spike lfp analysis

actions/stimulus-spike-lfp-response/attributes.yaml

@@ -1,5 +1,5 @@
 registered: '2019-10-15T17:17:48'
 data:
   results: results.csv
-  notebook: 10-calculate-stimulus-spike-lfp-response.ipynb
-  html: 10-calculate-stimulus-spike-lfp-response.html
+  notebook: 20_stimulus-spike-lfp-response.ipynb
+  html: 20_stimulus-spike-lfp-response.html

actions/stimulus-spike-lfp-response/data/10-calculate-stimulus-spike-lfp-response.html

@@ -13172,7 +13172,7 @@ div#notebook {
 
 
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>16:56:09 [I] klustakwik KlustaKwik2 version 0.2.6
+<pre>10:25:26 [I] klustakwik KlustaKwik2 version 0.2.6
 </pre>
 </div>
 </div>
@@ -13202,7 +13202,7 @@ div#notebook {
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">output</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="s1">&#39;output/stimulus-spike-lfp-response&#39;</span><span class="p">)</span>
-<span class="p">(</span><span class="n">output</span> <span class="o">/</span> <span class="s1">&#39;figures&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="p">(</span><span class="n">output</span> <span class="o">/</span> <span class="s1">&#39;data&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 
     </div>
@@ -13268,7 +13268,7 @@ div#notebook {
     <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">sd</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">m</span> <span class="o">/</span> <span class="n">sd</span><span class="p">)</span>
 
 
-<span class="k">def</span> <span class="nf">clean_lfp</span><span class="p">(</span><span class="n">anas</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">compute_clean_lfp</span><span class="p">(</span><span class="n">anas</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
     <span class="n">anas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">anas</span><span class="p">)</span>
 
     <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">anas</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
@@ -13303,7 +13303,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
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     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">find_theta_peak</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">):</span>
@@ -13324,7 +13324,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
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     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">compute_half_width</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">m_p</span><span class="p">,</span> <span class="n">m_f</span><span class="p">):</span>
@@ -13354,7 +13354,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
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     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">compute_stim_peak</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">s_f</span><span class="p">):</span>
@@ -13370,7 +13370,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
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     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">compute_spike_lfp</span><span class="p">(</span><span class="n">anas</span><span class="p">,</span> <span class="n">sptr</span><span class="p">,</span> <span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="p">,</span> <span class="n">NFFT</span><span class="p">):</span>
@@ -13383,7 +13383,7 @@ div#notebook {
         <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">anas</span><span class="o">.</span><span class="n">times</span> <span class="o">&gt;</span> <span class="n">t_start</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">anas</span><span class="o">.</span><span class="n">times</span> <span class="o">&lt;</span> <span class="n">t_stop</span><span class="p">)</span>
         <span class="n">anas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">anas</span><span class="p">)[</span><span class="n">mask</span><span class="p">,:]</span>
 
-    <span class="n">cleaned_anas</span> <span class="o">=</span> <span class="n">zscore</span><span class="p">(</span><span class="n">clean_lfp</span><span class="p">(</span><span class="n">anas</span><span class="p">))</span>
+    <span class="n">cleaned_anas</span> <span class="o">=</span> <span class="n">zscore</span><span class="p">(</span><span class="n">compute_clean_lfp</span><span class="p">(</span><span class="n">anas</span><span class="p">))</span>
     
     <span class="n">cleaned_anas</span> <span class="o">=</span> <span class="n">neo</span><span class="o">.</span><span class="n">AnalogSignal</span><span class="p">(</span>
         <span class="n">signal</span><span class="o">=</span><span class="n">cleaned_anas</span> <span class="o">*</span> <span class="n">units</span><span class="p">,</span> <span class="n">sampling_rate</span><span class="o">=</span><span class="n">sampling_rate</span><span class="p">,</span> <span class="n">t_start</span><span class="o">=</span><span class="n">t_start</span>
@@ -13405,7 +13405,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
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     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># action_id_0 = &#39;1833-200619-1&#39;</span>
@@ -13437,7 +13437,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[14]:</div>
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     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">NFFT</span> <span class="o">=</span> <span class="mi">4096</span><span class="o">*</span><span class="mi">2</span>
@@ -13451,7 +13451,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
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-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[15]:</div>
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     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
@@ -13459,7 +13459,7 @@ div#notebook {
     <span class="n">channel_group</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;channel_group&#39;</span><span class="p">]</span>
     <span class="n">unit_name</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;unit_name&#39;</span><span class="p">]</span>
     
-    <span class="n">lfp</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">lfp</span><span class="p">(</span><span class="n">action_id</span><span class="p">,</span> <span class="n">channel_group</span><span class="p">)</span>
+    <span class="n">lfp</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">lfp</span><span class="p">(</span><span class="n">action_id</span><span class="p">,</span> <span class="n">channel_group</span><span class="p">)</span> <span class="c1"># TODO consider choosing strongest stim response</span>
     
     <span class="n">sptr</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">spike_train</span><span class="p">(</span><span class="n">action_id</span><span class="p">,</span> <span class="n">channel_group</span><span class="p">,</span> <span class="n">unit_name</span><span class="p">)</span>
        
@@ -13470,7 +13470,8 @@ div#notebook {
     <span class="n">snls</span> <span class="o">=</span> <span class="n">signaltonoise</span><span class="p">(</span><span class="n">clean_lfp</span><span class="p">)</span>
     <span class="n">best_channel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">snls</span><span class="p">)</span>
     <span class="n">snl</span> <span class="o">=</span> <span class="n">snls</span><span class="p">[</span><span class="n">best_channel</span><span class="p">]</span>
-    <span class="n">p_xy</span> <span class="o">=</span> <span class="n">p_xys</span><span class="p">[:,</span><span class="n">best_channel</span><span class="p">]</span>
+    <span class="n">p_xy</span> <span class="o">=</span> <span class="n">p_xys</span><span class="p">[:,</span><span class="n">best_channel</span><span class="p">]</span><span class="o">.</span><span class="n">magnitude</span>
+    <span class="n">freq</span> <span class="o">=</span> <span class="n">freq</span><span class="o">.</span><span class="n">magnitude</span>
     
     <span class="n">theta_f</span><span class="p">,</span> <span class="n">theta_p_max</span> <span class="o">=</span> <span class="n">find_theta_peak</span><span class="p">(</span><span class="n">p_xy</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">theta_band_f1</span><span class="p">,</span> <span class="n">theta_band_f2</span><span class="p">)</span>
     
@@ -13520,7 +13521,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[16]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">units</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
@@ -13546,13 +13547,13 @@ div#notebook {
 
  
  
-<div id="cbc4a88c-0fa4-49d2-ad4a-a148cc94b80e"></div>
+<div id="fdf01411-110c-4f09-8a74-86966ed09bad"></div>
 <div class="output_subarea output_widget_view ">
 <script type="text/javascript">
-var element = $('#cbc4a88c-0fa4-49d2-ad4a-a148cc94b80e');
+var element = $('#fdf01411-110c-4f09-8a74-86966ed09bad');
 </script>
 <script type="application/vnd.jupyter.widget-view+json">
-{"model_id": "e28818ca79be42abaf9c2b6106580c75", "version_major": 2, "version_minor": 0}
+{"model_id": "564d1ef5339f46eebee42ec41cfcfe62", "version_major": 2, "version_minor": 0}
 </script>
 </div>
 
@@ -13570,22 +13571,141 @@ var element = $('#cbc4a88c-0fa4-49d2-ad4a-a148cc94b80e');
 </div>
 </div>
 
+<div class="output_area">
+
+    <div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="plot">plot<a class="anchor-link" href="#plot">&#182;</a></h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">coher</span><span class="p">,</span> <span class="n">freqs</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">units</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">units</span><span class="p">)):</span>
+    <span class="n">action_id</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;action&#39;</span><span class="p">]</span>
+    <span class="n">channel_group</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;channel_group&#39;</span><span class="p">]</span>
+    <span class="n">unit_name</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;unit_name&#39;</span><span class="p">]</span>
+    
+    <span class="n">name</span> <span class="o">=</span> <span class="n">f</span><span class="s1">&#39;</span><span class="si">{action_id}</span><span class="s1">_</span><span class="si">{channel_group}</span><span class="s1">_</span><span class="si">{unit_name}</span><span class="s1">&#39;</span>
+    
+    <span class="n">lfp</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">lfp</span><span class="p">(</span><span class="n">action_id</span><span class="p">,</span> <span class="n">channel_group</span><span class="p">)</span> <span class="c1"># TODO consider choosing strongest stim response</span>
+    
+    <span class="n">sptr</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">spike_train</span><span class="p">(</span><span class="n">action_id</span><span class="p">,</span> <span class="n">channel_group</span><span class="p">,</span> <span class="n">unit_name</span><span class="p">)</span>
+       
+    <span class="n">lim</span> <span class="o">=</span> <span class="n">get_lim</span><span class="p">(</span><span class="n">action_id</span><span class="p">)</span>
+
+    <span class="n">p_xys</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">clean_lfp</span> <span class="o">=</span> <span class="n">compute_spike_lfp</span><span class="p">(</span><span class="n">lfp</span><span class="p">,</span> <span class="n">sptr</span><span class="p">,</span> <span class="o">*</span><span class="n">lim</span><span class="p">,</span> <span class="n">NFFT</span><span class="o">=</span><span class="n">NFFT</span><span class="p">)</span>
+    
+    <span class="n">snls</span> <span class="o">=</span> <span class="n">signaltonoise</span><span class="p">(</span><span class="n">clean_lfp</span><span class="p">)</span>
+    <span class="n">best_channel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">snls</span><span class="p">)</span>
+    <span class="n">snl</span> <span class="o">=</span> <span class="n">snls</span><span class="p">[</span><span class="n">best_channel</span><span class="p">]</span>
+    <span class="n">p_xy</span> <span class="o">=</span> <span class="n">p_xys</span><span class="p">[:,</span><span class="n">best_channel</span><span class="p">]</span><span class="o">.</span><span class="n">magnitude</span>
+    <span class="n">freq</span> <span class="o">=</span> <span class="n">freq</span><span class="o">.</span><span class="n">magnitude</span>
+    
+    <span class="n">coher</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">name</span><span class="p">:</span> <span class="n">p_xy</span><span class="p">})</span>
+    <span class="n">freqs</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">name</span><span class="p">:</span> <span class="n">freq</span><span class="p">})</span>
+</pre></div>
+
+    </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
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+    <div class="prompt"></div>
+
+
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+
+ 
+ 
+<div id="0aa9c06f-773a-4f88-a2ad-e7de5c5ebe40"></div>
+<div class="output_subarea output_widget_view ">
+<script type="text/javascript">
+var element = $('#0aa9c06f-773a-4f88-a2ad-e7de5c5ebe40');
+</script>
+<script type="application/vnd.jupyter.widget-view+json">
+{"model_id": "b20a9a26e0864f578e1a9aa0c021999b", "version_major": 2, "version_minor": 0}
+</script>
+</div>
+
+</div>
+
+<div class="output_area">
+
+    <div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+</pre>
+</div>
+</div>
+
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-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[18]:</div>
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     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">debug</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">coher</span><span class="o">.</span><span class="n">to_feather</span><span class="p">(</span><span class="n">output</span> <span class="o">/</span> <span class="s1">&#39;data&#39;</span> <span class="o">/</span> <span class="s1">&#39;coherence.feather&#39;</span><span class="p">)</span>
+<span class="n">freqs</span><span class="o">.</span><span class="n">to_feather</span><span class="p">(</span><span class="n">output</span> <span class="o">/</span> <span class="s1">&#39;data&#39;</span> <span class="o">/</span> <span class="s1">&#39;freqs.feather&#39;</span><span class="p">)</span>
 </pre></div>
 
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+<div class="output_wrapper">
+<div class="output">
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+<div class="output_area">
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+    <div class="prompt"></div>
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+<div class="output_subarea output_text output_error">
+<pre>
+<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
+<span class="ansi-red-fg">AttributeError</span>                            Traceback (most recent call last)
+<span class="ansi-green-fg">&lt;ipython-input-18-bf8a91a90c5a&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span>
+<span class="ansi-green-fg">----&gt; 1</span><span class="ansi-red-fg"> </span>coher<span class="ansi-blue-fg">.</span>to_feather<span class="ansi-blue-fg">(</span>output <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">&#39;data&#39;</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">&#39;coherence.feather&#39;</span><span class="ansi-blue-fg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">      2</span> freqs<span class="ansi-blue-fg">.</span>to_feather<span class="ansi-blue-fg">(</span>output <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">&#39;data&#39;</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">&#39;freqs.feather&#39;</span><span class="ansi-blue-fg">)</span>
+
+<span class="ansi-red-fg">AttributeError</span>: &#39;dict&#39; object has no attribute &#39;to_feather&#39;</pre>
+</div>
+</div>
+
+</div>
+</div>
+
 </div>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
 </div><div class="inner_cell">

actions/stimulus-spike-lfp-response/data/10-calculate-stimulus-spike-lfp-response.ipynb

@@ -19,7 +19,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "16:56:09 [I] klustakwik KlustaKwik2 version 0.2.6\n"
+      "10:25:26 [I] klustakwik KlustaKwik2 version 0.2.6\n"
      ]
     }
    ],
@@ -68,7 +68,7 @@
    "outputs": [],
    "source": [
     "output = pathlib.Path('output/stimulus-spike-lfp-response')\n",
-    "(output / 'figures').mkdir(parents=True, exist_ok=True)"
+    "(output / 'data').mkdir(parents=True, exist_ok=True)"
    ]
   },
   {
@@ -122,7 +122,7 @@
     "    return np.where(sd == 0, 0, m / sd)\n",
     "\n",
     "\n",
-    "def clean_lfp(anas, width=500, threshold=2):\n",
+    "def compute_clean_lfp(anas, width=500, threshold=2):\n",
     "    anas = np.array(anas)\n",
     "\n",
     "    for ch in range(anas.shape[1]):\n",
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +217,7 @@
     "        mask = (anas.times > t_start) & (anas.times < t_stop)\n",
     "        anas = np.array(anas)[mask,:]\n",
     "\n",
-    "    cleaned_anas = zscore(clean_lfp(anas))\n",
+    "    cleaned_anas = zscore(compute_clean_lfp(anas))\n",
     "    \n",
     "    cleaned_anas = neo.AnalogSignal(\n",
     "        signal=cleaned_anas * units, sampling_rate=sampling_rate, t_start=t_start\n",
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -281,7 +281,7 @@
     "    channel_group = row['channel_group']\n",
     "    unit_name = row['unit_name']\n",
     "    \n",
-    "    lfp = data_loader.lfp(action_id, channel_group)\n",
+    "    lfp = data_loader.lfp(action_id, channel_group) # TODO consider choosing strongest stim response\n",
     "    \n",
     "    sptr = data_loader.spike_train(action_id, channel_group, unit_name)\n",
     "       \n",
@@ -292,7 +292,8 @@
     "    snls = signaltonoise(clean_lfp)\n",
     "    best_channel = np.argmax(snls)\n",
     "    snl = snls[best_channel]\n",
-    "    p_xy = p_xys[:,best_channel]\n",
+    "    p_xy = p_xys[:,best_channel].magnitude\n",
+    "    freq = freq.magnitude\n",
     "    \n",
     "    theta_f, theta_p_max = find_theta_peak(p_xy, freq, theta_band_f1, theta_band_f2)\n",
     "    \n",
@@ -337,13 +338,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e28818ca79be42abaf9c2b6106580c75",
+       "model_id": "564d1ef5339f46eebee42ec41cfcfe62",
        "version_major": 2,
        "version_minor": 0
       },
@@ -361,6 +362,13 @@
       "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/signal/spectral.py:1578: RuntimeWarning: invalid value encountered in true_divide\n",
       "  Cxy = np.abs(Pxy)**2 / Pxx / Pyy\n"
      ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -370,12 +378,86 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "%debug"
+    "# plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b20a9a26e0864f578e1a9aa0c021999b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "coher, freqs = {}, {}\n",
+    "for i, row in tqdm(units.iterrows(), total=len(units)):\n",
+    "    action_id = row['action']\n",
+    "    channel_group = row['channel_group']\n",
+    "    unit_name = row['unit_name']\n",
+    "    \n",
+    "    name = f'{action_id}_{channel_group}_{unit_name}'\n",
+    "    \n",
+    "    lfp = data_loader.lfp(action_id, channel_group) # TODO consider choosing strongest stim response\n",
+    "    \n",
+    "    sptr = data_loader.spike_train(action_id, channel_group, unit_name)\n",
+    "       \n",
+    "    lim = get_lim(action_id)\n",
+    "\n",
+    "    p_xys, freq, clean_lfp = compute_spike_lfp(lfp, sptr, *lim, NFFT=NFFT)\n",
+    "    \n",
+    "    snls = signaltonoise(clean_lfp)\n",
+    "    best_channel = np.argmax(snls)\n",
+    "    snl = snls[best_channel]\n",
+    "    p_xy = p_xys[:,best_channel].magnitude\n",
+    "    freq = freq.magnitude\n",
+    "    \n",
+    "    coher.update({name: p_xy})\n",
+    "    freqs.update({name: freq})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'dict' object has no attribute 'to_feather'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-bf8a91a90c5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcoher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'coherence.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfreqs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'data'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'freqs.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'to_feather'"
+     ]
+    }
+   ],
+   "source": [
+    "coher.to_feather(output / 'data' / 'coherence.feather')\n",
+    "freqs.to_feather(output / 'data' / 'freqs.feather')"
    ]
   },
   {