actions/stimulus-lfp-response

actions/stimulus-lfp-response/attributes.yaml

@@ -1,5 +1,5 @@
 registered: '2019-10-14T17:03:05'
 data:
   results: results.csv
-  notebook: 20_stimulus-lfp-response.ipynb
+  notebook: 10-calculate-stimulus-lfp-response.ipynb
-  html: 20_stimulus-lfp-response.html
+  html: 10-calculate-stimulus-lfp-response.html

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

@@ -13147,6 +13147,8 @@ div#notebook {
 <span class="kn">import</span> <span class="nn">quantities</span> <span class="k">as</span> <span class="nn">pq</span>
 <span class="kn">import</span> <span class="nn">shutil</span>
 <span class="kn">from</span> <span class="nn">distutils.dir_util</span> <span class="k">import</span> <span class="n">copy_tree</span>
+<span class="kn">import</span> <span class="nn">scipy</span>
+<span class="kn">import</span> <span class="nn">scipy.signal</span> <span class="k">as</span> <span class="nn">ss</span>
 
 <span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="k">import</span> <span class="n">find_peaks</span>
 <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="k">import</span> <span class="n">interp1d</span>
@@ -13171,7 +13173,9 @@ div#notebook {
 
 
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>10:26:07 [I] klustakwik KlustaKwik2 version 0.2.6
+<pre>19:43:24 [I] klustakwik KlustaKwik2 version 0.2.6
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:24: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
+Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`
 </pre>
 </div>
 </div>
@@ -13200,8 +13204,16 @@ div#notebook {
 <div class="prompt input_prompt">In&nbsp;[4]:</div>
 <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-lfp-response&#39;</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#############################</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>
+
+<span class="n">perform_zscore</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">perform_zscore</span><span class="p">:</span>
+    <span class="n">zscore_str</span> <span class="o">=</span> <span class="s2">&quot;-no-zscore&quot;</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">zscore_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+
+<span class="c1">#################################</span>
 </pre></div>
 
     </div>
@@ -13212,6 +13224,20 @@ div#notebook {
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
 <div class="prompt input_prompt">In&nbsp;[5]:</div>
+<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-lfp-response&#39;</span> <span class="o">+</span> <span class="n">zscore_str</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>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">identify_neurons</span> <span class="o">=</span> <span class="n">actions</span><span class="p">[</span><span class="s1">&#39;identify-neurons&#39;</span><span class="p">]</span>
@@ -13225,7 +13251,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">channel_groups</span> <span class="o">=</span> <span class="p">[]</span>
@@ -13242,7 +13268,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">channel_groups</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">channel_groups</span><span class="p">)</span>
@@ -13255,7 +13281,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_lim</span><span class="p">(</span><span class="n">action_id</span><span class="p">):</span>
@@ -13286,7 +13312,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">signaltonoise</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
@@ -13313,7 +13339,7 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">compute_energy</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>
@@ -13322,6 +13348,19 @@ div#notebook {
     <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">f</span> <span class="o">&gt;</span> <span class="n">f1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">f</span> <span class="o">&lt;</span> <span class="n">f2</span><span class="p">)</span>
     <span class="n">df</span> <span class="o">=</span> <span class="n">f</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">f</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
     <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">mask</span><span class="p">])</span> <span class="o">*</span> <span class="n">df</span>
+
+
+<span class="k">def</span> <span class="nf">compute_band_power</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>
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">f1</span><span class="p">)</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">p</span><span class="p">)):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span>
+    <span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="k">import</span> <span class="n">simps</span>
+    <span class="n">dx</span> <span class="o">=</span> <span class="n">f</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">f</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">f</span> <span class="o">&gt;</span> <span class="n">f1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">f</span> <span class="o">&lt;</span> <span class="n">f2</span><span class="p">)</span>
+    <span class="c1"># Compute the absolute power by approximating the area under the curve</span>
+    <span class="n">band_power</span> <span class="o">=</span> <span class="n">simps</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">mask</span><span class="p">],</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">)</span>
+    <span class="n">total_power</span> <span class="o">=</span> <span class="n">simps</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">)</span>
+    <span class="n">rel_power</span> <span class="o">=</span> <span class="n">band_power</span> <span class="o">/</span> <span class="n">total_power</span>
+    <span class="k">return</span> <span class="n">band_power</span><span class="p">,</span> <span class="n">rel_power</span>
 </pre></div>
 
     </div>
@@ -13331,11 +13370,13 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
 <div class="inner_cell">
     <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>
+<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>
-    <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">f</span> <span class="o">&gt;</span> <span class="mi">6</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">f</span> <span class="o">&lt;</span> <span class="mi">10</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">p</span><span class="p">)):</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+    <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">f</span> <span class="o">&gt;</span> <span class="n">f1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">f</span> <span class="o">&lt;</span> <span class="n">f2</span><span class="p">)</span>
     <span class="n">p_m</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
     <span class="n">f_m</span> <span class="o">=</span> <span class="n">f</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
     <span class="n">peaks</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">find_peaks</span><span class="p">(</span><span class="n">p_m</span><span class="p">)</span>
@@ -13350,52 +13391,40 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
 <div class="inner_cell">
     <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>
+<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">power</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">max_power</span><span class="p">,</span> <span class="n">max_frequency</span><span class="p">,</span> <span class="n">band</span><span class="p">,</span> <span class="n">band_width</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">m_p</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">max_power</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span>
+    
+    <span class="c1"># estimate baseline power</span>
+    <span class="n">low_baseline_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">freq</span> <span class="o">&gt;</span> <span class="n">band</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">band_width</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">freq</span> <span class="o">&lt;</span> <span class="n">band</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">high_baseline_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">freq</span> <span class="o">&gt;</span> <span class="n">band</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">freq</span> <span class="o">&lt;</span> <span class="n">band</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">band_width</span><span class="p">)</span>
+    <span class="n">baseline</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">power</span><span class="p">[</span><span class="n">low_baseline_mask</span><span class="p">],</span> <span class="n">power</span><span class="p">[</span><span class="n">high_baseline_mask</span><span class="p">]]))</span>
+    <span class="n">p</span> <span class="o">=</span> <span class="n">power</span> <span class="o">-</span> <span class="n">baseline</span>
+    <span class="n">m_p</span> <span class="o">=</span> <span class="n">max_power</span> <span class="o">-</span> <span class="n">baseline</span>
+    <span class="n">m_f</span> <span class="o">=</span> <span class="n">max_frequency</span>
+    <span class="n">f</span> <span class="o">=</span> <span class="n">freq</span>
+    
+    <span class="c1"># estimate half width</span>
     <span class="n">m_p_half</span> <span class="o">=</span> <span class="n">m_p</span> <span class="o">/</span> <span class="mi">2</span>
     <span class="n">half_p</span> <span class="o">=</span> <span class="n">p</span> <span class="o">-</span> <span class="n">m_p_half</span>
     <span class="n">idx_f</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">f</span> <span class="o">&lt;=</span> <span class="n">m_f</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
     <span class="n">idxs_p1</span><span class="p">,</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">half_p</span><span class="p">[:</span><span class="n">idx_f</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">idxs_p1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span>
     <span class="n">m1</span> <span class="o">=</span> <span class="n">idxs_p1</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
     <span class="n">idxs_p2</span><span class="p">,</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">half_p</span><span class="p">[</span><span class="n">idx_f</span><span class="p">:]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">idxs_p2</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span>
     <span class="n">m2</span> <span class="o">=</span> <span class="n">idxs_p2</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">+</span> <span class="n">idx_f</span>
-<span class="c1">#     plt.plot(f, p)</span>
+<span class="c1">#     assert p[m1] &lt; m_p_half &lt; p[m1+1], (p[m1], m_p_half, p[m1+1])</span>
-<span class="c1">#     plt.plot(m_f, m_p, marker=&#39;o&#39;, ls=&#39;none&#39;, markersize=10)</span>
+<span class="c1">#     assert p[m2] &gt; m_p_half &gt; p[m2+1], (p[m2], m_p_half, p[m2+1])</span>
-<span class="c1">#     plt.plot(f[m1], p[m1], marker=&#39;x&#39;, ls=&#39;none&#39;, markersize=10, c=&#39;r&#39;)</span>
-<span class="c1">#     plt.plot(f[m1+1], p[m1+1], marker=&#39;+&#39;, ls=&#39;none&#39;, markersize=10, c=&#39;r&#39;)</span>
-    
-<span class="c1">#     plt.plot(f[m2], p[m2], marker=&#39;x&#39;, ls=&#39;none&#39;, markersize=10, c=&#39;k&#39;)</span>
-<span class="c1">#     plt.plot(f[m2+1], p[m2+1], marker=&#39;+&#39;, ls=&#39;none&#39;, markersize=10, c=&#39;k&#39;)</span>
-    
-<span class="c1">#     plt.xlim(4,12)</span>
-    <span class="k">assert</span> <span class="n">p</span><span class="p">[</span><span class="n">m1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">m_p_half</span> <span class="o">&lt;</span> <span class="n">p</span><span class="p">[</span><span class="n">m1</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">m1</span><span class="p">],</span> <span class="n">m_p_half</span><span class="p">,</span> <span class="n">p</span><span class="p">[</span><span class="n">m1</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
-    <span class="k">assert</span> <span class="n">p</span><span class="p">[</span><span class="n">m2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">m_p_half</span> <span class="o">&gt;</span> <span class="n">p</span><span class="p">[</span><span class="n">m2</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">m2</span><span class="p">],</span> <span class="n">m_p_half</span><span class="p">,</span> <span class="n">p</span><span class="p">[</span><span class="n">m2</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
     
     <span class="n">f1</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">([</span><span class="n">half_p</span><span class="p">[</span><span class="n">m1</span><span class="p">],</span> <span class="n">half_p</span><span class="p">[</span><span class="n">m1</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]],</span> <span class="p">[</span><span class="n">f</span><span class="p">[</span><span class="n">m1</span><span class="p">],</span> <span class="n">f</span><span class="p">[</span><span class="n">m1</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]])(</span><span class="mi">0</span><span class="p">)</span>
     <span class="n">f2</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">([</span><span class="n">half_p</span><span class="p">[</span><span class="n">m2</span><span class="p">],</span> <span class="n">half_p</span><span class="p">[</span><span class="n">m2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]],</span> <span class="p">[</span><span class="n">f</span><span class="p">[</span><span class="n">m2</span><span class="p">],</span> <span class="n">f</span><span class="p">[</span><span class="n">m2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]])(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span>
+    <span class="k">return</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">,</span> <span class="n">m_p_half</span> <span class="o">+</span> <span class="n">baseline</span>
-</pre></div>
-
-    </div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[13]:</div>
-<div class="inner_cell">
-    <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>
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">s_f</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-    <span class="k">return</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">p</span><span class="p">)(</span><span class="n">s_f</span><span class="p">)</span>
 </pre></div>
 
     </div>
@@ -13408,8 +13437,18 @@ div#notebook {
 <div class="prompt input_prompt">In&nbsp;[14]:</div>
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">NFFT</span> <span class="o">=</span> <span class="mi">10000</span>
+<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>
-<span class="n">theta_band_f1</span><span class="p">,</span> <span class="n">theta_band_f2</span> <span class="o">=</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">10</span> 
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">s_f</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+    <span class="k">return</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">p</span><span class="p">)(</span><span class="n">s_f</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">compute_relative_peak</span><span class="p">(</span><span class="n">power</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">max_power</span><span class="p">,</span> <span class="n">band</span><span class="p">,</span> <span class="n">band_width</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="c1"># estimate baseline power</span>
+    <span class="n">low_baseline_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">freq</span> <span class="o">&gt;</span> <span class="n">band</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">band_width</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">freq</span> <span class="o">&lt;</span> <span class="n">band</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">high_baseline_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">freq</span> <span class="o">&gt;</span> <span class="n">band</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">freq</span> <span class="o">&lt;</span> <span class="n">band</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">band_width</span><span class="p">)</span>
+    <span class="n">baseline</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">power</span><span class="p">[</span><span class="n">low_baseline_mask</span><span class="p">],</span> <span class="n">power</span><span class="p">[</span><span class="n">high_baseline_mask</span><span class="p">]]))</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">max_power</span> <span class="o">-</span> <span class="n">baseline</span><span class="p">)</span> <span class="o">/</span> <span class="nb">abs</span><span class="p">(</span><span class="n">baseline</span><span class="p">)</span>
 </pre></div>
 
     </div>
@@ -13419,12 +13458,28 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[15]:</div>
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
 <div class="inner_cell">
     <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>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">theta_band_f1</span><span class="p">,</span> <span class="n">theta_band_f2</span> <span class="o">=</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">10</span> 
+</pre></div>
+
+    </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">psd_data</span><span class="p">,</span> <span class="n">freq_data</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
+
+<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">perform_zscore</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">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">&#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="n">clean_lfp</span> <span class="o">=</span> <span class="n">select_and_clean</span><span class="p">(</span><span class="n">lfp</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>
@@ -13434,19 +13489,30 @@ div#notebook {
     <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">mask</span> <span class="o">=</span> <span class="n">get_mask</span><span class="p">(</span><span class="n">lfp</span><span class="p">,</span> <span class="n">lim</span><span class="p">)</span>
-    <span class="n">signal</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">mask</span><span class="p">,</span> <span class="n">best_channel</span><span class="p">]</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span>
+    <span class="k">if</span> <span class="n">perform_zscore</span><span class="p">:</span>
+        <span class="n">signal</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">mask</span><span class="p">,</span> <span class="n">best_channel</span><span class="p">]</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">signal</span> <span class="o">=</span> <span class="n">clean_lfp</span><span class="p">[</span><span class="n">mask</span><span class="p">,</span> <span class="n">best_channel</span><span class="p">]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
     
-    <span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span> <span class="o">=</span> <span class="n">mlab</span><span class="o">.</span><span class="n">psd</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">Fs</span><span class="o">=</span><span class="n">lfp</span><span class="o">.</span><span class="n">sampling_rate</span><span class="o">.</span><span class="n">magnitude</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">window</span> <span class="o">=</span> <span class="mi">6</span> <span class="o">*</span> <span class="n">lfp</span><span class="o">.</span><span class="n">sampling_rate</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_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">)</span>
+<span class="c1">#     p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)</span>
+    <span class="n">freq</span><span class="p">,</span> <span class="n">p_xx</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">welch</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">fs</span><span class="o">=</span><span class="n">lfp</span><span class="o">.</span><span class="n">sampling_rate</span><span class="o">.</span><span class="n">magnitude</span><span class="p">,</span> <span class="n">nperseg</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">nfft</span><span class="o">=</span><span class="n">scipy</span><span class="o">.</span><span class="n">fftpack</span><span class="o">.</span><span class="n">next_fast_len</span><span class="p">(</span><span class="n">window</span><span class="p">))</span>
+    <span class="n">p_xx</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">p_xx</span><span class="p">)</span>
     
-    <span class="n">theta_energy</span> <span class="o">=</span> <span class="n">compute_energy</span><span class="p">(</span><span class="n">p_xx</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> <span class="c1"># theta band 6 - 10 Hz</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_xx</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>
     
-    <span class="n">theta_half_f1</span><span class="p">,</span> <span class="n">theta_half_f2</span> <span class="o">=</span> <span class="n">compute_half_width</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">theta_p_max</span><span class="p">,</span> <span class="n">theta_f</span><span class="p">)</span>
+    <span class="n">theta_bandpower</span><span class="p">,</span> <span class="n">theta_relpower</span> <span class="o">=</span> <span class="n">compute_band_power</span><span class="p">(</span><span class="n">p_xx</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>
+    
+    <span class="n">theta_relpeak</span> <span class="o">=</span> <span class="n">compute_relative_peak</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">theta_p_max</span><span class="p">,</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>
+        
+    <span class="n">theta_half_f1</span><span class="p">,</span> <span class="n">theta_half_f2</span><span class="p">,</span> <span class="n">theta_half_power</span> <span class="o">=</span> <span class="n">compute_half_width</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">theta_p_max</span><span class="p">,</span> <span class="n">theta_f</span><span class="p">,</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>
     
     <span class="n">theta_half_width</span> <span class="o">=</span> <span class="n">theta_half_f2</span> <span class="o">-</span> <span class="n">theta_half_f1</span>
     
-    <span class="n">theta_half_energy</span> <span class="o">=</span> <span class="n">compute_energy</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">theta_half_f1</span><span class="p">,</span> <span class="n">theta_half_f2</span><span class="p">)</span> <span class="c1"># theta band 6 - 10 Hz</span>
+    <span class="n">psd_data</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_xx</span><span class="p">})</span>
+    <span class="n">freq_data</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>
+
     
     <span class="c1"># stim</span>
     
@@ -13454,27 +13520,33 @@ div#notebook {
     
     <span class="n">stim_p_max</span> <span class="o">=</span> <span class="n">compute_stim_peak</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">stim_freq</span><span class="p">)</span>
     
-    <span class="n">stim_half_f1</span><span class="p">,</span> <span class="n">stim_half_f2</span> <span class="o">=</span> <span class="n">compute_half_width</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">stim_p_max</span><span class="p">,</span> <span class="n">stim_freq</span><span class="p">)</span>
+    <span class="n">stim_half_f1</span><span class="p">,</span> <span class="n">stim_half_f2</span><span class="p">,</span> <span class="n">stim_half_power</span> <span class="o">=</span> <span class="n">compute_half_width</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">stim_p_max</span><span class="p">,</span> <span class="n">stim_freq</span><span class="p">,</span> <span class="p">[</span><span class="n">stim_freq</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stim_freq</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
+    
     <span class="n">stim_half_width</span> <span class="o">=</span> <span class="n">stim_half_f2</span> <span class="o">-</span> <span class="n">stim_half_f1</span>
     
-    <span class="n">stim_energy</span> <span class="o">=</span> <span class="n">compute_energy</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">stim_half_f1</span><span class="p">,</span> <span class="n">stim_half_f2</span><span class="p">)</span>
+    <span class="n">stim_bandpower</span><span class="p">,</span> <span class="n">stim_relpower</span> <span class="o">=</span> <span class="n">compute_band_power</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">stim_freq</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stim_freq</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+    
+    <span class="n">stim_relpeak</span> <span class="o">=</span> <span class="n">compute_relative_peak</span><span class="p">(</span><span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span><span class="p">,</span> <span class="n">stim_p_max</span><span class="p">,</span> <span class="p">[</span><span class="n">stim_freq</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stim_freq</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
     
     <span class="n">result</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span>
         <span class="s1">&#39;signal_to_noise&#39;</span><span class="p">:</span> <span class="n">snl</span><span class="p">,</span>
         <span class="s1">&#39;best_channel&#39;</span><span class="p">:</span> <span class="n">best_channel</span><span class="p">,</span>
         <span class="s1">&#39;theta_freq&#39;</span><span class="p">:</span> <span class="n">theta_f</span><span class="p">,</span>
         <span class="s1">&#39;theta_peak&#39;</span><span class="p">:</span> <span class="n">theta_p_max</span><span class="p">,</span>
-        <span class="s1">&#39;theta_energy&#39;</span><span class="p">:</span> <span class="n">theta_energy</span><span class="p">,</span>
+        <span class="s1">&#39;theta_bandpower&#39;</span><span class="p">:</span> <span class="n">theta_bandpower</span><span class="p">,</span>
+        <span class="s1">&#39;theta_relpower&#39;</span><span class="p">:</span> <span class="n">theta_relpower</span><span class="p">,</span>
+        <span class="s1">&#39;theta_relpeak&#39;</span><span class="p">:</span> <span class="n">theta_relpeak</span><span class="p">,</span>
         <span class="s1">&#39;theta_half_f1&#39;</span><span class="p">:</span> <span class="n">theta_half_f1</span><span class="p">,</span> 
         <span class="s1">&#39;theta_half_f2&#39;</span><span class="p">:</span> <span class="n">theta_half_f2</span><span class="p">,</span>
         <span class="s1">&#39;theta_half_width&#39;</span><span class="p">:</span> <span class="n">theta_half_width</span><span class="p">,</span>
-        <span class="s1">&#39;theta_half_energy&#39;</span><span class="p">:</span> <span class="n">theta_half_energy</span><span class="p">,</span>
         <span class="s1">&#39;stim_freq&#39;</span><span class="p">:</span> <span class="n">stim_freq</span><span class="p">,</span>
         <span class="s1">&#39;stim_p_max&#39;</span><span class="p">:</span> <span class="n">stim_p_max</span><span class="p">,</span>
         <span class="s1">&#39;stim_half_f1&#39;</span><span class="p">:</span> <span class="n">stim_half_f1</span><span class="p">,</span> 
         <span class="s1">&#39;stim_half_f2&#39;</span><span class="p">:</span> <span class="n">stim_half_f2</span><span class="p">,</span>
         <span class="s1">&#39;stim_half_width&#39;</span><span class="p">:</span> <span class="n">stim_half_width</span><span class="p">,</span>
-        <span class="s1">&#39;stim_energy&#39;</span><span class="p">:</span> <span class="n">stim_energy</span>
+        <span class="s1">&#39;stim_bandpower&#39;</span><span class="p">:</span> <span class="n">stim_bandpower</span><span class="p">,</span>
+        <span class="s1">&#39;stim_relpower&#39;</span><span class="p">:</span> <span class="n">stim_relpower</span><span class="p">,</span>
+        <span class="s1">&#39;stim_relpeak&#39;</span><span class="p">:</span> <span class="n">stim_relpeak</span><span class="p">,</span>
     <span class="p">})</span>
     <span class="k">return</span> <span class="n">result</span>
 </pre></div>
@@ -13486,11 +13558,11 @@ div#notebook {
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[16]:</div>
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</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">channel_groups</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
-    <span class="n">channel_groups</span><span class="o">.</span><span class="n">progress_apply</span><span class="p">(</span><span class="n">process</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> 
+    <span class="n">channel_groups</span><span class="o">.</span><span class="n">progress_apply</span><span class="p">(</span><span class="n">process</span><span class="p">,</span> <span class="n">perform_zscore</span><span class="o">=</span><span class="n">perform_zscore</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> 
     <span class="n">left_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 
@@ -13512,13 +13584,13 @@ div#notebook {
 
  
  
-<div id="ccdb069e-4b63-4db1-9535-31624e57bc0e"></div>
+<div id="1e0ab9a5-d5fa-41a2-82b4-a29075688ad9"></div>
 <div class="output_subarea output_widget_view ">
 <script type="text/javascript">
-var element = $('#ccdb069e-4b63-4db1-9535-31624e57bc0e');
+var element = $('#1e0ab9a5-d5fa-41a2-82b4-a29075688ad9');
 </script>
 <script type="application/vnd.jupyter.widget-view+json">
-{"model_id": "e733903bb8d7406ea6c05bf5af093bfc", "version_major": 2, "version_minor": 0}
+{"model_id": "0a39fef20b724ef186ce6ce5299b3719", "version_major": 2, "version_minor": 0}
 </script>
 </div>
 
@@ -13529,8 +13601,19 @@ var element = $('#ccdb069e-4b63-4db1-9535-31624e57bc0e');
     <div class="prompt"></div>
 
 
-<div class="output_subarea output_stream output_stdout output_text">
+<div class="output_subarea output_stream output_stderr output_text">
-<pre>
+<pre>/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in greater
+  if __name__ == &#39;__main__&#39;:
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in less
+  if __name__ == &#39;__main__&#39;:
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in greater
+  # Remove the CWD from sys.path while we load stuff.
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in less
+  # Remove the CWD from sys.path while we load stuff.
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
+  out=out, **kwargs)
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in true_divide
+  ret = ret.dtype.type(ret / rcount)
 </pre>
 </div>
 </div>
@@ -13541,112 +13624,17 @@ var element = $('#ccdb069e-4b63-4db1-9535-31624e57bc0e');
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">psd</span><span class="p">,</span> <span class="n">freqs</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">psd_data</span><span class="p">)</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;psd.feather&#39;</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">channel_groups</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">channel_groups</span><span class="p">)):</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">freq_data</span><span class="p">)</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>
-    <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">action_group</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">&#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="n">clean_lfp</span> <span class="o">=</span> <span class="n">select_and_clean</span><span class="p">(</span><span class="n">lfp</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">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">mask</span> <span class="o">=</span> <span class="n">get_mask</span><span class="p">(</span><span class="n">lfp</span><span class="p">,</span> <span class="n">lim</span><span class="p">)</span>
-    <span class="n">signal</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">mask</span><span class="p">,</span> <span class="n">best_channel</span><span class="p">]</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span>
-    
-    <span class="n">p_xx</span><span class="p">,</span> <span class="n">freq</span> <span class="o">=</span> <span class="n">mlab</span><span class="o">.</span><span class="n">psd</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">Fs</span><span class="o">=</span><span class="n">lfp</span><span class="o">.</span><span class="n">sampling_rate</span><span class="o">.</span><span class="n">magnitude</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">psd</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">action_group</span><span class="p">:</span> <span class="n">p_xx</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">action_group</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">
-
-    <div class="prompt"></div>
-
-
-
-
-
- 
- 
-<div id="6a05ea1e-3782-40af-8458-e7713b9c2fb7"></div>
-<div class="output_subarea output_widget_view ">
-<script type="text/javascript">
-var element = $('#6a05ea1e-3782-40af-8458-e7713b9c2fb7');
-</script>
-<script type="application/vnd.jupyter.widget-view+json">
-{"model_id": "0184251a265a40a2a1e9910405f05db8", "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>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[18]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="p">(</span><span class="n">psd</span><span class="p">)</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;psd.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>
-
-    </div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area">
-
-    <div class="prompt"></div>
-
-
-<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-18-7660e1d3307d&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>psd<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;psd.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">
@@ -13660,7 +13648,7 @@ var element = $('#6a05ea1e-3782-40af-8458-e7713b9c2fb7');
 <div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">action</span> <span class="o">=</span> <span class="n">project</span><span class="o">.</span><span class="n">require_action</span><span class="p">(</span><span class="s2">&quot;stimulus-lfp-response&quot;</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">action</span> <span class="o">=</span> <span class="n">project</span><span class="o">.</span><span class="n">require_action</span><span class="p">(</span><span class="s2">&quot;stimulus-lfp-response&quot;</span> <span class="o">+</span> <span class="n">zscore_str</span><span class="p">)</span>
 </pre></div>
 
     </div>
@@ -13674,7 +13662,7 @@ var element = $('#6a05ea1e-3782-40af-8458-e7713b9c2fb7');
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">action</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="s1">&#39;parameters&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s1">&#39;NFFT&#39;</span><span class="p">:</span> <span class="n">NFFT</span><span class="p">,</span>
+    <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span>
     <span class="s1">&#39;theta_band_f1&#39;</span><span class="p">:</span> <span class="n">theta_band_f1</span><span class="p">,</span>
     <span class="s1">&#39;theta_band_f2&#39;</span><span class="p">:</span> <span class="n">theta_band_f2</span>
 <span class="p">}</span>

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

@@ -19,7 +19,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "10:26:07 [I] klustakwik KlustaKwik2 version 0.2.6\n"
+      "19:43:24 [I] klustakwik KlustaKwik2 version 0.2.6\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:24: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
+      "Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`\n"
      ]
     }
    ],
@@ -39,6 +41,8 @@
     "import quantities as pq\n",
     "import shutil\n",
     "from distutils.dir_util import copy_tree\n",
+    "import scipy\n",
+    "import scipy.signal as ss\n",
     "\n",
     "from scipy.signal import find_peaks\n",
     "from scipy.interpolate import interp1d\n",
@@ -66,8 +70,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output = pathlib.Path('output/stimulus-lfp-response')\n",
+    "#############################\n",
-    "(output / 'data').mkdir(parents=True, exist_ok=True)"
+    "\n",
+    "perform_zscore = True\n",
+    "\n",
+    "if not perform_zscore:\n",
+    "    zscore_str = \"-no-zscore\"\n",
+    "else:\n",
+    "    zscore_str = \"\"\n",
+    "\n",
+    "#################################"
    ]
   },
   {
@@ -75,6 +87,16 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "output = pathlib.Path('output/stimulus-lfp-response' + zscore_str)\n",
+    "(output / 'data').mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "identify_neurons = actions['identify-neurons']\n",
     "sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
@@ -82,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,17 +185,32 @@
     "        return np.nan\n",
     "    mask = (f > f1) & (f < f2)\n",
     "    df = f[1] - f[0]\n",
-    "    return np.sum(p[mask]) * df"
+    "    return np.sum(p[mask]) * df\n",
+    "\n",
+    "\n",
+    "def compute_band_power(p, f, f1, f2):\n",
+    "    if np.isnan(f1) or np.all(np.isnan(p)):\n",
+    "        return [np.nan] * 2\n",
+    "    from scipy.integrate import simps\n",
+    "    dx = f[1] - f[0]\n",
+    "    mask = (f > f1) & (f < f2)\n",
+    "    # Compute the absolute power by approximating the area under the curve\n",
+    "    band_power = simps(p[mask], dx=dx)\n",
+    "    total_power = simps(p, dx=dx)\n",
+    "    rel_power = band_power / total_power\n",
+    "    return band_power, rel_power"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def find_theta_peak(p, f):\n",
+    "def find_theta_peak(p, f, f1, f2):\n",
-    "    mask = (f > 6) & (f < 10)\n",
+    "    if np.all(np.isnan(p)):\n",
+    "        return np.nan, np.nan\n",
+    "    mask = (f > f1) & (f < f2)\n",
     "    p_m = p[mask]\n",
     "    f_m = f[mask]\n",
     "    peaks, _ = find_peaks(p_m)\n",
@@ -183,49 +220,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def compute_half_width(p, f, m_p, m_f):\n",
+    "def compute_half_width(power, freq, max_power, max_frequency, band, band_width=1):\n",
-    "    if np.isnan(m_p):\n",
+    "    if np.isnan(max_power):\n",
-    "        return np.nan, np.nan\n",
+    "        return [np.nan] * 3\n",
+    "    \n",
+    "    # estimate baseline power\n",
+    "    low_baseline_mask = (freq > band[0] - band_width) & (freq < band[0])\n",
+    "    high_baseline_mask = (freq > band[1]) & (freq < band[1] + band_width)\n",
+    "    baseline = np.mean(np.concatenate([power[low_baseline_mask], power[high_baseline_mask]]))\n",
+    "    p = power - baseline\n",
+    "    m_p = max_power - baseline\n",
+    "    m_f = max_frequency\n",
+    "    f = freq\n",
+    "    \n",
+    "    # estimate half width\n",
     "    m_p_half = m_p / 2\n",
     "    half_p = p - m_p_half\n",
     "    idx_f = np.where(f <= m_f)[0].max()\n",
     "    idxs_p1, = np.where(np.diff(half_p[:idx_f + 1] > 0) == 1)\n",
     "    if len(idxs_p1) == 0:\n",
-    "        return np.nan, np.nan\n",
+    "        return [np.nan] * 3\n",
     "    m1 = idxs_p1.max()\n",
     "    idxs_p2, = np.where(np.diff(half_p[idx_f:] > 0) == 1)\n",
-    "    m2 = idxs_p2.min() + idx_f    \n",
+    "    if len(idxs_p2) == 0:\n",
-    "#     plt.plot(f, p)\n",
+    "        return [np.nan] * 3\n",
-    "#     plt.plot(m_f, m_p, marker='o', ls='none', markersize=10)\n",
+    "    m2 = idxs_p2.min() + idx_f\n",
-    "#     plt.plot(f[m1], p[m1], marker='x', ls='none', markersize=10, c='r')\n",
+    "#     assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])\n",
-    "#     plt.plot(f[m1+1], p[m1+1], marker='+', ls='none', markersize=10, c='r')\n",
+    "#     assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])\n",
-    "    \n",
-    "#     plt.plot(f[m2], p[m2], marker='x', ls='none', markersize=10, c='k')\n",
-    "#     plt.plot(f[m2+1], p[m2+1], marker='+', ls='none', markersize=10, c='k')\n",
-    "    \n",
-    "#     plt.xlim(4,12)\n",
-    "    assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])\n",
-    "    assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])\n",
     "    \n",
     "    f1 = interp1d([half_p[m1], half_p[m1 + 1]], [f[m1], f[m1 + 1]])(0)\n",
     "    f2 = interp1d([half_p[m2], half_p[m2 + 1]], [f[m2], f[m2 + 1]])(0)\n",
-    "    return f1, f2"
+    "    return f1, f2, m_p_half + baseline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_stim_peak(p, f, s_f):\n",
-    "    if np.isnan(s_f):\n",
-    "        return np.nan\n",
-    "    return interp1d(f, p)(s_f)"
    ]
   },
   {
@@ -234,19 +263,41 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "NFFT = 10000\n",
+    "def compute_stim_peak(p, f, s_f):\n",
+    "    if np.isnan(s_f):\n",
+    "        return np.nan\n",
+    "    return interp1d(f, p)(s_f)\n",
+    "\n",
+    "\n",
+    "def compute_relative_peak(power, freq, max_power, band, band_width=1):\n",
+    "    # estimate baseline power\n",
+    "    low_baseline_mask = (freq > band[0] - band_width) & (freq < band[0])\n",
+    "    high_baseline_mask = (freq > band[1]) & (freq < band[1] + band_width)\n",
+    "    baseline = np.mean(np.concatenate([power[low_baseline_mask], power[high_baseline_mask]]))\n",
+    "    return (max_power - baseline) / abs(baseline)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "theta_band_f1, theta_band_f2 = 6, 10 "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def process(row):\n",
+    "psd_data, freq_data = {}, {}\n",
+    "\n",
+    "def process(row, perform_zscore):\n",
     "    action_id = row['action']\n",
     "    channel_group = row['channel_group']\n",
+    "    name =  f'{action_id}_{channel_group}'\n",
     "    lfp = data_loader.lfp(action_id, channel_group)\n",
     "    clean_lfp = select_and_clean(lfp)\n",
     "    snls = signaltonoise(clean_lfp)\n",
@@ -256,19 +307,30 @@
     "    lim = get_lim(action_id)\n",
     "    \n",
     "    mask = get_mask(lfp, lim)\n",
-    "    signal = zscore(clean_lfp[mask, best_channel].ravel())\n",
+    "    if perform_zscore:\n",
+    "        signal = zscore(clean_lfp[mask, best_channel].ravel())\n",
+    "    else:\n",
+    "        signal = clean_lfp[mask, best_channel].ravel()\n",
     "    \n",
-    "    p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)\n",
+    "    window = 6 * lfp.sampling_rate.magnitude\n",
     "    \n",
-    "    theta_f, theta_p_max = find_theta_peak(p_xx, freq)\n",
+    "#     p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)\n",
+    "    freq, p_xx = ss.welch(signal, fs=lfp.sampling_rate.magnitude, nperseg=window, nfft=scipy.fftpack.next_fast_len(window))\n",
+    "    p_xx = 10 * np.log10(p_xx)\n",
     "    \n",
-    "    theta_energy = compute_energy(p_xx, freq, theta_band_f1, theta_band_f2) # theta band 6 - 10 Hz\n",
+    "    theta_f, theta_p_max = find_theta_peak(p_xx, freq, theta_band_f1, theta_band_f2)\n",
     "    \n",
-    "    theta_half_f1, theta_half_f2 = compute_half_width(p_xx, freq, theta_p_max, theta_f)\n",
+    "    theta_bandpower, theta_relpower = compute_band_power(p_xx, freq, theta_band_f1, theta_band_f2)\n",
+    "    \n",
+    "    theta_relpeak = compute_relative_peak(p_xx, freq, theta_p_max, [theta_band_f1, theta_band_f2])\n",
+    "        \n",
+    "    theta_half_f1, theta_half_f2, theta_half_power = compute_half_width(p_xx, freq, theta_p_max, theta_f, [theta_band_f1, theta_band_f2])\n",
     "    \n",
     "    theta_half_width = theta_half_f2 - theta_half_f1\n",
     "    \n",
-    "    theta_half_energy = compute_energy(p_xx, freq, theta_half_f1, theta_half_f2) # theta band 6 - 10 Hz\n",
+    "    psd_data.update({name: p_xx})\n",
+    "    freq_data.update({name: freq})\n",
+    "\n",
     "    \n",
     "    # stim\n",
     "    \n",
@@ -276,133 +338,89 @@
     "    \n",
     "    stim_p_max = compute_stim_peak(p_xx, freq, stim_freq)\n",
     "    \n",
-    "    stim_half_f1, stim_half_f2 = compute_half_width(p_xx, freq, stim_p_max, stim_freq)\n",
+    "    stim_half_f1, stim_half_f2, stim_half_power = compute_half_width(p_xx, freq, stim_p_max, stim_freq, [stim_freq - 1, stim_freq + 1])\n",
+    "    \n",
     "    stim_half_width = stim_half_f2 - stim_half_f1\n",
     "    \n",
-    "    stim_energy = compute_energy(p_xx, freq, stim_half_f1, stim_half_f2)\n",
+    "    stim_bandpower, stim_relpower = compute_band_power(p_xx, freq, stim_freq - 1, stim_freq + 1)\n",
+    "    \n",
+    "    stim_relpeak = compute_relative_peak(p_xx, freq, stim_p_max, [stim_freq - 1, stim_freq + 1])\n",
     "    \n",
     "    result = pd.Series({\n",
     "        'signal_to_noise': snl,\n",
     "        'best_channel': best_channel,\n",
     "        'theta_freq': theta_f,\n",
     "        'theta_peak': theta_p_max,\n",
-    "        'theta_energy': theta_energy,\n",
+    "        'theta_bandpower': theta_bandpower,\n",
+    "        'theta_relpower': theta_relpower,\n",
+    "        'theta_relpeak': theta_relpeak,\n",
     "        'theta_half_f1': theta_half_f1, \n",
     "        'theta_half_f2': theta_half_f2,\n",
     "        'theta_half_width': theta_half_width,\n",
-    "        'theta_half_energy': theta_half_energy,\n",
     "        'stim_freq': stim_freq,\n",
     "        'stim_p_max': stim_p_max,\n",
     "        'stim_half_f1': stim_half_f1, \n",
     "        'stim_half_f2': stim_half_f2,\n",
     "        'stim_half_width': stim_half_width,\n",
-    "        'stim_energy': stim_energy\n",
+    "        'stim_bandpower': stim_bandpower,\n",
+    "        'stim_relpower': stim_relpower,\n",
+    "        'stim_relpeak': stim_relpeak,\n",
     "    })\n",
     "    return result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e733903bb8d7406ea6c05bf5af093bfc",
+       "model_id": "0a39fef20b724ef186ce6ce5299b3719",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "HBox(children=(IntProgress(value=0, max=704), HTML(value='')))"
+       "HBox(children=(IntProgress(value=0, max=696), HTML(value='')))"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in greater\n",
+      "  if __name__ == '__main__':\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in less\n",
+      "  if __name__ == '__main__':\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in greater\n",
+      "  # Remove the CWD from sys.path while we load stuff.\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in less\n",
+      "  # Remove the CWD from sys.path while we load stuff.\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
+      "  out=out, **kwargs)\n",
+      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
      ]
     }
    ],
    "source": [
     "results = channel_groups.merge(\n",
-    "    channel_groups.progress_apply(process, axis=1), \n",
+    "    channel_groups.progress_apply(process, perform_zscore=perform_zscore, axis=1), \n",
     "    left_index=True, right_index=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0184251a265a40a2a1e9910405f05db8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, max=704), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
    "source": [
-    "psd, freqs = {}, {}\n",
+    "pd.DataFrame(psd_data).to_feather(output / 'data' / 'psd.feather')\n",
-    "for i, row in tqdm(channel_groups.iterrows(), total=len(channel_groups)):\n",
+    "pd.DataFrame(freq_data).to_feather(output / 'data' / 'freqs.feather')"
-    "    action_id = row['action']\n",
-    "    channel_group = row['channel_group']\n",
-    "    action_group = f'{action_id}_{channel_group}'\n",
-    "    lfp = data_loader.lfp(action_id, channel_group)\n",
-    "    clean_lfp = select_and_clean(lfp)\n",
-    "    snls = signaltonoise(clean_lfp)\n",
-    "    best_channel = np.argmax(snls)\n",
-    "    snl = snls[best_channel]\n",
-    "    \n",
-    "    lim = get_lim(action_id)\n",
-    "    \n",
-    "    mask = get_mask(lfp, lim)\n",
-    "    signal = zscore(clean_lfp[mask, best_channel].ravel())\n",
-    "    \n",
-    "    p_xx, freq = mlab.psd(signal, Fs=lfp.sampling_rate.magnitude, NFFT=NFFT)\n",
-    "    psd.update({action_group: p_xx})\n",
-    "    freqs.update({action_group: 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-18-7660e1d3307d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpsd\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'psd.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": [
-    "(psd).to_feather(output / 'data' / 'psd.feather')\n",
-    "freqs.to_feather(output / 'data' / 'freqs.feather')"
    ]
   },
   {
@@ -418,7 +436,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "action = project.require_action(\"stimulus-lfp-response\")"
+    "action = project.require_action(\"stimulus-lfp-response\" + zscore_str)"
    ]
   },
   {
@@ -428,7 +446,7 @@
    "outputs": [],
    "source": [
     "action.modules['parameters'] = {\n",
-    "    'NFFT': NFFT,\n",
+    "    'window': 6,\n",
     "    'theta_band_f1': theta_band_f1,\n",
     "    'theta_band_f2': theta_band_f2\n",
     "}"
@@ -490,5 +508,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

actions/stimulus-lfp-response/modules/parameters.yaml

@@ -1,3 +1,3 @@
-NFFT: 10000
+window: 6
 theta_band_f1: 6
 theta_band_f2: 10