S3 method for class 'OpenSpecy' flatten_range(x, min = 2200, max = 2400, make_rel = TRUE, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Range restriction and flattening for spectra — restrict_range","text":"x OpenSpecy object. min vector minimum values range flattened. max vector maximum values range flattened. make_rel logical; output intensities normalized range [0, 1] using make_rel() function? ... additional arguments passed subfunctions; currently use.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Range restriction and flattening for spectra — restrict_range","text":"OpenSpecy object spectral intensities within specified ranges restricted flattened.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Range restriction and flattening for spectra — restrict_range","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Range restriction and flattening for spectra — restrict_range","text":"","code":"test_noise <- as_OpenSpecy(x = seq(400,4000, by = 10),                            spectra = data.frame(intensity = rnorm(361))) plot(test_noise)   restrict_range(test_noise, min = 1000, max = 2000) #>      wavenumber intensity #>           <num>     <num> #>   1:       1000 0.7779144 #>   2:       1010 0.4108821 #>   3:       1020 0.7862717 #>   4:       1030 0.4993344 #>   5:       1040 0.3027464 #>  ---                      #>  97:       1960 0.5593439 #>  98:       1970 0.4760214 #>  99:       1980 0.7164056 #> 100:       1990 0.7209565 #> 101:       2000 0.1502945 #>  #> $metadata #>        x     y  license    col_id                          file_id #>    <int> <int>   <char>    <char>                           <char> #> 1:     1     1 CC BY-NC intensity 5ef3b78d1f815177bb927948a30ed9d1  flattened_intensities <- flatten_range(test_noise, min = c(1000, 2000),                                        max = c(1500, 2500)) plot(flattened_intensities)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust wavelength to wavenumbers for Raman — adj_wave","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"Functions converting wave* units.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"","code":"adj_wave(x, ...)  # Default S3 method adj_wave(x, laser, ...)  # S3 method for class 'OpenSpecy' adj_wave(x, laser, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"x OpenSpecy object wavenumber units specified wavelength nm wavelength vector. laser wavelength nm Raman laser. ... additional arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"OpenSpecy object new units converted wavelength wavenumbers vector conversion.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"","code":"data(\"raman_hdpe\") raman_wavelength <- raman_hdpe raman_wavelength$wavenumber <- (-1*(raman_wavelength$wavenumber/10^7-1/530))^(-1) adj_wave(raman_wavelength, laser = 530) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b adj_wave(raman_wavelength$wavenumber, laser = 530) #>   [1]  301.040  304.632  308.221  311.810  315.398  318.983  322.566  326.150 #>   [9]  329.732  333.311  336.889  340.467  344.042  347.618  351.190  354.760 #>  [17]  358.332  361.900  365.466  369.031  372.597  376.160  379.721  383.280 #>  [25]  386.839  390.396  393.951  397.505  401.058  404.610  408.160  411.708 #>  [33]  415.256  418.801  422.346  425.887  429.429  432.970  436.507  440.044 #>  [41]  443.581  447.116  450.647  454.180  457.708  461.238  464.764  468.291 #>  [49]  471.815  475.339  478.859  482.379  485.899  489.416  492.931  496.447 #>  [57]  499.960  503.471  506.981  510.489  513.999  517.503  521.009  524.510 #>  [65]  528.014  531.513  535.014  538.510  542.008  545.502  548.997  552.488 #>  [73]  555.979  559.469  562.955  566.442  569.928  573.411  576.894  580.376 #>  [81]  583.854  587.331  590.811  594.286  597.759  601.231  604.705  608.174 #>  [89]  611.642  615.109  618.574  622.038  625.503  628.966  632.426  635.884 #>  [97]  639.342  642.798  646.252  649.706  653.158  656.608  660.058  663.506 #> [105]  666.953  670.398  673.842  677.285  680.727  684.165  687.602  691.040 #> [113]  694.476  697.911  701.345  704.775  708.204  711.634  715.063  718.490 #> [121]  721.912  725.336  728.760  732.180  735.599  739.018  742.434  745.849 #> [129]  749.265  752.675  756.088  759.498  762.906  766.313  769.719  773.124 #> [137]  776.527  779.929  783.330  786.729  790.127  793.524  796.919  800.312 #> [145]  803.705  807.097  810.485  813.874  817.262  820.647  824.032  827.416 #> [153]  830.797  834.179  837.557  840.936  844.312  847.688  851.061  854.433 #> [161]  857.806  861.175  864.543  867.910  871.278  874.642  878.005  881.367 #> [169]  884.728  888.087  891.445  894.802  898.157  901.511  904.864  908.216 #> [177]  911.567  914.914  918.262  921.609  924.954  928.296  931.639  934.981 #> [185]  938.320  941.659  944.995  948.332  951.665  954.999  958.330  961.660 #> [193]  964.991  968.318  971.644  974.971  978.295  981.617  984.940  988.258 #> [201]  991.578  994.896  998.212 1001.530 1004.840 1008.150 1011.460 1014.770 #> [209] 1018.080 1021.390 1024.700 1028.000 1031.300 1034.610 1037.910 1041.210 #> [217] 1044.510 1047.800 1051.100 1054.390 1057.680 1060.980 1064.270 1067.560 #> [225] 1070.840 1074.130 1077.420 1080.700 1083.980 1087.270 1090.550 1093.830 #> [233] 1097.100 1100.380 1103.650 1106.930 1110.200 1113.470 1116.740 1120.010 #> [241] 1123.280 1126.550 1129.810 1133.080 1136.340 1139.600 1142.860 1146.120 #> [249] 1149.370 1152.630 1155.890 1159.140 1162.390 1165.640 1168.890 1172.140 #> [257] 1175.390 1178.640 1181.880 1185.120 1188.370 1191.610 1194.850 1198.090 #> [265] 1201.320 1204.560 1207.790 1211.030 1214.260 1217.490 1220.720 1223.950 #> [273] 1227.180 1230.400 1233.630 1236.850 1240.070 1243.300 1246.520 1249.740 #> [281] 1252.950 1256.170 1259.380 1262.600 1265.810 1269.020 1272.230 1275.440 #> [289] 1278.650 1281.850 1285.060 1288.260 1291.460 1294.670 1297.870 1301.070 #> [297] 1304.260 1307.460 1310.660 1313.850 1317.040 1320.230 1323.420 1326.610 #> [305] 1329.800 1332.990 1336.170 1339.360 1342.540 1345.720 1348.900 1352.080 #> [313] 1355.260 1358.440 1361.610 1364.790 1367.960 1371.130 1374.300 1377.470 #> [321] 1380.640 1383.810 1386.970 1390.140 1393.300 1396.470 1399.630 1402.790 #> [329] 1405.950 1409.100 1412.260 1415.410 1418.570 1421.720 1424.870 1428.020 #> [337] 1431.170 1434.320 1437.470 1440.610 1443.760 1446.900 1450.040 1453.180 #> [345] 1456.320 1459.460 1462.590 1465.730 1468.870 1472.000 1475.130 1478.260 #> [353] 1481.400 1484.520 1487.650 1490.780 1493.900 1497.030 1500.150 1503.270 #> [361] 1506.390 1509.510 1512.630 1515.740 1518.860 1521.970 1525.090 1528.200 #> [369] 1531.310 1534.420 1537.530 1540.640 1543.740 1546.850 1549.950 1553.050 #> [377] 1556.160 1559.260 1562.360 1565.450 1568.550 1571.650 1574.740 1577.830 #> [385] 1580.920 1584.020 1587.110 1590.190 1593.280 1596.370 1599.450 1602.540 #> [393] 1605.620 1608.700 1611.780 1614.860 1617.940 1621.020 1624.090 1627.170 #> [401] 1630.240 1633.310 1636.380 1639.450 1642.520 1645.590 1648.650 1651.720 #> [409] 1654.780 1657.850 1660.910 1663.970 1667.030 1670.090 1673.150 1676.200 #> [417] 1679.260 1682.310 1685.360 1688.410 1691.460 1694.510 1697.560 1700.610 #> [425] 1703.650 1706.700 1709.740 1712.780 1715.830 1718.860 1721.900 1724.940 #> [433] 1727.980 1731.010 1734.050 1737.080 1740.110 1743.140 1746.170 1749.200 #> [441] 1752.230 1755.250 1758.280 1761.300 1764.330 1767.350 1770.370 1773.390 #> [449] 1776.410 1779.420 1782.440 1785.460 1788.470 1791.480 1794.490 1797.500 #> [457] 1800.510 1803.520 1806.530 1809.530 1812.540 1815.540 1818.540 1821.540 #> [465] 1824.550 1827.550 1830.540 1833.540 1836.540 1839.530 1842.520 1845.510 #> [473] 1848.510 1851.500 1854.490 1857.480 1860.460 1863.450 1866.430 1869.410 #> [481] 1872.400 1875.380 1878.360 1881.340 1884.320 1887.290 1890.270 1893.240 #> [489] 1896.220 1899.190 1902.160 1905.130 1908.100 1911.070 1914.030 1917.000 #> [497] 1919.960 1922.930 1925.890 1928.850 1931.810 1934.770 1937.430 1940.400 #> [505] 1943.360 1946.330 1949.300 1952.270 1955.230 1958.190 1961.160 1964.120 #> [513] 1967.080 1970.040 1972.990 1975.950 1978.910 1981.860 1984.820 1987.770 #> [521] 1990.720 1993.670 1996.620 1999.570 2002.520 2005.460 2008.410 2011.350 #> [529] 2014.290 2017.240 2020.180 2023.120 2026.050 2028.990 2031.930 2034.870 #> [537] 2037.800 2040.730 2043.660 2046.600 2049.520 2052.450 2055.380 2058.310 #> [545] 2061.230 2064.160 2067.080 2070.000 2072.930 2075.850 2078.770 2081.680 #> [553] 2084.600 2087.520 2090.430 2093.340 2096.260 2099.170 2102.080 2104.990 #> [561] 2107.900 2110.810 2113.710 2116.620 2119.520 2122.430 2125.330 2128.230 #> [569] 2131.130 2134.030 2136.930 2139.820 2142.720 2145.610 2148.510 2151.400 #> [577] 2154.290 2157.180 2160.070 2162.960 2165.850 2168.730 2171.620 2174.500 #> [585] 2177.390 2180.270 2183.150 2186.030 2188.910 2191.790 2194.670 2197.540 #> [593] 2200.420 2203.290 2206.160 2209.030 2211.900 2214.770 2217.640 2220.510 #> [601] 2223.380 2226.240 2229.110 2231.970 2234.830 2237.690 2240.550 2243.410 #> [609] 2246.270 2249.130 2251.980 2254.840 2257.690 2260.540 2263.390 2266.250 #> [617] 2269.100 2271.940 2274.790 2277.640 2280.490 2283.330 2286.170 2289.020 #> [625] 2291.860 2294.700 2297.540 2300.380 2303.210 2306.050 2308.890 2311.720 #> [633] 2314.550 2317.390 2320.220 2323.050 2325.880 2328.700 2331.530 2334.360 #> [641] 2337.180 2340.010 2342.830 2345.650 2348.470 2351.290 2354.110 2356.930 #> [649] 2359.750 2362.560 2365.380 2368.190 2371.000 2373.820 2376.630 2379.440 #> [657] 2382.250 2385.050 2387.860 2390.670 2393.470 2396.280 2399.080 2401.880 #> [665] 2404.680 2407.480 2410.280 2413.080 2415.870 2418.670 2421.460 2424.260 #> [673] 2427.050 2429.840 2432.630 2435.420 2438.210 2441.000 2443.790 2446.570 #> [681] 2449.360 2452.140 2454.920 2457.700 2460.480 2463.260 2466.040 2468.820 #> [689] 2471.600 2474.370 2477.150 2479.920 2482.690 2485.470 2488.240 2491.010 #> [697] 2493.770 2496.540 2499.310 2502.070 2504.840 2507.600 2510.370 2513.130 #> [705] 2515.890 2518.650 2521.410 2524.160 2526.920 2529.680 2532.430 2535.190 #> [713] 2537.940 2540.690 2543.440 2546.190 2548.940 2551.690 2554.440 2557.180 #> [721] 2559.930 2562.670 2565.420 2568.160 2570.900 2573.640 2576.380 2579.110 #> [729] 2581.850 2584.590 2587.320 2590.060 2592.790 2595.520 2598.250 2600.980 #> [737] 2603.720 2606.440 2609.170 2611.900 2614.620 2617.350 2620.070 2622.790 #> [745] 2625.510 2628.230 2630.950 2633.670 2636.390 2639.110 2641.820 2644.540 #> [753] 2647.250 2649.960 2652.680 2655.390 2658.100 2660.810 2663.510 2666.220 #> [761] 2668.930 2671.630 2674.340 2677.040 2679.740 2682.440 2685.140 2687.840 #> [769] 2690.540 2693.240 2695.940 2698.630 2701.330 2704.020 2706.710 2709.410 #> [777] 2712.100 2714.790 2717.470 2720.160 2722.850 2725.530 2728.220 2730.900 #> [785] 2733.590 2736.270 2738.950 2741.630 2744.310 2746.990 2749.670 2752.340 #> [793] 2755.020 2757.690 2760.370 2763.040 2765.710 2768.380 2771.050 2773.720 #> [801] 2776.390 2779.060 2781.720 2784.390 2787.050 2789.710 2792.380 2795.040 #> [809] 2797.700 2800.360 2803.020 2805.680 2808.330 2810.990 2813.640 2816.300 #> [817] 2818.950 2821.600 2824.250 2826.900 2829.550 2832.200 2834.850 2837.490 #> [825] 2840.140 2842.780 2845.430 2848.070 2850.710 2853.350 2855.990 2858.630 #> [833] 2861.270 2863.910 2866.540 2869.180 2871.810 2874.450 2877.080 2879.710 #> [841] 2882.340 2884.970 2887.600 2890.230 2892.860 2895.480 2898.110 2900.730 #> [849] 2903.350 2905.970 2908.600 2911.220 2913.840 2916.460 2919.070 2921.690 #> [857] 2924.310 2926.920 2929.540 2932.150 2934.760 2937.370 2939.980 2942.590 #> [865] 2945.200 2947.810 2950.420 2953.020 2955.630 2958.230 2960.830 2963.440 #> [873] 2966.040 2968.640 2971.240 2973.830 2976.430 2979.030 2981.630 2984.220 #> [881] 2986.810 2989.410 2992.000 2994.590 2997.180 2999.770 3002.360 3004.950 #> [889] 3007.530 3010.120 3012.700 3015.290 3017.870 3020.450 3023.040 3025.620 #> [897] 3028.200 3030.770 3033.350 3035.930 3038.500 3041.080 3043.650 3046.230 #> [905] 3048.800 3051.370 3053.940 3056.510 3059.080 3061.650 3064.210 3066.780 #> [913] 3069.350 3071.910 3074.470 3077.040 3079.600 3082.160 3084.720 3087.280 #> [921] 3089.830 3092.390 3094.950 3097.500 3100.060 3102.610 3105.160 3107.720 #> [929] 3110.270 3112.820 3115.370 3117.910 3120.460 3123.010 3125.560 3128.100 #> [937] 3130.640 3133.190 3135.730 3138.270 3140.810 3143.350 3145.890 3148.430 #> [945] 3150.960 3153.500 3156.030 3158.570 3161.100 3163.640 3166.170 3168.700 #> [953] 3171.230 3173.760 3175.300 3177.840 3180.380 3182.910 3185.450 3187.990 #> [961] 3190.520 3193.060 3195.590 3198.120"},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":null,"dir":"Reference","previous_headings":"","what":"Create OpenSpecy objects — as_OpenSpecy","title":"Create OpenSpecy objects — as_OpenSpecy","text":"Functions check object OpenSpecy, coerce possible.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create OpenSpecy objects — as_OpenSpecy","text":"","code":"as_OpenSpecy(x, ...)  # S3 method for class 'OpenSpecy' as_OpenSpecy(x, session_id = FALSE, ...)  # S3 method for class 'list' as_OpenSpecy(x, ...)  # S3 method for class 'hyperSpec' as_OpenSpecy(x, ...)  # S3 method for class 'data.frame' as_OpenSpecy(x, colnames = list(wavenumber = NULL, spectra = NULL), ...)  # Default S3 method as_OpenSpecy(   x,   spectra,   metadata = list(file_name = NULL, user_name = NULL, contact_info = NULL, organization =     NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL, material_form     = NULL, material_phase = NULL, material_producer = NULL, material_purity = NULL,     material_quality = NULL, material_color = NULL, material_other = NULL, cas_number =     NULL, instrument_used = NULL, instrument_accessories = NULL, instrument_mode = NULL,     intensity_units = NULL, spectral_resolution = NULL, laser_light_used = NULL,     number_of_accumulations = NULL,       total_acquisition_time_s = NULL,     data_processing_procedure = NULL, level_of_confidence_in_identification = NULL,     other_info = NULL, license = \"CC BY-NC\"),   attributes = list(intensity_unit = NULL, derivative_order = NULL, baseline = NULL,     spectra_type = NULL),   coords = \"gen_grid\",   session_id = FALSE,   ... )  is_OpenSpecy(x)  check_OpenSpecy(x)  OpenSpecy(x, ...)  gen_grid(n)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create OpenSpecy objects — as_OpenSpecy","text":"x depending method, list OpenSpecy parameters, vector wavenumbers spectra, data.frame full spectrum classic Open Specy format. session_id logical. Whether add session ID metadata. session ID based current session info metadata spectra return equal session info changes. Sometimes desirable. colnames names wavenumber column spectra column, makes assumptions based column names placement NULL. spectra spectral intensities formatted data.table one column per spectrum. metadata metadata spectrum one row per spectrum, see details. attributes list attributes describing critical aspects interpreting spectra. see details. coords spatial coordinates spectra. n number spectra generate spatial coordinate grid . ... additional arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create OpenSpecy objects — as_OpenSpecy","text":"as_OpenSpecy() OpenSpecy() returns three part lists described details. is_OpenSpecy() returns TRUE object OpenSpecy FALSE . gen_grid() returns data.table x y coordinates use generating spatial grid spectra one specified data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create OpenSpecy objects — as_OpenSpecy","text":"as_OpenSpecy() converts spectral datasets three part list; first vector wavenumbers spectra, second data.table spectral intensities ordered columns, third item another data.table metadata user provides harvested files . metadata argument may contain named list following details (* = minimum recommended). file_name* file name, defaults basename() specified user_name* User name, e.g. \"Win Cowger\" contact_info Contact information, e.g. \"1-513-673-8956, wincowger@gmail.com\" organization Affiliation, e.g. \"University California, Riverside\" citation Data citation, e.g. \"Primpke, S., Wirth, M., Lorenz, C., & Gerdts, G. (2018). Reference database design automated analysis microplastic samples based Fourier transform infrared (FTIR) spectroscopy. Analytical Bioanalytical Chemistry. doi:10.1007/s00216-018-1156-x \" spectrum_type* Raman FTIR spectrum_identity* Material/polymer analyzed, e.g. \"Polystyrene\" material_form Form material analyzed, e.g. textile fiber, rubber band, sphere, granule material_phase Phase material analyzed (liquid, gas, solid) material_producer Producer material analyzed, e.g. Dow material_purity Purity material analyzed, e.g. 99.98% material_quality Quality material analyzed, e.g. consumer product, manufacturer material, analytical standard, environmental sample material_color Color material analyzed, e.g. blue, #0000ff, (0, 0, 255) material_other material description, e.g. 5 µm diameter fibers, 1 mm spherical particles cas_number CAS number, e.g. 9003-53-6 instrument_used Instrument used, e.g. Horiba LabRam instrument_accessories Instrument accessories, e.g. Focal Plane Array, CCD instrument_mode Instrument modes/settings, e.g. transmission, reflectance intensity_units* Units intensity values spectrum, options transmittance, reflectance, absorbance spectral_resolution Spectral resolution, e.g. 4/cm laser_light_used Wavelength laser/light used, e.g. 785 nm number_of_accumulations Number accumulations, e.g 5 total_acquisition_time_s Total acquisition time (s), e.g. 10 s data_processing_procedure Data processing procedure, e.g. spikefilter, baseline correction, none level_of_confidence_in_identification Level confidence identification, e.g. 99% other_info information license license shared spectrum; defaults \"CC -NC\" (see https://creativecommons.org/licenses/-nc/4.0/ details). creative commons license allowed, example, CC0 CC session_id unique user session identifier; populated automatically paste(digest(Sys.info()), digest(sessionInfo()),   sep = \"/\") file_id unique file identifier; populated automatically digest(object[c(\"wavenumber\", \"spectra\")]) attributes argument may contain named list following details, set, used automate transformations warning messages: intensity_units supported options include \"absorbance\", \"transmittance\", \"reflectance\" derivative_order supported options include \"0\", \"1\", \"2\" baseline supported options include \"raw\" \"nobaseline\" spectra_type supported options include \"ftir\" \"raman\"","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create OpenSpecy objects — as_OpenSpecy","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create OpenSpecy objects — as_OpenSpecy","text":"","code":"data(\"raman_hdpe\")  # Inspect the spectra raman_hdpe # see how OpenSpecy objects print. #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b raman_hdpe$wavenumber # look at just the wavenumbers of the spectra. #>   [1]  301.040  304.632  308.221  311.810  315.398  318.983  322.566  326.150 #>   [9]  329.732  333.311  336.889  340.467  344.042  347.618  351.190  354.760 #>  [17]  358.332  361.900  365.466  369.031  372.597  376.160  379.721  383.280 #>  [25]  386.839  390.396  393.951  397.505  401.058  404.610  408.160  411.708 #>  [33]  415.256  418.801  422.346  425.887  429.429  432.970  436.507  440.044 #>  [41]  443.581  447.116  450.647  454.180  457.708  461.238  464.764  468.291 #>  [49]  471.815  475.339  478.859  482.379  485.899  489.416  492.931  496.447 #>  [57]  499.960  503.471  506.981  510.489  513.999  517.503  521.009  524.510 #>  [65]  528.014  531.513  535.014  538.510  542.008  545.502  548.997  552.488 #>  [73]  555.979  559.469  562.955  566.442  569.928  573.411  576.894  580.376 #>  [81]  583.854  587.331  590.811  594.286  597.759  601.231  604.705  608.174 #>  [89]  611.642  615.109  618.574  622.038  625.503  628.966  632.426  635.884 #>  [97]  639.342  642.798  646.252  649.706  653.158  656.608  660.058  663.506 #> [105]  666.953  670.398  673.842  677.285  680.727  684.165  687.602  691.040 #> [113]  694.476  697.911  701.345  704.775  708.204  711.634  715.063  718.490 #> [121]  721.912  725.336  728.760  732.180  735.599  739.018  742.434  745.849 #> [129]  749.265  752.675  756.088  759.498  762.906  766.313  769.719  773.124 #> [137]  776.527  779.929  783.330  786.729  790.127  793.524  796.919  800.312 #> [145]  803.705  807.097  810.485  813.874  817.262  820.647  824.032  827.416 #> [153]  830.797  834.179  837.557  840.936  844.312  847.688  851.061  854.433 #> [161]  857.806  861.175  864.543  867.910  871.278  874.642  878.005  881.367 #> [169]  884.728  888.087  891.445  894.802  898.157  901.511  904.864  908.216 #> [177]  911.567  914.914  918.262  921.609  924.954  928.296  931.639  934.981 #> [185]  938.320  941.659  944.995  948.332  951.665  954.999  958.330  961.660 #> [193]  964.991  968.318  971.644  974.971  978.295  981.617  984.940  988.258 #> [201]  991.578  994.896  998.212 1001.530 1004.840 1008.150 1011.460 1014.770 #> [209] 1018.080 1021.390 1024.700 1028.000 1031.300 1034.610 1037.910 1041.210 #> [217] 1044.510 1047.800 1051.100 1054.390 1057.680 1060.980 1064.270 1067.560 #> [225] 1070.840 1074.130 1077.420 1080.700 1083.980 1087.270 1090.550 1093.830 #> [233] 1097.100 1100.380 1103.650 1106.930 1110.200 1113.470 1116.740 1120.010 #> [241] 1123.280 1126.550 1129.810 1133.080 1136.340 1139.600 1142.860 1146.120 #> [249] 1149.370 1152.630 1155.890 1159.140 1162.390 1165.640 1168.890 1172.140 #> [257] 1175.390 1178.640 1181.880 1185.120 1188.370 1191.610 1194.850 1198.090 #> [265] 1201.320 1204.560 1207.790 1211.030 1214.260 1217.490 1220.720 1223.950 #> [273] 1227.180 1230.400 1233.630 1236.850 1240.070 1243.300 1246.520 1249.740 #> [281] 1252.950 1256.170 1259.380 1262.600 1265.810 1269.020 1272.230 1275.440 #> [289] 1278.650 1281.850 1285.060 1288.260 1291.460 1294.670 1297.870 1301.070 #> [297] 1304.260 1307.460 1310.660 1313.850 1317.040 1320.230 1323.420 1326.610 #> [305] 1329.800 1332.990 1336.170 1339.360 1342.540 1345.720 1348.900 1352.080 #> [313] 1355.260 1358.440 1361.610 1364.790 1367.960 1371.130 1374.300 1377.470 #> [321] 1380.640 1383.810 1386.970 1390.140 1393.300 1396.470 1399.630 1402.790 #> [329] 1405.950 1409.100 1412.260 1415.410 1418.570 1421.720 1424.870 1428.020 #> [337] 1431.170 1434.320 1437.470 1440.610 1443.760 1446.900 1450.040 1453.180 #> [345] 1456.320 1459.460 1462.590 1465.730 1468.870 1472.000 1475.130 1478.260 #> [353] 1481.400 1484.520 1487.650 1490.780 1493.900 1497.030 1500.150 1503.270 #> [361] 1506.390 1509.510 1512.630 1515.740 1518.860 1521.970 1525.090 1528.200 #> [369] 1531.310 1534.420 1537.530 1540.640 1543.740 1546.850 1549.950 1553.050 #> [377] 1556.160 1559.260 1562.360 1565.450 1568.550 1571.650 1574.740 1577.830 #> [385] 1580.920 1584.020 1587.110 1590.190 1593.280 1596.370 1599.450 1602.540 #> [393] 1605.620 1608.700 1611.780 1614.860 1617.940 1621.020 1624.090 1627.170 #> [401] 1630.240 1633.310 1636.380 1639.450 1642.520 1645.590 1648.650 1651.720 #> [409] 1654.780 1657.850 1660.910 1663.970 1667.030 1670.090 1673.150 1676.200 #> [417] 1679.260 1682.310 1685.360 1688.410 1691.460 1694.510 1697.560 1700.610 #> [425] 1703.650 1706.700 1709.740 1712.780 1715.830 1718.860 1721.900 1724.940 #> [433] 1727.980 1731.010 1734.050 1737.080 1740.110 1743.140 1746.170 1749.200 #> [441] 1752.230 1755.250 1758.280 1761.300 1764.330 1767.350 1770.370 1773.390 #> [449] 1776.410 1779.420 1782.440 1785.460 1788.470 1791.480 1794.490 1797.500 #> [457] 1800.510 1803.520 1806.530 1809.530 1812.540 1815.540 1818.540 1821.540 #> [465] 1824.550 1827.550 1830.540 1833.540 1836.540 1839.530 1842.520 1845.510 #> [473] 1848.510 1851.500 1854.490 1857.480 1860.460 1863.450 1866.430 1869.410 #> [481] 1872.400 1875.380 1878.360 1881.340 1884.320 1887.290 1890.270 1893.240 #> [489] 1896.220 1899.190 1902.160 1905.130 1908.100 1911.070 1914.030 1917.000 #> [497] 1919.960 1922.930 1925.890 1928.850 1931.810 1934.770 1937.430 1940.400 #> [505] 1943.360 1946.330 1949.300 1952.270 1955.230 1958.190 1961.160 1964.120 #> [513] 1967.080 1970.040 1972.990 1975.950 1978.910 1981.860 1984.820 1987.770 #> [521] 1990.720 1993.670 1996.620 1999.570 2002.520 2005.460 2008.410 2011.350 #> [529] 2014.290 2017.240 2020.180 2023.120 2026.050 2028.990 2031.930 2034.870 #> [537] 2037.800 2040.730 2043.660 2046.600 2049.520 2052.450 2055.380 2058.310 #> [545] 2061.230 2064.160 2067.080 2070.000 2072.930 2075.850 2078.770 2081.680 #> [553] 2084.600 2087.520 2090.430 2093.340 2096.260 2099.170 2102.080 2104.990 #> [561] 2107.900 2110.810 2113.710 2116.620 2119.520 2122.430 2125.330 2128.230 #> [569] 2131.130 2134.030 2136.930 2139.820 2142.720 2145.610 2148.510 2151.400 #> [577] 2154.290 2157.180 2160.070 2162.960 2165.850 2168.730 2171.620 2174.500 #> [585] 2177.390 2180.270 2183.150 2186.030 2188.910 2191.790 2194.670 2197.540 #> [593] 2200.420 2203.290 2206.160 2209.030 2211.900 2214.770 2217.640 2220.510 #> [601] 2223.380 2226.240 2229.110 2231.970 2234.830 2237.690 2240.550 2243.410 #> [609] 2246.270 2249.130 2251.980 2254.840 2257.690 2260.540 2263.390 2266.250 #> [617] 2269.100 2271.940 2274.790 2277.640 2280.490 2283.330 2286.170 2289.020 #> [625] 2291.860 2294.700 2297.540 2300.380 2303.210 2306.050 2308.890 2311.720 #> [633] 2314.550 2317.390 2320.220 2323.050 2325.880 2328.700 2331.530 2334.360 #> [641] 2337.180 2340.010 2342.830 2345.650 2348.470 2351.290 2354.110 2356.930 #> [649] 2359.750 2362.560 2365.380 2368.190 2371.000 2373.820 2376.630 2379.440 #> [657] 2382.250 2385.050 2387.860 2390.670 2393.470 2396.280 2399.080 2401.880 #> [665] 2404.680 2407.480 2410.280 2413.080 2415.870 2418.670 2421.460 2424.260 #> [673] 2427.050 2429.840 2432.630 2435.420 2438.210 2441.000 2443.790 2446.570 #> [681] 2449.360 2452.140 2454.920 2457.700 2460.480 2463.260 2466.040 2468.820 #> [689] 2471.600 2474.370 2477.150 2479.920 2482.690 2485.470 2488.240 2491.010 #> [697] 2493.770 2496.540 2499.310 2502.070 2504.840 2507.600 2510.370 2513.130 #> [705] 2515.890 2518.650 2521.410 2524.160 2526.920 2529.680 2532.430 2535.190 #> [713] 2537.940 2540.690 2543.440 2546.190 2548.940 2551.690 2554.440 2557.180 #> [721] 2559.930 2562.670 2565.420 2568.160 2570.900 2573.640 2576.380 2579.110 #> [729] 2581.850 2584.590 2587.320 2590.060 2592.790 2595.520 2598.250 2600.980 #> [737] 2603.720 2606.440 2609.170 2611.900 2614.620 2617.350 2620.070 2622.790 #> [745] 2625.510 2628.230 2630.950 2633.670 2636.390 2639.110 2641.820 2644.540 #> [753] 2647.250 2649.960 2652.680 2655.390 2658.100 2660.810 2663.510 2666.220 #> [761] 2668.930 2671.630 2674.340 2677.040 2679.740 2682.440 2685.140 2687.840 #> [769] 2690.540 2693.240 2695.940 2698.630 2701.330 2704.020 2706.710 2709.410 #> [777] 2712.100 2714.790 2717.470 2720.160 2722.850 2725.530 2728.220 2730.900 #> [785] 2733.590 2736.270 2738.950 2741.630 2744.310 2746.990 2749.670 2752.340 #> [793] 2755.020 2757.690 2760.370 2763.040 2765.710 2768.380 2771.050 2773.720 #> [801] 2776.390 2779.060 2781.720 2784.390 2787.050 2789.710 2792.380 2795.040 #> [809] 2797.700 2800.360 2803.020 2805.680 2808.330 2810.990 2813.640 2816.300 #> [817] 2818.950 2821.600 2824.250 2826.900 2829.550 2832.200 2834.850 2837.490 #> [825] 2840.140 2842.780 2845.430 2848.070 2850.710 2853.350 2855.990 2858.630 #> [833] 2861.270 2863.910 2866.540 2869.180 2871.810 2874.450 2877.080 2879.710 #> [841] 2882.340 2884.970 2887.600 2890.230 2892.860 2895.480 2898.110 2900.730 #> [849] 2903.350 2905.970 2908.600 2911.220 2913.840 2916.460 2919.070 2921.690 #> [857] 2924.310 2926.920 2929.540 2932.150 2934.760 2937.370 2939.980 2942.590 #> [865] 2945.200 2947.810 2950.420 2953.020 2955.630 2958.230 2960.830 2963.440 #> [873] 2966.040 2968.640 2971.240 2973.830 2976.430 2979.030 2981.630 2984.220 #> [881] 2986.810 2989.410 2992.000 2994.590 2997.180 2999.770 3002.360 3004.950 #> [889] 3007.530 3010.120 3012.700 3015.290 3017.870 3020.450 3023.040 3025.620 #> [897] 3028.200 3030.770 3033.350 3035.930 3038.500 3041.080 3043.650 3046.230 #> [905] 3048.800 3051.370 3053.940 3056.510 3059.080 3061.650 3064.210 3066.780 #> [913] 3069.350 3071.910 3074.470 3077.040 3079.600 3082.160 3084.720 3087.280 #> [921] 3089.830 3092.390 3094.950 3097.500 3100.060 3102.610 3105.160 3107.720 #> [929] 3110.270 3112.820 3115.370 3117.910 3120.460 3123.010 3125.560 3128.100 #> [937] 3130.640 3133.190 3135.730 3138.270 3140.810 3143.350 3145.890 3148.430 #> [945] 3150.960 3153.500 3156.030 3158.570 3161.100 3163.640 3166.170 3168.700 #> [953] 3171.230 3173.760 3175.300 3177.840 3180.380 3182.910 3185.450 3187.990 #> [961] 3190.520 3193.060 3195.590 3198.120 raman_hdpe$spectra # look at just the spectral intensities data.table. #>      intensity #>          <int> #>   1:        26 #>   2:        50 #>   3:        48 #>   4:        45 #>   5:        46 #>  ---           #> 960:        71 #> 961:        71 #> 962:        75 #> 963:        75 #> 964:        67 raman_hdpe$metadata # look at just the metadata of the spectra. #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  # Creating a list and transforming to OpenSpecy as_OpenSpecy(list(wavenumber = raman_hdpe$wavenumber,                   spectra = raman_hdpe$spectra,                   metadata = raman_hdpe$metadata[,-c(\"x\", \"y\")])) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id    col_id #>                              <char>    <char> #> 1: cb06ce2846b119d932fb6696479a445b intensity  # If you try to produce an OpenSpecy using an OpenSpecy it will just return # the same object. as_OpenSpecy(raman_hdpe) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  # Creating an OpenSpecy from a data.frame as_OpenSpecy(x = data.frame(wavenumber = raman_hdpe$wavenumber,                             spectra = raman_hdpe$spectra$intensity)) #> Ambiguous column names: taking 'spectra' data from all but the 'wavenumber' column; use 'colnames' to supply user-defined columnsFALSE #>      wavenumber spectra #>           <num>   <int> #>   1:    301.040      26 #>   2:    304.632      50 #>   3:    308.221      48 #>   4:    311.810      45 #>   5:    315.398      46 #>  ---                    #> 960:   3187.990      71 #> 961:   3190.520      71 #> 962:   3193.060      75 #> 963:   3195.590      75 #> 964:   3198.120      67 #>  #> $metadata #>        x     y  license  col_id                          file_id #>    <int> <int>   <char>  <char>                           <char> #> 1:     1     1 CC BY-NC spectra bf49db07d97b169635b8f8e17311cdbc  # Test that the spectrum is formatted as an OpenSpecy object. is_OpenSpecy(raman_hdpe) #> [1] TRUE is_OpenSpecy(raman_hdpe$spectra) #> [1] FALSE"},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Conform spectra to a standard wavenumber series — conform_spec","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"Spectra can conformed standard suite wavenumbers compared reference library merged spectra.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"","code":"conform_spec(x, ...)  # Default S3 method conform_spec(x, ...)  # S3 method for class 'OpenSpecy' conform_spec(x, range = NULL, res = 5, allow_na = F, type = \"interp\", ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"x list object class OpenSpecy. range vector new wavenumber values, can just supplied min max value. res spectral resolution adjusted NULL raw range used. allow_na logical; NA values places beyond wavenumbers dataset allowed? type type wavenumber adjustment make. \"interp\" results linear interpolation \"roll\" conducts nearest rolling join wavenumbers. \"mean_up\" works Spectra aggregated, take mean intensities within wavenumber specified. can maintain smaller peaks make spectra similar less resolved relatives. mean_up option still experimental. ... arguments passed approx()","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"adj_intens() returns data frame containing two columns named \"wavenumber\" \"intensity\"","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"","code":"data(\"raman_hdpe\") conform_spec(raman_hdpe, c(1000, 2000)) #>      wavenumber intensity #>           <num>     <num> #>   1:       1000  61.69439 #>   2:       1005  69.33837 #>   3:       1010  66.49849 #>   4:       1015  65.00000 #>   5:       1020  70.80060 #>  ---                      #> 197:       1980 100.32542 #> 198:       1985 114.12203 #> 199:       1990 122.80339 #> 200:       1995 106.29492 #> 201:       2000 115.16610 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalization and conversion of spectral data — adj_res","title":"Normalization and conversion of spectral data — adj_res","text":"adj_res() conform_res() helper functions align wavenumbers terms spectral resolution. adj_neg() converts numeric intensities y < 1 values >= 1, keeping absolute differences intensity values shifting value minimum intensity. make_rel() converts intensities y relative values 0 1 using standard normalization equation. na.rm TRUE, missing values removed computation proceeds.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalization and conversion of spectral data — adj_res","text":"","code":"adj_res(x, res = 1, fun = round)  conform_res(x, res = 5)  adj_neg(y, na.rm = FALSE)  mean_replace(y, na.rm = TRUE)  is_empty_vector(x)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalization and conversion of spectral data — adj_res","text":"x numeric vector R object coercible one .vector(x, \"numeric\"); x contain spectral wavenumbers. res spectral resolution supplied fun. fun function applied element x; defaults round() round specific resolution res. y numeric vector containing spectral intensities. na.rm logical. missing values removed?","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalization and conversion of spectral data — adj_res","text":"adj_res() conform_res() return numeric vector resolution-conformed wavenumbers. adj_neg() make_rel() return numeric vectors normalized intensity data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Normalization and conversion of spectral data — adj_res","text":"adj_res() conform_res() used Open Specy facilitate comparisons spectra different resolutions. adj_neg() used avoid errors arise log transforming spectra using adj_intens() functions. make_rel() used retain relative height proportions spectra avoiding large numbers can result spectral instruments.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Normalization and conversion of spectral data — adj_res","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalization and conversion of spectral data — adj_res","text":"","code":"adj_res(seq(500, 4000, 4), 5) #>   [1]  500  505  510  510  515  520  525  530  530  535  540  545  550  550  555 #>  [16]  560  565  570  570  575  580  585  590  590  595  600  605  610  610  615 #>  [31]  620  625  630  630  635  640  645  650  650  655  660  665  670  670  675 #>  [46]  680  685  690  690  695  700  705  710  710  715  720  725  730  730  735 #>  [61]  740  745  750  750  755  760  765  770  770  775  780  785  790  790  795 #>  [76]  800  805  810  810  815  820  825  830  830  835  840  845  850  850  855 #>  [91]  860  865  870  870  875  880  885  890  890  895  900  905  910  910  915 #> [106]  920  925  930  930  935  940  945  950  950  955  960  965  970  970  975 #> [121]  980  985  990  990  995 1000 1005 1010 1010 1015 1020 1025 1030 1030 1035 #> [136] 1040 1045 1050 1050 1055 1060 1065 1070 1070 1075 1080 1085 1090 1090 1095 #> [151] 1100 1105 1110 1110 1115 1120 1125 1130 1130 1135 1140 1145 1150 1150 1155 #> [166] 1160 1165 1170 1170 1175 1180 1185 1190 1190 1195 1200 1205 1210 1210 1215 #> [181] 1220 1225 1230 1230 1235 1240 1245 1250 1250 1255 1260 1265 1270 1270 1275 #> [196] 1280 1285 1290 1290 1295 1300 1305 1310 1310 1315 1320 1325 1330 1330 1335 #> [211] 1340 1345 1350 1350 1355 1360 1365 1370 1370 1375 1380 1385 1390 1390 1395 #> [226] 1400 1405 1410 1410 1415 1420 1425 1430 1430 1435 1440 1445 1450 1450 1455 #> [241] 1460 1465 1470 1470 1475 1480 1485 1490 1490 1495 1500 1505 1510 1510 1515 #> [256] 1520 1525 1530 1530 1535 1540 1545 1550 1550 1555 1560 1565 1570 1570 1575 #> [271] 1580 1585 1590 1590 1595 1600 1605 1610 1610 1615 1620 1625 1630 1630 1635 #> [286] 1640 1645 1650 1650 1655 1660 1665 1670 1670 1675 1680 1685 1690 1690 1695 #> [301] 1700 1705 1710 1710 1715 1720 1725 1730 1730 1735 1740 1745 1750 1750 1755 #> [316] 1760 1765 1770 1770 1775 1780 1785 1790 1790 1795 1800 1805 1810 1810 1815 #> [331] 1820 1825 1830 1830 1835 1840 1845 1850 1850 1855 1860 1865 1870 1870 1875 #> [346] 1880 1885 1890 1890 1895 1900 1905 1910 1910 1915 1920 1925 1930 1930 1935 #> [361] 1940 1945 1950 1950 1955 1960 1965 1970 1970 1975 1980 1985 1990 1990 1995 #> [376] 2000 2005 2010 2010 2015 2020 2025 2030 2030 2035 2040 2045 2050 2050 2055 #> [391] 2060 2065 2070 2070 2075 2080 2085 2090 2090 2095 2100 2105 2110 2110 2115 #> [406] 2120 2125 2130 2130 2135 2140 2145 2150 2150 2155 2160 2165 2170 2170 2175 #> [421] 2180 2185 2190 2190 2195 2200 2205 2210 2210 2215 2220 2225 2230 2230 2235 #> [436] 2240 2245 2250 2250 2255 2260 2265 2270 2270 2275 2280 2285 2290 2290 2295 #> [451] 2300 2305 2310 2310 2315 2320 2325 2330 2330 2335 2340 2345 2350 2350 2355 #> [466] 2360 2365 2370 2370 2375 2380 2385 2390 2390 2395 2400 2405 2410 2410 2415 #> [481] 2420 2425 2430 2430 2435 2440 2445 2450 2450 2455 2460 2465 2470 2470 2475 #> [496] 2480 2485 2490 2490 2495 2500 2505 2510 2510 2515 2520 2525 2530 2530 2535 #> [511] 2540 2545 2550 2550 2555 2560 2565 2570 2570 2575 2580 2585 2590 2590 2595 #> [526] 2600 2605 2610 2610 2615 2620 2625 2630 2630 2635 2640 2645 2650 2650 2655 #> [541] 2660 2665 2670 2670 2675 2680 2685 2690 2690 2695 2700 2705 2710 2710 2715 #> [556] 2720 2725 2730 2730 2735 2740 2745 2750 2750 2755 2760 2765 2770 2770 2775 #> [571] 2780 2785 2790 2790 2795 2800 2805 2810 2810 2815 2820 2825 2830 2830 2835 #> [586] 2840 2845 2850 2850 2855 2860 2865 2870 2870 2875 2880 2885 2890 2890 2895 #> [601] 2900 2905 2910 2910 2915 2920 2925 2930 2930 2935 2940 2945 2950 2950 2955 #> [616] 2960 2965 2970 2970 2975 2980 2985 2990 2990 2995 3000 3005 3010 3010 3015 #> [631] 3020 3025 3030 3030 3035 3040 3045 3050 3050 3055 3060 3065 3070 3070 3075 #> [646] 3080 3085 3090 3090 3095 3100 3105 3110 3110 3115 3120 3125 3130 3130 3135 #> [661] 3140 3145 3150 3150 3155 3160 3165 3170 3170 3175 3180 3185 3190 3190 3195 #> [676] 3200 3205 3210 3210 3215 3220 3225 3230 3230 3235 3240 3245 3250 3250 3255 #> [691] 3260 3265 3270 3270 3275 3280 3285 3290 3290 3295 3300 3305 3310 3310 3315 #> [706] 3320 3325 3330 3330 3335 3340 3345 3350 3350 3355 3360 3365 3370 3370 3375 #> [721] 3380 3385 3390 3390 3395 3400 3405 3410 3410 3415 3420 3425 3430 3430 3435 #> [736] 3440 3445 3450 3450 3455 3460 3465 3470 3470 3475 3480 3485 3490 3490 3495 #> [751] 3500 3505 3510 3510 3515 3520 3525 3530 3530 3535 3540 3545 3550 3550 3555 #> [766] 3560 3565 3570 3570 3575 3580 3585 3590 3590 3595 3600 3605 3610 3610 3615 #> [781] 3620 3625 3630 3630 3635 3640 3645 3650 3650 3655 3660 3665 3670 3670 3675 #> [796] 3680 3685 3690 3690 3695 3700 3705 3710 3710 3715 3720 3725 3730 3730 3735 #> [811] 3740 3745 3750 3750 3755 3760 3765 3770 3770 3775 3780 3785 3790 3790 3795 #> [826] 3800 3805 3810 3810 3815 3820 3825 3830 3830 3835 3840 3845 3850 3850 3855 #> [841] 3860 3865 3870 3870 3875 3880 3885 3890 3890 3895 3900 3905 3910 3910 3915 #> [856] 3920 3925 3930 3930 3935 3940 3945 3950 3950 3955 3960 3965 3970 3970 3975 #> [871] 3980 3985 3990 3990 3995 4000 conform_res(seq(500, 4000, 4)) #>   [1]  500  505  510  515  520  525  530  535  540  545  550  555  560  565  570 #>  [16]  575  580  585  590  595  600  605  610  615  620  625  630  635  640  645 #>  [31]  650  655  660  665  670  675  680  685  690  695  700  705  710  715  720 #>  [46]  725  730  735  740  745  750  755  760  765  770  775  780  785  790  795 #>  [61]  800  805  810  815  820  825  830  835  840  845  850  855  860  865  870 #>  [76]  875  880  885  890  895  900  905  910  915  920  925  930  935  940  945 #>  [91]  950  955  960  965  970  975  980  985  990  995 1000 1005 1010 1015 1020 #> [106] 1025 1030 1035 1040 1045 1050 1055 1060 1065 1070 1075 1080 1085 1090 1095 #> [121] 1100 1105 1110 1115 1120 1125 1130 1135 1140 1145 1150 1155 1160 1165 1170 #> [136] 1175 1180 1185 1190 1195 1200 1205 1210 1215 1220 1225 1230 1235 1240 1245 #> [151] 1250 1255 1260 1265 1270 1275 1280 1285 1290 1295 1300 1305 1310 1315 1320 #> [166] 1325 1330 1335 1340 1345 1350 1355 1360 1365 1370 1375 1380 1385 1390 1395 #> [181] 1400 1405 1410 1415 1420 1425 1430 1435 1440 1445 1450 1455 1460 1465 1470 #> [196] 1475 1480 1485 1490 1495 1500 1505 1510 1515 1520 1525 1530 1535 1540 1545 #> [211] 1550 1555 1560 1565 1570 1575 1580 1585 1590 1595 1600 1605 1610 1615 1620 #> [226] 1625 1630 1635 1640 1645 1650 1655 1660 1665 1670 1675 1680 1685 1690 1695 #> [241] 1700 1705 1710 1715 1720 1725 1730 1735 1740 1745 1750 1755 1760 1765 1770 #> [256] 1775 1780 1785 1790 1795 1800 1805 1810 1815 1820 1825 1830 1835 1840 1845 #> [271] 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 #> [286] 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 #> [301] 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 #> [316] 2075 2080 2085 2090 2095 2100 2105 2110 2115 2120 2125 2130 2135 2140 2145 #> [331] 2150 2155 2160 2165 2170 2175 2180 2185 2190 2195 2200 2205 2210 2215 2220 #> [346] 2225 2230 2235 2240 2245 2250 2255 2260 2265 2270 2275 2280 2285 2290 2295 #> [361] 2300 2305 2310 2315 2320 2325 2330 2335 2340 2345 2350 2355 2360 2365 2370 #> [376] 2375 2380 2385 2390 2395 2400 2405 2410 2415 2420 2425 2430 2435 2440 2445 #> [391] 2450 2455 2460 2465 2470 2475 2480 2485 2490 2495 2500 2505 2510 2515 2520 #> [406] 2525 2530 2535 2540 2545 2550 2555 2560 2565 2570 2575 2580 2585 2590 2595 #> [421] 2600 2605 2610 2615 2620 2625 2630 2635 2640 2645 2650 2655 2660 2665 2670 #> [436] 2675 2680 2685 2690 2695 2700 2705 2710 2715 2720 2725 2730 2735 2740 2745 #> [451] 2750 2755 2760 2765 2770 2775 2780 2785 2790 2795 2800 2805 2810 2815 2820 #> [466] 2825 2830 2835 2840 2845 2850 2855 2860 2865 2870 2875 2880 2885 2890 2895 #> [481] 2900 2905 2910 2915 2920 2925 2930 2935 2940 2945 2950 2955 2960 2965 2970 #> [496] 2975 2980 2985 2990 2995 3000 3005 3010 3015 3020 3025 3030 3035 3040 3045 #> [511] 3050 3055 3060 3065 3070 3075 3080 3085 3090 3095 3100 3105 3110 3115 3120 #> [526] 3125 3130 3135 3140 3145 3150 3155 3160 3165 3170 3175 3180 3185 3190 3195 #> [541] 3200 3205 3210 3215 3220 3225 3230 3235 3240 3245 3250 3255 3260 3265 3270 #> [556] 3275 3280 3285 3290 3295 3300 3305 3310 3315 3320 3325 3330 3335 3340 3345 #> [571] 3350 3355 3360 3365 3370 3375 3380 3385 3390 3395 3400 3405 3410 3415 3420 #> [586] 3425 3430 3435 3440 3445 3450 3455 3460 3465 3470 3475 3480 3485 3490 3495 #> [601] 3500 3505 3510 3515 3520 3525 3530 3535 3540 3545 3550 3555 3560 3565 3570 #> [616] 3575 3580 3585 3590 3595 3600 3605 3610 3615 3620 3625 3630 3635 3640 3645 #> [631] 3650 3655 3660 3665 3670 3675 3680 3685 3690 3695 3700 3705 3710 3715 3720 #> [646] 3725 3730 3735 3740 3745 3750 3755 3760 3765 3770 3775 3780 3785 3790 3795 #> [661] 3800 3805 3810 3815 3820 3825 3830 3835 3840 3845 3850 3855 3860 3865 3870 #> [676] 3875 3880 3885 3890 3895 3900 3905 3910 3915 3920 3925 3930 3935 3940 3945 #> [691] 3950 3955 3960 3965 3970 3975 3980 3985 3990 3995 4000 adj_neg(c(-1000, -1, 0, 1, 10)) #> [1]    1 1000 1001 1002 1011 make_rel(c(-1000, -1, 0, 1, 10)) #> [1] 0.0000000 0.9891089 0.9900990 0.9910891 1.0000000"},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Define features — collapse_spec","title":"Define features — collapse_spec","text":"Functions analyzing features, like particles, fragments, fibers, spectral map oriented OpenSpecy object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define features — collapse_spec","text":"","code":"collapse_spec(x, ...)  # Default S3 method collapse_spec(x, ...)  # S3 method for class 'OpenSpecy' collapse_spec(x, fun = median, column = \"feature_id\", ...)  def_features(x, ...)  # Default S3 method def_features(x, ...)  # S3 method for class 'OpenSpecy' def_features(   x,   features,   shape_kernel = c(3, 3),   shape_type = \"box\",   close = F,   close_kernel = c(4, 4),   close_type = \"box\",   img = NULL,   bottom_left = NULL,   top_right = NULL,   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define features — collapse_spec","text":"x OpenSpecy object fun function name collapse . column column name metadata collapse . features logical vector character vector describing spectra features (TRUE) (FALSE). character vector provided, represent different feature types present spectra. shape_kernel width height area pixels search connecting features, c(3,3) typically used larger numbers smooth connections particles . shape_type character, options shape used find connections c(\"box\", \"disc\", \"diamond\") close logical, whether closing performed using shape kernel estimating components. close_kernel width height area close using close option. close_type character, options shape used find connections c(\"box\", \"disc\", \"diamond\") img file location visual image corresponds spectral image. bottom_left two value vector specifying x,y location image pixels bottom left spectral map begins. y values top x values left right. top_right two value vector specifying x,y location visual image pixels top right spectral map extent . y values top x values left right. ... additional arguments passed subfunctions.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define features — collapse_spec","text":"OpenSpecy object appended metadata features collapsed features. units pixels. Metadata described . x x coordinate pixel centroid collapsed y y coordinate pixel centroid collapsed feature_id unique identifier feature area area pixels feature perimeter perimeter convex hull feature feret_min feret_max divided area feret_max largest dimension convex hull feature convex_hull_area area convex hull centroid_x mean x coordinate feature centroid_y mean y coordinate feature first_x first x coordinate feature first_y first y coordinate feature rand_x random x coordinate feature rand_y random y coordinate feature r using visual imagery overlay, red band value location g using visual imagery overlay, green band value location b using visual imagery overlay, blue band value location","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define features — collapse_spec","text":"def_features() accepts OpenSpecy object logical character vector describing pixels correspond particles. collapse_spec() takes OpenSpecy object particle-specific metadata (def_features()) collapses spectra function intensities unique particle. also updates metadata centroid coordinates, preserving feature information area Feret max.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Define features — collapse_spec","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define features — collapse_spec","text":"","code":"tiny_map <- read_extdata(\"CA_tiny_map.zip\") |> read_any() identified_map <- def_features(tiny_map, tiny_map$metadata$x == 0) collapse_spec(identified_map) #>      wavenumber          1        -88 #>           <num>      <num>      <num> #>   1:     717.42 -0.3454294 -0.1846190 #>   2:     725.13 -0.3505079 -0.2030258 #>   3:     732.85 -0.3595735 -0.2236059 #>   4:     740.56 -0.3662870 -0.2380186 #>   5:     748.28 -0.3689005 -0.2436821 #>  ---                                  #> 423:    3972.81 -0.4140871 -0.3092059 #> 424:    3980.52 -0.4108835 -0.3107601 #> 425:    3988.24 -0.4105957 -0.3180229 #> 426:    3995.95 -0.4125072 -0.3195935 #> 427:    4003.67 -0.4132366 -0.3126730 #>  #> $metadata #>        x     y feature_id  area perimeter feret_min feret_max convex_hull_area #>    <num> <num>     <char> <int>     <num>     <num>     <num>            <num> #> 1:     0     0          1    13        24         1        13                0 #> 2:     1     0        -88    NA        NA        NA        NA               NA #>          file_name  license #>             <char>   <char> #> 1: CA small UF.dat CC BY-NC #> 2: CA small UF.dat CC BY-NC #>                                                     description samples lines #>                                                          <char>   <num> <num> #> 1: Pixel 15, XPos=12549.72, YPos= 589.82, X=12362.22, Y= 614.82      16    13 #> 2: Pixel 15, XPos=12549.72, YPos= 589.82, X=12362.22, Y= 614.82      16    13 #>    bands header offset data type interleave                  z plot titles #>    <num>         <num>     <num>     <char>                         <char> #> 1:   427             0         4        bip Wavenumbers (cm-1), Absorbance #> 2:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>                  pixel size col_id #>                      <char> <char> #> 1: 0.000025000, 0.000025000    0_0 #> 2: 0.000025000, 0.000025000    0_1 #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/64639e89e58518f6b3eb1b73cdddb2ec #> 2: d1d242baa8fa46d0ce56c12a010b2596/64639e89e58518f6b3eb1b73cdddb2ec #>                             file_id centroid_x centroid_y first_x first_y #>                              <char>      <num>      <num>   <num>   <num> #> 1: 215a0fd7e0dc3b1001799214618ce6eb          0          6       0       0 #> 2: 215a0fd7e0dc3b1001799214618ce6eb          8          6       1       0 #>    rand_x rand_y #>     <num>  <num> #> 1:      0     10 #> 2:     14      1"},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic Open Specy Methods — head.OpenSpecy","title":"Generic Open Specy Methods — head.OpenSpecy","text":"Methods visualize convert OpenSpecy objects.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic Open Specy Methods — head.OpenSpecy","text":"","code":"# S3 method for class 'OpenSpecy' head(x, ...)  # S3 method for class 'OpenSpecy' print(x, ...)  # S3 method for class 'OpenSpecy' plot(x, ...)  # S3 method for class 'OpenSpecy' lines(x, ...)  # S3 method for class 'OpenSpecy' summary(object, ...)  # S3 method for class 'OpenSpecy' as.data.frame(x, ...)  # S3 method for class 'OpenSpecy' as.data.table(x, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic Open Specy Methods — head.OpenSpecy","text":"x OpenSpecy object. object OpenSpecy object. ... arguments passed respective default method.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic Open Specy Methods — head.OpenSpecy","text":"head(), print(), summary() return textual representation OpenSpecy object. plot() lines() return plot. .data.frame() .data.table() convert OpenSpecy objects tabular data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generic Open Specy Methods — head.OpenSpecy","text":"head() shows first lines OpenSpecy object. print() prints contents OpenSpecy object. summary() produces result summary OpenSpecy object. plot() produces matplot() OpenSpecy object; lines() adds new spectra .","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generic Open Specy Methods — head.OpenSpecy","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generic Open Specy Methods — head.OpenSpecy","text":"","code":"data(\"raman_hdpe\")  # Printing the OpenSpecy object print(raman_hdpe) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  # Displaying the first few lines of the OpenSpecy object head(raman_hdpe) #>    wavenumber intensity #>         <num>     <int> #> 1:    301.040        26 #> 2:    304.632        50 #> 3:    308.221        48 #> 4:    311.810        45 #> 5:    315.398        46 #> 6:    318.983        42  # Plotting the spectra plot(raman_hdpe)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":null,"dir":"Reference","previous_headings":"","what":"Create human readable timestamps — human_ts","title":"Create human readable timestamps — human_ts","text":"helper function creates human readable timestamps form %Y%m%d-%H%M%OS current time.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create human readable timestamps — human_ts","text":"","code":"human_ts()"},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create human readable timestamps — human_ts","text":"human_ts() returns character value respective timestamp.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create human readable timestamps — human_ts","text":"Human readable timestamps appended file names fields metadata shared Open Specy community.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create human readable timestamps — human_ts","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create human readable timestamps — human_ts","text":"","code":"human_ts() #> [1] \"20241023-220220\""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Interactive plots for OpenSpecy objects — plotly_spec","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"functions generate heatmaps, spectral plots, interactive plots OpenSpecy data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"","code":"plotly_spec(x, ...)  # Default S3 method plotly_spec(x, ...)  # S3 method for class 'OpenSpecy' plotly_spec(   x,   x2 = NULL,   line = list(color = \"rgb(255, 255, 255)\"),   line2 = list(dash = \"dot\", color = \"rgb(255,0,0)\"),   font = list(color = \"#FFFFFF\"),   plot_bgcolor = \"rgba(17, 0, 73, 0)\",   paper_bgcolor = \"rgb(0, 0, 0)\",   showlegend = FALSE,   make_rel = TRUE,   ... )  heatmap_spec(x, ...)  # Default S3 method heatmap_spec(x, ...)  # S3 method for class 'OpenSpecy' heatmap_spec(   x,   z = NULL,   sn = NULL,   cor = NULL,   min_sn = NULL,   min_cor = NULL,   select = NULL,   font = list(color = \"#FFFFFF\"),   plot_bgcolor = \"rgba(17, 0, 73, 0)\",   paper_bgcolor = \"rgb(0, 0, 0)\",   colorscale = \"Viridis\",   showlegend = FALSE,   type = \"interactive\",   ... )  interactive_plot(x, ...)  # Default S3 method interactive_plot(x, ...)  # S3 method for class 'OpenSpecy' interactive_plot(   x,   x2 = NULL,   select = NULL,   line = list(color = \"rgb(255, 255, 255)\"),   line2 = list(dash = \"dot\", color = \"rgb(255,0,0)\"),   font = list(color = \"#FFFFFF\"),   plot_bgcolor = \"rgba(17, 0, 73, 0)\",   paper_bgcolor = \"rgb(0, 0, 0)\",   colorscale = \"Viridis\",   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"x OpenSpecy object containing metadata spectral data first group. x2 optional second OpenSpecy object containing metadata spectral data second group. line list; line parameter x; passed add_trace(). line2 list; line parameter x2; passed font list; passed layout(). plot_bgcolor color value; passed layout(). paper_bgcolor color value; passed layout(). showlegend whether show legend passed make_rel logical, whether make spectra relative use raw values z optional numeric vector specifying intensity values heatmap. provided, function use intensity values OpenSpecy object. sn optional numeric value specifying signal--noise ratio threshold. provided along min_sn, regions SNR threshold excluded heatmap. cor optional numeric value specifying correlation threshold. provided along min_cor, regions correlation threshold excluded heatmap. min_sn optional numeric value specifying minimum signal--noise ratio inclusion heatmap. Regions SNR threshold excluded. min_cor optional numeric value specifying minimum correlation inclusion heatmap. Regions correlation threshold excluded. select optional index selected spectrum highlight heatmap. colorscale colorscale passed add_trace() can array one \"Blackbody\", \"Bluered\", \"Blues\", \"Cividis\", \"Earth\", \"Electric\", \"Greens\", \"Greys\", \"Hot\", \"Jet\", \"Picnic\", \"Portland\", \"Rainbow\", \"RdBu\", \"Reds\", \"Viridis\", \"YlGnBu\", \"YlOrRd\". type specification plot type either interactive static plot_ly(). ... arguments passed plot_ly().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"plotly heatmap object displaying OpenSpecy data. subplot containing heatmap spectra plot. plotly object displaying spectra OpenSpecy object(s).","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"","code":"data(\"raman_hdpe\") tiny_map <- read_extdata(\"CA_tiny_map.zip\") |> read_zip() plotly_spec(raman_hdpe)  {\"x\":{\"visdat\":{\"17862f39edb0\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"17862f39edb0\",\"attrs\":{\"17862f39edb0\":{\"mode\":\"lines\",\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"type\":\"scatter\"},\"17862f39edb0.1\":{\"mode\":\"lines\",\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"type\":\"scatter\",\"x\":{},\"y\":{},\"split\":{},\"line\":{\"color\":\"rgb(255, 255, 255)\"},\"name\":\"x1\",\"showlegend\":false,\"inherit\":true}},\"layout\":{\"margin\":{\"b\":40,\"l\":60,\"t\":25,\"r\":10},\"xaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"wavenumber [cm<sup>-1<\\/sup>]\",\"autorange\":\"reversed\"},\"yaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"intensity [-]\"},\"plot_bgcolor\":\"rgba(17, 0, 73, 0)\",\"paper_bgcolor\":\"rgb(0, 0, 0)\",\"legend\":{\"orientation\":\"h\",\"y\":1.1000000000000001},\"font\":{\"color\":\"#FFFFFF\"},\"hovermode\":\"closest\",\"showlegend\":false},\"source\":\"A\",\"config\":{\"modeBarButtonsToAdd\":[\"hoverclosest\",\"hovercompare\"],\"showSendToCloud\":false},\"data\":[{\"mode\":\"lines\",\"type\":\"scatter\",\"marker\":{\"color\":\"rgba(31,119,180,1)\",\"line\":{\"color\":\"rgba(31,119,180,1)\"}},\"error_y\":{\"color\":\"rgba(31,119,180,1)\"},\"error_x\":{\"color\":\"rgba(31,119,180,1)\"},\"line\":{\"color\":\"rgba(31,119,180,1)\"},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"mode\":\"lines\",\"type\":\"scatter\",\"x\":[301.04000000000002,304.63200000000001,308.221,311.81,315.39800000000002,318.983,322.56599999999997,326.14999999999998,329.73200000000003,333.31099999999998,336.88900000000001,340.46699999999998,344.04199999999997,347.61799999999999,351.19,354.75999999999999,358.33199999999999,361.89999999999998,365.46600000000001,369.03100000000001,372.59699999999998,376.16000000000003,379.721,383.27999999999997,386.839,390.39600000000002,393.95100000000002,397.505,401.05799999999999,404.61000000000001,408.16000000000003,411.70800000000003,415.25599999999997,418.80099999999999,422.346,425.887,429.42899999999997,432.97000000000003,436.50700000000001,440.04399999999998,443.58100000000002,447.11599999999999,450.64699999999999,454.18000000000001,457.70800000000003,461.238,464.76400000000001,468.291,471.815,475.339,478.85899999999998,482.37900000000002,485.899,489.416,492.93099999999998,496.447,499.95999999999998,503.471,506.98099999999999,510.48899999999998,513.99900000000002,517.50300000000004,521.00900000000001,524.50999999999999,528.01400000000001,531.51300000000003,535.01400000000001,538.50999999999999,542.00800000000004,545.50199999999995,548.99699999999996,552.48800000000006,555.97900000000004,559.46900000000005,562.95500000000004,566.44200000000001,569.928,573.41099999999994,576.89400000000001,580.37599999999998,583.85400000000004,587.33100000000002,590.81100000000004,594.28599999999994,597.75900000000001,601.23099999999999,604.70500000000004,608.17399999999998,611.64200000000005,615.10900000000004,618.57399999999996,622.03800000000001,625.50300000000004,628.96600000000001,632.42600000000004,635.88400000000001,639.34199999999998,642.798,646.25199999999995,649.70600000000002,653.15800000000002,656.60799999999995,660.05799999999999,663.50599999999997,666.95299999999997,670.39800000000002,673.84199999999998,677.28499999999997,680.72699999999998,684.16499999999996,687.60199999999998,691.03999999999996,694.476,697.91099999999994,701.34500000000003,704.77499999999998,708.20399999999995,711.63400000000001,715.06299999999999,718.49000000000001,721.91200000000003,725.33600000000001,728.75999999999999,732.17999999999995,735.59900000000005,739.01800000000003,742.43399999999997,745.84900000000005,749.26499999999999,752.67499999999995,756.08799999999997,759.49800000000005,762.90599999999995,766.31299999999999,769.71900000000005,773.12400000000002,776.52700000000004,779.92899999999997,783.33000000000004,786.72900000000004,790.12699999999995,793.524,796.91899999999998,800.31200000000001,803.70500000000004,807.09699999999998,810.48500000000001,813.87400000000002,817.26199999999994,820.64700000000005,824.03200000000004,827.41600000000005,830.79700000000003,834.17899999999997,837.55700000000002,840.93600000000004,844.31200000000001,847.68799999999999,851.06100000000004,854.43299999999999,857.80600000000004,861.17499999999995,864.54300000000001,867.90999999999997,871.27800000000002,874.64200000000005,878.005,881.36699999999996,884.72799999999995,888.08699999999999,891.44500000000005,894.80200000000002,898.15700000000004,901.51099999999997,904.86400000000003,908.21600000000001,911.56700000000001,914.91399999999999,918.26199999999994,921.60900000000004,924.95399999999995,928.29600000000005,931.63900000000001,934.98099999999999,938.32000000000005,941.65899999999999,944.995,948.33199999999999,951.66499999999996,954.99900000000002,958.33000000000004,961.65999999999997,964.99099999999999,968.31799999999998,971.64400000000001,974.971,978.29499999999996,981.61699999999996,984.94000000000005,988.25800000000004,991.57799999999997,994.89599999999996,998.21199999999999,1001.53,1004.84,1008.15,1011.46,1014.77,1018.08,1021.39,1024.7,1028,1031.3,1034.6099999999999,1037.9100000000001,1041.21,1044.51,1047.8,1051.0999999999999,1054.3900000000001,1057.6800000000001,1060.98,1064.27,1067.5599999999999,1070.8399999999999,1074.1300000000001,1077.4200000000001,1080.7,1083.98,1087.27,1090.55,1093.8299999999999,1097.0999999999999,1100.3800000000001,1103.6500000000001,1106.9300000000001,1110.2,1113.47,1116.74,1120.01,1123.28,1126.55,1129.8099999999999,1133.0799999999999,1136.3399999999999,1139.5999999999999,1142.8599999999999,1146.1199999999999,1149.3699999999999,1152.6300000000001,1155.8900000000001,1159.1400000000001,1162.3900000000001,1165.6400000000001,1168.8900000000001,1172.1400000000001,1175.3900000000001,1178.6400000000001,1181.88000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and write spectral data — write_spec","title":"Read and write spectral data — write_spec","text":"Functions reading writing spectral data OpenSpecy format. OpenSpecy objects lists components wavenumber, spectra, metadata. Currently supported formats .y()ml, .json, .csv, .rds.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read and write spectral data — write_spec","text":"","code":"write_spec(x, ...)  # Default S3 method write_spec(x, ...)  # S3 method for class 'OpenSpecy' write_spec(x, file, method = NULL, digits = getOption(\"digits\"), ...)  read_spec(file, method = NULL, ...)  as_hyperSpec(x)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read and write spectral data — write_spec","text":"x object class OpenSpecy. file file path read written . method optional; function used custom reader writer. Defaults appropriate function based file extension. digits number significant digits use formatting numeric values; defaults getOption(\"digits\"). ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read and write spectral data — write_spec","text":"read_spec() reads data formatted OpenSpecy object returns list object class OpenSpecy containing spectral data. write_spec() writes file object class OpenSpecy containing spectral data. as_hyperspec() converts OpenSpecy object hyperSpec-class object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read and write spectral data — write_spec","text":"Due floating point number errors may differences precision numbers returned using multiple devices .json .yaml files numbers nearly identical. readRDS() return exact object every time.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read and write spectral data — write_spec","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read and write spectral data — write_spec","text":"","code":"read_extdata(\"raman_hdpe.yml\") |> read_spec() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id      file_name    col_id #>                              <char>         <char>    <char> #> 1: cb06ce2846b119d932fb6696479a445b raman_hdpe.yml intensity read_extdata(\"raman_hdpe.json\") |> read_spec() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id       file_name    col_id #>                              <char>          <char>    <char> #> 1: cb06ce2846b119d932fb6696479a445b raman_hdpe.json intensity read_extdata(\"raman_hdpe.rds\") |> read_spec() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id      file_name #>                              <char>         <char> #> 1: cb06ce2846b119d932fb6696479a445b raman_hdpe.rds read_extdata(\"raman_hdpe.csv\") |> read_spec() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y      file_name  license    col_id #>    <int> <int>         <char>   <char>    <char> #> 1:     1     1 raman_hdpe.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  if (FALSE) { # \\dontrun{ data(raman_hdpe) write_spec(raman_hdpe, \"raman_hdpe.yml\") write_spec(raman_hdpe, \"raman_hdpe.json\") write_spec(raman_hdpe, \"raman_hdpe.rds\") write_spec(raman_hdpe, \"raman_hdpe.csv\")  # Convert an OpenSpecy object to a hyperSpec object hyper <- as_hyperSpec(raman_hdpe) } # }"},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":null,"dir":"Reference","previous_headings":"","what":"Make spectral intensities relative — make_rel","title":"Make spectral intensities relative — make_rel","text":"make_rel() converts intensities x relative values 0 1 using standard normalization equation. na.rm TRUE, missing values removed computation proceeds.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make spectral intensities relative — make_rel","text":"","code":"make_rel(x, ...)  # Default S3 method make_rel(x, na.rm = FALSE, ...)  # S3 method for class 'OpenSpecy' make_rel(x, na.rm = FALSE, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make spectral intensities relative — make_rel","text":"x numeric vector R OpenSpecy object na.rm logical. missing values removed? ... arguments passed make_rel().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make spectral intensities relative — make_rel","text":"make_rel() return numeric vectors (vector provided) OpenSpecy object normalized intensity data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make spectral intensities relative — make_rel","text":"make_rel() used retain relative height proportions spectra avoiding large numbers can result spectral instruments.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Make spectral intensities relative — make_rel","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make spectral intensities relative — make_rel","text":"","code":"make_rel(c(-1000, -1, 0, 1, 10)) #> [1] 0.0000000 0.9891089 0.9900990 0.9910891 1.0000000"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":null,"dir":"Reference","previous_headings":"","what":"Manage spectral libraries — check_lib","title":"Manage spectral libraries — check_lib","text":"functions import spectral libraries Open Specy already downloaded. CRAN allow deployment large datasets workaround using make sure everyone can easily get Open Specy functionality running desktop. Please see references using libraries. libraries accumulation massive amount effort independant groups attributed using data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manage spectral libraries — check_lib","text":"","code":"check_lib(   type = c(\"derivative\", \"nobaseline\", \"raw\", \"mediod\", \"model\"),   path = \"system\",   condition = \"warning\" )  get_lib(   type = c(\"derivative\", \"nobaseline\", \"raw\", \"medoid\", \"model\"),   path = \"system\",   ... )  load_lib(type, path = \"system\")  rm_lib(   type = c(\"derivative\", \"nobaseline\", \"raw\", \"mediod\", \"model\"),   path = \"system\" )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manage spectral libraries — check_lib","text":"type library type check/retrieve; defaults c(\"derivative\", \"nobaseline\", \"raw\", \"medoid\", \"model\") reads everything. path save look local library files; defaults \"system\" pointing system.file(\"extdata\", package = \"OpenSpecy\"). condition determines check_lib() warn (\"warning\", default) throw error (\"error\"). ... arguments passed osf_download().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manage spectral libraries — check_lib","text":"check_lib() get_lib() return messages ; load_lib() returns OpenSpecy object containing respective spectral reference library.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Manage spectral libraries — check_lib","text":"check_lib() checks see Open Specy reference library already exists users computer. get_lib() downloads Open Specy library OSF (doi:10.17605/OSF.IO/X7DPZ ). load_lib() load library global environment use Open Specy functions. rm_lib() removes libraries computer.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Manage spectral libraries — check_lib","text":"Bell IB, Clark RJH, Gibbs PJ (2010). “Raman Spectroscopic Library.” Christopher Ingold Laboratories, University College London, UK. https://www.chem.ucl.ac.uk/resources/raman/. Berzinš K, Sales RE, Barnsley JE, Walker G, Fraser-Miller SJ, Gordon KC (2020). “Low-Wavenumber Raman Spectral Database Pharmaceutical Excipients.” Vibrational Spectroscopy 107, 103021. doi:10.5281/zenodo.3614035 . Cabernard L, Roscher L, Lorenz C, Gerdts G, Primpke S (2018). “Comparison Raman Fourier Transform Infrared Spectroscopy Quantification Microplastics Aquatic Environment.” Environmental Science & Technology 52(22), 13279–13288. doi:10.1021/acs.est.8b03438 . Caggiani MC, Cosentino , Mangone (2016). “Pigments Checker version 3.0, handy set conservation scientists: free online Raman spectra database.” Microchemical Journal 129, 123–132. doi:10.1016/j.microc.2016.06.020 . Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy. Applied Spectroscopy.” Applied Spectroscopy 74(9), 1167–1183. doi:10.1177/0003702820923993 . Cowger, W (2023). “Library data.” OSF. doi:10.17605/OSF.IO/X7DPZ . Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi:10.1177/0003702820929064 . Cowger W, Roscher L, Chamas , Maurer B, Gehrke L, Jebens H, Gerdts G, Primpke S (2023). “High Throughput FTIR Analysis Macro Microplastics Plate Readers.” ChemRxiv Preprint. doi:10.26434/chemrxiv-2023-x88ss . De Frond H, Rubinovitz R, Rochman CM (2021). “µATR-FTIR Spectral Libraries Plastic Particles (FLOPP FLOPP-e) Analysis Microplastics.” Analytical Chemistry 93(48), 15878–15885. doi:10.1021/acs.analchem.1c02549 . El Mendili Y, Vaitkus , Merkys , Gražulis S, Chateigner D, Mathevet F, Gascoin S, Petit S, Bardeau JF, Zanatta M, Secchi M, Mariotto G, Kumar , Cassetta M, Lutterotti L, Borovin E, Orberger B, Simon P, Hehlen B, Le Guen M (2019). “Raman Open Database: first interconnected Raman–X-ray diffraction open-access resource material identification.” Journal Applied Crystallography, 52(3), 618–625. doi:10.1107/s1600576719004229 . Johnson TJ, Blake TA, Brauer CS, Su YF, Bernacki , Myers TL, Tonkyn RG, Kunkel BM, Ertel AB (2015). “Reflectance Spectroscopy Sample Identification: Considerations Quantitative Library Results Infrared Wavelengths.” International Conference Advanced Vibrational Spectroscopy (ICAVS 8). https://www.osti.gov/biblio/1452877. Lafuente R, Downs RT, Yang H, Stone N (2016). “power databases: RRUFF project.” Highlights Mineralogical Crystallography. doi:10.1515/9783110417104-003 . Munno K, De Frond H, O’Donnell B, Rochman CM (2020). “Increasing Accessibility Characterizing Microplastics: Introducing New Application-Based Spectral Libraries Plastic Particles (SLoPP SLoPP-E).” Analytical Chemistry 92(3), 2443–2451. doi:10.1021/acs.analchem.9b03626 . Myers TL, Brauer CS, Su YF, Blake TA, Johnson TJ, Richardson RL (2014). “influence particle size infrared reflectance spectra.” Proceedings Volume 9088, Algorithms Technologies Multispectral, Hyperspectral, Ultraspectral Imagery XX, 908809. doi:10.1117/12.2053350 . Myers TL, Brauer CS, Su YF, Blake TA, Tonkyn RG, Ertel AB, Johnson TJ, Richardson RL (2015). “Quantitative reflectance spectra solid powders function particle size.” Applied Optics 54(15), 4863–4875. doi:10.1364/ao.54.004863 . Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference database design automated analysis microplastic samples based Fourier transform infrared (FTIR) spectroscopy.” Analytical Bioanalytical Chemistry 410, 5131–-5141. doi:10.1007/s00216-018-1156-x . Roscher L, Fehres , Reisel L, Halbach M, Scholz-Böttcher B, Gerriets M, Badewien TH, Shiravani G, Wurpts , Primpke S, Gerdts G (2021). “Abundances large microplastics (L-MP, 500-5000 µm) surface waters Weser estuary German North Sea.” PANGAEA. doi:10.1594/PANGAEA.938143 . “Handbook Raman Spectra geology” (2023). http://www.geologie-lyon.fr/Raman/. “Scientific Workgroup Analysis Seized Drugs.” (2023). https://swgdrug.org/ir.htm. contribution spectra: Suja Sukumaran (Thermo Fisher Scientific), Aline Carvalho, Jennifer Lynch (NIST), Claudia Cella Dora Mehn (JRC), Horiba Scientific, USDA Soil Characterization Data (https://ncsslabdatamart.sc.egov.usda.gov), Archaeometrielabor, S.B. Engelsen (Royal Vet. Agricultural University, Denmark). Kimmel Center data collected provided Prof. Steven Weiner (Kimmel Center Archaeological Science, Weizmann Institute Science, Israel).","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manage spectral libraries — check_lib","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manage spectral libraries — check_lib","text":"","code":"if (FALSE) { # \\dontrun{ check_lib(\"derivative\") get_lib(\"derivative\")  spec_lib <- load_lib(\"derivative\") } # }"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Ignore or remove NA intensities — manage_na","title":"Ignore or remove NA intensities — manage_na","text":"Sometimes want keep remove NA values intensities allow spectra varying shapes analyzed together maintained single Open Specy object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ignore or remove NA intensities — manage_na","text":"","code":"manage_na(x, ...)  # Default S3 method manage_na(x, lead_tail_only = TRUE, ig = c(NA), ...)  # S3 method for class 'OpenSpecy' manage_na(x, lead_tail_only = TRUE, ig = c(NA), fun, type = \"ignore\", ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ignore or remove NA intensities — manage_na","text":"x numeric vector R OpenSpecy object. lead_tail_only logical whether look leading adn tailing values. ig character vector, values ignore. fun name function want run, used \"ignore\" type chosen. type character either \"ignore\" \"remove\". ... arguments passed fun.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ignore or remove NA intensities — manage_na","text":"manage_na() return logical vectors NA locations (vector provided) OpenSpecy object ignored removed NA values.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ignore or remove NA intensities — manage_na","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ignore or remove NA intensities — manage_na","text":"","code":"manage_na(c(NA, -1, NA, 1, 10)) #> [1]  TRUE FALSE FALSE FALSE FALSE manage_na(c(NA, -1, NA, 1, 10), lead_tail_only = FALSE) #> [1]  TRUE FALSE  TRUE FALSE FALSE manage_na(c(NA, 0, NA, 1, 10), lead_tail_only = FALSE, ig = c(NA,0)) #> [1]  TRUE  TRUE  TRUE FALSE FALSE data(raman_hdpe) raman_hdpe$spectra[[1]][1:10] <- NA  #would normally return all NA without na.rm = TRUE but doesn't here. manage_na(raman_hdpe, fun = make_rel) #>      wavenumber  intensity #>           <num>      <num> #>   1:    301.040         NA #>   2:    304.632         NA #>   3:    308.221         NA #>   4:    311.810         NA #>   5:    315.398         NA #>  ---                       #> 960:   3187.990 0.03870968 #> 961:   3190.520 0.03870968 #> 962:   3193.060 0.04387097 #> 963:   3195.590 0.04387097 #> 964:   3198.120 0.03354839 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  #will remove the first 10 values we set to NA manage_na(raman_hdpe, type = \"remove\") #>      wavenumber intensity #>           <num>     <int> #>   1:    336.889        48 #>   2:    340.467        44 #>   3:    344.042        49 #>   4:    347.618        52 #>   5:    351.190        48 #>  ---                      #> 950:   3187.990        71 #> 951:   3190.520        71 #> 952:   3193.060        75 #> 953:   3195.590        75 #> 954:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id    col_id #>                              <char>    <char> #> 1: cb06ce2846b119d932fb6696479a445b intensity"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Manage spectral objects — c_spec","title":"Manage spectral objects — c_spec","text":"c_spec() concatenates OpenSpecy objects. sample_spec() samples spectra OpenSpecy object. merge_map() merge two OpenSpecy objects spectral maps.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manage spectral objects — c_spec","text":"","code":"c_spec(x, ...)  # Default S3 method c_spec(x, ...)  # S3 method for class 'OpenSpecy' c_spec(x, ...)  # S3 method for class 'list' c_spec(x, range = NULL, res = 5, ...)  sample_spec(x, ...)  # Default S3 method sample_spec(x, ...)  # S3 method for class 'OpenSpecy' sample_spec(x, size = 1, prob = NULL, ...)  merge_map(x, ...)  # Default S3 method merge_map(x, ...)  # S3 method for class 'OpenSpecy' merge_map(x, ...)  # S3 method for class 'list' merge_map(x, origins = NULL, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manage spectral objects — c_spec","text":"x list OpenSpecy objects file paths. range numeric providing wavenumber ranges character argument called \"common\" let c_spec() find common wavenumber range supplied spectra. NULL interpret spectra wavenumber range. res defaults NULL, resolution want output wavenumbers . size number spectra sample. prob probabilities use sampling. origins list 2 value vectors x y coordinates offsets image. ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manage spectral objects — c_spec","text":"c_spec() sample_spec() return OpenSpecy objects.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manage spectral objects — c_spec","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manage spectral objects — c_spec","text":"","code":"# Concatenating spectra spectra <- lapply(c(read_extdata(\"raman_hdpe.csv\"),                     read_extdata(\"ftir_ldpe_soil.asp\")), read_any) common <- c_spec(spectra, range = \"common\", res = 5) range <- c_spec(spectra, range = c(1000, 2000), res = 5)  # Sampling spectra tiny_map <- read_any(read_extdata(\"CA_tiny_map.zip\")) sampled <- sample_spec(tiny_map, size = 3)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify and filter spectra — cor_spec","title":"Identify and filter spectra — cor_spec","text":"match_spec() joins two OpenSpecy objects metadata based similarity. cor_spec() correlates two OpenSpecy objects, typically one knowns one unknowns. ident_spec() retrieves top match values correlation matrix formats metadata. get_metadata() retrieves metadata OpenSpecy objects. max_cor_named() formats top correlation values correlation matrix named vector. filter_spec() filters Open Specy object. fill_spec() adds filler values OpenSpecy object intensities. os_similarity() EXPERIMENTAL, returns single similarity metric two OpenSpecy objects based method used.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify and filter spectra — cor_spec","text":"","code":"cor_spec(x, ...)  # Default S3 method cor_spec(x, ...)  # S3 method for class 'OpenSpecy' cor_spec(x, library, na.rm = T, conform = F, type = \"roll\", ...)  match_spec(x, ...)  # Default S3 method match_spec(x, ...)  # S3 method for class 'OpenSpecy' match_spec(   x,   library,   na.rm = T,   conform = F,   type = \"roll\",   top_n = NULL,   order = NULL,   add_library_metadata = NULL,   add_object_metadata = NULL,   fill = NULL,   ... )  ident_spec(   cor_matrix,   x,   library,   top_n = NULL,   add_library_metadata = NULL,   add_object_metadata = NULL,   ... )  get_metadata(x, ...)  # Default S3 method get_metadata(x, ...)  # S3 method for class 'OpenSpecy' get_metadata(x, logic, rm_empty = TRUE, ...)  max_cor_named(cor_matrix, na.rm = T)  filter_spec(x, ...)  # Default S3 method filter_spec(x, ...)  # S3 method for class 'OpenSpecy' filter_spec(x, logic, ...)  ai_classify(x, ...)  # Default S3 method ai_classify(x, ...)  # S3 method for class 'OpenSpecy' ai_classify(x, library, fill = NULL, ...)  fill_spec(x, ...)  # Default S3 method fill_spec(x, ...)  # S3 method for class 'OpenSpecy' fill_spec(x, fill, ...)  os_similarity(x, ...)  # Default S3 method os_similarity(x, ...)  # S3 method for class 'OpenSpecy' os_similarity(x, y, method = \"hamming\", na.rm = T, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify and filter spectra — cor_spec","text":"x OpenSpecy object, typically unknowns. library OpenSpecy glmnet object representing reference library spectra model use identification. na.rm logical; indicating whether missing values removed calculating correlations. Default TRUE. conform Whether conform spectra library wavenumbers . type type conformation make returned conform_spec() top_n integer; specifying number top matches return. NULL (default), matches returned. order OpenSpecy used sorting, ideally unprocessed one; NULL skips sorting. add_library_metadata name column library metadata joined; NULL want join. add_object_metadata name column object metadata joined; NULL want join. fill OpenSpecy object single spectrum used fill missing values alignment AI classification. cor_matrix correlation matrix object library, can returned cor_spec() logic logical numeric vector describing spectra keep. rm_empty logical; whether remove empty columns metadata. y OpenSpecy object perform similarity search x. method type similarity metric return. ... additional arguments passed cor().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify and filter spectra — cor_spec","text":"match_spec() ident_spec() return data.table-class() containing correlations spectra library. table three columns: object_id, library_id, match_val. row represents unique pairwise correlation spectrum object spectrum library. top_n specified, top top_n matches object spectrum returned. add_library_metadata .character, library metadata added output. add_object_metadata .character, object metadata added output. filter_spec() returns OpenSpecy object. fill_spec() returns OpenSpecy object. cor_spec() returns correlation matrix. get_metadata() returns data.table-class() metadata columns information. os_similarity() returns single numeric value representing type similarity metric requested. 'wavenumber' similarity based proportion wavenumber values overlap two objects, 'metadata' proportion metadata column names, 'hamming' something similar hamming distance discretize spectra OpenSpecy object wavenumber intensity values relate wavenumber intensity value distributions mean difference min-max normalized space. 'pca' tests distance OpenSpecy objects PCA space using first 4 component values calculating max-range normalized distance mean components. first two metrics pretty straightforward definitely ready go, 'hamming' 'pca' metrics pretty experimental appear working current test cases.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Identify and filter spectra — cor_spec","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify and filter spectra — cor_spec","text":"","code":"data(\"test_lib\")  unknown <- read_extdata(\"ftir_ldpe_soil.asp\") |>   read_any() |>   conform_spec(range = test_lib$wavenumber,                res = spec_res(test_lib)) |>   process_spec() cor_spec(unknown, test_lib) #>                                      intensity #> 00002e4e3fac430aa1fdfea6e26f85e4  0.5716650527 #> 00061dd49cbb71549cf3d530b894ba92 -0.0524107035 #> 0008a60c1af45a76ffb91b9cbe1a32e4  0.2120038819 #> 000902ae526db452960c5782843081b4 -0.0574671312 #> 00133c1f1531821f815431f72871eae9  0.3424853396 #> 0016677a99407c18717bf5d3eea9a0a2  0.1169383083 #> 00185609a3205a19225e1530ca98084e -0.0059672577 #> 001d3eb19976c785442a3e6fa8094bff -0.0063433127 #> 0026eee747d277edf308194c81179cb0 -0.0563867464 #> 0031bb13faea1e04b52ffbeca009e8ab  0.6675896750 #> 003b2e57d47a4225b8ea041c946cfc0a -0.0204737255 #> 0046ff947759e247f85dc63a2a25e097  0.0286048318 #> 0054792a45a11ccf7158fdf2c8873125  0.0889031245 #> 005c6a81975d747fe493032cac9dfaa2  0.2070905137 #> 0062d71f901713f2ac7a89c72fb186d4  0.2847788255 #> 00904e33ccbaa20fb68c1943d8303d82  0.0103191160 #> 00931a5ccadb549d463293381c974d79 -0.0182594153 #> 00b9f87c7d7c82675cb945fe4da37917 -0.0874221226 #> 00f4b09b9823187caf65b781b8be87b9 -0.0943789974 #> 010bfd4be6c24ced909f6de717c7b04e  0.0756006101 #> 015e8a21344a60a020226cfef344c584  0.0279306259 #> 016961c0d7def12f68239756d23a79a6 -0.0739530912 #> 019b8f3e4839f8fda1441f7d712ff12d -0.0088904495 #> 01b2fea6718855567e2e468a792b457f  0.0519834480 #> 02b8194bd714469666151a7c6b90b36c  0.0687712268 #> 03f1fa06cd6bc468ae5e406ca1d58dbb -0.0560522947 #> 041771facd71667d6bd6c9269d3701be  0.0502373982 #> 0481958e37c72c71f88d80e4dd69ac59  0.0405530434 #> 058a9af32053d14e876703a60a37498f -0.0172377431 #> 05c3cde45098e01cc44da3f72ada913e -0.0586996007 #> 06a2ed935a9b37b653717ff4a0ea62f7  0.0486159272 #> 06f7950194c7a42714cec488e378a820  0.1062184336 #> 087b8dc9fe95b7cab9d037304f0d39c8 -0.0071308393 #> 09b69fcfcf3c695299dd0f88743cf14a  0.0693075642 #> 0b10e6cc01c8e53f2221647a9c828164  0.5492755145 #> 0b8b5f10d7a9b2bd7d702cd4fdfd8a29  0.0198909283 #> 0e815860f0115ac34e2b46ac91e0c096  0.0219596516 #> 101b6ae86864958ddd95445d0ab01fe4  0.4308809802 #> 14ee1d85d0b1ee84fab0773329c45bd2 -0.0552288672 #> 1a31cfc8e25332f4771d03e8a862e217  0.0257980970 #> 23069d13c090ee5235874bd632467f9c  0.0239585112 #> 252b6f9c5fb83ec82246bc041cabd221 -0.0160335235 #> 263479214867d17e135a0d599b1f8354  0.5000294168 #> 2fa630bec93db824509be188494d79ad  0.1018184192 #> 35ce7ae774d57362dacb92cacd53b01c  0.0057562573 #> 3d35fd9a2766afaf4c088b615dffc350 -0.0009768496 #> 4762edfa07e6b28a4b90db83594f2b3d -0.0004834032 #> 4f1d701f4080b4c74acdaa04dea52dd1  0.0682211684 #> 5961a72282786fe43d8704780782f459  0.0629534493 #> 65792c9e80934e4692540a3e0fbfb552  0.0457114187 #> 6e013532253522149a0deb04373b79a2  0.1339615512 #> 7167a50e76f4a34dbb1d354babd674b0  0.0387929426 #> 76b3af06ab610907cea4dbe3aa4ec67e  0.0237972406 #> 889adce6238a677669b6523d74635d03  0.1267896304 #> 9f91f6441608ecca35f69d298e5351bd  0.0018181040 #> a692fd0b8b3b80b640ce4bbea8105570  0.0423592870 #> ec69350b1b7de35fd6b8cf65048e5fc0  0.1076174781  match_spec(unknown, test_lib, add_library_metadata = \"sample_name\",            top_n = 1) #> Key: <library_id> #>                          library_id object_id match_val     x     y #>                              <char>    <fctr>     <num> <int> <int> #> 1: 0031bb13faea1e04b52ffbeca009e8ab intensity 0.6675897    37     1 #>                         SpectrumID   Organization SpectrumType SpectrumIdentity #>                             <char>         <char>       <char>           <char> #> 1: HDPE_Sarah_#18_1s_20ac_10x_25mW J. Lynch, NIST        Raman             HDPE #>    Polymer.Category LibraryType  OWNER SpectralCollectionMode Preprocessing #>              <char>      <char> <char>                 <char>        <char> #> 1:      polyolefins    Polymers   <NA>                   <NA>          <NA> #>    InstrumentUsed RRUFFID IDEAL CHEMISTRY LOCALITY SOURCE STATUS    URL #>            <char>  <char>          <char>   <char> <char> <char> <char> #> 1:           <NA>    <NA>            <NA>     <NA>   <NA>   <NA>   <NA> #>    MEASURED CHEMISTRY OtherInformation JCAMP-DX YFACTOR YUNITS InstrumentMode #>                <char>           <char>   <char>  <char> <char>         <char> #> 1:               <NA>             <NA>     <NA>    <NA>   <NA>           <NA> #>    DELTAX Sponsor  Color   Cell     ID Comment cell_plate_id #>    <char>  <char> <char> <char> <char>  <char>        <char> #> 1:   <NA>    <NA>   <NA>   <NA>   <NA>    <NA>          <NA> #>    Form..film..foam.pliable..hard.  Brand   Item Location  Notes #>                             <char> <char> <char>   <char> <char> #> 1:                            <NA>   <NA>   <NA>     <NA>   <NA> #>    Longest.dimension  Width  Depth Particle.mass..mg. Tg..oC. Tm..oC. Onset.... #>               <char> <char> <char>             <char>  <char>  <char>    <char> #> 1:              <NA>   <NA>   <NA>               <NA>    <NA>    <NA>      <NA> #>    Enthalpy..J.g. Substance   Form Colour Filename SourceDatabase Method...49 #>            <char>    <char> <char> <char>   <char>         <char>      <char> #> 1:           <NA>      <NA>   <NA>   <NA>     <NA>           <NA>        <NA> #>      File  QA/QC Natural /Synthetic Plastic/other framework Abbreviation #>    <char> <char>             <char>        <char>    <char>       <char> #> 1:   <NA>   <NA>               <NA>          <NA>      <NA>         <NA> #>    Source ID Method...57 morphology  color final_polymer_assignment Citation #>       <char>      <char>     <char> <char>                   <char>   <char> #> 1:      <NA>        <NA>       <NA>   <NA>                     <NA>     <NA> #>    Plate Itteration SpectralResolution InstrumentAccessories Wavenumber_Range #>    <int>      <int>             <char>                <char>           <char> #> 1:    NA         NA               <NA>                  <NA>             <NA> #>    PIN_ID ORIENTATION ID merged database #>    <char>      <char>              <int> #> 1:   <NA>        <NA>                 NA #>    Database ID WWTP Paper/Kirstie/FLOPPE...70 #>                                         <int> #> 1:                                         NA #>    Database ID WWTP Paper/Kirstie/FLOPPE...71 ID (ESM1) Longest_dimension width #>                                         <int>     <int>             <int> <int> #> 1:                                         NA        NA                NA    NA #>    Cluster2018/WWTP PRESSURE TEMPERATURE MaterialForm MaterialProducer #>               <int>    <int>       <num>       <char>           <char> #> 1:               NA       NA          NA         <NA>             <NA> #>    NumberofAccumulations   TIME Ratio against background Truncated #>                    <int> <char>                   <char>    <char> #> 1:                    NA   <NA>                     <NA>      <NA> #>    ElementMultiply or ElementDivide Collection length Apodization #>                              <char>            <char>      <char> #> 1:                             <NA>              <NA>        <NA> #>    Bench serial number Subtraction or Addition Baseline Correction Smooth #>                 <char>                  <char>              <char> <char> #> 1:                <NA>                    <NA>                <NA>   <NA> #>    %Transmittance->Absorbance Sample gain Number of background scans #>                        <char>       <num>                      <int> #> 1:                       <NA>          NA                         NA #>    Background gain Sample spacing Number of scan points Resolution points #>              <num>          <num>                 <int>             <int> #> 1:              NA             NA                    NA                NA #>    Number of FFT points   ZPD Library ID Shortform Grating Hole size (nm) #>                   <int> <int>               <char>   <int>          <int> #> 1:                   NA    NA                 <NA>      NA             NA #>    Slit (um) Filter (%) Delay (s)   DATE MOLFORM $NIST SOURCE $NIST DOC FILE #>        <int>      <int>     <int> <char>  <char>       <char>         <char> #> 1:        NA         NA        NA   <NA>    <NA>         <NA>           <NA> #>    $NIST PSD FILE APERTURE BEAMSPLITTER DETECTOR (DIA. DET. PORT IN SPHERE) #>            <char>   <char>       <char>                              <char> #> 1:           <NA>     <NA>         <NA>                                <NA> #>    SPHERE DIAMETER ACQUISITION MODE SCANNER SPEED PHASE CORRECTION ZEROFILLING #>             <char>           <char>        <char>           <char>      <char> #> 1:            <NA>             <NA>          <NA>             <NA>        <NA> #>    WAVENUMBER ACCURACY LOW PASS FILTER SWITCH GAIN ON $NIST ID COADDED SCANS #>                 <char>          <char>         <char>   <char>         <int> #> 1:                <NA>            <NA>           <NA>     <NA>            NA #>    PHASE RESOLUTION    MW ContactInfo CASNumber MaterialQuality LaserLightUsed #>               <num> <num>      <char>    <char>          <char>         <char> #> 1:               NA    NA        <NA>      <NA>            <NA>           <NA> #>    TotalAcquisitionTime_s DataProcessingProcedure #>                    <char>                  <char> #> 1:                   <NA>                    <NA> #>    LevelofConfidenceinIdentification smoother baseline DENSITY new_label #>                               <char>    <int>    <int>   <num>    <char> #> 1:                              <NA>       NA       NA      NA      <NA> #>                polymer_class plastic_or_not #>                       <char>         <char> #> 1: Polyolefins (POLYALKENES)        plastic #>                                                   url_polymer_class #>                                                              <char> #> 1: https://www.polymerdatabase.com/polymer%20index/polyalkenes.html #>           polymer                                                url_polymer #>            <char>                                                     <char> #> 1: POLY(ETHYLENE) https://www.polymerdatabase.com/polymers/polyethylene.html #>                                                               url_more_info #>                                                                      <char> #> 1: https://www.polymerdatabase.com/polymer%20classes/Polyolefin%20type.html #>    snr_deriv                          file_id #>        <num>                           <char> #> 1:  1339.684 77f837c1640910f2879184d61ef1df48"},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Spectra — process_spec","title":"Process Spectra — process_spec","text":"process_spec() monolithic wrapper function spectral processing steps.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Spectra — process_spec","text":"","code":"process_spec(x, ...)  # Default S3 method process_spec(x, ...)  # S3 method for class 'OpenSpecy' process_spec(   x,   active = TRUE,   adj_intens = FALSE,   adj_intens_args = list(type = \"none\"),   conform_spec = TRUE,   conform_spec_args = list(range = NULL, res = 5, type = \"interp\"),   restrict_range = FALSE,   restrict_range_args = list(min = 0, max = 6000),   flatten_range = FALSE,   flatten_range_args = list(min = 2200, max = 2420),   subtr_baseline = FALSE,   subtr_baseline_args = list(type = \"polynomial\", degree = 8, raw = FALSE, baseline =     NULL),   smooth_intens = TRUE,   smooth_intens_args = list(polynomial = 3, window = 11, derivative = 1, abs = TRUE),   make_rel = TRUE,   make_rel_args = list(na.rm = TRUE),   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Spectra — process_spec","text":"x OpenSpecy object. active logical; indicating whether perform processing. TRUE, processing steps applied. FALSE, original data returned. adj_intens logical; describing whether adjust intensity units. adj_intens_args named list arguments passed adj_intens(). conform_spec logical; whether conform spectra new wavenumber range resolution. conform_spec_args named list arguments passed conform_spec(). restrict_range logical; indicating whether restrict wavenumber range spectra. restrict_range_args named list arguments passed restrict_range(). flatten_range logical; indicating whether flatten range around carbon dioxide region. flatten_range_args named list arguments passed flatten_range(). subtr_baseline logical; indicating whether subtract baseline spectra. subtr_baseline_args named list arguments passed subtr_baseline(). smooth_intens logical; indicating whether apply smoothing filter spectra. smooth_intens_args named list arguments passed smooth_intens(). make_rel logical; TRUE spectra automatically normalized make_rel(). make_rel_args named list arguments passed make_rel(). na.rm Whether allow NA set NA values ... arguments passed subfunctions.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Spectra — process_spec","text":"process_spec() returns OpenSpecy object processed spectra based specified parameters.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Process Spectra — process_spec","text":"","code":"data(\"raman_hdpe\") plot(raman_hdpe)  # Process spectra with range restriction and baseline subtraction process_spec(raman_hdpe,              restrict_range = TRUE,              restrict_range_args = list(min = 500, max = 3000),              subtr_baseline = TRUE,              subtr_baseline_args = list(type = \"polynomial\",                                         polynomial = 8)) |>   lines(col = \"darkred\")  # Process spectra with smoothing and derivative process_spec(raman_hdpe,              smooth_intens = TRUE,              smooth_intens_args = list(                polynomial = 3,                window = 11,                derivative = 1                )              ) |>   lines(col = \"darkgreen\")"},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample Raman spectrum — raman_hdpe","title":"Sample Raman spectrum — raman_hdpe","text":"Raman spectrum high-density polyethylene (HDPE) provided Horiba Scientific.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sample Raman spectrum — raman_hdpe","text":"threepart list class OpenSpecy containing:","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sample Raman spectrum — raman_hdpe","text":"Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi:10.1177/0003702820929064 .","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sample Raman spectrum — raman_hdpe","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample Raman spectrum — raman_hdpe","text":"","code":"data(raman_hdpe) print(raman_hdpe) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":null,"dir":"Reference","previous_headings":"","what":"Read ENVI data — read_envi","title":"Read ENVI data — read_envi","text":"function allows ENVI data import.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read ENVI data — read_envi","text":"","code":"read_envi(   file,   header = NULL,   spectral_smooth = F,   sigma = c(1, 1, 1),   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read ENVI data — read_envi","text":"file name binary file. header name ASCII header file. NULL, name header file guessed looking second file basename file .hdr extension. spectral_smooth logical value determines whether spectral smoothing performed. sigma spectral_smooth option applies 3d standard deviations gaussianSmooth function mmand package describe spectral smoothing occurs dimension. first two dimensions x y, third wavenumbers. metadata named list metadata; see as_OpenSpecy() details. ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read ENVI data — read_envi","text":"OpenSpecy object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read ENVI data — read_envi","text":"ENVI data usually consists two files, ASCII header binary data file. header contains information necessary correctly reading binary file via read.ENVI().","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read ENVI data — read_envi","text":"Zacharias Steinmetz, Claudia Beleites","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":null,"dir":"Reference","previous_headings":"","what":"Read spectral data — read_text","title":"Read spectral data — read_text","text":"Functions reading spectral data external file types. Currently supported reading formats .csv text files, .asp, .spa, .spc, .xyz, .jdx. Additionally, .0 (OPUS) .dat (ENVI) files supported via read_opus() read_envi(), respectively. read_zip() takes files listed . Note proprietary file formats like .0, .asp, .spa poorly supported likely still work cases.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read spectral data — read_text","text":"","code":"read_text(   file,   colnames = NULL,   method = \"fread\",   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_asp(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_spa(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_spc(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_jdx(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_extdata(file = NULL)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read spectral data — read_text","text":"file file read written . colnames character vector length = 2 indicating column names wavenumber intensity; NULL columns guessed. method submethod used reading text files; defaults fread() read.csv() works well. metadata named list metadata; see as_OpenSpecy() details. ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read spectral data — read_text","text":"read_*() functions return data frames containing two columns named \"wavenumber\" \"intensity\".","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read spectral data — read_text","text":"read_spc() read_jdx() wrappers around functions provided hyperSpec. functions adapted various online sources. Metadata harvested possible. many unique iterations spectral file formats may bugs file conversion. Please contact us identify .","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read spectral data — read_text","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read spectral data — read_text","text":"","code":"read_extdata(\"raman_hdpe.csv\") |> read_text() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y      file_name  license    col_id #>    <int> <int>         <char>   <char>    <char> #> 1:     1     1 raman_hdpe.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b read_extdata(\"raman_atacamit.spc\") |> read_spc() #>      wavenumber intensity #>           <num>     <num> #>   1:   117.7800  218.7200 #>   2:   119.4506  236.4000 #>   3:   121.1213  242.0400 #>   4:   122.7919  233.0500 #>   5:   124.4626  219.4200 #>  ---                      #> 555:  1043.3174   13.0066 #> 556:  1044.9881   12.4402 #> 557:  1046.6587   11.9187 #> 558:  1048.3294   11.3986 #> 559:  1050.0000   10.8148 #>  #> $metadata #>        x     y          file_name  license    col_id #>    <int> <int>             <char>   <char>    <char> #> 1:     1     1 raman_atacamit.spc CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: 2693c0b0295ce36c16b10e1e660241f2 read_extdata(\"ftir_ldpe_soil.asp\") |> read_asp() #>       wavenumber   intensity #>            <num>       <num> #>    1:   650.4205 0.027056019 #>    2:   652.2841 0.029133933 #>    3:   654.1478 0.032566057 #>    4:   656.0115 0.032249778 #>    5:   657.8751 0.028957002 #>   ---                        #> 1794:  3991.9788 0.005803670 #> 1795:  3993.8425 0.005495489 #> 1796:  3995.7062 0.005133253 #> 1797:  3997.5698 0.005595756 #> 1798:  3999.4335 0.005694630 #>  #> $metadata #>        x     y          file_name  license    col_id #>    <int> <int>             <char>   <char>    <char> #> 1:     1     1 ftir_ldpe_soil.asp CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: a33700bad0376ddab156980f473bac92 read_extdata(\"testdata_zipped.zip\") |> read_zip() #> [[1]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y                file_name  license    col_id #>    <int> <int>                   <char>   <char>    <char> #> 1:     1     1 testdata2 - Copy (2).csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #> [[2]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y            file_name  license    col_id #>    <int> <int>               <char>   <char>    <char> #> 1:     1     1 testdata2 - Copy.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #> [[3]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y     file_name  license    col_id #>    <int> <int>        <char>   <char>    <char> #> 1:     1     1 testdata2.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":null,"dir":"Reference","previous_headings":"","what":"Read spectral data from multiple files — read_any","title":"Read spectral data from multiple files — read_any","text":"Wrapper functions reading files batch.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read spectral data from multiple files — read_any","text":"","code":"read_any(file, ...)  read_many(file, ...)  read_zip(file, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read spectral data from multiple files — read_any","text":"file file read written . ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read spectral data from multiple files — read_any","text":"read_*() functions return OpenSpecy objects single spectrum map provided, otherwise provide list OpenSpecy objects.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read spectral data from multiple files — read_any","text":"read_any() provides single function quickly read supported formats, assumes file extension tell process spectra. read_zip() provides functionality reading spectral map files ENVI file format individual files zip folder. individual files, spectra concatenated. read_many() provides functionality reading multiple files character vector return list.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read spectral data from multiple files — read_any","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read spectral data from multiple files — read_any","text":"","code":"read_extdata(\"raman_hdpe.csv\") |> read_any() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y      file_name  license    col_id #>    <int> <int>         <char>   <char>    <char> #> 1:     1     1 raman_hdpe.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b read_extdata(\"ftir_ldpe_soil.asp\") |> read_any() #>       wavenumber   intensity #>            <num>       <num> #>    1:   650.4205 0.027056019 #>    2:   652.2841 0.029133933 #>    3:   654.1478 0.032566057 #>    4:   656.0115 0.032249778 #>    5:   657.8751 0.028957002 #>   ---                        #> 1794:  3991.9788 0.005803670 #> 1795:  3993.8425 0.005495489 #> 1796:  3995.7062 0.005133253 #> 1797:  3997.5698 0.005595756 #> 1798:  3999.4335 0.005694630 #>  #> $metadata #>        x     y          file_name  license    col_id #>    <int> <int>             <char>   <char>    <char> #> 1:     1     1 ftir_ldpe_soil.asp CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: a33700bad0376ddab156980f473bac92 read_extdata(\"testdata_zipped.zip\") |> read_many() #> [[1]] #> [[1]][[1]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y                file_name  license    col_id #>    <int> <int>                   <char>   <char>    <char> #> 1:     1     1 testdata2 - Copy (2).csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #> [[1]][[2]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y            file_name  license    col_id #>    <int> <int>               <char>   <char>    <char> #> 1:     1     1 testdata2 - Copy.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #> [[1]][[3]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y     file_name  license    col_id #>    <int> <int>        <char>   <char>    <char> #> 1:     1     1 testdata2.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #>  read_extdata(\"CA_tiny_map.zip\") |> read_many() #> [[1]] #>      wavenumber        0_0       0_1        0_2       0_3        0_4       0_5 #>           <num>      <num>     <num>      <num>     <num>      <num>     <num> #>   1:     717.42 -0.8599975 -1.176549 -0.8269680 -1.150691 -0.7874242 -1.131251 #>   2:     725.13 -0.8629220 -1.184673 -0.8241877 -1.145349 -0.7848697 -1.132279 #>   3:     732.85 -0.8684751 -1.201088 -0.8249527 -1.145835 -0.7878844 -1.138330 #>   4:     740.56 -0.8713333 -1.215284 -0.8285120 -1.151216 -0.7958543 -1.145293 #>   5:     748.28 -0.8721815 -1.216835 -0.8334568 -1.157416 -0.8058372 -1.147371 #>  ---                                                                           #> 423:    3972.81 -0.8804550 -1.293169 -0.8440830 -1.230502 -0.8018205 -1.190946 #> 424:    3980.52 -0.8789783 -1.295723 -0.8462818 -1.233429 -0.8030480 -1.190417 #> 425:    3988.24 -0.8772697 -1.305601 -0.8471817 -1.241330 -0.8022795 -1.192631 #> 426:    3995.95 -0.8773139 -1.310077 -0.8457789 -1.247904 -0.8011338 -1.193246 #> 427:    4003.67 -0.8778735 -1.303940 -0.8450245 -1.246171 -0.8017741 -1.188956 #>             0_6       0_7        0_8       0_9       0_10      0_11       0_12 #>           <num>     <num>      <num>     <num>      <num>     <num>      <num> #>   1: -0.8181180 -1.088998 -0.8292431 -1.072920 -0.8490217 -1.044781 -0.8055528 #>   2: -0.8157052 -1.094163 -0.8254585 -1.074778 -0.8455939 -1.042407 -0.8023769 #>   3: -0.8137981 -1.103946 -0.8287728 -1.076342 -0.8458889 -1.043109 -0.8070771 #>   4: -0.8150936 -1.110569 -0.8353058 -1.079404 -0.8477870 -1.045840 -0.8143305 #>   5: -0.8188222 -1.112912 -0.8405676 -1.084448 -0.8502600 -1.050660 -0.8213006 #>  ---                                                                           #> 423: -0.7831531 -1.134469 -0.7993639 -1.119114 -0.8128311 -1.076820 -0.8029224 #> 424: -0.7858623 -1.138433 -0.7996801 -1.123678 -0.8118886 -1.079553 -0.8038837 #> 425: -0.7878010 -1.146599 -0.7993538 -1.128633 -0.8121727 -1.081921 -0.8041241 #> 426: -0.7882353 -1.147445 -0.7993204 -1.130919 -0.8131239 -1.084749 -0.8037490 #> 427: -0.7870936 -1.141112 -0.8001940 -1.132363 -0.8130202 -1.086768 -0.8026713 #>            0_13       0_14       0_15        1_0       1_1        1_2 #>           <num>      <num>      <num>      <num>     <num>      <num> #>   1: -0.9851461 -0.7893320 -0.8729300 -0.7817467 -1.037279 -0.6797068 #>   2: -0.9868538 -0.7853272 -0.8692073 -0.7875991 -1.048603 -0.6794843 #>   3: -0.9862352 -0.7823833 -0.8676148 -0.7965024 -1.067380 -0.6840024 #>   4: -0.9870666 -0.7851717 -0.8697423 -0.8020197 -1.083057 -0.6892709 #>   5: -0.9920748 -0.7919775 -0.8769495 -0.8036770 -1.085820 -0.6939027 #>  ---                                                                  #> 423: -1.0179443 -0.7737747 -0.8834741 -0.8022860 -1.147436 -0.6932624 #> 424: -1.0177335 -0.7740544 -0.8812205 -0.8013673 -1.147939 -0.6938105 #> 425: -1.0158888 -0.7744763 -0.8804378 -0.8017516 -1.155403 -0.6935061 #> 426: -1.0132719 -0.7738099 -0.8825330 -0.8025478 -1.158695 -0.6918315 #> 427: -1.0116997 -0.7726955 -0.8838044 -0.8019281 -1.152465 -0.6914637 #>             1_3        1_4        1_5        1_6        1_7        1_8 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.9080905 -0.6090969 -0.8677020 -0.6418716 -0.8795404 -0.6977192 #>   2: -0.9057059 -0.6073114 -0.8766068 -0.6438789 -0.8903178 -0.6934057 #>   3: -0.9080038 -0.6117581 -0.8885315 -0.6456336 -0.9021218 -0.6992034 #>   4: -0.9134948 -0.6192009 -0.8989011 -0.6481388 -0.9077991 -0.7091047 #>   5: -0.9188258 -0.6264750 -0.9012332 -0.6513934 -0.9091613 -0.7153334 #>  ---                                                                   #> 423: -0.9997684 -0.6440702 -0.9665285 -0.6262602 -0.9431579 -0.6736915 #> 424: -1.0026237 -0.6445356 -0.9648960 -0.6271436 -0.9473616 -0.6741095 #> 425: -1.0109296 -0.6428438 -0.9644467 -0.6285679 -0.9552059 -0.6747956 #> 426: -1.0173761 -0.6405254 -0.9617673 -0.6288394 -0.9559735 -0.6754367 #> 427: -1.0150208 -0.6404064 -0.9559705 -0.6282292 -0.9501902 -0.6762650 #>             1_9       1_10       1_11       1_12       1_13       1_14 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.9222420 -0.7542071 -0.9433687 -0.7402943 -0.9332132 -0.7567033 #>   2: -0.9288093 -0.7519819 -0.9429623 -0.7404103 -0.9356532 -0.7536005 #>   3: -0.9341334 -0.7532891 -0.9469777 -0.7458370 -0.9353976 -0.7510077 #>   4: -0.9392776 -0.7558519 -0.9532304 -0.7527444 -0.9364607 -0.7533079 #>   5: -0.9439399 -0.7587993 -0.9586670 -0.7597492 -0.9411145 -0.7598255 #>  ---                                                                   #> 423: -0.9860366 -0.7275399 -0.9848989 -0.7450432 -0.9783244 -0.7571870 #> 424: -0.9909912 -0.7274999 -0.9867297 -0.7447987 -0.9787123 -0.7569463 #> 425: -0.9958966 -0.7286643 -0.9885115 -0.7436514 -0.9769683 -0.7576035 #> 426: -0.9968778 -0.7298300 -0.9915698 -0.7418910 -0.9733995 -0.7570600 #> 427: -0.9968597 -0.7295656 -0.9930958 -0.7406996 -0.9704962 -0.7558029 #>            1_15        2_0        2_1        2_2        2_3        2_4 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.8721603 -0.6341125 -0.8133104 -0.3901595 -0.5201151 -0.1980942 #>   2: -0.8671569 -0.6482702 -0.8365884 -0.4006985 -0.5343671 -0.2144724 #>   3: -0.8655049 -0.6606091 -0.8611584 -0.4139356 -0.5486515 -0.2310214 #>   4: -0.8679831 -0.6671489 -0.8794094 -0.4261746 -0.5613412 -0.2464838 #>   5: -0.8755950 -0.6697885 -0.8844213 -0.4350373 -0.5719566 -0.2595440 #>  ---                                                                   #> 423: -0.8880167 -0.6340107 -0.9397699 -0.4569200 -0.6700193 -0.2245990 #> 424: -0.8865166 -0.6315157 -0.9405787 -0.4589075 -0.6707723 -0.2224570 #> 425: -0.8867059 -0.6310282 -0.9495268 -0.4598360 -0.6808237 -0.2207177 #> 426: -0.8891636 -0.6334830 -0.9544747 -0.4595112 -0.6913007 -0.2204102 #> 427: -0.8905814 -0.6349432 -0.9491858 -0.4599341 -0.6900923 -0.2235211 #>             2_5        2_6        2_7        2_8        2_9       2_10 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.4099849 -0.2527224 -0.4977783 -0.3796127 -0.6079962 -0.5419455 #>   2: -0.4390349 -0.2684393 -0.5227140 -0.3849098 -0.6232019 -0.5477891 #>   3: -0.4656750 -0.2806208 -0.5420383 -0.3974718 -0.6357659 -0.5548076 #>   4: -0.4813786 -0.2890445 -0.5488055 -0.4114504 -0.6433610 -0.5590054 #>   5: -0.4849669 -0.2930531 -0.5470268 -0.4193472 -0.6473718 -0.5607016 #>  ---                                                                   #> 423: -0.5154879 -0.2002706 -0.5602914 -0.3915415 -0.7324996 -0.5452767 #> 424: -0.5099697 -0.1984237 -0.5632606 -0.3922307 -0.7367614 -0.5450110 #> 425: -0.5091169 -0.1995823 -0.5696034 -0.3931721 -0.7409591 -0.5462835 #> 426: -0.5092536 -0.2019320 -0.5696742 -0.3938283 -0.7417867 -0.5476882 #> 427: -0.5064613 -0.2023355 -0.5630394 -0.3950130 -0.7414424 -0.5475457 #>            2_11       2_12       2_13       2_14       2_15        3_0 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.7610027 -0.6229805 -0.8415435 -0.7059712 -0.8520501 -0.3957456 #>   2: -0.7661793 -0.6255116 -0.8423542 -0.7018267 -0.8484985 -0.4134940 #>   3: -0.7744415 -0.6338600 -0.8423946 -0.6993682 -0.8479570 -0.4314153 #>   4: -0.7820604 -0.6428714 -0.8442754 -0.7030330 -0.8511558 -0.4433241 #>   5: -0.7879329 -0.6515540 -0.8493459 -0.7104161 -0.8589715 -0.4486529 #>  ---                                                                   #> 423: -0.8237435 -0.6370554 -0.8907275 -0.7200700 -0.8903002 -0.4140871 #> 424: -0.8245081 -0.6377808 -0.8907435 -0.7203900 -0.8880396 -0.4108835 #> 425: -0.8265153 -0.6387181 -0.8892012 -0.7209251 -0.8868613 -0.4105957 #> 426: -0.8301792 -0.6393422 -0.8864406 -0.7207760 -0.8885221 -0.4125072 #> 427: -0.8323184 -0.6391008 -0.8829991 -0.7205515 -0.8889065 -0.4132366 #>             3_1         3_2        3_3          3_4        3_5          3_6 #>           <num>       <num>      <num>        <num>      <num>        <num> #>   1: -0.4706071 -0.03016547 -0.2407176  0.082791172 -0.2010814  0.065702595 #>   2: -0.5057679 -0.05242888 -0.2619650  0.057928637 -0.2434139  0.022760876 #>   3: -0.5376490 -0.07693648 -0.2832872  0.030258324 -0.2863251 -0.012867784 #>   4: -0.5588946 -0.09690103 -0.3049061  0.005039054 -0.3173652 -0.033973984 #>   5: -0.5629396 -0.10788352 -0.3245930 -0.014849934 -0.3317456 -0.044267047 #>  ---                                                                        #> 423: -0.6419066 -0.07969962 -0.4375644  0.015281066 -0.3982216  0.018667474 #> 424: -0.6423715 -0.08082324 -0.4397005  0.012953204 -0.3960228  0.017094024 #> 425: -0.6495159 -0.08243242 -0.4492351  0.011830383 -0.3970601  0.011106797 #> 426: -0.6525790 -0.08291471 -0.4578898  0.012850599 -0.3957864  0.005810447 #> 427: -0.6471939 -0.08415753 -0.4561356  0.012156048 -0.3883185  0.006481665 #>             3_7         3_8        3_9       3_10       3_11       3_12 #>           <num>       <num>      <num>      <num>      <num>      <num> #>   1: -0.1998793 -0.01399566 -0.2785261 -0.1904907 -0.5733052 -0.4793909 #>   2: -0.2449123 -0.03674952 -0.3024972 -0.2085340 -0.5838028 -0.4878016 #>   3: -0.2837758 -0.06542999 -0.3245068 -0.2265603 -0.5959360 -0.5013934 #>   4: -0.3092131 -0.09077036 -0.3431188 -0.2380186 -0.6060463 -0.5143723 #>   5: -0.3230577 -0.10647227 -0.3560015 -0.2436821 -0.6122009 -0.5251434 #>  ---                                                                    #> 423: -0.3698645 -0.01980718 -0.3975851 -0.1807054 -0.6520342 -0.4950348 #> 424: -0.3754378 -0.02023140 -0.4007568 -0.1797360 -0.6545247 -0.4978326 #> 425: -0.3863423 -0.02436409 -0.4062619 -0.1799946 -0.6574040 -0.4994102 #> 426: -0.3862761 -0.02749851 -0.4110578 -0.1800945 -0.6603731 -0.4983249 #> 427: -0.3753696 -0.02886785 -0.4138399 -0.1785197 -0.6617479 -0.4960453 #>            3_13       3_14       3_15         4_0        4_1        4_2 #>           <num>      <num>      <num>       <num>      <num>      <num> #>   1: -0.7665755 -0.6297212 -0.8223611 -0.06994937 -0.1948373 0.23507126 #>   2: -0.7730041 -0.6271325 -0.8182341 -0.09679992 -0.2343327 0.19651763 #>   3: -0.7757829 -0.6259195 -0.8187702 -0.12339281 -0.2791846 0.15964492 #>   4: -0.7789082 -0.6308877 -0.8228862 -0.14147647 -0.3141552 0.13575174 #>   5: -0.7851093 -0.6393642 -0.8300613 -0.15012543 -0.3275808 0.12282108 #>  ---                                                                    #> 423: -0.8091943 -0.6442823 -0.8769802 -0.10772412 -0.4471408 0.03666973 #> 424: -0.8092830 -0.6438907 -0.8746197 -0.10918775 -0.4476721 0.03725535 #> 425: -0.8063261 -0.6447943 -0.8734123 -0.10935027 -0.4572730 0.03887363 #> 426: -0.8015150 -0.6445024 -0.8748469 -0.11035092 -0.4627162 0.03930071 #> 427: -0.7982160 -0.6434273 -0.8757001 -0.11206047 -0.4563209 0.03814443 #>              4_3        4_4         4_5        4_6         4_7        4_8 #>            <num>      <num>       <num>      <num>       <num>      <num> #>   1: -0.02331112 0.26986513 -0.03277086 0.19152664 -0.06653646 0.12510364 #>   2: -0.06179452 0.23874061 -0.08068238 0.15103769 -0.10559224 0.10004297 #>   3: -0.10245890 0.20027897 -0.12478228 0.11091606 -0.15335120 0.06658314 #>   4: -0.13633749 0.16619243 -0.15494780 0.08322451 -0.18932059 0.03945834 #>   5: -0.15830097 0.14300099 -0.16945702 0.06948098 -0.20651995 0.02460175 #>  ---                                                                      #> 423: -0.39198825 0.05423059 -0.37294900 0.05756936 -0.32915789 0.07003556 #> 424: -0.39235875 0.05655045 -0.36823896 0.06076564 -0.33180121 0.07390746 #> 425: -0.39971539 0.05786621 -0.36747903 0.05863204 -0.33813080 0.07251260 #> 426: -0.40848437 0.05763303 -0.36740896 0.05429918 -0.33687240 0.06923430 #> 427: -0.41004351 0.05724512 -0.36362541 0.05428709 -0.32945213 0.06637439 #>             4_9         4_10       4_11       4_12       4_13       4_14 #>           <num>        <num>      <num>      <num>      <num>      <num> #>   1: -0.1101248  0.029535320 -0.3481123 -0.3506066 -0.6812164 -0.5529566 #>   2: -0.1527066  0.003534036 -0.3676054 -0.3633745 -0.6913838 -0.5519550 #>   3: -0.1881872 -0.025381880 -0.3941703 -0.3797562 -0.6990299 -0.5533080 #>   4: -0.2133017 -0.047853392 -0.4153597 -0.3951810 -0.7052442 -0.5597531 #>   5: -0.2292614 -0.060071301 -0.4268375 -0.4070649 -0.7121986 -0.5681785 #>  ---                                                                     #> 423: -0.2929688  0.017835222 -0.4555726 -0.3775359 -0.7328435 -0.5438783 #> 424: -0.2945867  0.019689614 -0.4546731 -0.3758536 -0.7310277 -0.5421190 #> 425: -0.2965241  0.016919773 -0.4559585 -0.3765136 -0.7278451 -0.5422699 #> 426: -0.2965332  0.012936818 -0.4604053 -0.3785573 -0.7247259 -0.5428804 #> 427: -0.2969316  0.011210520 -0.4643245 -0.3780981 -0.7230524 -0.5427254 #>            4_15        5_0         5_1        5_2         5_3        5_4 #>           <num>      <num>       <num>      <num>       <num>      <num> #>   1: -0.7702336 0.11460015 -0.03510817 0.34293243  0.11290687 0.42655033 #>   2: -0.7664602 0.08463844 -0.08284651 0.31294748  0.06828209 0.38676611 #>   3: -0.7678913 0.05434874 -0.13940026 0.27856693  0.01997435 0.33720395 #>   4: -0.7723041 0.03276557 -0.18394655 0.25130591 -0.01962078 0.29737791 #>   5: -0.7798773 0.02154485 -0.20320033 0.23551796 -0.04708247 0.27245685 #>  ---                                                                     #> 423: -0.8315062 0.03843024 -0.37421671 0.07204729 -0.36881086 0.07357048 #> 424: -0.8301061 0.03988123 -0.37570393 0.07378091 -0.36943567 0.07481265 #> 425: -0.8283818 0.03883109 -0.38774556 0.07472437 -0.38019809 0.07525086 #> 426: -0.8283017 0.03590450 -0.39282519 0.07751127 -0.39020240 0.07412328 #> 427: -0.8286900 0.03426958 -0.38598010 0.07995838 -0.38810304 0.07064135 #>              5_5        5_6          5_7        5_8         5_9       5_10 #>            <num>      <num>        <num>      <num>       <num>      <num> #>   1:  0.11753017 0.32303247  0.109790646 0.26948279  0.01834719 0.12938540 #>   2:  0.06776483 0.28515229  0.058814906 0.23558564 -0.02757971 0.09512787 #>   3:  0.01658157 0.24822997  0.007717322 0.20105782 -0.06737868 0.06089470 #>   4: -0.02130803 0.22373028 -0.032182094 0.17547110 -0.09359654 0.03801979 #>   5: -0.04069167 0.21401943 -0.059577677 0.16066670 -0.10749152 0.02652283 #>  ---                                                                       #> 423: -0.35792434 0.07230426 -0.308467656 0.08268414 -0.26994801 0.05796900 #> 424: -0.35649100 0.07285604 -0.310336679 0.08556347 -0.27288014 0.06006217 #> 425: -0.35849795 0.07272252 -0.320685357 0.08541255 -0.27874035 0.05942376 #> 426: -0.35917634 0.07051150 -0.325145066 0.08421563 -0.28221530 0.05715792 #> 427: -0.35430756 0.06807132 -0.319326639 0.08278632 -0.28278291 0.05654842 #>            5_11       5_12       5_13       5_14       5_15        6_0 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.1717014 -0.1969883 -0.5527974 -0.4865161 -0.7210248 0.18089953 #>   2: -0.2030258 -0.2211370 -0.5673119 -0.4884878 -0.7198143 0.15293343 #>   3: -0.2327487 -0.2465808 -0.5776873 -0.4922220 -0.7229511 0.12460253 #>   4: -0.2546774 -0.2635905 -0.5845709 -0.4998491 -0.7282363 0.10344046 #>   5: -0.2675154 -0.2707982 -0.5896380 -0.5077798 -0.7358948 0.08880223 #>  ---                                                                   #> 423: -0.2607206 -0.1880689 -0.6150235 -0.4683118 -0.7755380 0.13000481 #> 424: -0.2616906 -0.1876739 -0.6141766 -0.4669085 -0.7738265 0.13281861 #> 425: -0.2635156 -0.1880164 -0.6130396 -0.4668194 -0.7734500 0.13485433 #> 426: -0.2661669 -0.1878610 -0.6108111 -0.4666386 -0.7754002 0.13375901 #> 427: -0.2657728 -0.1861850 -0.6090330 -0.4672388 -0.7765176 0.13073599 #>              6_1       6_2         6_3        6_4         6_5        6_6 #>            <num>     <num>       <num>      <num>       <num>      <num> #>   1: -0.01905255 0.4086876  0.11888564 0.48320299  0.16874145 0.44582400 #>   2: -0.05683843 0.3770023  0.08798480 0.44471416  0.12288575 0.41802159 #>   3: -0.10118315 0.3361670  0.05229931 0.39902419  0.07216766 0.38312700 #>   4: -0.13947496 0.3031361  0.01808203 0.35593370  0.03447116 0.35206458 #>   5: -0.15685874 0.2850990 -0.01346898 0.32451251  0.01535929 0.32730293 #>  ---                                                                     #> 423: -0.34831402 0.1095677 -0.34016314 0.09610651 -0.34881091 0.08453228 #> 424: -0.34806740 0.1117842 -0.34011540 0.09830205 -0.34332514 0.08498331 #> 425: -0.35857370 0.1093288 -0.34659386 0.09833130 -0.34261155 0.08149479 #> 426: -0.36376283 0.1050092 -0.35318628 0.09690438 -0.34267443 0.07795043 #> 427: -0.35765105 0.1034839 -0.35265785 0.09591320 -0.33919519 0.07804901 #>              6_7        6_8         6_9       6_10         6_11         6_12 #>            <num>      <num>       <num>      <num>        <num>        <num> #>   1:  0.21916731 0.43569326  0.19325805 0.28649321  0.021155749  0.048674889 #>   2:  0.16726121 0.41328928  0.14087676 0.25508243 -0.009800109  0.024229340 #>   3:  0.11559406 0.37692678  0.10097029 0.22570246 -0.042084817 -0.007773926 #>   4:  0.07695936 0.34050414  0.07249004 0.20341654 -0.067647710 -0.031864427 #>   5:  0.05130208 0.31644794  0.05302528 0.18978401 -0.082216792 -0.040720206 #>  ---                                                                         #> 423: -0.30920589 0.09023030 -0.26208341 0.07535793 -0.207132086  0.040714394 #> 424: -0.31480187 0.08940946 -0.26722503 0.07533854 -0.203569978  0.040153250 #> 425: -0.32193980 0.08624943 -0.27583653 0.07458680 -0.207171693  0.038982097 #> 426: -0.31959346 0.08395305 -0.27948758 0.07265072 -0.215214461  0.038374390 #> 427: -0.31122905 0.08047207 -0.27833202 0.07338902 -0.218296513  0.039154116 #>            6_13       6_14       6_15        7_0         7_1       7_2 #>           <num>      <num>      <num>      <num>       <num>     <num> #>   1: -0.2817131 -0.3330086 -0.6200074 0.17514743 -0.09557199 0.4060850 #>   2: -0.3051751 -0.3411041 -0.6209924 0.14219251 -0.12218226 0.3676866 #>   3: -0.3258636 -0.3490969 -0.6252375 0.11172757 -0.15664205 0.3342102 #>   4: -0.3378293 -0.3588005 -0.6312293 0.09271412 -0.18458751 0.3104898 #>   5: -0.3440845 -0.3673511 -0.6388685 0.08287866 -0.19328009 0.2957985 #>  ---                                                                   #> 423: -0.3216147 -0.3059025 -0.6783990 0.14231284 -0.33671686 0.1486460 #> 424: -0.3206956 -0.3035870 -0.6754252 0.14542726 -0.33683294 0.1504826 #> 425: -0.3180229 -0.3014976 -0.6750098 0.14393683 -0.34644127 0.1512316 #> 426: -0.3146592 -0.3007114 -0.6782953 0.14145134 -0.35066342 0.1511961 #> 427: -0.3124145 -0.3003827 -0.6798649 0.14077842 -0.34340900 0.1495800 #>              7_3       7_4         7_5       7_6         7_7        7_8 #>            <num>     <num>       <num>     <num>       <num>      <num> #>   1:  0.10227323 0.4955553  0.16690482 0.4960505  0.22606452 0.51621252 #>   2:  0.06227709 0.4627217  0.11452152 0.4559644  0.19545102 0.47442657 #>   3:  0.02216644 0.4177838  0.07430249 0.4193894  0.15471040 0.42696881 #>   4: -0.01016116 0.3727280  0.04814187 0.3901626  0.11800983 0.39333984 #>   5: -0.02548500 0.3412454  0.03500795 0.3708753  0.09266277 0.37308314 #>  ---                                                                    #> 423: -0.31263500 0.1300725 -0.32309648 0.1092520 -0.30024305 0.10954092 #> 424: -0.31076008 0.1306683 -0.31835282 0.1115749 -0.30168831 0.10853294 #> 425: -0.31428114 0.1317825 -0.31976902 0.1091268 -0.30967656 0.10527629 #> 426: -0.32064453 0.1312346 -0.32075778 0.1039964 -0.31268883 0.10104267 #> 427: -0.32244471 0.1287172 -0.31694186 0.1019832 -0.30719063 0.09788156 #>             7_9       7_10        7_11      7_12       7_13       7_14 #>           <num>      <num>       <num>     <num>      <num>      <num> #>   1:  0.2669946 0.43942773  0.17619297 0.2079387 -0.1004790 -0.1541892 #>   2:  0.2206271 0.40604094  0.15137148 0.1752643 -0.1278359 -0.1677137 #>   3:  0.1714102 0.36487803  0.11816086 0.1402042 -0.1462014 -0.1811404 #>   4:  0.1325444 0.32844305  0.08585792 0.1160336 -0.1566433 -0.1942294 #>   5:  0.1089885 0.30745614  0.06451309 0.1055136 -0.1644206 -0.2030196 #>  ---                                                                   #> 423: -0.2565538 0.10326929 -0.18363880 0.1079208 -0.1985933 -0.1611951 #> 424: -0.2593665 0.10223637 -0.18624392 0.1098215 -0.1970126 -0.1608094 #> 425: -0.2634064 0.10008022 -0.18872470 0.1085010 -0.1931469 -0.1615037 #> 426: -0.2673599 0.09678093 -0.19275670 0.1058363 -0.1889191 -0.1614061 #> 427: -0.2727987 0.09452595 -0.19443908 0.1043969 -0.1866418 -0.1601589 #>            7_15          8_0        8_1       8_2         8_3       8_4 #>           <num>        <num>      <num>     <num>       <num>     <num> #>   1: -0.5388833  0.087934248 -0.1846190 0.3153276 -0.02330378 0.4506199 #>   2: -0.5411273  0.071245745 -0.2132872 0.2905726 -0.05313157 0.4143729 #>   3: -0.5462263  0.051149037 -0.2485526 0.2634043 -0.08097915 0.3769518 #>   4: -0.5518396  0.036638767 -0.2723057 0.2415955 -0.10360466 0.3495513 #>   5: -0.5576556  0.032449901 -0.2760711 0.2275214 -0.11793862 0.3316274 #>  ---                                                                    #> 423: -0.5895592  0.003167340 -0.3458221 0.1443959 -0.28520176 0.1748143 #> 424: -0.5865974  0.006487147 -0.3453450 0.1424087 -0.28314653 0.1798537 #> 425: -0.5859556  0.006562222 -0.3560074 0.1408821 -0.29136106 0.1807242 #> 426: -0.5888314  0.002249188 -0.3626761 0.1408205 -0.29821774 0.1776260 #> 427: -0.5904717 -0.003012084 -0.3560008 0.1408087 -0.29326501 0.1746140 #>              8_5       8_6         8_7       8_8        8_9      8_10 #>            <num>     <num>       <num>     <num>      <num>     <num> #>   1:  0.15336494 0.5026962  0.25929761 0.5448662  0.2914203 0.4914328 #>   2:  0.10891031 0.4816248  0.21767247 0.5082873  0.2498384 0.4499103 #>   3:  0.06503499 0.4589221  0.16304955 0.4671142  0.1989192 0.4126056 #>   4:  0.03334329 0.4330226  0.11946984 0.4280581  0.1616991 0.3867722 #>   5:  0.01523896 0.4074669  0.09858868 0.4038964  0.1397330 0.3701818 #>  ---                                                                  #> 423: -0.28495014 0.1534336 -0.25919485 0.1541284 -0.2198540 0.1424541 #> 424: -0.28366613 0.1540914 -0.25829354 0.1559501 -0.2187341 0.1477413 #> 425: -0.28505880 0.1516479 -0.26450428 0.1500032 -0.2210669 0.1490496 #> 426: -0.28465354 0.1485623 -0.26641917 0.1438126 -0.2242813 0.1446446 #> 427: -0.27912781 0.1472479 -0.26357475 0.1420557 -0.2270529 0.1418104 #>            8_11      8_12        8_13       8_14       8_15        9_0 #>           <num>     <num>       <num>      <num>      <num>      <num> #>   1:  0.2251298 0.3129992 -0.01930906 -0.1302575 -0.4997482 -0.1404191 #>   2:  0.1958722 0.2850758 -0.04237487 -0.1349224 -0.5037256 -0.1482290 #>   3:  0.1697999 0.2484965 -0.05983021 -0.1419368 -0.5089787 -0.1594115 #>   4:  0.1452528 0.2202435 -0.07422929 -0.1524901 -0.5153573 -0.1682616 #>   5:  0.1240693 0.2049263 -0.08699963 -0.1633783 -0.5238560 -0.1715885 #>  ---                                                                   #> 423: -0.1471200 0.1452986 -0.20291974 -0.2383699 -0.5793382 -0.2771131 #> 424: -0.1480197 0.1500701 -0.20409964 -0.2370797 -0.5767770 -0.2751649 #> 425: -0.1519214 0.1520038 -0.20630156 -0.2374736 -0.5755845 -0.2740324 #> 426: -0.1592668 0.1524178 -0.20555831 -0.2382059 -0.5769823 -0.2760093 #> 427: -0.1644321 0.1516853 -0.20182349 -0.2386301 -0.5775211 -0.2802055 #>             9_1       9_2        9_3       9_4         9_5       9_6 #>           <num>     <num>      <num>     <num>       <num>     <num> #>   1: -0.3880415 0.1964528 -0.1848320 0.3294951  0.06159078 0.5099736 #>   2: -0.4016658 0.1757130 -0.1974400 0.3060773  0.01614512 0.4669406 #>   3: -0.4255078 0.1511883 -0.2236059 0.2722496 -0.02511934 0.4293555 #>   4: -0.4447283 0.1303705 -0.2497362 0.2399727 -0.05653130 0.3961539 #>   5: -0.4463654 0.1197424 -0.2649184 0.2176039 -0.07012609 0.3755288 #>  ---                                                                 #> 423: -0.4726446 0.1334595 -0.2893841 0.2214528 -0.23276733 0.2040207 #> 424: -0.4710627 0.1328289 -0.2894299 0.2196637 -0.22747667 0.2037956 #> 425: -0.4767421 0.1337296 -0.2961083 0.2174471 -0.23046409 0.2013982 #> 426: -0.4798250 0.1356475 -0.3010665 0.2144648 -0.23281474 0.1966529 #> 427: -0.4757109 0.1343473 -0.2967418 0.2124132 -0.22850421 0.1938415 #>              9_7       9_8        9_9      9_10       9_11      9_12 #>            <num>     <num>      <num>     <num>      <num>     <num> #>   1:  0.17453891 0.5510354  0.2190386 0.4883946  0.2343115 0.3436384 #>   2:  0.13835642 0.5170754  0.2014254 0.4554164  0.1979440 0.3181358 #>   3:  0.10251222 0.4706872  0.1717279 0.4163522  0.1590499 0.2883997 #>   4:  0.07783717 0.4303708  0.1354595 0.3866921  0.1322260 0.2594654 #>   5:  0.06698119 0.4054990  0.1095207 0.3700014  0.1164159 0.2401658 #>  ---                                                                 #> 423: -0.20711856 0.1984591 -0.1697335 0.1919664 -0.1002033 0.1958435 #> 424: -0.20930032 0.1975257 -0.1713480 0.1919565 -0.1014204 0.1963937 #> 425: -0.21713974 0.1973275 -0.1762835 0.1880476 -0.1064350 0.1965378 #> 426: -0.21859702 0.1965215 -0.1810040 0.1842630 -0.1137590 0.1959506 #> 427: -0.21456438 0.1942090 -0.1817123 0.1841256 -0.1176966 0.1958615 #>             9_13       9_14       9_15       10_0       10_1         10_2 #>            <num>      <num>      <num>      <num>      <num>        <num> #>   1: -0.03961723 -0.2325946 -0.5138462 -0.3454294 -0.5610783 -0.003405581 #>   2: -0.06623416 -0.2391484 -0.5182777 -0.3505079 -0.5801501 -0.016585065 #>   3: -0.08755251 -0.2452990 -0.5224738 -0.3595735 -0.6064633 -0.035956357 #>   4: -0.10419883 -0.2539910 -0.5295882 -0.3662870 -0.6256430 -0.051071431 #>   5: -0.11806381 -0.2648067 -0.5404692 -0.3689005 -0.6284144 -0.058754291 #>  ---                                                                      #> 423: -0.20896144 -0.3554857 -0.5956661 -0.4831747 -0.6749610 -0.006982267 #> 424: -0.20708182 -0.3550624 -0.5945614 -0.4809514 -0.6780014 -0.005729941 #> 425: -0.20645618 -0.3566869 -0.5941038 -0.4795221 -0.6867968 -0.004676747 #> 426: -0.20509402 -0.3572714 -0.5957916 -0.4812161 -0.6908116 -0.004938770 #> 427: -0.20455551 -0.3560388 -0.5966506 -0.4826814 -0.6844195 -0.006340539 #>            10_3       10_4        10_5      10_6         10_7      10_8 #>           <num>      <num>       <num>     <num>        <num>     <num> #>   1: -0.3789502 0.13877778 -0.09041325 0.3947443  0.096415646 0.5012836 #>   2: -0.3927487 0.11653356 -0.12458400 0.3548783  0.067286357 0.4652751 #>   3: -0.4124519 0.08843436 -0.15803877 0.3201760  0.033194412 0.4181609 #>   4: -0.4331997 0.06170695 -0.18525149 0.2936058  0.004564124 0.3783152 #>   5: -0.4479685 0.04130512 -0.20156626 0.2748121 -0.016045203 0.3591099 #>  ---                                                                    #> 423: -0.4192245 0.21815482 -0.19248809 0.2559083 -0.168943003 0.2504518 #> 424: -0.4255278 0.22156312 -0.19656545 0.2575580 -0.171470091 0.2539417 #> 425: -0.4362897 0.22243653 -0.20338295 0.2567109 -0.178340837 0.2579956 #> 426: -0.4440649 0.22056223 -0.20394890 0.2551845 -0.180030599 0.2567944 #> 427: -0.4405728 0.21886383 -0.19539179 0.2556418 -0.175978899 0.2496768 #>             10_9     10_10       10_11     10_12       10_13      10_14 #>            <num>     <num>       <num>     <num>       <num>      <num> #>   1:  0.16088957 0.4022605  0.12462527 0.3303100 -0.07084840 -0.3302290 #>   2:  0.13614024 0.3802870  0.10487096 0.3162005 -0.08289412 -0.3306039 #>   3:  0.10655165 0.3530020  0.08353272 0.2910916 -0.09676377 -0.3353746 #>   4:  0.08416789 0.3325290  0.06740724 0.2647256 -0.10807587 -0.3462425 #>   5:  0.06685340 0.3216399  0.05747279 0.2419084 -0.12040044 -0.3579053 #>  ---                                                                    #> 423: -0.11701809 0.2348692 -0.06368053 0.2281845 -0.15948832 -0.4201501 #> 424: -0.12409801 0.2383129 -0.06182728 0.2300553 -0.15933338 -0.4195252 #> 425: -0.13336122 0.2388805 -0.06120444 0.2304868 -0.15755838 -0.4191516 #> 426: -0.13745880 0.2341105 -0.06617249 0.2291671 -0.15389903 -0.4183375 #> 427: -0.13737124 0.2278370 -0.07184657 0.2277900 -0.15289837 -0.4172875 #>           10_15       11_0       11_1       11_2       11_3        11_4 #>           <num>      <num>      <num>      <num>      <num>       <num> #>   1: -0.5502016 -0.4089794 -0.7035590 -0.1988441 -0.6013403 -0.04612614 #>   2: -0.5491410 -0.4128581 -0.7133205 -0.2056266 -0.6083025 -0.06301054 #>   3: -0.5519719 -0.4174158 -0.7311138 -0.2184241 -0.6193420 -0.08642311 #>   4: -0.5582104 -0.4215689 -0.7447819 -0.2302519 -0.6318524 -0.10882086 #>   5: -0.5680800 -0.4243726 -0.7436408 -0.2369744 -0.6402031 -0.12603880 #>  ---                                                                    #> 423: -0.6367587 -0.5125267 -0.8724214 -0.3014192 -0.6272018  0.09982392 #> 424: -0.6344557 -0.5105906 -0.8761734 -0.3029543 -0.6285866  0.10479824 #> 425: -0.6346116 -0.5097994 -0.8845704 -0.3034330 -0.6365673  0.10503902 #> 426: -0.6366958 -0.5112385 -0.8873740 -0.3033138 -0.6447878  0.10215051 #> 427: -0.6373948 -0.5125880 -0.8805217 -0.3047109 -0.6447583  0.09967330 #>            11_5      11_6        11_7      11_8         11_9     11_10 #>           <num>     <num>       <num>     <num>        <num>     <num> #>   1: -0.3021611 0.1909627 -0.06505151 0.3536376  0.001076699 0.2811520 #>   2: -0.3240646 0.1635235 -0.09604929 0.3317861 -0.020703882 0.2534717 #>   3: -0.3507667 0.1393459 -0.12998015 0.3020550 -0.043339010 0.2297511 #>   4: -0.3766404 0.1217387 -0.15413399 0.2728064 -0.061574921 0.2129553 #>   5: -0.3931606 0.1105335 -0.16765058 0.2570367 -0.073306367 0.2012097 #>  ---                                                                   #> 423: -0.2195135 0.2539870 -0.14811064 0.2794592 -0.083007313 0.2626795 #> 424: -0.2189381 0.2545430 -0.15172820 0.2852612 -0.081238411 0.2603240 #> 425: -0.2219534 0.2513329 -0.15867922 0.2861450 -0.083784021 0.2556774 #> 426: -0.2223453 0.2494126 -0.15801638 0.2827066 -0.087282449 0.2529730 #> 427: -0.2186473 0.2508664 -0.15099348 0.2778258 -0.093390450 0.2562906 #>            11_11     11_12      11_13      11_14      11_15       12_0 #>            <num>     <num>      <num>      <num>      <num>      <num> #>   1: -0.04550443 0.1613120 -0.2492418 -0.3870185 -0.5538554 -0.5889121 #>   2: -0.05919959 0.1532610 -0.2606162 -0.3864829 -0.5549448 -0.5879138 #>   3: -0.07607881 0.1355628 -0.2675352 -0.3900220 -0.5566438 -0.5898755 #>   4: -0.09350286 0.1189713 -0.2733023 -0.4005955 -0.5599077 -0.5909628 #>   5: -0.10829892 0.1061453 -0.2821340 -0.4124226 -0.5665730 -0.5905932 #>  ---                                                                   #> 423: -0.04777495 0.1684111 -0.2748755 -0.4656470 -0.6215362 -0.6319057 #> 424: -0.04765457 0.1688343 -0.2724172 -0.4665069 -0.6193126 -0.6320986 #> 425: -0.04815739 0.1672493 -0.2692471 -0.4676030 -0.6189794 -0.6328617 #> 426: -0.05001089 0.1658563 -0.2664106 -0.4671336 -0.6216958 -0.6342685 #> 427: -0.05160535 0.1668420 -0.2649757 -0.4658949 -0.6232929 -0.6346731 #>            12_1       12_2       12_3       12_4       12_5         12_6 #>           <num>      <num>      <num>      <num>      <num>        <num> #>   1: -0.9084547 -0.4400639 -0.7794662 -0.2471266 -0.5029922  0.001548128 #>   2: -0.9101344 -0.4375786 -0.7770191 -0.2589712 -0.5270271 -0.022970274 #>   3: -0.9202237 -0.4420333 -0.7809778 -0.2757573 -0.5534080 -0.042182062 #>   4: -0.9293817 -0.4489326 -0.7892566 -0.2921411 -0.5725283 -0.055214901 #>   5: -0.9286409 -0.4542748 -0.7958061 -0.3046873 -0.5792183 -0.064597100 #>  ---                                                                     #> 423: -1.0792398 -0.5509068 -0.7931226 -0.1663056 -0.3932111  0.167518958 #> 424: -1.0811239 -0.5530722 -0.7953201 -0.1666212 -0.3933381  0.164244816 #> 425: -1.0896326 -0.5551605 -0.8030046 -0.1681725 -0.3954019  0.159555063 #> 426: -1.0944729 -0.5558071 -0.8086336 -0.1697897 -0.3963541  0.157658920 #> 427: -1.0899518 -0.5560302 -0.8055803 -0.1712147 -0.3940453  0.159509078 #>            12_7       12_8       12_9        12_10      12_11      12_12 #>           <num>      <num>      <num>        <num>      <num>      <num> #>   1: -0.2636845 0.15986118 -0.2198299  0.012900840 -0.3475010 -0.1755732 #>   2: -0.2925263 0.13392644 -0.2365713  0.002008484 -0.3510732 -0.1797495 #>   3: -0.3226509 0.10030603 -0.2550317 -0.013462396 -0.3556432 -0.1896459 #>   4: -0.3424500 0.06911202 -0.2684755 -0.026638059 -0.3618752 -0.1994632 #>   5: -0.3514164 0.04984137 -0.2740572 -0.033214808 -0.3696662 -0.2079690 #>  ---                                                                     #> 423: -0.2231302 0.23791830 -0.1277080  0.222949252 -0.3009740 -0.2151229 #> 424: -0.2291479 0.23654796 -0.1266753  0.221743941 -0.3037770 -0.2165980 #> 425: -0.2400716 0.23113360 -0.1276943  0.217651576 -0.3070489 -0.2181160 #> 426: -0.2403418 0.22415788 -0.1321646  0.217663661 -0.3105931 -0.2197210 #> 427: -0.2322121 0.22267960 -0.1382107  0.219312802 -0.3126730 -0.2208407 #>           12_13      12_14      12_15 #>           <num>      <num>      <num> #>   1: -0.5086929 -0.4955095 -0.6490921 #>   2: -0.5129193 -0.4911957 -0.6464767 #>   3: -0.5125197 -0.4882756 -0.6439207 #>   4: -0.5119967 -0.4921051 -0.6446184 #>   5: -0.5161167 -0.4999369 -0.6508147 #>  ---                                  #> 423: -0.5514245 -0.5360808 -0.7430303 #> 424: -0.5530964 -0.5362788 -0.7415859 #> 425: -0.5516642 -0.5378205 -0.7405182 #> 426: -0.5466566 -0.5389180 -0.7409301 #> 427: -0.5412602 -0.5395846 -0.7407396 #>  #> $metadata #>          y     x       file_name  license #>      <num> <num>          <char>   <char> #>   1:     0     0 CA small UF.dat CC BY-NC #>   2:     0     1 CA small UF.dat CC BY-NC #>   3:     0     2 CA small UF.dat CC BY-NC #>   4:     0     3 CA small UF.dat CC BY-NC #>   5:     0     4 CA small UF.dat CC BY-NC #>  ---                                      #> 204:    12    11 CA small UF.dat CC BY-NC #> 205:    12    12 CA small UF.dat CC BY-NC #> 206:    12    13 CA small UF.dat CC BY-NC #> 207:    12    14 CA small UF.dat CC BY-NC #> 208:    12    15 CA small UF.dat CC BY-NC #>                                                       description samples lines #>                                   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d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>  ---                                                                   #> 204: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #> 205: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #> 206: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #> 207: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #> 208: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                               file_id #>                                <char> #>   1: 215a0fd7e0dc3b1001799214618ce6eb #>   2: 215a0fd7e0dc3b1001799214618ce6eb #>   3: 215a0fd7e0dc3b1001799214618ce6eb #>   4: 215a0fd7e0dc3b1001799214618ce6eb #>   5: 215a0fd7e0dc3b1001799214618ce6eb #>  ---                                  #> 204: 215a0fd7e0dc3b1001799214618ce6eb #> 205: 215a0fd7e0dc3b1001799214618ce6eb #> 206: 215a0fd7e0dc3b1001799214618ce6eb #> 207: 215a0fd7e0dc3b1001799214618ce6eb #> 208: 215a0fd7e0dc3b1001799214618ce6eb #>"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":null,"dir":"Reference","previous_headings":"","what":"Read spectral data from Bruker OPUS binary files — read_opus","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"Read file(s) acquired Bruker Vertex FTIR Instrument. function basically fork opus_read() https://github.com/pierreroudier/opusreader.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"","code":"read_opus(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   type = \"spec\",   digits = 1L,   atm_comp_minus4offset = FALSE )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"file character vector path file(s). metadata named list metadata; see as_OpenSpecy() details. type character vector spectra types extract OPUS binary file. Default \"spec\", extract final spectra, e.g. expressed absorbance (named AB Bruker OPUS programs). Possible additional values character vector supplied type \"spec_no_atm_comp\" (spectrum sample without compensation atmospheric gases, water vapor /carbon dioxide), \"sc_sample\" (single channel spectrum sample measurement), \"sc_ref\" (single channel spectrum reference measurement), \"ig_sample\" (interferogram sample measurement) \"ig_ref\" (interferogram reference measurement). digits Integer specifies number decimal places used round wavenumbers (values x-variables). atm_comp_minus4offset Logical whether spectra atmospheric compensation read offset -4 bytes Bruker OPUS files; default FALSE.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"OpenSpecy object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"type spectra returned function using type = \"spec\" depends setting Bruker instrument: typically, can either absorbance reflectance. type spectra extract file can also use Bruker's OPUS software naming conventions, follows: ScSm corresponds sc_sample ScRf corresponds sc_ref IgSm corresponds ig_sample IgRf corresponds ig_ref","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"Philipp Baumann, Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"","code":"read_extdata(\"ftir_ps.0\") |> read_opus() #>       wavenumber         V1 #>            <num>      <num> #>    1:   399.2239 0.01660202 #>    2:   401.1525 0.01790014 #>    3:   403.0811 0.01831616 #>    4:   405.0097 0.01894823 #>    5:   406.9384 0.02070733 #>   ---                       #> 2122:  4489.8223 0.02349887 #> 2123:  4491.7509 0.02352615 #> 2124:  4493.6796 0.02357077 #> 2125:  4495.6082 0.02362563 #> 2126:  4497.5368 0.02361715 #>  #> $metadata #>        x     y file_name  license                         unique_id #>    <int> <int>    <char>   <char>                            <char> #> 1:     1     1 ftir_ps.0 CC BY-NC sample1_PSnew_2020-10-28 14:33:02 #>        sample_id        date_time_sm        date_time_rf   sample_name #>           <char>              <POSc>              <POSc>        <char> #> 1: sample1_PSnew 2020-10-28 14:33:02 2020-10-28 14:23:20 sample1_PSnew #>    instr_name_range resolution_wn result_spc beamspl laser_wn #>              <char>         <int>     <char>  <char>    <num> #> 1:    VERTEX 70-MIR             4         AB     KBr 15799.24 #>                               spc_in_file zero_filling temp_scanner_sm #>                                    <char>        <int>           <num> #> 1: ig_sample;ig_ref;sc_sample;sc_ref;spec            2            27.9 #>    temp_scanner_rf hum_rel_sm hum_rel_rf hum_abs_sm hum_abs_rf col_id #>              <num>      <int>     <lgcl>     <lgcl>     <lgcl> <char> #> 1:            27.9          2         NA         NA         NA     V1 #>                                                           session_id #>                                                               <char> #> 1: d1d242baa8fa46d0ce56c12a010b2596/97535c1a8fde695071d79cd2d5cbd234 #>                             file_id #>                              <char> #> 1: 0e9318d04fdb3beb4cd61145b080ccb6"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":null,"dir":"Reference","previous_headings":"","what":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"Read single binary acquired Bruker Vertex FTIR Instrument","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"","code":"read_opus_raw(rw, type = \"spec\", atm_comp_minus4offset = FALSE)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"rw raw vector type character vector spectra types extract OPUS binary file. Default \"spec\", extract final spectra, e.g. expressed absorbance (named AB Bruker OPUS programs). Possible additional values character vector supplied type \"spec_no_atm_comp\" (spectrum sample without compensation atmospheric gases, water vapor /carbon dioxide), \"sc_sample\" (single channel spectrum sample measurement), \"sc_ref\" (single channel spectrum reference measurement), \"ig_sample\" (interferogram sample measurement) \"ig_ref\" (interferogram reference measurement). atm_comp_minus4offset logical; whether spectra atmospheric compensation read offset -4 bytes Bruker OPUS files. Default FALSE.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"list 10 elements: metadata data.frame containing metadata OPUS file. spec \"spec\" requested type option, matrix spectrum sample (otherwise set NULL). spec_no_atm_comp \"spec_no_atm_comp\" requested type option, matrix spectrum sample without atmospheric compensation (otherwise set NULL). sc_sample \"sc_sample\" requested type option, matrix single channel spectrum sample (otherwise set NULL). sc_ref \"sc_ref\" requested type option, matrix single channel spectrum reference (otherwise set NULL). ig_sample \"ig_sample\" requested type option, matrix interferogram sample (otherwise set NULL). ig_ref \"ig_ref\" requested type option, matrix interferogram reference (otherwise set NULL). wavenumbers \"spec\" \"spec_no_atm_comp\" requested type option, numeric vector wavenumbers spectrum sample (otherwise set NULL). wavenumbers_sc_sample \"sc_sample\" requested type option, numeric vector wavenumbers single channel spectrum sample (otherwise set NULL). wavenumbers_sc_ref \"sc_ref\" requested type option, numeric vector wavenumbers single channel spectrum reference (otherwise set NULL).","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"type spectra returned function using type = \"spec\" depends setting Bruker instrument: typically, can either absorbance reflectance. type spectra extract file can also use Bruker's OPUS software naming conventions, follows: ScSm corresponds sc_sample ScRf corresponds sc_ref IgSm corresponds ig_sample IgRf corresponds ig_ref","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"Philipp Baumann Pierre Roudier","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":null,"dir":"Reference","previous_headings":"","what":"Run Open Specy app — run_app","title":"Run Open Specy app — run_app","text":"wrapper function starts graphical user interface Open Specy.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run Open Specy app — run_app","text":"","code":"run_app(path = \"system\", log = TRUE, ref = \"main\", test_mode = FALSE, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run Open Specy app — run_app","text":"path store downloaded app files; defaults \"system\" pointing system.file(package = \"OpenSpecy\"). log logical; enables/disables logging tempdir() ref git reference; commit, tag, branch name. Defaults \"main\". change case errors. test_mode logical; internal testing . ... arguments passed runApp().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run Open Specy app — run_app","text":"function normally return value, see runGitHub().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Run Open Specy app — run_app","text":"running function Open Specy GUI open separate window computer browser.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Run Open Specy app — run_app","text":"Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run Open Specy app — run_app","text":"","code":"if (FALSE) { # \\dontrun{ run_app() } # }"},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"function calculates common signal noise metrics OpenSpecy objects.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"","code":"sig_noise(x, ...)  # Default S3 method sig_noise(x, ...)  # S3 method for class 'OpenSpecy' sig_noise(   x,   metric = \"run_sig_over_noise\",   na.rm = TRUE,   prob = 0.5,   step = 20,   breaks = seq(min(unlist(x$spectra)), max(unlist(x$spectra)), length =     ((nrow(x$spectra)^(1/3)) * (max(unlist(x$spectra)) - min(unlist(x$spectra))))/(2 *     IQR(unlist(x$spectra)))),   sig_min = NULL,   sig_max = NULL,   noise_min = NULL,   noise_max = NULL,   abs = T,   spatial_smooth = F,   sigma = c(1, 1),   threshold = NULL,   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"x OpenSpecy object. metric character; specifying desired metric calculate. Options include \"sig\" (mean intensity), \"noise\" (standard deviation intensity), \"sig_times_noise\" (absolute value signal times noise), \"sig_over_noise\" (absolute value signal / noise), \"run_sig_over_noise\" (absolute value signal / noise signal estimated max intensity noise estimated height low intensity region.), \"log_tot_sig\" (sum inverse log intensities, useful spectra  log units), \"tot_sig\" (sum intensities), \"entropy\" (Shannon entropy intensities).. na.rm logical; indicating whether missing values removed calculating signal noise. Default TRUE. prob numeric single value; probability retrieve quantile noise interpreted run_sig_over_noise option. step numeric; step size region look run_sig_over_noise option. breaks numeric; number positions breaks entropy calculation. Defaults infer decent value data. sig_min numeric; minimum wavenumber value signal region. sig_max numeric; maximum wavenumber value signal region. noise_min numeric; minimum wavenumber value noise region. noise_max numeric; maximum wavenumber value noise region. abs logical; whether return absolute value result spatial_smooth logical; whether spatially smooth sig/noise using xy coordinates gaussian smoother. sigma numeric; two value vector describing standard deviation smoother dimension, y specified first followed x, cases. threshold numeric; NULL, threshold set, otherwise use numeric value set target threshold true signal noise . function return logical value instead numeric threshold set. ... arguments passed subfunctions; currently used.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"numeric vector containing calculated metric spectrum OpenSpecy object logical value threshold set describing numbers equal (TRUE) threshold.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"","code":"data(\"raman_hdpe\")  sig_noise(raman_hdpe, metric = \"sig\") #> intensity  #>  101.1691  sig_noise(raman_hdpe, metric = \"noise\") #> intensity  #>  61.00777  sig_noise(raman_hdpe, metric = \"sig_times_noise\") #> intensity  #>    6172.1"},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth spectral intensities — smooth_intens","title":"Smooth spectral intensities — smooth_intens","text":"smoother can enhance signal noise ratio data useing Savitzky-Golay Whittaker-Henderson filter.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth spectral intensities — smooth_intens","text":"","code":"smooth_intens(x, ...)  # Default S3 method smooth_intens(x, ...)  # S3 method for class 'OpenSpecy' smooth_intens(   x,   polynomial = 3,   window = 11,   derivative = 1,   abs = TRUE,   lambda = 1600,   d = 2,   type = \"sg\",   lag = 2,   make_rel = TRUE,   ... )  calc_window_points(x, ...)  # Default S3 method calc_window_points(x, wavenum_width = 70, ...)  # S3 method for class 'OpenSpecy' calc_window_points(x, wavenum_width = 70, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth spectral intensities — smooth_intens","text":"x object class OpenSpecy vector calc_window_points(). polynomial polynomial order filter window number data points window, filter length (must odd). derivative derivative order want calculate derivative. Zero (default) derivative. abs logical; whether want calculate absolute value resulting output. lambda smoothing parameter Whittaker-Henderson smoothing, 50 results rough smoothing 10^4 results high level smoothing. d order differences use Whittaker-Henderson smoothing, typically set 2. type type smoothing use \"wh\" Whittaker-Henerson \"sg\" Savitzky-Golay. lag lag use numeric derivative calculation using Whittaker-Henderson. Greater values lead smoother derivatives, 1 2 common. make_rel logical; TRUE spectra automatically normalized make_rel(). wavenum_width width window want wavenumbers. ... arguments passed sgolay().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Smooth spectral intensities — smooth_intens","text":"smooth_intens() returns OpenSpecy object. calc_window_points() returns single numberic vector object number points needed fill window can passed smooth_intens(). many applications, reusable specifying static number points.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Smooth spectral intensities — smooth_intens","text":"Savitzky-Golay wrapper around filter function signal package improve integration Open Specy functions. typical good smooth can achieved 11 data point window 3rd 4th order polynomial. Whittaker-Henderson, code largely based whittaker() function pracma package. general Whittaker-Henderson expected slower robust Savitzky-Golay.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Smooth spectral intensities — smooth_intens","text":"Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Smooth spectral intensities — smooth_intens","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Smooth spectral intensities — smooth_intens","text":"","code":"data(\"raman_hdpe\")  smooth_intens(raman_hdpe) #>      wavenumber   intensity #>           <num>       <num> #>   1:    301.040 0.102500885 #>   2:    304.632 0.063171177 #>   3:    308.221 0.031695428 #>   4:    311.810 0.008073638 #>   5:    315.398 0.007614310 #>  ---                        #> 960:   3187.990 0.002890315 #> 961:   3190.520 0.005482884 #> 962:   3193.060 0.009382631 #> 963:   3195.590 0.014589555 #> 964:   3198.120 0.021103657 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  smooth_intens(raman_hdpe, window = calc_window_points(x = raman_hdpe, wavenum_width = 70)) #>      wavenumber  intensity #>           <num>      <num> #>   1:    301.040 0.06819092 #>   2:    304.632 0.05691608 #>   3:    308.221 0.04668103 #>   4:    311.810 0.03748578 #>   5:    315.398 0.02933032 #>  ---                       #> 960:   3187.990 0.01418184 #> 961:   3190.520 0.01677777 #> 962:   3193.060 0.01949510 #> 963:   3195.590 0.02233386 #> 964:   3198.120 0.02529402 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  smooth_intens(raman_hdpe, lambda = 1600, d = 2, lag = 2, type = \"wh\") #>      wavenumber  intensity #>           <num>      <num> #>   1:    301.040 0.09445155 #>   2:    304.632 0.08547362 #>   3:    308.221 0.07684707 #>   4:    311.810 0.06877658 #>   5:    315.398 0.06138149 #>  ---                       #> 960:   3187.990 0.07254546 #> 961:   3190.520 0.08301247 #> 962:   3193.060 0.09396644 #> 963:   3195.590 0.10515688 #> 964:   3198.120 0.11642380 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"Applies spatial smoothing OpenSpecy object using Gaussian filter.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"","code":"spatial_smooth(x, sigma = c(1, 1, 1), ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"x OpenSpecy object. sigma numeric vector specifying standard deviations Gaussian kernel x y dimensions, respectively. ... arguments passed methods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"OpenSpecy object smoothed spectra.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"function performs spatial smoothing spectral data OpenSpecy object. assumes spatial coordinates provided metadata element object, specifically x y columns, col_id column metadata matches column names spectra data.table.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":null,"dir":"Reference","previous_headings":"","what":"Spectral resolution — spec_res","title":"Spectral resolution — spec_res","text":"Helper function calculating spectral resolution wavenumber data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spectral resolution — spec_res","text":"","code":"spec_res(x, ...)  # Default S3 method spec_res(x, ...)  # S3 method for class 'OpenSpecy' spec_res(x, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spectral resolution — spec_res","text":"x numeric vector wavenumber data OpenSpecy object. ... arguments passed subfunctions; currently used.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spectral resolution — spec_res","text":"spec_res() returns single numeric value.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spectral resolution — spec_res","text":"spectral resolution minimum wavenumber, wavelength, frequency difference two lines spectrum can still distinguished.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spectral resolution — spec_res","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spectral resolution — spec_res","text":"","code":"data(\"raman_hdpe\")  spec_res(raman_hdpe) #> [1] 3.00527"},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Open Specy objects — split_spec","title":"Split Open Specy objects — split_spec","text":"Convert list Open Specy objects number spectra list Open Specy objects one spectrum .","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Open Specy objects — split_spec","text":"","code":"split_spec(x)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Open Specy objects — split_spec","text":"x list OpenSpecy objects","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Open Specy objects — split_spec","text":"list Open Specy objects 1 spectrum.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Open Specy objects — split_spec","text":"Function accept list Open Specy objects length split individual components. example list two objects, Open Specy one spectrum Open Specy 50 spectra return list length 51 Open Specy objects one spectrum.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split Open Specy objects — split_spec","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Open Specy objects — split_spec","text":"","code":"data(\"test_lib\") data(\"raman_hdpe\") listed <- list(test_lib, raman_hdpe) test <- split_spec(listed) test2 <- split_spec(list(test_lib))"},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Automated background subtraction for spectral data — subtr_baseline","title":"Automated background subtraction for spectral data — subtr_baseline","text":"baseline correction routine iteratively finds baseline spectrum using polynomial fitting accepts manual baseline.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automated background subtraction for spectral data — subtr_baseline","text":"","code":"subtr_baseline(x, ...)  # Default S3 method subtr_baseline(x, ...)  # S3 method for class 'OpenSpecy' subtr_baseline(   x,   type = \"polynomial\",   degree = 8,   raw = FALSE,   baseline,   make_rel = TRUE,   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automated background subtraction for spectral data — subtr_baseline","text":"x list object class OpenSpecy. type one \"polynomial\" \"manual\" depending whether want spectra corrected manual baseline polynomial baseline fitting. degree degree polynomial. Must less number unique points raw FALSE. Typically good fit can found 8th order polynomial. raw TRUE, use raw orthogonal polynomials. baseline OpenSpecy object containing baseline data subtracted. make_rel logical; TRUE spectra automatically normalized make_rel(). ... arguments passed poly().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automated background subtraction for spectral data — subtr_baseline","text":"subtr_baseline() returns data frame containing two columns named \"wavenumber\" \"intensity\".","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automated background subtraction for spectral data — subtr_baseline","text":"translation Michael Stephen Chen's MATLAB code written imodpolyfit routine Zhao et al. 2007.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Automated background subtraction for spectral data — subtr_baseline","text":"Chen MS (2020). Michaelstchen/ModPolyFit. MATLAB. Retrieved https://github.com/michaelstchen/modPolyFit (Original work published July 28, 2015) Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi:10.1366/000370207782597003 .","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automated background subtraction for spectral data — subtr_baseline","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automated background subtraction for spectral data — subtr_baseline","text":"","code":"data(\"raman_hdpe\")  subtr_baseline(raman_hdpe) #>      wavenumber   intensity #>           <num>       <num> #>   1:    301.040 0.000000000 #>   2:    304.632 0.010833354 #>   3:    308.221 0.008185951 #>   4:    311.810 0.000000000 #>   5:    315.398 0.000000000 #>  ---                        #> 960:   3187.990 0.037684523 #> 961:   3190.520 0.037684523 #> 962:   3193.060 0.043235847 #> 963:   3195.590 0.043235847 #> 964:   3198.120 0.032133199 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/test_lib.html","id":null,"dir":"Reference","previous_headings":"","what":"Test reference library — test_lib","title":"Test reference library — test_lib","text":"Reference library 29 FTIR 28 Raman spectra used examples internal testing.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/test_lib.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test reference library — test_lib","text":"OpenSpecy object; sample_name class spectra.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/test_lib.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Test reference library — test_lib","text":"Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/test_lib.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test reference library — test_lib","text":"","code":"data(\"test_lib\")"},{"path":"http://wincowger.com/OpenSpecy-package/news/index.html","id":"openspecy-120","dir":"Changelog","previous_headings":"","what":"OpenSpecy 1.2.0","title":"OpenSpecy 1.2.0","text":"CRAN release: 2024-09-14","code":""},{"path":"http://wincowger.com/OpenSpecy-package/news/index.html","id":"potentially-breaking-1-2-0","dir":"Changelog","previous_headings":"","what":"Potentially Breaking","title":"OpenSpecy 1.2.0","text":"Removed share data options functions. just weren’t useful users administrative thing. Keeping forced us incompatible webR.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/news/index.html","id":"major-1-2-0","dir":"Changelog","previous_headings":"","what":"Major","title":"OpenSpecy 1.2.0","text":"added support siMPle files. added support xyz files. added support img files. improved interactive plot popups. changed libraries downloaded avoid osfr pacakage. increased support options collapsing maps. avoid forcing min-max relative plots interactive mode. create static map option.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/news/index.html","id":"openspecy-110","dir":"Changelog","previous_headings":"","what":"OpenSpecy 1.1.0","title":"OpenSpecy 1.1.0","text":"CRAN release: 2024-06-13","code":""},{"path":"http://wincowger.com/OpenSpecy-package/news/index.html","id":"minor-improvements-1-1-0","dir":"Changelog","previous_headings":"","what":"Minor Improvements","title":"OpenSpecy 1.1.0","text":"updated 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Creative Commons may contacted creativecommons.org.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"how-to-interpret-the-reported-matches","dir":"Articles","previous_headings":"","what":"How to Interpret the Reported Matches","title":"Spectroscopy Advanced","text":"several important things consider interpreting spectral match including library source, Pearson’s r, metrics.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"the-library-source","dir":"Articles","previous_headings":"","what":"The Library Source","title":"Spectroscopy Advanced","text":"click spectrum, metadata Open Specy source displayed metadata window matches table. library different methodologies used develop . useful read library sources literature came . E.g. Chabuka et al. 2020 focuses weathered plastics, matching may suggest spectrum weathered polymer. Primpke et al. 2018 spectral range 2000, polymers may difficult differentiate . Make sure cite libraries use search publish results. authors kind enough make data open access used Open Specy return favor citing .","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"pearsons-r","dir":"Articles","previous_headings":"","what":"Pearson’s r","title":"Spectroscopy Advanced","text":"Correlation values used identify closest matches available current Open Specy spectral libraries improve material identification reduce sample processing times. Pearson’s r values range 0 - 1 0 completely different spectrum 1 exact match. general guidelines observed using Open Specy. matches > ~0.6 material may require additional processing may exist Open Specy library. Correlation values metric use assess spectra’s match material library, matches need make sense.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"things-to-consider-beyond-correlation","dir":"Articles","previous_headings":"","what":"Things to Consider beyond Correlation","title":"Spectroscopy Advanced","text":"Peak position height similarities important correlation need assessed manually. Peak position correlates specific bond types. Peak height correlates concentration compound. Therefore, peak height peak position match closely possible matched spectrum. peaks exist spectra trying interpret exist match, may additional materials identify. case, restrict processing range just unidentified peak try identify additional component (see also https://www.compoundchem.com/2015/02/05/irspectroscopy/). Also, check match metadata see match makes sense. Example: single fiber “cotton blend” since fibers make rest blend. Example: Cellophane degrade fibers, match fiber cellophane wouldn’t make sense. Example: analyzing particle room temperature, matched material liquid room temperature. material may component particle whole particle.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"how-specific-do-you-need-to-be-in-the-material-type-of-the-match","dir":"Articles","previous_headings":"","what":"How Specific Do You Need to be in the Material Type of the Match?","title":"Spectroscopy Advanced","text":"can choose specific classify substance (e.g. polyester, cellophane) general (e.g. synthetic, semi-synthetic, natural, etc.). choice depends research question. Using general groups can speed analysis time decrease information interpretation. identify materials generally, can often clump identities provided Open Specy suit needs. example, matches “polyester” “polypropylene” clumped category “plastic”.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"how-to-differentiate-between-similar-spectra","dir":"Articles","previous_headings":"","what":"How to Differentiate Between Similar Spectra?","title":"Spectroscopy Advanced","text":"One common challenge differentiating LDPE HDPE. , even low resolution instrument (MacroRAM, 2 cm-1 pixel-1), can still see differences. wide view, low, medium, high density PE samples look relatively similar (figures courtesy Bridget O'Donnell, Horiba Scientific):  , closer look 1450 cm-1 band reveals clear differences:  overlay , start see differences spectral regions :  , question , deal samples similar subtle differences? Usually, researchers use MVA techniques ’ve collected multiple reference spectra known samples (LDPE HDPE case). can develop models apply distinguish different types PE. reference database like Open Specy, complicated fact researchers measuring samples different instruments correspondingly different spectral responses spectral resolutions. makes even difficult accurately match definitively LDPE HDPE opposed generic ‘PE’. One possibility place emphasis (computational perspective) bands show difference (triplet 1450 cm-1) restricting range used match Open Specy. , much simpler option just match PE hit generic ‘PE’ specifically HDPE LDPE. Another challenge differentiating types nylons. , Raman pretty easy time distinguishing nylons. spectra recorded series nylons differences much distinguishable compared PE results (nylon 6, 6-6, 6-9, 6-10, 6-12 top bottom):  differences even pronounced overlay spectra:","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"what-to-do-when-matches-arent-making-sense","dir":"Articles","previous_headings":"","what":"What to Do When Matches Aren’t Making Sense","title":"Spectroscopy Advanced","text":"Double check baseline correction smoothing parameters result best processing data. Try reprocessing spectrum, limit specific peak regions higher signal noise ratio. Restrict spectral range include exclude questionable peaks peaks present previous matches. Restrict spectral range exclude things like CO2 (2200 cm-1) H2O (~1600 cm-1) spikes IR spectrum. nothing works determine quality match, may need measure spectrum material use another spectral analysis tool.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/advanced.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Spectroscopy Advanced","text":"Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy.” Applied Spectroscopy, 74(9), 1167–1183. doi: 10.1177/0003702820923993. Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi: 10.1177/0003702820929064. Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123. Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design Automated Analysis Microplastic Samples Based Fourier Transform Infrared (FTIR) Spectroscopy.” Analytical Bioanalytical Chemistry, 410(21), 5131–5141. doi: 10.1007/s00216-018-1156-x. Renner G, Schmidt TC, Schram J (2017). “New Chemometric Approach Automatic Identification Microplastics Environmental Compartments Based FT-IR Spectroscopy.” Analytical Chemistry, 89(22), 12045–12053. doi: 10.1021/acs.analchem.7b02472. Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639. Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi: 10.1366/000370207782597003.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"document-overview","dir":"Articles","previous_headings":"","what":"Document Overview","title":"Open Specy App Tutorial","text":"document outlines common workflow using Open Specy app highlights topics users often requesting tutorial . document followed sequentially beginning end, user better understanding every procedure involved using Open Specy R app tool interpreting spectra. takes approximately 45 minutes read follow along standard operating procedure first time. Afterward, knowledgeable users able thoroughly analyze spectra average speed 1 min-1 faster new batch automated procedures. looking documentation R package please see package vignette","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"running-the-app","dir":"Articles","previous_headings":"","what":"Running the App","title":"Open Specy App Tutorial","text":"get started Open Specy user interface, access https://openanalysis.org/openspecy/ start Shiny GUI directly computer R. using package, just need read library run command run_app().","code":"library(OpenSpecy) run_app()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"reading-data","dir":"Articles","previous_headings":"","what":"Reading Data","title":"Open Specy App Tutorial","text":"app open, data, click Browse top left hand corner Analyze Spectra tab.  uploading, indicate like share uploaded data using slider. selected, data uploaded tool automatically shared CC-4.0 license available researchers ventures use improve spectral analysis, build machine learning tools, etc. users may choose share need keep data private. switched , none uploaded data stored shared Open Specy.  Open Specy allows upload native Open Specy .y()ml, .json, .rds files. addition, .csv, .asp, .jdx, .0, .spa, .spc, .zip files can imported. .zip files can either contain multiple files individual spectra non-zip formats can contain .hdr .dat file form ENVI file spectral map. Open Specy .csv files always load correctly file types still development, though time files work perfectly. uploading .csv file, label column wavenumbers wavenumber name column intensities intensity. Wavenumber units must cm-1. columns used software. Always keep copy original file alteration preserve metadata raw data records. Sample data raman_hdpe best practice cross check files proprietary software came Open Specy use Open Specy. Due complexity proprietary file types, haven’t able make fully compatible yet. file working, please contact administrator share file can work integrating . specific steps converting instrument’s native files .csv can found software manual can check Spectragryph, supports many spectral file conversions see Spectragryph Tutorial. don’t data, can use test dataset.  .csv file HDPE Raman spectrum download computer. file can also used template formatting .csv data Open Specy accepted format.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"spectra","dir":"Articles","previous_headings":"Visualization","what":"Spectra","title":"Open Specy App Tutorial","text":"app spectral data uploaded, appear main window. plot selectable, zoomable, provides information hover. can also save .png file plot view using camera icon top right hover plot.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"maps","dir":"Articles","previous_headings":"Visualization","what":"Maps","title":"Open Specy App Tutorial","text":"Spectral maps can also visualized overlaid spectra addition spatial information can plotted heatmap. similar plot appear app upload multiple spectra spectral map. important note multiple spectra uploaded batch prescribed x y coordinates, can helpful visualizing summary statistics navigating vast amounts data shouldn’t confused data actually spatial coordinates. Hovering map reveal information signal noise correlation values. Clicking map provide selected spectrum underneath. app, colors heatmap either signal noise spectral identifications depending whether identification turned . Pixels black spectra pass signal-noise threshold /correlation threshold.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"processing","dir":"Articles","previous_headings":"","what":"Processing","title":"Open Specy App Tutorial","text":"goal processing increase signal noise ratio (S/N) without distorting shape relative size peaks. uploading data, can process data using intensity adjustment, baseline subtraction, smoothing, flattening, range selection save processed data. process button selected default processing initiate. absolute derivative transformation, kind magic, something similar smoothing, baseline subtraction, intensity correction simultaneously really quickly. clicking processing see spectrum update processed spectra.  view raw data , just deselect processing. Toggling processing can help make sure spectra processed correctly.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"threshold-signal-noise","dir":"Articles","previous_headings":"Processing","what":"Threshold Signal-Noise","title":"Open Specy App Tutorial","text":"Considering whether enough signal analyze spectra important. Classical spectroscopy recommend highest peak least 10 times baseline processed spectra begin analysis. spectra threshold, may want consider recollecting . practice, rarely able collect spectra good quality often use 5. Signal Noise technique searches spectra high low regions conducts division derive signal noise ratio. Signal Times Noise multiplies mean signal standard deviation signal Total Signal sums intensities. latter can really useful thresholding spectral maps identify particles discuss later.  analyzing spectra batch, recommend looking heatmap optimizing percent spectra signal noise threshold determine correct settings instead looking spectra individually. Good Signal tells percent data signal threshold.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"intensity-adjustment","dir":"Articles","previous_headings":"Processing","what":"Intensity Adjustment","title":"Open Specy App Tutorial","text":"Open Specy assumes intensity units absorbance units Open Specy can adjust reflectance transmittance spectra absorbance units using selection. transmittance adjustment uses log101/T\\log_{10} 1/T calculation correct system particle characteristics. reflectance adjustment use Kubelka-Munk equation (1−R)22R\\frac{(1-R)^2}{2R}. none selected, Open Specy assumes uploaded data absorbance spectrum apply adjustment.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"conforming","dir":"Articles","previous_headings":"Processing","what":"Conforming","title":"Open Specy App Tutorial","text":"Conforming spectra essential comparing reference library can useful summarizing data don’t need highly resolved spectrally. app, conforming happens behind scenes without user input make sure spectra user uploads library spectra compatible. set spectral resolution 8 tends pretty good lot applications 4 8 commonly used wavenumber resolutions.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"smoothing","dir":"Articles","previous_headings":"Processing","what":"Smoothing","title":"Open Specy App Tutorial","text":"value slider polynomial order Savitzky-Golay (SG) filter. Higher numbers lead wiggly fits thus less smooth, lower numbers lead smooth fits. SG filter fit moving window 11 data points default center point window replaced polynomial estimate. Larger windows produce smoother fits. derivative order set 1 default transforms spectra first derivative. zero order derivative derivative transformation. smoothing done well, peak shapes relative heights change. absolute value primarily useful first derivative spectra absolute value results absorbance-like spectrum set default.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"baseline-correction","dir":"Articles","previous_headings":"Processing","what":"Baseline Correction","title":"Open Specy App Tutorial","text":"goal baseline correction get non-peak regions spectra zero absorbance. higher polynomial order, wiggly fit baseline. baseline wiggly, wiggly fit remove peaks desired. baseline correction algorithm used Open Specy called “iModPolyfit” (Zhao et al. 2007). algorithm iteratively fits polynomial equations specified order whole spectrum. first fit iteration, peak regions often baseline fit. data peak region removed fit make sure baseline less likely fit peaks. iterative fitting terminates difference new previous fit small. example good baseline fit . OpenSpecy , notice app longer supports manual baseline correction, hard choice just didn’t make sense keep now moving toward high throughput automated methods. still exist R function though.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"range-selection","dir":"Articles","previous_headings":"Processing","what":"Range Selection","title":"Open Specy App Tutorial","text":"Sometimes instrument operates high noise ends spectrum , baseline fit produces distortions, regions interest analysis. Range selection accomplishes goals. look signal noise ratio specific instrument wavelength determine wavelength ranges use. Distortions due baseline fit can assessed looking process spectra. Additionally, can restrict range examine single peak subset peaks interests. maximum minimum wavenumbers specify range keep app.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"flattening-ranges","dir":"Articles","previous_headings":"Processing","what":"Flattening Ranges","title":"Open Specy App Tutorial","text":"Sometimes peaks really shouldn’t spectra can distort interpretation spectra don’t necessarily want remove regions analysis believe regions flat instead peak. One way deal replace peak values mean values around peak. purpose Flatten Range. default set flatten CO2 region FTIR spectra region often needs flattened atmospheric artifacts occur spectra.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"min-max-normalization","dir":"Articles","previous_headings":"Processing","what":"Min-Max Normalization","title":"Open Specy App Tutorial","text":"Min-Max normalization used throughout app one options user can specify. Often regard spectral intensities arbitrary min-max normalization allows us view spectra scale without drastically distorting shapes relative peak intensities. app, primarily used plotting processing step correlation analysis.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"downloading-processed-data","dir":"Articles","previous_headings":"Processing","what":"Downloading Processed Data","title":"Open Specy App Tutorial","text":"can download csv file processed data using Spectra option download. download version spectra whatever processing options selected time.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"identifying-spectra","dir":"Articles","previous_headings":"","what":"Identifying Spectra","title":"Open Specy App Tutorial","text":"uploading data processing (desired) can now identify spectrum. identify spectrum go Identification box. Pro tip: select Identification without uploading data app, ’ll able explore library .","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"reading-libraries","dir":"Articles","previous_headings":"Identifying Spectra","what":"Reading Libraries","title":"Open Specy App Tutorial","text":"options define strategy identification. ID Library inform library used. (default) search FTIR Raman libraries. Deriv search derivative transformed library. Baseline search baseline corrected library. line choose process spectra. Cor options use simple Pearson correlation search algorithm. AI currently experimental uses either multinomial model correlation mediod spectra library. Correlation thresholding set minimum value matching use ‘positive identification’ black pixels spectral map view lower threshold.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"matches","dir":"Articles","previous_headings":"Identifying Spectra","what":"Matches","title":"Open Specy App Tutorial","text":"Top matches app can assessed clicking cog right hand corner Spectra box. open side window matches sorted least similar. Clicking rows table add selected match spectra viewer. Using table’s filter options, can restrict range Pearson's r values search specific material types.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"selection-metadata","dir":"Articles","previous_headings":"Identifying Spectra","what":"Selection Metadata","title":"Open Specy App Tutorial","text":"Whatever match selected match table may additional metadata . metadata displayed plot. metadata may assist interpreting spectra. example, spectra metadata says liquid analyzing solid particle, spectrum may best match.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"match-plot","dir":"Articles","previous_headings":"Identifying Spectra","what":"Match Plot","title":"Open Specy App Tutorial","text":"plot dynamically updated selecting matches match table. red spectrum spectrum selected reference library white spectrum spectrum trying identify. Whenever new dataset uploaded, plot data table tab updated. plots can saved .png clicking camera button top plot.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"sharing-reference-data","dir":"Articles","previous_headings":"Identifying Spectra","what":"Sharing Reference Data","title":"Open Specy App Tutorial","text":"reference data AI models think useful sharing spectroscopy community OpenSpecy please contact website administrator discuss options collaborating.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"download-top-matches","dir":"Articles","previous_headings":"Identifying Spectra","what":"Download Top Matches","title":"Open Specy App Tutorial","text":"download top matches associated metadata, use top matches download option.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"characterizing-particles","dir":"Articles","previous_headings":"","what":"Characterizing Particles","title":"Open Specy App Tutorial","text":"Thresholding works shouldn’t used single particles. can threshold spectral maps using signal-noise values /correlation threshold. Whatever selection active inform spectra thresholded. get thresholded coincidence see spectral map. Pro tip: file gets downloaded OpenSpecy median spectra thresholded particles. can upload back app analyze independently.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"additional-app-features","dir":"Articles","previous_headings":"Characterizing Particles","what":"Additional App Features","title":"Open Specy App Tutorial","text":"Accessibility extremely important us making strives improve accessibility Open Specy spectroscopists. Please reach ideas improvement. added Google translate plugin pages app can easily translate app. know languages fully supported continue try improve translations.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/app.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Open Specy App Tutorial","text":"Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy.” Applied Spectroscopy, 74(9), 1167–1183. doi: 10.1177/0003702820923993. Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi: 10.1177/0003702820929064. Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123. Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design Automated Analysis Microplastic Samples Based Fourier Transform Infrared (FTIR) Spectroscopy.” Analytical Bioanalytical Chemistry, 410(21), 5131–5141. doi: 10.1007/s00216-018-1156-x. Renner G, Schmidt TC, Schram J (2017). “New Chemometric Approach Automatic Identification Microplastics Environmental Compartments Based FT-IR Spectroscopy.” Analytical Chemistry, 89(22), 12045–12053. doi: 10.1021/acs.analchem.7b02472. Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639. Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi: 10.1366/000370207782597003.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"document-overview","dir":"Articles","previous_headings":"","what":"Document Overview","title":"Open Specy Package Tutorial","text":"document outlines common workflow using Open Specy package highlights topics users often requesting tutorial . document followed sequentially beginning end, user better understanding every procedure involved using Open Specy R package tool interpreting spectra. takes approximately 45 minutes read follow along standard operating procedure first time. Afterward, knowledgeable users able thoroughly analyze spectra average speed 1 min-1 faster new batch automated procedures. Open Specy R package backbone Shiny app. choice start , use regular basis. tutorial talk R functions can use programatically analyze spectra. looking tutorial use app see tutorial.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Open Specy Package Tutorial","text":"installing R package, just need read library.","code":"library(OpenSpecy)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"running-the-app","dir":"Articles","previous_headings":"","what":"Running the App","title":"Open Specy Package Tutorial","text":"get started Open Specy user interface, access https://openanalysis.org/openspecy/ start Shiny GUI directly computer R. looking tutorial use app see tutorial.","code":"run_app()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"read-data","dir":"Articles","previous_headings":"","what":"Read Data","title":"Open Specy Package Tutorial","text":"following line code read data using package interprets reading function use based file extension. file type specific functions also read spectral data accordingly particular format mind. Open Specy allows upload native Open Specy .y()ml, .json, .rds files. addition, .csv, .asp, .jdx, .0, .spa, .spc, .zip files can imported. .zip files can either contain multiple files individual spectra non-zip formats can contain .hdr .dat file form ENVI file spectral map. Open Specy .csv files always load correctly file types still development, though time files work perfectly. uploading .csv file, label column wavenumbers wavenumber name column intensities intensity. Wavenumber units must cm-1. columns used software. Always keep copy original file alteration preserve metadata raw data records. best practice cross check files proprietary software came Open Specy use Open Specy. Due complexity proprietary file types, haven’t able make fully compatible yet. file working, please contact administrator share file can work integrating . specific steps converting instrument’s native files .csv can found software manual can check Spectragryph, supports many spectral file conversions. instructions, see Spectragryph Tutorial. don’t data, can use test dataset. also many onboard files can call test different formats: notice now R package reads files object class OpenSpecy. class created high throughput spectral analysis now also preserves spectral metadata. can even create scratch ’d like. Open Specy objects lists three components, wavenumber vector wavenumber values spectra corresponds rows spectra data.table column set spectral intensities. metadata data.table holds additional information spectra. row corresponds column spectra. generic functions built inspecting spectra:","code":"read_any(\"path/to/your/data\") read_text(\".csv\") read_asp(\".asp\") read_opus(\".0\") data(\"raman_hdpe\") spectral_map <- read_extdata(\"CA_tiny_map.zip\") |>    read_any() # preserves some metadata asp_example <- read_extdata(\"ftir_ldpe_soil.asp\") |>    read_any()  ps_example <- read_extdata(\"ftir_ps.0\") |>    read_any() # preserves some metadata csv_example <- read_extdata(\"raman_hdpe.csv\") |>    read_any() json_example <- read_extdata(\"raman_hdpe.json\") |>    read_any() # read in exactly as an OpenSpecy object scratch_OpenSpecy <- as_OpenSpecy(x = seq(1000,2000, by = 5),                                    spectra =  data.frame(runif(n = 201)),                                    metadata = list(file_name = \"fake_noise\")) # Access the wavenumbers scratch_OpenSpecy$wavenumber #>   [1] 1000 1005 1010 1015 1020 1025 1030 1035 1040 1045 1050 1055 1060 1065 1070 #>  [16] 1075 1080 1085 1090 1095 1100 1105 1110 1115 1120 1125 1130 1135 1140 1145 #>  [31] 1150 1155 1160 1165 1170 1175 1180 1185 1190 1195 1200 1205 1210 1215 1220 #>  [46] 1225 1230 1235 1240 1245 1250 1255 1260 1265 1270 1275 1280 1285 1290 1295 #>  [61] 1300 1305 1310 1315 1320 1325 1330 1335 1340 1345 1350 1355 1360 1365 1370 #>  [76] 1375 1380 1385 1390 1395 1400 1405 1410 1415 1420 1425 1430 1435 1440 1445 #>  [91] 1450 1455 1460 1465 1470 1475 1480 1485 1490 1495 1500 1505 1510 1515 1520 #> [106] 1525 1530 1535 1540 1545 1550 1555 1560 1565 1570 1575 1580 1585 1590 1595 #> [121] 1600 1605 1610 1615 1620 1625 1630 1635 1640 1645 1650 1655 1660 1665 1670 #> [136] 1675 1680 1685 1690 1695 1700 1705 1710 1715 1720 1725 1730 1735 1740 1745 #> [151] 1750 1755 1760 1765 1770 1775 1780 1785 1790 1795 1800 1805 1810 1815 1820 #> [166] 1825 1830 1835 1840 1845 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895 #> [181] 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 #> [196] 1975 1980 1985 1990 1995 2000 # Access the spectra scratch_OpenSpecy$spectra #>      runif.n...201. #>               <num> #>   1:    0.080750138 #>   2:    0.834333037 #>   3:    0.600760886 #>   4:    0.157208442 #>   5:    0.007399441 #>  ---                #> 197:    0.906051578 #> 198:    0.772730364 #> 199:    0.383370670 #> 200:    0.999652457 #> 201:    0.349299049 # Access the metadata scratch_OpenSpecy$metadata #>        x     y  file_name         col_id                          file_id #>    <int> <int>     <char>         <char>                           <char> #> 1:     1     1 fake_noise runif.n...201. 74944ff090b28e2fe0504f0e85d29ded # Performs checks to ensure that OpenSpecy objects are adhering to our standards; # returns TRUE if it passes.  check_OpenSpecy(scratch_OpenSpecy) #> [1] TRUE  # Checks only the object type to make sure it has OpenSpecy type is_OpenSpecy(scratch_OpenSpecy) #> [1] TRUE print(scratch_OpenSpecy) # shows the raw object #>      wavenumber runif.n...201. #>           <num>          <num> #>   1:       1000    0.080750138 #>   2:       1005    0.834333037 #>   3:       1010    0.600760886 #>   4:       1015    0.157208442 #>   5:       1020    0.007399441 #>  ---                           #> 197:       1980    0.906051578 #> 198:       1985    0.772730364 #> 199:       1990    0.383370670 #> 200:       1995    0.999652457 #> 201:       2000    0.349299049 #>  #> $metadata #>        x     y  file_name         col_id                          file_id #>    <int> <int>     <char>         <char>                           <char> #> 1:     1     1 fake_noise runif.n...201. 74944ff090b28e2fe0504f0e85d29ded summary(scratch_OpenSpecy) # summarizes the contents of the spectra #> $wavenumber #>  Length Min. Max.     Res. #>     201 1000 2000 4.975124 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1    0.004496308      0.9996525 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"         \"y\"         \"file_name\" \"col_id\"    \"file_id\" head(scratch_OpenSpecy) # shows the top wavenumbers and intensities #>    wavenumber runif.n...201. #>         <num>          <num> #> 1:       1000    0.080750138 #> 2:       1005    0.834333037 #> 3:       1010    0.600760886 #> 4:       1015    0.157208442 #> 5:       1020    0.007399441 #> 6:       1025    0.466393497"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"save-data","dir":"Articles","previous_headings":"","what":"Save Data","title":"Open Specy Package Tutorial","text":"Open Specy objects can saved accurately .rds, .yml, .json files. .rds reproducible native R file format floating point errors can happen .json .yml.","code":"write_spec(scratch_OpenSpecy, \"test_scratch_OpenSpecy.yml\", digits = 5) write_spec(scratch_OpenSpecy, \"test_scratch_OpenSpecy.json\", digits = 5)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"format-conversions","dir":"Articles","previous_headings":"","what":"Format Conversions","title":"Open Specy Package Tutorial","text":"Another great spectroscopy R package hyperSpec. actually depend functions several . currently making awesome new features want integrate well packages can easily used together. created as_hyperSpec function made sure as_OpenSpecy can convert hyperSpec objects.","code":"hyperspecy <- as_hyperSpec(scratch_OpenSpecy)"},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"spectra","dir":"Articles","previous_headings":"Visualization","what":"Spectra","title":"Open Specy Package Tutorial","text":"R, two ways visualize spectra, one quick efficient interactive. example quick efficient plotting.  example interactive plot. can plot two different datasets simultaneously compare.","code":"plot(scratch_OpenSpecy) # quick and efficient # This will min-max normalize your data even if it isn't already but are not # changing your underlying data plotly_spec(scratch_OpenSpecy, json_example)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"maps","dir":"Articles","previous_headings":"Visualization","what":"Maps","title":"Open Specy Package Tutorial","text":"Spectral maps can also visualized overlaid spectra addition spatial information can plotted heatmap. important note multiple spectra uploaded batch prescribed x y coordinates, can helpful visualizing summary statistics navigating vast amounts data shouldn’t confused data actually spatial coordinates. R user can control values form colors heatmap specifying z, handy put information want metadata reason. example just shows x values spectra. interactive plot heatmap spectra overlayed can generated interactive_plot() function. user can hover heatmap identify next row id interested update value select see spectrum.","code":"heatmap_spec(spectral_map,              z = spectral_map$metadata$x) interactive_plot(spectral_map, select = 100, z = spectral_map$metadata$x)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"combining-openspecy-objects","dir":"Articles","previous_headings":"","what":"Combining OpenSpecy Objects","title":"Open Specy Package Tutorial","text":"Sometimes several OpenSpecy objects want combine one. easiest way spectra exact format series wavenumbers. default settings c_spec assume case. different wavenumber ranges spectra want combine, can set range = \"common\" res equal wavenumber resolution want function collapse spectra whatever common range using linear interpolation.","code":"c_spec(list(asp_example, ps_example), range = \"common\", res = 8) |>    plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"filtering-openspecy-objects","dir":"Articles","previous_headings":"","what":"Filtering OpenSpecy Objects","title":"Open Specy Package Tutorial","text":"OpenSpecy objects can number spectra . create OpenSpecy subset spectra Open Specy object can use filter_spec function. Filtering allowed index number, name, using logical vector. Filtering update spectra metadata items OpenSpecy wavenumber.","code":"# Extract the 150th spectrum.  filter_spec(spectral_map, 150) #>      wavenumber         9_5 #>           <num>       <num> #>   1:     717.42  0.06159078 #>   2:     725.13  0.01614512 #>   3:     732.85 -0.02511934 #>   4:     740.56 -0.05653130 #>   5:     748.28 -0.07012609 #>  ---                        #> 423:    3972.81 -0.23276733 #> 424:    3980.52 -0.22747667 #> 425:    3988.24 -0.23046409 #> 426:    3995.95 -0.23281474 #> 427:    4003.67 -0.22850421 #>  #> $metadata #>        y     x       file_name  license #>    <num> <num>          <char>   <char> #> 1:     9     5 CA small UF.dat CC BY-NC #>                                                     description samples lines #>                                                          <char>   <num> <num> #> 1: Pixel 15, XPos=12549.72, YPos= 589.82, X=12362.22, Y= 614.82      16    13 #>    bands header offset data type interleave                  z plot titles #>    <num>         <num>     <num>     <char>                         <char> #> 1:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>                  pixel size col_id #>                      <char> <char> #> 1: 0.000025000, 0.000025000    9_5 #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/8965151f3f9c317aa66aaff74253501e #>                             file_id #>                              <char> #> 1: 215a0fd7e0dc3b1001799214618ce6eb # Extract the spectrum with column name \"8_5\".  filter_spec(spectral_map, \"8_5\") |>    print() #>      wavenumber         8_5 #>           <num>       <num> #>   1:     717.42  0.15336494 #>   2:     725.13  0.10891031 #>   3:     732.85  0.06503499 #>   4:     740.56  0.03334329 #>   5:     748.28  0.01523896 #>  ---                        #> 423:    3972.81 -0.28495014 #> 424:    3980.52 -0.28366613 #> 425:    3988.24 -0.28505880 #> 426:    3995.95 -0.28465354 #> 427:    4003.67 -0.27912781 #>  #> $metadata #>        y     x       file_name  license #>    <num> <num>          <char>   <char> #> 1:     8     5 CA small UF.dat CC BY-NC #>                                                     description samples lines #>                                                          <char>   <num> <num> #> 1: Pixel 15, XPos=12549.72, YPos= 589.82, X=12362.22, Y= 614.82      16    13 #>    bands header offset data type interleave                  z plot titles #>    <num>         <num>     <num>     <char>                         <char> #> 1:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>                  pixel size col_id #>                      <char> <char> #> 1: 0.000025000, 0.000025000    8_5 #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/8965151f3f9c317aa66aaff74253501e #>                             file_id #>                              <char> #> 1: 215a0fd7e0dc3b1001799214618ce6eb # Extract the spectra with a logical argument based on metadata filter_spec(spectral_map, spectral_map$metadata$y == 1) |>   plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"sampling-openspecy-objects","dir":"Articles","previous_headings":"","what":"Sampling OpenSpecy Objects","title":"Open Specy Package Tutorial","text":"Sometimes large suite examples spectra material want reduce number spectra run analysis simplicity running simulations procedures require first sample spectra contained OpenSpecy objects analysis. sample_spec function serves purpose.","code":"sample_spec(spectral_map, size = 5) |>   plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"processing","dir":"Articles","previous_headings":"","what":"Processing","title":"Open Specy Package Tutorial","text":"goal processing increase signal noise ratio (S/N) spectra without distorting shape, position, relative size peaks. loading data, can process data using intensity adjustment, baseline subtraction, smoothing, flattening, range selection. default settings absolute derivative transformation, kind magic, something similar smoothing, baseline subtraction, intensity correction simultaneously really quickly. process_spec() function monolithic function processing procedures optimized default result high signal noise cases, app. can compare processed unprocessed data overlay plot. want people use process_spec() function processing operations. processing functions can tuned using parameters single function set defaults nested processing functions way provides typically quality results. However, recognize nesting functions order operations can useful users control can also use individual functions operation ’d like. See explanations processing sub-function .","code":"processed <- process_spec(raman_hdpe,                           active = TRUE,                           adj_intens = FALSE,                           adj_intens_args = list(type = \"none\"),                           conform_spec = TRUE,                           conform_spec_args = list(range = NULL, res = 8,                                                    type = \"interp\"),                           restrict_range = FALSE,                           restrict_range_args = list(min = 0, max = 6000),                           flatten_range = FALSE,                           flatten_range_args = list(min = 2200, max = 2420),                           subtr_baseline = FALSE,                           subtr_baseline_args = list(type = \"polynomial\",                                                      degree = 8, raw = FALSE,                                                      baseline = NULL),                           smooth_intens = TRUE,                           smooth_intens_args = list(polynomial = 3, window = 11,                                                     derivative = 1, abs = TRUE),                           make_rel = TRUE) summary(processed) #> $wavenumber #>  Length Min. Max.     Res. #>     362  304 3192 7.977901 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1              0              1 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"                 \"y\"                 \"user_name\"         #> [4] \"spectrum_type\"     \"spectrum_identity\" \"organization\"      #> [7] \"license\"           \"session_id\"        \"file_id\" summary(raman_hdpe) #> $wavenumber #>  Length   Min.    Max.    Res. #>     964 301.04 3198.12 3.00527 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1             26            816 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"                 \"y\"                 \"user_name\"         #> [4] \"spectrum_type\"     \"spectrum_identity\" \"organization\"      #> [7] \"license\"           \"session_id\"        \"file_id\" plotly_spec(raman_hdpe, processed)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"threshold-signal-noise","dir":"Articles","previous_headings":"Processing","what":"Threshold Signal-Noise","title":"Open Specy Package Tutorial","text":"Considering whether enough signal analyze spectra important. Classical spectroscopy recommend highest peak least 10 times baseline processed spectra begin analysis. spectra threshold even processing, may want consider recollecting . practice, rarely able collect spectra good quality often use 5. “run_sig_over_noise” metric searches spectra high low regions conducts division derive signal noise ratio step specifies number intensities search. good way automatically calculate signal noise ratio. example can see signal noise ratio increased processing, goal processing generally maximize . “sig_times_noise” metric multiplies mean signal standard deviation signal “tot_sig” metric sums intensities. latter can really useful thresholding spectral maps identify particles discuss later. know signal region noise regions can specify sig_min, sig_max, noise_min, noise_max. analyzing spectra batch, recommend looking heatmap optimizing percent spectra signal noise threshold determine correct settings instead looking spectra individually. Setting min_sn threshold heatmap image color spectra sn value threshold.","code":"sig_noise(processed, metric = \"run_sig_over_noise\") >    sig_noise(raman_hdpe, metric = \"run_sig_over_noise\") spectral_map_p <- spectral_map |>   process_spec(flatten_range = T)  spectral_map_p$metadata$sig_noise <- sig_noise(spectral_map_p)  heatmap_spec(spectral_map_p, sn = spectral_map_p$metadata$sig_noise, min_sn = 5)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"intensity-adjustment","dir":"Articles","previous_headings":"Processing","what":"Intensity Adjustment","title":"Open Specy Package Tutorial","text":"Open Specy assumes intensity units absorbance units Open Specy can adjust reflectance transmittance spectra absorbance units. transmittance adjustment uses log101/T\\log_{10} 1/T calculation correct system particle characteristics. reflectance adjustment use Kubelka-Munk equation (1−R)22R\\frac{(1-R)^2}{2R}. respective R code scenario spectra doesn’t need intensity adjustment:","code":"trans_raman_hdpe <- raman_hdpe trans_raman_hdpe$spectra <- 2 - trans_raman_hdpe$spectra^2      rev_trans_raman_hdpe <- trans_raman_hdpe |>    adj_intens(type = \"transmittance\")  plotly_spec(trans_raman_hdpe, rev_trans_raman_hdpe)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"conforming","dir":"Articles","previous_headings":"Processing","what":"Conforming","title":"Open Specy Package Tutorial","text":"Conforming spectra essential comparing reference library can useful summarizing data don’t need highly resolved spectrally. set default spectral resolution 8 tends pretty good lot applications 4 8 commonly used wavenumber resolutions. several ways function can specified.","code":"conform_spec(raman_hdpe, res = 8) |> # convert res to 8 wavenumbers.   summary() #> $wavenumber #>  Length Min. Max.     Res. #>     362  304 3192 7.977901 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1       42.85152       602.4639 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"                 \"y\"                 \"user_name\"         #> [4] \"spectrum_type\"     \"spectrum_identity\" \"organization\"      #> [7] \"license\"           \"session_id\"        \"file_id\"  # Force one spectrum to have the exact same wavenumbers as another conform_spec(asp_example, range = ps_example$wavenumber, res = NULL) |>    summary() #> $wavenumber #>  Length     Min.     Max.     Res. #>    1736 651.8728 3998.025 1.927507 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>       1   0.0009781419      0.5156938 #>  #> $metadata #>   Min. Max. #> x    1    1 #> y    1    1 #> [1] \"x\"          \"y\"          \"file_name\"  \"license\"    \"col_id\"     #> [6] \"session_id\" \"file_id\"  # Specify the wavenumber resolution and use a rolling join instead of linear # approximation (faster for large datasets).  conform_spec(spectral_map, range = ps_example$wavenumber, res = 10,              type = \"roll\") |>    summary() #> $wavenumber #>  Length Min. Max.     Res. #>     329  720 4000 9.969605 #>  #> $spectra #>  Number Min. Intensity Max. Intensity #>     208      -1.317072       1.168225 #>  #> $metadata #>   Min. Max. #> x    0   15 #> y    0   12 #>  [1] \"y\"             \"x\"             \"file_name\"     \"license\"       #>  [5] \"description\"   \"samples\"       \"lines\"         \"bands\"         #>  [9] \"header offset\" \"data type\"     \"interleave\"    \"z plot titles\" #> [13] \"pixel size\"    \"col_id\"        \"session_id\"    \"file_id\""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"smoothing","dir":"Articles","previous_headings":"Processing","what":"Smoothing","title":"Open Specy Package Tutorial","text":"Savitzky-Golay filter used smoothing. Higher polynomial numbers lead wiggly fits thus less smoothing, lower numbers lead smooth fits. SG filter fit moving window 11 data points default center point window replaced polynomial estimate. Larger windows produce smoother fits. derivative order set 1 default transforms spectra first derivative. zero order derivative derivative transformation. smoothing done well, peak shapes relative heights change. absolute value primarily useful first derivative spectra absolute value results absorbance-like spectrum set default. ’ll notice new function using c_spec() used combine spectral objects one OpenSpecy object. Examples smoothing: Sample raman_hdpe spectrum different smoothing polynomials. Derivative transformation can happen function. Sample raman_hdpe spectrum different derivatives.","code":"none <- make_rel(raman_hdpe) p1 <- smooth_intens(raman_hdpe, polynomial = 1, derivative = 0, abs = F) p4 <- smooth_intens(raman_hdpe, polynomial = 4, derivative = 0, abs = F)  c_spec(list(none, p1, p4)) |>    plot() none <- make_rel(raman_hdpe) d1 <- smooth_intens(raman_hdpe, derivative = 1, abs = T) d2 <- smooth_intens(raman_hdpe, derivative = 2, abs = T)  c_spec(list(none, d1, d2)) |>    plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"baseline-correction","dir":"Articles","previous_headings":"Processing","what":"Baseline Correction","title":"Open Specy Package Tutorial","text":"goal baseline correction get non-peak regions spectra zero absorbance. higher polynomial order, wiggly fit baseline. baseline wiggly, wiggly fit remove peaks desired. baseline correction algorithm used Open Specy called “iModPolyfit” (Zhao et al. 2007). algorithm iteratively fits polynomial equations specified order whole spectrum. first fit iteration, peak regions often baseline fit. data peak region removed fit make sure baseline less likely fit peaks. iterative fitting terminates difference new previous fit small. example good baseline fit . Manual baseline correction can also specified providing baseline OpenSpecy object. Sample raman_hdpe spectrum different degrees background subtraction (Cowger et al., 2020).","code":"alternative_baseline <- smooth_intens(raman_hdpe, polynomial = 1, window = 51,                                       derivative = 0, abs = F, make_rel = F) |>   flatten_range(min = 2700, max = 3200, make_rel = F)  none <- make_rel(raman_hdpe) d2 <- subtr_baseline(raman_hdpe, type = \"manual\",                      baseline = alternative_baseline) d8 <- subtr_baseline(raman_hdpe, degree = 8)  c_spec(list(none, d2, d8)) |>    plot()"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"range-selection","dir":"Articles","previous_headings":"Processing","what":"Range Selection","title":"Open Specy Package Tutorial","text":"Sometimes instrument operates high noise ends spectrum , baseline fit produces distortions, regions interest analysis. Range selection accomplishes goals. look signal noise ratio specific instrument wavelength determine wavelength ranges use. Distortions due baseline fit can assessed looking process spectra. Additionally, can restrict range examine single peak subset peaks interests. Multiple ranges can specified simultaneously. Sample raman_hdpe spectrum different degrees range restriction.","code":"none <- make_rel(raman_hdpe) r1 <- restrict_range(raman_hdpe, min = 1000, max = 2000) r2 <- restrict_range(raman_hdpe, min = c(1000, 1800), max = c(1200, 2000))  compare_ranges <- c_spec(list(none, r1, r2), range = \"common\") # Common argument crops the ranges to the most common range between the spectra # when joining.   plot(compare_ranges)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"flattening-ranges","dir":"Articles","previous_headings":"Processing","what":"Flattening Ranges","title":"Open Specy Package Tutorial","text":"Sometimes peaks really shouldn’t spectra can distort interpretation spectra don’t necessarily want remove regions analysis believe regions flat instead peak. One way deal replace peak values mean values around peak. purpose flatten_range function. default set flatten CO2 region FTIR spectra region often needs flattened atmospheric artifacts occur spectra. Like restrict_range, R function can accept multiple ranges. Sample raman_hdpe spectrum different degrees background subtraction (Cowger et al., 2020).","code":"single <- filter_spec(spectral_map, 120) # Function to filter spectra by index                                          # number or name or a logical vector.  none <- make_rel(single) f1 <- flatten_range(single) #default flattening the CO2 region.  f2 <- flatten_range(single, min = c(1000, 2500), max = c(1200, 3000))  compare_flats <- c_spec(list(none, f1, f2))  plot(compare_flats)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"min-max-normalization","dir":"Articles","previous_headings":"Processing","what":"Min-Max Normalization","title":"Open Specy Package Tutorial","text":"Often regard spectral intensities arbitrary min-max normalization allows us view spectra scale without drastically distorting shapes relative peak intensities. package, processing functions min-max transform spectra default specify otherwise. plot shows two plots nearly identical except min-max transformed spectrum y axis ranges 0-1.","code":"relative <- make_rel(raman_hdpe)"},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"reading-libraries","dir":"Articles","previous_headings":"Identifying Spectra","what":"Reading Libraries","title":"Open Specy Package Tutorial","text":"Reference libraries spectra known identities. Open Specy library now 10,000 spectra getting large fit within R package size limit 5 MB. host reference libraries OSF function pull libraries automatically. Running get_lib download libraries package director can specify libraries want want . download libraries can load active environment. load one time. Libraries also OpenSpecy objects can use Open Specy object library.","code":"get_lib(type = \"derivative\") lib <- load_lib(type = \"derivative\")"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"matches","dir":"Articles","previous_headings":"Identifying Spectra","what":"Matches","title":"Open Specy Package Tutorial","text":"attempting use reference library identify spectra really important understand format reference library . OpenSpecy reference libraries Absorbance units. derivative absolute first derivative transformed, nobaseline baseline corrected, raw rawest form reference spectra (recommended except advanced uses). previously mentioned libraries Raman FTIR spectra . mediod mediod compressed library version absolute first derivative FTIR, model exception multinomial regression approach FTIR identification absolute derivative spectra. example use data(\"test_lib\") subsampled version absolute derivative library data(\"raman_hdpe\") unprocessed Raman spectrum absorbance units HDPE plastic. single spectra easy look spectra batch, also refer Thresholding Signal-Noise section guidance making sure batch spectra processed quality specs. spectra processed similarly library specifications, can identify spectra using match_spec(). libraries wavenumbers 8 cm^-1 resolution. Whichever library choose, need get spectra similar enough format use comparison. means conforming wavenumbers values using conform = T. Alternatively, done conformation processing. add_library_metadata add_object_metadata options specify column name metadata want add metadata top_n specifies many matches want. example just identified single spectrum library can also send OpenSpecy object multiple spectra. output matches data.table least 3 columns, object_id tells column names spectra x, library_id tells column names library matched . match_val value Pearson correlation coefficient (default) correlation specified ... using model identification option match_val model confidence. output example returned correct material type, HDPE, top match. using Pearson correlation, 0.7 good threshold use positive ID. example, top match greater threshold disregard matches. matches threshold, proclaim spectrum unknown identity. ’ll also notice example matched library Raman FTIR spectra Raman spectra highest hits, rationale lazily matching library . want just match library FTIR Raman spectra, can first filter library using filter_spec() using SpectrumType.","code":"data(\"test_lib\") data(\"raman_hdpe\")  processed <- process_spec(x = raman_hdpe,                            conform_spec = F, #We will conform during matching.                            smooth_intens = T #Conducts the default derivative transformation.                            )  # Check to make sure that the signal to noise ratio of the processed spectra is # greater than 10. print(sig_noise(processed) > 10) plotly_spec(raman_hdpe, processed) matches <- match_spec(x = processed, library = test_lib, conform = T,                       add_library_metadata = \"sample_name\", top_n = 5) print(matches[,c(\"object_id\", \"library_id\", \"match_val\", \"SpectrumType\",                  \"SpectrumIdentity\")])"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"library-metadata","dir":"Articles","previous_headings":"Identifying Spectra","what":"Library Metadata","title":"Open Specy Package Tutorial","text":"libraries created 100 variables metadata can onerous read especially given many variables NA values. created get_metadata() remedy removing columns metadata blank values. function return metadata top match matches. Remember, similar filter_spec(), can specify logic one thing time.","code":"get_metadata(x = test_lib, logic = matches[[1,\"library_id\"]], rm_empty = T)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"plot-matches","dir":"Articles","previous_headings":"Identifying Spectra","what":"Plot Matches","title":"Open Specy Package Tutorial","text":"Overlaying unknown spectra best matches can extremely useful identify peaks don’t fit reference library may need investigation. example shows great correspondence best match unknown spectrum. major peaks accounted correct relative height. two small peaks unknown spectrum near 500 accounted investigated call positive id HDPE.","code":"plotly_spec(processed, filter_spec(test_lib, logic = matches[[1,\"library_id\"]]))"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"sharing-reference-data","dir":"Articles","previous_headings":"Identifying Spectra","what":"Sharing Reference Data","title":"Open Specy Package Tutorial","text":"reference data AI models think useful sharing spectroscopy community OpenSpecy please contact package administrator discuss options collaborating.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"characterizing-particles","dir":"Articles","previous_headings":"","what":"Characterizing Particles","title":"Open Specy Package Tutorial","text":"Sometimes spectroscopy task want perform identify particles spectral map. especially common microplastic analysis spectral map used image sample spectral information used differentiate microplastic particles nonplastic particles. addition material id, one often wants measure shape size particles. brute force technique, one first identify every spectrum map, use thresholding image analysis measure particles. However, often , particles well separated image surface background spectra quite different particle spectra therefore can use thresholding priori identify measure particles, pass exemplary spectrum particle identification routine. important note bleeding edge theory technique may updating functions near future.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"brute-force","dir":"Articles","previous_headings":"Characterizing Particles","what":"Brute Force","title":"Open Specy Package Tutorial","text":"","code":"data(\"test_lib\") test_map <- read_any(read_extdata(\"CA_tiny_map.zip\"))  test_map_processed <- process_spec(test_map, conform_spec_args = list(   range = test_lib$wavenumber, res = NULL)   )  identities <- match_spec(test_map_processed, test_lib, order = test_map,                          add_library_metadata = \"sample_name\", top_n = 1)  features <- ifelse(identities$match_val > 0.7,                    tolower(identities$polymer_class), \"unknown\")  id_map <- def_features(x = test_map_processed, features = features)  id_map$metadata$identities <- features # Also should probably be implemented automatically in the function when a # character value is provided.   # Collapses spectra to their median for each particle test_collapsed <- collapse_spec(id_map)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"a-priori-particle-thresholding","dir":"Articles","previous_headings":"Characterizing Particles","what":"A Priori Particle Thresholding","title":"Open Specy Package Tutorial","text":"","code":"data(\"test_lib\") test_map <- read_any(read_extdata(\"CA_tiny_map.zip\"))  # Characterize the total signal as a threshold value.  snr <- sig_noise(test_map,metric = \"log_tot_sig\")  # Use this to find your particles and the sig_noise value to use for # thresholding.  heatmap_spec(test_map, z = snr)  # Set define the feature regions based on the threshold. 400 appeared to be # where I suspected my particle to be in the previous heatmap.  id_map <- def_features(x = test_map, features = snr > 400)   # Check that the thresholding worked as expected. heatmap_spec(id_map, z = id_map$metadata$feature_id)  # Collapse the spectra to their medians based on the threshold. Important to # note here that the particles with id -88 are anything from the FALSE values # so they should be your background.  collapsed_id_map <- id_map |>   collapse_spec()  # Process the collapsed spectra.  id_map_processed <- process_spec(collapsed_id_map, conform_spec_args = list(   range = test_lib$wavenumber, res = NULL)   )  # Check the spectra. plot(id_map_processed)  # Get the matches of the collapsed spectra for the particles. matches <- match_spec(id_map_processed, test_lib,                       add_library_metadata = \"sample_name\", top_n = 1)"},{"path":"http://wincowger.com/OpenSpecy-package/articles/sop.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Open Specy Package Tutorial","text":"Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy.” Applied Spectroscopy, 74(9), 1167–1183. doi: 10.1177/0003702820923993. Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi: 10.1177/0003702820929064. Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123. Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design Automated Analysis Microplastic Samples Based Fourier Transform Infrared (FTIR) Spectroscopy.” Analytical Bioanalytical Chemistry, 410(21), 5131–5141. doi: 10.1007/s00216-018-1156-x. Renner G, Schmidt TC, Schram J (2017). “New Chemometric Approach Automatic Identification Microplastics Environmental Compartments Based FT-IR Spectroscopy.” Analytical Chemistry, 89(22), 12045–12053. doi: 10.1021/acs.analchem.7b02472. Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639. Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi: 10.1366/000370207782597003.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Win Cowger. Maintainer, author, data contributor. Zacharias Steinmetz. Author. Nick Leong. Author. Andrea Faltynkova. Author, data contributor. Hannah Sherrod. Author. Andrew B Gray. Contributor. Hannah Hapich. Contributor. Jennifer Lynch. Contributor, data contributor. Hannah De Frond. Contributor, data contributor. Keenan Munno. Contributor, data contributor. Chelsea Rochman. Contributor, data contributor. Sebastian Primpke. Contributor, data contributor. Orestis Herodotou. Contributor. Mary C Norris. Contributor. Christine M Knauss. Contributor. Aleksandra Karapetrova. Contributor, data contributor, reviewer. Vesna Teofilovic. Contributor. Laura . T. Markley. Contributor. Shreyas Patankar. Contributor, data contributor. Rachel Kozloski. Contributor, data contributor. Samiksha Singh. Contributor. Katherine Lasdin. Contributor. Cristiane Vidal. Contributor. Clare Murphy-Hagan. Contributor. Philipp Baumann. Contributor. Pierre Roudier. Contributor. National Renewable Energy Laboratory. Funder. Possibility Lab. Funder.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cowger W, Steinmetz Z, Leong N, Faltynkova , Sherrod H (2024). OpenSpecy: Analyze, Process, Identify, Share Raman (FT)IR Spectra. R package version 1.2.0, https://wincowger.com/OpenSpecy-package/, https://github.com/wincowgerDEV/OpenSpecy-package/.","code":"@Manual{,   title = {OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra},   author = {Win Cowger and Zacharias Steinmetz and Nick Leong and Andrea Faltynkova and Hannah Sherrod},   year = {2024},   note = {R package version 1.2.0,     https://wincowger.com/OpenSpecy-package/},   url = {https://github.com/wincowgerDEV/OpenSpecy-package/}, }"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"open-specy-10","dir":"","previous_headings":"","what":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"Analyze, Process, Identify, Share Raman (FT)IR Spectra Raman (FT)IR spectral analysis tool plastic particles environmental samples (Cowger et al. 2021, doi: 10.1021/acs.analchem.1c00123). read_any(), Open Specy provides single function reading individual, batch, map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction flattening, intensity conversions, wavenumber alignment, min-max normalization. Spectra can identified batch using onboard reference library (Cowger et al. 2020, doi: 10.1177/0003702820929064) using match_spec(). Shiny app available via run_app() online https://openanalysis.org/openspecy/.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"OpenSpecy available CRAN GitHub.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"install-from-cran-stable-version","dir":"","previous_headings":"Installation","what":"Install from CRAN (stable version)","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"can install latest release OpenSpecy CRAN :","code":"install.packages(\"OpenSpecy\")"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"install-from-github-development-version","dir":"","previous_headings":"Installation","what":"Install from GitHub (development version)","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"install development version package, paste following code R console (requires devtools):","code":"if (!require(devtools)) install.packages(\"devtools\") devtools::install_github(\"wincowgerDEV/OpenSpecy-package\")"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"","code":"library(OpenSpecy) run_app()"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"simple-workflow-for-single-spectral-identification","dir":"","previous_headings":"","what":"Simple workflow for single spectral identification","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"See package vignette detailed standard operating procedure.","code":"# Fetch current spectral library from https://osf.io/x7dpz/ get_lib(\"derivative\")  # Load library into global environment spec_lib <- load_lib(\"derivative\")  # Read sample spectrum raman_hdpe <- read_extdata(\"raman_hdpe.csv\") |>    read_any()  # Look at the spectrum plotly_spec(raman_hdpe)  # Process the spectra and conform it to the library format raman_proc <- raman_hdpe |>   process_spec(conform_spec_args = list(range = spec_lib$wavenumbers),                 smooth_intens = T, make_rel = T)  # Compare raw and processed spectra plotly_spec(raman_hdpe, raman_proc)  top_matches <- match_spec(raman_proc, library = spec_lib, na.rm = T, top_n = 5,                           add_library_metadata = \"sample_name\",                           add_object_metadata = \"col_id\")  # Print the top 5 results with relevant metadata top_matches[, c(\"object_id\", \"library_id\", \"match_val\", \"SpectrumType\",                 \"SpectrumIdentity\")]  # Get all metadata for the matches get_metadata(spec_lib, logic = top_matches$library_id)"},{"path":"http://wincowger.com/OpenSpecy-package/index.html","id":"citations","dir":"","previous_headings":"","what":"Citations","title":"Analyze, Process, Identify, and Share Raman and (FT)IR Spectra","text":"Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123. Cowger W, Steinmetz Z, Leong N, Faltynkova , Sherrod H (2024). “OpenSpecy: Analyze, Process, Identify, Share Raman (FT)IR Spectra.” R package, 1.0.8. https://github.com/wincowgerDEV/OpenSpecy-package.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/OpenSpecy-package.html","id":null,"dir":"Reference","previous_headings":"","what":"OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra — OpenSpecy-package","title":"OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra — OpenSpecy-package","text":"Raman (FT)IR spectral analysis tool plastic particles environmental samples (Cowger et al. 2021, doi:10.1021/acs.analchem.1c00123 ). read_any(), Open Specy provides single function reading individual, batch, map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction flattening, intensity conversions, wavenumber alignment, min-max normalization. Spectra can identified batch using onboard reference library (Cowger et al. 2020, doi:10.1177/0003702820929064 ) using match_spec(). Shiny app available via run_app() online https://openanalysis.org/openspecy/.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/OpenSpecy-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra — OpenSpecy-package","text":"Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy.” Applied Spectroscopy, 74(9), 1167–1183. doi:10.1177/0003702820923993 . Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi:10.1177/0003702820929064 . Cowger, W (2023). “Library data.” OSF. doi:10.17605/OSF.IO/X7DPZ . Cowger W, Steinmetz Z, Gray , Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs Open Source Community: Open Specy Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi:10.1021/acs.analchem.1c00123 . Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design Automated Analysis Microplastic Samples Based Fourier Transform Infrared (FTIR) Spectroscopy.” Analytical Bioanalytical Chemistry, 410(21), 5131–5141. doi:10.1007/s00216-018-1156-x . Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639. Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi:10.1366/000370207782597003 .","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/OpenSpecy-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra — OpenSpecy-package","text":"Maintainer: Win Cowger wincowger@gmail.com (ORCID) [data contributor] Authors: Zacharias Steinmetz z.steinmetz@rptu.de (ORCID) Nick Leong (ORCID) Andrea Faltynkova (ORCID) [data contributor] Hannah Sherrod (ORCID) contributors: Andrew B Gray (ORCID) [contributor] Hannah Hapich (ORCID) [contributor] Jennifer Lynch (ORCID) [contributor, data contributor] Hannah De Frond (ORCID) [contributor, data contributor] Keenan Munno (ORCID) [contributor, data contributor] Chelsea Rochman (ORCID) [contributor, data contributor] Sebastian Primpke (ORCID) [contributor, data contributor] Orestis Herodotou [contributor] Mary C Norris [contributor] Christine M Knauss (ORCID) [contributor] Aleksandra Karapetrova (ORCID) [contributor, data contributor, reviewer] Vesna Teofilovic (ORCID) [contributor] Laura . T. Markley (ORCID) [contributor] Shreyas Patankar [contributor, data contributor] Rachel Kozloski (ORCID) [contributor, data contributor] Samiksha Singh [contributor] Katherine Lasdin [contributor] Cristiane Vidal (ORCID) [contributor] Clare Murphy-Hagan (ORCID) [contributor] Philipp Baumann info@spectral-cockpit.space (ORCID) [contributor] Pierre Roudier [contributor] National Renewable Energy Laboratory [funder] Possibility Lab [funder]","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust spectral intensities to standard absorbance units. — adj_intens","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"Converts reflectance transmittance intensity units absorbance units adjust log exp transformed units.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"","code":"adj_intens(x, ...)  # Default S3 method adj_intens(x, type = \"none\", make_rel = TRUE, log_exp = \"none\", ...)  # S3 method for class 'OpenSpecy' adj_intens(x, type = \"none\", make_rel = TRUE, log_exp = \"none\", ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"x list object class OpenSpecy. type character string specifying whether input spectrum absorbance units (\"none\", default) needs additional conversion \"reflectance\" \"transmittance\" data. make_rel logical; TRUE spectra automatically normalized make_rel(). log_exp character string specifying whether input needs log transformed \"log\", exp transformed \"exp\", (\"none\", default). ... arguments passed submethods; adj_neg() adj_intens() conform_res() conform_intens().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"adj_intens() returns data frame containing two columns named \"wavenumber\" \"intensity\".","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"Many Open Specy functions assume spectrum absorbance units. example, see subtr_baseline(). run functions properly, need first convert spectra transmittance reflectance absorbance using function. transmittance adjustment uses \\(log(1 / T)\\) calculation correct system particle characteristics. reflectance adjustment uses Kubelka-Munk equation \\((1 - R)^2 / 2R\\). assume reflectance intensity percent 1-100 first correct intensity dividing 100 fits form expected equation.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_intens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust spectral intensities to standard absorbance units. — adj_intens","text":"","code":"data(\"raman_hdpe\")  adj_intens(raman_hdpe) #>      wavenumber  intensity #>           <num>      <num> #>   1:    301.040 0.00000000 #>   2:    304.632 0.03037975 #>   3:    308.221 0.02784810 #>   4:    311.810 0.02405063 #>   5:    315.398 0.02531646 #>  ---                       #> 960:   3187.990 0.05696203 #> 961:   3190.520 0.05696203 #> 962:   3193.060 0.06202532 #> 963:   3195.590 0.06202532 #> 964:   3198.120 0.05189873 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Range restriction and flattening for spectra — restrict_range","title":"Range restriction and flattening for spectra — restrict_range","text":"restrict_range() restricts wavenumber ranges user specified values. Multiple ranges can specified inputting series max min values order. flatten_range() flatten ranges spectra peaks. Multiple ranges can specified inputting series max min values order.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Range restriction and flattening for spectra — restrict_range","text":"","code":"restrict_range(x, ...)  # Default S3 method restrict_range(x, ...)  # S3 method for class 'OpenSpecy' restrict_range(x, min, max, make_rel = TRUE, ...)  flatten_range(x, ...)  # Default S3 method flatten_range(x, ...)  # S3 method for class 'OpenSpecy' flatten_range(x, min = 2200, max = 2400, make_rel = TRUE, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Range restriction and flattening for spectra — restrict_range","text":"x OpenSpecy object. min vector minimum values range flattened. max vector maximum values range flattened. make_rel logical; output intensities normalized range [0, 1] using make_rel() function? ... additional arguments passed subfunctions; currently use.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Range restriction and flattening for spectra — restrict_range","text":"OpenSpecy object spectral intensities within specified ranges restricted flattened.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Range restriction and flattening for spectra — restrict_range","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Range restriction and flattening for spectra — restrict_range","text":"","code":"test_noise <- as_OpenSpecy(x = seq(400,4000, by = 10),                            spectra = data.frame(intensity = rnorm(361))) plot(test_noise)   restrict_range(test_noise, min = 1000, max = 2000) #>      wavenumber intensity #>           <num>     <num> #>   1:       1000 0.7779144 #>   2:       1010 0.4108821 #>   3:       1020 0.7862717 #>   4:       1030 0.4993344 #>   5:       1040 0.3027464 #>  ---                      #>  97:       1960 0.5593439 #>  98:       1970 0.4760214 #>  99:       1980 0.7164056 #> 100:       1990 0.7209565 #> 101:       2000 0.1502945 #>  #> $metadata #>        x     y  license    col_id                          file_id #>    <int> <int>   <char>    <char>                           <char> #> 1:     1     1 CC BY-NC intensity 5ef3b78d1f815177bb927948a30ed9d1  flattened_intensities <- flatten_range(test_noise, min = c(1000, 2000),                                        max = c(1500, 2500)) plot(flattened_intensities)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust wavelength to wavenumbers for Raman — adj_wave","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"Functions converting wave* units.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"","code":"adj_wave(x, ...)  # Default S3 method adj_wave(x, laser, ...)  # S3 method for class 'OpenSpecy' adj_wave(x, laser, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"x OpenSpecy object wavenumber units specified wavelength nm wavelength vector. laser wavelength nm Raman laser. ... additional arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"OpenSpecy object new units converted wavelength wavenumbers vector conversion.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/adj_wave.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adjust wavelength to wavenumbers for Raman — adj_wave","text":"","code":"data(\"raman_hdpe\") raman_wavelength <- raman_hdpe raman_wavelength$wavenumber <- (-1*(raman_wavelength$wavenumber/10^7-1/530))^(-1) adj_wave(raman_wavelength, laser = 530) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b adj_wave(raman_wavelength$wavenumber, laser = 530) #>   [1]  301.040  304.632  308.221  311.810  315.398  318.983  322.566  326.150 #>   [9]  329.732  333.311  336.889  340.467  344.042  347.618  351.190  354.760 #>  [17]  358.332  361.900  365.466  369.031  372.597  376.160  379.721  383.280 #>  [25]  386.839  390.396  393.951  397.505  401.058  404.610  408.160  411.708 #>  [33]  415.256  418.801  422.346  425.887  429.429  432.970  436.507  440.044 #>  [41]  443.581  447.116  450.647  454.180  457.708  461.238  464.764  468.291 #>  [49]  471.815  475.339  478.859  482.379  485.899  489.416  492.931  496.447 #>  [57]  499.960  503.471  506.981  510.489  513.999  517.503  521.009  524.510 #>  [65]  528.014  531.513  535.014  538.510  542.008  545.502  548.997  552.488 #>  [73]  555.979  559.469  562.955  566.442  569.928  573.411  576.894  580.376 #>  [81]  583.854  587.331  590.811  594.286  597.759  601.231  604.705  608.174 #>  [89]  611.642  615.109  618.574  622.038  625.503  628.966  632.426  635.884 #>  [97]  639.342  642.798  646.252  649.706  653.158  656.608  660.058  663.506 #> [105]  666.953  670.398  673.842  677.285  680.727  684.165  687.602  691.040 #> [113]  694.476  697.911  701.345  704.775  708.204  711.634  715.063  718.490 #> [121]  721.912  725.336  728.760  732.180  735.599  739.018  742.434  745.849 #> [129]  749.265  752.675  756.088  759.498  762.906  766.313  769.719  773.124 #> [137]  776.527  779.929  783.330  786.729  790.127  793.524  796.919  800.312 #> [145]  803.705  807.097  810.485  813.874  817.262  820.647  824.032  827.416 #> [153]  830.797  834.179  837.557  840.936  844.312  847.688  851.061  854.433 #> [161]  857.806  861.175  864.543  867.910  871.278  874.642  878.005  881.367 #> [169]  884.728  888.087  891.445  894.802  898.157  901.511  904.864  908.216 #> [177]  911.567  914.914  918.262  921.609  924.954  928.296  931.639  934.981 #> [185]  938.320  941.659  944.995  948.332  951.665  954.999  958.330  961.660 #> [193]  964.991  968.318  971.644  974.971  978.295  981.617  984.940  988.258 #> [201]  991.578  994.896  998.212 1001.530 1004.840 1008.150 1011.460 1014.770 #> [209] 1018.080 1021.390 1024.700 1028.000 1031.300 1034.610 1037.910 1041.210 #> [217] 1044.510 1047.800 1051.100 1054.390 1057.680 1060.980 1064.270 1067.560 #> [225] 1070.840 1074.130 1077.420 1080.700 1083.980 1087.270 1090.550 1093.830 #> [233] 1097.100 1100.380 1103.650 1106.930 1110.200 1113.470 1116.740 1120.010 #> [241] 1123.280 1126.550 1129.810 1133.080 1136.340 1139.600 1142.860 1146.120 #> [249] 1149.370 1152.630 1155.890 1159.140 1162.390 1165.640 1168.890 1172.140 #> [257] 1175.390 1178.640 1181.880 1185.120 1188.370 1191.610 1194.850 1198.090 #> [265] 1201.320 1204.560 1207.790 1211.030 1214.260 1217.490 1220.720 1223.950 #> [273] 1227.180 1230.400 1233.630 1236.850 1240.070 1243.300 1246.520 1249.740 #> [281] 1252.950 1256.170 1259.380 1262.600 1265.810 1269.020 1272.230 1275.440 #> [289] 1278.650 1281.850 1285.060 1288.260 1291.460 1294.670 1297.870 1301.070 #> [297] 1304.260 1307.460 1310.660 1313.850 1317.040 1320.230 1323.420 1326.610 #> [305] 1329.800 1332.990 1336.170 1339.360 1342.540 1345.720 1348.900 1352.080 #> [313] 1355.260 1358.440 1361.610 1364.790 1367.960 1371.130 1374.300 1377.470 #> [321] 1380.640 1383.810 1386.970 1390.140 1393.300 1396.470 1399.630 1402.790 #> [329] 1405.950 1409.100 1412.260 1415.410 1418.570 1421.720 1424.870 1428.020 #> [337] 1431.170 1434.320 1437.470 1440.610 1443.760 1446.900 1450.040 1453.180 #> [345] 1456.320 1459.460 1462.590 1465.730 1468.870 1472.000 1475.130 1478.260 #> [353] 1481.400 1484.520 1487.650 1490.780 1493.900 1497.030 1500.150 1503.270 #> [361] 1506.390 1509.510 1512.630 1515.740 1518.860 1521.970 1525.090 1528.200 #> [369] 1531.310 1534.420 1537.530 1540.640 1543.740 1546.850 1549.950 1553.050 #> [377] 1556.160 1559.260 1562.360 1565.450 1568.550 1571.650 1574.740 1577.830 #> [385] 1580.920 1584.020 1587.110 1590.190 1593.280 1596.370 1599.450 1602.540 #> [393] 1605.620 1608.700 1611.780 1614.860 1617.940 1621.020 1624.090 1627.170 #> [401] 1630.240 1633.310 1636.380 1639.450 1642.520 1645.590 1648.650 1651.720 #> [409] 1654.780 1657.850 1660.910 1663.970 1667.030 1670.090 1673.150 1676.200 #> [417] 1679.260 1682.310 1685.360 1688.410 1691.460 1694.510 1697.560 1700.610 #> [425] 1703.650 1706.700 1709.740 1712.780 1715.830 1718.860 1721.900 1724.940 #> [433] 1727.980 1731.010 1734.050 1737.080 1740.110 1743.140 1746.170 1749.200 #> [441] 1752.230 1755.250 1758.280 1761.300 1764.330 1767.350 1770.370 1773.390 #> [449] 1776.410 1779.420 1782.440 1785.460 1788.470 1791.480 1794.490 1797.500 #> [457] 1800.510 1803.520 1806.530 1809.530 1812.540 1815.540 1818.540 1821.540 #> [465] 1824.550 1827.550 1830.540 1833.540 1836.540 1839.530 1842.520 1845.510 #> [473] 1848.510 1851.500 1854.490 1857.480 1860.460 1863.450 1866.430 1869.410 #> [481] 1872.400 1875.380 1878.360 1881.340 1884.320 1887.290 1890.270 1893.240 #> [489] 1896.220 1899.190 1902.160 1905.130 1908.100 1911.070 1914.030 1917.000 #> [497] 1919.960 1922.930 1925.890 1928.850 1931.810 1934.770 1937.430 1940.400 #> [505] 1943.360 1946.330 1949.300 1952.270 1955.230 1958.190 1961.160 1964.120 #> [513] 1967.080 1970.040 1972.990 1975.950 1978.910 1981.860 1984.820 1987.770 #> [521] 1990.720 1993.670 1996.620 1999.570 2002.520 2005.460 2008.410 2011.350 #> [529] 2014.290 2017.240 2020.180 2023.120 2026.050 2028.990 2031.930 2034.870 #> [537] 2037.800 2040.730 2043.660 2046.600 2049.520 2052.450 2055.380 2058.310 #> [545] 2061.230 2064.160 2067.080 2070.000 2072.930 2075.850 2078.770 2081.680 #> [553] 2084.600 2087.520 2090.430 2093.340 2096.260 2099.170 2102.080 2104.990 #> [561] 2107.900 2110.810 2113.710 2116.620 2119.520 2122.430 2125.330 2128.230 #> [569] 2131.130 2134.030 2136.930 2139.820 2142.720 2145.610 2148.510 2151.400 #> [577] 2154.290 2157.180 2160.070 2162.960 2165.850 2168.730 2171.620 2174.500 #> [585] 2177.390 2180.270 2183.150 2186.030 2188.910 2191.790 2194.670 2197.540 #> [593] 2200.420 2203.290 2206.160 2209.030 2211.900 2214.770 2217.640 2220.510 #> [601] 2223.380 2226.240 2229.110 2231.970 2234.830 2237.690 2240.550 2243.410 #> [609] 2246.270 2249.130 2251.980 2254.840 2257.690 2260.540 2263.390 2266.250 #> [617] 2269.100 2271.940 2274.790 2277.640 2280.490 2283.330 2286.170 2289.020 #> [625] 2291.860 2294.700 2297.540 2300.380 2303.210 2306.050 2308.890 2311.720 #> [633] 2314.550 2317.390 2320.220 2323.050 2325.880 2328.700 2331.530 2334.360 #> [641] 2337.180 2340.010 2342.830 2345.650 2348.470 2351.290 2354.110 2356.930 #> [649] 2359.750 2362.560 2365.380 2368.190 2371.000 2373.820 2376.630 2379.440 #> [657] 2382.250 2385.050 2387.860 2390.670 2393.470 2396.280 2399.080 2401.880 #> [665] 2404.680 2407.480 2410.280 2413.080 2415.870 2418.670 2421.460 2424.260 #> [673] 2427.050 2429.840 2432.630 2435.420 2438.210 2441.000 2443.790 2446.570 #> [681] 2449.360 2452.140 2454.920 2457.700 2460.480 2463.260 2466.040 2468.820 #> [689] 2471.600 2474.370 2477.150 2479.920 2482.690 2485.470 2488.240 2491.010 #> [697] 2493.770 2496.540 2499.310 2502.070 2504.840 2507.600 2510.370 2513.130 #> [705] 2515.890 2518.650 2521.410 2524.160 2526.920 2529.680 2532.430 2535.190 #> [713] 2537.940 2540.690 2543.440 2546.190 2548.940 2551.690 2554.440 2557.180 #> [721] 2559.930 2562.670 2565.420 2568.160 2570.900 2573.640 2576.380 2579.110 #> [729] 2581.850 2584.590 2587.320 2590.060 2592.790 2595.520 2598.250 2600.980 #> [737] 2603.720 2606.440 2609.170 2611.900 2614.620 2617.350 2620.070 2622.790 #> [745] 2625.510 2628.230 2630.950 2633.670 2636.390 2639.110 2641.820 2644.540 #> [753] 2647.250 2649.960 2652.680 2655.390 2658.100 2660.810 2663.510 2666.220 #> [761] 2668.930 2671.630 2674.340 2677.040 2679.740 2682.440 2685.140 2687.840 #> [769] 2690.540 2693.240 2695.940 2698.630 2701.330 2704.020 2706.710 2709.410 #> [777] 2712.100 2714.790 2717.470 2720.160 2722.850 2725.530 2728.220 2730.900 #> [785] 2733.590 2736.270 2738.950 2741.630 2744.310 2746.990 2749.670 2752.340 #> [793] 2755.020 2757.690 2760.370 2763.040 2765.710 2768.380 2771.050 2773.720 #> [801] 2776.390 2779.060 2781.720 2784.390 2787.050 2789.710 2792.380 2795.040 #> [809] 2797.700 2800.360 2803.020 2805.680 2808.330 2810.990 2813.640 2816.300 #> [817] 2818.950 2821.600 2824.250 2826.900 2829.550 2832.200 2834.850 2837.490 #> [825] 2840.140 2842.780 2845.430 2848.070 2850.710 2853.350 2855.990 2858.630 #> [833] 2861.270 2863.910 2866.540 2869.180 2871.810 2874.450 2877.080 2879.710 #> [841] 2882.340 2884.970 2887.600 2890.230 2892.860 2895.480 2898.110 2900.730 #> [849] 2903.350 2905.970 2908.600 2911.220 2913.840 2916.460 2919.070 2921.690 #> [857] 2924.310 2926.920 2929.540 2932.150 2934.760 2937.370 2939.980 2942.590 #> [865] 2945.200 2947.810 2950.420 2953.020 2955.630 2958.230 2960.830 2963.440 #> [873] 2966.040 2968.640 2971.240 2973.830 2976.430 2979.030 2981.630 2984.220 #> [881] 2986.810 2989.410 2992.000 2994.590 2997.180 2999.770 3002.360 3004.950 #> [889] 3007.530 3010.120 3012.700 3015.290 3017.870 3020.450 3023.040 3025.620 #> [897] 3028.200 3030.770 3033.350 3035.930 3038.500 3041.080 3043.650 3046.230 #> [905] 3048.800 3051.370 3053.940 3056.510 3059.080 3061.650 3064.210 3066.780 #> [913] 3069.350 3071.910 3074.470 3077.040 3079.600 3082.160 3084.720 3087.280 #> [921] 3089.830 3092.390 3094.950 3097.500 3100.060 3102.610 3105.160 3107.720 #> [929] 3110.270 3112.820 3115.370 3117.910 3120.460 3123.010 3125.560 3128.100 #> [937] 3130.640 3133.190 3135.730 3138.270 3140.810 3143.350 3145.890 3148.430 #> [945] 3150.960 3153.500 3156.030 3158.570 3161.100 3163.640 3166.170 3168.700 #> [953] 3171.230 3173.760 3175.300 3177.840 3180.380 3182.910 3185.450 3187.990 #> [961] 3190.520 3193.060 3195.590 3198.120"},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":null,"dir":"Reference","previous_headings":"","what":"Create OpenSpecy objects — as_OpenSpecy","title":"Create OpenSpecy objects — as_OpenSpecy","text":"Functions check object OpenSpecy, coerce possible.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create OpenSpecy objects — as_OpenSpecy","text":"","code":"as_OpenSpecy(x, ...)  # S3 method for class 'OpenSpecy' as_OpenSpecy(x, session_id = FALSE, ...)  # S3 method for class 'list' as_OpenSpecy(x, ...)  # S3 method for class 'hyperSpec' as_OpenSpecy(x, ...)  # S3 method for class 'data.frame' as_OpenSpecy(x, colnames = list(wavenumber = NULL, spectra = NULL), ...)  # Default S3 method as_OpenSpecy(   x,   spectra,   metadata = list(file_name = NULL, user_name = NULL, contact_info = NULL, organization =     NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL, material_form     = NULL, material_phase = NULL, material_producer = NULL, material_purity = NULL,     material_quality = NULL, material_color = NULL, material_other = NULL, cas_number =     NULL, instrument_used = NULL, instrument_accessories = NULL, instrument_mode = NULL,     intensity_units = NULL, spectral_resolution = NULL, laser_light_used = NULL,     number_of_accumulations = NULL,       total_acquisition_time_s = NULL,     data_processing_procedure = NULL, level_of_confidence_in_identification = NULL,     other_info = NULL, license = \"CC BY-NC\"),   attributes = list(intensity_unit = NULL, derivative_order = NULL, baseline = NULL,     spectra_type = NULL),   coords = \"gen_grid\",   session_id = FALSE,   ... )  is_OpenSpecy(x)  check_OpenSpecy(x)  OpenSpecy(x, ...)  gen_grid(n)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create OpenSpecy objects — as_OpenSpecy","text":"x depending method, list OpenSpecy parameters, vector wavenumbers spectra, data.frame full spectrum classic Open Specy format. session_id logical. Whether add session ID metadata. session ID based current session info metadata spectra return equal session info changes. Sometimes desirable. colnames names wavenumber column spectra column, makes assumptions based column names placement NULL. spectra spectral intensities formatted data.table one column per spectrum. metadata metadata spectrum one row per spectrum, see details. attributes list attributes describing critical aspects interpreting spectra. see details. coords spatial coordinates spectra. n number spectra generate spatial coordinate grid . ... additional arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create OpenSpecy objects — as_OpenSpecy","text":"as_OpenSpecy() OpenSpecy() returns three part lists described details. is_OpenSpecy() returns TRUE object OpenSpecy FALSE . gen_grid() returns data.table x y coordinates use generating spatial grid spectra one specified data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create OpenSpecy objects — as_OpenSpecy","text":"as_OpenSpecy() converts spectral datasets three part list; first vector wavenumbers spectra, second data.table spectral intensities ordered columns, third item another data.table metadata user provides harvested files . metadata argument may contain named list following details (* = minimum recommended). file_name* file name, defaults basename() specified user_name* User name, e.g. \"Win Cowger\" contact_info Contact information, e.g. \"1-513-673-8956, wincowger@gmail.com\" organization Affiliation, e.g. \"University California, Riverside\" citation Data citation, e.g. \"Primpke, S., Wirth, M., Lorenz, C., & Gerdts, G. (2018). Reference database design automated analysis microplastic samples based Fourier transform infrared (FTIR) spectroscopy. Analytical Bioanalytical Chemistry. doi:10.1007/s00216-018-1156-x \" spectrum_type* Raman FTIR spectrum_identity* Material/polymer analyzed, e.g. \"Polystyrene\" material_form Form material analyzed, e.g. textile fiber, rubber band, sphere, granule material_phase Phase material analyzed (liquid, gas, solid) material_producer Producer material analyzed, e.g. Dow material_purity Purity material analyzed, e.g. 99.98% material_quality Quality material analyzed, e.g. consumer product, manufacturer material, analytical standard, environmental sample material_color Color material analyzed, e.g. blue, #0000ff, (0, 0, 255) material_other material description, e.g. 5 µm diameter fibers, 1 mm spherical particles cas_number CAS number, e.g. 9003-53-6 instrument_used Instrument used, e.g. Horiba LabRam instrument_accessories Instrument accessories, e.g. Focal Plane Array, CCD instrument_mode Instrument modes/settings, e.g. transmission, reflectance intensity_units* Units intensity values spectrum, options transmittance, reflectance, absorbance spectral_resolution Spectral resolution, e.g. 4/cm laser_light_used Wavelength laser/light used, e.g. 785 nm number_of_accumulations Number accumulations, e.g 5 total_acquisition_time_s Total acquisition time (s), e.g. 10 s data_processing_procedure Data processing procedure, e.g. spikefilter, baseline correction, none level_of_confidence_in_identification Level confidence identification, e.g. 99% other_info information license license shared spectrum; defaults \"CC -NC\" (see https://creativecommons.org/licenses/-nc/4.0/ details). creative commons license allowed, example, CC0 CC session_id unique user session identifier; populated automatically paste(digest(Sys.info()), digest(sessionInfo()),   sep = \"/\") file_id unique file identifier; populated automatically digest(object[c(\"wavenumber\", \"spectra\")]) attributes argument may contain named list following details, set, used automate transformations warning messages: intensity_units supported options include \"absorbance\", \"transmittance\", \"reflectance\" derivative_order supported options include \"0\", \"1\", \"2\" baseline supported options include \"raw\" \"nobaseline\" spectra_type supported options include \"ftir\" \"raman\"","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create OpenSpecy objects — as_OpenSpecy","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/as_OpenSpecy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create OpenSpecy objects — as_OpenSpecy","text":"","code":"data(\"raman_hdpe\")  # Inspect the spectra raman_hdpe # see how OpenSpecy objects print. #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b raman_hdpe$wavenumber # look at just the wavenumbers of the spectra. #>   [1]  301.040  304.632  308.221  311.810  315.398  318.983  322.566  326.150 #>   [9]  329.732  333.311  336.889  340.467  344.042  347.618  351.190  354.760 #>  [17]  358.332  361.900  365.466  369.031  372.597  376.160  379.721  383.280 #>  [25]  386.839  390.396  393.951  397.505  401.058  404.610  408.160  411.708 #>  [33]  415.256  418.801  422.346  425.887  429.429  432.970  436.507  440.044 #>  [41]  443.581  447.116  450.647  454.180  457.708  461.238  464.764  468.291 #>  [49]  471.815  475.339  478.859  482.379  485.899  489.416  492.931  496.447 #>  [57]  499.960  503.471  506.981  510.489  513.999  517.503  521.009  524.510 #>  [65]  528.014  531.513  535.014  538.510  542.008  545.502  548.997  552.488 #>  [73]  555.979  559.469  562.955  566.442  569.928  573.411  576.894  580.376 #>  [81]  583.854  587.331  590.811  594.286  597.759  601.231  604.705  608.174 #>  [89]  611.642  615.109  618.574  622.038  625.503  628.966  632.426  635.884 #>  [97]  639.342  642.798  646.252  649.706  653.158  656.608  660.058  663.506 #> [105]  666.953  670.398  673.842  677.285  680.727  684.165  687.602  691.040 #> [113]  694.476  697.911  701.345  704.775  708.204  711.634  715.063  718.490 #> [121]  721.912  725.336  728.760  732.180  735.599  739.018  742.434  745.849 #> [129]  749.265  752.675  756.088  759.498  762.906  766.313  769.719  773.124 #> [137]  776.527  779.929  783.330  786.729  790.127  793.524  796.919  800.312 #> [145]  803.705  807.097  810.485  813.874  817.262  820.647  824.032  827.416 #> [153]  830.797  834.179  837.557  840.936  844.312  847.688  851.061  854.433 #> [161]  857.806  861.175  864.543  867.910  871.278  874.642  878.005  881.367 #> [169]  884.728  888.087  891.445  894.802  898.157  901.511  904.864  908.216 #> [177]  911.567  914.914  918.262  921.609  924.954  928.296  931.639  934.981 #> [185]  938.320  941.659  944.995  948.332  951.665  954.999  958.330  961.660 #> [193]  964.991  968.318  971.644  974.971  978.295  981.617  984.940  988.258 #> [201]  991.578  994.896  998.212 1001.530 1004.840 1008.150 1011.460 1014.770 #> [209] 1018.080 1021.390 1024.700 1028.000 1031.300 1034.610 1037.910 1041.210 #> [217] 1044.510 1047.800 1051.100 1054.390 1057.680 1060.980 1064.270 1067.560 #> [225] 1070.840 1074.130 1077.420 1080.700 1083.980 1087.270 1090.550 1093.830 #> [233] 1097.100 1100.380 1103.650 1106.930 1110.200 1113.470 1116.740 1120.010 #> [241] 1123.280 1126.550 1129.810 1133.080 1136.340 1139.600 1142.860 1146.120 #> [249] 1149.370 1152.630 1155.890 1159.140 1162.390 1165.640 1168.890 1172.140 #> [257] 1175.390 1178.640 1181.880 1185.120 1188.370 1191.610 1194.850 1198.090 #> [265] 1201.320 1204.560 1207.790 1211.030 1214.260 1217.490 1220.720 1223.950 #> [273] 1227.180 1230.400 1233.630 1236.850 1240.070 1243.300 1246.520 1249.740 #> [281] 1252.950 1256.170 1259.380 1262.600 1265.810 1269.020 1272.230 1275.440 #> [289] 1278.650 1281.850 1285.060 1288.260 1291.460 1294.670 1297.870 1301.070 #> [297] 1304.260 1307.460 1310.660 1313.850 1317.040 1320.230 1323.420 1326.610 #> [305] 1329.800 1332.990 1336.170 1339.360 1342.540 1345.720 1348.900 1352.080 #> [313] 1355.260 1358.440 1361.610 1364.790 1367.960 1371.130 1374.300 1377.470 #> [321] 1380.640 1383.810 1386.970 1390.140 1393.300 1396.470 1399.630 1402.790 #> [329] 1405.950 1409.100 1412.260 1415.410 1418.570 1421.720 1424.870 1428.020 #> [337] 1431.170 1434.320 1437.470 1440.610 1443.760 1446.900 1450.040 1453.180 #> [345] 1456.320 1459.460 1462.590 1465.730 1468.870 1472.000 1475.130 1478.260 #> [353] 1481.400 1484.520 1487.650 1490.780 1493.900 1497.030 1500.150 1503.270 #> [361] 1506.390 1509.510 1512.630 1515.740 1518.860 1521.970 1525.090 1528.200 #> [369] 1531.310 1534.420 1537.530 1540.640 1543.740 1546.850 1549.950 1553.050 #> [377] 1556.160 1559.260 1562.360 1565.450 1568.550 1571.650 1574.740 1577.830 #> [385] 1580.920 1584.020 1587.110 1590.190 1593.280 1596.370 1599.450 1602.540 #> [393] 1605.620 1608.700 1611.780 1614.860 1617.940 1621.020 1624.090 1627.170 #> [401] 1630.240 1633.310 1636.380 1639.450 1642.520 1645.590 1648.650 1651.720 #> [409] 1654.780 1657.850 1660.910 1663.970 1667.030 1670.090 1673.150 1676.200 #> [417] 1679.260 1682.310 1685.360 1688.410 1691.460 1694.510 1697.560 1700.610 #> [425] 1703.650 1706.700 1709.740 1712.780 1715.830 1718.860 1721.900 1724.940 #> [433] 1727.980 1731.010 1734.050 1737.080 1740.110 1743.140 1746.170 1749.200 #> [441] 1752.230 1755.250 1758.280 1761.300 1764.330 1767.350 1770.370 1773.390 #> [449] 1776.410 1779.420 1782.440 1785.460 1788.470 1791.480 1794.490 1797.500 #> [457] 1800.510 1803.520 1806.530 1809.530 1812.540 1815.540 1818.540 1821.540 #> [465] 1824.550 1827.550 1830.540 1833.540 1836.540 1839.530 1842.520 1845.510 #> [473] 1848.510 1851.500 1854.490 1857.480 1860.460 1863.450 1866.430 1869.410 #> [481] 1872.400 1875.380 1878.360 1881.340 1884.320 1887.290 1890.270 1893.240 #> [489] 1896.220 1899.190 1902.160 1905.130 1908.100 1911.070 1914.030 1917.000 #> [497] 1919.960 1922.930 1925.890 1928.850 1931.810 1934.770 1937.430 1940.400 #> [505] 1943.360 1946.330 1949.300 1952.270 1955.230 1958.190 1961.160 1964.120 #> [513] 1967.080 1970.040 1972.990 1975.950 1978.910 1981.860 1984.820 1987.770 #> [521] 1990.720 1993.670 1996.620 1999.570 2002.520 2005.460 2008.410 2011.350 #> [529] 2014.290 2017.240 2020.180 2023.120 2026.050 2028.990 2031.930 2034.870 #> [537] 2037.800 2040.730 2043.660 2046.600 2049.520 2052.450 2055.380 2058.310 #> [545] 2061.230 2064.160 2067.080 2070.000 2072.930 2075.850 2078.770 2081.680 #> [553] 2084.600 2087.520 2090.430 2093.340 2096.260 2099.170 2102.080 2104.990 #> [561] 2107.900 2110.810 2113.710 2116.620 2119.520 2122.430 2125.330 2128.230 #> [569] 2131.130 2134.030 2136.930 2139.820 2142.720 2145.610 2148.510 2151.400 #> [577] 2154.290 2157.180 2160.070 2162.960 2165.850 2168.730 2171.620 2174.500 #> [585] 2177.390 2180.270 2183.150 2186.030 2188.910 2191.790 2194.670 2197.540 #> [593] 2200.420 2203.290 2206.160 2209.030 2211.900 2214.770 2217.640 2220.510 #> [601] 2223.380 2226.240 2229.110 2231.970 2234.830 2237.690 2240.550 2243.410 #> [609] 2246.270 2249.130 2251.980 2254.840 2257.690 2260.540 2263.390 2266.250 #> [617] 2269.100 2271.940 2274.790 2277.640 2280.490 2283.330 2286.170 2289.020 #> [625] 2291.860 2294.700 2297.540 2300.380 2303.210 2306.050 2308.890 2311.720 #> [633] 2314.550 2317.390 2320.220 2323.050 2325.880 2328.700 2331.530 2334.360 #> [641] 2337.180 2340.010 2342.830 2345.650 2348.470 2351.290 2354.110 2356.930 #> [649] 2359.750 2362.560 2365.380 2368.190 2371.000 2373.820 2376.630 2379.440 #> [657] 2382.250 2385.050 2387.860 2390.670 2393.470 2396.280 2399.080 2401.880 #> [665] 2404.680 2407.480 2410.280 2413.080 2415.870 2418.670 2421.460 2424.260 #> [673] 2427.050 2429.840 2432.630 2435.420 2438.210 2441.000 2443.790 2446.570 #> [681] 2449.360 2452.140 2454.920 2457.700 2460.480 2463.260 2466.040 2468.820 #> [689] 2471.600 2474.370 2477.150 2479.920 2482.690 2485.470 2488.240 2491.010 #> [697] 2493.770 2496.540 2499.310 2502.070 2504.840 2507.600 2510.370 2513.130 #> [705] 2515.890 2518.650 2521.410 2524.160 2526.920 2529.680 2532.430 2535.190 #> [713] 2537.940 2540.690 2543.440 2546.190 2548.940 2551.690 2554.440 2557.180 #> [721] 2559.930 2562.670 2565.420 2568.160 2570.900 2573.640 2576.380 2579.110 #> [729] 2581.850 2584.590 2587.320 2590.060 2592.790 2595.520 2598.250 2600.980 #> [737] 2603.720 2606.440 2609.170 2611.900 2614.620 2617.350 2620.070 2622.790 #> [745] 2625.510 2628.230 2630.950 2633.670 2636.390 2639.110 2641.820 2644.540 #> [753] 2647.250 2649.960 2652.680 2655.390 2658.100 2660.810 2663.510 2666.220 #> [761] 2668.930 2671.630 2674.340 2677.040 2679.740 2682.440 2685.140 2687.840 #> [769] 2690.540 2693.240 2695.940 2698.630 2701.330 2704.020 2706.710 2709.410 #> [777] 2712.100 2714.790 2717.470 2720.160 2722.850 2725.530 2728.220 2730.900 #> [785] 2733.590 2736.270 2738.950 2741.630 2744.310 2746.990 2749.670 2752.340 #> [793] 2755.020 2757.690 2760.370 2763.040 2765.710 2768.380 2771.050 2773.720 #> [801] 2776.390 2779.060 2781.720 2784.390 2787.050 2789.710 2792.380 2795.040 #> [809] 2797.700 2800.360 2803.020 2805.680 2808.330 2810.990 2813.640 2816.300 #> [817] 2818.950 2821.600 2824.250 2826.900 2829.550 2832.200 2834.850 2837.490 #> [825] 2840.140 2842.780 2845.430 2848.070 2850.710 2853.350 2855.990 2858.630 #> [833] 2861.270 2863.910 2866.540 2869.180 2871.810 2874.450 2877.080 2879.710 #> [841] 2882.340 2884.970 2887.600 2890.230 2892.860 2895.480 2898.110 2900.730 #> [849] 2903.350 2905.970 2908.600 2911.220 2913.840 2916.460 2919.070 2921.690 #> [857] 2924.310 2926.920 2929.540 2932.150 2934.760 2937.370 2939.980 2942.590 #> [865] 2945.200 2947.810 2950.420 2953.020 2955.630 2958.230 2960.830 2963.440 #> [873] 2966.040 2968.640 2971.240 2973.830 2976.430 2979.030 2981.630 2984.220 #> [881] 2986.810 2989.410 2992.000 2994.590 2997.180 2999.770 3002.360 3004.950 #> [889] 3007.530 3010.120 3012.700 3015.290 3017.870 3020.450 3023.040 3025.620 #> [897] 3028.200 3030.770 3033.350 3035.930 3038.500 3041.080 3043.650 3046.230 #> [905] 3048.800 3051.370 3053.940 3056.510 3059.080 3061.650 3064.210 3066.780 #> [913] 3069.350 3071.910 3074.470 3077.040 3079.600 3082.160 3084.720 3087.280 #> [921] 3089.830 3092.390 3094.950 3097.500 3100.060 3102.610 3105.160 3107.720 #> [929] 3110.270 3112.820 3115.370 3117.910 3120.460 3123.010 3125.560 3128.100 #> [937] 3130.640 3133.190 3135.730 3138.270 3140.810 3143.350 3145.890 3148.430 #> [945] 3150.960 3153.500 3156.030 3158.570 3161.100 3163.640 3166.170 3168.700 #> [953] 3171.230 3173.760 3175.300 3177.840 3180.380 3182.910 3185.450 3187.990 #> [961] 3190.520 3193.060 3195.590 3198.120 raman_hdpe$spectra # look at just the spectral intensities data.table. #>      intensity #>          <int> #>   1:        26 #>   2:        50 #>   3:        48 #>   4:        45 #>   5:        46 #>  ---           #> 960:        71 #> 961:        71 #> 962:        75 #> 963:        75 #> 964:        67 raman_hdpe$metadata # look at just the metadata of the spectra. #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  # Creating a list and transforming to OpenSpecy as_OpenSpecy(list(wavenumber = raman_hdpe$wavenumber,                   spectra = raman_hdpe$spectra,                   metadata = raman_hdpe$metadata[,-c(\"x\", \"y\")])) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id    col_id #>                              <char>    <char> #> 1: cb06ce2846b119d932fb6696479a445b intensity  # If you try to produce an OpenSpecy using an OpenSpecy it will just return # the same object. as_OpenSpecy(raman_hdpe) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  # Creating an OpenSpecy from a data.frame as_OpenSpecy(x = data.frame(wavenumber = raman_hdpe$wavenumber,                             spectra = raman_hdpe$spectra$intensity)) #> Ambiguous column names: taking 'spectra' data from all but the 'wavenumber' column; use 'colnames' to supply user-defined columnsFALSE #>      wavenumber spectra #>           <num>   <int> #>   1:    301.040      26 #>   2:    304.632      50 #>   3:    308.221      48 #>   4:    311.810      45 #>   5:    315.398      46 #>  ---                    #> 960:   3187.990      71 #> 961:   3190.520      71 #> 962:   3193.060      75 #> 963:   3195.590      75 #> 964:   3198.120      67 #>  #> $metadata #>        x     y  license  col_id                          file_id #>    <int> <int>   <char>  <char>                           <char> #> 1:     1     1 CC BY-NC spectra bf49db07d97b169635b8f8e17311cdbc  # Test that the spectrum is formatted as an OpenSpecy object. is_OpenSpecy(raman_hdpe) #> [1] TRUE is_OpenSpecy(raman_hdpe$spectra) #> [1] FALSE"},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Conform spectra to a standard wavenumber series — conform_spec","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"Spectra can conformed standard suite wavenumbers compared reference library merged spectra.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"","code":"conform_spec(x, ...)  # Default S3 method conform_spec(x, ...)  # S3 method for class 'OpenSpecy' conform_spec(x, range = NULL, res = 5, allow_na = F, type = \"interp\", ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"x list object class OpenSpecy. range vector new wavenumber values, can just supplied min max value. res spectral resolution adjusted NULL raw range used. allow_na logical; NA values places beyond wavenumbers dataset allowed? type type wavenumber adjustment make. \"interp\" results linear interpolation \"roll\" conducts nearest rolling join wavenumbers. \"mean_up\" works Spectra aggregated, take mean intensities within wavenumber specified. can maintain smaller peaks make spectra similar less resolved relatives. mean_up option still experimental. ... arguments passed approx()","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"adj_intens() returns data frame containing two columns named \"wavenumber\" \"intensity\"","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/conform_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conform spectra to a standard wavenumber series — conform_spec","text":"","code":"data(\"raman_hdpe\") conform_spec(raman_hdpe, c(1000, 2000)) #>      wavenumber intensity #>           <num>     <num> #>   1:       1000  61.69439 #>   2:       1005  69.33837 #>   3:       1010  66.49849 #>   4:       1015  65.00000 #>   5:       1020  70.80060 #>  ---                      #> 197:       1980 100.32542 #> 198:       1985 114.12203 #> 199:       1990 122.80339 #> 200:       1995 106.29492 #> 201:       2000 115.16610 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalization and conversion of spectral data — adj_res","title":"Normalization and conversion of spectral data — adj_res","text":"adj_res() conform_res() helper functions align wavenumbers terms spectral resolution. adj_neg() converts numeric intensities y < 1 values >= 1, keeping absolute differences intensity values shifting value minimum intensity. make_rel() converts intensities y relative values 0 1 using standard normalization equation. na.rm TRUE, missing values removed computation proceeds.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalization and conversion of spectral data — adj_res","text":"","code":"adj_res(x, res = 1, fun = round)  conform_res(x, res = 5)  adj_neg(y, na.rm = FALSE)  mean_replace(y, na.rm = TRUE)  is_empty_vector(x)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalization and conversion of spectral data — adj_res","text":"x numeric vector R object coercible one .vector(x, \"numeric\"); x contain spectral wavenumbers. res spectral resolution supplied fun. fun function applied element x; defaults round() round specific resolution res. y numeric vector containing spectral intensities. na.rm logical. missing values removed?","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalization and conversion of spectral data — adj_res","text":"adj_res() conform_res() return numeric vector resolution-conformed wavenumbers. adj_neg() make_rel() return numeric vectors normalized intensity data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Normalization and conversion of spectral data — adj_res","text":"adj_res() conform_res() used Open Specy facilitate comparisons spectra different resolutions. adj_neg() used avoid errors arise log transforming spectra using adj_intens() functions. make_rel() used retain relative height proportions spectra avoiding large numbers can result spectral instruments.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Normalization and conversion of spectral data — adj_res","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/data_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalization and conversion of spectral data — adj_res","text":"","code":"adj_res(seq(500, 4000, 4), 5) #>   [1]  500  505  510  510  515  520  525  530  530  535  540  545  550  550  555 #>  [16]  560  565  570  570  575  580  585  590  590  595  600  605  610  610  615 #>  [31]  620  625  630  630  635  640  645  650  650  655  660  665  670  670  675 #>  [46]  680  685  690  690  695  700  705  710  710  715  720  725  730  730  735 #>  [61]  740  745  750  750  755  760  765  770  770  775  780  785  790  790  795 #>  [76]  800  805  810  810  815  820  825  830  830  835  840  845  850  850  855 #>  [91]  860  865  870  870  875  880  885  890  890  895  900  905  910  910  915 #> [106]  920  925  930  930  935  940  945  950  950  955  960  965  970  970  975 #> [121]  980  985  990  990  995 1000 1005 1010 1010 1015 1020 1025 1030 1030 1035 #> [136] 1040 1045 1050 1050 1055 1060 1065 1070 1070 1075 1080 1085 1090 1090 1095 #> [151] 1100 1105 1110 1110 1115 1120 1125 1130 1130 1135 1140 1145 1150 1150 1155 #> [166] 1160 1165 1170 1170 1175 1180 1185 1190 1190 1195 1200 1205 1210 1210 1215 #> [181] 1220 1225 1230 1230 1235 1240 1245 1250 1250 1255 1260 1265 1270 1270 1275 #> [196] 1280 1285 1290 1290 1295 1300 1305 1310 1310 1315 1320 1325 1330 1330 1335 #> [211] 1340 1345 1350 1350 1355 1360 1365 1370 1370 1375 1380 1385 1390 1390 1395 #> [226] 1400 1405 1410 1410 1415 1420 1425 1430 1430 1435 1440 1445 1450 1450 1455 #> [241] 1460 1465 1470 1470 1475 1480 1485 1490 1490 1495 1500 1505 1510 1510 1515 #> [256] 1520 1525 1530 1530 1535 1540 1545 1550 1550 1555 1560 1565 1570 1570 1575 #> [271] 1580 1585 1590 1590 1595 1600 1605 1610 1610 1615 1620 1625 1630 1630 1635 #> [286] 1640 1645 1650 1650 1655 1660 1665 1670 1670 1675 1680 1685 1690 1690 1695 #> [301] 1700 1705 1710 1710 1715 1720 1725 1730 1730 1735 1740 1745 1750 1750 1755 #> [316] 1760 1765 1770 1770 1775 1780 1785 1790 1790 1795 1800 1805 1810 1810 1815 #> [331] 1820 1825 1830 1830 1835 1840 1845 1850 1850 1855 1860 1865 1870 1870 1875 #> [346] 1880 1885 1890 1890 1895 1900 1905 1910 1910 1915 1920 1925 1930 1930 1935 #> [361] 1940 1945 1950 1950 1955 1960 1965 1970 1970 1975 1980 1985 1990 1990 1995 #> [376] 2000 2005 2010 2010 2015 2020 2025 2030 2030 2035 2040 2045 2050 2050 2055 #> [391] 2060 2065 2070 2070 2075 2080 2085 2090 2090 2095 2100 2105 2110 2110 2115 #> [406] 2120 2125 2130 2130 2135 2140 2145 2150 2150 2155 2160 2165 2170 2170 2175 #> [421] 2180 2185 2190 2190 2195 2200 2205 2210 2210 2215 2220 2225 2230 2230 2235 #> [436] 2240 2245 2250 2250 2255 2260 2265 2270 2270 2275 2280 2285 2290 2290 2295 #> [451] 2300 2305 2310 2310 2315 2320 2325 2330 2330 2335 2340 2345 2350 2350 2355 #> [466] 2360 2365 2370 2370 2375 2380 2385 2390 2390 2395 2400 2405 2410 2410 2415 #> [481] 2420 2425 2430 2430 2435 2440 2445 2450 2450 2455 2460 2465 2470 2470 2475 #> [496] 2480 2485 2490 2490 2495 2500 2505 2510 2510 2515 2520 2525 2530 2530 2535 #> [511] 2540 2545 2550 2550 2555 2560 2565 2570 2570 2575 2580 2585 2590 2590 2595 #> [526] 2600 2605 2610 2610 2615 2620 2625 2630 2630 2635 2640 2645 2650 2650 2655 #> [541] 2660 2665 2670 2670 2675 2680 2685 2690 2690 2695 2700 2705 2710 2710 2715 #> [556] 2720 2725 2730 2730 2735 2740 2745 2750 2750 2755 2760 2765 2770 2770 2775 #> [571] 2780 2785 2790 2790 2795 2800 2805 2810 2810 2815 2820 2825 2830 2830 2835 #> [586] 2840 2845 2850 2850 2855 2860 2865 2870 2870 2875 2880 2885 2890 2890 2895 #> [601] 2900 2905 2910 2910 2915 2920 2925 2930 2930 2935 2940 2945 2950 2950 2955 #> [616] 2960 2965 2970 2970 2975 2980 2985 2990 2990 2995 3000 3005 3010 3010 3015 #> [631] 3020 3025 3030 3030 3035 3040 3045 3050 3050 3055 3060 3065 3070 3070 3075 #> [646] 3080 3085 3090 3090 3095 3100 3105 3110 3110 3115 3120 3125 3130 3130 3135 #> [661] 3140 3145 3150 3150 3155 3160 3165 3170 3170 3175 3180 3185 3190 3190 3195 #> [676] 3200 3205 3210 3210 3215 3220 3225 3230 3230 3235 3240 3245 3250 3250 3255 #> [691] 3260 3265 3270 3270 3275 3280 3285 3290 3290 3295 3300 3305 3310 3310 3315 #> [706] 3320 3325 3330 3330 3335 3340 3345 3350 3350 3355 3360 3365 3370 3370 3375 #> [721] 3380 3385 3390 3390 3395 3400 3405 3410 3410 3415 3420 3425 3430 3430 3435 #> [736] 3440 3445 3450 3450 3455 3460 3465 3470 3470 3475 3480 3485 3490 3490 3495 #> [751] 3500 3505 3510 3510 3515 3520 3525 3530 3530 3535 3540 3545 3550 3550 3555 #> [766] 3560 3565 3570 3570 3575 3580 3585 3590 3590 3595 3600 3605 3610 3610 3615 #> [781] 3620 3625 3630 3630 3635 3640 3645 3650 3650 3655 3660 3665 3670 3670 3675 #> [796] 3680 3685 3690 3690 3695 3700 3705 3710 3710 3715 3720 3725 3730 3730 3735 #> [811] 3740 3745 3750 3750 3755 3760 3765 3770 3770 3775 3780 3785 3790 3790 3795 #> [826] 3800 3805 3810 3810 3815 3820 3825 3830 3830 3835 3840 3845 3850 3850 3855 #> [841] 3860 3865 3870 3870 3875 3880 3885 3890 3890 3895 3900 3905 3910 3910 3915 #> [856] 3920 3925 3930 3930 3935 3940 3945 3950 3950 3955 3960 3965 3970 3970 3975 #> [871] 3980 3985 3990 3990 3995 4000 conform_res(seq(500, 4000, 4)) #>   [1]  500  505  510  515  520  525  530  535  540  545  550  555  560  565  570 #>  [16]  575  580  585  590  595  600  605  610  615  620  625  630  635  640  645 #>  [31]  650  655  660  665  670  675  680  685  690  695  700  705  710  715  720 #>  [46]  725  730  735  740  745  750  755  760  765  770  775  780  785  790  795 #>  [61]  800  805  810  815  820  825  830  835  840  845  850  855  860  865  870 #>  [76]  875  880  885  890  895  900  905  910  915  920  925  930  935  940  945 #>  [91]  950  955  960  965  970  975  980  985  990  995 1000 1005 1010 1015 1020 #> [106] 1025 1030 1035 1040 1045 1050 1055 1060 1065 1070 1075 1080 1085 1090 1095 #> [121] 1100 1105 1110 1115 1120 1125 1130 1135 1140 1145 1150 1155 1160 1165 1170 #> [136] 1175 1180 1185 1190 1195 1200 1205 1210 1215 1220 1225 1230 1235 1240 1245 #> [151] 1250 1255 1260 1265 1270 1275 1280 1285 1290 1295 1300 1305 1310 1315 1320 #> [166] 1325 1330 1335 1340 1345 1350 1355 1360 1365 1370 1375 1380 1385 1390 1395 #> [181] 1400 1405 1410 1415 1420 1425 1430 1435 1440 1445 1450 1455 1460 1465 1470 #> [196] 1475 1480 1485 1490 1495 1500 1505 1510 1515 1520 1525 1530 1535 1540 1545 #> [211] 1550 1555 1560 1565 1570 1575 1580 1585 1590 1595 1600 1605 1610 1615 1620 #> [226] 1625 1630 1635 1640 1645 1650 1655 1660 1665 1670 1675 1680 1685 1690 1695 #> [241] 1700 1705 1710 1715 1720 1725 1730 1735 1740 1745 1750 1755 1760 1765 1770 #> [256] 1775 1780 1785 1790 1795 1800 1805 1810 1815 1820 1825 1830 1835 1840 1845 #> [271] 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 #> [286] 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 #> [301] 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 #> [316] 2075 2080 2085 2090 2095 2100 2105 2110 2115 2120 2125 2130 2135 2140 2145 #> [331] 2150 2155 2160 2165 2170 2175 2180 2185 2190 2195 2200 2205 2210 2215 2220 #> [346] 2225 2230 2235 2240 2245 2250 2255 2260 2265 2270 2275 2280 2285 2290 2295 #> [361] 2300 2305 2310 2315 2320 2325 2330 2335 2340 2345 2350 2355 2360 2365 2370 #> [376] 2375 2380 2385 2390 2395 2400 2405 2410 2415 2420 2425 2430 2435 2440 2445 #> [391] 2450 2455 2460 2465 2470 2475 2480 2485 2490 2495 2500 2505 2510 2515 2520 #> [406] 2525 2530 2535 2540 2545 2550 2555 2560 2565 2570 2575 2580 2585 2590 2595 #> [421] 2600 2605 2610 2615 2620 2625 2630 2635 2640 2645 2650 2655 2660 2665 2670 #> [436] 2675 2680 2685 2690 2695 2700 2705 2710 2715 2720 2725 2730 2735 2740 2745 #> [451] 2750 2755 2760 2765 2770 2775 2780 2785 2790 2795 2800 2805 2810 2815 2820 #> [466] 2825 2830 2835 2840 2845 2850 2855 2860 2865 2870 2875 2880 2885 2890 2895 #> [481] 2900 2905 2910 2915 2920 2925 2930 2935 2940 2945 2950 2955 2960 2965 2970 #> [496] 2975 2980 2985 2990 2995 3000 3005 3010 3015 3020 3025 3030 3035 3040 3045 #> [511] 3050 3055 3060 3065 3070 3075 3080 3085 3090 3095 3100 3105 3110 3115 3120 #> [526] 3125 3130 3135 3140 3145 3150 3155 3160 3165 3170 3175 3180 3185 3190 3195 #> [541] 3200 3205 3210 3215 3220 3225 3230 3235 3240 3245 3250 3255 3260 3265 3270 #> [556] 3275 3280 3285 3290 3295 3300 3305 3310 3315 3320 3325 3330 3335 3340 3345 #> [571] 3350 3355 3360 3365 3370 3375 3380 3385 3390 3395 3400 3405 3410 3415 3420 #> [586] 3425 3430 3435 3440 3445 3450 3455 3460 3465 3470 3475 3480 3485 3490 3495 #> [601] 3500 3505 3510 3515 3520 3525 3530 3535 3540 3545 3550 3555 3560 3565 3570 #> [616] 3575 3580 3585 3590 3595 3600 3605 3610 3615 3620 3625 3630 3635 3640 3645 #> [631] 3650 3655 3660 3665 3670 3675 3680 3685 3690 3695 3700 3705 3710 3715 3720 #> [646] 3725 3730 3735 3740 3745 3750 3755 3760 3765 3770 3775 3780 3785 3790 3795 #> [661] 3800 3805 3810 3815 3820 3825 3830 3835 3840 3845 3850 3855 3860 3865 3870 #> [676] 3875 3880 3885 3890 3895 3900 3905 3910 3915 3920 3925 3930 3935 3940 3945 #> [691] 3950 3955 3960 3965 3970 3975 3980 3985 3990 3995 4000 adj_neg(c(-1000, -1, 0, 1, 10)) #> [1]    1 1000 1001 1002 1011 make_rel(c(-1000, -1, 0, 1, 10)) #> [1] 0.0000000 0.9891089 0.9900990 0.9910891 1.0000000"},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Define features — collapse_spec","title":"Define features — collapse_spec","text":"Functions analyzing features, like particles, fragments, fibers, spectral map oriented OpenSpecy object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define features — collapse_spec","text":"","code":"collapse_spec(x, ...)  # Default S3 method collapse_spec(x, ...)  # S3 method for class 'OpenSpecy' collapse_spec(x, fun = median, column = \"feature_id\", ...)  def_features(x, ...)  # Default S3 method def_features(x, ...)  # S3 method for class 'OpenSpecy' def_features(   x,   features,   shape_kernel = c(3, 3),   shape_type = \"box\",   close = F,   close_kernel = c(4, 4),   close_type = \"box\",   img = NULL,   bottom_left = NULL,   top_right = NULL,   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define features — collapse_spec","text":"x OpenSpecy object fun function name collapse . column column name metadata collapse . features logical vector character vector describing spectra features (TRUE) (FALSE). character vector provided, represent different feature types present spectra. shape_kernel width height area pixels search connecting features, c(3,3) typically used larger numbers smooth connections particles . shape_type character, options shape used find connections c(\"box\", \"disc\", \"diamond\") close logical, whether closing performed using shape kernel estimating components. close_kernel width height area close using close option. close_type character, options shape used find connections c(\"box\", \"disc\", \"diamond\") img file location visual image corresponds spectral image. bottom_left two value vector specifying x,y location image pixels bottom left spectral map begins. y values top x values left right. top_right two value vector specifying x,y location visual image pixels top right spectral map extent . y values top x values left right. ... additional arguments passed subfunctions.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define features — collapse_spec","text":"OpenSpecy object appended metadata features collapsed features. units pixels. Metadata described . x x coordinate pixel centroid collapsed y y coordinate pixel centroid collapsed feature_id unique identifier feature area area pixels feature perimeter perimeter convex hull feature feret_min feret_max divided area feret_max largest dimension convex hull feature convex_hull_area area convex hull centroid_x mean x coordinate feature centroid_y mean y coordinate feature first_x first x coordinate feature first_y first y coordinate feature rand_x random x coordinate feature rand_y random y coordinate feature r using visual imagery overlay, red band value location g using visual imagery overlay, green band value location b using visual imagery overlay, blue band value location","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define features — collapse_spec","text":"def_features() accepts OpenSpecy object logical character vector describing pixels correspond particles. collapse_spec() takes OpenSpecy object particle-specific metadata (def_features()) collapses spectra function intensities unique particle. also updates metadata centroid coordinates, preserving feature information area Feret max.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Define features — collapse_spec","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/def_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define features — collapse_spec","text":"","code":"tiny_map <- read_extdata(\"CA_tiny_map.zip\") |> read_any() identified_map <- def_features(tiny_map, tiny_map$metadata$x == 0) collapse_spec(identified_map) #>      wavenumber          1        -88 #>           <num>      <num>      <num> #>   1:     717.42 -0.3454294 -0.1846190 #>   2:     725.13 -0.3505079 -0.2030258 #>   3:     732.85 -0.3595735 -0.2236059 #>   4:     740.56 -0.3662870 -0.2380186 #>   5:     748.28 -0.3689005 -0.2436821 #>  ---                                  #> 423:    3972.81 -0.4140871 -0.3092059 #> 424:    3980.52 -0.4108835 -0.3107601 #> 425:    3988.24 -0.4105957 -0.3180229 #> 426:    3995.95 -0.4125072 -0.3195935 #> 427:    4003.67 -0.4132366 -0.3126730 #>  #> $metadata #>        x     y feature_id  area perimeter feret_min feret_max convex_hull_area #>    <num> <num>     <char> <int>     <num>     <num>     <num>            <num> #> 1:     0     0          1    13        24         1        13                0 #> 2:     1     0        -88    NA        NA        NA        NA               NA #>          file_name  license #>             <char>   <char> #> 1: CA small UF.dat CC BY-NC #> 2: CA small UF.dat CC BY-NC #>                                                     description samples lines #>                                                          <char>   <num> <num> #> 1: Pixel 15, XPos=12549.72, YPos= 589.82, X=12362.22, Y= 614.82      16    13 #> 2: Pixel 15, XPos=12549.72, YPos= 589.82, X=12362.22, Y= 614.82      16    13 #>    bands header offset data type interleave                  z plot titles #>    <num>         <num>     <num>     <char>                         <char> #> 1:   427             0         4        bip Wavenumbers (cm-1), Absorbance #> 2:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>                  pixel size col_id #>                      <char> <char> #> 1: 0.000025000, 0.000025000    0_0 #> 2: 0.000025000, 0.000025000    0_1 #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/3e1ed1ca8fc59e615a9d978fcca5e984 #> 2: 5a698732989a836ef6bdea187fd42e2c/3e1ed1ca8fc59e615a9d978fcca5e984 #>                             file_id centroid_x centroid_y first_x first_y #>                              <char>      <num>      <num>   <num>   <num> #> 1: 215a0fd7e0dc3b1001799214618ce6eb          0          6       0       0 #> 2: 215a0fd7e0dc3b1001799214618ce6eb          8          6       1       0 #>    rand_x rand_y #>     <num>  <num> #> 1:      0     10 #> 2:     14      1"},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic Open Specy Methods — head.OpenSpecy","title":"Generic Open Specy Methods — head.OpenSpecy","text":"Methods visualize convert OpenSpecy objects.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic Open Specy Methods — head.OpenSpecy","text":"","code":"# S3 method for class 'OpenSpecy' head(x, ...)  # S3 method for class 'OpenSpecy' print(x, ...)  # S3 method for class 'OpenSpecy' plot(x, ...)  # S3 method for class 'OpenSpecy' lines(x, ...)  # S3 method for class 'OpenSpecy' summary(object, ...)  # S3 method for class 'OpenSpecy' as.data.frame(x, ...)  # S3 method for class 'OpenSpecy' as.data.table(x, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic Open Specy Methods — head.OpenSpecy","text":"x OpenSpecy object. object OpenSpecy object. ... arguments passed respective default method.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generic Open Specy Methods — head.OpenSpecy","text":"head(), print(), summary() return textual representation OpenSpecy object. plot() lines() return plot. .data.frame() .data.table() convert OpenSpecy objects tabular data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generic Open Specy Methods — head.OpenSpecy","text":"head() shows first lines OpenSpecy object. print() prints contents OpenSpecy object. summary() produces result summary OpenSpecy object. plot() produces matplot() OpenSpecy object; lines() adds new spectra .","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generic Open Specy Methods — head.OpenSpecy","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/gen_OpenSpecy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generic Open Specy Methods — head.OpenSpecy","text":"","code":"data(\"raman_hdpe\")  # Printing the OpenSpecy object print(raman_hdpe) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  # Displaying the first few lines of the OpenSpecy object head(raman_hdpe) #>    wavenumber intensity #>         <num>     <int> #> 1:    301.040        26 #> 2:    304.632        50 #> 3:    308.221        48 #> 4:    311.810        45 #> 5:    315.398        46 #> 6:    318.983        42  # Plotting the spectra plot(raman_hdpe)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":null,"dir":"Reference","previous_headings":"","what":"Create human readable timestamps — human_ts","title":"Create human readable timestamps — human_ts","text":"helper function creates human readable timestamps form %Y%m%d-%H%M%OS current time.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create human readable timestamps — human_ts","text":"","code":"human_ts()"},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create human readable timestamps — human_ts","text":"human_ts() returns character value respective timestamp.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create human readable timestamps — human_ts","text":"Human readable timestamps appended file names fields metadata shared Open Specy community.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create human readable timestamps — human_ts","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/human_ts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create human readable timestamps — human_ts","text":"","code":"human_ts() #> [1] \"20241101-225933\""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Interactive plots for OpenSpecy objects — plotly_spec","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"functions generate heatmaps, spectral plots, interactive plots OpenSpecy data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"","code":"plotly_spec(x, ...)  # Default S3 method plotly_spec(x, ...)  # S3 method for class 'OpenSpecy' plotly_spec(   x,   x2 = NULL,   line = list(color = \"rgb(255, 255, 255)\"),   line2 = list(dash = \"dot\", color = \"rgb(255,0,0)\"),   font = list(color = \"#FFFFFF\"),   plot_bgcolor = \"rgba(17, 0, 73, 0)\",   paper_bgcolor = \"rgb(0, 0, 0)\",   showlegend = FALSE,   make_rel = TRUE,   ... )  heatmap_spec(x, ...)  # Default S3 method heatmap_spec(x, ...)  # S3 method for class 'OpenSpecy' heatmap_spec(   x,   z = NULL,   sn = NULL,   cor = NULL,   min_sn = NULL,   min_cor = NULL,   select = NULL,   font = list(color = \"#FFFFFF\"),   plot_bgcolor = \"rgba(17, 0, 73, 0)\",   paper_bgcolor = \"rgb(0, 0, 0)\",   colorscale = \"Viridis\",   showlegend = FALSE,   type = \"interactive\",   ... )  interactive_plot(x, ...)  # Default S3 method interactive_plot(x, ...)  # S3 method for class 'OpenSpecy' interactive_plot(   x,   x2 = NULL,   select = NULL,   line = list(color = \"rgb(255, 255, 255)\"),   line2 = list(dash = \"dot\", color = \"rgb(255,0,0)\"),   font = list(color = \"#FFFFFF\"),   plot_bgcolor = \"rgba(17, 0, 73, 0)\",   paper_bgcolor = \"rgb(0, 0, 0)\",   colorscale = \"Viridis\",   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"x OpenSpecy object containing metadata spectral data first group. x2 optional second OpenSpecy object containing metadata spectral data second group. line list; line parameter x; passed add_trace(). line2 list; line parameter x2; passed font list; passed layout(). plot_bgcolor color value; passed layout(). paper_bgcolor color value; passed layout(). showlegend whether show legend passed make_rel logical, whether make spectra relative use raw values z optional numeric vector specifying intensity values heatmap. provided, function use intensity values OpenSpecy object. sn optional numeric value specifying signal--noise ratio threshold. provided along min_sn, regions SNR threshold excluded heatmap. cor optional numeric value specifying correlation threshold. provided along min_cor, regions correlation threshold excluded heatmap. min_sn optional numeric value specifying minimum signal--noise ratio inclusion heatmap. Regions SNR threshold excluded. min_cor optional numeric value specifying minimum correlation inclusion heatmap. Regions correlation threshold excluded. select optional index selected spectrum highlight heatmap. colorscale colorscale passed add_trace() can array one \"Blackbody\", \"Bluered\", \"Blues\", \"Cividis\", \"Earth\", \"Electric\", \"Greens\", \"Greys\", \"Hot\", \"Jet\", \"Picnic\", \"Portland\", \"Rainbow\", \"RdBu\", \"Reds\", \"Viridis\", \"YlGnBu\", \"YlOrRd\". type specification plot type either interactive static plot_ly(). ... arguments passed plot_ly().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"plotly heatmap object displaying OpenSpecy data. subplot containing heatmap spectra plot. plotly object displaying spectra OpenSpecy object(s).","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/interactive_plots.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interactive plots for OpenSpecy objects — plotly_spec","text":"","code":"data(\"raman_hdpe\") tiny_map <- read_extdata(\"CA_tiny_map.zip\") |> read_zip() plotly_spec(raman_hdpe)  {\"x\":{\"visdat\":{\"17308993b11\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"17308993b11\",\"attrs\":{\"17308993b11\":{\"mode\":\"lines\",\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"type\":\"scatter\"},\"17308993b11.1\":{\"mode\":\"lines\",\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"type\":\"scatter\",\"x\":{},\"y\":{},\"split\":{},\"line\":{\"color\":\"rgb(255, 255, 255)\"},\"name\":\"x1\",\"showlegend\":false,\"inherit\":true}},\"layout\":{\"margin\":{\"b\":40,\"l\":60,\"t\":25,\"r\":10},\"xaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"wavenumber [cm<sup>-1<\\/sup>]\",\"autorange\":\"reversed\"},\"yaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"intensity [-]\"},\"plot_bgcolor\":\"rgba(17, 0, 73, 0)\",\"paper_bgcolor\":\"rgb(0, 0, 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and write spectral data — write_spec","title":"Read and write spectral data — write_spec","text":"Functions reading writing spectral data OpenSpecy format. OpenSpecy objects lists components wavenumber, spectra, metadata. Currently supported formats .y()ml, .json, .csv, .rds.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read and write spectral data — write_spec","text":"","code":"write_spec(x, ...)  # Default S3 method write_spec(x, ...)  # S3 method for class 'OpenSpecy' write_spec(x, file, method = NULL, digits = getOption(\"digits\"), ...)  read_spec(file, method = NULL, ...)  as_hyperSpec(x)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read and write spectral data — write_spec","text":"x object class OpenSpecy. file file path read written . method optional; function used custom reader writer. Defaults appropriate function based file extension. digits number significant digits use formatting numeric values; defaults getOption(\"digits\"). ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read and write spectral data — write_spec","text":"read_spec() reads data formatted OpenSpecy object returns list object class OpenSpecy containing spectral data. write_spec() writes file object class OpenSpecy containing spectral data. as_hyperspec() converts OpenSpecy object hyperSpec-class object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read and write spectral data — write_spec","text":"Due floating point number errors may differences precision numbers returned using multiple devices .json .yaml files numbers nearly identical. readRDS() return exact object every time.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read and write spectral data — write_spec","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/io_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read and write spectral data — write_spec","text":"","code":"read_extdata(\"raman_hdpe.yml\") |> read_spec() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id      file_name    col_id #>                              <char>         <char>    <char> #> 1: cb06ce2846b119d932fb6696479a445b raman_hdpe.yml intensity read_extdata(\"raman_hdpe.json\") |> read_spec() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id       file_name    col_id #>                              <char>          <char>    <char> #> 1: cb06ce2846b119d932fb6696479a445b raman_hdpe.json intensity read_extdata(\"raman_hdpe.rds\") |> read_spec() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id      file_name #>                              <char>         <char> #> 1: cb06ce2846b119d932fb6696479a445b raman_hdpe.rds read_extdata(\"raman_hdpe.csv\") |> read_spec() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y      file_name  license    col_id #>    <int> <int>         <char>   <char>    <char> #> 1:     1     1 raman_hdpe.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  if (FALSE) { # \\dontrun{ data(raman_hdpe) write_spec(raman_hdpe, \"raman_hdpe.yml\") write_spec(raman_hdpe, \"raman_hdpe.json\") write_spec(raman_hdpe, \"raman_hdpe.rds\") write_spec(raman_hdpe, \"raman_hdpe.csv\")  # Convert an OpenSpecy object to a hyperSpec object hyper <- as_hyperSpec(raman_hdpe) } # }"},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":null,"dir":"Reference","previous_headings":"","what":"Make spectral intensities relative — make_rel","title":"Make spectral intensities relative — make_rel","text":"make_rel() converts intensities x relative values 0 1 using standard normalization equation. na.rm TRUE, missing values removed computation proceeds.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make spectral intensities relative — make_rel","text":"","code":"make_rel(x, ...)  # Default S3 method make_rel(x, na.rm = FALSE, ...)  # S3 method for class 'OpenSpecy' make_rel(x, na.rm = FALSE, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make spectral intensities relative — make_rel","text":"x numeric vector R OpenSpecy object na.rm logical. missing values removed? ... arguments passed make_rel().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make spectral intensities relative — make_rel","text":"make_rel() return numeric vectors (vector provided) OpenSpecy object normalized intensity data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make spectral intensities relative — make_rel","text":"make_rel() used retain relative height proportions spectra avoiding large numbers can result spectral instruments.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Make spectral intensities relative — make_rel","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/make_rel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make spectral intensities relative — make_rel","text":"","code":"make_rel(c(-1000, -1, 0, 1, 10)) #> [1] 0.0000000 0.9891089 0.9900990 0.9910891 1.0000000"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":null,"dir":"Reference","previous_headings":"","what":"Manage spectral libraries — check_lib","title":"Manage spectral libraries — check_lib","text":"functions import spectral libraries Open Specy already downloaded. CRAN allow deployment large datasets workaround using make sure everyone can easily get Open Specy functionality running desktop. Please see references using libraries. libraries accumulation massive amount effort independant groups attributed using data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manage spectral libraries — check_lib","text":"","code":"check_lib(   type = c(\"derivative\", \"nobaseline\", \"raw\", \"medoid\", \"model\"),   path = \"system\",   condition = \"warning\" )  get_lib(   type = c(\"derivative\", \"nobaseline\", \"raw\", \"medoid\", \"model\"),   path = \"system\",   ... )  load_lib(type, path = \"system\")  rm_lib(   type = c(\"derivative\", \"nobaseline\", \"raw\", \"medoid\", \"model\"),   path = \"system\" )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manage spectral libraries — check_lib","text":"type library type check/retrieve; defaults c(\"derivative\", \"nobaseline\", \"raw\", \"medoid\", \"model\") reads everything. path save look local library files; defaults \"system\" pointing system.file(\"extdata\", package = \"OpenSpecy\"). condition determines check_lib() warn (\"warning\", default) throw error (\"error\"). ... arguments passed osf_download().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manage spectral libraries — check_lib","text":"check_lib() get_lib() return messages ; load_lib() returns OpenSpecy object containing respective spectral reference library.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Manage spectral libraries — check_lib","text":"check_lib() checks see Open Specy reference library already exists users computer. get_lib() downloads Open Specy library OSF (doi:10.17605/OSF.IO/X7DPZ ). load_lib() load library global environment use Open Specy functions. rm_lib() removes libraries computer.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Manage spectral libraries — check_lib","text":"Bell IB, Clark RJH, Gibbs PJ (2010). “Raman Spectroscopic Library.” Christopher Ingold Laboratories, University College London, UK. https://www.chem.ucl.ac.uk/resources/raman/. Berzinš K, Sales RE, Barnsley JE, Walker G, Fraser-Miller SJ, Gordon KC (2020). “Low-Wavenumber Raman Spectral Database Pharmaceutical Excipients.” Vibrational Spectroscopy 107, 103021. doi:10.5281/zenodo.3614035 . Cabernard L, Roscher L, Lorenz C, Gerdts G, Primpke S (2018). “Comparison Raman Fourier Transform Infrared Spectroscopy Quantification Microplastics Aquatic Environment.” Environmental Science & Technology 52(22), 13279–13288. doi:10.1021/acs.est.8b03438 . Caggiani MC, Cosentino , Mangone (2016). “Pigments Checker version 3.0, handy set conservation scientists: free online Raman spectra database.” Microchemical Journal 129, 123–132. doi:10.1016/j.microc.2016.06.020 . Chabuka BK, Kalivas JH (2020). “Application Hybrid Fusion Classification Process Identification Microplastics Based Fourier Transform Infrared Spectroscopy. Applied Spectroscopy.” Applied Spectroscopy 74(9), 1167–1183. doi:10.1177/0003702820923993 . Cowger, W (2023). “Library data.” OSF. doi:10.17605/OSF.IO/X7DPZ . Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi:10.1177/0003702820929064 . Cowger W, Roscher L, Chamas , Maurer B, Gehrke L, Jebens H, Gerdts G, Primpke S (2023). “High Throughput FTIR Analysis Macro Microplastics Plate Readers.” ChemRxiv Preprint. doi:10.26434/chemrxiv-2023-x88ss . De Frond H, Rubinovitz R, Rochman CM (2021). “µATR-FTIR Spectral Libraries Plastic Particles (FLOPP FLOPP-e) Analysis Microplastics.” Analytical Chemistry 93(48), 15878–15885. doi:10.1021/acs.analchem.1c02549 . El Mendili Y, Vaitkus , Merkys , Gražulis S, Chateigner D, Mathevet F, Gascoin S, Petit S, Bardeau JF, Zanatta M, Secchi M, Mariotto G, Kumar , Cassetta M, Lutterotti L, Borovin E, Orberger B, Simon P, Hehlen B, Le Guen M (2019). “Raman Open Database: first interconnected Raman–X-ray diffraction open-access resource material identification.” Journal Applied Crystallography, 52(3), 618–625. doi:10.1107/s1600576719004229 . Johnson TJ, Blake TA, Brauer CS, Su YF, Bernacki , Myers TL, Tonkyn RG, Kunkel BM, Ertel AB (2015). “Reflectance Spectroscopy Sample Identification: Considerations Quantitative Library Results Infrared Wavelengths.” International Conference Advanced Vibrational Spectroscopy (ICAVS 8). https://www.osti.gov/biblio/1452877. Lafuente R, Downs RT, Yang H, Stone N (2016). “power databases: RRUFF project.” Highlights Mineralogical Crystallography. doi:10.1515/9783110417104-003 . Munno K, De Frond H, O’Donnell B, Rochman CM (2020). “Increasing Accessibility Characterizing Microplastics: Introducing New Application-Based Spectral Libraries Plastic Particles (SLoPP SLoPP-E).” Analytical Chemistry 92(3), 2443–2451. doi:10.1021/acs.analchem.9b03626 . Myers TL, Brauer CS, Su YF, Blake TA, Johnson TJ, Richardson RL (2014). “influence particle size infrared reflectance spectra.” Proceedings Volume 9088, Algorithms Technologies Multispectral, Hyperspectral, Ultraspectral Imagery XX, 908809. doi:10.1117/12.2053350 . Myers TL, Brauer CS, Su YF, Blake TA, Tonkyn RG, Ertel AB, Johnson TJ, Richardson RL (2015). “Quantitative reflectance spectra solid powders function particle size.” Applied Optics 54(15), 4863–4875. doi:10.1364/ao.54.004863 . Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference database design automated analysis microplastic samples based Fourier transform infrared (FTIR) spectroscopy.” Analytical Bioanalytical Chemistry 410, 5131–-5141. doi:10.1007/s00216-018-1156-x . Roscher L, Fehres , Reisel L, Halbach M, Scholz-Böttcher B, Gerriets M, Badewien TH, Shiravani G, Wurpts , Primpke S, Gerdts G (2021). “Abundances large microplastics (L-MP, 500-5000 µm) surface waters Weser estuary German North Sea.” PANGAEA. doi:10.1594/PANGAEA.938143 . “Handbook Raman Spectra geology” (2023). http://www.geologie-lyon.fr/Raman/. “Scientific Workgroup Analysis Seized Drugs.” (2023). https://swgdrug.org/ir.htm. contribution spectra: Suja Sukumaran (Thermo Fisher Scientific), Aline Carvalho, Jennifer Lynch (NIST), Claudia Cella Dora Mehn (JRC), Horiba Scientific, USDA Soil Characterization Data (https://ncsslabdatamart.sc.egov.usda.gov), Archaeometrielabor, S.B. Engelsen (Royal Vet. Agricultural University, Denmark). Kimmel Center data collected provided Prof. Steven Weiner (Kimmel Center Archaeological Science, Weizmann Institute Science, Israel).","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manage spectral libraries — check_lib","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_lib.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manage spectral libraries — check_lib","text":"","code":"if (FALSE) { # \\dontrun{ check_lib(\"derivative\") get_lib(\"derivative\")  spec_lib <- load_lib(\"derivative\") } # }"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Ignore or remove NA intensities — manage_na","title":"Ignore or remove NA intensities — manage_na","text":"Sometimes want keep remove NA values intensities allow spectra varying shapes analyzed together maintained single Open Specy object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ignore or remove NA intensities — manage_na","text":"","code":"manage_na(x, ...)  # Default S3 method manage_na(x, lead_tail_only = TRUE, ig = c(NA), ...)  # S3 method for class 'OpenSpecy' manage_na(x, lead_tail_only = TRUE, ig = c(NA), fun, type = \"ignore\", ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ignore or remove NA intensities — manage_na","text":"x numeric vector R OpenSpecy object. lead_tail_only logical whether look leading adn tailing values. ig character vector, values ignore. fun name function want run, used \"ignore\" type chosen. type character either \"ignore\" \"remove\". ... arguments passed fun.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ignore or remove NA intensities — manage_na","text":"manage_na() return logical vectors NA locations (vector provided) OpenSpecy object ignored removed NA values.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ignore or remove NA intensities — manage_na","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ignore or remove NA intensities — manage_na","text":"","code":"manage_na(c(NA, -1, NA, 1, 10)) #> [1]  TRUE FALSE FALSE FALSE FALSE manage_na(c(NA, -1, NA, 1, 10), lead_tail_only = FALSE) #> [1]  TRUE FALSE  TRUE FALSE FALSE manage_na(c(NA, 0, NA, 1, 10), lead_tail_only = FALSE, ig = c(NA,0)) #> [1]  TRUE  TRUE  TRUE FALSE FALSE data(raman_hdpe) raman_hdpe$spectra[[1]][1:10] <- NA  #would normally return all NA without na.rm = TRUE but doesn't here. manage_na(raman_hdpe, fun = make_rel) #>      wavenumber  intensity #>           <num>      <num> #>   1:    301.040         NA #>   2:    304.632         NA #>   3:    308.221         NA #>   4:    311.810         NA #>   5:    315.398         NA #>  ---                       #> 960:   3187.990 0.03870968 #> 961:   3190.520 0.03870968 #> 962:   3193.060 0.04387097 #> 963:   3195.590 0.04387097 #> 964:   3198.120 0.03354839 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  #will remove the first 10 values we set to NA manage_na(raman_hdpe, type = \"remove\") #>      wavenumber intensity #>           <num>     <int> #>   1:    336.889        48 #>   2:    340.467        44 #>   3:    344.042        49 #>   4:    347.618        52 #>   5:    351.190        48 #>  ---                      #> 950:   3187.990        71 #> 951:   3190.520        71 #> 952:   3193.060        75 #> 953:   3195.590        75 #> 954:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id    col_id #>                              <char>    <char> #> 1: cb06ce2846b119d932fb6696479a445b intensity"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Manage spectral objects — c_spec","title":"Manage spectral objects — c_spec","text":"c_spec() concatenates OpenSpecy objects. sample_spec() samples spectra OpenSpecy object. merge_map() merge two OpenSpecy objects spectral maps.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manage spectral objects — c_spec","text":"","code":"c_spec(x, ...)  # Default S3 method c_spec(x, ...)  # S3 method for class 'OpenSpecy' c_spec(x, ...)  # S3 method for class 'list' c_spec(x, range = NULL, res = 5, ...)  sample_spec(x, ...)  # Default S3 method sample_spec(x, ...)  # S3 method for class 'OpenSpecy' sample_spec(x, size = 1, prob = NULL, ...)  merge_map(x, ...)  # Default S3 method merge_map(x, ...)  # S3 method for class 'OpenSpecy' merge_map(x, ...)  # S3 method for class 'list' merge_map(x, origins = NULL, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manage spectral objects — c_spec","text":"x list OpenSpecy objects file paths. range numeric providing wavenumber ranges character argument called \"common\" let c_spec() find common wavenumber range supplied spectra. NULL interpret spectra wavenumber range. res defaults NULL, resolution want output wavenumbers . size number spectra sample. prob probabilities use sampling. origins list 2 value vectors x y coordinates offsets image. ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manage spectral objects — c_spec","text":"c_spec() sample_spec() return OpenSpecy objects.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manage spectral objects — c_spec","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/manage_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manage spectral objects — c_spec","text":"","code":"# Concatenating spectra spectra <- lapply(c(read_extdata(\"raman_hdpe.csv\"),                     read_extdata(\"ftir_ldpe_soil.asp\")), read_any) common <- c_spec(spectra, range = \"common\", res = 5) range <- c_spec(spectra, range = c(1000, 2000), res = 5)  # Sampling spectra tiny_map <- read_any(read_extdata(\"CA_tiny_map.zip\")) sampled <- sample_spec(tiny_map, size = 3)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Identify and filter spectra — cor_spec","title":"Identify and filter spectra — cor_spec","text":"match_spec() joins two OpenSpecy objects metadata based similarity. cor_spec() correlates two OpenSpecy objects, typically one knowns one unknowns. ident_spec() retrieves top match values correlation matrix formats metadata. get_metadata() retrieves metadata OpenSpecy objects. max_cor_named() formats top correlation values correlation matrix named vector. filter_spec() filters Open Specy object. fill_spec() adds filler values OpenSpecy object intensities. os_similarity() EXPERIMENTAL, returns single similarity metric two OpenSpecy objects based method used.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identify and filter spectra — cor_spec","text":"","code":"cor_spec(x, ...)  # Default S3 method cor_spec(x, ...)  # S3 method for class 'OpenSpecy' cor_spec(x, library, na.rm = T, conform = F, type = \"roll\", ...)  match_spec(x, ...)  # Default S3 method match_spec(x, ...)  # S3 method for class 'OpenSpecy' match_spec(   x,   library,   na.rm = T,   conform = F,   type = \"roll\",   top_n = NULL,   order = NULL,   add_library_metadata = NULL,   add_object_metadata = NULL,   fill = NULL,   ... )  ident_spec(   cor_matrix,   x,   library,   top_n = NULL,   add_library_metadata = NULL,   add_object_metadata = NULL,   ... )  get_metadata(x, ...)  # Default S3 method get_metadata(x, ...)  # S3 method for class 'OpenSpecy' get_metadata(x, logic, rm_empty = TRUE, ...)  max_cor_named(cor_matrix, na.rm = T)  filter_spec(x, ...)  # Default S3 method filter_spec(x, ...)  # S3 method for class 'OpenSpecy' filter_spec(x, logic, ...)  ai_classify(x, ...)  # Default S3 method ai_classify(x, ...)  # S3 method for class 'OpenSpecy' ai_classify(x, library, fill = NULL, ...)  fill_spec(x, ...)  # Default S3 method fill_spec(x, ...)  # S3 method for class 'OpenSpecy' fill_spec(x, fill, ...)  os_similarity(x, ...)  # Default S3 method os_similarity(x, ...)  # S3 method for class 'OpenSpecy' os_similarity(x, y, method = \"hamming\", na.rm = T, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identify and filter spectra — cor_spec","text":"x OpenSpecy object, typically unknowns. library OpenSpecy glmnet object representing reference library spectra model use identification. na.rm logical; indicating whether missing values removed calculating correlations. Default TRUE. conform Whether conform spectra library wavenumbers . type type conformation make returned conform_spec() top_n integer; specifying number top matches return. NULL (default), matches returned. order OpenSpecy used sorting, ideally unprocessed one; NULL skips sorting. add_library_metadata name column library metadata joined; NULL want join. add_object_metadata name column object metadata joined; NULL want join. fill OpenSpecy object single spectrum used fill missing values alignment AI classification. cor_matrix correlation matrix object library, can returned cor_spec() logic logical numeric vector describing spectra keep. rm_empty logical; whether remove empty columns metadata. y OpenSpecy object perform similarity search x. method type similarity metric return. ... additional arguments passed cor().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identify and filter spectra — cor_spec","text":"match_spec() ident_spec() return data.table-class() containing correlations spectra library. table three columns: object_id, library_id, match_val. row represents unique pairwise correlation spectrum object spectrum library. top_n specified, top top_n matches object spectrum returned. add_library_metadata .character, library metadata added output. add_object_metadata .character, object metadata added output. filter_spec() returns OpenSpecy object. fill_spec() returns OpenSpecy object. cor_spec() returns correlation matrix. get_metadata() returns data.table-class() metadata columns information. os_similarity() returns single numeric value representing type similarity metric requested. 'wavenumber' similarity based proportion wavenumber values overlap two objects, 'metadata' proportion metadata column names, 'hamming' something similar hamming distance discretize spectra OpenSpecy object wavenumber intensity values relate wavenumber intensity value distributions mean difference min-max normalized space. 'pca' tests distance OpenSpecy objects PCA space using first 4 component values calculating max-range normalized distance mean components. first two metrics pretty straightforward definitely ready go, 'hamming' 'pca' metrics pretty experimental appear working current test cases.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Identify and filter spectra — cor_spec","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/match_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identify and filter spectra — cor_spec","text":"","code":"data(\"test_lib\")  unknown <- read_extdata(\"ftir_ldpe_soil.asp\") |>   read_any() |>   conform_spec(range = test_lib$wavenumber,                res = spec_res(test_lib)) |>   process_spec() cor_spec(unknown, test_lib) #>                                      intensity #> 00002e4e3fac430aa1fdfea6e26f85e4  0.5716650527 #> 00061dd49cbb71549cf3d530b894ba92 -0.0524107035 #> 0008a60c1af45a76ffb91b9cbe1a32e4  0.2120038819 #> 000902ae526db452960c5782843081b4 -0.0574671312 #> 00133c1f1531821f815431f72871eae9  0.3424853396 #> 0016677a99407c18717bf5d3eea9a0a2  0.1169383083 #> 00185609a3205a19225e1530ca98084e -0.0059672577 #> 001d3eb19976c785442a3e6fa8094bff -0.0063433127 #> 0026eee747d277edf308194c81179cb0 -0.0563867464 #> 0031bb13faea1e04b52ffbeca009e8ab  0.6675896750 #> 003b2e57d47a4225b8ea041c946cfc0a -0.0204737255 #> 0046ff947759e247f85dc63a2a25e097  0.0286048318 #> 0054792a45a11ccf7158fdf2c8873125  0.0889031245 #> 005c6a81975d747fe493032cac9dfaa2  0.2070905137 #> 0062d71f901713f2ac7a89c72fb186d4  0.2847788255 #> 00904e33ccbaa20fb68c1943d8303d82  0.0103191160 #> 00931a5ccadb549d463293381c974d79 -0.0182594153 #> 00b9f87c7d7c82675cb945fe4da37917 -0.0874221226 #> 00f4b09b9823187caf65b781b8be87b9 -0.0943789974 #> 010bfd4be6c24ced909f6de717c7b04e  0.0756006101 #> 015e8a21344a60a020226cfef344c584  0.0279306259 #> 016961c0d7def12f68239756d23a79a6 -0.0739530912 #> 019b8f3e4839f8fda1441f7d712ff12d -0.0088904495 #> 01b2fea6718855567e2e468a792b457f  0.0519834480 #> 02b8194bd714469666151a7c6b90b36c  0.0687712268 #> 03f1fa06cd6bc468ae5e406ca1d58dbb -0.0560522947 #> 041771facd71667d6bd6c9269d3701be  0.0502373982 #> 0481958e37c72c71f88d80e4dd69ac59  0.0405530434 #> 058a9af32053d14e876703a60a37498f -0.0172377431 #> 05c3cde45098e01cc44da3f72ada913e -0.0586996007 #> 06a2ed935a9b37b653717ff4a0ea62f7  0.0486159272 #> 06f7950194c7a42714cec488e378a820  0.1062184336 #> 087b8dc9fe95b7cab9d037304f0d39c8 -0.0071308393 #> 09b69fcfcf3c695299dd0f88743cf14a  0.0693075642 #> 0b10e6cc01c8e53f2221647a9c828164  0.5492755145 #> 0b8b5f10d7a9b2bd7d702cd4fdfd8a29  0.0198909283 #> 0e815860f0115ac34e2b46ac91e0c096  0.0219596516 #> 101b6ae86864958ddd95445d0ab01fe4  0.4308809802 #> 14ee1d85d0b1ee84fab0773329c45bd2 -0.0552288672 #> 1a31cfc8e25332f4771d03e8a862e217  0.0257980970 #> 23069d13c090ee5235874bd632467f9c  0.0239585112 #> 252b6f9c5fb83ec82246bc041cabd221 -0.0160335235 #> 263479214867d17e135a0d599b1f8354  0.5000294168 #> 2fa630bec93db824509be188494d79ad  0.1018184192 #> 35ce7ae774d57362dacb92cacd53b01c  0.0057562573 #> 3d35fd9a2766afaf4c088b615dffc350 -0.0009768496 #> 4762edfa07e6b28a4b90db83594f2b3d -0.0004834032 #> 4f1d701f4080b4c74acdaa04dea52dd1  0.0682211684 #> 5961a72282786fe43d8704780782f459  0.0629534493 #> 65792c9e80934e4692540a3e0fbfb552  0.0457114187 #> 6e013532253522149a0deb04373b79a2  0.1339615512 #> 7167a50e76f4a34dbb1d354babd674b0  0.0387929426 #> 76b3af06ab610907cea4dbe3aa4ec67e  0.0237972406 #> 889adce6238a677669b6523d74635d03  0.1267896304 #> 9f91f6441608ecca35f69d298e5351bd  0.0018181040 #> a692fd0b8b3b80b640ce4bbea8105570  0.0423592870 #> ec69350b1b7de35fd6b8cf65048e5fc0  0.1076174781  match_spec(unknown, test_lib, add_library_metadata = \"sample_name\",            top_n = 1) #> Key: <library_id> #>                          library_id object_id match_val     x     y #>                              <char>    <fctr>     <num> <int> <int> #> 1: 0031bb13faea1e04b52ffbeca009e8ab intensity 0.6675897    37     1 #>                         SpectrumID   Organization SpectrumType SpectrumIdentity #>                             <char>         <char>       <char>           <char> #> 1: HDPE_Sarah_#18_1s_20ac_10x_25mW J. Lynch, NIST        Raman             HDPE #>    Polymer.Category LibraryType  OWNER SpectralCollectionMode Preprocessing #>              <char>      <char> <char>                 <char>        <char> #> 1:      polyolefins    Polymers   <NA>                   <NA>          <NA> #>    InstrumentUsed RRUFFID IDEAL CHEMISTRY LOCALITY SOURCE STATUS    URL #>            <char>  <char>          <char>   <char> <char> <char> <char> #> 1:           <NA>    <NA>            <NA>     <NA>   <NA>   <NA>   <NA> #>    MEASURED CHEMISTRY OtherInformation JCAMP-DX YFACTOR YUNITS InstrumentMode #>                <char>           <char>   <char>  <char> <char>         <char> #> 1:               <NA>             <NA>     <NA>    <NA>   <NA>           <NA> #>    DELTAX Sponsor  Color   Cell     ID Comment cell_plate_id #>    <char>  <char> <char> <char> <char>  <char>        <char> #> 1:   <NA>    <NA>   <NA>   <NA>   <NA>    <NA>          <NA> #>    Form..film..foam.pliable..hard.  Brand   Item Location  Notes #>                             <char> <char> <char>   <char> <char> #> 1:                            <NA>   <NA>   <NA>     <NA>   <NA> #>    Longest.dimension  Width  Depth Particle.mass..mg. Tg..oC. Tm..oC. Onset.... #>               <char> <char> <char>             <char>  <char>  <char>    <char> #> 1:              <NA>   <NA>   <NA>               <NA>    <NA>    <NA>      <NA> #>    Enthalpy..J.g. Substance   Form Colour Filename SourceDatabase Method...49 #>            <char>    <char> <char> <char>   <char>         <char>      <char> #> 1:           <NA>      <NA>   <NA>   <NA>     <NA>           <NA>        <NA> #>      File  QA/QC Natural /Synthetic Plastic/other framework Abbreviation #>    <char> <char>             <char>        <char>    <char>       <char> #> 1:   <NA>   <NA>               <NA>          <NA>      <NA>         <NA> #>    Source ID Method...57 morphology  color final_polymer_assignment Citation #>       <char>      <char>     <char> <char>                   <char>   <char> #> 1:      <NA>        <NA>       <NA>   <NA>                     <NA>     <NA> #>    Plate Itteration SpectralResolution InstrumentAccessories Wavenumber_Range #>    <int>      <int>             <char>                <char>           <char> #> 1:    NA         NA               <NA>                  <NA>             <NA> #>    PIN_ID ORIENTATION ID merged database #>    <char>      <char>              <int> #> 1:   <NA>        <NA>                 NA #>    Database ID WWTP Paper/Kirstie/FLOPPE...70 #>                                         <int> #> 1:                                         NA #>    Database ID WWTP Paper/Kirstie/FLOPPE...71 ID (ESM1) Longest_dimension width #>                                         <int>     <int>             <int> <int> #> 1:                                         NA        NA                NA    NA #>    Cluster2018/WWTP PRESSURE TEMPERATURE MaterialForm MaterialProducer #>               <int>    <int>       <num>       <char>           <char> #> 1:               NA       NA          NA         <NA>             <NA> #>    NumberofAccumulations   TIME Ratio against background Truncated #>                    <int> <char>                   <char>    <char> #> 1:                    NA   <NA>                     <NA>      <NA> #>    ElementMultiply or ElementDivide Collection length Apodization #>                              <char>            <char>      <char> #> 1:                             <NA>              <NA>        <NA> #>    Bench serial number Subtraction or Addition Baseline Correction Smooth #>                 <char>                  <char>              <char> <char> #> 1:                <NA>                    <NA>                <NA>   <NA> #>    %Transmittance->Absorbance Sample gain Number of background scans #>                        <char>       <num>                      <int> #> 1:                       <NA>          NA                         NA #>    Background gain Sample spacing Number of scan points Resolution points #>              <num>          <num>                 <int>             <int> #> 1:              NA             NA                    NA                NA #>    Number of FFT points   ZPD Library ID Shortform Grating Hole size (nm) #>                   <int> <int>               <char>   <int>          <int> #> 1:                   NA    NA                 <NA>      NA             NA #>    Slit (um) Filter (%) Delay (s)   DATE MOLFORM $NIST SOURCE $NIST DOC FILE #>        <int>      <int>     <int> <char>  <char>       <char>         <char> #> 1:        NA         NA        NA   <NA>    <NA>         <NA>           <NA> #>    $NIST PSD FILE APERTURE BEAMSPLITTER DETECTOR (DIA. DET. PORT IN SPHERE) #>            <char>   <char>       <char>                              <char> #> 1:           <NA>     <NA>         <NA>                                <NA> #>    SPHERE DIAMETER ACQUISITION MODE SCANNER SPEED PHASE CORRECTION ZEROFILLING #>             <char>           <char>        <char>           <char>      <char> #> 1:            <NA>             <NA>          <NA>             <NA>        <NA> #>    WAVENUMBER ACCURACY LOW PASS FILTER SWITCH GAIN ON $NIST ID COADDED SCANS #>                 <char>          <char>         <char>   <char>         <int> #> 1:                <NA>            <NA>           <NA>     <NA>            NA #>    PHASE RESOLUTION    MW ContactInfo CASNumber MaterialQuality LaserLightUsed #>               <num> <num>      <char>    <char>          <char>         <char> #> 1:               NA    NA        <NA>      <NA>            <NA>           <NA> #>    TotalAcquisitionTime_s DataProcessingProcedure #>                    <char>                  <char> #> 1:                   <NA>                    <NA> #>    LevelofConfidenceinIdentification smoother baseline DENSITY new_label #>                               <char>    <int>    <int>   <num>    <char> #> 1:                              <NA>       NA       NA      NA      <NA> #>                polymer_class plastic_or_not #>                       <char>         <char> #> 1: Polyolefins (POLYALKENES)        plastic #>                                                   url_polymer_class #>                                                              <char> #> 1: https://www.polymerdatabase.com/polymer%20index/polyalkenes.html #>           polymer                                                url_polymer #>            <char>                                                     <char> #> 1: POLY(ETHYLENE) https://www.polymerdatabase.com/polymers/polyethylene.html #>                                                               url_more_info #>                                                                      <char> #> 1: https://www.polymerdatabase.com/polymer%20classes/Polyolefin%20type.html #>    snr_deriv                          file_id #>        <num>                           <char> #> 1:  1339.684 77f837c1640910f2879184d61ef1df48"},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Spectra — process_spec","title":"Process Spectra — process_spec","text":"process_spec() monolithic wrapper function spectral processing steps.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Spectra — process_spec","text":"","code":"process_spec(x, ...)  # Default S3 method process_spec(x, ...)  # S3 method for class 'OpenSpecy' process_spec(   x,   active = TRUE,   adj_intens = FALSE,   adj_intens_args = list(type = \"none\"),   conform_spec = TRUE,   conform_spec_args = list(range = NULL, res = 5, type = \"interp\"),   restrict_range = FALSE,   restrict_range_args = list(min = 0, max = 6000),   flatten_range = FALSE,   flatten_range_args = list(min = 2200, max = 2420),   subtr_baseline = FALSE,   subtr_baseline_args = list(type = \"polynomial\", degree = 8, raw = FALSE, baseline =     NULL),   smooth_intens = TRUE,   smooth_intens_args = list(polynomial = 3, window = 11, derivative = 1, abs = TRUE),   make_rel = TRUE,   make_rel_args = list(na.rm = TRUE),   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Spectra — process_spec","text":"x OpenSpecy object. active logical; indicating whether perform processing. TRUE, processing steps applied. FALSE, original data returned. adj_intens logical; describing whether adjust intensity units. adj_intens_args named list arguments passed adj_intens(). conform_spec logical; whether conform spectra new wavenumber range resolution. conform_spec_args named list arguments passed conform_spec(). restrict_range logical; indicating whether restrict wavenumber range spectra. restrict_range_args named list arguments passed restrict_range(). flatten_range logical; indicating whether flatten range around carbon dioxide region. flatten_range_args named list arguments passed flatten_range(). subtr_baseline logical; indicating whether subtract baseline spectra. subtr_baseline_args named list arguments passed subtr_baseline(). smooth_intens logical; indicating whether apply smoothing filter spectra. smooth_intens_args named list arguments passed smooth_intens(). make_rel logical; TRUE spectra automatically normalized make_rel(). make_rel_args named list arguments passed make_rel(). na.rm Whether allow NA set NA values ... arguments passed subfunctions.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Spectra — process_spec","text":"process_spec() returns OpenSpecy object processed spectra based specified parameters.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/process_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Process Spectra — process_spec","text":"","code":"data(\"raman_hdpe\") plot(raman_hdpe)  # Process spectra with range restriction and baseline subtraction process_spec(raman_hdpe,              restrict_range = TRUE,              restrict_range_args = list(min = 500, max = 3000),              subtr_baseline = TRUE,              subtr_baseline_args = list(type = \"polynomial\",                                         polynomial = 8)) |>   lines(col = \"darkred\")  # Process spectra with smoothing and derivative process_spec(raman_hdpe,              smooth_intens = TRUE,              smooth_intens_args = list(                polynomial = 3,                window = 11,                derivative = 1                )              ) |>   lines(col = \"darkgreen\")"},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample Raman spectrum — raman_hdpe","title":"Sample Raman spectrum — raman_hdpe","text":"Raman spectrum high-density polyethylene (HDPE) provided Horiba Scientific.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sample Raman spectrum — raman_hdpe","text":"threepart list class OpenSpecy containing:","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sample Raman spectrum — raman_hdpe","text":"Cowger W, Gray , Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review Processing Classification Techniques Images Spectra Microplastic Research.” Applied Spectroscopy, 74(9), 989–1010. doi:10.1177/0003702820929064 .","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sample Raman spectrum — raman_hdpe","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/raman_hdpe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample Raman spectrum — raman_hdpe","text":"","code":"data(raman_hdpe) print(raman_hdpe) #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":null,"dir":"Reference","previous_headings":"","what":"Read ENVI data — read_envi","title":"Read ENVI data — read_envi","text":"function allows ENVI data import.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read ENVI data — read_envi","text":"","code":"read_envi(   file,   header = NULL,   spectral_smooth = F,   sigma = c(1, 1, 1),   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read ENVI data — read_envi","text":"file name binary file. header name ASCII header file. NULL, name header file guessed looking second file basename file .hdr extension. spectral_smooth logical value determines whether spectral smoothing performed. sigma spectral_smooth option applies 3d standard deviations gaussianSmooth function mmand package describe spectral smoothing occurs dimension. first two dimensions x y, third wavenumbers. metadata named list metadata; see as_OpenSpecy() details. ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read ENVI data — read_envi","text":"OpenSpecy object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read ENVI data — read_envi","text":"ENVI data usually consists two files, ASCII header binary data file. header contains information necessary correctly reading binary file via read.ENVI().","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_envi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read ENVI data — read_envi","text":"Zacharias Steinmetz, Claudia Beleites","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":null,"dir":"Reference","previous_headings":"","what":"Read spectral data — read_text","title":"Read spectral data — read_text","text":"Functions reading spectral data external file types. Currently supported reading formats .csv text files, .asp, .spa, .spc, .xyz, .jdx. Additionally, .0 (OPUS) .dat (ENVI) files supported via read_opus() read_envi(), respectively. read_zip() takes files listed . Note proprietary file formats like .0, .asp, .spa poorly supported likely still work cases.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read spectral data — read_text","text":"","code":"read_text(   file,   colnames = NULL,   method = \"fread\",   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_asp(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_spa(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_spc(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_jdx(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   ... )  read_extdata(file = NULL)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read spectral data — read_text","text":"file file read written . colnames character vector length = 2 indicating column names wavenumber intensity; NULL columns guessed. method submethod used reading text files; defaults fread() read.csv() works well. metadata named list metadata; see as_OpenSpecy() details. ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read spectral data — read_text","text":"read_*() functions return data frames containing two columns named \"wavenumber\" \"intensity\".","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read spectral data — read_text","text":"read_spc() read_jdx() wrappers around functions provided hyperSpec. functions adapted various online sources. Metadata harvested possible. many unique iterations spectral file formats may bugs file conversion. Please contact us identify .","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read spectral data — read_text","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_ext.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read spectral data — read_text","text":"","code":"read_extdata(\"raman_hdpe.csv\") |> read_text() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y      file_name  license    col_id #>    <int> <int>         <char>   <char>    <char> #> 1:     1     1 raman_hdpe.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b read_extdata(\"raman_atacamit.spc\") |> read_spc() #>      wavenumber intensity #>           <num>     <num> #>   1:   117.7800  218.7200 #>   2:   119.4506  236.4000 #>   3:   121.1213  242.0400 #>   4:   122.7919  233.0500 #>   5:   124.4626  219.4200 #>  ---                      #> 555:  1043.3174   13.0066 #> 556:  1044.9881   12.4402 #> 557:  1046.6587   11.9187 #> 558:  1048.3294   11.3986 #> 559:  1050.0000   10.8148 #>  #> $metadata #>        x     y          file_name  license    col_id #>    <int> <int>             <char>   <char>    <char> #> 1:     1     1 raman_atacamit.spc CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: 2693c0b0295ce36c16b10e1e660241f2 read_extdata(\"ftir_ldpe_soil.asp\") |> read_asp() #>       wavenumber   intensity #>            <num>       <num> #>    1:   650.4205 0.027056019 #>    2:   652.2841 0.029133933 #>    3:   654.1478 0.032566057 #>    4:   656.0115 0.032249778 #>    5:   657.8751 0.028957002 #>   ---                        #> 1794:  3991.9788 0.005803670 #> 1795:  3993.8425 0.005495489 #> 1796:  3995.7062 0.005133253 #> 1797:  3997.5698 0.005595756 #> 1798:  3999.4335 0.005694630 #>  #> $metadata #>        x     y          file_name  license    col_id #>    <int> <int>             <char>   <char>    <char> #> 1:     1     1 ftir_ldpe_soil.asp CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: a33700bad0376ddab156980f473bac92 read_extdata(\"testdata_zipped.zip\") |> read_zip() #> [[1]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y                file_name  license    col_id #>    <int> <int>                   <char>   <char>    <char> #> 1:     1     1 testdata2 - Copy (2).csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #> [[2]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y            file_name  license    col_id #>    <int> <int>               <char>   <char>    <char> #> 1:     1     1 testdata2 - Copy.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #> [[3]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y     file_name  license    col_id #>    <int> <int>        <char>   <char>    <char> #> 1:     1     1 testdata2.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":null,"dir":"Reference","previous_headings":"","what":"Read spectral data from multiple files — read_any","title":"Read spectral data from multiple files — read_any","text":"Wrapper functions reading files batch.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read spectral data from multiple files — read_any","text":"","code":"read_any(file, ...)  read_many(file, ...)  read_zip(file, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read spectral data from multiple files — read_any","text":"file file read written . ... arguments passed submethods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read spectral data from multiple files — read_any","text":"read_*() functions return OpenSpecy objects single spectrum map provided, otherwise provide list OpenSpecy objects.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read spectral data from multiple files — read_any","text":"read_any() provides single function quickly read supported formats, assumes file extension tell process spectra. read_zip() provides functionality reading spectral map files ENVI file format individual files zip folder. individual files, spectra concatenated. read_many() provides functionality reading multiple files character vector return list.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read spectral data from multiple files — read_any","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_multi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read spectral data from multiple files — read_any","text":"","code":"read_extdata(\"raman_hdpe.csv\") |> read_any() #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y      file_name  license    col_id #>    <int> <int>         <char>   <char>    <char> #> 1:     1     1 raman_hdpe.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b read_extdata(\"ftir_ldpe_soil.asp\") |> read_any() #>       wavenumber   intensity #>            <num>       <num> #>    1:   650.4205 0.027056019 #>    2:   652.2841 0.029133933 #>    3:   654.1478 0.032566057 #>    4:   656.0115 0.032249778 #>    5:   657.8751 0.028957002 #>   ---                        #> 1794:  3991.9788 0.005803670 #> 1795:  3993.8425 0.005495489 #> 1796:  3995.7062 0.005133253 #> 1797:  3997.5698 0.005595756 #> 1798:  3999.4335 0.005694630 #>  #> $metadata #>        x     y          file_name  license    col_id #>    <int> <int>             <char>   <char>    <char> #> 1:     1     1 ftir_ldpe_soil.asp CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: a33700bad0376ddab156980f473bac92 read_extdata(\"testdata_zipped.zip\") |> read_many() #> [[1]] #> [[1]][[1]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y                file_name  license    col_id #>    <int> <int>                   <char>   <char>    <char> #> 1:     1     1 testdata2 - Copy (2).csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #> [[1]][[2]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y            file_name  license    col_id #>    <int> <int>               <char>   <char>    <char> #> 1:     1     1 testdata2 - Copy.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #> [[1]][[3]] #>      wavenumber intensity #>           <num>     <int> #>   1:    301.040        26 #>   2:    304.632        50 #>   3:    308.221        48 #>   4:    311.810        45 #>   5:    315.398        46 #>  ---                      #> 960:   3187.990        71 #> 961:   3190.520        71 #> 962:   3193.060        75 #> 963:   3195.590        75 #> 964:   3198.120        67 #>  #> $metadata #>        x     y     file_name  license    col_id #>    <int> <int>        <char>   <char>    <char> #> 1:     1     1 testdata2.csv CC BY-NC intensity #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b #>  #>  read_extdata(\"CA_tiny_map.zip\") |> read_many() #> [[1]] #>      wavenumber        0_0       0_1        0_2       0_3        0_4       0_5 #>           <num>      <num>     <num>      <num>     <num>      <num>     <num> #>   1:     717.42 -0.8599975 -1.176549 -0.8269680 -1.150691 -0.7874242 -1.131251 #>   2:     725.13 -0.8629220 -1.184673 -0.8241877 -1.145349 -0.7848697 -1.132279 #>   3:     732.85 -0.8684751 -1.201088 -0.8249527 -1.145835 -0.7878844 -1.138330 #>   4:     740.56 -0.8713333 -1.215284 -0.8285120 -1.151216 -0.7958543 -1.145293 #>   5:     748.28 -0.8721815 -1.216835 -0.8334568 -1.157416 -0.8058372 -1.147371 #>  ---                                                                           #> 423:    3972.81 -0.8804550 -1.293169 -0.8440830 -1.230502 -0.8018205 -1.190946 #> 424:    3980.52 -0.8789783 -1.295723 -0.8462818 -1.233429 -0.8030480 -1.190417 #> 425:    3988.24 -0.8772697 -1.305601 -0.8471817 -1.241330 -0.8022795 -1.192631 #> 426:    3995.95 -0.8773139 -1.310077 -0.8457789 -1.247904 -0.8011338 -1.193246 #> 427:    4003.67 -0.8778735 -1.303940 -0.8450245 -1.246171 -0.8017741 -1.188956 #>             0_6       0_7        0_8       0_9       0_10      0_11       0_12 #>           <num>     <num>      <num>     <num>      <num>     <num>      <num> #>   1: -0.8181180 -1.088998 -0.8292431 -1.072920 -0.8490217 -1.044781 -0.8055528 #>   2: -0.8157052 -1.094163 -0.8254585 -1.074778 -0.8455939 -1.042407 -0.8023769 #>   3: -0.8137981 -1.103946 -0.8287728 -1.076342 -0.8458889 -1.043109 -0.8070771 #>   4: -0.8150936 -1.110569 -0.8353058 -1.079404 -0.8477870 -1.045840 -0.8143305 #>   5: -0.8188222 -1.112912 -0.8405676 -1.084448 -0.8502600 -1.050660 -0.8213006 #>  ---                                                                           #> 423: -0.7831531 -1.134469 -0.7993639 -1.119114 -0.8128311 -1.076820 -0.8029224 #> 424: -0.7858623 -1.138433 -0.7996801 -1.123678 -0.8118886 -1.079553 -0.8038837 #> 425: -0.7878010 -1.146599 -0.7993538 -1.128633 -0.8121727 -1.081921 -0.8041241 #> 426: -0.7882353 -1.147445 -0.7993204 -1.130919 -0.8131239 -1.084749 -0.8037490 #> 427: -0.7870936 -1.141112 -0.8001940 -1.132363 -0.8130202 -1.086768 -0.8026713 #>            0_13       0_14       0_15        1_0       1_1        1_2 #>           <num>      <num>      <num>      <num>     <num>      <num> #>   1: -0.9851461 -0.7893320 -0.8729300 -0.7817467 -1.037279 -0.6797068 #>   2: -0.9868538 -0.7853272 -0.8692073 -0.7875991 -1.048603 -0.6794843 #>   3: -0.9862352 -0.7823833 -0.8676148 -0.7965024 -1.067380 -0.6840024 #>   4: -0.9870666 -0.7851717 -0.8697423 -0.8020197 -1.083057 -0.6892709 #>   5: -0.9920748 -0.7919775 -0.8769495 -0.8036770 -1.085820 -0.6939027 #>  ---                                                                  #> 423: -1.0179443 -0.7737747 -0.8834741 -0.8022860 -1.147436 -0.6932624 #> 424: -1.0177335 -0.7740544 -0.8812205 -0.8013673 -1.147939 -0.6938105 #> 425: -1.0158888 -0.7744763 -0.8804378 -0.8017516 -1.155403 -0.6935061 #> 426: -1.0132719 -0.7738099 -0.8825330 -0.8025478 -1.158695 -0.6918315 #> 427: -1.0116997 -0.7726955 -0.8838044 -0.8019281 -1.152465 -0.6914637 #>             1_3        1_4        1_5        1_6        1_7        1_8 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.9080905 -0.6090969 -0.8677020 -0.6418716 -0.8795404 -0.6977192 #>   2: -0.9057059 -0.6073114 -0.8766068 -0.6438789 -0.8903178 -0.6934057 #>   3: -0.9080038 -0.6117581 -0.8885315 -0.6456336 -0.9021218 -0.6992034 #>   4: -0.9134948 -0.6192009 -0.8989011 -0.6481388 -0.9077991 -0.7091047 #>   5: -0.9188258 -0.6264750 -0.9012332 -0.6513934 -0.9091613 -0.7153334 #>  ---                                                                   #> 423: -0.9997684 -0.6440702 -0.9665285 -0.6262602 -0.9431579 -0.6736915 #> 424: -1.0026237 -0.6445356 -0.9648960 -0.6271436 -0.9473616 -0.6741095 #> 425: -1.0109296 -0.6428438 -0.9644467 -0.6285679 -0.9552059 -0.6747956 #> 426: -1.0173761 -0.6405254 -0.9617673 -0.6288394 -0.9559735 -0.6754367 #> 427: -1.0150208 -0.6404064 -0.9559705 -0.6282292 -0.9501902 -0.6762650 #>             1_9       1_10       1_11       1_12       1_13       1_14 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.9222420 -0.7542071 -0.9433687 -0.7402943 -0.9332132 -0.7567033 #>   2: -0.9288093 -0.7519819 -0.9429623 -0.7404103 -0.9356532 -0.7536005 #>   3: -0.9341334 -0.7532891 -0.9469777 -0.7458370 -0.9353976 -0.7510077 #>   4: -0.9392776 -0.7558519 -0.9532304 -0.7527444 -0.9364607 -0.7533079 #>   5: -0.9439399 -0.7587993 -0.9586670 -0.7597492 -0.9411145 -0.7598255 #>  ---                                                                   #> 423: -0.9860366 -0.7275399 -0.9848989 -0.7450432 -0.9783244 -0.7571870 #> 424: -0.9909912 -0.7274999 -0.9867297 -0.7447987 -0.9787123 -0.7569463 #> 425: -0.9958966 -0.7286643 -0.9885115 -0.7436514 -0.9769683 -0.7576035 #> 426: -0.9968778 -0.7298300 -0.9915698 -0.7418910 -0.9733995 -0.7570600 #> 427: -0.9968597 -0.7295656 -0.9930958 -0.7406996 -0.9704962 -0.7558029 #>            1_15        2_0        2_1        2_2        2_3        2_4 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.8721603 -0.6341125 -0.8133104 -0.3901595 -0.5201151 -0.1980942 #>   2: -0.8671569 -0.6482702 -0.8365884 -0.4006985 -0.5343671 -0.2144724 #>   3: -0.8655049 -0.6606091 -0.8611584 -0.4139356 -0.5486515 -0.2310214 #>   4: -0.8679831 -0.6671489 -0.8794094 -0.4261746 -0.5613412 -0.2464838 #>   5: -0.8755950 -0.6697885 -0.8844213 -0.4350373 -0.5719566 -0.2595440 #>  ---                                                                   #> 423: -0.8880167 -0.6340107 -0.9397699 -0.4569200 -0.6700193 -0.2245990 #> 424: -0.8865166 -0.6315157 -0.9405787 -0.4589075 -0.6707723 -0.2224570 #> 425: -0.8867059 -0.6310282 -0.9495268 -0.4598360 -0.6808237 -0.2207177 #> 426: -0.8891636 -0.6334830 -0.9544747 -0.4595112 -0.6913007 -0.2204102 #> 427: -0.8905814 -0.6349432 -0.9491858 -0.4599341 -0.6900923 -0.2235211 #>             2_5        2_6        2_7        2_8        2_9       2_10 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.4099849 -0.2527224 -0.4977783 -0.3796127 -0.6079962 -0.5419455 #>   2: -0.4390349 -0.2684393 -0.5227140 -0.3849098 -0.6232019 -0.5477891 #>   3: -0.4656750 -0.2806208 -0.5420383 -0.3974718 -0.6357659 -0.5548076 #>   4: -0.4813786 -0.2890445 -0.5488055 -0.4114504 -0.6433610 -0.5590054 #>   5: -0.4849669 -0.2930531 -0.5470268 -0.4193472 -0.6473718 -0.5607016 #>  ---                                                                   #> 423: -0.5154879 -0.2002706 -0.5602914 -0.3915415 -0.7324996 -0.5452767 #> 424: -0.5099697 -0.1984237 -0.5632606 -0.3922307 -0.7367614 -0.5450110 #> 425: -0.5091169 -0.1995823 -0.5696034 -0.3931721 -0.7409591 -0.5462835 #> 426: -0.5092536 -0.2019320 -0.5696742 -0.3938283 -0.7417867 -0.5476882 #> 427: -0.5064613 -0.2023355 -0.5630394 -0.3950130 -0.7414424 -0.5475457 #>            2_11       2_12       2_13       2_14       2_15        3_0 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.7610027 -0.6229805 -0.8415435 -0.7059712 -0.8520501 -0.3957456 #>   2: -0.7661793 -0.6255116 -0.8423542 -0.7018267 -0.8484985 -0.4134940 #>   3: -0.7744415 -0.6338600 -0.8423946 -0.6993682 -0.8479570 -0.4314153 #>   4: -0.7820604 -0.6428714 -0.8442754 -0.7030330 -0.8511558 -0.4433241 #>   5: -0.7879329 -0.6515540 -0.8493459 -0.7104161 -0.8589715 -0.4486529 #>  ---                                                                   #> 423: -0.8237435 -0.6370554 -0.8907275 -0.7200700 -0.8903002 -0.4140871 #> 424: -0.8245081 -0.6377808 -0.8907435 -0.7203900 -0.8880396 -0.4108835 #> 425: -0.8265153 -0.6387181 -0.8892012 -0.7209251 -0.8868613 -0.4105957 #> 426: -0.8301792 -0.6393422 -0.8864406 -0.7207760 -0.8885221 -0.4125072 #> 427: -0.8323184 -0.6391008 -0.8829991 -0.7205515 -0.8889065 -0.4132366 #>             3_1         3_2        3_3          3_4        3_5          3_6 #>           <num>       <num>      <num>        <num>      <num>        <num> #>   1: -0.4706071 -0.03016547 -0.2407176  0.082791172 -0.2010814  0.065702595 #>   2: -0.5057679 -0.05242888 -0.2619650  0.057928637 -0.2434139  0.022760876 #>   3: -0.5376490 -0.07693648 -0.2832872  0.030258324 -0.2863251 -0.012867784 #>   4: -0.5588946 -0.09690103 -0.3049061  0.005039054 -0.3173652 -0.033973984 #>   5: -0.5629396 -0.10788352 -0.3245930 -0.014849934 -0.3317456 -0.044267047 #>  ---                                                                        #> 423: -0.6419066 -0.07969962 -0.4375644  0.015281066 -0.3982216  0.018667474 #> 424: -0.6423715 -0.08082324 -0.4397005  0.012953204 -0.3960228  0.017094024 #> 425: -0.6495159 -0.08243242 -0.4492351  0.011830383 -0.3970601  0.011106797 #> 426: -0.6525790 -0.08291471 -0.4578898  0.012850599 -0.3957864  0.005810447 #> 427: -0.6471939 -0.08415753 -0.4561356  0.012156048 -0.3883185  0.006481665 #>             3_7         3_8        3_9       3_10       3_11       3_12 #>           <num>       <num>      <num>      <num>      <num>      <num> #>   1: -0.1998793 -0.01399566 -0.2785261 -0.1904907 -0.5733052 -0.4793909 #>   2: -0.2449123 -0.03674952 -0.3024972 -0.2085340 -0.5838028 -0.4878016 #>   3: -0.2837758 -0.06542999 -0.3245068 -0.2265603 -0.5959360 -0.5013934 #>   4: -0.3092131 -0.09077036 -0.3431188 -0.2380186 -0.6060463 -0.5143723 #>   5: -0.3230577 -0.10647227 -0.3560015 -0.2436821 -0.6122009 -0.5251434 #>  ---                                                                    #> 423: -0.3698645 -0.01980718 -0.3975851 -0.1807054 -0.6520342 -0.4950348 #> 424: -0.3754378 -0.02023140 -0.4007568 -0.1797360 -0.6545247 -0.4978326 #> 425: -0.3863423 -0.02436409 -0.4062619 -0.1799946 -0.6574040 -0.4994102 #> 426: -0.3862761 -0.02749851 -0.4110578 -0.1800945 -0.6603731 -0.4983249 #> 427: -0.3753696 -0.02886785 -0.4138399 -0.1785197 -0.6617479 -0.4960453 #>            3_13       3_14       3_15         4_0        4_1        4_2 #>           <num>      <num>      <num>       <num>      <num>      <num> #>   1: -0.7665755 -0.6297212 -0.8223611 -0.06994937 -0.1948373 0.23507126 #>   2: -0.7730041 -0.6271325 -0.8182341 -0.09679992 -0.2343327 0.19651763 #>   3: -0.7757829 -0.6259195 -0.8187702 -0.12339281 -0.2791846 0.15964492 #>   4: -0.7789082 -0.6308877 -0.8228862 -0.14147647 -0.3141552 0.13575174 #>   5: -0.7851093 -0.6393642 -0.8300613 -0.15012543 -0.3275808 0.12282108 #>  ---                                                                    #> 423: -0.8091943 -0.6442823 -0.8769802 -0.10772412 -0.4471408 0.03666973 #> 424: -0.8092830 -0.6438907 -0.8746197 -0.10918775 -0.4476721 0.03725535 #> 425: -0.8063261 -0.6447943 -0.8734123 -0.10935027 -0.4572730 0.03887363 #> 426: -0.8015150 -0.6445024 -0.8748469 -0.11035092 -0.4627162 0.03930071 #> 427: -0.7982160 -0.6434273 -0.8757001 -0.11206047 -0.4563209 0.03814443 #>              4_3        4_4         4_5        4_6         4_7        4_8 #>            <num>      <num>       <num>      <num>       <num>      <num> #>   1: -0.02331112 0.26986513 -0.03277086 0.19152664 -0.06653646 0.12510364 #>   2: -0.06179452 0.23874061 -0.08068238 0.15103769 -0.10559224 0.10004297 #>   3: -0.10245890 0.20027897 -0.12478228 0.11091606 -0.15335120 0.06658314 #>   4: -0.13633749 0.16619243 -0.15494780 0.08322451 -0.18932059 0.03945834 #>   5: -0.15830097 0.14300099 -0.16945702 0.06948098 -0.20651995 0.02460175 #>  ---                                                                      #> 423: -0.39198825 0.05423059 -0.37294900 0.05756936 -0.32915789 0.07003556 #> 424: -0.39235875 0.05655045 -0.36823896 0.06076564 -0.33180121 0.07390746 #> 425: -0.39971539 0.05786621 -0.36747903 0.05863204 -0.33813080 0.07251260 #> 426: -0.40848437 0.05763303 -0.36740896 0.05429918 -0.33687240 0.06923430 #> 427: -0.41004351 0.05724512 -0.36362541 0.05428709 -0.32945213 0.06637439 #>             4_9         4_10       4_11       4_12       4_13       4_14 #>           <num>        <num>      <num>      <num>      <num>      <num> #>   1: -0.1101248  0.029535320 -0.3481123 -0.3506066 -0.6812164 -0.5529566 #>   2: -0.1527066  0.003534036 -0.3676054 -0.3633745 -0.6913838 -0.5519550 #>   3: -0.1881872 -0.025381880 -0.3941703 -0.3797562 -0.6990299 -0.5533080 #>   4: -0.2133017 -0.047853392 -0.4153597 -0.3951810 -0.7052442 -0.5597531 #>   5: -0.2292614 -0.060071301 -0.4268375 -0.4070649 -0.7121986 -0.5681785 #>  ---                                                                     #> 423: -0.2929688  0.017835222 -0.4555726 -0.3775359 -0.7328435 -0.5438783 #> 424: -0.2945867  0.019689614 -0.4546731 -0.3758536 -0.7310277 -0.5421190 #> 425: -0.2965241  0.016919773 -0.4559585 -0.3765136 -0.7278451 -0.5422699 #> 426: -0.2965332  0.012936818 -0.4604053 -0.3785573 -0.7247259 -0.5428804 #> 427: -0.2969316  0.011210520 -0.4643245 -0.3780981 -0.7230524 -0.5427254 #>            4_15        5_0         5_1        5_2         5_3        5_4 #>           <num>      <num>       <num>      <num>       <num>      <num> #>   1: -0.7702336 0.11460015 -0.03510817 0.34293243  0.11290687 0.42655033 #>   2: -0.7664602 0.08463844 -0.08284651 0.31294748  0.06828209 0.38676611 #>   3: -0.7678913 0.05434874 -0.13940026 0.27856693  0.01997435 0.33720395 #>   4: -0.7723041 0.03276557 -0.18394655 0.25130591 -0.01962078 0.29737791 #>   5: -0.7798773 0.02154485 -0.20320033 0.23551796 -0.04708247 0.27245685 #>  ---                                                                     #> 423: -0.8315062 0.03843024 -0.37421671 0.07204729 -0.36881086 0.07357048 #> 424: -0.8301061 0.03988123 -0.37570393 0.07378091 -0.36943567 0.07481265 #> 425: -0.8283818 0.03883109 -0.38774556 0.07472437 -0.38019809 0.07525086 #> 426: -0.8283017 0.03590450 -0.39282519 0.07751127 -0.39020240 0.07412328 #> 427: -0.8286900 0.03426958 -0.38598010 0.07995838 -0.38810304 0.07064135 #>              5_5        5_6          5_7        5_8         5_9       5_10 #>            <num>      <num>        <num>      <num>       <num>      <num> #>   1:  0.11753017 0.32303247  0.109790646 0.26948279  0.01834719 0.12938540 #>   2:  0.06776483 0.28515229  0.058814906 0.23558564 -0.02757971 0.09512787 #>   3:  0.01658157 0.24822997  0.007717322 0.20105782 -0.06737868 0.06089470 #>   4: -0.02130803 0.22373028 -0.032182094 0.17547110 -0.09359654 0.03801979 #>   5: -0.04069167 0.21401943 -0.059577677 0.16066670 -0.10749152 0.02652283 #>  ---                                                                       #> 423: -0.35792434 0.07230426 -0.308467656 0.08268414 -0.26994801 0.05796900 #> 424: -0.35649100 0.07285604 -0.310336679 0.08556347 -0.27288014 0.06006217 #> 425: -0.35849795 0.07272252 -0.320685357 0.08541255 -0.27874035 0.05942376 #> 426: -0.35917634 0.07051150 -0.325145066 0.08421563 -0.28221530 0.05715792 #> 427: -0.35430756 0.06807132 -0.319326639 0.08278632 -0.28278291 0.05654842 #>            5_11       5_12       5_13       5_14       5_15        6_0 #>           <num>      <num>      <num>      <num>      <num>      <num> #>   1: -0.1717014 -0.1969883 -0.5527974 -0.4865161 -0.7210248 0.18089953 #>   2: -0.2030258 -0.2211370 -0.5673119 -0.4884878 -0.7198143 0.15293343 #>   3: -0.2327487 -0.2465808 -0.5776873 -0.4922220 -0.7229511 0.12460253 #>   4: -0.2546774 -0.2635905 -0.5845709 -0.4998491 -0.7282363 0.10344046 #>   5: -0.2675154 -0.2707982 -0.5896380 -0.5077798 -0.7358948 0.08880223 #>  ---                                                                   #> 423: -0.2607206 -0.1880689 -0.6150235 -0.4683118 -0.7755380 0.13000481 #> 424: -0.2616906 -0.1876739 -0.6141766 -0.4669085 -0.7738265 0.13281861 #> 425: -0.2635156 -0.1880164 -0.6130396 -0.4668194 -0.7734500 0.13485433 #> 426: -0.2661669 -0.1878610 -0.6108111 -0.4666386 -0.7754002 0.13375901 #> 427: -0.2657728 -0.1861850 -0.6090330 -0.4672388 -0.7765176 0.13073599 #>              6_1       6_2         6_3        6_4         6_5        6_6 #>            <num>     <num>       <num>      <num>       <num>      <num> #>   1: -0.01905255 0.4086876  0.11888564 0.48320299  0.16874145 0.44582400 #>   2: -0.05683843 0.3770023  0.08798480 0.44471416  0.12288575 0.41802159 #>   3: -0.10118315 0.3361670  0.05229931 0.39902419  0.07216766 0.38312700 #>   4: -0.13947496 0.3031361  0.01808203 0.35593370  0.03447116 0.35206458 #>   5: -0.15685874 0.2850990 -0.01346898 0.32451251  0.01535929 0.32730293 #>  ---                                                                     #> 423: -0.34831402 0.1095677 -0.34016314 0.09610651 -0.34881091 0.08453228 #> 424: -0.34806740 0.1117842 -0.34011540 0.09830205 -0.34332514 0.08498331 #> 425: -0.35857370 0.1093288 -0.34659386 0.09833130 -0.34261155 0.08149479 #> 426: -0.36376283 0.1050092 -0.35318628 0.09690438 -0.34267443 0.07795043 #> 427: -0.35765105 0.1034839 -0.35265785 0.09591320 -0.33919519 0.07804901 #>              6_7        6_8         6_9       6_10         6_11         6_12 #>            <num>      <num>       <num>      <num>        <num>        <num> #>   1:  0.21916731 0.43569326  0.19325805 0.28649321  0.021155749  0.048674889 #>   2:  0.16726121 0.41328928  0.14087676 0.25508243 -0.009800109  0.024229340 #>   3:  0.11559406 0.37692678  0.10097029 0.22570246 -0.042084817 -0.007773926 #>   4:  0.07695936 0.34050414  0.07249004 0.20341654 -0.067647710 -0.031864427 #>   5:  0.05130208 0.31644794  0.05302528 0.18978401 -0.082216792 -0.040720206 #>  ---                                                                         #> 423: -0.30920589 0.09023030 -0.26208341 0.07535793 -0.207132086  0.040714394 #> 424: -0.31480187 0.08940946 -0.26722503 0.07533854 -0.203569978  0.040153250 #> 425: -0.32193980 0.08624943 -0.27583653 0.07458680 -0.207171693  0.038982097 #> 426: -0.31959346 0.08395305 -0.27948758 0.07265072 -0.215214461  0.038374390 #> 427: -0.31122905 0.08047207 -0.27833202 0.07338902 -0.218296513  0.039154116 #>            6_13       6_14       6_15        7_0         7_1       7_2 #>           <num>      <num>      <num>      <num>       <num>     <num> #>   1: -0.2817131 -0.3330086 -0.6200074 0.17514743 -0.09557199 0.4060850 #>   2: -0.3051751 -0.3411041 -0.6209924 0.14219251 -0.12218226 0.3676866 #>   3: -0.3258636 -0.3490969 -0.6252375 0.11172757 -0.15664205 0.3342102 #>   4: -0.3378293 -0.3588005 -0.6312293 0.09271412 -0.18458751 0.3104898 #>   5: -0.3440845 -0.3673511 -0.6388685 0.08287866 -0.19328009 0.2957985 #>  ---                                                                   #> 423: -0.3216147 -0.3059025 -0.6783990 0.14231284 -0.33671686 0.1486460 #> 424: -0.3206956 -0.3035870 -0.6754252 0.14542726 -0.33683294 0.1504826 #> 425: -0.3180229 -0.3014976 -0.6750098 0.14393683 -0.34644127 0.1512316 #> 426: -0.3146592 -0.3007114 -0.6782953 0.14145134 -0.35066342 0.1511961 #> 427: -0.3124145 -0.3003827 -0.6798649 0.14077842 -0.34340900 0.1495800 #>              7_3       7_4         7_5       7_6         7_7        7_8 #>            <num>     <num>       <num>     <num>       <num>      <num> #>   1:  0.10227323 0.4955553  0.16690482 0.4960505  0.22606452 0.51621252 #>   2:  0.06227709 0.4627217  0.11452152 0.4559644  0.19545102 0.47442657 #>   3:  0.02216644 0.4177838  0.07430249 0.4193894  0.15471040 0.42696881 #>   4: -0.01016116 0.3727280  0.04814187 0.3901626  0.11800983 0.39333984 #>   5: -0.02548500 0.3412454  0.03500795 0.3708753  0.09266277 0.37308314 #>  ---                                                                    #> 423: -0.31263500 0.1300725 -0.32309648 0.1092520 -0.30024305 0.10954092 #> 424: -0.31076008 0.1306683 -0.31835282 0.1115749 -0.30168831 0.10853294 #> 425: -0.31428114 0.1317825 -0.31976902 0.1091268 -0.30967656 0.10527629 #> 426: -0.32064453 0.1312346 -0.32075778 0.1039964 -0.31268883 0.10104267 #> 427: -0.32244471 0.1287172 -0.31694186 0.1019832 -0.30719063 0.09788156 #>             7_9       7_10        7_11      7_12       7_13       7_14 #>           <num>      <num>       <num>     <num>      <num>      <num> #>   1:  0.2669946 0.43942773  0.17619297 0.2079387 -0.1004790 -0.1541892 #>   2:  0.2206271 0.40604094  0.15137148 0.1752643 -0.1278359 -0.1677137 #>   3:  0.1714102 0.36487803  0.11816086 0.1402042 -0.1462014 -0.1811404 #>   4:  0.1325444 0.32844305  0.08585792 0.1160336 -0.1566433 -0.1942294 #>   5:  0.1089885 0.30745614  0.06451309 0.1055136 -0.1644206 -0.2030196 #>  ---                                                                   #> 423: -0.2565538 0.10326929 -0.18363880 0.1079208 -0.1985933 -0.1611951 #> 424: -0.2593665 0.10223637 -0.18624392 0.1098215 -0.1970126 -0.1608094 #> 425: -0.2634064 0.10008022 -0.18872470 0.1085010 -0.1931469 -0.1615037 #> 426: -0.2673599 0.09678093 -0.19275670 0.1058363 -0.1889191 -0.1614061 #> 427: -0.2727987 0.09452595 -0.19443908 0.1043969 -0.1866418 -0.1601589 #>            7_15          8_0        8_1       8_2         8_3       8_4 #>           <num>        <num>      <num>     <num>       <num>     <num> #>   1: -0.5388833  0.087934248 -0.1846190 0.3153276 -0.02330378 0.4506199 #>   2: -0.5411273  0.071245745 -0.2132872 0.2905726 -0.05313157 0.4143729 #>   3: -0.5462263  0.051149037 -0.2485526 0.2634043 -0.08097915 0.3769518 #>   4: -0.5518396  0.036638767 -0.2723057 0.2415955 -0.10360466 0.3495513 #>   5: -0.5576556  0.032449901 -0.2760711 0.2275214 -0.11793862 0.3316274 #>  ---                                                                    #> 423: -0.5895592  0.003167340 -0.3458221 0.1443959 -0.28520176 0.1748143 #> 424: -0.5865974  0.006487147 -0.3453450 0.1424087 -0.28314653 0.1798537 #> 425: -0.5859556  0.006562222 -0.3560074 0.1408821 -0.29136106 0.1807242 #> 426: -0.5888314  0.002249188 -0.3626761 0.1408205 -0.29821774 0.1776260 #> 427: -0.5904717 -0.003012084 -0.3560008 0.1408087 -0.29326501 0.1746140 #>              8_5       8_6         8_7       8_8        8_9      8_10 #>            <num>     <num>       <num>     <num>      <num>     <num> #>   1:  0.15336494 0.5026962  0.25929761 0.5448662  0.2914203 0.4914328 #>   2:  0.10891031 0.4816248  0.21767247 0.5082873  0.2498384 0.4499103 #>   3:  0.06503499 0.4589221  0.16304955 0.4671142  0.1989192 0.4126056 #>   4:  0.03334329 0.4330226  0.11946984 0.4280581  0.1616991 0.3867722 #>   5:  0.01523896 0.4074669  0.09858868 0.4038964  0.1397330 0.3701818 #>  ---                                                                  #> 423: -0.28495014 0.1534336 -0.25919485 0.1541284 -0.2198540 0.1424541 #> 424: -0.28366613 0.1540914 -0.25829354 0.1559501 -0.2187341 0.1477413 #> 425: -0.28505880 0.1516479 -0.26450428 0.1500032 -0.2210669 0.1490496 #> 426: -0.28465354 0.1485623 -0.26641917 0.1438126 -0.2242813 0.1446446 #> 427: -0.27912781 0.1472479 -0.26357475 0.1420557 -0.2270529 0.1418104 #>            8_11      8_12        8_13       8_14       8_15        9_0 #>           <num>     <num>       <num>      <num>      <num>      <num> #>   1:  0.2251298 0.3129992 -0.01930906 -0.1302575 -0.4997482 -0.1404191 #>   2:  0.1958722 0.2850758 -0.04237487 -0.1349224 -0.5037256 -0.1482290 #>   3:  0.1697999 0.2484965 -0.05983021 -0.1419368 -0.5089787 -0.1594115 #>   4:  0.1452528 0.2202435 -0.07422929 -0.1524901 -0.5153573 -0.1682616 #>   5:  0.1240693 0.2049263 -0.08699963 -0.1633783 -0.5238560 -0.1715885 #>  ---                                                                   #> 423: -0.1471200 0.1452986 -0.20291974 -0.2383699 -0.5793382 -0.2771131 #> 424: -0.1480197 0.1500701 -0.20409964 -0.2370797 -0.5767770 -0.2751649 #> 425: -0.1519214 0.1520038 -0.20630156 -0.2374736 -0.5755845 -0.2740324 #> 426: -0.1592668 0.1524178 -0.20555831 -0.2382059 -0.5769823 -0.2760093 #> 427: -0.1644321 0.1516853 -0.20182349 -0.2386301 -0.5775211 -0.2802055 #>             9_1       9_2        9_3       9_4         9_5       9_6 #>           <num>     <num>      <num>     <num>       <num>     <num> #>   1: -0.3880415 0.1964528 -0.1848320 0.3294951  0.06159078 0.5099736 #>   2: -0.4016658 0.1757130 -0.1974400 0.3060773  0.01614512 0.4669406 #>   3: -0.4255078 0.1511883 -0.2236059 0.2722496 -0.02511934 0.4293555 #>   4: -0.4447283 0.1303705 -0.2497362 0.2399727 -0.05653130 0.3961539 #>   5: -0.4463654 0.1197424 -0.2649184 0.2176039 -0.07012609 0.3755288 #>  ---                                                                 #> 423: -0.4726446 0.1334595 -0.2893841 0.2214528 -0.23276733 0.2040207 #> 424: -0.4710627 0.1328289 -0.2894299 0.2196637 -0.22747667 0.2037956 #> 425: -0.4767421 0.1337296 -0.2961083 0.2174471 -0.23046409 0.2013982 #> 426: -0.4798250 0.1356475 -0.3010665 0.2144648 -0.23281474 0.1966529 #> 427: -0.4757109 0.1343473 -0.2967418 0.2124132 -0.22850421 0.1938415 #>              9_7       9_8        9_9      9_10       9_11      9_12 #>            <num>     <num>      <num>     <num>      <num>     <num> #>   1:  0.17453891 0.5510354  0.2190386 0.4883946  0.2343115 0.3436384 #>   2:  0.13835642 0.5170754  0.2014254 0.4554164  0.1979440 0.3181358 #>   3:  0.10251222 0.4706872  0.1717279 0.4163522  0.1590499 0.2883997 #>   4:  0.07783717 0.4303708  0.1354595 0.3866921  0.1322260 0.2594654 #>   5:  0.06698119 0.4054990  0.1095207 0.3700014  0.1164159 0.2401658 #>  ---                                                                 #> 423: -0.20711856 0.1984591 -0.1697335 0.1919664 -0.1002033 0.1958435 #> 424: -0.20930032 0.1975257 -0.1713480 0.1919565 -0.1014204 0.1963937 #> 425: -0.21713974 0.1973275 -0.1762835 0.1880476 -0.1064350 0.1965378 #> 426: -0.21859702 0.1965215 -0.1810040 0.1842630 -0.1137590 0.1959506 #> 427: -0.21456438 0.1942090 -0.1817123 0.1841256 -0.1176966 0.1958615 #>             9_13       9_14       9_15       10_0       10_1         10_2 #>            <num>      <num>      <num>      <num>      <num>        <num> #>   1: -0.03961723 -0.2325946 -0.5138462 -0.3454294 -0.5610783 -0.003405581 #>   2: -0.06623416 -0.2391484 -0.5182777 -0.3505079 -0.5801501 -0.016585065 #>   3: -0.08755251 -0.2452990 -0.5224738 -0.3595735 -0.6064633 -0.035956357 #>   4: -0.10419883 -0.2539910 -0.5295882 -0.3662870 -0.6256430 -0.051071431 #>   5: -0.11806381 -0.2648067 -0.5404692 -0.3689005 -0.6284144 -0.058754291 #>  ---                                                                      #> 423: -0.20896144 -0.3554857 -0.5956661 -0.4831747 -0.6749610 -0.006982267 #> 424: -0.20708182 -0.3550624 -0.5945614 -0.4809514 -0.6780014 -0.005729941 #> 425: -0.20645618 -0.3566869 -0.5941038 -0.4795221 -0.6867968 -0.004676747 #> 426: -0.20509402 -0.3572714 -0.5957916 -0.4812161 -0.6908116 -0.004938770 #> 427: -0.20455551 -0.3560388 -0.5966506 -0.4826814 -0.6844195 -0.006340539 #>            10_3       10_4        10_5      10_6         10_7      10_8 #>           <num>      <num>       <num>     <num>        <num>     <num> #>   1: -0.3789502 0.13877778 -0.09041325 0.3947443  0.096415646 0.5012836 #>   2: -0.3927487 0.11653356 -0.12458400 0.3548783  0.067286357 0.4652751 #>   3: -0.4124519 0.08843436 -0.15803877 0.3201760  0.033194412 0.4181609 #>   4: -0.4331997 0.06170695 -0.18525149 0.2936058  0.004564124 0.3783152 #>   5: -0.4479685 0.04130512 -0.20156626 0.2748121 -0.016045203 0.3591099 #>  ---                                                                    #> 423: -0.4192245 0.21815482 -0.19248809 0.2559083 -0.168943003 0.2504518 #> 424: -0.4255278 0.22156312 -0.19656545 0.2575580 -0.171470091 0.2539417 #> 425: -0.4362897 0.22243653 -0.20338295 0.2567109 -0.178340837 0.2579956 #> 426: -0.4440649 0.22056223 -0.20394890 0.2551845 -0.180030599 0.2567944 #> 427: -0.4405728 0.21886383 -0.19539179 0.2556418 -0.175978899 0.2496768 #>             10_9     10_10       10_11     10_12       10_13      10_14 #>            <num>     <num>       <num>     <num>       <num>      <num> #>   1:  0.16088957 0.4022605  0.12462527 0.3303100 -0.07084840 -0.3302290 #>   2:  0.13614024 0.3802870  0.10487096 0.3162005 -0.08289412 -0.3306039 #>   3:  0.10655165 0.3530020  0.08353272 0.2910916 -0.09676377 -0.3353746 #>   4:  0.08416789 0.3325290  0.06740724 0.2647256 -0.10807587 -0.3462425 #>   5:  0.06685340 0.3216399  0.05747279 0.2419084 -0.12040044 -0.3579053 #>  ---                                                                    #> 423: -0.11701809 0.2348692 -0.06368053 0.2281845 -0.15948832 -0.4201501 #> 424: -0.12409801 0.2383129 -0.06182728 0.2300553 -0.15933338 -0.4195252 #> 425: -0.13336122 0.2388805 -0.06120444 0.2304868 -0.15755838 -0.4191516 #> 426: -0.13745880 0.2341105 -0.06617249 0.2291671 -0.15389903 -0.4183375 #> 427: -0.13737124 0.2278370 -0.07184657 0.2277900 -0.15289837 -0.4172875 #>           10_15       11_0       11_1       11_2       11_3        11_4 #>           <num>      <num>      <num>      <num>      <num>       <num> #>   1: -0.5502016 -0.4089794 -0.7035590 -0.1988441 -0.6013403 -0.04612614 #>   2: -0.5491410 -0.4128581 -0.7133205 -0.2056266 -0.6083025 -0.06301054 #>   3: -0.5519719 -0.4174158 -0.7311138 -0.2184241 -0.6193420 -0.08642311 #>   4: -0.5582104 -0.4215689 -0.7447819 -0.2302519 -0.6318524 -0.10882086 #>   5: -0.5680800 -0.4243726 -0.7436408 -0.2369744 -0.6402031 -0.12603880 #>  ---                                                                    #> 423: -0.6367587 -0.5125267 -0.8724214 -0.3014192 -0.6272018  0.09982392 #> 424: -0.6344557 -0.5105906 -0.8761734 -0.3029543 -0.6285866  0.10479824 #> 425: -0.6346116 -0.5097994 -0.8845704 -0.3034330 -0.6365673  0.10503902 #> 426: -0.6366958 -0.5112385 -0.8873740 -0.3033138 -0.6447878  0.10215051 #> 427: -0.6373948 -0.5125880 -0.8805217 -0.3047109 -0.6447583  0.09967330 #>            11_5      11_6        11_7      11_8         11_9     11_10 #>           <num>     <num>       <num>     <num>        <num>     <num> #>   1: -0.3021611 0.1909627 -0.06505151 0.3536376  0.001076699 0.2811520 #>   2: -0.3240646 0.1635235 -0.09604929 0.3317861 -0.020703882 0.2534717 #>   3: -0.3507667 0.1393459 -0.12998015 0.3020550 -0.043339010 0.2297511 #>   4: -0.3766404 0.1217387 -0.15413399 0.2728064 -0.061574921 0.2129553 #>   5: -0.3931606 0.1105335 -0.16765058 0.2570367 -0.073306367 0.2012097 #>  ---                                                                   #> 423: -0.2195135 0.2539870 -0.14811064 0.2794592 -0.083007313 0.2626795 #> 424: -0.2189381 0.2545430 -0.15172820 0.2852612 -0.081238411 0.2603240 #> 425: -0.2219534 0.2513329 -0.15867922 0.2861450 -0.083784021 0.2556774 #> 426: -0.2223453 0.2494126 -0.15801638 0.2827066 -0.087282449 0.2529730 #> 427: -0.2186473 0.2508664 -0.15099348 0.2778258 -0.093390450 0.2562906 #>            11_11     11_12      11_13      11_14      11_15       12_0 #>            <num>     <num>      <num>      <num>      <num>      <num> #>   1: -0.04550443 0.1613120 -0.2492418 -0.3870185 -0.5538554 -0.5889121 #>   2: -0.05919959 0.1532610 -0.2606162 -0.3864829 -0.5549448 -0.5879138 #>   3: -0.07607881 0.1355628 -0.2675352 -0.3900220 -0.5566438 -0.5898755 #>   4: -0.09350286 0.1189713 -0.2733023 -0.4005955 -0.5599077 -0.5909628 #>   5: -0.10829892 0.1061453 -0.2821340 -0.4124226 -0.5665730 -0.5905932 #>  ---                                                                   #> 423: -0.04777495 0.1684111 -0.2748755 -0.4656470 -0.6215362 -0.6319057 #> 424: -0.04765457 0.1688343 -0.2724172 -0.4665069 -0.6193126 -0.6320986 #> 425: -0.04815739 0.1672493 -0.2692471 -0.4676030 -0.6189794 -0.6328617 #> 426: -0.05001089 0.1658563 -0.2664106 -0.4671336 -0.6216958 -0.6342685 #> 427: -0.05160535 0.1668420 -0.2649757 -0.4658949 -0.6232929 -0.6346731 #>            12_1       12_2       12_3       12_4       12_5         12_6 #>           <num>      <num>      <num>      <num>      <num>        <num> #>   1: -0.9084547 -0.4400639 -0.7794662 -0.2471266 -0.5029922  0.001548128 #>   2: -0.9101344 -0.4375786 -0.7770191 -0.2589712 -0.5270271 -0.022970274 #>   3: -0.9202237 -0.4420333 -0.7809778 -0.2757573 -0.5534080 -0.042182062 #>   4: -0.9293817 -0.4489326 -0.7892566 -0.2921411 -0.5725283 -0.055214901 #>   5: -0.9286409 -0.4542748 -0.7958061 -0.3046873 -0.5792183 -0.064597100 #>  ---                                                                     #> 423: -1.0792398 -0.5509068 -0.7931226 -0.1663056 -0.3932111  0.167518958 #> 424: -1.0811239 -0.5530722 -0.7953201 -0.1666212 -0.3933381  0.164244816 #> 425: -1.0896326 -0.5551605 -0.8030046 -0.1681725 -0.3954019  0.159555063 #> 426: -1.0944729 -0.5558071 -0.8086336 -0.1697897 -0.3963541  0.157658920 #> 427: -1.0899518 -0.5560302 -0.8055803 -0.1712147 -0.3940453  0.159509078 #>            12_7       12_8       12_9        12_10      12_11      12_12 #>           <num>      <num>      <num>        <num>      <num>      <num> #>   1: -0.2636845 0.15986118 -0.2198299  0.012900840 -0.3475010 -0.1755732 #>   2: -0.2925263 0.13392644 -0.2365713  0.002008484 -0.3510732 -0.1797495 #>   3: -0.3226509 0.10030603 -0.2550317 -0.013462396 -0.3556432 -0.1896459 #>   4: -0.3424500 0.06911202 -0.2684755 -0.026638059 -0.3618752 -0.1994632 #>   5: -0.3514164 0.04984137 -0.2740572 -0.033214808 -0.3696662 -0.2079690 #>  ---                                                                     #> 423: -0.2231302 0.23791830 -0.1277080  0.222949252 -0.3009740 -0.2151229 #> 424: -0.2291479 0.23654796 -0.1266753  0.221743941 -0.3037770 -0.2165980 #> 425: -0.2400716 0.23113360 -0.1276943  0.217651576 -0.3070489 -0.2181160 #> 426: -0.2403418 0.22415788 -0.1321646  0.217663661 -0.3105931 -0.2197210 #> 427: -0.2322121 0.22267960 -0.1382107  0.219312802 -0.3126730 -0.2208407 #>           12_13      12_14      12_15 #>           <num>      <num>      <num> #>   1: -0.5086929 -0.4955095 -0.6490921 #>   2: -0.5129193 -0.4911957 -0.6464767 #>   3: -0.5125197 -0.4882756 -0.6439207 #>   4: -0.5119967 -0.4921051 -0.6446184 #>   5: -0.5161167 -0.4999369 -0.6508147 #>  ---                                  #> 423: -0.5514245 -0.5360808 -0.7430303 #> 424: -0.5530964 -0.5362788 -0.7415859 #> 425: -0.5516642 -0.5378205 -0.7405182 #> 426: -0.5466566 -0.5389180 -0.7409301 #> 427: -0.5412602 -0.5395846 -0.7407396 #>  #> $metadata #>          y     x       file_name  license #>      <num> <num>          <char>   <char> #>   1:     0     0 CA small UF.dat CC BY-NC #>   2:     0     1 CA small UF.dat CC BY-NC #>   3:     0     2 CA small UF.dat CC BY-NC #>   4:     0     3 CA small UF.dat CC BY-NC #>   5:     0     4 CA small UF.dat CC BY-NC #>  ---                                      #> 204:    12    11 CA small UF.dat CC BY-NC #> 205:    12    12 CA small UF.dat CC BY-NC #> 206:    12    13 CA small UF.dat CC BY-NC #> 207:    12    14 CA small UF.dat CC BY-NC #> 208:    12    15 CA small UF.dat CC BY-NC #>                                                       description samples lines #>                                   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interleave                  z plot titles #>      <num>         <num>     <num>     <char>                         <char> #>   1:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>   2:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>   3:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>   4:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>   5:   427             0         4        bip Wavenumbers (cm-1), Absorbance #>  ---                                                                         #> 204:   427             0         4        bip Wavenumbers (cm-1), Absorbance #> 205:   427             0         4        bip Wavenumbers (cm-1), Absorbance #> 206:   427             0         4        bip Wavenumbers (cm-1), Absorbance #> 207:   427             0         4        bip Wavenumbers (cm-1), Absorbance #> 208:   427             0         4        bip Wavenumbers (cm-1), 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5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>  ---                                                                   #> 204: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #> 205: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #> 206: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #> 207: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #> 208: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                               file_id #>                                <char> #>   1: 215a0fd7e0dc3b1001799214618ce6eb #>   2: 215a0fd7e0dc3b1001799214618ce6eb #>   3: 215a0fd7e0dc3b1001799214618ce6eb #>   4: 215a0fd7e0dc3b1001799214618ce6eb #>   5: 215a0fd7e0dc3b1001799214618ce6eb #>  ---                                  #> 204: 215a0fd7e0dc3b1001799214618ce6eb #> 205: 215a0fd7e0dc3b1001799214618ce6eb #> 206: 215a0fd7e0dc3b1001799214618ce6eb #> 207: 215a0fd7e0dc3b1001799214618ce6eb #> 208: 215a0fd7e0dc3b1001799214618ce6eb #>"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":null,"dir":"Reference","previous_headings":"","what":"Read spectral data from Bruker OPUS binary files — read_opus","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"Read file(s) acquired Bruker Vertex FTIR Instrument. function basically fork opus_read() https://github.com/pierreroudier/opusreader.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"","code":"read_opus(   file,   metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,     organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,     material_form = NULL, material_phase = NULL, material_producer = NULL,     material_purity = NULL, material_quality = NULL, material_color = NULL,     material_other = NULL, cas_number = NULL, instrument_used = NULL,     instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,     laser_light_used = NULL, number_of_accumulations = NULL,           total_acquisition_time_s = NULL, data_processing_procedure = NULL,     level_of_confidence_in_identification = NULL, other_info = NULL, license =     \"CC BY-NC\"),   type = \"spec\",   digits = 1L,   atm_comp_minus4offset = FALSE )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"file character vector path file(s). metadata named list metadata; see as_OpenSpecy() details. type character vector spectra types extract OPUS binary file. Default \"spec\", extract final spectra, e.g. expressed absorbance (named AB Bruker OPUS programs). Possible additional values character vector supplied type \"spec_no_atm_comp\" (spectrum sample without compensation atmospheric gases, water vapor /carbon dioxide), \"sc_sample\" (single channel spectrum sample measurement), \"sc_ref\" (single channel spectrum reference measurement), \"ig_sample\" (interferogram sample measurement) \"ig_ref\" (interferogram reference measurement). digits Integer specifies number decimal places used round wavenumbers (values x-variables). atm_comp_minus4offset Logical whether spectra atmospheric compensation read offset -4 bytes Bruker OPUS files; default FALSE.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"OpenSpecy object.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"type spectra returned function using type = \"spec\" depends setting Bruker instrument: typically, can either absorbance reflectance. type spectra extract file can also use Bruker's OPUS software naming conventions, follows: ScSm corresponds sc_sample ScRf corresponds sc_ref IgSm corresponds ig_sample IgRf corresponds ig_ref","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"Philipp Baumann, Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read spectral data from Bruker OPUS binary files — read_opus","text":"","code":"read_extdata(\"ftir_ps.0\") |> read_opus() #>       wavenumber         V1 #>            <num>      <num> #>    1:   399.2239 0.01660202 #>    2:   401.1525 0.01790014 #>    3:   403.0811 0.01831616 #>    4:   405.0097 0.01894823 #>    5:   406.9384 0.02070733 #>   ---                       #> 2122:  4489.8223 0.02349887 #> 2123:  4491.7509 0.02352615 #> 2124:  4493.6796 0.02357077 #> 2125:  4495.6082 0.02362563 #> 2126:  4497.5368 0.02361715 #>  #> $metadata #>        x     y file_name  license                         unique_id #>    <int> <int>    <char>   <char>                            <char> #> 1:     1     1 ftir_ps.0 CC BY-NC sample1_PSnew_2020-10-28 14:33:02 #>        sample_id        date_time_sm        date_time_rf   sample_name #>           <char>              <POSc>              <POSc>        <char> #> 1: sample1_PSnew 2020-10-28 14:33:02 2020-10-28 14:23:20 sample1_PSnew #>    instr_name_range resolution_wn result_spc beamspl laser_wn #>              <char>         <int>     <char>  <char>    <num> #> 1:    VERTEX 70-MIR             4         AB     KBr 15799.24 #>                               spc_in_file zero_filling temp_scanner_sm #>                                    <char>        <int>           <num> #> 1: ig_sample;ig_ref;sc_sample;sc_ref;spec            2            27.9 #>    temp_scanner_rf hum_rel_sm hum_rel_rf hum_abs_sm hum_abs_rf col_id #>              <num>      <int>     <lgcl>     <lgcl>     <lgcl> <char> #> 1:            27.9          2         NA         NA         NA     V1 #>                                                           session_id #>                                                               <char> #> 1: 5a698732989a836ef6bdea187fd42e2c/dbfcd304ac2251332e5abe934758a6a4 #>                             file_id #>                              <char> #> 1: 0e9318d04fdb3beb4cd61145b080ccb6"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":null,"dir":"Reference","previous_headings":"","what":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"Read single binary acquired Bruker Vertex FTIR Instrument","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"","code":"read_opus_raw(rw, type = \"spec\", atm_comp_minus4offset = FALSE)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"rw raw vector type character vector spectra types extract OPUS binary file. Default \"spec\", extract final spectra, e.g. expressed absorbance (named AB Bruker OPUS programs). Possible additional values character vector supplied type \"spec_no_atm_comp\" (spectrum sample without compensation atmospheric gases, water vapor /carbon dioxide), \"sc_sample\" (single channel spectrum sample measurement), \"sc_ref\" (single channel spectrum reference measurement), \"ig_sample\" (interferogram sample measurement) \"ig_ref\" (interferogram reference measurement). atm_comp_minus4offset logical; whether spectra atmospheric compensation read offset -4 bytes Bruker OPUS files. Default FALSE.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"list 10 elements: metadata data.frame containing metadata OPUS file. spec \"spec\" requested type option, matrix spectrum sample (otherwise set NULL). spec_no_atm_comp \"spec_no_atm_comp\" requested type option, matrix spectrum sample without atmospheric compensation (otherwise set NULL). sc_sample \"sc_sample\" requested type option, matrix single channel spectrum sample (otherwise set NULL). sc_ref \"sc_ref\" requested type option, matrix single channel spectrum reference (otherwise set NULL). ig_sample \"ig_sample\" requested type option, matrix interferogram sample (otherwise set NULL). ig_ref \"ig_ref\" requested type option, matrix interferogram reference (otherwise set NULL). wavenumbers \"spec\" \"spec_no_atm_comp\" requested type option, numeric vector wavenumbers spectrum sample (otherwise set NULL). wavenumbers_sc_sample \"sc_sample\" requested type option, numeric vector wavenumbers single channel spectrum sample (otherwise set NULL). wavenumbers_sc_ref \"sc_ref\" requested type option, numeric vector wavenumbers single channel spectrum reference (otherwise set NULL).","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"type spectra returned function using type = \"spec\" depends setting Bruker instrument: typically, can either absorbance reflectance. type spectra extract file can also use Bruker's OPUS software naming conventions, follows: ScSm corresponds sc_sample ScRf corresponds sc_ref IgSm corresponds ig_sample IgRf corresponds ig_ref","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/read_opus_raw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read a Bruker OPUS spectrum binary raw string — read_opus_raw","text":"Philipp Baumann Pierre Roudier","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":null,"dir":"Reference","previous_headings":"","what":"Run Open Specy app — run_app","title":"Run Open Specy app — run_app","text":"wrapper function starts graphical user interface Open Specy.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run Open Specy app — run_app","text":"","code":"run_app(path = \"system\", log = TRUE, ref = \"main\", test_mode = FALSE, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run Open Specy app — run_app","text":"path store downloaded app files; defaults \"system\" pointing system.file(package = \"OpenSpecy\"). log logical; enables/disables logging tempdir() ref git reference; commit, tag, branch name. Defaults \"main\". change case errors. test_mode logical; internal testing . ... arguments passed runApp().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run Open Specy app — run_app","text":"function normally return value, see runGitHub().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Run Open Specy app — run_app","text":"running function Open Specy GUI open separate window computer browser.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Run Open Specy app — run_app","text":"Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/run_app.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run Open Specy app — run_app","text":"","code":"if (FALSE) { # \\dontrun{ run_app() } # }"},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"function calculates common signal noise metrics OpenSpecy objects.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"","code":"sig_noise(x, ...)  # Default S3 method sig_noise(x, ...)  # S3 method for class 'OpenSpecy' sig_noise(   x,   metric = \"run_sig_over_noise\",   na.rm = TRUE,   prob = 0.5,   step = 20,   breaks = seq(min(unlist(x$spectra)), max(unlist(x$spectra)), length =     ((nrow(x$spectra)^(1/3)) * (max(unlist(x$spectra)) - min(unlist(x$spectra))))/(2 *     IQR(unlist(x$spectra)))),   sig_min = NULL,   sig_max = NULL,   noise_min = NULL,   noise_max = NULL,   abs = T,   spatial_smooth = F,   sigma = c(1, 1),   threshold = NULL,   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"x OpenSpecy object. metric character; specifying desired metric calculate. Options include \"sig\" (mean intensity), \"noise\" (standard deviation intensity), \"sig_times_noise\" (absolute value signal times noise), \"sig_over_noise\" (absolute value signal / noise), \"run_sig_over_noise\" (absolute value signal / noise signal estimated max intensity noise estimated height low intensity region.), \"log_tot_sig\" (sum inverse log intensities, useful spectra  log units), \"tot_sig\" (sum intensities), \"entropy\" (Shannon entropy intensities).. na.rm logical; indicating whether missing values removed calculating signal noise. Default TRUE. prob numeric single value; probability retrieve quantile noise interpreted run_sig_over_noise option. step numeric; step size region look run_sig_over_noise option. breaks numeric; number positions breaks entropy calculation. Defaults infer decent value data. sig_min numeric; minimum wavenumber value signal region. sig_max numeric; maximum wavenumber value signal region. noise_min numeric; minimum wavenumber value noise region. noise_max numeric; maximum wavenumber value noise region. abs logical; whether return absolute value result spatial_smooth logical; whether spatially smooth sig/noise using xy coordinates gaussian smoother. sigma numeric; two value vector describing standard deviation smoother dimension, y specified first followed x, cases. threshold numeric; NULL, threshold set, otherwise use numeric value set target threshold true signal noise . function return logical value instead numeric threshold set. ... arguments passed subfunctions; currently used.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"numeric vector containing calculated metric spectrum OpenSpecy object logical value threshold set describing numbers equal (TRUE) threshold.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/sig_noise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate signal and noise metrics for OpenSpecy objects — sig_noise","text":"","code":"data(\"raman_hdpe\")  sig_noise(raman_hdpe, metric = \"sig\") #> intensity  #>  101.1691  sig_noise(raman_hdpe, metric = \"noise\") #> intensity  #>  61.00777  sig_noise(raman_hdpe, metric = \"sig_times_noise\") #> intensity  #>    6172.1"},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth spectral intensities — smooth_intens","title":"Smooth spectral intensities — smooth_intens","text":"smoother can enhance signal noise ratio data useing Savitzky-Golay Whittaker-Henderson filter.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth spectral intensities — smooth_intens","text":"","code":"smooth_intens(x, ...)  # Default S3 method smooth_intens(x, ...)  # S3 method for class 'OpenSpecy' smooth_intens(   x,   polynomial = 3,   window = 11,   derivative = 1,   abs = TRUE,   lambda = 1600,   d = 2,   type = \"sg\",   lag = 2,   make_rel = TRUE,   ... )  calc_window_points(x, ...)  # Default S3 method calc_window_points(x, wavenum_width = 70, ...)  # S3 method for class 'OpenSpecy' calc_window_points(x, wavenum_width = 70, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth spectral intensities — smooth_intens","text":"x object class OpenSpecy vector calc_window_points(). polynomial polynomial order filter window number data points window, filter length (must odd). derivative derivative order want calculate derivative. Zero (default) derivative. abs logical; whether want calculate absolute value resulting output. lambda smoothing parameter Whittaker-Henderson smoothing, 50 results rough smoothing 10^4 results high level smoothing. d order differences use Whittaker-Henderson smoothing, typically set 2. type type smoothing use \"wh\" Whittaker-Henerson \"sg\" Savitzky-Golay. lag lag use numeric derivative calculation using Whittaker-Henderson. Greater values lead smoother derivatives, 1 2 common. make_rel logical; TRUE spectra automatically normalized make_rel(). wavenum_width width window want wavenumbers. ... arguments passed sgolay().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Smooth spectral intensities — smooth_intens","text":"smooth_intens() returns OpenSpecy object. calc_window_points() returns single numberic vector object number points needed fill window can passed smooth_intens(). many applications, reusable specifying static number points.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Smooth spectral intensities — smooth_intens","text":"Savitzky-Golay wrapper around filter function signal package improve integration Open Specy functions. typical good smooth can achieved 11 data point window 3rd 4th order polynomial. Whittaker-Henderson, code largely based whittaker() function pracma package. general Whittaker-Henderson expected slower robust Savitzky-Golay.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Smooth spectral intensities — smooth_intens","text":"Savitzky , Golay MJ (1964). “Smoothing Differentiation Data Simplified Least Squares Procedures.” Analytical Chemistry, 36(8), 1627–1639.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Smooth spectral intensities — smooth_intens","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/smooth_intens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Smooth spectral intensities — smooth_intens","text":"","code":"data(\"raman_hdpe\")  smooth_intens(raman_hdpe) #>      wavenumber   intensity #>           <num>       <num> #>   1:    301.040 0.102500885 #>   2:    304.632 0.063171177 #>   3:    308.221 0.031695428 #>   4:    311.810 0.008073638 #>   5:    315.398 0.007614310 #>  ---                        #> 960:   3187.990 0.002890315 #> 961:   3190.520 0.005482884 #> 962:   3193.060 0.009382631 #> 963:   3195.590 0.014589555 #> 964:   3198.120 0.021103657 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  smooth_intens(raman_hdpe, window = calc_window_points(x = raman_hdpe, wavenum_width = 70)) #>      wavenumber  intensity #>           <num>      <num> #>   1:    301.040 0.06819092 #>   2:    304.632 0.05691608 #>   3:    308.221 0.04668103 #>   4:    311.810 0.03748578 #>   5:    315.398 0.02933032 #>  ---                       #> 960:   3187.990 0.01418184 #> 961:   3190.520 0.01677777 #> 962:   3193.060 0.01949510 #> 963:   3195.590 0.02233386 #> 964:   3198.120 0.02529402 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b  smooth_intens(raman_hdpe, lambda = 1600, d = 2, lag = 2, type = \"wh\") #>      wavenumber  intensity #>           <num>      <num> #>   1:    301.040 0.09445155 #>   2:    304.632 0.08547362 #>   3:    308.221 0.07684707 #>   4:    311.810 0.06877658 #>   5:    315.398 0.06138149 #>  ---                       #> 960:   3187.990 0.07254546 #> 961:   3190.520 0.08301247 #> 962:   3193.060 0.09396644 #> 963:   3195.590 0.10515688 #> 964:   3198.120 0.11642380 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"Applies spatial smoothing OpenSpecy object using Gaussian filter.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"","code":"spatial_smooth(x, sigma = c(1, 1, 1), ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"x OpenSpecy object. sigma numeric vector specifying standard deviations Gaussian kernel x y dimensions, respectively. ... arguments passed methods.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"OpenSpecy object smoothed spectra.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"function performs spatial smoothing spectral data OpenSpecy object. assumes spatial coordinates provided metadata element object, specifically x y columns, col_id column metadata matches column names spectra data.table.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/spatial_smooth.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spatial Smoothing of OpenSpecy Objects — spatial_smooth","text":"Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":null,"dir":"Reference","previous_headings":"","what":"Spectral resolution — spec_res","title":"Spectral resolution — spec_res","text":"Helper function calculating spectral resolution wavenumber data.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spectral resolution — spec_res","text":"","code":"spec_res(x, ...)  # Default S3 method spec_res(x, ...)  # S3 method for class 'OpenSpecy' spec_res(x, ...)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spectral resolution — spec_res","text":"x numeric vector wavenumber data OpenSpecy object. ... arguments passed subfunctions; currently used.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spectral resolution — spec_res","text":"spec_res() returns single numeric value.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spectral resolution — spec_res","text":"spectral resolution minimum wavenumber, wavelength, frequency difference two lines spectrum can still distinguished.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spectral resolution — spec_res","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/spec_res.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spectral resolution — spec_res","text":"","code":"data(\"raman_hdpe\")  spec_res(raman_hdpe) #> [1] 3.00527"},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Open Specy objects — split_spec","title":"Split Open Specy objects — split_spec","text":"Convert list Open Specy objects number spectra list Open Specy objects one spectrum .","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Open Specy objects — split_spec","text":"","code":"split_spec(x)"},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Open Specy objects — split_spec","text":"x list OpenSpecy objects","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Open Specy objects — split_spec","text":"list Open Specy objects 1 spectrum.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Open Specy objects — split_spec","text":"Function accept list Open Specy objects length split individual components. example list two objects, Open Specy one spectrum Open Specy 50 spectra return list length 51 Open Specy objects one spectrum.","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split Open Specy objects — split_spec","text":"Zacharias Steinmetz, Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/split_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Open Specy objects — split_spec","text":"","code":"data(\"test_lib\") data(\"raman_hdpe\") listed <- list(test_lib, raman_hdpe) test <- split_spec(listed) test2 <- split_spec(list(test_lib))"},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Automated background subtraction for spectral data — subtr_baseline","title":"Automated background subtraction for spectral data — subtr_baseline","text":"baseline correction routine iteratively finds baseline spectrum using polynomial fitting accepts manual baseline.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automated background subtraction for spectral data — subtr_baseline","text":"","code":"subtr_baseline(x, ...)  # Default S3 method subtr_baseline(x, ...)  # S3 method for class 'OpenSpecy' subtr_baseline(   x,   type = \"polynomial\",   degree = 8,   raw = FALSE,   baseline,   make_rel = TRUE,   ... )"},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automated background subtraction for spectral data — subtr_baseline","text":"x list object class OpenSpecy. type one \"polynomial\" \"manual\" depending whether want spectra corrected manual baseline polynomial baseline fitting. degree degree polynomial. Must less number unique points raw FALSE. Typically good fit can found 8th order polynomial. raw TRUE, use raw orthogonal polynomials. baseline OpenSpecy object containing baseline data subtracted. make_rel logical; TRUE spectra automatically normalized make_rel(). ... arguments passed poly().","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automated background subtraction for spectral data — subtr_baseline","text":"subtr_baseline() returns data frame containing two columns named \"wavenumber\" \"intensity\".","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automated background subtraction for spectral data — subtr_baseline","text":"translation Michael Stephen Chen's MATLAB code written imodpolyfit routine Zhao et al. 2007.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Automated background subtraction for spectral data — subtr_baseline","text":"Chen MS (2020). Michaelstchen/ModPolyFit. MATLAB. Retrieved https://github.com/michaelstchen/modPolyFit (Original work published July 28, 2015) Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background Subtraction Algorithm Biomedical Raman Spectroscopy.” Applied Spectroscopy, 61(11), 1225–1232. doi:10.1366/000370207782597003 .","code":""},{"path":[]},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automated background subtraction for spectral data — subtr_baseline","text":"Win Cowger, Zacharias Steinmetz","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/subtr_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automated background subtraction for spectral data — subtr_baseline","text":"","code":"data(\"raman_hdpe\")  subtr_baseline(raman_hdpe) #>      wavenumber   intensity #>           <num>       <num> #>   1:    301.040 0.000000000 #>   2:    304.632 0.010833354 #>   3:    308.221 0.008185951 #>   4:    311.810 0.000000000 #>   5:    315.398 0.000000000 #>  ---                        #> 960:   3187.990 0.037684523 #> 961:   3190.520 0.037684523 #> 962:   3193.060 0.043235847 #> 963:   3195.590 0.043235847 #> 964:   3198.120 0.032133199 #>  #> $metadata #>        x     y  user_name spectrum_type spectrum_identity      organization #>    <int> <int>     <char>        <char>            <char>            <char> #> 1:     1     1 Win Cowger         Raman              HDPE Horiba Scientific #>     license                                                        session_id #>      <char>                                                            <char> #> 1: CC BY-NC 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf #>                             file_id #>                              <char> #> 1: cb06ce2846b119d932fb6696479a445b"},{"path":"http://wincowger.com/OpenSpecy-package/reference/test_lib.html","id":null,"dir":"Reference","previous_headings":"","what":"Test reference library — test_lib","title":"Test reference library — test_lib","text":"Reference library 29 FTIR 28 Raman spectra used examples internal testing.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/test_lib.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test reference library — test_lib","text":"OpenSpecy object; sample_name class spectra.","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/test_lib.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Test reference library — test_lib","text":"Win Cowger","code":""},{"path":"http://wincowger.com/OpenSpecy-package/reference/test_lib.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test reference library — test_lib","text":"","code":"data(\"test_lib\")"},{"path":"http://wincowger.com/OpenSpecy-package/news/index.html","id":"openspecy-120","dir":"Changelog","previous_headings":"","what":"OpenSpecy 1.2.0","title":"OpenSpecy 1.2.0","text":"CRAN release: 2024-09-14","code":""},{"path":"http://wincowger.com/OpenSpecy-package/news/index.html","id":"potentially-breaking-1-2-0","dir":"Changelog","previous_headings":"","what":"Potentially Breaking","title":"OpenSpecy 1.2.0","text":"Removed share data options functions. just weren’t useful users administrative thing. 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