actions/stimulus-spike-lfp-response/
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Mikkel Elle Lepperød
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">load_ext</span> autoreload
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<span class="kn">import</span> <span class="nn">expipe</span>
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<span class="kn">import</span> <span class="nn">os</span>
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<span class="kn">import</span> <span class="nn">pathlib</span>
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<span class="kn">import</span> <span class="nn">scipy</span>
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<span class="kn">import</span> <span class="nn">scipy.signal</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">exdir</span>
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<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<pre>10:25:26 [I] klustakwik KlustaKwik2 version 0.2.6
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_loader</span> <span class="o">=</span> <span class="n">dp</span><span class="o">.</span><span class="n">Data</span><span class="p">()</span>
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<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">'output/stimulus-spike-lfp-response'</span><span class="p">)</span>
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<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">'identify-neurons'</span><span class="p">]</span>
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<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>
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<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>
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<span class="n">m</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
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<span class="n">sd</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="n">ddof</span><span class="p">)</span>
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<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>
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<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>
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<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>
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<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>
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<span class="n">idxs</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="nb">abs</span><span class="p">(</span><span class="n">anas</span><span class="p">[:,</span> <span class="n">ch</span><span class="p">])</span> <span class="o">></span> <span class="n">threshold</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">idxs</span><span class="p">:</span>
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<span class="n">anas</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="n">width</span><span class="p">:</span><span class="n">idx</span><span class="o">+</span><span class="n">width</span><span class="p">,</span> <span class="n">ch</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># TODO AR model prediction</span>
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<span class="k">return</span> <span class="n">anas</span>
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<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>
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<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>
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<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>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># p = np.load('debug_p.npy')</span>
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<span class="c1"># f = np.load('debug_f.npy')</span>
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<span class="c1"># compute_half_width(p, f, 0.01038941, 30.30187709636872)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># plt.plot(f, p)</span>
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<span class="c1"># plt.xlim(29.9,30.6)</span>
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<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>
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<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>
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<span class="n">t_start</span> <span class="o">=</span> <span class="n">t_start</span> <span class="o">*</span> <span class="n">pq</span><span class="o">.</span><span class="n">s</span> <span class="k">if</span> <span class="n">t_start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">0</span> <span class="o">*</span> <span class="n">pq</span><span class="o">.</span><span class="n">s</span>
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<span class="n">sampling_rate</span> <span class="o">=</span> <span class="n">anas</span><span class="o">.</span><span class="n">sampling_rate</span>
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<span class="n">units</span> <span class="o">=</span> <span class="n">anas</span><span class="o">.</span><span class="n">units</span>
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<span class="k">if</span> <span class="n">t_start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">t_stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="n">t_stop</span> <span class="o">=</span> <span class="n">t_stop</span> <span class="o">*</span> <span class="n">pq</span><span class="o">.</span><span class="n">s</span>
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<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">></span> <span class="n">t_start</span><span class="p">)</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"><</span> <span class="n">t_stop</span><span class="p">)</span>
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<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>
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<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>
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<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>
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<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>
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<span class="p">)</span>
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<span class="n">sptr</span> <span class="o">=</span> <span class="n">neo</span><span class="o">.</span><span class="n">SpikeTrain</span><span class="p">(</span>
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<span class="n">sptr</span><span class="o">.</span><span class="n">times</span><span class="p">[(</span><span class="n">sptr</span><span class="o">.</span><span class="n">times</span> <span class="o">></span> <span class="n">t_start</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">sptr</span><span class="o">.</span><span class="n">times</span> <span class="o"><</span> <span class="n">cleaned_anas</span><span class="o">.</span><span class="n">times</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])],</span>
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<span class="n">t_start</span><span class="o">=</span><span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="o">=</span><span class="n">cleaned_anas</span><span class="o">.</span><span class="n">times</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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<span class="p">)</span>
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<span class="n">sigs</span><span class="p">,</span> <span class="n">freqs</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">sta</span><span class="o">.</span><span class="n">spike_field_coherence</span><span class="p">(</span><span class="n">cleaned_anas</span><span class="p">,</span> <span class="n">sptr</span><span class="p">,</span> <span class="o">**</span><span class="p">{</span><span class="s1">'nperseg'</span><span class="p">:</span> <span class="n">NFFT</span><span class="p">})</span>
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<span class="k">return</span> <span class="n">sigs</span><span class="p">,</span> <span class="n">freqs</span><span class="p">,</span> <span class="n">cleaned_anas</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># action_id_0 = '1833-200619-1'</span>
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<span class="c1"># action_id_1 = '1833-200619-2'</span>
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<span class="c1"># lfp_0 = data_loader.lfp(action_id_0, 6)</span>
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<span class="c1"># lfp_1 = data_loader.lfp(action_id_1, 6)</span>
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<span class="c1"># lim_0 = get_lim(action_id_0)</span>
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<span class="c1"># lim_1 = get_lim(action_id_1)</span>
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<span class="c1"># sptrs_0 = data_loader.spike_trains(action_id_0, 6)</span>
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<span class="c1"># sptrs_1 = data_loader.spike_trains(action_id_1, 6)</span>
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<span class="c1"># coher_0, freq_0, clean_lfp_0 = compute_spike_lfp(lfp_0, sptrs_0[163], *lim_0, 4096)</span>
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<span class="c1"># coher_1, freq_1, clean_lfp_1 = compute_spike_lfp(lfp_1, sptrs_1[28], *lim_1, 4096)</span>
|
||||
|
||||
<span class="c1"># best_channel_0 = np.argmax(signaltonoise(clean_lfp_0))</span>
|
||||
<span class="c1"># best_channel_1 = np.argmax(signaltonoise(clean_lfp_1))</span>
|
||||
|
||||
<span class="c1"># plt.plot(freq_0, coher_0[:,best_channel_0])</span>
|
||||
<span class="c1"># plt.plot(freq_1, coher_1[:,best_channel_1])</span>
|
||||
<span class="c1"># plt.xlim(0,20)</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 [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">4096</span><span class="o">*</span><span class="mi">2</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>
|
||||
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">compute_spike_lfp_coherence</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">NFFT</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sigs</span><span class="p">,</span> <span class="n">freqs</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">sta</span><span class="o">.</span><span class="n">spike_field_coherence</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="o">**</span><span class="p">{</span><span class="s1">'nperseg'</span><span class="p">:</span> <span class="n">NFFT</span><span class="p">})</span>
|
||||
<span class="k">return</span> <span class="n">sigs</span><span class="p">,</span> <span class="n">freqs</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
@ -13454,23 +13392,327 @@ div#notebook {
|
|||
<div class="prompt input_prompt">In [15]:</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="k">def</span> <span class="nf">butter_bandpass</span><span class="p">(</span><span class="n">lowcut</span><span class="p">,</span> <span class="n">highcut</span><span class="p">,</span> <span class="n">fs</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="n">nyq</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">fs</span>
|
||||
<span class="n">low</span> <span class="o">=</span> <span class="n">lowcut</span> <span class="o">/</span> <span class="n">nyq</span>
|
||||
<span class="n">high</span> <span class="o">=</span> <span class="n">highcut</span> <span class="o">/</span> <span class="n">nyq</span>
|
||||
<span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">signal</span><span class="o">.</span><span class="n">butter</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">],</span> <span class="n">btype</span><span class="o">=</span><span class="s1">'band'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">b</span><span class="p">,</span> <span class="n">a</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">butter_bandpass_filter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">lowcut</span><span class="p">,</span> <span class="n">highcut</span><span class="p">,</span> <span class="n">fs</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">butter_bandpass</span><span class="p">(</span><span class="n">lowcut</span><span class="p">,</span> <span class="n">highcut</span><span class="p">,</span> <span class="n">fs</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">signal</span><span class="o">.</span><span class="n">filtfilt</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">y</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">compute_spike_phase_func</span><span class="p">(</span><span class="n">lfp</span><span class="p">,</span> <span class="n">times</span><span class="p">,</span> <span class="n">return_degrees</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">x_a</span> <span class="o">=</span> <span class="n">hilbert</span><span class="p">(</span><span class="n">lfp</span><span class="p">)</span>
|
||||
<span class="n">x_phase</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">x_a</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">return_degrees</span><span class="p">:</span>
|
||||
<span class="n">x_phase</span> <span class="o">=</span> <span class="n">x_phase</span> <span class="o">*</span> <span class="mi">180</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span>
|
||||
<span class="k">return</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">x_phase</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">vonmises_kde</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">kappa</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.special</span> <span class="k">import</span> <span class="n">i0</span>
|
||||
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">n_bins</span><span class="p">)</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">n_bins</span><span class="p">)</span>
|
||||
<span class="c1"># integrate vonmises kernels</span>
|
||||
<span class="n">kde</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kappa</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">i0</span><span class="p">(</span><span class="n">kappa</span><span class="p">))</span>
|
||||
<span class="n">kde</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">trapz</span><span class="p">(</span><span class="n">kde</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="n">bins</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">bins</span><span class="p">,</span> <span class="n">kde</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">spike_phase_score</span><span class="p">(</span><span class="n">phase_bins</span><span class="p">,</span> <span class="n">density</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">math</span>
|
||||
<span class="kn">import</span> <span class="nn">pycircstat</span> <span class="k">as</span> <span class="nn">pc</span>
|
||||
<span class="n">ang</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">phase_bins</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="n">density</span><span class="p">)</span>
|
||||
<span class="n">vec_len</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">resultant_vector_length</span><span class="p">(</span><span class="n">phase_bins</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="n">density</span><span class="p">)</span>
|
||||
<span class="c1"># ci_lim = pc.mean_ci_limits(head_angle_bins, w=rate)</span>
|
||||
<span class="k">return</span> <span class="n">ang</span><span class="p">,</span> <span class="n">vec_len</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 [16]:</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_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="n">idxs</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="nb">abs</span><span class="p">(</span><span class="n">anas</span><span class="p">)</span> <span class="o">></span> <span class="n">threshold</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">idxs</span><span class="p">:</span>
|
||||
<span class="n">anas</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="n">width</span><span class="p">:</span><span class="n">idx</span><span class="o">+</span><span class="n">width</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># TODO AR model prediction</span>
|
||||
<span class="k">return</span> <span class="n">anas</span><span class="p">,</span> <span class="n">idxs</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">compute_clean_spikes</span><span class="p">(</span><span class="n">spikes</span><span class="p">,</span> <span class="n">idxs</span><span class="p">,</span> <span class="n">times</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="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">idxs</span><span class="p">:</span>
|
||||
<span class="n">t0</span> <span class="o">=</span> <span class="n">times</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="n">width</span><span class="p">]</span>
|
||||
<span class="n">stop</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">+</span> <span class="n">width</span>
|
||||
<span class="k">if</span> <span class="n">stop</span> <span class="o">></span> <span class="nb">len</span><span class="p">(</span><span class="n">times</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">stop</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">times</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<span class="n">t1</span> <span class="o">=</span> <span class="n">times</span><span class="p">[</span><span class="n">stop</span><span class="p">]</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">spikes</span> <span class="o">></span> <span class="n">t0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">spikes</span> <span class="o"><</span> <span class="n">t1</span><span class="p">)</span>
|
||||
<span class="n">spikes</span> <span class="o">=</span> <span class="n">spikes</span><span class="p">[</span><span class="o">~</span><span class="n">mask</span><span class="p">]</span>
|
||||
<span class="n">spikes</span> <span class="o">=</span> <span class="n">spikes</span><span class="p">[</span><span class="n">spikes</span> <span class="o"><=</span> <span class="n">times</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
|
||||
<span class="k">return</span> <span class="n">spikes</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">prepare_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">t_start</span> <span class="o">=</span> <span class="n">t_start</span> <span class="o">*</span> <span class="n">pq</span><span class="o">.</span><span class="n">s</span> <span class="k">if</span> <span class="n">t_start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">0</span> <span class="o">*</span> <span class="n">pq</span><span class="o">.</span><span class="n">s</span>
|
||||
<span class="n">sampling_rate</span> <span class="o">=</span> <span class="n">anas</span><span class="o">.</span><span class="n">sampling_rate</span>
|
||||
<span class="n">units</span> <span class="o">=</span> <span class="n">anas</span><span class="o">.</span><span class="n">units</span>
|
||||
<span class="n">times</span> <span class="o">=</span> <span class="n">anas</span><span class="o">.</span><span class="n">times</span>
|
||||
<span class="k">if</span> <span class="n">t_start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">t_stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">t_stop</span> <span class="o">=</span> <span class="n">t_stop</span> <span class="o">*</span> <span class="n">pq</span><span class="o">.</span><span class="n">s</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">times</span> <span class="o">></span> <span class="n">t_start</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">times</span> <span class="o"><</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">times</span> <span class="o">=</span> <span class="n">times</span><span class="p">[</span><span class="n">mask</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">signaltonoise</span><span class="p">(</span><span class="n">anas</span><span class="p">))</span>
|
||||
<span class="c1"># best_channel = np.random.choice(anas.shape[1])</span>
|
||||
|
||||
<span class="n">cleaned_anas</span><span class="p">,</span> <span class="n">idxs</span> <span class="o">=</span> <span class="n">compute_clean_lfp</span><span class="p">(</span><span class="n">anas</span><span class="p">[:,</span> <span class="n">best_channel</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>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="n">spike_units</span> <span class="o">=</span> <span class="n">sptr</span><span class="o">.</span><span class="n">units</span>
|
||||
<span class="n">spike_times</span> <span class="o">=</span> <span class="n">sptr</span><span class="o">.</span><span class="n">times</span>
|
||||
<span class="n">spike_times</span> <span class="o">=</span> <span class="n">compute_clean_spikes</span><span class="p">(</span><span class="n">spike_times</span><span class="p">,</span> <span class="n">idxs</span><span class="p">,</span> <span class="n">times</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sptr</span> <span class="o">=</span> <span class="n">neo</span><span class="o">.</span><span class="n">SpikeTrain</span><span class="p">(</span>
|
||||
<span class="n">spike_times</span><span class="p">[(</span><span class="n">spike_times</span> <span class="o">></span> <span class="n">t_start</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">spike_times</span> <span class="o"><</span> <span class="n">times</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])],</span> <span class="n">units</span><span class="o">=</span><span class="n">spike_units</span><span class="p">,</span>
|
||||
<span class="n">t_start</span><span class="o">=</span><span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="o">=</span><span class="n">times</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">cleaned_anas</span><span class="p">,</span> <span class="n">sptr</span><span class="p">,</span> <span class="n">best_channel</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 [17]:</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_spike_phase_func</span><span class="p">(</span><span class="n">lfp</span><span class="p">,</span> <span class="n">times</span><span class="p">,</span> <span class="n">return_degrees</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.fftpack</span> <span class="k">import</span> <span class="n">next_fast_len</span>
|
||||
<span class="n">x_a</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">signal</span><span class="o">.</span><span class="n">hilbert</span><span class="p">(</span>
|
||||
<span class="n">lfp</span><span class="p">,</span> <span class="n">next_fast_len</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">lfp</span><span class="p">)))[:</span><span class="nb">len</span><span class="p">(</span><span class="n">lfp</span><span class="p">)]</span>
|
||||
<span class="c1"># x_a = hilbert(lfp)</span>
|
||||
<span class="n">x_phase</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">x_a</span><span class="p">,</span> <span class="n">deg</span><span class="o">=</span><span class="n">return_degrees</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">x_phase</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">compute_spike_phase</span><span class="p">(</span><span class="n">lfp</span><span class="p">,</span> <span class="n">spikes</span><span class="p">,</span> <span class="n">flim</span><span class="o">=</span><span class="p">[</span><span class="mi">6</span><span class="p">,</span><span class="mi">10</span><span class="p">]):</span>
|
||||
|
||||
<span class="n">sample_rate</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="c1"># sometimes the position is recorded after LFP recording is ended</span>
|
||||
<span class="n">times</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">lfp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="n">sample_rate</span>
|
||||
|
||||
<span class="n">spikes</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">spikes</span><span class="p">)</span>
|
||||
<span class="n">spikes</span> <span class="o">=</span> <span class="n">spikes</span><span class="p">[(</span><span class="n">spikes</span> <span class="o">></span> <span class="n">times</span><span class="o">.</span><span class="n">min</span><span class="p">())</span> <span class="o">&</span> <span class="p">(</span><span class="n">spikes</span> <span class="o"><</span> <span class="n">times</span><span class="o">.</span><span class="n">max</span><span class="p">())]</span>
|
||||
|
||||
<span class="n">filtered_lfp</span> <span class="o">=</span> <span class="n">butter_bandpass_filter</span><span class="p">(</span>
|
||||
<span class="n">lfp</span><span class="o">.</span><span class="n">magnitude</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="o">*</span><span class="n">flim</span><span class="p">,</span> <span class="n">fs</span><span class="o">=</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
|
||||
|
||||
<span class="n">spike_phase_func</span> <span class="o">=</span> <span class="n">compute_spike_phase_func</span><span class="p">(</span><span class="n">filtered_lfp</span><span class="p">,</span> <span class="n">times</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">spike_phase_func</span><span class="p">(</span><span class="n">spikes</span><span class="p">),</span> <span class="n">filtered_lfp</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 [18]:</div>
|
||||
<div class="inner_cell">
|
||||
<div class="input_area">
|
||||
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># plt.figure(figsize=(16,9))</span>
|
||||
<span class="c1"># # lfp = data_loader.lfp('1833-200619-2', 6)</span>
|
||||
<span class="c1"># lfp = data_loader.lfp('1834-220319-3', 6)</span>
|
||||
<span class="c1"># # lfp = data_loader.lfp('1849-010319-4', 6)</span>
|
||||
<span class="c1"># times = np.arange(lfp.shape[0]) / lfp.sampling_rate.magnitude</span>
|
||||
<span class="c1"># clean_lfp, _ = compute_clean_lfp(lfp.magnitude[:, 0], threshold=2)</span>
|
||||
<span class="c1"># plt.plot(times,lfp[:,0])</span>
|
||||
<span class="c1"># plt.plot(times,clean_lfp)</span>
|
||||
|
||||
<span class="c1"># plt.figure(figsize=(16,9))</span>
|
||||
<span class="c1"># plt.psd(lfp[:,0].ravel(), Fs=1000, NFFT=10000)</span>
|
||||
<span class="c1"># plt.psd(clean_lfp, Fs=1000, NFFT=10000)</span>
|
||||
<span class="c1"># plt.xlim(0,100)</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 [19]:</div>
|
||||
<div class="inner_cell">
|
||||
<div class="input_area">
|
||||
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># plt.figure(figsize=(16,9))</span>
|
||||
|
||||
<span class="c1"># plt.plot(times,lfp[:,0])</span>
|
||||
<span class="c1"># # plt.plot(clean_lfp*100)</span>
|
||||
<span class="c1"># plt.plot(times[:-1], np.diff(lfp[:,0].magnitude.ravel()))</span>
|
||||
<span class="c1"># plt.xlim(64.5,65.5)</span>
|
||||
<span class="c1"># # plt.ylim(-250,250)</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 [20]:</div>
|
||||
<div class="inner_cell">
|
||||
<div class="input_area">
|
||||
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># # action_id_0, channel_0, unit_0 = '1833-200619-1', 6, 163</span>
|
||||
<span class="c1"># # action_id_1, channel_1, unit_1 = '1833-200619-2', 6, 28</span>
|
||||
<span class="c1"># action_id_0, channel_0, unit_0 = '1834-220319-3', 2, 46</span>
|
||||
<span class="c1"># action_id_1, channel_1, unit_1 = '1834-220319-4', 2, 60</span>
|
||||
<span class="c1"># lfp_0 = data_loader.lfp(action_id_0, channel_0)</span>
|
||||
<span class="c1"># lfp_1 = data_loader.lfp(action_id_1, channel_1)</span>
|
||||
|
||||
<span class="c1"># sample_rate_0 = lfp_0.sampling_rate</span>
|
||||
<span class="c1"># sample_rate_1 = lfp_1.sampling_rate</span>
|
||||
|
||||
<span class="c1"># lim_0 = get_lim(action_id_0)</span>
|
||||
<span class="c1"># lim_1 = get_lim(action_id_1)</span>
|
||||
|
||||
<span class="c1"># sptrs_0 = data_loader.spike_trains(action_id_0, channel_0)</span>
|
||||
|
||||
<span class="c1"># sptrs_1 = data_loader.spike_trains(action_id_1, channel_1)</span>
|
||||
|
||||
<span class="c1"># cleaned_lfps_0, sptr_0, best_channel_0 = prepare_spike_lfp(lfp_0, sptrs_0[unit_0], *lim_0)</span>
|
||||
|
||||
<span class="c1"># cleaned_lfps_1, sptr_1, best_channel_1 = prepare_spike_lfp(lfp_1, sptrs_1[unit_1], *lim_1)</span>
|
||||
|
||||
<span class="c1"># coher_0, freq_0 = compute_spike_lfp_coherence(cleaned_lfps_0, sptr_0, 4096)</span>
|
||||
|
||||
<span class="c1"># coher_1, freq_1 = compute_spike_lfp_coherence(cleaned_lfps_1, sptr_1, 4096)</span>
|
||||
|
||||
<span class="c1"># spike_phase_0, filtered_lfp_0 = compute_spike_phase(cleaned_lfps_0, sptrs_0[unit_0], flim=[6,10])</span>
|
||||
|
||||
<span class="c1"># spike_phase_1, filtered_lfp_1 = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[6,10])</span>
|
||||
|
||||
<span class="c1"># spike_phase_1_stim, filtered_lfp_1_stim = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[10.5,11.5])</span>
|
||||
|
||||
<span class="c1"># plt.figure()</span>
|
||||
<span class="c1"># plt.plot(freq_0, coher_0.ravel())</span>
|
||||
<span class="c1"># plt.plot(freq_1, coher_1.ravel())</span>
|
||||
<span class="c1"># plt.xlim(0,20)</span>
|
||||
|
||||
<span class="c1"># plt.figure()</span>
|
||||
<span class="c1"># bins_0, kde_0 = vonmises_kde(spike_phase_0, 100)</span>
|
||||
<span class="c1"># ang_0, vec_len_0 = spike_phase_score(bins_0, kde_0)</span>
|
||||
<span class="c1"># plt.polar(bins_0, kde_0, color='b')</span>
|
||||
<span class="c1"># plt.polar([ang_0, ang_0], [0, vec_len_0], color='b')</span>
|
||||
|
||||
<span class="c1"># bins_1, kde_1 = vonmises_kde(spike_phase_1, 100)</span>
|
||||
<span class="c1"># ang_1, vec_len_1 = spike_phase_score(bins_1, kde_1)</span>
|
||||
<span class="c1"># plt.polar(bins_1, kde_1, color='r')</span>
|
||||
<span class="c1"># plt.polar([ang_1, ang_1], [0, vec_len_1], color='r')</span>
|
||||
|
||||
<span class="c1"># bins_1_stim, kde_1_stim = vonmises_kde(spike_phase_1_stim, 100)</span>
|
||||
<span class="c1"># ang_1_stim, vec_len_1_stim = spike_phase_score(bins_1_stim, kde_1_stim)</span>
|
||||
<span class="c1"># plt.polar(bins_1_stim, kde_1_stim, color='k')</span>
|
||||
<span class="c1"># plt.polar([ang_1_stim, ang_1_stim], [0, vec_len_1_stim], color='k')</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 [ ]:</div>
|
||||
<div class="inner_cell">
|
||||
<div class="input_area">
|
||||
<div class=" highlight hl-ipython3"><pre><span></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 [21]:</div>
|
||||
<div class="inner_cell">
|
||||
<div class="input_area">
|
||||
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO fix artefact stuff from phase precession</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 [22]:</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">8192</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 [23]:</div>
|
||||
<div class="inner_cell">
|
||||
<div class="input_area">
|
||||
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">coherence_data</span><span class="p">,</span> <span class="n">freqency_data</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
|
||||
<span class="n">theta_kde_data</span><span class="p">,</span> <span class="n">theta_bins_data</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
|
||||
<span class="n">stim_kde_data</span><span class="p">,</span> <span class="n">stim_bins_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">action_id</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'action'</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">'channel_group'</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">'unit_name'</span><span class="p">]</span>
|
||||
|
||||
<span class="n">name</span> <span class="o">=</span> <span class="n">f</span><span class="s1">'</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">'</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">cleaned_lfp</span><span class="p">,</span> <span class="n">sptr</span><span class="p">,</span> <span class="n">best_channel</span> <span class="o">=</span> <span class="n">prepare_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">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">p_xys</span><span class="p">,</span> <span class="n">freq</span> <span class="o">=</span> <span class="n">compute_spike_lfp_coherence</span><span class="p">(</span><span class="n">cleaned_lfp</span><span class="p">,</span> <span class="n">sptr</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">p_xy</span> <span class="o">=</span> <span class="n">p_xys</span><span class="o">.</span><span class="n">magnitude</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</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>
|
||||
|
|
@ -13483,6 +13725,12 @@ div#notebook {
|
|||
|
||||
<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_xy</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">theta_spike_phase</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">compute_spike_phase</span><span class="p">(</span><span class="n">cleaned_lfp</span><span class="p">,</span> <span class="n">sptr</span><span class="p">,</span> <span class="n">flim</span><span class="o">=</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_bins</span><span class="p">,</span> <span class="n">theta_kde</span> <span class="o">=</span> <span class="n">vonmises_kde</span><span class="p">(</span><span class="n">theta_spike_phase</span><span class="p">)</span>
|
||||
<span class="n">theta_ang</span><span class="p">,</span> <span class="n">theta_vec_len</span> <span class="o">=</span> <span class="n">spike_phase_score</span><span class="p">(</span><span class="n">theta_bins</span><span class="p">,</span> <span class="n">theta_kde</span><span class="p">)</span>
|
||||
<span class="n">theta_kde_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">theta_kde</span><span class="p">})</span>
|
||||
<span class="n">theta_bins_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">theta_bins</span><span class="p">})</span>
|
||||
|
||||
<span class="c1"># stim</span>
|
||||
|
||||
<span class="n">stim_freq</span> <span class="o">=</span> <span class="n">compute_stim_freq</span><span class="p">(</span><span class="n">action_id</span><span class="p">)</span>
|
||||
|
|
@ -13494,8 +13742,19 @@ div#notebook {
|
|||
|
||||
<span class="n">stim_energy</span> <span class="o">=</span> <span class="n">compute_energy</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">stim_half_f1</span><span class="p">,</span> <span class="n">stim_half_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">stim_freq</span><span class="p">):</span>
|
||||
<span class="n">stim_spike_phase</span><span class="p">,</span> <span class="n">stim_bins</span><span class="p">,</span> <span class="n">stim_kde</span><span class="p">,</span> <span class="n">stim_ang</span><span class="p">,</span> <span class="n">stim_vec_len</span> <span class="o">=</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">5</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">stim_spike_phase</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">compute_spike_phase</span><span class="p">(</span><span class="n">cleaned_lfp</span><span class="p">,</span> <span class="n">sptr</span><span class="p">,</span> <span class="n">flim</span><span class="o">=</span><span class="p">[</span><span class="n">stim_freq</span> <span class="o">-</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="n">stim_freq</span> <span class="o">+</span> <span class="o">.</span><span class="mi">5</span><span class="p">])</span>
|
||||
<span class="n">stim_bins</span><span class="p">,</span> <span class="n">stim_kde</span> <span class="o">=</span> <span class="n">vonmises_kde</span><span class="p">(</span><span class="n">stim_spike_phase</span><span class="p">)</span>
|
||||
<span class="n">stim_ang</span><span class="p">,</span> <span class="n">stim_vec_len</span> <span class="o">=</span> <span class="n">spike_phase_score</span><span class="p">(</span><span class="n">stim_bins</span><span class="p">,</span> <span class="n">stim_kde</span><span class="p">)</span>
|
||||
<span class="n">stim_kde_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">stim_kde</span><span class="p">})</span>
|
||||
<span class="n">stim_bins_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">stim_bins</span><span class="p">})</span>
|
||||
|
||||
<span class="n">coherence_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_xy</span><span class="p">})</span>
|
||||
<span class="n">freqency_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="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">'signal_to_noise'</span><span class="p">:</span> <span class="n">snl</span><span class="p">,</span>
|
||||
<span class="s1">'best_channel'</span><span class="p">:</span> <span class="n">best_channel</span><span class="p">,</span>
|
||||
<span class="s1">'theta_freq'</span><span class="p">:</span> <span class="n">theta_f</span><span class="p">,</span>
|
||||
<span class="s1">'theta_peak'</span><span class="p">:</span> <span class="n">theta_p_max</span><span class="p">,</span>
|
||||
|
|
@ -13504,12 +13763,16 @@ div#notebook {
|
|||
<span class="s1">'theta_half_f2'</span><span class="p">:</span> <span class="n">theta_half_f2</span><span class="p">,</span>
|
||||
<span class="s1">'theta_half_width'</span><span class="p">:</span> <span class="n">theta_half_width</span><span class="p">,</span>
|
||||
<span class="s1">'theta_half_energy'</span><span class="p">:</span> <span class="n">theta_half_energy</span><span class="p">,</span>
|
||||
<span class="s1">'theta_ang'</span><span class="p">:</span> <span class="n">theta_ang</span><span class="p">,</span>
|
||||
<span class="s1">'theta_vec_len'</span><span class="p">:</span> <span class="n">theta_vec_len</span><span class="p">,</span>
|
||||
<span class="s1">'stim_freq'</span><span class="p">:</span> <span class="n">stim_freq</span><span class="p">,</span>
|
||||
<span class="s1">'stim_p_max'</span><span class="p">:</span> <span class="n">stim_p_max</span><span class="p">,</span>
|
||||
<span class="s1">'stim_half_f1'</span><span class="p">:</span> <span class="n">stim_half_f1</span><span class="p">,</span>
|
||||
<span class="s1">'stim_half_f2'</span><span class="p">:</span> <span class="n">stim_half_f2</span><span class="p">,</span>
|
||||
<span class="s1">'stim_half_width'</span><span class="p">:</span> <span class="n">stim_half_width</span><span class="p">,</span>
|
||||
<span class="s1">'stim_energy'</span><span class="p">:</span> <span class="n">stim_energy</span>
|
||||
<span class="s1">'stim_energy'</span><span class="p">:</span> <span class="n">stim_energy</span><span class="p">,</span>
|
||||
<span class="s1">'stim_ang'</span><span class="p">:</span> <span class="n">stim_ang</span><span class="p">,</span>
|
||||
<span class="s1">'stim_vec_len'</span><span class="p">:</span> <span class="n">stim_vec_len</span>
|
||||
<span class="p">})</span>
|
||||
<span class="k">return</span> <span class="n">result</span>
|
||||
</pre></div>
|
||||
|
|
@ -13521,7 +13784,7 @@ div#notebook {
|
|||
</div>
|
||||
<div class="cell border-box-sizing code_cell rendered">
|
||||
<div class="input">
|
||||
<div class="prompt input_prompt">In [16]:</div>
|
||||
<div class="prompt input_prompt">In [24]:</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>
|
||||
|
|
@ -13547,13 +13810,13 @@ div#notebook {
|
|||
|
||||
|
||||
|
||||
<div id="fdf01411-110c-4f09-8a74-86966ed09bad"></div>
|
||||
<div id="a0803bf8-bad9-4783-b06a-316f09ff8583"></div>
|
||||
<div class="output_subarea output_widget_view ">
|
||||
<script type="text/javascript">
|
||||
var element = $('#fdf01411-110c-4f09-8a74-86966ed09bad');
|
||||
var element = $('#a0803bf8-bad9-4783-b06a-316f09ff8583');
|
||||
</script>
|
||||
<script type="application/vnd.jupyter.widget-view+json">
|
||||
{"model_id": "564d1ef5339f46eebee42ec41cfcfe62", "version_major": 2, "version_minor": 0}
|
||||
{"model_id": "199c002aa9604451a318e3f471c9d892", "version_major": 2, "version_minor": 0}
|
||||
</script>
|
||||
</div>
|
||||
|
||||
|
|
@ -13565,8 +13828,9 @@ var element = $('#fdf01411-110c-4f09-8a74-86966ed09bad');
|
|||
|
||||
|
||||
<div class="output_subarea output_stream output_stderr output_text">
|
||||
<pre>/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/signal/spectral.py:1578: RuntimeWarning: invalid value encountered in true_divide
|
||||
<pre>/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/signal/spectral.py:1577: RuntimeWarning: invalid value encountered in true_divide
|
||||
Cxy = np.abs(Pxy)**2 / Pxx / Pyy
|
||||
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:28: RuntimeWarning: invalid value encountered in true_divide
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
|
|
@ -13585,97 +13849,54 @@ var element = $('#fdf01411-110c-4f09-8a74-86966ed09bad');
|
|||
</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">¶</a></h1>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell border-box-sizing code_cell rendered">
|
||||
<div class="input">
|
||||
<div class="prompt input_prompt">In [17]:</div>
|
||||
<div class="prompt input_prompt">In [25]:</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">'action'</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">'channel_group'</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">'unit_name'</span><span class="p">]</span>
|
||||
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># coher, freqs = {}, {}</span>
|
||||
<span class="c1"># for i, row in tqdm(units.iterrows(), total=len(units)):</span>
|
||||
<span class="c1"># action_id = row['action']</span>
|
||||
<span class="c1"># channel_group = row['channel_group']</span>
|
||||
<span class="c1"># unit_name = row['unit_name']</span>
|
||||
|
||||
<span class="n">name</span> <span class="o">=</span> <span class="n">f</span><span class="s1">'</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">'</span>
|
||||
<span class="c1"># name = f'{action_id}_{channel_group}_{unit_name}'</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="c1"># lfp = data_loader.lfp(action_id, channel_group) # 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="c1"># sptr = data_loader.spike_train(action_id, channel_group, unit_name)</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="c1"># lim = get_lim(action_id)</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="c1"># p_xys, freq, clean_lfp = compute_spike_lfp(lfp, sptr, *lim, NFFT=NFFT)</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="c1"># snls = signaltonoise(clean_lfp)</span>
|
||||
<span class="c1"># best_channel = np.argmax(snls)</span>
|
||||
<span class="c1"># snl = snls[best_channel]</span>
|
||||
<span class="c1"># p_xy = p_xys[:,best_channel].magnitude</span>
|
||||
<span class="c1"># freq = freq.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>
|
||||
<span class="c1"># coher.update({name: p_xy})</span>
|
||||
<span class="c1"># freqs.update({name: freq})</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="output_wrapper">
|
||||
<div class="output">
|
||||
|
||||
|
||||
<div class="output_area">
|
||||
|
||||
<div class="prompt"></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class="cell border-box-sizing code_cell rendered">
|
||||
<div class="input">
|
||||
<div class="prompt input_prompt">In [18]:</div>
|
||||
<div class="prompt input_prompt">In [26]:</div>
|
||||
<div class="inner_cell">
|
||||
<div class="input_area">
|
||||
<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">'data'</span> <span class="o">/</span> <span class="s1">'coherence.feather'</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">'data'</span> <span class="o">/</span> <span class="s1">'freqs.feather'</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">coherence_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">'data'</span> <span class="o">/</span> <span class="s1">'coherence.feather'</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">frequency_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">'data'</span> <span class="o">/</span> <span class="s1">'freqs.feather'</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">theta_kde_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">'data'</span> <span class="o">/</span> <span class="s1">'theta_kde.feather'</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">theta_bins_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">'data'</span> <span class="o">/</span> <span class="s1">'theta_bins.feather'</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">stim_kde_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">'data'</span> <span class="o">/</span> <span class="s1">'stim_kde.feather'</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">stim_bins_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">'data'</span> <span class="o">/</span> <span class="s1">'stim_bins.feather'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
@ -13694,12 +13915,15 @@ var element = $('#0aa9c06f-773a-4f88-a2ad-e7de5c5ebe40');
|
|||
<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"><ipython-input-18-bf8a91a90c5a></span> in <span class="ansi-cyan-fg"><module></span>
|
||||
<span class="ansi-green-fg">----> 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">'data'</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'coherence.feather'</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">'data'</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'freqs.feather'</span><span class="ansi-blue-fg">)</span>
|
||||
<span class="ansi-red-fg">NameError</span> Traceback (most recent call last)
|
||||
<span class="ansi-green-fg"><ipython-input-26-64114f1ea8bd></span> in <span class="ansi-cyan-fg"><module></span>
|
||||
<span class="ansi-green-intense-fg ansi-bold"> 1</span> pd<span class="ansi-blue-fg">.</span>DataFrame<span class="ansi-blue-fg">(</span>coherence_data<span class="ansi-blue-fg">)</span><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">'data'</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'coherence.feather'</span><span class="ansi-blue-fg">)</span>
|
||||
<span class="ansi-green-fg">----> 2</span><span class="ansi-red-fg"> </span>pd<span class="ansi-blue-fg">.</span>DataFrame<span class="ansi-blue-fg">(</span>frequency_data<span class="ansi-blue-fg">)</span><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">'data'</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'freqs.feather'</span><span class="ansi-blue-fg">)</span>
|
||||
<span class="ansi-green-intense-fg ansi-bold"> 3</span> pd<span class="ansi-blue-fg">.</span>DataFrame<span class="ansi-blue-fg">(</span>theta_kde_data<span class="ansi-blue-fg">)</span><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">'data'</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'theta_kde.feather'</span><span class="ansi-blue-fg">)</span>
|
||||
<span class="ansi-green-intense-fg ansi-bold"> 4</span> pd<span class="ansi-blue-fg">.</span>DataFrame<span class="ansi-blue-fg">(</span>theta_bins_data<span class="ansi-blue-fg">)</span><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">'data'</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'theta_bins.feather'</span><span class="ansi-blue-fg">)</span>
|
||||
<span class="ansi-green-intense-fg ansi-bold"> 5</span> pd<span class="ansi-blue-fg">.</span>DataFrame<span class="ansi-blue-fg">(</span>stim_kde_data<span class="ansi-blue-fg">)</span><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">'data'</span> <span class="ansi-blue-fg">/</span> <span class="ansi-blue-fg">'stim_kde.feather'</span><span class="ansi-blue-fg">)</span>
|
||||
|
||||
<span class="ansi-red-fg">AttributeError</span>: 'dict' object has no attribute 'to_feather'</pre>
|
||||
<span class="ansi-red-fg">NameError</span>: name 'frequency_data' is not defined</pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -12,17 +12,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"10:25:26 [I] klustakwik KlustaKwik2 version 0.2.6\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"%matplotlib inline\n",
|
||||
|
|
@ -32,6 +24,8 @@
|
|||
"import expipe\n",
|
||||
"import os\n",
|
||||
"import pathlib\n",
|
||||
"import scipy\n",
|
||||
"import scipy.signal\n",
|
||||
"import numpy as np\n",
|
||||
"import exdir\n",
|
||||
"import pandas as pd\n",
|
||||
|
|
@ -52,7 +46,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -63,7 +57,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -73,7 +67,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -84,7 +78,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -111,7 +105,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -119,22 +113,12 @@
|
|||
" a = np.asanyarray(a)\n",
|
||||
" m = a.mean(axis)\n",
|
||||
" sd = a.std(axis=axis, ddof=ddof)\n",
|
||||
" return np.where(sd == 0, 0, m / sd)\n",
|
||||
"\n",
|
||||
"\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",
|
||||
" idxs, = np.where(abs(anas[:, ch]) > threshold)\n",
|
||||
" for idx in idxs:\n",
|
||||
" anas[idx-width:idx+width, ch] = 0 # TODO AR model prediction\n",
|
||||
" return anas"
|
||||
" return np.where(sd == 0, 0, m / sd)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -148,7 +132,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -165,7 +149,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -191,7 +175,28 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# p = np.load('debug_p.npy')\n",
|
||||
"# f = np.load('debug_f.npy')\n",
|
||||
"# compute_half_width(p, f, 0.01038941, 30.30187709636872)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plt.plot(f, p)\n",
|
||||
"# plt.xlim(29.9,30.6)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -201,73 +206,16 @@
|
|||
" return interp1d(f, p)(s_f)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def compute_spike_lfp(anas, sptr, t_start, t_stop, NFFT):\n",
|
||||
"\n",
|
||||
" t_start = t_start * pq.s if t_start is not None else 0 * pq.s\n",
|
||||
" sampling_rate = anas.sampling_rate\n",
|
||||
" units = anas.units\n",
|
||||
" if t_start is not None and t_stop is not None:\n",
|
||||
" t_stop = t_stop * pq.s\n",
|
||||
" mask = (anas.times > t_start) & (anas.times < t_stop)\n",
|
||||
" anas = np.array(anas)[mask,:]\n",
|
||||
"\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",
|
||||
" )\n",
|
||||
"\n",
|
||||
" sptr = neo.SpikeTrain(\n",
|
||||
" sptr.times[(sptr.times > t_start) & (sptr.times < cleaned_anas.times[-1])],\n",
|
||||
" t_start=t_start, t_stop=cleaned_anas.times[-1]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" sigs, freqs = el.sta.spike_field_coherence(cleaned_anas, sptr, **{'nperseg': NFFT})\n",
|
||||
" return sigs, freqs, cleaned_anas"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# action_id_0 = '1833-200619-1'\n",
|
||||
"# action_id_1 = '1833-200619-2'\n",
|
||||
"# lfp_0 = data_loader.lfp(action_id_0, 6)\n",
|
||||
"# lfp_1 = data_loader.lfp(action_id_1, 6)\n",
|
||||
"\n",
|
||||
"# lim_0 = get_lim(action_id_0)\n",
|
||||
"# lim_1 = get_lim(action_id_1)\n",
|
||||
"\n",
|
||||
"# sptrs_0 = data_loader.spike_trains(action_id_0, 6)\n",
|
||||
"# sptrs_1 = data_loader.spike_trains(action_id_1, 6)\n",
|
||||
"\n",
|
||||
"# coher_0, freq_0, clean_lfp_0 = compute_spike_lfp(lfp_0, sptrs_0[163], *lim_0, 4096)\n",
|
||||
"# coher_1, freq_1, clean_lfp_1 = compute_spike_lfp(lfp_1, sptrs_1[28], *lim_1, 4096)\n",
|
||||
"\n",
|
||||
"# best_channel_0 = np.argmax(signaltonoise(clean_lfp_0))\n",
|
||||
"# best_channel_1 = np.argmax(signaltonoise(clean_lfp_1))\n",
|
||||
"\n",
|
||||
"# plt.plot(freq_0, coher_0[:,best_channel_0])\n",
|
||||
"# plt.plot(freq_1, coher_1[:,best_channel_1])\n",
|
||||
"# plt.xlim(0,20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"NFFT = 4096*2\n",
|
||||
"theta_band_f1, theta_band_f2 = 6, 10 "
|
||||
"def compute_spike_lfp_coherence(anas, sptr, NFFT):\n",
|
||||
"\n",
|
||||
" sigs, freqs = el.sta.spike_field_coherence(anas, sptr, **{'nperseg': NFFT})\n",
|
||||
" return sigs, freqs"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -276,23 +224,289 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def butter_bandpass(lowcut, highcut, fs, order=5):\n",
|
||||
" nyq = 0.5 * fs\n",
|
||||
" low = lowcut / nyq\n",
|
||||
" high = highcut / nyq\n",
|
||||
" b, a = scipy.signal.butter(order, [low, high], btype='band')\n",
|
||||
" return b, a\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
|
||||
" b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
|
||||
" y = scipy.signal.filtfilt(b, a, data)\n",
|
||||
" return y\n",
|
||||
"\n",
|
||||
"def compute_spike_phase_func(lfp, times, return_degrees=False):\n",
|
||||
" x_a = hilbert(lfp)\n",
|
||||
" x_phase = np.angle(x_a)\n",
|
||||
" if return_degrees:\n",
|
||||
" x_phase = x_phase * 180 / np.pi\n",
|
||||
" return interp1d(times, x_phase)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def vonmises_kde(data, kappa=100, n_bins=100):\n",
|
||||
" from scipy.special import i0\n",
|
||||
" bins = np.linspace(-np.pi, np.pi, n_bins)\n",
|
||||
" x = np.linspace(-np.pi, np.pi, n_bins)\n",
|
||||
" # integrate vonmises kernels\n",
|
||||
" kde = np.exp(kappa * np.cos(x[:, None] - data[None, :])).sum(1) / (2 * np.pi * i0(kappa))\n",
|
||||
" kde /= np.trapz(kde, x=bins)\n",
|
||||
" return bins, kde\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def spike_phase_score(phase_bins, density):\n",
|
||||
" import math\n",
|
||||
" import pycircstat as pc\n",
|
||||
" ang = pc.mean(phase_bins, w=density)\n",
|
||||
" vec_len = pc.resultant_vector_length(phase_bins, w=density)\n",
|
||||
" # ci_lim = pc.mean_ci_limits(head_angle_bins, w=rate)\n",
|
||||
" return ang, vec_len"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def compute_clean_lfp(anas, width=500, threshold=2):\n",
|
||||
" anas = np.array(anas)\n",
|
||||
" idxs, = np.where(abs(anas) > threshold)\n",
|
||||
" for idx in idxs:\n",
|
||||
" anas[idx-width:idx+width] = 0 # TODO AR model prediction\n",
|
||||
" return anas, idxs\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def compute_clean_spikes(spikes, idxs, times, width=500):\n",
|
||||
"\n",
|
||||
" for idx in idxs:\n",
|
||||
" t0 = times[idx-width]\n",
|
||||
" stop = idx + width\n",
|
||||
" if stop > len(times) - 1:\n",
|
||||
" stop = len(times) - 1 \n",
|
||||
" t1 = times[stop]\n",
|
||||
" mask = (spikes > t0) & (spikes < t1)\n",
|
||||
" spikes = spikes[~mask]\n",
|
||||
" spikes = spikes[spikes <= times[-1]]\n",
|
||||
" return spikes\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def prepare_spike_lfp(anas, sptr, t_start, t_stop):\n",
|
||||
"\n",
|
||||
" t_start = t_start * pq.s if t_start is not None else 0 * pq.s\n",
|
||||
" sampling_rate = anas.sampling_rate\n",
|
||||
" units = anas.units\n",
|
||||
" times = anas.times\n",
|
||||
" if t_start is not None and t_stop is not None:\n",
|
||||
" t_stop = t_stop * pq.s\n",
|
||||
" mask = (times > t_start) & (times < t_stop)\n",
|
||||
" anas = np.array(anas)[mask,:]\n",
|
||||
" times = times[mask]\n",
|
||||
"\n",
|
||||
" best_channel = np.argmax(signaltonoise(anas))\n",
|
||||
"# best_channel = np.random.choice(anas.shape[1])\n",
|
||||
" \n",
|
||||
" cleaned_anas, idxs = compute_clean_lfp(anas[:, best_channel])\n",
|
||||
" \n",
|
||||
" cleaned_anas = neo.AnalogSignal(\n",
|
||||
" signal=cleaned_anas * units, sampling_rate=sampling_rate, t_start=t_start\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" spike_units = sptr.units\n",
|
||||
" spike_times = sptr.times\n",
|
||||
" spike_times = compute_clean_spikes(spike_times, idxs, times)\n",
|
||||
"\n",
|
||||
" sptr = neo.SpikeTrain(\n",
|
||||
" spike_times[(spike_times > t_start) & (spike_times < times[-1])], units=spike_units,\n",
|
||||
" t_start=t_start, t_stop=times[-1]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" return cleaned_anas, sptr, best_channel"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def compute_spike_phase_func(lfp, times, return_degrees=False):\n",
|
||||
" from scipy.fftpack import next_fast_len\n",
|
||||
" x_a = scipy.signal.hilbert(\n",
|
||||
" lfp, next_fast_len(len(lfp)))[:len(lfp)]\n",
|
||||
"# x_a = hilbert(lfp)\n",
|
||||
" x_phase = np.angle(x_a, deg=return_degrees)\n",
|
||||
" return interp1d(times, x_phase)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def compute_spike_phase(lfp, spikes, flim=[6,10]):\n",
|
||||
" \n",
|
||||
" sample_rate = lfp.sampling_rate.magnitude\n",
|
||||
" \n",
|
||||
" # sometimes the position is recorded after LFP recording is ended\n",
|
||||
" times = np.arange(lfp.shape[0]) / sample_rate\n",
|
||||
" \n",
|
||||
" spikes = np.array(spikes)\n",
|
||||
" spikes = spikes[(spikes > times.min()) & (spikes < times.max())]\n",
|
||||
" \n",
|
||||
" filtered_lfp = butter_bandpass_filter(\n",
|
||||
" lfp.magnitude.ravel(), *flim, fs=sample_rate, order=3)\n",
|
||||
"\n",
|
||||
" spike_phase_func = compute_spike_phase_func(filtered_lfp, times)\n",
|
||||
" \n",
|
||||
" return spike_phase_func(spikes), filtered_lfp"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plt.figure(figsize=(16,9))\n",
|
||||
"# # lfp = data_loader.lfp('1833-200619-2', 6)\n",
|
||||
"# lfp = data_loader.lfp('1834-220319-3', 6)\n",
|
||||
"# # lfp = data_loader.lfp('1849-010319-4', 6)\n",
|
||||
"# times = np.arange(lfp.shape[0]) / lfp.sampling_rate.magnitude\n",
|
||||
"# clean_lfp, _ = compute_clean_lfp(lfp.magnitude[:, 0], threshold=2)\n",
|
||||
"# plt.plot(times,lfp[:,0])\n",
|
||||
"# plt.plot(times,clean_lfp)\n",
|
||||
"\n",
|
||||
"# plt.figure(figsize=(16,9))\n",
|
||||
"# plt.psd(lfp[:,0].ravel(), Fs=1000, NFFT=10000)\n",
|
||||
"# plt.psd(clean_lfp, Fs=1000, NFFT=10000)\n",
|
||||
"# plt.xlim(0,100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plt.figure(figsize=(16,9))\n",
|
||||
"\n",
|
||||
"# plt.plot(times,lfp[:,0])\n",
|
||||
"# # plt.plot(clean_lfp*100)\n",
|
||||
"# plt.plot(times[:-1], np.diff(lfp[:,0].magnitude.ravel()))\n",
|
||||
"# plt.xlim(64.5,65.5)\n",
|
||||
"# # plt.ylim(-250,250)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# # action_id_0, channel_0, unit_0 = '1833-200619-1', 6, 163\n",
|
||||
"# # action_id_1, channel_1, unit_1 = '1833-200619-2', 6, 28\n",
|
||||
"# action_id_0, channel_0, unit_0 = '1834-220319-3', 2, 46\n",
|
||||
"# action_id_1, channel_1, unit_1 = '1834-220319-4', 2, 60\n",
|
||||
"# lfp_0 = data_loader.lfp(action_id_0, channel_0)\n",
|
||||
"# lfp_1 = data_loader.lfp(action_id_1, channel_1)\n",
|
||||
"\n",
|
||||
"# sample_rate_0 = lfp_0.sampling_rate\n",
|
||||
"# sample_rate_1 = lfp_1.sampling_rate\n",
|
||||
"\n",
|
||||
"# lim_0 = get_lim(action_id_0)\n",
|
||||
"# lim_1 = get_lim(action_id_1)\n",
|
||||
"\n",
|
||||
"# sptrs_0 = data_loader.spike_trains(action_id_0, channel_0)\n",
|
||||
"\n",
|
||||
"# sptrs_1 = data_loader.spike_trains(action_id_1, channel_1)\n",
|
||||
"\n",
|
||||
"# cleaned_lfps_0, sptr_0, best_channel_0 = prepare_spike_lfp(lfp_0, sptrs_0[unit_0], *lim_0)\n",
|
||||
"\n",
|
||||
"# cleaned_lfps_1, sptr_1, best_channel_1 = prepare_spike_lfp(lfp_1, sptrs_1[unit_1], *lim_1)\n",
|
||||
"\n",
|
||||
"# coher_0, freq_0 = compute_spike_lfp_coherence(cleaned_lfps_0, sptr_0, 4096)\n",
|
||||
"\n",
|
||||
"# coher_1, freq_1 = compute_spike_lfp_coherence(cleaned_lfps_1, sptr_1, 4096)\n",
|
||||
"\n",
|
||||
"# spike_phase_0, filtered_lfp_0 = compute_spike_phase(cleaned_lfps_0, sptrs_0[unit_0], flim=[6,10])\n",
|
||||
"\n",
|
||||
"# spike_phase_1, filtered_lfp_1 = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[6,10])\n",
|
||||
"\n",
|
||||
"# spike_phase_1_stim, filtered_lfp_1_stim = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[10.5,11.5])\n",
|
||||
"\n",
|
||||
"# plt.figure()\n",
|
||||
"# plt.plot(freq_0, coher_0.ravel())\n",
|
||||
"# plt.plot(freq_1, coher_1.ravel())\n",
|
||||
"# plt.xlim(0,20)\n",
|
||||
"\n",
|
||||
"# plt.figure()\n",
|
||||
"# bins_0, kde_0 = vonmises_kde(spike_phase_0, 100)\n",
|
||||
"# ang_0, vec_len_0 = spike_phase_score(bins_0, kde_0)\n",
|
||||
"# plt.polar(bins_0, kde_0, color='b')\n",
|
||||
"# plt.polar([ang_0, ang_0], [0, vec_len_0], color='b')\n",
|
||||
"\n",
|
||||
"# bins_1, kde_1 = vonmises_kde(spike_phase_1, 100)\n",
|
||||
"# ang_1, vec_len_1 = spike_phase_score(bins_1, kde_1)\n",
|
||||
"# plt.polar(bins_1, kde_1, color='r')\n",
|
||||
"# plt.polar([ang_1, ang_1], [0, vec_len_1], color='r')\n",
|
||||
"\n",
|
||||
"# bins_1_stim, kde_1_stim = vonmises_kde(spike_phase_1_stim, 100)\n",
|
||||
"# ang_1_stim, vec_len_1_stim = spike_phase_score(bins_1_stim, kde_1_stim)\n",
|
||||
"# plt.polar(bins_1_stim, kde_1_stim, color='k')\n",
|
||||
"# plt.polar([ang_1_stim, ang_1_stim], [0, vec_len_1_stim], color='k')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# TODO fix artefact stuff from phase precession"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"NFFT = 8192\n",
|
||||
"theta_band_f1, theta_band_f2 = 6, 10 "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"coherence_data, freqency_data = {}, {}\n",
|
||||
"theta_kde_data, theta_bins_data = {}, {}\n",
|
||||
"stim_kde_data, stim_bins_data = {}, {}\n",
|
||||
"\n",
|
||||
"def process(row):\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",
|
||||
" cleaned_lfp, sptr, best_channel = prepare_spike_lfp(lfp, sptr, *lim)\n",
|
||||
" \n",
|
||||
" p_xys, freq = compute_spike_lfp_coherence(cleaned_lfp, sptr, NFFT=NFFT)\n",
|
||||
" \n",
|
||||
" p_xy = p_xys.magnitude.ravel()\n",
|
||||
" freq = freq.magnitude\n",
|
||||
" \n",
|
||||
" theta_f, theta_p_max = find_theta_peak(p_xy, freq, theta_band_f1, theta_band_f2)\n",
|
||||
|
|
@ -305,6 +519,12 @@
|
|||
" \n",
|
||||
" theta_half_energy = compute_energy(p_xy, freq, theta_half_f1, theta_half_f2) # theta band 6 - 10 Hz\n",
|
||||
" \n",
|
||||
" theta_spike_phase, _ = compute_spike_phase(cleaned_lfp, sptr, flim=[theta_band_f1, theta_band_f2])\n",
|
||||
" theta_bins, theta_kde = vonmises_kde(theta_spike_phase)\n",
|
||||
" theta_ang, theta_vec_len = spike_phase_score(theta_bins, theta_kde)\n",
|
||||
" theta_kde_data.update({name: theta_kde})\n",
|
||||
" theta_bins_data.update({name: theta_bins})\n",
|
||||
"\n",
|
||||
" # stim\n",
|
||||
" \n",
|
||||
" stim_freq = compute_stim_freq(action_id)\n",
|
||||
|
|
@ -316,8 +536,19 @@
|
|||
" \n",
|
||||
" stim_energy = compute_energy(p_xy, freq, stim_half_f1, stim_half_f2)\n",
|
||||
" \n",
|
||||
" if np.isnan(stim_freq):\n",
|
||||
" stim_spike_phase, stim_bins, stim_kde, stim_ang, stim_vec_len = [np.nan] * 5\n",
|
||||
" else:\n",
|
||||
" stim_spike_phase, _ = compute_spike_phase(cleaned_lfp, sptr, flim=[stim_freq - .5, stim_freq + .5])\n",
|
||||
" stim_bins, stim_kde = vonmises_kde(stim_spike_phase)\n",
|
||||
" stim_ang, stim_vec_len = spike_phase_score(stim_bins, stim_kde)\n",
|
||||
" stim_kde_data.update({name: stim_kde})\n",
|
||||
" stim_bins_data.update({name: stim_bins})\n",
|
||||
" \n",
|
||||
" coherence_data.update({name: p_xy})\n",
|
||||
" freqency_data.update({name: freq})\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",
|
||||
|
|
@ -326,30 +557,34 @@
|
|||
" 'theta_half_f2': theta_half_f2,\n",
|
||||
" 'theta_half_width': theta_half_width,\n",
|
||||
" 'theta_half_energy': theta_half_energy,\n",
|
||||
" 'theta_ang': theta_ang, \n",
|
||||
" 'theta_vec_len': theta_vec_len,\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_energy': stim_energy,\n",
|
||||
" 'stim_ang': stim_ang, \n",
|
||||
" 'stim_vec_len': stim_vec_len\n",
|
||||
" })\n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "564d1ef5339f46eebee42ec41cfcfe62",
|
||||
"model_id": "199c002aa9604451a318e3f471c9d892",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
|
||||
"HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
|
@ -359,8 +594,9 @@
|
|||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/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"
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/signal/spectral.py:1577: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" Cxy = np.abs(Pxy)**2 / Pxx / Pyy\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:28: RuntimeWarning: invalid value encountered in true_divide\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -377,87 +613,62 @@
|
|||
" left_index=True, right_index=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# plot"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 25,
|
||||
"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"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",
|
||||
"# 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",
|
||||
"# 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",
|
||||
"# 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",
|
||||
"# sptr = data_loader.spike_train(action_id, channel_group, unit_name)\n",
|
||||
" \n",
|
||||
" lim = get_lim(action_id)\n",
|
||||
"# lim = get_lim(action_id)\n",
|
||||
"\n",
|
||||
" p_xys, freq, clean_lfp = compute_spike_lfp(lfp, sptr, *lim, NFFT=NFFT)\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",
|
||||
"# 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})"
|
||||
"# coher.update({name: p_xy})\n",
|
||||
"# freqs.update({name: freq})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "'dict' object has no attribute 'to_feather'",
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'frequency_data' is not defined",
|
||||
"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'"
|
||||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-26-64114f1ea8bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoherence_data\u001b[0m\u001b[0;34m)\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[0;32m----> 2\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrequency_data\u001b[0m\u001b[0;34m)\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[0m\u001b[1;32m 3\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta_kde_data\u001b[0m\u001b[0;34m)\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'theta_kde.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta_bins_data\u001b[0m\u001b[0;34m)\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'theta_bins.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstim_kde_data\u001b[0m\u001b[0;34m)\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'stim_kde.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mNameError\u001b[0m: name 'frequency_data' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"coher.to_feather(output / 'data' / 'coherence.feather')\n",
|
||||
"freqs.to_feather(output / 'data' / 'freqs.feather')"
|
||||
"pd.DataFrame(coherence_data).to_feather(output / 'data' / 'coherence.feather')\n",
|
||||
"pd.DataFrame(frequency_data).to_feather(output / 'data' / 'freqs.feather')\n",
|
||||
"pd.DataFrame(theta_kde_data).to_feather(output / 'data' / 'theta_kde.feather')\n",
|
||||
"pd.DataFrame(theta_bins_data).to_feather(output / 'data' / 'theta_bins.feather')\n",
|
||||
"pd.DataFrame(stim_kde_data).to_feather(output / 'data' / 'stim_kde.feather')\n",
|
||||
"pd.DataFrame(stim_bins_data).to_feather(output / 'data' / 'stim_bins.feather')"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -545,5 +756,5 @@
|
|||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
|
|
|||
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actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_vec_len-ns_inhibited.svg (Stored with Git LFS)
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actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_vec_len-ns_not_inhibited.svg (Stored with Git LFS)
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actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_vec_len-ns_not_inhibited.svg (Stored with Git LFS)
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